Trend analysis and outcome prediction in mechanically ventilated XXXX : a nationwide population-based study in Taiwan.

@entity1 @entity2489
OBJECTIVE: To investigate the relationship between changes in @entity1 attributes and hospital attributes over time and to explore predictors of medical utilization and mortality rates in @entity2489 ( @entity2489 ) @entity1 in Taiwan. BACKGROUND: Providing effective medical care for @entity2489 @entity1 is challenging and requires good planning and effective clinical decision making policies. Most studies of @entity2489 , however, have only analyzed a single regional ventilator weaning center or respiratory care unit, high-quality population-based studies of @entity2489 trends and outcomes are scarce. METHODS: This population-based cohort study retrospectively analyzed 213,945 @entity2489 @entity1 treated during 2004-2009. RESULTS: During the study period, the percentages of @entity2489 @entity1 with the following characteristics significantly increased: age     65 years, treatment at a medical center, and treatment by a high-volume physician. In contrast, the percentages of @entity2489 @entity1 treated at local hospitals and by low-volume physicians significantly decreased (P<0.001). Age, gender, Deyo-Charlson co-morbidity index, teaching hospital, hospital level, hospital volume, and physician volume were significantly associated with @entity2489 outcome (P<0.001). Over the 6-year period analyzed in this study, the estimated mean hospital treatment cost increased 48.8% whereas mean length of stay decreased 13.9%. The estimated mean overall survival time for @entity2489 @entity1 was 16.4 months (SD 0.4 months), and the overall in-hospital 1-, 3-, and 5-year survival rates were 61.0%, 36.7%, 17.3%, and 9.6%, respectively. CONCLUSIONS: These population-based data revealed increases in the percentages of @entity2489 @entity1 treated at medical centers and by high-volume physicians, especially in younger @entity1 . Notably, although LOS for @entity2489 @entity1 decreased, hospital treatment costs increased. Healthcare providers and @entity1 should recognize that attributes of both the @entity1 and the hospital may affect outcomes.
