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Cylwik J, Celińska-Spodar M, Dudzic M. Individualized Perioperative Hemodynamic Management Using Hypotension Prediction Index Software and the Dynamics of Troponin and NTproBNP Concentration Changes in Patients Undergoing Oncological Abdominal Surgery. J Pers Med 2024; 14:211. [PMID: 38392644 PMCID: PMC10890224 DOI: 10.3390/jpm14020211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 02/08/2024] [Accepted: 02/14/2024] [Indexed: 02/24/2024] Open
Abstract
INTRODUCTION Abdominal oncologic surgeries pose significant risks due to the complexity of the surgery and patients' often weakened health, multiple comorbidities, and increased perioperative hazards. Hypotension is a major risk factor for perioperative cardiovascular complications, necessitating individualized management in modern anesthesiology. AIM This study aimed to determine the dynamics of changes in troponin and NTproBNP levels during the first two postoperative days in patients undergoing major cancer abdominal surgery with advanced hemodynamic monitoring including The AcumenTM Hypotension Prediction Index software (HPI) (Edwards Lifesciences, Irvine, CA, USA) and their association with the occurrence of postoperative cardiovascular complications. METHODS A prospective study was conducted, including 50 patients scheduled for abdominal cancer surgery who, due to the overall risk of perioperative complications (ASA class 3 or 4), were monitored using the HPI software. Hypotension was qualified as at least one ≥ 1 min episode of a MAP < 65 mm Hg. Preoperatively and 24 and 48 h after the procedure, the levels of NTproBNP and troponin were measured, and an ECG was performed. RESULTS We analyzed data from 46 patients and found that 82% experienced at least one episode of low blood pressure (MAP < 65 mmHg). However, the quality indices of hypotension were low, with a median time-weighted average MAP < 65 mmHg of 0.085 (0.03-0.19) mmHg and a median of 2 (2-1.17) minutes spent below MAP < 65 mmHg. Although the incidence of perioperative myocardial injury was 10%, there was no evidence to suggest a relationship with hypotension. Acute kidney injury was seen in 23.9% of patients, and it was significantly associated with a number of episodes of MAP < 50 mmHg. Levels of NTproBNP were significantly higher on the first postoperative day compared to preoperative values (285.8 [IQR: 679.8] vs. 183.9 [IQR: 428.1] pg/mL, p < 0.001). However, they decreased on the second day (276.65 [IQR: 609.4] pg/mL, p = 0.154). The dynamics of NTproBNP were similar for patients with and without heart failure, although those with heart failure had significantly higher preoperative concentrations (435.9 [IQR: 711.15] vs. 87 [IQR: 232.2] pg/mL, p < 0.001). Patients undergoing laparoscopic surgery showed a statistically significant increase in NTproBNP. CONCLUSIONS This study suggests that advanced HPI monitoring in abdominal cancer surgery effectively minimizes intraoperative hypotension with no significant NTproBNP or troponin perioperative dynamics, irrespective of preoperative heart failure.
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Affiliation(s)
- Jolanta Cylwik
- Anesthesiology and Intensive Care Unit, Mazovia Regional Hospital, 08-110 Siedlce, Poland
| | - Małgorzata Celińska-Spodar
- Anesthesiology and Intensive Care Unit, Mazovia Regional Hospital, 08-110 Siedlce, Poland
- Anesthesiology and Intensive Care Unit, The National Institute of Cardiology, 04-628 Warsaw, Poland
| | - Mariusz Dudzic
- Critical Care, Edwards Lifesciences, 00-807 Warsaw, Poland
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Dong Z, Chen X, Ritter J, Bai L, Huang J. American society of anesthesiologists physical status classification significantly affects the performances of machine learning models in intraoperative hypotension inference. J Clin Anesth 2024; 92:111309. [PMID: 37922642 PMCID: PMC10873053 DOI: 10.1016/j.jclinane.2023.111309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 09/24/2023] [Accepted: 10/24/2023] [Indexed: 11/07/2023]
Abstract
STUDY OBJECTIVE To explore how American Society of Anesthesiologists (ASA) physical status classification affects different machine learning models in hypotension prediction and whether the prediction uncertainty could be quantified. DESIGN Observational Studies SETTING: UofL health hospital PATIENTS: This study involved 562 hysterectomy surgeries performed on patients (≥ 18 years) between June 2020 and July 2021. INTERVENTIONS None MEASUREMENTS: Preoperative and intraoperative data is collected. Three parametric machine learning models, including Bayesian generalized linear model (BGLM), Bayesian neural network (BNN), a newly proposed BNN with multivariate mixed responses (BNNMR), and one nonparametric model, Gaussian Process (GP), were explored to predict patients' diastolic and systolic blood pressures (continuous responses) and patients' hypotensive event (binary response) for the next five minutes. Data was separated into American Society of Anesthesiologists (ASA) physical status class 1- 4 before being read in by four machine learning models. Statistical analysis and models' constructions are performed in Python. Sensitivity, specificity, and the confidence/credible intervals were used to evaluate the prediction performance of each model for each ASA physical status class. MAIN RESULTS ASA physical status classes require