1
|
Behnoush AH, Khalaji A, Rezaee M, Momtahen S, Mansourian S, Bagheri J, Masoudkabir F, Hosseini K. Machine learning-based prediction of 1-year mortality in hypertensive patients undergoing coronary revascularization surgery. Clin Cardiol 2023; 46:269-278. [PMID: 36588391 PMCID: PMC10018097 DOI: 10.1002/clc.23963] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 12/12/2022] [Accepted: 12/19/2022] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND Machine learning (ML) has shown promising results in all fields of medicine, including preventive cardiology. Hypertensive patients are at higher risk of mortality after coronary artery bypass graft (CABG) surgery; thus, we aimed to design and evaluate five ML models to predict 1-year mortality among hypertensive patients who underwent CABG. HYOTHESIS ML algorithms can significantly improve mortality prediction after CABG. METHODS Tehran Heart Center's CABG data registry was used to extract several baseline and peri-procedural characteristics and mortality data. The best features were chosen using random forest (RF) feature selection algorithm. Five ML models were developed to predict 1-year mortality: logistic regression (LR), RF, artificial neural network (ANN), extreme gradient boosting (XGB), and naïve Bayes (NB). The area under the curve (AUC), sensitivity, and specificity were used to evaluate the models. RESULTS Among the 8,493 hypertensive patients who underwent CABG (mean age of 68.27 ± 9.27 years), 303 died in the first year. Eleven features were selected as the best predictors, among which total ventilation hours and ejection fraction were the leading ones. LR showed the best prediction ability with an AUC of 0.82, while the least AUC was for the NB model (0.79). Among the subgroups, the highest AUC for LR model was for two age range groups (50-59 and 80-89 years), overweight, diabetic, and smoker subgroups of hypertensive patients. CONCLUSIONS All ML models had excellent performance in predicting 1-year mortality among CABG hypertension patients, while LR was the best regarding AUC. These models can help clinicians assess the risk of mortality in specific subgroups at higher risk (such as hypertensive ones).
Collapse
Affiliation(s)
- Amir Hossein Behnoush
- Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.,Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.,School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.,Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Amirmohammad Khalaji
- Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.,Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.,School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.,Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Malihe Rezaee
- Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.,Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.,Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.,School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Shahram Momtahen
- Department of Surgery, Tehran Heart Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Soheil Mansourian
- Department of Surgery, Tehran Heart Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Jamshid Bagheri
- Department of Surgery, Tehran Heart Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Farzad Masoudkabir
- Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.,Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Kaveh Hosseini
- Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.,Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| |
Collapse
|
2
|
Dong T, Sinha S, Zhai B, Fudulu DP, Chan J, Narayan P, Judge A, Caputo M, Dimagli A, Benedetto U, Angelini GD. Cardiac surgery risk prediction using ensemble machine learning to incorporate legacy risk scores: A benchmarking study. Digit Health 2023; 9:20552076231187605. [PMID: 37492033 PMCID: PMC10363892 DOI: 10.1177/20552076231187605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 06/23/2023] [Indexed: 07/27/2023] Open
Abstract
Objective The introduction of new clinical risk scores (e.g. European System for Cardiac Operative Risk Evaluation (EuroSCORE) II) superseding original scores (e.g. EuroSCORE I) with different variable sets typically result in disparate datasets due to high levels of missingness for new score variables prior to time of adoption. Little is known about the use of ensemble learning to incorporate disparate data from legacy scores. We tested the hypothesised that Homogenenous and Heterogeneous Machine Learning (ML) ensembles will have better performance than ensembles of Dynamic Model Averaging (DMA) for combining knowledge from EuroSCORE I legacy data with EuroSCORE II data to predict cardiac surgery risk. Methods Using the National Adult Cardiac Surgery Audit dataset, we trained 12 different base learner models, based on two different variable sets from either EuroSCORE I (LogES) or EuroScore II (ES II), partitioned by the time of score adoption (1996-2016 or 2012-2016) and evaluated on holdout set (2017-2019). These base learner models were ensembled using nine different combinations of six ML algorithms to produce homogeneous or heterogeneous ensembles. Performance was assessed using a consensus metric. Results Xgboost homogenous ensemble (HE) was the highest performing model (clinical effectiveness metric (CEM) 0.725) with area under the curve (AUC) (0.8327; 95% confidence interval (CI) 0.8323-0.8329) followed by Random Forest HE (CEM 0.723; AUC 0.8325; 95%CI 0.8320-0.8326). Across different heterogenous ensembles, significantly better performance was obtained by combining siloed datasets across time (CEM 0.720) than building ensembles of either 1996-2011 (t-test adjusted, p = 1.67×10-6) or 2012-2019 (t-test adjusted, p = 1.35×10-193) datasets alone. Conclusions Both homogenous and heterogenous ML ensembles performed significantly better than DMA ensemble of Bayesian Update models. Time-dependent ensemble combination of variables, having differing qualities according to time of score adoption, enabled previously siloed data to be combined, leading to increased power, clinical interpretability of variables and usage of data.
