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Objective To quantify differences in hospital-associated costs, and accompanying travel costs and productivity losses, before and after withdrawing TNF-α inhibitors (TNFi) in JIA patients. Methods This was a retrospective analysis of prospectively collected data from electronic medical records of paediatric JIA patients treated with TNFi, which were immediately discontinued, spaced (increased treatment interval) or tapered (reduced subsequent doses). Costs of hospital-associated resource use (consultations, medication, radiology procedures, laboratory testing, procedures under general anaesthesia, hospitalization) and associated travel costs and productivity losses were quantified during clinically inactive disease until TNFi withdrawal (pre-withdrawal period) and compared with costs during the first and second year after withdrawal initiation (first and second year post-withdrawal). Results Fifty-six patients were included of whom 26 immediately discontinued TNFi, 30 spaced and zero tapered. Mean annual costs were €9165/patient on active treatment (pre-withdrawal) and decreased significantly to €5063/patient (−44.8%) and €6569/patient (−28.3%) in the first and second year post-withdrawal, respectively (P  < 0.05). Of these total annual costs, travel costs plus productivity losses were €834/patient, €1180/patient, and €1320/patient in the three periods respectively. Medication comprised 80.7%, 61.5% and 72.4% of total annual costs in the pre-withdrawal, first and second year post-withdrawal period, respectively. Conclusion In the first two years after initiating withdrawal, the total annual costs were decreased compared with the pre-withdrawal period. However, cost reductions were lower in the second year compared with the first year post-withdrawal, primarily due to restarting or intensifying biologics. To support biologic withdrawal decisions, future research should assess the full long-term societal cost impacts, and include all biologics. [ABSTRACT FROM AUTHOR]

Introduction Asthma is the most common diagnosis in military personnel who endorse chronic dyspnea. Service members have unique occupational risk factors, and there is concern that airborne exposures in the deployed environment as well as other occupational exposures may contribute to the development of asthma or exacerbate pre-existing disease. Asthma phenotyping with clinical biomarkers such as serum immunoglobulin E (IgE) levels and eosinophil (EOS) counts is useful in defining treatment strategies for the management of asthma. This study sought to characterize the phenotype of medically separated military personnel with career-limiting asthma to define potential management strategies and guide future research evaluating the unexplained prevalence of asthma in this population. Materials and Methods A retrospective chart review of active duty service members (ADSM) who underwent fitness for duty evaluation via medical evaluation board between 2005 and 2016 and were separated with a minimum 30% conditional disability rating for asthma was performed. Only ADSM who were diagnosed with asthma by a pulmonologist and had spirometry data available were included in the analysis. Demographics, spirometry data, and laboratory data to include IgE levels, radioallergosorbent panels, and EOS counts were analyzed from the DoD electronic medical record. Results A total of 141 service members were evaluated with a mean age of 42 ± 6.8 years, mean serum EOS count of 300 ± 358 cells/μL, and mean IgE level of 305 ± 363 IU/mL. The patients were further categorized into 4 subgroups based on serum EOS count and IgE level: group A with IgE < 100 IU/mL and EOS < 300 cells/μL (n  = 45; 33%), group B with IgE > 100 IU/mL and EOS < 300 cells/μL (n  = 44; 32%), group C with IgE < 100 IU/mL and EOS > 300 cells/μL (n  = 6; 1%), and group D with IgE > 100 IU/mL, EOS > 300 cells/μL (n  = 46; 34%). Among the cohorts, there were no statistically significant differences in demographics, body mass index, spirometry, smoking history, or disability rating. Conclusion The majority of ADSM with a defined asthma history do not have concordant elevations in serum IgE and blood EOS suggestive of a Th2-high phenotype. Asthma in this population is heterogeneous, and phenotyping using clinical biomarkers may be useful to define optimal treatment strategies. [ABSTRACT FROM AUTHOR]

Purpose To analyze the appropriateness of triple-antithrombotic therapy based on the 2020 American College of Cardiology (ACC) consensus statement while evaluating safety outcomes for patients with respect to adverse events. Methods A single-center, retrospective chart review was conducted using electronic medical records from December 18, 2020, to August 31, 2022. The primary endpoint was the rate of appropriateness for triple-antithrombotic therapy in patients discharged from Ochsner LSU Health Shreveport. Appropriateness was a composite endpoint extrapolated from the 2020 ACC consensus statement. For therapy to be defined as appropriate, patients had to have had the correct therapy indication, medications, dosing, and 30-day duration. Secondary safety endpoints included the percentage of patients rehospitalized at 14 and 30 days, the rate of major bleeding events, and the percentage of patients on gastrointestinal prophylaxis while on triple-antithrombotic therapy. Results A total of 93 patients were included in the study, of whom 31 (33%) received appropriate triple-antithrombotic therapy. Prolonged duration of triple-antithrombotic therapy was the most common reason that therapy did not meet the primary endpoint. The readmission rate due to bleeding was 2.2% at 14 days and 6.5% at 30 days. Within 30 days of initiation of triple therapy, 4.3% of patients endured major bleeding as defined by the International Society on Thrombosis and Hemostasis and 2 patients died. Conclusion In this single-center study, triple-antithrombotic therapy appropriately adhered to the 2020 ACC consensus statement for one-third of patients discharged on this therapy. [ABSTRACT FROM AUTHOR]

Background Population screening for colorectal cancer (CRC) remains low, requiring alternative approaches for increasing participation. Opportunistic screening of hospitalized patients may aid in increasing uptake rates. Objective To assess whether inpatients can be recruited for opportunistic CRC screening using fecal immunochemical testing (FIT). Methods Inpatient charts were prospectively reviewed for study eligibility on admission of patients to the medical wards of 3 hospitals in Winnipeg, Canada. Eligible patients were approached for participation and consent. Inoculated FIT specimen collection tubes were sent to the hospital laboratory for testing. Patients with positive FIT results received a follow-up colonoscopy. Results In total, 1542 inpatient charts were screened for eligibility; 53 patients were identified for enrollment (51.9% were male; median age, 59 years), of whom 13 patients consented to participate but only 7 provided a stool specimen. One of those 7 patients had a positive FIT result. The overall screening rate was low, at 0.45%. The primary reason for exclusion of patients was age (outside of the range of 50-75 years), followed by patients having recent gastrointestinal bleeding and/or known intestinal diseases. Conclusions Our data suggest that it is infeasible to recruit inpatients for opportunistic CRC screening in routine clinical practice. [ABSTRACT FROM AUTHOR]

Objectives Iron-deficiency anemia (IDA) is a common health problem worldwide, and up to 10% of adult patients with incidental IDA may have gastrointestinal cancer. A diagnosis of IDA can be established through a combination of laboratory tests, but it is often underrecognized until a patient becomes symptomatic. Based on advances in machine learning, we hypothesized that we could reduce the time to diagnosis by developing an IDA prediction model. Our goal was to develop 3 neural networks by using retrospective longitudinal outpatient laboratory data to predict the risk of IDA 3 to 6 months before traditional diagnosis. Methods We analyzed retrospective outpatient electronic health record data between 2009 and 2020 from an academic medical center in northern Texas. We included laboratory features from 30,603 patients to develop 3 types of neural networks: artificial neural networks, long short-term memory cells, and gated recurrent units. The classifiers were trained using the Adam Optimizer across 200 random training-validation splits. We calculated accuracy, area under the receiving operating characteristic curve, sensitivity, and specificity in the testing split. Results Although all models demonstrated comparable performance, the gated recurrent unit model outperformed the other 2, achieving an accuracy of 0.83, an area under the receiving operating characteristic curve of 0.89, a sensitivity of 0.75, and a specificity of 0.85 across 200 epochs. Conclusions Our results showcase the feasibility of employing deep learning techniques for early prediction of IDA in the outpatient setting based on sequences of laboratory data, offering a substantial lead time for clinical intervention. [ABSTRACT FROM AUTHOR]

Objective To develop a machine learning-based prediction model for identifying hyperuricemic participants at risk of developing gout. Methods A retrospective nationwide Israeli cohort study used the Clalit Health Insurance database of 473 124 individuals to identify adults 18 years or older with at least two serum urate measurements exceeding 6.8 mg/dl between January 2007 and December 2022. Patients with a prior gout diagnosis or on gout medications were excluded. Patients' demographic characteristics, community and hospital diagnoses, routine medication prescriptions and laboratory results were used to train a risk prediction model. A machine learning model, XGBoost, was developed to predict the risk of gout. Feature selection methods were used to identify relevant variables. The model's performance was evaluated using the receiver operating characteristic area under the curve (ROC AUC) and precision-recall AUC. The primary outcome was the diagnosis of gout among hyperuricemic patients. Results Among the 301 385 participants with hyperuricemia included in the analysis, 15 055 (5%) were diagnosed with gout. The XGBoost model had a ROC-AUC of 0.781 (95% CI 0.78–0.784) and precision-recall AUC of 0.208 (95% CI 0.195–0.22). The most significant variables associated with gout diagnosis were serum uric acid levels, age, hyperlipidemia, non-steroidal anti-inflammatory drugs and diuretic purchases. A compact model using only these five variables yielded a ROC-AUC of 0.714 (95% CI 0.706–0.723) and a negative predictive value (NPV) of 95%. Conclusions The findings of this cohort study suggest that a machine learning-based prediction model had relatively good performance and high NPV for identifying hyperuricemic participants at risk of developing gout. [ABSTRACT FROM AUTHOR]

