Racial Disparities in Pulse Oximeter Device Inaccuracy and Estimated Clinical Impact on COVID-19 Treatment Course.

Arterial blood oxygen saturation as measured by pulse oximetry (peripheral oxygen saturation (SpO2)) may be differentially less accurate for people with darker skin pigmentation, which could potentially affect the course of coronavirus disease 2019 (COVID-19) treatment. We analyzed pulse oximeter accuracy and its association with COVID-19 treatment outcomes using electronic health record data from Sutter Health, a large, mixed-payer, integrated health-care delivery system in Northern California. We analyzed 2 cohorts: 1) 43,753 non-Hispanic White (NHW) or non-Hispanic Black/African-American (NHB) adults with concurrent arterial blood gas oxygen saturation/SpO2 measurements taken between January 2020 and February 2021; and 2) 8,735 adults who went to a hospital emergency department with COVID-19 between July 2020 and February 2021. Pulse oximetry systematically overestimated blood oxygenation by 1% more in NHB individuals than in NHW individuals. For people with COVID-19, this was associated with lower admission probability (-3.1 percentage points), dexamethasone treatment (-3.1 percentage points), and supplemental oxygen treatment (-4.5 percentage points), as well as increased time to treatment: 37.2 minutes before dexamethasone initiation and 278.5 minutes before initiation of supplemental oxygen. These results call for additional investigation of pulse oximeters and suggest that current guidelines for development, testing, and calibration of these devices should be revisited, investigated, and revised.

A population-health approach to characterizing migraine by comorbidity: Results from the Mindfulness and Migraine Cohort Study.

BACKGROUND: The heterogeneity of migraine has been reported extensively, with identified subgroups usually based on symptoms. Grouping individuals with migraine and similar comorbidity profiles has been suggested, however such segmentation methods have not been tested using real-world clinical data. OBJECTIVE: To gain insights into natural groupings of patients with migraine using latent class analysis based on electronic health record-determined comorbidities. METHODS: Retrospective electronic health record data analysis of primary-care patients at Sutter Health, a large open healthcare system in Northern California, USA. We identified migraine patients over a five-year time period (2015-2019) and extracted 29 comorbidities. We then applied latent class analysis to identify comorbidity-based natural subgroups. RESULTS: We identified 95,563 patients with migraine and found seven latent classes, summarized by their predominant comorbidities and population share: fewest comorbidities (61.8%), psychiatric (18.3%), some comorbidities (10.0%), most comorbidities - no cardiovascular (3.6%), vascular (3.1%), autoimmune/joint/pain (2.2%), and most comorbidities (1.0%). We found minimal demographic differences across classes. CONCLUSION: Our study found groupings of migraine patients based on comorbidity that have the potential to be used to guide targeted treatment strategies and the development of new therapies.

Persistence of Disparities Among Racially/Ethnically Marginalized Groups in the Coronavirus Disease 2019 Pandemic Regardless of Statewide Shelter-in-Place Policies: An Analysis From Northern California.

To measure disparities in coronavirus disease 2019 (COVID-19) hospitalization and intensive care unit (ICU) transfer among racially/ethnically marginalized groups before and after implementation of the California statewide shelter-in-place (SIP) policy, we conducted a retrospective cohort study within a health-care system in California. COVID-19 patients diagnosed from January 1, 2020, to August 31, 2020, were identified from electronic health records. We examined hospitalizations and ICU transfers by race/ethnicity and pandemic period using logistic regression. Among 16,520 people with COVID-19 (mean age = 46.6 (standard deviation, 18.4) years; 54.2% women), during the post-SIP period, patients were on average younger and a larger proportion were Hispanic. In adjusted models, odds of hospitalization were 20% lower post-SIP as compared with the SIP period, yet all non-White groups had higher odds (odds ratios = 1.6-2.1) than non-Hispanic White individuals, regardless of period. Among hospitalized patients, odds of ICU transfer were 33% lower post-SIP than during SIP. Hispanic and Asian patients had higher odds than non-Hispanics. Disparities in hospitalization persisted and ICU risk became more pronounced for Asian and Hispanic patients post-SIP. Policy-makers should consider ways to proactively address racial/ethnic inequities in risk when considering future population-level policy interventions for public health crises.

