Artificial Intelligence for the Prediction of In-Hospital Clinical Deterioration: A Systematic Review

Abstract

This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. OBJECTIVES:To analyze the available literature on the performance of artificial intelligence-generated clinical models for the prediction of serious life-threatening events in non-ICU adult patients and evaluate their potential clinical usage. DATA SOURCES:The PubMed database was searched for relevant articles in English literature from January 1, 2000, to January 23, 2022. Search terms, including artificial intelligence, machine learning, deep learning, and deterioration, were both controlled terms and free-text terms. STUDY SELECTION:We performed a systematic search reporting studies that showed performance of artificial intelligence-based models with outcome mortality and clinical deterioration. DATA EXTRACTION:Two review authors independently performed study selection and data extraction. Studies with the same outcome were grouped, namely mortality and various forms of deterioration (including ICU admission, adverse events, and cardiac arrests). Meta-analysis was planned in case sufficient data would be extracted from each study and no considerable heterogeneity between studies was present. DATA SYNTHESIS:In total, 45 articles were included for analysis, in which multiple methods of artificial intelligence were used. Twenty-four articles described models for the prediction of mortality and 21 for clinical deterioration. Due to heterogeneity of study characteristics (patient cohort, outcomes, and prediction models), meta-analysis could not be performed. The main reported measure of performance was the area under the receiver operating characteristic (AUROC) (n = 38), of which 33 (87%) had an AUROC greater than 0.8. The highest reported performance in a model predicting mortality had an AUROC of 0.935 and an area under the precision-recall curve of 0.96. CONCLUSIONS:Currently, a growing number of studies develop and analyzes artificial intelligence-based prediction models to predict critical illness and deterioration. We show that artificial intelligence-based prediction models have an overall good performance in predicting deterioration of patients. However, external validation of existing models and its performance in a clinical setting is highly recommended.

Development of a population‐level prediction model for intensive care unit (ICU) survivorship and mortality in older adults: A population‐based cohort study

“…In the process of development of the risk scores, we also identified the variables associated with increased risk of mortality. These predictors included older age, diagnoses of COPD, CHF, and laboratory abnormalities in multiple organ systems (such as liver, kidney, and blood) 13,14 . By their design, however, prior studies relied on in‐hospital or in‐ICU clinical data, whereas our study includes variables that may be predictive of mortality even when measured in the outpatient, pre‐ICU phases of care.…”

“…12 There have been recent advancements in the field of risk prediction models using EHR data to predict ICU mortality or identify community dwelling adults at risk for critical illness. [13][14][15][16][17][18][19][20][21] However, many of these prediction tools include mortality within their definition of critical illness with limited discriminant ability to identify patients who will be ICU survivors. 14,15,[22][23][24] Predictive risk scores that can discriminate among these outcomes hold potential to improve current care pathways in two ways: (a) early identification of older adults in a community or health system at highest risk for ICU admission allowing recruitment and follow up in cohort studies; and, (b) development of novel health services programs and infrastructure by critical care stakeholders to advance the care for populations at risk for future critical illness and PICS.…”

“…[13][14][15][16][17][18][19][20][21] However, many of these prediction tools include mortality within their definition of critical illness with limited discriminant ability to identify patients who will be ICU survivors. 14,15,[22][23][24] Predictive risk scores that can discriminate among these outcomes hold potential to improve current care pathways in two ways: (a) early identification of older adults in a community or health system at highest risk for ICU admission allowing recruitment and follow up in cohort studies; and, (b) development of novel health services programs and infrastructure by critical care stakeholders to advance the care for populations at risk for future critical illness and PICS. With the above goals in mind, we conducted this study using EHR data from a large cohort of older adults seen in the primary care setting to develop and validate a multivariable and scalable risk prediction tool for ICU survivorship and mortality.…”

“…Electronic health record (EHR) data offers the possibility of developing predictive, scalable, and generalizable models for prognostication 12 . There have been recent advancements in the field of risk prediction models using EHR data to predict ICU mortality or identify community dwelling adults at risk for critical illness 13–21 . However, many of these prediction tools include mortality within their definition of critical illness with limited discriminant ability to identify patients who will be ICU survivors 14,15,22–24 .…”

Evaluating the Quality of Postpartum Hemorrhage Nursing Care Plans Generated by Artificial Intelligence Models

“…20,21 Among medical AI models (eg, MedQA, MedicalGPT, Med-PaLM), Med-PaLM stands out as the AI model demonstrating the best clinical knowledge performance. 21 The potential applications of AI in various nursing domains such as education 22,23 and nursing care 6,7,24 are currently under intensive research. The health care sector is a transformational industry that requires the evaluation of the best methods for integrating Al.…”