Frailty Assessment and Perioperative Major Adverse Cardiovascular Events After Noncardiac Surgery

Predicting perioperative myocardial injury/infarction after noncardiac surgery in patients under surgical and medical co-management: a prospective cohort study

BACKGROUND

Perioperative myocardial injury/infarction (PMI) following noncardiac surgery is a frequent cardiac complication. This study aims to evaluate PMI risk and explore preoperative assessment tools of PMI in patients at increased cardiovascular (CV) risk who underwent noncardiac surgery under the surgical and medical co-management (SMC) model.

METHODS

A prospective cohort study that included consecutive patients at increased CV risk who underwent intermediate- or high-risk noncardiac surgery at the Second Medical Center, Chinese PLA General Hospital, between January 2017 and December 2022. All patients were treated with perioperative management by the SMC team. The SMC model was initiated when surgical intervention was indicated and throughout the entire perioperative period. The incidence, risk factors, and impact of PMI on 30-day mortality were analyzed. The ability of the Revised Cardiac Risk Index (RCRI), frailty, and their combination to predict PMI was evaluated.

RESULTS

613 eligible patients (mean [standard deviation, SD] age 73.3[10.9] years, 94.6% male) were recruited consecutively. Under SMC, PMI occurred in 24/613 patients (3.9%). Patients with PMI had a higher rate of 30-day mortality than patients without PMI (29.2% vs. 0.7%, p = 0.00). The FRAIL Scale for frailty was independently associated with an increased risk for PMI (odds ratio = 5.91; 95% confidence interval [CI], 2.34-14.93; p = 0.00). The RCRI demonstrated adequate discriminatory capacity for predicting PMI (area under the curve [AUC], 0.78; 95% CI, 0.67-0.88). Combining frailty with the RCRI further increased the accuracy of predicting PMI (AUC, 0.87; 95% CI, 0.81-0.93).

CONCLUSIONS

The incidence of PMI was relatively low in high CV risk patients undergoing intermediate- or high-risk noncardiac surgery under SMC. The RCRI adequately predicted PMI. Combining frailty with the RCRI further increased the accuracy of PMI predictions, achieving excellent discriminatory capacity. These findings may aid personalized evaluation and management of high-risk patients who undergo intermediate- or high-risk noncardiac surgery.

The impact of frailty on short-term prognosis in discharged adult stroke patients: A multicenter prospective cohort study

BACKGROUND

Frailty is commonly observed in stroke patients and it is associated with adverse outcomes. However, there remains a gap in longitudinal studies investigating the causal relationship between baseline frailty and short-term prognosis in discharged adult stroke patients.

OBJECTIVE

To examine the causal impact of frailty on non-elective readmission and major adverse cardiac and cerebral events, and investigate its associations with cognitive impairment and post-stroke disability.

DESIGN

A multicenter prospective cohort study.

SETTING

Two tertiary hospitals in Central and Northwest China.

PARTICIPANTS

667 adult stroke patients in stroke units were included from January 2022 to June 2022.

METHODS

Baseline frailty was assessed by the Frailty Scale. Custom-designed questions were utilized to assess non-elective readmission and major adverse cardiac and cerebral events as primary outcomes. Cognitive impairment, assessed using the Mini-Mental State Examination Scale (MMSE), and post-stroke disability, measured with the Modified Rankin Scale (mRS), were considered secondary outcomes at a 3-month follow-up. The impact of baseline frailty on non-elective readmission and major adverse cardiac and cerebral events was examined using bivariate and multiple Cox regression analyses. Furthermore, associations between baseline frailty and cognitive impairment, or post-stroke disability, were investigated through generalized linear models.

