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PurposeHeterogeneity has been observed in outcomes of hospitalized patients with coronavirus disease 2019 (COVID-19). Identification of clinical phenotypes may facilitate tailored therapy and improve outcomes. The purpose of this study is to identify specific clinical phenotypes across COVID-19 patients and compare admission characteristics and outcomes.MethodsThis is a retrospective analysis of COVID-19 patients from March 7, 2020 to August 25, 2020 at 14 U.S. hospitals. Ensemble clustering was performed on 33 variables collected within 72 hours of admission. Principal component analysis was performed to visualize variable contributions to clustering. Multinomial regression models were fit to compare patient comorbidities across phenotypes. Multivariable models were fit to estimate associations between phenotype and in-hospital complications and clinical outcomes.ResultsThe database included 1,022 hospitalized patients with COVID-19. Three clinical phenotypes were identified (I, II, III), with 236 [23.1%] patients in phenotype I, 613 [60%] patients in phenotype II, and 173 [16.9%] patients in phenotype III. Patients with respiratory comorbidities were most commonly phenotype III (p = 0.002), while patients with hematologic, renal, and cardiac (all pConclusionWe identified three clinical COVID-19 phenotypes, reflecting patient populations with different comorbidities, complications, and clinical outcomes. Future research is needed to determine the utility of these phenotypes in clinical practice and trial design.

Summary: Background: Many patients receive guideline-discordant inhaler regimens after chronic obstructive pulmonary disease (COPD) hospitalization. Geography and fragmented care across multiple providers likely influence prescription of guideline-discordant inhaler regimens, but these have not been comprehensively studied. We assessed patient-level differences in guideline-discordant inhaler regimens by rurality, drive time to pulmonary specialty care, and fragmented care. Methods: Retrospective cohort analysis using national Veterans Health Administration (VA) data among patients who received primary care and prescriptions from the VA. Patients hospitalized for COPD exacerbation between 2017 and 2020 were assessed for guideline-discordant inhaler regimens in the subsequent 3 months. Guideline-discordant inhaler regimens were defined as short-acting inhaler/s only, inhaled corticosteroid (ICS) monotherapy, long-acting beta-agonist (LABA) monotherapy, ICS + LABA, long-acting muscarinic antagonist (LAMA) monotherapy, or LAMA + ICS. Rural residence and drive time to the closest pulmonary specialty care were obtained from geocoded addresses. Fragmented care was defined as hospitalization outside the VA. We used multivariable logistic regression models to assess associations between rurality, drive time, fragmentated care, and guideline-discordant inhaler regimens. Models were adjusted for age, sex, race/ethnicity, Charlson Comorbidity Index, Area Deprivation Index, and region. Findings: Of 33,785 patients, 16,398 (48.6%) received guideline-discordant inhaler regimens 3 months after hospitalization. Rural residents had higher odds of guideline-discordant inhalers regimens compared to their urban counterparts (adjusted odds ratio [aOR] 1.18 [95% CI: 1.12–1.23]). The odds of receiving guideline-discordant inhaler regimens increased with longer drive time to pulmonary specialty care (aOR 1.38 [95% CI: 1.30–1.46] for drive time >90 min compared to

OBJECTIVES/GOALS: To assess barriers and recommendations for improving delivery of care for chronic obstructive pulmonary disease (COPD) in rural clinics, we assessed COPD care metrics and obtained perspectives of primary care providers (PCPs) who practice in rural clinics. METHODS/STUDY POPULATION: Quantitative retrospective analysis of patients with COPD using VA data. We included patients whose primary care clinic is located in a rural VA Midwest Health Care Network (HCN) facility and quantified binary measures for receipt of: spirometry, pulmonary specialty care, and optimal inhaler therapy (regimen with LAMA or LABA monotherapy) Qualitative semi-structured individual interviews of PCPs (physicians, nurse practitioners, and physician assistants) whose clinics are located in a rural VA Midwest HCN facility. We elicited perceived barriers to and recommendations for receiving spirometry, pulmonary specialty care, and optimal inhaler therapy from PCPs. RESULTS/ANTICIPATED RESULTS: 6,350 rural patients had a new diagnosis of COPD in 2016-2019. 48.4% had spirometry, 14.4% had pulmonary encounters, and among patients who were prescribed long-acting inhaler therapy, 48.8% received optimal inhaler regimens. Rural PCPs (n=14) highlighted lack of access to spirometry, pulmonary specialty care, and clinic staff support in local clinics and suggested: 1) leveraging the expertise of pharmacists in COPD management and 2) improving access to resources, including use of telehealth technologies. DISCUSSION/SIGNIFICANCE: Less than 50% of rural COPD patients received recommended diagnostic testing and therapy. Resource limitations in rural clinics were the main barrier. The main recommendation was to leverage pharmacists’ expertise in COPD care. These findings provide a pathway forward to improving rural COPD care.