The migraine signature study: Methods and baseline results.

OBJECTIVE: To characterize patients who utilize services for migraine in a large integrated health care network, and describe patterns of care and utilization. BACKGROUND: Within health care systems, migraine is a common reason for seeking primary and neurology care, but relatively little is documented about who seeks care and the factors that explain variation in utilization. METHODS: We conducted a retrospective cohort study using electronic health record (EHR) data from Sutter Health primary care (PC) patients who had at least one office visit to a PC clinic between 2013 and 2017. Migraine status was ascertained from diagnosis codes and medication orders. Control status was assigned to those with no evidence of care for any type of headache. We divided the primary care migraine cohort into two groups: those who received all their care for migraine from PC (denoted PC-M) and those who had ≥1 encounter with a neurologist for migraine (denoted N-M). Migraine cases were also designated as having preexisting migraine if they had an encounter with a migraine diagnosis within (±) 6 months of their first study period PC visit and, otherwise, designated as first migraine consult. Two levels of contrasts included: patients with migraine and controls; and within the group of patients with migraine, PC-M and N-M groups. Comorbid conditions were determined from EHR encounter diagnosis codes. RESULTS: We identified 94,149 patients with migraine (including 21,525 N-M and 72,624 PC-M) and 1,248,763 controls. Comorbidities: Proportions of psychiatric [29.8% (n = 28,054) vs. 11.8% (n = 147,043)], autoimmune [(4.4% (n = 4162) vs. 2.6% (n = 31,981)], pain [13.2% (n = 12,439) vs. 5.8% (n = 72,049)], respiratory [24.6% (n = 23,186) vs. 12.3% (n = 153,692)], neurologic [2.9% (n = 2688) vs. 0.9% (n = 11,321)], and cerebrovascular [1.0% (n = 945) vs. 0.6% (n = 7500)] conditions were higher in the migraine group compared to controls, all p < 0.001. Among patients with migraine, the N-M group was similar to the PC-M group in sex, age, ethnicity, and marital status, but were more likely to have preexisting migraine (49.9% (n = 10,734) vs. 36.2% (n = 26,317), p < 0.001). Proportions of comorbid conditions were higher among the N-M group than the PC-M group {psychiatric [38.5% (n = 8291) vs. 27.2% (n = 19,763)], autoimmune [6.3% (n = 1365) vs. 3.9% (n = 2797)], pain [19.6% (n = 4218) vs. 11.3% (n = 8211)], respiratory [30.3% (n = 6516) vs. 23.0% (n = 16,670)], neurologic [6.0% (n = 1288) vs. 1.9% (n = 1400)], cardiovascular [9.7% (n = 2091) vs. 7.0% (n = 5076)], and cerebrovascular [2.3% (n = 500) vs. 0.6% (n = 445)], all p < 0.001}. Medications: During the study period, 82.6% (n = 77,762) of patients with migraine received ≥1 prescription order for an acute migraine medication [89.4% (n = 19,250) of N-M vs. 80.6% (n = 58,512) of PC]. Opioids were prescribed to 52.9% (n = 49,837) of patients with migraine [63.5% (n = 13,669) for N-M and 49.8% (n = 36,168) for PC-M patients). During the study period, 61.4% (n = 57,810) of patients received ≥1 prescription for a migraine preventive medication [81.4% (n = 17,521) of N-M and 55.5% (n = 40,289) of PC-M patients]. The most commonly prescribed classes of preventive medications were antidepressants. CONCLUSIONS: Among patients with migraine in a large health system, those who were also cared for in neurology were more likely to receive both acute and preventive medication migraine orders than those patients who did not see a neurologist, with triptans and antidepressants the most commonly prescribed classes of acute and preventive pharmacotherapies, respectively. Opioids were prescribed to approximately half of the total sample and more common in the N-M group. Adjusting for demographics, patients with migraine had higher rates of nearly every comorbidity we assessed and were more likely to utilize services compared to those without migraine. Overall, patients with migraine also cared for in neurology practices used more of all health care resource types under consideration and had more medical issues, which may be due in some part to a more severe, frequent and disabling disease state compared to those who sought care exclusively from PC practices.

