PMC:7417201 / 9829-15324 JSONTXT

{"project":"LitCovid-PD-FMA-UBERON","denotations":[{"id":"T20","span":{"begin":1723,"end":1727},"obj":"Body_part"},{"id":"T21","span":{"begin":1773,"end":1778},"obj":"Body_part"},{"id":"T22","span":{"begin":2547,"end":2562},"obj":"Body_part"},{"id":"T23","span":{"begin":3465,"end":3469},"obj":"Body_part"},{"id":"T24","span":{"begin":3545,"end":3550},"obj":"Body_part"},{"id":"T25","span":{"begin":4187,"end":4202},"obj":"Body_part"},{"id":"T26","span":{"begin":4975,"end":4979},"obj":"Body_part"},{"id":"T27","span":{"begin":5030,"end":5035},"obj":"Body_part"},{"id":"T28","span":{"begin":5472,"end":5487},"obj":"Body_part"}],"attributes":[{"id":"A20","pred":"fma_id","subj":"T20","obj":"http://purl.org/sig/ont/fma/fma256135"},{"id":"A21","pred":"fma_id","subj":"T21","obj":"http://purl.org/sig/ont/fma/fma9576"},{"id":"A22","pred":"fma_id","subj":"T22","obj":"http://purl.org/sig/ont/fma/fma49893"},{"id":"A23","pred":"fma_id","subj":"T23","obj":"http://purl.org/sig/ont/fma/fma256135"},{"id":"A24","pred":"fma_id","subj":"T24","obj":"http://purl.org/sig/ont/fma/fma9576"},{"id":"A25","pred":"fma_id","subj":"T25","obj":"http://purl.org/sig/ont/fma/fma49893"},{"id":"A26","pred":"fma_id","subj":"T26","obj":"http://purl.org/sig/ont/fma/fma256135"},{"id":"A27","pred":"fma_id","subj":"T27","obj":"http://purl.org/sig/ont/fma/fma9576"},{"id":"A28","pred":"fma_id","subj":"T28","obj":"http://purl.org/sig/ont/fma/fma49893"}],"text":"Results\nA total of 1361 stress SPECT-MPI were considered, including all tests performed during the pandemic between February and May 2020 and those performed in the corresponding months of the prior 3 years (2017, 2018, and 2019).\nThe number of stress SPECT-MPI studies performed during the pandemic (n = 123) was significantly lower (P \u003c 0.0001) compared with the mean number of procedures in the corresponding months of the years 2017, 2018, and 2019 (n = 413) (Fig. 1). However, the percentage of abnormal stress SPECT-MPI studies was similar (P = 0.65) during the pandemic (36%) compared with mean percentage value of the corresponding months of the years 2017, 2018, and 2019 (34%) (Fig. 2). Of note, given the mean number of 139 abnormal stress SPECT-MPI in the previous 3 years and the number of 44 abnormal tests during the pandemic, there was a 68% of abnormal test missed. Baseline characteristics of the entire population according to the year of SPECT-MPI execution are reported on Table 1. Most of the considered variables were comparable between the two study periods.\nFig. 1 Number of stress SPECT-MPI procedures performed during COVID-19 pandemic and during corresponding months of the years 2017, 2018, and 2019 (P for trend \u003c 0.0001)\nFig. 2 Prevalence of abnormal stress SPECT-MPI studies during COVID-19 pandemic and during the corresponding months of the years 2017, 2018, and 2019. The percentage of abnormal stress SPECT-MPI studies was similar (P = 0.65)\nTable 1 Baseline characteristics of overall population according to the year of MPI execution\nAll (n = 1361) 2020 (n = 123) 2017–2019 (n = 1238) P value\nAge (years) 64 ± 11 64 ± 10 64 ± 11 0.14\nMale gender, n (%) 439 (32) 29 (24) 410 (33) \u003c .05\nBody mass index (kg/m2) 29 ± 5 29 ± 5 29 ± 6 0.43\nChest pain, n (%) 468 (34) 37 (30) 431 (35) 0.29\nDiabetes, n (%) 437 (32) 47 (38) 390 (32) 0.12\nDyspnea, n (%) 624 (46) 49 (40) 575 (46) 0.16\nFamily history, n (%) 729 (54) 60 (49) 669 (54) 0.26\nHypertension, n (%) 1070 (79) 102 (83) 968 (78) 0.22\nHyperlipidemia, n (%) 949 (70) 92 (75) 857 (69) 0.20\nSmoking, n (%) 416 (31) 26 (21) 390 (32) \u003c .05\nKnown CAD, n (%) 593 (44) 52 (42) 541 (44) 0.76\nStress type \u003c .0001\n  Physical exercise test, n (%) 519 (38) 11 (9) 508 (41)\n  Pharmacological test, n (%) 842 (62) 112 (91) 730 (59)\nAbnormal MPI, n (%) 462 (34) 44 (36) 418 (34) 0.65\nTotal perfusion defect \u003e 10% 246 (53) 27 (61) 219 (52) 0.26\nValues are expressed as mean value ± standard deviation or as number (percentage) of subjects. MPI myocardial perfusion imaging, CAD coronary artery disease\nCharacteristics of overall population are also described according to stress SPECT-MPI results during COVID-19 pandemic and during the corresponding months of 2017–2019 (Table 2). In both study periods, the percentage of patients with hyperlipidemia and known CAD was higher in patients with abnormal compared with those with normal stress SPECT-MPI findings, while during 2017–2019, patients with abnormal SPECT-MPI were older (P \u003c 0.05) and had higher prevalence of diabetes (P \u003c 0.005) and hypertension (P \u003c 0.001).\nTable 2 Clinical characteristics of overall population according to MPI results during COVID-19 emergency and during 2017–2019 3-year’s corresponding months\n2020 2017–2019\nNormal (n = 79) Abnormal (n = 44) P value Normal (n = 820) Abnormal (n = 418) P value\nAge (years) 64 ± 11 64 ± 8 0.77 63 ± 11 65 ± 11 \u003c .05\nMale gender, n (%) 23 (38) 6 (43) 0.05 337 (41) 73 (17) \u003c .001\nBody mass index (kg/m2) 28.9 ± 4.9 28.1 ± 4.4 0.38 29.1 ± 6.1 27.9 ± 4.2 \u003c .001\nChest pain, n (%) 22 (38) 15 (43) 0.47 308 (38) 123 (29) \u003c .005\nDiabetes, n (%) 28 (19) 19 (36) 0.40 234 (29) 156 (37) \u003c .005\nDyspnea, n (%) 29 (22) 20 (25) 0.34 375 (46) 200 (48) 0.48\nFamily history, n (%) 36 (48) 24 (41) 0.34 446 (54) 223 (53) 0.73\nHypertension, n (%) 66 (62) 36 (76) 0.81 614 (75) 354 (85) \u003c .001\nHyperlipidemia, n (%) 54 (55) 38 (57) \u003c .05 540 (66) 317 (76) \u003c .001\nSmoking, n (%) 15 (55) 11 (57) 0.43 258 (31) 132 (32) 0.97\nKnown CAD n (%) 21 (55) 31 (57) \u003c .001 224 (27) 317 (76) \u003c .001\nValues are expressed as mean value ± standard deviation or as number (percentage) of subjects. MPI myocardial perfusion imaging, CAD coronary artery disease\nAt multivariable logistic regression analysis (Table 3), hypertension (P \u003c 0.05) and known CAD (P \u003c 0.001) were significantly associated with abnormal stress SPECT-MPI during both COVID-19 pandemic and the corresponding months of the previous 3 years. No significant interactions among risk factors were found. During the 2017–2019 period, also male gender (P \u003c 0.001) and diabetes (P \u003c 0.05) were associated with abnormal stress SPECT-MPI.\nTable 3 Multivariable logistic regression analysis with abnormal MPI as dependent variable during COVID-19 emergency and during 2017–2019 3-year’s corresponding months\n2020 2017–2019\nβ coefficient SE P value β coefficient SE P value\nAge 0.005 0.023 0.834 − 0.004 0.007 0.60\nMale gender 0.162 0.639 0.799 0.671 0.166 \u003c .001\nBody mass index − 0.013 0.049 0.785 − 0.024 0.014 0.08\nChest pain 0.542 0.508 0.286 − 0.264 0.149 0.08\nDiabetes 0.565 0.466 0.225 0.310 0.148 \u003c .05\nFamily history 0.180 0.454 0.692 − 0.163 0.150 0.28\nDyspnea 0.362 0.382 0.343 − 0.085 0.120 0.48\nHypertension − 1.573 0.709 \u003c .05 0.376 0.196 \u003c .05\nHyperlipidemia 0.822 0.621 0.186 − 0.239 0.167 0.15\nSmoking 0.619 0.533 0.245 − 0.102 0.152 0.50\nKnown CAD 0.137 0.541 \u003c.001 1.994 0.154 \u003c .001\nSE standard error, MPI myocardial perfusion imaging, CAD coronary artery disease"}

{"project":"LitCovid-PD-UBERON","denotations":[{"id":"T22","span":{"begin":1773,"end":1778},"obj":"Body_part"},{"id":"T23","span":{"begin":2547,"end":2562},"obj":"Body_part"},{"id":"T24","span":{"begin":2556,"end":2562},"obj":"Body_part"},{"id":"T25","span":{"begin":3545,"end":3550},"obj":"Body_part"},{"id":"T26","span":{"begin":4187,"end":4202},"obj":"Body_part"},{"id":"T27","span":{"begin":4196,"end":4202},"obj":"Body_part"},{"id":"T28","span":{"begin":5030,"end":5035},"obj":"Body_part"},{"id":"T29","span":{"begin":5472,"end":5487},"obj":"Body_part"},{"id":"T30","span":{"begin":5481,"end":5487},"obj":"Body_part"}],"attributes":[{"id":"A22","pred":"uberon_id","subj":"T22","obj":"http://purl.obolibrary.org/obo/UBERON_0001443"},{"id":"A23","pred":"uberon_id","subj":"T23","obj":"http://purl.obolibrary.org/obo/UBERON_0001621"},{"id":"A24","pred":"uberon_id","subj":"T24","obj":"http://purl.obolibrary.org/obo/UBERON_0001637"},{"id":"A25","pred":"uberon_id","subj":"T25","obj":"http://purl.obolibrary.org/obo/UBERON_0001443"},{"id":"A26","pred":"uberon_id","subj":"T26","obj":"http://purl.obolibrary.org/obo/UBERON_0001621"},{"id":"A27","pred":"uberon_id","subj":"T27","obj":"http://purl.obolibrary.org/obo/UBERON_0001637"},{"id":"A28","pred":"uberon_id","subj":"T28","obj":"http://purl.obolibrary.org/obo/UBERON_0001443"},{"id":"A29","pred":"uberon_id","subj":"T29","obj":"http://purl.obolibrary.org/obo/UBERON_0001621"},{"id":"A30","pred":"uberon_id","subj":"T30","obj":"http://purl.obolibrary.org/obo/UBERON_0001637"}],"text":"Results\nA total of 1361 stress SPECT-MPI were considered, including all tests performed during the pandemic between February and May 2020 and those performed in the corresponding months of the prior 3 years (2017, 2018, and 2019).\nThe number of stress SPECT-MPI studies performed during the pandemic (n = 123) was significantly lower (P \u003c 0.0001) compared with the mean number of procedures in the corresponding months of the years 2017, 2018, and 2019 (n = 413) (Fig. 1). However, the percentage of abnormal stress SPECT-MPI studies was similar (P = 0.65) during the pandemic (36%) compared with mean percentage value of the corresponding months of the years 2017, 2018, and 2019 (34%) (Fig. 2). Of note, given the mean number of 139 abnormal stress SPECT-MPI in the previous 3 years and the number of 44 abnormal tests during the pandemic, there was a 68% of abnormal test missed. Baseline characteristics of the entire population according to the year of SPECT-MPI execution are reported on Table 1. Most of the considered variables were comparable between the two study periods.