RESULTS

A total of 5 participants died, 12 had major adverse cardiac and cerebral events, and 57 had non-selective readmission among 667 adult stroke patients. Frailty was an independent risk factor for non-selective readmission (hazard ratio [HR]: 2.71, 95 % confidence interval [CI]: 1.59, 4.62) and major adverse cardiac and cerebral events (HR: 3.77, 95 % CI: 1.07, 13.22) for stroke patients. Baseline frailty was correlated with cognitive impairment (regression coefficient [β]: -2.68, 95 % CI: -3.78, -1.58) adjusting for socio-demographic and clinical factors and follow-up interval. However, the relationship between frailty and cognitive impairment did not reach statistical significance when further adjusting for baseline MMSE (β: -0.39, 95 % CI: -1.43, 0.64). Moreover, baseline frailty was associated with post-stroke disability (β: 0.36, 95 % CI: 0.08, 0.65) adjusting for socio-demographic and clinical variables, follow-up interval, and baseline mRS.

CONCLUSIONS

The finding highlights the importance of assessing baseline frailty in discharged adult stroke patients, as it is significantly associated with non-elective readmission, major adverse cardiac and cerebral events, and post-stroke disability at 3 months. These results highlight the crucial role of screening and evaluating frailty status in improving short-term prognosis for adult stroke patients. Interventions should be developed to address baseline frailty and mitigate the short-term prognosis of stroke.

TWEETABLE ABSTRACT

Baseline frailty predicts non-elective readmission, major adverse cardiac and cerebral events, and post-stroke disability in adult stroke patients. @haiyanhexyyy.

Catheter-based therapy for high-risk or intermediate-risk pulmonary embolism: death and re-hospitalization

BACKGROUND AND AIMS

Catheter-based therapies (CBTs) have been developed as a treatment option in patients with pulmonary embolism (PE). There remains a paucity of data to inform decision-making in patients with intermediate-risk or high-risk PE. The aim of this study was to characterize in-hospital and readmission outcomes in patients with intermediate-risk or high-risk PE treated with vs. without CBT in a large retrospective registry.

METHODS

Patients hospitalized with intermediate-risk or high-risk PE were identified using the 2017-20 National Readmission Database. In-hospital outcomes included death and bleeding and 30- and 90-day readmission outcomes including all-cause, venous thromboembolism (VTE)-related and bleeding-related readmissions. Inverse probability of treatment weighting (IPTW) was utilized to compare outcomes between CBT and no CBT.

RESULTS

A total of 14 903 [2076 (13.9%) with CBT] and 42 829 [8824 (20.6%) with CBT] patients with high-risk and intermediate-risk PE were included, respectively. Prior to IPTW, patients with CBT were younger and less likely to have cancer and cardiac arrest, receive systemic thrombolysis, or be on mechanical ventilation. In the IPTW logistic regression model, CBT was associated with lower odds of in-hospital death in high-risk [odds ratio (OR) 0.83, 95% confidence interval (CI) 0.80-0.87] and intermediate-risk PE (OR 0.76, 95% CI 0.70-0.83). Patients with high-risk PE treated with CBT were associated with lower risk of 90-day all-cause [hazard ratio (HR) 0.77, 95% CI 0.71-0.83] and VTE (HR 0.46, 95% CI 0.34-0.63) readmission. Patients with intermediate-risk PE treated with CBT were associated with lower risk of 90-day all-cause (HR 0.75, 95% CI 0.72-0.79) and VTE (HR 0.66, 95% CI 0.57-0.76) readmission.

CONCLUSIONS

Among patients with high-risk or intermediate-risk PE, CBT was associated with lower in-hospital death and 90-day readmission. Prospective, randomized trials are needed to confirm these findings.

Frailty-aware surgical care: Validation of Hospital Frailty Risk Score (HFRS) in older surgical patients

Introduction

Frailty has an important impact on the health outcomes of older patients, and frailty screening is recommended as part of perioperative evaluation. The Hospital Frailty Risk Score (HFRS) is a validated tool that highlights frailty risk using 109 International Classification of Diseases, 10th revision (ICD-10) codes. In this study, we aim to compare HFRS to the Charlson Comorbidity Index (CCI) and validate HFRS as a predictor of adverse outcomes in Asian patients admitted to surgical services.

