The surgical patient of yesterday, today, and tomorrow-a time-trend analysis based on a cohort of 8.7 million surgical patients

BACKGROUND

Global healthcare delivery is challenged by the aging population and the increase in obesity and type 2 diabetes. The extent to which such trends affect the cohort of patients the authors surgically operate on remains to be elucidated. Comprising of 8.7 million surgical patients, the American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) database can be analyzed to investigate the echo of general population dynamics and forecast future trends.

MATERIAL AND METHODS

The authors reviewed the ACS-NSQIP database (2008-2020) in its entirety, extracting patient age, BMI, and diabetes prevalence. Based on these data, the authors forecasted future trends up to 2030 using a drift model.

RESULTS

During the review period, median age increased by 3 years, and median BMI by 0.9 kg/m2. The proportion of patients with overweight, obesity class I, and class II rates increased. The prevalence of diabetes rose between 2008 (14.9%) and 2020 (15.3%). The authors forecast the median age in 2030 to reach 61.5 years and median BMI to climb to 29.8 kg/m2. Concerningly, in 2030, eight of ten surgical patients are projected to have a BMI above normal. Diabetes prevalence is projected to rise to 15.6% over the next decade.

CONCLUSION

General population trends echo in the field of surgery, with the surgical cohort aging at an alarmingly rapid rate and increasingly suffering from obesity and diabetes. These trends show no sign of abating without dedicated efforts and call for urgent measures and fundamental re-structuring for improved future surgical care.

Postoperative short-term mortality between insulin-treated and non-insulin-treated patients with diabetes after non-cardiac surgery: a systematic review and meta-analysis

Background

Diabetes mellitus is an independent risk factor for postoperative complications. It has been reported that insulin-treated diabetes is associated with increased postoperative mortality compared to non-insulin-treated diabetes after cardiac surgery; however, it is unclear whether this finding is applicable to non-cardiac surgery.

Objective

We aimed to assess the effects of insulin-treated and non-insulin-treated diabetes on short-term mortality after non-cardiac surgery.

Methods

Our study was a systematic review and meta-analysis of observational studies. PubMed, CENTRAL, EMBASE, and ISI Web of Science databases were searched from inception to February 22, 2021. Cohort or case-control studies that provided information on postoperative short-term mortality in insulin-treated diabetic and non-insulin-treated diabetic patients were included. We pooled the data with a random-effects model. The Grading of Recommendations, Assessment, Development, and Evaluation system was used to rate the quality of evidence.

Results

Twenty-two cohort studies involving 208,214 participants were included. Our study suggested that insulin-treated diabetic patients was associated with a higher risk of 30-day mortality than non-insulin-treated diabetic patients [19 studies with 197,704 patients, risk ratio (RR) 1.305; 95% confidence interval (CI), 1.127 to 1.511; p < 0.001]. The studies were rated as very low quality. The new pooled result only slightly changed after seven simulated missing studies were added using the trim-and-fill method (RR, 1.260; 95% CI, 1.076-1.476; p = 0.004). Our results also showed no significant difference between insulin-treated diabetes and non-insulin-treated diabetes regarding in-hospital mortality (two studies with 9,032 patients, RR, 0.970; 95% CI, 0.584-1.611; p = 0.905).

Conclusion

Very-low-quality evidence suggests that insulin-treated diabetes was associated with increased 30-day mortality after non-cardiac surgery. However, this finding is non-definitive because of the influence of confounding factors.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021246752, identifier: CRD42021246752.

Major Adverse Cardiac Events after Elective Infrarenal Endovascular Aortic Aneurysm Repair

OBJECTIVE

There is a significant cardiac morbidity and mortality after endovascular aneurysm repair (EVAR). However, information about long-term risk of cardiac events after EVAR and potential predictors is lacking. Therefore, the aim of this study was to determine incidence and predictors of major adverse cardiac events (MACE) at one- and five-years after elective EVAR for infrarenal abdominal aortic aneurysms.