Collapse
Affiliation(s)
- Tim Dong
- Translational Health Sciences, Bristol Heart Institute, University of Bristol, Bristol, UK
| | - Shubhra Sinha
- Translational Health Sciences, Bristol Heart Institute, University of Bristol, Bristol, UK
| | - Ben Zhai
- School of Computing Science, Northumbria University, Newcastle upon Tyne, UK
| | - Daniel P Fudulu
- Translational Health Sciences, Bristol Heart Institute, University of Bristol, Bristol, UK
| | - Jeremy Chan
- Translational Health Sciences, Bristol Heart Institute, University of Bristol, Bristol, UK
| | - Pradeep Narayan
- Department of Cardiac Surgery, Rabindranath Tagore International Institute of Cardiac Sciences, Kolkata, India
| | - Andy Judge
- Translational Health Sciences, Bristol Heart Institute, University of Bristol, Bristol, UK
| | - Massimo Caputo
- Translational Health Sciences, Bristol Heart Institute, University of Bristol, Bristol, UK
| | - Arnaldo Dimagli
- Translational Health Sciences, Bristol Heart Institute, University of Bristol, Bristol, UK
| | - Umberto Benedetto
- Translational Health Sciences, Bristol Heart Institute, University of Bristol, Bristol, UK
| | - Gianni D Angelini
- Translational Health Sciences, Bristol Heart Institute, University of Bristol, Bristol, UK
| |
Collapse
|
3
|
Khaleghi Rad M, Pirmoradi MM, Doosti-Irani A, Thiruganasambandamoorthy V, Mirfazaelian H. The performance of HEAR score for identification of low-risk chest pain: a systematic review and meta-analysis. Eur J Emerg Med 2022; 29:173-187. [PMID: 35446265 DOI: 10.1097/mej.0000000000000921] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Chest pain is one of the most common presentations to the emergency department (ED) and HEART score (history, ECG, age, risk factors, and cardiac troponin) is recommended for risk stratification. It has been proposed that the sum of four items with no troponin (HEAR score) below 2 can be used safely to lower testing and reduce length of stay. To assess the performance of the HEAR score in hospital and prehospital settings, we performed a systematic review and meta-analysis. English studies on the performance of the HEAR score in patients with acute chest pain were included. They were excluded if data are inaccessible. MEDLINE, Embase, Evidence-Based Medicine Reviews, Scopus, and web of science were searched from 1946 to July 2021. The quality of studies was assessed using Quality Assessment of Diagnostic Accuracy Studies version 2. Acute coronary syndrome or major adverse cardiac events prediction were outcomes of interest. The performance indices with 95% confidence intervals (CIs) were extracted. Inverse variance and the random-effects model were used to report the results. Of the 692 articles on the HEAR score, 10 studies were included in the analysis with 33 843 patients. Studies were at low to moderate risk of bias. Three studies were in prehospital and three were retrospective. The pooling of data on the HEAR score showed that the sensitivity at the HEAR<2, <3, and <4 cutoffs in the ED were 99.03% (95% CI, 98.29-99.77), 97.54% (95% CI, 94.50-100), and 91.80% (95% CI, 84.62-98.98), respectively. The negative predictive values (NPVs) for the above cutoffs were 99.84% (95% CI, 99.72-99.95), 99.75% (95% CI, 99.65-99.85), and 99.57% (95% CI, 99.11-100), respectively. Of note, for the HEAR<2, negative likelihood ratio was 0.07 (95% CI, 0.02-0.12). In the prehospital, at the HEAR<4 cutoff, the pooled sensitivity and NPV were 85.01% (95% CI, 80.56-89.47) and 91.48% (95% CI, 87.10-95.87), respectively. This study showed that in the ED, the HEAR score<2 can be used for an early discharge strategy. Currently, this score cannot be recommended in prehospital setting. Prospero (CRD42021273710).
