Nevin Manimala

J Am Acad Orthop Surg Glob Res Rev. 2025 Nov 12;9(11). doi: 10.5435/JAAOSGlobal-D-25-00319. eCollection 2025 Nov 1.

ABSTRACT

BACKGROUND: Anterior cruciate ligament (ACL) reconstruction is a common surgery, following which pain control medications are often prescribed. In recent years, efforts have been made to minimize opioids and other nonnarcotic medications as multimodal regimens evolve following such surgeries.

METHODS: Opioid-naïve ACL reconstruction patients were identified from the PearlDiver M165Ortho data set. Those with a history of substance abuse were excluded. Prescriptions of pain management medications were evaluated in the 90 days following surgery per 1000 ACL reconstructions and grouped into the following categories: opioids, benzodiazepines, NSAIDs, serotonin norepinephrine reuptake inhibitor/tricyclic antidepressant/antiepileptic, tramadol, gabapentinoid, and nonbenzodiazepine muscle relaxant.Trends for annual prescriptions and morphine milligram equivalents were defined. Multivariable analysis was performed to determine factors independently associated with narcotic prescriptions.

RESULTS: A total of 101,331 ACL reconstruction patients met study inclusion criteria. In the 90 days following surgery, opioid prescriptions decreased from 402.7 per 1,000 ACL reconstructions in 2010 to 153.5 in 2021 (-61.9%). Prescriptions of other pain management drugs on aggregate decreased from 298.0 in 2010 to 129.8 in 2021 (-56.4%). Among patients who received opioids in the 90 days postoperatively, morphine milligram equivalents prescribed per 1000 ACL reconstructions decreased from 277,941 in 2010 to 39,640 in 2021 (-85.7%).On multivariate analysis, the strongest predictors of postoperative opioid prescriptions were younger age (odds ratio [OR] 1.30 per decade decrease, P < 0.0001), male sex (relative to female, OR 1.39, P < 0.0001), patient comorbidity (per two-point decrease in Elixhauser Comorbidity Index, OR 1.25, P < 0.0001), and region of the country where surgery was performed (relative to west, Northeast OR 1.20, South OR 1.22, Midwest OR 1.41, P = 0.0006, P = 0.0026, P = 0.0002, respectively). Neither having the use of regional nerve blocks nor having multiple concomittent knee procedures affected postoperative opioid prescriptions.

CONCLUSION: Fewer prescriptions of both narcotic and nonnarcotic medications following ACL reconstruction had been written over the years from 2010 to 2021, likely in favor of nonprescription over-the-counter analgesics including NSAIDs and acetaminophen. There may be opportunities to further reduce opioid prescribing following ACL reconstruction, particularly among patients receiving regional nerve blocks or those undergoing isolated ACL reconstruction.

PMID:41237369 | DOI:10.5435/JAAOSGlobal-D-25-00319

JMIR Med Inform. 2025 Nov 14;13:e79307. doi: 10.2196/79307.

ABSTRACT

BACKGROUND: Missing data are a common challenge in electronic health record (EHR)-based prediction modeling. Traditional imputation methods may not suit prediction or machine learning models, and real-world use requires workflows that are implementable for both model development and real-time prediction.

OBJECTIVE: We evaluated methods for handling missing data when using EHR data to build clinical prediction models for patients admitted to the pediatric intensive care unit (PICU).

METHODS: Using EHR data containing missing values from an academic medical center PICU, we generated a synthetic complete dataset. From this, we created 300 datasets with missing data under varying mechanisms and proportions of missingness for the outcomes of (1) successful extubation (binary) and (2) blood pressure (continuous). We assessed strategies to address missing data including simple methods (eg, last observation carried forward [LOCF]), complex methods (eg, random forest multiple imputation), and native support for missing values in outcome prediction models.

RESULTS: Across 886 patients and 1220 intubation events, 18.2% of original data were missing. LOCF had the lowest imputation error, followed by random forest imputation (average mean squared error [MSE] improvement over mean imputation: 0.41 [range: 0.30, 0.50] and 0.33 [0.21, 0.43], respectively). LOCF generally outperformed other imputation methods across outcome metrics and models (mean improvement: 1.28% [range: -0.07%, 7.2%]). Imputation methods showed more performance variability for the binary outcome (balanced accuracy coefficient of variation: 0.042) than the continuous outcome (mean squared error coefficient of variation: 0.001).

