Observational studyCritical care (London, England)2018
Multicenter derivation and validation of an early warning score for acute respiratory failure or death in the hospital.
Observational study in Critical care (London, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 36 papers, 5 of them syntheses 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.
Prevention of Severe Acute Respiratory Failure in Patients With PROOFcheck - an Electronic Checklist to Prevent Organ Failure
Investigating Incidental Pulmonary Nodules in Underserved Communities
Who cites it
36 citing papers in PubMed, 5 syntheses or guidelines pooled it, 65 citations in OpenAlex.
- Factors underpinning the performance of implemented artificial intelligence-based patient deterioration prediction systems: reasons for selection and implications for hospitals and researchers.Journal of the American Medical Informatics Association : JAMIA · 2025Pooled it
- Systematic review and longitudinal analysis of implementing Artificial Intelligence to predict clinical deterioration in adult hospitals: what is known and what remains uncertain.Journal of the American Medical Informatics Association : JAMIA · 2024Pooled it
- Early warning score validation methodologies and performance metrics: a systematic review.BMC medical informatics and decision making · 2020Pooled it
- Early warning scores for detecting deterioration in adult hospital patients: systematic review and critical appraisal of methodology.BMJ (Clinical research ed.) · 2020Pooled it
- Development and validation of early warning score system: A systematic literature review.Journal of biomedical informatics · 2020Pooled it
- Machine learning time-to-event algorithms for predicting the duration of ventilation after cardiac surgery.JTCVS open · 2026Article
- Observational
- Can we predict the future of respiratory failure prediction?Critical care (London, England) · 2025Review
- Improving acute care outcome in internal medicine: the role of early stabilization and intermediate care unit.Internal and emergency medicine · 2025Observational
- Artificial Intelligence-Based Early Prediction of Acute Respiratory Failure in the Emergency Department Using Biosignal and Clinical Data.Yonsei medical journal · 2025Article
- Early hypoxia prediction in diseased patients via wheezing sounds in respiration: a prospective cohort study.Frontiers in medicine · 2025Article
- Blood urea nitrogen-to-albumin ratio independently predicts 30-day mortality in acute respiratory failure patients: a retrospective cohort study.Journal of thoracic disease · 2024Article
- Early Warning Systems for Critical Illness Outside the Intensive Care Unit.Critical care clinics · 2024Review
- Nonlinear relationship between platelet count and 30-day in-hospital mortality in ICU acute respiratory failure patients: a multicenter retrospective cohort study.European journal of medical research · 2024Article
- Development and validation of a prospective study to predict the risk of readmission within 365 days of respiratory failure: based on a random survival forest algorithm combined with COX regression modeling.BMC pulmonary medicine · 2024Article
- Development and Validation of a Machine Learning Algorithm Using Clinical Pages to Predict Imminent Clinical Deterioration.Journal of general internal medicine · 2024Observational
- Development of an automated, general-purpose prediction tool for postoperative respiratory failure using machine learning: A retrospective cohort study.Journal of clinical anesthesia · 2023Article
- Development of a dynamic prediction model for unplanned ICU admission and mortality in hospitalized patients.PLOS digital health · 2023Article
- Development and validation of nomogram for unplanned ICU admission in patients with dilated cardiomyopathy.Frontiers in cardiovascular medicine · 2023Article
- A new index, Respiratory Insufficiency index and Modified Early Warning Scores predict extubation failure.Canadian journal of respiratory therapy : CJRT = Revue canadienne de la therapie respiratoire : RCTR · 2023Article
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 at 4 institutions in 2 countries.
Funding
Abstract
backgroundAcute respiratory failure occurs frequently in hospitalized patients and often starts before ICU admission. A risk stratification tool to predict mortality and risk for mechanical ventilation (MV) may allow for earlier evaluation and intervention. We developed and validated an automated electronic health record (EHR)-based model-Accurate Prediction of Prolonged Ventilation (APPROVE)-to identify patients at risk of death or respiratory failure requiring >= 48 h of MV.
methodsThis was an observational study of adults admitted to four hospitals in 2013 or a fifth hospital in 2017. Clinical data were extracted from the EHRs. The 2013 patients were randomly split 50:50 into a derivation/validation cohort. The qualifying event was death or intubation leading to MV >= 48 h. Random forest method was used in model derivation. APPROVE was calculated retrospectively whenever data were available in 2013, and prospectively every 4 h after hospital admission in 2017. The Modified Early Warning Score (MEWS) and National Early Warning Score (NEWS) were calculated at the same times as APPROVE. Clinicians were not alerted except for APPROVE in 2017cohort.
resultsThere were 68,775 admissions in 2013 and 2258 in 2017. APPROVE had an area under the receiver operator curve of 0.87 (95% CI 0.85-0.88) in 2013 and 0.90 (95% CI 0.84-0.95) in 2017, which is significantly better than the MEWS and NEWS in 2013 but similar to the MEWS and NEWS in 2017. At a threshold of > 0.25, APPROVE had similar sensitivity and positive predictive value (PPV) (sensitivity 63% and PPV 21% in 2013 vs 64% and 16%, respectively, in 2017). Compared to APPROVE in 2013, at a threshold to achieve comparable PPV (19% at MEWS > 4 and 22% at NEWS > 6), the MEWS and NEWS had lower sensitivity (16% for MEWS and NEWS). Similarly in 2017, at a comparable sensitivity threshold (64% for APPROVE > 0.25 and 67% for MEWS and NEWS > 4), more patients who triggered an alert developed the event with APPROVE (PPV 16%) while achieving a lower false positive rate (FPR 5%) compared to the MEWS (PPV 7%, FPR 14%) and NEWS (PPV 4%, FPR 25%).
conclusionsAn automated EHR model to identify patients at high risk of MV or death was validated retrospectively and prospectively, and was determined to be feasible for real-time risk identification.
trial registrationClinicalTrials.gov, NCT02488174 . Registered on 18 March 2015.
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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.