Background As patients with congenital heart disease (CHD) are living longer, understanding the comorbidities they develop as they age is increasingly important. However, there are no published population‐based estimates of the comorbidity burden among the US adult patients with CHD. Methods and Results Using the IBM MarketScan commercial claims database from 2010 to 2016, we identified adults aged ≥18 years with CHD and 2 full years of continuous enrollment. These were frequency matched with adults without CHD within categories jointly defined by age, sex, and dates of enrollment in the database. A total of 40 127 patients with CHD met the inclusion criteria (mean [SD] age, 36.8 [14.6] years; and 48.2% were women). Adults with CHD were nearly twice as likely to have any comorbidity than those without CHD (P

ABSTRACT IMPACT: Our research focuses on determining rural-urban disparities in chronic obstructive pulmonary disease (COPD) management to improve COPD health outcomes in rural areas. OBJECTIVES/GOALS: Several methods exist to distinguish rural from urban areas, but it is not clear which method relates most directly to rural-urban health care disparities. To address this, we compared different measures of rurality to measures of chronic obstructive pulmonary disease (COPD) processes of care among a national sample of veterans. METHODS/STUDY POPULATION: Retrospective analysis of patients with COPD (2016-2019 by ICD-10 codes) using national Veterans Affairs (VA) data. We assessed rurality by: 1) patient’s residential address, 2) assigned primary care clinic address, and 3) drive time from the patient’s residence to closest primary care clinic. Rurality designations of the residential address and primary care clinic address into urban, rural, and highly rural areas are based on the Rural Urban Commuting Area (RUCA) codes. The dependent variables were binary outcomes of: 1) documentation of a pulmonary clinic encounter and 2) evidence of spirometry to confirm the diagnosis of COPD. RESULTS/ANTICIPATED RESULTS: Of 6,765,951 veterans, 1,157,002 (17%) had COPD (Table 1). Although approximately 40% of patients with COPD reside in addresses that are rural and highly rural, a large majority are assigned to primary care clinics in urban areas (82.8%) and reside within 30 minutes to the closest primary care clinic (76.7%) (Table 2). Compared to defining rurality based on patient’s residential address or drive time to closest primary care, defining rurality based on the assigned primary care clinic address was associated with a larger disparity in rates of pulmonary encounter. In contrast, the drive time from the patient’s residence to the closest primary care was the strongest predictor of receipt of spirometry (Figure 1 and Table 3). DISCUSSION/SIGNIFICANCE OF FINDINGS: Estimates of the severity of rural-urban disparities varied based on the definition of rurality used. For two process measures, definitions of rurality based on where the patient received primary care generated more evidence of disparities than definitions based solely on the patient’s residential address.

Safety-net hospitals (SNHs) may gain little financial benefit from the rapidly spreading adoption of public reporting and pay-for-performance, but may feel compelled to participate (and bear the costs of data collection) to meet public expectations of transparency and accountability. To better understand the concerns that SNH administrators have regarding public reporting and pay-for-performance, we interviewed 37 executives at randomly selected California SNHs. The main concerns noted by SNH executives were that human and financial resource constraints made it difficult for SNHs to accurately measure their performance. Additionally, some executives felt that market-driven public reporting and pay-for-performance may focus on clinical areas and incentive structures that may not be high-priority clinical areas for SNHs. Executives at SNHs suggested several policy responses to these concerns—such as offering training programs for SNH data collectors—that could be relatively inexpensive and might improve the cost-benefit ratio of public reporting and pay-for-performance programs.

