Indication for Surgery, the Revised Cardiac Risk Index, and 1-Year Mortality

Comparative analysis of perioperative outcomes in endovascular abdominal aortic aneurysm repair: Fascia iliaca block versus general anesthesia, a retrospective study

OBJECTIVE

This retrospective study aimed to compare rates of perioperative mortality and morbidity, especially pulmonary complication, between endovascular aneurysm repair (EVAR) performed under general anesthesia (GA) and under fascia iliaca block (FIB).

METHODS

Patients diagnosed with infrarenal abdominal aortic aneurysm (AAA) who were treated with EVAR were included. Retrospective review of electronic medical records was performed. Patient characteristics, operative details, and postoperative results including mortality and morbidity within 30 days were collected. Statistical analysis to compare postoperative outcomes between EVAR under FIB and EVAR under GA was performed. A univariate analysis was conducted to identify factors associated with increased 30-day mortality.

RESULTS

This study included 119 patients, 75 in the FIB group and 44 in the GA group. Most patients were male, with 62 (82.5%) in the FIB group and 31 (70.2%) in the GA group, and most patients were hypertensive, with 57 (76%) in the FIB group and 36 (81.8%) in the GA group. Smoking and coronary artery disease (CAD) was more prevalent in the FIB group, p < .05. Thirty-day mortality was not significantly different between the FIB group and the GA group (1 (1.3%) vs 2 (4.5%), p = .554). Pulmonary complication was lower in the FIB group than in the GA group (1.3% vs 11.4%, p = .026). ICU stay was shorter in the FIB group than in the GA group (0.2 vs 4.5 days, p = .012). Univariate analysis showed that chronic obstructive pulmonary disease (COPD) was associated with higher 30-day mortality.

CONCLUSIONS

Endovascular aneurysm repair under FIB was feasible. Compared to GA, this approach resulted in lower postoperative pulmonary complications and shorter ICU stay.

Coronary artery calcium score and coronary computed tomography angiography predict one-year mortality in patients with type 2 diabetes and peripheral artery disease undergoing partial foot amputation

METHODS

This is a single-center prospective cohort study including 199 consecutive patients with T2D, PAD (mean age 62.3 ± 7.2 years; 62.8% males), and preoperative CACS and CCTA undergoing PFA and followed-up over 1 year.

RESULTS

Over a period of 1 year follow-up, a total of 35 (17.6%) participants died. The area under ROC curve to predict mortality for the CACS was 0.835 (95% CI:0.769-0.900), for CCTA 0.858 (95% CI:0.788-0.927). After adjustment for confounders, compared to no-stenosis on CCTA (reference), the risk of all-cause mortality in non-obstructive coronary atery disease (CAD) increased (HR = 1.38, 95% CI [0.75-12.86], p = .284), 1-vessel obstructive CAD (HR = 8.13, 95% CI [0.87-75.88], p = .066), 2-vessels (HR = 10.94, 95% CI [1.03-115.8], p = .047), and 3-vessels (HR = 45.73, 95% CI [4.6-454.7], p = .001) respectively. Increasing levels of CACS tended to be associated with increased risk of all-cause mortality (HR = 1.002, 95% CI [1.0-1.003], p = .061). 61/95 patients with obstructive CAD underwent coronary revascularization.

CONCLUSIONS

Coronary artery calcium score and CCTA have a high predictive value for 1-year all-cause mortality in T2D patients undergoing minor amputations and may be considered for preoperative risk assessment allowing timely preventive interventions.

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.

Sex-related disparities in intervention rates and type of intervention in patients with aortic and peripheral arterial diseases in the National Inpatient Sample Database

OBJECTIVE

The first annual Women's Vascular Summit highlighted sex- and gender-related knowledge gaps in vascular disease diagnosis and treatment. This finding suggests an opportunity for further research to improve care and outcomes in people who identify as women, specifically. The purpose of this study was to a large national dataset to identify all operations performed for abdominal aortic aneurysm (AAA), carotid artery stenosis (CAS), and peripheral arterial disease (PAD) in the United States, and to provide data on sex-related disparities in treatment.

METHODS

All hospitalizations of adult patients (≥18 years old) diagnosed with AAA, CAS, or PAD who underwent vascular surgery from 2000 to 2016 were identified in the Healthcare Cost and Utilization Project National Inpatient Sample. Sex-stratified U.S. Census data and sex-specific population disease prevalence estimates from the National Institute of Health and Agency for Healthcare Research and Quality were used to calculate the number of U.S. adults with AAA, CAS, and PAD. Sex-stratified rates of surgery and incidence rate ratios were estimated using Poisson regression. Among those undergoing surgery, multivariable logistic regression was used to assess differences in endovascular vs open approach.