CONCLUSIONS: Traditional imputation methods for inferential statistics, such as multiple imputation, may not be optimal for prediction models. The amount of missingness influenced performance more than the missingness mechanism. In datasets with frequent measurements, LOCF and native support for missing values in machine learning models offer reasonable performance for handling missingness at minimal computational cost in predictive analyses.

PMID:41237368 | DOI:10.2196/79307

JMIR Infodemiology. 2025 Nov 14;5:e74317. doi: 10.2196/74317.

ABSTRACT

BACKGROUND: A high prevalence of dry eye disease (DED) has intensified public health concerns in Taiwan. With the growing reliance on online resources for health information, platforms such as Google Trends (GT) provide a valuable method for capturing public interest. This approach also allows for the exploration of potential associations between public interest in DED and environmental parameters, which may further elucidate underlying factors contributing to the disease’s rising prevalence.

OBJECTIVE: This study aims to (1) analyze public interest in DED in Taiwan using GT data, (2) investigate correlations between search interest and environmental parameters, and (3) identify shifts in the focus of search over time.

METHODS: We analyzed GT data from December 2018 to July 2024, focusing on relative search volume (RSV) for DED across Taiwan and its 6 special municipalities. Temporal trends in RSV were assessed using spline regression models, and monthly variations were assessed using the Kruskal-Wallis test. The Spearman correlation analysis was used to evaluate the association between RSV and environmental parameters, while dynamic time warping analysis clarified the temporal alignment of RSV with these parameters. Rising search queries were analyzed to identify shifts in public interest over time. Furthermore, top Google search results for DED-related keywords were assessed for topic coverage, quality, and readability.

RESULTS: A significant rising trend in RSV for DED was observed over the study period in Taiwan (mean instantaneous derivative=0.445; P<.001) and across all 6 special municipalities. Environmental parameters such as methane (CH4), total hydrocarbons, and nonmethane hydrocarbons were identified as novel pollutants strongly correlated with RSV (P<.001), along with known pollutants such as nitric oxide (NO), nitrogen dioxide (NO2), sulfur dioxide (SO2), nitrogen oxides (NOx), and carbon monoxide (CO). Dynamic time warping analysis revealed the strongest temporal alignment was between RSV and hydrocarbons, including CH4 and total hydrocarbons, further emphasizing their potential role in influencing public interest. Assessment of web-based DED information of 80 websites revealed generally low quality (DISCERN score: mean 2.14, SD 0.40), and the average readability corresponded to a college reading level (grade: mean 21.1, SD 4.5). Rising search queries shifted from diagnostic and treatment methods before the COVID-19 pandemic to natural remedies during the COVID-19 lockdown and self-diagnosis and treatment options after the pandemic. Gaps were also identified between public interest and the availability of online information.

CONCLUSIONS: Public interest in DED has increased significantly in Taiwan from 2018 to 2024, with hydrocarbons identified as strongly associated environmental parameters. The shifts in related queries reflect changing public interest, accentuating the need for health care information that aligns with public interest and addresses gaps in available resources.

PMID:41237348 | DOI:10.2196/74317

JMIR Cardio. 2025 Nov 14;9:e79239. doi: 10.2196/79239.

ABSTRACT

BACKGROUND: The management of heart failure (HF) requires complex, data-driven decision-making. Although electronic health record (EHR) systems and clinical decision support (CDS) tools can streamline access to essential clinical information, it remains unclear which EHR elements and tools cardiologists and general medicine physicians prioritize when caring for patients with HF.

OBJECTIVE: This study aims to identify these elements and tools to improve the user interface design of future EHR applications.