Background Chat Generative Pre-trained Transformer (ChatGPT) and other ChatBots have emerged as tools for interacting with information in manners resembling natural human speech. Consequently, the technology is used across various disciplines, including business, education, and even in biomedical sciences. There is a need to better understand how ChatGPT can be used to advance gerontology research. Therefore, we evaluated ChatGPT responses to questions on specific topics in gerontology research, and brainstormed recommendations for its use in the field. Methods We conducted semistructured brainstorming sessions to identify uses of ChatGPT in gerontology research. We divided a team of multidisciplinary researchers into 4 topical groups: (a) gero-clinical science, (b) basic geroscience, (c) informatics as it relates to electronic health records, and (d) gero-technology. Each group prompted ChatGPT on a theory-, methods-, and interpretation-based question and rated responses for accuracy and completeness based on standardized scales. Results ChatGPT responses were rated by all groups as generally accurate. However, the completeness of responses was rated lower, except by members of the informatics group, who rated responses as highly comprehensive. Conclusions ChatGPT accurately depicts some major concepts in gerontological research. However, researchers have an important role in critically appraising the completeness of its responses. Having a single generalized resource like ChatGPT may help summarize the preponderance of evidence in the field to identify gaps in knowledge and promote cross-disciplinary collaboration. [ABSTRACT FROM AUTHOR]

Background Many adolescents with type 1 diabetes experience diabetes distress which is associated with suboptimal glycemic and psychosocial outcomes. The ways in which adolescents respond to diabetes distress may serve as a risk or protective factor for these outcomes, but few studies have examined the coping strategies adolescents use to manage diabetes distress. Purpose To examine the association of coping strategies with glycemic and psychosocial outcomes among adolescents experiencing diabetes distress. Methods Participants included 198 adolescents with elevated diabetes distress who completed baseline data for a randomized controlled trial (M age = 15.3 ± 1.4, 58% female, 58% non-Hispanic White, M A1c = 9.1 ± 2.1%). Adolescents reported on their use of coping strategies related to diabetes stressors, including primary control engagement coping (e.g. problem-solving), secondary control engagement coping (e.g. positive thinking), and disengagement coping (e.g. avoidance). Adolescents also completed measures of diabetes distress, quality of life, and resilience. HbA1c data were extracted from electronic medical records and at-home kits. Results Higher use of primary control engagement coping was associated with better glycemic and psychosocial outcomes. Secondary control engagement coping was associated with better psychosocial outcomes but not glycemic outcomes. Greater use of disengagement coping strategies was associated with poorer glycemic and psychosocial outcomes. All associations were significant after adjusting for adolescent sex, age, race/ethnicity, and continuous glucose monitor use. Conclusions These results build on prior findings by including a more diverse sample of adolescents and highlight the value of promoting engagement coping strategies and discouraging the use of disengagement coping strategies among adolescents experiencing diabetes distress. Clinical Trial information NCT03845465. [ABSTRACT FROM AUTHOR]

Background There are limited data on the risk of new-onset anxiety disorders in patients with hidradenitis suppurativa (HS). Objectives To compare the risk of new-onset anxiety disorder in patients with HS and controls, and to describe risk factors for the development of anxiety in patients with HS. Methods We carried out a retrospective cohort analysis of a US electronic health records database between 2011 and 2020. Adults newly diagnosed with HS at a dermatology or primary care visit and control participants were included. The primary outcome was a new diagnosis of generalized anxiety disorder, phobic disorders, panic disorder or unspecified anxiety. Cox proportional hazards regression was used to compare the crude risk of any anxiety disorder between groups and to assess the independent association with HS while controlling for potential demographic, clinical and healthcare-related confounders. Results Among 9597 patients with HS and 959 493 controls, the incidence rate (IR) of anxiety was 5.74 and 3.86 per 100 person-years (PY), respectively. The crude risk among all patients was 48% higher for those with HS vs. controls [hazard ratio (HR) 1.48, 95% confidence interval (CI) 1.40–1.55]. When stratified by index encounter type, patients with HS had 2.43 (95% CI 2.13–2.77) times the risk of anxiety disorder than dermatology controls and 1.46 (95%CI 1.38–1.55) times the risk than primary care controls. The adjusted HR for patients with HS vs. controls was 1.11 (95% CI 1.05–1.17) overall, 1.26 (95% CI 1.07–1.48) in the dermatology subgroup and 1.07 (95% CI 1.01–1.13) in the primary care subgroup. Risk factors for an incident anxiety diagnosis among patients with HS included depression (HR 1.69, 95% CI 1.48–1.93), female sex (HR 1.41, 95% CI 1.23–1.60), younger age (HR 0.87 per 10-year increase, 95% CI 0.84–0.90), White race, in the Medicaid insurance programme (HR 1.22, 95% CI 1.07–1.40), tobacco smoking (HR 1.16, 95% CI 1.03–1.31) and having one or more emergency department visits in the year before a HS diagnosis. Absolute IRs of anxiety disorders were highest among patients with HS who were aged 18–29 years (7.10 per 100 PY), female (6.34 per 100 PY) and White (6.79 per 100 PY). Conclusions HS is independently associated with an increased risk of anxiety disorders. An increased risk remains but is attenuated when confounders are controlled for. The relative risk may be particularly high in patients managed by dermatologists. [ABSTRACT FROM AUTHOR]

BACKGROUND People with HIV (human immunodeficiency virus; PWH) have higher rates of cardiovascular disease than people without HIV. However, limited information exists about hypertension prevalence and associated risk factors in PWH. METHODS This cross-sectional study included adult patients in the 2022 IQVIATM Ambulatory Electronic Medical Record—US data. HIV was identified based on ≥2 HIV diagnosis codes or a positive HIV test. Hypertension was identified by diagnosis codes, ≥2 blood pressure (BP) readings ≥130/80 mm Hg, or an antihypertensive medication prescription. Among those with hypertension, control was defined as the most recent BP < 130/80 mm Hg. Logistic models using the marginal standardization method were used to estimate adjusted prevalence ratios (aPR) of hypertension and hypertension control among all patients and PWH specifically, controlling for covariates. RESULTS Of 7,533,379 patients, 19,102 (0.3%) had HIV. PWH had higher hypertension prevalence (66% vs. 54%, aPR:1.14, 95% CI: 1.13–1.15) compared with people without HIV. Among persons with hypertension, PWH were more likely to have controlled hypertension (aPR: 1.10, 95% CI: 1.07–1.13) compared with people without HIV. Among PWH, those from the South were more likely to have hypertension (aPR: 1.07, 95% CI: 1.02–1.12) than PWH from the Northeast, while Black PWH were less likely to have controlled hypertension (aPR: 0.72, 95% CI: 0.67–0.77) than White PWH. CONCLUSIONS PWH were more likely to have hypertension than people without HIV. Geographic and racial disparities in hypertension prevalence and control were observed among PWH. Optimal care for PWH includes comprehensive strategies to screen for, prevent, and manage hypertension. [ABSTRACT FROM AUTHOR]

Objective To assess the performance of large language models (LLMs) for zero-shot disambiguation of acronyms in clinical narratives. Materials and Methods Clinical narratives in English, German, and Portuguese were applied for testing the performance of four LLMs: GPT-3.5, GPT-4, Llama-2-7b-chat, and Llama-2-70b-chat. For English, the anonymized Clinical Abbreviation Sense Inventory (CASI, University of Minnesota) was used. For German and Portuguese, at least 500 text spans were processed. The output of LLM models, prompted with contextual information, was analyzed to compare their acronym disambiguation capability, grouped by document-level metadata, the source language, and the LLM. Results On CASI, GPT-3.5 achieved 0.91 in accuracy. GPT-4 outperformed GPT-3.5 across all datasets, reaching 0.98 in accuracy for CASI, 0.86 and 0.65 for two German datasets, and 0.88 for Portuguese. Llama models only reached 0.73 for CASI and failed severely for German and Portuguese. Across LLMs, performance decreased from English to German and Portuguese processing languages. There was no evidence that additional document-level metadata had a significant effect. Conclusion For English clinical narratives, acronym resolution by GPT-4 can be recommended to improve readability of clinical text by patients and professionals. For German and Portuguese, better models are needed. Llama models, which are particularly interesting for processing sensitive content on premise, cannot yet be recommended for acronym resolution. [ABSTRACT FROM AUTHOR]

