2024 AHA/ACC/ACS/ASNC/HRS/SCA/SCCT/SCMR/SVM Guideline for Perioperative Cardiovascular Management for Noncardiac Surgery: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines.

AIM: The "2024 AHA/ACC/ACS/ASNC/HRS/SCA/SCCT/SCMR/SVM Guideline for Perioperative Cardiovascular Management for Noncardiac Surgery" provides recommendations to guide clinicians in the perioperative cardiovascular evaluation and management of adult patients undergoing noncardiac surgery. METHODS: A comprehensive literature search was conducted from August 2022 to March 2023 to identify clinical studies, reviews, and other evidence conducted on human subjects that were published in English from MEDLINE (through PubMed), EMBASE, the Cochrane Library, the Agency for Healthcare Research and Quality, and other selected databases relevant to this guideline. STRUCTURE: Recommendations from the "2014 ACC/AHA Guideline on Perioperative Cardiovascular Evaluation and Management of Patients Undergoing Noncardiac Surgery" have been updated with new evidence consolidated to guide clinicians; clinicians should be advised this guideline supersedes the previously published 2014 guideline. In addition, evidence-based management strategies, including pharmacological therapies, perioperative monitoring, and devices, for cardiovascular disease and associated medical conditions, have been developed.

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.

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.

Multicentre validation of a machine learning model for predicting respiratory failure after noncardiac surgery.

BACKGROUND: Postoperative respiratory failure is a serious complication that could benefit from early accurate identification of high-risk patients. We developed and validated a machine learning model to predict postoperative respiratory failure, defined as prolonged (>48 h) mechanical ventilation or reintubation after surgery. METHODS: Easily extractable electronic health record (EHR) variables that do not require subjective assessment by clinicians were used. From EHR data of 307,333 noncardiac surgical cases, the model, trained with a gradient boosting algorithm, utilised a derivation cohort of 99,025 cases from Seoul National University Hospital (2013-9). External validation was performed using three separate cohorts A-C from different hospitals comprising 208,308 cases. Model performance was assessed by area under the receiver operating characteristic (AUROC) curve and area under the precision-recall curve (AUPRC), a measure of sensitivity and precision at different thresholds. RESULTS: The model included eight variables: serum albumin, age, duration of anaesthesia, serum glucose, prothrombin time, serum creatinine, white blood cell count, and body mass index. Internally, the model achieved an AUROC of 0.912 (95% confidence interval [CI], 0.908-0.915) and AUPRC of 0.113. In external validation cohorts A, B, and C, the model achieved AUROCs of 0.879 (95% CI, 0.876-0.882), 0.872 (95% CI, 0.870-0.874), and 0.931 (95% CI, 0.925-0.936), and AUPRCs of 0.029, 0.083, and 0.124, respectively. CONCLUSIONS: Utilising just eight easily extractable variables, this machine learning model demonstrated excellent discrimination in both internal and external validation for predicting postoperative respiratory failure. The model enables personalised risk stratification and facilitates data-driven clinical decision-making.

Prediction, prognosis, and professionalism in perioperative medicine.

Guidelines provide a framework to take better care of our patients. They are published by different professional groups and are based on all the research done for us by hardworking colleagues. Compiling a guideline is an enormous amount of work and is generally done with the utmost care. However, recommendations often require a subjective interpretation of published research, where personal and academic interests can influence the outcome. We discuss two recently published guidelines on perioperative cardiovascular assessment that led to different conclusions on some important areas of patient care.

Ward monitoring 4.0: real-time metabolic insights from continuous glucose monitoring into perioperative organ dysfunction.

The now-routine clinical deployment of continuous glucose monitoring has demonstrated benefit in real-world settings. We make the case that continuous glucose monitoring can help re-examine, at scale, the role that (stress) hyperglycaemia plays in fuelling organ dysfunction after tissue trauma. Provided robust perioperative data do emerge, well-established continuous glucose monitoring technology could soon help transform the perioperative landscape.

Preoperative N-terminal pro-B-type natriuretic peptide and myocardial injury after stopping or continuing renin-angiotensin system inhibitors in noncardiac surgery: a prespecified analysis of a phase 2 randomised controlled multicentre trial.

