Association Between Surgeon/Anesthesiologist Sex Discordance and 1-year Mortality Among Adults Undergoing Noncardiac Surgery: A Population-based Retrospective Cohort Study.

OBJECTIVE: To investigate the association between surgeon-anesthesiologist sex discordance and patient mortality after noncardiac surgery. BACKGROUND: Evidence suggests different practice patterns exist among female and male physicians. However, the influence of physician sex on team-based practices in the operating room and subsequent patient outcomes remains unclear in the context of noncardiac surgery. METHODS: We conducted a population-based, retrospective cohort study of adult Ontario residents who underwent index, inpatient noncardiac surgery between January 2007 and December 2017. The primary exposure was physician sex discordance (ie, the surgeon and anesthesiologist were of the opposite sex). The primary outcome was 1-year mortality. The association between physician sex discordance and patient outcomes was modeled using multivariable Cox proportional hazard regression with adjustment for relevant physician, patient, and hospital characteristics. RESULTS: Of 541,209 patients, 158,084 (29.2%) were treated by sex-discordant physician teams. Physician sex discordance was associated with a lower rate of mortality at 1 year [5.2% vs. 5.7%; adjusted HR: 0.95 (0.91-0.99)]. Patients treated by teams composed of female surgeons and male anesthesiologists were more likely to be alive at 1 year than those treated by all-male physician teams [adjusted HR: 0.90 (0.81-0.99)]. CONCLUSIONS: Noncardiac surgery patients had a lower likelihood of 1-year mortality when treated by sex-discordant surgeon-anesthesiologist teams. The likelihood of mortality was further reduced if the surgeon was female. Further research is needed to explore the underlying mechanisms of these observations and design strategies to diversify operating room teams to optimize performance and patient outcomes.

Comorbidity clusters and in-hospital outcomes in patients admitted with acute myocardial infarction in the USA: A national population-based study.

BACKGROUND: The prevalence of multimorbidity in patients with acute myocardial infarction (AMI) is increasing. It is unclear whether comorbidities cluster into distinct phenogroups and whether are associated with clinical trajectories. METHODS: Survey-weighted analysis of the United States Nationwide Inpatient Sample (NIS) for patients admitted with a primary diagnosis of AMI in 2018. In-hospital outcomes included mortality, stroke, bleeding, and coronary revascularisation. Latent class analysis of 21 chronic conditions was used to identify comorbidity classes. Multivariable logistic and linear regressions were fitted for associations between comorbidity classes and outcomes. RESULTS: Among 416,655 AMI admissions included in the analysis, mean (±SD) age was 67 (±13) years, 38% were females, and 76% White ethnicity. Overall, hypertension, coronary heart disease (CHD), dyslipidaemia, and diabetes were common comorbidities, but each of the identified five classes (C) included ≥1 predominant comorbidities defining distinct phenogroups: cancer/coagulopathy/liver disease class (C1); least burdened (C2); CHD/dyslipidaemia (largest/referent group, (C3)); pulmonary/valvular/peripheral vascular disease (C4); diabetes/kidney disease/heart failure class (C5). Odds ratio (95% confidence interval [CI]) for mortality ranged between 2.11 (1.89-2.37) in C2 to 5.57 (4.99-6.21) in C1. For major bleeding, OR for C1 was 4.48 (3.78; 5.31); for acute stroke, ORs ranged between 0.75 (0.60; 0.94) in C2 to 2.76 (2.27; 3.35) in C1; for coronary revascularization, ORs ranged between 0.34 (0.32; 0.36) in C1 to 1.41 (1.30; 1.53) in C4. CONCLUSIONS: We identified distinct comorbidity phenogroups that predicted in-hospital outcomes in patients admitted with AMI. Some conditions overlapped across classes, driven by the high comorbidity burden. Our findings demonstrate the predictive value and potential clinical utility of identifying patients with AMI with specific comorbidity clustering.

Ethnic Disparities in ST-Segment Elevation Myocardial Infarction Outcomes and Processes of Care in Patients With and Without Standard Modifiable Cardiovascular Risk Factors: A Nationwide Cohort Study.