Collapse
Affiliation(s)
- Mahsan Khaleghi Rad
- Department of Emergency Medicine, Tehran University of Medical Sciences, Tehran
| | | | - Amin Doosti-Irani
- Department of Epidemiology, School of Public Health and Modeling of Non-communicable Diseases Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
| | | | - Hadi Mirfazaelian
- Department of Emergency Medicine, Tehran University of Medical Sciences, Tehran
| |
Collapse
|
4
|
Yu Y, Peng C, Zhang Z, Shen K, Zhang Y, Xiao J, Xi W, Wang P, Rao J, Jin Z, Wang Z. Machine Learning Methods for Predicting Long-Term Mortality in Patients After Cardiac Surgery. Front Cardiovasc Med 2022; 9:831390. [PMID: 35592400 PMCID: PMC9110683 DOI: 10.3389/fcvm.2022.831390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 03/21/2022] [Indexed: 11/21/2022] Open
Abstract
Objective: This study aims to construct and validate several machine learning (ML) algorithms to predict long-term mortality and identify risk factors in unselected patients post-cardiac surgery. Methods The Medical Information Mart for Intensive Care (MIMIC-III) database was used to perform a retrospective administrative database study. Candidate predictors consisted of the demographics, comorbidity, vital signs, laboratory test results, scoring systems, and treatment information on the first day of ICU admission. Four-year mortality was set as the study outcome. We used the ML methods of logistic regression (LR), artificial neural network (NNET), naïve bayes (NB), gradient boosting machine (GBM), adapting boosting (Ada), random forest (RF), bagged trees (BT), and eXtreme Gradient Boosting (XGB). The prognostic capacity and clinical utility of these ML models were compared using the area under the receiver operating characteristic curves (AUC), calibration curves, and decision curve analysis (DCA). Results Of 7,368 patients in MIMIC-III included in the final cohort, a total of 1,337 (18.15%) patients died during a 4-year follow-up. Among 65 variables extracted from the database, a total of 25 predictors were selected using recursive feature elimination and included in the subsequent analysis. The Ada model performed best among eight models in both discriminatory ability with the highest AUC of 0.801 and goodness of fit (visualized by calibration curve). Moreover, the DCA shows that the net benefit of the RF, Ada, and BT models surpassed that of other ML models for almost all threshold probability values. Additionally, through the Ada technique, we determined that red blood cell distribution width (RDW), blood urea nitrogen (BUN), SAPS II, anion gap (AG), age, urine output, chloride, creatinine, congestive heart failure, and SOFA were the Top 10 predictors in the feature importance rankings. Conclusions The Ada model performs best in predicting 4-year mortality after cardiac surgery among the eight ML models, which might have significant application in the development of early warning systems for patients following operations.
Collapse
Affiliation(s)
- Yue Yu
- Department of Cardiothoracic Surgery, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Chi Peng
- Department of Health Statistics, Naval Medical University, Shanghai, China
| | - Zhiyuan Zhang
- Department of Cardiothoracic Surgery, No. 988 Hospital of Joint Logistic Support Force of PLA, Zhengzhou, China
| | - Kejia Shen
- Department of Personnel Administration, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Yufeng Zhang
- Department of Cardiothoracic Surgery, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Jian Xiao
- Department of Cardiothoracic Surgery, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Wang Xi
- Department of Cardiothoracic Surgery, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Pei Wang
- Department of Cardiothoracic Surgery, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Jin Rao
- Department of Cardiothoracic Surgery, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Zhichao Jin
- Department of Health Statistics, Naval Medical University, Shanghai, China
- *Correspondence: Zhichao Jin
| | - Zhinong Wang
- Department of Cardiothoracic Surgery, Changzheng Hospital, Naval Medical University, Shanghai, China
- Zhinong Wang
| |
Collapse
|
5
|
Fan Y, Dong J, Wu Y, Shen M, Zhu S, He X, Jiang S, Shao J, Song C. Development of machine learning models for mortality risk prediction after cardiac surgery. Cardiovasc Diagn Ther 2022; 12:12-23. [PMID: 35282663 PMCID: PMC8898685 DOI: 10.21037/cdt-21-648] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Accepted: 12/28/2021] [Indexed: 02/12/2024]
Abstract
BACKGROUND We developed machine learning models that combine preoperative and intraoperative risk factors to predict mortality after cardiac surgery. METHODS Machine learning involving random forest, neural network, support vector machine, and gradient boosting machine was developed and compared with the risk scores of EuroSCORE I and II, Society of Thoracic Surgeons (STS), as well as a logistic regression model. Clinical data were collected from patients undergoing adult cardiac surgery at the First Medical Centre of Chinese PLA General Hospital between December 2008 and December 2017. The primary outcome was post-operative mortality. Model performance was estimated using several metrics, including sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC). The visualization algorithm was implemented using Shapley's additive explanations. RESULTS A total of 5,443 patients were enrolled during the study period. The mean EuroSCORE II score was 3.7%, and the actual in-hospital mortality rate was 2.7%. For predicting operative mortality after cardiac surgery, the AUC scores were 0.87, 0.79, 0.81, and 0.82 for random forest, neural network, support vector machine, and gradient boosting machine, compared with 0.70, 0.73, 0.71, and 0.74 for EuroSCORE I and II, STS, and logistic regression model. Shapley's additive explanations analysis of random forest yielded the top-20 predictors and individual-level explanations for each prediction. CONCLUSIONS Machine learning models based on available clinical data may be superior to clinical scoring tools in predicting postoperative mortality in patients following cardiac surgery. Explanatory models show the potential to provide personalized risk profiles for individuals by accounting for the contribution of influencing factors. Additional prospective multicenter studies are warranted to confirm the clinical benefit of these machine learning-driven models.
