Dataset Information

Derivation and Validation of a Geriatric-Sensitive Perioperative Cardiac Risk Index.

ABSTRACT: BACKGROUND:Surgical patients aged 65 and over face a higher risk of cardiac complications from noncardiac surgery. The Revised Cardiac Risk Index (RCRI) and the Gupta Myocardial Infarction or Cardiac Arrest (MICA) calculator are widely used to predict this risk, but they are not specifically designed to predict MICA in geriatric patients. Our hypothesis is that a new geriatric-sensitive index, derived from geriatric data, will capture this population's unique response to risk factors. METHODS AND RESULTS:The model was developed using the NSQIP (National Surgical Quality Improvement Program) 2013 geriatric cohort (N=584,931) (210,914 age ?65) and validated on the NSQIP 2012 geriatric cohort (N= 485,426) (172,905 age ?65). Least Angle Shrinkage and Selection Operator regression was used for initial variable selection. The Geriatric-Sensitive Cardiac Risk Index (GSCRI) was then evaluated in the 2012 data set. The area under the curve (AUC) was compared among the GSCRI, RCRI, and Gupta MICA in the 2012 data set. The GSCRI had an AUC of 0.76 in the validation cohort among geriatric patients. When the Gupta MICA was tested on geriatric patients in the validation cohort, a significant deterioration (?17%) was noted, as well as a significant underestimation of the risk. The GSCRI AUC of 0.76 in the geriatric subset was significantly greater (P<0.001) than those in the RCRI (AUC=0.63) or Gupta MICA (AUC=0.70) models, outperforming the RCRI and Gupta MICA models in geriatric patients by 13% and 6%, respectively, with a ?AUC and P-value of 0.13 (P<0.001), and 0.06 (P<0.001). CONCLUSIONS:The GSCRI is a significantly better predictor of cardiac risk in geriatric patients undergoing noncardiac surgery.

SUBMITTER: Alrezk R

PROVIDER: S-EPMC5721761 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Derivation and Validation of a Geriatric-Sensitive Perioperative Cardiac Risk Index.

Alrezk Rami R Jackson Nicholas N Al Rezk Mohanad M Elashoff Robert R Weintraub Nancy N Elashoff David D Fonarow Gregg C GC

Journal of the American Heart Association 20171116 11

<h4>Background</h4>Surgical patients aged 65 and over face a higher risk of cardiac complications from noncardiac surgery. The Revised Cardiac Risk Index (RCRI) and the Gupta Myocardial Infarction or Cardiac Arrest (MICA) calculator are widely used to predict this risk, but they are not specifically designed to predict MICA in geriatric patients. Our hypothesis is that a new geriatric-sensitive index, derived from geriatric data, will capture this population's unique response to risk factors.<h4 ...[more]

PMID: 29146612

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Project description:BackgroundPostoperative delirium is an important problem for surgical inpatients and was the target of a multidisciplinary quality improvement project at our institution. We developed and tested a semiautomated delirium risk stratification instrument, Age, WORLD backwards, Orientation, iLlness severity, Surgery-specific risk (AWOL-S), in 3 independent cohorts from our tertiary care hospital and describe its performance characteristics and impact on clinical care.MethodsThe risk stratification instrument was derived with elective surgical patients who were admitted at least overnight and received at least 1 postoperative delirium screen (Nursing Delirium Screening Scale [NuDESC] or Confusion Assessment Method for the Intensive Care Unit [CAM-ICU]) and preoperative cognitive screening tests (orientation to place and ability to spell WORLD backward). Using data pragmatically collected between December 7, 2016, and June 15, 2017, we derived a logistic regression model predicting probability of delirium in the first 7 postoperative hospital days. A priori predictors included age, cognitive screening, illness severity or American Society of Anesthesiologists physical status, and surgical delirium risk. We applied model odds ratios to 2 subsequent cohorts ("validation" and "sustained performance") and assessed performance using area under the receiver operator characteristic curves (AUC-ROC). A post hoc sensitivity analysis assessed performance in emergency and preadmitted patients. Finally, we retrospectively evaluated the use of benzodiazepines and anticholinergic medications in patients who screened at high risk for delirium.ResultsThe logistic regression model used to derive odds ratios for the risk prediction tool included 2091 patients. Model AUC-ROC was 0.71 (0.67-0.75), compared with 0.65 (0.58-0.72) in the validation (n = 908) and 0.75 (0.71-0.78) in the sustained performance (n = 3168) cohorts. Sensitivity was approximately 75% in the derivation and sustained performance cohorts; specificity was approximately 59%. The AUC-ROC for emergency and preadmitted patients was 0.71 (0.67-0.75; n = 1301). After AWOL-S was implemented clinically, patients at high risk for delirium (n = 3630) had 21% (3%-36%) lower relative risk of receiving an anticholinergic medication perioperatively after controlling for secular trends.ConclusionsThe AWOL-S delirium risk stratification tool has moderate accuracy for delirium prediction in a cohort of elective surgical patients, and performance is largely unchanged in emergent/preadmitted surgical patients. Using AWOL-S risk stratification as a part of a multidisciplinary delirium reduction intervention was associated with significantly lower rates of perioperative anticholinergic but not benzodiazepine, medications in those at high risk for delirium. AWOL-S offers a feasible starting point for electronic medical record-based postoperative delirium risk stratification and may serve as a useful paradigm for other institutions.