METHODS: This study used a user-centered design research approach to understand physician workflows and decision-making needs in HF care. A cross-sectional online survey was administered to 302 physicians, comprising 150 cardiologists (including 15 HF specialists) and 152 general medicine physicians. Respondents reported their use of EHR variables (eg, medication lists, laboratory results, diagnostic tests, problem lists, clinical notes) for decision-making in HF care, as well as their time spent in the EHR before, during, and after patient visits along with their use of predictive models and patient-reported outcome questionnaire. Descriptive analyses, χ2 tests, and t tests were conducted to compare groups, with statistical significance set at P<.05.

RESULTS: A total of 302 health care providers participated in the survey, nearly evenly split between cardiologists (49.7%, 150/302) and general medicine physicians (50.3%, 152/302). Both groups consistently relied on medication lists, vital signs, laboratory results, diagnostic tests, problem lists, and clinical notes for HF decision-making. Cardiologists placed greater emphasis on diagnostic tests for inpatient HF care (mean [SD] overall frequency, 4.66 [0.50] vs 4.44 [0.64]; P=.012) and outpatient HF care (mean [SD] overall frequency, 4.67 [0.55] vs 4.35 [0.71], P<.001). In contrast, general medicine physicians relied more on problem lists for inpatient HF care (mean [SD] overall frequency, 4.63 [0.58] vs 4.43 [0.72], P=.034), with no significant difference in the outpatient setting (P>.05). Both groups underutilized standardized questionnaires and predictive models, with only 20.1% (29/144) of cardiologists and 4.5% (6/133) of general medicine physicians using standardized questionnaires (P<.001).

CONCLUSIONS: Both physician groups depend on medication lists, laboratory results, diagnostic tests, and problem lists. Cardiologists prioritize diagnostic tests, whereas general medicine physicians more often use problem lists. Low use of questionnaires and predictive models highlights the need for better integration of these tools. Future EHR design interface should tailor functionalities to accommodate these differing priorities and optimize HF care.

PMID:41237334 | DOI:10.2196/79239

JMIR Public Health Surveill. 2025 Nov 14;11:e78728. doi: 10.2196/78728.

ABSTRACT

BACKGROUND: The COVID-19 pandemic has significantly affected the mental health of older adults, particularly through increased loneliness and psychological distress. While various contributing factors have been studied, the role of preference for solitude as a potential predictor and mediator remains poorly understood.

OBJECTIVE: This national cross-sectional study aimed to examine predictors of loneliness and psychological distress among older adults during the pandemic, with a specific focus on preference for solitude and its mediating role between pandemic-specific stressors and self-efficacy.

METHODS: A total of 2053 Croatian residents aged 65 years and older were recruited using snowball sampling. Validated instruments were used, including the UCLA Loneliness Scale, Preference for Solitude Scale, Clinical Outcomes in Routine Evaluation-Outcome Measure, General Self-Efficacy Scale, and Pandemic-Specific Stressors Questionnaire for Older Adults. Hierarchical regression and path analysis were used, with statistical significance set at P<.05.

RESULTS: For loneliness, the final model explained 30.7% of the variance, with a coefficient of determination (R²) of 0.307 and a root mean square error of 0.708 (P<.001). Significant predictors included marital status (eg, never married: B=0.390, P<.001; psychological problems: B=0.020, P<.001; functionality: B=-0.037, P<.001; and social distancing: B=0.014, P<.001). Preference for solitude was also a significant predictor of loneliness (B=0.011; P<.001). For psychological distress, the final model explained 30.8% of the variance (R²=0.308; root mean square error=14.872; P<.001). Self-efficacy emerged as the strongest negative predictor of distress (B=-1.066; P<.001), whereas preference for solitude was a positive predictor (B=2.403; P<.001). The variable “spending several hours alone per day” was associated with lower levels of distress (B=-3.509; P=.003), while “frequent or superficial interactions with acquaintances (eg, at least once a week)” were related to higher distress (B=4.321, P=.002). Path analysis revealed that both social distancing and exposure to infection had a significant direct effect on self-efficacy: negative in the case of social distancing (β=-.391; P<.001) and positive for exposure to infection (β=.386; P<.001). However, preference for solitude did not significantly mediate either relationship, as indicated by nonsignificant indirect effects (β=-.006; P=.09 and β=.002; P=.48, respectively).

