PMC:2940023 / 12340-22207 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/2940023","sourcedb":"PMC","sourceid":"2940023","source_url":"https://www.ncbi.nlm.nih.gov/pmc/2940023","text":"Multivariate logistic regression analysis\nFive published prediction rules were validated (Tables 2 and 3): Diamond \u0026 Forrester [22], Pryor et al. [23], Morise et al. 1994 [24], Morise et al. 1997 [17] and Shaw et al. [25]. The Diamond \u0026 Forrester prediction rule includes age, sex and type of chest pain, all of which were significant predictors of obstructive CAD in our dataset and with an area under the ROC curve (AUC) of 0.798. Including CTCS increased the AUC to 0.890, which was a statistically significant improvement (p \u003c 0.001). In the expanded model age and sex were no longer significant predictors (Table 2).\nTable 2 Comparison of multivariate logistic regression models\nVariables Model 1: Diamond \u0026 Forrester 1979 Model 2: Pryor et al. 1993 Model 3: Morise et al. 1994\nNo calcium score Calcium score No calcium score Calcium score No calcium score Calcium score\nOR 95% CI OR 95% CI OR 95% CI OR 95% CI OR 95% CI OR 95% CI\nln CTCSa (CT calcium score) 1.97 1.60, 2.41 1.93 1.55, 2.41 1.87 1.52, 2.29\nAge 1.05 1.02, 1.08 0.98 0.95, 1.02 1.02 0.95, 1.09 0.93 0.86, 1.01 1.05 1.01, 1.08 0.98 0.95, 1.02\nMale sex 2.97 1.57, 5.61 1.64 0.79, 3.42 0.19 0.00, 12.20 0.02 0.00, 1.91 3.48 1.77, 6.83 2.05 0.94, 4.45\nTypical chest pain 6.61 3.67, 11.89 5.34 2.70, 10.56 5.44 2.90, 10.18 5.22 2.52, 10.81 5.44 2.94, 10.05 4.91 2.43, 9.91\nSmoking 0.08 0.00, 9.40 0.06 0.00, 11.40\nDyslipidaemia 10.90 0.19, 610.99 2.43 0.03, 195.86 3.03 1.63, 5.61 1.94 0.96, 3.94\nDiabetes 2.81 1.07, 7.38 2.14 0.71, 6.42 2.61 1.02, 6.73 2.22 0.74, 6.65\nAge–smoking 1.04 0.97, 1.13 1.03 0.95, 1.12\nAge–dyslipidaemia 0.98 0.92, 1.05 1.00 0.93, 1.07\nSex–smoking 1.77 0.36, 8.84 3.82 0.66, 22.09\nAge–sex 1.05 0.98, 1.12 1.07 1.00, 1.15\nOestrogen\nHypertension\nFamily history\nDyslipidaemia–family history\nObesity\nBMI\n \nAUCb 0.798 0.742, 0.854 0.890 0.851, 0.930 0.838 0.789, 0.887 0.901 0.863, 0.938 0.831 0.780, 0.881 0.899 0.861, 0.937\nLR testc p \u003c 0.001 p \u003c 0.001 p \u003c 0.001\nOdds ratios (ORs) in bold typeface are statistically significant\naNatural logarithm of CTCS + 1\nbArea under the receiver operating characteristic curve\ncLikelihood ratio test comparing model without CTCS and model including CTCS\nTable 3 Comparison of multivariate logistic regression models\nVariables Model 4: Morise et al. 1997 Model 5: Shaw et al. 1998\nNo calcium score Calcium score No calcium score Calcium score\nOR 95% CI OR 95% CI OR 95% CI OR 95% CI\nln CTCSa (CT calcium score) 1.87 1.50, 2.31 1.86 1.51, 2.30\nIntercept\nAge 1.05 1.01, 1.09 0.99 0.95, 1.03 1.05 1.02, 1.08 0.98 0.95, 1.02\nMale sex 3.15 1.29, 7.70 1.37 0.48, 3.95 3.42 1.74, 6.74 2.05 0.94, 4.45\nTypical pain 5.56 2.94, 10.51 4.82 2.36, 9.86 5.50 2.96, 10.21 4.91 2.44, 9.90\nSmoking 1.53 0.72, 3.24 1.03 0.44, 2.39 1.63 0.80, 3.30 1.04 0.47, 2.27\nDyslipidaemia 3.20 1.29, 7.95 1.80 0.62, 5.22 3.04 1.63, 5.66 1.95 0.96, 3.94\nDiabetes 2.66 0.98, 7.26 2.01 0.61, 6.61 2.85 1.10, 7.39 2.24 0.74, 6.75\nAge–smoking\nAge–dyslipidaemia\nSex–smoking\nAge–sex\nOestrogen 0.78 0.33, 1.86 0.53 0.19, 1.48\nHypertension 1.83 0.93, 3.60 1.31 0.62, 2.79\nFamily History 2.02 0.77, 5.29 1.14 0.38, 3.39\nDyslipidaemia–family history 0.74 0.21, 2.60 1.15 0.28, 4.82\nObesity 0.88 0.31, 