Implementing Telestroke in the Inpatient Setting: Identifying Factors for Success.

BACKGROUND: Inpatient telestroke programs have emerged as a solution to provide timely stroke care in underserved areas, but their successful implementation and factors influencing their effectiveness remain underexplored. This study aimed to qualitatively evaluate the perspectives of inpatient clinicians located at spoke hospitals participating in a newly established inpatient telestroke program to identify implementation barriers and facilitators. METHODS: This was a formative evaluation relying on semistructured qualitative interviews with 16 inpatient providers (physicians and nurse practitioners) at 5 spoke sites of a hub-and-spoke inpatient telestroke program. The Integrated-Promoting Action on Research Implementation in Health Services framework guided data analysis, focusing on the innovation, recipients, context, and facilitation aspects of implementation. Interviews were transcribed and coded using thematic analysis. RESULTS: Fifteen themes were identified in the data and mapped to the Integrated-Promoting Action on Research Implementation in Health Services framework. Themes related to the innovation (the telestroke program) included easy access to stroke specialists, the benefits of limiting patient transfers, concerns about duplicating tests, and challenges of timing inpatient telestroke visits and notes to align with discharge workflow. Themes pertaining to recipients (care team members and patients) were communication gaps between teams, concern about the supervision of inpatient telestroke advanced practice providers and challenges with nurse empowerment. With regard to the context (hospital and system factors), providers highlighted familiarity with telehealth technologies as a facilitator to implementing inpatient telestroke, yet highlighted resource limitations in smaller facilities. Facilitation (program implementation) was recognized as crucial for education, standardization, and buy-in. CONCLUSIONS: Understanding barriers and facilitators to implementation is crucial to determining where programmatic changes may need to be made to ensure the success and sustainment of inpatient telestroke services.

Evaluation of a Clinical Decision Support Tool to Guide Adoption of the American Heart Association Telemetry Monitoring Practice Standards.

BACKGROUND: The objectives of this study were to (1) evaluate telemetry use pre- and postimplementation of clinical decision support tools to support American Heart Association practice standards for telemetry monitoring and (2) understand the factors that may contribute to variation of telemetry monitoring in practice. METHODS AND RESULTS: First, we captured overall variability in telemetry use pre- and postimplementation of the clinical decision support intervention. We then conducted semistructured interviews with telemetry-ordering providers to identify key barriers and facilitators to adoption. During the study period, 399 physicians met criteria for inclusion and were divided into excessive and nonexcessive orderers. Distribution of telemetry use was bimodal. Among nonexcessive users, 24.4% of patient days were with telemetry compared with 51.6% among excessive users. On average, both excessive (6.1% reduction) and nonexcessive users (2.8% reduction) decreased telemetry use postimplementation, and these reductions were sustained over a 16-month period. Sixteen interviews were conducted. Physicians believed that the tool was successful because it caused them to more closely consider if telemetry was indicated for each patient. Physicians also voiced frustration with interruptions to their workflow, and some noted that they commonly use telemetry outside of practice standards to monitor patients who were acutely but not critically ill. CONCLUSIONS: Embedding telemetry practice standards into the electronic health record in the form of clinical decision support is effective at reducing excess telemetry use. Although the intervention was well received, there are persistent barriers, such as preexisting views on telemetry and existing workflow habits, that may inhibit higher adoption of standards.

Validation of a Proprietary Deterioration Index Model and Performance in Hospitalized Adults.

