Predicting the risk of falls in advance can benefit the quality of care and potentially reduce mortality and morbidity in the older population. The aim of this study was to construct and validate an electronic health record-based fall risk predictive tool to identify elders at a higher risk of falls. The one-year fall prediction model was developed using the machine-learning-based algorithm, XGBoost, and tested on an independent validation cohort. The data were collected from electronic health records (EHR) of Maine from 2016 to 2018, comprising 265,225 older patients (≥65 years of age). This model attained a validated C-statistic of 0.807, where 50 % of the identified high-risk true positives were confirmed to fall during the first 94 days of next year. The model also captured in advance 58.01 % and 54.93 % of falls that happened within the first 30 and 30-60 days of next year. The identified high-risk patients of fall showed conditions of severe disease comorbidities, an enrichment of fall-increasing cardiovascular and mental medication prescriptions and increased historical clinical utilization, revealing the complexity of the underlying fall etiology. The XGBoost algorithm captured 157 impactful predictors into the final predictive model, where cognitive disorders, abnormalities of gait and balance, Parkinson's disease, fall history and osteoporosis were identified as the top-5 strongest predictors of the future fall event. By using the EHR data, this risk assessment tool attained an improved discriminative ability and can be immediately deployed in the health system to provide automatic early warnings to older adults with increased fall risk and identify their personalized risk factors to facilitate customized fall interventions.

Objective: To assess the longitudinal metabolic patterns during the evolution of bronchopulmonary dysplasia (BPD) development. Methods: A case-control dataset of preterm infants (<32-week gestation) was obtained from a multicenter database, including 355 BPD cases and 395 controls. A total of 72 amino acid (AA) and acylcarnitine (AC) variables, along with infants’ calorie intake and growth outcomes, were measured on day of life 1, 7, 28, and 42. Logistic regression, clustering methods, and random forest statistical modeling were utilized to identify metabolic variables significantly associated with BPD development and to investigate their longitudinal patterns that are associated with BPD development. Results: A panel of 27 metabolic variables were observed to be longitudinally associated with BPD development. The involved metabolites increased from 1 predominant different AC by day 7 to 19 associated AA and AC compounds by day 28 and 16 metabolic features by day 42. Citrulline, alanine, glutamate, tyrosine, propionylcarnitine, free carnitine, acetylcarnitine, hydroxybutyrylcarnitine, and most median-chain ACs (C₅:C₁₀) were the most associated metabolites down-regulated in BPD babies over the early days of life, whereas phenylalanine, methionine, and hydroxypalmitoylcarnitine were observed to be up-regulated in BPD babies. Most calorie intake and growth outcomes revealed similar longitudinal patterns between BPD cases and controls over the first 6 weeks of life, after gestational adjustment. When combining with birth weight, the derived metabolic-based discriminative model observed some differences between those with and without BPD development, with c-statistics of 0.869 and 0.841 at day 7 and 28 of life on the test data. Conclusions: The metabolic panel we describe identified some metabolic differences in the blood associated with BPD pathogenesis. Further work is needed to determine whether these compounds could facilitate the monitoring and/or investigation of early-life metabolic status in the lung and other tissues for the prevention and management of BPD.

Preeclampsia (PE) is a condition that poses a significant risk of maternal mortality and multiple organ failure during pregnancy. Early prediction of PE can enable timely surveillance and interventions, such as low-dose aspirin administration. In this study, conducted at Stanford Health Care, we examined a cohort of 60 pregnant women and collected 478 urine samples between gestational weeks 8 and 20 for comprehensive metabolomic profiling. By employing liquid chromatography mass spectrometry (LCMS/MS), we identified the structures of seven out of 26 metabolomics biomarkers detected. Utilizing the XGBoost algorithm, we developed a predictive model based on these seven metabolomics biomarkers to identify individuals at risk of developing PE. The performance of the model was evaluated using 10-fold cross-validation, yielding an area under the receiver operating characteristic curve of 0.856. Our findings suggest that measuring urinary metabolomics biomarkers offers a noninvasive approach to assess the risk of PE prior to its onset.