Collapse
Affiliation(s)
- Yunlong Fan
- Medical School of Chinese PLA, Beijing, China
- Department of Cardiovascular Surgery, the First Medical Centre of Chinese PLA General Hospital, Beijing, China
| | - Junfeng Dong
- Department of Organ Transplantation, Changzhen Hospital, Navy Medical University, Shanghai, China
| | - Yuanbin Wu
- Medical School of Chinese PLA, Beijing, China
- Department of Cardiovascular Surgery, the First Medical Centre of Chinese PLA General Hospital, Beijing, China
| | - Ming Shen
- Department of Cardiology, The First Hospital of Hebei Medical University, Shijiazhuang, China
| | - Siming Zhu
- Medical School of Chinese PLA, Beijing, China
- Department of Cardiovascular Surgery, the First Medical Centre of Chinese PLA General Hospital, Beijing, China
| | - Xiaoyi He
- Medical School of Chinese PLA, Beijing, China
- Department of Cardiovascular Surgery, the First Medical Centre of Chinese PLA General Hospital, Beijing, China
| | - Shengli Jiang
- Department of Cardiovascular Surgery, the First Medical Centre of Chinese PLA General Hospital, Beijing, China
| | | | - Chao Song
- Medical School of Chinese PLA, Beijing, China
- Department of Cardiovascular Surgery, the First Medical Centre of Chinese PLA General Hospital, Beijing, China
| |
Collapse
|
6
|
Volk L, Chao J, Dombrovskiy V, Ikegami H, Russo MJ, Lemaire A, Lee LY. Impact of risk factors on in-hospital mortality for octogenarians undergoing cardiac surgery. J Card Surg 2021; 36:2400-2406. [PMID: 33821496 DOI: 10.1111/jocs.15532] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 12/22/2020] [Accepted: 01/21/2021] [Indexed: 12/21/2022]
Abstract
BACKGROUND Octogenarians undergoing cardiac surgery have higher mortality than their younger counterparts. OBJECTIVES To determine if various risk factors have the same effect on mortality in octogenarians as in younger patients. METHODS The National Inpatient Sample data set from 2004 to 2014 was queried to select patients aged 65 years and older who underwent either coronary artery bypass grafting (CABG), valvular heart surgery (VHS), or both (CABG + VHS) within 10 days of hospital admission. The patients were divided into two groups 65-79 years and 80 years and greater. Hospital mortality, patient demographics, comorbidities, and type of hospital admission was evaluated and compared using χ2 and multivariable logistic regressions. RESULTS About 397,713 patients were identified including 86,345 (21.7%) aged 80 and above. Octogenarians had higher in-hospital mortality for all procedures: CABG (4.94% vs. 2.39%, p < .001), VHS (5.49% vs. 4.08%, p < .001), and CABG + VHS (7.59% vs. 5.95%, p < .001), and this relationship persisted when gender, race, comorbidities, and type of hospital admission were controlled for: CABG (odds ratio [OR] = 1.71; 95% confidence interval [CI] 1.62-1.81); VHS (OR = 1.18; 95% CI 1.11-1.27); and CABH + VHS (OR = 1.17; 95%CI 1.10-1.26). Female gender, renal, or heart failure, nonelective admission, and CABG + VHS were associated with increased risk of in-hospital mortality. Octogenarians had higher rates of these factors (p < .001). The effect size of renal and heart failure and type of surgery was smaller for octogenarians. CONCLUSIONS Octogenarians undergoing cardiac surgery have higher rates of nonelective admissions, renal and heart failure, and female gender, which are most strongly associated with in-hospital mortality. Differing effect sizes suggest that certain risk factors, such as renal and heart failure, contribute more to mortality in younger patients.
Collapse
Affiliation(s)
- Lindsay Volk
- Department of Cardiothoracic Surgery, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA
| | - Joshua Chao
- Department of Cardiothoracic Surgery, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA
| | - Viktor Dombrovskiy
- Department of Cardiothoracic Surgery, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA
| | - Hirohisa Ikegami
- Department of Cardiothoracic Surgery, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA
| | - Mark J Russo
- Department of Cardiothoracic Surgery, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA
| | - Anthony Lemaire
- Department of Cardiothoracic Surgery, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA
| | - Leonard Y Lee
- Department of Cardiothoracic Surgery, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA
| |
Collapse
|
7
|
Provenchère S, Chevalier A, Ghodbane W, Bouleti C, Montravers P, Longrois D, Iung B. Is the EuroSCORE II reliable to estimate operative mortality among octogenarians? PLoS One 2017; 12:e0187056. [PMID: 29145434 PMCID: PMC5690588 DOI: 10.1371/journal.pone.0187056] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2017] [Accepted: 10/12/2017] [Indexed: 11/30/2022] Open
Abstract
Objectives Concerns have been raised about the predictive performance (PP) of the EuroSCORE I (ES I) to estimate operative mortality (OM) of patients aged ≥80. The EuroSCORE II (ES II) has been described to have better PP of OM but external validations are scarce. Furthermore, the PP of ES II has not been investigated among the octogenarians. The goal of the study was to compare the PP of ES II and ES I among the overall population and patients ≥ 80. Methods The ES I and ES II were computed for 7161 consecutive patients who underwent major cardiac surgery in a 7-year period. Discrimination was assessed by using the c- index and calibration with the Hosmer-Lemeshow (HL) and calibration plot by comparing predicted and observed mortality. Results From the global cohort of 7161 patients, 832 (12%) were ≥80. The mean values of ES I and ES II were 7.4±9.4 and 5.2±9.1 respectively for the whole cohort, 6.3±8.6 and 4.7±8.5 for the patients <80, 15.1±11.8 and 8.5±11.0 for the patients ≥80. The mortality was 9.38% (≥80) versus 5.18% (<80). The discriminatory power was good for the two algorithms among the whole population and the <80 but less satisfying among the ≥80 (AUC 0.64 [0.58–0.71] for ES I and 0.67 [0.60–0.73] for the ES II without significant differences (p = 0.35) between the two scores. For the octogenarians, the ES II had a fair calibration until 10%-predicted values and over-predicted beyond. Conclusions The ES II has a better PP than the ES I among patients <80. Its discrimination and calibration are less satisfying in patients ≥80, showing an overestimation in the elderly at very high-surgical risk. Nevertheless, it shows an acceptable calibration until 10%- predicted mortality.
