Guideline-discordant inhaler regimens after COPD hospitalization: associations with rurality, drive time to care, and fragmented care - a United States cohort study.

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 <30 min). Fragmented care was also associated with higher odds of guideline-discordant inhaler regimens (aOR 1.56 [95% CI: 1.48-1.63]). Interpretation: Rurality, long drive time to care, and fragmented care were associated with greater prescription of guideline-discordant inhaler regimens after COPD hospitalization. These findings highlight the need to understand challenges in delivering evidence-based care. Funding: NIHNCATS grants KL2TR002492 and UL1TR002494.

Sociodemographic and Geographic Risk Factors for All-Cause Mortality in Patients with COPD.

Background: Chronic obstructive pulmonary disease (COPD) is a leading cause of mortality worldwide. Identifying both individual and community risk factors associated with higher mortality is essential to improve outcomes. Few population-based studies of mortality in COPD include both individual characteristics and community risk factors. Objective: We used geocoded, patient-level data to describe the associations between individual demographics, neighborhood socioeconomic status, and all-cause mortality. Methods: We performed a nationally representative retrospective cohort analysis of all patients enrolled in the Veteran Health Administration with at least one ICD-9 or ICD-10 code for COPD in 2016-2019. We obtained demographic characteristics, comorbidities, and geocoded residential address. Area Deprivation Index and rurality were classified using individual geocoded residential addresses. We used logistic regression models to assess the association between these characteristics and age-adjusted all-cause mortality. Results: Of 1,106,163 COPD patients, 33.4% were deceased as of January 2021. In age-adjusted models, having more comorbidities, Black/African American race (OR 1.09 [95% CI: 1.08-1.11]), and higher neighborhood disadvantage (OR 1.30 [95% CI: 1.28-1.32]) were associated with all-cause mortality. Female sex (OR 0.67 [95% CI: 0.65-0.69]), Asian race (OR 0.64, [95% CI: 0.59-0.70]), and living in a more rural area were associated with lower odds of all-cause mortality. After adjusting for age, comorbidities, neighborhood socioeconomic status, and rurality, the association with Black/African American race reversed. Conclusion: All-cause mortality in COPD patients is disproportionately higher in patients living in poorer neighborhoods and urban areas, suggesting the impact of social determinants of health on COPD outcomes. Black race was associated with higher age-adjusted all-cause mortality, but this association was abrogated after adjusting for gender, socioeconomic status, comorbidities, and urbanicity. Future studies should focus on exploring mechanisms by which disparities arise and developing interventions to address these.

Prediction of in-hospital mortality among intensive care unit patients using modified daily Laboratory-based Acute Physiology Scores, version 2 (LAPS2).

Background: Mortality prediction for intensive care unit (ICU) patients frequently relies on single acuity measures based on ICU admission physiology without accounting for subsequent clinical changes. Objectives: Evaluate novel models incorporating modified admission and daily, time-updating Laboratory-based Acute Physiology Scores, version 2 (LAPS2) to predict in-hospital mortality among ICU patients. Research design: Retrospective cohort study. Subjects: All ICU patients in five hospitals from October 2017 through September 2019. Measures: We used logistic regression, penalized logistic regression, and random forest models to predict in-hospital mortality within 30 days of ICU admission using admission LAPS2 alone in patient-level and patient-day-level models, or admission and daily LAPS2 at the patient-day level. Multivariable models included patient and admission characteristics. We performed internal-external validation using four hospitals for training and the fifth for validation, repeating analyses for each hospital as the validation set. We assessed performance using scaled Brier scores (SBS), c-statistics, and calibration plots. Results: The cohort included 13,993 patients and 120,101 ICU days. The patient-level model including the modified admission LAPS2 without daily LAPS2 had an SBS of 0.175 (95% CI 0.148-0.201) and c-statistic of 0.824 (95% CI 0.808-0.840). Patient-day-level models including daily LAPS2 consistently outperformed models with modified admission LAPS2 alone. Among patients with <50% predicted mortality, daily models were better calibrated than models with modified admission LAPS2 alone. Conclusions: Models incorporating daily, time-updating LAPS2 to predict mortality among an ICU population perform as well or better than models incorporating modified admission LAPS2 alone.

