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Risk Factors Associated with Perioperative Myocardial Infarction in Major Open Vascular Surgery.

ABSTRACT: BACKGROUND:Among patients undergoing noncardiac surgery, major vascular surgery is associated with a high risk of perioperative myocardial infarction (MI). Currently, there are no perioperative MI risk calculators accounting for intraoperative and postoperative risk factors in vascular surgery patients. We aimed to investigate specific risk factors for perioperative MI after major open vascular surgery to determine which patients are at highest risk of MI and the association of perioperative MI with perioperative transfusion. METHODS:This statewide, retrospective cohort study analyzed risk factors for perioperative MI in major open vascular surgery between July 2012 and December 2015 using the Michigan Surgical Quality Collaborative, a multicenter quality collaborative. Patients were identified using current procedure terminology codes including open abdominal aortic aneurysm repairs (oAAA), aortobifemoral bypasses (AFB), and lower extremity bypasses (LEB). Rates of myocardial infarction were described for each procedure. A priori, preoperative, intraoperative, and postoperative variables were evaluated using univariate and multivariable statistics after adjusting for intraoperative factors including anesthesia type, intraoperative blood loss, intraoperative transfusion, and intraoperative vasopressor medications. RESULTS:A total of 3,689 patients underwent major open vascular surgery, including 375 oAAA, 392 AFB, and 2,922 LEB procedures. The overall incidence of MI was 2.4%, varying from 1.8% for aortobifemoral bypass, 2.4% for lower extremity bypass, and 3.7% for open abdominal aortic aneurysm repair. Although preoperative risk factors for myocardial infarction included age, American Society of Anesthesiologists score, diabetes, coronary artery disease, congestive heart failure, use of beta blocker, lower preoperative hematocrit, and surgical priority (urgent/emergent cases), after adjusting for intraoperative risk factors, all preoperative risk factors were not significant with the exception of surgical priority. After adjusting for intraoperative factors, only surgical priority (odds ratio [OR] = 1.70, 95% confidence interval [CI] [1.01-2.85], P < 0.001) and postoperative transfusion (OR = 2.65, 95% CI [1.59-4.44], P < 0.001) was associated with myocardial infarction, and higher nadir hematocrit was inversely associated with myocardial infarction (OR = 0.89, 95% CI [0.85-0.94], P < 0.001). CONCLUSIONS:Among vascular surgery patients undergoing major open vascular surgery, surgical priority was the only preoperative risk factors independently associated with MI, and only postoperative variables such as nadir hematocrit and postoperative transfusion were associated with MI. This suggests minimizing intraoperative blood loss and prioritizing early intraoperative transfusion may be the potential targets for process improvement.

SUBMITTER: Sutzko DC

PROVIDER: S-EPMC5805566 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Risk Factors Associated with Perioperative Myocardial Infarction in Major Open Vascular Surgery.

Sutzko Danielle C DC Andraska Elizabeth A EA Obi Andrea T AT Henke Peter K PK Osborne Nicholas H NH

Annals of vascular surgery 20170908

<h4>Background</h4>Among patients undergoing noncardiac surgery, major vascular surgery is associated with a high risk of perioperative myocardial infarction (MI). Currently, there are no perioperative MI risk calculators accounting for intraoperative and postoperative risk factors in vascular surgery patients. We aimed to investigate specific risk factors for perioperative MI after major open vascular surgery to determine which patients are at highest risk of MI and the association of periopera ...[more]

PMID: 28893702

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Project description:ImportancePatients who survive acute myocardial infarction (AMI) have a high risk of subsequent major cardiovascular events. Efforts to identify risk factors for recurrence have primarily focused on the period immediately following AMI admission.ObjectivesTo identify risk factors and develop and evaluate a risk model that predicts 1-year cardiovascular events after AMI.Design, setting, and participantsProspective cohort study. Patients with AMI (n?=?4227), aged 18 years or older, discharged alive from 53 acute-care hospitals across China from January 1, 2013, to July 17, 2014. Patients were randomly divided into samples: training (50% [2113 patients]), test (25% [1057 patients]), and validation (25% [1057 patients]). Risk factors were identified by a Cox model with Markov chain Monte Carlo simulation and further evaluated by latent class analysis. Analyses were conducted from May 1, 2017, to January 21, 2018.Main outcomes and measuresMajor cardiovascular events, including recurrent AMI, stroke, heart failure, and death, within 1 year after discharge for the index AMI hospitalization.ResultsThe mean (SD) age of the cohort was 60.8 (11.8) years and 994 of 4227 patients (23.5%) were female. Common comorbidities included hypertension (2358 patients [55.8%]), coronary heart disease (1798 patients [42.5%]), and dyslipidemia (1290 patients [30.5%]). One-year event rates were 8.1% (95% CI, 6.91%-9.24%), 9.0% (95% CI, 7.22%-10.70%), and 6.4% (95% CI, 4.89%-7.85%) for the training, test, and validation samples, respectively. Nineteen risk factors comprising 15 unique variables (age, education, prior AMI, prior ventricular tachycardia or fibrillation, hypertension, angina, prearrival medical assistance, >4 hours from onset of symptoms to admission, ejection fraction, renal dysfunction, heart rate, systolic blood pressure, white blood cell count, blood glucose, and in-hospital complications) were identified. In the training, test, and validation samples, respectively, the risk model had C statistics of 0.79 (95% CI, 0.75-0.83), 0.73 (95% CI, 0.68-0.78), and 0.77 (95% CI, 0.70-0.83) and a predictive range of 1.2% to 33.9%, 1.2% to 37.9%, and 1.3% to 34.3%. The C statistic was 0.69 (95% CI, 0.65-0.74) for the latent class model in the training data. The risk model stratified 11.3%, 81.0%, and 7.7% of patients to high-, average-, and low-risk groups, with respective probabilities of 0.32, 0.06, and 0.01 for 1-year events.Conclusions and relevanceNineteen risk factors were identified, and a model was developed and evaluated to predict risk of 1-year cardiovascular events after AMI. This may aid clinicians in identifying high-risk patients who would benefit most from intensive follow-up and aggressive risk factor reduction.