BACKGROUND: Patients with elevated preoperative plasma N-terminal pro-B-type natriuretic peptide (NT-proBNP >100 pg ml-1) experience more complications after noncardiac surgery. Individuals prescribed renin-angiotensin system (RAS) inhibitors for cardiometabolic disease are at particular risk of perioperative myocardial injury and complications. We hypothesised that stopping RAS inhibitors before surgery increases the risk of perioperative myocardial injury, depending on preoperative risk stratified by plasma NT-proBNP concentrations. METHODS: In a preplanned analysis of a phase 2a trial in six UK centres, patients ≥60 yr old undergoing elective noncardiac surgery were randomly assigned either to stop or continue RAS inhibitors before surgery. The pharmacokinetic profile of individual RAS inhibitors determined for how long they were stopped before surgery. The primary outcome, masked to investigators, clinicians, and patients, was myocardial injury (plasma high-sensitivity troponin-T ≥15 ng L-1 or a ≥5 ng L-1 increase, when preoperative high-sensitivity troponin-T ≥15 ng L-1) within 48 h after surgery. The co-exposures of interest were preoperative plasma NT-proBNP (< or >100 pg ml -1) and stopping or continuing RAS inhibitors. RESULTS: Of 241 participants, 101 (41.9%; mean age 71 [7] yr; 48% females) had preoperative NT-proBNP >100 pg ml -1 (median 339 [160-833] pg ml-1), of whom 9/101 (8.9%) had a formal diagnosis of cardiac failure. Myocardial injury occurred in 63/101 (62.4%) subjects with NT-proBNP >100 pg ml-1, compared with 45/140 (32.1%) subjects with NT-proBNP <100 pg ml -1 {odds ratio (OR) 3.50 (95% confidence interval [CI] 2.05-5.99); P<0.0001}. For subjects with preoperative NT-proBNP <100 pg ml-1, 30/75 (40%) who stopped RAS inhibitors had myocardial injury, compared with 15/65 (23.1%) who continued RAS inhibitors (OR for stopping 2.22 [95% CI 1.06-4.65]; P=0.03). For preoperative NT-proBNP >100 pg ml-1, myocardial injury rates were similar regardless of stopping (62.2%) or continuing (62.5%) RAS inhibitors (OR for stopping 0.98 [95% CI 0.44-2.22]). CONCLUSIONS: Stopping renin-angiotensin system inhibitors in lower-risk patients (preoperative NT-proBNP <100 pg ml -1) increased the likelihood of myocardial injury before noncardiac surgery.

Association of preoperative beta-blocker use and cardiac complications after major noncardiac surgery: a prospective cohort study.

INTRODUCTION: Cardiac complications after major noncardiac surgery are common and associated with high morbidity and mortality. How preoperative use of beta-blockers may impact perioperative cardiac complications remains unclear. METHODS: In a multicentre prospective cohort study, preoperative beta-blocker use was ascertained in consecutive patients at elevated cardiovascular risk undergoing major noncardiac surgery. Cardiac complications were prospectively monitored and centrally adjudicated by two independent experts. The primary endpoint was perioperative myocardial infarction or injury attributable to a cardiac cause (cardiac PMI) within the first three postoperative days. The secondary endpoints were major adverse cardiac events (MACE), defined as a composite of myocardial infarction, acute heart failure, life-threatening arrhythmia, and cardiovascular death and all-cause death after 365 days. We used inverse probability of treatment weighting to account for differences between patients receiving beta-blockers and those who did not. RESULTS: A total of 3839/10 272 (37.4%) patients (mean age 74 yr; 44.8% female) received beta-blockers before surgery. Patients on beta-blockers were older, and more likely to be male with established cardiorespiratory and chronic kidney disease. Cardiac PMI occurred in 1077 patients, with a weighted odds ratio of 1.03 (95% confidence interval [CI] 0.94-1.12, P=0.55) for patients on beta-blockers. Within 365 days of surgery, 971/10 272 (9.5%) MACE had occurred, with a weighted hazard ratio of 0.99 (95% CI 0.83-1.18, P=0.90) for patients on beta-blockers. CONCLUSION: Preoperative use of beta-blockers was not associated with decreased cardiac complications including cardiac perioperative myocardial infarction or injury and major adverse cardiac event. Additionally, preoperative use of beta-blockers was not associated with increased all-cause death within 30 and 365 days. CLINICAL TRIAL REGISTRATION: NCT02573532.

Preoperative risk prediction models for acute kidney injury after noncardiac surgery: an independent external validation cohort study.