Importance: The Deterioration Index (DTI), used by hospitals for predicting patient deterioration, has not been extensively validated externally, raising concerns about performance and equitable predictions. Objective: To locally validate DTI performance and assess its potential for bias in predicting patient clinical deterioration. Design, Setting, and Participants: This retrospective prognostic study included 13 737 patients admitted to 8 heterogenous Midwestern US hospitals varying in size and type, including academic, community, urban, and rural hospitals. Patients were 18 years or older and admitted between January 1 and May 31, 2021. Exposure: DTI predictions made every 15 minutes. Main Outcomes and Measures: Deterioration, defined as the occurrence of any of the following while hospitalized: mechanical ventilation, intensive care unit transfer, or death. Performance of the DTI was evaluated using area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (AUPRC). Bias measures were calculated across demographic subgroups. Results: A total of 5 143 513 DTI predictions were made for 13 737 patients across 14 834 hospitalizations. Among 13 918 encounters, the mean (SD) age of patients was 60.3 (19.2) years; 7636 (54.9%) were female, 11 345 (81.5%) were White, and 12 392 (89.0%) were of other ethnicity than Hispanic or Latino. The prevalence of deterioration was 10.3% (n = 1436). The DTI produced AUROCs of 0.759 (95% CI, 0.756-0.762) at the observation level and 0.685 (95% CI, 0.671-0.700) at the encounter level. Corresponding AUPRCs were 0.039 (95% CI, 0.037-0.040) at the observation level and 0.248 (95% CI, 0.227-0.273) at the encounter level. Bias measures varied across demographic subgroups and were 14.0% worse for patients identifying as American Indian or Alaska Native and 19.0% worse for those who chose not to disclose their ethnicity. Conclusions and Relevance: In this prognostic study, the DTI had modest ability to predict patient deterioration, with varying degrees of performance at the observation and encounter levels and across different demographic groups. Disparate performance across subgroups suggests the need for more transparency in model training data and reinforces the need to locally validate externally developed prediction models.

Predictors of discharge disposition and mortality following hospitalization with SARS-CoV-2 infection.

IMPORTANCE: The SARS-CoV-2 pandemic has overwhelmed hospital capacity, prioritizing the need to understand factors associated with type of discharge disposition. OBJECTIVE: Characterization of disposition associated factors following SARS-CoV-2. DESIGN: Retrospective study of SARS-CoV-2 positive patients from March 7th, 2020, to May 4th, 2022, requiring hospitalization. SETTING: Midwest academic health-system. PARTICIPANTS: Patients above the age 18 years admitted with PCR + SARS-CoV-2. INTERVENTION: None. MAIN OUTCOMES: Discharge to home versus PAC (inpatient rehabilitation facility (IRF), skilled-nursing facility (SNF), long-term acute care (LTACH)), or died/hospice while hospitalized (DH). RESULTS: We identified 62,279 SARS-CoV-2 PCR+ patients; 6,248 required hospitalizations, of whom 4611(73.8%) were discharged home, 985 (15.8%) to PAC and 652 (10.4%) died in hospital (DH). Patients discharged to PAC had a higher median age (75.7 years, IQR: 65.6-85.1) compared to those discharged home (57.0 years, IQR: 38.2-69.9), and had longer mean length of stay (LOS) 14.7 days, SD: 14.0) compared to discharge home (5.8 days, SD: 5.9). Older age (RRR:1.04, 95% CI:1.041-1.055), and higher Elixhauser comorbidity index [EI] (RRR:1.19, 95% CI:1.168-1.218) were associated with higher rate of discharge to PAC versus home. Older age (RRR:1.069, 95% CI:1.060-1.077) and higher EI (RRR:1.09, 95% CI:1.071-1.126) were associated with more frequent DH versus home. Blacks, Asians, and Hispanics were less likely to be discharged to PAC (RRR, 0.64 CI 0.47-0.88), (RRR 0.48 CI 0.34-0.67) and (RRR 0.586 CI 0.352-0.975). Having alpha variant was associated with less frequent PAC discharge versus home (RRR 0.589 CI 0.444-780). The relative risks for DH were lower with a higher platelet count 0.998 (CI 0.99-0.99) and albumin levels 0.342 (CI 0.26-0.45), and higher with increased CRP (RRR 1.006 CI 1.004-1.007) and D-Dimer (RRR 1.070 CI 1.039-1.101). Increased albumin had lower risk to PAC discharge (RRR 0.630 CI 0.497-0.798. An increase in D-Dimer (RRR1.033 CI 1.002-1.064) and CRP (RRR1.002 CI1.001-1.004) was associated with higher risk of PAC discharge. A breakthrough (BT) infection was associated with lower likelihood of DH and PAC. CONCLUSION: Older age, higher EI, CRP and D-Dimer are associated with PAC and DH discharges following hospitalization with COVID-19 infection. BT infection reduces the likelihood of being discharged to PAC and DH.

