Logue Everett, Smucker William, Regan Christine
From the Department of Family Medicine, Summa Health System, Akron, OH.
J Am Board Fam Med. 2016 Jan-Feb;29(1):50-9. doi: 10.3122/jabfm.2016.01.150127.
The purpose of this study was to identify data available at the time of hospital admission that predict readmission risk.
We performed a retrospective multiple regression analysis of 958 adult, nonpregnant patients admitted to the Family Medicine Service between June 2012 and October 2013. Data were abstracted from hospital administrative sources and electronic medical records. The outcome was 30-day hospital readmission. Candidate readmission predictors included polypharmacy (≥6 medicines), Charlson comorbidity index, age, sex, insurance status, emergency department use, smoking, nursing report of cognitive issues, patient report of social support or financial issues, and a history of heart failure, pneumonia, or chronic obstructive pulmonary disease.
Patients at the Family Medicine Service had a 14% readmission risk. Bivariate analysis showed that high Charlson scores (≥5), polypharmacy, heart failure, pneumonia, or chronic obstructive pulmonary disease each increased readmission risk (P < .05). A logistic model showed an estimated odds ratio for readmission for high Charlson scores of 1.7 (95% confidence interval, 1.1-2.6) and of 2.1 for polypharmacy (95% confidence interval, 1.3-3.7). The model yielded a readmission risk estimate of 6% if neither a high Charlson score nor polypharmacy was present, 9% if only the Charlson score was high, 12% if only polypharmacy was present, and 19% if both were present. The receiver operating characteristics curve for the 2-factor model yielded an estimated area under the curve of 85%. Cross-validation supported this result.
Polypharmacy and higher Charlson score at admission predict readmission risk as well as or better than published risk prediction models. The model could help to conserve limited resources and to target interventions for reducing readmission among the highest-risk patients.
本研究旨在确定入院时可获取的能预测再入院风险的数据。
我们对2012年6月至2013年10月间入住家庭医学科的958例成年非妊娠患者进行了回顾性多元回归分析。数据从医院行政记录和电子病历中提取。观察指标为30天内再次入院情况。再入院预测指标候选因素包括多种药物联用（≥6种药物）、查尔森合并症指数、年龄、性别、保险状况、急诊科就诊情况、吸烟、认知问题护理报告、患者社会支持或财务问题报告以及心力衰竭、肺炎或慢性阻塞性肺疾病病史。
家庭医学科患者的再入院风险为14%。双变量分析显示，高查尔森评分（≥5）、多种药物联用、心力衰竭、肺炎或慢性阻塞性肺疾病均增加再入院风险（P < 0.05）。逻辑模型显示，高查尔森评分的再入院估计比值比为1.7（95%置信区间，1.1 - 2.6），多种药物联用为2.1（95%置信区间，1.3 - 3.7）。若既无高查尔森评分也无多种药物联用，该模型得出的再入院风险估计值为6%；若仅查尔森评分高，为9%；若仅存在多种药物联用，为12%；若两者均存在，为19%。双因素模型的受试者工作特征曲线下面积估计值为85%。交叉验证支持该结果。
入院时多种药物联用和较高的查尔森评分预测再入院风险的效果与已发表的风险预测模型相当或更佳。该模型有助于节约有限资源，并针对最高风险患者进行干预以降低再入院率。
