Medicaid expansion in California and breast cancer incidence across neighborhoods with varying social vulnerabilities.

PURPOSE: To investigate changes in breast cancer incidence rates associated with Medicaid expansion in California. METHODS: We extracted yearly census tract-level population counts and cases of breast cancer diagnosed among women aged between 20 and 64 years in California during years 2010-2017. Census tracts were classified into low, medium and high groups according to their social vulnerability index (SVI). Using a difference-in-difference (DID) approach with Poisson regression models, we estimated the incidence rate, incidence rate ratio (IRR) during the pre- (2010-2013) and post-expansion periods (2014-2017), and the relative IRR (DID estimates) across three groups of neighborhoods. RESULTS: Prior to the Medicaid expansion, the overall incidence rate was 93.61, 122.03, and 151.12 cases per 100,000 persons among tracts with high, medium, and low-SVI, respectively; and was 96.49, 122.07, and 151.66 cases per 100,000 persons during the post-expansion period, respectively. The IRR between high and low vulnerability neighborhoods was 0.62 and 0.64 in the pre- and post-expansion period, respectively, and the relative IRR was 1.03 (95% CI 1.00 to 1.06, p = 0.026). In addition, significant DID estimate was only found for localized breast cancer (relative IRR = 1.05; 95% CI, 1.01 to 1.09, p = 0.049) between high and low-SVI neighborhoods, not for regional and distant cancer stage. CONCLUSIONS: The Medicaid expansion had differential impact on breast cancer incidence across neighborhoods in California, with the most pronounced increase found for localized cancer stage in high-SVI neighborhoods. Significant pre-post change was only found for localized breast cancer between high and low-SVI neighborhoods.

Power calculation for detecting interaction effect in cross-sectional stepped-wedge cluster randomized trials: an important tool for disparity research.

BACKGROUND: The stepped-wedge cluster randomized trial (SW-CRT) design has become popular in healthcare research. It is an appealing alternative to traditional cluster randomized trials (CRTs) since the burden of logistical issues and ethical problems can be reduced. Several approaches for sample size determination for the overall treatment effect in the SW-CRT have been proposed. However, in certain situations we are interested in examining the heterogeneity in treatment effect (HTE) between groups instead. This is equivalent to testing the interaction effect. An important example includes the aim to reduce racial disparities through healthcare delivery interventions, where the focus is the interaction between the intervention and race. Sample size determination and power calculation for detecting an interaction effect between the intervention status variable and a key covariate in the SW-CRT study has not been proposed yet for binary outcomes. METHODS: We utilize the generalized estimating equation (GEE) method for detecting the heterogeneity in treatment effect (HTE). The variance of the estimated interaction effect is approximated based on the GEE method for the marginal models. The power is calculated based on the two-sided Wald test. The Kauermann and Carroll (KC) and the Mancl and DeRouen (MD) methods along with GEE (GEE-KC and GEE-MD) are considered as bias-correction methods. RESULTS: Among three approaches, GEE has the largest simulated power and GEE-MD has the smallest simulated power. Given cluster size of 120, GEE has over 80% statistical power. When we have a balanced binary covariate (50%), simulated power increases compared to an unbalanced binary covariate (30%). With intermediate effect size of HTE, only cluster sizes of 100 and 120 have more than 80% power using GEE for both correlation structures. With large effect size of HTE, when cluster size is at least 60, all three approaches have more than 80% power. When we compare an increase in cluster size and increase in the number of clusters based on simulated power, the latter has a slight gain in power. When the cluster size changes from 20 to 40 with 20 clusters, power increases from 53.1% to 82.1% for GEE; 50.6% to 79.7% for GEE-KC; and 48.1% to 77.1% for GEE-MD. When the number of clusters changes from 20 to 40 with cluster size of 20, power increases from 53.1% to 82.1% for GEE; 50.6% to 81% for GEE-KC; and 48.1% to 79.8% for GEE-MD. CONCLUSIONS: We propose three approaches for cluster size determination given the number of clusters for detecting the interaction effect in SW-CRT. GEE and GEE-KC have reasonable operating characteristics for both intermediate and large effect size of HTE.

Malnutrition risk assessment using a machine learning-based screening tool: A multicentre retrospective cohort.

