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出院带药数量可预测30天内再次入院:一项队列研究。

The number of discharge medications predicts thirty-day hospital readmission: a cohort study.

作者信息

Picker David, Heard Kevin, Bailey Thomas C, Martin Nathan R, LaRossa Gina N, Kollef Marin H

机构信息

Division of Pulmonary & Critical Care Medicine, Washington University School of Medicine, 660 South Euclid Ave., Campus Box 8052, St. Louis, MO, 63110, USA.

Center for Clinical Excellence, BJC Learning Institute (BLI), 8300 Eager Road, St. Louis, MO, 63144, USA.

出版信息

BMC Health Serv Res. 2015 Jul 23;15:282. doi: 10.1186/s12913-015-0950-9.

Abstract

BACKGROUND

Hospital readmission occurs often and is difficult to predict. Polypharmacy has been identified as a potential risk factor for hospital readmission. However, the overall impact of the number of discharge medications on hospital readmission is still undefined.

METHODS

To determine whether the number of discharge medications is predictive of thirty-day readmission using a retrospective cohort study design performed at Barnes-Jewish Hospital from January 15, 2013 to May 9, 2013. The primary outcome assessed was thirty-day hospital readmission. We also assessed potential predictors of thirty-day readmission to include the number of discharge medications.

RESULTS

The final cohort had 5507 patients of which 1147 (20.8 %) were readmitted within thirty days of their hospital discharge date. The number of discharge medications was significantly greater for patients having a thirty-day readmission compared to those without a thirty-day readmission (7.2 ± 4.1 medications [7.0 medications (4.0 medications, 10.0 medications)] versus 6.0 ± 3.9 medications [6.0 medications (3.0 medications, 9.0 medications)]; P < 0.001). There was a statistically significant association between increasing numbers of discharge medications and the prevalence of thirty-day hospital readmission (P < 0.001). Multiple logistic regression identified more than six discharge medications to be independently associated with thirty-day readmission (OR, 1.26; 95 % CI, 1.17-1.36; P = 0.003). Other independent predictors of thirty-day readmission were: more than one emergency department visit in the previous six months, a minimum hemoglobin value less than or equal to 9 g/dL, presence of congestive heart failure, peripheral vascular disease, cirrhosis, and metastatic cancer. A risk score for thirty-day readmission derived from the logistic regression model had good predictive accuracy (AUROC = 0.661 [95 % CI, 0.643-0.679]).

CONCLUSIONS

The number of discharge medications is associated with the prevalence of thirty-day hospital readmission. A risk score, that includes the number of discharge medications, accurately predicts patients at risk for thirty-day readmission. Our findings suggest that relatively simple and accessible parameters can identify patients at high risk for hospital readmission potentially distinguishing such individuals for interventions to minimize readmissions.

摘要

背景

医院再入院情况经常发生且难以预测。多种药物联合使用已被确定为医院再入院的一个潜在风险因素。然而,出院带药数量对医院再入院的总体影响仍不明确。

方法

采用回顾性队列研究设计,于2013年1月15日至2013年5月9日在巴恩斯犹太医院进行,以确定出院带药数量是否可预测30天再入院情况。评估的主要结局是30天医院再入院。我们还评估了30天再入院的潜在预测因素,包括出院带药数量。

结果

最终队列有5507例患者,其中1147例(20.8%)在出院日期后30天内再次入院。与未在30天内再入院的患者相比,30天内再入院患者的出院带药数量显著更多(7.2±4.1种药物[7.0种药物(4.0种药物,10.0种药物)]对6.0±3.9种药物[6.0种药物(3.0种药物,9.0种药物)];P<0.001)。出院带药数量增加与30天医院再入院患病率之间存在统计学显著关联(P<0.001)。多因素逻辑回归分析确定,超过6种出院带药与30天再入院独立相关(比值比,1.26;95%置信区间,1.17 - 1.36;P = 0.003)。30天再入院的其他独立预测因素为:前6个月内急诊就诊超过1次、最低血红蛋白值小于或等于9 g/dL、存在充血性心力衰竭、外周血管疾病、肝硬化和转移性癌症。从逻辑回归模型得出的30天再入院风险评分具有良好的预测准确性(曲线下面积 = 0.661 [95%置信区间,0.643 - 0.679])。

结论

出院带药数量与医院30天再入院患病率相关。一个包含出院带药数量的风险评分能准确预测有30天再入院风险的患者。我们的研究结果表明,相对简单且易于获取的参数可识别出有医院再入院高风险的患者,这可能有助于区分这些个体以便进行干预,从而尽量减少再入院情况。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cea/4512093/c5f54af8a37b/12913_2015_950_Fig1_HTML.jpg

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