Clinical utility of the revised cardiac risk index in older Chinese patients with known coronary artery disease

Machine learning-based predictive models for perioperative major adverse cardiovascular events in patients with stable coronary artery disease undergoing noncardiac surgery

BACKGROUND AND OBJECTIVE

Accurate prediction of perioperative major adverse cardiovascular events (MACEs) is crucial, as it not only aids clinicians in comprehensively assessing patients' surgical risks and tailoring personalized surgical and perioperative management plans, but also for information-based shared decision-making with patients and efficient allocation of medical resources. This study developed and validated a machine learning (ML) model using accessible preoperative clinical data to predict perioperative MACEs in stable coronary artery disease (SCAD) patients undergoing noncardiac surgery (NCS).

METHODS

We collected data from 9171 adult SCAD patients who underwent NCS and extracted 64 preoperative variables. First, the optimal data imputation, resampling, and feature selection methods were compared and selected to deal with missing data values and imbalances. Then, nine independent machine learning models (logistic regression (LR), support vector machine, Gaussian Naive Bayes (GNB), random forest, gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), light gradient boosting machine, categorical boosting (CatBoost), and deep neural network) and a stacking ensemble model were constructed and compared with the validated Revised Cardiac Risk Index's (RCRI) model for predictive performance, which was evaluated using the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPRC), calibration curve, and decision curve analysis (DCA). To reduce overfitting and enhance robustness, we performed hyperparameter tuning and 5-fold cross-validation. Finally, the Shapley additive interpretation (SHAP) method and a partial dependence plot (PDP) were used to determine the optimal ML model.

RESULTS

Of the 9,171 patients, 514 (5.6 %) developed MACEs. 24 significant preoperative features were selected for model development and evaluation. All ML models performed well, with AUROC above 0.88 and AUPRC above 0.39, outperforming the AUROC (0.716) and AUPRC (0.185) of RCRI (P < 0.001). The best independent model was XGBoost (AUROC = 0.898, AUPRC = 0.479). The calibration curve accurately predicted the risk of MACEs (Brier score = 0.040), and the DCA results showed that XGBoost had a high net benefit for predicting MACEs. The top-ranked stacking ensemble model, consisting of CatBoost, GBDT, GNB, and LR, proved to be the best (AUROC 0.894, AUPRC 0.485). We identified the top 20 most important features using the mean absolute SHAP values and depicted their effects on model predictions using PDP.

CONCLUSIONS

This study combined missing-value imputation, feature screening, unbalanced data processing, and advanced machine learning methods to successfully develop and verify the first ML-based perioperative MACEs prediction model for patients with SCAD, which is more accurate than RCRI and enables effective identification of high-risk patients and implementation of targeted interventions to reduce the incidence of MACEs.

Perioperative Focused Transthoracic Echocardiogram Evaluations for Elderly Hip Fractures: A Narrative Review of Literature and Recommendations

Multiple comorbidities and limited information at first contact with elderly hip-fracture patients have made it difficult to create safe perioperative plans. Various risk-stratification calculators, laboratory tests and imaging modalities are used to aid anesthesiologists in identifying which patients may need further evaluation and testing before surgery. Delaying surgical intervention in this population for >24 to 48 hours significantly increase perioperative complications such as myocardial infarction, deep venous thrombosis, pulmonary embolism, or pneumonia. Transthoracic echocardiograms (TTEs) are commonly used to identify pertinent cardiac pathologies that could alter anesthetic management. However, their use can often delay care, and its clinical utility has remained a subject of debate. Point-of-care ultrasound (POCUS) has been recognized as an effective tool to efficiently screen patients who might have underlying cardiac pathologies. Thus, anesthesiologists should utilize POCUS skill sets to guide their clinical decision-making and perioperative planning.