Method

A retrospective study of electronic health records (EHR) was undertaken in patients aged 65 years and above who were discharged from surgical services between 1 April 2022 to 31 July 2022. Patients were stratified into low (HFRS <5), interme-diate (HFRS 5-15) and high (HFRS >15) risk of frailty.

Results

Those at high risk of frailty were older and more likely to be men. They were also likely to have more comorbidities and a higher CCI than those at low risk of frailty. High HFRS scores were associated with an increased risk of adverse outcomes, such as mortality, hospital length of stay (LOS) and 30-day readmission. When used in combination with CCI, there was better prediction of mortality at 90 and 270 days, and 30-day readmission.

Conclusion

To our knowledge, this is the first validation of HFRS in Singapore in surgical patients and confirms that high-risk HFRS predicts long LOS (≥7days), increased unplanned hospital readmissions (both 30-day and 270-day) and increased mortality (inpatient, 10-day, 30-day, 90-day, 270-day) compared with those at low risk of frailty.

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.

The frail patient undergoing cardiac surgery: lessons learned and future perspectives

Frailty is a geriatric condition characterized by the reduction of the individual's homeostatic reserves. It determines an increased vulnerability to endogenous and exogenous stressors and can lead to poor outcomes. It is an emerging concept in perioperative medicine, since an increasing number of patients undergoing surgical interventions are older and the traditional models of care seem to be inadequate to satisfy these patients' emerging clinical needs. Nowadays, the progressive technical and clinical improvements allow to offer cardiac operations to an older, sicker and frail population. For these reasons, a multidisciplinary team involving cardiac surgeons, clinical cardiologists, anesthesiologists, and geriatricians, is often needed to assess, select and provide tailored care to these high-risk frail patients to optimize clinical outcomes. There is unanimous agreement that frailty assessment may capture the individual's biological decline and the heterogeneity in risk profile for poor health-related outcomes among people of the same age. However, since commonly used preoperative scores for cardiac surgery fail to capture frailty, a specific preoperative assessment with dedicated tools is warranted to correctly recognize, measure and quantify frailty in these patients. On the contrary, pre-operative and post-operative interventions can reduce the risk of complications and support patient recovery promoting surgical resilience. Minimally invasive cardiac procedures aim to reduce surgical trauma and may be associated with better clinical outcome in this specific sub-group of high-risk patients. Among postoperative adverse events, the occurrence of delirium represents a risk factor for several unfavorable outcomes including mortality and subsequent cognitive decline. Its presence should be carefully recognized, triggering an adequate, evidence based, treatment. There is evidence, from several cross-section and longitudinal studies, that frailty and delirium may frequently overlap, with frailty serving both as a predisposing factor and as an outcome of delirium and delirium being a marker of a latent condition of frailty. In conclusion, frail patients are at increased risk to experience poor outcome after cardiac surgery. A multidisciplinary approach aimed to recognize more vulnerable individuals, optimize pre-operative conditions, reduce surgical invasivity and improve post-operative recovery is required to obtain optimal long-term outcome.

Automated Electronic Frailty Index-Identified Frailty Status and Associated Postsurgical Adverse Events

Importance

Electronic frailty index (eFI) is an automated electronic health record (EHR)-based tool that uses a combination of clinical encounters, diagnosis codes, laboratory workups, medications, and Medicare annual wellness visit data as markers of frailty status. The association of eFI with postanesthesia adverse outcomes has not been evaluated.

Objective

To examine the association of frailty, calculated as eFI at the time of the surgical procedure and categorized as fit, prefrail, or frail, with adverse events after elective noncardiac surgery.

Design, Setting, and Participants

This cohort study was conducted at a tertiary care academic medical center in Winston-Salem, North Carolina. The cohort included patients 55 years or older who underwent noncardiac surgery of at least 1 hour in duration between October 1, 2017, and June 30, 2021.

