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.

BackgroundThe Covid-19 pandemic has become a global public health crisis and providing optimal patient care while preventing a collapse of the health care system is a principal objective worldwide.ObjectiveTo develop and validate a prognostic model based on routine hematological parameters to predict uncomplicated disease progression to support the decision for an earlier discharge.DesignDevelopment and refinement of a multivariable logistic regression model with subsequent external validation. The time course of several hematological variables until four days after admission were used as predictors. Variables were first selected based on subject matter knowledge; their number was further reduced using likelihood ratio-based backward elimination in random bootstrap samples.SettingModel development based on three Austrian hospitals, validation cohorts from two Austrian and one Swedish hospital.ParticipantsModel development based on 363 survivors and 78 non-survivors of Covid-19 hospitalized in Austria. External validation based on 492 survivors and 61 non-survivors hospitalized in Austria and Sweden.OutcomeIn-hospital death.Main ResultsThe final model includes age, fever upon admission, parameters derived from C-reactive protein (CRP) concentration, platelet count and creatinine concentration, approximating their baseline values (CRP, creatinine) and change over time (CRP, platelet count). In Austrian validation cohorts both discrimination and calibration of this model were good, with c indices of 0.93 (95% CI 0.90 - 0.96) in a cohort from Vienna and 0.93 (0.88 - 0.98) in one from Linz. The model performance seems independent of how long symptoms persisted before admission. In a small Swedish validation cohort, the model performance was poorer (p = 0.008) compared with Austrian cohorts with a c index of 0.77 (0.67 - 0.88), potentially due to substantial differences in patient demographics and clinical routine.ConclusionsHere we describe a formula, requiring only variables routinely acquired in hospitals, which allows to estimate death probabilities of hospitalized patients with Covid-19. The model could be used as a decision support for earlier discharge of low-risk patients to reduce the burden on the health care system. The model could further be used to monitor whether patients should be admitted to hospital in countries with health care systems with emphasis on outpatient care (e.g. Sweden).

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Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has undergone different molecular changes, sprouting genetic variants of the original wildtype. Clinical comparisons between patients infected with alpha versus delta are scarce. Methods In this retrospective observational study, adult patients hospitalized with coronavirus disease 2019 (COVID-19) due to confirmed SARS-CoV‑2 alpha or delta infection were included. Patient characteristics, virologic and laboratory parameters, as well as the clinical course were compared in patients infected with alpha vs. delta variants. Results A total of 106 patients infected with alpha and 215 patients infected with delta were included. Patients infected with the delta variant were admitted to hospital earlier after symptom onset (6 vs. 7 days, p < 0.001). Blood levels of C‑reactive protein (43.3 vs. 62.9 mg/l, p = 0.02) and neutrophil count (3.81 vs. 4.53 G/l, p = 0.06) were lower in delta patients. Furthermore, at hospital admission cycle threshold (CT) values were significantly lower in patients infected with the delta variant (22.3 vs. 24.9, p < 0.001). Patients infected with the delta variant needed supplemental oxygen less often during disease course (50% vs. 64%, p = 0.02). Furthermore, there was a statistically non-significant trend towards a lower ICU admission rate among delta patients (16% vs. 24%, p = 0.08) Conclusion Patients diagnosed with the delta variant were admitted to the hospital earlier, had a less severe course of disease and a higher viral replication on admission. This may provide a window of opportunity for antivirals in the hospital setting.