Interventions and contextual factors to improve retention in care for patients with hypertension in primary care: Hermeneutic systematic review.

BACKGROUND: Regular engagement over time in hypertension care, or retention, is a crucial but understudied step in optimizing patient outcomes. This systematic review leverages a hermeneutic methodology to identify, evaluate, and quantify the effects of interventions and contextual factors for improving retention for patients with hypertension. METHODS: We searched for articles that were published between 2000 and 2022 from multiple electronic databases, including MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, clinicaltrials.gov, and WHO International Trials Registry. We followed the latest version of the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guideline to report the findings for this review. We also synthesized the findings using a hermeneutic methodology for systematic reviews, which used an iterative process to review, integrate, analyze, and interpret evidence. RESULTS: From 4686 screened titles and abstracts, 18 unique studies from 9 countries were identified, including 10 (56%) randomized controlled trials (RCTs), 3 (17%) cluster RCTs, and 5 (28%) non-RCT studies. The number of participants ranged from 76 to 1562. The overall mean age range was 41-67 years, and the proportion of female participants ranged from 0% to 100%. Most (n = 17, 94%) studies used non-physician personnel to implement the proposed interventions. Fourteen studies (78%) implemented multilevel combinations of interventions. Education and training, team-based care, consultation, and Short Message Service reminders were the most common interventions tested. CONCLUSIONS: This review presents the most comprehensive findings on retention in hypertension care to date and fills the gaps in the literature, including the effectiveness of interventions, their components, and contextual factors. Adaptation of and implementing HIV care models, such differentiated service delivery, may be more effective and merit further study. REGISTRATION: CRD42021291368. PROTOCOL REGISTRATION: PROSPERO 2021 CRD42021291368. Available at: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=291368.

Declining Participation in Primary Care Quality Improvement Research: A Qualitative Study.

PURPOSE: There are numerous supportive quality improvement (QI) projects to facilitate the implementation of evidence-based practices in primary care, but recruiting physician practices to join these projects is challenging, costly, and time consuming. We aimed to identify factors leading primary care practices to decline participation in QI projects, and strategies to improve the feasibility and attractiveness of QI projects in the future. METHODS: For this qualitative study, we contacted 109 representatives of practices that had declined participation in 1 of 4 Agency for Healthcare Research and Quality-funded EvidenceNOW projects. The representatives were invited to participate in a 15-minute interview or complete a 5-question questionnaire. Thematic analysis was used to organize and characterize findings. RESULTS: Representatives from 31 practices (28.4% of those contacted) responded. Overwhelmingly, respondents indicated that staff turnover, staffing shortages, and general time constraints, exacerbated by the pandemic, prevented participation in the QI projects. Challenges with electronic health records, an expectation of greater financial compensation for participation, and confidence in the practices' current care practices were secondary reasons for declining participation. Tying participation to value-based programs and offering greater compensation were identified as strategies to facilitate recruitment. None of the respondents' recommendations, however, addressed the primary issues of staffing challenges and time constraints. CONCLUSIONS: Staffing challenges and general time constraints, exacerbated by the pandemic, are compromising primary care practices' ability to engage in QI research projects. To encourage participation, policy makers should consider direct supports for primary care, which may also help to alleviate burnout.

Multiancestral polygenic risk score for pediatric asthma.

BACKGROUND: Asthma is the most common chronic condition in children and the third leading cause of hospitalization in pediatrics. The genome-wide association study catalog reports 140 studies with genome-wide significance. A polygenic risk score (PRS) with predictive value across ancestries has not been evaluated for this important trait. OBJECTIVES: This study aimed to train and validate a PRS relying on genetic determinants for asthma to provide predictions for disease occurrence in pediatric cohorts of diverse ancestries. METHODS: This study applied a Bayesian regression framework method using the Trans-National Asthma Genetic Consortium genome-wide association study summary statistics to derive a multiancestral PRS score, used one Electronic Medical Records and Genomics (eMERGE) cohort as a training set, used a second independent eMERGE cohort to validate the score, and used the UK Biobank data to replicate the findings. A phenome-wide association study was performed using the PRS to identify shared genetic etiology with other phenotypes. RESULTS: The multiancestral asthma PRS was associated with asthma in the 2 pediatric validation datasets. Overall, the multiancestral asthma PRS has an area under the curve (AUC) of 0.70 (95% CI, 0.69-0.72) in the pediatric validation 1 and AUC of 0.66 (0.65-0.66) in the pediatric validation 2 datasets. We found significant discrimination across pediatric subcohorts of European (AUC, 95% CI, 0.60 and 0.66), African (AUC, 95% CI, 0.61 and 0.66), admixed American (AUC, 0.64 and 0.70), Southeast Asian (AUC, 0.65), and East Asian (AUC, 0.73) ancestry. Pediatric participants with the top 5% PRS had 2.80 to 5.82 increased odds of asthma compared to the bottom 5% across the training, validation 1, and validation 2 cohorts when adjusted for ancestry. Phenome-wide association study analysis confirmed the strong association of the identified PRS with asthma (odds ratio, 2.71, PFDR = 3.71 × 10-65) and related phenotypes. CONCLUSIONS: A multiancestral PRS for asthma based on Bayesian posterior genomic effect sizes identifies increased odds of pediatric asthma.