Mind the clinical-analytic gap: Electronic health records and COVID-19 pandemic response.

Data quality is essential to the success of the most simple and the most complex analysis. In the context of the COVID-19 pandemic, large-scale data sharing across the US and around the world has played an important role in public health responses to the pandemic and has been crucial to understanding and predicting its likely course. In California, hospitals have been required to report a large volume of daily data related to COVID-19. In order to meet this need, electronic health records (EHRs) have played an important role, but the challenges of reporting high-quality data in real-time from EHR data sources have not been explored. We describe some of the challenges of utilizing EHR data for this purpose from the perspective of a large, integrated, mixed-payer health system in northern California, US. We emphasize some of the inadequacies inherent to EHR data using several specific examples, and explore the clinical-analytic gap that forms the basis for some of these inadequacies. We highlight the need for data and analytics to be incorporated into the early stages of clinical crisis planning in order to utilize EHR data to full advantage. We further propose that lessons learned from the COVID-19 pandemic can result in the formation of collaborative teams joining clinical operations, informatics, data analytics, and research, ultimately resulting in improved data quality to support effective crisis response.

Racial and Ethnic Disparities in Opioid Prescribing for Long Bone Fractures at Discharge From the Emergency Department: A Cross-sectional Analysis of 22 Centers From a Health Care Delivery System in Northern California.

STUDY OBJECTIVE: We examine racial and ethnic differences in opioid prescribing and dosing for long bone fractures at emergency department (ED) discharge. METHODS: We conducted an electronic health records-based cross-sectional study of adults with long bone fractures who presented to the ED across 22 sites from a health care delivery system (2016 to 2017). We examined differences in opioid prescribing at ED discharge and, among patients with a prescription, differences in opioid dosing (measured as morphine milligram equivalents) by race/ethnicity, using regression modeling with statistical adjustment for patient, fracture, and prescriber characteristics. RESULTS: A total of 11,576 patients with long bone fractures were included in the study; 64.4% were non-Hispanic white; 16.4%, 7.3%, 5.8%, and 5.1%, respectively, were Hispanic, Asian, black, and of other or unknown race; and 65.6% received an opioid at discharge. After adjusting for other factors, rates of opioid prescribing were not different by race/ethnicity; however, among patients with an opioid prescription, total morphine milligram equivalent units prescribed were 4.3%, 6.0%, and 8.1% less for Hispanics, blacks, and Asians relative to non-Hispanic whites. CONCLUSION: Racial and ethnic minority groups with long bone fractures receive similar frequencies of opioid prescriptions at discharge, with a small potency difference. How this affects pain relief and why it happens is unclear.

Impact of home-based, patient-centered support for people with advanced illness in an open health system: A retrospective claims analysis of health expenditures, utilization, and quality of care at end of life.

BACKGROUND: Home-based care coordination and support programs for people with advanced illness work alongside usual care to promote personal care goals, which usually include a preference for home-based end-of-life care. More research is needed to confirm the efficacy of these programs, especially when disseminated on a large scale. Advanced Illness Management is one such program, implemented within a large open health system in northern California, USA. AIM: To evaluate the impact of Advanced Illness Management on end-of-life resource utilization, cost of care, and care quality, as indicators of program success in supporting patient care goals. DESIGN: A retrospective-matched observational study analyzing medical claims in the final 3 months of life. SETTING/PARTICIPANTS: Medicare fee-for-service 2010-2014 decedents in northern California, USA. RESULTS: Final month total expenditures for Advanced Illness Management enrollees ( N = 1352) were reduced by US$4824 (US$3379, US$6268) and inpatient payments by US$6127 (US$4874, US$7682). Enrollees also experienced 150 fewer hospitalizations/1000 (101, 198) and 1361 fewer hospital days/1000 (998, 1725). The percentage of hospice enrollees increased by 17.9 percentage points (14.7, 21.0), hospital deaths decreased by 8.2 percentage points (5.5, 10.8), and intensive care unit deaths decreased by 7.1 percentage points (5.2, 8.9). End-of-life chemotherapy use and non-inpatient expenditures in months 2 and 3 prior to death did not differ significantly from the control group. CONCLUSION: Advanced Illness Management has a positive impact on inpatient utilization, cost of care, hospice enrollment, and site of death. This suggests that home-based support programs for people with advanced illness can be successful on a large scale in supporting personal end-of-life care choices.