\nFig. 1 Number of stress SPECT-MPI procedures performed during COVID-19 pandemic and during corresponding months of the years 2017, 2018, and 2019 (P for trend \u003c 0.0001)\nFig. 2 Prevalence of abnormal stress SPECT-MPI studies during COVID-19 pandemic and during the corresponding months of the years 2017, 2018, and 2019. The percentage of abnormal stress SPECT-MPI studies was similar (P = 0.65)\nTable 1 Baseline characteristics of overall population according to the year of MPI execution\nAll (n = 1361) 2020 (n = 123) 2017–2019 (n = 1238) P value\nAge (years) 64 ± 11 64 ± 10 64 ± 11 0.14\nMale gender, n (%) 439 (32) 29 (24) 410 (33) \u003c .05\nBody mass index (kg/m2) 29 ± 5 29 ± 5 29 ± 6 0.43\nChest pain, n (%) 468 (34) 37 (30) 431 (35) 0.29\nDiabetes, n (%) 437 (32) 47 (38) 390 (32) 0.12\nDyspnea, n (%) 624 (46) 49 (40) 575 (46) 0.16\nFamily history, n (%) 729 (54) 60 (49) 669 (54) 0.26\nHypertension, n (%) 1070 (79) 102 (83) 968 (78) 0.22\nHyperlipidemia, n (%) 949 (70) 92 (75) 857 (69) 0.20\nSmoking, n (%) 416 (31) 26 (21) 390 (32) \u003c .05\nKnown CAD, n (%) 593 (44) 52 (42) 541 (44) 0.76\nStress type \u003c .0001\n  Physical exercise test, n (%) 519 (38) 11 (9) 508 (41)\n  Pharmacological test, n (%) 842 (62) 112 (91) 730 (59)\nAbnormal MPI, n (%) 462 (34) 44 (36) 418 (34) 0.65\nTotal perfusion defect \u003e 10% 246 (53) 27 (61) 219 (52) 0.26\nValues are expressed as mean value ± standard deviation or as number (percentage) of subjects. MPI myocardial perfusion imaging, CAD coronary artery disease\nCharacteristics of overall population are also described according to stress SPECT-MPI results during COVID-19 pandemic and during the corresponding months of 2017–2019 (Table 2). In both study periods, the percentage of patients with hyperlipidemia and known CAD was higher in patients with abnormal compared with those with normal stress SPECT-MPI findings, while during 2017–2019, patients with abnormal SPECT-MPI were older (P \u003c 0.05) and had higher prevalence of diabetes (P \u003c 0.005) and hypertension (P \u003c 0.001).\nTable 2 Clinical characteristics of overall population according to MPI results during COVID-19 emergency and during 2017–2019 3-year’s corresponding months\n2020 2017–2019\nNormal (n = 79) Abnormal (n = 44) P value Normal (n = 820) Abnormal (n = 418) P value\nAge (years) 64 ± 11 64 ± 8 0.77 63 ± 11 65 ± 11 \u003c .05\nMale gender, n (%) 23 (38) 6 (43) 0.05 337 (41) 73 (17) \u003c .001\nBody mass index (kg/m2) 28.9 ± 4.9 28.1 ± 4.4 0.38 29.1 ± 6.1 27.9 ± 4.2 \u003c .001\nChest pain, n (%) 22 (38) 15 (43) 0.47 308 (38) 123 (29) \u003c .005\nDiabetes, n (%) 28 (19) 19 (36) 0.40 234 (29) 156 (37) \u003c .005\nDyspnea, n (%) 29 (22) 20 (25) 0.34 375 (46) 200 (48) 0.48\nFamily history, n (%) 36 (48) 24 (41) 0.34 446 (54) 223 (53) 0.73\nHypertension, n (%) 66 (62) 36 (76) 0.81 614 (75) 354 (85) \u003c .001\nHyperlipidemia, n (%) 54 (55) 38 (57) \u003c .05 540 (66) 317 (76) \u003c .001\nSmoking, n (%) 15 (55) 11 (57) 0.43 258 (31) 132 (32) 0.97\nKnown CAD n (%) 21 (55) 31 (57) \u003c .001 224 (27) 317 (76) \u003c .001\nValues are expressed as mean value ± standard deviation or as number (percentage) of subjects. MPI myocardial perfusion imaging, CAD coronary artery disease\nAt multivariable logistic regression analysis (Table 3), hypertension (P \u003c 0.05) and known CAD (P \u003c 0.001) were significantly associated with abnormal stress SPECT-MPI during both COVID-19 pandemic and the corresponding months of the previous 3 years. No significant interactions among risk factors were found. During the 2017–2019 period, also male gender (P \u003c 0.001) and diabetes (P \u003c 0.05) were associated with abnormal stress SPECT-MPI.\nTable 3 Multivariable logistic regression analysis with abnormal MPI as dependent variable during COVID-19 emergency and during 2017–2019 3-year’s corresponding months\n2020 2017–2019\nβ coefficient SE P value β coefficient SE P value\nAge 0.005 0.023 0.834 − 0.004 0.007 0.60\nMale gender 0.162 0.639 0.799 0.671 0.166 \u003c .001\nBody mass index − 0.013 0.049 0.785 − 0.024 0.014 0.08\nChest pain 0.542 0.508 0.286 − 0.264 0.149 0.08\nDiabetes 0.565 0.466 0.225 0.310 0.148 \u003c .05\nFamily history 0.180 0.454 0.692 − 0.163 0.150 0.28\nDyspnea 0.362 0.382 0.343 − 0.085 0.120 0.48\nHypertension − 1.573 0.709 \u003c .05 0.376 0.196 \u003c .05\nHyperlipidemia 0.822 0.621 0.186 − 0.239 0.167 0.15\nSmoking 0.619 0.533 0.245 − 0.102 0.152 0.50\nKnown CAD 0.137 0.541 \u003c.001 1.994 0.154 \u003c .001\nSE standard error, MPI myocardial perfusion imaging, CAD coronary artery disease"}

{"project":"LitCovid-PD-MONDO","denotations":[{"id":"T54","span":{"begin":1145,"end":1153},"obj":"Disease"},{"id":"T55","span":{"begin":1314,"end":1322},"obj":"Disease"},{"id":"T56","span":{"begin":1822,"end":1830},"obj":"Disease"},{"id":"T57","span":{"begin":1968,"end":1980},"obj":"Disease"},{"id":"T58","span":{"begin":2021,"end":2035},"obj":"Disease"},{"id":"T59","span":{"begin":2127,"end":2130},"obj":"Disease"},{"id":"T61","span":{"begin":2543,"end":2546},"obj":"Disease"},{"id":"T63","span":{"begin":2547,"end":2570},"obj":"Disease"},{"id":"T64","span":{"begin":2556,"end":2570},"obj":"Disease"},{"id":"T65","span":{"begin":2673,"end":2681},"obj":"Disease"},{"id":"T66","span":{"begin":2806,"end":2820},"obj":"Disease"},{"id":"T67","span":{"begin":2831,"end":2834},"obj":"Disease"},{"id":"T69","span":{"begin":3039,"end":3047},"obj":"Disease"},{"id":"T70","span":{"begin":3064,"end":3076},"obj":"Disease"},{"id":"T71","span":{"begin":3177,"end":3185},"obj":"Disease"},{"id":"T72","span":{"begin":3609,"end":3617},"obj":"Disease"},{"id":"T73","span":{"begin":3796,"end":3808},"obj":"Disease"},{"id":"T74","span":{"begin":3862,"end":3876},"obj":"Disease"},{"id":"T75","span":{"begin":3996,"end":3999},"obj":"Disease"},{"id":"T77","span":{"begin":4183,"end":4186},"obj":"Disease"},{"id":"T79","span":{"begin":4187,"end":4210},"obj":"Disease"},{"id":"T80","span":{"begin":4196,"end":4210},"obj":"Disease"},{"id":"T81","span":{"begin":4268,"end":4280},"obj":"Disease"},{"id":"T82","span":{"begin":4302,"end":4305},"obj":"Disease"},{"id":"T84","span":{"begin":4391,"end":4399},"obj":"Disease"},{"id":"T85","span":{"begin":4584,"end":4592},"obj":"Disease"},{"id":"T86","span":{"begin":4750,"end":4758},"obj":"Disease"},{"id":"T87","span":{"begin":4849,"end":4851},"obj":"Disease"},{"id":"T88","span":{"begin":4874,"end":4876},"obj":"Disease"},{"id":"T89","span":{"begin":5078,"end":5086},"obj":"Disease"},{"id":"T90","span":{"begin":5220,"end":5232},"obj":"Disease"},{"id":"T91","span":{"begin":5271,"end":5285},"obj":"Disease"},{"id":"T92","span":{"begin":5374,"end":5377},"obj":"Disease"},{"id":"T94","span":{"begin":5415,"end":5417},"obj":"Disease"},{"id":"T95","span":{"begin":5468,"end":5471},"obj":"Disease"},{"id":"T97","span":{"begin":5472,"end":5495},"obj":"Disease"},{"id":"T98","span":{"begin":5481,"end":5495},"obj":"Disease"}],"attributes":[{"id":"A54","pred":"mondo_id","subj":"T54","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A55","pred":"mondo_id","subj":"T55","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A56","pred":"mondo_id","subj":"T56","obj":"http://purl.obolibrary.org/obo/MONDO_0005015"},{"id":"A57","pred":"mondo_id","subj":"T57","obj":"http://purl.obolibrary.org/obo/MONDO_0005044"},{"id":"A58","pred":"mondo_id","subj":"T58","obj":"http://purl.obolibrary.org/obo/MONDO_0021187"},{"id":"A59","pred":"mondo_id","subj":"T59","obj":"http://purl.obolibrary.org/obo/MONDO_0005010"},{"id":"A60","pred":"mondo_id","subj":"T59","obj":"http://purl.obolibrary.org/obo/MONDO_0018922"},{"id":"A61","pred":"mondo_id","subj":"T61","obj":"http://purl.obolibrary.org/obo/MONDO_0005010"},{"id":"A62","pred":"mondo_id","subj":"T61","obj":"http://purl.obolibrary.org/obo/MONDO_0018922"},{"id":"A63","pred":"mondo_id","subj":"T63","obj":"http://purl.obolibrary.org/obo/MONDO_0005010"},{"id":"A64","pred":"mondo_id","subj":"T64","obj":"http://purl.obolibrary.org/obo/MONDO_0000473"},{"id":"A65","pred":"mondo_id","subj":"T65","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A66","pred":"mondo_id","subj":"T66","obj":"http://purl.obolibrary.org/obo/MONDO_0021187"},{"id":"A67","pred":"mondo_id","subj":"T67","obj":"http://purl.obolibrary.org/obo/MONDO_0005010"},{"id":"A68","pred":"mondo_id","subj":"T67","obj":"http://purl.obolibrary.org/obo/MONDO_0018922"},{"id":"A69","pred":"mondo_id","subj":"T69","obj":"http://purl.obolibrary.org/obo/MONDO_0005015"},{"id":"A70","pred":"mondo_id","subj":"T70","obj":"http://purl.obolibrary.org/obo/MONDO_0005044"},{"id":"A71","pred":"mondo_id","subj":"T71","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A72","pred":"mondo_id","subj":"T72","obj":"http://purl.obolibrary.org/obo/MONDO_0005015"},{"id":"A73","pred":"mondo_id","subj":"T73","obj":"http://purl.obolibrary.org/obo/MONDO_0005044"},{"id":"A74","pred":"mondo_id","subj":"T74","obj":"http://purl.obolibrary.org/obo/MONDO_0021187"},{"id":"A75","pred":"mondo_id","subj":"T75","obj":"http://purl.obolibrary.org/obo/MONDO_0005010"},{"id":"A76","pred":"mondo_id","subj":"T75","obj":"http://purl.obolibrary.org/obo/MONDO_0018922"},{"id":"A77","pred":"mondo_id","subj":"T77","obj":"http://purl.obolibrary.org/obo/MONDO_0005010"},{"id":"A78","pred":"mondo_id","subj":"T77","obj":"http://purl.obolibrary.org/obo/MONDO_0018922"},{"id":"A79","pred":"mondo_id","subj":"T79","obj":"http://purl.obolibrary.org/obo/MONDO_0005010"},{"id":"A80","pred":"mondo_id","subj":"T80","obj":"http://purl.obolibrary.org/obo/MONDO_0000473"},{"id":"A81","pred":"mondo_id","subj":"T81","obj":"http://purl.obolibrary.org/obo/MONDO_0005044"},{"id":"A82","pred":"mondo_id","subj":"T82","obj":"http://purl.obolibrary.org/obo/MONDO_0005010"},{"id":"A83","pred":"mondo_id","subj":"T82","obj":"http://purl.obolibrary.org/obo/MONDO_0018922"},{"id":"A84","pred":"mondo_id","subj":"T84","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A85","pred":"mondo_id","subj":"T85","obj":"http://purl.obolibrary.org/obo/MONDO_0005015"},{"id":"A86","pred":"mondo_id","subj":"T86","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A87","pred":"mondo_id","subj":"T87","obj":"http://purl.obolibrary.org/obo/MONDO_0002125"},{"id":"A88","pred":"mondo_id","subj":"T88","obj":"http://purl.obolibrary.org/obo/MONDO_0002125"},{"id":"A89","pred":"mondo_id","subj":"T89","obj":"http://purl.obolibrary.org/obo/MONDO_0005015"},{"id":"A90","pred":"mondo_id","subj":"T90","obj":"http://purl.obolibrary.org/obo/MONDO_0005044"},{"id":"A91","pred":"mondo_id","subj":"T91","obj":"http://purl.obolibrary.org/obo/MONDO_0021187"},{"id":"A92","pred":"mondo_id","subj":"T92","obj":"http://purl.obolibrary.org/obo/MONDO_0005010"},{"id":"A93","pred":"mondo_id","subj":"T92","obj":"http://purl.obolibrary.org/obo/MONDO_0018922"},{"id":"A94","pred":"mondo_id","subj":"T94","obj":"http://purl.obolibrary.org/obo/MONDO_0002125"},{"id":"A95","pred":"mondo_id","subj":"T95","obj":"http://purl.obolibrary.org/obo/MONDO_0005010"},{"id":"A96","pred":"mondo_id","subj":"T95","obj":"http://purl.obolibrary.org/obo/MONDO_0018922"},{"id":"A97","pred":"mondo_id","subj":"T97","obj":"http://purl.obolibrary.org/obo/MONDO_0005010"},{"id":"A98","pred":"mondo_id","subj":"T98","obj":"http://purl.obolibrary.org/obo/MONDO_0000473"}],"text":"Results\nA total of 1361 stress SPECT-MPI were considered, including all tests performed during the pandemic between February and May 2020 and those performed in the corresponding months of the prior 3 years (2017, 2018, and 2019).