Predicting Hospital Mortality, Length of Stay, and Transfer to Long-Term Care for Injured Patients

التفاصيل البيبلوغرافية
العنوان: Predicting Hospital Mortality, Length of Stay, and Transfer to Long-Term Care for Injured Patients
المؤلفون: Louise Ryan, David E. Clark, F.L. Lucas
المصدر: Journal of Trauma: Injury, Infection & Critical Care. 62:592-600
بيانات النشر: Ovid Technologies (Wolters Kluwer Health), 2007.
سنة النشر: 2007
مصطلحات موضوعية: Male, Patient Transfer, Pediatrics, medicine.medical_specialty, Poison control, Critical Care and Intensive Care Medicine, Occupational safety and health, Injury Severity Score, Outcome Assessment, Health Care, Epidemiology, Injury prevention, medicine, Humans, Hospital Mortality, Patient transfer, Aged, Aged, 80 and over, Coma, Models, Statistical, business.industry, Length of Stay, Middle Aged, Long-Term Care, Patient Discharge, Long-term care, Wounds and Injuries, Female, Surgery, medicine.symptom, business
الوصف: BACKGROUND: Using hospital length of stay (LOS) to measure trauma care efficiency is complicated by short LOS resulting from early mortality or transfer to long-term care (LTC). METHODS: Records from the 1999 to 2003 National Trauma Data Bank were used to create a multistate model divided into four time periods, each with constant rates of death, discharge home, and LTC transfer. Estimated hospital mortality and LOS for patient subgroups were calculated from this model, and time-varying covariate effects were estimated. RESULTS: A total of 369,829 cases with adequate data were available. Early mortality was increased most by hypotension or coma, and also by anatomic injury severity or penetrating mechanism, but these effects diminished with time; age remained a strong predictor of mortality at any time but sex was insignificant. Rates of discharge home decreased with time, whereas rates of LTC transfer peaked at 6 to 11 days. Increased age strongly predicted transfer to LTC, whereas penetrating or burn mechanisms made it less likely. Predicted and observed outcomes were similar for multiple subgroups, and about 17% of individual variation in LOS was explained by the model. CONCLUSIONS: Multistate models of patient status can accurately predict mortality and resource use after injury, and describe time-varying effects of other factors. Language: en
تدمد: 0022-5282
DOI: 10.1097/01.ta.0000257239.15436.29
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a0ca6daf5c9fbf4e8bb9752a5091307c
https://doi.org/10.1097/01.ta.0000257239.15436.29
رقم الانضمام: edsair.doi.dedup.....a0ca6daf5c9fbf4e8bb9752a5091307c
قاعدة البيانات: OpenAIRE
الوصف
تدمد:00225282
DOI:10.1097/01.ta.0000257239.15436.29