BACKGROUND: Malnutrition is associated with increased morbidity, mortality, and healthcare costs. Early detection is important for timely intervention. This paper assesses the ability of a machine learning screening tool (MUST-Plus) implemented in registered dietitian (RD) workflow to identify malnourished patients early in the hospital stay and to improve the diagnosis and documentation rate of malnutrition. METHODS: This retrospective cohort study was conducted in a large, urban health system in New York City comprising six hospitals serving a diverse patient population. The study included all patients aged ≥ 18 years, who were not admitted for COVID-19 and had a length of stay of ≤ 30 days. RESULTS: Of the 7736 hospitalisations that met the inclusion criteria, 1947 (25.2%) were identified as being malnourished by MUST-Plus-assisted RD evaluations. The lag between admission and diagnosis improved with MUST-Plus implementation. The usability of the tool output by RDs exceeded 90%, showing good acceptance by users. When compared pre-/post-implementation, the rate of both diagnoses and documentation of malnutrition showed improvement. CONCLUSION: MUST-Plus, a machine learning-based screening tool, shows great promise as a malnutrition screening tool for hospitalised patients when used in conjunction with adequate RD staffing and training about the tool. It performed well across multiple measures and settings. Other health systems can use their electronic health record data to develop, test and implement similar machine learning-based processes to improve malnutrition screening and facilitate timely intervention.

Latent class analysis of eHealth behaviors among adults with epilepsy.

OBJECTIVE: The objective of this study was to determine the proportions of uptake and factors associated with electronic health (eHealth) behaviors among adults with epilepsy. METHODS: The 2013, 2015, and 2017 National Health Interview Surveys were analyzed. We assessed the proportions of use of five domains of eHealth in those with epilepsy: looked up health information on the internet, filled a prescription on the internet, scheduled a medical appointment on the internet, communicated with a health care provider via email, and used chat groups to learn about health topics. Multivariate logistic regressions were conducted to identify factors associated with any eHealth behaviors among those with active epilepsy. Latent class analysis was performed to identify underlying patterns of eHealth activity. Survey participants were classified into three discrete classes: (1) frequent, (2) infrequent, and (3) nonusers of eHealth. Multinomial logistic regression was performed to identify factors associated with frequency of eHealth use. RESULTS: There were 1770 adults with epilepsy, of whom 65.87% had at least one eHealth behavior in the prior year. By domain, 62.61% looked up health information on the internet, 15.81% filled a prescription on the internet, 14.95% scheduled a medical appointment on the internet, 17.20% communicated with a health care provider via email, and 8.27% used chat groups to learn about health topics. Among those with active epilepsy, female sex, more frequent computer usage, and internet usage were associated with any eHealth behavior. Female sex and frequent computer use were associated with frequent eHealth use as compared to nonusers. SIGNIFICANCE: A majority of persons with epilepsy were found to use at least one form of eHealth. Various technological and demographic factors were associated with eHealth behaviors. Individuals with lower eHealth behaviors should be provided with targeted interventions that address barriers to the adoption of these technologies.

Longitudinal Assessment of Association Between Tobacco Use and Tobacco Dependence Among Adults: Latent Class Analysis of the Population Assessment of Tobacco and Health Study Waves 1-4.

INTRODUCTION: With increasing tobacco product varieties, understanding tobacco use (TU) profiles and their associations with tobacco dependence (TD) has also become increasingly challenging. AIMS AND METHODS: We aimed to identify TU profiles and their associations with TD over time, and to identify subgroups with high risk of TD. We included 3463 adult recent tobacco users who had complete TU and TD data across waves 1-4 of the Population Assessment of Tobacco and Health (PATH) study. We used a composite index of TD and a summed TD score from an established 16-item TD measure. We applied a latent class analysis to identify TU profiles based on participants' usage of eight common tobacco product groups at each survey wave and to check the stability of the TU profiles over time. We then used generalized estimating equations regressions to evaluate the longitudinal TU-TD association, adjusting for potential confounders. RESULTS: We identified three distinct TU profiles that remained consistent across four survey waves: Dominant cigarette users (62%-68%), poly users with high propensity of using traditional cigarettes, e-cigarettes, and cigars (24%-31%), and dominant smokeless product users (7%-9%). Covariate-adjusted models showed that TD was significantly lower among the poly users and the dominant smokeless users, compared to that among the dominant cigarette users. CONCLUSIONS: Both TU profiles and their associations with TD were stable over time at the population level. Poly users and smokeless product users were consistently associated with lower TD than cigarette-dominant users, suggesting the need for tailored tobacco cessation interventions for users with different TU profiles. IMPLICATIONS: The finding of consistent TU profiles across four survey waves extends the current literature in capturing TU patterns in an evolving tobacco product landscape. The finding of the overall higher level of TD among the cigarette-dominant users compared to the other TU latent profiles (the Cig+eCig+Cigar dominant poly users and the dominant smokeless product users) can help identify high-risk groups for potential interventions. Our application of innovative statistical methods to high-quality longitudinal data from the PATH study helps improve the understanding of the dynamic TU-TD relationship over time.

Preinjury Health Status of Adults With Traumatic Brain Injury: A Preliminary Matched Case-Control Study.

