Predicting in-hospital mortality from Coronavirus Disease 2019: A simple validated app for clinical use

Abstract

Backgrounds Validated tools for predicting individual in-hospital mortality of COVID-19 are lacking. We aimed to develop and to validate a simple clinical prediction rule for early identification of in-hospital mortality of patients with COVID-19. Methods and findings We enrolled 2191 consecutive hospitalized patients with COVID-19 from three Italian dedicated units (derivation cohort: 1810 consecutive patients from Bergamo and Pavia units; validation cohort: 381 consecutive patients from Rome unit). The outcome was in-hospital mortality. Fine and Gray competing risks multivariate model (with discharge as a competing event) was used to develop a prediction rule for in-hospital mortality. Discrimination and calibration were assessed by the area under the receiver operating characteristic curve (AUC) and by Brier score in both the derivation and validation cohorts. Seven variables were independent risk factors for in-hospital mortality: age (Hazard Ratio [HR] 1.08, 95% Confidence Interval [CI] 1.07–1.09), male sex (HR 1.62, 95%CI 1.30–2.00), duration of symptoms before hospital admission <10 days (HR 1.72, 95%CI 1.39–2.12), diabetes (HR 1.21, 95%CI 1.02–1.45), coronary heart disease (HR 1.40 95% CI 1.09–1.80), chronic liver disease (HR 1.78, 95%CI 1.16–2.72), and lactate dehydrogenase levels at admission (HR 1.0003, 95%CI 1.0002–1.0005). The AUC was 0.822 (95%CI 0.722–0.922) in the derivation cohort and 0.820 (95%CI 0.724–0.920) in the validation cohort with good calibration. The prediction rule is freely available as a web-app (COVID-CALC: https://sites.google.com/community.unipa.it/covid-19riskpredictions/c19-rp). Conclusions A validated simple clinical prediction rule can promptly and accurately assess the risk for in-hospital mortality, improving triage and the management of patients with COVID-19.

Chest CT and Clinical Features of COVID-19 Patients in the Early Stage of the Epidemic: An Observational Study from China

“…Fever [15,19], shortness of breath/dyspnea [19,20] and gastrointestinal symptoms seems to be important risk factors for severity of COVID-19 [21]. What's more, some comorbidities, such as Hypertension, Diabetes, obesity, Metabolic Syndrome, COPD and so on, may increase severe outcome [22][23][24][25][26][27][28][29][30][31]. The Charlson Comorbidity index (CCI) score has been identi ed in studies as a prognostic factor for COVID-19-related death [32].…”

Limitations of Only Reporting the Odds Ratio in the Age of Precision Medicine: A Deterministic Simulation Study

“…To demonstrate the negative impact (with respect to predictive modeling) of collecting naturally continuous features as dichotomous, we included a brief study using real world COVID-19 data from a recent publication (18). The training set included 1,810 patients from Bergamo and Pavia; the test set had 381 patients from Rome.…”

Derivation and Validation of a Clinical Score to Predict Death Among Non-Palliative Covid-19 Patients Presenting to Emergency Departments: The Ccedrrn Covid Mortality Score

“…In contrast to prior models, we excluded patients with palliative goals of care, for whom invasive mechanical ventilation was not offered to ensure our model did not predict patients who were expected to succumb, or ineligible for the highest level of critical care. (9)(10)(11)13,14,(20)(21)(22)(23)(24) This avoids the potential for self-fulfilling prophecy bias, whereby the prognostic model predicts the outcome that occurred as a result of a decision to withhold life-sustaining measures. (25)(26)(27) Prior models were derived or validated during the early pandemic while COVID-19 testing was restricted to those with severe disease, and did not include consecutive eligible patients, both of which may have resulted in selection bias.…”

“…Two other rules also identified liver disease as a mortality risk factor, perhaps due to the potential for virus-induced liver inflammation. (14,20,28) Other prognostic decision rules have used similar analytic approaches, (20,22,23) but had lower predictive performance with c-statistics ranging from 0.80 to 0.82, and were based on patients from the early pandemic. Other rules have incorporated measures of hypoxemia or respiratory support, corroborating their strong predictive power.…”

“…Most other published rules use imaging or laboratory tests, which precludes their use as a first-line triage tool in the ED where decisions on the appropriateness of intubation and mechanical ventilation may have to be made on arrival. (9)(10)(11)(12)(13)(14) Our objective was to develop and validate a clinical score that accurately predicts mortality among non-palliative COVID-19 patients, using clinical variables that are readily available on ED arrival.…”