RESULTS

Over 16 years, there were 1,021,684 hospitalizations for vascular surgery: 13% AAA (21% female, 79% male), 40% CAS (42% female, 58% male), and 47% PAD (42% female, 58% male). Females were older than males at time of surgery (median age, 71.3 years vs 69.7 years) and less likely to have private insurance (18% vs 23%); minimal differences were seen across race/ethnicity, comorbidities, and hospital characteristics. After accounting for disease prevalence, females were still 25% less likely to undergo surgery for AAA and 30% less likely to undergo surgery for PAD compared with males with the same disease. These results were consistent over time. After adjustment, females, compared with males, were less likely to receive an endovascular procedure compared with open for AAA or CAS, and more likely to receive one for PAD.

CONCLUSIONS

From 2000 to 2016 in the United States, females were less likely to undergo intervention for AAA and PAD than males. This finding is particularly significant for PAD, because the prevalence is the same for both sexes, indicating that females are likely undertreated for PAD. Additionally, females were less likely to undergo endovascular surgery for AAA and more likely to undergo endovascular surgery for PAD than males. These findings suggest that improvement in AAA and PAD identification and management in females may improve outcomes.

The Relationship between the Lee Score and Postoperative Mortality in Patients with Proximal Femur Fractures

Objective  To verify the predictive value of the Lee score for mortality in a one-year period after proximal femur fracture surgery. The present study also evaluated the isolated predictive capacity of other variables. Methods  A sample of 422 patients with surgically-treated proximal femur fractures was evaluated. Data was collected through a review of medical records, appointments, and contact by telephone. Results  The Lee score was applied to 99.3% of the patients with proximal femur fractures submitted to surgical treatment. The mortality rate was of 22% of the sample, and the majority were classified as class I risk. The Lee score had no significant association with mortality ( p  = 0.515). High levels of serum creatinine ( p  = 0.001) and age ( p  = 0.000) were directly associated with death. Conclusion  The Lee score was not predictive of mortality in a one-year period after proximal femur fracture surgery; however, a statistical significance was observed between age and serum creatinine levels, considered separately, and death.

Revised Cardiac Risk Index as a Predictor for Myocardial Infarction and Cardiac Arrest Following Posterior Lumbar Decompression

STUDY DESIGN

A retrospective analysis of prospectively collected data.

OBJECTIVE

The aim of this study was to determine the ability of Revised Cardiac Risk Index (RCRI) to predict adverse cardiac events following posterior lumbar decompression (PLD).

SUMMARY OF BACKGROUND DATA

PLD is an increasingly common procedure used to treat a variety of degenerative spinal conditions. The RCRI is used to predict risk for cardiac events following noncardiac surgery. There is a paucity of literature that directly addresses the relationship between RCRI and outcomes following PLD, specifically, the discriminative ability of the RCRI to predict adverse postoperative cardiac events.

METHODS

ACS-NSQIP was utilized to identify patients undergoing PLD from 2006 to 2014. Fifty-two thousand sixty-six patients met inclusion criteria. Multivariate and ROC analysis was utilized to identify associations between RCRI and postoperative complications.

RESULTS

Membership in the RCRI=1 cohort was a predictor for myocardial infarction (MI) [odds ratio (OR) = 3.3, P = 0.002] and cardiac arrest requiring cardiopulmonary resuscitation (CPR) (OR = 3.4, P = 0.013). Membership in the RCRI = 2 cohort was a predictor for MI (OR = 5.9, P = 0.001) and cardiac arrest requiring CPR (OR = 12.5), Membership in the RCRI = 3 cohort was a predictor for MI (OR = 24.9) and cardiac arrest requiring CPR (OR = 26.9, P = 0.006). RCRI had a good discriminative ability to predict both MI [area under the curve (AUC) = 0.876] and cardiac arrest requiring CPR (AUC = 0.855). The RCRI had a better discriminative ability to predict these outcomes that did ASA status, which had discriminative abilities of "fair" (AUC = 0.799) and "poor" (AUC = 0.674), respectively. P < 0.001 unless otherwise specified.

CONCLUSION

RCRI was predictive of cardiac events following PLD, and RCRI had a better discriminative ability to predict MI and cardiac arrest requiring CPR than did ASA status. Consideration of the RCRI as a component of preoperative surgical risk stratification can minimize patient morbidity and mortality. Studies such as this can allow for implementation of guidelines that better estimate the preoperative risk profile of surgical patients.

LEVEL OF EVIDENCE

3.

Revised Cardiac Risk Index versus ASA Status as a Predictor for Noncardiac Events After Posterior Lumbar Decompression

BACKGROUND

The Revised Cardiac Risk Index (RCRI) was designed to predict risk for cardiac events after noncardiac surgery. However, there is a paucity of literature that directly addresses the relationship between RCRI and noncardiac outcomes after posterior lumbar decompression (PLD). The objective of this study is to determine the ability of RCRI to predict noncardiac adverse events after PLD.