Exposure

Frailty calculated by the eFI tool. Preoperative eFI scores were calculated based on available data 1 day prior to the procedure and categorized as fit (eFI score: ≤0.10), prefrail (eFI score: >0.10 to ≤0.21), or frail (eFI score: >0.21).

Main Outcomes and Measures

The primary outcome was a composite of the following 8 adverse component events: 90-item Patient Safety Indicators (PSI 90) score, hospital-acquired conditions, in-hospital mortality, 30-day mortality, 30-day readmission, 30-day emergency department visit after surgery, transfer to a skilled nursing facility after surgery, or unexpected intensive care unit admission after surgery. Secondary outcomes were each of the component events of the composite.

Results

Of the 33 449 patients (median [IQR] age, 67 [61-74] years; 17 618 females [52.7%]) included, 11 563 (34.6%) were classified as fit, 15 928 (47.6%) as prefrail, and 5958 (17.8%) as frail. Using logistic regression models that were adjusted for age, sex, race and ethnicity, and comorbidity burden, patients with prefrail (odds ratio [OR], 1.24; 95% CI, 1.18-1.30; P < .001) and frail (OR, 1.71; 95% CI, 1.58-1.82; P < .001) statuses were more likely to experience postoperative adverse events compared with patients with a fit status. Subsequent adjustment for all other potential confounders or covariates did not alter this association. For every increase in eFI of 0.03 units, the odds of a composite of postoperative adverse events increased by 1.06 (95% CI, 1.03-1.13; P < .001).

Conclusions and Relevance

This cohort study found that frailty, as measured by an automatically calculated index integrated within the EHR, was associated with increased risk of adverse events after noncardiac surgery. Deployment of eFI tools may support screening and possible risk modification, especially in patients who undergo high-risk surgery.

RAI-measured frailty predicts non-home discharge following metastatic brain tumor resection: national inpatient sample analysis of 20,185 patients

PURPOSE

Preoperative risk stratification for patients undergoing metastatic brain tumor resection (MBTR) is based on established tumor-, patient-, and disease-specific risk factors for outcome prognostication. Frailty, or decreased baseline physiologic reserve, is a demonstrated independent risk factor for adverse outcomes following MBTR. The present study sought to assess the impact of frailty, measured by the Risk Analysis Index (RAI), on MBTR outcomes.

METHODS

All MBTR were queried from the National Inpatient Sample (NIS) from 2019 to 2020 using diagnosis and procedural codes. The relationship between preoperative RAI frailty score and our primary outcome - non-home discharge (NHD) - and secondary outcomes - complication rates, extended length of stay (eLOS), and mortality - were analyzed via univariate and multivariable analyses. Discriminatory accuracy was tested by computation of concordance statistics in area under the receiver operating characteristic (AUROC) curve analysis.

RESULTS

There were 20,185 MBTR patients from the NIS database from 2019 to 2020. Each patient's frailty status was stratified by RAI score: 0-20 (robust): 34%, 21-30 (normal): 35.1%, 31-40 (very frail): 13.9%, 41+ (severely frail): 16.8%. Compared to robust patients, severely frail patients demonstrated increased complication rates (12.2% vs. 6.8%, p < 0.001), eLOS (37.6% vs. 13.2%, p < 0.001), NHD (52.0% vs. 20.6%, p < 0.001), and mortality (9.9% vs. 4.1%, p < 0.001). AUROC curve analysis demonstrated good discriminatory accuracy of RAI-measured frailty in predicting NHD after MBTR (C-statistic = 0.67).

CONCLUSION

Increasing RAI-measured frailty status is significantly associated with increased complication rates, eLOS, NHD, and mortality following MBTR. Preoperative frailty assessment using the RAI may aid in preoperative surgical planning and risk stratification for patient selection.