Investigating the Benefit-Risk Profile of Drugs: From Spontaneous Reporting Systems to Real-World Data for Pharmacovigilance.

Rapid and detailed post-marketing surveillance of drugs and vaccine is required to enable assessment of their real-world safety and effectiveness. Spontaneous reporting from healthcare professionals and citizens is recognized as the basic method in the passive post-marketing surveillance of drugs and vaccines, allowing the identification of rare adverse drug reactions (ADRs) and adverse events following immunization (AEFIs). According to the current law, online platforms for ADRs and AEFI reporting and related databases are available in every country and at the global level. Recently, the use of electronic health records and the establishment of networks of databases as different sources of real-world data is emerging allowing high-quality, large-scale evaluations and providing real-world evidence on questions of clinical and regulatory interests. Here, we summarize the adverse event pharmacovigilance reporting systems in place at the global, European and in some European countries, and provide examples from recent literature of how the analysis of pharmacovigilance reports can provide evidence for unexpected and novel adverse drug reactions. Furthermore, we discuss the role of real-world data to generate real-world evidence in pharmacovigilance and regulatory activities.

Using precision periodontal health care chart (PPHCC) to improve periodontal health.

OBJECTIVES: Developing a Precision Periodontal Health Care Chart (PPHCC) in the electronic dental record (EDR) system and evaluating its clinical usability and effects on clinical outcomes. MATERIALS AND METHODS: A survey with ten questions based on the System Usability Scale (SUS) and six questions about assessing clinical impact was used to evaluate the satisfaction of periodontitis patients and care providers with PPHCC before and after non-surgical periodontal therapy (NSPT). The clinical outcomes, including probing depth (PD), interdental clinical attachment loss (CAL), and bleeding on probing (BOP), in patients who used PPHCC (PC) were compared to those in patients without using PPHCC (control). The associations between risk assessments included in PPHCC and clinical outcomes of NSPT were also analyzed. RESULTS: The mean scores of SUS questions at the initial periodontal examination were 74.26 ± 18.89 (n = 37) for patients and 88.31 ± 14.14 (n = 37) for care providers. The mean scores of SUS questions at re-evaluation were 74.84 ± 17.78 (n = 16) for patients and 89.63 ± 13.48 (n = 20) for care providers. The changes in the percentages of teeth with interdental CAL 1-2 mm (p = 0.019) and CAL 3-4 mm (p = 0.026) at the re-evaluation visit were significantly different between the PC and control groups, but the other parameters were not. CONCLUSIONS: Both patients and care providers were satisfied with using PPHCC in the clinic. However, the short-term clinical outcomes in the PC group were similar to those in the control group. CLINICAL RELEVANCE: PPHCC, as a tool for delivering clinical and educational information, can motivate patients to control periodontitis and assist clinicians in making a personalized treatment plan.

Machine learning-based delirium prediction in surgical in-patients: a prospective validation study.

Objective: Delirium is a syndrome that leads to severe complications in hospitalized patients, but is considered preventable in many cases. One of the biggest challenges is to identify patients at risk in a hectic clinical routine, as most screening tools cause additional workload. The aim of this study was to validate a machine learning (ML)-based delirium prediction tool on surgical in-patients undergoing a systematic assessment of delirium. Materials and Methods: 738 in-patients of a vascular surgery, a trauma surgery and an orthopedic surgery department were screened for delirium using the DOS scale twice a day over their hospital stay. Concurrently, delirium risk was predicted by the ML algorithm in real-time for all patients at admission and evening of admission. The prediction was performed automatically based on existing EHR data and without any additional documentation needed. Results: 103 patients (14.0%) were screened positive for delirium using the DOS scale. Out of them, 85 (82.5%) were correctly identified by the ML algorithm. Specificity was slightly lower, detecting 463 (72.9%) out of 635 patients without delirium. The AUROC of the algorithm was 0.883 (95% CI, 0.8523-0.9147). Discussion: In this prospective validation study, the implemented machine-learning algorithm was able to detect patients with delirium in surgical departments with high discriminative performance. Conclusion: In future, this tool or similar decision support systems may help to replace time-intensive screening tools and enable efficient prevention of delirium.

Reliable prediction of childhood obesity using only routinely collected EHRs may be possible.

