Ambulatory Cardiology or General Internal Medicine Assessment Prior to Scheduled Major Vascular Surgery is Associated with Improved Outcomes.

OBJECTIVE: To characterize the association between ambulatory cardiology or general internal medicine (GIM) assessment prior to surgery and outcomes following scheduled major vascular surgery. BACKGROUND: Cardiovascular risk assessment and management prior to high-risk surgery remains an evolving area of care. METHODS: This is population-based retrospective cohort study of all adults who underwent scheduled major vascular surgery in Ontario, Canada, April 1, 2004-March 31, 2019. Patients who had an ambulatory cardiology and/or GIM assessment within 6 months prior to surgery were compared to those who did not. The primary outcome was 30-day mortality. Secondary outcomes included: composite of 30-day mortality, myocardial infarction or stroke; 30-day cardiovascular death; 1-year mortality; composite of 1-year mortality, myocardial infarction or stroke; and 1-year cardiovascular death. Cox proportional hazard regression using inverse probability of treatment weighting (IPTW) was used to mitigate confounding by indication. RESULTS: Among 50,228 patients, 20,484 (40.8%) underwent an ambulatory assessment prior to surgery: 11,074 (54.1%) with cardiology, 8,071 (39.4%) with GIM and 1,339 (6.5%) with both. Compared to patients who did not, those who underwent an assessment had a higher Revised Cardiac Risk Index (N with Index over 2= 4,989[24.4%] vs. 4,587[15.4%], P<0.001) and more frequent pre-operative cardiac testing (N=7,772[37.9%] vs. 6,113[20.6%], P<0.001) but, lower 30-day mortality (N=551[2.7%] vs. 970[3.3%], P<0.001). After application of IPTW, cardiology or GIM assessment prior to surgery remained associated with a lower 30-day mortality (weighted Hazard Ratio [95%CI] = 0.73 [0.65-0.82]) and a lower rate of all secondary outcomes. CONCLUSIONS: Major vascular surgery patients assessed by a cardiology or GIM physician prior to surgery have better outcomes than those who are not. Further research is needed to better understand potential mechanisms of benefit.

Recent Cannabis Use and Accelerometer-Measured Physical Activity and Sedentary Behavior Among Young-to-Midlife Adults: An Analysis of the National Health and Nutrition Examination Survey from 2011 to 2014.

Introduction: Cannabis use has been associated with reduced physical activity and increased sedentary behavior in adolescents. In adults, however, there is no conclusive evidence of such an association, and existing studies have primarily relied on self-reported activity measures. As cannabis use increases globally, a deeper understanding of its relationship with activity levels may inform clinical counseling and guidelines. This study investigated the association between recent cannabis use and accelerometer-measured activity. Methods: Data were obtained from the National Health and Nutrition Examination Survey (NHANES) from 2011 to 2014. We included adults in the United States who responded to a cannabis questionnaire and had at least 4 days of activity data from an ActiGraph GT3X+ accelerometer, which comprised participants from 18 to 59 years. The primary exposure was any self-reported cannabis use in the past 30 days. The primary outcome was daily sedentary time and secondary outcomes were daily light physical activity (LPA) and moderate-to-vigorous physical activity (MVPA). Analyses were performed with multivariable quasi-Poisson regression models. Results: Of 4666 included adults, 658 (14.1%) reported recent cannabis use. After covariate adjustment, recent cannabis use was not associated with daily sedentary time (adjusted incidence rate ratio [aIRR] 0.99, 95% confidence interval [CI]: 0.98-1.01) or daily MVPA time (aIRR 1.01, 95% CI: 0.98-1.04). Daily LPA time was 4% greater with recent cannabis use (aIRR 1.04, 95% CI: 1.01-1.06). Conclusion: Recent cannabis use in young to midlife adults was not associated with accelerometer-measured sedentary or MVPA time, but it was associated with a marginal increase in LPA time of unclear clinical significance. Our findings provide evidence against existing concerns that cannabis use independently promotes sedentary behavior and decreases physical activity. Future prospective studies are needed to determine if these findings generalize to specific populations using cannabis including chronic pain patients.

Predicting Outcomes Following Lower Extremity Endovascular Revascularization Using Machine Learning.