distinct models to accurately predict intraoperative blood pressures and hypotensive events. Overall, high sensitivity (above 0.85) and low uncertainty can be achieved by all models for ASA class 4 patients. In contrast, models trained without controlling ASA classes yielded lower sensitivity (below 0.5) and larger uncertainty. Particularly, in terms of predicting binary hypotensive event, for ASA physical status class 1, BNNMR yields the highest sensitivity of 1. For classes 2 and 3, BNN has the highest sensitivity of 0.429 and 0.415, respectively. For class 4, BNNMR and GP are tied with the highest sensitivity of 0.857. On the other hand, the sensitivity is just 0.031, 0.429, 0.165 and 0.305 for BNNMR, BNN, GBLM and GP models respectively, when training data is not divided by ASA physical status classes. In terms of predicting systolic blood pressure, the GP regression yields the lowest root mean squared errors (RMSE) of 2.072, 7.539, 9.214 and 0.295 for ASA physical status classes 1, 2, 3 and 4, respectively, but a RMSE of 126.894 if model is trained without controlling the ASA physical status class. The RMSEs for other models are far higher. RMSEs are 2.175, 13.861, 17.560 and 22.426 for classes 1, 2, 3 and 4 respectively for the BGLM. In terms of predicting diastolic blood pressure, the GP regression yields the lowest RMSEs of 2.152, 6.573, 5.371 and 0.831 for ASA physical status classes 1, 2, 3 and 4, respectively; RMSE of 8.084 if model is trained without controlling the ASA physical status class. The RMSEs for other models are far higher. Finally, in terms of the width of the 95% confidence interval of the mean prediction for systolic and diastolic blood pressures, GP regression gives narrower confidence interval with much smaller margin of error across all four ASA physical status classes. CONCLUSIONS Different ASA physical status classes present different data distributions, and thus calls for distinct machine learning models to improve prediction accuracy and reduce predictive uncertainty. Uncertainty quantification enabled by Bayesian inference provides valuable information for clinicians as an additional metric to evaluate performance of machine learning models for medical decision making.
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Affiliation(s)
- Zehua Dong
- Department of Industrial and Systems Engineering, University at Buffalo, United States of America.
| | - Xiaoyu Chen
- Department of Industrial and Systems Engineering, University at Buffalo, United States of America.
| | - Jodie Ritter
- Department of Industrial Engineering, University of Louisville, United States of America.
| | - Lihui Bai
- Department of Industrial Engineering, University of Louisville, United States of America.
| | - Jiapeng Huang
- Department of Anesthesiology & Perioperative Medicine, University of Louisville, United States of America.
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Khanna AK, O'Connell NS, Ahuja S, Saha AK, Harris L, Cusson BD, Faris A, Huffman CS, Vallabhajosyula S, Clark CJ, Segal S, Wells BJ, Kirkendall ES, Sessler DI. Incidence, severity and detection of blood pressure and heart rate perturbations in postoperative ward patients after noncardiac surgery. J Clin Anesth 2023; 89:111159. [PMID: 37295123 DOI: 10.1016/j.jclinane.2023.111159] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 05/22/2023] [Accepted: 05/28/2023] [Indexed: 06/12/2023]
Abstract
STUDY OBJECTIVE We sought to determine changes in continuous mean and systolic blood pressure and heart rate in a cohort of non-cardiac surgical patients recovering on the postoperative ward. Furthermore, we estimated the proportion of vital signs changes that would remain undetected with intermittent vital signs checks. DESIGN Retrospective cohort. SETTING Post-operative general ward. PATIENTS 14,623 adults recovering from non-cardiac surgical procedures. INTERVENTIONS & MEASUREMENTS Using a wireless, noninvasive monitor, we recorded postoperative blood pressure and heart rate at 15-s intervals and encouraged nursing intervention as clinically indicated. MAIN RESULTS 7% of our cohort of 14,623 patients spent >15 sustained minutes with a MAP <65 mmHg, and 23% had MAP <75 mmHg for 15 sustained minutes. Hypertension was more common, with 67% of patients spending at least 60 sustained minutes with MAP >110 mmHg. Systolic pressures <90 mmHg were present for 15 sustained minutes in about a fifth of all patients, and 40% of patients had pressures >160 mmHg sustained for 30 min. 40% of patients were tachycardic with heart rates >100 beats/min for at least continuous 15 min and 15% of patients were bradycardic at a threshold of <50 beats/min for 5 sustained minutes. Conventional vital sign assessments at 4-h intervals would have missed 54% of mean pressure episodes <65 mmHg sustained >15 min, 20% of episodes of mean pressures >130 mmHg sustained >30 min, 36% of episodes of heart rate > 120 beats/min sustained <10 min, and 68% of episodes of heart rate sustained <40 beats per minute for >3 min. CONCLUSIONS Substantial hemodynamic disturbances persisted despite implementing continuous portable ward monitoring coupled with nursing alarms and interventions. A significant proportion of these changes would have gone undetected using traditional intermittent monitoring. Better understanding of effective responses to alarms and appropriate interventions on hospital wards remains necessary.
