|Year : 2020 | Volume
| Issue : 4 | Page : 332-333
COVID journal review
Niranjan Karthik Senthil Kumar, Sharmila Devi Vadivelu
Department of Cornea, Regional Institute of Ophthalmology and Government Ophthalmic Hospital, Chennai, Tamil Nadu, India
|Date of Submission||19-Nov-2020|
|Date of Acceptance||23-Nov-2020|
|Date of Web Publication||16-Dec-2020|
Dr. Niranjan Karthik Senthil Kumar
Regional Institute of Ophthalmology and Government Ophthalmic Hospital, Chennai, Old 132, Rukmani Lakshmipathi Road, Pudupet, Komaleeswaranpet, Egmore, Chennai, Tamil Nadu
Source of Support: None, Conflict of Interest: None
|How to cite this article:|
Senthil Kumar NK, Vadivelu SD. COVID journal review. TNOA J Ophthalmic Sci Res 2020;58:332-3
| Risk Stratification of Patients Admitted to Hospital with Coronavirus Disease 2019 using the International Severe Acute Respiratory and Emerging Infection Consortium World Health Organization Clinical Characterisation Protocol: Development and Validation of the 4C Mortality Score|| |
Knight SR, Ho A, Pius R, Buchan I, Carson G, Drake TM, et al. Risk stratification of patients admitted to hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: development and validation of the 4C Mortality Score. bmj 2020;370.
Disease resulting from infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a high mortality rate with deaths predominantly caused by respiratory failure. The aim of the study is to develop and validate a pragmatic risk score, the 4C (Coronavirus Clinical Characterisation Consortium) Mortality Score, to predict mortality in patients admitted to hospital with coronavirus disease 2019 (COVID-19).
Materials and methods
It is a prospective observational cohort study carried out by the International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) World Health Organization Clinical Characterisation Protocol UK (performed by the ISARIC Coronavirus Clinical Characterisation Consortium) in 260 hospitals across England, Scotland, and Wales. Model training was performed on a cohort of patients recruited between February 6 and May 20, 2020, with validation conducted on a second cohort of patients recruited after model development between May 21 and June 29, 2020. The participants in the study included adults (age = 18 years) admitted to hospital with COVID-19 at least 4 weeks before final data extraction. The main outcome measure is inhospital mortality.
Totally 35,463 patients were included in the derivation dataset (mortality rate: 32.2%) and 22,361 in the validation dataset (mortality rate: 30.1%). The final 4C Mortality Score included eight variables readily available at initial hospital assessment: age, sex, number of comorbidities, respiratory rate, peripheral oxygen saturation, level of consciousness, urea level, and C-reactive protein (score range: 0–21 points). The 4C Mortality Score showed high discrimination for mortality (derivation cohort: area under the receiver operating characteristic curve = 0.79, 95% confidence interval = 0.78–0.79; validation cohort: 0.77, 0.76–0.77) with excellent calibration (validation: calibration-in-the-large = 0, slope = 1.0). Patients with a score of at least 15 (n = 4158, 19%) had a 62% mortality (positive predictive value: 62%) compared with 1% mortality for those with a score of 3 or less (n = 1650, 7%; negative predictive value: 99%). Discriminatory performance was higher than 15 preexisting risk stratification scores (area under the receiver operating characteristic curve range: 0.61–0.76), with scores developed in other COVID-19 cohorts often performing poorly (range: 0.63–0.73).
The 4C Mortality Score [Table 1] uses patient demographics, clinical observations, and blood parameters that are commonly available at the time of hospital admission and can accurately characterize the population of patients at high risk of death in hospital. The score compared favorably with other models, including best-in-class machine learning techniques, and showed consistent performance across the validation cohorts, including good clinical utility in a decision curve analysis. The 4C Mortality Score has several methodological advantages over current COVID-19 prognostic scores. The use of penalized regression methods and an event-to-variable ratio > 100 reduce the risk of overfitting. 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. Of course, a model developed in a specific dataset should describe that dataset best. In addition, 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.
|Table 1: Final 4C Mortality Score for inhospital mortality in patients with coronavirus disease 2019|
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First, evaluation of the predictive performance of several existing scores that require a large number of parameters (for example, APACHE II48) was not possible, as well as several other COVID-19 prognostic scores that use computed tomography findings or uncommonly measured biomarkers. Second, 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.
An easy-to-use risk stratification score has been developed and validated based on commonly available parameters at hospital presentation. The 4C Mortality Score outperformed existing scores, showed utility to directly inform clinical decision-making, and can be used to stratify patients admitted to hospital with COVID-19 into different management groups. The score should be further validated to determine its applicability in other populations.
With several scoring scales available for assessing the risk for progression in the disease process, each having its own limitations, the 4C risk assessment is successful in providing a handy tool for the clinicians in crucial decision-making in the management of SARS-CoV-2 to decrease the morbidity and mortality associated with it.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.