BACKGROUND: Lower extremity endovascular revascularization for peripheral artery disease carries nonnegligible perioperative risks; however, outcome prediction tools remain limited. Using machine learning, we developed automated algorithms that predict 30-day outcomes following lower extremity endovascular revascularization. METHODS AND RESULTS: The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent lower extremity endovascular revascularization (angioplasty, stent, or atherectomy) for peripheral artery disease between 2011 and 2021. Input features included 38 preoperative demographic/clinical variables. The primary outcome was 30-day postprocedural major adverse limb event (composite of major reintervention, untreated loss of patency, or major amputation) or death. Data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, 6 machine learning models were trained using preoperative features. The primary model evaluation metric was area under the receiver operating characteristic curve. Overall, 21 886 patients were included, and 30-day major adverse limb event/death occurred in 1964 (9.0%) individuals. The best performing model for predicting 30-day major adverse limb event/death was extreme gradient boosting, achieving an area under the receiver operating characteristic curve of 0.93 (95% CI, 0.92-0.94). In comparison, logistic regression had an area under the receiver operating characteristic curve of 0.72 (95% CI, 0.70-0.74). The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.09. The top 3 predictive features in our algorithm were (1) chronic limb-threatening ischemia, (2) tibial intervention, and (3) congestive heart failure. CONCLUSIONS: Our machine learning models accurately predict 30-day outcomes following lower extremity endovascular revascularization using preoperative data with good discrimination and calibration. Prospective validation is warranted to assess for generalizability and external validity.

Incidence and risk factors of chronic postoperative pain in same-day surgery: A prospective cohort study.

BACKGROUND: The amount of same-day surgery has increased markedly worldwide in recent decades, but there remains limited evidence on chronic postsurgical pain in this setting. METHODS: We assessed pain 90 days after ambulatory surgery in an international, multicentre prospective cohort study of patients ≥45 years old with comorbidities or ≥65 years old. Pain was assessed using the Brief Pain Inventory. Chronic postsurgical pain was defined as a change ≥1 in self-rated average pain at the surgical site between baseline and 90 days, and moderate to severe chronic postsurgical pain as a score ≥4 in self-rated average pain at the surgical site at 90 days. Risk factors for chronic postsurgical pain were identified using multivariable logistic regression. RESULTS: Between November 2021 and January 2023, a total of 2054 participants were included, and chronic postsurgical pain occurred in 12% of participants, of whom 93.1% had new chronic pain at the surgical site (i.e., participants without pain prior to surgery). Moderate to severe chronic postsurgical pain occurred in 9% of overall participants. Factors associated with chronic postsurgical pain were: active smoking (OR 1.82; 95% CI 1.20 to 2.76), orthopaedic surgery (OR 4.7; 95% CI 2.24 to 9.7), plastic surgery (OR 4.3; 95% CI 1.97 to 9.2), breast surgery (OR 2.74; 95% CI 1.29 to 5.8), vascular surgery (OR 2.71; 95% CI 1.09 to 6.7), and ethnicity (i.e., Hispanic/Latino ethnicity OR 3.41; 95% CI 1.68 to 6.9 and First Nations/Native persons OR 4.0; 95% CI 1.05 to 15.4). CONCLUSIONS: Persistent postsurgical pain after same-day surgery is common, usually moderate to severe in nature, and occurs mostly in patients without chronic pain prior to surgery.

Days alive and out of hospital for adult female and male cardiac surgery patients: a population-based cohort study.

BACKGROUND: Research shows women experience higher mortality than men after cardiac surgery but information on sex-differences during postoperative recovery is limited. Days alive and out of hospital (DAH) combines death, readmission and length of stay, and may better quantify sex-differences during recovery. This main objective is to evaluate (i) how DAH at 30-days varies between sex and surgical procedure, (ii) DAH responsiveness to patient and surgical complexity, and (iii) longer-term prognostic value of DAH. METHODS: We evaluated 111,430 patients (26% female) who underwent one of three types of cardiac surgery (isolated coronary artery bypass [CABG], isolated non-CABG, combination procedures) between 2009 - 2019. Primary outcome was DAH at 30 days (DAH30), secondary outcomes were DAH at 90 days (DAH90) and 180 days (DAH180). Data were stratified by sex and surgical group. Unadjusted and risk-adjusted analyses were conducted to determine the association of DAH with patient-, surgery-, and hospital-level characteristics. Patients were divided into two groups (below and above the 10th percentile) based on the number of days at DAH30. Proportion of patients below the 10th percentile at DAH30 that remained in this group at DAH90 and DAH180 were determined. RESULTS: DAH30 were lower for women compared to men (22 vs. 23 days), and seen across all surgical groups (isolated CABG 23 vs. 24, isolated non-CABG 22 vs. 23, combined surgeries 19 vs. 21 days). Clinical risk factors including multimorbidity, socioeconomic status and surgical complexity were associated with lower DAH30 values, but women showed lower values of DAH30 compared to men for many factors. Among patients in the lowest 10th percentile at DAH30, 80% of both females and males remained in the lowest 10th percentile at 90 days, while 72% of females and 76% males remained in that percentile at 180 days. CONCLUSION: DAH is a responsive outcome to differences in patient and surgical risk factors. Further research is needed to identify new care pathways to reduce disparities in outcomes between male and female patients.

Preoperative depression and outcomes after metabolic and bariatric surgery: A systematic narrative review.