Electronic health record data use in the assessment of quality indicators for glucocorticoid osteoporosis screening in systemic lupus erythematosus.

OBJECTIVE: To determine whether electronic health record (EHR) data components could be identified and used to assess bone health quality indicators in patients with systemic lupus erythematosus as a foundation for population health management. METHODS: We identified patients in our EHR system who had diagnosis codes for lupus from 2012 to 2017 and characterized them based on the frequency and dosage of prescribed glucocorticoid medications. The medical records of patients who received repeated high-dose glucocorticoid orders were further reviewed for osteoporosis, osteoporotic fractures, receipt of appropriate preventive screening, and orders for protective medications based on established quality indicators. Descriptive statistics were calculated to summarize results. RESULTS: We identified 617 patients with a lupus diagnosis; 414 received glucocorticoid prescriptions, 189 received chronic, high-dose; and 83 received chronic, low-dose prescription orders. Of those with chronic high-dose glucocorticoid prescriptions, 14% had an osteoporosis diagnosis, 3% had an osteoporotic fracture, 51% received a prescription for calcium/vitamin D, 43% had bone mineral density screening orders, 20% received a spine radiograph order, 29% had a documented T-score, 12% received a prescription for osteoporosis medication, and 6% had a documented osteoporosis screening. We were able to identify data elements in the EHR for all nine components of the osteoporosis management quality indicator. CONCLUSIONS: It is possible to identify data in the EHR for all attributes of the quality indicator for osteoporosis in lupus patients who receive chronic high-dose glucocorticoids. However, missing data and need to extract data from text-based notes may make development of population management tools challenging.

Considerations Before Selecting a Stepped-Wedge Cluster Randomized Trial Design for a Practice Improvement Study.

PURPOSE: Despite the growing popularity of stepped-wedge cluster randomized trials (SW-CRTs) for practice-based research, the design's advantages and challenges are not well documented. The objective of this study was to identify the advantages and challenges of the SW-CRT design for large-scale intervention implementations in primary care settings. METHODS: The EvidenceNOW: Advancing Heart Health initiative, funded by the Agency for Healthcare Research and Quality, included a large collection of SW-CRTs. We conducted qualitative interviews with 17 key informants from EvidenceNOW grantees to identify the advantages and challenges of using SW-CRT design. RESULTS: All interviewees reported that SW-CRT can be an effective study design for large-scale intervention implementations. Advantages included (1) incentivized recruitment, (2) staggered resource allocation, and (3) statistical power. Challenges included (1) time-sensitive recruitment, (2) retention, (3) randomization requirements and practice preferences, (4) achieving treatment schedule fidelity, (5) intensive data collection, (6) the Hawthorne effect, and (7) temporal trends. CONCLUSIONS: The challenges experienced by EvidenceNOW grantees suggest that certain favorable real-world conditions constitute a context that increases the odds of a successful SW-CRT. An existing infrastructure can support the recruitment of many practices. Strong retention plans are needed to continue to engage sites waiting to start the intervention. Finally, study outcomes should be ones already captured in routine practice; otherwise, funders and investigators should assess the feasibility and cost of data collection.VISUAL ABSTRACT.

Remdesivir for Severe Coronavirus Disease 2019 (COVID-19) Versus a Cohort Receiving Standard of Care.

