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Introduction: Diabetic polyneuropathy (DPN) is a chronic complication that affects up to 50% of diabetics. There is a need for Rasayana, a specialized Ayurvedic drug with active potential that is helpful in the management of DPN. Despite these serious complications, the development of targeted therapies for DPN is lacking. Methods: A single-centric, randomized, comparative open clinical trial was conducted in patients with DPN. Thirty participants were randomized (n = 15 in the control group and n = 15 in the trial group). Koshtashodhana (~mild therapeutic purgation) with Nimbamrutadi Eranda Taila to all patients, followed by Vangeshawara Rasa with a dose of 125 mg twice a day with water in the trial group and Guduchyadi Kashaya with a dose of 40 mL twice a day with an equal quantity of water in the control group for a duration of 45 days. Patients were assessed on signs and symptoms, the toronto clinical scoring system (TCSS), 10 g monofilament, and laboratory investigations during each follow-up for 15 days. Results: Thirty participants were analyzed (n = 15 each). In trial groups, improvements in subjective parameters, sensory motor examinations, frequency of urination, fasting blood sugar, and postprandial blood sugar were obtained (P < 0.05). The effects of both groups were comparable. Conclusion: Vangeshwara Rasa, a Herbo-mineral drug possessing Rasayana potential, has been proven to be safe and effective in the management of DPN. The drug is free from any adverse effects, and 45 days of administration give significant relief from the signs and symptoms of DPN. The present study proves that the results of both the trial group, i.e., Vangeshwara Rasa (Group A), and the control group, i.e., Guduchyadi Kashaya (Group B), were comparable.

Objective To evaluate if a machine learning approach can accurately predict antidepressant treatment outcome using electronic health records (EHRs) from patients with depression. Method This study examined 808 patients with depression at a New York City‐based outpatient mental health clinic between June 13, 2016 and June 22, 2020. Antidepressant treatment outcome was defined based on trend in depression symptom severity over time and was categorized as either “Recovering” or “Worsening” (i.e., non‐Recovering), measured by the slope of individual‐level Patient Health Questionnaire‐9 (PHQ‐9) score trajectory spanning 6 months following treatment initiation. A patient was designated as “Recovering” if the slope is less than 0 and as “Worsening” if the slope was no less than 0. Multiple machine learning (ML) models including L2 norm regularized Logistic Regression, Naive Bayes, Random Forest, and Gradient Boosting Decision Tree (GBDT) were used to predict treatment outcome based on additional data from EHRs, including demographics and diagnoses. Shapley Additive Explanations were applied to identify the most important predictors. Results The GBDT achieved the best results of predicting “Recovering” (AUC: 0.7654 ± 0.0227; precision: 0.6002 ± 0.0215; recall: 0.5131 ± 0.0336). When excluding patients with low PHQ‐9 scores (

Abstract Left ventricular ejection fraction (EF) is a key measure in the diagnosis and treatment of heart failure (HF) and many patients experience changes in EF overtime. Large-scale analysis of longitudinal changes in EF using electronic health records (EHRs) is limited. In a multi-site retrospective study using EHR data from three academic medical centers, we investigated longitudinal changes in EF measurements in patients diagnosed with HF. We observed significant variations in baseline characteristics and longitudinal EF change behavior of the HF cohorts from a previous study that is based on HF registry data. Data gathered from this longitudinal study were used to develop multiple machine learning models to predict changes in ejection fraction measurements in HF patients. Across all three sites, we observed higher performance in predicting EF increase over a 1-year duration, with similarly higher performance predicting an EF increase of 30% from baseline compared to lower percentage increases. In predicting EF decrease we found moderate to high performance with low confidence for various models. Among various machine learning models, XGBoost was the best performing model for predicting EF changes. Across the three sites, the XGBoost model had an F1-score of 87.2, 89.9, and 88.6 and AUC of 0.83, 0.87, and 0.90 in predicting a 30% increase in EF, and had an F1-score of 95.0, 90.6, 90.1 and AUC of 0.54, 0.56, 0.68 in predicting a 30% decrease in EF. Among features that contribute to predicting EF changes, baseline ejection fraction measurement, age, gender, and heart diseases were found to be statistically significant.