Project description:ObjectiveThe objective of this study was to develop and to validate the Vascular Quality Initiative (VQI) Cardiac Risk Index (CRI) for prediction of postoperative myocardial infarction (POMI) after vascular surgery.MethodsWe developed risk models for in-hospital POMI after 88,791 nonemergent operations from the VQI registry, including carotid endarterectomy (CEA; n = 45,340), infrainguinal bypass (INFRA; n = 18,054), suprainguinal bypass (SUPRA; n = 2678), endovascular aneurysm repair (EVAR; n = 18,539), and open abdominal aortic aneurysm repair (OAAA repair; n = 4180). Multivariable logistic regression was used to create an all-procedure and four procedure-specific risk calculators based on the derivation cohort from 2012 to 2014 (N = 61,236). Generalizability of the all-procedure model was evaluated by applying it to each procedure subtype. The models were validated using a cohort (N = 27,555) from January 2015 to February 2016. Model discrimination was measured by area under the receiver operating characteristic curve (AUC), and performance was validated by bootstrapping 5000 iterations. The VQI CRI calculator was made available on the Internet and as a free smart phone app available through QxCalculate.ResultsOverall POMI incidence was 1.6%, with variation by procedure type as follows: CEA, 0.8%; EVAR, 1.0%; INFRA, 2.6%; SUPRA, 3.1%; and OAAA repair, 4.3% (P < .001). Predictors of POMI in the all-procedure model included age, operation type, coronary artery disease, congestive heart failure, diabetes, creatinine concentration >1.8 mg/dL, stress test status, and body mass index (AUC, 0.75; 95% confidence interval [CI], 0.73-0.76). The all-procedure model demonstrated only minimally reduced accuracy when it was applied to each procedure, with the following AUCs: CEA, 0.65 (95% CI, 0.59-0.70); INFRA, 0.69 (95% CI, 0.64-0.73); EVAR, 0.72 (95% CI, 0.65-0.80); SUPRA, 0.62 (95% CI, 0.52-0.72); and OAAA, 0.63 (95% CI, 0.56-0.70). Procedure-specific models had unique predictors and showed improved prediction compared with the all-procedure model, with the following AUCs: CEA, 0.69 (95% CI, 0.66-0.72); INFRA, 0.75 (95% CI, 0.73-0.78); EVAR, 0.76 (95% CI, 0.73-0.80); and OAAA, 0.72 (95% CI, 0.69-0.77). Bias-corrected AUC (95% CI) from internal validation for the models was as follows: all procedures, 0.75 (0.73-0.76); CEA, 0.68 (0.65-0.71); INFRA, 0.74 (0.72-0.76); EVAR, 0.73 (0.70-0.78); and OAAA repair, 0.68 (0.65-0.73).ConclusionsThe VQI CRI is a useful and valid clinical decision-making tool to predict POMI after vascular surgery. Procedure-specific models improve accuracy when they include unique risk factors.