BACKGROUND: Numerous models have been developed to predict acute kidney injury (AKI) after noncardiac surgery, yet there is a lack of independent validation and comparison among them. METHODS: We conducted a systematic literature search to review published risk prediction models for AKI after noncardiac surgery. An independent external validation was performed using a retrospective surgical cohort at a large Chinese hospital from January 2019 to October 2022. The cohort included patients undergoing a wide range of noncardiac surgeries with perioperative creatinine measurements. Postoperative AKI was defined according to the Kidney Disease Improving Global Outcomes creatinine criteria. Model performance was assessed in terms of discrimination (area under the receiver operating characteristic curve, AUROC), calibration (calibration plot), and clinical utility (net benefit), before and after model recalibration through intercept and slope updates. A sensitivity analysis was conducted by including patients without postoperative creatinine measurements in the validation cohort and categorising them as non-AKI cases. RESULTS: Nine prediction models were evaluated, each with varying clinical and methodological characteristics, including the types of surgical cohorts used for model development, AKI definitions, and predictors. In the validation cohort involving 13,186 patients, 650 (4.9%) developed AKI. Three models demonstrated fair discrimination (AUROC between 0.71 and 0.75); other models had poor or failed discrimination. All models exhibited some miscalibration; five of the nine models were well-calibrated after intercept and slope updates. Decision curve analysis indicated that the three models with fair discrimination consistently provided a positive net benefit after recalibration. The results were confirmed in the sensitivity analysis. CONCLUSIONS: We identified three models with fair discrimination and potential clinical utility after recalibration for assessing the risk of acute kidney injury after noncardiac surgery.

Implementation of a software-based decision support tool for guideline-appropriate preoperative evaluation: a prospective agreement study.

BACKGROUND: Guideline adherence in the medical field leaves room for improvement. Digitalised decision support helps improve compliance. However, the complex nature of the guidelines makes implementation in clinical practice difficult. METHODS: This single-centre prospective study included 204 adult ASA physical status 3-4 patients undergoing elective noncardiac surgery at a German university hospital. Agreement of clearance for surgery between a guideline expert and a digital guideline support tool was investigated. The decision made by the on-duty anaesthetists (standard approach) was assessed for agreement with the expert in a cross-over design. The main outcome was the level of agreement between digital guideline support and the expert. RESULTS: The digital guideline support approach cleared 18.1% of the patients for surgery, the standard approach cleared 74.0%, and the expert approach cleared 47.5%. Agreement of the expert decision with digital guideline support (66.7%) and the standard approach (67.6%) was fair (Cohen's kappa 0.37 [interquartile range 0.26-0.48] vs 0.31 [0.21-0.42], P=0.6). Taking the expert decision as a benchmark, correct clearance using digital guideline support was 50.5%, and correct clearance using the standard approach was 44.6%. Digital guideline support incorrectly asked for additional examinations in 31.4% of the patients, whereas the standard approach did not consider conditions that would have justified additional examinations before surgery in 29.4%. CONCLUSIONS: Strict guideline adherence for clearance for surgery through digitalised decision support inadequately considered patients, clinical context. Vague formulations, weak recommendations, and low-quality evidence complicate guideline translation into explicit rules. CLINICAL TRIAL REGISTRATION: NCT04058769.

Heart failure diagnostic accuracy, intraoperative fluid management, and postoperative acute kidney injury: a single-centre prospective observational study.

BACKGROUND: The accurate diagnosis of heart failure (HF) before major noncardiac surgery is frequently challenging. The impact of diagnostic accuracy for HF on intraoperative practice patterns and clinical outcomes remains unknown. METHODS: We performed an observational study of adult patients undergoing major noncardiac surgery at an academic hospital from 2015 to 2019. A preoperative clinical diagnosis of HF was defined by keywords in the preoperative assessment or a diagnosis code. Medical records of patients with and without HF clinical diagnoses were reviewed by a multispecialty panel of physician experts to develop an adjudicated HF reference standard. The exposure of interest was an adjudicated diagnosis of heart failure. The primary outcome was volume of intraoperative fluid administered. The secondary outcome was postoperative acute kidney injury (AKI). RESULTS: From 40 659 surgeries, a stratified subsample of 1018 patients were reviewed by a physician panel. Among patients with adjudicated diagnoses of HF, those without a clinical diagnosis (false negatives) more commonly had preserved left ventricular ejection fractions and fewer comorbidities. Compared with false negatives, an accurate diagnosis of HF (true positives) was associated with 470 ml (95% confidence interval: 120-830; P=0.009) lower intraoperative fluid administration and lower risk of AKI (adjusted odds ratio:0.39, 95% confidence interval 0.18-0.89). For patients without adjudicated diagnoses of HF, non-HF was not associated with differences in either fluids administered or AKI. CONCLUSIONS: An accurate preoperative diagnosis of heart failure before noncardiac surgery is associated with reduced intraoperative fluid administration and less acute kidney injury. Targeted efforts to improve preoperative diagnostic accuracy for heart failure may improve perioperative outcomes.