Various approaches have been proposed to adjust for differences in enrollee risk in health plans. Because risk-selection strategies may have different effects on enrollment, we simulated three types of selection—dumping, skimming, and stinting. Concurrent diagnosis-based risk adjustment, and a hybrid using concurrent adjustment for about 8% of the cases and prospective adjustment for the rest, perform markedly better than prospective or demographic adjustments, both in terms of R 2 and the extent to which plans experience unwarranted gains or losses. The simulation approach offers a valuable tool for analysts in assessing various risk-adjustment strategies under different selection situations.

OBJECTIVES:. Providers vary in their impact on clinical outcomes, but this is rarely accounted for in healthcare research. By failing to identify the provider responsible for a patient’s care, investigators miss an opportunity to account for nonrandom variation in outcomes. Prior methods of identifying responsible providers have relied on manual chart review, which is time-consuming and expensive, or analysis of claims data, which has been demonstrated to be inaccurate. To address these gaps, we sought to develop an algorithm using electronic health record (EHR) data to identify the responsible provider for each day of a patient’s hospitalization. DESIGN:. A multicenter retrospective cohort study. SETTING:. Midwest healthcare system. PATIENTS:. Hospitalized patients and their providers. INTERVENTIONS:. None. MEASUREMENTS AND MAIN RESULTS:. We first confirmed high inter-rater reliability of manual chart review to identify the responsible provider. Using manual chart review as the gold standard, we then assessed the accuracy of an automated algorithm in a set of randomly selected patients. The agreement between two independent physicians in their determination of the responsible provider by chart review was 100%. Among 200 randomly selected patients, the algorithm identified the same responsible provider as the physician chart reviewer on 93% (3372/3626; 95% CI, 92–94%) of patient-days. CONCLUSIONS:. Readily available EHR data can be used to assign patients to providers daily with a high degree of accuracy. This methodology could be applied in healthcare research to identify sources of variation other than the intervention being studied.

Abstract Background Opioids and multimodal analgesia are widely administered to manage postoperative pain. However, little is known on how improvements in inpatient pain control are correlated with high-risk (> 90 daily OME) discharge opioid prescriptions for opioid naïve surgical patients. Methods We conducted a retrospective observational study of adult opioid-naïve patients undergoing surgery from June 2012 through December 2018 at a large academic medical center. We used multivariate logistic regression to assess whether multimodal analgesic drugs consumed in the 24 h prior to discharge was associated with a reduction in high-risk opioid discharge prescriptions. We identified other risk factors for receiving a high-risk discharge opioid prescription. Results Among the 32,511 patients, 83% of patients were discharged with an opioid prescription. In 2013, 34.1% of patients with a discharge opioid prescription received a high-risk prescription and this declined to 17.7% by 2018. Use of multimodal analgesic agents during the final 24 h of hospitalization increased each year, with over 80% receiving at least one multimodal analgesic agent by 2018. The median OME consumed in the 24 h prior to discharge peaked in 2013 at 31 and steadily decreased to 19.8 by 2018. There was a significant association between the use of acetaminophen in the 24 h prior to discharge and a high-risk prescription at discharge (p < 0.01). OMEs consumed in the 24 h prior to discharge was a significant predictor of receiving a high-risk discharge prescription, even at low doses. Other factors associated with receipt of a high-risk discharge opioid prescription included male gender, race, history of anxiety disorder, and discharge service. Discussion Use of multimodal analgesia regimens in hospitalized surgical patients in the 24 h prior to hospital discharge increased between 2012 and 2018. Simultaneously, opioid use prior to hospital discharge decreased. Despite these gains, approximately one in five discharge prescriptions was high-risk (> 90 daily OME). In addition, we found that prescribing of discharge opioids above inpatient opioid requirements remains common in opioid naive surgical patients. Conclusion Providers should account for pre-discharge opioid consumption and use of multimodal analgesia when considering the total and daily OME’s that may be appropriate for an individual surgical patient on the discharge opioid prescription.