METHODS

The American College of Surgeons National Surgical Quality Improvement Program was used to identify patients undergoing PLD from 2006 to 2014. Multivariate and receiver operating characteristic analysis was used to identify associations between RCRI and postoperative complications.

RESULTS

A total of 52,066 patients met the inclusion criteria. Membership in the RCRI=1 cohort independently predicted unplanned intubation, ventilation >48 hours, progressive renal insufficiency, acute renal failure, urinary tract infection (UTI), sepsis, septic shock, and readmission. Membership in the RCRI=2 cohort independently predicted for superficial surgical site infection, pneumonia, unplanned intubation, ventilation >48 hours, bleeding transfusion, progressive renal insufficiency, acute renal failure, UTI, sepsis, septic shock, and readmission. Membership in the RCRI=3 cohort independently predicted unplanned intubation (odds ratio [OR], 11.8), ventilation >48 hours (OR, 23.0), acute renal failure (OR, 84.5), and UTI (OR, 3.6). RCRI had a poor discriminative ability (DA) (area under the curve = 0.623), and American Society of Anesthesiologists status had a fair DA (area under the curve = 0.770) to predict a composite of noncardiac complications.

CONCLUSIONS

RCRI was predictive of a wide range of noncardiac complications after PLD but had a diminished DA to predict a composite of any noncardiac complication than did American Society of Anesthesiologists score. Consideration of the RCRI as a component of preoperative surgical risk stratification can minimize patient morbidity and mortality after lumbar decompression.

Pre-operative predictors of poor outcomes in patients undergoing surgical lower extremity revascularisation - Retrospective cohort study

BACKGROUND

Surgical lower extremity revascularisation (LER) can lead to poor outcomes that include delayed hospital discharge, in-hospital mortality, major amputations and readmissions. The aim of this study was to identify pre-operative predictors associated with these poor clinical outcomes.

MATERIALS AND METHODS

All patients (n = 635; mean age 69; male 67.4%) who underwent surgical LER over a 5 year period in a single tertiary vascular institution were identified. Patients considered to have suffered a poor outcome (Group A) included all in-hospital mortality and major amputations, delayed discharges with a length of stay (LOS) over one standard deviation above the mean or any readmission under any specialty within 12 months. Group A included 247 patients (38.9%) and the good outcome group included the remaining 388 patients (61.1%) from which a sample of 99 patients were selected as controls (Group B).

RESULTS

Mean LOS for the entire study group was 14.4 ± 17.5 days, 12 month readmission rate was 29.1% and in-hospital mortality and major amputation rate was 2.7% and 1.4%, respectively. Pre-admission residence other than own home (OR 9.0; 95% CI 1.2-70.1; P = 0.036), atherosclerotic disease burden (OR 2.2; 95% CI 1.3-3.8; P = 0.003) and tissue loss (OR 3.0; 95% CI 1.6-5.3; P < 0.001) were identified as independent, statistically significant pre-operative predictors of poor outcome. Following discharge, group B patients had a significantly higher rate of amputation free survival and graft infection free survival (P < 0.001) compared to group A.

CONCLUSION

Recognition of pre-operative predictors of poor outcome should inform case selection and identify high risk patients requiring intensive perioperative optimisation and post discharge follow up.

The PER (Preoperative Esophagectomy Risk) Score: A Simple Risk Score to Predict Short-Term and Long-Term Outcome in Patients with Surgically Treated Esophageal Cancer

Esophageal resection in patients with esophageal cancer (EC) is still associated with high mortality and morbidity rates. We aimed to develop a simple preoperative risk score for the prediction of short-term and long-term outcomes for patients with EC treated by esophageal resection. In total, 498 patients suffering from esophageal carcinoma, who underwent esophageal resection, were included in this retrospective cohort study. Three preoperative esophagectomy risk (PER) groups were defined based on preoperative functional evaluation of different organ systems by validated tools (revised cardiac risk index, model for end-stage liver disease score, and pulmonary function test). Clinicopathological parameters, morbidity, and mortality as well as disease-free survival (DFS) and overall survival (OS) were correlated to the PER score. The PER score significantly predicted the short-term outcome of patients with EC who underwent esophageal resection. PER 2 and PER 3 patients had at least double the risk of morbidity and mortality compared to PER 1 patients. Furthermore, a higher PER score was associated with shorter DFS (P < 0.001) and OS (P < 0.001). The PER score was identified as an independent predictor of tumor recurrence (hazard ratio [HR] 2.1; P < 0.001) and OS (HR 2.2; P < 0.001). The PER score allows preoperative objective allocation of patients with EC into different risk categories for morbidity, mortality, and long-term outcomes. Thus, multicenter studies are needed for independent validation of the PER score.

