Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19

Machine learning (ML) models require large datasets which may be siloed across different healthcare institutions. Using federated learning, a ML technique that avoids locally aggregating raw clinical data across multiple institutions, we predict mortality within seven days in hospitalized COVID-19 patients. Patient data was collected from Electronic Health Records (EHRs) from five hospitals within the Mount Sinai Health System (MSHS). Logistic Regression with L1 regularization (LASSO) and Multilayer Perceptron (MLP) models were trained using local data at each site, a pooled model with combined data from all five sites, and a federated model that only shared parameters with a central aggregator. Both the federated LASSO and federated MLP models performed better than their local model counterparts at four hospitals. The federated MLP model also outperformed the federated LASSO model at all hospitals. Federated learning shows promise in COVID-19 EHR data to develop robust predictive models without compromising patient privacy.

Characterization of Patients Who Return to Hospital Following Discharge from Hospitalization For COVID-19

BackgroundData on patients with coronavirus disease 2019 (COVID-19) who return to hospital after discharge are scarce. Characterization of these patients may inform post-hospitalization care. Methods and FindingsRetrospective cohort study of patients with confirmed SARS-CoV-2 discharged alive from five hospitals in New York City with index hospitalization between February 27th-April 12th, 2020, with follow-up of [≥]14 days. Significance was defined as P<0.05 after multiplying P by 125 study-wide comparisons. Of 2,864 discharged patients, 103 (3.6%) returned for emergency care after a median of 4.5 days, with 56 requiring inpatient readmission. The most common reason for return was respiratory distress (50%). Compared to patients who did not return, among those who returned there was a higher proportion of COPD (6.8% vs 2.9%) and hypertension (36% vs 22.1%). Patients who returned also had a shorter median length of stay (LOS) during index hospitalization (4.5 [2.9,9.1] vs. 6.7 [3.5, 11.5] days; Padjusted=0.006), and were less likely to have required intensive care on index hospitalization (5.8% vs 19%; Padjusted=0.001). A trend towards association between absence of in-hospital anticoagulation on index admission and return to hospital was also observed (20.9% vs 30.9%, Padjusted=0.064). On readmission, rates of intensive care and death were 5.8% and 3.6%, respectively. ConclusionsReturn to hospital after admission for COVID-19 was infrequent within 14 days of discharge. The most common cause for return was respiratory distress. Patients who returned had higher proportion of COPD and hypertension with shorter LOS on index hospitalization, and a trend towards lower rates of in-hospital treatment-dose anticoagulation. Future studies should focus on whether these comorbid conditions, longer LOS and anticoagulation are associated with reduced readmissions.

Acute Kidney Injury in Hospitalized Patients with COVID-19

ImportancePreliminary reports indicate that acute kidney injury (AKI) is common in coronavirus disease (COVID)-19 patients and is associated with worse outcomes. AKI in hospitalized COVID-19 patients in the United States is not well-described. ObjectiveTo provide information about frequency, outcomes and recovery associated with AKI and dialysis in hospitalized COVID-19 patients. DesignObservational, retrospective study. SettingAdmitted to hospital between February 27 and April 15, 2020. ParticipantsPatients aged [≥]18 years with laboratory confirmed COVID-19 ExposuresAKI (peak serum creatinine increase of 0.3 mg/dL or 50% above baseline). Main Outcomes and MeasuresFrequency of AKI and dialysis requirement, AKI recovery, and adjusted odds ratios (aOR) with mortality. We also trained and tested a machine learning model for predicting dialysis requirement with independent validation. ResultsA total of 3,235 hospitalized patients were diagnosed with COVID-19. AKI occurred in 1406 (46%) patients overall and 280 (20%) with AKI required renal replacement therapy. The incidence of AKI (admission plus new cases) in patients admitted to the intensive care unit was 68% (553 of 815). In the entire cohort, the proportion with stages 1, 2, and 3 AKI were 35%, 20%, 45%, respectively. In those needing intensive care, the respective proportions were 20%, 17%, 63%, and 34% received acute renal replacement therapy. Independent predictors of severe AKI were chronic kidney disease, systolic blood pressure, and potassium at baseline. In-hospital mortality in patients with AKI was 41% overall and 52% in intensive care. The aOR for mortality associated with AKI was 9.6 (95% CI 7.4-12.3) overall and 20.9 (95% CI 11.7-37.3) in patients receiving intensive care. 56% of patients with AKI who were discharged alive recovered kidney function back to baseline. The area under the curve (AUC) for the machine learned predictive model using baseline features for dialysis requirement was 0.79 in a validation test. Conclusions and RelevanceAKI is common in patients hospitalized with COVID-19, associated with worse mortality, and the majority of patients that survive do not recover kidney function. A machine-learned model using admission features had good performance for dialysis prediction and could be used for resource allocation. Key PointsO_ST_ABSQuestionC_ST_ABSWhat is incidence and outcomes of acute kidney injury (AKI) in patients hospitalized with COVID-19? FindingsIn this observational study of 3,235 hospitalized patients with COVID-19 in New York City, AKI occurred in 46% of patients and 20% of those patients required dialysis. AKI was associated with increased mortality. 44% of patients discharged alive had residual acute kidney disease. A machine learned predictive model using baseline features for dialysis requirement had an AUC Of 0.79. MeaningAKI was common in patients with COVID-19, associated with increased mortality, and nearly half of patients had acute kidney disease on discharge.

