Category: Nevin Manimala

Obstet Gynecol. 2024 Oct 24. doi: 10.1097/AOG.0000000000005772. Online ahead of print.

ABSTRACT

OBJECTIVE: To examine changes in availability of procedural abortion, especially in the second and third trimesters of pregnancy, since the U.S. Supreme Court ended federal protections for abortion in its Dobbs v. Jackson Women’s Health Organization decision in 2022.

METHODS: We used the Advancing New Standards in Reproductive Health Abortion Facility Database, a national database of all publicly advertising abortion facilities, to document trends in service availability from 2021 to 2023. We calculated summary statistics to describe facility gestational limits for procedural abortion for the United States and by state, subregion, and region, and we examined the number and proportion of facilities that offer procedural abortion in the second or third trimester of pregnancy.

RESULTS: From 2021 to 2023, the total number of publicly advertising facilities providing procedural abortion decreased 11.0%, from 473 to 421. Overall, one-quarter of facilities (n=115) that had been providing procedural abortion in 2021 ceased providing services, and an additional 99 decreased their gestational limits. In contrast, 73 facilities increased their gestational limits, and 64 new facilities began providing or publicly advertising procedural abortion services. The number of facilities offering procedural abortion later in pregnancy decreased (327 to 309 providing 14 weeks of gestation or later, 60 to 50 providing 24 weeks of gestation or later), although the proportion of all facilities providing these services held steady. The greatest changes were in the South, where many facilities closed.

CONCLUSION: There have been substantial reductions in the number and distribution of facilities offering procedural abortion since the Dobbs decision, with critical decreases in the availability of later abortion services. Some facilities are positioning themselves to meet the needs of patients by opening new facilities, publicly advertising their services, or extending their gestational limits.

PMID:39447180 | DOI:10.1097/AOG.0000000000005772

JMIR Form Res. 2024 Oct 24;8:e56376. doi: 10.2196/56376.

ABSTRACT

BACKGROUND: High levels of physical activity are key to improving health outcomes, yet many people fail to take action. Using pedometers to target steps per day and providing financial incentives is a simple and scalable approach to promoting public health. However, conventional pedometers do not account for “intensity” and “duration,” making it challenging to efficiently increase people’s moderate-to-vigorous physical activity (MVPA), which is expected to improve health outcomes. Based on these rationales, we developed a smartphone app that sets step cadence as a goal (defined as a daily challenge of walking more than 1500 steps in 15 minutes twice a day, which is a heuristic threshold for moderate physical activity) and provides financial incentive when the challenge is met.

OBJECTIVE: This study aimed to evaluate the feasibility of our novel app and explore whether its use can increase users’ daily MVPA.

METHODS: A single-arm pre-post study evaluated the feasibility and efficacy of the app. A total of 15 participants used app 1 (an app without financial incentives) for the first period (4 weeks) and then switched to app 2 (an app with financial incentives) for the second period (4 weeks). The primary outcome was the difference between the first and second periods in the number of successful challenge attempts per week. Secondary outcomes were differences between the first and second periods in daily steps and distance walked. Exploratory outcomes included the difference between the first and second periods in daily “heart points” as measured by Google Fit, a publicly available app that measures users’ daily MVPA.

RESULTS: The number of successful challenge attempts per week increased significantly compared to the first period (5.6 times per week vs 0.7 times per week; P<.001). Although not statistically significant, there was a trend toward an increase in the mean steps per day and distance walked per day (6586 steps per day vs 5950 steps per day; P=.19; and 4.69 km per day vs 3.85 km per day; P=.09, respectively). An exploratory end point examining daily MVPA by “heart points” collected from Google Fit also showed a significant increase compared to the first period (22.7 points per day vs 12.8 points per day; P=.02).

CONCLUSIONS: Our app using step cadence as a goal and providing financial incentives seemed feasible and could be an effective app to increase users’ daily MVPA. Based on the results of this study, we are motivated to conduct a confirmatory study with a broader and larger number of participants.

TRIAL REGISTRATION: UMIN 000050518; https://center6.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000057420.

PMID:39447165 | DOI:10.2196/56376

JMIR Form Res. 2024 Oct 24;8:e53510. doi: 10.2196/53510.

