Tag: pubmed

Contemp Clin Trials Commun. 2024 Nov 4;42:101388. doi: 10.1016/j.conctc.2024.101388. eCollection 2024 Dec.

ABSTRACT

This paper proposes a platform trial for conducting A/B tests with multiple arms and interim monitoring to investigate the impact of several factors on the expected sample size and probability of early stopping. We examined the performance of three stopping boundaries: O’Brien Fleming (OBF) stopping for either futility or difference (both), Pocock stopping for futility only, and fixed sample size design. We simulated twelve scenarios of different orders of arms based on various effect sizes, as well as considered 1 or 3 interim looks. The overall findings are summarizing in a flowchart to provide intuitive guidance for the design of the platform based on the simulation. We found that it is better to use OBF stopping for both if there is any effective variant and the trial is sufficiently powered to detect the expected effect size. If the study is underpowered to detect a difference, we recommend fixed sample size design to gather as much information as possible, however if the expected sample size is important to minimize, we recommend using Pocock boundaries with futility monitoring. Our results aimed at helping high-tech companies conduct their own studies without requiring extensive knowledge of clinical trial design and statistical methodology.

PMID:39611005 | PMC:PMC11602995 | DOI:10.1016/j.conctc.2024.101388

J Med Internet Res. 2024 Nov 11. doi: 10.2196/59921. Online ahead of print.

ABSTRACT

BACKGROUND: An increasing aging population, accompanied by a shortage of residential aged care homes and workforce, and consumer feedback, has driven a growing interest in enabling older people to age in place through home-based care. In this context, smart home technologies for remote health monitoring have gained popularity in supporting older people to live in their own homes.

OBJECTIVE: This study aims to investigate the impact of smart home monitoring on multiple outcomes, including quality of life, activities of daily living, and depressive symptoms among older people living in their own homes over a 12-month period.

METHODS: We conducted an open-label parallel-group randomized controlled trial. The control group continued to receive their existing care from aged care service providers. Meanwhile the intervention group, in addition to receiving their usual aged care services, had their activities of daily living monitored using a smart home platform. Surveys including Adult Social Care Outcomes Toolkit (ASCOT), EuroQol-5 Dimensions-5 Levels (EQ-5D-5L), Katz Index of Independence in Activities of Daily Living (Katz ADL), Lawton Instrumental Activities of Daily Living Scale (IADL), and Geriatric Depression Scale (GDS) were conducted at baseline, 6 and 12 months from baseline. Linear mixed-effects models were used to compare the difference between the intervention and control groups, with the ASCOT as the primary outcome measure.

RESULTS: The data from 130 participants were used in the analysis, with no significant differences in baseline characteristics between the control group (n = 61) and the intervention group (n = 69). In comparison to the control group, the intervention group had a higher ASCOT score at the 6-month assessment (mean difference: 0.045; 95% CI: 0.001 to 0.089; Cohen d: 0.377). However, this difference did not persist at the 12-month assessment (mean difference: 0.031; 95% CI: -0.014 to 0.076; Cohen d: 0.259). There were no significant differences in EQ-5D-5L, Katz ADL, IADL, and GDS observed between the intervention and control groups at the 6-month and 12-month assessments.

CONCLUSIONS: The study demonstrates that smart home monitoring can improve social care-related quality of life for older people living in their own homes. However, the improvement was not sustained over the long term. The lack of statistically significant findings and diminished long-term improvements may be attributed to the influence of the COVID-19 pandemic during the later stage of the trial. Further research with a larger sample size is needed to evaluate the effect of smart home monitoring on broader quality of life measures.

CLINICALTRIAL: Australian New Zealand Clinical Trials Registry ACTRN12618000829213; https://tinyurl.com/2n6a75em.

INTERNATIONAL REGISTERED REPORT: RR2-10.2196/31970.

PMID:39608020 | DOI:10.2196/59921

Hum Factors. 2024 Nov 28:187208241302475. doi: 10.1177/00187208241302475. Online ahead of print.

ABSTRACT

OBJECTIVE: This study aims to compare the relative sensitivity between scene-independent and scene-dependent eye metrics in assessing trainees’ performance in simulated psychomotor tasks.