Background: Early identification of children at high risk of obesity can provide clinicians with the information needed to provide targeted lifestyle counseling to high-risk children at a critical time to change the disease course. Objectives: This study aimed to develop predictive models of childhood obesity, applying advanced machine learning methods to a large unaugmented electronic health record (EHR) dataset. This work improves on other studies that have (i) relied on data not routinely available in EHRs (like prenatal data), (ii) focused on single-age predictions, or (iii) not been rigorously validated. Methods: A customized sequential deep-learning model to predict the development of obesity was built, using EHR data from 36,191 diverse children aged 0-10 years. The model was evaluated using extensive discrimination, calibration, and utility analysis; and was validated temporally, geographically, and across various subgroups. Results: Our results are mostly better or comparable to similar studies. Specifically, the model achieved an AUROC above 0.8 in all cases (with most cases around 0.9) for predicting obesity within the next 3 years for children 2-7 years of age. Validation results show the model's robustness and top predictors match important risk factors of obesity. Conclusions: Our model can predict the risk of obesity for young children at multiple time points using only routinely collected EHR data, greatly facilitating its integration into clinical care. Our model can be used as an objective screening tool to provide clinicians with insights into a patient's risk for developing obesity so that early lifestyle counseling can be provided to prevent future obesity in young children.

Prevention of adverse HIV treatment outcomes: machine learning to enable proactive support of people at risk of HIV care disengagement in Tanzania.

OBJECTIVES: This study aimed to develop a machine learning (ML) model to predict disengagement from HIV care, high viral load or death among people living with HIV (PLHIV) with the goal of enabling proactive support interventions in Tanzania. The algorithm addressed common challenges when applying ML to electronic medical record (EMR) data: (1) imbalanced outcome distribution; (2) heterogeneity across multisite EMR data and (3) evolving virological suppression thresholds. DESIGN: Observational study using a national EMR database. SETTING: Conducted in two regions in Tanzania, using data from the National HIV Care database. PARTICIPANTS: The study included over 6 million HIV care visit records from 295 961 PLHIV in two regions in Tanzania's National HIV Care database from January 2015 to May 2023. RESULTS: Our ML model effectively identified PLHIV at increased risk of adverse outcomes. Key predictors included past disengagement from care, antiretroviral therapy (ART) status (which tracks a patient's engagement with ART across visits), age and time on ART. The downsampling approach we implemented effectively managed imbalanced data to reduce prediction bias. Site-specific algorithms performed better compared with a universal approach, highlighting the importance of tailoring ML models to local contexts. A sensitivity analysis confirmed the model's robustness to changes in viral load suppression thresholds. CONCLUSIONS: ML models leveraging large-scale databases of patient data offer significant potential to identify PLHIV for interventions to enhance engagement in HIV care in resource-limited settings. Tailoring algorithms to local contexts and flexibility towards evolving clinical guidelines are essential for maximising their impact.

Factors Associated With Treatment Escalation for Psoriasis: An Analysis of Electronic Health Records Data.

Background: Electronic health records (EHRs) offer the possibility of using data entry templates to simultaneously document routine clinical care and capture disease-specific measures as discrete data elements that can be used for health services research (HSR). The objective of this study was to determine factors associated with meaningful treatment escalation (MTE) of psoriasis as a pilot study for future real-world HSR studies. Methods: We conducted a retrospective, observational cohort study of psoriasis patients by using data collected during routine clinical care from an EHR using EpiCare® SmartForms. The psoriasis SmartForm records psoriasis disease severity measures and descriptive findings to generate visit notes. These data were extracted and analyzed to identify factors associated with MTE, defined as changing or adding, phototherapy, systemic, or biologic therapy. Results: 473 psoriasis patients met study criteria; 239 underwent MTE between their first and third observed visits. Patients who experienced MTE had more severe disease at Visit 1-assessed by BSA, pPGA, oPGA, and a patient-reported disease severity measure--than patients who did not experience MTE. Other factors associated with MTE included use of topicals only or no active treatment at Visit 1, palmoplantar disease, and involvement of other difficult-to-treat body areas. Patients who underwent MTE experienced larger improvements in disease severity than those who did not. Conclusions: This study highlights how data collected during routine clinical practice can be readily used for real-world retrospective HSR when disease measures are captured as discrete elements. This approach could provide a cost-effective platform to conduct real-world HSR.

The Development of an Electronic Medical Record System to Improve Quality of Care for Individuals With Type 1 Diabetes in Rwanda: Qualitative Study.