Preoperative depression is prevalent among patients undergoing metabolic and bariatric surgery (MBS) and is a potentially modifiable risk factor. However, the impact of preoperative depression on MBS outcomes has not been systematically reviewed. A search of MEDLINE, Embase, Cochrane, and PsychINFO (inception to June 2023) was conducted for studies reporting associations between preoperative depression and any clinical or patient-reported outcomes after MBS. Eighteen studies (5 prospective and 13 retrospective) reporting on 5933 participants were included. Most participants underwent gastric bypass or sleeve gastrectomy. Meta-analyses were not conducted due to heterogeneity in reported outcomes; findings were instead synthesized using a narrative and tabular approach. Across 13 studies (n = 3390) the associations between preoperative depression and weight loss outcomes at 6-72 months were mixed overall. This may be related to differences in cohort characteristics, outcome definitions, and instruments used to measure depression. A small number of studies reported that preoperative depression was associated with lower quality of life, worse acute pain, and more perioperative complications after surgery. Most of the included studies were deemed to be at high risk of bias, resulting in low or very low certainty of evidence according to the Risk of Bias In Non-randomized Studies - of Exposure (ROBINS-E) tool. While the impact of preoperative depression on weight loss after MBS remains unclear, there is early evidence that depression has negative consequences on other patient-important outcomes. Adequately powered studies using more sophisticated statistical methods are needed to accurately estimate these associations.

Machine Learning to Predict Outcomes of Endovascular Intervention for Patients With PAD.

Importance: Endovascular intervention for peripheral artery disease (PAD) carries nonnegligible perioperative risks; however, outcome prediction tools are limited. Objective: To develop machine learning (ML) algorithms that can predict outcomes following endovascular intervention for PAD. Design, Setting, and Participants: This prognostic study included patients who underwent endovascular intervention for PAD between January 1, 2004, and July 5, 2023, with 1 year of follow-up. Data were obtained from the Vascular Quality Initiative (VQI), a multicenter registry containing data from vascular surgeons and interventionalists at more than 1000 academic and community hospitals. From an initial cohort of 262 242 patients, 26 565 were excluded due to treatment for acute limb ischemia (n = 14 642) or aneurysmal disease (n = 3456), unreported symptom status (n = 4401) or procedure type (n = 2319), or concurrent bypass (n = 1747). Data were split into training (70%) and test (30%) sets. Exposures: A total of 112 predictive features (75 preoperative [demographic and clinical], 24 intraoperative [procedural], and 13 postoperative [in-hospital course and complications]) from the index hospitalization were identified. Main Outcomes and Measures: Using 10-fold cross-validation, 6 ML models were trained using preoperative features to predict 1-year major adverse limb event (MALE; composite of thrombectomy or thrombolysis, surgical reintervention, or major amputation) or death. The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). After selecting the best performing algorithm, additional models were built using intraoperative and postoperative data. Results: Overall, 235 677 patients who underwent endovascular intervention for PAD were included (mean [SD] age, 68.4 [11.1] years; 94 979 [40.3%] female) and 71 683 (30.4%) developed 1-year MALE or death. The best preoperative prediction model was extreme gradient boosting (XGBoost), achieving the following performance metrics: AUROC, 0.94 (95% CI, 0.93-0.95); accuracy, 0.86 (95% CI, 0.85-0.87); sensitivity, 0.87; specificity, 0.85; positive predictive value, 0.85; and negative predictive value, 0.87. In comparison, logistic regression had an AUROC of 0.67 (95% CI, 0.65-0.69). The XGBoost model maintained excellent performance at the intraoperative and postoperative stages, with AUROCs of 0.94 (95% CI, 0.93-0.95) and 0.98 (95% CI, 0.97-0.99), respectively. Conclusions and Relevance: In this prognostic study, ML models were developed that accurately predicted outcomes following endovascular intervention for PAD, which performed better than logistic regression. These algorithms have potential for important utility in guiding perioperative risk-mitigation strategies to prevent adverse outcomes following endovascular intervention for PAD.

Complication Rates of Central Venous Catheters: A Systematic Review and Meta-Analysis.