Advanced chronic kidney disease after surgery and the contribution of acute kidney disease: a national observational cohort study.

BACKGROUND: Limited knowledge exists regarding long-term renal outcomes after noncardiac surgery. This study investigated the incidence of, and risk factors for, developing advanced chronic kidney disease (CKD) and major adverse kidney events within 1 yr of surgery in a nationwide cohort. METHODS: Adults without renal dysfunction before noncardiac surgery in Sweden were included between 2007 and 2013 in this observational multicentre cohort study. We analysed data from a national surgical database linked to several national and quality outcome registries. Associations of perioperative risk factors with advanced CKD (estimated glomerular filtration rate [eGFR] <30 ml min-1 1.73 m-2) and major adverse kidney events within 1 yr (MAKE365, comprising eGFR <30 ml min-1 1.73 m-2, chronic dialysis, death) were quantified. RESULTS: Of 237,124 patients, 1597 (0.67%) developed advanced CKD and 16,789 (7.1%) developed MAKE365. Risk factors for advanced CKD included higher ASA physical status, urological surgery, extended surgical duration, prolonged postoperative hospital stay, repeated surgery, and postoperative use of renin-angiotensin-aldosterone system blockers. Advanced acute kidney disease (AKD) (eGFR <30 ml min-1 1.73 m-2 within 90 postoperative days) occurred in 1661 (0.70%) patients and was associated with advanced CKD (subdistribution hazard ratio [SHR] 44.5, 95% confidence interval [CI] 38.7-51.1) and MAKE365 (hazard ratio [HR] 6.60, 95% CI 6.07-7.17). Among patients with advanced AKD after surgery 36% developed advanced CKD at 1 yr after surgery and 51% developed MAKE365. CONCLUSIONS: Advanced CKD within 1 yr after surgery is uncommon but clinically important in patients without preoperative renal dysfunction. Advanced AKD after surgery constitutes a major risk factor for advanced CKD and MAKE365.

Visualising myocardial injury after noncardiac surgery: a case series using postoperative cardiovascular MRI.

Myocardial injury after noncardiac surgery (MINS) and perioperative myocardial injury are associated with increased morbidity and mortality. Both are diagnosed by a perioperative increase in troponin, yet there is controversy if MINS is a genuine myocardial insult. We applied postoperative cardiovascular magnetic resonance T2 mapping techniques to visualise acute myocardial injury (i.e. oedema) in six patients with multiple cardiovascular risk factors who underwent aortic surgery. The burden of myocardial oedema was substantially higher in four patients with elevated troponin qualifying for MINS, compared with patients without MINS. The data and images suggest that MINS represents genuine myocardial injury.

Serum metabolism characteristics of patients with myocardial injury after noncardiac surgery explored by the untargeted metabolomics approach.

BACKGROUND: Myocardial injury after noncardiac surgery (MINS) is one of the most common complications associated with postoperative adverse cardiovascular outcomes and mortality. However, MINS often fails to be timely diagnosed due to the absence of clinical symptoms and limited diagnostic methods. The metabolomic analysis might be an efficient way to discover new biomarkers of MINS. Characterizing the metabolomic features of MINS patients may provide new insight into the diagnosis of MINS. METHODS: In this study, serum samples from 20 matched patients with or without MINS (n = 10 per group) were subjected to untargeted metabolomics analysis to investigate comprehensive metabolic information. Differential metabolites were identified, and the enriched metabolic pathway was determined based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. RESULTS: A comprehensive analysis revealed 124 distinct metabolites, predominantly encompassing lipids, amino acids and other compounds. The observed modifications in metabolic pathways in patients with or without MINS showed significant clustering in cholesterol metabolism, aldosterone synthesis and secretion, primary bile acid biosynthesis, as well as cysteine and methionine metabolism. Four specific metabolites (taurocholic acid, L-pyroglutamic acid, taurochenodeoxycholic acid, and pyridoxamine) exhibited promising potential as biomarkers for prognosticating MINS. CONCLUSIONS: This study contributes valuable insights into the metabolomic features of MINS and the discovery of potential biomarkers which may help the early diagnosis of MINS. The identified metabolites and altered pathways offer valuable insights into the molecular underpinnings of MINS, paving the way for improved diagnostic approaches and potential intervention strategies.

