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Cartailler J, Beaucote V, Trillat B, Gayat E, Le Guen M, Vallee A, Fischler M. Deciphering the explanatory potential of blood pressure variables on post-operative length of stay through hierarchical clustering: A retrospective monocentric study. PLoS One 2024; 19:e0308910. [PMID: 39269941 PMCID: PMC11398650 DOI: 10.1371/journal.pone.0308910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Accepted: 07/29/2024] [Indexed: 09/15/2024] Open
Abstract
OBJECTIVE Mean arterial pressure is widely used as the variable to monitor during anesthesia. But there are many other variables proposed to define intraoperative arterial hypotension. The goal of the present study was to search arterial pressure variables linked with prolonged postoperative length of stay (pLOS). DESIGN Retrospective cohort study of adult patients having received general anesthesia for a scheduled non-cardiac surgical procedure between 15th July 2017 and 31st December 2019. METHODS pLOS was defined as a stay longer than the median (main outcome), adjusted for surgery type and duration. 330 arterial pressure variables were analyzed and organized through a clustering approach. An unsupervised hierarchical aggregation method for optimal cluster determination, employing Kendall's tau coefficients and a penalized Bayes information criterion was used. Variables were ranked using the absolute standardized mean distance (aSMD) to measure their effect on pLOS. Finally, after multivariate independence analysis, the number of variables was reduced to three. RESULTS Our study examined 9,516 patients. When LOS is defined as strictly greater than the median, 34% of patients experienced pLOS. Key arterial pressure variables linked with this definition of pLOS included the difference between the highest and lowest pulse pressure values computed throughout the surgery (aSMD[95%CI] = 0.39[0.31-0.40], p<0.001), the accumulated time pulse pressure above 61mmHg (aSMD = 0.21[0.17-0.25], p<0.001), and the lowest MAP during surgery (aSMD = 0.20[0.16-0.24], p<0.001). CONCLUSIONS By applying a clustering approach, three arterial pressure variables were associated with pLOS. This scalable method can be applied to various dichotomized outcomes.
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Affiliation(s)
- Jérôme Cartailler
- Department of Anesthesiology and Critical Care Medicine, Hôpital Lariboisière, Paris, France
- U942 MASCOT, Inserm, Université Paris Cité, Paris, France
| | - Victor Beaucote
- Department of Anesthesiology and Critical Care Medicine, Hôpital Lariboisière, Paris, France
| | - Bernard Trillat
- Department of Information Systems, Hôpital Foch, Suresnes, France
| | - Etienne Gayat
- Department of Anesthesiology and Critical Care Medicine, Hôpital Lariboisière, Paris, France
- U942 MASCOT, Inserm, Université Paris Cité, Paris, France
| | - Morgan Le Guen
- Department of Anesthesiology, Hôpital Foch, Suresnes, France
- Université Paris-Saclay, Gif-sur-Yvette, France
| | - Alexandre Vallee
- Department of Epidemiology and Public Health, Hôpital Foch, Suresnes, France
| | - Marc Fischler
- Department of Anesthesiology, Hôpital Foch, Suresnes, France
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Gupta S, Singh A, Sharma A. Exploiting moving slope features of PPG derivatives for estimation of mean arterial pressure. Biomed Eng Lett 2023; 13:1-9. [PMID: 36711158 PMCID: PMC9873885 DOI: 10.1007/s13534-022-00247-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 08/24/2022] [Accepted: 09/17/2022] [Indexed: 02/01/2023] Open
Abstract
Monitoring Mean Arterial Pressure (MAP) helps calculate the arteries' flow, resistance, and pressure. It allows doctors to check how well the blood flows through our body and reaches all major organs. Photoplethysmogram technology is gaining momentum and popularity in smart wearable devices to monitor cuff-less blood pressure (BP). However, the performance reliability of the existing PPG-based BP estimation devices is still poor. Inaccuracy in estimating systolic and diastolic blood pressure leads to an overall imprecision in resultant MAP values. Hence, there is a need for robust and reliable MAP estimation algorithms. This work exploits the moving slope features of PPG contour in its first and second derivatives that directly correlate with MAP and does not require estimating systolic and diastolic blood pressure values. The proposed approach is evaluated using two different data sets (i.e., MIMIC-I and MIMIC-II) to demonstrate the robustness and reliability of the work for personalized non-invasive BP monitoring devices to estimate MAP directly. A mean absolute error of 1.28 mmHg and a standard deviation of 2.50 mmHg is obtained with MIMIC-II data-set using GridSearchCV random forest regressor that outperformed most of the existing related works.
