ArticleJMIR medical informatics2021
Predicting Intensive Care Transfers and Other Unforeseen Events: Analytic Model Validation Study and Comparison to Existing Methods.
Article in JMIR medical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Post-Market Surveillance of Predictive Decision Support Tools in the Presence of Confounding Medical Interventions using Causal Inference.Research square · 2026Article
- Clinical profiles and predictors of intensive care unit transfer among children hospitalized with severe Plasmodium falciparum malaria in a tertiary care hospital in Libreville, Gabon: a retrospective study.BMC infectious diseases · 2026Article
- Prospective external validation of a deep-learning-based early-warning system for major adverse events in general wards in South Korea.Acute and critical care · 2025Article
- A deep learning model for clinical outcome prediction using longitudinal inpatient electronic health records.JAMIA open · 2025Article
- Beyond Unplanned ICU Transfers: Linking a Revised Definition of Deterioration to Patient Outcomes.Critical care medicine · 2024Article
- Prediction of episode of hemodynamic instability using an electrocardiogram based analytic: a retrospective cohort study.BMC anesthesiology · 2023Article
- Prospective, multicenter validation of the deep learning-based cardiac arrest risk management system for predicting in-hospital cardiac arrest or unplanned intensive care unit transfer in patients admitted to general wards.Critical care (London, England) · 2023Article
- Validation of a Proprietary Deterioration Index Model and Performance in Hospitalized Adults.JAMA network open · 2023Article
- The Ward or the Intensive Care Unit: Is It All Relative?Annals of the American Thoracic Society · 2023Article
- Risk Assessment of COVID-19 Cases in Emergency Departments and Clinics With the Use of Real-World Data and Artificial Intelligence: Observational Study.JMIR formative research · 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
- Detection of Hemodynamic Status Using an Analytic Based on an Electrocardiogram Lead Waveform.Critical care explorations · 2022Article
- Early identification of patients admitted to hospital for covid-19 at risk of clinical deterioration: model development and multisite external validation study.BMJ (Clinical research ed.) · 2022Article
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Abstract
backgroundCOVID-19 has led to an unprecedented strain on health care facilities across the United States. Accurately identifying patients at an increased risk of deterioration may help hospitals manage their resources while improving the quality of patient care. Here, we present the results of an analytical model, Predicting Intensive Care Transfers and Other Unforeseen Events (PICTURE), to identify patients at high risk for imminent intensive care unit transfer, respiratory failure, or death, with the intention to improve the prediction of deterioration due to COVID-19.
objectiveThis study aims to validate the PICTURE model's ability to predict unexpected deterioration in general ward and COVID-19 patients, and to compare its performance with the Epic Deterioration Index (EDI), an existing model that has recently been assessed for use in patients with COVID-19.
methodsThe PICTURE model was trained and validated on a cohort of hospitalized non-COVID-19 patients using electronic health record data from 2014 to 2018. It was then applied to two holdout test sets: non-COVID-19 patients from 2019 and patients testing positive for COVID-19 in 2020. PICTURE results were aligned to EDI and NEWS scores for head-to-head comparison via area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve. We compared the models' ability to predict an adverse event (defined as intensive care unit transfer, mechanical ventilation use, or death). Shapley values were used to provide explanations for PICTURE predictions.
resultsIn non-COVID-19 general ward patients, PICTURE achieved an AUROC of 0.819 (95% CI 0.805-0.834) per observation, compared to the EDI's AUROC of 0.763 (95% CI 0.746-0.781; n=21,740; P<.001). In patients testing positive for COVID-19, PICTURE again outperformed the EDI with an AUROC of 0.849 (95% CI 0.820-0.878) compared to the EDI's AUROC of 0.803 (95% CI 0.772-0.838; n=607; P<.001). The most important variables influencing PICTURE predictions in the COVID-19 cohort were a rapid respiratory rate, a high level of oxygen support, low oxygen saturation, and impaired mental status (Glasgow Coma Scale).
conclusionsThe PICTURE model is more accurate in predicting adverse patient outcomes for both general ward patients and COVID-19 positive patients in our cohorts compared to the EDI. The ability to consistently anticipate these events may be especially valuable when considering potential incipient waves of COVID-19 infections. The generalizability of the model will require testing in other health care systems for validation.
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