Faculty Bibliography

BACKGROUND:Because of the strong link between childhood obesity and adulthood obesity comorbidities, and the difficulty in decreasing body mass index (BMI) later in life, effective strategies are needed to address this condition in early childhood. The ability to predict obesity before age five could be a useful tool, allowing prevention strategies to focus on high risk children. The few existing prediction models for obesity in childhood have primarily employed data from longitudinal cohort studies, relying on difficult to collect data that are not readily available to all practitioners. Instead, we utilized real-world unaugmented electronic health record (EHR) data from the first two years of life to predict obesity status at age five, an approach not yet taken in pediatric obesity research. METHODS AND FINDINGS/RESULTS:We trained a variety of machine learning algorithms to perform both binary classification and regression. Following previous studies demonstrating different obesity determinants for boys and girls, we similarly developed separate models for both groups. In each of the separate models for boys and girls we found that weight for length z-score, BMI between 19 and 24 months, and the last BMI measure recorded before age two were the most important features for prediction. The best performing models were able to predict obesity with an Area Under the Receiver Operator Characteristic Curve (AUC) of 81.7% for girls and 76.1% for boys. CONCLUSIONS:We were able to predict obesity at age five using EHR data with an AUC comparable to cohort-based studies, reducing the need for investment in additional data collection. Our results suggest that machine learning approaches for predicting future childhood obesity using EHR data could improve the ability of clinicians and researchers to drive future policy, intervention design, and the decision-making process in a clinical setting.

Background/UNASSIGNED:Despite evidence that biological and genetic factors contribute strongly to obesity, many healthcare providers still attribute obesity more to controllable behavioral issues rather than factors outside a person's control. We evaluated whether medical school students' beliefs about obesity correlate with ability to effectively counsel patients with obesity. Methods/UNASSIGNED:Clerkship-year medical students at NYU School of Medicine completed an Objective Structured Clinical Experience (OSCE) that tests ability to effectively counsel standardized actor-patients with obesity. We surveyed these students to evaluate their beliefs about the causes of obesity and their attitudes towards people with obesity. We analyzed correlations between student beliefs, negative obesity attitudes, and OSCE performance. Results/UNASSIGNED: < 0.05). Conclusions/UNASSIGNED:Attribution of obesity to external factors correlated with greater ability to counsel patients with obesity, suggesting that educating providers on the biological causes of obesity could help reduce bias and improve provider care.

Background: The risk-benefit ratio of ACE-I/ARB therapy after an AKI episode is unclear.
Method(s): We studied 1570 patients recently discharged from hospital and enrolled in a multi-center prospective cohort study (ASSESS-AKI). Follow-up began 3 months after index hospitalization and continued through November 2018. Half of the participants had AKI during the index hospitalization. ACE-I/ARB use and covariates were ascertained 3 months after discharge from the index hospitalization. We used multivariable Cox regression adjusting for demographics, cardiovascular disease, diabetes mellitus, heart failure (HF), blood pressure, urine protein to creatinine ratio, and eGFR to examine the association between ACE-I/ARB use and subsequent death, AKI (>=50% difference between peak and nadir inpatient serum creatinine), renal progression (ESRD or halving of eGFR), and adjudicated HF events.
Result(s): Among study participants who did not have AKI during index hospitalization (N=806), mean age was 65 years, mean eGFR 74 ml/min/1.73m2, and 45% self-reported use of ACE-I/ARB 3 months after hospitalization. Among study participants who did have AKI during index hospitalization (N=764), mean age was 64 years, mean eGFR 65 ml/min/1.73m2, and 50% self-reported use of ACE-I/ARB 3 months after hospitalization. Mean follow-up time was 3.6 years. ACE-I/ARB therapy 3 months after an AKI hospitalization was associated with a lower risk of another hospitalized AKI event and a lower risk of death (Table).
Conclusion(s): Use of ACE-I/ARB in survivors of hospitalized AKI was not associated with increased risk of subsequent AKI but was associated with lower risk of death