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.

We sought to assess longitudinal electronic health records (EHRs) using machine learning (ML) methods to computationally derive probable Alzheimer's Disease (AD) and related dementia subphenotypes.

The objective of this study was to investigate the potential association between the use of four frequently prescribed drug classes, namely antihypertensive drugs, statins, selective serotonin reuptake inhibitors, and proton-pump inhibitors, and the likelihood of disease progression from mild cognitive impairment (MCI) to dementia using electronic health records (EHRs). We conducted a retrospective cohort study using observational EHRs from a cohort of approximately 2 million patients seen at a large, multi-specialty urban academic medical center in New York City, USA between 2008 and 2020 to automatically emulate the randomized controlled trials. For each drug class, two exposure groups were identified based on the prescription orders documented in the EHRs following their MCI diagnosis. During follow-up, we measured drug efficacy based on the incidence of dementia and estimated the average treatment effect (ATE) of various drugs. To ensure the robustness of our findings, we confirmed the ATE estimates via bootstrapping and presented associated 95% confidence intervals (CIs). Our analysis identified 14,269 MCI patients, among whom 2501 (17.5%) progressed to dementia. Using average treatment estimation and bootstrapping confirmation, we observed that drugs including rosuvastatin (ATE = - 0.0140 [- 0.0191, - 0.0088], p value < 0.001), citalopram (ATE = - 0.1128 [- 0.125, - 0.1005], p value < 0.001), escitalopram (ATE = - 0.0560 [- 0.0615, - 0.0506], p value < 0.001), and omeprazole (ATE = - 0.0201 [- 0.0299, - 0.0103], p value < 0.001) have a statistically significant association in slowing the progression from MCI to dementia. The findings from this study support the commonly prescribed drugs in altering the progression from MCI to dementia and warrant further investigation.

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.

There is a lack of research on how to best incorporate home-based self-measured blood pressure (SMBP) measurements, combined with other patient-generated health data (PGHD), into electronic health record (EHR) systems in a way that promotes primary care workflow without burdening the primary care team with irrelevant or superfluous data.

The adoption rate of electronic health records (EHRs) in hospitals has become a main index to measure digitalization in medicine in each country.

Cancer patients are particularly vulnerable to COVID-19, partially owing to their compromised immune systems and curbed or cut cancer healthcare services caused by the pandemic. As a result, cancer caregivers may have to shoulder triple crises: the COVID-19 pandemic, pronounced healthcare needs from the patient, and elevated need for care from within. While technology-based health interventions have the potential to address unique challenges cancer caregivers face amid COVID-19, limited insights are available. Thus, to bridge this gap, we aim to identify technology-based interventions designed for cancer caregivers and report the characteristics and effects of these interventions concerning cancer caregivers' distinctive challenges amid COVID-19.

Cancer patients are particularly vulnerable to COVID-19, partially owing to their compromised immune systems and curbed or cut cancer healthcare services caused by the pandemic. As a result, cancer caregivers may have to shoulder triple crises: the COVID-19 pandemic, pronounced healthcare needs from the patient, and elevated need for care from within. While technology-based health interventions have the potential to address unique challenges cancer caregivers face amid COVID-19, limited insights are available. Thus, to bridge this gap, we aim to identify technology-based interventions designed for cancer caregivers and report the characteristics and effects of these interventions concerning cancer caregivers' distinctive challenges amid COVID-19.

This study aims to systematically investigate the impact of ultrasound angiography (UA) combined with fine needle aspiration (FNA) for the diagnosis of thyroid nodules (TNs).

This study aimed to identify, analyze, and summarize the clinical efficacy of virtual reality (VR) distraction therapy for oral treatment in different hospital settings in contrast to medical interventions that induce anxiety and pain. Furthermore, this review aimed to determine the implications for research and clinical practice of VR distraction therapy.