BACKGROUND: Electronic medical record (EMR) systems have the potential to improve the quality of care and clinical outcomes for individuals with chronic and complex diseases. However, studies on the development and use of EMR systems for type 1 (T1) diabetes management in sub-Saharan Africa are few. OBJECTIVE: The aim of this study is to analyze the need for improvements in the care processes that can be facilitated by an EMR system and to develop an EMR system for increasing quality of care and clinical outcomes for individuals with T1 diabetes in Rwanda. METHODS: A qualitative, cocreative, and multidisciplinary approach involving local stakeholders, guided by the framework for complex public health interventions, was applied. Participant observation and the patient's personal experiences were used as case studies to understand the clinical care context. A focus group discussion and workshops were conducted to define the features and content of an EMR. The data were analyzed using thematic analysis. RESULTS: The identified themes related to feature requirements were (1) ease of use, (2) automatic report preparation, (3) clinical decision support tool, (4) data validity, (5) patient follow-up, (6) data protection, and (7) training. The identified themes related to content requirements were (1) treatment regimen, (2) mental health, and (3) socioeconomic and demographic conditions. A theory of change was developed based on the defined feature and content requirements to demonstrate how these requirements could strengthen the quality of care and improve clinical outcomes for people with T1 diabetes. CONCLUSIONS: The EMR system, including its functionalities and content, can be developed through an inclusive and cocreative process, which improves the design phase of the EMR. The development process of the EMR system is replicable, but the solution needs to be customized to the local context.

Mapping the evaluation of the electronic health system PEC e-SUS APS in Brazil: a scoping review protocol.

BACKGROUND: The Brazilian Ministry of Health has developed and provided the Citizen's Electronic Health Record (PEC e-SUS APS), a health information system freely available for utilization by all municipalities. Given the substantial financial investment being made to enhance the quality of health services in the country, it is crucial to understand how users evaluate this product. Consequently, this scoping review aims to map studies that have evaluated the PEC e-SUS APS. METHODS: This scoping review is guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P) framework, as well as by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Checklist extension for scoping reviews (PRISMA-ScR). The research question was framed based on the "CoCoPop" mnemonic (Condition, Context, Population). The final question posed is, "How has the Citizen's Electronic Health Record (PEC e-SUS APS) been evaluated?" The search strategy will be executed across various databases (LILACS, PubMed/MEDLINE, Scopus, Web of Science, ACM Digital Library, and IEEE Digital Library), along with gray literature from ProQuest Dissertation and Theses Global and Google Scholar, with assistance from a professional healthcare librarian skilled in supporting systematic reviews. The database search will encompass the period from 2013 to 2024. Articles included will be selected by three independent reviewers in two stages, and the findings will undergo a descriptive analysis and synthesis following a "narrative review" approach. Independent reviewers will chart the data as outlined in the literature. DISCUSSION: The implementation process for the PEC e-SUS APS can be influenced by the varying characteristics of the over 5500 Brazilian municipalities. These factors and other challenges encountered by health professionals and managers may prove pivotal for a municipality's adoption of the PEC e-SUS APS system. With the literature mapping to be obtained from this review, vital insights into how users have evaluated the PEC will be obtained. SYSTEMATIC REVIEW REGISTRATION: The protocol has been registered prospectively at the Open Science Framework platform under the number 10.17605/OSF.IO/NPKRU.

Real-world data to support post-market safety and performance of embolization coils: evidence generation from a medical device manufacturer and data institute partnership.