Trials suggest patients with ST-elevation myocardial infarction (STEMI) without 'standard modifiable cardiovascular risk factors' (SMuRFs) have poorer outcomes, but the role of ethnicity has not been investigated. We analyzed 118,177 STEMI patients using the Myocardial Ischaemia National Audit Project (MINAP) registry. Clinical characteristics and outcomes were analyzed using hierarchical logistic regression models; patients with ≥1 SMuRF (n = 88,055) were compared with 'SMuRFless' patients (n = 30,122), with subgroup analysis comparing outcomes of White and Ethnic minority patients. SMuRFless patients had higher incidence of major adverse cardiovascular events (MACE) (odds ratio, OR: 1.09, 95% CI 1.02-1.16) and in-hospital mortality (OR: 1.09, 95% CI 1.01-1.18) after adjusting for demographics, Killip classification, cardiac arrest, and comorbidities. When additionally adjusting for invasive coronary angiography (ICA) and revascularisation (percutaneous coronary intervention (PCI) or coronary artery bypass grafts surgery (CABG)), results for in-hospital mortality were no longer significant (OR 1.05, 95% CI .97-1.13). There were no significant differences in outcomes according to ethnicity. Ethnic minority patients were more likely to undergo revascularisation with ≥1 SMuRF (88 vs 80%, P < .001) or SMuRFless (87 vs 77%, P < .001. Ethnic minority patients were more likely undergo ICA and revascularisation regardless of SMuRF status.

Considerations on the Management of Acute Postoperative Ischemia After Cardiac Surgery: A Scientific Statement From the American Heart Association.

Acute postoperative myocardial ischemia (PMI) after cardiac surgery is an infrequent event that can evolve rapidly and become a potentially life-threatening complication. Multiple factors are associated with acute PMI after cardiac surgery and may vary by the type of surgical procedure performed. Although the criteria defining nonprocedural myocardial ischemia are well established, there are no universally accepted criteria for the diagnosis of acute PMI. In addition, current evidence on the management of acute PMI after cardiac surgery is sparse and generally of low methodological quality. Once acute PMI is suspected, prompt diagnosis and treatment are imperative, and options range from conservative strategies to percutaneous coronary intervention and redo coronary artery bypass grafting. In this document, a multidisciplinary group including experts in cardiac surgery, cardiology, anesthesiology, and postoperative care summarizes the existing evidence on diagnosis and treatment of acute PMI and provides clinical guidance.

Outcomes of ST elevation myocardial infarction in patients with cancer: a nationwide study.

AIMS: To assess processes of care and clinical outcomes in cancer patients with ST elevation myocardial infarction (STEMI) according to cancer type. METHODS AND RESULTS: This is a national population-based study of patients admitted with STEMI in the UK between January 2005 and March 2019. Data were obtained from the National Heart Attack Myocardial Infarction National Audit Project (MINAP) registry and the Hospital Episode Statistics registry. We identified 353 448 STEMI-indexed admissions between 2005 and 2019. Of those, 8581 (2.4%) had active cancer. Prostate cancer (29% of STEMI patients with cancer) was the most common cancer followed by haematologic malignancies (14%) and lung cancer (13%). Cancer patients were less likely to receive invasive coronary revascularization (60.0% vs. 71.6%, P < 0.001] and had higher in-hospital death [odd ratio (OR) 1.39, 95% confidence interval (CI) 1.25-1.54] and bleeding (OR 1.23, 95% CI 1.03-1.46). Cancer patients had higher mortality at 30 days (HR 2.39, 95% CI 2.19-2.62) and 1 year (HR 3.73, 95% CI 3.58-3.89). Lung cancer was the cancer associated with the highest risk of death in the hospital (OR 1.75, 95% CI 1.39-2.22) and at 1 year (OR 8.08, 95% CI 7.44-8.78). Colon cancer (OR 1.98, 95% CI 1.24-3.14) was the main cancer associated with major bleeding. All common cancer types were associated with higher mortality at 1 year. Cardiovascular death (62%) was the main cause of death in the first 30 days, while cancer (52%) was the main cause of death within 1 year. CONCLUSION: STEMI patients with cancer have a higher risk of short- and long-term mortality, particularly lung cancer. Colon cancer is the main cancer associated with major bleeding. Cardiovascular disease was the main cause of death in the first month, whereas cancer was the main cause of death within 1 year.