BACKGROUND: We compared the efficacy of the antiviral agent, remdesivir, versus standard-of-care treatment in adults with severe coronavirus disease 2019 (COVID-19) using data from a phase 3 remdesivir trial and a retrospective cohort of patients with severe COVID-19 treated with standard of care. METHODS: GS-US-540-5773 is an ongoing phase 3, randomized, open-label trial comparing two courses of remdesivir (remdesivir-cohort). GS-US-540-5807 is an ongoing real-world, retrospective cohort study of clinical outcomes in patients receiving standard-of-care treatment (non-remdesivir-cohort). Inclusion criteria were similar between studies: patients had confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, were hospitalized, had oxygen saturation ≤94% on room air or required supplemental oxygen, and had pulmonary infiltrates. Stabilized inverse probability of treatment weighted multivariable logistic regression was used to estimate the treatment effect of remdesivir versus standard of care. The primary endpoint was the proportion of patients with recovery on day 14, dichotomized from a 7-point clinical status ordinal scale. A key secondary endpoint was mortality. RESULTS: After the inverse probability of treatment weighting procedure, 312 and 818 patients were counted in the remdesivir- and non-remdesivir-cohorts, respectively. At day 14, 74.4% of patients in the remdesivir-cohort had recovered versus 59.0% in the non-remdesivir-cohort (adjusted odds ratio [aOR] 2.03: 95% confidence interval [CI]: 1.34-3.08, P < .001). At day 14, 7.6% of patients in the remdesivir-cohort had died versus 12.5% in the non-remdesivir-cohort (aOR 0.38, 95% CI: .22-.68, P = .001). CONCLUSIONS: In this comparative analysis, by day 14, remdesivir was associated with significantly greater recovery and 62% reduced odds of death versus standard-of-care treatment in patients with severe COVID-19. CLINICAL TRIALS REGISTRATION: NCT04292899 and EUPAS34303.

Pleiotropy of systemic lupus erythematosus risk alleles and cardiometabolic disorders: A phenome-wide association study and inverse-variance weighted meta-analysis.

OBJECTIVES: To test the hypothesis that genetic predisposition to systemic lupus erythematosus (SLE) increases the risk of cardiometabolic disorders. METHODS: Using 41 single nucleotide polymorphisms (SNPs) associated with SLE, we calculated a weighted genetic risk score (wGRS) for SLE. In a large biobank we tested the association between this wGRS and 9 cardiometabolic phenotypes previously associated with SLE: atrial fibrillation, ischemic stroke, coronary artery disease, type 1 and type 2 diabetes, obesity, chronic kidney disease, hypertension, and hypercholesterolemia. Additionally, we performed a phenome-wide association analysis (pheWAS) to discover novel clinical associations with a genetic predisposition to SLE. Findings were replicated in the Electronic Medical Records and Genomics (eMERGE) Network. To further define the association between SLE-related risk alleles and the selected cardiometabolic phenotypes, we performed an inverse variance weighted regression (IVWR) meta-analysis. RESULTS: The wGRS for SLE was calculated in 74,759 individuals of European ancestry. Among the pre-selected phenotypes, the wGRS was significantly associated with type 1 diabetes (OR [95%CI] =1.11 [1.06, 1.17], P-value = 1.05x10-5). In the PheWAS, the wGRS was associated with several autoimmune phenotypes, kidney disorders, and skin neoplasm; but only the associations with autoimmune phenotypes were replicated. In the IVWR meta-analysis, SLE-related risk alleles were nominally associated with type 1 diabetes (P = 0.048) but the associations were heterogeneous and did not meet the adjusted significance threshold. CONCLUSION: A weighted GRS for SLE was associated with an increased risk of several autoimmune-related phenotypes including type I diabetes but not with cardiometabolic disorders.

Effects of 2 Forms of Practice Facilitation on Cardiovascular Prevention in Primary Care: A Practice-randomized, Comparative Effectiveness Trial.

