Tag: nevin manimala

South Med J. 2025 Oct;118(10):657-662. doi: 10.14423/SMJ.0000000000001889.

ABSTRACT

OBJECTIVES: Patients with a history of massive weight loss (MWL) frequently undergo body-contouring surgery such as abdominoplasty, and the safety profile of this procedure is well accepted. The deep inferior epigastric artery perforator (DIEP) flap is a procedure where excess abdominal tissue is used to reconstruct the breast. The abdominal muscles are preserved by isolating the flap on vascular perforators to the abdominal skin and adipose tissue, whereas in abdominoplasty, the same tissue is removed and discarded. In this study, the abdominal-contouring outcomes of patients who underwent DIEP breast reconstruction following MWL were compared with the abdominal contouring outcomes of those who received abdominoplasty following MWL.

METHODS: A propensity-matched retrospective cohort study was performed comparing MWL patients who underwent either DIEP flap breast reconstruction after breast cancer treatments with mastectomy or abdominoplasty. Patients were matched for multiple preoperative variables. Statistical analysis included an independent-samples t test and the Fisher exact test for univariate analysis and multivariate analysis for predictive variables of postoperative complications.

RESULTS: Eighteen patients with a history of MWL who underwent DIEP flaps were matched to 18 patients who underwent abdominoplasty. Patient data for the DIEP cohort were obtained from a database of 314 patients and a total of 484 flaps performed at our institution. Patient data for the abdominoplasty cohort were obtained from a database of 155 patients who underwent abdominoplasty at our institution. Groups differed on body mass index and total body weight loss (P = 0.008 and P = 0.002, respectively), but they did not differ in excess body weight loss (P = 0.094). All abdominoplasty patients and 50% of the DIEP patients had undergone bariatric surgery. Complication rates at the abdominal site were similar between the two groups (DIEP 33% vs body-contouring surgery 39%, P = 0.73).

CONCLUSIONS: Patients with DIEP procedures were found to have abdominal complication rates similar to those who received standard abdominoplasty. This information can be used by plastic surgeons to counsel MWL patients considering DIEP that their chance of a postoperative abdominal complication is similar to abdominal body-contouring procedures.

PMID:41072029 | DOI:10.14423/SMJ.0000000000001889

JMIR Res Protoc. 2025 Oct 10;14:e77224. doi: 10.2196/77224.

ABSTRACT

BACKGROUND: Drug-drug interactions (DDIs) are a major concern, especially for older adults taking multiple medications. Although Health Canada and the US Food and Drug Administration (FDA) use population-based studies to identify adverse drug events, detecting harmful DDIs is challenging due to the millions of potential drug combinations. Traditional pharmacoepidemiologic studies are slow and inefficient, often missing important harmful DDIs.

OBJECTIVE: This protocol outlines a novel approach to efficiently identify harmful DDIs using administrative health care data.

METHODS: Using high-throughput computing, we will conduct multiple population-based, new-user cohort studies using Ontario’s linked administrative health care data. The cohorts will be selected from the population of Ontario residents aged 66 years and older who filled at least one oral outpatient drug prescription from 2002 to 2023. In each cohort, the exposed group will comprise individuals who are regular users of one drug (drug A) who start a new prescription for a second drug (drug B); the referent group will comprise regular users of drug A not taking drug B. We will evaluate 74 acute outcomes within 30 days of cohort entry, including hospitalizations, emergency department visits, and mortality. Propensity score methods will balance exposed and referent groups on more than 400 baseline health characteristics. Modified Poisson and binomial regression models will estimate risk ratios (RRs) and risk differences (RDs). To ensure findings are both statistically and clinically meaningful, we will apply prespecified thresholds for effect sizes (eg, lower bounds of 95% CIs≥1.33 for RRs and ≥0.1% for RDs) and control the false discovery rate at 5% using the Benjamini-Hochberg procedure to address multiplicity. Subgroup and sensitivity analyses, including negative control outcomes and E-values, will assess robustness.