Purpose: Pancreatic cancer is a common digestive neoplasm with a high fatality rate. We performed this systematic review and meta-analysis of clinical randomized phase III trials to explore the efficacy and safety of gemcitabine plus anti-angiogenesis therapy versus gemcitabine monotherapy for locally advanced or metastatic pancreatic cancer. Methods: We searched PubMed, Embase and the Cochrane Library to identify eligible studies. Data were collected for the period from January 1, 2000 to August 20, 2018. Hazard ratios (HRs) and odds ratios (ORs) were used as main evaluation parameters. Results: A total of eight eligible studies with 3,586 individuals were included in the present meta-analysis. The results showed that the combination of gemcitabine plus anti-angiogenesis therapy had a significant effect on progression-free survival (HR = 0.92, 95% CI: 0.86 - 1.00, P = 0.04), but led to no significant difference in the overall survival (HR = 0.96, 95% CI: 0.88 - 1.05, P = 0.38). In terms of safety, gemcitabine plus anti-angiogenesis therapy did not increase the rate of grade 3-4 common adverse effects except for hypertension. Conclusions: Although gemcitabine plus anti-angiogenesis therapy might prolong the progression-free survival in locally advanced or metastatic pancreatic cancer, these successful results did not translate into a significant improvement in the overall survival or change in the clinical guidelines.

(1) Background: Topical non-steroidal anti-inflammatory drugs (NSAIDs) are one of the primary drugs for treating musculoskeletal pain. However, there are currently no evidence-based recommendations about drug selection, drug administration, drug interactions, and use in special populations or other pharmacology-related content of such medications. To this end, the Chinese Pharmaceutical Association Hospital Pharmacy Professional Committee developed multidisciplinary guidelines on using topical NSAIDs to treat musculoskeletal pain. (2) Methods: The guidelines development process followed the World Health Organization guideline development handbook, the GRADE methodology, and the statement of Reporting Items for Practice Guidelines in Healthcare. The guideline panel used the Delphi method to identify six clinical questions to be addressed in the guidelines. An independent systematic review team conducted a systematic search and integration of evidence. (3) Results: Based on the balance between the benefits and harms of an intervention, the quality of the evidence, patient preferences and values, and resource utilization, the guideline panel developed 11 recommendations and nine expert consensuses on using topical NSAIDs to treat acute and chronic musculoskeletal pain. (4) Conclusions: Based on the effectiveness and overall safety of topical NSAIDs, we recommend patients with musculoskeletal pain use topical NSAIDs and suggest high-risk patients use topical NSAIDs, such as those with other diseases or receiving other concurrent treatments. The evidenced-based guidelines on topical NSAIDs for musculoskeletal pain incorporated a pharmacist perspective. The guidelines have the potential to facilitate the rational use of topical NSAIDs. The guideline panel will monitor the relevant evidence and update the recommendations accordingly.

Previous clinical studies have reported that ultrasound-guided fine needle aspiration cytology (UGFNAC) can be used for the diagnosis of thyroid nodules (TN) effectively. However, no study has systematically explored its diagnosis accuracy in patients with TN. Thus, this study will assess its diagnosis accuracy for TN.

As a complicated metabolic disorder, type 2 diabetes mellitus (T2DM) is becoming a major health concern worldwide. Drugs including acarbose, saxagliptin and vildagliptin are applied, but their efficacy is still required to be compared. Therefore, the study aimed to evaluate the efficacy and safety of acarbose, saxagliptin and vildagliptin in the treatment of T2DM. Ninety patients diagnosed with T2DM were treated with acarbose, saxagliptin and vildagliptin, respectively (30 patients for each drug). All patients were examined at 0, 4 and 12 weeks after treatment with vital signs recorded. Fasting blood glucose and blood biochemical indices were analyzed. In addition, fecal samples were taken for microbial macrogenome sequencing and safety evaluation within 12 weeks after treatment. Blood glucose level decreased at 4 and 12 weeks after treatment, and the total cholesterol (TC) and high-density lipoprotein (HDL) levels at 12 weeks were different. Genus abundance of intestinal flora was altered at different time points. Acarbose increased Butyricimonas level first and then decreased it during drug treatment. Saxagliptin increased Megamonas and decreased Turicibacter genus level gradually. Pseudomonas, Klebsiella, Blautia, Faecalibacterium and Roseburia levels fluctuated after Vildagliptin treatment, which increased fasting C-peptide level greater than the other two drugs. Saxagliptin showed higher adverse reactions than acarbose and vildagliptin. Collectively, acarbose, vildagliptin, and saxagliptin can effectively reduce the HbA1c level and affect the intestinal flora distribution in T2DM patients, and the adverse reactions of acarbose and vildagliptin are less than saxagliptin, providing alternative strategies for the treatment of T2DM.

Maintenance therapy with the poly(ADP-ribose) polymerase (PARP) inhibitor olaparib provided a substantial progression-free survival (PFS) benefit compared with placebo in patients with newly diagnosed advanced ovarian cancer and a BRCA mutation (BRCAm) who were in clinical complete or partial response following platinum-based chemotherapy in the Phase III SOLO1 global study. This led to the approval of maintenance olaparib in China, USA, EU, Japan and other countries, in the newly diagnosed setting. This separate China cohort of the SOLO1 study investigated the efficacy and safety of maintenance olaparib within the Chinese population.

