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AHRQ Research Studies
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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
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1 to 2 of 2 Research Studies DisplayedAngraal S, Mortazavi BJ, Gupta A
Machine learning prediction of mortality and hospitalization in heart failure with preserved ejection fraction.
This study developed models to predict the risk of death and hospitalization in patients with heart failure (HF) with preserved ejection fraction (HFpEF). Data was used from the TOPCAT (Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist) clinical trial. Five methods: logistic regression with a forward selection of variables; logistic regression with a lasso regularization for variable selection; random forest (RF); gradient descent boosting; and support vector machine, were used to train models for assessing risks of mortality and HF hospitalization through 3 years of follow-up and were validated using 5-fold cross-validation. RF was found to be the best performing model for predicting mortality and HF hospitalization. Blood urea nitrogen levels, body mass index, and Kansas City Cardiomyopathy Questionnaire (KCCQ) subscale scores were strongly associated with mortality, while hemoglobin level, blood urea nitrogen, time since previous HF hospitalization, and KCCQ scores were the most significant predictors of HF hospitalization.
AHRQ-funded; HS023000.
Citation: Angraal S, Mortazavi BJ, Gupta A .
Machine learning prediction of mortality and hospitalization in heart failure with preserved ejection fraction.
JACC Heart Fail 2020 Jan;8(1):12-21. doi: 10.1016/j.jchf.2019.06.013..
Keywords: Heart Disease and Health, Cardiovascular Conditions, Mortality, Hospitalization, Risk, Health Status, Health Information Technology (HIT)
Park JS, Bateni SB, Bold RJ
The modified frailty index to predict morbidity and mortality for retroperitoneal sarcoma resections.
The researchers performed a retrospective analysis of patients with a diagnosis of primary malignant retroperitoneal neoplasm who underwent surgical resection. The modified frailty index (mFI) was calculated according to standard published methods. Their data demonstrate that the majority of patients undergoing retroperitoneal sarcoma resections have few, if any, comorbidities. The mFI was a limited predictor of overall and serious complications and was not a significant predictor of mortality.
AHRQ-funded; HS022236.
Citation: Park JS, Bateni SB, Bold RJ .
The modified frailty index to predict morbidity and mortality for retroperitoneal sarcoma resections.
J Surg Res 2017 Sep;217:191-97. doi: 10.1016/j.jss.2017.05.025.
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Keywords: Cancer, Elderly, Health Status, Mortality, Risk