Objectives: The aim of this study was to develop a single blood test that could determine gestational age and estimate the risk of preterm birth by measuring serum metabolites. We hypothesised that serial metabolic modelling of serum analytes throughout pregnancy could be used to describe fetal gestational age and project preterm birth with a high degree of precision.Study design: A retrospective cohort study.Setting: Two medical centres from the USA.Participants: Thirty-six patients (20 full-term, 16 preterm) enrolled at Stanford University were used to develop gestational age and preterm birth risk algorithms, 22 patients (9 full-term, 13 preterm) enrolled at the University of Alabama were used to validate the algorithms.Outcome measures: Maternal blood was collected serially throughout pregnancy. Metabolic datasets were generated using mass spectrometry.Results: A model to determine gestational age was developed (R²=0.98) and validated (R²=0.81). 66.7% of the estimates fell within ±1 week of ultrasound results during model validation. Significant disruptions from full-term pregnancy metabolic patterns were observed in preterm pregnancies (R²=−0.68). A separate algorithm to predict preterm birth was developed using a set of 10 metabolic pathways that resulted in an area under the curve of 0.96 and 0.92, a sensitivity of 0.88 and 0.86, and a specificity of 0.96 and 0.92 during development and validation testing, respectively.Conclusions: In this study, metabolic profiling was used to develop and test a model for determining gestational age during full-term pregnancy progression, and to determine risk of preterm birth. With additional patient validation studies, these algorithms may be used to identify at-risk pregnancies prompting alterations in clinical care, and to gain biological insights into the pathophysiology of preterm birth. Metabolic pathway-based pregnancy modelling is a novel modality for investigation and clinical application development.

Background Though Aspirin and intravenous immunoglobulin (IVIG) remain the standard treatments for Kawasaki Disease (KD) to minimize coronary artery damage, the duration and dosage of aspirin are inconsistent across hospitals. However, the lack of multi-center randomized trials prevents definitive answers to the impact of high-dose aspirin. Methods This clinical trial was structured as a prospective, evaluator-blinded, multi-center randomized controlled trial with two parallel arms, aiming to assess the effectiveness of IVIG as a standalone primary therapy of KD in comparison to the combination of IVIG with high-dose aspirin therapy. KD patients were enrolled between September, 2016 and August, 2019. A final cohort of 134 patients were randomly assigned to the standard and test groups with 69 and 65 patients, respectively. The Standard group received IVIG (2 g/kg) along with aspirin (80-100 mg/kg/day) until fever subsided for 48 hours. The test group received IVIG (2 g/kg) alone. Following the initial treatment, both groups received a daily aspirin dose (3-5 mg/kg) for six weeks. The primary outcome measure was the occurrence of coronary artery lesions (CAL) at the 6-8 weeks mark. The secondary outcome is IVIG resistance. Results The overall rate of CAL in test group decreased from 10.8% at diagnosis to 1.5% and 3.1% at 6 weeks and 6 months, respectively. The CAL rate of standard group declined from 13.0% to 2.9% and 1.4%, with no statistically significant difference (P>0.1) in the frequency of CAL between the two groups. Furthermore, no statistically significant differences were found for treatment (P>0.1) and prevention (P>0.1) effect between the two groups. Conclusions This marks the first prospective multi-center randomized controlled trial comparing the standard treatment of KD using IVIG plus high-dose aspirin against IVIG alone. Our analysis indicates that addition of high-dose aspirin during initial IVIG treatment is neither statistically significant nor clinically meaningful for CAL reduction. Registration URL: http://www.clinicaltrials.gov; identifier: NCT02951234 What is New? This study represents the first multi-center randomized controlled trial investigating the efficacy of high-dose aspirin or intravenous immunoglobulin (IVIG) during the acute stage of KD. This study assessed the impact of discontinuing high-dose aspirin (80–100 mg/kg/day) on the occurrence of CAL during the acute phase treatment of Kawasaki Disease. No significant differences were observed between high-dose aspirin plus IVIG treatment and IVIG alone treatment in terms of the frequency of abnormal coronary artery abnormalities. Additionally, our analysis revealed no statistically significant differences in either the treatment effect (the number of cases successfully treated) or prevention effect (the prevention of new cases) between these two treatments. What Are the Clinical Implications? Comparison analysis indicated the non-inferiority between two groups with or without high-dose aspirin. Administering the standard 2 g/kg/day IVIG without high-dose aspirin (80–100 mg/kg/day) during the acute phase therapy for KD does not increase the risk of coronary artery lesions, which are a primary cause of morbidity and mortality in KD patients. Addition of high-dose aspirin during initial IVIG treatment is not statistically significant or clinically meaningful.