RESULTS: In a preliminary analysis, we identified approximately 3.8 million older adults who filled prescriptions for over 500 unique medications during the study period (2002-2023), and therefore, approximately 200,000 potential drug combinations will be available for study. The initial drug pair cohorts had a median of 583 new users per cohort (IQR 237-2130); the median overlap in drug pair prescriptions was 57 days (IQR 30-90). The protocol was finalized on August 30, 2025, and outlines the analysis of data from 2002 to 2023. The analysis is scheduled to be completed by fall 2026, with results interpreted in 2027. The final manuscript submission is planned for December 2028.

CONCLUSIONS: This study aims to identify credible signals of harmful DDIs in older adults in routine care. This study will use an innovative approach that leverages data from provincial administrative health care databases and integrates high-throughput computing and rigorous pharmacoepidemiologic methods to generate robust real-world evidence that can inform safer prescribing practices and regulatory decision-making.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/77224.

PMID:41072015 | DOI:10.2196/77224

JMIR Form Res. 2025 Oct 10;9:e80027. doi: 10.2196/80027.

ABSTRACT

BACKGROUND: Cognitive difficulties are common and can interfere with daily functioning. While digital cognitive training apps are widely used, few studies have examined whether personalized tools support perceived improvements in cognitive functioning, daily functioning, and overall mental fitness among general adult users.

OBJECTIVE: The purpose of this secondary analysis was to explore the self-reported cognitive benefits of Elevate, a commercial, personalized cognitive training app developed to support cognitive functioning, as well as engagement with the app. We aimed to (1) describe demographics, engagement metrics, and self-reported improvements; (2) examine associations between app engagement and self-reported improvements in cognitive functioning skills directly targeted by the app; and (3) examine associations between app engagement and self-reported improvements in daily functioning and overall mental fitness as potential transfer effects of cognitive training.

METHODS: Adult Elevate users (aged ≥18 years) who used the app at least twice in the previous 30 days completed a brief web-based survey on perceived cognitive, functional, and mental fitness improvements. Responses were linked to objective app use data, including total active weeks, mean active days per week, and mean time per day. Ordinal logistic regressions tested associations between engagement metrics and self-reported outcomes controlling for demographic variables. A Bonferroni correction was applied to adjust for multiple comparisons.

RESULTS: A total of 3367 adult Elevate users were included. Participants were primarily middle-aged (mean 55, SD 16 y), White (2557/3336, 76.65%), and female (2184/3362, 64.96%), with 67.72% (2274/3358) holding at least a college degree. Using the app across more weeks was associated with a greater likelihood of reporting improvements in all cognitive skills (odds ratios [ORs] 1.0014-1.0027, 95% CI 1.0006-1.0036), several areas of daily functioning (eg, motivation and task efficiency; ORs 1.0014-1.0017, 95% CI 1.0006-1.0026), and overall mental fitness (OR 1.0021, 95% CI 1.0012-1.0031). More days of use per week were linked to improvement in math only (OR 1.15, 95% CI 1.09-1.22), whereas spending more time per day was associated with improvements in speaking, reading, math, motivation, personal progress, and mental fitness (ORs 1.02-1.04, 95% CI 1.01-1.06).

CONCLUSIONS: Greater use of the Elevate app was linked to self-reported improvements in cognitive skills, daily functioning, and overall mental fitness. These findings suggest that personalized, adaptive cognitive training apps such as Elevate may serve as scalable tools for enhancing everyday cognitive and functional well-being. Future research should use rigorous, longitudinal methods to confirm these effects and clarify which app features drive meaningful outcomes.

PMID:41072012 | DOI:10.2196/80027

JMIR Med Inform. 2025 Oct 10;13:e71994. doi: 10.2196/71994.

ABSTRACT

BACKGROUND: Machine learning (ML) has shown great potential in recognizing complex disease patterns and supporting clinical decision-making. Diabetic foot ulcers (DFUs) represent a significant multifactorial medical problem with high incidence and severe outcomes, providing an ideal example for a comprehensive framework that encompasses all essential steps for implementing ML in a clinically relevant fashion.