\nThe number of stress SPECT-MPI studies performed during the pandemic (n = 123) was significantly lower (P \u003c 0.0001) compared with the mean number of procedures in the corresponding months of the years 2017, 2018, and 2019 (n = 413) (Fig. 1). However, the percentage of abnormal stress SPECT-MPI studies was similar (P = 0.65) during the pandemic (36%) compared with mean percentage value of the corresponding months of the years 2017, 2018, and 2019 (34%) (Fig. 2). Of note, given the mean number of 139 abnormal stress SPECT-MPI in the previous 3 years and the number of 44 abnormal tests during the pandemic, there was a 68% of abnormal test missed. Baseline characteristics of the entire population according to the year of SPECT-MPI execution are reported on Table 1. Most of the considered variables were comparable between the two study periods.\nFig. 1 Number of stress SPECT-MPI procedures performed during COVID-19 pandemic and during corresponding months of the years 2017, 2018, and 2019 (P for trend \u003c 0.0001)\nFig. 2 Prevalence of abnormal stress SPECT-MPI studies during COVID-19 pandemic and during the corresponding months of the years 2017, 2018, and 2019. The percentage of abnormal stress SPECT-MPI studies was similar (P = 0.65)\nTable 1 Baseline characteristics of overall population according to the year of MPI execution\nAll (n = 1361) 2020 (n = 123) 2017–2019 (n = 1238) P value\nAge (years) 64 ± 11 64 ± 10 64 ± 11 0.14\nMale gender, n (%) 439 (32) 29 (24) 410 (33) \u003c .05\nBody mass index (kg/m2) 29 ± 5 29 ± 5 29 ± 6 0.43\nChest pain, n (%) 468 (34) 37 (30) 431 (35) 0.29\nDiabetes, n (%) 437 (32) 47 (38) 390 (32) 0.12\nDyspnea, n (%) 624 (46) 49 (40) 575 (46) 0.16\nFamily history, n (%) 729 (54) 60 (49) 669 (54) 0.26\nHypertension, n (%) 1070 (79) 102 (83) 968 (78) 0.22\nHyperlipidemia, n (%) 949 (70) 92 (75) 857 (69) 0.20\nSmoking, n (%) 416 (31) 26 (21) 390 (32) \u003c .05\nKnown CAD, n (%) 593 (44) 52 (42) 541 (44) 0.76\nStress type \u003c .0001\n  Physical exercise test, n (%) 519 (38) 11 (9) 508 (41)\n  Pharmacological test, n (%) 842 (62) 112 (91) 730 (59)\nAbnormal MPI, n (%) 462 (34) 44 (36) 418 (34) 0.65\nTotal perfusion defect \u003e 10% 246 (53) 27 (61) 219 (52) 0.26\nValues are expressed as mean value ± standard deviation or as number (percentage) of subjects. MPI myocardial perfusion imaging, CAD coronary artery disease\nCharacteristics of overall population are also described according to stress SPECT-MPI results during COVID-19 pandemic and during the corresponding months of 2017–2019 (Table 2). In both study periods, the percentage of patients with hyperlipidemia and known CAD was higher in patients with abnormal compared with those with normal stress SPECT-MPI findings, while during 2017–2019, patients with abnormal SPECT-MPI were older (P \u003c 0.05) and had higher prevalence of diabetes (P \u003c 0.005) and hypertension (P \u003c 0.001).\nTable 2 Clinical characteristics of overall population according to MPI results during COVID-19 emergency and during 2017–2019 3-year’s corresponding months\n2020 2017–2019\nNormal (n = 79) Abnormal (n = 44) P value Normal (n = 820) Abnormal (n = 418) P value\nAge (years) 64 ± 11 64 ± 8 0.77 63 ± 11 65 ± 11 \u003c .05\nMale gender, n (%) 23 (38) 6 (43) 0.05 337 (41) 73 (17) \u003c .001\nBody mass index (kg/m2) 28.9 ± 4.9 28.1 ± 4.4 0.38 29.1 ± 6.1 27.9 ± 4.2 \u003c .001\nChest pain, n (%) 22 (38) 15 (43) 0.47 308 (38) 123 (29) \u003c .005\nDiabetes, n (%) 28 (19) 19 (36) 0.40 234 (29) 156 (37) \u003c .005\nDyspnea, n (%) 29 (22) 20 (25) 0.34 375 (46) 200 (48) 0.48\nFamily history, n (%) 36 (48) 24 (41) 0.34 446 (54) 223 (53) 0.73\nHypertension, n (%) 66 (62) 36 (76) 0.81 614 (75) 354 (85) \u003c .001\nHyperlipidemia, n (%) 54 (55) 38 (57) \u003c .05 540 (66) 317 (76) \u003c .001\nSmoking, n (%) 15 (55) 11 (57) 0.43 258 (31) 132 (32) 0.97\nKnown CAD n (%) 21 (55) 31 (57) \u003c .001 224 (27) 317 (76) \u003c .001\nValues are expressed as mean value ± standard deviation or as number (percentage) of subjects. MPI myocardial perfusion imaging, CAD coronary artery disease\nAt multivariable logistic regression analysis (Table 3), hypertension (P \u003c 0.05) and known CAD (P \u003c 0.001) were significantly associated with abnormal stress SPECT-MPI during both COVID-19 pandemic and the corresponding months of the previous 3 years. No significant interactions among risk factors were found. During the 2017–2019 period, also male gender (P \u003c 0.001) and diabetes (P \u003c 0.05) were associated with abnormal stress SPECT-MPI.\nTable 3 Multivariable logistic regression analysis with abnormal MPI as dependent variable during COVID-19 emergency and during 2017–2019 3-year’s corresponding months\n2020 2017–2019\nβ coefficient SE P value β coefficient SE P value\nAge 0.005 0.023 0.834 − 0.004 0.007 0.60\nMale gender 0.162 0.639 0.799 0.671 0.166 \u003c .001\nBody mass index − 0.013 0.049 0.785 − 0.024 0.014 0.08\nChest pain 0.542 0.508 0.286 − 0.264 0.149 0.08\nDiabetes 0.565 0.466 0.225 0.310 0.148 \u003c .05\nFamily history 0.180 0.454 0.692 − 0.163 0.150 0.28\nDyspnea 0.362 0.382 0.343 − 0.085 0.120 0.48\nHypertension − 1.573 0.709 \u003c .05 0.376 0.196 \u003c .05\nHyperlipidemia 0.822 0.621 0.186 − 0.239 0.167 0.15\nSmoking 0.619 0.533 0.245 − 0.102 0.152 0.50\nKnown CAD 0.137 0.541 \u003c.001 1.994 0.154 \u003c .001\nSE standard error, MPI myocardial perfusion imaging, CAD coronary artery disease"}

{"project":"LitCovid-PD-CLO","denotations":[{"id":"T79","span":{"begin":8,"end":9},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T80","span":{"begin":72,"end":77},"obj":"http://purl.obolibrary.org/obo/UBERON_0000473"},{"id":"T81","span":{"begin":214,"end":218},"obj":"http://purl.obolibrary.org/obo/CLO_0001185"},{"id":"T82","span":{"begin":438,"end":442},"obj":"http://purl.obolibrary.org/obo/CLO_0001185"},{"id":"T83","span":{"begin":578,"end":580},"obj":"http://purl.obolibrary.org/obo/CLO_0001313"},{"id":"T84","span":{"begin":666,"end":670},"obj":"http://purl.obolibrary.org/obo/CLO_0001185"},{"id":"T85","span":{"begin":682,"end":684},"obj":"http://purl.obolibrary.org/obo/CLO_0001302"},{"id":"T86","span":{"begin":815,"end":820},"obj":"http://purl.obolibrary.org/obo/UBERON_0000473"},{"id":"T87","span":{"begin":852,"end":853},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T88","span":{"begin":870,"end":874},"obj":"http://purl.obolibrary.org/obo/UBERON_0000473"},{"id":"T89","span":{"begin":1214,"end":1218},"obj":"http://purl.obolibrary.org/obo/CLO_0001185"},{"id":"T90","span":{"begin":1387,"end":1391},"obj":"http://purl.obolibrary.org/obo/CLO_0001185"},{"id":"T91","span":{"begin":1648,"end":1650},"obj":"http://purl.obolibrary.org/obo/CLO_0053733"},{"id":"T92","span":{"begin":1664,"end":1666},"obj":"http://purl.obolibrary.org/obo/CLO_0053733"},{"id":"T93","span":{"begin":1672,"end":1676},"obj":"http://purl.obolibrary.org/obo/UBERON_0003101"},{"id":"T94","span":{"begin":1672,"end":1676},"obj":"http://www.ebi.ac.uk/efo/EFO_0000970"},{"id":"T95","span":{"begin":1773,"end":1778},"obj":"http://www.ebi.ac.uk/efo/EFO_0000965"},{"id":"T96","span":{"begin":1796,"end":1798},"obj":"http://purl.obolibrary.org/obo/CLO_0001302"},{"id":"T97","span":{"begin":1813,"end":1815},"obj":"http://purl.obolibrary.org/obo/CLO_0001000"},{"id":"T98","span":{"begin":1998,"end":2001},"obj":"http://purl.obolibrary.org/obo/CLO_0054060"},{"id":"T99","span":{"begin":2147,"end":2149},"obj":"http://purl.obolibrary.org/obo/CLO_0001407"},{"id":"T100","span":{"begin":2209,"end":2213},"obj":"http://purl.obolibrary.org/obo/UBERON_0000473"},{"id":"T101","span":{"begin":2230,"end":2232},"obj":"http://purl.obolibrary.org/obo/CLO_0053733"},{"id":"T102","span":{"begin":2242,"end":2244},"obj":"http://purl.obolibrary.org/obo/CLO_0053794"},{"id":"T103","span":{"begin":2264,"end":2268},"obj":"http://purl.obolibrary.org/obo/UBERON_0000473"},{"id":"T104","span":{"begin":2323,"end":2326},"obj":"http://purl.obolibrary.org/obo/CLO_0001377"},{"id":"T105","span":{"begin":2328,"end":2330},"obj":"http://purl.obolibrary.org/obo/CLO_0001302"},{"id":"T106","span":{"begin":2336,"end":2338},"obj":"http://purl.obolibrary.org/obo/CLO_0001313"},{"id":"T107","span":{"begin":2345,"end":2347},"obj":"http://purl.obolibrary.org/obo/CLO_0001302"},{"id":"T108","span":{"begin":2392,"end":2394},"obj":"http://purl.obolibrary.org/obo/CLO_0050509"},{"id":"T109","span":{"begin":2400,"end":2403},"obj":"http://purl.obolibrary.org/obo/CLO_0001195"},{"id":"T110","span":{"begin":2405,"end":2407},"obj":"http://purl.obolibrary.org/obo/CLO_0001407"},{"id":"T111","span":{"begin":2556,"end":2562},"obj":"http://purl.obolibrary.org/obo/UBERON_0001637"},{"id":"T112","span":{"begin":2556,"end":2562},"obj":"http://www.ebi.ac.uk/efo/EFO_0000814"},{"id":"T113","span":{"begin":3331,"end":3338},"obj":"http://purl.obolibrary.org/obo/CLO_0007929"},{"id":"T114","span":{"begin":3365,"end":3367},"obj":"http://