Project description:National guidelines on carotid endarterectomy (CEA) for asymptomatic patients state that the procedure should be performed with a ? 3% risk of perioperative death or stroke. We developed and validated a multivariate model of risk of death or stroke within 30 days of CEA for asymptomatic disease and a related clinical prediction rule.We analyzed asymptomatic cases in a population-based cohort of CEAs performed in Medicare beneficiaries in New York State. Medical records were abstracted for sociodemographics, neurologic history, disease severity, diagnostic imaging data, comorbidities, and deaths and strokes within 30 days of surgery. We used multivariate logistic regression to identify independent predictors of perioperative death or stroke. The CEA-8 clinical risk score was derived from the final model.Among the 6553 patients, the mean age was 74 years, 55% were male, 62% had coronary artery disease, and 22% had a history of distant stroke or transient ischemic attack. The perioperative rate of death or stroke was 3.0%. Multivariable predictors of perioperative events were female sex (odds ratio [OR] = 1.5; 95% CI, 1.1 to 1.9), nonwhite race (OR = 1.8; 95% CI, 1.1 to 2.9), severe disability (OR = 3.7; 95% CI, 1.8 to 7.7), congestive heart failure (OR = 1.6; 95% CI, 1.1 to 2.4), coronary artery disease (OR = 1.6; 95% CI, 1.2 to 2.2), valvular heart disease (OR = 1.5; 95% CI, 1.1 to 2.3), a distant history of stroke or transient ischemic attack (OR = 1.5; 95% CI, 1.1 to 2.0), and a nonoperated stenosis ? 50% (OR = 1.8; 95% CI, 1.3 to 2.3). The CEA-8 risk score stratified patients with a predicted probability of death or stroke rate from 0.6% to 9.6%.Several sociodemographic, neurologic severity, and comorbidity factors predicted the risk of perioperative death or stroke in asymptomatic patients. The CEA-8 risk score can help clinicians calculate a predicted probability of complications for an individual patient to help inform the decision about revascularization.

Project description:BackgroundThere is an unmet need for non-invasive biomarkers for the diagnosis of nonalcoholic steatohepatitis (NASH) in non-specialized settings. We aimed to develop and validate a non-invasive test for diagnosing NASH in individuals with biopsy-proven nonalcoholic fatty liver disease (NAFLD).MethodsWe developed a non-invasive test named the acNASH index that combines serum creatinine and aspartate aminotransferase levels in a derivation cohort of 390 Chinese NAFLD patients admitted to the hepatology center of the First Affiliated Hospital of Wenzhou Medical University (China) between December 2016 and September 2019 and subsequently validated in five external cohorts of different ethnicities of patients with biopsy-confirmed NAFLD (pooled n=1,089).FindingsThe performance of the acNASH index for identifying NASH (defined as NAFLD activity score ≥5 with score of ≥1 for each steatosis, lobular inflammation and ballooning) was good in the derivation cohort with an area under receiver operating characteristics (AUROC) of 0·818 (95%CI 0·777-0·860). A cutoff of acNASH index <4·15 gave a sensitivity (Se) of 91%, a specificity (Sp) of 48% and a negative predictive value (NPV) of 83% for ruling-out NASH, conversely, a cutoff of acNASH >7·73 gave a Sp of 91%, Se of 53% and a positive predictive value (PPV) of 85% for ruling-in NASH. In the pooled validation cohort (n=1,089), the diagnostic performance of the index was also good with AUROC=0·805 (95%CI 0·780-0·830), NPV of 93% for ruling-out NASH and PPV of 73% for ruling-in NASH. Subgroup analyses showed similar performance in patients with diabetes or subjects with normal serum transaminase levels.InterpretationThe acNASH index shows promising utility as a simple non-invasive biomarker for diagnosing NASH among adults with biopsy-proven NAFLD of different ethnicities from different countries.FundingThe National Natural Science Foundation of China (82070588), High Level Creative Talents from Department of Public Health in Zhejiang Province (S2032102600032) and Project of New Century 551 Talent Nurturing in Wenzhou.

