Nevin Manimala

PLoS One. 2024 Jul 23;19(7):e0301319. doi: 10.1371/journal.pone.0301319. eCollection 2024.

ABSTRACT

BACKGROUND: Heart failure (HF) is becoming a growing public health concern. Diagnostic tests for determining the severity of HF often come with high costs and require specialized expertise, which makes it difficult to assess HF severity, especially in low-income countries or at primary healthcare facilities. Recently, red blood cell distribution width (RDW) has emerged as a promising, easily accessible marker associated with HF severity. The study aimed to assess changes in RDW levels in HF patients and the diagnostic value of RDW in detecting acute heart failure (AHF) among HF patients.

METHODS: We conducted a cross-sectional examination involving 351 participants divided into HF and non-HF cohorts. HF was defined and categorized according to the diagnostic and treatment guidelines for AHF and chronic heart failure (CHF) set forth by the European Society of Cardiology (2021). Univariate and multivariate analysis of factors associated with AHF was performed.

RESULTS: The study revealed that HF patients displayed higher median RDW levels (14.90% [13.70-17.00]) compared to non-HF individuals (13.00% [12.23-13.78]). RDW was notably elevated in HF patients with left ventricular ejection fraction < 50% compared to those with left ventricular ejection fraction ≥ 50%. ROC curve analysis of RDW for AHF detection identified a cutoff value of 13.85%, with a sensitivity of 86.05% and specificity of 47.18%, statistically significant at p < 0.001. RDW > 13.85% was identified as an independent risk factor for AHF in patients with HF, with odds ratios of 2.644 (95% CI, 1.190-5.875; p = 0.017).

CONCLUSION: The study revealed significant RDW variations in patients with CHF and AHF compared to the control group. These findings suggest that RDW could be a biomarker for detecting HF severity.

PMID:39042640 | DOI:10.1371/journal.pone.0301319

JMIR Cardio. 2024 Jul 23;8:e54994. doi: 10.2196/54994.

ABSTRACT

BACKGROUND: Patients with heart failure (HF) are the most commonly readmitted group of adult patients in Germany. Most patients with HF are readmitted for noncardiovascular reasons. Understanding the relevance of HF management outside the hospital setting is critical to understanding HF and factors that lead to readmission. Application of machine learning (ML) on data from statutory health insurance (SHI) allows the evaluation of large longitudinal data sets representative of the general population to support clinical decision-making.

OBJECTIVE: This study aims to evaluate the ability of ML methods to predict 1-year all-cause and HF-specific readmission after initial HF-related admission of patients with HF in outpatient SHI data and identify important predictors.

METHODS: We identified individuals with HF using outpatient data from 2012 to 2018 from the AOK Baden-Württemberg SHI in Germany. We then trained and applied regression and ML algorithms to predict the first all-cause and HF-specific readmission in the year after the first admission for HF. We fitted a random forest, an elastic net, a stepwise regression, and a logistic regression to predict readmission by using diagnosis codes, drug exposures, demographics (age, sex, nationality, and type of coverage within SHI), degree of rurality for residence, and participation in disease management programs for common chronic conditions (diabetes mellitus type 1 and 2, breast cancer, chronic obstructive pulmonary disease, and coronary heart disease). We then evaluated the predictors of HF readmission according to their importance and direction to predict readmission.

RESULTS: Our final data set consisted of 97,529 individuals with HF, and 78,044 (80%) were readmitted within the observation period. Of the tested modeling approaches, the random forest approach best predicted 1-year all-cause and HF-specific readmission with a C-statistic of 0.68 and 0.69, respectively. Important predictors for 1-year all-cause readmission included prescription of pantoprazole, chronic obstructive pulmonary disease, atherosclerosis, sex, rurality, and participation in disease management programs for type 2 diabetes mellitus and coronary heart disease. Relevant features for HF-specific readmission included a large number of canonical HF comorbidities.

CONCLUSIONS: While many of the predictors we identified were known to be relevant comorbidities for HF, we also uncovered several novel associations. Disease management programs have widely been shown to be effective at managing chronic disease; however, our results indicate that in the short term they may be useful for targeting patients with HF with comorbidity at increased risk of readmission. Our results also show that living in a more rural location increases the risk of readmission. Overall, factors beyond comorbid disease were relevant for risk of HF readmission. This finding may impact how outpatient physicians identify and monitor patients at risk of HF readmission.

