Record Data Research Articles

Symmetric and Asymmetric Expansion of the Weibull Distribution: Features and Applications to Complete, Upper Record, and Type-II Right-Censored Data

This paper introduces a new continuous lifetime model called the Odd Flexible Weibull-Weibull (OFW-W) distribution, which features three parameters. The new model is capable of modeling both symmetric and asymmetric datasets, regardless of whether they are positively or negatively skewed. Its hazard rate functions can exhibit various behaviors, including increasing, decreasing, unimodal, or bathtub-shaped. The key characteristics of the OFW-W model are discussed, including the quantile function, median, reliability and hazard rate functions, kurtosis and skewness, mean waiting (residual) lifetimes, moments, and entropies. The unknown parameters of the model are estimated using eight different techniques. A comprehensive simulation study evaluates the performance of these estimators based on bias, mean squared error (MSE), and mean relative error (MRE). The practical usefulness of the OFW-W distribution is demonstrated through four real datasets from the fields of engineering and medicine, including complete data, upper record data, and type-II right-censored data. Comparisons with five other lifetime distributions reveal that the OFW-W model exhibits superior flexibility and capability in fitting various data types, highlighting its advantages and improvements. In conclusion, we anticipate that the OFW-W model will prove valuable in various applications, including human health, environmental studies, reliability theory, actuarial science, and medical sciences, among others.

Methods for modeling gestational weight gain: empirical application using electronic health record data from a safety net population

BackgroundUnderstanding the risks and effects of gestational weight gain (GWG) is a prominent area of perinatal research but approaches for quantifying GWG are evolving and remain underdeveloped, especially in clinical settings for underserved demographic subgroups. To fill this gap, we demonstrated and compared six GWG metrics across pre-pregnancy BMI classifications: total GWG, trimester-specific linear rate of GWG, adherence to total and trimester-specific recommendations, area under the curve, and GWG for gestational age z-scores.MethodsWe used clinical data on 44,801 pregnant people from community-based health care organizations with extensive longitudinal measures and substantial representation of understudied subgroups.ResultsTotal GWG was lower in individuals with higher pre-pregnancy BMI; yet more temporally resolved analyses revealed differences in trimester-specific weight change. Differences included common first trimester weight loss in people with pre-pregnancy class II or III obesity and substantial first trimester weight gain in people with pre-pregnancy underweight, with the greatest pre-pregnancy BMI-related variation in GWG occurring in the second trimester. These differences are reflected to varying degrees in the AUC and GWG z-score metrics.ConclusionsOur findings inform development of GWG guidelines within BMI categories, especially in obesity subclasses and underweight, and selection, refinement, and application of GWG metrics in future research. GWG metrics differ to varying degrees across BMI categories in a population consisting of several underserved subgroups: pregnant people of color, with larger body sizes, or with lower incomes. Stronger evidence on safe levels of first trimester weight loss and obesity class-specific recommendations is needed.

Effect of nirmatrelvir/ritonavir (Paxlovid) on hospitalization among adults with COVID-19: An electronic health record-based target trial emulation from N3C.

