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Li Q, Lv H, Chen Y, Shen J, Shi J, Zhou C. Hybrid feature selection in a machine learning predictive model for perioperative myocardial injury in noncoronary cardiac surgery with cardiopulmonary bypass. Perfusion 2024:2676591241253459. [PMID: 38733257 DOI: 10.1177/02676591241253459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/13/2024]
Abstract
BACKGROUND Perioperative myocardial injury (PMI) is associated with increased mobility and mortality after noncoronary cardiac surgery. However, limited studies have developed a predictive model for PMI. Therefore, we used hybrid feature selection (FS) methods to establish a predictive model for PMI in noncoronary cardiac surgery with cardiopulmonary bypass (CPB). METHODS This was a single-center retrospective study conducted at the Fuwai Hospital in China. Patients aged 18-70 years who underwent elective noncoronary surgery with CPB at our institution from December 2018 to April 2021 were enrolled. The primary outcome was PMI, defined as the postoperative cardiac troponin I (cTnI) levels exceeding 220 times of upper reference limit (URL). Statistical analyses were conducted by Python (Python Software Foundation, version 3.9.7 and integrated development environment Jupyter Notebook 1.1.0) and SPSS software version 26.0 (IBM Corp., Armonk, New York, USA). RESULTS A total of 1130 patients were eventually eligible for this study. The incidence of PMI was 20.3% (229/1130) in the overall patients, 20.6% (163/791) in the training dataset, and 19.5% (66/339) in the testing dataset. The logistic regression model performed the best AUC of 0.6893 (95 CI%: 0.6371-0.7382) by the traditional selection method, and the random forest model performed the best AUC of 0.6937 (95 CI%: 0.6416-0.7423) by the union of Wrapper and Embedded method, and the CatBoost model performed the best AUC of 0.6828 (95 CI%: 0.6304-0.7320) by the union of Embedded and forward logistic regression technique, and the Naïve Bayes model achieved the best AUC with 0.7254 (95 CI%: 0.6746-0.7723) by forwarding logistic regression method. Moreover, the decision tree, KNeighborsClassifier, and support vector machine models performed the worse AUC in all selection forms. Furthermore, the SHapley Additive exPlanations plot showed that prolonged CPB, aortic clamp time, and preoperative low platelets count were strongly related to the PMI risk. CONCLUSIONS In total, four category feature selection methods were utilized, comprising five individual selection techniques and 15 combined methods. Notably, the combination of logistic regression and embedded methods demonstrated outstanding performance in predicting PMI risk. We also concluded that the machine learning model, including random forest, catboost, and Naive Bayes, were suitable candidates for establishing PMI predictive model. Nevertheless, additional investigation and validation are imperative for substantiating these finding.
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Affiliation(s)
- Qian Li
- Department of Anesthesiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Bejing, China
| | - Hong Lv
- Department of Anesthesiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Bejing, China
| | - Yuye Chen
- Department of Anesthesiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Bejing, China
| | - Jingjia Shen
- Department of Anesthesiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Bejing, China
| | - Jia Shi
- Department of Anesthesiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Bejing, China
| | - Chenghui Zhou
- Department of Anesthesiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Bejing, China
- Center for Anesthesiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
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Chang Y, Zhou M, Huang J, Wang Y, Shao J. Incidence and risk factors of postoperative acute myocardial injury in noncardiac patients: A systematic review and meta-analysis. PLoS One 2023; 18:e0286431. [PMID: 37319136 PMCID: PMC10270363 DOI: 10.1371/journal.pone.0286431] [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: 03/09/2023] [Accepted: 05/16/2023] [Indexed: 06/17/2023] Open
Abstract
INTRODUCTION Postoperative myocardial injury after noncardiac surgery is common and is associated with short- and long-term morbidity and mortality. However, the incidence and risk factors for postoperative acute myocardial injury (POAMI) are currently unknown due to inconsistent definitions. METHODS We systematically searched PubMed and Web of Science to identify studies that applied the change value of preoperative and postoperative cardiac troponins to define cardiac injury. We estimated the pooled incidence, risk factors, and 30-day and long-term mortality of POAMI in noncardiac patients. The study protocol was registered with PROSPERO, CRD42023401607. RESULTS Ten cohorts containing 11,494 patients were included for analysis. The pooled incidence of POAMI was 20% (95% CI: 16% to 23%). Preoperative hypertension (OR: 1.47; 95% CI: 1.30 to 1.66), cardiac failure (OR: 2.63; 95% CI: 2.01 to 3.44), renal impairment (OR: 1.66; 95% CI: 1.48 to 1.86), diabetes (OR: 1.43; 95% CI: 1.27 to 1.61), and preoperative beta-blocker intake (OR: 1.65; 95% CI: 1.10 to 2.49) were the risk factors for POAMI. Age (mean difference: 2.08 years; 95% CI: -0.47 to 4.62), sex (male, OR: 1.16; 95% CI: 0.77 to 1.76), body mass index (mean difference: 0.35; 95% CI: -0.86 to 1.57), preoperative coronary artery disease (OR: 2.10; 95% CI: 0.85 to 5.21), stroke (OR: 0.90; 95% CI: 0.50 to 1.59) and preoperative statins intake (OR: 0.65; 95% CI: 0.21 to 2.02) were not associated with POAMI. Patients with POAMI had higher preoperative hsTnT levels (mean difference: 5.92 ng/L; 95% CI: 4.17 to 7.67) and lower preoperative hemoglobin levels (mean difference: -1.29 g/dL; 95% CI: -1.43 to -1.15) than patients without. CONCLUSION Based on this meta-analysis, approximately 1 in 5 of noncardiac patients develop POAMI. However, the lack of a universally recognized definition for POAMI, which incorporates diverse cardiac biomarkers and patient groups, poses a challenge in accurately characterizing its incidence, risk factors, and clinical outcomes.