OBJECTIVE: To discern whether there is evidence that individuals who sustained a traumatic brain injury (TBI) had the greater odds of preexisting health conditions and/or poorer health behaviors than matched controls without TBI. SETTING: Brain Injury Inpatient Rehabilitation Unit at Mount Sinai Hospital. Midlife in the United States (MIDUS) control data were collected via random-digit-dialing phone survey. PARTICIPANTS: TBI cases were enrolled in the TBI Health Study and met at least 1 of the following 4 injury severity criteria: abnormal computed tomography scan; Glasgow Coma Scale score between 3 and 12; loss of consciousness greater than 30 minutes; or post-TBI amnesia longer than 24 hours. Sixty-two TBI cases and 171 matched MIDUS controls were included in the analyses; controls were excluded if they reported having a history of head injury. DESIGN: Matched case-control study. MAIN MEASURES: Self-reported measures of depression symptoms, chronic pain, health status, alcohol use, smoking status, abuse of controlled substances, physical activity, physical health composite score, and behavioral health composite score. RESULTS: Pre-index injury depression was nearly 4 times higher in TBI cases than in matched controls (OR= 3.98, 95% CI, 1.71-9.27; P = .001). We found no significant differences in the odds of self-reporting 3 or more medical health conditions in year prior to index injury (OR = 1.52; 95% CI, 0.82-2.81; P = .183) or reporting more risky health behaviors (OR = 1.48; 95% CI; 0.75-2.91; P = .254]) in individuals with TBI than in controls. CONCLUSION: These preliminary findings suggest that the odds of depression in the year prior to index injury far exceed those reported in matched controls. Further study in larger samples is required to better understand the relative odds of prior health problems in those who sustain a TBI, with a goal of elucidating the implications of preinjury health on post-TBI disease burden.

Trends in neoadjuvant chemotherapy versus surgery-first in stage I HER2-positive breast cancer patients in the National Cancer DataBase (NCDB).

BACKGROUND: Neoadjuvant chemotherapy (NAC) is the standard of care for locally advanced HER2 + breast cancer (BC). Optimal sequencing of treatment (NAC vs. surgery first) is less clear cut in stage I (T1N0) HER2 + BC, where information from surgical pathology could impact adjuvant treatment decisions. Utilizing the NCDB, we evaluated the trend of NAC use compared to upfront surgery in patients with small HER2 + BC. METHODS: We identified NCDB female patients diagnosed with T1 N0 HER2 + BC from 2010 through 2015. Prevalence ratios (PR) using multivariable robust Poisson regression models were calculated to measure the association between baseline characteristics and the receipt of NAC. Analysis of trends over time was denoted by annual percent change (APC) of NAC versus surgery upfront. RESULTS: Of the 14,949 that received chemotherapy and anti-HER2 therapy during the study period, overall 1281 (8.6%) received NAC and 13,668 (91.4%) received adjuvant treatment. Patients receiving NAC increased annually from 4.2% in 2010 to 17.3% in 2015, with the most rapid increase occurring between years 2013 (8.5%) and 2014 (14.2%). The greatest increase was seen in patients with cT1c tumors with an APC of 37.8% over the study period (95% CI 29.0, 47.3%, p < 0.01), although a significant trend was likewise seen in patients with cT1a (APC = 26.1%,95% CI 1.59, 56.6%), and cT1b (APC = 27.4%, 95% CI 18.0, 37.7%) tumors. Predictors of neoadjuvant therapy receipt were age younger than 50 (PR = 1.69, 95% CI 1.52, 1.89), Mountain/Pacific area (PR = 1.24, 95% CI 1.05, 1.46), and estrogen receptor negativity (ER- PR + : PR = 2.01, 95% CI 1.51, 2.68; ER- PR- : PR = 1.49, 95% CI 1.32, 1.69). CONCLUSIONS: Neoadjuvant therapy for T1 N0 HER2 + BC increased over the study period and was mostly due increased use in clinical T1c tumors. This may be consistent with secular change in Pertuzumab treatment following FDA approval in 2013.

Pre-existing Disparities and Potential Implications for the Rapid Expansion of Telemedicine in Response to the Coronavirus Disease 2019 Pandemic.

BACKGROUND: Concerns exist regarding exacerbation of existing disparities in health care access with the rapid implementation of telemedicine during the coronavirus disease 2019 (COVID-19) pandemic. However, data on pre-existing disparities in telemedicine utilization is currently lacking. OBJECTIVE: We aimed to study: (1) the prevalence of outpatient telemedicine visits before the COVID-19 pandemic by patient subgroups based on age, comorbidity burden, residence rurality, and median household income; and (2) associated diagnosis categories. RESEARCH DESIGN: This was a retrospective cohort study. SUBJECT: Commercial claims data from the Truven MarketScan database (2014-2018) representing n=846,461,609 outpatient visits. MEASURES: We studied characteristics and utilization of outpatient telemedicine services before the COVID-19 pandemic by patient subgroups based on age, comorbidity burden, residence rurality, and median household income. Disparities were assessed in unadjusted and adjusted (regression) analyses. RESULTS: With overall telemedicine uptake of 0.12% (n=1,018,092/846,461,609 outpatient visits) we found that pre-COVID-19 disparities in telemedicine use became more pronounced over time with lower use in patients who were older, had more comorbidities, were in rural areas, and had lower median household incomes (all trends and effect estimates P<0.001). CONCLUSION: These results contextualize pre-existing disparities in telemedicine use and are crucial in the monitoring of potential disparities in telemedicine access and subsequent outcomes after the rapid expansion of telemedicine during the COVID-19 pandemic.