Importance: Central venous catheters (CVCs) are commonly used but are associated with complications. Quantifying complication rates is essential for guiding CVC utilization decisions. Objective: To summarize current rates of CVC-associated complications. Data Sources: MEDLINE, Embase, CINAHL, and CENTRAL databases were searched for observational studies and randomized clinical trials published between 2015 to 2023. Study Selection: This study included English-language observational studies and randomized clinical trials of adult patients that reported complication rates of short-term centrally inserted CVCs and data for 1 or more outcomes of interest. Studies that evaluated long-term intravascular devices, focused on dialysis catheters not typically used for medication administration, or studied catheters placed by radiologists were excluded. Data Extraction and Synthesis: Two reviewers independently extracted data and assessed risk of bias. Bayesian random-effects meta-analysis was applied to summarize event rates. Rates of placement complications (events/1000 catheters with 95% credible interval [CrI]) and use complications (events/1000 catheter-days with 95% CrI) were estimated. Main Outcomes and Measures: Ten prespecified complications associated with CVC placement (placement failure, arterial puncture, arterial cannulation, pneumothorax, bleeding events requiring action, nerve injury, arteriovenous fistula, cardiac tamponade, arrhythmia, and delay of ≥1 hour in vasopressor administration) and 5 prespecified complications associated with CVC use (malfunction, infection, deep vein thrombosis [DVT], thrombophlebitis, and venous stenosis) were assessed. The composite of 4 serious complications (arterial cannulation, pneumothorax, infection, or DVT) after CVC exposure for 3 days was also assessed. Results: Of 11 722 screened studies, 130 were included in the analyses. Seven of 15 prespecified complications were meta-analyzed. Placement failure occurred at 20.4 (95% CrI, 10.9-34.4) events per 1000 catheters placed. Other rates of CVC placement complications (per 1000 catheters) were arterial canulation (2.8; 95% CrI, 0.1-10), arterial puncture (16.2; 95% CrI, 11.5-22), and pneumothorax (4.4; 95% CrI, 2.7-6.5). Rates of CVC use complications (per 1000 catheter-days) were malfunction (5.5; 95% CrI, 0.6-38), infection (4.8; 95% CrI, 3.4-6.6), and DVT (2.7; 95% CrI, 1.0-6.2). It was estimated that 30.2 (95% CrI, 21.8-43.0) in 1000 patients with a CVC for 3 days would develop 1 or more serious complication (arterial cannulation, pneumothorax, infection, or DVT). Use of ultrasonography was associated with lower rates of arterial puncture (risk ratio [RR], 0.20; 95% CrI, 0.09-0.44; 13.5 events vs 68.8 events/1000 catheters) and pneumothorax (RR, 0.25; 95% CrI, 0.08-0.80; 2.4 events vs 9.9 events/1000 catheters). Conclusions and Relevance: Approximately 3% of CVC placements were associated with major complications. Use of ultrasonography guidance may reduce specific risks including arterial puncture and pneumothorax.

Predicting outcomes following lower extremity open revascularization using machine learning.

Lower extremity open revascularization is a treatment option for peripheral artery disease that carries significant peri-operative risks; however, outcome prediction tools remain limited. Using machine learning (ML), we developed automated algorithms that predict 30-day outcomes following lower extremity open revascularization. The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent lower extremity open revascularization for chronic atherosclerotic disease between 2011 and 2021. Input features included 37 pre-operative demographic/clinical variables. The primary outcome was 30-day major adverse limb event (MALE; composite of untreated loss of patency, major reintervention, or major amputation) or death. Our data were split into training (70%) and test (30%) sets. Using tenfold cross-validation, we trained 6 ML models. Overall, 24,309 patients were included. The primary outcome of 30-day MALE or death occurred in 2349 (9.3%) patients. Our best performing prediction model was XGBoost, achieving an area under the receiver operating characteristic curve (95% CI) of 0.93 (0.92-0.94). The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.08. Our ML algorithm has potential for important utility in guiding risk mitigation strategies for patients being considered for lower extremity open revascularization to improve outcomes.

Postoperative troponin surveillance to detect myocardial infarction: an observational cohort modelling study.

BACKGROUND: Clinical presentation of postoperative myocardial infarction (POMI) is often silent. Several international guidelines recommend routine troponin surveillance in patients at risk. We compared how these different guidelines select patients for surveillance after noncardiac surgery with our established risk stratification model. METHODS: We used outcome data from two prospective studies: Measurement of Exercise Tolerance before Surgery (METS) and Troponin Elevation After Major non-cardiac Surgery (TEAMS). We compared the major American, Canadian, and European guideline recommendations for troponin surveillance with our established risk stratification model. For each guideline and model, we quantified the number of patients requiring monitoring, % POMI detected, sensitivity, specificity, diagnostic odds ratio, and number needed to screen (NNS). RESULTS: METS and TEAMS contributed 2350 patients, of whom 319 (14%) had myocardial injury, 61 (2.5%) developed POMI, and 14 (0.6%) died. Our risk stratification model selected fewer patients for troponin monitoring (20%), compared with the Canadian (78%) and European (79%) guidelines. The sensitivity to detect POMI was highest with the Canadian and European guidelines (0.85; 95% confidence interval [CI] 0.74-0.92). Specificity was highest using the American guidelines (0.91; 95% CI 0.90-0.92). Our risk stratification model had the best diagnostic odds ratio (2.5; 95% CI 1.4-4.2) and a lower NNS (21 vs 35) compared with the guidelines. CONCLUSIONS: Most postoperative myocardial infarctions were detected by the Canadian and European guidelines but at the cost of low specificity and a higher number of patients undergoing screening. Patient selection based on our risk stratification model was optimal.