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Affiliation(s)
| | - Anurag Singh
- IIIT Naya Raipur, Raipur, Chhattisgarh 493661 India
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Flick M, Bergholz A, Sierzputowski P, Vistisen ST, Saugel B. What is new in hemodynamic monitoring and management? J Clin Monit Comput 2022; 36:305-313. [PMID: 35394584 PMCID: PMC9122861 DOI: 10.1007/s10877-022-00848-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 03/10/2022] [Indexed: 01/20/2023]
Affiliation(s)
- Moritz Flick
- Department of Anesthesiology, Center of Anesthesiology and Intensive Care Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Alina Bergholz
- Department of Anesthesiology, Center of Anesthesiology and Intensive Care Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Pawel Sierzputowski
- Department of Anesthesiology, Center of Anesthesiology and Intensive Care Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Simon T Vistisen
- Department of Anaesthesiology and Intensive Care, Aarhus University Hospital, Aarhus, Denmark
| | - Bernd Saugel
- Department of Anesthesiology, Center of Anesthesiology and Intensive Care Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany. .,Outcomes Research Consortium, Cleveland, Ohio, USA.
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Charlton PH, Paliakaitė B, Pilt K, Bachler M, Zanelli S, Kulin D, Allen J, Hallab M, Bianchini E, Mayer CC, Terentes-Printzios D, Dittrich V, Hametner B, Veerasingam D, Žikić D, Marozas V. Assessing hemodynamics from the photoplethysmogram to gain insights into vascular age: a review from VascAgeNet. Am J Physiol Heart Circ Physiol 2022; 322:H493-H522. [PMID: 34951543 PMCID: PMC8917928 DOI: 10.1152/ajpheart.00392.2021] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 12/21/2021] [Accepted: 12/21/2021] [Indexed: 12/07/2022]
Abstract
The photoplethysmogram (PPG) signal is widely measured by clinical and consumer devices, and it is emerging as a potential tool for assessing vascular age. The shape and timing of the PPG pulse wave are both influenced by normal vascular aging, changes in arterial stiffness and blood pressure, and atherosclerosis. This review summarizes research into assessing vascular age from the PPG. Three categories of approaches are described: 1) those which use a single PPG signal (based on pulse wave analysis), 2) those which use multiple PPG signals (such as pulse transit time measurement), and 3) those which use PPG and other signals (such as pulse arrival time measurement). Evidence is then presented on the performance, repeatability and reproducibility, and clinical utility of PPG-derived parameters of vascular age. Finally, the review outlines key directions for future research to realize the full potential of photoplethysmography for assessing vascular age.
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Affiliation(s)
- Peter H Charlton
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- Research Centre for Biomedical Engineering, University of London, London, United Kingdom
| | - Birutė Paliakaitė
- Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania
| | - Kristjan Pilt
- Department of Health Technologies, Tallinn University of Technology, Tallinn, Estonia
| | - Martin Bachler
- Biomedical Systems, Center for Health and Bioresources, AIT Austrian Institute of Technology, Seibersdorf, Austria
| | - Serena Zanelli
- Laboratoire Analyze, Géométrie et Applications, University Sorbonne Paris Nord, Paris, France
- Axelife, Redon, France
| | - Dániel Kulin
- Institute of Translational Medicine, Semmelweis University, Budapest, Hungary
- E-Med4All Europe, Limited, Budapest, Hungary
| | - John Allen
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, United Kingdom
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Magid Hallab
- Axelife, Redon, France
- Centre de recherche et d'Innovation, Clinique Bizet, Paris, France
| | | | - Christopher C Mayer
- Biomedical Systems, Center for Health and Bioresources, AIT Austrian Institute of Technology, Seibersdorf, Austria
| | - Dimitrios Terentes-Printzios
- Hypertension and Cardiometabolic Unit, First Department of Cardiology, Hippokration Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Verena Dittrich
- Redwave Medical, Gesellschaft mit beschränkter Haftung, Jena, Germany
| | - Bernhard Hametner
- Biomedical Systems, Center for Health and Bioresources, AIT Austrian Institute of Technology, Seibersdorf, Austria
| | - Dave Veerasingam
- Department of Cardiothoracic Surgery, Galway University Hospitals, Galway, Ireland
| | - Dejan Žikić
- Faculty of Medicine, Institute of Biophysics, University of Belgrade, Belgrade, Serbia
| | - Vaidotas Marozas
- Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania
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