Project description:ObjectivesIdentification of patient subpopulations susceptible to develop myocardial infarction (MI) or, conversely, those displaying either intrinsic cardioprotective phenotypes or highly responsive to protective interventions remain high-priority knowledge gaps. We sought to identify novel common genetic variants associated with perioperative MI in patients undergoing coronary artery bypass grafting using genome-wide association methodology.Setting107 secondary and tertiary cardiac surgery centres across the USA.ParticipantsWe conducted a stage I genome-wide association study (GWAS) in 1433 ethnically diverse patients of both genders (112 cases/1321 controls) from the Genetics of Myocardial Adverse Outcomes and Graft Failure (GeneMAGIC) study, and a stage II analysis in an expanded population of 2055 patients (225 cases/1830 controls) combined from the GeneMAGIC and Duke Perioperative Genetics and Safety Outcomes (PEGASUS) studies. Patients undergoing primary non-emergent coronary bypass grafting were included.Primary and secondary outcome measuresThe primary outcome variable was perioperative MI, defined as creatine kinase MB isoenzyme (CK-MB) values ≥10× upper limit of normal during the first postoperative day, and not attributable to preoperative MI. Secondary outcomes included postoperative CK-MB as a quantitative trait, or a dichotomised phenotype based on extreme quartiles of the CK-MB distribution.ResultsFollowing quality control and adjustment for clinical covariates, we identified 521 single nucleotide polymorphisms in the stage I GWAS analysis. Among these, 8 common variants in 3 genes or intergenic regions met p<10(-5) in stage II. A secondary analysis using CK-MB as a quantitative trait (minimum p=1.26×10(-3) for rs609418), or a dichotomised phenotype based on extreme CK-MB values (minimum p=7.72×10(-6) for rs4834703) supported these findings. Pathway analysis revealed that genes harbouring top-scoring variants cluster in pathways of biological relevance to extracellular matrix remodelling, endoplasmic reticulum-to-Golgi transport and inflammation.ConclusionsUsing a two-stage GWAS and pathway analysis, we identified and prioritised several potential susceptibility loci for perioperative MI.

Project description:IntroductionPremature myocardial infarction (MI) generally refers to MI in men ≤55 years or women ≤65 years. Premature MI is a major contributor to cardiovascular disease (CVD), which claimed 17.6 million lives globally in 2016. Reducing premature MI and CVD is a key priority for all nations; however, there is sparse synthesis of information on risk factors associated with premature MI. To address this knowledge gap, we are conducting a systematic review to describe the association between risk factors (demographics, lifestyle factors and biomarkers) and premature MI.Methods and analysisThe following databases were searched from inception to June 2018: CENTRAL, CINAHL, Clinical Trials, EMBASE and MEDLINE. We will include original research articles (case-control, cohort and cross-sectional studies) that report a quantitative relationship between at least one risk factor and premature MI. Two investigators will use predetermined selection criteria and independently screen articles based on title and abstract (primary screening). Articles that meet selection criteria will undergo full-text screening based on criteria used for primary screening (secondary screening). Data will be extracted using predetermined data extraction forms. The Newcastle-Ottawa Scale for case-control and cohort studies will be used to evaluate the risk of bias and will be adapted for cross-sectional studies. Whenever feasible, data will be summarised into a random-effects meta-analysis.Ethics and disseminationTo our knowledge, this will be the first study to synthesise results on the relationship between risk factors and premature MI. These findings will inform healthcare providers on factors associated with risk of premature MI and potentially improve primary prevention efforts by guiding development of interventions. These findings will be summarised and presented at conferences and through publication in a peer-reviewed journal.Prospero registration numberCRD42018076862.

Project description:ImportanceStudies of acute myocardial infarction (AMI) occurring outside the hospital have informed approaches to addressing risk, treatment, and patient outcomes. Similar insights for in-hospital AMI are lacking.ObjectiveTo determine the incidence, risk factors, and outcomes associated with in-hospital AMI.Design, setting, and participantsCohort, nested case-control, and matched cohort study of patients hospitalized in US Veterans Health Administration facilities between July 2007 and September 2009. The incidence of in-hospital AMI was determined from a complete cohort of in-hospital AMI relative to the total number of inpatient admissions. From the in-hospital AMI cohort, detailed medical record review was performed on 687 cases and 687 individually matched controls. Risk factors and outcomes associated with in-hospital AMI were determined from matched comparison of in-hospital AMI cases to hospitalized controls.ExposuresCandidate risk factors for in-hospital AMI included characteristics at the time of admission and in-hospital variables prior to the index date.Main outcomes and measuresIn the determination of the incidence and risk factors associated with in-hospital AMI, the outcome of interest was in-hospital AMI. All-cause mortality was the main outcome of interest following in-hospital AMI.ResultsA total of 5556 patients with in-hospital AMI (mean [SD] age, 73 [10] years; 5456 [98.2%] male) were identified among 1.3 million admissions, with an incidence of 4.27 in-hospital AMI events per 1000 admissions. Independent risk factors associated with in-hospital AMI included intensive care unit setting, history of coronary artery disease, heart rate greater than 100 beats/min, hemoglobin level less than 8 g/dL, and white blood cell count 14 000/μL or greater. Compared with the matched control group, mortality was significantly higher for patients with in-hospital AMI (in-hospital mortality, 26.4% vs 4.2%; 30-day mortality, 33.0% vs 10.0%; 1-year mortality, 59.2% vs 34.4%).Conclusions and relevanceIn-hospital AMI was common and associated with common cardiovascular risk factors and markers of acute illness. Patient outcomes following in-hospital AMI were poor, with 1-year mortality approaching 60%. Further study of in-hospital AMI may yield opportunities to reduce in-hospital AMI risk and improve patient outcomes.

Dataset Information

Risk Factors Associated with Perioperative Myocardial Infarction in Major Open Vascular Surgery.

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Risk Factors Associated with Perioperative Myocardial Infarction in Major Open Vascular Surgery.

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