Derivation and validation of pragmatic clinical models to predict hospital length of stay after cardiac surgery in Ontario, Canada: a population-based cohort study.

BACKGROUND: Cardiac surgery is resource intensive and often requires multidisciplinary involvement to facilitate discharge. To facilitate evidence-based resource planning, we derived and validated clinical models to predict postoperative hospital length of stay (LOS). METHODS: We used linked, population-level databases with information on all Ontario residents and included patients aged 18 years or older who underwent coronary artery bypass grafting, valvular or thoracic aorta surgeries between October 2008 and September 2019. The primary outcome was hospital LOS. The models were derived by using patients who had surgery before Sept. 30, 2016, and validated after that date. To address the rightward skew in LOS data and to identify top-tier resource users, we used logistic regression to derive a model to predict the likelihood of LOS being more than the 98th percentile (> 30 d), and γ regression in the remainder to predict continuous LOS in days. We used backward stepwise variable selection for both models. RESULTS: Among 105 193 patients, 2422 (2.3%) had an LOS of more than 30 days. Factors predicting prolonged LOS included age, female sex, procedure type and urgency, comorbidities including frailty, high-risk acute coronary syndrome, heart failure, reduced left ventricular ejection fraction and psychiatric and pulmonary circulatory disease. The C statistic was 0.92 for the prolonged LOS model and the mean absolute error was 2.4 days for the continuous LOS model. INTERPRETATION: We derived and validated clinical models to identify top-tier resource users and predict continuous LOS with excellent accuracy. Our models could be used to benchmark clinical performance based on expected LOS, rationally allocate resources and support patient-centred operative decision-making.

Palliative Care Use in Patients With Acute Myocardial Infarction and Do-Not-Resuscitate Status From a Nationwide Inpatient Cohort.

OBJECTIVE: To examine the predictors, treatments, and outcomes of the use of palliative care in patients hospitalized with acute myocardial infarction (AMI) who had a do-not-resuscitate (DNR) order. PATIENTS AND METHODS: Using the National (Nationwide) Inpatient Sampling database for 2015-2018, we examined the predictors, in-hospital procedures, and outcomes of palliative care recipients among patients with AMI who had a DNR order. RESULTS: We identified 339,270 admissions with AMI that had a DNR order, including patients who received palliative care (n=113,215 [33.4%]). Compared with patients who did not receive palliative care, these patients were more frequently younger (median age, 81 vs 83 years; P<.001), were less likely to be female (50.9% [57,626 of 113,215] vs 54.7% [123,652 of 226,055]; P<.001), and were more likely to present with cardiac arrest (11.6% [13,133 of 113,215] vs 6.9% [15,598 of 226,055]; P<.001). Patients were more likely to receive palliative care at a large (odds ratio [OR], 1.47; 95% CI, 1.44 to 1.50) or teaching (OR, 2.10; 95% CI, 2.04 to 2.16) hospitals compared with small or rural ones. Patients receiving palliative care were less likely to be treated invasively, with reduced rates of invasive coronary angiography (OR, 0.46; 95% CI, 0.45 to 0.47) and percutaneous coronary intervention (OR, 0.47; 95% CI, 0.45 to 0.48), and were more likely to die in the hospital (52.4% [59,325 of 113,215] vs 22.9% [51,766 of 226,055]). CONCLUSION: In patients who had a DNR status and were hospitalized and received a diagnosis of AMI, only one-third received palliative care.

The association between cancer diagnosis, care, and outcomes in 1 million patients hospitalized for acute pulmonary embolism.

