Predicting body mass index in early childhood using data from the first 1000 days.

Few existing efforts to predict childhood obesity have included risk factors across the prenatal and early infancy periods, despite evidence that the first 1000 days is critical for obesity prevention. In this study, we employed machine learning techniques to understand the influence of factors in the first 1000 days on body mass index (BMI) values during childhood. We used LASSO regression to identify 13 features in addition to historical weight, height, and BMI that were relevant to childhood obesity. We then developed prediction models based on support vector regression with fivefold cross validation, estimating BMI for three time periods: 30-36 (N = 4204), 36-42 (N = 4130), and 42-48 (N = 2880) months. Our models were developed using 80% of the patients from each period. When tested on the remaining 20% of the patients, the models predicted children's BMI with high accuracy (mean average error [standard deviation] = 0.96[0.02] at 30-36 months, 0.98 [0.03] at 36-42 months, and 1.00 [0.02] at 42-48 months) and can be used to support clinical and public health efforts focused on obesity prevention in early life.

Digital detection of dementia (D3): a study protocol for a pragmatic cluster-randomized trial examining the application of patient-reported outcomes and passive clinical decision support systems.

BACKGROUND: Early detection of Alzheimer's disease and related dementias (ADRD) in a primary care setting is challenging due to time constraints and stigma. The implementation of scalable, sustainable, and patient-driven processes may improve early detection of ADRD; however, there are competing approaches; information may be obtained either directly from a patient (e.g., through a questionnaire) or passively using electronic health record (EHR) data. In this study, we aim to identify the benefit of a combined approach using a pragmatic cluster-randomized clinical trial. METHODS: We have developed a Passive Digital Marker (PDM), based on machine learning algorithms applied to EHR data, and paired it with a patient-reported outcome (the Quick Dementia Rating Scale or QDRS) to rapidly share an identified risk of impairment to a patient's physician. Clinics in both south Florida and Indiana will be randomly assigned to one of three study arms: 1200 patients in each of the two populations will be administered either the PDM, the PDM with the QDRS, or neither, for a total of 7200 patients across all clinics and populations. Both incidence of ADRD diagnosis and acceptance into ADRD diagnostic work-up regimens is hypothesized to increase when patients are administered both the PDM and QDRS. Physicians performing the work-up regimens will be blind to the study arm of the patient. DISCUSSION: This study aims to test the accuracy and effectiveness of the two scalable approaches (PDM and QDRS) for the early detection of ADRD among older adults attending primary care practices. The data obtained in this study may lead to national early detection and management program for ADRD as an efficient and beneficial method of reducing the current and future burden of ADRD, as well as improving the annual rate of newly documented ADRD in primary care practices. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT05231954 . Registered February 9, 2022.

A Novel Conceptual Architecture for Person-Centered Health Records.

Personal health records available to patients today suffer from multiple limitations, such as information fragmentation, a one-size-fits-all approach and a focus on data gathered over time and by institution rather than health conditions. This makes it difficult for patients to effectively manage their health, for these data to be enriched with relevant information from external sources and for clinicians to support them in that endeavor. We propose a novel conceptual architecture for person-centered health record information systems that transcends many of these limitations and capitalizes on the emerging trend of socially-driven information systems. Our proposed personal health record system is personalized on demand to the conditions of each individual patient; organized to facilitate the tracking and review of the patient's conditions; and able to support patient-community interactions, thereby promoting community engagement in scientific studies, facilitating preventive medicine, and accelerating the translation of research findings.