Prevalence of Medical Contraindications to Nirmatrelvir/Ritonavir in a Cohort of Hospitalized and Nonhospitalized Patients With COVID-19.

This analysis describes the prevalence of contraindications to nirmatrelvir/ritonavir among 66 007 patients with coronavirus disease 2019 in a large health care system. A possible contradiction was present in 9830 patients (14.8%), with the prevalence of contraindications increasing with higher acuity of illness.

Predictors of Postacute Sequelae of COVID-19 Development and Rehabilitation: A Retrospective Study.

OBJECTIVE: To examine the frequency of postacute sequelae of SARS-CoV-2 (PASC) and the factors associated with rehabilitation utilization in a large adult population with PASC. DESIGN: Retrospective study. SETTING: Midwest hospital health system. PARTICIPANTS: 19,792 patients with COVID-19 from March 10, 2020, to January 17, 2021. INTERVENTION: Not applicable. MAIN OUTCOME MEASURES: Descriptive analyses were conducted across the entire cohort along with an adult subgroup analysis. A logistic regression was performed to assess factors associated with PASC development and rehabilitation utilization. RESULTS: In an analysis of 19,792 patients, the frequency of PASC was 42.8% in the adult population. Patients with PASC compared with those without had a higher utilization of rehabilitation services (8.6% vs 3.8%, P<.001). Risk factors for rehabilitation utilization in patients with PASC included younger age (odds ratio [OR], 0.99; 95% confidence interval [CI], 0.98-1.00; P=.01). In addition to several comorbidities and demographics factors, risk factors for rehabilitation utilization solely in the inpatient population included male sex (OR, 1.24; 95% CI, 1.02-1.50; P=.03) with patients on angiotensin-converting-enzyme inhibitors or angiotensin-receptor blockers 3 months prior to COVID-19 infections having a decreased risk of needing rehabilitation (OR, 0.80; 95% CI, 0.64-0.99; P=.04). CONCLUSIONS: Patients with PASC had higher rehabilitation utilization. We identified several clinical and demographic factors associated with the development of PASC and rehabilitation utilization.

A 12-hospital prospective evaluation of a clinical decision support prognostic algorithm based on logistic regression as a form of machine learning to facilitate decision making for patients with suspected COVID-19.

OBJECTIVE: To prospectively evaluate a logistic regression-based machine learning (ML) prognostic algorithm implemented in real-time as a clinical decision support (CDS) system for symptomatic persons under investigation (PUI) for Coronavirus disease 2019 (COVID-19) in the emergency department (ED). METHODS: We developed in a 12-hospital system a model using training and validation followed by a real-time assessment. The LASSO guided feature selection included demographics, comorbidities, home medications, vital signs. We constructed a logistic regression-based ML algorithm to predict "severe" COVID-19, defined as patients requiring intensive care unit (ICU) admission, invasive mechanical ventilation, or died in or out-of-hospital. Training data included 1,469 adult patients who tested positive for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) within 14 days of acute care. We performed: 1) temporal validation in 414 SARS-CoV-2 positive patients, 2) validation in a PUI set of 13,271 patients with symptomatic SARS-CoV-2 test during an acute care visit, and 3) real-time validation in 2,174 ED patients with PUI test or positive SARS-CoV-2 result. Subgroup analysis was conducted across race and gender to ensure equity in performance. RESULTS: The algorithm performed well on pre-implementation validations for predicting COVID-19 severity: 1) the temporal validation had an area under the receiver operating characteristic (AUROC) of 0.87 (95%-CI: 0.83, 0.91); 2) validation in the PUI population had an AUROC of 0.82 (95%-CI: 0.81, 0.83). The ED CDS system performed well in real-time with an AUROC of 0.85 (95%-CI, 0.83, 0.87). Zero patients in the lowest quintile developed "severe" COVID-19. Patients in the highest quintile developed "severe" COVID-19 in 33.2% of cases. The models performed without significant differences between genders and among race/ethnicities (all p-values > 0.05). CONCLUSION: A logistic regression model-based ML-enabled CDS can be developed, validated, and implemented with high performance across multiple hospitals while being equitable and maintaining performance in real-time validation.

