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Davies SJ, Sessler DI, Jian Z, Fleming NW, Mythen M, Maheshwari K, Veelo DP, Vlaar APJ, Settels J, Scheeren T, van der Ster BJP, Sander M, Cannesson M, Hatib F. Comparison of Differences in Cohort (Forward) and Case Control (Backward) Methodologic Approaches for Validation of the Hypotension Prediction Index. Anesthesiology 2024; 141:443-452. [PMID: 38557791 PMCID: PMC11323758 DOI: 10.1097/aln.0000000000004989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 03/18/2024] [Indexed: 04/04/2024]
Abstract
BACKGROUND The Hypotension Prediction Index (the index) software is a machine learning algorithm that detects physiologic changes that may lead to hypotension. The original validation used a case control (backward) analysis that has been suggested to be biased. This study therefore conducted a cohort (forward) analysis and compared this to the original validation technique. METHODS A retrospective analysis of data from previously reported studies was conducted. All data were analyzed identically with two different methodologies, and receiver operating characteristic curves were constructed. Both backward and forward analyses were performed to examine differences in area under the receiver operating characteristic curves for the Hypotension Prediction Index and other hemodynamic variables to predict a mean arterial pressure (MAP) less than 65 mmHg for at least 1 min 5, 10, and 15 min in advance. RESULTS The analysis included 2,022 patients, yielding 4,152,124 measurements taken at 20-s intervals. The area under the curve for the index predicting hypotension analyzed by backward and forward methodologies respectively was 0.957 (95% CI, 0.947 to 0.964) versus 0.923 (95% CI, 0.912 to 0.933) 5 min in advance, 0.933 (95% CI, 0.924 to 0.942) versus 0.923 (95% CI, 0.911 to 0.933) 10 min in advance, and 0.929 (95% CI, 0.918 to 0.938) versus 0.926 (95% CI, 0.914 to 0.937) 15 min in advance. No variable other than MAP had an area under the curve greater than 0.7. The areas under the curve using forward analysis for MAP predicting hypotension 5, 10, and 15 min in advance were 0.932 (95% CI, 0.920 to 0.940), 0.929 (95% CI, 0.918 to 0.938), and 0.932 (95% CI, 0.921 to 0.940), respectively. The R2 for the variation in the index due to MAP was 0.77. CONCLUSIONS Using an updated methodology, the study found that the utility of the Hypotension Prediction Index to predict future hypotensive events is high, with an area under the receiver operating characteristics curve similar to that of the original validation method. EDITOR’S PERSPECTIVE
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Affiliation(s)
- Simon J. Davies
- Department of Anaesthesia, Critical Care and Perioperative Medicine, York and Scarborough Teaching Hospitals National Health Service Foundation Trust, York, United Kingdom; and Centre for Health and Population Science, Hull York Medical School, York, United Kingdom
| | | | | | - Neal W. Fleming
- University of California–Davis School of Medicine, Sacramento, California
| | - Monty Mythen
- Edwards Lifesciences, Irvine, California; and University College London/University College London Hospital, National Institute of Health Research Biomedical Research Centre, London, United Kingdom
| | - Kamal Maheshwari
- Department of Outcomes Research, Cleveland Clinic, Cleveland, Ohio
| | - Denise P. Veelo
- Departments of Anaesthesia and Intensive Care, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Alexander P. J. Vlaar
- Departments of Anaesthesia and Intensive Care, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | | | - Thomas Scheeren
- Edwards Lifesciences, Irvine, California; and Department of Anesthesiology, University Medical Centre Groningen, Groningen, The Netherlands
| | - B. J. P. van der Ster
- Departments of Anaesthesia and Intensive Care, Amsterdam University Medical Center, Amsterdam, The Netherlands; and Erasmus Medical Center, Rotterdam, The Netherlands
| | - Michael Sander
- Department of Anaesthesiology, Intensive Care Medicine and Pain Medicine, University Hospital Giessen, Giessen, Germany
| | - Maxime Cannesson
- Department of Anesthesiology and Perioperative Medicine, UCLA, California
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Dong S, Wang Q, Wang S, Zhou C, Wang H. Hypotension prediction index for the prevention of hypotension during surgery and critical care: A narrative review. Comput Biol Med 2024; 170:107995. [PMID: 38325215 DOI: 10.1016/j.compbiomed.2024.107995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 12/17/2023] [Accepted: 01/13/2024] [Indexed: 02/09/2024]
Abstract
Surgeons and anesthesia clinicians commonly face a hemodynamic disturbance known as intraoperative hypotension (IOH), which has been linked to more severe postoperative outcomes and increases mortality rates. Increased occurrence of IOH has been positively associated with mortality and incidence of myocardial infarction, stroke, and organ dysfunction hypertension. Hence, early detection and recognition of IOH is meaningful for perioperative management. Currently, when hypotension occurs, clinicians use vasopressor or fluid therapy to intervene as IOH develops but interventions should be taken before hypotension occurs; therefore, the Hypotension Prediction Index (HPI) method can be used to help clinicians further react to the IOH process. This literature review evaluates the HPI method, which can reliably predict hypotension several minutes before a hypotensive event and is beneficial for patients' outcomes.
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Affiliation(s)
- Siwen Dong
- The Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Qing Wang
- Department of Anesthesiology, Tongde Hospital of Zhejiang Province, Hangzhou 310012, China
| | - Shuai Wang
- The Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Congcong Zhou
- Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou 310016, China; Biosensor National Special Laboratory, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China
| | - Hongwei Wang
- The Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou 310053, China; Department of Anesthesiology, Tongde Hospital of Zhejiang Province, Hangzhou 310012, China.
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