BACKGROUND: Eye metrics have been extensively studied for skill assessment and training in psychomotor tasks, including aviation, driving, and surgery. These metrics can be categorized as scene-independent or scene-dependent, based on whether predefined areas of interest are considered. There is a paucity of direct comparisons between these metric types, particularly in their ability to assess performance during early training.

METHOD: Thirteen medical students practiced the peg transfer task in the Fundamentals of Laparoscopic Surgery. Scene-independent and scene-dependent eye metrics, completion time, and tool motion metrics were derived from eye-tracking data and task videos. K-means clustering of nine eye metrics identified three groups of practice trials with similar gaze behaviors, corresponding to three performance levels verified by completion time and tool motion metrics. A random forest model using eye metrics estimated classification accuracy and determined the feature importance of the eye metrics.

RESULTS: Scene-dependent eye metrics demonstrated a clearer linear trend with performance levels than scene-independent metrics. The random forest model achieved 88.59% accuracy, identifying the top four predictors of performance as scene-dependent metrics, whereas the two least effective predictors were scene-independent metrics.

CONCLUSION: Scene-dependent eye metrics are overall more sensitive than scene-independent ones for assessing trainee performance in simulated psychomotor tasks.

APPLICATION: The study’s findings are significant for advancing eye metrics in psychomotor skill assessment and training, enhancing operator competency, and promoting safe operations.

PMID:39608016 | DOI:10.1177/00187208241302475

Radiol Oncol. 2024 Nov 28;58(4):535-543. doi: 10.2478/raon-2024-0059. eCollection 2024 Dec 1.

ABSTRACT

BACKGROUND: In patients with right-sided breast cancer the liver can be partially irradiated during adjuvant radiotherapy (RT). We aimed to determine breast cancer RT effects on liver using with magnetic resonance elastography (MRE) and biological results.

PATIENTS AND METHODS: This retrospective study enrolled 34 patients diagnosed with right-sided breast cancer who underwent adjuvant RT. Liver segment assessments were conducted using MRE for all participants. Additionally, a complete blood count and liver enzyme analysis were performed for each patient. All measurements were taken both prior to the initiation and upon completion of RT.

RESULTS: A statistically significant difference was found in ALT (p = 0.015), ALP (p = 0.026), total protein (p = 0.037), and albumin (p = 0.004) levels before and after RT. The highest mean liver stiffness (kPa) value was recorded in segment 8, while the lowest was observed in segment 6. A weak but statistically significant positive correlation was found between segment 5 stiffness and liver volume (p = 0.039). Additionally, a statistically significant positive correlation was detected between ALP levels and the stiffness values in segment 4A (p = 0.020) and segment 6 (p = 0.003). Conversely, a weak negative correlation was observed between the stiffness values in segment 8 and post-RT total protein levels (p = 0.031).

CONCLUSIONS: MRE can help us identify the level of fibrotic stiffness in the liver segments within the RT area without establishing clinical symptoms. MRE can support the clinician in evaluating the liver functions of right breast cancer patients who underwent RT. We assume these results will facilitate new studies with a large number of patients on MRE imaging at certain intervals in the follow-up of patients with right breast cancer who received RT before the development of radiation-induced liver disease (RILD).

PMID:39608011 | DOI:10.2478/raon-2024-0059

Radiol Oncol. 2024 Nov 28;58(4):573-579. doi: 10.2478/raon-2024-0056. eCollection 2024 Dec 1.

ABSTRACT

BACKGROUND: To compare the diagnostic accuracy and predictive value of aneurysm sac volume measurement versus maximum diameter measurement of abdominal aortic aneurysm sac after endovascular aneurysm repair (EVAR) in patients with type II endoleak.

PATIENTS AND METHODS: Retrospective study on a cohort of 103 patients who presented with a type II endoleak after EVAR for infrarenal abdominal aortic aneurysm. Maximum diameter and volumetric measurements were calculated on computed tomography follow-up scans at 3 months and 1 year after index surgery. Pearson correlation coefficient was used to determine linear association between diameter and volume; Mann-Whitney U test was used to compare patients with and without later intervention for type II endoleak with regard to diameter and volume change.

