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BackgroundEarly hospital readmission for patients with cirrhosis continues to challenge the healthcare system. Risk stratification may help tailor resources, but existing models were designed using small, single-institution cohorts or had modest performance.AimsWe leveraged a large clinical database from the Department of Veterans Affairs (VA) to design a readmission risk model for patients hospitalized with cirrhosis. Additionally, we analyzed potentially modifiable or unexplored readmission risk factors.MethodsA national VA retrospective cohort of patients with a history of cirrhosis hospitalized for any reason from January 1, 2006, to November 30, 2013, was developed from 123 centers. Using 174 candidate variables within demographics, laboratory results, vital signs, medications, diagnoses and procedures, and healthcare utilization, we built a 47-variable penalized logistic regression model with the outcome of all-cause 30-day readmission. We excluded patients who left against medical advice, transferred to a non-VA facility, or if the hospital length of stay was greater than 30days. We evaluated calibration and discrimination across variable volume and compared the performance to recalibrated preexisting risk models for readmission.ResultsWe analyzed 67,749 patients and 179,298 index hospitalizations. The 30-day readmission rate was 23%. Ascites was the most common cirrhosis-related cause of index hospitalization and readmission. The AUC of the model was 0.670 compared to existing models (0.649, 0.566, 0.577). The Brier score of 0.165 showed good calibration.ConclusionOur model achieved better discrimination and calibration compared to existing models, even after local recalibration. Assessment of calibration by variable parsimony revealed performance improvements for increasing variable inclusion well beyond those detectable for discrimination.

Advanced regression and machine learning models can provide personalized risk predictions to support clinical decision-making. We aimed to understand whether modeling methods impact the tendency of calibration to deteriorate as patient populations shift over time, with the goal of informing model updating practices. We developed models for 30-day hospital mortality using seven common regression and machine learning methods. Models were developed on 2006 admissions to Department of Veterans Affairs hospitals and validated on admissions in 2007-2013. All models maintained discrimination. Calibration was stable for the neural network model and declined for all other models. The L-2 penalized logistic regression and random forest models experienced smaller magnitudes of calibration drift than the other regression models. Calibration drift was linked with a changing case mix rather than shifts in predictoroutcome associations or outcome rate. Model updating protocols will need to be tailored to variations in calibration drift across methods.

Few studies have explored adoption of patient portals for pediatric patients outside primary care or disease-specific applications. We examined use of patient-provider messaging in a patient portal across pediatric specialties during the three years after implementation of pediatric portal accounts at Vanderbilt University Medical Center. We determined the number of patient-initiated message threads and clinic visits for pediatric specialties and percentage of these outpatient interactions (i.e., message threads + clinic visits) done through messaging. Generalized estimating equations measured the likelihood of message-based interaction. During the study period, pediatric families initiated 33,503 messages and participated in 318,386 clinic visits. The number of messages sent (and messaging percentage of outpatient interaction) increased each year from 2,860 (2.7%) to 18,772 (17%). Primary care received 4,368 messages (3.4% of outpatient interactions); pediatric subspecialties, 29,135 (13.0%). Rapid growth in messaging volume over time was seen in primary care and most pediatric specialties (OR>1.0; p

Patient portal adoption has rapidly increased, and portal usage has been associated with patients' sociodemographics, health literacy, and education. Research on patient portals has primarily focused on the outpatient setting. We explored whether health literacy and education were associated with portal usage in an inpatient population. Among 60,159 admissions in 2012-2013, 23.3% of patients reported limited health literacy; 50.4% reported some post-secondary education; 34.4% were registered for the portal; and 23.4% of registered patients used the portal during hospitalization. Probability of registration and inpatient portal use increased with educational attainment. Health literacy was associated with registration but not inpatient use. Among admissions with inpatient use, educational attainment was associated with viewing health record data, and health literacy was associated use of appointment and health education tools. The inpatient setting may provide an opportunity to overcome barriers to patient portal adoption and reduce disparities in use of health information technologies.