Project description:OBJECTIVES:The visceral adiposity index (VAI), an indirect marker of visceral adipose tissue, serves as a model associated with cardiometabolic risk, but has limitations regarding the Asian population. We sought to develop a new VAI (NVAI) for the Korean population and compare it to VAI for prediction of atherosclerotic cardiovascular disease (ASCVD) risk and development of major cardiovascular diseases (CVD) and stroke. METHODS:Patients (969) who underwent visceral fat area measurement were analyzed. After exclusion, 539 patients (142 men, 397 women) were randomly divided into internal (n = 374) and external validation (n = 165) data set. The NVAI was developed using univariate and multivariate logistic regression with backward selection of predictors. Receiver operating characteristic (ROC) curve analysis and comparison of the area under the curve (AUC) verified the better predictor of ASCVD risk score. Additionally, nationwide population-based cross-sectional survey data (Korean National Health and Nutrition Examination Survey [KNHANES] 2008-2010, n = 29,235) was used to validate the NVAI's ability to predict ASCVD risk and major CVD and stroke. RESULTS:The NVAI better reflected visceral fat area in internal and external data sets, with AUCs of 0.911 (95% confidence interval [CI]: 0.882-0.940) and 0.879 (95% CI: 0.828-0.931), respectively. NVAI better discriminated for ASCVD risk (AUC = 0.892, 95% CI: 0.846-0.938) compared to VAI (0.559, 95% CI: 0.439-0.679). The NVAI also better predicted MI or angina, and stroke with AUCs of 0.771 (95% CI: 0.752-0.789), and 0.812 (95% CI: 0.794-0.830), respectively, compared with waist circumference (WC), body mass index (BMI), TG to HDL ratio, and VAI via KNHANES, in a statistically significant manner. CONCLUSIONS:The NVAI has advantages as a predictor of visceral obesity and is significantly associated with ASCVD risks and development of major CVD and stroke in the Korean population. The NVAI could be a screening tool for improved risk estimation related to visceral obesity.

Project description:ImportanceSevere acute kidney injury (AKI) is a serious postoperative complication. A tool for predicting the risk of AKI requiring kidney replacement therapy (KRT) after major noncardiac surgery might assist with patient counseling and targeted use of measures to reduce this risk.ObjectiveTo derive and validate a predictive model for AKI requiring KRT after major noncardiac surgery.Design, setting, and participantsIn this prognostic study, 5 risk prediction models were derived and internally validated in a population-based cohort of adults without preexisting kidney failure who underwent noncardiac surgery in Alberta, Canada, between January 1, 2004, and December 31, 2013. The best performing model and corresponding risk index were externally validated in a population-based cohort of adults without preexisting kidney failure who underwent noncardiac surgery in Ontario, Canada, between January 1, 2007, and December 31, 2017. Data analysis was conducted from September 1, 2019, to May 31, 2021.ExposuresDemographic characteristics, surgery type, laboratory measures, and comorbidities before surgery.Main outcomes and measuresAcute kidney injury requiring KRT within 14 days after surgery. Discrimination was assessed using the C statistic; calibration was assessed using calibration intercept and slope. Logistic recalibration was used to optimize model calibration in the external validation cohort.ResultsThe derivation cohort included 92 114 patients (52.2% female; mean [SD] age, 62.3 [18.0] years), and the external validation cohort included 709 086 patients (50.8% female; mean [SD] age, 61.0 [16.0] years). A total of 529 patients (0.6%) developed postoperative AKI requiring KRT in the derivation cohort, and 2956 (0.4%) developed postoperative AKI requiring KRT in the external validation cohort. The following factors were consistently associated with the risk of AKI requiring KRT: younger age (40-69 years: odds ratio [OR], 2.07 [95% CI, 1.69-2.53]; <40 years: OR, 3.73 [95% CI, 2.61-5.33]), male sex (OR, 1.55; 95% CI, 1.28-1.87), surgery type (colorectal: OR, 4.86 [95% CI, 3.28-7.18]; liver or pancreatic: OR, 6.46 [95% CI, 3.85-10.83]; other abdominal: OR, 2.19 [95% CI, 1.66-2.89]; abdominal aortic aneurysm repair: OR, 19.34 [95% CI, 14.31-26.14]; other vascular: OR, 7.30 [95% CI, 5.48-9.73]; thoracic: OR, 3.41 [95% CI, 2.07-5.59]), lower estimated glomerular filtration rate (OR, 0.97; 95% CI, 0.97-0.97 per 1 mL/min/1.73 m2 increase), lower hemoglobin concentration (OR, 0.99; 95% CI, 0.98-0.99 per 0.1 g/dL increase), albuminuria (mild: OR, 1.88 [95% CI, 1.52-2.33]; heavy: OR, 3.74 [95% CI, 2.98-4.69]), history of myocardial infarction (OR, 1.63; 95% CI, 1.32-2.03), and liver disease (mild: OR, 2.32 [95% CI, 1.66-3.24]; moderate or severe: OR, 4.96 [95% CI, 3.58-6.85]). In external validation, a final model including these variables showed excellent discrimination (C statistic, 0.95; 95% CI, 0.95-0.96), with sensitivity of 21.2%, specificity of 99.9%, positive predictive value of 38.1%, and negative predictive value of 99.7% at a predicted risk threshold of 10% or greater.Conclusions and relevanceThe findings suggest that this risk model can predict AKI requiring KRT after noncardiac surgery using routine preoperative data. The model may be feasible for implementation in clinical perioperative risk stratification for severe AKI.

Dataset Information

Derivation and Validation of a Geriatric-Sensitive Perioperative Cardiac Risk Index.

Publications

Derivation and Validation of a Geriatric-Sensitive Perioperative Cardiac Risk Index.

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