Postpartum psychiatric readmissions: A nationwide study in women with and without epilepsy.

OBJECTIVE: To assess whether epilepsy is associated with increased odds of 30-day readmission due to psychiatric illness during the postpartum period. METHODS: The 2014 Nationwide Readmissions Database and the International Classification of Disease, Ninth Revision, Clinical Modification codes were used to identify postpartum women up to 50 years old in the United States, including the subgroup with epilepsy. The primary outcome was 30-day readmission and was categorized as (1) readmission due to psychiatric illness, (2) readmission due to all other causes, or (3) no readmission. Secondary outcome was diagnosis at readmission. The association of the primary outcome and presence of epilepsy was examined using multinomial logistic regression. RESULTS: Of 1 558 875 women with admissions for delivery identified, 6745 (.45%) had epilepsy. Thirteen of every 10 000 women had 30-day psychiatric readmissions in the epilepsy group compared to one of every 10 000 in the no-epilepsy group (p < .0001). Of every 10 000 women with epilepsy, 256 had 30-day readmissions due to other causes compared to 115 of every 10 000 women in the no-epilepsy group (p < .0001). The odds ratio for readmission due to psychiatric illness was 10.13 (95% confidence interval = 5.48-18.72) in those with epilepsy compared to those without. Top psychiatric causes for 30-day readmissions among women with epilepsy were mood disorders, schizophrenia and other psychotic disorders, and substance-related disorders. SIGNIFICANCE: This large-scale study demonstrated that postpartum women with epilepsy have higher odds of readmission due to a psychiatric illness compared to women without epilepsy. Postpartum treatment strategies and interventions to prevent psychiatric readmissions are necessary in this vulnerable population.

Safety of Tranexamic Acid in Hip and Knee Arthroplasty in High-risk Patients.

BACKGROUND: With increasing use of tranexamic acid in total hip and knee arthroplasties, safety concerns remain. Using national claims data, this study examined tranexamic acid use in patients with preexisting comorbidities. The hypothesis was that tranexamic acid use is not associated with increased complication risk in hip and knee arthroplasty patients with comorbidities. METHODS: Among 765,011 total hip/knee arthroplasties (2013 to 2016, Premier Healthcare claims), tranexamic acid use was assessed in three high-risk groups: group I with patients with a history of venous thromboembolism, myocardial infarction, seizures, or ischemic stroke/transient ischemic attack (n = 27,890); group II with renal disease (n = 44,608); and group III with atrial fibrillation (n = 45,952). The coprimary outcomes were blood transfusion and new-onset "composite complications" (venous thromboembolism, myocardial infarction, seizures, and ischemic stroke/transient ischemic attack). Associations between tranexamic acid use and outcomes were measured separately by high-risk group. The odds ratios and Bonferroni-adjusted 99.9% CIs are reported. RESULTS: Overall, 404,974 patients (52.9%) received tranexamic acid, with similar frequencies across high-risk groups I (13,004 of 27,890 [46.6%]), II (22,424 of 44,608 [50.3%]), and III (22,379 of 45,952 [48.7%]). Tranexamic acid use was associated with decreased odds of blood transfusion in high-risk groups I (721 of 13,004 [5.5%] vs. 2,293 of 14,886 [15.4%]; odds ratio, 0.307; 99.9% CI, 0.258 to 0.366), group II (2,045 of 22,424 [9.1%] vs. 5,159 of 22,184 [23.3%]; odds ratio, 0.315; 99.9% CI, 0.263 to 0.378), and group III (1,325 of 22,379 [5.9%] vs. 3,773 of 23,573 [16.0%]; odds ratio, 0.321; 99.9% CI, 0.266 to 0.389); all adjusted comparisons P < 0.001. No increased odds of composite complications were observed in high-risk group I (129 of 13,004 [1.0%] vs. 239 of 14,886 [1.6%]; odds ratio, 0.89, 99.9% CI, 0.49 to 1.59), group II (238 of 22,424 [1.1%] vs. 369 of 22,184 [1.7%]; odds ratio, 0.98; 99.9% CI, 0.58 to 1.67), and group III (187 of 22,379 [0.8%] vs. 290 of 23,573 [1.2%]; odds ratio, 0.93; 99.9% CI, 0.54 to 1.61); all adjusted comparisons P > 0.999. CONCLUSIONS: Although effective in reducing blood transfusions, tranexamic acid is not associated with increased complications, irrespective of patient high-risk status at baseline.