Objectives Phenotyping is a core task in observational health research utilizing electronic health records (EHRs). Developing an accurate algorithm demands substantial input from domain experts, involving extensive literature review and evidence synthesis. This burdensome process limits scalability and delays knowledge discovery. We investigate the potential for leveraging large language models (LLMs) to enhance the efficiency of EHR phenotyping by generating high-quality algorithm drafts. Materials and Methods We prompted four LLMs—GPT-4 and GPT-3.5 of ChatGPT, Claude 2, and Bard—in October 2023, asking them to generate executable phenotyping algorithms in the form of SQL queries adhering to a common data model (CDM) for three phenotypes (ie, type 2 diabetes mellitus, dementia, and hypothyroidism). Three phenotyping experts evaluated the returned algorithms across several critical metrics. We further implemented the top-rated algorithms and compared them against clinician-validated phenotyping algorithms from the Electronic Medical Records and Genomics (eMERGE) network. Results GPT-4 and GPT-3.5 exhibited significantly higher overall expert evaluation scores in instruction following, algorithmic logic, and SQL executability, when compared to Claude 2 and Bard. Although GPT-4 and GPT-3.5 effectively identified relevant clinical concepts, they exhibited immature capability in organizing phenotyping criteria with the proper logic, leading to phenotyping algorithms that were either excessively restrictive (with low recall) or overly broad (with low positive predictive values). Conclusion GPT versions 3.5 and 4 are capable of drafting phenotyping algorithms by identifying relevant clinical criteria aligned with a CDM. However, expertise in informatics and clinical experience is still required to assess and further refine generated algorithms. [ABSTRACT FROM AUTHOR]

Objective The use of electronic health records (EHRs) for clinical risk prediction is on the rise. However, in many practical settings, the limited availability of task-specific EHR data can restrict the application of standard machine learning pipelines. In this study, we investigate the potential of leveraging language models (LMs) as a means to incorporate supplementary domain knowledge for improving the performance of various EHR-based risk prediction tasks. Methods We propose two novel LM-based methods, namely "LLaMA2-EHR" and "Sent-e-Med." Our focus is on utilizing the textual descriptions within structured EHRs to make risk predictions about future diagnoses. We conduct a comprehensive comparison with previous approaches across various data types and sizes. Results Experiments across 6 different methods and 3 separate risk prediction tasks reveal that employing LMs to represent structured EHRs, such as diagnostic histories, results in significant performance improvements when evaluated using standard metrics such as area under the receiver operating characteristic (ROC) curve and precision-recall (PR) curve. Additionally, they offer benefits such as few-shot learning, the ability to handle previously unseen medical concepts, and adaptability to various medical vocabularies. However, it is noteworthy that outcomes may exhibit sensitivity to a specific prompt. Conclusion LMs encompass extensive embedded knowledge, making them valuable for the analysis of EHRs in the context of risk prediction. Nevertheless, it is important to exercise caution in their application, as ongoing safety concerns related to LMs persist and require continuous consideration. [ABSTRACT FROM AUTHOR]

In the 40 years since Steere and colleagues first described Lyme disease, the illness has increased in incidence and distribution to become the most common vector-borne disease in the United States. Public health officials have developed, implemented, and revised surveillance systems to describe and monitor the condition. Much has been learned about the epidemiology of the illness, despite practical and logistical constraints that have encumbered the collection and interpretation of surveillance data. Future development of automated data collection from electronic health records as a source of surveillance and clinical information will address practical challenges and help answer ongoing questions about complications and persistent symptoms. Robust surveillance will be essential to monitor the effectiveness and safety of future vaccines and other preventive measures. [ABSTRACT FROM AUTHOR]

Background Streptococci are a common cause of infective endocarditis (IE). We aimed to evaluate the performance of the HANDOC score to identify patients at high risk for IE and the Duke clinical criteria of the European Society of Cardiology (ESC; 2015 and 2023 versions) and the 2023 version from the International Society of Cardiovascular Infectious Diseases (ISCVID) in diagnosing IE among patients with streptococcal bacteremia. Methods This retrospective study included adult patients with streptococcal bacteremia hospitalized at Lausanne University Hospital. Episodes were classified as IE by the Endocarditis Team. A HANDOC score >2 classified patients as high risk for IE. Results Among 851 episodes with streptococcal bacteremia, IE was diagnosed in 171 episodes (20%). Among 607 episodes with non-β-hemolytic streptococci, 213 (35%) had HANDOC scores >2 points; 132 (22%) had IE. The sensitivity of the HANDOC score to identify episodes at high risk for IE was 95% (95% confidence interval [CI], 90%–98%), the specificity 82% (95% CI, 78%–85%), and the negative predictive value (NPV) 98% (95% CI, 96%–99%). 2015 Duke-ESC, 2023 Duke-ISCVID, and 2023 Duke-ESC clinical criteria classified 114 (13%), 145 (17%), and 126 (15%) episodes as definite IE, respectively. Sensitivity (95% CI) for the 2015 Duke-ESC, 2023 Duke-ISCVID, and 2023 Duke-ESC clinical criteria was calculated at 65% (57%–72%), 81% (74%–86%), and 73% (65%–79%), respectively, with specificity (95% CI) at 100% (98%–100%), 99% (98%–100%), and 99% (98%–100%), respectively. Conclusions The HANDOC score showed an excellent NPV to identify episodes at high risk for IE. Among the different versions of the Duke criteria, the 2023 Duke-ISCVID version fared better for the diagnosis of IE among streptococcal bacteremia. [ABSTRACT FROM AUTHOR]

Background Cryptococcal meningitis (CM) causes substantial mortality in African countries with a high prevalence of human immunodeficiency virus (HIV), despite advances in disease management and increasing antiretroviral therapy (ART) coverage. Reliable diagnosis of CM is cheap and more accessible than other indicators of advanced HIV disease burden such as CD4 testing or investigation for disseminated tuberculosis; therefore, monitoring CM incidence has the potential to serve as a valuable metric of HIV programmatic success. Methods Botswana national meningitis surveillance data from 2015 to 2022 were obtained from electronic health records. All electronic laboratory records from cerebrospinal fluid samples analyzed within government healthcare facilities in Botswana were extracted from a central online repository. Adjustments for missing data were made through triangulation with prospective cohort study datasets. CM case frequency was enumerated using a case definition and incidence calculated using national census data. Results A total of 1744 episodes of CM were identified; incidence declined from 15.0 (95% confidence interval [CI], 13.4–16.7) cases/100 000 person-years in 2015 to 7.4 (95% CI, 6.4–8.6) cases/100 000 person-years in 2022. However, the rate of decline slowed following the introduction of universal treatment in 2016. The highest incidence was observed in men and individuals aged 40–44 years. The proportion of cases diagnosed through cryptococcal antigen testing increased from 35.5% to 86.3%. Conclusions CM incidence has decreased in Botswana following expansion of ART coverage but persists at a stubbornly high incidence. Most cases are now diagnosed through the cheap and easy-to-use cryptococcal antigen test, highlighting the potential of using CM as key metric of program success in the Treat All era. [ABSTRACT FROM AUTHOR]

Purpose Results of the 2023 ASHP National Survey of Pharmacy Practice in Hospital Settings are presented. Methods Pharmacy directors at 1,497 general and children's medical-surgical hospitals in the United States were surveyed using a mixed-mode method of contact by email and mail. Survey completion was online using Qualtrics. IQVIA supplied data on hospital characteristics; the survey sample was drawn from IQVIA's hospital database. Results The response rate was 21.6%. Inpatient pharmacists independently prescribe medications in 26.7% of hospitals. Advanced analytics are used in 5.7% of hospitals. Basic analytics are used in 87.3% of hospitals. Pharmacists work in ambulatory or primary care clinics in 54.2% of hospitals operating outpatient clinics. Most hospitals (86.1%) use automated dispensing cabinets as the primary method of maintenance dose distribution. Machine-readable coding is used in 73.6% of hospitals to verify doses during dispensing in the pharmacy. Autoverification functionality in the electronic health record system is used in 73.4% of hospitals. Most hospitals report some integration of pharmacy services to optimize patient care transitions (60.0%), while 24.9% report no integration. Traditional technician activities still predominate, but more advanced roles are emerging. Technologies to assist sterile product preparation are used in 62.8% of hospitals. Conclusion Drug distribution continues to trend toward decentralized models with medications available closer to patients. Technologies are enabling this transition to occur without a significant negative impact on patient safety. The pharmacy workforce is stable, and more advanced responsibilities are being assigned to pharmacy technicians, enabling pharmacists to increase their clinical role. [ABSTRACT FROM AUTHOR]