OBJECTIVE: This paper aims to provide a framework for the proper use of ML algorithms to predict clinical outcomes of multifactorial diseases and their treatments.

METHODS: The comparison of ML models was performed on a DFU dataset. The selection of patient characteristics associated with wound healing was based on outcomes of statistical tests, that is, ANOVA and chi-square test, and validated on expert recommendations. Imputation and balancing of patient records were performed with MIDAS (Multiple Imputation with Denoising Autoencoders) Touch and adaptive synthetic sampling, respectively. Logistic regression, support vector machine (SVM), k-nearest neighbors, random forest (RF), extreme gradient boosting (XGBoost), Bayesian additive regression trees, and artificial neural network were trained, cross-validated, and optimized using random sampling on the patient dataset. To evaluate model calibration and clinical utility, calibration curves, Brier scores, and decision curve analysis (DCA) were performed.

RESULTS: The exploratory dataset consisted of 700 patient records with 199 variables. After dataset cleaning, the variables used for model training included age, smoking status, toe systolic pressure, blood pressure, oxygen saturation, hemoglobin, hemoglobin A_1c, estimated glomerular filtration rate, wound location, diabetes type, Texas wound classification, neuropathy, and wound area measurement. The SVM obtained a stable accuracy of 0.853 (95% CI 0.810-0.896) with an area under the receiver operating characteristic curve of 0.922 (95% CI 0.889-0.955). The RF and XGBoost acquired an accuracy of 0.838 (95% CI 0.793-0.883) and 0.815 (95% CI 0.768-0.862), respectively, with areas under the receiver operating characteristic curve of 0.917 (95% CI 0.883-0.951) for RF and 0.889 (95% CI 0.849-0.929) for XGBoost. SVM, RF, and XGBoost were well-calibrated, with average Brier scores around 0.127 (SD 0.013). DCA showed that the SVM provided the highest net clinical benefit across relevant risk thresholds.

CONCLUSIONS: Handling missing values, feature selection, and addressing class imbalance are critical components of the key steps in developing ML applications for clinical research. Seven models were selected for comparing their predictive power regarding complete wound healing, and each model representing a different branch in ML. In this initial DFU dataset used as an example, the SVM achieved the best performance in predicting clinical outcomes, followed by RF and XGBoost. The model’s calibration and clinical utility were determined through calibration curves, Brier scores, and DCA, demonstrating its potential relevance in clinical decision-making.

PMID:41072008 | DOI:10.2196/71994

Online J Public Health Inform. 2025 Oct 10;17:e71720. doi: 10.2196/71720.

ABSTRACT

BACKGROUND: Bacterial vaginosis (BV) is the most common cause of vaginal discharge in people of childbearing age in the United States. More information about what patients do and do not like about the most common BV products and the extent to which they reduce BV symptoms is important for understanding patients’ health and the current treatment landscape for BV.

OBJECTIVE: Using data from online drug review forums, this study’s objectives were to (1) quantitatively characterize the patient voice via sentiments (positive to negative) and emotions about the three most common Food and Drug Administration (FDA)-approved treatments for BV-oral metronidazole (OM), vaginal metronidazole (VM), vaginal clindamycin (VC)-and (2) qualitatively summarize themes characterizing the patient-perceived impact of BV and BV products.

METHODS: Data for this mixed methods descriptive study came from 1645 users’ reviews of BV products posted on WebMD.com and Drugs.com. Reviewer attributes, reviewer-submitted star ratings, and sentiment analysis (SA) using word-emotion association were analyzed with descriptive statistics and bivariate associations. A traditional qualitative analysis using qualitative description was also performed.

RESULTS: Most reviewers were female (n=629, 99.4%), between the ages of 18 and 44 years, and reported using BV products for less than 1 month, though qualitative results suggested most reported recurrent BV infections. Quantitative results revealed reviewers’ preference for vaginal products. The mean star ratings for VC were significantly higher when compared to OM and VM. VC reviews had the highest proportion of positive emotion words compared to OM and VM. Qualitative results for VC supported the quantitative findings: favorable themes related to perceptions of value, effectiveness in alleviating symptoms, and minimal side effects. Additionally, despite some concerns related to the cost of VC, reviewers said they would use the medication again. Other qualitative findings supported BV medical education campaigns for patients and providers on BV treatment.