METHODS

Baseline, perioperative and postoperative information of 320 patients was evaluated. The primary outcome was the incidence of MACE after EVAR, which was defined as acute coronary syndrome, unstable angina pectoris, de novo atrial fibrillation, hospitalization for heart failure, mitral valve insufficiency, revascularization (including PCI and CABG), as well as cardiovascular and non-cardiovascular death. Kaplan Meier analysis was performed to determine incidences of MACE, MACE excluding non-cardiovascular death and cardiac events by excluding non-cardiovascular and vascular death from MACE. Predictors of MACE were identified using univariate and multivariate binary regression analysis.

RESULTS

Through one- and five-years follow-up after EVAR, freedom from MACE was 89.4% (standard error (SE) 0.018) and 59.8% (SE 0.033), freedom from MACE excluding non-cardiovascular death was 94.7% (SE 0.013) and 77.5% (SE 0.030) and freedom from cardiac events was 96.0% (SE 0.011) and 79.1% (SE 0.030), respectively. Predictors for MACE within one-year were American Society of Anaesthesiologists (ASA) score 3 or 4 (OR, 3.17; 95% CI, 1.52-6.59) and larger abdominal aortic diameter (OR, 1.04; 95% CI, 1.01-1.08). History of atrial fibrillation (OR, 0.14; 95% CI, 0.03-0.60) was a negative predictor factor. Predictors for MACE through five-years were history of heart failure (OR, 4.10; 95% CI 1.36-12.32) and valvular heart disease (OR, 2.31; 95% CI, 0.97-5.51), ASA score 3 or 4 (OR, 1.66; 95% CI, 0.96-2.88) and older age (OR, 1.04; 95% CI, 1.01-1.08).

CONCLUSION

MACE is a common complication during the first five-years after elective EVAR. Cardiac diseases at baseline are strong predictors for long-term MACE and potentially helpful in optimizing future post-operative long-term follow-up.

Diabetes mellitus and perioperative outcomes: a scoping review of the literature

BACKGROUND

Diabetes mellitus (DM) is frequently encountered in the perioperative period. DM may increase the risk of adverse perioperative outcomes owing to the potential vascular complications of DM. We conducted a scoping review to examine the association between DM and adverse perioperative outcomes.

METHODS

A systematic search strategy of the published literature was built and applied in multiple databases. Observational studies examining the association between DM and adverse perioperative outcomes were included. Abstract screening determined full texts suitable for inclusion. Core information was extracted from each of the included studies including study design, definition of DM, type of DM, surgical specialties, and outcomes. Only primary outcomes are reported in this review.

RESULTS

The search strategy identified 2363 records. Of those, 61 were included and 28 were excluded with justification. DM was mostly defined by either haemoglobin A1c (HbA1c) or blood glucose values (19 studies each). Other definitions included 'prior diagnosis' or use of medication. In 17 studies the definition was unclear. Type 2 DM was the most frequently studied subtype. Five of seven studies found DM was associated with mortality, 5/13 reported an association with 'complications' (as a composite measure), and 12/17 studies found DM was associated with 'infection'. Overall, 33/61 studies reported that DM was associated with the primary outcome measure.

CONCLUSION

Diabetes mellitus is inconsistently defined in the published literature, which limits the potential for pooled analysis. Further research is necessary to determine which cohort of patients with DM are most at risk of adverse postoperative outcomes, and how control influences this association.

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.

Diagnosis and Management of Patients With Myocardial Injury After Noncardiac Surgery: A Scientific Statement From the American Heart Association

Myocardial injury after noncardiac surgery is defined by elevated postoperative cardiac troponin concentrations that exceed the 99th percentile of the upper reference limit of the assay and are attributable to a presumed ischemic mechanism, with or without concomitant symptoms or signs. Myocardial injury after noncardiac surgery occurs in ≈20% of patients who have major inpatient surgery, and most are asymptomatic. Myocardial injury after noncardiac surgery is independently and strongly associated with both short-term and long-term mortality, even in the absence of clinical symptoms, electrocardiographic changes, or imaging evidence of myocardial ischemia consistent with myocardial infarction. Consequently, surveillance of myocardial injury after noncardiac surgery is warranted in patients at high risk for perioperative cardiovascular complications. This scientific statement provides diagnostic criteria and reviews the epidemiology, pathophysiology, and prognosis of myocardial injury after noncardiac surgery. This scientific statement also presents surveillance strategies and treatment approaches.