Purpose We describe the implementation and ongoing maintenance of CYP2C19 and CYP2D6 focused pharmacogenetic (PGx) testing to guide antidepressant and antianxiety medication prescriptions in a large rural, nonprofit health system. Summary Depression and anxiety are common psychiatric conditions. Sanford Health implemented PGx testing for metabolism of cytochrome P450 (CYP) isozymes 2C19 and 2D6 in 2014 to inform prescribing for multiple medications, including antidepressant and antianxiety therapies. As guidelines, genotype to phenotype translation, panel offerings, and other resources are updated, we adapt our approach. We make educational and informational materials available to providers and patients. Pharmacogenomic clinical pharmacists review PGx results with discrete values and provide guidance documentation in the electronic medical record. A robust clinical decision support system is in place to provide interruptive alerts, noninterruptive alerts, and genomic indicators. A referral-based interdisciplinary clinic is also available to provide in-depth education to patients regarding PGx results and implications. Additionally, partnering with our health plan has expanded access to PGx testing for patients with anxiety or depression. Conclusion The implementation and maintenance of Sanford Health's PGx program to guide antidepressant and antianxiety medication use continues to evolve and requires a multipronged approach relying on both human and informatics-based resources. [ABSTRACT FROM AUTHOR]

Aims Patients with acute coronary syndrome (ACS) often experience reduced health-related quality of life (HRQOL), which may be attributable to the disease severity and psychological stress. While illness perception is speculated to be a potential pathway underlying these relationships, evidence supporting this mechanism remains limited. This study aimed to investigate the relationships between disease severity, psychological stress, and HRQOL and whether these relationships are mediated by illness perception in patients with ACS. Methods and results Data were collected from June to July 2019 and June to September 2020 in the cardiology departments of four public hospitals in China. Eligible patients completed measures of disease severity, psychological stress, illness perception, HRQOL, and socio-demographic and clinical characteristics. Data were analysed employing hierarchical multiple regression and structural equation modelling. This study included 405 participants (mean age 60.63 years, 67.4% male). After controlling for socio-demographic and clinical covariates, higher levels of disease severity (β = 0.115 , P = 0.024) and psychological stress (β = −0.209, P ＜0.001) were associated with poorer HRQOL; however, the relationships became non-significant after adding illness perception into the regression model. Structural equation modelling analysis suggested that illness perception played a mediating role between disease severity, psychological stress, and HRQOL, accounting for 45.95% and 65.79% of the total effects, respectively. Conclusion This study found that illness perception mediated the relationships between disease severity, psychological stress, and HRQOL among patients with ACS. Improving patients' HRQOL should consider its important influencing factors with a focus on promoting positive illness perception. [ABSTRACT FROM AUTHOR]

Background This study aimed to assess the prognosis of people with chronic kidney disease (CKD) in Japan using the Kidney Disease: Improving Global Outcomes (KDIGO) heatmap. Methods The prognoses of individuals with estimated glomerular filtration rates (eGFR) <90 mL/min/1.73 m2 were evaluated based on the KDIGO heatmap using an electronic medical record database in Japan. The primary outcome was major adverse cardiovascular events (MACE), a composite of myocardial infarction (MI), stroke, heart failure (HF) hospitalization and in-hospital death (referred to as MACE1). Additionally, ad hoc MACE2 (MI hospitalization, stroke hospitalization, HF hospitalization and in-hospital death) was examined. The secondary outcome was the renal outcome. Results Of the 543 606 individuals included, the mean age was 61.6 ± 15.3 years, 50.1% were male and 40.9% lacked urine protein results. The risk of MACEs increased independently with both eGFR decline and increasing proteinuria from the early KDIGO stages: hazard ratios (95% confidence interval) of MACE1 and MACE2, compared with G2A1 were 1.16 (1.12–1.20) and 1.17 (1.11–1.23), respectively, for G3aA1, and 1.17 (1.12–1.21) and 1.35 (1.28–1.43), respectively, for G2A2. This increased up to 2.83 (2.54–3.15) and 3.43 (3.00–3.93), respectively, for G5A3. Risks of renal outcomes also increased with CKD progression. Conclusions This study is the first to demonstrate the applicability of the KDIGO heatmap in assessing cardiovascular and renal risk in Japan. The risk increased from the early stages of CKD, indicating the importance of early diagnosis and intervention through appropriate testing. [ABSTRACT FROM AUTHOR]

Anaemia is common in chronic kidney disease (CKD) and has a significant impact on quality of life (QoL), work productivity and outcomes. Current management includes oral or intravenous iron and erythropoiesis-stimulating agents (ESAs), to which hypoxia-inducible factor prolyl hydroxylase inhibitors (HIF-PHIs) have been recently added, increasing the available therapeutic options. In randomised controlled trials, only intravenous iron improved cardiovascular outcome, while some ESAs were associated with increased adverse cardiovascular events. Despite therapeutic advances, several challenges and unmet needs remain in the current management of anaemia of CKD. In particular, clinical practice does not include an assessment of QoL, which prompted a group of European nephrologists and representatives of patient advocacy groups to revisit the current approach. In this consensus document, the authors propose a move towards a more holistic, personalised and long-term approach, based on existing evidence. The focus of treatment should be on improving QoL without increasing the risk of adverse cardiovascular events, and tailoring management strategies to the needs of the individual. In addition, the authors discuss the suitability of a currently available anaemia of CKD–specific health-related QoL measure for inclusion in the routine clinical management of anaemia of CKD. The authors also outline the logistics and challenges of incorporating such a measure into electronic health records and how it may be used to improve QoL for people with anaemia of CKD. [ABSTRACT FROM AUTHOR]

Background This study estimated the relative vaccine effectiveness (rVE) of the MF59-adjuvanted trivalent influenza vaccine (aTIV) versus high-dose trivalent inactivated influenza vaccine (HD-TIV) for prevention of influenza-related medical encounters (IRMEs) during the 2019–2020 United States (US) influenza season stratified by the cumulative number of influenza risk factors. A secondary objective evaluated outpatient IRMEs and influenza- and pneumonia-related hospitalizations. Methods This retrospective cohort study included US adults ≥65 years old vaccinated with aTIV or HD-TIV between 1 August 2019 and 31 January 2020. Electronic health records linked to claims were used to ascertain exposure, covariates, risk factors, and outcomes. Multivariable adjusted odds ratios (ORs) were derived using inverse probability of treatment–weighted samples to calculate rVEs independently for individuals with 0, ≥1, 1–2, or ≥3 risk factors. Results The study included 1 115 725 aTIV and 2 561 718 HD-TIV recipients. For the primary outcome of any IRME, the analysis found comparable effectiveness between aTIV and HD-TIV (rVE, 5.2% [95% confidence interval {CI}, −5.9% to 15.1%]) among those with 0 risk factors, whereas aTIV was more effective than HD-TIV among patients with ≥1, 1–2, or ≥3 risk factors (12.5% [95% CI, 10.0%–15.0%], 18.4% [95% CI, 13.7%–22.9%], and 10.4% [7.4%–13.3%], respectively). The same trends were observed for the secondary outcomes. Conclusions This study demonstrated comparable effectiveness of aTIV and HD-TIV among individuals with no identified risk factors and higher effectiveness of aTIV compared with HD-TIV in preventing any IRMEs, outpatient IRMEs, and influenza- or pneumonia-related hospitalizations among those with at least 1 or multiple high-risk factors in adults ≥65 years old. [ABSTRACT FROM AUTHOR]

Background Isoniazid-resistant, rifampin-susceptible tuberculosis (Hr-TB) is associated with poor treatment outcomes and higher rates of acquisition of further drug resistance during treatment. Due to a lack of widespread diagnostics, Hr-TB is frequently undetected and its epidemiology is incompletely understood. Methods We studied the molecular epidemiology of Hr-TB among all patients diagnosed with culture-positive pulmonary tuberculosis between January 1 and June 30, 2017, at an urban referral tuberculosis clinic in Port-au-Prince, Haiti. Demographic and clinical data were extracted from the electronic medical record. Archived diagnostic Mycobacterium tuberculosis isolates were tested for genotypic and phenotypic isoniazid resistance using the Genotype MTBDR plus assay (Hain, Nehren, Germany) and culture-based testing, respectively. All isoniazid-resistant isolates and a randomly selected subset of isoniazid-susceptible isolates underwent whole-genome sequencing to confirm the presence of mutations associated with isoniazid resistance, to validate use of Genotype MTBDR plus in this population, and to identify potential transmission links between isoniazid-resistant isolates. Results and Conclusions Among 845 patients with culture-positive pulmonary tuberculosis in Haiti, 65 (7.7%) had Hr-TB based on the Genotype MTBDR plus molecular assay. Age < 20 years was significantly associated with Hr-TB (odds ratio, 2.39; 95% confidence interval, 1.14, 4.70; P =.015). Thirteen (20%) isoniazid-resistant isolates were found in 5 putative transmission clusters based on a single nucleotide polymorphism distance of ≤ 5. No patients in these transmission clusters were members of the same household. Adolescents are at higher risk for Hr-TB. Strains of isoniazid-resistant M tuberculosis are actively circulating in Haiti and transmission is likely occurring in community settings. [ABSTRACT FROM AUTHOR]