OBJECTIVES: To evaluate the clinical care provided to cancer patients hospitalized for acute pulmonary embolism (PE), as well as the association between type of cancer, in-hospital care, and clinical outcomes. METHODS: This study examined the in-hospital care (systemic thrombolysis, catheter-directed thrombolysis, and surgical thrombectomy/embolectomy) and clinical outcomes (mortality, major bleeding, and hemorrhagic stroke) among adults hospitalized due to acute PE between October 2015 to December 2018 using the National Inpatient Sample (NIS). Multivariable logistic regression analysis was used to determine adjusted odds ratios (aOR) with 95% confidence interval (95% CI). RESULTS: Of 1,090,130 hospital records included in the analysis, 216,825 (19.9%) had current cancer diagnoses, including lung (4.7%), hematological (2.5%), colorectal (1.6%), breast (1.3%), prostate (0.8%), and 'other' cancer (9.0%). Cancer patients had lower adjusted odds of receiving systemic thrombolysis, catheter-directed therapy, and surgical thrombectomy/embolectomy compared with their non-cancer counterparts (P < 0.001), except for systemic thrombolysis (aOR 0.96, 95% CI 0.85-1.09, P = 0.553) and catheter-directed therapy (aOR 0.82, 95% CI 0.67-1.00, P = 0.053) for prostate cancer. Cancer patients had greater odds of mortality (P < 0.05). Lung cancer patients had the highest odds of mortality (aOR 2.68, 95% CI 2.61-2.76, P < 0.001) and hemorrhagic stroke (aOR 1.75, 95% CI 1.61-1.90, P < 0.001), while colorectal cancer patients had the greatest odds of bleeding (aOR 2.04, 95% CI 1.94-2.15, P < 0.001). CONCLUSION: Among those hospitalized for PE, cancer diagnoses were associated with lower odds of invasive management and poorer in-hospital outcomes, with metastatic status being an especially important determinant. Appropriateness of care could not be assessed in this study.

Team principles for successful interdisciplinary research teams.

Interdisciplinary research teams can be extremely beneficial when addressing difficult clinical problems. The incorporation of conceptual and methodological strategies from a variety of research disciplines and health professions yields transformative results. In this setting, the long-term goal of team science is to improve patient care, with emphasis on population health outcomes. However, team principles necessary for effective research teams are rarely taught in health professional schools. To form successful interdisciplinary research teams in cardio-oncology and beyond, guiding principles and organizational recommendations are necessary. Cardiovascular disease results in annual direct costs of $220 billion (about $680 per person in the US) and is the leading cause of death for cancer survivors, including adult survivors of childhood cancers. Optimizing cardio-oncology research in interdisciplinary research teams has the potential to aid in the investigation of strategies for saving hundreds of thousands of lives each year in the United States and mitigating the annual cost of cardiovascular disease. Despite published reports on experiences developing research teams across organizations, specialties and settings, there is no single journal article that compiles principles for cardiology or cardio-oncology research teams. In this review, recurring threads linked to working as a team, as well as optimal methods, advantages, and problems that arise when managing teams are described in the context of career development and research. The worth and hurdles of a team approach, based on practical lessons learned from establishing our multidisciplinary research team and information gleaned from relevant specialties in the development of a successful team are presented.

Derivation and validation of predictive indices for cardiac readmission after coronary and valvular surgery - A multicenter study.

Objective: To derive and validate models to predict the risk of a cardiac readmission within one year after specific cardiac surgeries using information that is commonly available from hospital electronic medical records. Methods: In this retrospective cohort study, we derived and externally validated clinical models to predict the likelihood of cardiac readmissions within one-year of isolated CABG, AVR, and combined CABG+AVR in Ontario, Canada, using multiple clinical registries and routinely collected administrative databases. For all adult patients who underwent these procedures, multiple Fine and Gray subdistribution hazard models were derived within a competing-risk framework using the cohort from April 2015 to March 2018 and validated in an independent cohort (April 2018 to March 2020). Results: For the model that predicted post-CABG cardiac readmission, the c-statistic was 0.73 in the derivation cohort and 0.70 in the validation cohort at one-year. For the model that predicted post-AVR cardiac readmission, the c-statistic was 0.74 in the derivation and 0.73 in the validation cohort at one-year. For the model that predicted cardiac readmission following CABG+AVR, the c-statistic was 0.70 in the derivation and 0.66 in the validation cohort at one-year. Conclusions: Prediction of one-year cardiac readmission for isolated CABG, AVR, and combined CABG+AVR can be achieved parsimoniously using multidimensional data sources. Model discrimination was better than existing models derived from single and multicenter registries.