Nirmatrelvir with ritonavir (Paxlovid) is indicated for patients with Coronavirus Disease 2019 (COVID-19) who are at risk for progression to severe disease due to the presence of one or more risk factors. Millions of treatment courses have been prescribed in the United States alone. Paxlovid was highly effective at preventing hospitalization and death in clinical trials. Several studies have found a protective association in real-world data, but they variously used less recent study periods, correlational methods, and small, local cohorts. Their estimates also varied widely. The real-world effectiveness of Paxlovid remains uncertain, and it is unknown whether its effect is homogeneous across demographic strata. This study leverages electronic health record data in the National COVID Cohort Collaborative's (N3C) repository to investigate disparities in Paxlovid treatment and to emulate a target trial assessing its effectiveness in reducing severe COVID-19 outcomes. This target trial emulation used a cohort of 703,647 patients with COVID-19 seen at 34 clinical sites across the United States between April 1, 2022 and August 28, 2023. Treatment was defined as receipt of a Paxlovid prescription within 5 days of the patient's COVID-19 index date (positive test or diagnosis). To emulate randomization, we used the clone-censor-weight technique with inverse probability of censoring weights to balance a set of covariates including sex, age, race and ethnicity, comorbidities, community well-being index (CWBI), prior healthcare utilization, month of COVID-19 index, and site of care provision. The primary outcome was hospitalization; death was a secondary outcome. We estimated that Paxlovid reduced the risk of hospitalization by 39% (95% confidence interval (CI) [36%, 41%]; p < 0.001), with an absolute risk reduction of 0.9 percentage points (95% CI [0.9, 1.0]; p < 0.001), and reduced the risk of death by 61% (95% CI [55%, 67%]; p < 0.001), with an absolute risk reduction of 0.2 percentage points (95% CI [0.1, 0.2]; p < 0.001). We also conducted stratified analyses by vaccination status and age group. Absolute risk reduction for hospitalization was similar among patients that were vaccinated and unvaccinate, but was much greater among patients aged 65+ years than among younger patients. We observed disparities in Paxlovid treatment, with lower rates among black and Hispanic or Latino patients, and within socially vulnerable communities. This study's main limitation is that it estimates causal effects using observational data and could be biased by unmeasured confounding. In this study of Paxlovid's real-world effectiveness, we observed that Paxlovid is effective at preventing hospitalization and death, including among vaccinated patients, and particularly among older patients. This remains true in the era of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Omicron subvariants. However, disparities in Paxlovid treatment rates imply that the benefit of Paxlovid's effectiveness is not equitably distributed.

Development of a Perioperative Risk Mortality Calculator for Humanitarian Surgical Care.

Risk models to predict perioperative mortality rates (POMR) are critical to surgical quality improvement yet are not widely adapted for use in humanitarian and low-resource settings (LRS). We developed a POMR and corresponding nomogram and calculator for use in humanitarian surgical care. Electronic health record data from a high-income academic medical center from 2015 to 2019 were retrospectively extracted, selecting variables and operations specific to LRS. This development dataset was used to create a logistic regression POMR model, which was then prospectively validated using data from 2022 to 2023 from the same institution. EHR from a total of 49,277 patients were used. The model fitted eight variables feasibly obtainable in LRS: age >65years (OR=4.05 and 95% CI: 3.71-4.43), male sex (OR=1.32 and 95% CI: 1.25-1.40), GCS < 13 (OR=5.20 and 95% CI: 4.73-5.73), glucose > 200mg/dL (OR=2.19 and 95% CI: 2.01-2.38), Hgb ≤ 11g/dL (OR=2.65 and 95% CI: 2.43-2.89), HR>120 bpm (OR=2.49 and 95% CI: 2.35-2.64), T>38 degrees Celsius (OR=1.32 and 95% CI: 1.19-1.45), and SBP > 180mmHg (OR=1.18 and 95% CI: 1.02-1.37). The model demonstrated a high area under the curve (0.847, 0.867, and 0.925), sensitivity (0.739, 0.886, and 0.844), specificity (0.807, 0.780, and 0.864), and negative predictive value (0.750, 0.997, and 0.999) on training, holdout, and prospective validation sets. We validated a POMR model for use in LRS using eight variables that are readily available in the target environment. This model's predictors and accompanying clinical tools of an Excel calculator and nomogram make it simultaneously comprehensive and accessible in LRS.

Engagement in child psychiatry department appointments: An analysis of electronic medical records in one safety-net hospital in New England, USA.

This study examined whether being scheduled in a screening clinic versus scheduled directly with a long-term provider to conduct a mental health intake (MHI) is associated with engagement in child psychiatry services in New England, USA. We used electronic medical record data from one safety-net hospital serving a predominantly low-income and minoritised population. The study sample included 815 youths aged 0 to 25years, referred or scheduled for a MHI between 1 January 2016 and 31 December 2016. We used chi-square and t-tests to examine the association between referral pathways and engagement, logistic regression to understand the relationship between youth's socio-demographic characteristics and referral pathways, and logistic and Poisson regressions to assess potential moderating effects of socio-demographic characteristics on engagement. The mean age of the study population was 12years; 46% were female, and the majority had public health insurance (84%) and lived in high social vulnerability areas (65%). Less than half of the youth attended the first scheduled MHI visit. Those scheduled with the screening clinic were less likely than those scheduled with the provider to ever attend a MHI appointment. Spanish-speakers were more likely to be directly scheduled with a provider (Odds Ratio, OR 0.48; 95% CI: 0.32, 0.73), while those with public health insurance were more likely to be scheduled with the screening clinic (OR 0.56; 95% CI: 0.43, 0.96). Spanish-speaking status and areas social vulnerability scores moderated the relationship between the referral pathway and engagement in psychiatric appointments. The study highlights the need for psychiatric services to evaluate how MHI referral procedures may mitigate barriers to care and facilitate engagement for youth at high risk of not attending psychiatric service appointments.