Modifiable risky health behaviors, such as tobacco use, excessive alcohol use, being overweight, lack of physical activity, and unhealthy eating habits, are some of the major factors for developing chronic health conditions. Social media platforms have become indispensable means of communication in the digital era. They provide an opportunity for individuals to express themselves, as well as share their health-related concerns with peers and health care providers, with respect to risky behaviors. Such peer interactions can be utilized as valuable data sources to better understand inter-and intrapersonal psychosocial mediators and the mechanisms of social influence that drive behavior change.

Overly restrictive eligibility criteria for clinical trials may limit the generalizability of the trial results to their target real-world patient populations. We developed a novel machine learning approach using large collections of real-world data (RWD) to better inform clinical trial eligibility criteria design. We extracted patients' clinical events from electronic health records (EHRs), which include demographics, diagnoses, and drugs, and assumed certain compositions of these clinical events within an individual's EHRs can determine the subphenotypes-homogeneous clusters of patients, where patients within each subgroup share similar clinical characteristics. We introduced an outcome-guided probabilistic model to identify those subphenotypes, such that the patients within the same subgroup not only share similar clinical characteristics but also at similar risk levels of encountering severe adverse events (SAEs). We evaluated our algorithm on two previously conducted clinical trials with EHRs from the OneFlorida+ Clinical Research Consortium. Our model can clearly identify the patient subgroups who are more likely to suffer or not suffer from SAEs as subphenotypes in a transparent and interpretable way. Our approach identified a set of clinical topics and derived novel patient representations based on them. Each clinical topic represents a certain clinical event composition pattern learned from the patient EHRs. Tested on both trials, patient subgroup (#SAE=0) and patient subgroup (#SAE>0) can be well-separated by k-means clustering using the inferred topics. The inferred topics characterized as likely to align with the patient subgroup (#SAE>0) revealed meaningful combinations of clinical features and can provide data-driven recommendations for refining the exclusion criteria of clinical trials. The proposed supervised topic modeling approach can infer the clinical topics from the subphenotypes with or without SAEs. The potential rules for describing the patient subgroups with SAEs can be further derived to inform the design of clinical trial eligibility criteria.

Genetic mutations may play an important role in the progression and invasion of thyroid carcinoma (TC), and their coexistence may result in mutational synergy. The presence of the BRAFV600E mutation, as well as mutations affecting the TERT promoter, RAS, CHEK2 and RET/PTC, may all have an impact on prognosis. The aim of this study was to explore whether synergy between the coexistent mutations predicts histopathological prognostic factors that influence disease outcome.

There is a growing interest in the role of immune and inflammatory responses in mental disorders (MDs). Mean platelet volume (MPV) is an extensively utilized hemogram parameter that reflects systemic inflammation and immune function. Our research sought to determine whether a connection exists between MPV and various types of MDs.

This systematic and meta-analytic review aimed to investigate the effects of Tai Chi Chuan (TCC) on the cognitive function of the elderly with cognitive impairment and to analyze the moderators of these effects. We searched eight electronic databases for randomized controlled trials on the effects of TCC on cognitive function, published up to June 14, 2020. The PEDro scale was used to evaluate the methodological quality of the included literature. Stata14.0 software was used for meta-analysis, subgroup analysis, and publication bias testing. A total of 19 studies and 1,970 samples were included. The methodological quality of the included literature was fair to good, and there was no publication bias. Overall, the research shows that the effect of TCC on the elderly with cognitive impairment is statistically significant (SMD = 0.31, p < 0.0001). Five of the cognitive function subdomains were significant moderators [Q (5) = 15.66, p=0.008], and the effect size (ES) was the largest for global cognitive function (SMD = 0.41), followed by executive function (SMD = 0.33), memory (SMD = 0.31), and verbal fluency (SMD = 0.27). Regarding the exercise prescription variables, results were significantly moderated by the length of exercise training [Q (2) = 6.00, p=0.05], with ESs largest for moderate length (SMD = 0.41), followed by short length (SMD = 0.40) and long length (SMD = 0.29). However, the results were not moderated by session time or frequency. TCC can improve multiple cognitive functions of the elderly with cognitive impairment. The intervention effects are moderated by exercise length, but not by exercise session time and frequency.