Influence of age on perioperative major adverse cardiovascular events and mortality risks in elective non-cardiac surgery

Major cardiovascular events under biologic psoriasis therapies: a 19-year real-world analysis of FAERS data

Objective

Over the years when biologic psoriasis therapies (TNF inhibitors, IL-12/23 inhibitors, IL-23 inhibitors, and IL-17 inhibitors) have been used in psoriasis patients, reports of major cardiovascular events (MACEs) have emerged. This study aims to investigate the association between MACEs and biologic psoriasis therapies by using information reported to the US Food and Drug Administration Adverse Event Reporting System (FAERS).

Methods

FAERS data (January 2004 to December 2022) were reviewed. For each drug-event pair, the proportional reporting ratio (PRR) and the multi-item gamma Poisson shrinker (MGPS) algorithms were used to identify drug-adverse event associations.

Results

We filtered the query for indication and identified 173,330 reports with psoriasis indication in FAERS throughout the analyzed time frame. MACEs occurred in 4,206 patients treated with biologics. All the four biological classes had an elevated and similar reporting rates for MACEs relative to other alternative psoriasis treatments (PRR from 2.10 to 4.26; EB05 from 1.15 to 2.45). The descending order of association was IL-12/23 inhibitors>IL-17 inhibitors>IL-23 inhibitors>TNF inhibitors. The signal strength for myocardial infarction (PRR, 2.86; χ2, 296.27; EBGM 05, 1.13) was stronger than that for stroke, cardiac fatality, and death. All the biological classes demonstrated a little higher EBGM 05 score≥1 for the MACEs in patients aged 45-64 years. The time-to-onset of MACEs was calculated with a median of 228 days.

Conclusions

Analysis of adverse event reports in the FAERS reflects the potential risk of MACEs associated with the real-world use of biological therapies in comparison to other alternative psoriasis treatments. Future long-term and well-designed studies are needed to further our knowledge regarding the cardiovascular safety profile of these agents.

Development and Internal Validation of a Nomogram for Predicting Postoperative Cardiac Events in Elderly Hip Fracture Patients

Purpose

Postoperative cardiac events (PCEs) are among the main adverse events after hip fracture surgery in the elderly. Existing cardiac risk assessment tools have some limitations and are not specifically designed for elderly patients undergoing hip fracture surgery. This study aimed to develop and internally validate a nomogram for prediction of PCEs in these patients.

Patients and Methods

We performed a retrospective study of 992 patients aged ≥65 years undergoing hip fracture surgery in our hospital from July 2015 to December 2021. Patients' demographics and clinical data were collected. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to select predictors, and multivariate logistic regression was employed to construct a nomogram. Internal validation was performed by bootstrapping. The discriminatory ability of the model was determined by the area under the receiver operating characteristic curve (AUC). The calibration and clinical utility of the model were assessed. The predictive power and clinical benefit of the nomogram were compared with the Revised Cardiac Risk Index (RCRI).

Results

The nomogram was constructed including seven variables: general anesthesia, the American Society of Anesthesiologists (ASA) classification, history of heart failure, history of severe arrhythmia, history of coronary artery disease, preoperative platelet count, and serum creatinine. The nomogram had an excellent predictive ability (AUC = 0.875, 95% confidence interval [CI]: 0.828-0.918). Satisfactory calibration was shown by calibration plots and the Hosmer-Lemeshow goodness-of-fit test (P = 0.520). Clinical usefulness was confirmed by decision curve analysis and clinical impact curve. The predictive power and clinical utility of the nomogram were superior to RCRI.

Conclusion

We developed an easy-to-use nomogram for prediction of PCEs in elderly hip fracture patients. This prediction model could effectively identify patients at high risk of PCEs and may be useful for perioperative management optimization.

Machine learning-based model for predicting major adverse cardiovascular and cerebrovascular events in patients aged 65 years and older undergoing noncardiac surgery

BACKGROUND

Few evidence-based prediction models have been developed for predicting major adverse cardiovascular and cerebrovascular events (MACCE) in patients aged 65 years or older undergoing noncardiac surgery. In this study, we aimed to analyze the risk factors for perioperative MACCE in patients aged 65 years or older undergoing noncardiac surgery and construct a prediction model.