A New Risk Prediction Model for the Assessment of Myocardial Injury in Elderly Patients Undergoing Non-Elective Surgery

Background: Currently, recommended pre-operative risk assessment models including the revised cardiac risk index (RCRI) are not very effective in predicting postoperative myocardial damage after non-elective surgery, especially for elderly patients. Aims: This study aimed to create a new risk prediction model to assess myocardial injury after non-cardiac surgery (MINS) in elderly patients and compare it with the RCRI, a well-known pre-operative risk prediction model. Materials and Methods: This retrospective study included 370 elderly patients who were over 65 years of age and had non-elective surgery in a tertiary hospital. Each patient underwent detailed physical evaluations before the surgery. The study cohort was divided into two groups: patients who had MINS and those who did not. Results: In total, 13% (48 out of 370 patients) of the patients developed MINS. Multivariable analysis revealed that creatinine, lymphocyte, aortic regurgitation (moderate-severe), stroke, hemoglobin, ejection fraction, and D-dimer were independent determinants of MINS. By using these parameters, a model called "CLASHED" was developed to predict postoperative MINS. The ROC analysis comparison demonstrated that the new risk prediction model was significantly superior to the RCRI in predicting MINS in elderly patients undergoing non-elective surgery (AUC: 0.788 vs. AUC: 0.611, p < 0.05). Conclusions: Our study shows that the new risk preoperative model successfully predicts MINS in elderly patients undergoing non-elective surgery. In addition, this new model is found to be superior to the RCRI in predicting MINS.

Development and validation of a novel score for predicting perioperative major adverse cardiovascular events in patients with stable coronary artery disease undergoing noncardiac surgery

BACKGROUND

A model developed specifically for stable coronary artery disease (SCAD) patients to predict perioperative major adverse cardiovascular events (MACE) has not been previously reported.

METHODS

The derivation cohort consisted of 5780 patients with SCAD undergoing noncardiac surgery at the First Affiliated Hospital of Zhejiang University School of Medicine, from January 1, 2013 until May 31, 2021. The validation cohort consisted of 2677 similar patients from June 1, 2021 to May 31, 2023. The primary outcome was a composite of MACEs (death, resuscitated cardiac arrest, myocardial infarction, heart failure, and stroke) intraoperatively or during hospitalization postoperatively.

RESULTS

Six predictors, including Creatinine >90 μmol/L, Hemoglobin <110 g/L, Albumin <40 g/L, Leukocyte >10 ×109/L, high-risk Surgery (general abdominal or vascular), and American Society of Anesthesiologists (ASA) class (III or IV), were selected in the final model (CHALSA score). Each patient was assigned a CHALSA score of 0, 1, 2, 3, or > 3 according to the number of predictors present. The incidence of perioperative MACEs increased steadily across the CHALSA score groups in both the derivation (0.5%, 1.4%, 2.9%, 6.8%, and 23.4%, respectively; p < 0.001) and validation (0.3%, 1.5%, 4.1%, 9.2%, and 29.2%, respectively; p < 0.001) cohorts. The CHALSA score had a higher discriminatory ability than the revised cardiac risk index (C statistic: 0.827 vs. 0.695 in the validation dataset; p < 0.001).

CONCLUSIONS

The CHALSA score showed good validity in an external dataset and will be a valuable bedside tool to guide the perioperative management of patients with SCAD undergoing noncardiac surgery.

Risk stratification of surgical and cardiovascular complications in non-cardiac surgery: prognostic value of recommended scales

Aim. To assess the prognostic value of current scales and indices for risk stratification of any surgical and cardiovascular complications (CVC) in patients undergoing non-cardiac surgical interventions.

Material and methods. This single-center cohort retrospective study was conducted in patients who underwent non-cardiac surgery in 2018 and 2020. Surgical postoperative complications (POCs) were assessed according to the Clavien-Dindo classification. CVCs included any cardio-vascular events (CVEs), major adverse cardiac events (MACE), ST-T abnormalities on the electrocardiogram (ECG), decompensated heart failure (HF), arrhythmias, episodes of hypotension or hypertension, delirium, bleeding, thromboembolic events (TEEs). Risk stratification of POCs was carried out using recommended prognostic scales and indices. Their prognostic significance was assessed using ROC analysis with assessment of the area under the curve (AUC).