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Affiliation(s)
- Ashish K Khanna
- Department of Anesthesiology, Wake Forest University School of Medicine, Winston-Salem, NC, USA; Perioperative Outcomes and Informatics Collaborative (POIC), Winston-Salem, NC, USA; Outcomes Research Consortium, Cleveland, OH, USA.
| | - Nathaniel S O'Connell
- Department of Biostatistics and Data Science, Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
| | - Sanchit Ahuja
- Outcomes Research Consortium, Cleveland, OH, USA; Department of Cardiothoracic Anesthesiology, Anesthesiology Institute, Cleveland Clinic, Cleveland, OH and Department of Outcomes Research, Cleveland Clinic, Cleveland, OH, USA.
| | - Amit K Saha
- Department of Anesthesiology, Wake Forest University School of Medicine, Winston-Salem, NC, USA; Perioperative Outcomes and Informatics Collaborative (POIC), Winston-Salem, NC, USA.
| | - Lynnette Harris
- Department of Anesthesiology, Wake Forest University School of Medicine, Winston-Salem, NC, USA; Perioperative Outcomes and Informatics Collaborative (POIC), Winston-Salem, NC, USA.
| | - Bruce D Cusson
- Department of Anesthesiology, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
| | - Ann Faris
- Perioperative Outcomes and Informatics Collaborative (POIC), Winston-Salem, NC, USA; Center for Nursing Research, Atrium Health Wake Forest Baptist, Winston-Salem, NC, USA.
| | - Carolyn S Huffman
- Perioperative Outcomes and Informatics Collaborative (POIC), Winston-Salem, NC, USA; Center for Nursing Research, Atrium Health Wake Forest Baptist, Winston-Salem, NC, USA.
| | - Saraschandra Vallabhajosyula
- Perioperative Outcomes and Informatics Collaborative (POIC), Winston-Salem, NC, USA; Section of Cardiovascular Medicine, Department of Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
| | - Clancy J Clark
- Department of Surgery, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
| | - Scott Segal
- Department of Anesthesiology, Wake Forest University School of Medicine, Winston-Salem, NC, USA; Perioperative Outcomes and Informatics Collaborative (POIC), Winston-Salem, NC, USA.
| | - Brian J Wells
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA; Center for Biomedical Informatics, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
| | - Eric S Kirkendall
- Department of Pediatrics, Wake Forest University School of Medicine, Winston-Salem, NC, USA; Center for Healthcare Innovation, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
| | - Daniel I Sessler
- Department of Outcomes Research, Cleveland Clinic, Cleveland, OH, USA.