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Affiliation(s)
- Yuan Chang
- Department of Anesthesiology, the First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Mengjiao Zhou
- Department of Anesthesiology, the First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Jing Huang
- Department of Anesthesiology, the First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yanqiong Wang
- Department of Anesthesiology, the First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Jianlin Shao
- Department of Anesthesiology, the First Affiliated Hospital of Kunming Medical University, Kunming, China
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Vetrugno L, Boero E, Bignami E, Cortegiani A, Raineri SM, Spadaro S, Moro F, D’Incà S, D’Orlando L, Agrò FE, Bernardinetti M, Forfori F, Corradi F, Pregnolato S, Mosconi M, Bellini V, Franchi F, Mongelli P, Leonardi S, Giuffrida C, Tescione M, Bruni A, Garofalo E, Longhini F, Cammarota G, De Robertis E, Giglio G, Urso F, Bove T. Association between preoperative evaluation with lung ultrasound and outcome in frail elderly patients undergoing orthopedic surgery for hip fractures: study protocol for an Italian multicenter observational prospective study (LUSHIP). Ultrasound J 2021; 13:30. [PMID: 34100124 PMCID: PMC8184059 DOI: 10.1186/s13089-021-00230-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 05/25/2021] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Hip fracture is one of the most common orthopedic causes of hospital admission in frail elderly patients. Hip fracture fixation in this class of patients is considered a high-risk procedure. Preoperative physical examination, plasma natriuretic peptide levels (BNP, Pro-BNP), and cardiovascular scoring systems (ASA-PS, RCRI, NSQIP-MICA) have all been demonstrated to underestimate the risk of postoperative complications. We designed a prospective multicenter observational study to assess whether preoperative lung ultrasound examination can predict better postoperative events thanks to the additional information they provide in the form of "indirect" and "direct" cardiac and pulmonary lung ultrasound signs. METHODS LUSHIP is an Italian multicenter prospective observational study. Patients will be recruited on a nation-wide scale in the 12 participating centers. Patients aged > 65 years undergoing spinal anesthesia for hip fracture fixation will be enrolled. A lung ultrasound score (LUS) will be generated based on the examination of six areas of each lung and ascribing to each area one of the four recognized aeration patterns-each of which is assigned a subscore of 0, 1, 2, or 3. Thus, the total score will have the potential to range from a minimum of 0 to a maximum of 36. The association between 30-day postoperative complications of cardiac and/or pulmonary origin and the overall mortality will be studied. Considering the fact that cardiac complications in patients undergoing hip surgery occur in approx. 30% of cases, to achieve 80% statistical power, we will need a sample size of 877 patients considering a relative risk of 1.5. CONCLUSIONS Lung ultrasound (LU), as a tool within the anesthesiologist's armamentarium, is becoming increasingly widespread, and its use in the preoperative setting is also starting to become more common. Should the study demonstrate the ability of LU to predict postoperative cardiac and pulmonary complications in hip fracture patients, a randomized clinical trial will be designed with the scope of improving patient outcome. Trial registration ClinicalTrials.gov, NCT04074876. Registered on August 30, 2019.