Comparison of Multidimensional Frailty Instruments for Estimation of Long-term Patient-Centered Outcomes After Cardiac Surgery.

Importance: Little is known about the performance of available frailty instruments in estimating patient-relevant outcomes after cardiac surgery. Objective: To examine how well the Johns Hopkins Adjusted Clinical Groups (ACG) frailty indicator, the Hospital Frailty Risk Score (HFRS), and the Preoperative Frailty Index (PFI) estimate long-term patient-centered outcomes after cardiac surgery. Design, Setting, and Participants: This retrospective cohort study was conducted in Ontario, Canada, among residents 18 years and older who underwent coronary artery bypass grafting or aortic, mitral or tricuspid valve, or thoracic aorta surgery between October 2008 and March 2017. Long-term care residents, those with discordant surgical encounters, and those receiving dialysis or dependent on a ventilator within 90 days were excluded. Statistical analysis was conducted from July 2021 to January 2022. Main Outcomes and Measures: The primary outcome was patient-defined adverse cardiovascular and noncardiovascular events (PACE), defined as the composite of severe stroke, heart failure, long-term care admission, new-onset dialysis, and ventilator dependence. Secondary outcomes included mortality and individual PACE events. The association between frailty and PACE was examined using cause-specific hazard models with death as a competing risk, and the association between frailty and death was examined using Cox models. Areas under the receiver operating characteristic curve (AUROC) were determined over 10 years of follow-up for each frailty instrument. Results: Of 88 456 patients (22 924 [25.9%] female; mean [SD] age, 66.3 [11.1] years), 14 935 (16.9%) were frail according to ACG criteria, 63 095 (71.3%) according to HFRS, and 76 754 (86.8%) according to PFI. Patients with frailty were more likely to be older, female, and rural residents; to have lower income and multimorbidity; and to undergo urgent surgery. Patients meeting ACG criteria (hazard ratio [HR], 1.66; 95% CI, 1.60-1.71) and those with higher HFRS scores (HR per 1.0-point increment, 1.10; 95% CI, 1.09-1.10) and PFI scores (HR per 0.1-point increment, 1.75; 95% CI, 1.73-1.78) had higher rates of PACE. Similar magnitudes of association were observed for each frailty instrument with death and individual PACE components. The HFRS had the highest AUROC for estimating PACE during the first 2 years and death during the first 4 years, after which the PFI had the highest AUROC. Conclusions and Relevance: These findings could help to tailor the use of frailty instruments by outcome and follow-up duration, thus optimizing preoperative risk stratification, patient-centered decision-making, candidate selection for prehabilitation, and personalized monitoring and health resource planning in patients undergoing cardiac surgery.

IGFBP7 as a preoperative predictor of congestive acute kidney injury after cardiac surgery.

OBJECTIVE: Congestive acute kidney injury (c-AKI) refers to AKI in the presence of right ventricular failure (RVF) and is a highly morbid complication of cardiac surgery. However, treatment has traditionally been reactive rather than proactive due to limited modalities to predict this complication. The objective of this study was to investigate the ability of insulin-like growth-factor binding protein 7 (IGFBP7), to predict c-AKI, AKI and RVF in patients undergoing cardiac surgery, as compared to N-terminal prohormone B-type natriuretic peptide (NT-pro-BNP) and pulmonary artery pulsatility index (PAPi). METHODS: This prospective nested case-control study consisted of 350 adult patients who underwent elective cardiac surgery. The outcomes were c-AKI, AKI and RVF. Unadjusted and adjusted conditional logistic regression models and areas under the receiver operating characteristic curve (AUC) were used to assess the predictive performance of each marker. RESULTS: For the prediction of c-AKI, the unadjusted IGPBP7 model had an AUC of 0.81, as compared with 0.51 for NT-pro-BNP and 0.61 for PAPi. The adjusted c-AKI models had AUCs of 0.90 for IGFBP7, 0.87 for NT-pro-BNP and 0.77 for PAPi. For AKI and RVF, the predictive performance of IGFBP7 was moderate and exceeded that of NT-pro-BNP and PAPi in univariable analysis. IGFBP7 remained a robust independent predictor of all outcomes in multivariable analysis, whereas the other markers did not. CONCLUSIONS: IGFBP7 is a promising biomarker for prediction of AKI, RVF and c-AKI and could have value for preoperative optimisation and risk stratification of patients undergoing cardiac surgery.

