Elevated plasma Complement Factor H Regulating Protein 5 is associated with venous thromboembolism and COVID-19 severity

Venous thromboembolism (VTE), comprising both deep vein thrombosis (DVT) and pulmonary embolism (PE) is a common, multi-causal disease with potentially serious short- and long-term complications. In clinical practice, there is a need for improved plasma biomarker-based tools for VTE diagnosis and risk prediction. We used multiplex proteomics profiling to screen plasma from patients with suspected acute VTE, and a case-control study of patients followed up after ending anticoagulant treatment for a first VTE. With replication in 5 independent studies, together totalling 1137 patients and 1272 controls, we identify Complement Factor H Related Protein (CFHR5), a regulator of the alternative pathway of complement activation, as a novel VTE associated plasma biomarker. Using GWAS analysis of 2967 individuals we identified a genome-wide significant pQTL signal on chr1q31.3 associated with CFHR5 levels. We showed that higher CFHR5 levels are associated with increased thrombin generation in patient plasma and that recombinant CFHR5 enhances platelet activation in vitro. Thrombotic complications are a frequent feature of COVID-19; in hospitalised patients we found CFHR5 levels at baseline were associated with short-time prognosis of disease severity, defined as maximum level of respiratory support needed during hospital stay. Our results indicate a clinically important role for regulation of the alternative pathway of complement activation in the pathogenesis of VTE and pulmonary complications in acute COVID-19. Thus, CFHR5 is a potential diagnostic and/or risk predictive plasma biomarker reflecting underlying pathology in VTE and acute COVID-19.

Development and external validation of a logistic regression derived formula based on repeated routine hematological measurements predicting survival of hospitalized Covid-19 patients

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).