Machine Learning to Predict Mortality and Critical Events in COVID-19 Positive New York City Patients

Coronavirus 2019 (COVID-19), caused by the SARS-CoV-2 virus, has become the deadliest pandemic in modern history, reaching nearly every country worldwide and overwhelming healthcare institutions. As of April 20, there have been more than 2.4 million confirmed cases with over 160,000 deaths. Extreme case surges coupled with challenges in forecasting the clinical course of affected patients have necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods for achieving this are lacking. In this paper, we use electronic health records from over 3,055 New York City confirmed COVID-19 positive patients across five hospitals in the Mount Sinai Health System and present a decision tree-based machine learning model for predicting in-hospital mortality and critical events. This model is first trained on patients from a single hospital and then externally validated on patients from four other hospitals. We achieve strong performance, notably predicting mortality at 1 week with an AUC-ROC of 0.84. Finally, we establish model interpretability by calculating SHAP scores to identify decisive features, including age, inflammatory markers (procalcitonin and LDH), and coagulation parameters (PT, PTT, D-Dimer). To our knowledge, this is one of the first models with external validation to both predict outcomes in COVID-19 patients with strong validation performance and identify key contributors in outcome prediction that may assist clinicians in making effective patient management decisions. One-Sentence SummaryWe identify clinical features that robustly predict mortality and critical events in a large cohort of COVID-19 positive patients in New York City.

Clinical Characteristics of Hospitalized Covid-19 Patients in New York City

BackgroundThe coronavirus 2019 (Covid-19) pandemic is a global public health crisis, with over 1.6 million cases and 95,000 deaths worldwide. Data are needed regarding the clinical course of hospitalized patients, particularly in the United States. MethodsDemographic, clinical, and outcomes data for patients admitted to five Mount Sinai Health System hospitals with confirmed Covid-19 between February 27 and April 2, 2020 were identified through institutional electronic health records. We conducted a descriptive study of patients who had in-hospital mortality or were discharged alive. ResultsA total of 2,199 patients with Covid-19 were hospitalized during the study period. As of April 2nd, 1,121 (51%) patients remained hospitalized, and 1,078 (49%) completed their hospital course. Of the latter, the overall mortality was 29%, and 36% required intensive care. The median age was 65 years overall and 75 years in those who died. Pre-existing conditions were present in 65% of those who died and 46% of those discharged. In those who died, the admission median lymphocyte percentage was 11.7%, D-dimer was 2.4 ug/ml, C-reactive protein was 162 mg/L, and procalcitonin was 0.44 ng/mL. In those discharged, the admission median lymphocyte percentage was 16.6%, D-dimer was 0.93 ug/ml, C-reactive protein was 79 mg/L, and procalcitonin was 0.09 ng/mL. ConclusionsThis is the largest and most diverse case series of hospitalized patients with Covid-19 in the United States to date. Requirement of intensive care and mortality were high. Patients who died typically had pre-existing conditions and severe perturbations in inflammatory markers.