METHODS

In this nested case-control study, a total of 342 Chinese patients who were aged ≥ 65 years and underwent medium- or high-risk noncardiac surgery in our hospital were included. There were 84 cases with MACCE (the MACCE group) and 258 without MACCE (the control group). Univariable logistic regression analysis was performed to identify the risk factors for MACCE. Least absolute shrinkage and selection operator (LASSO) regression was used to screen the variables. Nomogram was constructed using the selected variables. Machine learning methods, including Decision Tree, XGBoost, Support Vector Machine, K-nearest Neighbor, and Neural network, was used to establish, validate, and compare the performance of different prediction models.

RESULTS

A prediction model based on nine variables, including age ≥ 85 years, history of ischemic chest pain, symptoms of decompensated heart failure, high-risk surgery, intraoperative minimum systolic blood pressure, postoperative systolic blood pressure, Cr levels over 2.0 mg/dL, left ventricular ejection fraction, and perioperative blood transfusion, was constructed. This LASSO logistic regression model showed good discriminatory ability to predict MACCE (area under the curve = 0.89; 95% confidence interval, 0.818 - 0.963) and fit to the test set (Hosmer-Lemeshow, χ2 = 7.4053, P = 0.4936). The decision curve analysis showed a positive net benefit of the new model. Compared with logistic regression model, the XGBoost model showed better prediction ability (area under the curve = 0.903). A preoperative prediction model based on five variables, including age ≥ 85 years, symptoms of decompensated heart failure, ischemic chest pain, high-risk type of surgery and Cr levels over 2.0 mg/dL was also constructed. This model showed good discriminatory ability to predict MACCE before surgery (area under the curve = 0.720 [95% CI, 0.591-0.848]. Both models compared with the modified RCRI score had improvement in reclassification.

CONCLUSION

By analyzing Chinese patients aged ≥ 65 years undergoing medium- or high-risk noncardiac surgery, the risk factors for perioperative MACCE were identified. Then, simple prediction models were constructed and validated, which showed good prediction performance and may be used as a decision-making assistant tool for clinicians. These findings provide a basis for preventing and improving the perioperative management of MACCE.

Perioperative cardiac risk reduction in non cardiac surgery

For patients undergoing nonemergent noncardiac surgery, care must be taken to identify patients at increased risk of major adverse cardiovascular events, as these remain a significant source of perioperative morbidity and mortality. Identification of at-risk patients requires careful attention to risk factors including assessment of functional status, medical comorbidities, and a medication assessment. After identification, to minimize perioperative cardiac risk, care should be taken through a combination of appropriate medication management, close monitoring for cardiovascular ischemic events, and optimization of pre-existing medical conditions. There are multiple society guidelines that aim to mitigate risk of cardiovascular morbidity and mortality in patients undergoing nonemergent noncardiac surgery. However, the rapid evolution of medical literature often creates gaps between the existing evidence and best practice recommendations. In this review, we aim to reconcile the recommendations made in the guidelines from the major cardiovascular and anesthesiology societies from the USA, Canada, and Europe, and to provide updated recommendations based on new evidence.

Coronary CT angiography for preoperative evaluation of non-cardiac surgery in patients with thoracic tumors: preliminary exploratory analysis in a retrospective cohort

PURPOSE

Noninvasive coronary CT angiography (CCTA) was used to retrospectively analyze the characteristics of coronary artery disease (CAD) in patients with thoracic tumors and the impact of the results on clinical surgery decision-making, thus increasing the understanding of perioperative cardiac risk evaluation.

METHOD

A total of 779 patients (age 68.6 ± 6.6 years) with thoracic tumor (lung, esophageal, and mediastinal tumor) scheduled for non-cardiac surgery were retrospectively enrolled. Patients were divided into two groups: accepted or canceled surgery. Clinical data and CCTA results were compared between the two groups, and multivariate logistic regression analysis was performed to determine predictors of the events of cancellations of scheduled surgeries.

RESULTS

634 patients (81.4%) had non-significant CAD and 145 patients (18.6%) had significant CAD. Single‑, 2‑, and 3‑ vessel disease was found in 173 (22.2%), 93 (11.9%) and 50 (6.4%) patients, respectively. 500 (64.2%), 96 (12.3%), 96 (12.3%), 56 (7.2%) and 31 (4.0%) patients were rated as CACS 0, 1-99, 100-399, 400-999 and > 1000, respectively. Cancellations of scheduled procedures continue to increase based on the severity of the stenosis and the number of major coronary artery stenosis. The degree of stenosis and the number of vascular stenosis were independent predictors of cancelling scheduled surgery.