Association between preoperative frailty and myocardial injury after noncardiac surgery in geriatric patients: study protocol for a prospective, multicentre, real-world observational, cohort trial.

INTRODUCTION: Frailty has become a worldwide health burden that has a large influence on public health and clinical practice. The incidence of frailty is anticipated to increase as the ageing population increases. Myocardial injury after noncardiac surgery (MINS) is associated with short-term and long-term mortality. However, the incidence of MINS in frail geriatric patients is unknown. METHODS AND ANALYSIS: This prospective, multicentre, real-world observational cohort study will be conducted at 18 designated centres in China from January 2023 to December 2024, with an anticipated sample size of 856 patients aged 65 years and older who are scheduled to undergo noncardiac surgery. The primary outcome will be the incidence of MINS. MINS is defined as a fourth-generation plasma cardiac troponin T (cTnT) concentration ≥ 0.03 ng/mL exhibited at least once within 30 days after surgery, with or without symptoms of myocardial ischaemia. All data will be collected via electronic data acquisition. DISCUSSION: This study will explore the incidence of MINS in frail patients. The characteristics, predictive factors and 30-day outcomes of MINS in frail patients will be further investigated to lay the foundation for identifying clinical interventions. CLINICAL TRIAL REGISTRATION: https://beta. CLINICALTRIALS: gov/study/NCT05635877 , NCT05635877.

A simple machine learning model for the prediction of acute kidney injury following noncardiac surgery in geriatric patients: a prospective cohort study.

BACKGROUND: Surgery in geriatric patients often poses risk of major postoperative complications. Acute kidney injury (AKI) is a common complication following noncardiac surgery and is associated with increased mortality. Early identification of geriatric patients at high risk of AKI could facilitate preventive measures and improve patient prognosis. This study used machine learning methods to identify important features and predict AKI following noncardiac surgery in geriatric patients. METHODS: The data for this study were obtained from a prospective cohort. Patients aged ≥ 65 years who received noncardiac surgery from June 2019 to December 2021 were enrolled. Data were split into training set (from June 2019 to March 2021) and internal validation set (from April 2021 to December 2021) by time. The least absolute shrinkage and selection operator (LASSO) regularization algorithm and the random forest recursive feature elimination algorithm (RF-RFE) were used to screen important predictors. Models were trained through extreme gradient boosting (XGBoost), random forest, and LASSO. The SHapley Additive exPlanations (SHAP) package was used to interpret the machine learning model. RESULTS: The training set included 6753 geriatric patients. Of these, 250 (3.70%) patients developed AKI. The XGBoost model with RF-RFE selected features outperformed other models with an area under the precision-recall curve (AUPRC) of 0.505 (95% confidence interval [CI]: 0.369-0.626) and an area under the receiver operating characteristic curve (AUROC) of 0.806 (95%CI: 0.733-0.875). The model incorporated ten predictors, including operation site and hypertension. The internal validation set included 3808 geriatric patients, and 96 (2.52%) patients developed AKI. The model maintained good predictive performance with an AUPRC of 0.431 (95%CI: 0.331-0.524) and an AUROC of 0.845 (95%CI: 0.796-0.888) in the internal validation. CONCLUSIONS: This study developed a simple machine learning model and a web calculator for predicting AKI following noncardiac surgery in geriatric patients. This model may be a valuable tool for guiding preventive measures and improving patient prognosis. TRIAL REGISTRATION: The protocol of this study was approved by the Committee of Ethics from West China Hospital of Sichuan University (2019-473) with a waiver of informed consent and registered at www.chictr.org.cn (ChiCTR1900025160, 15/08/2019).

