Current best evidence for clinical care (more info)
BACKGROUND: Previous published prognostic models for COVID-19 patients have been suggested to be prone to bias due to unrepresentativeness of patient population, lack of external validation, inappropriate statistical analyses, or poor reporting. A high-quality and easy-to-use prognostic model to predict in-hospital mortality for COVID-19 patients could support physicians to make better clinical decisions.
METHODS: Fine-Gray models were used to derive a prognostic model to predict in-hospital mortality (treating discharged alive from hospital as the competing event) in COVID-19 patients using two retrospective cohorts (n = 1008) in Wuhan, China from January 1 to February 10, 2020. The proposed model was internally evaluated by bootstrap approach and externally evaluated in an external cohort (n = 1031).
RESULTS: The derivation cohort was a case-mix of mild-to-severe hospitalized COVID-19 patients (43.6% females, median age 55). The final model (PLANS), including five predictor variables of platelet count, lymphocyte count, age, neutrophil count, and sex, had an excellent predictive performance (optimism-adjusted C-index: 0.85, 95% CI: 0.83 to 0.87; averaged calibration slope: 0.95, 95% CI: 0.82 to 1.08). Internal validation showed little overfitting. External validation using an independent cohort (47.8% female, median age 63) demonstrated excellent predictive performance (C-index: 0.87, 95% CI: 0.85 to 0.89; calibration slope: 1.02, 95% CI: 0.92 to 1.12). The averaged predicted cumulative incidence curves were close to the observed cumulative incidence curves in patients with different risk profiles.
CONCLUSIONS: The PLANS model based on five routinely collected predictors would assist clinicians in better triaging patients and allocating healthcare resources to reduce COVID-19 fatality.
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This is a very interesting derivation/validation of a mortality prediction score for COVID-19. The authors propose that this would be helpful in allocation of resources and triage, but offer no evidence of that. The data required are easy and common to obtain. The calculations are cumbersome. This should not modify clinical practice at this time. It is important to note that the model has significant error (overestimate of mortality) for the more severe patients. This might lead to improper decision-making if this model was implemented prior to proper validation.
This prognostic model for predicting in-hospital mortality in patients with COVID-19 pneumonia was developed on the large derivation and validation populations with robust methodology and acknowledgement of the limitations. The simplicity of using readily available admission lab measures and age to predict in-hospital mortality make it attractive. The on-line calculator is also easy to use. If this model is generalizable outside the Wuhan region remains to be seen.
This is good quality research providing a clinically useful model using routinely collected predictors that would assist clinicians in better triaging of COVID-19 patients to help reduce fatality.