From the Medical Physics and Bioengineering Department, Division of Internal Medicine and Office of the CEO, St. James's Hospital, Dublin 8, Ireland
From the Medical Physics and Bioengineering Department, Division of Internal Medicine and Office of the CEO, St. James's Hospital, Dublin 8, Ireland.
QJM. 2015 May;108(5):387-96. doi: 10.1093/qjmed/hcu217. Epub 2014 Oct 21.
Chronic disabling disease is present in nearly 90% of emergency medical admissions. We have examined its impact on outcomes and costs in one institution, using a database of episodes collected prospectively over 12 years.
All emergency admissions (66,933 episodes; 36,271 patients) to St James' Hospital over a 12-year period (2002-13) were evaluated in relation to 30-day in-hospital mortality, length of stay (LOS) and hospital costs. Predictor variables (identified univariately) were entered into a multi-variable logistic regression model to predict 30-day in-hospital mortality. The data were also modelled as count data (absolute LOS, total cost) using zero-truncated Poisson regression.
Acute illness severity was the best independent predictor of mortality; chronic disabling disease was an independent predictor (P < 0.001) for patients with 4+ disabling conditions. Age, adjusted for other predictors, was only independently predictive of mortality for patient 85+ years. Chronic disabling disease was an independent predictor of LOS increasing linearly with incidence rate ratios of 1.35 (95% CI: 1.29, 1.42), 1.59 (95% CI: 1.51, 1.66), 1.73 (95% CI: 1.65, 1.83) and 1.74 (95% CI: 1.65, 1.84) for those with 1, 2, 3 or 4+ disabling conditions, respectively. Age, as a predictor of LOS was strongly correlated with the presence of disabling disease. Chronic disabling disease independently predicted costs non-linearly; those with 2 or more disabling conditions had particularly high total hospital costs.
Chronic disabling disease is an independent predictor of hospital LOS and costs in unselected emergency admissions; adjusted for illness severity, it is only a mortality predictor for those with multiple disabling conditions.
慢性致残性疾病几乎存在于近 90%的急诊入院患者中。我们使用前瞻性收集的 12 年期间的病例数据库,研究了其对某一机构的结果和成本的影响。
对圣詹姆斯医院在 12 年(2002-13 年)期间的所有急诊入院(66933 例;36271 例患者)与 30 天院内死亡率、住院时间(LOS)和医院费用进行了评估。使用单变量识别预测变量,并将其输入多变量逻辑回归模型以预测 30 天院内死亡率。数据也作为计数数据(绝对 LOS、总费用)使用零截断泊松回归进行建模。
急性疾病严重程度是死亡率的最佳独立预测因子;慢性致残性疾病是患有 4+致残疾病的患者的独立预测因子(P<0.001)。对于 85 岁以上的患者,年龄在调整其他预测因子后仅可独立预测死亡率。慢性致残性疾病是 LOS 的独立预测因子,其发生率比(IRR)分别为 1.35(95%CI:1.29,1.42)、1.59(95%CI:1.51,1.66)、1.73(95%CI:1.65,1.83)和 1.74(95%CI:1.65,1.84),随着残疾状况发生率的增加呈线性增加,对于残疾状况为 1、2、3 或 4+的患者。作为 LOS 的预测因子,年龄与致残性疾病的存在密切相关。慢性致残性疾病对成本的预测是非线性的;具有 2 种或更多致残性疾病的患者的总住院费用特别高。
慢性致残性疾病是未选择的急诊入院患者 LOS 和费用的独立预测因子;在调整疾病严重程度后,它仅预测患有多种致残性疾病的患者的死亡率。