RESULTS: The correlation between diameter and volume measurement was high (Rho: 0.890-0.980 with P < 0.0001). In 38 out of 103 patients (37%) with type II endoleak, a later intervention for endoleak management was performed; early diameter (P = 0.097), or volume (P = 0.387) change could not predict risk for later intervention.

CONCLUSIONS: Both diameter and volume measurements can be used in the imaging follow-up of patients with endoleak type II after EVAR; however early changes in diameter or volume of the aneurysm sac cannot predict late intervention for type II endoleak.

PMID:39608008 | DOI:10.2478/raon-2024-0056

Radiol Oncol. 2024 Nov 28;58(4):494-500. doi: 10.2478/raon-2024-0062. eCollection 2024 Dec 1.

ABSTRACT

BACKGROUND: Whole-body positron emission tomography/magnetic resonance imaging (WB-PET/MRI) is increasingly used in the initial evaluation of oncology patients. The purpose of this study was to compare the diagnostic performance of WB MRI sequences, attenuation-corrected raw data positron-emission tomography (AC PET), and PET/MRI fused images to detect bone metastases.

PATIENTS AND METHODS: We included 765 consecutive oncologic patients who received WB-PET/MRI from between January 2017 and September 2023. The presence of bone metastases was assessed using the individual sequences by two radiologists. Interobserver agreement was calculated. A receiver operating characteristic (ROC) analysis was performed to assess the performance of each individual sequence and fused images.

RESULTS: Interobserver agreement for the detection of bone metastases on all sequences ranged from good to very good. The reading of the combination of MRI sequences with PET images showed statistically significantly better performance than the reading of individual MRI sequences and PET component only. Contrast enhanced T1 W Volume-interpolated breath-hold examination (CE T1W VIBE) sequence superior to PET for the detection of bone metastasis, but the statistical significance was not as high as with T1W-PET and CE T1W-PET fused images. The highest performance was achieved by the fused CE T1W-PET images with sensitivity of 100%, specificity of 92%, PPV of 96%, and NPV of 100%.

CONCLUSIONS: The combination of these CE T1W VIBE sequences with PET images have the highest diagnostic performance in detecting bone metastases in oncologic patients. This sequence should be integrated in WB-PET/MRI acquisitions for initial staging of cancer.

PMID:39608007 | DOI:10.2478/raon-2024-0062

Radiol Oncol. 2024 Nov 28;58(4):501-508. doi: 10.2478/raon-2024-0060. eCollection 2024 Dec 1.

ABSTRACT

BACKGROUND: Multiparametric magnetic resonance imaging (mpMRI) is a prerequisite for targeted prostate biopsy. The aim of our study was to evaluate the performance and learning curve of the mpMRI-transrectal ultrasound (TRUS) software image fusion (MRI-TRUS fusion) biopsy (BX) process in the first year after its introduction in our urology department.

PATIENTS AND METHODS: MRI-TRUS fusion BX was performed in 293 patients with at least one Prostate Imaging-Reporting and Data System (PIRADS) ≥3 lesion. The proportion of patients and lesions with positive histopathologic result for prostate cancer (PCa) was analyzed. The learning curve for MRI-TRUS fusion BX was assessed at institutional and individual level. Positive BX lesions were further analyzed by PIRADS and Gleason scores.

RESULTS: The proportion of patients with positive histopathologic results for targeted BX, systematic BX, and combined BX was 53.9%, 47.9%, and 63.5%, respectively. The chi-square test for the proportion of PCa positive patients showed no significant difference between the time-based patient groups at the institutional level and no significant difference between individual urologists. PIRADS score (p < 0.001), total PSA concentration (p = 0.05), prostate volume (p < 0.001) and number of cores per lesion (p = 0.034) were significant predictors of a positive histopathologic result in a lesion-based analysis. Clinically significant PCa (csPCa) was confirmed in 34.7% of the 412 BX lesions and 76.4% of the 187 positive PCa lesions.

CONCLUSIONS: MRI-TRUS fusion targeted BX significantly improves the overall rate of PCa detection compared with systematic BX alone. No steep learning curve was observed in our urologists. The proportion of lesions with clinically insignificant PCa was low, limiting overdiagnosis of PCa.

PMID:39608006 | DOI:10.2478/raon-2024-0060

JMIR Form Res. 2024 Nov 28;8:e63262. doi: 10.2196/63262.