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Affiliation(s)
- Luigi Vetrugno
- Department of Medicine, University of Udine, Via Colugna no. 50, 33100 Udine, Italy
- University-Hospital of Friuli Centrale, ASFC, P.le S. Maria della Misericordia no. 15, 33100 Udine, Italy
| | - Enrico Boero
- Anesthesia and Intensive Care Unit, San Giovanni Bosco Hospital, Turin, Italy
| | - Elena Bignami
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Andrea Cortegiani
- Department of Surgical, Oncological and Oral Science (Di.Chir.On.S), University of Palermo, Palermo, Italy
- Department of Anesthesia Intensive Care and Emergency, Policlinico Paolo Giaccone, Palermo, Italy
| | - Santi Maurizio Raineri
- Department of Surgical, Oncological and Oral Science (Di.Chir.On.S), University of Palermo, Palermo, Italy
- Department of Anesthesia Intensive Care and Emergency, Policlinico Paolo Giaccone, Palermo, Italy
| | - Savino Spadaro
- Department of translational medicine, Anesthesia and Intensive Care, University of Ferrara, Ferrara, Italy
| | - Federico Moro
- Department of translational medicine, Anesthesia and Intensive Care, University of Ferrara, Ferrara, Italy
| | - Stefano D’Incà
- Department of Medicine, University of Udine, Via Colugna no. 50, 33100 Udine, Italy
| | - Loris D’Orlando
- Department of Medicine, University of Udine, Via Colugna no. 50, 33100 Udine, Italy
| | - Felice Eugenio Agrò
- Department of Medicine, Unit of Anesthesia Intensive Care Pain Management, Università Campus Bio-Medico Di Roma, Rome, Italy
| | - Mattia Bernardinetti
- Department of Medicine, Unit of Anesthesia Intensive Care Pain Management, Università Campus Bio-Medico Di Roma, Rome, Italy
| | - Francesco Forfori
- Department of Surgical, Medical and Molecular Pathology and Critical Care Medicine, University of Pisa, Pisa, Italy
| | - Francesco Corradi
- Department of Anesthesia and Intensive Care, Ente Ospedaliero Ospedali Galliera, Genova, Italy
| | - Sandro Pregnolato
- Department of Clinical-Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy
| | - Mario Mosconi
- Orthopedics and Traumatology, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Valentina Bellini
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Federico Franchi
- Department of Medicine, Surgery and Neuroscience, Anesthesiology and Intensive Care, University of Siena, Siena, Italy
| | - Pierpaolo Mongelli
- Department of Medicine, Surgery and Neuroscience, Anesthesiology and Intensive Care, University of Siena, Siena, Italy
| | | | | | - Marco Tescione
- Anesthesia and Intensive Care Unit, Grande Ospedale Metropolitano, Reggio Calabria, Italy
| | - Andrea Bruni
- Anesthesia and Intensive Care Unit, Department of Medical and Surgical Science, Magna Graecia University, Catanzaro, Italy
| | - Eugenio Garofalo
- Anesthesia and Intensive Care Unit, Department of Medical and Surgical Science, Magna Graecia University, Catanzaro, Italy
| | - Federico Longhini
- Anesthesia and Intensive Care Unit, Department of Medical and Surgical Science, Magna Graecia University, Catanzaro, Italy
| | - Gianmaria Cammarota
- Section of Anaesthesia, Analgesia, and Intensive Care, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Edoardo De Robertis
- Section of Anaesthesia, Analgesia, and Intensive Care, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Giuseppe Giglio
- University-Hospital of Friuli Centrale, ASFC, P.le S. Maria della Misericordia no. 15, 33100 Udine, Italy
| | - Felice Urso
- Anesthesia and Intensive Care Unit, San Giovanni Bosco Hospital, Turin, Italy
| | - Tiziana Bove
- Department of Medicine, University of Udine, Via Colugna no. 50, 33100 Udine, Italy
- University-Hospital of Friuli Centrale, ASFC, P.le S. Maria della Misericordia no. 15, 33100 Udine, Italy
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Cavaliere F, Allegri M, Apan A, Calderini E, Carassiti M, Cohen E, Coluzzi F, DI Marco P, Langeron O, Rossi M, Spieth P, Turnbull D. A year in review in Minerva Anestesiologica 2020. Anesthesia, analgesia, and perioperative medicine. Minerva Anestesiol 2021; 87:253-265. [PMID: 33599441 DOI: 10.23736/s0375-9393.21.15570-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Franco Cavaliere
- IRCCS A. Gemelli University Polyclinic Foundation, Sacred Heart Catholic University, Rome Italy -
| | - Massimo Allegri
- Unit of Pain Therapy of Column and Athlete, Policlinic of Monza, Monza, Italy
| | - Alparslan Apan
- Department of Anesthesiology and Intensive Care, Faculty of Medicine, University of Giresun, Giresun, Turkey
| | - Edoardo Calderini
- Unit of Women-Child Anesthesia and Intensive Care, Maggiore Polyclinic Hospital, Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Massimiliano Carassiti
- Unit of Anesthesia, Intensive Care and Pain Management, Campus Bio-Medico University Hospital, Rome, Italy
| | - Edmond Cohen
- Department of Anesthesiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Thoracic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Flaminia Coluzzi
- Department of Medical and Surgical Sciences and Biotechnologies, Sapienza University, Polo Pontino, Latina, Italy.,Unit of Anesthesia, Intensive Care and Pain Medicine, Sant'Andrea University Hospital, Rome, Italy
| | - Pierangelo DI Marco
- Department of Internal Anesthesiologic and Cardiovascular Clinical Studies, Sapienza University, Rome, Italy
| | - Olivier Langeron
- Department of Anesthesia and Intensive Care, Henri Mondor University Hospital, Assistance Publique - Hôpitaux de Paris (AP-HP), University Paris-Est Créteil (UPEC), Paris, France
| | - Marco Rossi
- Institute of Anesthesia and Intensive Care, Sacred Heart Catholic University, Rome, Italy
| | - Peter Spieth
- Department of Anesthesiology and Critical Care Medicine, University Hospital Dresden, Dresden, Germany
| | - David Turnbull
- Department of Anaesthetics and Neuro Critical Care, Royal Hallamshire Hospital, Sheffield, UK
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