purl.obolibrary.org/obo/CLO_0053733"},{"id":"T115","span":{"begin":3385,"end":3387},"obj":"http://purl.obolibrary.org/obo/CLO_0053733"},{"id":"T116","span":{"begin":3393,"end":3401},"obj":"http://purl.obolibrary.org/obo/CLO_0001040"},{"id":"T117","span":{"begin":3402,"end":3406},"obj":"http://purl.obolibrary.org/obo/UBERON_0003101"},{"id":"T118","span":{"begin":3402,"end":3406},"obj":"http://www.ebi.ac.uk/efo/EFO_0000970"},{"id":"T119","span":{"begin":3446,"end":3448},"obj":"http://purl.obolibrary.org/obo/CLO_0053794"},{"id":"T120","span":{"begin":3545,"end":3550},"obj":"http://www.ebi.ac.uk/efo/EFO_0000965"},{"id":"T121","span":{"begin":3557,"end":3565},"obj":"http://purl.obolibrary.org/obo/CLO_0007926"},{"id":"T122","span":{"begin":3637,"end":3639},"obj":"http://purl.obolibrary.org/obo/CLO_0001313"},{"id":"T123","span":{"begin":3690,"end":3692},"obj":"http://purl.obolibrary.org/obo/CLO_0050507"},{"id":"T124","span":{"begin":3721,"end":3723},"obj":"http://purl.obolibrary.org/obo/CLO_0001382"},{"id":"T125","span":{"begin":3752,"end":3754},"obj":"http://purl.obolibrary.org/obo/CLO_0001313"},{"id":"T126","span":{"begin":3756,"end":3758},"obj":"http://purl.obolibrary.org/obo/CLO_0001382"},{"id":"T127","span":{"begin":3764,"end":3766},"obj":"http://purl.obolibrary.org/obo/CLO_0053794"},{"id":"T128","span":{"begin":3824,"end":3826},"obj":"http://purl.obolibrary.org/obo/CLO_0001313"},{"id":"T129","span":{"begin":3954,"end":3956},"obj":"http://purl.obolibrary.org/obo/CLO_0053733"},{"id":"T130","span":{"begin":3976,"end":3979},"obj":"http://purl.obolibrary.org/obo/CLO_0054061"},{"id":"T131","span":{"begin":4034,"end":4036},"obj":"http://purl.obolibrary.org/obo/CLO_0050509"},{"id":"T132","span":{"begin":4196,"end":4202},"obj":"http://purl.obolibrary.org/obo/UBERON_0001637"},{"id":"T133","span":{"begin":4196,"end":4202},"obj":"http://www.ebi.ac.uk/efo/EFO_0000814"},{"id":"T134","span":{"begin":4556,"end":4560},"obj":"http://purl.obolibrary.org/obo/UBERON_0003101"},{"id":"T135","span":{"begin":4556,"end":4560},"obj":"http://www.ebi.ac.uk/efo/EFO_0000970"},{"id":"T136","span":{"begin":4926,"end":4930},"obj":"http://purl.obolibrary.org/obo/UBERON_0003101"},{"id":"T137","span":{"begin":4926,"end":4930},"obj":"http://www.ebi.ac.uk/efo/EFO_0000970"},{"id":"T138","span":{"begin":5030,"end":5035},"obj":"http://www.ebi.ac.uk/efo/EFO_0000965"},{"id":"T139","span":{"begin":5481,"end":5487},"obj":"http://purl.obolibrary.org/obo/UBERON_0001637"},{"id":"T140","span":{"begin":5481,"end":5487},"obj":"http://www.ebi.ac.uk/efo/EFO_0000814"}],"text":"Results\nA total of 1361 stress SPECT-MPI were considered, including all tests performed during the pandemic between February and May 2020 and those performed in the corresponding months of the prior 3 years (2017, 2018, and 2019).\nThe number of stress SPECT-MPI studies performed during the pandemic (n = 123) was significantly lower (P \u003c 0.0001) compared with the mean number of procedures in the corresponding months of the years 2017, 2018, and 2019 (n = 413) (Fig. 1). However, the percentage of abnormal stress SPECT-MPI studies was similar (P = 0.65) during the pandemic (36%) compared with mean percentage value of the corresponding months of the years 2017, 2018, and 2019 (34%) (Fig. 2). Of note, given the mean number of 139 abnormal stress SPECT-MPI in the previous 3 years and the number of 44 abnormal tests during the pandemic, there was a 68% of abnormal test missed. Baseline characteristics of the entire population according to the year of SPECT-MPI execution are reported on Table 1. Most of the considered variables were comparable between the two study periods.\nFig. 1 Number of stress SPECT-MPI procedures performed during COVID-19 pandemic and during corresponding months of the years 2017, 2018, and 2019 (P for trend \u003c 0.0001)\nFig. 2 Prevalence of abnormal stress SPECT-MPI studies during COVID-19 pandemic and during the corresponding months of the years 2017, 2018, and 2019. The percentage of abnormal stress SPECT-MPI studies was similar (P = 0.65)\nTable 1 Baseline characteristics of overall population according to the year of MPI execution\nAll (n = 1361) 2020 (n = 123) 2017–2019 (n = 1238) P value\nAge (years) 64 ± 11 64 ± 10 64 ± 11 0.14\nMale gender, n (%) 439 (32) 29 (24) 410 (33) \u003c .05\nBody mass index (kg/m2) 29 ± 5 29 ± 5 29 ± 6 0.43\nChest pain, n (%) 468 (34) 37 (30) 431 (35) 0.29\nDiabetes, n (%) 437 (32) 47 (38) 390 (32) 0.12\nDyspnea, n (%) 624 (46) 49 (40) 575 (46) 0.16\nFamily history, n (%) 729 (54) 60 (49) 669 (54) 0.26\nHypertension, n (%) 1070 (79) 102 (83) 968 (78) 0.22\nHyperlipidemia, n (%) 949 (70) 92 (75) 857 (69) 0.20\nSmoking, n (%) 416 (31) 26 (21) 390 (32) \u003c .05\nKnown CAD, n (%) 593 (44) 52 (42) 541 (44) 0.76\nStress type \u003c .0001\n  Physical exercise test, n (%) 519 (38) 11 (9) 508 (41)\n  Pharmacological test, n (%) 842 (62) 112 (91) 730 (59)\nAbnormal MPI, n (%) 462 (34) 44 (36) 418 (34) 0.65\nTotal perfusion defect \u003e 10% 246 (53) 27 (61) 219 (52) 0.26\nValues are expressed as mean value ± standard deviation or as number (percentage) of subjects. MPI myocardial perfusion imaging, CAD coronary artery disease\nCharacteristics of overall population are also described according to stress SPECT-MPI results during COVID-19 pandemic and during the corresponding months of 2017–2019 (Table 2). In both study periods, the percentage of patients with hyperlipidemia and known CAD was higher in patients with abnormal compared with those with normal stress SPECT-MPI findings, while during 2017–2019, patients with abnormal SPECT-MPI were older (P \u003c 0.05) and had higher prevalence of diabetes (P \u003c 0.005) and hypertension (P \u003c 0.001).\nTable 2 Clinical characteristics of overall population according to MPI results during COVID-19 emergency and during 2017–2019 3-year’s corresponding months\n2020 2017–2019\nNormal (n = 79) Abnormal (n = 44) P value Normal (n = 820) Abnormal (n = 418) P value\nAge (years) 64 ± 11 64 ± 8 0.77 63 ± 11 65 ± 11 \u003c .05\nMale gender, n (%) 23 (38) 6 (43) 0.05 337 (41) 73 (17) \u003c .001\nBody mass index (kg/m2) 28.9 ± 4.9 28.1 ± 4.4 0.38 29.1 ± 6.1 27.9 ± 4.2 \u003c .001\nChest pain, n (%) 22 (38) 15 (43) 0.47 308 (38) 123 (29) \u003c .005\nDiabetes, n (%) 28 (19) 19 (36) 0.40 234 (29) 156 (37) \u003c .005\nDyspnea, n (%) 29 (22) 20 (25) 0.34 375 (46) 200 (48) 0.48\nFamily history, n (%) 36 (48) 24 (41) 0.34 446 (54) 223 (53) 0.73\nHypertension, n (%) 66 (62) 36 (76) 0.81 614 (75) 354 (85) \u003c .001\nHyperlipidemia, n (%) 54 (55) 38 (57) \u003c .05 540 (66) 317 (76) \u003c .001\nSmoking, n (%) 15 (55) 11 (57) 0.43 258 (31) 132 (32) 0.97\nKnown CAD n (%) 21 (55) 31 (57) \u003c .001 224 (27) 317 (76) \u003c .001\nValues are expressed as mean value ± standard deviation or as number (percentage) of subjects. MPI myocardial perfusion imaging, CAD coronary artery disease\nAt multivariable logistic regression analysis (Table 3), hypertension (P \u003c 0.05) and known CAD (P \u003c 0.001) were significantly associated with abnormal stress SPECT-MPI during both COVID-19 pandemic and the corresponding months of the previous 3 years. No significant interactions among risk factors were found. During the 2017–2019 period, also male gender (P \u003c 0.001) and diabetes (P \u003c 0.05) were associated with abnormal stress SPECT-MPI.\nTable 3 Multivariable logistic regression analysis with abnormal MPI as dependent variable during COVID-19 emergency and during 2017–2019 3-year’s corresponding months\n2020 2017–2019\nβ coefficient SE P value β coefficient SE P value\nAge 0.005 0.023 0.834 − 0.004 0.007 0.60\nMale gender 0.162 0.639 0.799 0.671 0.166 \u003c .001\nBody mass index − 0.013 0.049 0.785 − 0.024 0.014 0.08\nChest pain 0.542 0.508 0.286 − 0.264 0.149 0.08\nDiabetes 0.565 0.466 0.225 0.310 0.148 \u003c .05\nFamily history 0.180 0.454 0.692 − 0.163 0.150 0.28\nDyspnea 0.362 0.382 0.343 − 0.085 0.120 0.48\nHypertension − 1.573 0.709 \u003c .05 0.376 0.196 \u003c .05\nHyperlipidemia 0.822 0.621 0.186 − 0.239 0.167 0.15\nSmoking 0.619 0.533 0.245 − 0.102 0.152 0.50\nKnown CAD 0.137 0.541 \u003c.001 1.994 0.154 \u003c .001\nSE standard error, MPI myocardial perfusion imaging, CAD coronary artery disease"}

{"project":"LitCovid-PD-CHEBI","denotations":[{"id":"T18","span":{"begin":4556,"end":4560},"obj":"Chemical"},{"id":"T19","span":{"begin":4849,"end":4851},"obj":"Chemical"},{"id":"T20","span":{"begin":4874,"end":4876},"obj":"Chemical"},{"id":"T21","span":{"begin":5415,"end":5417},"obj":"Chemical"}],"attributes":[{"id":"A18","pred":"chebi_id","subj":"T18","obj":"http://purl.obolibrary.org/obo/CHEBI_30780"},{"id":"A19","pred":"chebi_id","subj":"T19","obj":"http://purl.obolibrary.org/obo/CHEBI_74813"},{"id":"A20","pred":"chebi_id","subj":"T20","obj":"http://purl.obolibrary.org/obo/CHEBI_74813"},{"id":"A21","pred":"chebi_id","subj":"T21","obj":"http://purl.obolibrary.org/obo/CHEBI_74813"}],"text":"Results\nA total of 1361 stress SPECT-MPI were considered, including all tests performed during the pandemic between February and May 2020 and those performed in the corresponding months of the prior 3 years (2017, 2018, and 2019).\nThe number of stress SPECT-MPI studies performed during the pandemic (n = 123) was significantly lower (P \u003c 0.0001) compared with the mean number of procedures in the corresponding months of the years 2017, 2018, and 2019 (n = 413) (Fig. 1). However, the percentage of abnormal stress SPECT-MPI studies was similar (P = 0.65) during the pandemic (36%) compared with mean percentage value of the corresponding months of the years 2017, 2018, and 2019 (34%) (Fig. 2). Of note, given the mean number of 139 abnormal stress SPECT-MPI in the previous 3 years and the number of 44 abnormal tests during the pandemic, there was a 68% of abnormal test missed. Baseline characteristics of the entire population according to the year of SPECT-MPI execution are reported on Table 1. Most of the considered variables were comparable between the two study periods.