CONCLUSIONS

For patients with thoracic tumors scheduled for non-cardiac surgery, the results suggested by CCTA significantly influenced surgery planning and facilitated to reduce perioperative cardiovascular events.

External Validation of the Revised Cardiac Risk Index and the Geriatric-Sensitive Perioperative Cardiac Risk Index in Oldest Old Patients Following Surgery Under Spinal Anaesthesia; a Retrospective Cross-Sectional Cohort Study

Background

The Revised Cardiac Risk Index (RCRI) and the Geriatric Sensitive Cardiac Risk Index (GSCRI) estimate the risk of postoperative major adverse cardiac events (MACE) regardless of the type of anesthesia and without specifying the oldest old patients. Since spinal anesthesia (SA) is a preferred technique in geriatrics, we aimed to test the external validity of these indices in patients ≥ 80 years old who underwent surgery under SA and tried to identify other potential risk factors for postoperative MACE.

Methods

The performance of both indices to estimate postoperative in-hospital MACE risk was tested through discrimination, calibration, and clinical utility. We also investigated the correlation between both indices and postoperative ICU admission and length of hospital stay (LOS).

Results

The MACE incidence was 7.5%. Both indices had limited discriminative (AUC for RCRI and GSCRI were 0.69 and 0.68, respectively) and predictive abilities. The regression analysis showed that patients with atrial fibrillation (AF) were 3.77 and those with trauma surgery were 2.03 times more likely to exhibit MACE, and the odds of MACE increased by 9% for each additional year above 80. Introducing these factors into both indices (multivariable models) increased the discriminative ability (AUC reached 0.798 and 0.777 for RCRI and GSCRI, respectively). Bootstrap analysis showed that the predictive ability of the multivariate GSCRI but not the multivariate RCRI improved. Decision curve analysis (DCA) showed that multivariate GSCRI had superior clinical utility when compared with multivariate RCRI. Both indices correlated poorly with postoperative ICU admission and LOS.

Conclusion

Both indices had limited predictive and discriminative ability to estimate postoperative in-hospital MACE risk and correlated poorly with postoperative ICU admission and LOS, following surgery under SA in the oldest-old patients. Updated versions by introducing age, AF, and trauma surgery improved the GSCRI performance but not the RCRI.

A novel radical prostatectomy specific index (PSI) for the prediction of major cardiovascular events following surgery

PURPOSE

Prostate cancer patients tend to be older with multiple comorbidities and are thus at increased risk for postoperative cardiovascular events after radical prostatectomy (RP). Thus, proper patient selection strategies are essential to decide for or against a surgical approach. We aimed to derive a prostatectomy specific index (PSI) for patients undergoing RP and compare its performance to universally used indices.

METHODS

The cohort was derived from National Surgical Quality Improvement Program database between 2005 and 2012. The primary outcome was incidence of major adverse cardiovascular events at 30 days post-surgery including: death, myocardial infarction, or stroke. A multivariable logistic regression model was constructed, performance and calibration were evaluated using a ROC analysis and the Hosmer-Lemeshow test, the PSI index was derived and compared to the RCRI and AUB-HAS2 indices.

RESULTS

A total of 17,299 patients were included in our cohort, with a mean age of 62 ± 7.4 years. Seventy three patients had a cardiac event post RP. The final PSI index encompassed six variables: history of heart disease, age, anemia, American society of anesthesiology class, surgical approach, and hypertension. The PSI ROC analysis provided C-statistic = 0.72, calibration R² = 0.99 and proper goodness of fit. In comparison, the C-statistics of RCRI and AUB-HAS2 were found to be 0.57 and 0.65, respectively (p value < 0.001).

CONCLUSION

The PSI model is a procedure tailored index for prediction of major cardiovascular events post RP. It was calibrated using a large national database aiming to optimize treatment selection strategies for prostate cancer patients.