2.48 0.65 0.20, 2.12\nBMI 0.99 0.88, 1.13 1.05 0.91, 1.22\n \nAUCb 0.840 0.792, 0.889 0.898 0.859, 0.936 0.833 0.783, 0.883 0.899 0.861, 0.937\nLR testc p \u003c 0.001 p \u003c 0.001\nOdds ratios (ORs) in bold typeface are statistically significant\naNatural logarithm of CTCS + 1\nbArea under the receiver operating characteristic curve\ncLikelihood ratio test comparing model without CTCS and model including CTCS\nPryor et al. analysed age, sex, type of chest pain, smoking, dyslipidaemia, diabetes and the interaction between age and smoking, age and dyslipidaemia, sex and smoking, and age and sex, of which type of chest pain and the presence of diabetes were significant predictors. This model resulted in an AUC of 0.838. After including CTCS, the AUC increased to 0.901 which was a statistically significant improvement (p \u003c 0.001). In the expanded model diabetes was no longer a significant predictor (Table 2).\nMorise et al. (1994) included diabetes and dyslipidaemia in addition to the variables used by Diamond \u0026 Forrester and resulted in an AUC of 0.831. All variables were significant predictors of the presence of obstructive CAD. After including CTCS, type of chest pain was the only variable that remained significant. After inclusion of CTCS the AUC increased to 0.899, which was a statistically significant improvement (p \u003c 0.001) (Table 2).\nMorise et al. (1997) assessed age, sex, type of chest pain, smoking, dyslipidaemia, diabetes, oestrogen status, hypertension, family history, obesity, BMI and the interaction between dyslipidaemia and family history. This model resulted in an AUC of 0.840. Age, sex, type of chest pain and dyslipidaemia were significant predictors. After including CTCS, the AUC increased to 0.898, which was a significant model improvement (p \u003c 0.001). Age, sex and dyslipidaemia were no longer significant predictors after the addition of CTCS (Table 3).\nShaw et al. considered age, sex, typical chest pain, smoking, dyslipidaemia and diabetes and resulted in an AUC of 0.833. After including CTCS, only type of chest pain remained a significant predictor and the AUC increased to 0.899 which was a statistically significant improvement (p \u003c 0.001) (Table 3).\nReclassification tables for the Diamond \u0026 Forrester model and the Pryor model are presented in Tables 4 and 5. The addition of CTCS to Diamond \u0026 Forrester resulted in reclassification of 47.2% of patients of whom 73.3% were correctly reclassified. The reclassification calibration statistic (RCS) indicated a strong lack of fit for the Diamond \u0026 Forrester model (p \u003c 0.00001) which decreased substantially when CTCS was added to the model (p \u003c 0.01). The NRI (net reclassification improvement) was 33.6% (p \u003c 0.0001) and the IDI (integrated discrimination improvement) was also statistically significant (18.8%, p \u003c 0.001) indicating improvement in the classification of cases and non-cases in probability categories and improvement in discrimination between cases and non-cases.