\nFig. 1 Number of stress SPECT-MPI procedures performed during COVID-19 pandemic and during corresponding months of the years 2017, 2018, and 2019 (P for trend \u003c 0.0001)\nFig. 2 Prevalence of abnormal stress SPECT-MPI studies during COVID-19 pandemic and during the corresponding months of the years 2017, 2018, and 2019. The percentage of abnormal stress SPECT-MPI studies was similar (P = 0.65)\nTable 1 Baseline characteristics of overall population according to the year of MPI execution\nAll (n = 1361) 2020 (n = 123) 2017–2019 (n = 1238) P value\nAge (years) 64 ± 11 64 ± 10 64 ± 11 0.14\nMale gender, n (%) 439 (32) 29 (24) 410 (33) \u003c .05\nBody mass index (kg/m2) 29 ± 5 29 ± 5 29 ± 6 0.43\nChest pain, n (%) 468 (34) 37 (30) 431 (35) 0.29\nDiabetes, n (%) 437 (32) 47 (38) 390 (32) 0.12\nDyspnea, n (%) 624 (46) 49 (40) 575 (46) 0.16\nFamily history, n (%) 729 (54) 60 (49) 669 (54) 0.26\nHypertension, n (%) 1070 (79) 102 (83) 968 (78) 0.22\nHyperlipidemia, n (%) 949 (70) 92 (75) 857 (69) 0.20\nSmoking, n (%) 416 (31) 26 (21) 390 (32) \u003c .05\nKnown CAD, n (%) 593 (44) 52 (42) 541 (44) 0.76\nStress type \u003c .0001\n  Physical exercise test, n (%) 519 (38) 11 (9) 508 (41)\n  Pharmacological test, n (%) 842 (62) 112 (91) 730 (59)\nAbnormal MPI, n (%) 462 (34) 44 (36) 418 (34) 0.65\nTotal perfusion defect \u003e 10% 246 (53) 27 (61) 219 (52) 0.26\nValues are expressed as mean value ± standard deviation or as number (percentage) of subjects. MPI myocardial perfusion imaging, CAD coronary artery disease\nCharacteristics of overall population are also described according to stress SPECT-MPI results during COVID-19 pandemic and during the corresponding months of 2017–2019 (Table 2). In both study periods, the percentage of patients with hyperlipidemia and known CAD was higher in patients with abnormal compared with those with normal stress SPECT-MPI findings, while during 2017–2019, patients with abnormal SPECT-MPI were older (P \u003c 0.05) and had higher prevalence of diabetes (P \u003c 0.005) and hypertension (P \u003c 0.001).\nTable 2 Clinical characteristics of overall population according to MPI results during COVID-19 emergency and during 2017–2019 3-year’s corresponding months\n2020 2017–2019\nNormal (n = 79) Abnormal (n = 44) P value Normal (n = 820) Abnormal (n = 418) P value\nAge (years) 64 ± 11 64 ± 8 0.77 63 ± 11 65 ± 11 \u003c .05\nMale gender, n (%) 23 (38) 6 (43) 0.05 337 (41) 73 (17) \u003c .001\nBody mass index (kg/m2) 28.9 ± 4.9 28.1 ± 4.4 0.38 29.1 ± 6.1 27.9 ± 4.2 \u003c .001\nChest pain, n (%) 22 (38) 15 (43) 0.47 308 (38) 123 (29) \u003c .005\nDiabetes, n (%) 28 (19) 19 (36) 0.40 234 (29) 156 (37) \u003c .005\nDyspnea, n (%) 29 (22) 20 (25) 0.34 375 (46) 200 (48) 0.48\nFamily history, n (%) 36 (48) 24 (41) 0.34 446 (54) 223 (53) 0.73\nHypertension, n (%) 66 (62) 36 (76) 0.81 614 (75) 354 (85) \u003c .001\nHyperlipidemia, n (%) 54 (55) 38 (57) \u003c .05 540 (66) 317 (76) \u003c .001\nSmoking, n (%) 15 (55) 11 (57) 0.43 258 (31) 132 (32) 0.97\nKnown CAD n (%) 21 (55) 31 (57) \u003c .001 224 (27) 317 (76) \u003c .001\nValues are expressed as mean value ± standard deviation or as number (percentage) of subjects. MPI myocardial perfusion imaging, CAD coronary artery disease\nAt multivariable logistic regression analysis (Table 3), hypertension (P \u003c 0.05) and known CAD (P \u003c 0.001) were significantly associated with abnormal stress SPECT-MPI during both COVID-19 pandemic and the corresponding months of the previous 3 years. No significant interactions among risk factors were found. During the 2017–2019 period, also male gender (P \u003c 0.001) and diabetes (P \u003c 0.05) were associated with abnormal stress SPECT-MPI.\nTable 3 Multivariable logistic regression analysis with abnormal MPI as dependent variable during COVID-19 emergency and during 2017–2019 3-year’s corresponding months\n2020 2017–2019\nβ coefficient SE P value β coefficient SE P value\nAge 0.005 0.023 0.834 − 0.004 0.007 0.60\nMale gender 0.162 0.639 0.799 0.671 0.166 \u003c .001\nBody mass index − 0.013 0.049 0.785 − 0.024 0.014 0.08\nChest pain 0.542 0.508 0.286 − 0.264 0.149 0.08\nDiabetes 0.565 0.466 0.225 0.310 0.148 \u003c .05\nFamily history 0.180 0.454 0.692 − 0.163 0.150 0.28\nDyspnea 0.362 0.382 0.343 − 0.085 0.120 0.48\nHypertension − 1.573 0.709 \u003c .05 0.376 0.196 \u003c .05\nHyperlipidemia 0.822 0.621 0.186 − 0.239 0.167 0.15\nSmoking 0.619 0.533 0.245 − 0.102 0.152 0.50\nKnown CAD 0.137 0.541 \u003c.001 1.994 0.154 \u003c .001\nSE standard error, MPI myocardial perfusion imaging, CAD coronary artery disease"}

{"project":"LitCovid-sentences","denotations":[{"id":"T70","span":{"begin":0,"end":7},"obj":"Sentence"},{"id":"T71","span":{"begin":8,"end":230},"obj":"Sentence"},{"id":"T72","span":{"begin":231,"end":472},"obj":"Sentence"},{"id":"T73","span":{"begin":473,"end":696},"obj":"Sentence"},{"id":"T74","span":{"begin":697,"end":882},"obj":"Sentence"},{"id":"T75","span":{"begin":883,"end":1002},"obj":"Sentence"},{"id":"T76","span":{"begin":1003,"end":1082},"obj":"Sentence"},{"id":"T77","span":{"begin":1083,"end":1251},"obj":"Sentence"},{"id":"T78","span":{"begin":1252,"end":1402},"obj":"Sentence"},{"id":"T79","span":{"begin":1403,"end":1477},"obj":"Sentence"},{"id":"T80","span":{"begin":1478,"end":1571},"obj":"Sentence"},{"id":"T81","span":{"begin":1572,"end":1630},"obj":"Sentence"},{"id":"T82","span":{"begin":1631,"end":1671},"obj":"Sentence"},{"id":"T83","span":{"begin":1672,"end":1722},"obj":"Sentence"},{"id":"T84","span":{"begin":1723,"end":1772},"obj":"Sentence"},{"id":"T85","span":{"begin":1773,"end":1821},"obj":"Sentence"},{"id":"T86","span":{"begin":1822,"end":1868},"obj":"Sentence"},{"id":"T87","span":{"begin":1869,"end":1914},"obj":"Sentence"},{"id":"T88","span":{"begin":1915,"end":1967},"obj":"Sentence"},{"id":"T89","span":{"begin":1968,"end":2020},"obj":"Sentence"},{"id":"T90","span":{"begin":2021,"end":2073},"obj":"Sentence"},{"id":"T91","span":{"begin":2074,"end":2120},"obj":"Sentence"},{"id":"T92","span":{"begin":2121,"end":2168},"obj":"Sentence"},{"id":"T93","span":{"begin":2169,"end":2188},"obj":"Sentence"},{"id":"T94","span":{"begin":2189,"end":2245},"obj":"Sentence"},{"id":"T95","span":{"begin":2246,"end":2302},"obj":"Sentence"},{"id":"T96","span":{"begin":2303,"end":2353},"obj":"Sentence"},{"id":"T97","span":{"begin":2354,"end":2413},"obj":"Sentence"},{"id":"T98","span":{"begin":2414,"end":2508},"obj":"Sentence"},{"id":"T99","span":{"begin":2509,"end":2570},"obj":"Sentence"},{"id":"T100","span":{"begin":2571,"end":2750},"obj":"Sentence"},{"id":"T101","span":{"begin":2751,"end":3089},"obj":"Sentence"},{"id":"T102","span":{"begin":3090,"end":3246},"obj":"Sentence"},{"id":"T103","span":{"begin":3247,"end":3261},"obj":"Sentence"},{"id":"T104","span":{"begin":3262,"end":3347},"obj":"Sentence"},{"id":"T105","span":{"begin":3348,"end":3401},"obj":"Sentence"},{"id":"T106","span":{"begin":3402,"end":3464},"obj":"Sentence"},{"id":"T107","span":{"begin":3465,"end":3544},"obj":"Sentence"},{"id":"T108","span":{"begin":3545,"end":3608},"obj":"Sentence"},{"id":"T109","span":{"begin":3609,"end":3670},"obj":"Sentence"},{"id":"T110","span":{"begin":3671,"end":3729},"obj":"Sentence"},{"id":"T111","span":{"begin":3730,"end":3795},"obj":"Sentence"},{"id":"T112","span":{"begin":3796,"end":3861},"obj":"Sentence"},{"id":"T113","span":{"begin":3862,"end":3930},"obj":"Sentence"},{"id":"T114","span":{"begin":3931,"end":3989},"obj":"Sentence"},{"id":"T115","span":{"begin":3990,"end":4053},"obj":"Sentence"},{"id":"T116","span":{"begin":4054,"end":4148},"obj":"Sentence"},{"id":"T117","span":{"begin":4149,"end":4210},"obj":"Sentence"},{"id":"T118","span":{"begin":4211,"end":4462},"obj":"Sentence"},{"id":"T119","span":{"begin":4463,"end":4521},"obj":"Sentence"},{"id":"T120","span":{"begin":4522,"end":4651},"obj":"Sentence"},{"id":"T121","span":{"begin":4652,"end":4819},"obj":"Sentence"},{"id":"T122","span":{"begin":4820,"end":4834},"obj":"Sentence"},{"id":"T123","span":{"begin":4835,"end":4884},"obj":"Sentence"},{"id":"T124","span":{"begin":4885,"end":4925},"obj":"Sentence"},{"id":"T125","span":{"begin":4926,"end":4974},"obj":"Sentence"},{"id":"T126","span":{"begin":4975,"end":5029},"obj":"Sentence"},{"id":"T127","span":{"begin":5030,"end":5077},"obj":"Sentence"},{"id":"T128","span":{"begin":5078,"end":5122},"obj":"Sentence"},{"id":"T129","span":{"begin":5123,"end":5174},"obj":"Sentence"},{"id":"T130","span":{"begin":5175,"end":5219},"obj":"Sentence"},{"id":"T131","span":{"begin":5220,"end":5270},"obj":"Sentence"},{"id":"T132","span":{"begin":5271,"end":5322},"obj":"Sentence"},{"id":"T133","span":{"begin":5323,"end":5367},"obj":"Sentence"},{"id":"T134","span":{"begin":5368,"end":5414},"obj":"Sentence"},{"id":"T135","span":{"begin":5415,"end":5495},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"Results\nA total of 1361 stress SPECT-MPI were considered, including all tests performed during the pandemic between February and May 2020 and those performed in the corresponding months of the prior 3 years (2017, 2018, and 2019).\nThe number of stress SPECT-MPI studies performed during the pandemic (n = 123) was significantly lower (P \u003c 0.0001) compared with the mean number of procedures in the corresponding months of the years 2017, 2018, and 2019 (n = 413) (Fig. 1). However, the percentage of abnormal stress SPECT-MPI studies was similar (P = 0.65) during the pandemic (36%) compared with mean percentage value of the corresponding months of the years 2017, 2018, and 2019 (34%) (Fig. 2). Of note, given the mean number of 139 abnormal stress SPECT-MPI in the previous 3 years and the number of 44 abnormal tests during the pandemic, there was a 68% of abnormal test missed. Baseline characteristics of the entire population according to the year of SPECT-MPI execution are reported on Table 1. Most of the considered variables were comparable between the two study periods.