Introduction & Objectives: There are a range of mechanisms and systems underlying urinary bladder contractions, with interactions between nerves, endogenously released chemicals and receptors all playing a part in the overall function1,2. However, when contraction is inhibited, such as with underactive bladder, it can be challenging to work out which mechanisms may be at fault. Nonetheless, it is feasible that parasympathomimetics (muscarinic agonists and acetylcholinesterase inhibitors) might be beneficial for enhancing contractions of the urinary bladder, which is of particular interest in the pharmaceutical treatment of underactive bladder. This study systematically reviewed and meta-analysed the current literature to assess the effectiveness of muscarinic agonists and acetylcholinesterase inhibitors for the treatment of underactive bladder. Methods: An a priori protocol was registered in PROSPERO, and database searches were undertaken within Embase, PubMed and CENTRAL. The study included both randomised and non-randomised controlled trials that incorporated patients with underactive bladder. Articles that compared any parasympathomimetic medication to a placebo, no treatment, or other pharmaceuticals were included. Risk ratios, mean differences and odds ratios were calculated from the extracted data. Results: 3024 participants were included across twelve trials. There were significant differences between selected comparators and parasympathomimetics (favouring parasympathomimetics) in the number of patients with urinary retention, although no overall difference was identified to mean postvoid volume. There was a significant difference at up to 1-week post-intervention, favouring parasympathomimetics, but no difference at 1-month post-intervention. There were no differences identified in adverse events. Conclusions: The literature is not clear as hypothesised regarding whether the prescription of parasympathomimetics is evidence-based for the treatment of underactive bladder3. The overall evidence is of low quality, and studies tended to end without long-term follow-ups. However, there are some observed benefits, with no major identified risk factors from parasympathomimetic prescriptions. Although it is not entirely possible to draw clear evidence-based conclusions from the current literature, this study has identified a clear need for well-controlled clinical trials to be conducted in the future. [ABSTRACT FROM AUTHOR]

Abstract Substance use disorders (SUDs) commonly co-occur with mental illness. However, the ongoing addiction crisis raises the question of how opioid use disorder (OUD) impacts healthcare utilization relative to other SUDs. This study examines the utilization patterns of patients with major depressive disorder (MDD) and: (1) co-occurring OUD (MDD-OUD); (2) a co-occurring SUD other than OUD (MDD-NOUD); and (3) no co-occurring SUD (MDD-NSUD). We analyzed electronic health records (EHRs) derived from multiple health systems across the New York City (NYC) metropolitan area between January 2008 and December 2017. 11,275 patients aged ≥18 years with a gap of 30–180 days between 2 consecutive MDD diagnoses and an antidepressant prescribed 0–180 days after any MDD diagnosis were selected, and prevalence of any SUD was 24%. Individuals were stratified into comparison groups and matched on age, gender, and select underlying comorbidities. Prevalence rates and encounter frequencies were measured and compared across outpatient, inpatient, and emergency department (ED) settings. Our key findings showed that relative to other co-occurring SUDs, OUD was associated with larger increases in the rates and odds of using substance-use-related services in all settings, as well as services that integrate mental health and substance abuse treatments in inpatient and ED settings. OUD was also associated with larger increases in total encounters across all settings. These findings and our proposed policy recommendations could inform efforts towards targeted OUD interventions, particularly for individuals with underlying mental illness whose treatment and recovery are often more challenging.

Abstract Objective To identify depression subphenotypes from Electronic Health Records (EHRs) using machine learning methods, and analyze their characteristics with respect to patient demographics, comorbidities, and medications. Materials and Methods Using EHRs from the INSIGHT Clinical Research Network (CRN) database, multiple machine learning (ML) algorithms were applied to analyze 11 275 patients with depression to discern depression subphenotypes with distinct characteristics. Results Using the computational approaches, we derived three depression subphenotypes: Phenotype_A (n = 2791; 31.35%) included patients who were the oldest (mean (SD) age, 72.55 (14.93) years), had the most comorbidities, and took the most medications. The most common comorbidities in this cluster of patients were hyperlipidemia, hypertension, and diabetes. Phenotype_B (mean (SD) age, 68.44 (19.09) years) was the largest cluster (n = 4687; 52.65%), and included patients suffering from moderate loss of body function. Asthma, fibromyalgia, and Chronic Pain and Fatigue (CPF) were common comorbidities in this subphenotype. Phenotype_C (n = 1452; 16.31%) included patients who were younger (mean (SD) age, 63.47 (18.81) years), had the fewest comorbidities, and took fewer medications. Anxiety and tobacco use were common comorbidities in this subphenotype. Conclusion Computationally deriving depression subtypes can provide meaningful insights and improve understanding of depression as a heterogeneous disorder. Further investigation is needed to assess the utility of these derived phenotypes to inform clinical trial design and interpretation in routine patient care.