PMID:39042456 | DOI:10.2196/54994

JMIR Public Health Surveill. 2024 Jul 23;10:e54281. doi: 10.2196/54281.

ABSTRACT

Infectious disease (ID) cohorts are key to advancing public health surveillance, public policies, and pandemic responses. Unfortunately, ID cohorts often lack funding to store and share clinical-epidemiological (CE) data and high-dimensional laboratory (HDL) data long term, which is evident when the link between these data elements is not kept up to date. This becomes particularly apparent when smaller cohorts fail to successfully address the initial scientific objectives due to limited case numbers, which also limits the potential to pool these studies to monitor long-term cross-disease interactions within and across populations. CE data from 9 arbovirus (arthropod-borne viruses) cohorts in Latin America were retrospectively harmonized using the Maelstrom Research methodology and standardized to Clinical Data Interchange Standards Consortium (CDISC). We created a harmonized and standardized meta-cohort that contains CE and HDL data from 9 arbovirus studies from Latin America. To facilitate advancements in cross-population inference and reuse of cohort data, the Reconciliation of Cohort Data for Infectious Diseases (ReCoDID) Consortium harmonized and standardized CE and HDL from 9 arbovirus cohorts into 1 meta-cohort. Interested parties will be able to access data dictionaries that include information on variables across the data sets via Bio Studies. After consultation with each cohort, linked harmonized and curated human cohort data (CE and HDL) will be made accessible through the European Genome-phenome Archive platform to data users after their requests are evaluated by the ReCoDID Data Access Committee. This meta-cohort can facilitate various joint research projects (eg, on immunological interactions between sequential flavivirus infections and for the evaluation of potential biomarkers for severe arboviral disease).

PMID:39042429 | DOI:10.2196/54281

J Med Internet Res. 2024 Jul 23;26:e55542. doi: 10.2196/55542.

ABSTRACT

BACKGROUND: The diagnosis of inflammatory rheumatic diseases (IRDs) is often delayed due to unspecific symptoms and a shortage of rheumatologists. Digital diagnostic decision support systems (DDSSs) have the potential to expedite diagnosis and help patients navigate the health care system more efficiently.

OBJECTIVE: The aim of this study was to assess the diagnostic accuracy of a mobile artificial intelligence (AI)-based symptom checker (Ada) and a web-based self-referral tool (Rheport) regarding IRDs.

METHODS: A prospective, multicenter, open-label, crossover randomized controlled trial was conducted with patients newly presenting to 3 rheumatology centers. Participants were randomly assigned to complete a symptom assessment using either Ada or Rheport. The primary outcome was the correct identification of IRDs by the DDSSs, defined as the presence of any IRD in the list of suggested diagnoses by Ada or achieving a prespecified threshold score with Rheport. The gold standard was the diagnosis made by rheumatologists.

RESULTS: A total of 600 patients were included, among whom 214 (35.7%) were diagnosed with an IRD. Most frequent IRD was rheumatoid arthritis with 69 (11.5%) patients. Rheport’s disease suggestion and Ada’s top 1 (D1) and top 5 (D5) disease suggestions demonstrated overall diagnostic accuracies of 52%, 63%, and 58%, respectively, for IRDs. Rheport showed a sensitivity of 62% and a specificity of 47% for IRDs. Ada’s D1 and D5 disease suggestions showed a sensitivity of 52% and 66%, respectively, and a specificity of 68% and 54%, respectively, concerning IRDs. Ada’s diagnostic accuracy regarding individual diagnoses was heterogenous, and Ada performed considerably better in identifying rheumatoid arthritis in comparison to other diagnoses (D1: 42%; D5: 64%). The Cohen κ statistic of Rheport for agreement on any rheumatic disease diagnosis with Ada D1 was 0.15 (95% CI 0.08-0.18) and with Ada D5 was 0.08 (95% CI 0.00-0.16), indicating poor agreement for the presence of any rheumatic disease between the 2 DDSSs.

CONCLUSIONS: To our knowledge, this is the largest comparative DDSS trial with actual use of DDSSs by patients. The diagnostic accuracies of both DDSSs for IRDs were not promising in this high-prevalence patient population. DDSSs may lead to a misuse of scarce health care resources. Our results underscore the need for stringent regulation and drastic improvements to ensure the safety and efficacy of DDSSs.