\nFig. 1 Number of stress SPECT-MPI procedures performed during COVID-19 pandemic and during corresponding months of the years 2017, 2018, and 2019 (P for trend \u003c 0.0001)\nFig. 2 Prevalence of abnormal stress SPECT-MPI studies during COVID-19 pandemic and during the corresponding months of the years 2017, 2018, and 2019. The percentage of abnormal stress SPECT-MPI studies was similar (P = 0.65)\nTable 1 Baseline characteristics of overall population according to the year of MPI execution\nAll (n = 1361) 2020 (n = 123) 2017–2019 (n = 1238) P value\nAge (years) 64 ± 11 64 ± 10 64 ± 11 0.14\nMale gender, n (%) 439 (32) 29 (24) 410 (33) \u003c .05\nBody mass index (kg/m2) 29 ± 5 29 ± 5 29 ± 6 0.43\nChest pain, n (%) 468 (34) 37 (30) 431 (35) 0.29\nDiabetes, n (%) 437 (32) 47 (38) 390 (32) 0.12\nDyspnea, n (%) 624 (46) 49 (40) 575 (46) 0.16\nFamily history, n (%) 729 (54) 60 (49) 669 (54) 0.26\nHypertension, n (%) 1070 (79) 102 (83) 968 (78) 0.22\nHyperlipidemia, n (%) 949 (70) 92 (75) 857 (69) 0.20\nSmoking, n (%) 416 (31) 26 (21) 390 (32) \u003c .05\nKnown CAD, n (%) 593 (44) 52 (42) 541 (44) 0.76\nStress type \u003c .0001\n  Physical exercise test, n (%) 519 (38) 11 (9) 508 (41)\n  Pharmacological test, n (%) 842 (62) 112 (91) 730 (59)\nAbnormal MPI, n (%) 462 (34) 44 (36) 418 (34) 0.65\nTotal perfusion defect \u003e 10% 246 (53) 27 (61) 219 (52) 0.26\nValues are expressed as mean value ± standard deviation or as number (percentage) of subjects. MPI myocardial perfusion imaging, CAD coronary artery disease\nCharacteristics of overall population are also described according to stress SPECT-MPI results during COVID-19 pandemic and during the corresponding months of 2017–2019 (Table 2). In both study periods, the percentage of patients with hyperlipidemia and known CAD was higher in patients with abnormal compared with those with normal stress SPECT-MPI findings, while during 2017–2019, patients with abnormal SPECT-MPI were older (P \u003c 0.05) and had higher prevalence of diabetes (P \u003c 0.005) and hypertension (P \u003c 0.001).\nTable 2 Clinical characteristics of overall population according to MPI results during COVID-19 emergency and during 2017–2019 3-year’s corresponding months\n2020 2017–2019\nNormal (n = 79) Abnormal (n = 44) P value Normal (n = 820) Abnormal (n = 418) P value\nAge (years) 64 ± 11 64 ± 8 0.77 63 ± 11 65 ± 11 \u003c .05\nMale gender, n (%) 23 (38) 6 (43) 0.05 337 (41) 73 (17) \u003c .001\nBody mass index (kg/m2) 28.9 ± 4.9 28.1 ± 4.4 0.38 29.1 ± 6.1 27.9 ± 4.2 \u003c .001\nChest pain, n (%) 22 (38) 15 (43) 0.47 308 (38) 123 (29) \u003c .005\nDiabetes, n (%) 28 (19) 19 (36) 0.40 234 (29) 156 (37) \u003c .005\nDyspnea, n (%) 29 (22) 20 (25) 0.34 375 (46) 200 (48) 0.48\nFamily history, n (%) 36 (48) 24 (41) 0.34 446 (54) 223 (53) 0.73\nHypertension, n (%) 66 (62) 36 (76) 0.81 614 (75) 354 (85) \u003c .001\nHyperlipidemia, n (%) 54 (55) 38 (57) \u003c .05 540 (66) 317 (76) \u003c .001\nSmoking, n (%) 15 (55) 11 (57) 0.43 258 (31) 132 (32) 0.97\nKnown CAD n (%) 21 (55) 31 (57) \u003c .001 224 (27) 317 (76) \u003c .001\nValues are expressed as mean value ± standard deviation or as number (percentage) of subjects. MPI myocardial perfusion imaging, CAD coronary artery disease\nAt multivariable logistic regression analysis (Table 3), hypertension (P \u003c 0.05) and known CAD (P \u003c 0.001) were significantly associated with abnormal stress SPECT-MPI during both COVID-19 pandemic and the corresponding months of the previous 3 years. No significant interactions among risk factors were found. During the 2017–2019 period, also male gender (P \u003c 0.001) and diabetes (P \u003c 0.05) were associated with abnormal stress SPECT-MPI.\nTable 3 Multivariable logistic regression analysis with abnormal MPI as dependent variable during COVID-19 emergency and during 2017–2019 3-year’s corresponding months\n2020 2017–2019\nβ coefficient SE P value β coefficient SE P value\nAge 0.005 0.023 0.834 − 0.004 0.007 0.60\nMale gender 0.162 0.639 0.799 0.671 0.166 \u003c .001\nBody mass index − 0.013 0.049 0.785 − 0.024 0.014 0.08\nChest pain 0.542 0.508 0.286 − 0.264 0.149 0.08\nDiabetes 0.565 0.466 0.225 0.310 0.148 \u003c .05\nFamily history 0.180 0.454 0.692 − 0.163 0.150 0.28\nDyspnea 0.362 0.382 0.343 − 0.085 0.120 0.48\nHypertension − 1.573 0.709 \u003c .05 0.376 0.196 \u003c .05\nHyperlipidemia 0.822 0.621 0.186 − 0.239 0.167 0.15\nSmoking 0.619 0.533 0.245 − 0.102 0.152 0.50\nKnown CAD 0.137 0.541 \u003c.001 1.994 0.154 \u003c .001\nSE standard error, MPI myocardial perfusion imaging, CAD coronary artery disease"}

{"project":"LitCovid-PD-HP","denotations":[{"id":"T12","span":{"begin":1773,"end":1783},"obj":"Phenotype"},{"id":"T13","span":{"begin":1869,"end":1876},"obj":"Phenotype"},{"id":"T14","span":{"begin":1968,"end":1980},"obj":"Phenotype"},{"id":"T15","span":{"begin":2021,"end":2035},"obj":"Phenotype"},{"id":"T16","span":{"begin":2806,"end":2820},"obj":"Phenotype"},{"id":"T17","span":{"begin":3064,"end":3076},"obj":"Phenotype"},{"id":"T18","span":{"begin":3545,"end":3555},"obj":"Phenotype"},{"id":"T19","span":{"begin":3671,"end":3678},"obj":"Phenotype"},{"id":"T20","span":{"begin":3796,"end":3808},"obj":"Phenotype"},{"id":"T21","span":{"begin":3862,"end":3876},"obj":"Phenotype"},{"id":"T22","span":{"begin":4268,"end":4280},"obj":"Phenotype"},{"id":"T23","span":{"begin":5030,"end":5040},"obj":"Phenotype"},{"id":"T24","span":{"begin":5175,"end":5182},"obj":"Phenotype"},{"id":"T25","span":{"begin":5220,"end":5232},"obj":"Phenotype"},{"id":"T26","span":{"begin":5271,"end":5285},"obj":"Phenotype"}],"attributes":[{"id":"A12","pred":"hp_id","subj":"T12","obj":"http://purl.obolibrary.org/obo/HP_0100749"},{"id":"A13","pred":"hp_id","subj":"T13","obj":"http://purl.obolibrary.org/obo/HP_0002094"},{"id":"A14","pred":"hp_id","subj":"T14","obj":"http://purl.obolibrary.org/obo/HP_0000822"},{"id":"A15","pred":"hp_id","subj":"T15","obj":"http://purl.obolibrary.org/obo/HP_0003077"},{"id":"A16","pred":"hp_id","subj":"T16","obj":"http://purl.obolibrary.org/obo/HP_0003077"},{"id":"A17","pred":"hp_id","subj":"T17","obj":"http://purl.obolibrary.org/obo/HP_0000822"},{"id":"A18","pred":"hp_id","subj":"T18","obj":"http://purl.obolibrary.org/obo/HP_0100749"},{"id":"A19","pred":"hp_id","subj":"T19","obj":"http://purl.obolibrary.org/obo/HP_0002094"},{"id":"A20","pred":"hp_id","subj":"T20","obj":"http://purl.obolibrary.org/obo/HP_0000822"},{"id":"A21","pred":"hp_id","subj":"T21","obj":"http://purl.obolibrary.org/obo/HP_0003077"},{"id":"A22","pred":"hp_id","subj":"T22","obj":"http://purl.obolibrary.org/obo/HP_0000822"},{"id":"A23","pred":"hp_id","subj":"T23","obj":"http://purl.obolibrary.org/obo/HP_0100749"},{"id":"A24","pred":"hp_id","subj":"T24","obj":"http://purl.obolibrary.org/obo/HP_0002094"},{"id":"A25","pred":"hp_id","subj":"T25","obj":"http://purl.obolibrary.org/obo/HP_0000822"},{"id":"A26","pred":"hp_id","subj":"T26","obj":"http://purl.obolibrary.org/obo/HP_0003077"}],"text":"Results\nA total of 1361 stress SPECT-MPI were considered, including all tests performed during the pandemic between February and May 2020 and those performed in the corresponding months of the prior 3 years (2017, 2018, and 2019).\nThe number of stress SPECT-MPI studies performed during the pandemic (n = 123) was significantly lower (P \u003c 0.0001) compared with the mean number of procedures in the corresponding months of the years 2017, 2018, and 2019 (n = 413) (Fig. 1). However, the percentage of abnormal stress SPECT-MPI studies was similar (P = 0.65) during the pandemic (36%) compared with mean percentage value of the corresponding months of the years 2017, 2018, and 2019 (34%) (Fig. 2). Of note, given the mean number of 139 abnormal stress SPECT-MPI in the previous 3 years and the number of 44 abnormal tests during the pandemic, there was a 68% of abnormal test missed. Baseline characteristics of the entire population according to the year of SPECT-MPI execution are reported on Table 1. Most of the considered variables were comparable between the two study periods.\nFig. 1 Number of stress SPECT-MPI procedures performed during COVID-19 pandemic and during corresponding months of the years 2017, 2018, and 2019 (P for trend \u003c 0.0001)\nFig. 2 Prevalence of abnormal stress SPECT-MPI studies during COVID-19 pandemic and during the corresponding months of the years 2017, 2018, and 2019. The percentage of abnormal stress SPECT-MPI studies was similar (P = 0.65)\nTable 1 Baseline characteristics of overall population according to the year of MPI execution\nAll (n = 1361) 2020 (n = 123) 2017–2019 (n = 1238) P value\nAge (years) 64 ± 11 64 ± 10 64 ± 11 0.14\nMale gender, n (%) 439 (32) 29 (24) 410 (33) \u003c .05\nBody mass index (kg/m2) 29 ± 5 29 ± 5 29 ± 6 0.43\nChest pain, n (%) 468 (34) 37 (30) 431 (35) 0.29\nDiabetes, n (%) 437 (32) 47 (38) 390 (32) 0.12\nDyspnea, n (%) 624 (46) 49 (40) 575 (46) 0.16\nFamily history, n (%) 729 (54) 60 (49) 669 (54) 0.26\nHypertension, n (%) 1070 (79) 102 (83) 968 (78) 0.22\nHyperlipidemia, n (%) 949 (70) 92 (75) 857 (69) 0.20\nSmoking, n (%) 416 (31) 26 (21) 390 (32) \u003c .05\nKnown CAD, n (%) 593 (44) 52 (42) 541 (44) 0.76\nStress type \u003c .0001\n  Physical exercise test, n (%) 519 (38) 11 (9) 508 (41)\n  Pharmacological test, n (%) 842 (62) 112 (91) 730 (59)\nAbnormal MPI, n (%) 462 (34) 44 (36) 418 (34) 0.65\nTotal perfusion defect \u003e 10% 246 (53) 27 (61) 219 (52) 0.26\nValues are expressed as mean value ± standard deviation or as number (percentage) of subjects. MPI myocardial perfusion imaging, CAD coronary artery disease\nCharacteristics of overall population are also described according to stress SPECT-MPI results during COVID-19 pandemic and during the corresponding months of 2017–2019 (Table 2). In both study periods, the percentage of patients with hyperlipidemia and known CAD was higher in patients with abnormal compared with those with normal stress SPECT-MPI findings, while during 2017–2019, patients with abnormal SPECT-MPI were older (P \u003c 0.05) and had higher prevalence of diabetes (P \u003c 0.005) and hypertension (P \u003c 0.001).