BACKGROUND: Recognizing the limitations of pre-market clinical data, regulatory authorities have embraced total product lifecycle management with post-market surveillance (PMS) data to assess medical device safety and performance. One method of proactive PMS involves the analysis of real-world data (RWD) through retrospective review of electronic health records (EHR). Because EHRs are patient-centered and focused on providing tools that clinicians use to determine care rather than collecting information on individual medical products, the process of transforming RWD into real-world evidence (RWE) can be laborious, particularly for medical devices with broad clinical use and extended clinical follow-up. This study describes a method to extract RWD from EHR to generate RWE on the safety and performance of embolization coils. METHODS: Through a partnership between a non-profit data institute and a medical device manufacturer, information on implantable embolization coils' use was extracted, linked, and analyzed from clinical data housed in an electronic data warehouse from the state of Indiana's largest health system. To evaluate the performance and safety of the embolization coils, technical success and safety were defined as per the Society of Interventional Radiology guidelines. A multi-prong strategy including electronic and manual review of unstructured (clinical chart notes) and structured data (International Classification of Disease codes), was developed to identify patients with relevant devices and extract data related to the endpoints. RESULTS: A total of 323 patients were identified as treated using Cook Medical Tornado, Nester, or MReye embolization coils between 1 January 2014 and 31 December 2018. Available clinical follow-up for these patients was 1127 ± 719 days. Indications for use, adverse events, and procedural success rates were identified via automated extraction of structured data along with review of available unstructured data. The overall technical success rate was 96.7%, and the safety events rate was 5.3% with 18 major adverse events in 17 patients. The calculated technical success and safety rates met pre-established performance goals (≥ 85% for technical success and ≤ 12% for safety), highlighting the relevance of this surveillance method. CONCLUSIONS: Generating RWE from RWD requires careful planning and execution. The process described herein provided valuable longitudinal data for PMS of real-world device safety and performance. This cost-effective approach can be translated to other medical devices and similar RWD database systems.

Accurately identifying incident cases of venous thromboembolism in the electronic health record: Performance of a novel phenotyping algorithm.

BACKGROUND: Accurate identification of incident venous thromboembolism (VTE) for quality improvement and health services research is challenging. The purpose of this study was to evaluate the performance of a novel incident VTE phenotyping algorithm defined using standard terminologies, requiring three key indicators documented in the electronic health record (EHR): VTE diagnostic code, VTE-related imaging procedure code, and anticoagulant medication code. METHODS: Retrospective chart reviews were conducted to assess the performance of the algorithm using a random sample of phenotype(+) and phenotype(-) diagnostic encounters from primary care practices and acute care sites affiliated with five hospitals across a large integrated care delivery system in Massachusetts. The performance of the algorithm was evaluated by calculating the positive predictive value (PPV), negative predictive value (NPV), sensitivity, and specificity, using the phenotype(+) and phenotype(-) diagnostic encounters sample and target population data. RESULTS: Based on gold-standard manual chart review, the algorithm had a PPV of 95.2 % (95 % CI: 93.1-96.8 %), NPV of 97.1 % (95 % CI: 95.3-98.4 %), sensitivity of 91.7 % (95 % CI: 90.8-92.6 %), and specificity of 98.4 % (95 % CI: 98.1-98.6 %). The algorithm systematically misclassified a low number of specific types of encounters, highlighting potential areas for improvement. CONCLUSIONS: This novel phenotyping algorithm offers an accurate approach for identifying incident VTE in general populations using EHR data and standard terminologies, and accurately identifies the specific encounter and date of diagnosis of the incident VTE. This approach can be used for measurement of incident VTE to drive quality improvement, research to expand the evidence, and development of quality metrics and clinical decision support to improve the diagnostic process.

Enrollment strategies in the era of digital revolution: Experience from a remote health management program.

We share our experience on the strategies implemented for identifying and enrolling participants in a randomized remote implementation trial. We aimed to evaluate the effectiveness of various digital and traditional screening and outreach methods in participant enrollment. This study focuses on understanding the success and challenges associated with different approaches to patient engagement.

Evaluation of pharmacogenetic automated clinical decision support for clopidogrel.

Aim: Clopidogrel requires CYP2C19 activation to have antiplatelet effects. Pharmacogenetic testing to identify patients with impaired CYP2C19 function can be coupled with clinical decision support (CDS) alerts to guide antiplatelet prescribing. We evaluated the impact of alerts on clopidogrel prescribing.Materials & methods: We retrospectively analyzed data for 866 patients in which CYP2C19-clopidogrel CDS was deployed at a single healthcare system during 2015-2023.Results: Analyses included 2,288 alerts. CDS acceptance rates increased from 24% in 2015 to 63% in 2023 (p < 0.05). Adjusted analyses also showed higher acceptance rates when clopidogrel had been ordered for a percutaneous intervention (OR: 28.7, p < 0.001) and when cardiologists responded to alerts (OR: 2.11, p = 0.001).Conclusion: CDS for CYP2C19-clopidogrel was effective in reducing potential drug-gene interactions. Its influence varied by clinician specialty and medication indications.

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A Clinician and Electronic Health Record Wearable Device Intervention to Increase Physical Activity in Patients With Obesity: Formative Qualitative Study.