MUST-Plus: A Machine Learning Classifier That Improves Malnutrition Screening in Acute Care Facilities.

OBJECTIVE: Malnutrition among hospital patients, a frequent, yet under-diagnosed problem is associated with adverse impact on patient outcome and health care costs. Development of highly accurate malnutrition screening tools is, therefore, essential for its timely detection, for providing nutritional care, and for addressing the concerns related to the suboptimal predictive value of the conventional screening tools, such as the Malnutrition Universal Screening Tool (MUST). We aimed to develop a machine learning (ML) based classifier (MUST-Plus) for more accurate prediction of malnutrition. METHOD: A retrospective cohort with inpatient data consisting of anthropometric, lab biochemistry, clinical data, and demographics from adult (≥ 18 years) admissions at a large tertiary health care system between January 2017 and July 2018 was used. The registered dietitian (RD) nutritional assessments were used as the gold standard outcome label. The cohort was randomly split (70:30) into training and test sets. A random forest model was trained using 10-fold cross-validation on training set, and its predictive performance on test set was compared to MUST. RESULTS: In all, 13.3% of admissions were associated with malnutrition in the test cohort. MUST-Plus provided 73.07% (95% confidence interval [CI]: 69.61%-76.33%) sensitivity, 76.89% (95% CI: 75.64%-78.11%) specificity, and 83.5% (95% CI: 82.0%-85.0%) area under the receiver operating curve (AUC). Compared to classic MUST, MUST-Plus demonstrated 30% higher sensitivity, 6% higher specificity, and 17% increased AUC. CONCLUSIONS: ML-based MUST-Plus provided superior performance in identifying malnutrition compared to the classic MUST. The tool can be used for improving the operational efficiency of RDs by timely referrals of high-risk patients.

Impact of pre-transplant carbapenem-resistant Enterobacterales colonization and/or infection on solid organ transplant outcomes.

The impact of pre-transplant (SOT) carbapenem-resistant Enterobacterales (CRE) colonization or infection on post-SOT outcomes is unclear. We conducted a multi-center, international, cohort study of SOT recipients, with microbiologically diagnosed CRE colonization and/or infection pre-SOT. Sixty adult SOT recipients were included (liver n = 30, hearts n = 17). Klebsiella pneumoniae (n = 47, 78%) was the most common pre-SOT CRE species. Median time from CRE detection to SOT was 2.32 months (IQR 0.33-10.13). Post-SOT CRE infection occurred in 40% (n = 24/60), at a median of 9 days (IQR 7-17), and most commonly due to K pneumoniae (n = 20/24, 83%). Of those infected, 62% had a surgical site infection, and 46% had bloodstream infection. Patients with post-SOT CRE infection more commonly had a liver transplant (16, 67% vs. 14, 39%; p =.0350) or pre-SOT CRE BSI (11, 46% vs. 7, 19%; p =.03). One-year post-SOT survival was 77%, and those with post-SOT CRE infection had a 50% less chance of survival vs. uninfected (0.86, 95% CI, 0.76-0.97 vs. 0.34, 95% CI 0.08-1.0, p =.0204). Pre-SOT CRE infection or colonization is not an absolute contraindication to SOT and is more common among abdominal SOT recipients, those with pre-SOT CRE BSI, and those with early post-SOT medical and surgical complications.

Examining the role of healthcare access in racial/ethnic disparities in receipt of provider-patient discussions about smoking: A latent class analysis.

Using insurance as a single indicator of healthcare access in examining the association between race/ethnicity and healthcare encounter-based interventions for smoking may not be adequate. In this study, we assessed the role of healthcare access using multifactorial measures in accounting for racial/ethnic disparities in the receipt of provider-patient discussions, defined as either being asked about smoking or advised to quit smoking by providers. We identified adult current smokers from the 2015 National Health Interview Survey. We first conducted a latent class analysis (LCA) to identify the underlying patterns of healthcare access measured by 13 indicators of healthcare access and utilization. We then used a propensity score - based weighting approach to examine racial/ethnic disparities in receiving provider-patient discussions about smoking or quitting in stratified groups by the distinct healthcare access clusters. Out of the 4134 adult current smokers who visited a doctor or a healthcare provider during the past 12 months, 3265 (79.90%) participants were classified as having high healthcare access and 869 (20.10%) participants as having low healthcare access. Compared to non-Hispanic whites, Hispanics had significantly lower odds of being asked about smoking (OR 0.46, 95% CI (0.27-0.77)) and being advised to quit (OR 0.57, 95% CI (0.34-0.97)) in the low access group, but neither association was significant in the high access group. In addition to increasing health insurance coverage, reducing other healthcare access barriers for Hispanics will likely facilitate provider-patient discussion and promote tobacco cessation among Hispanic smokers.