Drive Time and Receipt of Guideline-Recommended Screening, Diagnosis, and Treatment.

Importance: Many patients do not receive recommended services. Drive time to health care services may affect receipt of guideline-recommended care, but this has not been comprehensively studied. Objective: To assess associations between drive time to care and receipt of guideline-recommended screening, diagnosis, and treatment interventions. Design, Setting, and Participants: This cohort study used administrative data from the National Veterans Health Administration (VA) data merged with Medicare data. Eligible participants were patients using VA services between January 2016 and December 2019. Women ages 65 years or older without underlying bone disease were assessed for osteoporosis screening. Patients with new diagnosis of chronic obstructive pulmonary disease (COPD) indicated by at least 2 encounter codes for COPD or at least 1 COPD-related hospitalization were assessed for receipt of diagnostic spirometry. Patients hospitalized for ischemic heart disease were assessed for cardiac rehabilitation treatment. Exposures: Drive time from each patient's residential address to the closest VA facility where the service was available, measured using geocoded addresses. Main Outcomes and Measures: Binary outcome at the patient level for receipt of osteoporosis screening, spirometry, and cardiac rehabilitation. Multivariable logistic regression models were used to assess associations between drive time and receipt of services. Results: Of 110 780 eligible women analyzed, 36 431 (32.9%) had osteoporosis screening (mean [SD] age, 66.7 [5.4] years; 19 422 [17.5%] Black, 63 403 [57.2%] White). Of 281 130 patients with new COPD diagnosis, 145 249 (51.7%) had spirometry (mean [SD] age, 68.2 [11.5] years; 268 999 [95.7%] men; 37 834 [13.5%] Black, 217 608 [77.4%] White). Of 73 146 patients hospitalized for ischemic heart disease, 11 171 (15.3%) had cardiac rehabilitation (mean [SD] age, 70.0 [10.8] years; 71 217 [97.4%] men; 15 213 [20.8%] Black, 52 144 [71.3%] White). The odds of receiving recommended services declined as drive times increased. Compared with patients with a drive time of 30 minutes or less, patients with a drive time of 61 to 90 minutes had lower odds of receiving osteoporosis screening (adjusted odds ratio [aOR], 0.90; 95% CI, 0.86-0.95) and spirometry (aOR, 0.90; 95% CI, 0.88-0.92) while patients with a drive time of 91 to 120 minutes had lower odds of receiving cardiac rehabilitation (aOR, 0.80; 95% CI, 0.74-0.87). Results were similar in analyses restricted to urban patients or patients whose primary care clinic was in a tertiary care center. Conclusions and Relevance: In this retrospective cohort study, longer drive time was associated with less frequent receipt of guideline-recommended services across multiple components of care. To improve quality of care and health outcomes, health systems and clinicians should adopt strategies to mitigate travel burden, even for urban patients.

Anesthesia Care for Cataract Surgery in Medicare Beneficiaries.