OBJECTIVES:. To evaluate whether different approaches in note text preparation (known as preprocessing) can impact machine learning model performance in the case of mortality prediction ICU. DESIGN:. Clinical note text was used to build machine learning models for adults admitted to the ICU. Preprocessing strategies studied were none (raw text), cleaning text, stemming, term frequency-inverse document frequency vectorization, and creation of n-grams. Model performance was assessed by the area under the receiver operating characteristic curve. Models were trained and internally validated on University of California San Francisco data using 10-fold cross validation. These models were then externally validated on Beth Israel Deaconess Medical Center data. SETTING:. ICUs at University of California San Francisco and Beth Israel Deaconess Medical Center. SUBJECTS:. Ten thousand patients in the University of California San Francisco training and internal testing dataset and 27,058 patients in the external validation dataset, Beth Israel Deaconess Medical Center. INTERVENTIONS:. None. MEASUREMENTS AND MAIN RESULTS:. Mortality rate at Beth Israel Deaconess Medical Center and University of California San Francisco was 10.9% and 7.4%, respectively. Data are presented as area under the receiver operating characteristic curve (95% CI) for models validated at University of California San Francisco and area under the receiver operating characteristic curve for models validated at Beth Israel Deaconess Medical Center. Models built and trained on University of California San Francisco data for the prediction of inhospital mortality improved from the raw note text model (AUROC, 0.84; CI, 0.80–0.89) to the term frequency-inverse document frequency model (AUROC, 0.89; CI, 0.85–0.94). When applying the models developed at University of California San Francisco to Beth Israel Deaconess Medical Center data, there was a similar increase in model performance from raw note text (area under the receiver operating characteristic curve at Beth Israel Deaconess Medical Center: 0.72) to the term frequency-inverse document frequency model (area under the receiver operating characteristic curve at Beth Israel Deaconess Medical Center: 0.83). CONCLUSIONS:. Differences in preprocessing strategies for note text impacted model discrimination. Completing a preprocessing pathway including cleaning, stemming, and term frequency-inverse document frequency vectorization resulted in the preprocessing strategy with the greatest improvement in model performance. Further study is needed, with particular emphasis on how to manage author implicit bias present in note text, before natural language processing algorithms are implemented in the clinical setting.

We used data from a statewide public health-health system collaboration to describe trends in COVID-19 vaccination rates by racial and ethnic groups among people experiencing homelessness or incarceration in Minnesota. Vaccination completion rates among the general population and people incarcerated in state prisons were substantially higher than those among people experiencing homelessness or jail incarceration.

Evidence suggests that hydroxychloroquine (HCQ) retinal toxicity is more common than previously thought. Adhering to careful weight-based dosing can significantly reduce the risk of this adverse event and is recommended in recent guidelines. We used electronic health record data from a large health system to examine HCQ dosing over a 5-year period and identify risk factors associated with higher dosage of HCQ. We constructed a longitudinal, retrospective cohort of patients with HCQ prescriptions (1681 patients with 3490 prescribing events) between 2012 and 2016. We measured HCQ dosing patterns relative to guidelines (<6.5 and <5.0 mg/kg) over time and used longitudinal multivariate mixed effects logistic regression to identify sociodemographic, clinical and health system factors associated with receiving higher than recommended doses of HCQ. The proportion of patients receiving doses above 6.5 mg/kg decreased from 12% in 2012 to 7% by 2016. Similarly, the proportion of patients with doses above 5.0 mg/kg fell from 38% in 2012 to 30% in 2016. Low body weight (<68 kg) was strongly associated with receiving doses of HCQ above 6.5 mg/kg across all time points, even after adjusting for other factors (odds ratios ranging from 13.2 to 21.0). Although the proportion of patients receiving higher than recommended HCQ doses has declined over a period of 5 years, a substantial number of individuals remain at increased risk for toxicity. Given the widespread use of HCQ in immune-mediated diseases, our study suggests that interventions aimed to ensure appropriate dosing are warranted to improve patient safety.