CONCLUSIONS: Overall, people want a BV treatment that is easy to use, quickly alleviates symptoms, and has minimal side effects. Patients use product reviews to inform their decision-making about BV treatment, ask and seek answers to health-related questions, and share their experiences, presenting a unique opportunity for comprehensive patient education through clinical encounters or public health outreach efforts.

PMID:41072007 | DOI:10.2196/71720

J Invasive Cardiol. 2025 Oct 10. doi: 10.25270/jic/25.00225. Online ahead of print.

ABSTRACT

OBJECTIVES: There are limited data on the use of excimer laser coronary atherectomy (ELCA) in conjunction with other calcium modification devices (intravascular lithotripsy [IVL], rotational/orbital atherectomy [RA/OA]). The aim of this analysis was to compare the use of ELCA as a sole device for coronary intervention with ELCA in combination with additional calcium modification devices.

METHODS: This was a retrospective analysis of all patients treated with ELCA (either as a sole modification device or in conjunction with another calcium modification device) at a single high-volume center. Data and comparisons between ELCA alone and each of the combination therapies (with IVL, with RA/OA, with both IVL and RA/OA) were presented and compared using statistical methods appropriate to the data type.

RESULTS: This analysis included 98 interventions using ELCA (67 as a sole device, 22 with IVL, 6 with RA/OA, 3 with IVL and RA/OA). ELCA alone or in conjunction with IVL were most utilized for in stent restenosis/underexpansion compared with ELCA in conjunction with RA/OA +/-IVL, which were used more frequently for uncrossable/calcified lesions. The frequency of coronary artery perforation across the entire cohort was 4.1%. Target vessel revascularization frequency was 9.2%, and target vessel myocardial infarction was 3.1% at a median of 1051 days, with no statistically significant differences between the device groups.

CONCLUSIONS: ELCA combination therapies have a potential role in certain complex cases and though these are associated with higher risk, they can be safely performed in selected centers using radial access with good medium-term outcomes.

PMID:41071994 | DOI:10.25270/jic/25.00225

JMIR Form Res. 2025 Oct 10;9:e70495. doi: 10.2196/70495.

ABSTRACT

BACKGROUND: The 6-minute walk test (6MWT) measures exercise capacity in cardiorespiratory, neurological, and musculoskeletal conditions. It consists of observing how far a patient can walk in 6 minutes and is usually performed in a corridor in a clinic. During the COVID-19 pandemic, as health care systems cancelled nonurgent outpatient appointments, many tests were conducted online. At Oxford University Hospitals National Health Service Foundation Trust, patients followed up on by cardiovascular outpatient clinics were asked to use the open-source Timed Walk app to perform the 6MWT in their community as a substitute for the regular tests in the clinic.

OBJECTIVE: This study aimed to assess the clinical usefulness of the app within the context of the pandemic.

METHODS: Consented patients were invited to perform a 6MWT outdoors using the app at least once a month and report the results through periodic telephone calls and visits. Clinical decisions made for the same cohort were registered, with a focus on the effect of the app in supporting decision-making. Data collected through the app during the study period were compared with 6MWTs performed in the prepandemic period.

RESULTS: This study was conducted between October 2021 and December 2022. A total of 55 participants consented (n=25, 45% female; mean age 44.80, SD 17.49 y). In total, 741 events were logged. A total of 51 medical decisions were made for 25 patients; in 41% (21/51) of the decisions, the app played a role, affecting 44% (11/25) of the patients. Between 2018 and 2022, a cohort of 49 patients for whom data were available performed 63 6MWTs in the clinic (18 in 2021), whereas the same patients performed 605 tests using the app in 2022 (ie, October 2021 to December 2022).