Collapse
Affiliation(s)
- Sophie Provenchère
- Département d’Anesthésie-Réanimation, APHP, Hôpital Bichat-Claude Bernard, Paris, France
- INSERM Centre d’Investigation Clinique 1425, APHP, Hôpital Bichat-Claude Bernard, Paris, France
- * E-mail:
| | - Arnaud Chevalier
- Département d’Anesthésie-Réanimation, APHP, Hôpital Bichat-Claude Bernard, Paris, France
| | - Walid Ghodbane
- Département de Chirurgie Cardiaque, APHP, Hôpital Bichat-Claude Bernard, Paris, France
| | - Claire Bouleti
- Département de Cardiologie, APHP, Hôpital Bichat-Claude Bernard, Paris, France
| | - Philippe Montravers
- Département d’Anesthésie-Réanimation, APHP, Hôpital Bichat-Claude Bernard, Paris, France
- Université Paris 7-Diderot, Paris, France
| | - Dan Longrois
- Département d’Anesthésie-Réanimation, APHP, Hôpital Bichat-Claude Bernard, Paris, France
- Université Paris 7-Diderot, Paris, France
- INSERM 1148, Paris, France
| | - Bernard Iung
- Département de Cardiologie, APHP, Hôpital Bichat-Claude Bernard, Paris, France
- Université Paris 7-Diderot, Paris, France
- INSERM 1148, Paris, France
| |
Collapse
|
8
|
Porizka M, Kunstyr J, Vanek T, Nejedly M, Buckova J, Mokrejs J, Mosna F. Postoperative Outcome of High-Risk Octogenarians Undergoing Cardiac Surgery: A Multicenter Observational Retrospective Study. Ann Thorac Cardiovasc Surg 2017; 23:188-195. [PMID: 28539544 DOI: 10.5761/atcs.oa.16-00280] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
PURPOSE Cardiac surgery in octogenarians with severely deteriorated functional status is increasingly common, but outcome data are still limited. The aim of this study was to compare postoperative outcome, survival, and quality of life of low-, medium-, and high-risk octogenarians undergoing cardiac surgery. METHODS In all, 285 Czech octogenarians who underwent any cardiac surgical procedure between January 2011 and December 2012 were included in the study. Five out of all twelve national adult cardiac surgical centers participated in the study, representing almost half of all octogenarians operated in our country in that period. Patients' perioperative data were analyzed retrospectively. Follow-up was performed by interviewing patients by telephone. RESULTS There was higher 30-day mortality (20% vs. 6.4% vs. 5.2%, respectively, p <0.001), lower 2-year survival (60% vs. 84.0% vs. 85.4%, respectively, p <0.05), and lower Karnofsky score (44.4 vs. 70.1 vs. 70.6, respectively, p <0.001) in high-risk group compared with medium- and low-risk groups, respectively. Greater improvement in New York Heart Association (NYHA) status was noted in high- and medium-risk groups compared with low-risk group (51% vs. 45% vs. 24%, respectively, p <0.05). CONCLUSION High perioperative mortality, poor 2-year survival, and low postoperative quality of life have been observed in high-risk octogenarians undergoing cardiac surgery.
