Machine Learning Prediction of Mortality and Hospitalization in Heart Failure With Preserved Ejection Fraction.

OBJECTIVES: This study sought to develop models for predicting mortality and heart failure (HF) hospitalization for outpatients with HF with preserved ejection fraction (HFpEF) in the TOPCAT (Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist) trial. BACKGROUND: Although risk assessment models are available for patients with HF with reduced ejection fraction, few have assessed the risks of death and hospitalization in patients with HFpEF. METHODS: The following 5 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. Model discrimination and calibration were estimated using receiver-operating characteristic curves and Brier scores, respectively. The top prediction variables were assessed by using the best performing models, using the incremental improvement of each variable in 5-fold cross-validation. RESULTS: The RF was the best performing model with a mean C-statistic of 0.72 (95% confidence interval [CI]: 0.69 to 0.75) for predicting mortality (Brier score: 0.17), and 0.76 (95% CI: 0.71 to 0.81) for HF hospitalization (Brier score: 0.19). Blood urea nitrogen levels, body mass index, and Kansas City Cardiomyopathy Questionnaire (KCCQ) subscale scores were strongly associated with mortality, whereas hemoglobin level, blood urea nitrogen, time since previous HF hospitalization, and KCCQ scores were the most significant predictors of HF hospitalization. CONCLUSIONS: These models predict the risks of mortality and HF hospitalization in patients with HFpEF and emphasize the importance of health status data in determining prognosis. (Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist [TOPCAT]; NCT00094302).

Endocrinological and auxological abnormalities in young children with optic nerve hypoplasia: a prospective study.

OBJECTIVE: To determine the prevalence of endocrinopathies, neuroradiographical findings, and growth derangements in young children with optic nerve hypoplasia (ONH). STUDY DESIGN: A prospective observational study examined the prevalence of endocrinopathies at study enrollment and growth patterns in children with ONH. Subjects (n = 47, mean +/- SD 15.2 +/- 10.6 months) were followed until 59.0 +/- 6.2 months of age. RESULTS: The prevalence of endocrinopathies was 71.7%: 64.1% of subjects had growth hormone (GH) axis abnormalities, 48.5% hyperprolactinemia, 34.9% hypothyroidism, 17.1% adrenal insufficiency, and 4.3% diabetes insipidus (DI). Endocrinopathies were not associated with ONH laterality, absence of the septum pellucidum, or pituitary abnormalities on neuroimaging. End height standard deviation score (SDS) was similar to start length SDS independent of GH surrogate status. A significant increase in end weight SDS was found for the cohort (p < .001). A body mass index (BMI) >85th percentile was noted in 44.4% of the cohort and in 52.1% of subjects with GH axis abnormalities. Initial hyperprolactinemia was positively associated with increased end BMI SDS (p = .004). CONCLUSIONS: These prospective findings confirm the high prevalence of pituitary endocrinopathies in children with ONH reported in previous retrospective studies. Our data reveal that some of these children maintain normal height velocity despite GH axis abnormalities, and, as a group, they are at high risk for increased BMI.