Major adverse cardiovascular events following partial nephrectomy: a procedure-specific risk index

Introduction:

Partial nephrectomy (PN) is associated with a non-negligible risk of postoperative cardiovascular morbidity and mortality. Identification of high-risk patients may enable optimization of perioperative management and consideration of alternative approaches. The authors aim to develop a procedure-specific cardiovascular risk index for PN patients and compare its performance to the widely used revised cardiac risk index (RCRI) and AUB-HAS2 cardiovascular risk index.

Methods:

The cohort was derived from the American College of Surgeons – National Surgical Quality Improvement Program (ACS-NSQIP) database. The primary outcome was the incidence of major adverse cardiovascular events (MACE), defined as 30-day postoperative incidence of myocardial infarction, stroke, or mortality. A multivariate logistic regression model was constructed; performance and calibration were evaluated using an ROC analysis and the Hosmer–Lemeshow test and compared to the RCRI and the AUB-HAS2 index.

Results:

In a cohort of 4795 patients, MACE occurred in 52 (1.1%) patients. A univariate analysis yielded 13 eligible variables for entry into the multivariate model. The final PN-A₄CH model utilized six variables: Age ⩾75 years, ASA class >2, Anemia, surgical Approach, Creatinine >1.5, and history of Heart disease. Index ROC analysis provided a C-statistic of 0.81, calibration R² was 0.99, and sensitivity was 85%. In comparison, the RCRI and AUB-HAS2 C-statistics were 0.59 and 0.68, respectively.

Conclusion:

This study proposes a novel procedure-specific cardiovascular risk index. The PN-A₄CH index demonstrated good predictive ability and excellent calibration using a large national database and may enable further individualization of patient care and optimization of patient selection.

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.

The predictive value of high-sensitive troponin I for perioperative risk in patients undergoing gastrointestinal tumor surgery

BACKGROUND

The incidence of cardiovascular events in perioperative period of gastrointestinal tumor surgery cannot be ignored, and studies have shown that level of postoperative troponin is related to the postoperative risk of non-cardiac surgery. However, the relationship between pre-operative troponin levels and perioperative risk of gastrointestinal tumor surgery is unclear. Thus, we aimed to evaluate the value of high-sensitive cardiac troponin I (hs-cTnI) prior to gastrointestinal tumor surgery for perioperative risk assessment.

METHODS

In this retrospective cohort study, 1259 patients who underwent gastrointestinal tumor surgery and had been tested for hs-cTnI on admission within 7 days prior to surgery were retrospectively recruited from January 2018 to June 2020. The primary combined endpoint including in-hospital all-cause mortality, acute myocardial infarction, cardiac arrest or ventricular fibrillation and acute decompensated heart failure. The secondary endpoint included total hospital stay and requirement of intensive care treatment.

FINDINGS

Compared with patients with normal hs-cTnI, those with elevated hs-cTnI (> 0·028 ng/ml) were more likely to experience the combined endpoint (28·2% versus 2·7%, P < 0·001) and there was also an increasing rate of in mortality in elevated hs-cTnI group (2·4% versus 0·3%, P = 0·057). The length of total hospital stay was significantly longer in patients with elevated hs-cTnI (24·8 ± 16·3 versus 19·5 ± 7·9, P = 0·003) and the number of patients requiring intensive care treatment was also higher (22·6% versus 4·2%, P < 0·001). The area under the ROC curve assessing hs-cTnI in predicting in-hospital mortality was 0·787 [95% confidence interval (CI) 0·612-0·963, P = 0·015] and for combined endpoint was 0·822 [95% CI 0·766-0·879, P < 0·001]. Hs-cTnI > 0·028 ng/ml was associated with significantly higher cardiovascular event rate in patients with the revised cardiac index ≤ 1. The positive likelihood ratio of hs-cTnI (> 0·028 ng/ml) for predicting combined endpoint reaches 10.5 in patients with Lee index = 0. In multivariate logistic analyses, hs-cTnI was one of the best predictors for the combined endpoint [odds ratio (OR) 5·924 (95%CI: 2·869-12·233), P < 0·001].

INTERPRETATION

Hs-cTnI provides powerful prognostic information for patients undergoing gastrointestinal tumor surgery, and therefore provides reliable prognostic information incremental to revised cardiac index.

