CONTEXT: Lower respiratory tract infection (LRI) is a leading cause of mortality and hospitalization in nursing home residents. Treatment decisions may be aided by a clinical prediction rule that identifies residents at low and high risk of mortality. OBJECTIVE: To identify patient characteristics predictive of 30-day mortality in nursing home residents with an LRI. DESIGN, SETTING, AND PATIENTS: Prospective cohort study of 1406 episodes of LRI in 1044 residents of 36 nursing homes in central Missouri and the St Louis, Mo, area between August 15, 1995, and September 30, 1998. MAIN OUTCOME MEASURE: Thirty-day all-cause mortality. RESULTS: Thirty-day mortality was 14.7% (n = 207). In a logistic analysis, using generalized estimating equations to adjust for clustering, we developed an 8-variable model to predict 30-day mortality, including serum urea nitrogen, white blood cell count, body mass index, pulse rate, activities of daily living status, absolute lymphocyte count of less than 800/microL (0.8 x 10(9)/L), male sex, and deterioration in mood over 90 days. In validation testing, the model exhibited reasonable discrimination (c =.76) and calibration (nonsignificant Hosmer-Lemeshow goodness-of-fit statistic, P =.54). A point score based on this model's variables fit to the entire data set closely matched observed mortality. Fifty-two percent of residents had low (score of 0-4) or relatively low (score of 5-6) predicted 30-day mortality, with 2.2% and 6.2% actual mortality, respectively. CONCLUSIONS: Our model distinguishes nursing home residents at relatively low risk for mortality due to LRI. If independently validated, our findings could help physicians identify nursing home residents in need of different therapeutic approaches for LRI.
CONTEXT: Lower respiratory tract infection (LRI) is a leading cause of mortality and hospitalization in nursing home residents. Treatment decisions may be aided by a clinical prediction rule that identifies residents at low and high risk of mortality. OBJECTIVE: To identify patient characteristics predictive of 30-day mortality in nursing home residents with an LRI. DESIGN, SETTING, AND PATIENTS: Prospective cohort study of 1406 episodes of LRI in 1044 residents of 36 nursing homes in central Missouri and the St Louis, Mo, area between August 15, 1995, and September 30, 1998. MAIN OUTCOME MEASURE: Thirty-day all-cause mortality. RESULTS: Thirty-day mortality was 14.7% (n = 207). In a logistic analysis, using generalized estimating equations to adjust for clustering, we developed an 8-variable model to predict 30-day mortality, including serum ureanitrogen, white blood cell count, body mass index, pulse rate, activities of daily living status, absolute lymphocyte count of less than 800/microL (0.8 x 10(9)/L), male sex, and deterioration in mood over 90 days. In validation testing, the model exhibited reasonable discrimination (c =.76) and calibration (nonsignificant Hosmer-Lemeshow goodness-of-fit statistic, P =.54). A point score based on this model's variables fit to the entire data set closely matched observed mortality. Fifty-two percent of residents had low (score of 0-4) or relatively low (score of 5-6) predicted 30-day mortality, with 2.2% and 6.2% actual mortality, respectively. CONCLUSIONS: Our model distinguishes nursing home residents at relatively low risk for mortality due to LRI. If independently validated, our findings could help physicians identify nursing home residents in need of different therapeutic approaches for LRI.
Authors: Robin L Kruse; David R Mehr; Jenny T van der Steen; Marcel E Ooms; Richard W Madsen; Ashley K Sherman; Ralph B D'Agostino; Gerrit van der Wal; Miel W Ribbe Journal: Ann Fam Med Date: 2005 Sep-Oct Impact factor: 5.166
Authors: Robin L Kruse; Gregory F Petroski; David R Mehr; Jane Banaszak-Holl; Orna Intrator Journal: J Am Geriatr Soc Date: 2013-10-28 Impact factor: 5.562
Authors: Katie S Murray; Megan Prunty; Alex Henderson; Tyler Haden; Naveen Pokala; Bin Ge; Mark Wakefield; Gregory F Petroski; David R Mehr; Robin L Kruse Journal: Urology Date: 2018-08-01 Impact factor: 2.649