ABSTRACT

BACKGROUND: Physician use of stigmatizing language in the clinical documentation of hospitalized adults with opioid use is common. However, patient factors associated with stigmatizing language in this setting remain poorly characterized.

OBJECTIVE: This study aimed to determine whether specific demographic factors and clinical outcomes are associated with the presence of stigmatizing language by physicians in the clinical documentation of encounters with opioid-related ICD-10 (International Statistical Classification of Diseases, Tenth Revision) codes.

METHODS: Hospital encounters with one or more associated opioid-related ICD-10 admission diagnoses on the hospital medicine service during the 2020 calendar year were analyzed for the presence of stigmatizing language in history and physical and discharge summaries. Multivariable adjusted logistic regression models were used to determine associations of age, race, gender, medication for addiction treatment use, against medical advice discharge, homelessness, comorbid polysubstance use, comorbid psychiatric disorder, comorbid chronic pain, cost, and 30-day readmission with the presence of stigmatizing language.

RESULTS: A total of 221 encounters were identified, of which 64 (29%) encounters had stigmatizing language present in physician documentation. Most stigmatizing language was due to use of “substance abuse” rather than the preferred term “substance use” (63/66 instances). Polysubstance use and homelessness were independently associated with the presence of stigmatizing language (adjusted odds ratio [aOR] 7.83; 95% CI 3.42-19.24 and aOR 2.44; 95% CI 1.03-5.90) when controlling for chronic pain and other covariates.

CONCLUSIONS: Among hospital medicine encounters with an opioid-related diagnosis, stigmatizing language by physicians in clinical documentation was common and independently associated with comorbid polysubstance use and homelessness.

PMID:39447164 | DOI:10.2196/53510

JMIR Form Res. 2024 Oct 24;8:e58418. doi: 10.2196/58418.

ABSTRACT

BACKGROUND: Recent advancements in large language models (LLMs) have accelerated their use across various domains. Psychiatric interviews, which are goal-oriented and structured, represent a significantly underexplored area where LLMs can provide substantial value. In this study, we explore the application of LLMs to enhance psychiatric interviews by analyzing counseling data from North Korean defectors who have experienced traumatic events and mental health issues.

OBJECTIVE: This study aims to investigate whether LLMs can (1) delineate parts of the conversation that suggest psychiatric symptoms and identify those symptoms, and (2) summarize stressors and symptoms based on the interview dialogue transcript.

METHODS: Given the interview transcripts, we align the LLMs to perform 3 tasks: (1) extracting stressors from the transcripts, (2) delineating symptoms and their indicative sections, and (3) summarizing the patients based on the extracted stressors and symptoms. These 3 tasks address the 2 objectives, where delineating symptoms is based on the output from the second task, and generating the summary of the interview incorporates the outputs from all 3 tasks. In this context, the transcript data were labeled by mental health experts for the training and evaluation of the LLMs.

RESULTS: First, we present the performance of LLMs in estimating (1) the transcript sections related to psychiatric symptoms and (2) the names of the corresponding symptoms. In the zero-shot inference setting using the GPT-4 Turbo model, 73 out of 102 transcript segments demonstrated a recall mid-token distance d<20 for estimating the sections associated with the symptoms. For evaluating the names of the corresponding symptoms, the fine-tuning method demonstrates a performance advantage over the zero-shot inference setting of the GPT-4 Turbo model. On average, the fine-tuning method achieves an accuracy of 0.82, a precision of 0.83, a recall of 0.82, and an F1-score of 0.82. Second, the transcripts are used to generate summaries for each interviewee using LLMs. This generative task was evaluated using metrics such as Generative Evaluation (G-Eval) and Bidirectional Encoder Representations from Transformers Score (BERTScore). The summaries generated by the GPT-4 Turbo model, utilizing both symptom and stressor information, achieve high average G-Eval scores: coherence of 4.66, consistency of 4.73, fluency of 2.16, and relevance of 4.67. Furthermore, it is noted that the use of retrieval-augmented generation did not lead to a significant improvement in performance.

