ReviewBMC medical informatics and decision making2021
Predicting clinical outcomes among hospitalized COVID-19 patients using both local and published models.
Review in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Lithium in the time of COVID: forever vigilant.International journal of bipolar disorders · 2024Article
- Identification and Prediction of Clinical Phenotypes in Hospitalized Patients With COVID-19: Machine Learning From Medical Records.JMIR formative research · 2023Article
- Evaluation of a fast-and-frugal clinical decision algorithm ('pathways') on clinical outcomes in hospitalised patients with COVID-19 treated with anticoagulants.Journal of evaluation in clinical practice · 2023Observational
- Correlates of Coronavirus Disease 2019 Inpatient Mortality at a Southern California Community Hospital With a Predominantly Hispanic/Latino Adult Population.Open forum infectious diseases · 2023Article
- Lessons learned: Development of COVID-19 clinical staging models at a large urban research institution.Journal of clinical and translational science · 2023Article
- Biomarkers extracted by fully automated body composition analysis from chest CT correlate with SARS-CoV-2 outcome severity.Scientific reports · 2022Article
- A process mining- deep learning approach to predict survival in a cohort of hospitalized COVID-19 patients.BMC medical informatics and decision making · 2022Article
- A Multimodal Approach for the Risk Prediction of Intensive Care and Mortality in Patients with COVID-19.Diagnostics (Basel, Switzerland) · 2021Article
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Authors and funding
9 authors.
Funding
Abstract
backgroundMany models are published which predict outcomes in hospitalized COVID-19 patients. The generalizability of many is unknown. We evaluated the performance of selected models from the literature and our own models to predict outcomes in patients at our institution.
methodsWe searched the literature for models predicting outcomes in inpatients with COVID-19. We produced models of mortality or criticality (mortality or ICU admission) in a development cohort. We tested external models which provided sufficient information and our models using a test cohort of our most recent patients. The performance of models was compared using the area under the receiver operator curve (AUC).
resultsOur literature review yielded 41 papers. Of those, 8 were found to have sufficient documentation and concordance with features available in our cohort to implement in our test cohort. All models were from Chinese patients. One model predicted criticality and seven mortality. Tested against the test cohort, internal models had an AUC of 0.84 (0.74-0.94) for mortality and 0.83 (0.76-0.90) for criticality. The best external model had an AUC of 0.89 (0.82-0.96) using three variables, another an AUC of 0.84 (0.78-0.91) using ten variables. AUC's ranged from 0.68 to 0.89. On average, models tested were unable to produce predictions in 27% of patients due to missing lab data.
conclusionDespite differences in pandemic timeline, race, and socio-cultural healthcare context some models derived in China performed well. For healthcare organizations considering implementation of an external model, concordance between the features used in the model and features available in their own patients may be important. Analysis of both local and external models should be done to help decide on what prediction method is used to provide clinical decision support to clinicians treating COVID-19 patients as well as what lab tests should be included in order sets.
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