Importance: Cataract surgery in the US is routinely performed with anesthesia care, whereas anesthesia care for other elective, low-risk, outpatient procedures is applied more selectively. Objective: To identify predictors of anesthesia care in Medicare beneficiaries undergoing cataract surgery and evaluate anesthesia care for cataract surgery compared with other elective, low-risk, outpatient procedures. Design, Setting, and Participants: This population-based, retrospective observational cohort study included Medicare beneficiaries 66 years or older who underwent cataract surgery in 2017. The data were analyzed from August 2020 through May 2021. Interventions (for clinical trials) or Exposures (for observational studies): Anesthesia care during elective, low-risk, outpatient procedures. Main Outcomes and Measures: Prevalence of anesthesia care during cataract surgery compared with other low-risk procedures; association of anesthesia care with patient, clinician, and health system characteristics; and proportion of patients experiencing a systemic complication within 7 days of cataract surgery compared with patients undergoing other low-risk procedures. Results: Among 36 652 cataract surgery patients, the mean (SD) age was 74.7 (6.1) years; 21 690 (59.2%) were female; 2200 (6.6%) were Black and 32 049 (87.4%) were White. Anesthesia care was more common among patients undergoing cataract surgery compared with patients undergoing other low-risk procedures (89.8% vs range of <1% to 70.2%). Neither the patient's age (adjusted odds ratio, 1.01; 95% CI, 1.00-1.02; P = .01) nor Charlson Comorbidity Index (CCI) score (CCI of ≥3: adjusted odds ratio, 1.06; 95% CI, 0.95-1.18; P = .28; reference, CCI score of 0-1) was strongly associated with anesthesia care for cataract surgery, but a model comprising a single variable identifying the ophthalmologist predicted anesthesia care with a C statistic of 0.96. Approximately 6.0% of ophthalmologists never used anesthesia care, 76.6% always used anesthesia care, and 17.4% used it for only a subset of patients. Fewer cataract surgery patients experienced systemic complications within 7 days (2833 [7.7%]), even when limited to patients of ophthalmologists who never used anesthesia care (108 [7.4%]), than patients undergoing other low-risk procedures (range, 13.2%-52.2%). Conclusions and Relevance: The results of this cohort study suggest that systemic complications occurred less frequently after cataract surgery compared with other elective, low-risk, outpatient procedures during which anesthesia care was less commonly used. Anesthesia care was not associated with patient characteristics, such as older age or worse health status, but with the ophthalmologists' usual approach to cataract surgery sedation. The study findings suggest an opportunity to use anesthesia care more selectively in patients undergoing cataract surgery.

Beyond Access: Factors Associated With Spirometry Underutilization Among Patients With a Diagnosis of COPD in Urban Tertiary Care Centers.

Rationale: Many patients with suspected chronic obstructive pulmonary disease (COPD) do not undergo spirometry to confirm the diagnosis. Underutilization is often attributed to barriers to accessing spirometry. Objective: Our objective wasto identify factors associated with spirometry underutilization for patients who are less likely to face access barriers related to travel, insurance, and availability of spirometry. Methods: A retrospective analysis was conducted of patients enrolled in the Veterans Health Administration and living in urban areas with a new diagnosis of COPD between 2012 to 2015, reducing out-of-pocket cost and travel barriers, respectively. We included only patients whose primary care clinic was located in an academically affiliated tertiary level facility with spirometry available. We used logistic regression to estimate associations between patient characteristics and receipt of spirometry within 2 years before or after COPD diagnosis. Results: Of 24,300 patients, 59.7% had spirometry. Compared to patients <55 years, patients 75-84 years had an adjusted odds ratio (aOR) of undergoing spirometry of 0.80 (95% confidence interval [CI]:0.72-0.90), while patients ≥85 years had an aOR of 0.47 (95%CI: 0.40-0.54). Compared to patients with a Charlson Comorbidity Index (CCI) ≥3, patients with a CCI of 0 had an aOR of 0.60 (95%CI:0.54-0.67). Patients who had not seen a pulmonary specialist had lower odds of receiving spirometry (aOR 0.38 [95%CI:0.35-0.41]). Conclusion: Spirometry underutilization persists among patients who are less likely to have access barriers related to travel, insurance, and availability of spirometry. Spirometry underutilization is associated with older age, not having received pulmonary care, and having fewer comorbidities. COPD care quality initiatives will need to address these factors.

COVID-19 Vaccination Of People Experiencing Homelessness And Incarceration In Minnesota.

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.

Geometrical picture of the electron-electron correlation at the large-D limit.

In electronic structure calculations, the correlation energy is defined as the difference between the mean field and the exact solution of the non relativistic Schrödinger equation. Such an error in the different calculations is not directly observable as there is no simple quantum mechanical operator, apart from correlation functions, that correspond to such quantity. Here, we use the dimensional scaling approach, in which the electrons are localized at the large-dimensional scaled space, to describe a geometric picture of the electronic correlation. Both, the mean field, and the exact solutions at the large-D limit have distinct geometries. Thus, the difference might be used to describe the correlation effect. Moreover, correlations can be also described and quantified by the entanglement between the electrons, which is a strong correlation without a classical analog. Entanglement is directly observable and it is one of the most striking properties of quantum mechanics and bounded by the area law for local gapped Hamiltonians of interacting many-body systems. This study opens up the possibility of presenting a geometrical picture of the electron-electron correlations and might give a bound on the correlation energy. The results at the large-D limit and at D = 3 indicate the feasibility of using the geometrical picture to get a bound on the electron-electron correlations.