BACKGROUND: Effective quality improvement (QI) strategies are needed for small practices. OBJECTIVE: The objective of this study was to compare practice facilitation implementing point-of-care (POC) QI strategies alone versus facilitation implementing point-of-care plus population management (POC+PM) strategies on preventive cardiovascular care. DESIGN: Two arm, practice-randomized, comparative effectiveness study. PARTICIPANTS: Small and mid-sized primary care practices. INTERVENTIONS: Practices worked with facilitators on QI for 12 months to implement POC or POC+PM strategies. MEASURES: Proportion of eligible patients in a practice meeting "ABCS" measures: (Aspirin) Aspirin/antiplatelet therapy for ischemic vascular disease, (Blood pressure) Controlling High Blood Pressure, (Cholesterol) Statin Therapy for the Prevention and Treatment of Cardiovascular Disease, and (Smoking) Tobacco Use: Screening and Cessation Intervention, and the Change Process Capability Questionnaire. Measurements were performed at baseline, 12, and 18 months. RESULTS: A total of 226 practices were randomized, 179 contributed follow-up data. The mean proportion of patients meeting each performance measure was greater at 12 months compared with baseline: Aspirin 0.04 (95% confidence interval: 0.02-0.06), Blood pressure 0.04 (0.02-0.06), Cholesterol 0.05 (0.03-0.07), Smoking 0.05 (0.02-0.07); P<0.001 for each. Improvements were sustained at 18 months. At 12 months, baseline-adjusted difference-in-differences in proportions for the POC+PM arm versus POC was: Aspirin 0.02 (-0.02 to 0.05), Blood pressure -0.01 (-0.04 to 0.03), Cholesterol 0.03 (0.00-0.07), and Smoking 0.02 (-0.02 to 0.06); P>0.05 for all. Change Process Capability Questionnaire improved slightly, mean change 0.30 (0.09-0.51) but did not significantly differ across arms. CONCLUSION: Facilitator-led QI promoting population management approaches plus POC improvement strategies was not clearly superior to POC strategies alone.

Making work visible for electronic phenotype implementation: Lessons learned from the eMERGE network.

BACKGROUND: Implementation of phenotype algorithms requires phenotype engineers to interpret human-readable algorithms and translate the description (text and flowcharts) into computable phenotypes - a process that can be labor intensive and error prone. To address the critical need for reducing the implementation efforts, it is important to develop portable algorithms. METHODS: We conducted a retrospective analysis of phenotype algorithms developed in the Electronic Medical Records and Genomics (eMERGE) network and identified common customization tasks required for implementation. A novel scoring system was developed to quantify portability from three aspects: Knowledge conversion, clause Interpretation, and Programming (KIP). Tasks were grouped into twenty representative categories. Experienced phenotype engineers were asked to estimate the average time spent on each category and evaluate time saving enabled by a common data model (CDM), specifically the Observational Medical Outcomes Partnership (OMOP) model, for each category. RESULTS: A total of 485 distinct clauses (phenotype criteria) were identified from 55 phenotype algorithms, corresponding to 1153 customization tasks. In addition to 25 non-phenotype-specific tasks, 46 tasks are related to interpretation, 613 tasks are related to knowledge conversion, and 469 tasks are related to programming. A score between 0 and 2 (0 for easy, 1 for moderate, and 2 for difficult portability) is assigned for each aspect, yielding a total KIP score range of 0 to 6. The average clause-wise KIP score to reflect portability is 1.37 ±â€¯1.38. Specifically, the average knowledge (K) score is 0.64 ±â€¯0.66, interpretation (I) score is 0.33 ±â€¯0.55, and programming (P) score is 0.40 ±â€¯0.64. 5% of the categories can be completed within one hour (median). 70% of the categories take from days to months to complete. The OMOP model can assist with vocabulary mapping tasks. CONCLUSION: This study presents firsthand knowledge of the substantial implementation efforts in phenotyping and introduces a novel metric (KIP) to measure portability of phenotype algorithms for quantifying such efforts across the eMERGE Network. Phenotype developers are encouraged to analyze and optimize the portability in regards to knowledge, interpretation and programming. CDMs can be used to improve the portability for some 'knowledge-oriented' tasks.

Practice Facilitators' and Leaders' Perspectives on a Facilitated Quality Improvement Program.

PURPOSE: Practice facilitation is a promising approach to helping practices implement quality improvements. Our purpose was to describe practice facilitators' and practice leaders' perspectives on implementation of a practice facilitator-supported quality improvement program and describe where their perspectives aligned and diverged. METHODS: We conducted interviews with practice leaders and practice facilitators who participated in a program that included 35 improvement strategies aimed at the ABCS of heart health (aspirin use in high-risk individuals, blood pressure control, cholesterol management, and smoking cessation). Rapid qualitative analysis was used to collect, organize, and analyze the data. RESULTS: We interviewed 17 of the 33 eligible practice leaders, and the 10 practice facilitators assigned to those practices. Practice leaders and practice facilitators both reported value in the program's ability to bring needed, high-quality resources to practices. Practice leaders appreciated being able to set the schedule for facilitation and select among the 35 interventions. According to practice facilitators, however, relying on practice leaders to set the pace of the intervention resulted in a lower level of program intensity than intended. Practice leaders preferred targeted assistance, particularly electronic health record documentation guidance and linkages to state smoking cessation programs. Practice facilitators reported that the easiest interventions were those that did not alter care practices. CONCLUSIONS: The dual perspectives of practice leaders and practice facilitators provide a more holistic picture of enablers and barriers to program implementation. There may be greater opportunities to assist small practices through simple, targeted practice facilitator-supported efforts rather than larger, comprehensive quality improvement projects.