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

BACKGROUND: Perioperative myocardial injury/infarction (PMI) following noncardiac surgery is a frequent cardiac complication. This study aims to evaluate PMI risk and explore preoperative assessment tools of PMI in patients at increased cardiovascular (CV) risk who underwent noncardiac surgery under the surgical and medical co-management (SMC) model. METHODS: A prospective cohort study that included consecutive patients at increased CV risk who underwent intermediate- or high-risk noncardiac surgery at the Second Medical Center, Chinese PLA General Hospital, between January 2017 and December 2022. All patients were treated with perioperative management by the SMC team. The SMC model was initiated when surgical intervention was indicated and throughout the entire perioperative period. The incidence, risk factors, and impact of PMI on 30-day mortality were analyzed. The ability of the Revised Cardiac Risk Index (RCRI), frailty, and their combination to predict PMI was evaluated. RESULTS: 613 eligible patients (mean [standard deviation, SD] age 73.3[10.9] years, 94.6% male) were recruited consecutively. Under SMC, PMI occurred in 24/613 patients (3.9%). Patients with PMI had a higher rate of 30-day mortality than patients without PMI (29.2% vs. 0.7%, p = 0.00). The FRAIL Scale for frailty was independently associated with an increased risk for PMI (odds ratio = 5.91; 95% confidence interval [CI], 2.34-14.93; p = 0.00). The RCRI demonstrated adequate discriminatory capacity for predicting PMI (area under the curve [AUC], 0.78; 95% CI, 0.67-0.88). Combining frailty with the RCRI further increased the accuracy of predicting PMI (AUC, 0.87; 95% CI, 0.81-0.93). CONCLUSIONS: The incidence of PMI was relatively low in high CV risk patients undergoing intermediate- or high-risk noncardiac surgery under SMC. The RCRI adequately predicted PMI. Combining frailty with the RCRI further increased the accuracy of PMI predictions, achieving excellent discriminatory capacity. These findings may aid personalized evaluation and management of high-risk patients who undergo intermediate- or high-risk noncardiac surgery.

Effect of dexmedetomidine on postoperative high-sensitivity cardiac troponin T in patients undergoing video-assisted thoracoscopic surgery: a prospective, randomised controlled trial.

BACKGROUND: One-lung ventilation and intrathoracic operations during thoracoscopic surgery often result in intraoperative hypoxaemia and haemodynamic fluctuations, resulting in perioperative myocardial injury. Dexmedetomidine, an alpha-2 (α-2) agonist, has demonstrated myocardial protection. We hypothesize that the routine intravenous administration of dexmedetomidine could reduce the extent of myocardial injury during video-assisted thoracoscopic surgery (VATS). METHODS: The study included patients aged ≥ 45 years, classified as American Society of Anesthesiologists physical status I-III, who underwent general anesthesia for video-assisted thoracoscopic surgery. The patients were randomly assigned to either the intervention group, receiving general anesthesia with dexmedetomidine, or the control group, receiving general anesthesia without dexmedetomidine. Patients in the intervention group received a loading dose of dexmedetomidine (0.5 µg·kg-1) before anesthesia induction, followed by a continuous infusion (0.5 µg·kg-1·h-1) until the completion of the surgery. Placebos (saline) were administered for the control group to match the treatment. The primary outcome assessed was the high-sensitivity cardiac troponin T on postoperative day 1. Additionally, the incidence of myocardial injury after noncardiac surgery (MINS) was noted. RESULTS: A total of 110 participants completed this study. The median [interquartile range (IQR)] concentration of hs-cTnT on postoperative day 1 was lower in the intervention group compared with the control group (7 [6-9] vs. 8 [7-11] pg·ml-1; difference in medians,1 pg·ml-1; 95% confidence interval [CI], 0 to 2; P = 0.005). Similarly, on postoperative day 3, the median [IQR] concentration of hs-cTnT in the intervention group was also lower than that in the control group (6 [5-7] vs. 7 [6-9]; difference in medians,1 pg·ml-1; 95%CI, 0 to 2; P = 0.011). Although the incidence of MINS was not statistically significant (the intervention group vs. the control group, 3.8% vs. 9.1%, P = 0.465), there was a decreasing trend in the incidence of MINS in the intervention group. CONCLUSION: The administration of perioperative dexmedetomidine in patients ≥ 45 years undergoing video-assisted thoracoscopic surgery could lower the release of postoperative hs-cTnT without reducing incidence of myocardial injury. TRIAL REGISTRATION: chictr.org.cn (ChiCTR2200063193); prospectively registered 1 September 2022.