A novel telemonitoring device for improving diabetes control: protocol and results from a randomized clinical trial.

BACKGROUND: Telemedicine is one approach to managing patients with chronic illness. Several telephone-based monitoring studies of diabetes patients have shown improved glycosylated hemoglobin (HbA1c), blood pressure (BP), and low-density lipoprotein (LDL) levels. The purpose of this study was to evaluate an investigational in-home telemetry device for improving glucose and BP control over 6 months for patients with type 2 diabetes. The device was used to transmit weekly blood glucose, weight, and BP readings to a diabetes care manager. SUBJECTS AND METHODS: We conducted a two-arm, parallel-comparison, single-blind, randomized controlled trial among Kaiser Permanente Northern California members 18-75 years old with type 2 diabetes mellitus and entry HbA1c levels between 7.5% and 10.0%. Participants were randomly assigned to either the telemonitoring arm or the usual care arm. RESULTS: We observed very small, nonsignificant changes in fructosamine (telemonitoring, -54.9 µmol; usual care, -59.4 µmol) and systolic BP (telemonitoring, -6.3 mm Hg; usual care, -3.2 mm Hg) from baseline to 6 weeks in both groups. At 6 months, we observed no significant intergroup differences in change from baseline for HbA1c, fructosamine, or self-efficacy. However, LDL cholesterol in the telemonitoring arm decreased more than in the usual care arm (-17.1 mg/dL versus -5.4 mg/dL; P=0.045). CONCLUSIONS: Although HbA1c improved significantly over 6 months in both groups, the difference in improvement between the groups was not significant. This lack of significance may be due to the relatively healthy status of the volunteers in our study and to the high level of care provided by the care managers in the Santa Rosa, CA clinic. Further study in subgroups of less healthy diabetes patients is recommended.

Learning health system, positive deviance analysis, and electronic health records: Synergy for a learning health system.

Introduction: Over the past decade, numerous efforts have encouraged the realization of the learning health system (LHS) in the United States. Despite these efforts, and promising aims of the LHS, the full potential and value of research conducted within LHSs have yet to be realized. New technology coupled with a catalyzing global pandemic have spurred momentum. In addition, the LHS has lacked a consistent framework within which "best evidence" can be identified. Positive deviance analysis, itself reinvigorated by recent advances in health information technology (IT) and ubiquitous adoption of electronic health records (EHRs), may finally provide a framework through which LHSs can be operationalized and optimized. Methods: We describe the synergy between positive deviance and the LHS and how they may be integrated to achieve a continuous cycle of health system improvement. Results: As we describe below, the positive deviance approach focuses on learning from high-performing teams and organizations. Conclusion: Such learning can be enabled by EHRs and health IT, providing a lens into how digital clinical interventions are successfully developed and deployed.

Transcriptome-wide association study identifies novel candidate susceptibility genes for migraine.