Engaging Primary Care Practices in Studies of Improvement: Did You Budget Enough for Practice Recruitment?

PURPOSE: The methods and costs to enroll small primary care practices in large, regional quality improvement initiatives are unknown. We describe the recruitment approach, cost, and resources required to recruit and enroll 500 practices in the Northwest and Midwest regional cooperatives participating in the Agency for Healthcare Research and Quality (AHRQ)-funded initiative, EvidenceNOW: Advancing Heart Health in Primary Care. METHODS: The project management team of each cooperative tracked data on recruitment methods used for identifying and connecting with practices. We developed a cost-of-recruitment template and used it to record personnel time and associated costs of travel and communication materials. RESULTS: A total of 3,669 practices were contacted during the 14- to 18-month recruitment period, resulting in 484 enrolled practices across the 6 states served by the 2 cooperatives. The average number of interactions per enrolled practice was 7, with a total of 29,100 hours and a total cost of $2.675 million, or $5,529 per enrolled practice. Prior partnerships predicted recruiting almost 1 in 3 of these practices as contrasted to 1 in 20 practices without a previous relationship or warm hand-off. CONCLUSIONS: Recruitment of practices for large-scale practice quality improvement transformation initiatives is difficult and costly. The cost of recruiting practices without existing partnerships is expensive, costing 7 times more than reaching out to familiar practices. Investigators initiating and studying practice quality improvement initiatives should budget adequate funds to support high-touch recruitment strategies, including building trusted relationships over a long time frame, for a year or more.

Community-Based Participatory Research and System Dynamics Modeling for Improving Retention in Hypertension Care.

Importance: The high prevalence of hypertension calls for broad, multisector responses that foster prevention and care services, with the goal of leveraging high-quality treatment as a means of reducing hypertension incidence. Health care system improvements require stakeholder input from across the care continuum to identify gaps and inform interventions that improve hypertension care service, delivery, and retention; system dynamics modeling offers a participatory research approach through which stakeholders learn about system complexity and ways to model sustainable system-level improvements. Objective: To assess the association of simulated interventions with hypertension care retention rates in the Nigerian primary health care system using system dynamics modeling. Design, Setting, and Participants: This decision analytical model used a participatory research approach involving stakeholder workshops conducted in July and October 2022 to gather insights and inform the development of a system dynamics model designed to simulate the association of various interventions with retention in hypertension care. The study focused on the primary health care system in Nigeria, engaging stakeholders from various sectors involved in hypertension care, including patients, community health extension workers, nurses, pharmacists, researchers, administrators, policymakers, and physicians. Exposure: Simulated intervention packages. Main Outcomes and Measures: Retention rate in hypertension care at 12, 24, and 36 months, modeled to estimate the effectiveness of the interventions. Results: A total of 16 stakeholders participated in the workshops (mean [SD] age, 46.5 [8.6] years; 9 [56.3%] male). Training of health care workers was estimated to be the most effective single implementation strategy for improving retention in hypertension care in Nigeria, with estimated retention rates of 29.7% (95% CI, 27.8%-31.2%) at 12 months and 27.1% (95% CI, 26.0%-28.3%) at 24 months. Integrated intervention packages were associated with the greatest improvements in hypertension care retention overall, with modeled retention rates of 72.4% (95% CI, 68.4%-76.4%), 68.1% (95% CI, 64.5%-71.7%), and 67.1% (95% CI, 64.5%-71.1%) at 12, 24, and 36 months, respectively. Conclusions and Relevance: This decision analytical model study showed that community-based participatory research could be used to estimate the potential effectiveness of interventions for improving retention in hypertension care. Integrated intervention packages may be the most promising strategies.

Detection of Personal and Family History of Suicidal Thoughts and Behaviors using Deep Learning and Natural Language Processing: A Multi-Site Study.