Evaluation of PCORnet as an Approach to Accessing Electronic Health Record (EHR) Data for Cleft Outcomes Research: Advantages and Limitations.

Objective: To evaluate the feasibility of using the National Patient-Centered Clinical Research Network (PCORnet®) as a source of electronic health record (EHR) data for cleft outcomes research. Design: Exploratory retrospective analysis of multi-year, administrative and clinical, structured data stored in PCORnet. Setting: Academic institution with an ACPA-approved cleft and craniofacial team. Patients/Participants: Encounter-level data pertaining to patients with orofacial clefts treated at this center between 2010 and 2018. Outcome Measures: (1) Ability of PCORnet to report metrics such as the following: number of new and returning patients per year; demographics; phenotype; procedures; readmission or reoperation within 30 days; etc. (2) Accuracy of selected metrics, compared with manual chart review. Results: PCORnet is useful for the calculation of simple process metrics such as patient demographics, phenotype mix, case mix, and number of readmissions. However, as it lacks access to clinical notes, PCORnet alone cannot provide more detailed information. Phenotypic classification (based on ICD codes) and procedural description (based on CPT®) are subject to inaccuracy. A 1-2 year delay in data upload to PCORnet may be rate-limiting for certain applications. Multi-institutional queries were feasible. Conclusions: PCORnet does not include all necessary data elements from the EHR. While very convenient for the tabulation of simple process metrics, especially from multiple institutions, supplemental data collection will be required for meaningful cleft outcomes research. Cleft teams whose institutions participate in PCORnet might choose to store the supplemental data as "sidecars" alongside the standard PCORnet database tables, which would allow for future PCORnet queries to be more informative and impactful.

Conflicting data and new ways of digitising Byzantine prosopography

AbstractThis paper aims to illustrate the abilities and practicability of digital prosopographical databases through the case of historical dates. Numerous historical databases were created in the last few decades, and they applied different models to record and store historical data in an effective way. Although the developers and researchers of these projects faced several problems due to a conflict between the characteristics of structured databases and the nature of historical evidence and historical studies. The paper compares the methodologies of two databases, PBW and RELEVEN through the cases of the death of John of Bari and Bisantius Guirdeliku. The primary sources provide conflicting dates of these events. Thus, the analysis can address the issues of interpretation and authority behind a piece of information.

Performance of an Algorithm Grading Surgery-Related Adverse Events According to the Clavien-Dindo Classification.

To assess performance of an algorithm for automated grading of surgery-related adverse events (AEs) according to Clavien-Dindo (C-D) classification. Surgery-related AEs are common, lead to increased morbidity for patients, and raise healthcare costs. Resource-intensive manual chart review is still standard and to our knowledge algorithms using electronic health record (EHR) data to grade AEs according to C-D classification have not been explored. The algorithm was developed in a research database containing all EHR data of Karolinska University Hospital Stockholm and returns a C-D grade for each AE within 30 days. This raw score was used to grade postoperative recovery of 1,379 elective colorectal procedures according to C-D classification and Comprehensive Complication Index® (CCI). Agreement with manual annotation of colorectal surgeon (gold standard) and research nurse (current practice) was assessed in a random sample of 399 procedures. For the C-D classification, kappa was 0.77 (95%CI 0.71-0.84) for algorithm vs surgeon and 0.74 (95%CI 0.67-0.82) for algorithm vs nurse. The kappa value increased to 0.89 (95%CI 0.84-0.95) after correction of misclassified annotations of surgeon. The intraclass correlation for CCI between algorithm and surgeon was 0.89 (95%CI 0.87-0.91) after correction and 0.76 (95%CI 0.71-0.80) for algorithm vs nurse. The performance of the algorithm motivates in our opinion implementation to real-time data under continuous scientific evaluation of the impact on AEs in different types of surgery. In the future, local EHR data could be used to enhance risk prediction with machine learning techniques.