Cystatin C or creatinine for pre-operative assessment of kidney function and risk of post-operative acute kidney injury: a secondary analysis of the METS cohort study.

Background: Post-operative acute kidney injury (PO-AKI) is a common surgical complication consistently associated with subsequent morbidity and mortality. Prior kidney dysfunction is a major risk factor for PO-AKI, however it is unclear whether serum creatinine, the conventional kidney function marker, is optimal in this population. Serum cystatin C is a kidney function marker less affected by body composition and might provide better prognostic information in surgical patients. Methods: This was a pre-defined, secondary analysis of a multi-centre prospective cohort study of pre-operative functional capacity. Participants were aged ≥40 years, undergoing non-cardiac surgery. We assessed the association of pre-operative estimated glomerular filtration rate (eGFR) calculated using both serum creatinine and serum cystatin C with PO-AKI within 3 days after surgery, defined by KDIGO creatinine changes. The adjusted analysis accounted for established AKI risk factors. Results: A total of 1347 participants were included (median age 65 years, interquartile range 56-71), of whom 775 (58%) were male. A total of 82/1347 (6%) patients developed PO-AKI. These patients were older, had higher prevalence of cardiovascular disease and related medication, were more likely to have intra-abdominal procedures, had more intraoperative transfusion, and were more likely to be dead at 1 year after surgery 6/82 (7.3%) vs 33/1265 (2.7%) (P = .038). Pre-operative eGFR was lower in AKI than non-AKI patients using both creatinine and cystatin C. When both measurements were considered in a single age- and sex-adjusted model, eGFR-Cysc was strongly associated with PO-AKI, with increasing risk of AKI as eGFR-Cysc decreased below 90, while eGFR-Cr was no longer significantly associated. Conclusions: Data from over 1000 prospectively recruited surgical patients confirms pre-operative kidney function as major risk factor for PO-AKI. Of the kidney function markers available, compared with creatinine, cystatin C had greater strength of association with PO-AKI and merits further assessment in pre-operative assessment of surgical risk.

Sustained Mood Improvement with Laughing Gas Exposure (SMILE): Study protocol for a randomized placebo-controlled pilot trial of nitrous oxide for treatment-resistant depression.

BACKGROUND: Nitrous oxide has shown potentially as an efficacious intervention for treatment-resistant depression, yet there remains insufficient evidence pertaining to repeated administration of nitrous oxide over time and active placebo-controlled studies with optimal blinding. Thus, we aim to examine the feasibility and preliminary efficacy of a six-week follow up study examining the effects of a 4 week course of weekly administered nitrous oxide as compared to the active placebo, midazolam. METHODS: In this randomized, active placebo-controlled, pilot trial, 40 participants with treatment-resistant depression will receive either inhaled nitrous oxide (1 hour at 50% concentration) plus intravenous saline (100mL) or inhaled oxygen (1 hour at 50% concentration) plus intravenous midazolam (0.02 mg/kg in 100mL, up to 2mg) once per week, for 4 consecutive weeks. Participants will be followed up for 6 weeks starting from the first treatment visit. Primary feasibility outcomes include recruitment rate, withdrawal rate, adherence, missing data, and adverse events. The primary exploratory clinical outcome is change in Montgomery-Åsberg Depression Rating Scale (MADRS) score at day 42 of the study. Other exploratory clinical outcomes include remission (defined as MADRS score <10), response (defined as ≥ 50% reduction in MADRS score), and adverse side effects. DISCUSSION: This pilot study will provide valuable information regarding the feasibility and preliminary efficacy of repeated nitrous oxide administration over time for treatment-resistant depression. If feasible, this study will inform the design of a future definitive trial of nitrous oxide as an efficacious and fast-acting treatment for treatment-resistant depression. TRIAL REGISTRATION: ClinicalTrials.gov NCT04957368. Registered on July 12, 2021.

Admitting Hospital Influences on Withdrawal of Life-Sustaining Treatment Decision for Patients With Severe Traumatic Brain Injury.

BACKGROUND AND OBJECTIVES: Withdrawal of life-sustaining treatment (WLST) in severe traumatic brain injury (TBI) is complex, with a paucity of standardized guidelines. We aimed to assess the variability in WLST practices between trauma centers in North America. METHODS: This retrospective study used data from trauma centers through the American College of Surgeons Trauma Quality Improvement Program between 2017 and 2020. We included adult patients (>16 years) with severe TBI and a documented decision for WLST. We constructed a series of hierarchical logistic regression models to adjust for patient, injury, and hospital attributes influencing WLST; residual between-center variability was characterized using the median odds ratio. The impact of disparate WLST practices was further assessed by ranking centers by their conditional random intercept and assessing mortality, length of stay, and WLST between quartiles. RESULTS: We identified a total of 85 511 subjects with severe TBI treated across 510 trauma centers, of whom 20 300 (24%) had WLST. Patient-level factors associated with increased likelihood of WLST were advanced age, White race, self-pay, or Medicare insurance status (compared with private insurance). Black race was associated with reduced tendency for WLST. Treatment in nonprofit centers and higher-severity intracranial and extracranial injuries, midline shift, and pupil asymmetry also increased the likelihood for WLST. After adjustment for patient and hospital attributes, the median odds ratio was 1.45 (1.41-1.49 95% CI), suggesting residual variation in WLST between centers. When centers were grouped into quartiles by their propensity for WLST, there was increased adjusted mortality and shorter length of stay in fourth compared with first quartile centers. CONCLUSION: We highlighted the presence of contextual phenomena associated with disparate WLST practice patterns between trauma centers after adjustment for case-mix and hospital attributes. These findings highlight a need for standardized WLST guidelines to improve equity of care provision for patients with severe TBI.