CONCLUSIONS: The use of the Timed Walk app for remote 6MWTs allowed clinicians to obtain frequent and objective indications of the status of the patients during the pandemic, compensating for the absence of regular clinic appointments and providing 33 times more tests than in the prepandemic period. These tests supported approximately half of the clinical decisions made regarding the consented patients, showing that the app is useful in clinical practice.

PMID:41071986 | DOI:10.2196/70495

JMIR Diabetes. 2025 Oct 10;10:e68948. doi: 10.2196/68948.

ABSTRACT

BACKGROUND: Exercise is an important aspect of diabetes self-management. Patients with type 1 diabetes frequently struggle with exercise-induced hyperglycemia and hypoglycemia, decreasing their willingness to exercise.

OBJECTIVE: We aim to build accurate and easy-to-deploy models to forecast exercise-induced glycemic events in real-world settings.

METHODS: We analyzed free-living data from the Type 1 Diabetes Exercise Initiative study, where adults with type 1 diabetes wore a continuous glucose monitor (CGM) while performing video-guided exercises (30-minute exercises at least 6 times over 4 weeks), along with concurrent detailed phenotyping of their insulin program and diet. We built models to predict glycemic events (blood glucose ≤54 mg/dL, ≤70 mg/dL, ≥200 mg/dL, and ≥250 mg/dL) during and 1 hour post exercise with variables from 4 data modalities, such as demographic and clinical (eg, glycated hemoglobin; CGM (blood glucose value and their summary statistics); carbohydrate intake and insulin administration; and exercise type, duration, and intensity. We used repeated stratified nested cross-validation for model selection and performance estimation. We evaluated the relative contribution of the 4 input data modalities for predicting glycemic events, which informs the cost and benefit for including them in the decision support tool for risk prediction. We also evaluated other important aspects related to model translation into decision support tools, including model calibration and sensitivity to noisy inputs.

RESULTS: Our models were built based on 1901 exercise episodes for 329 participants. The median age for the participants was 34 (IQR 26-48) years. Of the participants, 74.8% (246/329) are female and 94.5% (311/329) are White. A total of 182/329 (55.3%) participants used a closed-loop insulin delivery system, while the rest used a pump without a closed-loop system. Models incorporating information from all 4 data modalities showed excellent predictive performance with cross-validated area under the receiver operating curves (AUROCs) ranging from mean 0.880 (SD 0.057) to mean 0.992 (SD 0.001) for different glycemic events. Models built with CGM data alone have statistically indistinguishable performance compared to models using all data modalities, indicating the other 3 data modalities do not add additional information with respect to predicting exercise-related glycemic events. The models based solely on CGM data also showed outstanding calibration (Brier score ≤0.08) and resilience to noisy input.

CONCLUSIONS: We successfully constructed models to forecast exercise-induced glycemic events using only CGM data as input with excellent predictive performance, calibration, and robustness. In addition, these models are based on automatically captured CGM data, thus easy to deploy and maintain and incurring minimal user burden, enabling model translation into a decision support tool.

PMID:41071985 | DOI:10.2196/68948

JMIR Mhealth Uhealth. 2025 Oct 10;13:e72372. doi: 10.2196/72372.

ABSTRACT

BACKGROUND: Head and neck cancer (HNC) survivors face profound functional and quality-of-life deficits due to disease- and treatment-related sequelae, ranging from mild fatigue to debilitating dysphagia. Wearable technology, by monitoring biometric data such as step counts or providing swallowing biofeedback, offers a unique method for tracking and monitoring the negative effects of HNC.

OBJECTIVE: The aim of this study was to explore the current applications of wearable technology in HNC.

METHODS: A scoping review was conducted following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. A search strategy was built, and a literature search was performed across 5 databases. The initial search yielded 5256 studies, which underwent a 2-phase screening process: title and abstract review followed by full-text review. Inclusion criteria included peer-reviewed, English-language articles published between January 2002 and April 2024 that used wearable technology in HNC care. After full-text review, 9 studies met the inclusion criteria. Data were manually extracted and synthesized narratively.