FHIR PIT: a geospatial and spatiotemporal data integration pipeline to support subject-level clinical research

BackgroundEnvironmental exposures such as airborne pollutant exposures and socio-economic indicators are increasingly recognized as important to consider when conducting clinical research using electronic health record (EHR) data or other sources of clinical data such as survey data. While numerous public sources of geospatial and spatiotemporal data are available to support such research, the data are challenging to work with due to inconsistencies in file formats and spatiotemporal resolutions, computational challenges with large file sizes, and a lack of tools for patient- or subject-level data integration.ResultsWe developed FHIR PIT (HL7® Fast Healthcare Interoperability Resources Patient data Integration Tool) as an open-source, modular, data-integration software pipeline that consumes EHR data in FHIR® format and integrates the data at the level of the patient or subject with environmental exposures data of varying spatiotemporal resolutions and file formats. We applied FHIR PIT to generate “integrated feature tables” containing patient- or subject-level EHR data integrated with environmental exposures data on two cohorts: one on patients with asthma and related common pulmonary disorders; and a second on patients with primary ciliary dyskinesia and related rare pulmonary disorders. The data were then exposed via the open Integrated Clinical and Environmental Exposures Service, which was then queried to explore relationships between exposures to two representative airborne pollutants (particulate matter and ozone) and annual emergency department or inpatient visits for respiratory issues. We found that hospitalizations for respiratory issues were more common among patients exposed to relatively high levels of particulate matter and ozone and were higher overall among patients with primary ciliary dyskinesia than among patients with asthma.ConclusionsOur manuscript describes a major release of FHIR PIT v1.0 and includes a technical demonstration use case and a clinical application on the use of FHIR PIT to support research on environmental exposures and health outcomes related to asthma and primary ciliary dyskinesia. For application of the tool to common data models (CDMs) other than FHIR, we offer open-source conversion tools to map from the PCORnet, i2b2, and OMOP CDMs to FHIR.

EXPRESS: Quality, Necessity, or Accessibility? Impact of Dockless Bike Sharing on Ineffective Use of Medical Resources

In recent years, patients have increasing autonomy in medical decision-making. With great autonomy in selecting medical institutions, patients with low-risk diseases (LRD) may opt for treatment in prestigious hospitals, which limits the access of patients with high-risk diseases (HRD) to these hospitals. Moreover, due to limited medical knowledge and a fear of uncertainty regarding diseases, patients with greater medical autonomy may request various unnecessary medical tests to alleviate their health-related anxiety. This study takes a novel patients’ perspective to investigate how dockless bike sharing, which addresses the “last-mile problem” and improves geographical accessibility to medical institutions, affects two types of ineffective use of medical resources: disease-institution mismatch and overuse of medical tests. By introducing the concept of quality-accessibility trade-off in patients’ decision-making processes, we shed light on the psychological mechanism underlying the effect of dockless bike sharing on patients’ medical institution choices. We also introduce a new concept, perceived necessity of medical tests, to explain how dockless bike sharing reduces unnecessary medical tests. In Study 1, we designed a quasi-experiment and applied a difference-in-differences model based on real-world electronic health record data to show that dockless bike sharing increased the likelihood of patients choosing hospitals over clinics and decreased the number of unnecessary medical tests. In Studies 2a, 2b, 3a, and 3b, four randomized controlled experiments were employed to replicate the findings and reveal the underlying mechanisms. In sum, we found that dockless bike sharing acted as a “double-edged sword” in terms of its impact on exacerbating disease-institution mismatch and alleviating the overuse of medical tests. This study makes significant theoretical contributions to the Operations Management (OM), sharing economy, and medical resource management literature. It also offers important managerial insights for governments, shared mobility platforms, and healthcare providers.

Early Results of Implementing Rapid Methadone Titration for Hospitalized Patients: A Case Series.