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Hoppe P, Burfeindt C, Reese PC, Briesenick L, Flick M, Kouz K, Pinnschmidt H, Hapfelmeier A, Sessler DI, Saugel B. Chronic arterial hypertension and nocturnal non-dipping predict postinduction and intraoperative hypotension: A secondary analysis of a prospective study. J Clin Anesth 2022; 79:110715. [DOI: 10.1016/j.jclinane.2022.110715] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 02/23/2022] [Accepted: 02/27/2022] [Indexed: 12/25/2022]
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Shimada T, Cohen B, Shah K, Mosteller L, Bravo M, Ince I, Esa WAS, Cywinski J, Sessler DI, Ruetzler K, Turan A. Associations between intraoperative and post-anesthesia care unit hypotension and surgical ward hypotension. J Clin Anesth 2021; 75:110495. [PMID: 34560444 DOI: 10.1016/j.jclinane.2021.110495] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 08/03/2021] [Accepted: 08/23/2021] [Indexed: 01/18/2023]
Abstract
STUDY OBJECTIVE To test whether patients who experience hypotension in the post-anesthesia care unit or during surgery are most likely to experience hypotension on surgical wards. DESIGN A prediction study using data from two randomized controlled trials. SETTING Operating room, post-anesthesia care unit, and surgical ward. PATIENTS 550 adult patients having abdominal surgery with ASA physical status I-IV. INTERVENTIONS Blood pressure measurement per routine intraoperatively, and with continuous non-invasive monitoring postoperatively. MEASUREMENTS The primary predictors were minimum mean arterial pressure (<60, <65, <70 and < 80 mmHg) and minimum systolic blood pressure (<70, <75, <80, <85 mmHg) in the post-anesthesia care unit. The secondary predictors were intraoperative minimum blood pressures with the same thresholds as the primary ones. Our outcome was ward hypotension defined as mean pressure < 70 mmHg or systolic pressure < 85 mmHg. A threshold was considered clinically useful if both sensitivity and specificity exceeded 0.75. MAIN RESULTS Minimum mean and systolic pressures in the post-anesthesia care unit similarly predicted ward mean or systolic hypotension, with the areas under the curves near 0.74. The best performing threshold was mean pressure < 80 mmHg in the post-anesthesia care unit which had a sensitivity of 0.41 (95% confidence interval [CI], 0.35, 0.47) and specificity of 0.91 (95% CI, 0.87, 0.94) for ward mean pressure < 70 mmHg and a sensitivity of 0.44 (95% CI, 0.37, 0.51) and specificity of 0.88 (95% CI, 0.84, 0.91) for ward systolic pressure < 85 mmHg. The areas under the curves using intraoperative hypotension to predict ward hypotension were roughly similar at about 0.60, with correspondingly low sensitivity and specificity. CONCLUSIONS Intraoperative hypotension poorly predicted ward hypotension. Pressures in the post-anesthesia care unit were more predictive, but the combination of sensitivity and specificity remained poor. Unless far better predictors are identified, all surgical inpatients should be considered at risk for postoperative hypotension.
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Affiliation(s)
- Tetsuya Shimada
- Department of OUTCOMES RESEARCH, Cleveland Clinic, Cleveland, OH, United States; Department of Anesthesiology, National Hospital Organization, Murayama Medical Center, Musashimurayama, Tokyo, Japan; Department of Anesthesiology, National Defense Medical College, Tokorozawa, Saitama, Japan
| | - Barak Cohen
- Department of OUTCOMES RESEARCH, Cleveland Clinic, Cleveland, OH, United States; Division of Anesthesia, Intensive Care and Pain Management, Tel-Aviv Medical Center, Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Karan Shah
- Department of OUTCOMES RESEARCH, Cleveland Clinic, Cleveland, OH, United States; Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, United States
| | - Lauretta Mosteller
- Department of OUTCOMES RESEARCH, Cleveland Clinic, Cleveland, OH, United States
| | - Mauro Bravo
- Department of OUTCOMES RESEARCH, Cleveland Clinic, Cleveland, OH, United States
| | - Ilker Ince
- Department of OUTCOMES RESEARCH, Cleveland Clinic, Cleveland, OH, United States; Anesthesiology Clinical Research Office, Ataturk University, Erzurum, Turkey
| | - Wael Ali Sakr Esa
- Department of General Anesthesia, Cleveland Clinic, Cleveland, OH, United States
| | - Jacek Cywinski
- Department of General Anesthesia, Cleveland Clinic, Cleveland, OH, United States
| | - Daniel I Sessler
- Department of OUTCOMES RESEARCH, Cleveland Clinic, Cleveland, OH, United States
| | - Kurt Ruetzler
- Department of OUTCOMES RESEARCH, Cleveland Clinic, Cleveland, OH, United States; Department of General Anesthesia, Cleveland Clinic, Cleveland, OH, United States
| | - Alparslan Turan
- Department of OUTCOMES RESEARCH, Cleveland Clinic, Cleveland, OH, United States; Department of General Anesthesia, Cleveland Clinic, Cleveland, OH, United States.
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Is a Mean Arterial Pressure Less Than 65 mm Hg an Appropriate Indicator of the Quality of Anesthesia Care? Anesth Analg 2021; 132:942-945. [PMID: 33723192 DOI: 10.1213/ane.0000000000005281] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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