CONCLUSIONS: Psychological problems and reduced functionality emerged as the strongest predictors of loneliness among older adults during the pandemic. Self-efficacy was the most important protective factor against psychological distress. Although preference for solitude may have adaptive benefits, in the context of this study, it was associated with increased loneliness and distress during enforced isolation. These findings suggest that public health interventions should balance respect for individual preferences with the provision of active support for vulnerable populations during crises.

PMID:41237326 | DOI:10.2196/78728

Br J Radiol. 2025 Nov 14:tqaf276. doi: 10.1093/bjr/tqaf276. Online ahead of print.

ABSTRACT

OBJECTIVES: The aim of this study was to explore whether PET/MR-derived parameters would classify hypopharyngeal squamous cell carcinoma (HSCC) by p53 status.

METHODS: Patients with HSCC were prospectively enrolled and underwent neck integrated PET/MR imaging. Patients were classified into p53-mutated group and non-p53-mutated group based on P53 immunohistochemistry. PET/MR-derived parameters were measured by two evaluators and then compared using Mann-Whitney U nonparametric test. Receiver operating characteristic (ROC) curves and areas under the curve (AUCs) were used to determine the efficacies of those parameters with statistical differences for predicting p53 status in HSCC. P < 0.05 was statistically significant.

RESULTS: This study enrolled 21 patients with biopsy-proven HSCC (p53-mutated group [n = 12], non-p53-mutated group [n = 9]). Good-excellent interobserver agreement for measurements was observed with intraclass correlation coefffcients (ICCs) ranging from 0.844 to 0.999. Statistically significant differences were observed in maximum standardized uptake value (SUVmax) and minimal apparent diffusion coefficient value (ADCmin) between two groups. AUCs for SUVmax and ADCmin were 0.787 and 0.801, respectively. The combined use of both yielded a maximum AUC of 0.898.

CONCLUSIONS: SUVmax and ADCmin can serve as independent biomarkers to predict p53 status in HSCC. Furthermore, SUVmax combined ADCmin holds promise for improved predictive performance.

ADVANCES IN KNOWLEDGE: SUVmax and ADCmin derived from PET/MR show promise in predicting p53 status in HSCC.

PMID:41237313 | DOI:10.1093/bjr/tqaf276

JBJS Rev. 2025 Nov 14;13(11). doi: 10.2106/JBJS.RVW.25.00131. eCollection 2025 Nov 1.

ABSTRACT

BACKGROUND: Sleep disturbances are common after total joint arthroplasty and can impair recovery, increase complications, and reduce patient satisfaction. Nonpharmacological interventions (NPIs) may offer safer alternatives to medications, but their effectiveness in improving postoperative sleep remains unclear. The aim of this study was to systematically evaluate the impact of NPIs on sleep outcomes following hip or knee arthroplasty.

METHODS: We conducted a systematic review in November 2024 across PubMed, Scopus, Web of Science, and Embase for studies investigating NPIs related to sleep outcomes after hip or knee arthroplasty. Data extraction and quality assessment were performed independently using the National Institutes of Health tools. A meta-analysis was conducted on studies reporting Pittsburgh Sleep Quality Index scores, and the mean differences (MDs) with 95% confidence intervals (CIs) were calculated. Heterogeneity was assessed by the Cochran Q statistic and the I2 test.

RESULTS: Ten studies (n = 1,545; mean age 65.69 years, 54.43% female) were included. NPIs were categorized into nursing-based interventions, environmental controls, relaxation techniques, neuromodulation, and movement restriction. The pooled analysis of 6 studies (n = 760) showed that NPIs significantly improved sleep quality compared with controls (MD = -2.61; 95% CI -3.27 to -1.95; p < 0.00001; I2 = 86%). Subgroup analysis revealed the greatest benefit from nursing-based interventions (MD = -3.06; 95% CI -3.39 to -2.73; I2 = 30%), while environmental interventions showed a smaller but significant effect (MD = -1.58; 95% CI -2.75 to -0.40; I2 = 79%). Functional, psychological, and quality-of-life outcomes showed variable results across studies.

CONCLUSION: NPIs, particularly nursing-based interventions and environmental controls, appear effective in improving postoperative sleep after joint arthroplasty. However, heterogeneity and limited high-quality evidence warrant further randomized trials with standardized protocols and objective sleep measures.