Results. POC risk stratification was performed in 2937 patients. There was following prognostic value of scales and indices: Physiological and Operative Severity Score for the enUmeration of Mortality and morbidity (POSSUM) score — AUC of 0,990, 0,808, 0,825, 0,841, 0,808, 0,793, 0,701, 0,776, 0,744 in predicting Clavien-Dindo grade 5, 4, HF, delirium, TEEs, MACE, ST-T abnormalities, arrhythmias, bleeding, respectively; Surgical Outcome Risk Tool (SORT) — AUC of 0,973, 0,740, 0,890, 0,763, 0,721, 0,716, 0,700 in predicting Clavien-Dindo grade 5, 4, delirium, MACE, HF, arrhythmia, TEEs, respectively; American Society of Anesthesiologists (ASA) — AUC of 0,648, 0,600, 0,658 for HF, ST-T abnormalities, arrhythmias, respectively; Charlson comorbidity index — AUC of 0,819, 0,950, 0,789, 0,788, 0,706, 0,771, 0,898 in predicting Clavien-Dindo grade 5, 4, MACE, HF, ST-T abnormalities, arrhythmias, delirium; surgical risk score associated with the risk of cardiac events — AUC of 0,989, 0,887, 0,728 for Clavien-Dindo grade 3, 5, MACE, respectively; reconstructed Revised Cardiac Risk Index (rRCRI) — AUC of 0,916 and 0,979, 0,762, 0,741, 0,737 in predicting Clavien-Dindo grade 3, 5, HF, arrhythmia, delirium, respectively; National Surgical Quality Improvement Program Myocardial Infarction & Cardiac Arrest (NSQIP MICA) — AUC of 0,705, 0,757, 0,718 for arrhythmia, delirium, TEEs, respectively; total cardiovascular risk according to 2022 European Society of Cardiology (ESC) guidelines — AUC of 0,942, 0,726, 0,701, 0,748, 0,785 for Clavien-Dindo grade 5, MACE, ST-T abnormalities, arrhythmias, delirium, respectively; Caprini score — AUC of 0,718 and Venous ThromboEmbolism and Bleeding (VTE-Bleed) — AUC of 0,722 in predicting TEEs; simplified Bleeding Independently associated with Mortality after noncardiac Surgery (BIMS) index — AUC of 0,729 for stratification of bleeding. In assessment of total risk of Clavien-Dindo surgical complications and any CVEs, none of the scales showed a predictive value of >0,7.

Conclusion. Any CVE stratification requires improvement of current tools and development of novel prognostic tools.

Efficacy of polyethylene glycol loxenatide for type 2 diabetes mellitus patients: a systematic review and meta-analysis

Objective: Some studies have proved that polyethylene glycol loxenatide (PEG-Loxe) has significant effects on controlling blood glucose and body weight in patients with type 2 diabetes mellitus (T2DM), but there is still some controversy over the improvement of blood lipid profiles (BLP) and blood pressure (BP), and more evidences are needed to verify such effects. Therefore, this study was conducted to provide a comprehensive evaluation of the efficacy of PEG-Loxe in improving blood glucose (BG), BLP, BP, body mass index (BMI), and body weight (BW) in patients with T2DM for clinical reference. Methods: Randomized controlled trials (RCT) in which PEG-Loxe was applied to treat T2DM were retrieved by searching PubMed, Cochrane Library, Embase, Medline, Scopus, Web of Science, China National Knowledge Infrastructure, China Scientific Journal, Wanfang Data, and SinoMed databases. Outcome measures included BG, BLP, BP, BMI, and BW. RevMan 5.3 software was used to perform data analysis. Results: Eighteen trials were identified involving 2,166 patients. In experimental group 1,260 patients received PEG-Loxe alone or with other hypoglycemic agents, while in control group 906 patients received placebo or other hypoglycemic agents. In the overall analysis, PEG-Loxe significantly reduced the levels of glycosylated hemoglobin (HbA1c), fasting plasma glucose (FPG), 2-h postprandial blood glucose (2-h PBG), BMI, and BW compared with control group. However, it had no obvious effect on total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), systolic blood pressure (SBP), and diastolic blood pressure (DBP). Conclusion: PEG-Loxe has better hypoglycemic effects compared with placebo in patients with T2DM, but could not significantly improved TG, LDL-C, HDL-C, SBP, and DBP. And the combination of conventional hypoglycemic drugs (CHD) and PEG-Loxe could more effectively improve the levels of HbA1c, FPG, 2-h PBG, TC, TG, BMI, and BW compared with CHD in T2DM patients. Systematic Review Registration: www.inplasy.com, identifier INPLASY202350106.

Association of Cardiac Valve Calcification and 1-year Mortality after Lower-extremity Amputation in Diabetic Patients: A Retrospective Study

BACKGROUND

Cardiac valve calcification predisposes patients to a higher risk of adverse cardiovascular events. This study aimed to investigate the association between cardiac valve calcification and 1-year mortality in diabetic patients after lower-extremity amputation.