Effectiveness of BNT162b2 and mRNA-1273 Second Doses and Boosters for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Infection and SARS-CoV-2-Related Hospitalizations: A Statewide Report From the Minnesota Electronic Health Record Consortium.

Using vaccine data combined with electronic health records, we report that mRNA boosters provide greater protection than a 2-dose regimen against SARS-CoV-2 infection and related hospitalizations. The benefit of a booster was more evident in the elderly and those with comorbidities.

Minnesota Electronic Health Record Consortium COVID-19 Project: Informing Pandemic Response Through Statewide Collaboration Using Observational Data.

OBJECTIVE: Robust disease and syndromic surveillance tools are underdeveloped in the United States, as evidenced by limitations and heterogeneity in sociodemographic data collection throughout the COVID-19 pandemic. To monitor the COVID-19 pandemic in Minnesota, we developed a federated data network in March 2020 using electronic health record (EHR) data from 8 multispecialty health systems. MATERIALS AND METHODS: In this serial cross-sectional study, we examined patients of all ages who received a COVID-19 polymerase chain reaction test, had symptoms of a viral illness, or received an influenza test from January 3, 2016, through November 7, 2020. We evaluated COVID-19 testing rates among patients with symptoms of viral illness and percentage positivity among all patients tested, in aggregate and by zip code. We stratified results by patient and area-level characteristics. RESULTS: Cumulative COVID-19 positivity rates were similar for people aged 12-64 years (range, 15.1%-17.6%) but lower for adults aged ≥65 years (range, 9.3%-10.7%). We found notable racial and ethnic disparities in positivity rates early in the pandemic, whereas COVID-19 positivity was similarly elevated across most racial and ethnic groups by the end of 2020. Positivity rates remained substantially higher among Hispanic patients compared with other racial and ethnic groups throughout the study period. We found similar trends across area-level income and rurality, with disparities early in the pandemic converging over time. PRACTICE IMPLICATIONS: We rapidly developed a distributed data network across Minnesota to monitor the COVID-19 pandemic. Our findings highlight the utility of using EHR data to monitor the current pandemic as well as future public health priorities. Building partnerships with public health agencies can help ensure data streams are flexible and tailored to meet the changing needs of decision makers.

Recent Trends in Admission Diagnosis and Related Mortality in the Medically Critically Ill.

PURPOSE: With decades of declining ICU mortality, we hypothesized that the outcomes and distribution of diseases cared for in the ICU have changed and we aimed to further characterize them. STUDY DESIGN AND METHODS: A retrospective cohort analysis of 287,154 nonsurgical-critically ill adults, from 237 U.S. ICUs, using the manually abstracted Cerner APACHE Outcomes database from 2008 to 2016 was performed. Surgical patients, rare admission diagnoses (<100 occurrences), and low volume hospitals (<100 total admissions) were excluded. Diagnoses were distributed into mutually exclusive organ system/disease-based categories based on admission diagnosis. Multi-level mixed-effects negative binomial regression was used to assess temporal trends in admission, in-hospital mortality, and length of stay (LOS). RESULTS: The number of ICU admissions remained unchanged (IRR 0.99, 0.98-1.003) while certain organ system/disease groups increased (toxicology [25%], hematologic/oncologic [55%] while others decreased (gastrointestinal [31%], pulmonary [24%]). Overall risk-adjusted in-hospital mortality was unchanged (IRR 0.98, 0.96-1.0004). Risk-adjusted ICU LOS (Estimate -0.06 days/year, -0.07 to -0.04) decreased. Risk-adjusted mortality varied significantly by disease. CONCLUSION: Risk-adjusted ICU mortality rate did not change over the study period, but there was evidence of shifting disease burden across the critical care population. Our data provides useful information regarding future ICU personnel and resource needs.

Emergency Department Length of Stay and Mortality in Critically Injured Patients.