With the increase in illicit fentanyl use in the USA, hospitals face challenges managing opioid withdrawal and opioid use disorder (OUD). To improve opioid withdrawal and OUD treatment among hospitalized patients with daily fentanyl use, we developed a rapid methadone titration (RMT) protocol. We describe development, implementation, and outcomes during the first 12weeks. We analyzed electronic health record data of hospitalizations seen by the Addiction Consult Team (ACT) for methadone initiation between 9/11/23 and 12/3/23. Adults aged 18-64 reporting daily fentanyl use, desiring methadone, and without end-stage organ damage or critical illness were RMT eligible. We characterized patients who received RMT, abstracting demographic and clinical characteristics, adverse events graded by the Common Terminology Criteria for Adverse Events(CTCAE), and methadone and additional full agonist opioid (FAO) dosing. Our primary outcome was adverse events. Secondary outcomes included median time to 100mg of methadone, FAO dosing, and self-directed discharge. ACT assessed 55 hospitalizations representing 47 patients for RMT eligibility. Among these, 19 (34.5%) hospitalizations representing 17 patients were eligible for and received RMT. Four (21.2%) hospitalizations that received RMT had sedation events, and all were mild or moderate grade by CTCAE. Hospitalizations achieved a median methadone dose of 100mg by day 6, with FAO doses peaking on day 5. One (5.3%) hospitalization had a self-directed discharge. With careful patient selection and ACT evaluation, a RMT protocol for hospitalized patients with fentanyl use disorder experienced few adverse events other than mild-moderate sedation, even among those receiving FAO and those with concurrent substance use disorders.

Mental Health Utilization Among Transgender Veterans

Compared with cisgender (CG) individuals, transgender and gender-diverse (TGD) individuals experience substantial social and economic disparities that can result in adverse mental health consequences. It is critical to understand potential barriers to care and to address the causes of the disparities in the future. To characterize mental health care utilization among TGD veterans with depression. This cohort study used electronic health record data from the US Department of Veterans Affairs (VA) to create a 1:3 age group-matched and VA facility-matched nationwide cohort of TGD and CG veterans with documentation of depression during 2018 to 2020. Data analysis was performed from January to November 2023. TGD identity was ascertained by diagnosis of a gender identity disorder. The primary outcome was mental health care utilization, including counts of outpatient (in specialty care and primary care settings), telehealth, emergency department, and inpatient visits in this cohort. Descriptive statistics were used to characterize counts of mental health utilization, and statistically significant differences between TGD and CG veterans were tested using χ2 and Fisher exact tests. Wilcoxon rank-sum tests were used to test for differences in utilization between the 2 groups. Adjusted logistic regression, controlling for age group, administrative sex, race, Charlson Comorbidity Index, and number of mental health medications (eg, antidepressant, antipsychotic, and anxiolytic medications), was also used to compare utilization between TGD and CG veterans. Among 10 564 veterans with depression (mean [SD] age, 46.4 [15.2] years; 8050 male [76.2%]), 2643 TGD veterans were matched with 7921 CG veterans. TGD veterans had 6 more specialty mental health visits per year than CG veterans (mean [SD], 13.93 [20.08] vs 8.46 [14.96] visits a year; median [range], 7.14 [0.00-246.30] vs 3.76 [0.00-202.38] visits per year). In adjusted models, compared with CG veterans, TGD veterans were 2.6 times more likely to have an outpatient mental-health visit (odds ratio, 2.60; 95% CI, 2.16-3.15). In this cohort study of veterans with depression, TGD veterans had significantly higher utilization of mental health services compared with CG veterans, even after adjusting for several relevant health factors. Different health system resources may be required to meet the needs of this population. Further studies are needed to understand the determinants of these disparities and subsequently how to address them.

Preliminary Effectiveness of a Telehealth-Delivered Exercise Program in Older Adults Living With and Beyond Cancer: Retrospective Study.