Hyperglycemia inhibition of endothelial miR-140-3p mediates angiogenic dysfunction in diabetes mellitus

OBJECTIVE

MicroRNAs (miRNAs) have emerged as promising regulators of diabetes mellitus (DM)-induced angiogenic dysfunction in endothelial cells (ECs), but information vis-à-vis the functional roles of distinct miRNAs remain surprisingly scarce. The current study was designed to elucidate the expression and function of miR-140-3p in diabetic ECs.

METHODS

miR-140-3p expression was evaluated in DM mouse model and in human ECs using RT-qPCR, Northern blot and RNA fluorescent in situ hybridization. Effects of miR-140-3p manipulation on ECs function were evaluated using cell proliferation, migration and in vitro tube formation assay. Regulation of FOXK2 transcription by miR-140-3p was determined by luciferase reporter assay and site-directed mutagenesis.

RESULTS

miR-140-3p expression was significantly down-regulated in high glucose-challenged ECs. Under normal conditions, miR-140-3p knockdown impaired endothelial proliferation and migration, and endothelial tube formation. Mechanistically, miR-140-3p exhibited its proangiogenic effects through directly inhibiting the expression of the forkhead transcription factor FOXK2. From a therapeutic standpoint, shRNA-mediated stable inhibition of FOXK2 effectively corrected miR-140-3p deficiency-induced impairment of ECs proliferation and in vitro angiogenesis.

CONCLUSION

Endothelial miR-140-3p positive regulates ECs function by directly targeting FOXK2 signaling. Deregulation of miR-140-3p/FOXK2 cascade by hyperglycemia thus serves as an important contributor to angiogenic dysfunction in DM.

Major Cardiac Events in Patients Admitted to Intensive Care After Vascular Noncardiac Surgery: A Retrospective Cohort

Introduction. Patients proposed to vascular noncardiac surgery (VS) have several comorbidities associated with major adverse cardiac events (MACE). We evaluated incidence, predictors, and outcomes, and compared different scores to predict MACE after VS. Methods. We included all patients admitted from 2006 to 2013. Perioperative MACE included cardiac arrhythmias, myocardial infarction (MI), cardiogenic pulmonary edema (CPE), acute heart failure (AHF), and cardiac arrest (CA). Lee Revised Cardiac Risk Index (RCRI), Vascular Quality Initiative (VQI-CRI), Vascular Study Group of New England (VSG-CRI), and South African Vascular Surgical (SAVS-CRI) Cardiac Risk Indexes were calculated and analyzed. We performed multivariate logistic regression to assess independent predictors with calculation of odds ratio (OR) and 95% confidence interval (CI). To reduce overfitting, we used leave-one-out cross-validation approach. The Predictive ability of scores was tested using area under receiver operating characteristic curve (AUROC). Results. A total of 928 patients were included. We observed 81 MACE (28 MI, 22 arrhythmias, 10 CPE, 9 AHF, 12 CA) in 60 patients (6.5%): 3.3% in intermediate-risk surgery and 9.8% in high-risk surgery. Previous history of coronary artery disease (OR = 3.2, CI = 1.8-5.7), atrial fibrillation (OR = 5.1, CI = 2.4-11.0), insulin-treated diabetes mellitus (OR = 3.26, CI = 1.51-7.06), mechanical ventilation (OR = 2.75, CI = 1.41-4.63), and heart rate (OR = 1.02, CI = 1.01-1.03) at admission were considered independent risk factors in multivariate analysis. The AUROC of our model was 0.79, compared with RCRI (0.66), VSG-CRI (0.69), VQI-CRI (0.71), and SAVS-CRI (0.73). Conclusions. Observed MACE were within predicted range (1% to 5% after intermediate-risk surgery and >5% after high-risk surgery). SAVS-CRI and VQI-CRI had slightly better predictive capacity than VSG-CRI or RCRI.

Incidence of Myocardial Infarction After High-Risk Vascular Operations in Adults

Importance

Advances in perioperative cardiac management and an increase in the number of endovascular procedures have made significant contributions to patients and postoperative myocardial infarction (POMI) risk following high-risk vascular procedures. Whether these changes have translated into real-world improvements in POMI incidence remain unknown.