TRIAL REGISTRATION: German Register of Clinical Trials DRKS00017642; https://drks.de/search/en/trial/DRKS00017642.

PMID:39042425 | DOI:10.2196/55542

JMIR Res Protoc. 2024 Jul 23;13:e54129. doi: 10.2196/54129.

ABSTRACT

BACKGROUND: Person-centered care (PCC) within HIV treatment services has demonstrated potential to overcome inequities in HIV service access while improving treatment outcomes. Despite PCC being widely considered a best practice, no consensus exists on its assessment and measurement. This study in Zambia builds upon previous research that informed development of a framework for PCC and a PCC assessment tool (PCC-AT).

OBJECTIVE: This mixed methods study aims to examine the preliminary effectiveness of the PCC-AT through assessing the association between client HIV service delivery indicators and facility PCC-AT scores. We hypothesize that facilities with higher PCC-AT scores will demonstrate more favorable HIV treatment continuity, viral load (VL) coverage, and viral suppression in comparison to those of facilities with lower PCC-AT scores.

METHODS: We will implement the PCC-AT at 30 randomly selected health facilities in the Copperbelt and Central provinces of Zambia. For each study facility, data will be gathered from 3 sources: (1) PCC-AT scores, (2) PCC-AT action plans, and (3) facility characteristics, along with service delivery data. Quantitative analysis, using STATA, will include descriptive statistics on the PCC-AT results stratified by facility characteristics. Cross-tabulations and/or regression analysis will be used to determine associations between scores and treatment continuity, VL coverage, and/or viral suppression. Qualitative data will be collected via action planning, with detailed notes collected and recorded into an action plan template. Descriptive coding and emerging themes will be analyzed with NVivo software.

RESULTS: As of May 2024, we enrolled 29 facilities in the study and data analysis from the key informant interviews is currently underway. Results are expected to be published by September 2024.

CONCLUSIONS: Assessment and measurement of PCC within HIV treatment settings is a novel approach that offers HIV treatment practitioners the opportunity to examine their services and identify actions to improve PCC performance. Study results and the PCC-AT will be broadly disseminated for use among all project sites in Zambia as well as other HIV treatment programs, in addition to making the PCC-AT publicly available to global HIV practitioners.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/54129.

PMID:39042423 | DOI:10.2196/54129

JAMA Netw Open. 2024 Jul 1;7(7):e2421637. doi: 10.1001/jamanetworkopen.2024.21637.

NO ABSTRACT

PMID:39042413 | DOI:10.1001/jamanetworkopen.2024.21637

JAMA Netw Open. 2024 Jul 1;7(7):e2424096. doi: 10.1001/jamanetworkopen.2024.24096.

NO ABSTRACT

PMID:39042411 | DOI:10.1001/jamanetworkopen.2024.24096

JAMA Netw Open. 2024 Jul 1;7(7):e2421717. doi: 10.1001/jamanetworkopen.2024.21717.

ABSTRACT

IMPORTANCE: Understanding gender differences in electronic health record (EHR) use among surgeons is crucial for addressing potential disparities in workload, compensation, and physician well-being.

OBJECTIVE: To investigate gender differences in EHR usage patterns.

DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study examined data from an EHR system (Epic Signal) at a single academic hospital from January to December 2022. Participants included 224 attending surgeons with patient encounters in the outpatient setting. Statistical analysis was performed from May 2023 to April 2024.

EXPOSURES: Surgeon’s gender.

MAIN OUTCOMES AND MEASURES: The primary outcome variables were progress note length, documentation length, time spent in medical records, and time spent documenting patient encounters. Continuous variables were summarized with median and IQR and assessed via the Kruskal-Wallis test. Categorical variables were summarized using proportion and frequency and compared using the χ2 test. Multivariate linear regression was used with primary EHR usage variables as dependent variables and surgeon characteristics as independent variables.