BACKGROUND: The number of individuals using digital health devices has grown in recent years. A higher rate of use in patients suggests that primary care providers (PCPs) may be able to leverage these tools to effectively guide and monitor physical activity (PA) for their patients. Despite evidence that remote patient monitoring (RPM) may enhance obesity interventions, few primary care practices have implemented programs that use commercial digital health tools to promote health or reduce complications of the disease. OBJECTIVE: This formative study aimed to assess the perceptions, needs, and challenges of implementation of an electronic health record (EHR)-integrated RPM program using wearable devices to promote patient PA at a large urban primary care practice to prepare for future intervention. METHODS: Our team identified existing workflows to upload wearable data to the EHR (Epic Systems), which included direct Fitbit (Google) integration that allowed for patient PA data to be uploaded to the EHR. We identified pictorial job aids describing the clinical workflow to PCPs. We then performed semistructured interviews with PCPs (n=10) and patients with obesity (n=8) at a large urban primary care clinic regarding their preferences and barriers to the program. We presented previously developed pictorial aids with instructions for (1) providers to complete an order set, set step-count goals, and receive feedback and (2) patients to set up their wearable devices and connect them to their patient portal account. We used rapid qualitative analysis during and after the interviews to code and develop key themes for both patients and providers that addressed our research objective. RESULTS: In total, 3 themes were identified from provider interviews: (1) providers' knowledge of PA prescription is focused on general guidelines with limited knowledge on how to tailor guidance to patients, (2) providers were open to receiving PA data but were worried about being overburdened by additional patient data, and (3) providers were concerned about patients being able to equitably access and participate in digital health interventions. In addition, 3 themes were also identified from patient interviews: (1) patients received limited or nonspecific guidance regarding PA from providers and other resources, (2) patients want to share exercise metrics with the health care team and receive tailored PA guidance at regular intervals, and (3) patients need written resources to support setting up an RPM program with access to live assistance on an as-needed basis. CONCLUSIONS: Implementation of an EHR-based RPM program and associated workflow is acceptable to PCPs and patients but will require attention to provider concerns of added burdensome patient data and patient concerns of receiving tailored PA guidance. Our ongoing work will pilot the RPM program and evaluate feasibility and acceptability within a primary care setting.

Health professionals' perceptions of electronic health records system: a mixed method study in Ghana.

BACKGROUND: Electronic Health Record systems (EHRs) offer significant benefits and have transformed healthcare in developed countries. However, their implementation and adoption in low- and middle-income countries (LMICs) remains low due to challenges and competing interests. Health professionals' perception of EHRs can influence their adoption and continued use. The objectives of this study are to explore the perception of health professionals regarding implemented EHR systems in three hospitals in Ghana and identify factors influencing their perception and satisfaction. METHODS: In this study, we employed a concurrent mixed method design to collect data from study participants from May to June 2023. The quantitative part employed a descriptive-survey and the qualitative (in-depth interview) techniques was applied. After obtaining written informed consent from each respondent, a structured survey questionnaire was filled out by the health professionals from three hospitals. An a priori power calculation was used to determine the sample size for the quantitative component. Two hundred and sixty-three (263) health professionals completed the questionnaire from the three facilities. A purposive sampling technique was used to select fifteen [1] participants for the interviews. A semi-structured interview guide was used for the in-depth interviews. The interviews were audio recorded, transcribed, and coded into themes using QSR Nvivo 12 software before thematic content analysis. RESULTS: Our findings revealed that 213 (80.99%) health professionals perceived the EHRs as beneficial to patients and were generally satisfied. An overwhelming majority, 197 (74.90%) of the health professionals, were satisfied with its use and expressed interest in continuing to use the system. The majority of health professionals viewed the EHRs to have improved their work and workflow processes and provided the desired results. However, few other health professionals were dissatisfied with the system because they viewed the EHRs as frustrating due to unstable internet connectivity and power supply. Other concerns were related to the privacy and confidentiality of patient information. They believe access to patient information should be on a need-to-know basis, and patient information should not be accessible to all other clinicians except those involved directly in their care processes. CONCLUSION: The study revealed that health professionals have a positive perception of the implemented EHRs, are highly satisfied with them, and are interested in continuing to use them. However, health professionals' concerns about the unstable power supply, poor internet connectivity, security, and confidentiality of patient's information need attention, to mitigate their frustrations and boost their confidence in the system.

Use of electronic health records to characterize patients with uncontrolled hypertension in two large health system networks.