METHODS

This was a retrospective study conducted on the clinical data of diabetic patients who underwent lower-extremity amputation admitted to the Sun Yat-sen Memorial Hospital, Guangzhou, Guangdong, China for diabetic foot ulcers needed lower extremity amputation surgery between July 2017 and March 2021. Detailed preoperative medical assessments were performed and recorded. Cardiac valve calcification was assessed using echocardiography at baseline. Oneyear follow-up assessments were conducted and included clinical visits, hospital record assessments, and telephone reviews to obtain the survival status of patients.

RESULTS

Ninety-three diabetic patients participated in the study. The 1-year follow-up mortality rate after amputation was 24.7%. Compared to the survival group, the prevalence of cardiac valve calcification and the Revised Cardiac Risk Index (RCRI) were higher in the mortality group. In the Cox regression analysis, cardiac valvular calcification (HR=3.427, 95% CI=1.125- 10.443, p =0.030) was found to be an independent predictor of all-cause mortality after amputation. In addition, the patients with both aortic valve calcification and mitral annular calcification had a higher all-cause mortality rate (50%). Receiver operator characteristic curve analysis showed a stronger predictive ability when using a combination of calcified valve number and RCRI (AUC=0.786 95%, CI=0.676-0.896, p =0.000).

CONCLUSION

In diabetic patients after lower-extremity amputation, cardiac valve calcification was associated with all-cause mortality during 1-year follow-up. Combination of calcified valve number and RCRI score showed a stronger predictive value for mortality.

Development and validation of a prediction model to predict major adverse cardiovascular events in elderly patients undergoing noncardiac surgery: A retrospective cohort study

BACKGROUND AND AIMS

Current existing predictive tools have limitations in predicting major adverse cardiovascular events (MACEs) in elderly patients. We will build a new prediction model to predict MACEs in elderly patients undergoing noncardiac surgery by using traditional statistical methods and machine learning algorithms.

METHODS

MACEs were defined as acute myocardial infarction (AMI), ischemic stroke, heart failure and death within 30 days after surgery. Clinical data from 45,102 elderly patients (≥65 years old), who underwent noncardiac surgery from two independent cohorts, were used to develop and validate the prediction models. A traditional logistic regression and five machine learning models (decision tree, random forest, LGBM, AdaBoost, and XGBoost) were compared by the area under the receiver operating characteristic curve (AUC). In the traditional prediction model, the calibration was assessed using the calibration curve and the patients' net benefit was measured by decision curve analysis (DCA).

RESULTS

Among 45,102 elderly patients, 346 (0.76%) developed MACEs. The AUC of this traditional model was 0.800 (95% CI, 0.708-0.831) in the internal validation set, and 0.768 (95% CI, 0.702-0.835) in the external validation set. In the best machine learning prediction model-AdaBoost model, the AUC in the internal and external validation set was 0.778 and 0.732, respectively. Besides, for the traditional prediction model, the calibration curve of model performance accurately predicted the risk of MACEs (Hosmer and Lemeshow, p = 0.573), the DCA results showed that the nomogram had a high net benefit for predicting postoperative MACEs.

CONCLUSIONS

This prediction model based on the traditional method could accurately predict the risk of MACEs after noncardiac surgery in elderly patients.

Using preoperative N-terminal pro-B-type natriuretic peptide levels for predicting major adverse cardiovascular events and myocardial injury after noncardiac surgery in Chinese advanced-age patients

BACKGROUND

N-terminal pro-B-type natriuretic peptide (NT-proBNP) is often viewed as an indicator for heart failure. However, the prognostic association and the predictive utility of NT-proBNP for postoperative major adverse cardiovascular events (MACEs) and myocardial injury after noncardiac surgery (MINS) among older patients are unclear.

METHODS

In this study, we included 5033 patients aged 65 years or older who underwent noncardiac surgery with preoperative NT-proBNP recorded. Logistic regression was adopted to model the associations between preoperative NT-proBNP and the risk of MACEs and MINS. The receiver operating characteristic curve was used to determine the predictive value of NT-proBNP.