Effect of a national guideline on postoperative troponin surveillance: a retrospective cohort study.

PURPOSE: We aimed to evaluate the effect of the 2017 Canadian Cardiovascular Society (CCS) guidelines on troponin surveillance after noncardiac surgery. METHODS: This was a single-centre, retrospective, observational study. Patients aged 40 yr or older undergoing intermediate- to high-risk elective noncardiac surgery between 2016 and 2021 were included. We compared the number and percentage of troponin tests ordered before and after the guidelines were published and compared patient characteristics, specifically cardiovascular comorbidity, using odds ratio's (OR) with 95% confidence intervals (CIs). Outcomes were myocardial injury, myocardial infarction (MI), and in-hospital mortality. RESULTS: The cohort included 36,386 patients and the median age was 63 yr. Between 2016 and 2018, troponin surveillance was done in 2,461 (13%) of the 19,046 patients, compared with 2,398 (14%) of the 17,340 patients who had surgery between 2019 and 2021 (OR, 1.08; 95% CI, 1.02 to 1.15). Patients who had surgery in the second period had less cardiovascular comorbidity; the adjusted OR for troponin surveillance was 1.14 (95% CI, 1.07 to 1.21). In the two periods, troponin was elevated in 561 (2.9%) and 470 (2.7%) patients, an MI was documented in 54 (0.3%) and 36 (0.2%) patients, and 95 (0.5%) and 73 (0.4%) patients died, respectively. After adjustment for baseline differences in the two periods, the ORs for MI and mortality were 0.83 (95% CI, 0.54 to 1.27) and 0.88 (95% CI, 0.64 to 1.19), respectively. CONCLUSION: Although the odds of troponin ordering were slightly but significantly higher after publication of the CCS guidelines, the odds for detecting an MI and for mortality did not change.

RéSUMé: OBJECTIF: Notre objectif était d'évaluer l'effet des lignes directrices 2017 de la Société canadienne de cardiologie (SCC) sur le monitorage de la troponine après une chirurgie non cardiaque. MéTHODE: Il s'agissait d'une étude observationnelle rétrospective monocentrique. Les patient·es âgé·es de 40 ans ou plus bénéficiant d'une chirurgie non cardiaque non urgente à risque intermédiaire à élevé entre 2016 et 2021 ont été inclus·es. Nous avons comparé le nombre et le pourcentage de tests de troponine prescrits avant et après la publication des lignes directrices et comparé les caractéristiques des patient·es, en particulier la comorbidité cardiovasculaire, en utilisant le rapport de cotes (RC) avec des intervalles de confiance (IC) à 95 %. Les critères d'évaluation comprenaient les lésions myocardiques, l'infarctus du myocarde (IM) et la mortalité hospitalière. RéSULTATS: La cohorte comprenait 36 386 personnes et l'âge médian était de 63 ans. Entre 2016 et 2018, le monitorage de la troponine a été réalisé chez 2461 (13 %) des 19 046 patient·es, contre 2398 (14 %) des 17 340 patient·es opéré·es entre 2019 et 2021 (RC, 1,08; IC 95%, 1,02 à 1,15). Les patient·es qui ont bénéficié d'une intervention chirurgicale au cours de la deuxième période présentaient moins de comorbidité cardiovasculaire; le RC ajusté pour le monitorage de la troponine était de 1,14 (IC 95 %, 1,07 à 1,21). Au cours des deux périodes, la troponine était élevée chez 561 (2,9 %) et 470 (2,7 %) patient·es, un IM a été documenté chez 54 (0,3 %) et 36 (0,2 %) patient·es, et 95 (0,5 %) et 73 (0,4 %) patient·es sont décédé·es, respectivement. Après ajustement pour tenir compte des différences initiales au cours des deux périodes, les RC pour l'IM et la mortalité étaient de 0,83 (IC 95 %, 0,54 à 1,27) et de 0,88 (IC 95 %, 0,64 à 1,19), respectivement. CONCLUSION: Bien que les probabilités de commande de troponine aient été légèrement mais significativement plus élevées après la publication des lignes directrices de la SCC, les probabilités de détection d'un IM et de mortalité n'ont pas changé.