Collapse
Affiliation(s)
- Michal Porizka
- General University Hospital, Department of Anaesthesiology, Resuscitation and Intensive Medicine, The First Faculty of Medicine, Charles University in Prague, Prague, Czech Republic
| | - Jan Kunstyr
- General University Hospital, Department of Anaesthesiology, Resuscitation and Intensive Medicine, The First Faculty of Medicine, Charles University in Prague, Prague, Czech Republic
| | - Tomas Vanek
- University Hospital Kralovske Vinohrady, Department of Cardiac Surgery, Third Faculty of Medicine, Charles University in Prague, Prague, Czech Republic
| | - Michal Nejedly
- University Hospital Kralovske Vinohrady, Department of Cardiac Surgery, Third Faculty of Medicine, Charles University in Prague, Prague, Czech Republic
| | - Jana Buckova
- Na Homolce Hospital, Department of Cardiology, Prague, Czech Republic
| | - Jiri Mokrejs
- Faculty Hospital Pilsen, Department of Cardiac Surgery, Charles University in Prague, Pilsen-Bory, Czech Republic
| | - Frantisek Mosna
- Motol University Hospital, Department of Anaesthesiology and Intensive Care Medicine, The Second Faculty of Medicine, Charles University in Prague, Prague, Czech Republic
| |
Collapse
|
9
|
Allyn J, Allou N, Augustin P, Philip I, Martinet O, Belghiti M, Provenchere S, Montravers P, Ferdynus C. A Comparison of a Machine Learning Model with EuroSCORE II in Predicting Mortality after Elective Cardiac Surgery: A Decision Curve Analysis. PLoS One 2017; 12:e0169772. [PMID: 28060903 PMCID: PMC5218502 DOI: 10.1371/journal.pone.0169772] [Citation(s) in RCA: 97] [Impact Index Per Article: 13.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2016] [Accepted: 12/21/2016] [Indexed: 12/15/2022] Open
Abstract
Background The benefits of cardiac surgery are sometimes difficult to predict and the decision to operate on a given individual is complex. Machine Learning and Decision Curve Analysis (DCA) are recent methods developed to create and evaluate prediction models. Methods and finding We conducted a retrospective cohort study using a prospective collected database from December 2005 to December 2012, from a cardiac surgical center at University Hospital. The different models of prediction of mortality in-hospital after elective cardiac surgery, including EuroSCORE II, a logistic regression model and a machine learning model, were compared by ROC and DCA. Of the 6,520 patients having elective cardiac surgery with cardiopulmonary bypass, 6.3% died. Mean age was 63.4 years old (standard deviation 14.4), and mean EuroSCORE II was 3.7 (4.8) %. The area under ROC curve (IC95%) for the machine learning model (0.795 (0.755–0.834)) was significantly higher than EuroSCORE II or the logistic regression model (respectively, 0.737 (0.691–0.783) and 0.742 (0.698–0.785), p < 0.0001). Decision Curve Analysis showed that the machine learning model, in this monocentric study, has a greater benefit whatever the probability threshold. Conclusions According to ROC and DCA, machine learning model is more accurate in predicting mortality after elective cardiac surgery than EuroSCORE II. These results confirm the use of machine learning methods in the field of medical prediction.
Collapse
Affiliation(s)
- Jérôme Allyn
- Réanimation Polyvalente, Centre Hospitalier Universitaire Félix Guyon, Saint-Denis, France
- Département d'Anesthésie Réanimation, APHP, CHU Bichat-Claude Bernard, Paris, France
- * E-mail:
| | - Nicolas Allou
- Réanimation Polyvalente, Centre Hospitalier Universitaire Félix Guyon, Saint-Denis, France
- Département d'Anesthésie Réanimation, APHP, CHU Bichat-Claude Bernard, Paris, France
| | - Pascal Augustin
- Département d'Anesthésie Réanimation, APHP, CHU Bichat-Claude Bernard, Paris, France
| | - Ivan Philip
- Département d'Anesthésie Réanimation, APHP, CHU Bichat-Claude Bernard, Paris, France
- Département d'Anesthésie Réanimation, Institut Mutualiste Montsouris, 42 boulevard Jourdan, Paris, France
| | - Olivier Martinet
- Réanimation Polyvalente, Centre Hospitalier Universitaire Félix Guyon, Saint-Denis, France
| | - Myriem Belghiti
- Réanimation Polyvalente, Centre Hospitalier Universitaire Félix Guyon, Saint-Denis, France
| | - Sophie Provenchere
- Département d'Anesthésie Réanimation, APHP, CHU Bichat-Claude Bernard, Paris, France
| | - Philippe Montravers
- Département d'Anesthésie Réanimation, APHP, CHU Bichat-Claude Bernard, Paris, France
- Université Denis Diderot, PRESS Sorbonne Cité, Paris, France
| | - Cyril Ferdynus
- Unité de Soutien Méthodologique, CHU de La Réunion, Saint-Denis, France
- INSERM, CIC 1410, Saint-Pierre, France
| |
Collapse
|
10
|
Augustin P, Tanaka S, Chhor V, Provenchère S, Arnaudovski D, Ibrahim H, Dilly MP, Allou N, Montravers P, Philip I. Prognosis of Prolonged Intensive Care Unit Stay After Aortic Valve Replacement for Severe Aortic Stenosis in Octogenarians. J Cardiothorac Vasc Anesth 2016; 30:1555-1561. [PMID: 27720290 DOI: 10.1053/j.jvca.2016.07.029] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2016] [Indexed: 11/11/2022]
Abstract
OBJECTIVES Octogenarians considered for cardiac surgery encounter more complications than other patients. Postoperative complications raise the question of continuation of high-cost care for patients with limited life expectancy. Duration of hospitalization in intensive care after cardiac surgery may differ between octogenarians and other patients. The objectives were evaluating the mortality rate of octogenarians experiencing prolonged hospitalization in intensive care and defining the best cut-off for prolonged intensive care unit length of stay. DESIGN A single-center observational study. SETTING A postoperative surgical intensive care unit in a tertiary teaching hospital in Paris, France. PARTICIPANTS All consecutive patients older than 80 years considered for aortic valve replacement for aortic stenosis were included. MEASUREMENTS AND MAIN RESULTS Mortality rate was determined among patients experiencing prolonged stay in intensive care with organ failure and without organ failure. An ROC curve determined the optimal cut-off defining prolonged hospitalization in intensive care according to the occurrence of postoperative complications. Multivariate analysis determined risk factors for early death or prolonged intensive care stay. The optimal cut-off defining prolonged intensive care unit length of stay was 4 days. Low ventricular ejection fraction (odds ratio [OR] = 0.95; 95% confidence interval [CI] 0.96-0.83; p = 0.0016), coronary disease (OR = 2.34; 95% CI 1.19-4.85; p = 0.014), and need for catecholamine (OR = 2.79; 95% CI 1.33-5.88; p = 0.0068) were associated with eventful postoperative course. There was not a hospitalization duration beyond which the prognosis significantly worsened. CONCLUSIONS Prolonged length of stay in ICU without organ failure is not associated with increased mortality. No specific duration of hospitalization in intensive care was associated with increased mortality. Continuation of care should be discussed on an individual basis.