Hospital readmissions in older adults with epilepsy in the US - A population-based study.

OBJECTIVE: Our objective was to determine proportions, causes, and predictors of 30-day readmissions among older adults with epilepsy. Understanding predictors of readmissions may inform future interventions aimed at reducing avoidable hospitalizations in this vulnerable population. METHODS: Individuals 65 years or older with epilepsy were identified using previously validated ICD-9-CM codes in any diagnostic position in the 2014 Nationwide Readmissions Database. Proportions of 30-day readmissions and causes of readmissions in older adults with epilepsy were compared to both older adults without and younger adults (18-64 years old) with epilepsy. We identified predictors of readmission in older adults with epilepsy using logistic regression. RESULTS: There were 92,030 older adults with, 3,166,852 older adults without, and 168,622 younger adults with epilepsy. Proportions of readmissions were higher in older adults with (16.2%) than older adults without (12.5%) and younger adults with epilepsy (15.1%). The main cause of readmission for older adults with and without epilepsy was septicemia, and epilepsy/seizure in younger adults with epilepsy. Predictors of 30-day readmissions in older adults with epilepsy were: non-elective admissions (OR 1.37, 95%CI 1.27-1.48), public insurance (Medicaid vs. private insurance OR 1.19, 95%CI 1.02-1.39; Medicare vs. private insurance OR 1.11, 95%CI 1.00-1.22), lower median household income for patient's zip code ($1-$39,999 vs. $66,000 + OR 1.15, 95% CI 1.08-1.22), hospital location in large metropolitan areas (OR 1.22, 95%CI 1.05-1.42), higher Charlson-Deyo comorbidity index (OR 1.11, 95%CI 1.10-1.02), and male sex (OR 1.04, 95%CI 1.00-1.09). SIGNIFICANCE: Our findings suggest that targeted interventions to reduce the risk of infection may potentially reduce readmission in older people with epilepsy, similarly to those without. Provision of coordinated care and appropriate discharge planning may reduce readmissions particularly in those who are males, are of lower socioeconomic status and with more comorbidities.

The co-occurrence of dementia in those with epilepsy is associated with 30-day readmission - A population-based study.

OBJECTIVE: Dementia and epilepsy often co-occur and are associated with poor health outcomes and increased healthcare utilization. The literature on the association between readmission and co-occurrence of dementia and epilepsy is scant. Our objective was to determine if dementia in patients with epilepsy >40 years old is associated with 30-day hospital readmission, in-hospital mortality, discharge disposition, and length-of-stay. METHODS: This retrospective cohort study used the 2014 Nationwide Readmissions Database, containing data from hospital discharges across the US and readmissions. Epilepsy and dementia were identified using previously validated ICD-9-CM codes. Primary outcome was 30-day readmission, analyzed with univariable and multivariable logistic regressions. Secondary outcomes were discharge disposition, in-hospital mortality, and length-of-stay, analyzed with univariable multinomial logistic, univariable logistic, and univariable ordinary least squared regressions, respectively. The top ten causes of readmission in each group were compared as well. All analyses accounted for survey weights, cluster, and stratum. RESULTS: Patients with epilepsy with dementia (n = 15,588) had longer hospital stays [15% (95%CI 10-20%)], and higher odds of readmission [OR 1.11 (95%CI 1.05-1.17)], transfer to another facility [OR 2.18 (95%CI 1.93-2.46)], and in-hospital mortality [OR 1.50 (95%CI 1.25-1.79)] compared to those without dementia (n = 186,289).The top two causes of readmission were septicemia (dementia: 14.81%; no dementia: 9.45%) and epilepsy/convulsions (dementia: 5.91%; no dementia: 6.25%). Other top 10 causes of readmissions in those with epilepsy and dementia which were not present in those without dementia included delirium (5.21%), urinary tract infections (4.98%), and aspiration pneumonitis (4.29%). SIGNIFICANCE: Dementia in epilepsy is associated with worse outcomes, including higher in-hospital mortality and higher readmissions. Potentially preventable causes of readmission in those with epilepsy and dementia were identified, including septicemia, delirium, urinary tract infection, and aspiration pneumonitis. Future studies are needed to inform interventions aimed at decreasing premature mortality and reducing potentially preventable readmissions in this vulnerable population.

Readmission for cardiac and non-cardiac causes among adults with epilepsy or multiple sclerosis - A nationwide analysis.