CONCLUSIONS: LLMs, using either (1) appropriate prompting techniques or (2) fine-tuning methods with data labeled by mental health experts, achieved an accuracy of over 0.8 for the symptom delineation task when measured across all segments in the transcript. Additionally, they attained a G-Eval score of over 4.6 for coherence in the summarization task. This research contributes to the emerging field of applying LLMs in psychiatric interviews and demonstrates their potential effectiveness in assisting mental health practitioners.

PMID:39447159 | DOI:10.2196/58418

JMIR Med Inform. 2024 Oct 24;12:e57035. doi: 10.2196/57035.

ABSTRACT

Before deploying a clinical prediction model (CPM) in clinical practice, its performance needs to be demonstrated in the population of intended use. This is also called “targeted validation.” Many CPMs developed in tertiary settings may be most useful in secondary care, where the patient case mix is broad and practitioners need to triage patients efficiently. However, since structured or rich datasets of sufficient quality from secondary to assess the performance of a CPM are scarce, a validation gap exists that hampers the implementation of CPMs in secondary care settings. In this viewpoint, we highlight the importance of targeted validation and the use of CPMs in secondary care settings and discuss the potential and challenges of using electronic health record (EHR) data to overcome the existing validation gap. The introduction of software applications for text mining of EHRs allows the generation of structured “big” datasets, but the imperfection of EHRs as a research database requires careful validation of data quality. When using EHR data for the development and validation of CPMs, in addition to widely accepted checklists, we propose considering three additional practical steps: (1) involve a local EHR expert (clinician or nurse) in the data extraction process, (2) perform validity checks on the generated datasets, and (3) provide metadata on how variables were constructed from EHRs. These steps help to generate EHR datasets that are statistically powerful, of sufficient quality and replicable, and enable targeted development and validation of CPMs in secondary care settings. This approach can fill a major gap in prediction modeling research and appropriately advance CPMs into clinical practice.

PMID:39447145 | DOI:10.2196/57035

Neurology. 2024 Nov 26;103(10):e210004. doi: 10.1212/WNL.0000000000210004. Epub 2024 Oct 24.

ABSTRACT

BACKGROUND AND OBJECTIVES: Recent technological advances have enabled visualizing in vivo a subset of chronic active brain lesions in persons with multiple sclerosis (pwMS), referred to as “paramagnetic rim lesions” (PRLs), with iron-sensitive MRI. PRLs predict future clinical disease progression, making them a promising clinical and translational imaging marker. However, it is unknown how disease progression is modified by PRL evolution (PRL disappearance, new PRL appearance). This is key to understanding MS pathophysiology and may help inform selection of sensitive endpoints for clinical trials targeting chronic active inflammation. To this end, we assessed the longitudinal associations between PRL disappearance and new PRL appearance and clinical disability progression and brain atrophy.

METHODS: PwMS and healthy controls (HCs) were included from a larger prospective, longitudinal cohort study at the University at Buffalo if they had available 3T MRI and clinical visits at baseline and follow-up timepoints. PwMS with sufficient clinical data for confirmed disability progression (CDP) analysis were included in a Disability Progression Cohort, and pwMS and HCs with brain volumetry data at baseline and follow-up were included in MS and HC Brain Atrophy cohorts. PRLs were assessed at baseline and follow-up and assigned as disappearing, newly appearing, or persisting at follow-up. Linear models were fit to compare annualized PRL disappearance rates or new PRL appearance (yes/no) with annualized rates of CDP and progression independent of relapse activity (PIRA) or with annualized rates of brain atrophy, adjusting for covariates including baseline PRL number and follow-up time. Statistical analyses were corrected for false discovery rate (FDR; i.e., q-value).

RESULTS: In total, 160 pwMS (73.8% female; mean baseline age 46.6 ± 11.4 years; mean baseline disease duration 13.8 ± 10.6 years; median follow-up time 5.6 [interquartile range 5.2-7.8] years; 26.9% progressive MS) and 27 HCs (74.1% female; mean baseline age 43.9 ± 13.6 years; median follow-up time 5.4 [5.2-5.6] years) were enrolled. Greater PRL disappearance rates were associated with reduced rates of CDP (β mean = -0.262, 95% CI -0.475 to -0.049, q = 0.028) and PIRA (β mean = -0.283, 95% CI -0.492 to -0.073, q = 0.036), and new PRL appearance was associated with increased rates of PIRA (β mean = 0.223, 95% CI 0.049-0.398, q = 0.036). By contrast, no associations between new PRL appearance or PRL disappearance and brain volume changes survived FDR correction (q > 0.05).