Perioperative Cardiac Risk Reduction in Noncardiac Surgery

Major adverse cardiovascular events are a significant source of morbidity and mortality in the perioperative setting, estimated to occur in approximately 5% of patients undergoing nonemergent noncardiac surgery. To minimize the incidence and impact of these events, careful attention must be paid to preoperative cardiovascular assessment to identify patients at high risk of cardiovascular complications. Once identified, cardiovascular risk reduction is achieved through optimization of medical conditions, appropriate management of medication, and careful monitoring to allow for early identification of-and intervention for-any new conditions that would increase the risk of adverse cardiovascular outcomes. The major cardiovascular and anesthesiology societies in the United States, Europe, and Canada have published guidelines for perioperative management of patients undergoing noncardiac surgery. However, since publication of these guidelines, there has been a practice-changing evolution in the medical literature. In this review, we attempt to reconcile the recommendations made in these 3 comprehensive guidelines, while updating recommendations, based on new evidence, when available.

[Perioperative Medicine in Visceral Surgery in the Elderly Patient from an Anaesthesiological Perspective]

Demographic change is leading to an increasing number of old patients both in our society and in hospitals. With increasing age, not only the number of pre-existing conditions increases, but also the postoperative complication rate and mortality. Ultimately, however, it is not age that is decisive, but the condition of the patient and his or her capacity to face the physical and mental challenges of a surgical procedure. Frail patients are particularly at risk of complications, and an essential strategy - known as prehabilitation - is to put them in a better state pre-operatively through physical and mental training, as well as nutritional counselling. Delirium is one of the most frequent postoperative complications. Measures such as refraining from premedication with benzodiazepines, measuring the depth of anaesthesia, refraining from long-acting opioids, performing fast-track surgery, and providing glasses/hearing aids quickly postoperatively can reduce the risk of delirium. Close interdisciplinary consultation between surgeons, anaesthetists, geriatricians and physiotherapists is essential to coordinate the perioperative procedure and reduce the perioperative risk for elderly patients.

Differences in Outcomes After Anesthesia-Related Adverse Events in Older and Younger Patients

Because more older adults undergo surgical procedures, it is incumbent on us to learn how to provide them with the safest possible perioperative care. We conducted a retrospective cohort study at a large tertiary care center to determine whether outcomes after anesthesia-related adverse events differed between patients aged 65 years and older versus patients under age 65. One thousand four hundred twenty-four cases were referred to the Performance Improvement committee of the Department of Anesthesiology from the years 2007-2015. After exclusions of cases that were not anesthesia-related, could not be identified, or were duplicates, 747 cases with anesthesia-related adverse events were included in the study. Two hundred eighty-six were aged 65 years and older and 461 were under age 65. Anesthesia-related adverse events occurred more commonly in the postoperative period in older patients relative to younger patients (37.7% vs. 21.9%, p = .001), and older patients had a greater incidence of mortality compared with a propensity-matched group of younger patients (adjusted odds ratio 1.87 [1.14-3.12], p < .05). We concluded that older patients have a greater likelihood of mortality as a result of suffering an anesthesia-related adverse event and may benefit from increased vigilance in the postoperative period.

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.

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

Objectives

The revised Cardiac Risk Index (RCRI) is the most widely used risk prediction tool for postoperative cardiac adverse events. We aim to explore the predictive ability of the RCRI in older Chinese patients with coronary artery disease (CAD) undergoing noncardiac surgery, which has not been previously evaluated.

Methods

We performed a multicenter, prospective study. We enrolled a total of 1,202 patients, aged >60 years, with a history of CAD who underwent noncardiac surgery. Perioperative data were extracted from an electronic database. The primary end point was defined as an occurrence of a postoperative major cardiac event (PoMCE) within 30 days. Logistic regression analysis was performed to evaluate the performance of the RCRI. A modified RCRI was created and compared with the original RCRI with regard to its ability to predict postoperative cardiac events.

Results

Of the enrolled patients, 4.3% experienced PoMCE. Most components of the RCRI were not predictive of postoperative cardiac events with the exception of insulin-dependent diabetes mellitus (odds ratio =2.38, 95% CI: 1.11-5.11; P=0.03). The RCRI performed no better than chance (area under the curve =0.53; 95% CI: 0.45-0.61) in identifying patients' cardiac risk. The modified score had a higher discriminatory ability toward PoMCE (c index, 0.69 versus 0.53; P<0.01).

Conclusion

The original RCRI shows poor predictive ability in Chinese patients with CAD undergoing noncardiac surgery.