Aortic stenosis post-COVID-19: a mathematical model on waiting lists and mortality.

OBJECTIVES: To provide estimates for how different treatment pathways for the management of severe aortic stenosis (AS) may affect National Health Service (NHS) England waiting list duration and associated mortality. DESIGN: We constructed a mathematical model of the excess waiting list and found the closed-form analytic solution to that model. From published data, we calculated estimates for how the strategies listed under Interventions may affect the time to clear the backlog of patients waiting for treatment and the associated waiting list mortality. SETTING: The NHS in England. PARTICIPANTS: Estimated patients with AS in England. INTERVENTIONS: (1) Increasing the capacity for the treatment of severe AS, (2) converting proportions of cases from surgery to transcatheter aortic valve implantation and (3) a combination of these two. RESULTS: In a capacitated system, clearing the backlog by returning to pre-COVID-19 capacity is not possible. A conversion rate of 50% would clear the backlog within 666 (533-848) days with 1419 (597-2189) deaths while waiting during this time. A 20% capacity increase would require 535 (434-666) days, with an associated mortality of 1172 (466-1859). A combination of converting 40% cases and increasing capacity by 20% would clear the backlog within a year (343 (281-410) days) with 784 (292-1324) deaths while awaiting treatment. CONCLUSION: A strategy change to the management of severe AS is required to reduce the NHS backlog and waiting list deaths during the post-COVID-19 'recovery' period. However, plausible adaptations will still incur a substantial wait to treatment and many hundreds dying while waiting.

Prediction of acute kidney injury risk after cardiac surgery: using a hybrid machine learning algorithm.

BACKGROUND: Acute kidney injury (AKI) is a serious complication after cardiac surgery. We derived and internally validated a Machine Learning preoperative model to predict cardiac surgery-associated AKI of any severity and compared its performance with parametric statistical models. METHODS: We conducted a retrospective study of adult patients who underwent major cardiac surgery requiring cardiopulmonary bypass between November 1st, 2009 and March 31st, 2015. AKI was defined according to the KDIGO criteria as stage 1 or greater, within 7 days of surgery. We randomly split the cohort into derivation and validation datasets. We developed three AKI risk models: (1) a hybrid machine learning (ML) algorithm, using Random Forests for variable selection, followed by high performance logistic regression; (2) a traditional logistic regression model and (3) an enhanced logistic regression model with 500 bootstraps, with backward variable selection. For each model, we assigned risk scores to each of the retained covariate and assessed model discrimination (C statistic) and calibration (Hosmer-Lemeshow goodness-of-fit test) in the validation datasets. RESULTS: Of 6522 included patients, 1760 (27.0%) developed AKI. The best performance was achieved by the hybrid ML algorithm to predict AKI of any severity. The ML and enhanced statistical models remained robust after internal validation (C statistic = 0.75; Hosmer-Lemeshow p = 0.804, and AUC = 0.74, Hosmer-Lemeshow p = 0.347, respectively). CONCLUSIONS: We demonstrated that a hybrid ML model provides higher accuracy without sacrificing parsimony, computational efficiency, or interpretability, when compared with parametric statistical models. This score-based model can easily be used at the bedside to identify high-risk patients who may benefit from intensive perioperative monitoring and personalized management strategies.

Establishing an interdisciplinary research team for cardio-oncology artificial intelligence informatics precision and health equity.

