Author: Alkaabi, Salem; Alnuaimi, Asma; Alharbi, Mariam; Amari, Mohammed A; Ganapathy, Rajiv; Iqbal, Imran; Nauman, Javaid; Oulhaj, Abderrahim
Title: A clinical risk score to predict in-hospital mortality in critically ill patients with COVID-19: a retrospective cohort study Cord-id: k463ntq8 Document date: 2021_8_26
ID: k463ntq8
Snippet: OBJECTIVES: To identify factors influencing the mortality risk in critically ill patients with COVID-19, and to develop a risk prediction score to be used at admission to intensive care unit (ICU). DESIGN: A multicentre cohort study. SETTING AND PARTICIPANTS: 1542 patients with COVID-19 admitted to ICUs in public hospitals of Abu Dhabi, United Arab Emirates between 1 March 2020 and 22 July 2020. MAIN OUTCOMES AND MEASURES: The primary outcome was time from ICU admission until death. We used comp
Document: OBJECTIVES: To identify factors influencing the mortality risk in critically ill patients with COVID-19, and to develop a risk prediction score to be used at admission to intensive care unit (ICU). DESIGN: A multicentre cohort study. SETTING AND PARTICIPANTS: 1542 patients with COVID-19 admitted to ICUs in public hospitals of Abu Dhabi, United Arab Emirates between 1 March 2020 and 22 July 2020. MAIN OUTCOMES AND MEASURES: The primary outcome was time from ICU admission until death. We used competing risk regression models and Least Absolute Shrinkage and Selection Operator to identify the factors, and to construct a risk score. Predictive ability of the score was assessed by the area under the receiver operating characteristic curve (AUC), and the Brier score using 500 bootstraps replications. RESULTS: Among patients admitted to ICU, 196 (12.7%) died, 1215 (78.8%) were discharged and 131 (8.5%) were right-censored. The cumulative mortality incidence was 14% (95% CI 12.17% to 15.82%). From 36 potential predictors, we identified seven factors associated with mortality, and included in the risk score: age (adjusted HR (AHR) 1.98; 95% CI 1.71 to 2.31), neutrophil percentage (AHR 1.71; 95% CI 1.27 to 2.31), lactate dehydrogenase (AHR 1.31; 95% CI 1.15 to 1.49), respiratory rate (AHR 1.31; 95% CI 1.15 to 1.49), creatinine (AHR 1.19; 95% CI 1.11 to 1.28), Glasgow Coma Scale (AHR 0.70; 95% CI 0.63 to 0.78) and oxygen saturation (SpO(2)) (AHR 0.82; 95% CI 0.74 to 0.91). The mean AUC was 88.1 (95% CI 85.6 to 91.6), and the Brier score was 8.11 (95% CI 6.74 to 9.60). We developed a freely available web-based risk calculator (https://icumortalityrisk.shinyapps.io/ICUrisk/). CONCLUSION: In critically ill patients with COVID-19, we identified factors associated with mortality, and developed a risk prediction tool that showed high predictive ability. This tool may have utility in clinical settings to guide decision-making, and may facilitate the identification of supportive therapies to improve outcomes.
Search related documents:
Co phrase search for related documents- absolute shrinkage and liver disease cardiovascular disease: 1
- absolute shrinkage and logistic regression model: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19
- absolute shrinkage and machine learning: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24
- absolute shrinkage and machine learning method: 1
- absolute shrinkage lasso selection operator and liver disease: 1
- absolute shrinkage lasso selection operator and liver disease cardiovascular disease: 1
- absolute shrinkage lasso selection operator and logistic regression model: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14
- absolute shrinkage lasso selection operator and machine learning: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21
- absolute shrinkage lasso selection operator and machine learning method: 1
- abu dhabi health and liver disease cardiovascular disease: 1
- abu dhabi health department and liver disease cardiovascular disease: 1
- accurately classify and machine learning: 1, 2, 3, 4, 5, 6, 7, 8
- accurately classify and machine learning method: 1
- active cancer and liver dysfunction: 1
- active cancer and logistic regression model: 1, 2, 3, 4
- active cancer and logistic regression model proportional hazard model: 1
- active cancer and machine learning: 1
- liver disease and logistic regression model: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15
- liver disease and machine learning: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11
Co phrase search for related documents, hyperlinks ordered by date