Faculty Bibliography

Favourable body composition has been associated with higher dietary protein intake. However, little is known regarding this relationship in a population of Chinese Americans (CHA), who have lower BMI compared with other populations. The aim of the present study was to assess the relationship between dietary protein intake, fat mass (FM) and fat-free mass (FFM) in CHA. Data were from the Chinese American Cardiovascular Health Assessment (CHA CHA) 2010-2011 (n 1707); dietary intake was assessed using an adapted and validated FFQ. Body composition was assessed using bioelectrical impedance analysis. The associations between protein intake (% energy intake) and BMI, percentage FM (FM%), percentage FFM (FFM%), FM index (FMI) and FFM index (FFMI) were examined using multiple linear regression adjusted for age, sex, physical activity, acculturation, total energy intake, sedentary time, smoking status, education, employment and income. There was a significant positive association between dietary protein and BMI (B = 0·056, 95 % CI 0·017, 0·104; P = 0·005), FM (B = 0·106, 95 % CI 0·029, 0·184; P = 0·007), FM% (B = 0·112, 95 % CI 0·031, 0·194; P = 0·007) and FMI (B = 0·045, 95 % CI 0·016, 0·073; P = 0·002). There was a significant negative association between dietary protein and FFM% (B = -0·116, 95 % CI -0·196, -0·036; P = 0·004). In conclusion, higher dietary protein intake was associated with higher adiposity; however, absolute FFM and FFMI were not associated with dietary protein intake. Future work examining the relationship between protein source (i.e. animal) and body composition is warranted in this population of CHA.

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: The growing opioid overdose epidemic has grappled the nation with the CDC now reporting that drug overdose deaths have become the most common cause of death for young people. Medical education has historically ignored substance use disorders, and though they generally require all medical students to learn basic life support, they have not taught how to respond to opioid overdoses. Further, medical education is moving towards modalities which utilize adult learning theory. One such modality are online modules. However, there are few studies comparing their outcomes with traditional lectures. Previously, the authors compared in-person and online training of medical students to respond to opioid overdoses using naloxone in a non-randomized controlled setting, which showed no meaningful differences in knowledge, attitudes, and preparedness outcomes for students. In this paper, the authors attempt to use a randomized controlled trial to compare the two educational modalities at a second urban medical school.
Objective(s): The author's primary objective was to demonstrate non-inferiority of online compared to in-person training for knowledge. Our secondary objective were to show non-inferiority of online compared to in-person training attitudes, and preparedness.
Method(s): Our study received IRB exemption as an education intervention. As a part of a transition to clinical clerkships curriculum used for second year medical students, second year medical students in an urban medical school were randomized into training sessions by the office of medical education without foreknowledge of the planned study. Students taking the online training were provided with a link to online modules with pre- and post-tests and video based lectures. Students randomized to the in-person training group took a pre-test just prior to receiving an oral lecture, and then immediately completed a post-test. Paired student's t-tests were used to compare measurements for each group in knowledge, attitudes, and preparedness, and Cohen's D was used to measure the effect size of the change. We calculated 99% confidence intervals for each measure and utilized a margin of non-inferiority of 5%.
Result(s): The in-person group demonstrated a statistically significant increase in knowledge, a non-statistically significant decrease in self-reported preparedness, and a small non-statistically significant increase in attitudes, see Table 1. The online group demonstrated a statistically significant increase in knowledge and self-reported preparedness, without a statistically significant change in attitudes, see Table 1. 99% CIs were [-0.20, 1.09] for knowledge, [6.51, 10.93] for preparedness, and [-2.32, 1.59] for attitudes, see Figure 1.
Conclusion(s): Online training for opioid overdose prevention training provided non-inferior outcomes for knowledge, preparedness, and attitudes. This study supports the use of online opioid overdose prevention training as a non-inferior alternative to in-person training

BACKGROUND:Heart Failure (HF) is accompanied by a high cost of care and gloomy prognosis despite recent advances in its management. Therefore, efforts to minimize HF rehospitalizations is a major focus of several studies. METHODS:We conducted a retrospective cohort study of 140 patients 18 years and above who had baseline clinical parameters, echocardiography, NT-ProBNP, troponin I and other laboratory parameters following a 3-year electronic medical record review. Patients with coronary artery disease, preserved ejection fraction, pulmonary embolism, cancer, and end-stage renal disease were excluded. RESULTS:Of the 140 patients admitted with HF with reduced Ejection Fraction (HFrEF) secondary to non-ischemic cardiomyopathy, 15 were re-hospitalized within 30 days of discharge while 42 were rehospitalized within 6 months after discharge for decompensated HF. Receiver operating characteristic (ROC) cutoff points were obtained for NT-ProBNP at 5178 pg/ml and serum troponin I at 0.045 ng/ml. After Cox regression analysis, patients with HFrEF who had higher hemoglobin levels had reduced odds of re-hospitalization (p = 0.007) within 30 days after discharge. NT-ProBNP and troponin I were independent predictors of re-hospitalization at 6 months after discharge (p = 0.047 and p = 0.02), respectively, after Cox regression analysis. CONCLUSION/CONCLUSIONS:Troponin I and NT-ProBNP at admission are the best predictors of re-hospitalization 6 months after discharge among patients with HFrEF. Hemoglobin is the only predictor of 30 -day rehospitalization among HFrEF patients in this study. High-risk patients may require aggressive therapy to improve outcomes.