RESULTS: This study included 222 529 patient encounters by 224 attending surgeons, of whom 68 (30%) were female and 156 (70%) were male. The median (IQR) time in practice was 14.0 (7.8-24.3) years. Male surgeons had more median (IQR) appointments per month (78.3 [39.2-130.6] vs 57.8 [25.7-89.8]; P = .005) and completed more medical records per month compared with female surgeons (43.0 [21.8-103.9] vs 29.1 [15.9-48.1]; P = .006). While there was no difference in median (IQR) time spent in the EHR system per month (664.1 [301.0-1299.1] vs 635.0 [315.6-1192.0] minutes; P = .89), female surgeons spent more time logged into the system both outside of 7am to 7pm (36.4 [7.8-67.6] vs 14.1 [5.4-52.2] min/mo; P = .05) and outside of scheduled clinic hours (134.8 [58.9-310.1] vs 105.2 [40.8-214.3] min/mo; P = .05). Female surgeons spent more median (IQR) time per note (4.8 [2.6-7.1] vs 2.5 [0.9-4.2] minutes; P < .001) compared with male surgeons. Male surgeons had a higher number of median (IQR) days logged in per month (17.7 [13.8-21.3] vs 15.7 [10.7-19.7] days; P = .03). Female surgeons wrote longer median (IQR) inpatient progress notes (6025.1 [3692.1-7786.7] vs 4307.7 [2808.9-5868.4] characters/note; P = .001) and had increased outpatient document length (6321.1 [4079.9-7825.0] vs 4445.3 [2934.7-6176.7] characters/note; P < .001). Additionally, female surgeons wrote a higher fraction of the notes manually (17% vs 12%; P = .006). After using multivariable linear regression models, male gender was associated with reduced character length for both documentations (regression coefficient, -1106.9 [95% CI, -1981.5 to -232.3]; P = .01) and progress notes (regression coefficient, -1119.0 [95% CI, -1974.1 to -263.9]; P = .01). Male gender was positively associated with total hospital medical records completed (regression coefficient, 47.3 [95% CI, 28.3-66.3]; P < .001). There was no difference associated with gender for time spent in each note, time spent outside of 7 am to 7 pm, or time spent outside scheduled clinic hours.

CONCLUSIONS AND RELEVANCE: This cross-sectional study of EHR data found that female surgeons spent more time documenting patient encounters, wrote longer notes, and spent more time in the EHR system compared with male surgeons. These findings have important implications for understanding the differential burdens faced by female surgeons, including potential contributions to burnout and payment disparities.

PMID:39042410 | DOI:10.1001/jamanetworkopen.2024.21717

JAMA Netw Open. 2024 Jul 1;7(7):e2421724. doi: 10.1001/jamanetworkopen.2024.21724.

ABSTRACT

IMPORTANCE: Universal screening to identify unfavorable lipid levels is recommended for US children aged 9 to 11 years and adolescents aged 17 to 21 years (hereafter, young adults); however, screening benefits in these individuals have been questioned. Current use of lipid screening and prevalence of elevated lipid measurements among US youths is not well understood.

OBJECTIVE: To investigate the prevalence of ambulatory pediatric lipid screening and elevated or abnormal lipid measurements among US screened youths by patient characteristic and test type.

DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study used data from the IQVIA Ambulatory Electronic Medical Record database and included youths aged 9 to 21 years with 1 or more valid measurement of height and weight during the observation period (2018-2021). Body mass index (BMI) was calculated and categorized using standard pediatric BMI percentiles (9-19 years) and adult BMI categories (≥20 years). The data were analyzed from October 6, 2022, to January 18, 2023.

MAIN OUTCOMES AND MEASURES: Lipid measurements were defined as abnormal if 1 or more of the following test results was identified: total cholesterol (≥200 mg/dL), low-density lipoprotein cholesterol (≥130 mg/dL), very low-density lipoprotein cholesterol (≥31 mg/dL), non-high-density lipoprotein cholesterol (≥145 mg/dL), and triglycerides (≥100 mg/dL for children aged 9 years or ≥130 mg/dL for patients aged 10-21 years). After adjustment for age group, sex, race and ethnicity, and BMI category, adjusted prevalence ratios (aPRs) and 95% CIs were calculated.

RESULTS: Among 3 226 002 youths (23.9% aged 9-11 years, 34.8% aged 12-16 years, and 41.3% aged 17-21 years; 1 723 292 females [53.4%]; 60.0% White patients, 9.5% Black patients, and 2.4% Asian patients), 11.3% had 1 or more documented lipid screening tests. The frequency of lipid screening increased by age group (9-11 years, 9.0%; 12-16 years, 11.1%; 17-21 years, 12.9%) and BMI category (range, 9.2% [healthy weight] to 21.9% [severe obesity]). Among those screened, 30.2% had abnormal lipid levels. Compared with youths with a healthy weight, prevalence of an abnormal result was higher among those with overweight (aPR, 1.58; 95% CI, 1.56-1.61), moderate obesity (aPR, 2.16; 95% CI, 2.14-2.19), and severe obesity (aPR, 2.53; 95% CI, 2.50-2.57).