Self-reported fitness as a measure of perioperative cardiovascular risk: tension between subjective and objective assessments persists.

Despite recent high-quality international studies, the optimal sum and sequence of subjective and objective assessments that build the complex picture of fitness for surgery remains to be defined. Physicians' subjective assessment of patient fitness after a typical unstructured interview has poor prognostic accuracy in predicting the risk of major cardiovascular events after noncardiac surgery. How does self-reported fitness assessed by structured questionnaire compare as an indicator of perioperative cardiovascular risk? Here we discuss the latest evidence in this evolving and fundamental aspect of perioperative care.

Postoperative myocardial injury phenotypes and self-reported disability in patients undergoing noncardiac surgery: a multicentre observational study.

BACKGROUND: Postoperative myocardial injury (PMI) comprises a spectrum of mechanisms resulting in troponin release. The impact of different PMI phenotypes on postoperative disability remains unknown. METHODS: This was a multicentre prospective cohort study including patients aged ≥50 yr undergoing elective major noncardiac surgery. Patients were stratified in five groups based on the occurrence of PMI and clinical information on postoperative adverse events: PMI classified as myocardial infarction (MI; according to fourth definition), PMI plus adverse event other than MI, clinically silent PMI (PMI without adverse events), adverse events without PMI, and neither PMI nor an adverse event (reference). The primary endpoint was 6-month self-reported disability (assessed by WHO Disability Assessment Schedule 2.0 [WHODAS]). Disability-free survival was defined as WHODAS ≤16%. RESULTS: We included 888 patients of mean age 69 (range 53-91) yr, of which 356 (40%) were women; 151 (17%) patients experienced PMI, and 625 (71%) experienced 6-month disability-free survival. Patients with PMI, regardless of its phenotype, had higher preoperative disability scores than patients without PMI (difference in WHODAS; ß: 3.3, 95% confidence interval [CI]: 0.5-6.2), but scores remained stable after surgery (ß: 1.2, 95% CI: -3.2-5.6). Before surgery, patients with MI (n=36, 4%) were more disabled compared with patients without PMI and no adverse events (ß: 5.5, 95% CI: 0.3-10.8). At 6 months, patients with MI and patients without PMI but with adverse events worsened in disability score (ß: 11.2, 95% CI: 2.3-20.2; ß: 8.1, 95% CI: 3.0-13.2, respectively). Patients with clinically silent PMI did not change in disability score at 6 months (ß: 1.39, 95% CI: -4.50-7.29, P=0.642). CONCLUSIONS: Although patients with postoperative myocardial injury had higher preoperative self-reported disability, disability scores did not change at 6 months after surgery. However, patients experiencing myocardial infarction worsened in disability score after surgery.

Using machine learning to predict outcomes following suprainguinal bypass.