RESULTS

A total of 5033 patients were enrolled, 63 patients (1.25%) and 525 patients (10.43%) had incident postoperative MACEs and MINS, respectively. Analysis of the receiver operating characteristic curve indicated that the cutoff values of ln (NT-proBNP) for MACEs and MINS were 5.16 (174 pg/mL) and 5.30 (200 pg/mL), respectively. Adding preoperative ln (NT-proBNP) to the Revised Cardiac Risk Index score and the Cardiac and Stroke Risk Model boosted the area under the receiver operating characteristic curves from 0.682 to 0.726 and 0.787 to 0.804, respectively. The inclusion of preoperative NT-proBNP in the prediction models significantly increased the reclassification and discrimination.

CONCLUSIONS

Increased preoperative NT-proBNP was associated with a higher risk of postoperative MACEs and MINS. The inclusion of NT-proBNP enhances the predictive ability of the preexisting models.

The comparative and added prognostic value of biomarkers to the Revised Cardiac Risk Index for preoperative prediction of major adverse cardiac events and all-cause mortality in patients who undergo noncardiac surgery

BACKGROUND

The Revised Cardiac Risk Index (RCRI) is a widely acknowledged prognostic model to estimate preoperatively the probability of developing in-hospital major adverse cardiac events (MACE) in patients undergoing noncardiac surgery. However, the RCRI does not always make accurate predictions, so various studies have investigated whether biomarkers added to or compared with the RCRI could improve this.

OBJECTIVES

Primary: To investigate the added predictive value of biomarkers to the RCRI to preoperatively predict in-hospital MACE and other adverse outcomes in patients undergoing noncardiac surgery. Secondary: To investigate the prognostic value of biomarkers compared to the RCRI to preoperatively predict in-hospital MACE and other adverse outcomes in patients undergoing noncardiac surgery. Tertiary: To investigate the prognostic value of other prediction models compared to the RCRI to preoperatively predict in-hospital MACE and other adverse outcomes in patients undergoing noncardiac surgery.

SEARCH METHODS

We searched MEDLINE and Embase from 1 January 1999 (the year that the RCRI was published) until 25 June 2020. We also searched ISI Web of Science and SCOPUS for articles referring to the original RCRI development study in that period.

SELECTION CRITERIA

We included studies among adults who underwent noncardiac surgery, reporting on (external) validation of the RCRI and: - the addition of biomarker(s) to the RCRI; or - the comparison of the predictive accuracy of biomarker(s) to the RCRI; or - the comparison of the predictive accuracy of the RCRI to other models. Besides MACE, all other adverse outcomes were considered for inclusion.

DATA COLLECTION AND ANALYSIS

We developed a data extraction form based on the CHARMS checklist. Independent pairs of authors screened references, extracted data and assessed risk of bias and concerns regarding applicability according to PROBAST. For biomarkers and prediction models that were added or compared to the RCRI in ≥ 3 different articles, we described study characteristics and findings in further detail. We did not apply GRADE as no guidance is available for prognostic model reviews.

MAIN RESULTS

We screened 3960 records and included 107 articles.   Over all objectives we rated risk of bias as high in ≥ 1 domain in 90% of included studies, particularly in the analysis domain. Statistical pooling or meta-analysis of reported results was impossible due to heterogeneity in various aspects: outcomes used, scale by which the biomarker was added/compared to the RCRI, prediction horizons and studied populations.  Added predictive value of biomarkers to the RCRI Fifty-one studies reported on the added value of biomarkers to the RCRI. Sixty-nine different predictors were identified derived from blood (29%), imaging (33%) or other sources (38%). Addition of NT-proBNP, troponin or their combination improved the RCRI for predicting MACE (median delta c-statistics: 0.08, 0.14 and 0.12 for NT-proBNP, troponin and their combination, respectively). The median total net reclassification index (NRI) was 0.16 and 0.74 after addition of troponin and NT-proBNP to the RCRI, respectively. Calibration was not reported. To predict myocardial infarction, the median delta c-statistic when NT-proBNP was added to the RCRI was 0.09, and 0.06 for prediction of all-cause mortality and MACE combined. For BNP and copeptin, data were not sufficient to provide results on their added predictive performance, for any of the outcomes. Comparison of the predictive value of biomarkers to the RCRI  Fifty-one studies assessed the predictive performance of biomarkers alone compared to the RCRI. We identified 60 unique predictors derived from blood (38%), imaging (30%) or other sources, such as the American Society of Anesthesiologists (ASA) classification (32%). Predictions were similar between the ASA classification and the RCRI for all studied outcomes. In studies different from those identified in objective 1, the median delta c-statistic was 0.15 and 0.12 in favour of  BNP and NT-proBNP alone, respectively, when compared to the RCRI, for the prediction of MACE. For C-reactive protein, the predictive performance was similar to the RCRI. For other biomarkers and outcomes, data were insufficient to provide summary results. One study reported on calibration and none on reclassification. Comparison of the predictive value of other prognostic models to the RCRI   Fifty-two articles compared the predictive ability of the RCRI to other prognostic models. Of these, 42% developed a new prediction model, 22% updated the RCRI, or another prediction model, and 37% validated an existing prediction model. None of the other prediction models showed better performance in predicting MACE than the RCRI. To predict myocardial infarction and cardiac arrest, ACS-NSQIP-MICA had a higher median delta c-statistic of 0.11 compared to the RCRI. To predict all-cause mortality, the median delta c-statistic was 0.15 higher in favour of ACS-NSQIP-SRS compared to the RCRI. Predictive performance was not better for CHADS₂, CHA₂DS₂-VASc, R₂CHADS₂, Goldman index, Detsky index or VSG-CRI compared to the RCRI for any of the outcomes. Calibration and reclassification were reported in only one and three studies, respectively.

