ArticleThe Lancet. Digital health2020
Clinical features of COVID-19 mortality: development and validation of a clinical prediction model.
Article in The Lancet. Digital health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 151 papers, 1 of them a synthesis that pooled it.
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
151 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Comprehensive Review of Artificial Intelligence in Prevention and Treatment of COVID-19 Pandemic.Frontiers in genetics · 2022Pooled it
- Machine Learning-Based Prediction of Brain Metastasis at Initial Diagnosis in Small-Cell Lung Cancer: Model Development and SHAP Interpretation Study.Cancer reports (Hoboken, N.J.) · 2026Article
- Artificial intelligence-based algorithm for predicting outcomes in early-stage lung cancer: An annotation-free imaging artificial intelligence study.JTCVS open · 2026Article
- Viral associations in multiple sclerosis: pathogenetic mechanisms and therapeutic implications.Virology journal · 2026Review
- Evaluating Biomedical Feature Fusion on Machine Learning's Predictability and Interpretability of COVID-19 Severity Types: Model Development, Interpretation, and Validation.JMIR formative research · 2026Article
- Practical adaptability of a pre-hospital prognostic prediction model for patients following out-of-hospital cardiac arrest during the COVID-19 pandemic.BMJ health & care informatics · 2026Article
- Data mining approach to predicting of death in patients with COVID-19.Journal of education and health promotion · 2026Article
- Development and validation of a clinical wearable deep learning based continuous inhospital deterioration prediction model.Nature communications · 2025Article
- Weekend Effect and Predictors of Mortality for Patients Presenting to Emergency Department with COVID-19 Infection.Journal of personalized medicine · 2025Article
- AI-derived CT biomarker score for robust COVID-19 mortality prediction across multiple waves and regions using machine learning.Scientific reports · 2025Article
- A machine learning-based risk prediction model for diabetic oral ulceration.BMC oral health · 2025Article
- Early Detection of Elevated Ketone Bodies in Type 1 Diabetes Using Insulin and Glucose Dynamics Across Age Groups: Model Development Study.JMIR diabetes · 2025Article
- Predicting coronavirus disease 2019 severity using explainable artificial intelligence techniques.Scientific reports · 2025Article
- Lymphocyte Inhibition Mechanisms and Immune Checkpoints in COVID-19: Insights into Prognostic Markers and Disease Severity.Medicina (Kaunas, Lithuania) · 2025Observational
- Comparative performance of twelve machine learning models in predicting COVID-19 mortality risk in children: a population-based retrospective cohort study in Brazil.PeerJ. Computer science · 2025Article
- Deployable machine learning-based decision support system for tracheostomy in acute burn patients.Burns & trauma · 2025Article
- Machine learning-based prediction model for brain metastasis in patients with extensive-stage small cell lung cancer.Scientific reports · 2024Article
- Prospective and External Validation of Machine Learning Models for Short- and Long-Term Mortality in Acutely Admitted Patients Using Blood Tests.Journal of clinical medicine · 2024Article
- Innovation through Artificial Intelligence in Triage Systems for Resource Optimization in Future Pandemics.Biomimetics (Basel, Switzerland) · 2024Article
- Neuroimaging and natural language processing-based classification of suicidal thoughts in major depressive disorder.Translational psychiatry · 2024Article
91 more citing papers are in PubMed but not listed here.
Corrections and comments
- Commented on by
- Update of
Authors and funding
6 authors.
Funding
Abstract
Background: The COVID-19 pandemic has affected millions of individuals and caused hundreds of thousands of deaths worldwide. Predicting mortality among patients with COVID-19 who present with a spectrum of complications is very difficult, hindering the prognostication and management of the disease. We aimed to develop an accurate prediction model of COVID-19 mortality using unbiased computational methods, and identify the clinical features most predictive of this outcome. Methods: In this prediction model development and validation study, we applied machine learning techniques to clinical data from a large cohort of patients with COVID-19 treated at the Mount Sinai Health System in New York City, NY, USA, to predict mortality. We analysed patient-level data captured in the Mount Sinai Data Warehouse database for individuals with a confirmed diagnosis of COVID-19 who had a health system encounter between March 9 and April 6, 2020. For initial analyses, we used patient data from March 9 to April 5, and randomly assigned (80:20) the patients to the development dataset or test dataset 1 (retrospective). Patient data for those with encounters on April 6, 2020, were used in test dataset 2 (prospective). We designed prediction models based on clinical features and patient characteristics during health system encounters to predict mortality using the development dataset. We assessed the resultant models in terms of the area under the receiver operating characteristic curve (AUC) score in the test datasets. Findings: Using the development dataset (n=3841) and a systematic machine learning framework, we developed a COVID-19 mortality prediction model that showed high accuracy (AUC=0·91) when applied to test datasets of retrospective (n=961) and prospective (n=249) patients. This model was based on three clinical features: patient's age, minimum oxygen saturation over the course of their medical encounter, and type of patient encounter (inpatient Interpretation: An accurate and parsimonious COVID-19 mortality prediction model based on three features might have utility in clinical settings to guide the management and prognostication of patients affected by this disease. External validation of this prediction model in other populations is needed. Funding: National Institutes of Health.
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.