BACKGROUND: Improving hypertension control is a public health priority. However, consistent identification of uncontrolled hypertension using computable definitions in electronic health records (EHR) across health systems remains uncertain. METHODS: In this retrospective cohort study, we applied two computable definitions to the EHR data to identify patients with controlled and uncontrolled hypertension and to evaluate differences in characteristics, treatment, and clinical outcomes between these patient populations. We included adult patients (≥ 18 years) with hypertension (based on either ICD-10 codes of hypertension or two elevated blood pressure [BP] measurements) receiving ambulatory care within Yale-New Haven Health System (YNHHS; a large US health system) and OneFlorida Clinical Research Consortium (OneFlorida; a Clinical Research Network comprised of 16 health systems) between October 2015 and December 2018. We identified patients with controlled and uncontrolled hypertension based on either a single BP measurement from a randomly selected visit or all BP measurements recorded between hypertension identification and the randomly selected visit). RESULTS: Overall, 253,207 and 182,827 adults at YNHHS and OneFlorida were identified as having hypertension. Of these patients, 83.1% at YNHHS and 76.8% at OneFlorida were identified using ICD-10-CM codes, whereas 16.9% and 23.2%, respectively, were identified using elevated BP measurements (≥ 140/90 mmHg). A total of 24.1% of patients at YNHHS and 21.6% at OneFlorida had both diagnosis code for hypertension and elevated blood pressure measurements. Uncontrolled hypertension was observed among 32.5% and 43.7% of patients at YNHHS and OneFlorida, respectively. Uncontrolled hypertension was disproportionately higher among Black patients when compared with White patients (38.9% versus 31.5% in YNHHS; p < 0.001; 49.7% versus 41.2% in OneFlorida; p < 0.001). Medication prescription for hypertension management was more common in patients with uncontrolled hypertension when compared with those with controlled hypertension (overall treatment rate: 39.3% versus 37.3% in YNHHS; p = 0.04; 42.2% versus 34.8% in OneFlorida; p < 0.001). Patients with controlled and uncontrolled hypertension had similar incidence rates of deaths, CVD events, and healthcare visits at 3, 6, 12, and 24 months. The two computable definitions generated consistent results. CONCLUSIONS: While the current EHR systems are not fully optimized for disease surveillance and stratification, our findings illustrate the potential of leveraging EHR data to conduct digital population surveillance in the realm of hypertension management.

Longitudinal study of the manifestations and mechanisms of technology-related prescribing errors in pediatrics.

OBJECTIVES: To examine changes in technology-related errors (TREs), their manifestations and underlying mechanisms at 3 time points after the implementation of computerized provider order entry (CPOE) in an electronic health record; and evaluate the clinical decision support (CDS) available to mitigate the TREs at 5-years post-CPOE. MATERIALS AND METHODS: Prescribing errors (n = 1315) of moderate, major, or serious potential harm identified through review of 35 322 orders at 3 time points (immediately, 1-year, and 4-years post-CPOE) were assessed to identify TREs at a tertiary pediatric hospital. TREs were coded using the Technology-Related Error Mechanism classification. TRE rates, percentage of prescribing errors that were TREs, and mechanism rates were compared over time. Each TRE was tested in the CPOE 5-years post-implementation to assess the availability of CDS to mitigate the error. RESULTS: TREs accounted for 32.5% (n = 428) of prescribing errors; an adjusted rate of 1.49 TREs/100 orders (95% confidence interval [CI]: 1.06, 1.92). At 1-year post-CPOE, the rate of TREs was 40% lower than immediately post (incident rate ratio [IRR]: 0.60; 95% CI: 0.41, 0.89). However, at 4-years post, the TRE rate was not significantly different to baseline (IRR: 0.80; 95% CI: 0.59, 1.08). "New workflows required by the CPOE" was the most frequent TRE mechanism at all time points. CDS was available to mitigate 32.7% of TREs. DISCUSSION: In a pediatric setting, TREs persisted 4-years post-CPOE with no difference in the rate compared to immediately post-CPOE. CONCLUSION: Greater attention is required to address TREs to enhance the safety benefits of systems.

Asthma exacerbations in Aspergillus fumigatus-sensitized adults with asthma differs by race.