LEVEL OF EVIDENCE: Therapeutic Level III. See Instructions for Authors for a complete description of levels of evidence.

PMID:41237292 | DOI:10.2106/JBJS.RVW.25.00131

JMIR Cancer. 2025 Nov 14;11:e67911. doi: 10.2196/67911.

ABSTRACT

BACKGROUND: Cutting-edge oncology care often depends on patients’ ability to use rapidly evolving health technology. Digital health literacy (DHL; the capacity to understand health-related information with electronic media) is an emerging, yet underexplored social determinant of health in patients with cancer.

OBJECTIVE: We aimed to characterize sociodemographic and clinical factors associated with DHL in patients with cancer and explore whether a single-item screener could be derived from a widely-used DHL questionnaire to detect low DHL.

METHODS: Patients (N=105) who received systemic treatment in the past year for colorectal carcinoma (CRC) or non-Hodgkin lymphoma (NHL) were recruited through collaborating clinics. Participants self-reported DHL using the eHealth Literacy Scale (eHEALS). They also reported general health literacy and sociodemographic and clinical characteristics. Correlations and group comparisons (independent sample t tests and χ2 tests, as appropriate) were used to evaluate links between DHL and sociodemographic and clinical characteristics. Receiver operating characteristic (ROC) curve analysis was used to determine whether a single eHEALS item could effectively screen for low DHL (eHEALS score ≤20).

RESULTS: Patients with a lower education level (Spearman ρ=0.29; P=.004) and lower general health literacy (r=0.25; P=.009) had lower DHL. Patients with NHL reported lower DHL than those with CRC (t103=2.72; P=.008). Additionally, the subset of patients who reported participation in a clinical trial (n=10) exhibited lower DHL than nonparticipants (t100=3.08; P=.003). Other sociodemographic and clinical characteristics were not significantly associated with DHL (all P>.21). The ROC curve analysis showed that eHEALS item 4 (“I know where to find helpful health resources on the Internet”) was a strong predictor of high versus low DHL (area under the curve=0.975, 95% CI 0.949-1.00; P<.001).

CONCLUSIONS: In this convenience sample, DHL varied based on cancer type, education level, general health literacy, and clinical trial participation. Furthermore, we found that a single item from the eHEALS has strong potential for identifying those with low DHL. These findings may inform which patients have higher need for or may benefit from DHL interventions and suggest avenues for detecting low DHL in oncology clinics.

PMID:41237283 | DOI:10.2196/67911

JMIR Mhealth Uhealth. 2025 Nov 14;13:e72009. doi: 10.2196/72009.

ABSTRACT

BACKGROUND: Canada, Mexico, and the United States are primary transit destinations for migrants in the Western Hemisphere. Migrants face barriers to accessing health services, including HIV and AIDS and sexually transmitted infection (STI) prevention. Mobile apps may enhance public health access for these populations.

OBJECTIVE: This study aims to systematically identify and evaluate mobile apps supporting HIV and STI prevention in Canada, Mexico, and the United States.

METHODS: An environmental scan of 357 mobile apps from the Google Play and Apple App stores was conducted on June 18, 2024, following the rigorous 6-step framework proposed by Fernández-Sánchez to ensure a systematic and comprehensive evaluation of apps for HIV and STI prevention. Predefined inclusion and exclusion criteria were applied, resulting in 6 eligible apps. Each app was assessed using the 29-item Mobile App Rating Scale (MARS), scored on a 5-point Likert scale (1=inadequate, 5=excellent), and categorized as high (3), medium (2), or low (1) based on mean scores. Internal consistency was excellent (Cronbach α=0.90), and interrater reliability demonstrated near-perfect agreement (Cohen κ=0.862). Data analyses were performed using SPSS (version 27; IBM Corp).