Study objective: A multi-institutional interdisciplinary team was created to develop a research group focused on leveraging artificial intelligence and informatics for cardio-oncology patients. Cardio-oncology is an emerging medical field dedicated to prevention, screening, and management of adverse cardiovascular effects of cancer/ cancer therapies. Cardiovascular disease is a leading cause of death in cancer survivors. Cardiovascular risk in these patients is higher than in the general population. However, prediction and prevention of adverse cardiovascular events in individuals with a history of cancer/cancer treatment is challenging. Thus, establishing an interdisciplinary team to create cardiovascular risk stratification clinical decision aids for integration into electronic health records for oncology patients was considered crucial. Design/setting/participants: Core team members from the Medical College of Wisconsin (MCW), University of Wisconsin-Milwaukee (UWM), and Milwaukee School of Engineering (MSOE), and additional members from Cleveland Clinic, Mayo Clinic, and other institutions have joined forces to apply high-performance computing in cardio-oncology. Results: The team is comprised of clinicians and researchers from relevant complementary and synergistic fields relevant to this work. The team has built an epidemiological cohort of ~5000 cancer survivors that will serve as a database for interdisciplinary multi-institutional artificial intelligence projects. Conclusion: Lessons learned from establishing this team, as well as initial findings from the epidemiology cohort, are presented. Barriers have been broken down to form a multi-institutional interdisciplinary team for health informatics research in cardio-oncology. A database of cancer survivors has been created collaboratively by the team and provides initial insight into cardiovascular outcomes and comorbidities in this population.

Derivation and validation of a clinical risk score to predict death among patients awaiting cardiac surgery in Ontario, Canada: a population-based study.

BACKGROUND: Surgical delay may result in unintended harm to patients needing cardiac surgery, who are at risk for death if their condition is left untreated. Our objective was to derive and internally validate a clinical risk score to predict death among patients awaiting major cardiac surgery. METHODS: We used the CorHealth Ontario Registry and linked ICES health administrative databases with information on all Ontario residents to identify patients aged 18 years or more who were referred for isolated coronary artery bypass grafting (CABG), valvular procedures, combined CABG-valvular procedures or thoracic aorta procedures between Oct. 1, 2008, and Sept. 30, 2019. We used a hybrid modelling approach with the random forest method for initial variable selection, followed by backward stepwise logistic regression modelling for clinical interpretability and parsimony. We internally validated the logistic regression model, termed the CardiOttawa Waitlist Mortality Score, using 200 bootstraps. RESULTS: Of the 112 266 patients referred for cardiac surgery, 269 (0.2%) died while awaiting surgery (118/72 366 [0.2%] isolated CABG, 81/24 461 [0.3%] valvular procedures, 63/12 046 [0.5%] combined CABG-valvular procedures and 7/3393 [0.2%] thoracic aorta procedures). Age, sex, surgery type, left main stenosis, Canadian Cardiovascular Society classification, left ventricular ejection fraction, heart failure, atrial fibrillation, dialysis, psychosis and operative priority were predictors of waitlist mortality. The model discriminated (C-statistic 0.76 [optimism-corrected 0.73]). It calibrated well in the overall cohort (Hosmer-Lemeshow p = 0.2) and across surgery types. INTERPRETATION: The CardiOttawa Waitlist Mortality Score is a simple clinical risk model that predicts the likelihood of death while awaiting cardiac surgery. It has the potential to provide data-driven decision support for managing access to cardiac care and preserve system capacity during the COVID-19 pandemic, the recovery period and beyond.

A standardized definition for right ventricular failure in cardiac surgery patients.

Right ventricular failure (RVF) is a significant cause of mortality and morbidity after cardiac surgery. Despite its prognostic importance, RVF remains under investigated and without a universally accepted definition in the perioperative setting. We foresee that the provision of a standardized perioperative definition for RVF based on practical and objective criteria will help to improve quality of care through early detection and facilitate the generalization of RVF research to advance this field. This article provides an overview of RVF aetiology, pathophysiology, current diagnostic modalities, as well as a summary of existing RVF definitions. This is followed by our proposal for a standardized definition of perioperative RVF, one that captures RV structural and functional abnormalities through a multimodal approach based on anatomical, echocardiographic, and haemodynamic criteria that are readily available in the perioperative setting (Central Image).

Patient Sex and Postoperative Outcomes after Inpatient Intraabdominal Surgery: A Population-based Retrospective Cohort Study.