Patient Heterogeneity and the J-Curve Relationship Between Time-to-Antibiotics and the Outcomes of Patients Admitted With Bacterial Infection.

OBJECTIVES: Sepsis remains a leading and preventable cause of hospital utilization and mortality in the United States. Despite updated guidelines, the optimal definition of sepsis as well as optimal timing of bundled treatment remain uncertain. Identifying patients with infection who benefit from early treatment is a necessary step for tailored interventions. In this study, we aimed to illustrate clinical predictors of time-to-antibiotics among patients with severe bacterial infection and model the effect of delay on risk-adjusted outcomes across different sepsis definitions. DESIGN: A multicenter retrospective observational study. SETTING: A seven-hospital network including academic tertiary care center. PATIENTS: Eighteen thousand three hundred fifteen patients admitted with severe bacterial illness with or without sepsis by either acute organ dysfunction (AOD) or systemic inflammatory response syndrome positivity. MEASUREMENTS AND MAIN RESULTS: The primary exposure was time to antibiotics. We identified patient predictors of time-to-antibiotics including demographics, chronic diagnoses, vitals, and laboratory results and determined the impact of delay on a composite of inhospital death or length of stay over 10 days. Distribution of time-to-antibiotics was similar across patients with and without sepsis. For all patients, a J-curve relationship between time-to-antibiotics and outcomes was observed, primarily driven by length of stay among patients without AOD. Patient characteristics provided good to excellent prediction of time-to-antibiotics irrespective of the presence of sepsis. Reduced time-to-antibiotics was associated with improved outcomes for all time points beyond 2.5 hours from presentation across sepsis definitions. CONCLUSIONS: Antibiotic timing is a function of patient factors regardless of sepsis criteria. Similarly, we show that early administration of antibiotics is associated with improved outcomes in all patients with severe bacterial illness. Our findings suggest identifying infection is a rate-limiting and actionable step that can improve outcomes in septic and nonseptic patients.

Responding to COVID-19 Through Interhospital Resource Coordination: A Mixed-Methods Evaluation.

OBJECTIVES: The COVID-19 pandemic stressed hospital operations, requiring rapid innovations to address rise in demand and specialized COVID-19 services while maintaining access to hospital-based care and facilitating expertise. We aimed to describe a novel hospital system approach to managing the COVID-19 pandemic, including multihospital coordination capability and transfer of COVID-19 patients to a single, dedicated hospital. METHODS: We included patients who tested positive for SARS-CoV-2 by polymerase chain reaction admitted to a 12-hospital network including a dedicated COVID-19 hospital. Our primary outcome was adherence to local guidelines, including admission risk stratification, anticoagulation, and dexamethasone treatment assessed by differences-in-differences analysis after guideline dissemination. We evaluated outcomes and health care worker satisfaction. Finally, we assessed barriers to safe transfer including transfer across different electronic health record systems. RESULTS: During the study, the system admitted a total of 1209 patients. Of these, 56.3% underwent transfer, supported by a physician-led System Operations Center. Patients who were transferred were older (P = 0.001) and had similar risk-adjusted mortality rates. Guideline adherence after dissemination was higher among patients who underwent transfer: admission risk stratification (P < 0.001), anticoagulation (P < 0.001), and dexamethasone administration (P = 0.003). Transfer across electronic health record systems was a perceived barrier to safety and reduced quality. Providers positively viewed our transfer approach. CONCLUSIONS: With standardized communication, interhospital transfers can be a safe and effective method of cohorting COVID-19 patients, are well received by health care providers, and have the potential to improve care quality.