PURPOSE: The aim of this study was to determine proportions of 30-day cardiac readmissions in adults with epilepsy compared to multiple sclerosis (MS) or those with neither condition. Predictors and causes of readmissions were also examined. METHODS: We used the 2014 Nationwide Readmissions Database and ICD-9-CM codes to identify people with epilepsy, MS, and without epilepsy or MS. Multinomial logistic regressions were fitted to: (1) examine association between 30-day readmissions and epilepsy, MS or neither, and (2) to describe causes and predictors of 30-day readmission for cardiac readmissions in epilepsy. RESULTS: Out of 6,870,508 adults admitted in 2014, 202,938 (2.98%) had epilepsy and 29,556 (0.45%) had MS. The proportion of 30-day readmission for epilepsy and MS were, respectively: (1) due to cardiac causes (0.17% vs. 0.13%); (2) due to other causes (13.89% vs. 10.61%). The odds of 30-day cardiac readmission in those with epilepsy and MS were lower compared to those without either condition (OR = 0.64, 95% CI 0.57-0.73, p < 0.0001; OR = 0.60, 95% CI 0.43-0.84, p = 0.003). Among those with epilepsy, increasing age (OR = 1.03, 95% CI 1.02-1.04, p < 0.0001) and a Charlson comorbidity index ≥1 (OR = 1.79, 95% CI 1.24-2.60, p = 0.002) were associated with higher odds of 30-day cardiac readmission. A higher proportion of those with epilepsy readmitted within 30-days due to cardiac causes died in hospital (10.09%) compared to those with MS (not reportable due to cell frequency <10) or without epilepsy or MS (5.61%). CONCLUSION: Those admitted to a hospital and living with epilepsy had a higher proportion of cardiac readmissions and death in hospital when compared to those living with MS, and the determinants are likely multifactorial. These findings are important and need to be further explored to identify strategies to prevent readmissions due to any cause and treatments that could reduce mortality.

Differential Perioperative Outcomes in Patients With Obstructive Sleep Apnea, Obesity, or a Combination of Both Undergoing Open Colectomy: A Population-Based Observational Study.

BACKGROUND: An increasing body of evidence demonstrates an association between obstructive sleep apnea (OSA) and adverse perioperative outcomes. However, large-scale data on open colectomies are lacking. Moreover, the interaction of obesity with OSA is unknown. This study examines the impact of OSA, obesity, or a combination of both, on perioperative complications in patients undergoing open colectomy. We hypothesized that while both obesity and OSA individually increase the likelihood for perioperative complications, the overlap of the 2 conditions is associated with the highest risk. METHODS: Patients undergoing open colectomies were identified using the national Premier Healthcare claims-based Database (2006-2016; n = 340,047). Multilevel multivariable models and relative excess risk due to interaction (RERI) analysis quantified the impact of OSA, obesity, or both on length and cost of hospitalization, respiratory and cardiac complications, intensive care unit (ICU) admission, mechanical ventilation, and inhospital mortality. RESULTS: Nine thousand twenty-eight (2.7%) patients had both OSA and obesity diagnoses; 10,137 (3.0%) had OSA without obesity; and 33,692 (9.9%) had obesity without OSA. Although there were overlapping confidence intervals in the binary outcomes, the risk increase was found highest for OSA with obesity, intermediate for obesity without OSA, and lowest for OSA without obesity. The strongest effects were seen for respiratory complications: odds ratio (OR), 2.41 (2.28-2.56), OR, 1.40 (1.31-1.49), and OR, 1.50 (1.45-1.56), for OSA with obesity, OSA without obesity, and obesity without OSA, respectively (all P < .0001). RERI analysis revealed a supraadditive effect of 0.51 (95% confidence interval [CI], 0.34-0.68) for respiratory complications, 0.11 (-0.04 to 0.26) for cardiac complications, 0.30 (0.14-0.45) for ICU utilization, 0.34 (0.21-0.47) for mechanical ventilation utilization, and 0.26 (0.15-0.37) for mortality in patients with both OSA and obesity, compared to the sum of the conditions' individual risks. Inhospital mortality was significantly higher in patients with both OSA and obesity (OR [CI], 1.21 [1.07-1.38]) but not in the other groups. CONCLUSIONS: Both OSA and obesity are individually associated with adverse perioperative outcomes, with a supraadditive effect if both OSA and obesity are present. Interventions, screening, and perioperative precautionary measures should be tailored to the respective risk profile. Moreover, both conditions appear to be underreported compared to the general population, highlighting the need for stringent perioperative screening, documentation, and reporting.

Developing an Institute for Health Care Delivery Science: successes, challenges, and solutions in the first five years.