RESULTS: The included studies examined 3 main types of wearable devices: radioactivity (2 studies), physical activity (4 studies), and throat physiology monitors (3 studies). Radioactivity monitors detected residual radioactivity and thyroidal radioiodine uptake. They demonstrated potential to reduce radioactivity exposure risk and personalize radiation doses for patients with thyroid cancer. Physical activity monitors tracked step counts, heart rate, and sleep habits. Low step counts were significantly associated with increased anxiety, radiation-related toxicity, hospital admission rates, and feeding tube placement. One study also linked poor sleep patterns to declines in quality of life. Throat physiology monitors measured pharyngeal electromyography data as well as extrinsic laryngeal muscle movements. Throat sensors achieved high accuracy in classifying swallowing events and translating muscle movements into speech. While earliest in the development continuum, they are promising tools for swallowing and vocal rehabilitation therapy. Barriers to wearable adoption included wearable discomfort, technical difficulties, and patient withdrawal due to treatment side effects. As the definition of wearable adherence varied widely, we propose that future studies report wearable adherence as “percentage of prescribed wear time achieved” to facilitate cross-study comparisons.

CONCLUSIONS: Wearable technology may enhance treatment monitoring, prognostication, and rehabilitation in head and neck oncology. Radioactivity and physical activity monitors provide actionable insights for clinical decision-making, while throat physiology monitors offer innovative solutions for speech and swallowing therapy. However, challenges such as device adherence, data integration, and patient comfort must be addressed to realize their full potential. Future research should prioritize larger, longitudinal studies, standardized adherence metrics, and consider the integration of artificial intelligence to refine predictive capabilities. By overcoming these barriers, wearable technology could transform survivorship care, improving functional outcomes and quality of life for patients with HNC.

PMID:41071984 | DOI:10.2196/72372

J Med Internet Res. 2025 Oct 10;27:e73142. doi: 10.2196/73142.

ABSTRACT

BACKGROUND: Stroke frequently leads to various functional impairments. Both virtual reality (VR) and mirror therapy (MT) have shown efficacy in stroke rehabilitation. In recent years, the combination of these 2 approaches has emerged as a potential treatment for patients with stroke.

OBJECTIVE: This study aimed to evaluate the efficacy of combined immersive and nonimmersive VR with MT in stroke rehabilitation.

METHODS: Five electronic databases were systematically searched for relevant papers published up to January 2025. Randomized controlled trials (RCTs) that investigated the combination treatment of VR and MT for patients with stroke were included. A gray literature search was also conducted. The risk of bias and the certainty of the evidence were assessed using the Cochrane Collaboration’s tool and the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) guidelines, respectively.

RESULTS: A total of 475 patients from 14 RCTs were included, of which 7 were eligible for meta-analysis. Meta-analysis revealed significant improvements in upper extremity (UE) motor function and hand dexterity, as evidenced by the Fugl-Meyer Assessment-Upper Extremity (FMA-UE; mean difference, MD 3.50, 95% CI 1.47 to 5.53; P=.<001), the manual function test (MD 2.15, 95% CI 1.22 to 3.09; P<.001), and the Box and Block Test (MD 1.09, 95% CI 0.14 to 2.05; P=.03). Subgroup analyses based on disease duration (>6 months or not) revealed significant differences in the FMA-UE outcome. However, the pooled FMA-UE improvement did not consistently exceed the established minimal clinically important difference (4.25-7.25), indicating that while statistically significant, the clinical significance of the observed effect remains uncertain. Narrative evidence also suggested potential benefits for lower extremity function, dynamic balance, and quality of life, though these findings were not meta-analyzed and should be interpreted with caution.

CONCLUSIONS: Moderate-quality evidence supports combined VR and MT as a promising nonpharmacological intervention to improve upper extremity function and hand dexterity in stroke rehabilitation. While the intervention demonstrates statistically significant effects, it does not reach the minimum clinically important difference for the FMA-UE outcome. Preliminary descriptive evidence indicates possible advantages for lower extremity function, balance, and quality of life.

PMID:41071983 | DOI:10.2196/73142