Overcoming gaps: regional collaborative to optimize capacity management and predict length of stay of patients admitted with COVID-19.

OBJECTIVE: Ensuring an efficient response to COVID-19 requires a degree of inter-system coordination and capacity management coupled with an accurate assessment of hospital utilization including length of stay (LOS). We aimed to establish optimal practices in inter-system data sharing and LOS modeling to support patient care and regional hospital operations. MATERIALS AND METHODS: We completed a retrospective observational study of patients admitted with COVID-19 followed by 12-week prospective validation, involving 36 hospitals covering the upper Midwest. We developed a method for sharing de-identified patient data across systems for analysis. From this, we compared 3 approaches, generalized linear model (GLM) and random forest (RF), and aggregated system level averages to identify features associated with LOS. We compared model performance by area under the ROC curve (AUROC). RESULTS: A total of 2068 patients were included and used for model derivation and 597 patients for validation. LOS overall had a median of 5.0 days and mean of 8.2 days. Consistent predictors of LOS included age, critical illness, oxygen requirement, weight loss, and nursing home admission. In the validation cohort, the RF model (AUROC 0.890) and GLM model (AUROC 0.864) achieved good to excellent prediction of LOS, but only marginally better than system averages in practice. CONCLUSION: Regional sharing of patient data allowed for effective prediction of LOS across systems; however, this only provided marginal improvement over hospital averages at the aggregate level. A federated approach of sharing aggregated system capacity and average LOS will likely allow for effective capacity management at the regional level.

Metabolic surgery may protect against admission for COVID-19 in persons with nonalcoholic fatty liver disease.

BACKGROUND: SARS-CoV-2 (COVID-19) disease causes significant morbidity and mortality through increased inflammation and thrombosis. Nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) are states of chronic inflammation and indicate advanced metabolic disease. OBJECTIVE: The purpose of this observational study was to characterize the risk of hospitalization for COVID-19 in patients with NAFLD/NASH and evaluate the mitigating effect of various metabolic treatments. SETTING: Retrospective analysis of electronic medical record data of 26,896 adults from a 12-hospital Midwest healthcare system with a positive COVID-19 polymerase chain reaction (PCR) test from March 1, 2020, to January 26, 2021. METHODS: Variable selection was guided by the least absolute shrinkage and selection operator (LASSO) method, and multiple imputation was used to account for missing data. Multivariable logistic regression and competing risk models were used to assess the odds of being hospitalized within 45 days of a COVID-19 diagnosis. Analysis assessed the risk of hospitalization among patients with a prescription for metformin and statin use within the 3 months prior to the COVID-19 PCR result, history of home glucagon-like peptide 1 receptor agonist (GLP-1 RA) use, and history of metabolic and bariatric surgery (MBS). Interactions were assessed by sex and race. RESULTS: A history of NAFLD/NASH was associated with increased odds of admission for COVID-19 (odds ratio [OR], 1.88; 95% confidence interval [CI], 1.57-2.26; P < .001) and mortality (OR, 1.96; 95% CI, 1.45-2.67; P < .001). Each additional year of having NAFLD/NASH was associated with a significant increased risk of being hospitalized for COVID-19 (OR, 1.24; 95% CI, 1.14-1.35; P < .001). NAFLD/NASH increased the risk of hospitalization in men, but not women, and increased the risk of hospitalization in all multiracial/multiethnic subgroups. Medication treatments for metabolic syndrome were associated with significantly reduced risk of admission (OR, .81; 95% CI, .67-.99; P < .001 for home metformin use; OR, .71; 95% CI, .65-.83; P < .001 for home statin use). MBS was associated with a significant decreased risk of admission (OR, .48; 95% CI, .33-.69; P < .001). CONCLUSIONS: NAFLD/NASH is a significant risk factor for hospitalization for COVID-19 and appears to account for risk attributed to obesity. Other significant risks include factors associated with socioeconomic status and other co-morbidities, such as history of venous thromboembolism. Treatments for metabolic disease mitigated risks from NAFLD/NASH. More research is needed to confirm the risk associated with visceral adiposity, and patients should be screened for and informed of treatments for metabolic syndrome.