OBJECTIVE: Multicenter data from 2 decades ago demonstrated that critically ill and injured patients spending more than 6 hours in the emergency department (ED) before transfer to the intensive care unit (ICU) had higher mortality rates. A contemporary analysis of ED length of stay in critically injured patients at American College of Surgeons' Trauma Quality Improvement Program (ACS-TQIP) centers was performed to test whether prolonged ED length of stay is still associated with mortality. METHODS: This was an observational cohort study of critically injured patients admitted directly to ICU from the ED in ACS-TQIP centers from 2010-2015. Spending more than 6 hours in the ED was defined as prolonged ED length of stay. Patients with prolonged ED length of stay were matched to those with non-prolonged ED length of stay and mortality was compared. MAIN RESULTS: A total of 113,097 patients were directly admitted from the ED to the ICU following injury. The median ED length of stay was 167 minutes. Prolonged ED length of stay occurred in 15,279 (13.5%) of patients. Women accounted for 29.4% of patients with prolonged ED length of stay but only 25.8% of patients with non-prolonged ED length of stay, P < 0.0001. Mortality rates were similar after matching-4.5% among patients with prolonged ED length of stay versus 4.2% among matched controls. Multivariable logistic regression of the matched cohorts demonstrated prolonged ED length of stay was not associated with mortality. However, women had higher adjusted mortality compared to men Odds Ratio = 1.41, 95% Confidence Interval 1.28 -1.61, P < 0.0001. CONCLUSION: Prolonged ED length of stay is no longer associated with mortality among critically injured patients. Women are more likely to have prolonged ED length of stay and mortality.

The association of multimodal analgesia and high-risk opioid discharge prescriptions in opioid-naive surgical patients.

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.

Thermodynamic feasibility of shipboard conversion of marine plastics to blue diesel for self-powered ocean cleanup.

Collecting and removing ocean plastics can mitigate their environmental impacts; however, ocean cleanup will be a complex and energy-intensive operation that has not been fully evaluated. This work examines the thermodynamic feasibility and subsequent implications of hydrothermally converting this waste into a fuel to enable self-powered cleanup. A comprehensive probabilistic exergy analysis demonstrates that hydrothermal liquefaction has potential to generate sufficient energy to power both the process and the ship performing the cleanup. Self-powered cleanup reduces the number of roundtrips to port of a waste-laden ship, eliminating the need for fossil fuel use for most plastic concentrations. Several cleanup scenarios are modeled for the Great Pacific Garbage Patch (GPGP), corresponding to 230 t to 11,500 t of plastic removed yearly; the range corresponds to uncertainty in the surface concentration of plastics in the GPGP. Estimated cleanup times depends mainly on the number of booms that can be deployed in the GPGP without sacrificing collection efficiency. Self-powered cleanup may be a viable approach for removal of plastics from the ocean, and gaps in our understanding of GPGP characteristics should be addressed to reduce uncertainty.

Dimensional Interpolation for Random Walk.

We employ a simple and accurate dimensional interpolation formula for the shapes of random walks at D = 3 and D = 2 based on the analytically known solutions at both limits D = ∞ and D = 1. The results obtained for the radius of gyration of an arbitrary shaped object have about 2% error compared with accurate numerical results at D = 3 and D = 2. We also calculated the asphericity for a three-dimensional random walk using the dimensional interpolation formula. The results agree very well with the numerically simulated results. The method is general and can be used to estimate other properties of random walks.

Impact of Different Approaches to Preparing Notes for Analysis With Natural Language Processing on the Performance of Prediction Models in Intensive Care.

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.

Racial and Ethnic Disparities in Hospital Admissions from COVID-19: Determining the Impact of Neighborhood Deprivation and Primary Language.