This article discusses the prevalence of female genital schistosomiasis (FGS) among long-term sub-Saharan African migrants in Europe. FGS is a condition characterized by gynecological signs and symptoms, such as menstrual disturbances and vulvovaginal discomfort, that can occur in women with urinary Schistosoma haematobium infection. The study found that African migrant women with a positive Schistosoma serology often experienced gynecological and urinary complaints, despite living in non-endemic European countries for many years. The findings suggest that chronic genitourinary schistosomiasis may be an underestimated condition in non-endemic countries, with significant implications for the sexual and reproductive health of migrant women. Further research is needed to develop a gold standard diagnostic and determine the appropriate treatment for chronic FGS. [Extracted from the article]

Out of all the racial groups in the United States, people who identify as American Indian and Alaska Native (AI/AN) have disproportionately worse health as a result of living in poverty. The preponderance of research connects poor health with a socioeconomic perspective, which might create prejudice against AI/AN. As already known, AI/AN's high rates of obesity, diabetes, and stroke in comparison with that of other ethnic groups are mainly derived from their impoverished economic conditions that have forced them to consume the food distributed by the U.S. government. When minority health is discussed generally, the ethnic density perspective explains a minority population's positive health despite low socioeconomic status. This perspective helps researchers and practitioners understand the connections of psychological and social factors with physical health and demonstrates positive health effects on minority groups. Despite the high correlation between ethnic density and health having been validated, little to no research has explored AI/AN's health from this perspective. Using 13,064 electronic health records, this research tests the relationship between AI/AN density and health outcomes. This article introduces an innovative analytical strategy (i.e. a data mining technique), which is ideal for discovering frequently appearing health outcomes in a group. The finding reveals positive relationships between health outcomes and AI/AN density. [ABSTRACT FROM AUTHOR]

Sepsis is a leading cause of pediatric mortality and timely antibiotic administration has been shown to improve outcomes. In this retrospective review of a single center sepsis dataset, we identified younger age and female sex as more likely to have delays in antibiotics. [ABSTRACT FROM AUTHOR]

Purpose This project compared the time to complete investigational prescription drug order verification by investigational drug service (IDS) pharmacists in an onsite vs offsite (hybrid) staffing model, evaluating the impact of remote work on verification time. Methods Parenteral IDS drug orders from August 2019 through June 2022 were analyzed. Electronic medical records were timestamped for each order, first verification, second verification, and medication administration. The project was divided into four 6-month time periods to allow for 2 independent comparisons of onsite vs hybrid staffing models. A survey was given retrospectively to IDS pharmacists for the last hybrid timeframe to assess work satisfaction and burnout. Results A total of 4,398 orders were evaluated. The time from order entry to first verification was 14 minutes (n = 1,248) during the first onsite time period compared to 13 minutes (n = 1,042) during the first hybrid period (P = 0.003). The time for order verification was similar between the second onsite and hybrid periods (12 minutes [n = 1,041] vs 10 minutes [n = 1,067], respectively; P < 0.0001). The staff satisfaction survey for the hybrid model showed high levels of job satisfaction and no self-reported burnout. Conclusion The remote hybrid staffing model did not result in a meaningful change in the time from order placement to medication administration within the IDS pharmacy. Hybrid staffing also resulted in greater job satisfaction among IDS pharmacists. This study highlights the benefits of implementing remote work practices in the pharmacy practice setting. [ABSTRACT FROM AUTHOR]

Background Although frailty is associated with a range of adverse health outcomes, its association with the risk of hospital-treated infections is uncertain. Methods A total of 416 220 participants from the UK Biobank were included in this prospective cohort study. Fried phenotype was adopted to evaluate frailty, which included 5 aspects (gait speed, physical activity, grip strength, exhaustion, and weight). More than 800 infectious diseases were identified based on electronic health records. Cox proportional models were used to estimate the associations. Results During a median 12.3 years (interquartile range 11.4–13.2) of follow-up (4 747 345 person-years), there occurred 77 988 (18.7%) hospital-treated infections cases. In the fully adjusted model, compared with participants with nonfrail, the hazard ratios (HRs) (95% confidence intervals [CIs]) of those with prefrail and frail for overall hospital-treated infections were 1.22 (1.20, 1.24) and 1.78 (1.72–1.84), respectively. The attributable risk proportion of prefrail and frail were 18.03% and 43.82%. Similarly, compared to those without frailty, the HRs (95% CIs) of those with frailty for bacterial infections were 1.76 (1.70–1.83), for viral infections were 1.62 (1.44–1.82), and for fungal infections were 1.75 (1.47–2.08). No association was found between frailty and parasitic infections (HR: 1.17; 95% CI: 0.62–2.20). Conclusions Frailty was significantly associated with a higher risk of hospital-treated infections, except for parasitic infections. Studies evaluating the effectiveness of implementing frailty assessments are needed to confirm our results. [ABSTRACT FROM AUTHOR]

Massive medical data are indispensable for training diagnostic models to provide high-quality health monitoring services. The methods for sharing data in existing works involve securely and essentially copying data but often overlook the integration and efficiency of data storage, exchange and application. In this paper, we propose a Secure Decentralized Trading Alliance (SDTA) to encompass the entire process holistically. With monetary incentives, we formulate a chain-net structure for recording data digests and authentic transactions, thereby transforming data sharing into data trading without duplicating data storage. Data privacy is promised by encryption. To manage and employ encrypted medical data, users can update and search their encrypted data using an index and keywords, subsequently retrieving data within the SDTA framework. It is realized by a novel dynamic searchable symmetric encryption (SSE) with an |$l$| -level access strategy, which confines users to data pertinent solely to them, thus circumventing unnecessary data leakage. We scrutinize the storage efficiency and prove the fairness and security of SDTA. Finally, we generate datasets of varying sizes, where the time required to search for a single keyword is approximately 0.04 s with 1 000 000 (keyword, identifier) pairs, showing it quite acceptable. [ABSTRACT FROM AUTHOR]

Background Parkinson's disease (PD) is a common neurodegenerative disorder in older adults that is associated with neuroinflammation, oxidative stress, and characterized by loss of dopaminergic cells. Illicit stimulants increase oxidative stress and are associated with increased risk of PD. Prescription stimulants are similar in mechanism to illicit stimulants, yet their influence on PD is not well described. This study aims to determine if prescription stimulants influence risk of PD among older adults with attention-deficit and hyperactivity disorder (ADHD). Methods We implemented a retrospective observational cohort design utilizing the TriNetX database which sources from the electronic health records of 57 healthcare organizations. Inclusion criteria were ADHD diagnosis and age ≥50. Those exposed to stimulants and the unexposed controls were matched based on demographics and known risk factors for PD. The outcome of interest was the incidence of PD over a 30-year follow-up period. We utilized TriNetX software for hazard ratio (HR) analysis. Results Among the 59,471 individuals treated with prescription stimulants 131 of them developed PD, and there were 272 individuals who developed PD that were not prescribed stimulants. This analysis yielded a HR of 0.419 (HR = 0.419 [95% CI 0.34, 0.516], P = 0.0013). Conclusion Illicit stimulants are associated with increased risk of PD, but this study suggests prescribed stimulants may not impart that same risk. The reduced risk in this cohort may be due to protection from illicit substance use and oxidative stress, however additional study exploring the relationship between prescription stimulants and PD is warranted. [ABSTRACT FROM AUTHOR]

Background Posttraumatic stress disorder (PTSD) has significant morbidity and economic costs. This study describes the prevalence and characteristics of patients with PTSD using primary care electronic medical record (EMR) data. Methods This retrospective cross-sectional study used EMR data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). This study included 1,574 primary care providers located in 7 Canadian provinces. There were 689,301 patients that visited a CPCSSN provider between 1 January 2017 and 31 December 2019. We describe associations between PTSD and patient characteristics using descriptive statistics, chi-square, and multiple logistic regression models. Results Among the 689,301 patients included, 8,817 (1.3%, 95% CI 1.2–1.3) had a diagnosis of PTSD. On multiple logistic regression analysis, patients with depression (OR 4.4, 95% CI 4.2–4.7, P < 0.001), alcohol abuse/dependence (OR 1.7, 95% CI 1.6–1.9, P < 0.001), and/or drug abuse/dependence (OR 2.6, 95% CI 2.5–2.8, P < 0.001) had significantly higher odds of PTSD compared with patients without those conditions. Patients residing in community areas considered the most material deprived (OR 2.1, 95% CI 1.5–2.1, P < 0.001) or the most socially deprived (OR 2.8, 95% CI 2.7–5.3, P < 0.001) had higher odds of being diagnosed with PTSD compared with patients in the least deprived areas. Conclusions The prevalence of PTSD in Canadian primary care is 1.3% (95% CI 1.25–1.31). Using EMR records we confirmed the co-occurrence of PTSD with other mental health conditions within primary care settings suggesting benefit for improved screening and evidence-based resources to manage PTSD. [ABSTRACT FROM AUTHOR]

