We project a marked increase in demand for LT for NASH given population obesity trends. Continued public health efforts to curb obesity prevalence are needed to reduce the projected future burden of NASH. (Hepatology 2017).

We have characterized several important predictors of deceased donor kidney discard. Better understanding of factors that lead to increased deceased donor kidney discard can allow for targeted interventions to reduce discard.

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BACKGROUND: Readmissions after radical cystectomy are common, burdensome, and poorly understood. For these reasons, the authors conducted a population-based study that focused on the causes of and time to readmission after radical cystectomy. METHODS: Using Surveillance, Epidemiology, and End Results-Medicare data, at total of 1782 patients who underwent radical cystectomy from 2003 through 2009 were identified. A piecewise exponential model was used to examine reasons for readmission as well as patient and clinical factors associated with the timing of readmission. RESULTS: One in 4 patients (25.5%) were readmitted within 30 days of discharge after radical cystectomy. Compared with patients without readmission, those readmitted were similar with regard to age, sex, and race. Readmitted patients had more complications (33.8% vs 13.9%; P <.001) and were more likely to have been discharged to skilled nursing facilities from their index admission (P <.001). The average time to readmission and subsequent length of stay were 11.5 days and 6.7 days, respectively. The majority of readmissions (67.4%) occurred within 2 weeks of discharge, 66.8% had emergency department charges, and 25.9% involved intensive care unit use. Although the spectrum of reasons for readmission varied over the 4 weeks after discharge, the most common included infection (51.4%), failure to thrive (36.3%), and urinary (33.2%) and gastrointestinal (23.1%) etiologies; 95.8% of patients had 1 of these diagnosis groups present at the time of readmission. CONCLUSIONS: Readmissions after radical cystectomy are common and time-dependent. Interventions to prevent and reduce the readmission burden after cystectomy likely need to focus on the first 2 weeks after discharge, take into consideration the spectrum of reasons for readmission, and target high-risk individuals.

Purpose To determine whether dynamic and personalized schedules of visual field (VF) testing and intraocular pressure (IOP) measurements result in an improvement in disease progression detection compared with fixed interval schedules for performing these tests when evaluating patients with open-angle glaucoma (OAG). Design Secondary analyses using longitudinal data from two randomized controlled trials. Participants 571 participants from Advanced Glaucoma Intervention Study (AGIS) and Collaborative Initial Glaucoma Treatment Study (CIGTS). Methods Perimetric and tonometric data were obtained for AGIS and CIGTS trial participants and used to parameterize and validate a Kalman filter model. The Kalman filter updates knowledge about each participant’s disease dynamics as additional VF tests and IOP measurements are obtained. After incorporating the most recent VF and IOP measurements, the model forecasts each participant’s disease dynamics into the future and characterizes the forecasting error. To determine personalized schedules for future VF tests and IOP measurements, we developed an algorithm by combining the Kalman filter for state estimation with the predictive power of logistic regression to identify OAG progression. The algorithm was compared against 1, 1.5, and 2 year fixed interval schedules of obtaining VF and IOP measurements. Main Outcome Measures Length of diagnostic delay in detecting OAG progression, efficiency of detecting progression, number of VF and IOP measurements needed to assess for progression. Results Participants were followed in the AGIS and CIGTS trials for a mean (standard deviation) of 6.5 (2.8) years. Our forecasting model achieved a 29% increased efficiency in identifying OAG progression (p<0.0001) and detected OAG progression 57% sooner (reduced diagnostic delay) (p= 0.02) than following a fixed yearly monitoring schedule, without increasing the number of VF tests and IOP measurements required. The model performed well on patients with mild and advanced disease. The model performed significantly more testing on patients who exhibited OAG progression than non-progressing patients (1.3 vs. 1.0 tests per year; p<0.0001). Conclusion Use of dynamic and personalized testing schedules can enhance the efficiency of OAG progression detection and reduce diagnostic delay as compared with yearly fixed monitoring intervals. If further validation studies confirm these findings, such algorithms may be able to greatly enhance OAG management.