BACKGROUND: Intraabdominal surgeries are frequently performed procedures that lead to a high volume of unplanned readmissions and postoperative complications. Patient sex may be a determinant of adverse outcomes in this population, possibly due to differences in biology or care delivery, but it is understudied. The authors hypothesized that there would be no association between patient sex and the risk of postoperative adverse outcomes in intraabdominal surgery. METHODS: This retrospective, population-based cohort study involved adult inpatients aged 18 yr or older who underwent intraabdominal surgeries in Ontario, Canada, between April 2009 and March 2016. The authors studied the association of patient sex on the primary composite outcome of all-cause death, hospital readmission, or major postoperative complications, all within 30 postoperative days. Inverse probability of exposure weighting based on propensity scores (computed using demographic characteristics such as rural residence status and median neighborhood income quintile, common comorbidities, and surgery- and hospital-specific characteristics) was used to estimate the adjusted association of sex on outcomes. RESULTS: The cohort included 215,846 patients (52.3% female). The primary outcome was observed in 24,712 (21.9%) females and 25,486 (24.7%) males (unadjusted risk difference, 2.8% [95% CI, 2.5 to 3.2%]; P < 0.001). After adjustment, the association between the male sex and the primary outcome was not statistically significant (adjusted risk difference, -0.2% [95% CI, -0.5 to 0.2%]; P = 0.378). CONCLUSIONS: In a large population of intraabdominal surgical patients, there was no differential risk between sexes in the composite outcome of all-cause death, hospital readmission, or major postoperative complications, all within 30 postoperative days.

Association Between Handover of Anesthesiology Care and 1-Year Mortality Among Adults Undergoing Cardiac Surgery.

Importance: Handovers of anesthesia care from one anesthesiologist to another is an important intraoperative event. Despite its association with adverse events after noncardiac surgery, its impact in the context of cardiac surgery remains unclear. Objective: To compare the outcomes of patients who were exposed to anesthesia handover vs those who were unexposed to anesthesia handover during cardiac surgery. Design, Setting, and Participants: This retrospective cohort study in Ontario, Canada, included Ontario residents who were 18 years or older and had undergone coronary artery bypass grafting or aortic, mitral, tricuspid valve, or thoracic aorta surgical procedures between 2008 and 2019. Exclusion criteria were non-Ontario residency status and other concomitant procedures. Statistical analysis was conducted from April 2021 to June 2021, and data collection occurred between November 2020 to January 2021. Exposures: Complete handover of anesthesia care, where the case is completed by the replacement anesthesiologist. Main Outcomes and Measures: The coprimary outcomes were mortality within 30 days and 1 year after surgery. Secondary outcomes were patient-defined adverse cardiac and noncardiac events (PACE), intensive care unit (ICU), and hospital lengths of stay (LOS). Inverse probability of treatment weighting based on the propensity score was used to estimate adjusted effect measures. Mortality was assessed using a Cox proportional hazard model, PACE using a cause-specific hazard model with death as a competing risk, and LOS using Poisson regression. Results: Of the 102 156 patients in the cohort, 25 207 (24.7%) were women; the mean (SD) age was 66.4 (10.8) years; and 72 843 of surgical procedures (71.3%) were performed in teaching hospitals. Handover occurred in 1926 patients (1.9%) and was associated with higher risks of 30-day mortality (hazard ratio [HR], 1.89; 95% CI, 1.41-2.54) and 1-year mortality (HR, 1.66; 95% CI, 1.31-2.12), as well as longer ICU (risk ratio [RR], 1.43; 95% CI, 1.22-1.68) and hospital (RR, 1.17; 95% CI, 1.06-1.28) LOS. There was no statistically significant association between handover and PACE (30 days: HR 1.09; 95% CI, 0.79-1.49; 1 year: HR 0.89; 95% CI, 0.70-1.13). Conclusions and Relevance: Handover of anesthesia care during cardiac surgical procedures was associated with higher 30-day and 1-year mortality rates and increased health care resource use. Further research is needed to evaluate and systematically improve the handover process qualitatively.