DISCUSSION: Our results show that resolution of existing PRLs and lack of new PRLs are associated with improved clinical outcomes. These findings further motivate the need for novel therapies targeting microglia-mediated brain inflammation and adoption of clinical strategies to prevent appearance of new PRL.

PMID:39447104 | DOI:10.1212/WNL.0000000000210004

JCO Glob Oncol. 2024 Oct;10:e2400131. doi: 10.1200/GO.24.00131. Epub 2024 Oct 24.

ABSTRACT

PURPOSE: Cancer continues to be a significant public health concern. Sub-Saharan Africa (SSA) struggles with a lack of proper infrastructure and adequate cancer care workforce. This has led to some countries relying on referrals of cancer care to countries with higher income levels. In some instances, patients refer themselves. Some countries have made it their goal to attract patients from other countries, a term that has been referred to as medical tourism. In this article, we explore the current status of oncology-related medical tourism in SSA.

METHODS: This was a cross-sectional study. The study participants included oncologists, surgeons, and any other physicians who take care of patients with cancer. A predesigned questionnaire was distributed through African Organization for Research and Training in Cancer member mailing list and through study team personal contacts and social media.

RESULTS: A total of 52 participants from 17 African countries with a 1.6:2 male to female ratio responded to the survey. Most (55.8%) of the respondents were from Eastern African countries. The majority (92%) of study participants reported that they knew patients who referred themselves abroad, whereas 75% referred patients abroad, and the most common (94%) referral destination was India. The most common (93%) reason for referral was perception of a higher quality of care in foreign health institutions.

CONCLUSION: The findings suggest the need to improve local health care systems including building trust of the system among general population. The study highlights potential financial toxicity, and it adds to the current emphasis on return of investment on homegrown workforce and cancer treatment infrastructure.

PMID:39447099 | DOI:10.1200/GO.24.00131

JCO Precis Oncol. 2024 Oct;8:e2400145. doi: 10.1200/PO.24.00145. Epub 2024 Oct 24.

ABSTRACT

PURPOSE: Current clinical risk stratification methods for localized prostate cancer are suboptimal, leading to over- and undertreatment. Recently, machine learning approaches using digital histopathology have shown superior prognostic ability in phase III trials. This study aims to develop a clinically usable risk grouping system using multimodal artificial intelligence (MMAI) models that outperform current National Comprehensive Cancer Network (NCCN) risk groups.

MATERIALS AND METHODS: The cohort comprised 9,787 patients with localized prostate cancer from eight NRG Oncology randomized phase III trials, treated with radiation therapy, androgen deprivation therapy, and/or chemotherapy. Locked MMAI models, which used digital histopathology images and clinical data, were applied to each patient. Expert consensus on cut points defined low-, intermediate-, and high-risk groups on the basis of 10-year distant metastasis rates of 3% and 10%, respectively. The MMAI’s reclassification and prognostic performance were compared with the three-tier NCCN risk groups.

RESULTS: The median follow-up for censored patients was 7.9 years. According to NCCN risk categories, 30.4% of patients were low-risk, 25.5% intermediate-risk, and 44.1% high-risk. The MMAI risk classification identified 43.5% of patients as low-risk, 34.6% as intermediate-risk, and 21.8% as high-risk. MMAI reclassified 1,039 (42.0%) patients initially categorized by NCCN. Despite the MMAI low-risk group being larger than the NCCN low-risk group, the 10-year metastasis risks were comparable: 1.7% (95% CI, 0.2 to 3.2) for NCCN and 3.2% (95% CI, 1.7 to 4.7) for MMAI. The overall 10-year metastasis risk for NCCN high-risk patients was 16.6%, with MMAI further stratifying this group into low-, intermediate-, and high-risk, showing metastasis rates of 3.4%, 8.2%, and 26.3%, respectively.

CONCLUSION: The MMAI risk grouping system expands the population of men identified as having low metastatic risk and accurately pinpoints a high-risk subset with elevated metastasis rates. This approach aims to prevent both overtreatment and undertreatment in localized prostate cancer, facilitating shared decision making.