OBJECTIVE: Suprainguinal bypass for peripheral artery disease (PAD) carries important surgical risks; however, outcome prediction tools remain limited. We developed machine learning (ML) algorithms that predict outcomes following suprainguinal bypass. METHODS: The Vascular Quality Initiative database was used to identify patients who underwent suprainguinal bypass for PAD between 2003 and 2023. We identified 100 potential predictor variables from the index hospitalization (68 preoperative [demographic/clinical], 13 intraoperative [procedural], and 19 postoperative [in-hospital course/complications]). The primary outcomes were major adverse limb events (MALE; composite of untreated loss of patency, thrombectomy/thrombolysis, surgical revision, or major amputation) or death at 1 year following suprainguinal bypass. Our data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, we trained six ML models using preoperative features (Extreme Gradient Boosting [XGBoost], random forest, Naïve Bayes classifier, support vector machine, artificial neural network, and logistic regression). The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). The best performing algorithm was further trained using intra- and postoperative data. Model robustness was evaluated using calibration plots and Brier scores. Performance was assessed on subgroups based on age, sex, race, ethnicity, rurality, median Area Deprivation Index, symptom status, procedure type, prior intervention for PAD, concurrent interventions, and urgency. RESULTS: Overall, 16,832 patients underwent suprainguinal bypass, and 3136 (18.6%) developed 1-year MALE or death. Patients with 1-year MALE or death were older (mean age, 64.9 vs 63.5 years; P < .001) with more comorbidities, had poorer functional status (65.7% vs 80.9% independent at baseline; P < .001), and were more likely to have chronic limb-threatening ischemia (67.4% vs 47.6%; P < .001) than those without an outcome. Despite being at higher cardiovascular risk, they were less likely to receive acetylsalicylic acid or statins preoperatively and at discharge. Our best performing prediction model at the preoperative stage was XGBoost, achieving an AUROC of 0.92 (95% confidence interval [CI], 0.91-0.93). In comparison, logistic regression had an AUROC of 0.67 (95% CI, 0.65-0.69). Our XGBoost model maintained excellent performance at the intra- and postoperative stages, with AUROCs of 0.93 (95% CI, 0.92-0.94) and 0.98 (95% CI, 0.97-0.99), respectively. Calibration plots showed good agreement between predicted and observed event probabilities with Brier scores of 0.12 (preoperative), 0.11 (intraoperative), and 0.10 (postoperative). Of the top 10 predictors, nine were preoperative features including chronic limb-threatening ischemia, previous procedures, comorbidities, and functional status. Model performance remained robust on all subgroup analyses. CONCLUSIONS: We developed ML models that accurately predict outcomes following suprainguinal bypass, performing better than logistic regression. Our algorithms have potential for important utility in guiding perioperative risk mitigation strategies to prevent adverse outcomes following suprainguinal bypass.

Predicting Outcomes Following Endovascular Abdominal Aortic Aneurysm Repair Using Machine Learning.

OBJECTIVE: To develop machine learning (ML) models that predict outcomes following endovascular aneurysm repair (EVAR) for abdominal aortic aneurysm (AAA). BACKGROUND: EVAR carries non-negligible perioperative risks; however, there are no widely used outcome prediction tools. METHODS: The National Surgical Quality Improvement Program targeted database was used to identify patients who underwent EVAR for infrarenal AAA between 2011 and 2021. Input features included 36 preoperative variables. The primary outcome was 30-day major adverse cardiovascular event (composite of myocardial infarction, stroke, or death). Data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, 6 ML models were trained using preoperative features. The primary model evaluation metric was area under the receiver operating characteristic curve. Model robustness was evaluated with calibration plot and Brier score. Subgroup analysis was performed to assess model performance based on age, sex, race, ethnicity, and prior AAA repair. RESULTS: Overall, 16,282 patients were included. The primary outcome of 30-day major adverse cardiovascular event occurred in 390 (2.4%) patients. Our best-performing prediction model was XGBoost, achieving an area under the receiver operating characteristic curve (95% CI) of 0.95 (0.94-0.96) compared with logistic regression [0.72 [0.70-0.74)]. The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.06. Model performance remained robust on all subgroup analyses. CONCLUSIONS: Our newer ML models accurately predict 30-day outcomes following EVAR using preoperative data and perform better than logistic regression. Our automated algorithms can guide risk mitigation strategies for patients being considered for EVAR.

Psychological Distress After Inpatient Noncardiac Surgery: A Secondary Analysis of the Measurement of Exercise Tolerance Before Surgery Prospective Cohort Study.

OBJECTIVE: To describe the incidence and natural progression of psychological distress after major surgery. BACKGROUND: The recovery process after surgery imposes physical and mental burdens that put patients at risk of psychological distress. Understanding the natural course of psychological distress after surgery is critical to supporting the timely and tailored management of high-risk individuals. METHODS: We conducted a secondary analysis of the "Measurement of Exercise Tolerance before Surgery" multicentre cohort study (Canada, Australia, New Zealand, and the UK). Measurement of Exercise Tolerance before Surgery recruited adult participants (≥40 years) undergoing elective inpatient noncardiac surgery and followed them for 1 year. The primary outcome was the severity of psychological distress measured using the anxiety-depression item of EQ-5D-3L. We used cumulative link mixed models to characterize the time trajectory of psychological distress among relevant patient subgroups. We also explored potential predictors of severe and/or worsened psychological distress at 1 year using multivariable logistic regression models. RESULTS: Of 1546 participants, moderate-to-severe psychological distress was reported by 32.6% of participants before surgery, 27.3% at 30 days after surgery, and 26.2% at 1 year after surgery. Psychological distress appeared to improve over time among females [odds ratio (OR): 0.80, 95% CI: 0.65-0.95] and patients undergoing orthopedic procedures (OR: 0.73, 95% CI: 0.55-0.91), but not among males (OR: 0.87, 95% CI: 0.87-1.07) or patients undergoing nonorthopedic procedures (OR: 0.95, 95% CI: 0.87-1.04). Among the average middle-aged adult, there were no time-related changes (OR: 0.94, 97% CI: 0.75-1.13), whereas the young-old (OR: 0.89, 95% CI: 0.79-0.99) and middle-old (OR: 0.87, 95% CI: 0.73-1.01) had small improvements. Predictors of severe and/or worsened psychological distress at 1 year were younger age, poor self-reported functional capacity, smoking history, and undergoing open surgery. CONCLUSIONS: One-third of adults experience moderate to severe psychological distress before major elective noncardiac surgery. This distress tends to persist or worsen over time among select patient subgroups.