Objective: Personal and family history of suicidal thoughts and behaviors (PSH and FSH, respectively) are significant risk factors associated with future suicide events. These are often captured in narrative clinical notes in electronic health records (EHRs). Collaboratively, Weill Cornell Medicine (WCM), Northwestern Medicine (NM), and the University of Florida (UF) developed and validated deep learning (DL)-based natural language processing (NLP) tools to detect PSH and FSH from such notes. The tool's performance was further benchmarked against a method relying exclusively on ICD-9/10 diagnosis codes. Materials and Methods: We developed DL-based NLP tools utilizing pre-trained transformer models Bio_ClinicalBERT and GatorTron, and compared them with expert-informed, rule-based methods. The tools were initially developed and validated using manually annotated clinical notes at WCM. Their portability and performance were further evaluated using clinical notes at NM and UF. Results: The DL tools outperformed the rule-based NLP tool in identifying PSH and FHS. For detecting PSH, the rule-based system obtained an F1-score of 0.75 ± 0.07, while the Bio_ClinicalBERT and GatorTron DL tools scored 0.83 ± 0.09 and 0.84 ± 0.07, respectively. For detecting FSH, the rule-based NLP tool's F1-score was 0.69 ± 0.11, compared to 0.89 ± 0.10 for Bio_ClinicalBERT and 0.92 ± 0.07 for GatorTron. For the gold standard corpora across the three sites, only 2.2% (WCM), 9.3% (NM), and 7.8% (UF) of patients reported to have an ICD-9/10 diagnosis code for suicidal thoughts and behaviors prior to the clinical notes report date. The best performing GatorTron DL tool identified 93.0% (WCM), 80.4% (NM), and 89.0% (UF) of patients with documented PSH, and 85.0%(WCM), 89.5%(NM), and 100%(UF) of patients with documented FSH in their notes. Discussion: While PSH and FSH are significant risk factors for future suicide events, little effort has been made previously to identify individuals with these history. To address this, we developed a transformer based DL method and compared with conventional rule-based NLP approach. The varying effectiveness of the rule-based tools across sites suggests a need for improvement in its dictionary-based approach. In contrast, the performances of the DL tools were higher and comparable across sites. Furthermore, DL tools were fine-tuned using only small number of annotated notes at each site, underscores its greater adaptability to local documentation practices and lexical variations. Conclusion: Variations in local documentation practices across health care systems pose challenges to rule-based NLP tools. In contrast, the developed DL tools can effectively extract PSH and FSH information from unstructured clinical notes. These tools will provide clinicians with crucial information for assessing and treating patients at elevated risk for suicide who are rarely been diagnosed.

Rapid identification of inflammatory arthritis and associated adverse events following immune checkpoint therapy: a machine learning approach.

Introduction: Immune checkpoint inhibitor-induced inflammatory arthritis (ICI-IA) poses a major clinical challenge to ICI therapy for cancer, with 13% of cases halting ICI therapy and ICI-IA being difficult to identify for timely referral to a rheumatologist. The objective of this study was to rapidly identify ICI-IA patients in clinical data and assess associated immune-related adverse events (irAEs) and risk factors. Methods: We conducted a retrospective study of the electronic health records (EHRs) of 89 patients who developed ICI-IA out of 2451 cancer patients who received ICI therapy at Northwestern University between March 2011 to January 2021. Logistic regression and random forest machine learning models were trained on all EHR diagnoses, labs, medications, and procedures to identify ICI-IA patients and EHR codes indicating ICI-IA. Multivariate logistic regression was then used to test associations between ICI-IA and cancer type, ICI regimen, and comorbid irAEs. Results: Logistic regression and random forest models identified ICI-IA patients with accuracies of 0.79 and 0.80, respectively. Key EHR features from the random forest model included ICI-IA relevant features (joint pain, steroid prescription, rheumatoid factor tests) and features suggesting comorbid irAEs (thyroid function tests, pruritus, triamcinolone prescription). Compared to 871 adjudicated ICI patients who did not develop arthritis, ICI-IA patients had higher odds of developing cutaneous (odds ratio [OR]=2.66; 95% Confidence Interval [CI] 1.63-4.35), endocrine (OR=2.09; 95% CI 1.15-3.80), or gastrointestinal (OR=2.88; 95% CI 1.76-4.72) irAEs adjusting for demographics, cancer type, and ICI regimen. Melanoma (OR=1.99; 95% CI 1.08-3.65) and renal cell carcinoma (OR=2.03; 95% CI 1.06-3.84) patients were more likely to develop ICI-IA compared to lung cancer patients. Patients on nivolumab+ipilimumab were more likely to develop ICI-IA compared to patients on pembrolizumab (OR=1.86; 95% CI 1.01-3.43). Discussion: Our machine learning models rapidly identified patients with ICI-IA in EHR data and elucidated clinical features indicative of comorbid irAEs. Patients with ICI-IA were significantly more likely to also develop cutaneous, endocrine, and gastrointestinal irAEs during their clinical course compared to ICI therapy patients without ICI-IA.

