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August 5, 2024Journal of Translational Medicine44 citationsOpen Access

Predictive ability of hypotension prediction index and machine learning methods in intraoperative hypotension: a systematic review and meta-analysis

IMIda MohammadiSFShahryar Rajai FirouzabadiMHMelika Hosseinpour

Structured PICO

Do AI models, including the Hypotension Prediction Index (HPI), accurately predict and reduce intraoperative hypotension in patients undergoing surgery?

P
Population
Patients undergoing any type of surgery across 43 included studies
I
Intervention
Artificial Intelligence (AI) models predicting intraoperative hypotension (IOH), including the Hypotension Prediction Index (HPI) and non-HPI models
C
Comparator
Standard monitoring or non-use of AI models
O
Outcome
Cumulative duration of IOH per patient, time weighted average of mean arterial pressure < 65 (TWA-MAP < 65), area under the threshold of mean arterial pressure (AUT-MAP), and area under the receiver operating characteristics curve (AUROC)surrogate

The Hypotension Prediction Index and other AI models demonstrate strong predictive performance for intraoperative hypotension, with HPI use associated with reduced hypotension duration.

Abstract

INTRODUCTION: Intraoperative Hypotension (IOH) poses a substantial risk during surgical procedures. The integration of Artificial Intelligence (AI) in predicting IOH holds promise for enhancing detection capabilities, providing an opportunity to improve patient outcomes. This systematic review and meta analysis explores the intersection of AI and IOH prediction, addressing the crucial need for effective monitoring in surgical settings. METHOD: A search of Pubmed, Scopus, Web of Science, and Embase was conducted. Screening involved two-phase assessments by independent reviewers, ensuring adherence to predefined PICOS criteria. Included studies focused on AI models predicting IOH in any type of surgery. Due to the high number of studies evaluating the hypotension prediction index (HPI), we conducted two sets of meta-analyses: one involving the HPI studies and one including non-HPI studies. In the HPI studies the following outcomes were analyzed: cumulative duration of IOH per patient, time weighted average of mean arterial pressure < 65 (TWA-MAP < 65), area under the threshold of mean arterial pressure (AUT-MAP), and area under the receiver operating characteristics curve (AUROC). In the non-HPI studies, we examined the pooled AUROC of all AI models other than HPI. RESULTS: 43 studies were included in this review. Studies showed significant reduction in IOH duration, TWA-MAP < 65 mmHg, and AUT-MAP < 65 mmHg in groups where HPI was used. AUROC for HPI algorithms demonstrated strong predictive performance (AUROC = 0.89, 95CI). Non-HPI models had a pooled AUROC of 0.79 (95CI: 0.74, 0.83). CONCLUSION: HPI demonstrated excellent ability to predict hypotensive episodes and hence reduce the duration of hypotension. Other AI models, particularly those based on deep learning methods, also indicated a great ability to predict IOH, while their capacity to reduce IOH-related indices such as duration remains unclear.

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Cite This Study

Mohammadi et al. (2024) studied this question.

synapsesocial.com/papers/6a1bc48f4ebd09f3dfa8f916https://doi.org/10.1186/s12967-024-05481-4
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