Context Primary hyperparathyroidism (PHPT) increases the risk of bone loss, debilitating fractures, kidney stones, impaired renal function, and neurocognitive symptoms. Studies describing the natural history of PHPT have been limited to small samples, single institutions, or specific populations. Objective We assessed the natural history of PHPT through a large, diverse national cohort from an electronic health record dataset representing more than 100 million patients. Methods The TriNetX database was queried for adult patients with PHPT. We extracted demographics, comorbidities, and longitudinal biochemistries. Primary outcomes included major osteoporotic fracture (MOF) and chronic kidney disease (CKD). Outcomes were stratified by treatment strategy (surgical parathyroidectomy [PTX] vs nonsurgical) and age. Results Among 50 958 patients with PHPT, 26.5% were treated surgically at a median of 0.3 years postdiagnosis. At diagnosis, median age was 65 years, 74.0% were female, and median calcium level was 10.9 mg/dL. Black and older patients underwent PTX less frequently than White and younger patients. MOF 10-year incidence was 5.20% (PTX) and 7.91% (nonsurgical), with median 1.7-year delay with PTX compared to nonsurgical. PTX-associated MOF absolute risk reduction was 0.83% (age < 65 years) and 3.33% (age ≥ 65 years). CKD 10-year incidence was 21.2% (PTX) and 33.6% (nonsurgical), with median 1.9-year delay with PTX. PTX-associated CKD absolute risk reduction was 12.2% (age < 65 years) and 9.5% (age ≥ 65 years). Conclusion We report 1 of the largest, representative, population-based natural histories of PHPT with different management strategies. A minority of patients underwent PTX, especially in older age. Patients managed surgically had lower incidence of fracture and CKD, and older patients experienced differential benefit. [ABSTRACT FROM AUTHOR]

This document has been developed to provide a guide for basic and advanced reporting in paediatric echocardiography. Furthermore, it aims to help clinicians in the interpretation of echocardiographic measurements and functional data for estimating the severity of disease in different paediatric age groups. The following topics will be reviewed and discussed in the present document: (i) the general principle in constructing a paediatric echocardiographic report, (ii) the basic elements to be included, and (iii) the potential and limitation of currently employed tools used for disease severity quantification during paediatric reporting. A guide for the interpretation of Z-scores will be provided. Use and interpretation of parameters employed for quantification of ventricular systolic function will be discussed. Difficulties in the adoption of adult parameters for the study of diastolic function and valve defects at different ages and pressure and loading conditions will be outlined, with pitfalls for the assessment listed. A guide for careful use of prediction scores for complex congenital heart disease will be provided. Examples of basic and advanced (disease-specific) formats for reporting in paediatric echocardiography will be provided. This document should serve as a comprehensive guide to (i) structure a comprehensive paediatric echocardiographic report; (ii) identify the basic morphological details, measures, and functional parameters to be included during echocardiographic reporting; and (iii) correctly interpret measurements and functional data for estimating disease severity. [ABSTRACT FROM AUTHOR]

Background After months of few mpox cases, an increase in cases was reported in Chicago during May 2023, predominantly among fully vaccinated (FV) patients. We investigated the outbreak scope, differences between vaccinated and unvaccinated patients, and hypotheses for monkeypox virus (MPXV) infection after vaccination. Methods We interviewed patients and reviewed medical records to assess demographic, behavioral, and clinical characteristics; mpox vaccine status; and vaccine administration routes. We evaluated serum antibody levels after infection and compared patient viral genomes with MPXV sequences in available databases. We discussed potential vaccine compromise with partners who manufactured, handled, and administered the vaccine associated with breakthrough infections. Results During 18 March–27 June 2023, we identified 49 mpox cases; 57% of these mpox patients were FV. FV patients received both JYNNEOS doses subcutaneously (57%), intradermally (7%), or via heterologous administration (36%). FV patients had more median sex partners (3; interquartile range [IQR] = 1–4) versus not fully vaccinated patients (1; IQR = 1–2). Thirty-six of 37 sequenced specimens belonged to lineage B.1.20 of clade IIb MPXV, which did not demonstrate any amino acid changes relative to B.1, the predominant lineage from May 2022. Vaccinated patients demonstrated expected humoral antibody responses; none were hospitalized. No vaccine storage excursions were identified. Approximately 63% of people at risk for mpox in Chicago were FV during this period. Conclusions Our investigation indicated that cases were likely due to frequent behaviors associated with mpox transmission, even with relatively high vaccine effectiveness and vaccine coverage. Cases after vaccination might occur in similar populations. [ABSTRACT FROM AUTHOR]

Background Carbapenemase-producing Enterobacterales (CPE) are challenging in healthcare, with resistance to multiple classes of antibiotics. This study describes the emergence of imipenemase (IMP)–encoding CPE among diverse Enterobacterales species between 2016 and 2019 across a London regional network. Methods We performed a network analysis of patient pathways, using electronic health records, to identify contacts between IMP-encoding CPE–positive patients. Genomes of IMP-encoding CPE isolates were overlaid with patient contacts to imply potential transmission events. Results Genomic analysis of 84 Enterobacterales isolates revealed diverse species (predominantly Klebsiella spp, Enterobacter spp, and Escherichia coli); 86% (72 of 84) harbored an IncHI2 plasmid carrying bla IMP and colistin resistance gene mcr-9 (68 of 72). Phylogenetic analysis of IncHI2 plasmids identified 3 lineages showing significant association with patient contacts and movements between 4 hospital sites and across medical specialties, which was missed in initial investigations. Conclusions Combined, our patient network and plasmid analyses demonstrate an interspecies, plasmid-mediated outbreak of bla IMPCPE, which remained unidentified during standard investigations. With DNA sequencing and multimodal data incorporation, the outbreak investigation approach proposed here provides a framework for real-time identification of key factors causing pathogen spread. Plasmid-level outbreak analysis reveals that resistance spread may be wider than suspected, allowing more interventions to stop transmission within hospital networks. Summary This was an investigation, using integrated pathway networks and genomics methods, of the emergence of imipenemase-encoding carbapenemase-producing Enterobacterales among diverse Enterobacterales species between 2016 and 2019 in patients across a London regional hospital network, which was missed on routine investigations. [ABSTRACT FROM AUTHOR]

Background (1,3)- β-D-glucan (BDG) testing is one of the noninvasive tests to aid diagnosis of invasive fungal infections (IFIs). The study results have been heterogenous, and diagnostic performance varies depending on the risks for IFI. Thus, it is important to select appropriate patients for BDG testing to prevent false-positive results. An algorithmic diagnostic stewardship intervention was instituted at a single academic medical center to improve BDG test utilization. Methods The BDG test order in the electronic health record was replaced with the BDG test request order, which required approval to process the actual test order. The approval criteria were (1) immunocompromised or intensive care unit patient and (2) on empiric antifungal therapy, or inability to undergo invasive diagnostic procedures. A retrospective observational study was conducted to evaluate the efficacy of the intervention by comparing the number of BDG tests performed between 1 year pre- and post-intervention. Safety was assessed by chart review of the patients for whom BDG test requests were deemed inappropriate and rejected. Results The number of BDG tests performed per year decreased by 85% from 156 in the pre-intervention period to 24 in the post-intervention period. The average monthly number of BDG tests performed was significantly lower between those periods (P =.002). There was no delay in IFI diagnosis or IFI-related deaths in the patients whose BDG test requests were rejected. The sustained effectiveness of the intervention was observed for 5 years. Conclusions Institution of the diagnostic stewardship intervention successfully and safely improved BDG test utilization. [ABSTRACT FROM AUTHOR]

Background Physiologic and immunologic adaptations in pregnancy may increase the risk of adverse outcomes from respiratory viral infections. However, data are limited on longer-term outcomes after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in pregnancy prior to widespread vaccine availability. Methods Using electronic health record data, we retrospectively compared 6-, 12-, and 18-month outcomes including death and rehospitalization between pregnant and nonpregnant reproductive-aged individuals hospitalized for SARS-CoV-2 infection between 2020 and 2021 at 2 academic referral hospitals. Results There were 190 nonpregnant and 70 pregnant participants. Mean age was 31 years for pregnant and 34 years for nonpregnant participants. For pregnant patients, mean gestational age at coronavirus disease 2019 (COVID-19) diagnosis was 36 weeks, 54% delivered by cesarean, and 97% delivered a live birth. Compared to pregnant participants, nonpregnant participants had a higher prevalence of baseline comorbidities and a higher proportion received mechanical ventilation (84% vs 55%). Index hospitalization complications (31% vs 17%) and mortality (3% vs 0%) were more common in nonpregnant participants. Over 18 months following index hospitalization, 39 (21%) nonpregnant and 5 (7%) pregnant participants were readmitted, most for infection (28/44 [64%]). Most readmissions occurred within 6 months. There were no posthospitalization deaths in the pregnant group. Conclusions Pregnant people with severe COVID-19 disease had a low rate of severe adverse outcomes after index hospitalization. The low readmission rate is reassuring that pregnant individuals may not be at higher risk for long-term severe adverse health outcomes after COVID-19 compared to the nonpregnant reproductive-aged population, possibly because any increased risk conferred by pregnancy resolves soon after delivery. [ABSTRACT FROM AUTHOR]