Collapse
Affiliation(s)
- Pascal Augustin
- Département d'Anesthésie Réanimation Chirurgicale, Groupe Hospitalier Bichat Claude Bernard, Assistance Publique-Hôpitaux de Paris, Université Paris 7, Denis Diderot, Paris, France.
| | - Sebastien Tanaka
- Département d'Anesthésie Réanimation Chirurgicale, Groupe Hospitalier Bichat Claude Bernard, Assistance Publique-Hôpitaux de Paris, Université Paris 7, Denis Diderot, Paris, France
| | - Vibol Chhor
- †Département d'Anesthésie Réanimation Chirurgicale, Hôpital Européen Georges Pompidou, Assistance Publique-Hôpitaux de Paris, Université Paris 5, René Descartes, Paris, France
| | - Sophie Provenchère
- Département d'Anesthésie Réanimation Chirurgicale, Groupe Hospitalier Bichat Claude Bernard, Assistance Publique-Hôpitaux de Paris, Université Paris 7, Denis Diderot, Paris, France
| | - Darko Arnaudovski
- Département d'Anesthésie Réanimation Chirurgicale, Groupe Hospitalier Bichat Claude Bernard, Assistance Publique-Hôpitaux de Paris, Université Paris 7, Denis Diderot, Paris, France
| | - Hassan Ibrahim
- Département d'Anesthésie Réanimation Chirurgicale, Groupe Hospitalier Bichat Claude Bernard, Assistance Publique-Hôpitaux de Paris, Université Paris 7, Denis Diderot, Paris, France
| | - Marie-Pierre Dilly
- Département d'Anesthésie Réanimation Chirurgicale, Groupe Hospitalier Bichat Claude Bernard, Assistance Publique-Hôpitaux de Paris, Université Paris 7, Denis Diderot, Paris, France
| | - Nicolas Allou
- Département d'Anesthésie Réanimation Chirurgicale, Groupe Hospitalier Bichat Claude Bernard, Assistance Publique-Hôpitaux de Paris, Université Paris 7, Denis Diderot, Paris, France
| | - Philippe Montravers
- Département d'Anesthésie Réanimation Chirurgicale, Groupe Hospitalier Bichat Claude Bernard, Assistance Publique-Hôpitaux de Paris, Université Paris 7, Denis Diderot, Paris, France
| | - Ivan Philip
- Département d'Anesthésie Réanimation Chirurgicale, Groupe Hospitalier Bichat Claude Bernard, Assistance Publique-Hôpitaux de Paris, Université Paris 7, Denis Diderot, Paris, France; ‡Service d'Anesthésie, Institut Mutualiste Montsouris, Paris, France
| |
Collapse
|
11
|
Bonnet V, Boisselier C, Saplacan V, Belin A, Gérard JL, Fellahi JL, Hanouz JL, Fischer MO. The role of age and comorbidities in postoperative outcome of mitral valve repair: A propensity-matched study. Medicine (Baltimore) 2016; 95:e3938. [PMID: 27336886 PMCID: PMC4998324 DOI: 10.1097/md.0000000000003938] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
The average age of patients undergoing mitral valve repair is increasing each year. This retrospective study aimed to compare postoperative complications of mitral valve repair (known to be especially high-risk) between 2 age groups: under and over the age of 80.Patients who underwent mitral valve repair were divided into 2 groups: group 1 (<80 years old) and group 2 (≥80 years old). Baseline characteristics, pre- and postoperative hemodynamic data, surgical characteristics, and postoperative follow-up data until hospital discharge were collected.A total of 308 patients were included: 264 in group 1 (age 63 ± 13 years) and 44 in group 2 (age 83 ± 2 years). Older patients had more comorbidities (atrial fibrillation, history of cardiac decompensation, systemic hypertension, pulmonary hypertension, and chronic kidney disease) and they presented more postoperative complications (50.0% vs 33.7%; P = 0.043), with a longer hospital stay (8.9 ± 6.9 vs 6.6 ± 4.6 days; P = 0.005). To assess the burden of age, a propensity score was awarded to postoperative complications. Active smoking, chronic pulmonary disease, chronic kidney disease, associated ischemic heart disease, obesity, and cardio pulmonary by-pass duration were described as independent risk factors. When matched on this propensity score, there was no difference in morbidity or mortality between group 1 and group 2.Older patients suffered more postoperative complications, which were related to their comorbidities and not only to their age.