PMID:39447096 | DOI:10.1200/PO.24.00145

Shock. 2024 Oct 21. doi: 10.1097/SHK.0000000000002474. Online ahead of print.

ABSTRACT

INTRODUCTION: The prehospital environment is fraught with operational constraints, making it difficult to assess the need for resources such as lifesaving interventions (LSI) for adults with traumatic injuries. While invasive methods such as lactate have been found to be highly predictive for estimating injury severity and resource requirements, noninvasive methods, to include continuous vital signs (VS), have the potential to provide prognostic information that can be quickly acquired, interpreted, and incorporated into decision making. In this work, we hypothesized that an analysis of continuous VS would have predictive capacity comparable to lactate and other laboratory tests for the prediction of injury severity, need for LSIs and intensive care unit (ICU) admission.

METHODS: In this pre-planned secondary analysis of 300 prospectively enrolled patients, venous blood samples were collected in the prehospital environment aboard a helicopter and analyzed with a portable lab device. Patients were transported to the primary adult resource center for trauma in the state of Maryland. Continuous VS were simultaneously collected. Descriptive statistics were used to describe the cohort and predictive models were constructed using a regularized gradient boosting framework with 10-fold cross-validation with additional analyses using Shapley additive explanations (SHAP).

RESULTS: Complete VS and laboratory data from 166 patients were available for analysis. The continuous VS models had better performance for prediction of receiving LSIs and ICU length of stay compared to single (initial) VS measurements. The continuous VS models had comparable performance to models using only laboratory tests in predicting discharge within 24 hours (continuous VS model: AUROC 0.71; 95% CI, 0.68-0.75 vs. lactate model: AUROC 0.65; 95% CI, 0.68; 95% CI, 0.66-0.71). The model using all laboratory data yielded the highest sensitivity and sensitivity (AUROC 0.77; 95% CI, 0.74-0.81).

DISCUSSION: The results from this study suggest that continuous VS obtained from autonomous monitors in an aeromedical environment may be helpful for predicting LSIs and the critical care requirements for traumatically injured adults. The collection and use of noninvasively obtained physiological data during the early stages of prehospital care may be useful for in developing user-friendly early warning systems for identifying potentially unstable trauma patients.

PMID:39447081 | DOI:10.1097/SHK.0000000000002474

J Natl Cancer Inst. 2024 Oct 24:djae265. doi: 10.1093/jnci/djae265. Online ahead of print.

ABSTRACT

BACKGROUND: Previous epidemiologic studies have reported an association of serum immunoglobulin E (IgE) levels with reduced glioma risk, but the association between IgE and glioma prognosis has not been characterized. This study aimed to examine how sex, tumor subtype, and IgE class modulate the association of serum IgE levels with glioma risk and survival.

METHODS: We conducted a case-control study using participants from the University of California, San Francisco Adult Glioma Study (1997-2010). Serum IgE levels for total, respiratory and food allergy were measured in adults diagnosed with glioma (n = 1319) and cancer-free controls (n = 1139) matched based on age, sex, and race and ethnicity. Logistic regression was adjusted for patient demographics to assess the association between IgE levels and glioma risk. Multivariable Cox regression adjusted for patient-specific and tumor-specific factors compared survival between the elevated and normal IgE groups. All statistical tests were 2-sided.

RESULTS: Elevated total IgE was associated with reduced risk of IDH-wildtype (RR = 0.78, 95% CI: 0.71-0.86) and IDH-mutant glioma (RR = 0.73, 95% CI: 0.63-0.85). In multivariable Cox regression, positive respiratory IgE was associated with improved survival for IDH-wildtype glioma (RR = 0.79, 95% CI: 0.67-0.93). The reduction in mortality risk was significant in females only (RR = 0.75, 95% CI: 0.57-0.98) with an improvement in median survival of 6.9 months (P<.001).

CONCLUSION: Elevated serum IgE was associated with improved prognosis for IDH-wildtype glioma, with a more pronounced protective effect in females than males, which has implications for the future study of IgE-based immunotherapies for glioma.

PMID:39447063 | DOI:10.1093/jnci/djae265