ABSTRACT

BACKGROUND: Artificial intelligence (AI) tools hold much promise for mental health care by increasing the scalability and accessibility of care. However, current development and evaluation practices of AI tools limit their meaningfulness for health care contexts and therefore also the practical usefulness of such tools for professionals and clients alike.

OBJECTIVE: The aim of this study is to demonstrate the evaluation of an AI monitoring tool that detects the need for more intensive care in a web-based grief intervention for older mourners who have lost their spouse, with the goal of moving toward meaningful evaluation of AI tools in e-mental health.

METHODS: We leveraged the insights from three evaluation approaches: (1) the F1-score evaluated the tool’s capacity to classify user monitoring parameters as either in need of more intensive support or recommendable to continue using the web-based grief intervention as is; (2) we used linear regression to assess the predictive value of users’ monitoring parameters for clinical changes in grief, depression, and loneliness over the course of a 10-week intervention; and (3) we collected qualitative experience data from e-coaches (N=4) who incorporated the monitoring in their weekly email guidance during the 10-week intervention.

RESULTS: Based on n=174 binary recommendation decisions, the F1-score of the monitoring tool was 0.91. Due to minimal change in depression and loneliness scores after the 10-week intervention, only 1 linear regression was conducted. The difference score in grief before and after the intervention was included as a dependent variable. Participants’ (N=21) mean score on the self-report monitoring and the estimated slope of individually fitted growth curves and its standard error (ie, participants’ response pattern to the monitoring questions) were used as predictors. Only the mean monitoring score exhibited predictive value for the observed change in grief (R2=1.19, SE 0.33; t16=3.58, P=.002). The e-coaches appreciated the monitoring tool as an opportunity to confirm their initial impression about intervention participants, personalize their email guidance, and detect when participants’ mental health deteriorated during the intervention.

CONCLUSIONS: The monitoring tool evaluated in this paper identified a need for more intensive support reasonably well in a nonclinical sample of older mourners, had some predictive value for the change in grief symptoms during a 10-week intervention, and was appreciated as an additional source of mental health information by e-coaches who supported mourners during the intervention. Each evaluation approach in this paper came with its own set of limitations, including (1) skewed class distributions in prediction tasks based on real-life health data and (2) choosing meaningful statistical analyses based on clinical trial designs that are not targeted at evaluating AI tools. However, combining multiple evaluation methods facilitates drawing meaningful conclusions about the clinical value of AI monitoring tools for their intended mental health context.

PMID:39608005 | DOI:10.2196/63262

J Med Internet Res. 2024 Nov 28;26:e53320. doi: 10.2196/53320.

ABSTRACT

BACKGROUND: Nationally, COVID-19 spurred the uptake of telehealth to facilitate patients’ access to medical care, especially among individuals living in geographically isolated areas. Despite the potential benefits of telehealth to address health care access barriers and enhance health outcomes, there are still disparities in the accessibility and utilization of telehealth services. Hence, identifying facilitators and barriers to telehealth should be prioritized to ensure that disparities are mitigated rather than exacerbated.

OBJECTIVE: This study aims to identify factors associated with ever use of telehealth in Nebraska, a primarily rural state with a significant portion of its population living in nonmetropolitan areas.

METHODS: A stratified random sample of Nebraska households (n=5300), with oversampling of census tracts with at least 30% African American, Hispanic, or Native American populations, received a mailed survey (English and Spanish) with web-based response options about social determinants of health and health care access (October 2020-March 2021). Survey weights were used for all calculations. Chi-square tests were used to compare telehealth use (yes or no) by participant sociodemographic, health, and access variables. Robust Poisson regression models were used to compute prevalence odds ratios (POR) with 95% CIs of telehealth use after controlling for socioeconomic, demographic, and health conditions.

