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LitCovid-PubTator

LitCovid-sentences

{"project":"LitCovid-sentences","denotations":[{"id":"T176","span":{"begin":0,"end":39},"obj":"Sentence"},{"id":"T177","span":{"begin":40,"end":245},"obj":"Sentence"},{"id":"T178","span":{"begin":246,"end":422},"obj":"Sentence"},{"id":"T179","span":{"begin":423,"end":566},"obj":"Sentence"},{"id":"T180","span":{"begin":567,"end":668},"obj":"Sentence"},{"id":"T181","span":{"begin":669,"end":1100},"obj":"Sentence"},{"id":"T182","span":{"begin":1101,"end":1267},"obj":"Sentence"},{"id":"T183","span":{"begin":1268,"end":1385},"obj":"Sentence"},{"id":"T184","span":{"begin":1386,"end":1491},"obj":"Sentence"},{"id":"T185","span":{"begin":1492,"end":1596},"obj":"Sentence"},{"id":"T186","span":{"begin":1597,"end":1623},"obj":"Sentence"},{"id":"T187","span":{"begin":1624,"end":2073},"obj":"Sentence"},{"id":"T188","span":{"begin":2074,"end":2211},"obj":"Sentence"},{"id":"T189","span":{"begin":2212,"end":2288},"obj":"Sentence"},{"id":"T190","span":{"begin":2289,"end":2470},"obj":"Sentence"},{"id":"T191","span":{"begin":2471,"end":2573},"obj":"Sentence"},{"id":"T192","span":{"begin":2574,"end":2773},"obj":"Sentence"},{"id":"T193","span":{"begin":2774,"end":2885},"obj":"Sentence"},{"id":"T194","span":{"begin":2886,"end":3041},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"Strengths and limitations of this study\nThe ISARIC WHO CCP-UK study represents a large prospectively collected cohort admitted to hospital with covid-19 and reflects the clinical data available in most economically developed healthcare settings. We derived a clinically applicable prediction score with clear methods and tested it against existing risk stratification scores in a large patient cohort admitted to hospital. The score compared favourably with other prognostic tools, with good to excellent discrimination, calibration, and performance characteristics.\nThe 4C Mortality Score has several methodological advantages over current covid-19 prognostic scores. The use of penalised regression methods and an event-to-variable ratio greater than 100 reduce the risk of overfitting.4546 The use of parameters commonly available at first assessment increases its clinical applicability, avoiding the requirement for markers often only available after a patient has been admitted to a critical care facility.447 Of course a model developed in a specific dataset should describe that dataset best. However, by including comparisons with pre-existing models, reassurance is provided that equivalent performance cannot be delivered with a simple tool already in use.\nAdditionally, in a pandemic, baseline infection rates and patient characteristics might change by time and geography. This motivated the temporal and geographical validation, which is crucial to the reporting of this study. These sensitivity analyses showed that score performance continued to be robust over time and geography.\nOur study has limitations. Firstly, we were unable to evaluate the predictive performance of several existing scores that require a large number of parameters (for example, APACHE II48), as well as several other covid-19 prognostic scores that use computed tomography findings or uncommonly measured biomarkers.5 Additionally, several potentially relevant comorbidities, such as hypertension, previous myocardial infarction, and stroke,16 were not included in data collection. The inclusion of these comorbidities might have impacted upon or improved the performance and generalisability of the 4C Mortality Score.\nSecondly, a proportion of recruited patients (3.3%) had incomplete episodes. Selection bias is possible if patients with incomplete episodes, such as those with prolonged hospital admission, had a differential mortality risk to those with completed episodes. Nevertheless, the size of our patient cohort compares favourably to other datasets for model creation. The patient cohort on which the 4C Mortality Score was derived comprised patients admitted to hospital who were seriously ill (mortality rate of 32.2%) and were of advanced age (median age 73 years). This model is not for use in the community and could perform differently in populations at lower risk of death. Further external validation is required to determine whether the 4C Mortality Score is generalisable among younger patients and in settings outside the UK."}

LitCovid-PD-HP

{"project":"LitCovid-PD-HP","denotations":[{"id":"T20","span":{"begin":1976,"end":1988},"obj":"Phenotype"},{"id":"T21","span":{"begin":1999,"end":2020},"obj":"Phenotype"},{"id":"T22","span":{"begin":2026,"end":2032},"obj":"Phenotype"}],"attributes":[{"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_0001658"},{"id":"A22","pred":"hp_id","subj":"T22","obj":"http://purl.obolibrary.org/obo/HP_0001297"}],"text":"Strengths and limitations of this study\nThe ISARIC WHO CCP-UK study represents a large prospectively collected cohort admitted to hospital with covid-19 and reflects the clinical data available in most economically developed healthcare settings. We derived a clinically applicable prediction score with clear methods and tested it against existing risk stratification scores in a large patient cohort admitted to hospital. The score compared favourably with other prognostic tools, with good to excellent discrimination, calibration, and performance characteristics.\nThe 4C Mortality Score has several methodological advantages over current covid-19 prognostic scores. The use of penalised regression methods and an event-to-variable ratio greater than 100 reduce the risk of overfitting.4546 The use of parameters commonly available at first assessment increases its clinical applicability, avoiding the requirement for markers often only available after a patient has been admitted to a critical care facility.447 Of course a model developed in a specific dataset should describe that dataset best. However, by including comparisons with pre-existing models, reassurance is provided that equivalent performance cannot be delivered with a simple tool already in use.\nAdditionally, in a pandemic, baseline infection rates and patient characteristics might change by time and geography. This motivated the temporal and geographical validation, which is crucial to the reporting of this study. These sensitivity analyses showed that score performance continued to be robust over time and geography.\nOur study has limitations. Firstly, we were unable to evaluate the predictive performance of several existing scores that require a large number of parameters (for example, APACHE II48), as well as several other covid-19 prognostic scores that use computed tomography findings or uncommonly measured biomarkers.5 Additionally, several potentially relevant comorbidities, such as hypertension, previous myocardial infarction, and stroke,16 were not included in data collection. The inclusion of these comorbidities might have impacted upon or improved the performance and generalisability of the 4C Mortality Score.\nSecondly, a proportion of recruited patients (3.3%) had incomplete episodes. Selection bias is possible if patients with incomplete episodes, such as those with prolonged hospital admission, had a differential mortality risk to those with completed episodes. Nevertheless, the size of our patient cohort compares favourably to other datasets for model creation. The patient cohort on which the 4C Mortality Score was derived comprised patients admitted to hospital who were seriously ill (mortality rate of 32.2%) and were of advanced age (median age 73 years). This model is not for use in the community and could perform differently in populations at lower risk of death. Further external validation is required to determine whether the 4C Mortality Score is generalisable among younger patients and in settings outside the UK."}

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PMC:7116472 / 28335-31376 JSON TXT