Association between postinduction hypotension and postoperative mortality: a single-centre retrospective cohort study.

PURPOSE: We aimed to elucidate whether postinduction hypotension (PIH), defined as hypotension between anesthesia induction and skin incision, and intraoperative hypotension (IOH) are associated with postoperative mortality. METHODS: We conducted a retrospective cohort study of adult patients with an ASA Physical Status I-IV who underwent noncardiac and nonobstetric surgery under general anesthesia between 2015 and 2021 at Nagoya City University Hospital. The primary and secondary outcomes were 30-day and 90-day postoperative mortality, respectively. We calculated four hypotensive indices (with time proportion of the area under the threshold being the primary exposure variable) to evaluate the association between hypotension (defined as a mean blood pressure < 65 mm Hg) and mortality using multivariable logistic regression models. We used propensity score matching and RUSBoost (random under-sampling and boosting), a machine-learning model for imbalanced data, for sensitivity analyses. RESULTS: Postinduction hypotension and IOH were observed in 82% and 84% of patients, respectively. The 30-day and 90-day postoperative mortality rates were 0.4% (52/14,210) and 1.0% (138/13,334), respectively. Postinduction hypotension was not associated with 30-day mortality (adjusted odds ratio [aOR], 1.03; 95% confidence interval [CI], 0.93 to 1.13; P = 0.60) and 90-day mortality (aOR, 1.01; 95% CI, 0.94 to 1.07; P = 0.82). Conversely, IOH was associated with 30-day mortality (aOR, 1.19; 95% CI, 1.12 to 1.27; P < 0.001) and 90-day mortality (aOR, 1.12; 95% CI, 1.06 to 1.19; P < 0.001). Sensitivity analyses supported the association of IOH but not PIH with postoperative mortality. CONCLUSION: Despite limitations, including power and residual confounding, postoperative mortality was associated with IOH but not with PIH.

RéSUMé: OBJECTIF: Nous avons cherché à déterminer si l'hypotension post-induction (HPI), définie comme une hypotension entre l'induction de l'anesthésie et l'incision cutanée, et l'hypotension peropératoire (HPO) étaient associées à la mortalité postopératoire. MéTHODE: Nous avons mené une étude de cohorte rétrospective de patient·es adultes ayant un statut physique I-IV selon l'ASA et ayant bénéficié d'une chirurgie non cardiaque et non obstétricale sous anesthésie générale entre 2015 et 2021 à l'Hôpital universitaire de la ville de Nagoya. Les critères d'évaluation principal et secondaire étaient la mortalité postopératoire à 30 et 90 jours, respectivement. Nous avons calculé quatre indices d'hypotension (la proportion temporelle de la zone sous le seuil étant la principale variable d'exposition) pour évaluer l'association entre l'hypotension (définie comme une tension artérielle moyenne < 65 mm Hg) et la mortalité à l'aide de modèles de régression logistique multivariée. Nous avons utilisé l'appariement par score de propension et le RUSBoost (sous-échantillonnage et boosting aléatoire), un modèle d'apprentissage automatique pour les données déséquilibrées, pour les analyses de sensibilité. RéSULTATS: Une HPI et une HPO ont été observées chez 82 % et 84 % des patient·es, respectivement. Les taux de mortalité postopératoire à 30 et 90 jours étaient respectivement de 0,4 % (52/14 210) et de 1,0 % (138/13 334). L'hypotension post-induction n'était pas associée à la mortalité à 30 jours (rapport de cotes ajusté [RCa], 1,03; intervalle de confiance [IC] à 95 %, 0,93 à 1,13; P = 0,60) et à la mortalité à 90 jours (RCa, 1,01; IC 95 %, 0,94 à 1,07; P = 0,82). À l'inverse, l'HPO était associée à une mortalité à 30 jours (RCa, 1,19; IC 95 %, 1,12 à 1,27; P < 0,001) et à la mortalité à 90 jours (RCa, 1,12; IC 95 %, 1,06 à 1,19; P < 0,001). Les analyses de sensibilité ont confirmé l'association de l'HPO, mais pas de l'HPI, avec la mortalité postopératoire. CONCLUSION: Malgré les limitations, y compris la puissance et persistance de facteurs confondants, la mortalité postopératoire était associée à l'hypotension peropératoire mais pas à l'hypotension post-induction seule.