Background:
Predicting outcomes of COVID-19 patients at an early stage is critical for optimized clinical care and resource management, especially during a pandemic. Although multiple machine learning models have been proposed to address this issue, based on the need for extensive data pre-processing and feature engineering, these models have not been validated or implemented outside of the original study site.
Methods:
In this study, we propose CovRNN, recurrent neural network (RNN)-based models to predict COVID-19 patients' outcomes, using their available electronic health record (EHR) data on admission, without the need for specific feature selection or missing data imputation. CovRNN is designed to predict three outcomes: in-hospital mortality, need for mechanical ventilation, and long length of stay (LOS >7 days). Predictions are made for time-to-event risk scores (survival prediction) and all-time risk scores (binary prediction). Our models were trained and validated using heterogeneous and de-identified data of 247,960 COVID-19 patients from 87 healthcare systems, derived from the Cerner® Real-World Dataset (CRWD). External validation was performed using three test sets (approximately 53,000 patients). Further, the transferability of CovRNN was validated using 36,140 de-identified patients' data derived from the Optum® de-identified COVID-19 Electronic Health Record v.1015 dataset (2007-2020).
Findings:
CovRNN shows higher performance than do traditional models. It achieved an area under the receiving operating characteristic (AUROC) of 93% for mortality and mechanical ventilation predictions on the CRWD test set (vs. 91.5% and 90% for light gradient boost machine (LGBM) and logistic regression (LR), respectively) and 86.5% for prediction of LOS > 7 days (vs. 81.7% and 80% for LGBM and LR, respectively). For survival prediction, CovRNN achieved a C-index of 86% for mortality and 92.6% for mechanical ventilation. External validation confirmed AUROCs in similar ranges.
Interpretation:
Trained on a large heterogeneous real-world dataset, our CovRNN model showed high prediction accuracy, good calibration, and transferability through consistently good performance on multiple external datasets. Our results demonstrate the feasibility of a COVID-19 predictive model that delivers high accuracy without the need for complex feature engineering.
Background
: Studies have shown conflicting results on the efficacy of tocilizumab (TCZ) for patients with COVID-19, with many confounders of clinical status and limited duration of the observation. Here, we evaluate the real-world long-term efficacy of TCZ in COVID-19 patients.
Methods
: We conducted a retrospective study of hospitalized adult patients with COVID-19 using a large US-based multicenter COVID-19 database (Cerner Real-World Data; updated in September, 2020). The TCZ group was defined as patients who received at least one dose of the drug. Matching weight (MW) and a propensity score weighting method were used to balance confounding factors.
Results
: A total of 20,399 patients were identified. 1,510 and 18,899 were in the TCZ and control groups, respectively. After MW adjustment, no statistically significant differences in all-cause mortality were found for the TCZ vs. control group (Hazard Ratio [HR]:0.76, p=0.06). Survival curves suggested a better trend in short-term observation, driven from a subgroup of patients requiring oxygen masks, BIPAP or CPAP.
Conclusion:
We observed a temporal (early) benefit of TCZ, especially in patients on non-invasive high-flow supplemental oxygen. However, the benefit effects faded with longer observation. The long-term benefits and risks of TCZ should be carefully evaluated with follow-up studies.
INTRODUCTION:
Antithrombotic therapy is often interrupted before the placement of a percutaneous endoscopic gastrostomy (PEG) tube because of potentially increased risk of hemorrhagic events. The aim of our study was to evaluate the risk of bleeding events and overall complication rates after PEG in patients on uninterrupted antiplatelet and anticoagulation therapy in a high-volume center.
METHODS:
Data regarding demographics, diagnoses, comorbidities, and clinical outcomes pertinent to PEG were collected from 2010 to 2016. Furthermore, data regarding antithrombotic therapy along with the rate of minor or major complications including bleeding associated with this procedure were analyzed. Significant bleeding was defined as postprocedure bleeding from PEG site requiring a blood transfusion and/or surgical/endoscopic intervention.
RESULTS:
We included 1,613 consecutive PEG procedures in this study, of which 1,540 patients (95.5%) received some form of uninterrupted antithrombotic therapy. Of those patients, 535 (34.7%) were on aspirin, 256 (16.6%) on clopidogrel, and 119 (7.7%) on both aspirin and clopidogrel. Subcutaneous heparin was uninterrupted in 980 (63.6%), intravenous heparin in 34 (2.1%), warfarin in 168 (10.9%), and direct-acting oral anticoagulation in 82 (5.3%) patients who overlapped on multiple drugs. We observed 6 significant bleeding events in the entire cohort (0.39%), and all were in subcutaneous heparin groups either alone or in combination with aspirin. No clinically significant bleeding was noted in patients on uninterrupted aspirin, warfarin, clopidogrel, or direct-acting oral anticoagulation groups. Only 5 patients (0.31%) had PEG-related mortality.
DISCUSSION:
The risk of significant bleeding associated with the PEG placement was minimal in patients on uninterrupted periprocedural antithrombotic therapy.
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