Morbidity and Mortality Trends of Pancreatitis: An Observational Study.

Background: Pancreatitis accounts for more than $2.5 billion of healthcare costs and remains the most common gastrointestinal (GI) admission. Few contemporary studies have assessed temporal trends of incidence, complications, management, and outcomes for acute pancreatitis in hospitalized patients at the national level. Methods: We used data from one of the largest hospital-based databases available in the United States, the Healthcare Cost and Utilization Project's (HCUP) State Inpatient Database, from 10 states between 2008 and 2015. We included patients with a diagnosis of acute pancreatitis (ICD-9 CM 577.0). Patient- and hospital-level data were used to estimate incidence and inpatient mortality rates. Results: From 80,736,256 hospitalizations, 929,914 (1.15%) cases of acute pancreatitis were identified, 186,226 (20.2%) of which were caused by gallbladder disease). The median age was 53 years (interquartile range [IQR], 41-67) and 50.8% were men. In-hospital mortality was 2.5% and crude mortality rates declined from 2.9% to 2.0% over the study period. Admission year remained significant after adjusting for patient demographics and comorbidities (odds ratio [OR], 0.90; 95% confidence interval [CI], 0.89-0.90; p < 0.001). Gallbladder disease was associated with decreased odds of mortality (OR, 0.60; 95% CI, 0.57-0.62). Median length of stay was four days (IQR, 2-7) and decreased over time. The rates of surgical and endoscopic interventions were highest in 2011 (peak incidence of 16.1% and 9.5%, respectively) and have been decreasing since. Surgical providers were, on average, more likely than medical providers to perform surgery in both those with and without gallbladder disease etiology (gallbladder disease OR, 7.11; 95% CI, 5.46-9.25; non-gallbladder disease OR, 20.50; 95% CI, 16.81-25.01), endoscopy (gallbladder disease OR, 1.22; 95% CI, 0.87-1.72; non-gallbladder disease OR, 1.60; 95% CI, 1.18-2.16), or both (gallbladder disease OR, 7.00; 95% CI, 5.22-9.37; non-gallbladder disease OR, 8.85; 95% CI, 5.61-13.96). Conclusions: The incidence of pancreatitis, from 2008 to 2015, has increased whereas inpatient mortality (i.e., case fatality) has decreased. Understanding temporal trends in outcomes and management along with provider, hospital, and regional variation can better identify areas for future research and collaboration in managing these patients.

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.

Super fragmented: a nationally representative cross-sectional study exploring the fragmentation of inpatient care among super-utilizers.