RESULTS: The overall response rate was 20.8% (1101/5300). About 25.5% of Nebraska adults had ever used telehealth (urban 26.4%, rural 20.8%), despite 97% of respondents reporting internet access (98.3% urban, 90.5% rural). In the chi-square analysis, telehealth use was statistically significantly more common (P<.05) among those who are aged <45 years (32.4%), female (30.7%), and non-Hispanic (25.9%); with at least a bachelor’s degree (32.6%); who had a routine checkup (30.2%) or health care visit other than a routine checkup (34.2%); and with any chronic health conditions (29.6%) but did not differ (P≥.05) by race, marital status, income, insurance, having a primary care provider, or 1-way travel time for medical visits. In univariate models, internet access, age, sex, ethnicity, education, any health care visit in the past year, and no chronic health condition were significant (P<.05). When adjusted, education (POR 1.87, 95% CI 0.33-10.63) and sex (1.38, 0.93-2.04) were not significant, but internet access (5.43, 1.62-18.16), age <45 (5.33, 2.22-12.81) and 45-64 years (9.05, 2.37-34.62), non-Hispanic ethnicity (7.40, 2.39-22.90), any health care visit (2.43, 1.23-4.79), and any chronic condition (1.73, 1.09-2.76) were significantly associated with having ever used telehealth.

CONCLUSIONS: This study highlights disparities in telehealth use. Despite high coverage, internet access was a significant predictor of telehealth use, highlighting the role of the digital divide in telehealth access and use. Telehealth use was significantly less prevalent among older adults, people without chronic health conditions, and Hispanic individuals. Targeted interventions that address barriers to telehealth use and improve health care access are warranted.

PMID:39608002 | DOI:10.2196/53320

J Med Internet Res. 2024 Nov 28;26:e53932. doi: 10.2196/53932.

ABSTRACT

BACKGROUND: To address geographic barriers to specialty care access for services such as cardiology, the Veterans Health Administration (VA) has implemented a novel, regionalized telehealth care hub. The Clinical Resource Hub (CRH) model extends care, including cardiology services, to individuals in low-access communities across the region. Little is known, however, about the reach of such programs.

OBJECTIVE: This study aimed to describe the initial CRH program implementation in terms of growth in users and clinical encounters, as well as the association between user characteristics and the use of CRH cardiology care, in VA’s Sierra Pacific region (Northern California, Nevada, and the Pacific Islands).

METHODS: We compared patients who used CRH cardiology services (CRH users) to those using non-CRH cardiology services (CRH nonusers) in the Sierra Pacific region between July 15, 2021, and March 31, 2023. After characterizing changes in the numbers of CRH users and nonusers and clinical encounters over the study period, we used multivariable logistic regression to estimate the association between patient-level factors and the odds of being a CRH user.

RESULTS: There were 804 CRH users over the study period, with 1961 CRH encounters concentrated at 3 main CRH sites. The CRH program comprised a minority of cardiology users and encounters in the region, with 19,583 CRH nonusers with 83,489 encounters. The numbers of CRH patients and encounters both increased at a steady-to-increasing rate over the study period, with increases of 37% (n=292 vs n=213) in users and 64% (n=584 vs n=356) in encounters in the first quarter of 2023 compared with the last quarter of 2022. Among CRH users, 8.3% (67/804) were female and 41.4% (333/804) were aged ≥75 years, compared with 4.3% (840/19,583) and 49% (9600/19,583), respectively, among CRH nonusers. The proportions of rural (users: 205/804, 25.5%; nonusers: 4936/19,583, 25.2%), highly disabled (users: 387/804, 48.1%; nonusers: 9246/19,583, 47.2%), and low-income (users: 165/804, 20.5%; nonusers: 3941/19,583, 20.1%) veterans in both groups were similar. In multivariable logistic models, adjusted odds ratios of using CRH were higher for female veterans (1.70, 95% CI 1.29-2.24) and lower for older veterans (aged ≥75 years; 0.33, 95% CI 0.23-0.47). Rural veterans also had a higher adjusted odds ratio of using CRH (1.19, 95% CI 1.00-1.42; P=.046).

CONCLUSIONS: The VA’s Sierra Pacific CRH cardiology program grew substantially in its first 2 years of operation, serving disproportionately more female and rural veterans and similar proportions of highly disabled and low-income veterans compared to conventional VA care. This model appears to be effective for overcoming specialty care access barriers for certain individuals, although targeted efforts may be required to reach older veterans. While this study focuses on a single region, specialty, and health care system, lessons from implementing regionalized telehealth hub models may be applicable to other settings.

PMID:39607997 | DOI:10.2196/53932