Genome-wide association studies (GWASs) have identified more than 130 genetic susceptibility loci for migraine; however, how most of these loci impact migraine development is unknown. To identify novel genes associated with migraine and interpret the transcriptional products of those genes, we conducted a transcriptome-wide association study (TWAS). We performed tissue-specific and multi-tissue TWAS analyses to assess associations between imputed gene expression from 53 tissues and migraine susceptibility using FUSION software. Meta-analyzed GWAS summary statistics from 26,052 migraine cases and 487,214 controls, all of European ancestry and from two cohorts (the Kaiser Permanente GERA and the UK Biobank), were used. We evaluated the associations for genes after conditioning on variant-level effects from GWAS, and we tested for colocalization of GWAS migraine-associated loci and expression quantitative trait loci (eQTLs). Across tissue-specific and multi-tissue analyses, we identified 53 genes for which genetically predicted gene expression was associated with migraine after correcting for multiple testing. Of these 53 genes, 10 (ATF5, CNTNAP1, KTN1-AS1, NEIL1, NEK4, NNT, PNKP, RUFY2, TUBG2, and VAT1) did not overlap known migraine-associated loci identified from GWAS. Tissue-specific analysis identified 45 gene-tissue pairs and cardiovascular tissues represented the highest proportion of the Bonferroni-significant gene-tissue pairs (n = 22 [49%]), followed by brain tissues (n = 6 [13%]), and gastrointestinal tissues (n = 4 [9%]). Colocalization analyses provided evidence of shared genetic variants underlying eQTL and GWAS signals in 18 of the gene-tissue pairs (40%). Our TWAS reports novel genes for migraine and highlights the important contribution of brain, cardiovascular, and gastrointestinal tissues in migraine susceptibility.

Impact of a Statewide Livestock Antibiotic Use Policy on Resistance in Human Urine Escherichia coli Isolates: A Synthetic Control Analysis.

BACKGROUND: On 1 January 2018, California implemented Senate Bill 27 (SB27), banning, for the first time in the United States, routine preventive use of antibiotics in food-animal production and any antibiotic use without a veterinarian's prescription. OBJECTIVES: Our objective was to assess whether SB27 was associated with decreased antimicrobial resistance among E. coli isolated from human urine. METHODS: We used U.S. nationwide monthly state-level data from BD Insights Research Database (Becton, Dickinson, and Co.) spanning 1 January 2013 to 30 June 2021 on antibiotic-resistance patterns of 30-d nonduplicate E. coli isolated from urine. Tested antibiotic classes included aminoglycosides, extended-spectrum cephalosporins (ESC), fluoroquinolones, and tetracyclines. Counts of tested and not-susceptible (resistant and intermediate, hereafter resistant) urine isolates were available by sex, age group (<65, 65+ year), month, and state. We applied a synthetic control approach to estimate the causal effect of SB27 on resistance patterns. Our approach created a synthetic California based on a composite of other states without the policy change and contrasted its counterfactual postpolicy trends with the observed postpolicy trends in California. FINDINGS: We included 7.1 million E. coli urine isolates, 90% among women, across 33 states. From 2013 to 2017, the median (interquartile range) resistance percentages in California were 11.9% (7.4, 17.6), 13.8% (5.8, 20.0), 24.6% (9.6, 36.4), 7.9% (2.1, 13.1), for aminoglycosides, ESC, fluoroquinolones, and tetracyclines, respectively. SB27 was associated with a 7.1% reduction in ESC resistance (p-value for joint null: <0.01), but no change in resistance to aminoglycosides, fluoroquinolones, or tetracyclines. DISCUSSION: Further research is needed to determine the role of SB27 in the observed reduction in ESC resistance E. coli in human populations, particularly as additional states implement similar legislation. https://doi.org/10.1289/EHP11221.

Ambient temperature and risk of urinary tract infection in California: A time-stratified case-crossover study using electronic health records.

BACKGROUND: In the United States (US), urinary tract infections (UTI) lead to more than 10 million office visits each year. Temperature and season are potentially important risk factors for UTI, particularly in the context of climate change. METHODS: We examined the relationship between ambient temperature and outpatient UTI diagnoses among patients followed from 2015 to 2017 in two California healthcare systems: Kaiser Permanente Southern California (KPSC) and Sutter Health in Northern California. We identified UTI diagnoses in adult patients using diagnostic codes and laboratory records from electronic health records. We abstracted patient age, sex, season of diagnosis, and linked community-level Index of Concentration at the Extremes (ICE-I, a measure of wealth and poverty concentration) based on residential address. Daily county-level average ambient temperature was assembled from the Parameter-elevation Regressions on Independent Slopes Model (PRISM). We implemented distributed lag nonlinear models (DLNM) to assess the association between UTI and lagged daily temperatures. Main analyses were confined to women. In secondary analyses, we stratified by season, healthcare system, and community-level ICE-I. RESULTS: We observed 787,186 UTI cases (89% among women). We observed a threshold association between ambient temperature and UTI among women: an increase in daily temperature from the 5th percentile (6.0 ˚C) to the mean (16.2 ˚C) was associated with a 3.2% (95% CI: 2.4, 3.9%) increase in same-day UTI diagnosis rate, whereas an increase from the mean to 95th percentile was associated with no change in UTI risk (0.0%, 95% CI: -0.7, 0.6%). In secondary analyses, we observed the clearest monotonic increase in the rate of UTI diagnosis with higher temperatures in the fall. Associations did not differ meaningfully by healthcare system or community-level ICE-I. Results were robust to alternate model specifications. DISCUSSION: Increasing temperature was related to higher rate of outpatient UTI, particularly in the shoulder seasons (spring, autumn).

