ArticleAmerican journal of critical care : an official publication, American Association of Critical-Care Nurses2018
Advancing In-Hospital Clinical Deterioration Prediction Models.
Article in American journal of critical care : an official publication, American Association of Critical-Care Nurses, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning techniques for mortality prediction in emergency departments: a systematic review.BMJ open · 2021Pooled it
- Application of machine learning in predicting survival outcomes involving real-world data: a scoping review.BMC medical research methodology · 2023Article
- ECMO PAL: using deep neural networks for survival prediction in venoarterial extracorporeal membrane oxygenation.Intensive care medicine · 2023Article
- Nursing science as a federally-recognized STEM degree: A call to action for the United States with global implications.International journal of nursing studies advances · 2022Article
- Validation of an Automatic Tagging System for Identifying Respiratory and Hemodynamic Deterioration Events in the Intensive Care Unit.Healthcare informatics research · 2021Article
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Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundEarly warning systems lack robust evidence that they improve patients' outcomes, possibly because of their limitation of predicting binary rather than time-to-event outcomes.
objectivesTo compare the prediction accuracy of 2 statistical modeling strategies (logistic regression and Cox proportional hazards regression) and 2 machine learning strategies (random forest and random survival forest) for in-hospital cardiopulmonary arrest.
methodsRetrospective cohort study with prediction model development from deidentified electronic health records at an urban academic medical center.
resultsThe classification models (logistic regression and random forest) had statistical recall and precision similar to or greater than those of the time-to-event models (Cox proportional hazards regression and random survival forest). However, the time-to-event models provided predictions that could potentially better indicate to clinicians whether and when a patient is likely to experience cardiopulmonary arrest.
conclusionsAs early warning scoring systems are refined, they must use the best analytical methods that both model the underlying phenomenon and provide an understandable prediction.
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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.