Deep learning for identifying personal and family history of suicidal thoughts and behaviors from EHRs.

Personal and family history of suicidal thoughts and behaviors (PSH and FSH, respectively) are significant risk factors associated with suicides. Research is limited in automatic identification of such data from clinical notes in Electronic Health Records. This study developed deep learning (DL) tools utilizing transformer models (Bio_ClinicalBERT and GatorTron) to detect PSH and FSH in clinical notes derived from three academic medical centers, and compared their performance with a rule-based natural language processing tool. For detecting PSH, the rule-based approach obtained an F1-score of 0.75 ± 0.07, while the Bio_ClinicalBERT and GatorTron DL tools scored 0.83 ± 0.09 and 0.84 ± 0.07, respectively. For detecting FSH, the rule-based approach achieved an F1-score of 0.69 ± 0.11, compared to 0.89 ± 0.10 for Bio_ClinicalBERT and 0.92 ± 0.07 for GatorTron. Across sites, the DL tools identified more than 80% of patients at elevated risk for suicide who remain undiagnosed and untreated.

Risk factors affecting polygenic score performance across diverse cohorts.

Apart from ancestry, personal or environmental covariates may contribute to differences in polygenic score (PGS) performance. We analyzed effects of covariate stratification and interaction on body mass index (BMI) PGS (PGSBMI) across four cohorts of European (N=491,111) and African (N=21,612) ancestry. Stratifying on binary covariates and quintiles for continuous covariates, 18/62 covariates had significant and replicable R2 differences among strata. Covariates with the largest differences included age, sex, blood lipids, physical activity, and alcohol consumption, with R2 being nearly double between best and worst performing quintiles for certain covariates. 28 covariates had significant PGSBMI-covariate interaction effects, modifying PGSBMI effects by nearly 20% per standard deviation change. We observed overlap between covariates that had significant R2 differences among strata and interaction effects - across all covariates, their main effects on BMI were correlated with their maximum R2 differences and interaction effects (0.56 and 0.58, respectively), suggesting high-PGSBMI individuals have highest R2 and increase in PGS effect. Using quantile regression, we show the effect of PGSBMI increases as BMI itself increases, and that these differences in effects are directly related to differences in R2 when stratifying by different covariates. Given significant and replicable evidence for context-specific PGSBMI performance and effects, we investigated ways to increase model performance taking into account non-linear effects. Machine learning models (neural networks) increased relative model R2 (mean 23%) across datasets. Finally, creating PGSBMI directly from GxAge GWAS effects increased relative R2 by 7.8%. These results demonstrate that certain covariates, especially those most associated with BMI, significantly affect both PGSBMI performance and effects across diverse cohorts and ancestries, and we provide avenues to improve model performance that consider these effects.

Age-stratified comorbid and pharmacologic analysis of patients with glioblastoma.

Background: Increased age is a strong and unfavorable prognostic factor for patients with glioblastoma (GBM). However, the relationships between stratified patient age, comorbidities, and medications have yet to be explored in GBM patient survival analyses. Objective: To evaluate co-morbid conditions, tumor-related symptoms, medication prescriptions, and subject age for patients with GBM and to establish potential targets for prospective studies. Methods: Electronic health records for 565 patients with IDHwt GBM were evaluated at a single center between January 1, 2000 and August 9, 2021 were retrospectively assessed. Data were stratified by MGMT promoter methylation status when available and were used to construct multivariable time-dependent cox models and intra-cohort hazards. Results: Younger (<65 years of age) but not older (≥65 years) GBM patients demonstrated a worse prognosis with movement related disabilities (P < 0.0001), gait/balance difficulty (P = 0.04) and weakness (P = 0.007), as well as psychiatric conditions, mental health disorders (P = 0.002) and anxiety (P = 0.001). In contrast, older but not younger GBM patients demonstrated a worse prognosis with epilepsy (P = 0.039). Both groups had worse survival with confusion/altered mental status (P = 0.023 vs < 0.000) and an improved survival with a Temozolomide prescription. Older but not younger GBM patients experienced an improved hazard with a prescription of ace-inhibitor medications (P = 0.048). Conclusion: Age-dependent novel associations between clinical symptoms and medications prescribed for co-morbid conditions were demonstrated in patients with GBM. The results of the current work support future mechanistic studies that investigate the negative relationship(s) between increased age, comorbidities, and drug therapies for differential clinical decision-making across the lifespan of patients with GBM.