Exercise can attenuate the deleterious combined effects of cancer treatment and aging among older adults with cancer, yet exercise participation is low. Telehealth exercise may improve exercise engagement by decreasing time and transportation barriers; however, the utility of telehealth exercise among older adults with cancer is not well established. We aimed to evaluate the preliminary effectiveness of a one-on-one, supervised telehealth exercise program on physical function, muscular endurance, balance, and flexibility among older adults with cancer. In this retrospective study, we analyzed electronic health record data collected from the Personal Optimism With Exercise Recovery clinical exercise program delivered via telehealth among older adults with cancer (≥65 y) who completed a virtual initial program telehealth assessment between March 2020 and December 2021. The virtual initial assessment included the following measures: 30-second chair stand test, 30-second maximum push-up test, 2-minute standing march, single leg stance, plank, chair sit and reach, shoulder range of motion, and the clock test. All baseline measures were repeated after 12-weeks of telehealth exercise. Change scores were calculated for all assessments and compared to minimal clinically important difference (MCID) values for assessments with published MCIDs. Paired samples t tests (2-tailed) were conducted to determine change in assessment outcomes. Older adults with cancer who chose to participate in the telehealth exercise program (N=68) were 71.8 (SD 5.3) years of age on average (range 65-92 y). The 3 most common cancer types in this sample were breast (n=13), prostate (n=13), and multiple myeloma (n=8). All cancer stages were represented in this sample with stage II (n=16, 23.5%) and III (n=18, 26.5%) being the most common. A follow-up telehealth assessment was completed by 29.4% (n=20) of older adults with cancer. Among those who completed a follow-up telehealth assessment, there were significant increases in the 30-second chair stand (n=19; mean change +2.00 repetitions, 95% CI 0.12 to 3.88) and 30-second maximum push-up scores (n=20; mean change +2.85 repetitions, 95% CI 1.60 to 4.11). There were no significant differences for the 2-minute standing march, plank, single leg stance, sit and reach, shoulder mobility, or clock test (P>.05). Nine (47.3%) older adults with cancer had a change in 30-second chair stand scores greater than the MCID of 2 repetitions. Our findings suggest a one-on-one, supervised telehealth exercise program may positively influence measures of physical function, muscular endurance, balance, and flexibility among older adults with cancer, but more adequately powered trials are needed to confirm these findings.

Pola Peresepan Obat Diare pada Pasien Balita di Instalasi Rawat Jalan RSUD Kajen Kabupaten Pekalongan Periode Januari – Juni 2024

Diarrhea generally attacks toddlers under 5 years of age because toddlers' immune systems are still in the weak category, so they are more susceptible to exposure to bacteria that cause diarrhea. The Central Java Provincial Health Service recorded 179,172 or 46.3% of the highest cases of diarrhea found in health facilities. The aim of this research was to determine the prescribing patterns used for toddler patients in the outpatient installation of Kajen Regional Hospital, Pekalongan Regency. This type of research is non-experimental using a descriptive design with a quantitative approach and collecting data retrospectively using medical record data. The inclusion criteria in this study were diarrhea patients aged 0-5 years in the outpatient installation of Kajen Regional Hospital during January – June 2024 who received treatment for diarrhea without a diagnosis of chronic disease using purposive sampling data collection techniques. The research was conducted on 66 patients who met the inclusion criteria with the results of the characteristics of the patients who suffered the most from diarrhea being 56.06% male, 63.64% aged 0-1 year. The most frequently prescribed class of drugs is zinc at 47.06% and the least prescribed class of drugs is oral rehydration at 0.84%.

Variation in Hypertension Control by Race and Ethnicity, and Geography in US Veterans.

Hypertension control and related cardiovascular outcomes among Americans remain suboptimal, and differ by race, ethnicity, and geography. Healthcare access is one of multiple critical factors in hypertension control. Understanding the degree to which healthcare access, versus other factors, produce these outcomes can inform policies and interventions to improve cardiovascular outcomes and reduce disparities. Department of Veterans Affairs Healthcare System data provide a unique opportunity to understand residual racial and ethnic differences in hypertension control after accounting for healthcare access. Our objective was to describe pre-pandemic post-Affordable Care Act implementation hypertension control by geographic sector and race and ethnicity, and assess spatial clustering of hypertension control. A secondary data analysis of hypertension control among US veterans (n=1 619 414) nationwide and in 4 US territories was conducted using electronic health record data. Age- and sex-adjusted regression models estimated overall and race- and ethnicity-specific rates by geographic sector. We created choropleth maps of hypertension control rates and assessed spatial autocorrelation. Hypertension control rates varied across sectors by race and ethnicity (range, 44.1%-97.5%); Black veterans, followed by American Indian or Alaska Native veterans, had the lowest mean control rates (72.5% and 75.4%, respectively). There was clustering of low hypertension control rates for Black veterans in the Pacific Northwest, Southwest, Missouri, Kansas, and Arkansas, and for American Indian or Alaska Native veterans in the West and Southwest. Hypertension control rates varied geographically for veteran groups experiencing racial and ethnic disparities. Geographic areas with concentrations of low rates of hypertension control should be a focus for interventions to address racial and ethnic disparities.