Withdrawal of Life-Sustaining Treatment for Pediatric Patients With Severe Traumatic Brain Injury.

Importance: The decision to withdraw life-sustaining treatment for pediatric patients with severe traumatic brain injury (TBI) is challenging for clinicians and families with limited evidence quantifying existing practices. Given the lack of standardized clinical guidelines, variable practice patterns across trauma centers seem likely. Objective: To evaluate the factors influencing decisions to withdraw life-sustaining treatment across North American trauma centers for pediatric patients with severe TBI and to quantify any existing between-center variability in withdrawal of life-sustaining treatment practices. Design, Setting, and Participants: This retrospective cohort study used data collected from 515 trauma centers through the American College of Surgeons Trauma Quality Improvement Program between 2017 and 2020. Pediatric patients younger than 19 years with severe TBI and a documented decision for withdrawal of life-sustaining treatment were included. Data were analyzed from January to May 2023. Main Outcomes and Measures: A random intercept multilevel logistic regression model was used to quantify patient, injury, and hospital characteristics associated with the decision to withdraw life-sustaining treatment; the median odds ratio was used to characterize residual between-center variability. Centers were ranked by their conditional random intercepts and quartile-specific adjusted mortalities were computed. Results: A total of 9803 children (mean [SD] age, 12.6 [5.7]; 2920 [29.8%] female) with severe TBI were identified, 1003 of whom (10.2%) had a documented decision to withdraw life-sustaining treatment. Patient-level factors associated with an increase in likelihood of withdrawal of life-sustaining treatment were young age (younger than 3 years), higher severity intracranial and extracranial injuries, and mechanism of injury related to firearms. Following adjustment for patient and hospital attributes, the median odds ratio was 1.54 (95% CI, 1.46-1.62), suggesting residual variation in withdrawal of life-sustaining treatment between centers. When centers were grouped into quartiles by their propensity for withdrawal of life-sustaining treatment, adjusted mortality was higher for fourth-quartile compared to first-quartile centers (odds ratio, 1.66; 95% CI, 1.45-1.88). Conclusions and Relevance: Several patient and injury factors were associated with withdrawal of life-sustaining treatment decision-making for pediatric patients with severe TBI in this study. Variation in withdrawal of life-sustaining treatment practices between trauma centers was observed after adjustment for case mix; this variation was associated with differences in risk-adjusted mortality rates. Taken together, these findings highlight the presence of inconsistent approaches to withdrawal of life-sustaining treatment in children, which speaks to the need for guidelines to address this significant practice pattern variation.

Using Machine Learning (XGBoost) to Predict Outcomes After Infrainguinal Bypass for Peripheral Artery Disease.

OBJECTIVE: To develop machine learning (ML) algorithms that predict outcomes after infrainguinal bypass. BACKGROUND: Infrainguinal bypass for peripheral artery disease carries significant surgical risks; however, outcome prediction tools remain limited. METHODS: The Vascular Quality Initiative database was used to identify patients who underwent infrainguinal bypass for peripheral artery disease between 2003 and 2023. We identified 97 potential predictor variables from the index hospitalization [68 preoperative (demographic/clinical), 13 intraoperative (procedural), and 16 postoperative (in-hospital course/complications)]. The primary outcome was 1-year major adverse limb event (composite of surgical revision, thrombectomy/thrombolysis, or major amputation) or death. Our data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, we trained 6 ML models using preoperative features. The primary model evaluation metric was the area under the receiver operating characteristic curve (AUROC). The top-performing algorithm was further trained using intraoperative and postoperative features. Model robustness was evaluated using calibration plots and Brier scores. RESULTS: Overall, 59,784 patients underwent infrainguinal bypass, and 15,942 (26.7%) developed 1-year major adverse limb event/death. The best preoperative prediction model was XGBoost, achieving an AUROC (95% CI) of 0.94 (0.93-0.95). In comparison, logistic regression had an AUROC (95% CI) of 0.61 (0.59-0.63). Our XGBoost model maintained excellent performance at the intraoperative and postoperative stages, with AUROCs (95% CI's) of 0.94 (0.93-0.95) and 0.96 (0.95-0.97), respectively. Calibration plots showed good agreement between predicted and observed event probabilities with Brier scores of 0.08 (preoperative), 0.07 (intraoperative), and 0.05 (postoperative). CONCLUSIONS: ML models can accurately predict outcomes after infrainguinal bypass, outperforming logistic regression.