Background Existing publicly-reported readmission measures are condition-specific, representing < 20% of adult hospitalizations. An all-condition measure may better measure quality and promote innovation. Objective To develop an all-condition, hospital-wide readmission measure. Design Measure development Setting 4,821 US hospitals. Patients Medicare Fee for Service (FFS) beneficiaries ≥ 65 years. Measurements Hospital-level, risk-standardized unplanned readmissions within 30 days of discharge. The measure uses Medicare FFS claims and is a composite of five specialty-based risk-standardized rates for medicine, surgery/gynecology, cardiorespiratory, cardiovascular and neurology cohorts. We randomly split the 2007–2008 admissions for development and validation. Models were adjusted for age, principal diagnosis and comorbidity. We examined calibration in Medicare and all-payer data, and compared hospital rankings in the development and validation samples. Results The development dataset contained 8,018,949 admissions associated with 1,276,165 unplanned readmissions (15.9%). The median hospital risk-standardized unplanned readmission rate was 15.8 (range 11.6–21.9). The five specialty cohort models accurately predicted readmission risk in both Medicare and all-payer datasets for average risk patients but slightly overestimated readmission risk at the extremes. Overall hospital risk-standardized readmission rates did not differ statistically in the split samples (p=0.7 for difference in rank) and 76% of hospitals’ validation set rankings were within two deciles of the development rank (24% >2 deciles). Of hospitals ranking in the top or bottom deciles, 90% remained within two deciles (10% >2 deciles), and 82% remained within one decile (18% > 1 decile). Limitations Risk-adjustment was limited to that available in claims data. Conclusions We developed a claims-based hospital-wide unplanned readmission measure for profiling hospitals that produced reasonably consistent results in different datasets and was similarly calibrated in both Medicare and all-payer data. Primary funding source Centers for Medicare & Medicaid Services

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Background It is desirable not to include planned readmissions in readmission measures because they represent deliberate, scheduled care. Objectives To develop an algorithm to identify planned readmissions, describe its performance characteristics and identify improvements. Design Consensus-driven algorithm development and chart review validation study at 7 acute care hospitals in 2 health systems. Patients For development, all discharges qualifying for the publicly-reported hospital-wide readmission measure. For validation, all qualifying same-hospital readmissions that were characterized by the algorithm as planned, and a random sampling of same-hospital readmissions that were characterized as unplanned. Measurements We calculated weighted sensitivity and specificity, and positive and negative predictive values of the algorithm (version 2.1), compared to gold standard chart review. Results In consultation with 27 experts, we developed an algorithm that characterizes 7.8% of readmissions as planned. For validation we reviewed 634 readmissions. The weighted sensitivity of the algorithm was 45.1% overall; 50.9% in large teaching centers and 40.2% in smaller community hospitals. The weighted specificity was 95.9%, positive predictive value was 51.6% and negative predictive value was 94.7%. We identified 4 minor changes to improve algorithm performance. The revised algorithm had a weighted sensitivity 49.8% (57.1% at large hospitals), weighted specificity 96.5%, positive predictive value 58.7%, and negative predictive value 94.5%. Positive predictive value was poor for the two most common potentially planned procedures: diagnostic cardiac catheterization (25%) and procedures involving cardiac devices (33%). Conclusions An administrative claims-based algorithm to identify planned readmissions is feasible and can facilitate public reporting of primarily unplanned readmissions.

“Taste-like” tuft cells in the intestine trigger type 2 immunity in response to worm infection. The secretion of interleukin-13 (IL-13) from type 2 innate lymphoid cells (ILC2) represents a key step in the tuft cell–ILC2 cell–intestinal epithelial cell circuit that drives the clearance of worms from the gut via type 2 immune responses. Hallmark features of type 2 responses include tissue remodeling, such as tuft and goblet cell expansion, and villus atrophy, yet it remains unclear if additional molecular changes in the gut epithelium facilitate the clearance of worms from the gut. Using gut organoids, we demonstrated that IL-4 and IL-13, two type 2 cytokines with similar functions, not only induced the classical type 2 responses (e.g., tuft cell expansion) but also drastically up-regulated the expression of gasdermin C genes (Gsdmcs). Using an in vivo worm-induced type 2 immunity model, we confirmed the up-regulation of Gsdmcs in Nippostrongylus brasiliensis–infected wild-type C57BL/6 mice. Consistent with gasdermin family members being principal effectors of pyroptosis, overexpression of Gsdmc2 in human embryonic kidney 293 (HEK293) cells triggered pyroptosis and lytic cell death. Moreover, in intestinal organoids treated with IL-4 or IL-13, or in wild-type mice infected with N. brasiliensis, lytic cell death increased, which may account for villus atrophy observed in worm-infected mice. Thus, we propose that the up-regulated Gsdmc family may be major effectors for type 2 responses in the gut and that Gsdmc-mediated pyroptosis may provide a conduit for the release of antiparasitic factors from enterocytes to facilitate the clearance of worms.

Purpose Tea is one of the most commonly consumed beverages worldwide. To date, observational data from prospective cohort studies investigating the relationship between green and black tea intake and prostate cancer risk are sparse and equivocal. In a population-based, prospective cohort study of Chinese men in Singapore, we investigated the relationship between green and black tea intake and prostate cancer risk. Methods Tea consumption data for 27,293 men were collected at baseline (between 1993 and 1998) using a validated food frequency questionnaire. After an average of 11.2 years of follow-up, 298 men had developed prostate cancer. Proportional hazards regression methods were used to assess the associations between tea intake and prostate cancer risk. Results There was no association between daily green tea intake and prostate cancer risk, compared with no green tea intake [hazard ratio (HR) = 1.08; 95 % confidence interval (CI) 0.79, 1.47]. For black tea, a statistically significant positive association and trend were observed for daily intake compared with no black tea intake (HR = 1.41, 95 % CI 1.03, 1.92; p for trend <0.01) Conclusions Few prospective data are available from populations that have both a high level and wide range of black and green tea intake; this study represents a unique opportunity to evaluate their individual effects on prostate cancer risk. Our findings support the notion that green tea intake does not protect against prostate cancer and that black tea intake may increase prostate cancer risk.