ArticleNature communications2021
Real-time prediction of COVID-19 related mortality using electronic health records.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
32 citing papers in PubMed.
- Evidence-Guided Multimodal Risk Prediction Framework for Severe COVID-19 Outcomes Using EHR and CT Imaging for COVID-19 Clinical Decision Support.Bioengineering (Basel, Switzerland) · 2026Article
- Exploring patient motivations and preferences for medical data sharing with researchers: a simulation study using the iAgree platform.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Assessing the comorbidity between asthma and depression through polygenic risk scoring and time-to-event models.BMC medicine · 2026Article
- Medical laboratory data-based models: opportunities, obstacles, and solutions.Journal of translational medicine · 2025Review
- Profiling short-term longitudinal severity progression and associated genes in COVID-19 patients using EHR and single-cell analysis.Scientific reports · 2025Article
- Disease progression associated cytokines in COVID-19 patients with deteriorating and recovering health conditions.Scientific reports · 2024Article
- Article
- In-hospital real-time prediction of COVID-19 severity regardless of disease phase using electronic health records.PloS one · 2024Article
- Developing deep learning-based strategies to predict the risk of hepatocellular carcinoma among patients with nonalcoholic fatty liver disease from electronic health records.medRxiv : the preprint server for health sciences · 2023Article
- Use of Natural Language Processing of Patient-Initiated Electronic Health Record Messages to Identify Patients With COVID-19 Infection.JAMA network open · 2023Article
- Patient and researcher stakeholder preferences for use of electronic health record data: a qualitative study to guide the design and development of a platform to honor patient preferences.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Article
- Peak Plasma Levels of mtDNA Serve as a Predictive Biomarker for COVID-19 in-Hospital Mortality.Journal of clinical medicine · 2022Article
- Predicting health crises from early warning signs in patient medical records.Scientific reports · 2022Article
- Article
- An ML prediction model based on clinical parameters and automated CT scan features for COVID-19 patients.Scientific reports · 2022Article
- Conditional generation of medical time series for extrapolation to underrepresented populations.PLOS digital health · 2022Article
- Preparing for the next pandemic via transfer learning from existing diseases with hierarchical multi-modal BERT: a study on COVID-19 outcome prediction.Scientific reports · 2022Article
- A Scalable Risk-Scoring System Based on Consumer-Grade Wearables for Inpatients With COVID-19: Statistical Analysis and Model Development.JMIR formative research · 2022Article
- Recurrent neural network models (CovRNN) for predicting outcomes of patients with COVID-19 on admission to hospital: model development and validation using electronic health record data.The Lancet. Digital health · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Coronavirus disease 2019 (COVID-19) is a respiratory disease with rapid human-to-human transmission caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Due to the exponential growth of infections, identifying patients with the highest mortality risk early is critical to enable effective intervention and prioritisation of care. Here, we present the COVID-19 early warning system (CovEWS), a risk scoring system for assessing COVID-19 related mortality risk that we developed using data amounting to a total of over 2863 years of observation time from a cohort of 66 430 patients seen at over 69 healthcare institutions. On an external cohort of 5005 patients, CovEWS predicts mortality from 78.8% (95% confidence interval [CI]: 76.0, 84.7%) to 69.4% (95% CI: 57.6, 75.2%) specificity at sensitivities greater than 95% between, respectively, 1 and 192 h prior to mortality events. CovEWS could enable earlier intervention, and may therefore help in preventing or mitigating COVID-19 related mortality.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.