A Model Predicting Mortality of Hospitalized Covid-19 Patients Four Days After Admission: Development, Internal and Temporal-External Validation

Abstract

ObjectiveTo develop and validate a prognostic model for in-hospital mortality after four days based on age, fever at admission and five haematological parameters routinely measured in hospitalized Covid-19 patients during the first four days after admission.MethodsHaematological parameters measured during the first 4 days after admission were subjected to a linear mixed model to obtain patient-specific intercepts and slopes for each parameter. A prediction model was built using logistic regression with variable selection and shrinkage factor estimation supported by bootstrapping. Model development was based on 481 survivors and 97 non-survivors, hospitalized before the occurrence of mutations. Internal validation was done by 10-fold cross-validation. The model was temporally-externally validated in 299 survivors and 42 non-survivors hospitalized when the Alpha variant (B.1.1.7) was prevalent.ResultsThe final model included age, fever on admission as well as the slope or intercept of lactate dehydrogenase, platelet count, C-reactive protein, and creatinine. Tenfold cross validation resulted in a mean area under the receiver operating characteristic curve (AUROC) of 0.92, a mean calibration slope of 1.0023 and a Brier score of 0.076. At temporal-external validation, application of the previously developed model showed an AUROC of 0.88, a calibration slope of 0.95 and a Brier score of 0.073. Regarding the relative importance of the variables, the (apparent) variation in mortality explained by the six variables deduced from the haematological parameters measured during the first four days is higher (explained variation 0.295) than that of age (0.210).ConclusionsThe presented model requires only variables routinely acquired in hospitals, which allows immediate and wide-spread use as a decision support for earlier discharge of low-risk patients to reduce the burden on the health care system.Clinical Trial RegistrationAustrian Coronavirus Adaptive Clinical Trial (ACOVACT); ClinicalTrials.gov, identifier NCT04351724.

Assess and validate predictive performance of models for in-hospital mortality in COVID-19 patients: A retrospective cohort study in the Netherlands comparing the value of registry data with high-granular electronic health records

“…This might be a result from dependences and correlations that are specific for our set of predictors. Various prognostic models of mortality among patients with COVID-19 have been proposed [1] , [18] , [25] , [26] , [27] , [28] , [29] . Their predictive performance varied from fair (AUROC 0.7-0.8) to excellent (AUROC > 0.9).…”

“…Their predictive performance varied from fair (AUROC 0.7-0.8) to excellent (AUROC > 0.9). However, many studies showed high risk of bias [1] and only few temporally validate the models [27] , [28] , [29] . Our performance in the temporal validation is in line with another externally-validated model developed on EHR data [30] , as well as other studies that take into account readily availability data [31] , [32] .…”

COVID-19 hospitalizations and patients' age at admission: The neglected importance of data variability for containment policies

“…This information was not considered in our analysis because it was incomplete and underreported, especially during the first phase of the pandemic. However, the literature has extensively demonstrated that patient age is the leading predictor of the most important outcomes of COVID-19 ( 48 ).…”

External Validation of Mortality Scores among High-Risk COVID-19 Patients: A Romanian Retrospective Study in the First Pandemic Year

“…A systematic review released in July 2020 found many published prognostic scores estimating the mortality risk in COVID-19 patients, with a high or unclear risk of bias, of which the 4CM Score was considered promising [ 5 ]. Other scores were recently proposed [ 6 , 7 , 8 , 9 ]. The worldwide applicability of these predictive scores remains an open question because healthcare systems and patient profiles differ between countries, and may impact the scores’ performance [ 10 , 11 , 12 , 13 , 14 , 15 ].…”