BACKGROUND: the mortality is high in acutely ill oldest old patients. Understanding the prognostic factors which influence mortality will help clinicians make appropriate management decisions. METHODS: we analysed prospective mortality audit data (November 2008 to January 2009) to identify variables associated with in-patient mortality in oldest old. We selected those with P < 0.10 from univariate analysis and determined at which cut-point they served as the strongest predictor of mortality. Using these cut-off points, we constructed multivariate logistic regression models. A 5-point score was derived from cut-off points which were significantly associated with mortality tested in a smaller independent re-audit sample conducted in October 2011. RESULTS: a total of 405 patients (mean 93.5 ± 2.7 years) were included in the study. The mean length of stay was 18.5 ± 42.4 days and 13.8% died as in-patients. Variables (cut-off values) found to be significantly associated with in-patient mortality were admission sodium (>145 mmol/l), urea (≥14 mmol/l), respiratory rate (>20/min) and shock index (>1.0): creating a 5-point score (NaURSE: NaURS in the Elderly). The crude mortality rates were 9.5, 19.9, 34.4, 66.7, and 100% for scores 0, 1, 2, 3 and 4, respectively. Using the cut-off point of ≥2, the NaURSE score has a specificity of 87% (83.1-90.3) and sensitivity of 39% (28.5-50.0), with an AUC value of 0.69 (0.63-0.76). An external independent validation study (n = 121) showed similar results. CONCLUSIONS: the NaURSE score may be particularly useful in identifying oldest old who are likely to die in that admission to guide appropriate care.
BACKGROUND: the mortality is high in acutely ill oldest old patients. Understanding the prognostic factors which influence mortality will help clinicians make appropriate management decisions. METHODS: we analysed prospective mortality audit data (November 2008 to January 2009) to identify variables associated with in-patient mortality in oldest old. We selected those with P < 0.10 from univariate analysis and determined at which cut-point they served as the strongest predictor of mortality. Using these cut-off points, we constructed multivariate logistic regression models. A 5-point score was derived from cut-off points which were significantly associated with mortality tested in a smaller independent re-audit sample conducted in October 2011. RESULTS: a total of 405 patients (mean 93.5 ± 2.7 years) were included in the study. The mean length of stay was 18.5 ± 42.4 days and 13.8% died as in-patients. Variables (cut-off values) found to be significantly associated with in-patient mortality were admission sodium (>145 mmol/l), urea (≥14 mmol/l), respiratory rate (>20/min) and shock index (>1.0): creating a 5-point score (NaURSE: NaURS in the Elderly). The crude mortality rates were 9.5, 19.9, 34.4, 66.7, and 100% for scores 0, 1, 2, 3 and 4, respectively. Using the cut-off point of ≥2, the NaURSE score has a specificity of 87% (83.1-90.3) and sensitivity of 39% (28.5-50.0), with an AUC value of 0.69 (0.63-0.76). An external independent validation study (n = 121) showed similar results. CONCLUSIONS: the NaURSE score may be particularly useful in identifying oldest old who are likely to die in that admission to guide appropriate care.
Authors: Phyo Kyaw Myint; Shubin Sheng; Ying Xian; Roland A Matsouaka; Mathew J Reeves; Jeffrey L Saver; Deepak L Bhatt; Gregg C Fonarow; Lee H Schwamm; Eric E Smith Journal: J Am Heart Assoc Date: 2018-09-18 Impact factor: 5.501