Methods for assessing fracture risk prediction models: experience with FRAX in a large integrated health care delivery system.

Area under the receiver operating characteristics (AUROC) curve is often used to evaluate risk models. However, reclassification tests provide an alternative assessment of model performance. We performed both evaluations on results from FRAX (World Health Organization Collaborating Centre for Metabolic Bone Diseases, University of Sheffield, UK), a fracture risk tool, using Kaiser Permanente Northern California women older than 50yr with bone mineral density (BMD) measured during 1997-2003. We compared FRAX performance with and without BMD in the model. Among 94,489 women with mean follow-up of 6.6yr, 1579 (1.7%) sustained a hip fracture. Overall, AUROCs were 0.83 and 0.84 for FRAX without and with BMD, suggesting that BMD did not contribute to model performance. AUROC decreased with increasing age, and BMD contributed significantly to higher AUROC among those aged 70yr and older. Using an 81% sensitivity threshold (optimum level from receiver operating characteristic curve, corresponding to 1.2% cutoff), 35% of those categorized above were reassigned below when BMD was added. In contrast, only 10% of those categorized below were reassigned to the higher risk category when BMD was added. The net reclassification improvement was 5.5% (p<0.01). Two versions of this risk tool have similar AUROCs, but alternative assessments indicate that addition of BMD improves performance. Multiple methods should be used to evaluate risk tool performance with less reliance on AUROC alone.

Measuring and Promoting SARS-CoV-2 Vaccine Equity: Development of a COVID-19 Vaccine Equity Index.

Purpose: The coronavirus pandemic has created the greatest public health crisis in a century, causing >500,000 deaths in the United States alone. Minoritized and socioeconomically disadvantaged groups have borne a disproportionate burden of severe illness, hospitalization, and death from COVID-19. Recently developed FDA-approved vaccines have been shown to significantly reduce severe COVID-19-related outcomes. Vaccination campaigns have the potential to advance health equity by prioritizing allocation to those at highest risk while striving for herd immunity. Large integrated health systems have been faced with the daunting task of meeting the rapidly evolving needs of diverse patient populations for the provision of population-based testing, treatment, education, and now vaccine distribution. We have designed a COVID-19 vaccine equity index (CVEI) to guide health system vaccination strategy. Methods: We considered proportion unvaccinated within a health care system. We then used real-time readily available electronic health record (EHR) COVID-19 testing positivity and proportion hospitalized to measure burden of illness by race/ethnicity. We used conditional probability and statistical theory to measure equity for unvaccinated individuals and to derive an index to highlight these inequities for specific subgroups. Results: We present an illustrative hypothetical example using simulated data for which we calculated the CVEI for non-Hispanic White, non-Hispanic Black, non-Hispanic Asian, and Hispanic patients. In the example, non-Hispanic Black and Hispanic patients had inequitable outcomes. Conclusion: The index can be widely implemented to promote more equitable outcomes among racial/ethnic groups, reducing morbidity and mortality within the overall population as we pursue the collective goal of herd immunity through mass vaccination.

New and sex-specific migraine susceptibility loci identified from a multiethnic genome-wide meta-analysis.