Gambaran Penggunaan Obat Antiinflamasi Non Steroid Pada Pasien Penderita Osteoarthritis di Instalasi Rawat Jalan RSUD Kajen Periode Januari – Oktober 2024

Basic Health Research data in 2018 recorded that the rate of osteoarthritis sufferers in Central Java was around 6.78% and the rate of osteoarthritis sufferers in Pekalongan Regency was around 5.37%. Treatment of osteoarthritis can be done pharmacologically by administering non-steroidal anti-inflammatory drugs. The purpose of this study is to find out the description of the use of non-steroidal anti-inflammatory drugs in patients with osteoarthritis in the outpatient unit of Kajen Hospital, Pekalongan Regency. This type of research is non-experimental using a descriptive design with a quantitative approach and retrospective data collection using medical record data. The inclusion criteria in this study are patients with osteoarthritis in the outpatient unit of Kajen Hospital during January-October 2024 diagnosed with or without comorbidities. The data collection technique is purposive sampling. This study was conducted on 300 patients who met the inclusion criteria with the most characteristic results experienced by osteoarthritis patients aged 55-64 years, 45.00% were female, 74.64% were diagnosed with comorbidities, 93.00%. The most widely used non-steroidal anti-inflammatory drugs in the treatment of osteoarthritis in the outpatient installation of Kajen Hospital are Meloxicam 15 mg 48.67% and the most widely used non-steroidal anti-inflammatory drugs are COX-2 Selective Agents 89.67%.

Leveraging Natural Language Processing and Machine Learning Methods for Adverse Drug Event Detection in Electronic Health/Medical Records: A Scoping Review.

Natural language processing (NLP) and machine learning (ML) techniques may help harness unstructured free-text electronic health record (EHR) data to detect adverse drug events (ADEs) and thus improve pharmacovigilance. However, evidence of their real-world effectiveness remains unclear. To summarise the evidence on the effectiveness of NLP/ML in detecting ADEs from unstructured EHR data and ultimately improve pharmacovigilance in comparison to other data sources. A scoping review was conducted by searching six databases in July 2023. Studies leveraging NLP/ML to identify ADEs from EHR were included. Titles/abstracts were screened by two independent researchers as were full-text articles. Data extraction was conducted by one researcher and checked by another. A narrative synthesis summarises the research techniques, ADEs analysed, model performance and pharmacovigilance impacts. Seven studies met the inclusion criteria covering a wide range of ADEs and medications. The utilisation of rule-based NLP, statistical models, and deep learning approaches was observed. Natural language processing/ML techniques with unstructured data improved the detection of under-reported adverse events and safety signals. However, substantial variability was noted in the techniques and evaluation methods employed across the different studies and limitations exist in integrating the findings into practice. Natural language processing (NLP) and machine learning (ML) have promising possibilities in extracting valuable insights with regard to pharmacovigilance from unstructured EHR data. These approaches have demonstrated proficiency in identifying specific adverse events and uncovering previously unknown safety signals that would not have been apparent through structured data alone. Nevertheless, challenges such as the absence of standardised methodologies and validation criteria obstruct the widespread adoption of NLP/ML for pharmacovigilance leveraging of unstructured EHR data.

Development and Validation of a Routine Electronic Health Record-Based Delirium Prediction Model for Surgical Patients Without Dementia: Retrospective Case-Control Study.

Postoperative delirium (POD) is a common complication after major surgery and is associated with poor outcomes in older adults. Early identification of patients at high risk of POD can enable targeted prevention efforts. However, existing POD prediction models require inpatient data collected during the hospital stay, which delays predictions and limits scalability. This study aimed to develop and externally validate a machine learning-based prediction model for POD using routine electronic health record (EHR) data. We identified all surgical encounters from 2014 to 2021 for patients aged 50 years and older who underwent an operation requiring general anesthesia, with a length of stay of at least 1 day at 3 Indiana hospitals. Patients with preexisting dementia or mild cognitive impairment were excluded. POD was identified using Confusion Assessment Method records and delirium International Classification of Diseases (ICD) codes. Controls without delirium or nurse-documented confusion were matched to cases by age, sex, race, and year of admission. We trained logistic regression, random forest, extreme gradient boosting (XGB), and neural network models to predict POD using 143 features derived from routine EHR data available at the time of hospital admission. Separate models were developed for each hospital using surveillance periods of 3 months, 6 months, and 1 year before admission. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). Each model was internally validated using holdout data and externally validated using data from the other 2 hospitals. Calibration was assessed using calibration curves. The study cohort included 7167 delirium cases and 7167 matched controls. XGB outperformed all other classifiers. AUROCs were highest for XGB models trained on 12 months of preadmission data. The best-performing XGB model achieved a mean AUROC of 0.79 (SD 0.01) on the holdout set, which decreased to 0.69-0.74 (SD 0.02) when externally validated on data from other hospitals. Our routine EHR-based POD prediction models demonstrated good predictive ability using a limited set of preadmission and surgical variables, though their generalizability was limited. The proposed models could be used as a scalable, automated screening tool to identify patients at high risk of POD at the time of hospital admission.