\nTable 2 Clinical characteristics of overall population according to MPI results during COVID-19 emergency and during 2017–2019 3-year’s corresponding months\n2020 2017–2019\nNormal (n = 79) Abnormal (n = 44) P value Normal (n = 820) Abnormal (n = 418) P value\nAge (years) 64 ± 11 64 ± 8 0.77 63 ± 11 65 ± 11 \u003c .05\nMale gender, n (%) 23 (38) 6 (43) 0.05 337 (41) 73 (17) \u003c .001\nBody mass index (kg/m2) 28.9 ± 4.9 28.1 ± 4.4 0.38 29.1 ± 6.1 27.9 ± 4.2 \u003c .001\nChest pain, n (%) 22 (38) 15 (43) 0.47 308 (38) 123 (29) \u003c .005\nDiabetes, n (%) 28 (19) 19 (36) 0.40 234 (29) 156 (37) \u003c .005\nDyspnea, n (%) 29 (22) 20 (25) 0.34 375 (46) 200 (48) 0.48\nFamily history, n (%) 36 (48) 24 (41) 0.34 446 (54) 223 (53) 0.73\nHypertension, n (%) 66 (62) 36 (76) 0.81 614 (75) 354 (85) \u003c .001\nHyperlipidemia, n (%) 54 (55) 38 (57) \u003c .05 540 (66) 317 (76) \u003c .001\nSmoking, n (%) 15 (55) 11 (57) 0.43 258 (31) 132 (32) 0.97\nKnown CAD n (%) 21 (55) 31 (57) \u003c .001 224 (27) 317 (76) \u003c .001\nValues are expressed as mean value ± standard deviation or as number (percentage) of subjects. MPI myocardial perfusion imaging, CAD coronary artery disease\nAt multivariable logistic regression analysis (Table 3), hypertension (P \u003c 0.05) and known CAD (P \u003c 0.001) were significantly associated with abnormal stress SPECT-MPI during both COVID-19 pandemic and the corresponding months of the previous 3 years. No significant interactions among risk factors were found. During the 2017–2019 period, also male gender (P \u003c 0.001) and diabetes (P \u003c 0.05) were associated with abnormal stress SPECT-MPI.\nTable 3 Multivariable logistic regression analysis with abnormal MPI as dependent variable during COVID-19 emergency and during 2017–2019 3-year’s corresponding months\n2020 2017–2019\nβ coefficient SE P value β coefficient SE P value\nAge 0.005 0.023 0.834 − 0.004 0.007 0.60\nMale gender 0.162 0.639 0.799 0.671 0.166 \u003c .001\nBody mass index − 0.013 0.049 0.785 − 0.024 0.014 0.08\nChest pain 0.542 0.508 0.286 − 0.264 0.149 0.08\nDiabetes 0.565 0.466 0.225 0.310 0.148 \u003c .05\nFamily history 0.180 0.454 0.692 − 0.163 0.150 0.28\nDyspnea 0.362 0.382 0.343 − 0.085 0.120 0.48\nHypertension − 1.573 0.709 \u003c .05 0.376 0.196 \u003c .05\nHyperlipidemia 0.822 0.621 0.186 − 0.239 0.167 0.15\nSmoking 0.619 0.533 0.245 − 0.102 0.152 0.50\nKnown CAD 0.137 0.541 \u003c.001 1.994 0.154 \u003c .001\nSE standard error, MPI myocardial perfusion imaging, CAD coronary artery disease"}

{"project":"LitCovid-PubTator","denotations":[{"id":"135","span":{"begin":1145,"end":1153},"obj":"Disease"},{"id":"137","span":{"begin":1314,"end":1322},"obj":"Disease"},{"id":"146","span":{"begin":2127,"end":2165},"obj":"Gene"},{"id":"147","span":{"begin":1779,"end":1783},"obj":"Disease"},{"id":"148","span":{"begin":1822,"end":1830},"obj":"Disease"},{"id":"149","span":{"begin":1869,"end":1876},"obj":"Disease"},{"id":"150","span":{"begin":1968,"end":1980},"obj":"Disease"},{"id":"151","span":{"begin":2021,"end":2035},"obj":"Disease"},{"id":"152","span":{"begin":2169,"end":2175},"obj":"Disease"},{"id":"153","span":{"begin":2303,"end":2315},"obj":"Disease"},{"id":"161","span":{"begin":3551,"end":3555},"obj":"Disease"},{"id":"162","span":{"begin":3609,"end":3617},"obj":"Disease"},{"id":"163","span":{"begin":3671,"end":3678},"obj":"Disease"},{"id":"164","span":{"begin":3796,"end":3808},"obj":"Disease"},{"id":"165","span":{"begin":3862,"end":3876},"obj":"Disease"},{"id":"167","span":{"begin":3177,"end":3185},"obj":"Disease"},{"id":"178","span":{"begin":2831,"end":2834},"obj":"Gene"},{"id":"179","span":{"begin":2792,"end":2800},"obj":"Species"},{"id":"180","span":{"begin":2849,"end":2857},"obj":"Species"},{"id":"181","span":{"begin":2955,"end":2963},"obj":"Species"},{"id":"182","span":{"begin":2673,"end":2681},"obj":"Disease"},{"id":"183","span":{"begin":2806,"end":2820},"obj":"Disease"},{"id":"184","span":{"begin":3039,"end":3047},"obj":"Disease"},{"id":"185","span":{"begin":3064,"end":3076},"obj":"Disease"},{"id":"193","span":{"begin":5374,"end":5377},"obj":"Gene"},{"id":"194","span":{"begin":4825,"end":4876},"obj":"Disease"},{"id":"195","span":{"begin":5036,"end":5040},"obj":"Disease"},{"id":"196","span":{"begin":5078,"end":5086},"obj":"Disease"},{"id":"197","span":{"begin":5175,"end":5182},"obj":"Disease"},{"id":"198","span":{"begin":5220,"end":5232},"obj":"Disease"},{"id":"199","span":{"begin":5271,"end":5285},"obj":"Disease"},{"id":"201","span":{"begin":4750,"end":4758},"obj":"Disease"},{"id":"204","span":{"begin":5415,"end":5417},"obj":"Disease"},{"id":"205","span":{"begin":5468,"end":5495},"obj":"Disease"},{"id":"210","span":{"begin":4302,"end":4305},"obj":"Gene"},{"id":"211","span":{"begin":4268,"end":4280},"obj":"Disease"},{"id":"212","span":{"begin":4391,"end":4399},"obj":"Disease"},{"id":"213","span":{"begin":4584,"end":4592},"obj":"Disease"}],"attributes":[{"id":"A135","pred":"tao:has_database_id","subj":"135","obj":"MESH:C000657245"},{"id":"A137","pred":"tao:has_database_id","subj":"137","obj":"MESH:C000657245"},{"id":"A146","pred":"tao:has_database_id","subj":"146","obj":"Gene:730249"},{"id":"A147","pred":"tao:has_database_id","subj":"147","obj":"MESH:D010146"},{"id":"A148","pred":"tao:has_database_id","subj":"148","obj":"MESH:D003920"},{"id":"A149","pred":"tao:has_database_id","subj":"149","obj":"MESH:D004417"},{"id":"A150","pred":"tao:has_database_id","subj":"150","obj":"MESH:D006973"},{"id":"A151","pred":"tao:has_database_id","subj":"151","obj":"MESH:D006949"},{"id":"A152","pred":"tao:has_database_id","subj":"152","obj":"MESH:D000079225"},{"id":"A153","pred":"tao:has_database_id","subj":"153","obj":"MESH:C535740"},{"id":"A161","pred":"tao:has_database_id","subj":"161","obj":"MESH:D010146"},{"id":"A162","pred":"tao:has_database_id","subj":"162","obj":"MESH:D003920"},{"id":"A163","pred":"tao:has_database_id","subj":"163","obj":"MESH:D004417"},{"id":"A164","pred":"tao:has_database_id","subj":"164","obj":"MESH:D006973"},{"id":"A165","pred":"tao:has_database_id","subj":"165","obj":"MESH:D006949"},{"id":"A167","pred":"tao:has_database_id","subj":"167","obj":"MESH:C000657245"},{"id":"A178","pred":"tao:has_database_id","subj":"178","obj":"Gene:730249"},{"id":"A179","pred":"tao:has_database_id","subj":"179","obj":"Tax:9606"},{"id":"A180","pred":"tao:has_database_id","subj":"180","obj":"Tax:9606"},{"id":"A181","pred":"tao:has_database_id","subj":"181","obj":"Tax:9606"},{"id":"A182","pred":"tao:has_database_id","subj":"182","obj":"MESH:C000657245"},{"id":"A183","pred":"tao:has_database_id","subj":"183","obj":"MESH:D006949"},{"id":"A184","pred":"tao:has_database_id","subj":"184","obj":"MESH:D003920"},{"id":"A185","pred":"tao:has_database_id","subj":"185","obj":"MESH:D006973"},{"id":"A193","pred":"tao:has_database_id","subj":"193","obj":"Gene:730249"},{"id":"A194","pred":"tao:has_database_id","subj":"194","obj":"MESH:C000657245"},{"id":"A195","pred":"tao:has_database_id","subj":"195","obj":"MESH:D010146"},{"id":"A196","pred":"tao:has_database_id","subj":"196","obj":"MESH:D003920"},{"id":"A197","pred":"tao:has_database_id","subj":"197","obj":"MESH:D004417"},{"id":"A198","pred":"tao:has_database_id","subj":"198","obj":"MESH:D006973"},{"id":"A199","pred":"tao:has_database_id","subj":"199","obj":"MESH:D006949"},{"id":"A201","pred":"tao:has_database_id","subj":"201","obj":"MESH:C000657245"},{"id":"A205","pred":"tao:has_database_id","subj":"205","obj":"MESH:D003324"},{"id":"A210","pred":"tao:has_database_id","subj":"210","obj":"Gene:730249"},{"id":"A211","pred":"tao:has_database_id","subj":"211","obj":"MESH:D006973"},{"id":"A212","pred":"tao:has_database_id","subj":"212","obj":"MESH:C000657245"},{"id":"A213","pred":"tao:has_database_id","subj":"213","obj":"MESH:D003920"}],"namespaces":[{"prefix":"Tax","uri":"https://www.ncbi.nlm.nih.gov/taxonomy/"},{"prefix":"MESH","uri":"https://id.nlm.nih.gov/mesh/"},{"prefix":"Gene","uri":"https://www.ncbi.nlm.nih.gov/gene/"},{"prefix":"CVCL","uri":"https://web.expasy.org/cellosaurus/CVCL_"}],"text":"Results\nA total of 1361 stress SPECT-MPI were considered, including all tests performed during the pandemic between February and May 2020 and those performed in the corresponding months of the prior 3 years (2017, 2018, and 2019).\nThe number of stress SPECT-MPI studies performed during the pandemic (n = 123) was significantly lower (P \u003c 0.0001) compared with the mean number of procedures in the corresponding months of the years 2017, 2018, and 2019 (n = 413) (Fig. 1). However, the percentage of abnormal stress SPECT-MPI studies was similar (P = 0.65) during the pandemic (36%) compared with mean percentage value of the corresponding months of the years 2017, 2018, and 2019 (34%) (Fig. 2). Of note, given the mean number of 139 abnormal stress SPECT-MPI in the previous 3 years and the number of 44 abnormal tests during the pandemic, there was a 68% of abnormal test missed. Baseline characteristics of the entire population according to the year of SPECT-MPI execution are reported on Table 1. Most of the considered variables were comparable between the two study periods.