BACKGROUND: Super-utilizers with 4 or more admissions per year frequently receive low-quality care and disproportionately contribute to healthcare costs. Inpatient care fragmentation (admission to multiple different hospitals) in this population has not been well described. OBJECTIVE: To determine the prevalence of super-utilizers who receive fragmented care across different hospitals and to describe associated risks, costs, and health outcomes. RESEARCH DESIGN: We analyzed inpatient data from the Health Care Utilization Project's State Inpatient and Emergency Department database from 6 states from 2013. After identifying hospital super-utilizers, we stratified by the number of different hospitals visited in a 1-year period. We determined how patient demographics, costs, and outcomes varied by degree of fragmentation. We then examined how fragmentation would influence a hospital's ability to identify super-utilizers. SUBJECTS: Adult patients with 4 or more inpatient stays in 1 year. MEASURES: Patient demographics, cost, 1-year hospital reported mortality, and probability that a single hospital could correctly identify a patient as a super-utilizer. RESULTS: Of the 167,515 hospital super-utilizers, 97,404 (58.1%) visited more than 1 hospital in a 1-year period. Fragmentation was more likely among younger, non-white, low-income, under-insured patients, in population-dense areas. Patients with fragmentation were more likely to be admitted for chronic disease management, psychiatric illness, and substance abuse. Inpatient fragmentation was associated with higher yearly costs and lower likelihood of being identified as a super-utilizer. CONCLUSIONS: Inpatient care fragmentation is common among super-utilizers, disproportionately affects vulnerable populations. It is associated with high yearly costs and a decreased probability of correctly identifying super-utilizers.

We Should Do More to Offer Evidence-Based Treatment for an Important Modifiable Risk Factor for COVID-19: Obesity.

Observational studies, from multiple countries, repeatedly demonstrate an association between obesity and severe COVID-19, which is defined as need for hospitalization, intensive care unit admission, invasive mechanical ventilation (IMV) or death. Meta-analysis of studies from China, USA, and France show odds ratio (OR) of 2.31 (95% CI 1.3-4.1) for obesity and severe COVID-19. Other studies show OR of 12.1 (95% CI 3.25-45.1) for mortality and OR of 7.36 (95% CI 1.63-33.14) for need for IMV for patients with body mass index (BMI) ≥ 35 kg/m2. Obesity is the only modifiable risk factor that is not routinely treated but treatment can lead to improvement in visceral adiposity, insulin sensitivity, and mortality risk. Increasing the awareness of the association between obesity and COVID-19 risk in the general population and medical community may serve as the impetus to make obesity identification and management a higher priority.

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.

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

Background Despite past and ongoing efforts to achieve health equity in the United States, persistent disparities in socioeconomic status along with multilevel racism maintain disparate outcomes and appear to be amplified by COVID-19. Objective Measure socioeconomic factors and primary language effects on the risk of COVID-19 severity across and within racial/ethnic groups. Design Retrospective cohort study. Setting Health records of 12 Midwest hospitals and 60 clinics in the U.S. between March 4, 2020 to August 19, 2020. Patients PCR+ COVID-19 patients. Exposures Main exposures included race/ethnicity, area deprivation index (ADI), and primary language. Main Outcomes and Measures The primary outcome was COVID-19 severity using hospitalization within 45 days of diagnosis. Logistic and competing-risk regression models (censored at 45 days and accounting for the competing risk of death prior to hospitalization) 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 5,577 COVID-19 patients 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. 2,049 [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 continued concern that racism contributes to disparities during COVID-19 while also highlighting the underappreciated role primary language plays in COVID-19 severity across and within minority groups.

Insurance Coverage Predicts Mortality in Patients Transferred Between Hospitals: a Cross-Sectional Study.

BACKGROUND: Patients transferred between hospitals are at high risk of adverse events and mortality. The relationship between insurance status, transfer practices, and outcomes has not been definitively characterized. OBJECTIVE: To identify the association between insurance coverage and mortality of patients transferred between hospitals. DESIGN: We conducted a single-institution observational study, and validated results using a national administrative database of inter-hospital transfers. SETTING: Three ICUs at an academic tertiary care center validated by a nationally representative sample of inter-hospital transfers. PATIENTS: The single-institution analysis included 652 consecutive patients transferred from 57 hospitals between 2011 and 2012. The administrative database included 353,018 patients transferred between 437 hospitals. MEASUREMENTS: Adjusted inpatient mortality and 24-h mortality, stratified by insurance status. RESULTS: Of 652 consecutive transfers to three ICUs, we observed that uninsured patients had higher adjusted inpatient mortality (OR 2.67, p = 0.021) when controlling for age, race, gender, Apache-II, and whether the patient was transferred from an ED. Uninsured were more likely to be transferred from ED (OR 2.3, p = 0.026), and earlier in their hospital course (3.9 vs 2.0 days, p = 0.002). Using an administrative dataset, we validated these observations, finding that the uninsured had higher adjusted inpatient mortality (OR 1.24, 95% CI 1.13-1.36, p < 0.001) and higher mortality within 24 h (OR 1.33 95% CI 1.11-1.60, p < 0.002). The increase in mortality was independent of patient demographics, referral patterns, or diagnoses. LIMITATIONS: This is an observational study where transfer appropriateness cannot be directly assessed. CONCLUSIONS: Uninsured patients are more likely to be transferred from an ED and have higher mortality. These data suggest factors that drive inter-hospital transfer of uninsured patients have the potential to exacerbate outcome disparities.