Objective

To examine the temporal trends of myocardial infarction (MI) following high-risk vascular procedures.

Design, Setting, and Participants

A retrospective cohort study was performed using data collected from January 1, 2005, to December 31, 2013, in the American College of Surgeons National Surgery Quality Improvement Program database, to which participating hospitals across the United States report their preoperative, operative, and 30-day outcome data. A total of 90 303 adults who underwent a high-risk vascular procedure-open aortic surgery or infrainguinal bypass-during the study period were identified. Patients were divided into cohorts based on their year of operation, and their baseline cardiac risk factors and incidence of POMI were compared. Cases from 2005 to 2014 in the database were eligible for inclusion if one of their Current Procedural Terminology codes matched any of the operations identified as a high-risk vascular procedure. Data analysis took place from August 1, 2016, to November 15, 2016.

Exposures

The main exposure was the year of the operation. Other variables of interest included demographics, comorbidities, and other risk factors for MI.

Main Outcomes and Measures

Primary outcome of interest was the incidence of POMI.

Results

Of the 90 303 patients included in the study, 22 836 (25.3%) had undergone open aortic surgery and 67 467 (74.7%) had had infrainguinal bypass. The open aortic cohort comprised 16 391 men (71.9%), had a mean (SD) age of 69.1 (11.5) years, and was predominantly white (18 440 patients [80.8%] self-identified as white race/ethnicity). The infrainguinal bypass cohort included 41 845 men (62.1%), had a mean (SD) age of 66.7 (11.7) years, and had 51 043 patients (75.7%) who self-identified as white race/ethnicity. During the study period, patients who underwent open aortic procedures were more likely to be classified as American Society of Anesthesiologists class IV (7426 patients [32.6%] vs 15 683 [23.3%] for the infrainguinal bypass cohort) or class V (1131 [5.0%] vs 206 [0.3%]; P < .001) and to undergo emergency procedures (4852 [21.3%] vs 4954 [7.3%]; P < .001). The open aortic procedure cohort also experienced significantly higher actual incidence of POMI (464 [3.0%] vs 1270 [1.9%]; P < .001). From 2009 to 2014, the incidence of POMI demonstrated no substantial temporal change (2.7% in 2009 to 3.1% in 2014; P = .64 for trend). Postoperative MI was consistently associated with poor prognosis, with a 3.62-fold (95% CI, 2.25-5.82) to 11.77-fold (95% CI, 6.10-22.72) increased odds of cardiac arrest and a 3.01-fold (95% CI, 2.08-4.36) to 6.66-fold (95% CI, 4.66-9.52) increased odds of mortality.

Conclusions and Relevance

The incidence of MI did not significantly decrease in the past decade and has been consistently associated with worse clinical outcomes. Further inquiry into why advanced perioperative care did not reduce cardiac complications is important to quality improvement efforts.

Fasting hyperglycemia increases in-hospital mortality risk in nondiabetic female patients with acute myocardial infarction: a retrospective study

Previous studies had shown that elevated admission plasma glucose (APG) could increase mortality rate and serious complications of acute myocardial infarction (AMI), but whether fasting plasma glucose (FPG) had the same role remains controversial. In this retrospective study, 253 cases of AMI patients were divided into diabetic (n = 87) and nondiabetic group (n = 166). Our results showed that: compared with the nondiabetic patients, diabetic patients had higher APG, FPG, higher plasma triglyceride, higher rates of painless AMI (P < 0.01), non-ST-segment elevation myocardial infarction (NSTEMI), and reinfraction (P < 0.05). They also had lower high density lipoprotein cholesterol and rate of malignant arrhythmia, but in-hospital mortality rate did not differ significantly (P > 0.05). While nondiabetic patients were subgrouped in terms of APG and FPG (cut points were 11.1 mmol/L and 7.0 mmol/L, resp.), the mortality rate had significant difference (P < 0.01), whereas glucose level lost significance in diabetic group. Multivariate logistic regression analysis showed that FPG (OR: 2.014; 95% confidence interval: 1.296-3.131; p < 0.01) but not APG was independent predictor of in-hospital mortality for nondiabetic patients. These results indicate that FPG can be an independent predictor for mortality in nondiabetic female patients with AMI.