AUTHORS' CONCLUSIONS

Studies included in this review suggest that the predictive performance of the RCRI in predicting MACE is improved when NT-proBNP, troponin or their combination are added. Other studies indicate that BNP and NT-proBNP, when used in isolation, may even have a higher discriminative performance than the RCRI. There was insufficient evidence of a difference between the predictive accuracy of the RCRI and other prediction models in predicting MACE. However, ACS-NSQIP-MICA and ACS-NSQIP-SRS outperformed the RCRI in predicting myocardial infarction and cardiac arrest combined, and all-cause mortality, respectively. Nevertheless, the results cannot be interpreted as conclusive due to high risks of bias in a majority of papers, and pooling was impossible due to heterogeneity in outcomes, prediction horizons, biomarkers and studied populations. Future research on the added prognostic value of biomarkers to existing prediction models should focus on biomarkers with good predictive accuracy in other settings (e.g. diagnosis of myocardial infarction) and identification of biomarkers from omics data. They should be compared to novel biomarkers with so far insufficient evidence compared to established ones, including NT-proBNP or troponins. Adherence to recent guidance for prediction model studies (e.g. TRIPOD; PROBAST) and use of standardised outcome definitions in primary studies is highly recommended to facilitate systematic review and meta-analyses in the future.

Perioperative Cardiac Complications in Patients Over 80 Years of Age with Coronary Artery Disease Undergoing Noncardiac Surgery: The Incidence and Risk Factors

Purpose

Ever-increasing noncardiac surgeries are performed in patients aged 80 years or over with coronary artery disease (CAD). The objective of the study was to explore the incidence and risk factors of perioperative cardiac complications (PCCs) for the oldest-old patients with CAD undergoing noncardiac surgery, which have not been evaluated previously.

Patients and Methods

A total of 547 patients, aged over 80 years, with a history of CAD who underwent noncardiac surgery were enrolled in this retrospective study. Perioperative clinical variables were extracted from the electronic medical records database. The primary outcome was the occurrence of PCCs intraoperatively or within 30 days postoperatively, defined as any of the following complications: acute coronary syndrome, heart failure, new-onset severe arrhythmia, nonfatal cardiac arrest, and cardiac death. Multivariate logistic regression analysis and multivariate Cox regression model were both performed to estimate the risk factors of PCCs. The incidence of PCCs overtime was illustrated by the Kaplan-Meier curve with a stratified Log-rank test.

Results

One hundred six (19.4%) patients developed at least one PCC, and 15 (2.7%) patients developed cardiac death. The independent risk factors contributing to PCCs were age ≧85 years; body mass index ≧30 kg/m²; the history of angina within 6 months; metabolic equivalents <4; hypertension without regular treatment; preoperative ST-T segment abnormality; anesthesia time >3 h and drainage ≧200 mL within 24 h postoperatively.

Conclusion

The incidence of PCCs in elderly patients over 80 years with CAD who underwent noncardiac surgery was high. Comprehensive preoperative evaluation, skilled surgical technique, and regular postoperative monitoring may help to reduce the occurrence of PCCs in this high-risk population.