BACKGROUND: Despite past and ongoing efforts to achieve health equity in the USA, racial and ethnic disparities persist and appear to be exacerbated by COVID-19. OBJECTIVE: Evaluate neighborhood-level deprivation and English language proficiency effect on disproportionate outcomes seen in racial and ethnic minorities diagnosed with COVID-19. DESIGN: Retrospective cohort study SETTING: Health records of 12 Midwest hospitals and 60 clinics in Minnesota between March 4, 2020, and August 19, 2020 PATIENTS: Polymerase chain reaction-positive COVID-19 patients EXPOSURES: Area Deprivation Index (ADI) and primary language MAIN MEASURES: The primary outcome was COVID-19 severity, using hospitalization within 45 days of diagnosis as a marker of severity. Logistic and competing-risk regression models assessed the effects of neighborhood-level deprivation (using the ADI) and primary language. Within race, effects of ADI and primary language were measured using logistic regression. RESULTS: A total of 5577 individuals infected with SARS-CoV-2 were included; 866 (n = 15.5%) were hospitalized within 45 days of diagnosis. Hospitalized patients were older (60.9 vs. 40.4 years, p < 0.001) and more likely to be male (n = 425 [49.1%] vs. 2049 [43.5%], p = 0.002). Of those requiring hospitalization, 43.9% (n = 381), 19.9% (n = 172), 18.6% (n = 161), and 11.8% (n = 102) were White, Black, Asian, and Hispanic, respectively. Independent of ADI, minority race/ethnicity was associated with COVID-19 severity: Hispanic patients (OR 3.8, 95% CI 2.72-5.30), Asians (OR 2.39, 95% CI 1.74-3.29), and Blacks (OR 1.50, 95% CI 1.15-1.94). ADI was not associated with hospitalization. Non-English-speaking (OR 1.91, 95% CI 1.51-2.43) significantly increased odds of hospital admission across and within minority groups. CONCLUSIONS: Minority populations have increased odds of severe COVID-19 independent of neighborhood deprivation, a commonly suspected driver of disparate outcomes. Non-English-speaking accounts for differences across and within minority populations. These results support the ongoing need to determine the mechanisms that contribute to disparities during COVID-19 while also highlighting the underappreciated role primary language plays in COVID-19 severity among minority groups.

Characterizing COVID-19 clinical phenotypes and associated comorbidities and complication profiles.

PURPOSE: Heterogeneity 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. METHODS: This 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. RESULTS: The 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 p<0.001) comorbidities were most commonly phenotype I. Adjusted odds of respiratory, renal, hepatic, metabolic (all p<0.001), and hematological (p = 0.02) complications were highest for phenotype I. Phenotypes I and II were associated with 7.30-fold (HR:7.30, 95% CI:(3.11-17.17), p<0.001) and 2.57-fold (HR:2.57, 95% CI:(1.10-6.00), p = 0.03) increases in hazard of death relative to phenotype III. CONCLUSION: We 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.

Is Better Patient Knowledge Associated with Different Treatment Preferences? A Survey of Patients with Stable Coronary Artery Disease.

BACKGROUND: In stable coronary artery disease (CAD), shared decision-making (SDM) is encouraged when deciding whether to pursue percutaneous coronary intervention (PCI) given similar cardiovascular outcomes between PCI and medical therapy. However, it remains unclear whether improving patient-provider communication and patient knowledge, the main tenets of SDM, changes patient preferences or the treatment chosen. We explored the relationships between patient-provider communication, patient knowledge, patient preferences, and the treatment received. METHODS: We surveyed stable CAD patients referred for elective cardiac catheterization at seven hospitals from 6/2016 to 9/2018. Surveys assessed patient-provider communication, medical knowledge, and preferences for treatment and decision-making. We verified treatments received by chart review. We used linear and logistic regression to examine relationships between patient-provider communication and knowledge, knowledge and preference, and preference and treatment received. RESULTS: Eighty-seven patients completed the survey. More discussion of the benefits and risks of both medical therapy and PCI associated with higher patient knowledge scores (ß=0.28, p<0.01). Patient knowledge level was not associated with preference for PCI (OR=0.78, 95% CI 0.57-1.03, p=0.09). Black patients had more than four times the odds of preferring medical therapy to PCI (OR=4.49, 1.22-18.45, p=0.03). Patients preferring medical therapy were not significantly less likely to receive PCI (OR=0.67, 0.16-2.52, p=0.57). CONCLUSIONS: While communicating the risks of PCI may improve patient knowledge, this knowledge may not affect patient treatment preferences. Rather, other factors such as race may be significantly more influential on a patient's treatment preferences. Furthermore, patient preferences are still not well reflected in the treatment received. Improving shared decision-making in stable CAD therefore may require not only increasing patient education but also better understanding and including a patient's background and pre-existing beliefs.