Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association · 2016
A Diagnostic Score for Insulin Resistance in Nondiabetic Patients with Ischemic Stroke or Transient Ischemic Attack.
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Xu Jin, Viscoli Catherine M, Ford Gary A, Gorman Mark, Kernan Walter N, IRIS Trial Investigators
Аннотация
GOAL: We sought to develop an instrument to screen for insulin resistance in nondiabetic patients with recent ischemic stroke or transient ischemic attack (TIA).
MATERIALS AND METHODS: Subjects were 7262 nondiabetic patients aged greater than or equal to 40 years with ischemic strokes or TIA within the past 6 months. Features were analyzed in bivariate analysis for association with insulin resistance, measured by the homeostasis model assessment of insulin resistance (HOMA-IR). Features significantly associated with HOMA-IR (P < .05) were entered into multivariable analysis. The magnitudes of regression coefficients from the multivariable model were used to assign point values for 2 diagnostic scoring instruments: a basic instrument that did not incorporate laboratory test values and an enhanced instrument that did. The performance of the instruments was tested using receiver operating characteristic (ROC) analysis.
FINDINGS: In the basic model, 5 features were retained in the multivariable regression analysis: male gender, abdominal obesity, body mass index (BMI), elevated waist-to-hip ratio, and systolic blood pressure. In the enhanced model, 4 features were retained in the multivariable regression analysis: BMI, abdominal obesity, fasting glucose greater than or equal to 100 mg/dL, and triglyceride/high-density lipoprotein ratio. In the basic model, the area under the ROC curve (aROC) was .73 in the validation cohort. In the enhanced model, the aROC was .78 in the validation cohort.
CONCLUSIONS: Our 2 scoring systems performed well in identifying stroke patients with insulin resistance, but they are probably not sufficiently accurate for high-stake clinical decisions. We suggest strategies for improving the accuracy of future instruments.
MATERIALS AND METHODS: Subjects were 7262 nondiabetic patients aged greater than or equal to 40 years with ischemic strokes or TIA within the past 6 months. Features were analyzed in bivariate analysis for association with insulin resistance, measured by the homeostasis model assessment of insulin resistance (HOMA-IR). Features significantly associated with HOMA-IR (P < .05) were entered into multivariable analysis. The magnitudes of regression coefficients from the multivariable model were used to assign point values for 2 diagnostic scoring instruments: a basic instrument that did not incorporate laboratory test values and an enhanced instrument that did. The performance of the instruments was tested using receiver operating characteristic (ROC) analysis.
FINDINGS: In the basic model, 5 features were retained in the multivariable regression analysis: male gender, abdominal obesity, body mass index (BMI), elevated waist-to-hip ratio, and systolic blood pressure. In the enhanced model, 4 features were retained in the multivariable regression analysis: BMI, abdominal obesity, fasting glucose greater than or equal to 100 mg/dL, and triglyceride/high-density lipoprotein ratio. In the basic model, the area under the ROC curve (aROC) was .73 in the validation cohort. In the enhanced model, the aROC was .78 in the validation cohort.
CONCLUSIONS: Our 2 scoring systems performed well in identifying stroke patients with insulin resistance, but they are probably not sufficiently accurate for high-stake clinical decisions. We suggest strategies for improving the accuracy of future instruments.
Предметные рубрики MeSH
AdultAgedFemaleHumansMaleMiddle AgedArea Under CurveBiomarkersBlood GlucoseBlood PressureBody Mass IndexBrain IschemiaGlucose Metabolism DisordersInsulinInsulin ResistanceIschemic Attack, TransientLipoproteins, HDLMultivariate AnalysisObesity, AbdominalPredictive Value of TestsRandomized Controlled Trials as TopicReproducibility of ResultsRisk FactorsROC CurveSex FactorsStrokeTriglyceridesWaist-Hip Ratio