\nFig. 1 Number of stress SPECT-MPI procedures performed during COVID-19 pandemic and during corresponding months of the years 2017, 2018, and 2019 (P for trend \u003c 0.0001)\nFig. 2 Prevalence of abnormal stress SPECT-MPI studies during COVID-19 pandemic and during the corresponding months of the years 2017, 2018, and 2019. The percentage of abnormal stress SPECT-MPI studies was similar (P = 0.65)\nTable 1 Baseline characteristics of overall population according to the year of MPI execution\nAll (n = 1361) 2020 (n = 123) 2017–2019 (n = 1238) P value\nAge (years) 64 ± 11 64 ± 10 64 ± 11 0.14\nMale gender, n (%) 439 (32) 29 (24) 410 (33) \u003c .05\nBody mass index (kg/m2) 29 ± 5 29 ± 5 29 ± 6 0.43\nChest pain, n (%) 468 (34) 37 (30) 431 (35) 0.29\nDiabetes, n (%) 437 (32) 47 (38) 390 (32) 0.12\nDyspnea, n (%) 624 (46) 49 (40) 575 (46) 0.16\nFamily history, n (%) 729 (54) 60 (49) 669 (54) 0.26\nHypertension, n (%) 1070 (79) 102 (83) 968 (78) 0.22\nHyperlipidemia, n (%) 949 (70) 92 (75) 857 (69) 0.20\nSmoking, n (%) 416 (31) 26 (21) 390 (32) \u003c .05\nKnown CAD, n (%) 593 (44) 52 (42) 541 (44) 0.76\nStress type \u003c .0001\n  Physical exercise test, n (%) 519 (38) 11 (9) 508 (41)\n  Pharmacological test, n (%) 842 (62) 112 (91) 730 (59)\nAbnormal MPI, n (%) 462 (34) 44 (36) 418 (34) 0.65\nTotal perfusion defect \u003e 10% 246 (53) 27 (61) 219 (52) 0.26\nValues are expressed as mean value ± standard deviation or as number (percentage) of subjects. MPI myocardial perfusion imaging, CAD coronary artery disease\nCharacteristics of overall population are also described according to stress SPECT-MPI results during COVID-19 pandemic and during the corresponding months of 2017–2019 (Table 2). In both study periods, the percentage of patients with hyperlipidemia and known CAD was higher in patients with abnormal compared with those with normal stress SPECT-MPI findings, while during 2017–2019, patients with abnormal SPECT-MPI were older (P \u003c 0.05) and had higher prevalence of diabetes (P \u003c 0.005) and hypertension (P \u003c 0.001).\nTable 2 Clinical characteristics of overall population according to MPI results during COVID-19 emergency and during 2017–2019 3-year’s corresponding months\n2020 2017–2019\nNormal (n = 79) Abnormal (n = 44) P value Normal (n = 820) Abnormal (n = 418) P value\nAge (years) 64 ± 11 64 ± 8 0.77 63 ± 11 65 ± 11 \u003c .05\nMale gender, n (%) 23 (38) 6 (43) 0.05 337 (41) 73 (17) \u003c .001\nBody mass index (kg/m2) 28.9 ± 4.9 28.1 ± 4.4 0.38 29.1 ± 6.1 27.9 ± 4.2 \u003c .001\nChest pain, n (%) 22 (38) 15 (43) 0.47 308 (38) 123 (29) \u003c .005\nDiabetes, n (%) 28 (19) 19 (36) 0.40 234 (29) 156 (37) \u003c .005\nDyspnea, n (%) 29 (22) 20 (25) 0.34 375 (46) 200 (48) 0.48\nFamily history, n (%) 36 (48) 24 (41) 0.34 446 (54) 223 (53) 0.73\nHypertension, n (%) 66 (62) 36 (76) 0.81 614 (75) 354 (85) \u003c .001\nHyperlipidemia, n (%) 54 (55) 38 (57) \u003c .05 540 (66) 317 (76) \u003c .001\nSmoking, n (%) 15 (55) 11 (57) 0.43 258 (31) 132 (32) 0.97\nKnown CAD n (%) 21 (55) 31 (57) \u003c .001 224 (27) 317 (76) \u003c .001\nValues are expressed as mean value ± standard deviation or as number (percentage) of subjects. MPI myocardial perfusion imaging, CAD coronary artery disease\nAt multivariable logistic regression analysis (Table 3), hypertension (P \u003c 0.05) and known CAD (P \u003c 0.001) were significantly associated with abnormal stress SPECT-MPI during both COVID-19 pandemic and the corresponding months of the previous 3 years. No significant interactions among risk factors were found. During the 2017–2019 period, also male gender (P \u003c 0.001) and diabetes (P \u003c 0.05) were associated with abnormal stress SPECT-MPI.\nTable 3 Multivariable logistic regression analysis with abnormal MPI as dependent variable during COVID-19 emergency and during 2017–2019 3-year’s corresponding months\n2020 2017–2019\nβ coefficient SE P value β coefficient SE P value\nAge 0.005 0.023 0.834 − 0.004 0.007 0.60\nMale gender 0.162 0.639 0.799 0.671 0.166 \u003c .001\nBody mass index − 0.013 0.049 0.785 − 0.024 0.014 0.08\nChest pain 0.542 0.508 0.286 − 0.264 0.149 0.08\nDiabetes 0.565 0.466 0.225 0.310 0.148 \u003c .05\nFamily history 0.180 0.454 0.692 − 0.163 0.150 0.28\nDyspnea 0.362 0.382 0.343 − 0.085 0.120 0.48\nHypertension − 1.573 0.709 \u003c .05 0.376 0.196 \u003c .05\nHyperlipidemia 0.822 0.621 0.186 − 0.239 0.167 0.15\nSmoking 0.619 0.533 0.245 − 0.102 0.152 0.50\nKnown CAD 0.137 0.541 \u003c.001 1.994 0.154 \u003c .001\nSE standard error, MPI myocardial perfusion imaging, CAD coronary artery disease"}

{"project":"LitCovid-PMC-OGER-BB","denotations":[{"id":"T65","span":{"begin":1145,"end":1153},"obj":"SP_7"},{"id":"T66","span":{"begin":1314,"end":1322},"obj":"SP_7"},{"id":"T67","span":{"begin":2248,"end":2263},"obj":"CHEBI:52217;CHEBI:52217"},{"id":"T68","span":{"begin":2513,"end":2523},"obj":"UBERON:0002349"},{"id":"T69","span":{"begin":2547,"end":2562},"obj":"UBERON:0001621"},{"id":"T70","span":{"begin":2673,"end":2681},"obj":"SP_7"},{"id":"T50079","span":{"begin":1145,"end":1153},"obj":"SP_7"},{"id":"T44925","span":{"begin":1314,"end":1322},"obj":"SP_7"},{"id":"T52972","span":{"begin":2248,"end":2263},"obj":"CHEBI:52217;CHEBI:52217"},{"id":"T82716","span":{"begin":5438,"end":5448},"obj":"UBERON:0002349"},{"id":"T1355","span":{"begin":5472,"end":5487},"obj":"UBERON:0001621"}],"text":"Results\nA total of 1361 stress SPECT-MPI were considered, including all tests performed during the pandemic between February and May 2020 and those performed in the corresponding months of the prior 3 years (2017, 2018, and 2019).\nThe number of stress SPECT-MPI studies performed during the pandemic (n = 123) was significantly lower (P \u003c 0.0001) compared with the mean number of procedures in the corresponding months of the years 2017, 2018, and 2019 (n = 413) (Fig. 1). However, the percentage of abnormal stress SPECT-MPI studies was similar (P = 0.65) during the pandemic (36%) compared with mean percentage value of the corresponding months of the years 2017, 2018, and 2019 (34%) (Fig. 2). Of note, given the mean number of 139 abnormal stress SPECT-MPI in the previous 3 years and the number of 44 abnormal tests during the pandemic, there was a 68% of abnormal test missed. Baseline characteristics of the entire population according to the year of SPECT-MPI execution are reported on Table 1. Most of the considered variables were comparable between the two study periods.\nFig. 1 Number of stress SPECT-MPI procedures performed during COVID-19 pandemic and during corresponding months of the years 2017, 2018, and 2019 (P for trend \u003c 0.0001)\nFig. 2 Prevalence of abnormal stress SPECT-MPI studies during COVID-19 pandemic and during the corresponding months of the years 2017, 2018, and 2019. The percentage of abnormal stress SPECT-MPI studies was similar (P = 0.65)\nTable 1 Baseline characteristics of overall population according to the year of MPI execution\nAll (n = 1361) 2020 (n = 123) 2017–2019 (n = 1238) P value\nAge (years) 64 ± 11 64 ± 10 64 ± 11 0.14\nMale gender, n (%) 439 (32) 29 (24) 410 (33) \u003c .05\nBody mass index (kg/m2) 29 ± 5 29 ± 5 29 ± 6 0.43\nChest pain, n (%) 468 (34) 37 (30) 431 (35) 0.29\nDiabetes, n (%) 437 (32) 47 (38) 390 (32) 0.12\nDyspnea, n (%) 624 (46) 49 (40) 575 (46) 0.16\nFamily history, n (%) 729 (54) 60 (49) 669 (54) 0.26\nHypertension, n (%) 1070 (79) 102 (83) 968 (78) 0.22\nHyperlipidemia, n (%) 949 (70) 92 (75) 857 (69) 0.20\nSmoking, n (%) 416 (31) 26 (21) 390 (32) \u003c .05\nKnown CAD, n (%) 593 (44) 52 (42) 541 (44) 0.76\nStress type \u003c .0001\n  Physical exercise test, n (%) 519 (38) 11 (9) 508 (41)\n  Pharmacological test, n (%) 842 (62) 112 (91) 730 (59)\nAbnormal MPI, n (%) 462 (34) 44 (36) 418 (34) 0.65\nTotal perfusion defect \u003e 10% 246 (53) 27 (61) 219 (52) 0.26\nValues are expressed as mean value ± standard deviation or as number (percentage) of subjects. MPI myocardial perfusion imaging, CAD coronary artery disease\nCharacteristics of overall population are also described according to stress SPECT-MPI results during COVID-19 pandemic and during the corresponding months of 2017–2019 (Table 2). In both study periods, the percentage of patients with hyperlipidemia and known CAD was higher in patients with abnormal compared with those with normal stress SPECT-MPI findings, while during 2017–2019, patients with abnormal SPECT-MPI were older (P \u003c 0.05) and had higher prevalence of diabetes (P \u003c 0.005) and hypertension (P \u003c 0.001).\nTable 2 Clinical characteristics of overall population according to MPI results during COVID-19 emergency and during 2017–2019 3-year’s corresponding months\n2020 2017–2019\nNormal (n = 79) Abnormal (n = 44) P value Normal (n = 820) Abnormal (n = 418) P value\nAge (years) 64 ± 11 64 ± 8 0.77 63 ± 11 65 ± 11 \u003c .05\nMale gender, n (%) 23 (38) 6 (43) 0.05 337 (41) 73 (17) \u003c .001\nBody mass index (kg/m2) 28.9 ± 4.9 28.1 ± 4.4 0.38 29.1 ± 6.1 27.9 ± 4.2 \u003c .001\nChest pain, n (%) 22 (38) 15 (43) 0.47 308 (38) 123 (29) \u003c .005\nDiabetes, n (%) 28 (19) 19 (36) 0.40 234 (29) 156 (37) \u003c .005\nDyspnea, n (%) 29 (22) 20 (25) 0.34 375 (46) 200 (48) 0.48\nFamily history, n (%) 36 (48) 24 (41) 0.34 446 (54) 223 (53) 0.73\nHypertension, n (%) 66 (62) 36 (76) 0.81 614 (75) 354 (85) \u003c .001\nHyperlipidemia, n (%) 54 (55) 38 (57) \u003c .05 540 (66) 317 (76) \u003c .001\nSmoking, n (%) 15 (55) 11 (57) 0.43 258 (31) 132 (32) 0.97\nKnown CAD n (%) 21 (55) 31 (57) \u003c .001 224 (27) 317 (76) \u003c .001\nValues are expressed as mean value ± standard deviation or as number (percentage) of subjects. MPI myocardial perfusion imaging, CAD coronary artery disease\nAt multivariable logistic regression analysis (Table 3), hypertension (P \u003c 0.05) and known CAD (P \u003c 0.001) were significantly associated with abnormal stress SPECT-MPI during both COVID-19 pandemic and the corresponding months of the previous 3 years. No significant interactions among risk factors were found. During the 2017–2019 period, also male gender (P \u003c 0.001) and diabetes (P \u003c 0.05) were associated with abnormal stress SPECT-MPI.\nTable 3 Multivariable logistic regression analysis with abnormal MPI as dependent variable during COVID-19 emergency and during 2017–2019 3-year’s corresponding months\n2020 2017–2019\nβ coefficient SE P value β coefficient SE P value\nAge 0.005 0.023 0.834 − 0.004 0.007 0.60\nMale gender 0.162 0.639 0.799 0.671 0.166 \u003c .001\nBody mass index − 0.013 0.049 0.785 − 0.024 0.014 0.08\nChest pain 0.542 0.508 0.286 − 0.264 0.149 0.08\nDiabetes 0.565 0.466 0.225 0.310 0.148 \u003c .05\nFamily history 0.180 0.454 0.692 − 0.163 0.150 0.28\nDyspnea 0.362 0.382 0.343 − 0.085 0.120 0.48\nHypertension − 1.573 0.709 \u003c .05 0.376 0.196 \u003c .05\nHyperlipidemia 0.822 0.621 0.186 − 0.239 0.167 0.15\nSmoking 0.619 0.533 0.245 − 0.102 0.152 0.50\nKnown CAD 0.137 0.541 \u003c.001 1.994 0.154 \u003c .001\nSE standard error, MPI myocardial perfusion imaging, CAD coronary artery disease"}