RESULTS: All 6 apps were available in Canada, Mexico, and the United States, with 33.3% (2/6) from Google Play, 16.7% (1/6) from Apple, and 50% (3/6) from both platforms. MARS evaluation revealed high quality ratings for engagement (83.0%), functionality (88.9%), aesthetics (83.3%), and information quality (100%), as well as high subjective quality (83.3%) and app-specific quality (88.9%). Life4Me+ was the highest-rated app (4.6), while HIV-TEST received the lowest rating (3.4). Most apps (5/6, 83.3%) were only available in English, and 16.7% (1/6) supported multiple languages, which may limit accessibility for non-English-speaking migrant populations. In addition, 83.3% (5/6) were updated in 2024, 33.3% (2/6) were linked to nongovernmental organization, 16.7% (1/6) to a university, and 50% (3/6) had no clear affiliation. Regarding their focus, 50% (3/6) addressed STI prevention, diagnosis, and treatment, 16.7% (1/6) combined HIV and STI prevention, and 33.3% (2/6) provided pre-exposure prophylaxis-related resources.

CONCLUSIONS: These 6 apps stand out for their high functionality, engagement, and accessibility, establishing themselves as effective tools for HIV and STI prevention education among migrant populations. This study highlights the critical role of digital resources in addressing public health challenges faced by vulnerable and minority groups. Integrating these apps into health promotion strategies is essential to improve health literacy and encourage preventive behaviors. Moreover, ensuring the quality, credibility, linguistic diversity, and continuous updating of these digital interventions is crucial to achieving a real and sustained impact on public health. Policies should promote clear standards that guarantee accessibility, transparency, and accuracy, thereby facilitating access to health care services in complex migratory contexts.

PMID:41237280 | DOI:10.2196/72009

JMIR Mhealth Uhealth. 2025 Nov 14;13:e67721. doi: 10.2196/67721.

ABSTRACT

BACKGROUND: The health impact of summer heat on older adults is a growing public concern, yet the physiological responses, particularly changes in resting heart rate (RHR), and the role of personal heat adaptation behaviors remain underexplored. Wearable devices offer an opportunity to objectively monitor physiological responses and evaluate the effectiveness of adaptation strategies in real-world settings.

OBJECTIVE: This study aimed to quantify the short-term association between summer temperatures and RHR in older adults and to examine how individual heat adaptation behaviors modify this relationship, with additional consideration of personal characteristics such as age, sex, BMI, and chronic disease status.

METHODS: We conducted a panel study among 83 community-dwelling older adults (≥65 y) in Taipei City during the summer of 2021 (May to September). Participants wore Garmin smartwatches to continuously monitor heart rate. Daily RHR was defined as the lowest 30-minute average heart rate. In September, heat adaptation behaviors were assessed via structured telephone interviews. Ambient temperature and relative humidity were obtained from a nearby monitoring station. Linear mixed-effect models were used to estimate temperature-RHR associations, and interaction terms were included to examine behavioral modifications. Subgroup analyses were conducted to explore effect modification by individual characteristics such as age, sex, BMI, and chronic disease status.

RESULTS: Each 1 °C increase in daily mean temperature over lag days 0-1 was associated with a 0.11 (95% CI 0.07-0.15; P<.001) beats/min increase in RHR. After mutual adjustment for behaviors, several heat adaptation strategies showed significant protective effects, including reducing physical activity (β=-.15, P=.001), drinking cold beverages (β=-.24, P<.001), increasing naps or sleep duration (β=-.28, P=.003), drinking additional water ≥500 mL (β=-.10, P=.02), using air conditioner (AC) before (β=-.15, P=.002) and during sleep (β=-.13, P=.007), and using electric fans during sleep (β=-.12, P=.01). Subgroup analyses revealed stronger effects for certain behaviors in vulnerable populations: reduced physical activity was particularly beneficial for those with higher BMI; AC use and cold beverage intake were more effective in people with diabetes; increased naps yielded the largest benefits in individuals with hypertension; and the use of AC or fans during sleep was especially protective for older adults and females.

CONCLUSIONS: Summer heat is associated with elevated RHR in older adults, but this effect can be mitigated through targeted heat adaptation behaviors. Smartwatch monitoring provides a feasible and informative approach for capturing physiological changes, supporting the development of personalized heat-health recommendations for aging populations in a warming climate.

PMID:41237278 | DOI:10.2196/67721