Using implementation science to help integrate preoperative evidence-based measures into practice for vascular surgery patients

The preoperative workup is a necessary and crucial evaluation for patients undergoing major vascular surgery. It is important to assess for likelihood of perioperative adverse events and then implement evidence-based measures to reduce identified medical comorbidities and improve the quality of patient care and outcomes after surgery. Although there are numerous opportunities to implement evidence-based processes during the preoperative period, there are many barriers that can prevent vascular surgeons from achieving these goals. This review will discuss how an implementation science-based approach can be used by members of the vascular surgery team to identify appropriate preoperative evidence-based interventions for diverse practice settings and to overcome barriers and allow integration of these interventions as part of the routine preoperative workup.

The association between ASA status and other risk stratification models on postoperative intensive care unit outcomes

BACKGROUND

There is limited medical literature investigating the association between perioperative risk stratification methods and surgical intensive care unit (SICU) outcomes. Our hypothesis contends that routine assessments such as higher ASA physical status classification, surgical risk as defined by American College of Cardiology/American Heart Association guidelines, and simplified Revised Cardiac Index (SRCI) can reliably be associated with SICU outcomes.

METHODS

We performed a chart review of all patients 18 years or older admitted to the SICU between October 1, 2010, and March 1, 2011. We collected demographic and preoperative clinical data: age, sex, ASA physical status class, surgical risk, and SRCI. Outcome data included our primary end point, SICU length of stay, and secondary end points: mechanical ventilation and vasopressor treatment duration, number of acquired organ dysfunctions (NOD), readmission to the intensive care unit (ICU) within 7 days, SICU mortality, and 30-day mortality. Regression analysis and nonparametric tests were used, and P < 0.05 was considered significant.

RESULTS

We screened 239 patients and included 220 patients in the study. The patients' mean age was 58 ± 16 years. There were 32% emergent surgery and 5% readmissions to the SICU within 7 days. The SICU mortality and the 30-day mortality were 3.2%. There was a significant difference between SICU length of stay (2.9 ± 2.1 vs 5.9 ± 7.4, P = 0.007), mechanical ventilation (0.9 ± 2.0 vs 3.4 ± 6.8, P = 0.01), and NOD (0 [0-2] vs 1 [0-5], P < 0.001) based on ASA physical status class (≤ 2 vs ≥ 3). Outcomes significantly associated with ASA physical status class after adjusting for confounders were: SICU length of stay (incidence rate ratio [IRR] = 1.79, 95% confidence interval [CI], 1.35-2.39, P < 0.001), mechanical ventilation (IRR = 2.57, 95% CI, 1.69-3.92, P < 0.001), vasopressor treatment (IRR = 3.57, 95% CI, 1.84-6. 94, P < 0.001), NOD (IRR = 1.71, 95% CI, 1.46-1.99, P < 0.001), and readmission to ICU (odds ratio = 3.39, 95% CI, 1.04-11.09, P = 0.04). We found significant association between surgery risk and NOD (IRR = 1.56, 95% CI, 1.29-1.89, P < 0.001, and adjusted IRR = 1.31, 95% CI, 1.05-1.64, P = 0.02). SRCI was not significantly associated with SICU outcomes.

CONCLUSIONS

Our study revealed that ASA physical status class is associated with increased SICU length of stay, mechanical ventilation, vasopressor treatment duration, NOD, readmission to ICU, and surgery risk is associated with NOD.

Outcome after hepatectomy-delirium as an independent predictor for mortality

Background

Most studies that follow up hepatectomy cases are limited in scope to an investigation of mortality and morbidity rates or the costs and length of hospital stay. In this study the authors aimed to characterize the quality of life and to evaluate mortality and its determinants after hepatectomy.

Methods

This prospective study was carried in a Post-Anaesthesia Care Unit (PACU) over 15 months, and 70 patients submitted to hepatectomy were enrolled. Demographic and peri-operative characteristics were evaluated for associations with mortality. At admission and 6 months after discharge, patients completed a Short Form-36 questionnaire (SF-36) and have their independence in Activities of Daily Living (ADL) was evaluated. Binary and multiple logistic regression analyses were used to evaluate of associations with mortality, and the Wilcoxon signed rank test was used to compare SF-36 scores before and after 6 months after hepatectomy.

Results

The mortality rate was 19% at 6 months. Multivariate analysis identified postoperative delirium as an independent determinant for mortality. Six months after discharge, 46% patients stated that their health in general was better or much better than that 1 year previously. Six months after hepatectomy, patients had worse scores in the physical function domain of SF-36; however, scores for all the other domains did not differ. At this time point, patients were more dependent in instrumental ADL than before surgery (32% versus 7%, p = 0.027).

Conclusion

This study identified postoperative delirium as an independent risk factor for mortality 6 months after hepatectomy. After 6 months, survivors were more dependent in instrumental ADL tasks and had worse scores in the physical function domain of SF-36.