This study describes decentralized recruitment and enrollment for a COVID-19 treatment trial, while comparing 5 primary recruitment methods: search engine ads, paid advertising within a national testing company, paid advertising within a regional testing company, electronic health record messages, and word of mouth. These are compared across patient demographics, efficiency, and cost. Clinical Trials Registration NCT04510194. [ABSTRACT FROM AUTHOR]

Motivation Electronic health records (EHRs) represent a comprehensive resource of a patient's medical history. EHRs are essential for utilizing advanced technologies such as deep learning (DL), enabling healthcare providers to analyze extensive data, extract valuable insights, and make precise and data-driven clinical decisions. DL methods such as recurrent neural networks (RNN) have been utilized to analyze EHR to model disease progression and predict diagnosis. However, these methods do not address some inherent irregularities in EHR data such as irregular time intervals between clinical visits. Furthermore, most DL models are not interpretable. In this study, we propose two interpretable DL architectures based on RNN, namely time-aware RNN (TA-RNN) and TA-RNN-autoencoder (TA-RNN-AE) to predict patient's clinical outcome in EHR at the next visit and multiple visits ahead, respectively. To mitigate the impact of irregular time intervals, we propose incorporating time embedding of the elapsed times between visits. For interpretability, we propose employing a dual-level attention mechanism that operates between visits and features within each visit. Results The results of the experiments conducted on Alzheimer's Disease Neuroimaging Initiative (ADNI) and National Alzheimer's Coordinating Center (NACC) datasets indicated the superior performance of proposed models for predicting Alzheimer's Disease (AD) compared to state-of-the-art and baseline approaches based on F2 and sensitivity. Additionally, TA-RNN showed superior performance on the Medical Information Mart for Intensive Care (MIMIC-III) dataset for mortality prediction. In our ablation study, we observed enhanced predictive performance by incorporating time embedding and attention mechanisms. Finally, investigating attention weights helped identify influential visits and features in predictions. Availability and implementation https://github.com/bozdaglab/TA-RNN. [ABSTRACT FROM AUTHOR]

Motivation The emergence of COVID-19 (C19) created incredible worldwide challenges but offers unique opportunities to understand the physiology of its risk factors and their interactions with complex disease conditions, such as metabolic syndrome. To address the challenges of discovering clinically relevant interactions, we employed a unique approach for epidemiological analysis powered by redescription-based topological data analysis (RTDA). Results Here, RTDA was applied to Explorys data to discover associations among severe C19 and metabolic syndrome. This approach was able to further explore the probative value of drug prescriptions to capture the involvement of RAAS and hypertension with C19, as well as modification of risk factor impact by hyperlipidemia (HL) on severe C19. RTDA found higher-order relationships between RAAS pathway and severe C19 along with demographic variables of age, gender, and comorbidities such as obesity, statin prescriptions, HL, chronic kidney failure, and disproportionately affecting Black individuals. RTDA combined with CuNA (cumulant-based network analysis) yielded a higher-order interaction network derived from cumulants that furthered supported the central role that RAAS plays. TDA techniques can provide a novel outlook beyond typical logistic regressions in epidemiology. From an observational cohort of electronic medical records, it can find out how RAAS drugs interact with comorbidities, such as hypertension and HL, of patients with severe bouts of C19. Where single variable association tests with outcome can struggle, TDA's higher-order interaction network between different variables enables the discovery of the comorbidities of a disease such as C19 work in concert. Availability and Implementation Code for performing TDA/RTDA is available in https://github.com/IBM/Matilda and code for CuNA can be found in https://github.com/BiomedSciAI/Geno4SD/. Supplementary Information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Background ChatGPT is a free artificial intelligence (AI)-based natural language processing tool that generates complex responses to inputs from users. Objectives To determine whether ChatGPT is able to generate high-quality responses to patient-submitted questions in the patient portal. Methods Patient-submitted questions and the corresponding responses from their dermatology physician were extracted from the electronic medical record for analysis. The questions were input into ChatGPT (version 3.5) and the outputs extracted for analysis, with manual removal of verbiage pertaining to ChatGPT's inability to provide medical advice. Ten blinded reviewers (seven physicians and three nonphysicians) rated and selected their preference in terms of 'overall quality', 'readability', 'accuracy', 'thoroughness' and 'level of empathy' of the physician- and ChatGPT-generated responses. Results Thirty-one messages and responses were analysed. Physician-generated responses were vastly preferred over the ChatGPT -responses by the physician and nonphysician reviewers and received significantly higher ratings for 'readability' and 'level of empathy'. Conclusions The results of this study suggest that physician-generated responses to patients' portal messages are still preferred over ChatGPT, but generative AI tools may be helpful in generating the first drafts of responses and providing information on education resources for patients. [ABSTRACT FROM AUTHOR]

More severe atopic dermatitis and psoriasis are associated with a higher cumulative impact on quality of life, multimorbidity and healthcare costs. Proactive, early intervention in those most at risk of severe disease may reduce this cumulative burden and modify the disease trajectory to limit progression. The lack of reliable biomarkers for this at-risk group represents a barrier to such a paradigm shift in practice. To expedite discovery and validation, the BIOMarkers in Atopic Dermatitis and Psoriasis (BIOMAP) consortium (a large-scale European, interdisciplinary research initiative) has curated clinical and molecular data across diverse study designs and sources including cross-sectional and cohort studies (small-scale studies through to large multicentre registries), clinical trials, electronic health records and large-scale population-based biobanks. We map all dataset disease severity instruments and measures to three key domains (symptoms, inflammatory activity and disease course), and describe important codependencies and relationships across variables and domains. We prioritize definitions for more severe disease with reference to international consensus, reference standards and/or expert opinion. Key factors to consider when analysing datasets across these diverse study types include explicit early consideration of biomarker purpose and clinical context, candidate biomarkers associated with disease severity at a particular point in time and over time and how they are related, taking the stage of biomarker development into account when selecting disease severity measures for analyses, and validating biomarker associations with disease severity outcomes using both physician- and patient-reported measures and across domains. The outputs from this exercise will ensure coherence and focus across the BIOMAP consortium so that mechanistic insights and biomarkers are clinically relevant, patient-centric and more generalizable to current and future research efforts. [ABSTRACT FROM AUTHOR]

Background Atopic dermatitis (AD) is a common inflammatory disease of the skin that begins early in life and can be lifelong. The purpose of our study was to evaluate whether fetal exposure and/or early-life exposure of a child to antibiotics increases the risk of early-onset AD. Objectives We hypothesize that antibiotic exposure in utero or early in life (e.g. first 90 days) increases the likelihood that children develop AD. Methods Utilizing a large, prospectively collected electronic medical records database, we studied the association of antibiotic exposure received in utero or very early in life and the relative risk of onset of AD in a population-based cohort study. Associations were estimated using proportional hazards models as hazard ratios (HRs) with 95% confidence intervals (CIs). Results The risk of AD in childhood was increased after in utero or early-life antibiotic exposure. For any in utero antibiotic exposure the HR (CI) was 1.38 (1.36–1.39). However, penicillin demonstrated the strongest association with AD for both in utero exposure [1.43 (1.41–1.44)] and for childhood exposure [1.81 (1.79–1.82)]. HRs were higher in children born to mothers without AD than in those with AD pointing to effect modification by maternal AD status. Conclusions Children born to mothers exposed to antibiotics while in utero had, depending on the mother's history of AD, approximately a 20–40% increased risk of developing AD. Depending on the antibiotic, children who received antibiotics early in life had a 40–80% increased risk of developing AD. Our study supports and refines the association between incident AD and antibiotic administration. It also adds population-based support to therapeutic attempts to treat AD by modifying the skin microbiome. [ABSTRACT FROM AUTHOR]

Background Prediction models can identify fall-prone individuals. Prediction models can be based on either data from research cohorts (cohort-based) or routinely collected data (RCD-based). We review and compare cohort-based and RCD-based studies describing the development and/or validation of fall prediction models for community-dwelling older adults. Methods Medline and Embase were searched via Ovid until January 2023. We included studies describing the development or validation of multivariable prediction models of falls in older adults (60+). Both risk of bias and reporting quality were assessed using the PROBAST and TRIPOD, respectively. Results We included and reviewed 28 relevant studies, describing 30 prediction models (23 cohort-based and 7 RCD-based), and external validation of two existing models (one cohort-based and one RCD-based). The median sample sizes for cohort-based and RCD-based studies were 1365 [interquartile range (IQR) 426–2766] versus 90 441 (IQR 56 442–128 157), and the ranges of fall rates were 5.4% to 60.4% versus 1.6% to 13.1%, respectively. Discrimination performance was comparable between cohort-based and RCD-based models, with the respective area under the receiver operating characteristic curves ranging from 0.65 to 0.88 versus 0.71 to 0.81. The median number of predictors in cohort-based final models was 6 (IQR 5–11); for RCD-based models, it was 16 (IQR 11–26). All but one cohort-based model had high bias risks, primarily due to deficiencies in statistical analysis and outcome determination. Conclusions Cohort-based models to predict falls in older adults in the community are plentiful. RCD-based models are yet in their infancy but provide comparable predictive performance with no additional data collection efforts. Future studies should focus on methodological and reporting quality. [ABSTRACT FROM AUTHOR]