BACKGROUND: Allergic sensitization to mold is a risk factor for poor asthma outcomes, but whether it accounts for disparities in asthma outcomes according to race or socioeconomic status is not well-studied. OBJECTIVE: We sought to 1) identify factors associated with allergic sensitization to molds and 2) evaluate associations of sensitization to molds with asthma exacerbations after stratifying by race. METHODS: We conducted a retrospective cohort study of adults with asthma who had an outpatient visit in a large health system between 1/1/2017-6/30/2023 and received aeroallergen testing to Aspergillus fumigatus, Penicillium, Alternaria, and Cladosporium. We used logistic regression models to evaluate factors associated with 1) mold sensitization and 2) the effect of mold sensitization on asthma exacerbations in the 12 months before testing, overall and then stratified by race. RESULTS: 2,732 patients met inclusion criteria. Sensitization to each mold was negatively associated with being a woman (odds ratios (ORs)≤0.59, p≤0.001 in five models) and positively associated with Black race (ORs≥2.16 versus White, p<0.0005 in five models). In the full cohort, sensitization to molds were not associated with asthma exacerbations (ORs 0.95-1.40, p≥0.003 in five models and all above the corrected p-value threshold). Among 1,032 Black patients, sensitization to Aspergillus fumigatus, but not to other molds, was associated with increased odds of asthma exacerbations (OR 2.04, p<0.0005). CONCLUSION: Being a man and Black race were associated with allergic sensitization to molds. Sensitization to Aspergillus fumigatus was associated with asthma exacerbations among Black patients but not the overall cohort, suggesting that Aspergillus fumigatus allergy is a source of disparities in asthma outcomes according to race.

Addressing the implementation challenge of risk prediction model due to missing risk factors: The submodel approximation approach.

Clinical prediction models have been widely acknowledged as informative tools providing evidence-based support for clinical decision making. However, prediction models are often underused in clinical practice due to many reasons including missing information upon real-time risk calculation in electronic health records (EHR) system. Existing literature to address this challenge focuses on statistical comparison of various approaches while overlooking the feasibility of their implementation in EHR. In this article, we propose a novel and feasible submodel approach to address this challenge for prediction models developed using the model approximation (also termed "preconditioning") method. The proposed submodel coefficients are equivalent to the corresponding original prediction model coefficients plus a correction factor. Comprehensive simulations were conducted to assess the performance of the proposed method and compared with the existing "one-step-sweep" approach as well as the imputation approach. In general, the simulation results show the preconditioning-based submodel approach is robust to various heterogeneity scenarios and is comparable to the imputation-based approach, while the "one-step-sweep" approach is less robust under certain heterogeneity scenarios. The proposed method was applied to facilitate real-time implementation of a prediction model to identify emergency department patients with acute heart failure who can be safely discharged home.

Hypertension-Associated Expenditures Among Privately Insured US Adults in 2021.

BACKGROUND: There are no recent estimates for hypertension-associated medical expenditures. This study aims to estimate hypertension-associated incremental medical expenditures among privately insured US adults. METHODS: We conducted a retrospective cohort study using IQVIA's Ambulatory Electronic Medical Records-US data set linked with PharMetrics Plus claims data. Among privately insured adults aged 18 to 64 years, hypertension was identified as having ≥1 diagnosis code or ≥2 blood pressure measurements of ≥140/90 mm Hg, or ≥1 antihypertensive medication in 2021. Annual total expenditures (in 2021 $US) were estimated using a generalized linear model with gamma distribution and log-link function adjusting for demographic characteristics and cooccurring conditions. Out-of-pocket expenditures were estimated using a 2-part model that included logistic and generalized linear model regression. Overlap propensity score weights from logistic regression were used to obtain a balanced sample on hypertension status. RESULTS: Among the 393 018 adults, 156 556 (40%) were identified with hypertension. Compared with individuals without hypertension, those with hypertension had $2926 (95% CI, $2681-$3170) higher total expenditures and $328 (95% CI, $300-$355) higher out-of-pocket expenditures. Adults with hypertension had higher total inpatient ($3272 [95% CI, $1458-$5086]) and outpatient ($2189 [95% CI, $2009-$2369]) expenditures when compared with those without hypertension. Hypertension-associated incremental total expenditures were higher for women ($3242 [95% CI, $2915-$3569]) than for men ($2521 [95% CI, $2139-$2904]). CONCLUSIONS: Among privately insured US adults, hypertension was associated with higher medical expenditures, including higher inpatient and out-of-pocket expenditures. These findings may help assess the economic value of interventions effective in preventing hypertension.