OBJECTIVES: In a population-based study, we examined time trends in chronic liver disease (CLD)-related hospitalizations in a large and diverse metroplex. METHODS: We examined all CLD-related inpatient encounters (2000–2015) in Dallas–Fort Worth (DFW) using data from the DFW council collaborative that captures claims data from 97% of all hospitalizations in DFW (10.7 million regional patients). RESULTS: There were 83,539 CLD-related hospitalizations in 48,580 unique patients across 84 hospitals. The age and gender standardized annual rate of CLD-related hospitalization increased from 48.9 per 100,000 in 2000 to 125.7 per 100,000 in 2014. Mean age at hospitalization increased from 54.0 (14.1) to 58.5 (13.5) years; the proportion of CLD patients above 65 years increased from 24.2% to 33.1%. HCV-related hospitalizations plateaued, whereas an increase was seen in hospitalizations related to alcohol (9.1 to 22.7 per 100,000) or fatty liver (1.4 per 100,000 to 19.5 per 100,000). The prevalence of medical comorbidities increased for CLD patients: coronary artery disease (4.8% to 14.3%), obesity (2.8% to 14.6%), chronic kidney disease (2.8% to 18.2%), and diabetes (18.0% to 33.2%). Overall hospitalizations with traditional complications of portal hypertension (ascites, varices, and peritonitis) remained stable over time. However, hospitalization with complications related to infection increased from 54.7% to 66.4%, and renal failure increased by sevenfold (2.7% to 19.5%). CONCLUSIONS: CLD-related hospitalizations have increased twofold over the last decade. Hospitalized CLD patients are older and sicker with multiple chronic conditions. Traditional complications of portal hypertension have been superseded by infection and renal failure, warranting a need to redefine what it means to have decompensated CLD.

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ObjectivesOpioid-induced respiratory depression (OIRD) and oversedation are rare but potentially devastating adverse events in hospitalised patients. We investigated which features predict an individual patient’s risk of OIRD or oversedation; and developed a risk stratification tool that can be used to aid point-of-care clinical decision-making.DesignRetrospective observational study.SettingTwelve acute care hospitals in a large not-for-profit integrated delivery system.ParticipantsAll inpatients ≥18 years admitted between 1 July 2016 and 30 June 2018 who received an opioid during their stay (163 190 unique hospitalisations).Main outcome measuresThe primary outcome was occurrence of sedation or respiratory depression severe enough that emergent reversal with naloxone was required, as determined from medical record review; if naloxone reversal was unsuccessful or if there was no evidence of hypoxic encephalopathy or death due to oversedation, it was not considered an oversedation event.ResultsAge, sex, body mass index, chronic obstructive pulmonary disease, concurrent sedating medication, renal insufficiency, liver insufficiency, opioid naïvety, sleep apnoea and surgery were significantly associated with risk of oversedation. The strongest predictor was concurrent administration of another sedating medication (adjusted HR, 95% CI=3.88, 2.48 to 6.06); the most common such medications were benzodiazepines (29%), antidepressants (22%) and gamma-aminobutyric acid analogue (14.7%). The c-statistic for the final model was 0.755. The 24-point Oversedation Risk Criteria (ORC) score developed from the model stratifies patients as high (>20%, ≥21 points), moderate (11%–20%, 10–20 points) and low risk (≤10%, <10 points).ConclusionsThe ORC risk score identifies patients at high risk for OIRD or oversedation from routinely collected data, enabling targeted monitoring for early detection and intervention. It can also be applied to preventive strategies—for example, clinical decision support offered when concurrent prescriptions for opioids and other sedating medications are entered that shows how the chosen combination impacts the patient’s risk.

INTRODUCTION:The burden of liver disease is substantial and increasing; the impact of comorbid chronic diseases on the clinical course of patients with compensated and decompensated cirrhosis is not well-defined. The aim of this study was to examine the individual and additive impact of comorbid chronic diseases on mortality in patients with cirrhosis.METHODS:In this population-based study, we used Cox proportional hazards modeling with time-dependent covariates to assess the impact of comorbid chronic diseases (diabetes mellitus, chronic kidney disease, and cardiovascular disease [CVD]) on mortality in patients with cirrhosis in a large, diverse Metroplex.RESULTS:There were 35,361 patients with cirrhosis (mean age 59.5 years, 41.8% females, 29.7% non-White, and 17.5% Hispanic ethnicity). Overall, the presence of chronic comorbidities was 1 disease (28.9%), 2 diseases (17.5%), and 3 diseases (12.6%) with a majority having CVD (45%). Adjusted risk of mortality progressively increased with an increase in chronic diseases from 1 (hazard ratio [HR] 2.5, 95% confidence interval [CI] 2.23–2.8) to 2 (HR 3.27.95% CI 2.9–3.69) to 3 (HR 4.52, 95% CI 3.99–5.12) diseases. Survival of patients with compensated cirrhosis and 3 chronic diseases was similar to subsets of decompensated cirrhosis (67.7% as compared with decompensated cirrhosis with 1–3 conditions, 61.9%–63.9%).DISCUSSION:In patients with cirrhosis, a focus on comorbid chronic disease(s) as potential management targets may help avoid premature mortality, regardless of etiology. Multidisciplinary care early in the clinical course of cirrhosis is needed in addition to the current focus on management of complications of portal hypertension.

Context:Collaborative networks support the goals of a learning health system by sharing, aggregating, and analyzing data to facilitate identification of best practices care across delivery organizations. This case study describes the infrastructure and process developed by an integrated health delivery system to successfully prepare and submit a complex data set to a large national collaborative network.Case Description:We submitted four years of data for a diverse population of patients in specific clinical areas: diabetes, chronic heart failure, sepsis, and hip, knee, and spine. The most recent submission included 19 tables, more than 376,000 unique patients, and almost 5 million patient encounters. Data was extracted from multiple clinical and administrative systems.Lessons Learned:We found that a structured process with documentation was key to maintaining communication, timelines, and quality in a large-scale data submission to a national collaborative network. The three key components of this process were the experienced project team, documentation, and communication. We used a formal QA and feedback process to track and review data. Overall, the data submission was resource intensive and required an incremental approach to data quality.Conclusion:Participation in collaborative networks can be time and resource intense, however it can serve as a catalyst to increase the technical data available to the learning health system.