Clinical associations with a polygenic predisposition to benign lower white blood cell counts.

Polygenic variation unrelated to disease contributes to interindividual variation in baseline white blood cell (WBC) counts, but its clinical significance is uncharacterized. We investigated the clinical consequences of a genetic predisposition toward lower WBC counts among 89,559 biobank participants from tertiary care centers using a polygenic score for WBC count (PGSWBC) comprising single nucleotide polymorphisms not associated with disease. A predisposition to lower WBC counts was associated with a decreased risk of identifying pathology on a bone marrow biopsy performed for a low WBC count (odds-ratio = 0.55 per standard deviation increase in PGSWBC [95%CI, 0.30-0.94], p = 0.04), an increased risk of leukopenia (a low WBC count) when treated with a chemotherapeutic (n = 1724, hazard ratio [HR] = 0.78 [0.69-0.88], p = 4.0 × 10-5) or immunosuppressant (n = 354, HR = 0.61 [0.38-0.99], p = 0.04). A predisposition to benign lower WBC counts was associated with an increased risk of discontinuing azathioprine treatment (n = 1,466, HR = 0.62 [0.44-0.87], p = 0.006). Collectively, these findings suggest that there are genetically predisposed individuals who are susceptible to escalations or alterations in clinical care that may be harmful or of little benefit.

Identification and assessment of classification criteria attributes for systemic lupus erythematosus in a regional medical record data network.

OBJECTIVE: To assess the application and utility of algorithms designed to detect features of SLE in electronic health record (EHR) data in a multisite, urban data network. METHODS: Using the Chicago Area Patient-Centered Outcomes Research Network (CAPriCORN), a Clinical Data Research Network (CDRN) containing data from multiple healthcare sites, we identified patients with at least one positively identified criterion from three SLE classification criteria sets developed by the American College of Rheumatology (ACR) in 1997, the Systemic Lupus International Collaborating Clinics (SLICC) in 2012, and the European Alliance of Associations for Rheumatology and the ACR in 2019 using EHR-based algorithms. To measure the algorithms' performance in this data setting, we first evaluated whether the number of clinical encounters for SLE was associated with a greater quantity of positively identified criteria domains using Poisson regression. We next quantified the amount of SLE criteria identified at a single healthcare institution versus all sites to assess the amount of SLE-related information gained from implementing the algorithms in a CDRN. RESULTS: Patients with three or more SLE encounters were estimated to have documented 2.77 (2.73 to 2.80) times the number of positive SLE attributes from the 2012 SLICC criteria set than patients without an SLE encounter via Poisson regression. Patients with three or more SLE-related encounters and with documented care from multiple institutions were identified with more SLICC criteria domains when data were included from all CAPriCORN sites compared with a single site (p<0.05). CONCLUSIONS: The positive association observed between amount of SLE-related clinical encounters and the number of criteria domains detected suggests that the algorithms used in this study can be used to help describe SLE features in this data environment. This work also demonstrates the benefit of aggregating data across healthcare institutions for patients with fragmented care.

Rationale and design for Healthy Hearts for Michigan (HH4M): A pragmatic single-arm hybrid effectiveness-implementation study.

Background: The burden of cardiovascular disease (CVD) is particularly high in several US states, which include the state of Michigan. Hypertension and smoking are two major risk factors for mortality due to CVD. Rural Michigan is disproportionally affected by CVD and by primary care shortages. The Healthy Hearts for Michigan (HH4M) study aims to promote hypertension management and smoking cessation through practice facilitation and quality improvement efforts and is part of the multi-state EvidenceNOW: Building State Capacity initiative to provide external support to primary care practices to improve care delivery. Methods: Primary care practices in rural and underserved areas of Michigan were recruited to join HH4M, a pragmatic, single-arm hybrid Type 2 effectiveness-implementation study during which practice facilitation was delivered at the practice level for 12 months, followed by a 3-month maintenance period. Results: Fifty-four practices were enrolled over a 12-month recruitment period. At baseline, the mean proportion (standard deviation) of patients at the practice level meeting the clinical quality measures were: blood pressure, 0.72 (0.12); tobacco screening, 0.80 (0.30); tobacco cessation intervention, 0.57 (0.28); tobacco screening and cessation intervention: 0.78 (0.26). Conclusion: This three-year research program will evaluate the ability of rural and medically underserved primary care practices to implement the quality improvement model by identifying drivers of and barriers to sustainable implementation, and test whether the model improves (a) blood pressure control and (b) tobacco use screening and cessation.