Evaluating risk stratification scoring systems to predict mortality in patients with COVID-19
Open Access
- 14 September 2021
- journal article
- review article
- Published by BMJ in BMJ Health & Care Informatics
- Vol. 28 (1), e100389
- https://doi.org/10.1136/bmjhci-2021-100389
Abstract
Background The COVID-19 pandemic has necessitated efficient and accurate triaging of patients for more effective allocation of resources and treatment. Objectives The objectives are to investigate parameters and risk stratification tools that can be applied to predict mortality within 90 days of hospital admission in patients with COVID-19. Methods A literature search of original studies assessing systems and parameters predicting mortality of patients with COVID-19 was conducted using MEDLINE and EMBASE. Results 589 titles were screened, and 76 studies were found investigating the prognostic ability of 16 existing scoring systems (area under the receiving operator curve (AUROC) range: 0.550–0.966), 38 newly developed COVID-19-specific prognostic systems (AUROC range: 0.6400–0.9940), 15 artificial intelligence (AI) models (AUROC range: 0.840–0.955) and 16 studies on novel blood parameters and imaging. Discussion Current scoring systems generally underestimate mortality, with the highest AUROC values found for APACHE II and the lowest for SMART-COP. Systems featuring heavier weighting on respiratory parameters were more predictive than those assessing other systems. Cardiac biomarkers and CT chest scans were the most commonly studied novel parameters and were independently associated with mortality, suggesting potential for implementation into model development. All types of AI modelling systems showed high abilities to predict mortality, although none had notably higher AUROC values than COVID-19-specific prediction models. All models were found to have bias, including lack of prospective studies, small sample sizes, single-centre data collection and lack of external validation. Conclusion The single parameters established within this review would be useful to look at in future prognostic models in terms of the predictive capacity their combined effect may harness.Keywords
This publication has 36 references indexed in Scilit:
- Sociodemographic and clinical determinants of in-facility case fatality rate for 938 adult Ebola patients treated at Sierra Leone Ebola treatment centerBMC Infectious Diseases, 2020
- Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort studyThe Lancet, 2020
- A Real-world Prospective Study of Mother-to-child Transmission of HBV in China Using a Mobile Health Application (Shield 01)Journal of Clinical and Translational Hepatology, 2020
- PHD1 controls muscle mTORC1 in a hydroxylation-independent manner by stabilizing leucyl tRNA synthetaseNature Communications, 2020
- U1 snRNP regulates cancer cell migration and invasion in vitroNature Communications, 2020
- Brain Intelligence: Go beyond Artificial IntelligenceMobile Networks and Applications, 2017
- Scores and scales used in emergency medicine. Practicability in toxicology2014
- Deep Learning: Methods and ApplicationsFoundations and Trends® in Signal Processing, 2014
- Receiver Operating Characteristic (ROC) Curve Analysis for Medical Diagnostic Test Evaluation.2013
- Glossary of TermsMachine Learning, 1998