Background Recording and coding of ageing syndromes in hospital records is known to be suboptimal. Natural Language Processing algorithms may be useful to identify diagnoses in electronic healthcare records to improve the recording and coding of these ageing syndromes, but the feasibility and diagnostic accuracy of such algorithms are unclear. Methods We conducted a systematic review according to a predefined protocol and in line with Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. Searches were run from the inception of each database to the end of September 2023 in PubMed, Medline, Embase, CINAHL, ACM digital library, IEEE Xplore and Scopus. Eligible studies were identified via independent review of search results by two coauthors and data extracted from each study to identify the computational method, source of text, testing strategy and performance metrics. Data were synthesised narratively by ageing syndrome and computational method in line with the Studies Without Meta-analysis guidelines. Results From 1030 titles screened, 22 studies were eligible for inclusion. One study focussed on identifying sarcopenia, one frailty, twelve falls, five delirium, five dementia and four incontinence. Sensitivity (57.1%–100%) of algorithms compared with a reference standard was reported in 20 studies, and specificity (84.0%–100%) was reported in only 12 studies. Study design quality was variable with results relevant to diagnostic accuracy not always reported, and few studies undertaking external validation of algorithms. Conclusions Current evidence suggests that Natural Language Processing algorithms can identify ageing syndromes in electronic health records. However, algorithms require testing in rigorously designed diagnostic accuracy studies with appropriate metrics reported. [ABSTRACT FROM AUTHOR]

Background Before the COVID-19 pandemic, a need for a uniform approach to health information (HI) knowledge in population health analysis across Europe was evident. The Population Health Information Research Infrastructure (PHIRI) emerged as a proactive initiative to strengthen European HI capacities. This article describes the achievements of PHIRI, highlighting its capacity-building activities and their contribution towards a sustainable strategy for the implementation of the European Health Data Space (EHDS). Methods PHIRI collaboration established a work package for skill-building activities in population health in partnership with other organizations. Activities included webinars, workshops, sessions, training schools and courses for researchers and public administration workers from Europe and beyond. The primary goal of the activities was to examine the impact of COVID-19 on European health systems at both local and national levels, including healthcare facilities and policymaking entities. Results Twelve activities were organized between October 2020 and the summer of 2023. In March 2023, the Spring School on Health Information was organized to share the knowledge achieved from PHIRI and other European Union-related projects. This event also validated the European Health Information Training Programme. Conclusions PHIRI's findings emphasized the importance of equipping the workforce with core HI skills to improve health systems' preparedness and resilience. Through this research, it is possible to propose a strategy for building capacity that emphasizes the importance of providing training in human-machine dynamics. This approach will contribute to the sustainable implementation of the EHDS. [ABSTRACT FROM AUTHOR]

Aims Risk assessment is essential in the prevention of cardiovascular disease. In patients with recent acute coronary syndrome (ACS) or coronary revascularization, risk prediction tools, like the European Society of Cardiology guideline recommended SMART-REACH risk score, are increasingly used to predict the risk of recurrent cardiovascular events enabling risk-based personalized prevention. However, little is known about the association between risk stratification and the social and healthcare costs at a population level. This study evaluated the associations between baseline SMART-REACH risk scores, long-term recurrent clinical events, cumulative costs, and post-index event LDL-C goal attainment in patients with recent ACS and/or revascularization. Methods and results This retrospective study used electronic health records and was conducted in the North Karelia region of Finland. The study cohort included all patients aged 45–85 admitted to a hospital for ACS or who underwent percutaneous coronary intervention or coronary artery bypass surgery between 1 January 2017 and 31 December 2021. Patients were divided into quintiles based on their baseline SMART-REACH risk scores to examine the associations between predicted 5-year scores and selected clinical and economic outcomes. In addition, simple age-based stratification was conducted as a sensitivity analysis. The observed 5-year cumulative incidence of recurrent events ranged from 20% in the lowest to 41% in the highest risk quintile, whereas the corresponding predicted risks ranged from 13% to 51%, and cumulative 5-year mean total costs per patient ranged from 15 827 to 46 182€, respectively. Both monitoring and attainment of low LDL-C values were suboptimal. Conclusion The use of the SMART-REACH quintiles as a population-level risk stratification tool successfully stratified patients into subgroups with different cumulative numbers of recurrent events and cumulative total costs. However, more research is needed to define clinically and economically optimal threshold values for a population-level stratification. [ABSTRACT FROM AUTHOR]

Objective This study evaluates regularization variants in logistic regression (L1, L2, ElasticNet, Adaptive L1, Adaptive ElasticNet, Broken adaptive ridge [BAR], and Iterative hard thresholding [IHT]) for discrimination and calibration performance, focusing on both internal and external validation. Materials and Methods We use data from 5 US claims and electronic health record databases and develop models for various outcomes in a major depressive disorder patient population. We externally validate all models in the other databases. We use a train-test split of 75%/25% and evaluate performance with discrimination and calibration. Statistical analysis for difference in performance uses Friedman's test and critical difference diagrams. Results Of the 840 models we develop, L1 and ElasticNet emerge as superior in both internal and external discrimination, with a notable AUC difference. BAR and IHT show the best internal calibration, without a clear external calibration leader. ElasticNet typically has larger model sizes than L1. Methods like IHT and BAR, while slightly less discriminative, significantly reduce model complexity. Conclusion L1 and ElasticNet offer the best discriminative performance in logistic regression for healthcare predictions, maintaining robustness across validations. For simpler, more interpretable models, L0-based methods (IHT and BAR) are advantageous, providing greater parsimony and calibration with fewer features. This study aids in selecting suitable regularization techniques for healthcare prediction models, balancing performance, complexity, and interpretability. [ABSTRACT FROM AUTHOR]

Objectives To develop recommendations regarding the use of weights to reduce selection bias for commonly performed analyses using electronic health record (EHR)-linked biobank data. Materials and methods We mapped diagnosis (ICD code) data to standardized phecodes from 3 EHR-linked biobanks with varying recruitment strategies: All of Us (AOU; n  = 244 071), Michigan Genomics Initiative (MGI; n  = 81 243), and UK Biobank (UKB; n  = 401 167). Using 2019 National Health Interview Survey data, we constructed selection weights for AOU and MGI to represent the US adult population more. We used weights previously developed for UKB to represent the UKB-eligible population. We conducted 4 common analyses comparing unweighted and weighted results. Results For AOU and MGI, estimated phecode prevalences decreased after weighting (weighted-unweighted median phecode prevalence ratio [MPR]: 0.82 and 0.61), while UKB estimates increased (MPR: 1.06). Weighting minimally impacted latent phenome dimensionality estimation. Comparing weighted versus unweighted phenome-wide association study for colorectal cancer, the strongest associations remained unaltered, with considerable overlap in significant hits. Weighting affected the estimated log-odds ratio for sex and colorectal cancer to align more closely with national registry-based estimates. Discussion Weighting had a limited impact on dimensionality estimation and large-scale hypothesis testing but impacted prevalence and association estimation. When interested in estimating effect size, specific signals from untargeted association analyses should be followed up by weighted analysis. Conclusion EHR-linked biobanks should report recruitment and selection mechanisms and provide selection weights with defined target populations. Researchers should consider their intended estimands, specify source and target populations, and weight EHR-linked biobank analyses accordingly. [ABSTRACT FROM AUTHOR]

Objectives We sought to create a computational pipeline for attaching geomarkers, contextual or geographic measures that influence or predict health, to electronic health records at scale, including developing a tool for matching addresses to parcels to assess the impact of housing characteristics on pediatric health. Materials and Methods We created a geomarker pipeline to link residential addresses from hospital admissions at Cincinnati Children's Hospital Medical Center (CCHMC) between July 2016 and June 2022 to place-based data. Linkage methods included by date of admission, geocoding to census tract, street range geocoding, and probabilistic address matching. We assessed 4 methods for probabilistic address matching. Results We characterized 124 244 hospitalizations experienced by 69 842 children admitted to CCHMC. Of the 55 684 hospitalizations with residential addresses in Hamilton County, Ohio, all were matched to 7 temporal geomarkers, 97% were matched to 79 census tract-level geomarkers and 13 point-level geomarkers, and 75% were matched to 16 parcel-level geomarkers. Parcel-level geomarkers were linked using our exact address matching tool developed using the best-performing linkage method. Discussion Our multimodal geomarker pipeline provides a reproducible framework for attaching place-based data to health data while maintaining data privacy. This framework can be applied to other populations and in other regions. We also created a tool for address matching that democratizes parcel-level data to advance precision population health efforts. Conclusion We created an open framework for multimodal geomarker assessment by harmonizing and linking a set of over 100 geomarkers to hospitalization data, enabling assessment of links between geomarkers and hospital admissions. [ABSTRACT FROM AUTHOR]