CONCLUSIONS AND RELEVANCE: In this cross-sectional study of prevalence of lipid screening among US youths aged 9 to 21 years, approximately 1 in 10 were screened. Among them, abnormal lipid levels were identified in 1 in 3 youths overall and 1 in 2 youths with severe obesity. Health care professionals should consider implementing lipid screening among children aged 9 to 11 years, young adults aged 17 to 21 years, and all youths at high cardiovascular risk.

PMID:39042409 | DOI:10.1001/jamanetworkopen.2024.21724

JAMA Netw Open. 2024 Jul 1;7(7):e2422634. doi: 10.1001/jamanetworkopen.2024.22634.

ABSTRACT

IMPORTANCE: Advancements in assisted reproductive technology (ART) have led to an increase in gestational carrier (GC) pregnancies. However, the perinatal outcomes of GC pregnancies remain understudied, necessitating a deeper understanding of their associated risks.

OBJECTIVE: To assess maternal characteristics and obstetric outcomes associated with GC pregnancies.

DATA SOURCES: A comprehensive systematic search of publications published before October 31, 2023, using PubMed, Web of Science, Scopus, and Cochrane Library databases was conducted.

STUDY SELECTION: Two authors selected studies examining obstetric characteristics and outcomes in GC pregnancies with 24 or more weeks’ gestation. Studies with insufficient outcome information, unavailable data on gestational surrogacies, and non-English language studies were excluded.

DATA EXTRACTION AND SYNTHESIS: Adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, 2 investigators extracted and synthesized both quantitative and qualitative data. Both fixed-effect and random-effect analysis were used to pool data.

MAIN OUTCOMES AND MEASURES: The primary outcomes were obstetric characteristics and outcomes, including hypertensive disorders, preterm birth, and low birth weight. Secondary outcomes included severe maternal morbidity and mortality associated with GC pregnancies.

RESULTS: Six studies from 2011 to 2023 involving 28 300 GC pregnancies and 1 270 662 non-GC pregnancies were included. GCs accounted for 2.5% of in vitro fertilization cycles (59 502 of 2 374 154 cycles) and 3.8% of ART pregnancies (26 759 of 701 047 ART pregnancies). GC pregnancies were more likely to be conceived by frozen embryo transfer compared with non-GC ART pregnancies (odds ratio [OR], 2.84; 95% CI, 1.56-5.15), and rates of single embryo transfer were similar between the 2 groups (OR, 1.18; 95% CI, 0.94-1.48). GCs were rarely nulliparous (6 of 361 patients [1.7%]) and were more likely to have multifetal pregnancies compared with non-GC ART patients (OR, 1.18; 95% CI, 1.02-1.35). Comparator studies revealed lower odds of cesarean delivery (adjusted OR [aOR], 0.42; 95% CI, 0.27-0.65) and comparable rates of hypertensive disorders (aOR, 0.86; 95% CI, 0.45-1.64), preterm birth (aOR, 0.82; 95% CI, 0.68-1.00), and low birth weight (aOR, 0.79; 95% CI, 0.50-1.26) in GC pregnancies vs non-GC ART pregnancies. Comparatively, GC pregnancies had higher odds of hypertensive disorders (aOR, 1.44; 95% CI, 1.13-1.84) vs general (non-GC ART and non-ART) pregnancies with comparable cesarean delivery risk (aOR, 1.06; 95% CI, 0.90-1.25). Preterm birth and low birth weight data lacked a comparative group using multivariate analysis. Severe maternal morbidity and maternal mortality were rare among GCs.

CONCLUSIONS AND RELEVANCE: In this systematic review and meta-analysis, although GC pregnancies had slightly improved outcomes compared with non-GC ART pregnancies, they posed higher risks than general pregnancies. Contributing factors may include ART procedures and increased rates of multiple gestations which influence adverse perinatal outcomes in GC pregnancies.

PMID:39042408 | DOI:10.1001/jamanetworkopen.2024.22634