Analysis of Repeat Hepatitis C Viremia After Sustained Virologic Response in a Large Cohort of U.S. Veterans.

Chronic hepatitis C virus (HCV) infection affects >1% of the U.S. population, higher among U.S. Veterans. Direct-acting antiviral (DAA) medications are effective for viral cure, termed sustained virologic response (SVR), but repeat viremia after SVR is recognized. Prior work has been limited by use of electronic medical record data. We aim to better understand repeat viremia in the DAA era through detailed chart review. We identified 1,129 individuals from Veteran's Health Administration (VHA) who achieved SVR using DAA therapy but subsequently had detectable serum HCV nucleic acid. A random subset of 110 were chart reviewed and assigned one of four categories using laboratory, diagnosis, and chart review data: definite reinfection (25.5%), probable reinfection (25.5%), false positive (11.8%), and presumed late relapse (37.3%). We conducted between-group analysis of variance to identify demographic, behavioral, and laboratory features specific to each. In our medical record cohort (n=1,129), substance use and unstable housing were common and median time to repeat viremia was 1.9 years. In our chart review cohort (n=110), younger age (18-34) and substance use were more frequent in definite or probable reinfection. Presumed relapse had comparatively more comorbid hepatocellular carcinoma (HCC) (20%, p<0.05) and more than half occurred prior to 1 year. The unique category of false positive has not previously been reported. This study deepens understanding of HCV reinfection and relapse and highlights important features including the HCV and opioid syndemic, contribution of laboratory error, possibility of a viral reservoir in HCC, and clinical engagement implications for those with ongoing risk.

Bicalutamide does not raise transaminases clinically significantly compared to alternative anti-androgen regimens among transfeminine adolescents and young adults: a retrospective cohort study

Background Bicalutamide is a potential anti-androgen for transgender individuals with feminizing embodiment goals, but use is limited because of hepatotoxicity in cisgender men with prostate cancer. This study compared transaminase changes in transfeminine adolescents and young adults (AYA) using low dose bicalutamide with individuals using another androgen blockade. Methods A retrospective analysis was conducted using electronic health record data for patients starting gender affirming hormone therapy with at least 10 months of follow-up between 2015 and 2023. The primary outcome was change in alanine aminotransferase (ALT) and aspartate aminotransferase (AST) from baseline. Linear mixed models compared change in ALT and AST from baseline and maximum ALT and AST values in bicalutamide and comparison groups. Secondary outcomes included % individuals with ALT and AST elevation more than 1, 2, or 3 times the upper limit of normal (ULN) (Fisher’s exact test), standardized mean estradiol dose by group (t test), and Tanner staging of breast tissue by group (Fisher’s exact test). Results Eighty-four transfeminine AYA (median age 18) taking bicalutamide were compared to 69 transfeminine AYA (median age 19) taking GnRH agonists, spironolactone or no agent in addition to estradiol. In linear mixed models adjusted for baseline age, race, BMI, baseline ALT or AST, and alcohol use, there were no clinically significant differences in delta or maximum ALT or AST in bicalutamide and comparison groups. No individuals had AST or ALT levels > 3x ULN though % with AST > ULN was higher for bicalutamide (10.7 v 1.5%, p = 0.02). Estradiol doses and Tanner stages were similar between groups among individuals receiving pediatric care. Conclusion Bicalutamide was not associated with a clinically significant change in transaminases as compared with other anti-androgen regimens over one year. Bicalutamide appears to be a safe anti-androgen for transfeminine individuals at low dose with close monitoring and deserves further study.