Medical knowledge is increasing at an exponential rate. At the same time, unexplained variations in practice and patient outcomes and unacceptable rates of medical errors and inefficiencies in health care delivery have emerged. Our Institute for Health Care Delivery Science (I-HDS) began in 2014 as a novel platform to conduct multidisciplinary healthcare delivery research. We followed ten strategies to develop a successful institute with excellence in methodology and strong understanding of the value of team science. Our work was organized around five hubs: 1) Quality/Process Improvement and Systematic Review, 2) Comparative Effectiveness Research, Pragmatic Clinical Trials, and Predictive Analytics, 3) Health Economics and Decision Modeling, 4) Qualitative, Survey, and Mixed Methods, and 5) Training and Mentoring. In the first 5 years of the I-HDS, we have identified opportunities for change in clinical practice through research using our health system's electronic health record (EHR) data, and designed programs to educate clinicians in the value of research to improve patient care and recognize efficiencies in processes. Testing the value of several model interventions has guided prioritization of evidence-based quality improvements. Some of the changes in practice have already been embedded in the EHR workflow successfully. Development and sustainability of the I-HDS has been fostered by a mix of internal and external funding, including philanthropic foundations. Challenges remain due to the highly competitive funding environment and changes needed to adapt the EHR to healthcare delivery research. Further stakeholder engagement and culture change working with hospital leadership and I-HDS core and affiliate members continues.

Administration of tranexamic acid during total shoulder arthroplasty is not associated with increased risk of complications in patients with a history of thrombotic events.

INTRODUCTION: Tranexamic acid (TXA) has been shown to reduce blood loss and transfusion risk in various orthopedic surgeries including shoulder arthroplasty. However, concerns still exist regarding its use in patients with a history of thrombotic events. Using national claims data, we aimed to study the safety of TXA administration in shoulder arthroplasty patients with a history of thrombotic events. METHODS: We used retrospective national claims data (Premier Healthcare) on 71,174 patients who underwent a total or reverse shoulder arthroplasty between 2010 and 2016. TXA use was evaluated specifically within a subgroup of patients with a history of thrombotic events such as myocardial infarction, deep venous thrombosis, pulmonary embolism, transient ischemic attack, or ischemic stroke. Studied outcomes were blood transfusion need, complications (including acute renal failure, new onset myocardial infarction, deep venous thrombosis, pulmonary embolism, transient ischemic attack, or ischemic stroke), and cost and length of hospitalization. Mixed-effects models measured the association between TXA use and outcomes, separately in patients with and without a history of thrombotic events. Odds ratios (OR) or percent change for continuous outcomes with 95% confidence intervals (CI) were reported. RESULTS: Overall, TXA was used in 13.7% (n = 9735) of patients, whereas 10.5% (n = 7475) of patients had a history of a thrombotic event. After adjustment for relevant covariates, TXA use (compared with no TXA use) in patients without a history of thrombotic events was associated with decreased odds of blood transfusions (OR, 0.48; CI, 0.24-0.98; P = .0444), whereas no increased odds for complications were observed (OR, 0.83; CI, 0.40-1.76; P = .6354). Similar results were observed in patients with a history of thrombotic events. Moreover, in this subgroup, TXA use was associated with a slight reduction in hospitalization cost (-8.9% CI: -13.1%; -4.6%; P < .0001; group median $18,830). CONCLUSIONS: Among shoulder arthroplasty patients, TXA use was not associated with increased complication odds, independent of a history of thrombotic events. These findings are in support of wider TXA use.

Peripheral nerve block use in ankle arthroplasty and ankle arthrodesis: utilization patterns and impact on outcomes.

PURPOSE: Ankle arthrodesis and total ankle arthroplasty (TAA) are often associated with significant postoperative pain. While this may be mitigated by the use of peripheral nerve blocks (PNB), large-scale data are lacking. Using national data, we aimed to evaluate PNB utilization pattern and its impact on outcomes. METHODS: This retrospective cohort study utilized data from the nationwide database (2006-2016) on TAA (n = 5,290) and ankle arthrodesis (n = 14,709) procedures. PNB use was defined from billing; outcomes included opioid utilization, length and cost of stay, discharge to a skilled nurse facility, and opioid-related complications. Mixed-effects models estimated the association between PNB use and outcomes, separate by procedure type and inpatient/outpatient setting. We report odds ratios and 95% confidence intervals (CI). RESULTS: Overall, PNB was utilized in 8.7% of TAA and 9.9% of ankle arthrodesis procedures, with increased utilization from 2006 to 2016 of 2.6% to 11.3% and 5.2% to 12.0%, respectively. After adjustment for relevant covariates, PNB use was significantly associated with decreased total opioid utilization specifically in the inpatient setting in TAA ( - 16.9% CI  - 23.9%;  - 9.1%) and ankle arthrodesis procedures ( - 18.9% CI  - 24.4;  - 13.0%), this was particularly driven by a decrease in opioid utilization on the day of surgery. No clinically relevant effects were observed for other outcomes. CONCLUSION: PNB utilization is associated with substantial reductions in opioid utilization, particularly in the inpatient setting. Our study is in support of a wider use of this analgesic technique, which may translate into more benefits in terms of clinical outcomes and resource utilization. LEVEL OF EVIDENCE: III.