Migraine is a common disabling primary headache disorder that is ranked as the most common neurological cause of disability worldwide. Women present with migraine much more frequently than men, but the reasons for this difference are unknown. Migraine heritability is estimated to up to 57%, yet much of the genetic risk remains unaccounted for, especially in non-European ancestry populations. To elucidate the etiology of this common disorder, we conduct a multiethnic genome-wide association meta-analysis of migraine, combining results from the GERA and UK Biobank cohorts, followed by a European-ancestry meta-analysis using public summary statistics. We report 79 loci associated with migraine, of which 45 were novel. Sex-stratified analyses identify three additional novel loci (CPS1, PBRM1, and SLC25A21) specific to women. This large multiethnic migraine study provides important information that may substantially improve our understanding of the etiology of migraine susceptibility.

Enhancing the use of EHR systems for pragmatic embedded research: lessons from the NIH Health Care Systems Research Collaboratory.

OBJECTIVE: We identified challenges and solutions to using electronic health record (EHR) systems for the design and conduct of pragmatic research. MATERIALS AND METHODS: Since 2012, the Health Care Systems Research Collaboratory has served as the resource coordinating center for 21 pragmatic clinical trial demonstration projects. The EHR Core working group invited these demonstration projects to complete a written semistructured survey and used an inductive approach to review responses and identify EHR-related challenges and suggested EHR enhancements. RESULTS: We received survey responses from 20 projects and identified 21 challenges that fell into 6 broad themes: (1) inadequate collection of patient-reported outcome data, (2) lack of structured data collection, (3) data standardization, (4) resources to support customization of EHRs, (5) difficulties aggregating data across sites, and (6) accessing EHR data. DISCUSSION: Based on these findings, we formulated 6 prerequisites for PCTs that would enable the conduct of pragmatic research: (1) integrate the collection of patient-centered data into EHR systems, (2) facilitate structured research data collection by leveraging standard EHR functions, usable interfaces, and standard workflows, (3) support the creation of high-quality research data by using standards, (4) ensure adequate IT staff to support embedded research, (5) create aggregate, multidata type resources for multisite trials, and (6) create re-usable and automated queries. CONCLUSION: We are hopeful our collection of specific EHR challenges and research needs will drive health system leaders, policymakers, and EHR designers to support these suggestions to improve our national capacity for generating real-world evidence.

Early Weight Loss and Treatment Response: Data From a Lifestyle Change Program in Clinical Practice.

INTRODUCTION: The purpose of this study was to develop and validate a predictive model for the early identification of nonresponders to a 12-month lifestyle change program in clinical practice. METHODS: Investigators identified lifestyle change program participants in the electronic health records of a large healthcare delivery system between 2010 and 2017. Nonresponse was defined as weight gain or no weight loss at 12 months from the program initiation (baseline). Logistic regression with percentage weight change at 2-12 weeks from baseline was used as an independent predictor of nonresponse. Baseline demographics and clinical characteristics were also tested as potential predictors. The authors performed ten-fold cross-validation for model assessment and examined model performance with the area under the receiver operating characteristic curve, sensitivity, specificity, and positive and negative predictive values. The analyses were conducted in 2019. RESULTS: Among 947 program participants, 30% were classified as nonresponders at 12 months. The model with the best discrimination of responders from nonresponders included weight change at 12 weeks from baseline as the sole predictor (area under the receiver operating characteristic curve, 0.789). Sensitivity and positive predictive value were maximized at 0.56 (specificity and negative predictive value, 0.81 each). CONCLUSIONS: In a cohort of lifestyle change program participants from clinical practice, percentage weight change at 12 weeks from baseline can serve as a single indicator of nonresponse at the completion of the 12-month program. Clinicians can easily apply this algorithm to identify and assess participants in potential need of adjunctive or alternative therapy to maximize treatment outcomes.

Disparities In Outcomes Among COVID-19 Patients In A Large Health Care System In California.