Information handoff and outcomes of critically ill patients transferred between hospitals.

PURPOSE: Patients transferred between hospitals are at high risk of adverse events and mortality. This study aims to identify which components of the transfer handoff process are important predictors of adverse events and mortality. MATERIALS AND METHODS: We conducted a retrospective, observational study of 335 consecutive patient transfers to 3 intensive care units at an academic tertiary referral center. We assessed the relationship between handoff documentation completeness and patient outcomes. The primary outcome was in-hospital mortality. Secondary outcomes included adverse events, duplication of labor, disposition error, and length of stay. RESULTS: Transfer documentation was frequently absent with overall completeness of 58.3%. Adverse events occurred in 42% of patients within 24 hours of arrival, with an overall in-hospital mortality of 17.3%. Higher documentation completeness was associated with reduced in-hospital mortality (odds ratio [OR], 0.07; 95% confidence interval [CI], 0.02 to 0.38; P = .002), reduced adverse events (coefficient, -2.08; 95% CI, -2.76 to -1.390; P < .001), and reduced duplication of labor (OR, 0.19; 95% CI, 0.04 to 0.88; P = .033) when controlling for severity of illness. CONCLUSIONS: Documentation completeness is associated with improved outcomes and resource utilization in patients transferred between hospitals.

Myeloid mineralocorticoid receptor during experimental ischemic stroke: effects of model and sex.

BACKGROUND: Mineralocorticoid receptor (MR) antagonists have protective effects in the brain during experimental ischemic stroke, and we have previously demonstrated a key role for myeloid MR during stroke pathogenesis. In this study, we explore both model- and sex-specific actions of myeloid MR during ischemic stroke. METHODS AND RESULTS: The MR antagonist eplerenone significantly reduced the infarct size in male (control, 99.5 mm(3); eplerenone, 74.2 mm(3); n=8 to 12 per group) but not female (control, 84.0 mm(3); eplerenone, 83.7 mm(3); n=6 to 7 per group) mice after transient (90-minute) middle cerebral artery occlusion. In contrast to MR antagonism, genetic ablation of myeloid MR in female mice significantly reduced infarct size (myeloid MR knockout, 9.4 mm(3) [5.4 to 36.6]; control, 66.0 mm(3) [50.0 to 81.4]; n=6 per group) after transient middle cerebral artery occlusion. This was accompanied by reductions in inflammatory gene expression and improvement in neurological function. In contrast to ischemia-reperfusion, myeloid MR-knockout mice were not protected from permanent middle cerebral artery occlusion. The infarct size and inflammatory response after permanent occlusion showed no evidence of protection by myeloid MR knockout in photothrombotic and intraluminal filament models of permanent occlusion. CONCLUSIONS: These studies demonstrate that MR antagonism is protective in male but not female mice during transient middle cerebral artery occlusion, whereas genetic ablation of myeloid MR is protective in both male and female mice. They also highlight important mechanistic differences in the role of myeloid cells in different models of stroke and confirm that specific myeloid phenotypes play key roles in stroke protection.