Collapse
Affiliation(s)
- Vincent Bonnet
- Pôle Réanimations Anesthésie SAMU/SMUR, CHU de Caen, Avenue de la Côte de Nacre
| | - Clément Boisselier
- Pôle Réanimations Anesthésie SAMU/SMUR, CHU de Caen, Avenue de la Côte de Nacre
| | | | - Annette Belin
- Department of Cardiology, University Hospital of Caen, Caen
| | - Jean-Louis Gérard
- Pôle Réanimations Anesthésie SAMU/SMUR, CHU de Caen, Avenue de la Côte de Nacre
| | - Jean-Luc Fellahi
- Department of Anaesthesiology and Critical Care, Hôpital Cardiologique Louis Pradel, Avenue du Doyen Lepine
- Faculty of Medicine, University of Lyon 1 Claude Bernard, Lyon
| | - Jean-Luc Hanouz
- Pôle Réanimations Anesthésie SAMU/SMUR, CHU de Caen, Avenue de la Côte de Nacre
- EA 4650, Université de Caen Basse-Normandie, Esplanade de la Paix, CS, Caen, France
| | - Marc-Olivier Fischer
- Pôle Réanimations Anesthésie SAMU/SMUR, CHU de Caen, Avenue de la Côte de Nacre
- EA 4650, Université de Caen Basse-Normandie, Esplanade de la Paix, CS, Caen, France
| |
Collapse
|
12
|
Mikkelsen MM, Johnsen SP, Nielsen PH, Jakobsen CJ. The EuroSCORE in Western Denmark: A Population-Based Study. J Cardiothorac Vasc Anesth 2012; 26:258-64. [DOI: 10.1053/j.jvca.2011.09.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2011] [Indexed: 11/11/2022]
|
13
|
Oliveira SM, Correia AS, Paiva M, Gonçalves A, Pereira M, Alves E, Dias P, Almeida R, Abreu A, Pinho P. Long-term survival, autonomy, and quality of life of elderly patients undergoing aortic valve replacement. J Card Surg 2011; 27:20-3. [PMID: 22211277 DOI: 10.1111/j.1540-8191.2011.01360.x] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
AIMS We reviewed the long-term survival, autonomy, and quality of life (QoL) of elderly patients undergoing aortic valve replacement (AVR). METHODS Records of patients ≥75 years old that underwent AVR from 2002 to 2006 were retrospectively analyzed. Functional status was classified with Barthel Index (BI). QoL was presumed as the self-perception of well-being after AVR. Independent predictors of mortality were identified using the Cox proportional hazards model. RESULTS We included 114 patients, with a mean age of 78.5 ± 2.5 years. Seventy (59.8%) patients were females. Mean additive and logistic EuroSCORE were 7 ± 2 and 9 ± 7, respectively. Follow-up on vital status was achieved for 113 (99.1%) patients after a mean period of 47.2 ± 23.4 months. Twenty-seven (23.7%) patients died (including three operative deaths). Survival up to one, three, and five years of follow-up was 94.4%, 86.7%, and 76.1%, respectively. Multivariate analysis showed that pulmonary hypertension and diabetes were independent predictors of all-cause mortality. Information on BI score and QoL was obtained for 77 (89.5%) and patients. Among those, 69 (89.6%) were autonomous according to BI and 72 (93.5%) considered having had an improvement in QoL. CONCLUSION Patients ≥75 years old undergoing AVR presented good medium-term survival. Predictors of an adverse outcome were significant pulmonary hypertension and diabetes. At follow-up, most achieved improvement of QoL and remained autonomous. These results stress that excellent long-term outcomes with AVR can be achieved in appropriately selected elderly patients.
Collapse
|
14
|
Lebreton G, Merle S, Inamo J, Hennequin JL, Sanchez B, Rilos Z, Roques F. Limitations in the inter-observer reliability of EuroSCORE: what should change in EuroSCORE II? Eur J Cardiothorac Surg 2011; 40:1304-8. [DOI: 10.1016/j.ejcts.2011.02.067] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2010] [Revised: 02/23/2011] [Accepted: 02/25/2011] [Indexed: 10/18/2022] Open
|