As the novel coronavirus disease (COVID-19) pandemic spreads throughout the United States, evidence is mounting that racial and ethnic minorities and socioeconomically disadvantaged groups are bearing a disproportionate burden of illness and death. We conducted a retrospective cohort analysis of COVID-19 patients at Sutter Health, a large integrated health system in northern California, to measure potential disparities. We used Sutter's integrated electronic health record to identify adults with suspected and confirmed COVID-19, and we used multivariable logistic regression to assess risk of hospitalization, adjusting for known risk factors, such as race/ethnicity, sex, age, health, and socioeconomic variables. We analyzed 1,052 confirmed cases of COVID-19 from the period January 1-April 8, 2020. Among our findings, we observed that compared with non-Hispanic white patients, non-Hispanic African American patients had 2.7 times the odds of hospitalization, after adjustment for age, sex, comorbidities, and income. We explore possible explanations for this, including societal factors that either result in barriers to timely access to care or create circumstances in which patients view delaying care as the most sensible option. Our study provides real-world evidence of racial and ethnic disparities in the presentation of COVID-19.

Effectiveness of a Group-Based Lifestyle Change Program Versus Usual Care: An Electronic Health Record, Propensity Score-Matched Cohort Study.

INTRODUCTION: Translational lifestyle change programs for community and clinical settings have been available for a decade, yet there are limited data on their comparative effectiveness. This study examines the effectiveness of a Centers for Disease Control and Prevention-aligned lifestyle change program relative to usual care in clinical practice. METHODS: This was an electronic health record-based retrospective cohort study conducted in a community-based healthcare system. Investigators identified adult program participants and usual-care patients in the electronic health record between 2010 and 2018 and defined their index date (baseline) as the first lifestyle change program encounter or a random encounter date, respectively. Participants were matched 1:2 to usual-care patients on baseline demographics and clinical characteristics using propensity-score methods. Changes in body weight and blood pressure were examined from baseline through 24 months. RESULTS: The authors identified 2,833 program participants and 438,432 usual-care patients meeting study eligibility criteria. A total of 2,833 program participants were matched to 4,776 usual-care patients; the average age was 54 years, and 80% of the participants were female. Program participation was associated with a 1.9- and 1.6-fold higher prevalence of clinically meaningful (≥5%) weight loss at 12- and 24-month follow-up than usual care and a higher prevalence of blood pressure control at 12 months but not at 24 months. Patients without type 2 diabetes at baseline had more pronounced outcomes than those with type 2 diabetes. CONCLUSIONS: This study demonstrates the effectiveness of an evidence-based, Centers for Disease Control and Prevention-aligned lifestyle change program in reducing cardiometabolic risk factors compared with usual care in clinical practice, with long-term reductions in weight and transient reductions in blood pressure.

Short-term weight trajectories and long-term weight outcomes from a lifestyle intervention in real-world clinical practice.

Centers for Disease Control and Prevention aligned lifestyle change programs are effective in promoting weight loss among those with elevated cardiometabolic risk; yet, variability in weight outcomes among participants is high. Little is known about heterogeneity of short-term weight changes among participants in real-world clinical practice. We sought to identify short-term weight trajectory clusters among lifestyle change program participants in real-world clinical practice and to examine the relationship between cluster membership and long-term weight outcomes. We identified participants from the electronic health records (2010-2017) with weight measured ≤30 days prior to program initiation (baseline) and in four intervals (3-week segments) in the 12 weeks after baseline. Clustering analysis was performed to identify distinct trajectories in percent weight change over 12 weeks. Cluster-specific differences in weight change at 12 and 52 weeks were assessed. Among 1,148 participants, across 18 clinic sites, three clusters were identified: minimal-to-no weight loss (MWL), delayed-minimal weight loss (DWL), and steady-moderate weight loss (SWL), corresponding to mean weight changes of 0.4%, -2.3%, and -4.8% at 12 weeks follow-up, respectively. Mean weight changes were 0.4%, -1.8%, and -5.1% for MWL, DWL, and SWL clusters, respectively, at 52 weeks follow-up, which correlated in direction and magnitude with short-term weight changes. Clustering analysis reveals heterogeneous, short-term weight trajectories among lifestyle change program participants in real-world clinical practice. Given the relationship between the magnitudes of short- and long-term weight change, individual participant weight trajectories may be useful in identifying potential non-responders in need of adjunctive or alternative therapy.