\nTable 4 Probability of coronary artery disease: reclassification table after addition of CTCS to the Diamond and Forrester (model 1)\nProbability category based on model 1 Probability category based on model 1 + CTCS Total, n (%)\n\u003c30% ≥30–50% ≥50–70% ≥70%\n\u003c30%\n N (%) 51 (67.1) 19 (25.0%) 4 (5.2) 2 (2.6) 76 (29.9)\n Observed probability, % 11.8 36.8 50.0 100.0 22.4\n≥30–50%\n N (%) 25 (39.7) 13 (20.6) 19 (30.2) 6 (9.5) 63 (28.4)\n Observed probability, % 0.0 23.1 68.4 50.0 30.2\n≥50–70%\n N (%) 4 (11.1) 5 (13.9) 6 (16.7) 21 (58.3) 36 (14.2)\n Observed probability, % 25 80.0 50.0 66.7 61.1\n≥70%\n N (%) 5 (6.3) 2 (2.5) 8 (10.1) 64 (81.0) 79 (31.1)\n Observed probability, % 0.0 50.0 37.5 95.3 82.3\n \nTotal\n N (%) 85 (33.5) 39 (15.4) 37 (14.6) 93 (36.6) 254 (100.0)\n Observed probability, % 8.2 38.5 56.8 86.0 48.4\nCTCS computed tomography coronary calcium score\nTable 5 Probability of coronary artery disease: reclassification table after addition of CTCS to the model published by Pryor et al. (model 2)\nProbability category based on model 2 Probability category based on model 2 + CTCS Total, n (%)\n\u003c30% ≥30–50% ≥50–70% ≥70%\n\u003c30%\n N (%) 76 (85.4) 10 (11.2) 2 (2.3) 1 (1.1) 89 (35.0)\n Observed probability, % 9.2 40.0 100.0 100.0 15.7\n≥30–50%\n N (%) 10 (27.0) 10 (27.0) 12 (32.4) 5 (13.5) 37 (14.6)\n Observed probability, % 10.0 20.0 50.0 100.0 37.8\n≥50–70%\n N (%) 7 (13.5) 6 (11.5) 15 (28.9) 24 (46.2) 52 (20.5)\n Observed probability, % 0.0 50.0 73.3 66.7 57.7\n≥70%\n N (%) 3 (4.0) 3 (4.0) 9 (11.8) 61 (80.3) 76 (29.9)\n Observed probability, % 33.3 0.0 66.7 95.0 85.5\n \nTotal\n N (%) 96 (37.8) 29 (11.4) 38 (15.0) 91 (35.8) 254 (100.0)\n Observed probability, % 9.4 31.0 65.8 87.9 48.4\nCTCS computed tomography coronary calcium score\nFor the model by Pryor et al., 36.2% of the patients were reclassified, of whom 54.3% were correctly classified. The RCS indicated a lack of fit (p = 0.01), which decreased when CTCS was added to the model (p = 0.03). The NRI was 24.0% (p \u003c 0.0001) and the IDI was 14.8% (p \u003c 0.001).\nThe reclassification measures for all models are presented in Table 6.\nTable 6 Reclassification measures obtained by adding CTCS to the existing prediction models\nModel Reclassification percentages % Correcta Chi-squared excluding CTCSb p value Chi-squared including CTCSc p value NRI, % p value for NRI Reclassification improvement (cases)d Reclassification improvement (non-cases)d IDI, % p value for IDI\nOverall Reclassified from ≥30–50% Reclassified from ≥50–70%\n1 47.2 79.4 83.3 73.3 25.01 \u003c0.00001 10.91 \u003c0.01 33.6 \u003c0.0001 \u003c0.0001 0.23 18.8 \u003c0.001\n2 36.2 73.0 71.2 54.3 6.57 0.01 4.70 0.03 24.0 0.001 \u003c0.001 0.31 14.8 \u003c0.001\n3 38.2 77.5 75.5 63.9 4.14 0.04 3.31 0.07 21.8 0.005 \u003c0.001 0.58 14.9 \u003c0.001\n4 34.3 74.4 74.0 83.9 3.82 0.05 1.58 0.21 24.9 \u003c0.001 \u003c0.001 0.29 13.3 \u003c0.001\n5 37.8 77.5 75.9 69.8 3.65 0.06 5.14 0.02 22.6 0.003 \u003c0.001 0.58 14.4 \u003c0.001\nCTCS computed tomography coronary calcium score, NRI net reclassification improvement, IDI integrated discrimination improvement\naIf the predicted probability of obstructive CAD of the model including CTCS was closer to the observed probability of CAD compared with the prediction of the model without CTCS, the reclassification was considered to be correct\nbReclassification calibration (Hosmer–Lemeshow) statistic for model without CTCS, using cells from the reclassification table with at least 20 observations\ncReclassification calibration statistic for models including CTCS, using cells from the reclassification table with at least 20 observations\ndThe reclassification improvement is defined as the difference in proportions of patients moving up and down for cases and non-cases 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