田纳西州中风患者的医院出院处置情况。
Hospital Discharge Disposition of Stroke Patients in Tennessee.
作者信息
Cho Jin S, Hu Zhen, Fell Nancy, Heath Gregory W, Qayyum Rehan, Sartipi Mina
机构信息
From the Departments of Computer Science and Engineering, Physical Therapy, Health and Human Performance, University of Tennessee, Chattanooga, and Erlanger Health System, Chattanooga, Tennessee.
出版信息
South Med J. 2017 Sep;110(9):594-600. doi: 10.14423/SMJ.0000000000000694.
OBJECTIVES
Early determination of hospital discharge disposition status at an acute admission is extremely important for stroke management and the eventual outcomes of patients with stroke. We investigated the hospital discharge disposition of patients with stroke residing in Tennessee and developed a predictive tool for clinical adoption. Our investigational aims were to evaluate the association of selected patient characteristics with hospital discharge disposition status and predict such status at the time of an acute stroke admission.
METHODS
We analyzed 127,581 records of patients with stroke hospitalized between 2010 and 2014. Logistic regression was used to generate odds ratios with 95% confidence intervals to examine the factor outcome association. An easy-to-use clinical predictive tool was built by using integer-based risk scores derived from coefficients of multivariable logistic regression.
RESULTS
Among the 127,581 records of patients with stroke, 86,114 (67.5%) indicated home discharge and 41,467 (32.5%) corresponded to facility discharge. All considered patient characteristics had significant correlations with hospital discharge disposition status. Patients were at greater odds of being discharged to another facility if they were women; older; black; patients with a subarachnoid or intracerebral hemorrhage; those with the comorbidities of diabetes mellitus, heart disease, hypertension, chronic kidney disease, arrhythmia, or depression; those transferred from another hospital; or patients with Medicare as the primary payer. A predictive tool had a discriminatory capability with area under the curve estimates of 0.737 and 0.724 for derivation and validation cohorts, respectively.
CONCLUSIONS
Our investigation revealed that the hospital discharge disposition pattern of patients with stroke in Tennessee was associated with the key patient characteristics of selected demographics, clinical indicators, and insurance status. These analyses resulted in the development of an easy-to-use predictive tool for early determination of hospital discharge disposition status.
目的
急性入院时尽早确定出院处置状态对卒中管理及卒中患者的最终结局极为重要。我们调查了田纳西州卒中患者的医院出院处置情况，并开发了一种供临床应用的预测工具。我们的研究目的是评估选定的患者特征与医院出院处置状态之间的关联，并在急性卒中入院时预测该状态。
方法
我们分析了2010年至2014年间住院的127581例卒中患者的记录。采用逻辑回归生成比值比及95%置信区间，以检验因素与结局的关联。通过使用基于多变量逻辑回归系数得出的整数风险评分构建了一个易于使用的临床预测工具。
结果
在127581例卒中患者记录中，86114例（67.5%）显示出院回家，41467例（32.5%）对应转至其他机构出院。所有考虑的患者特征均与医院出院处置状态显著相关。如果患者为女性、年龄较大、为黑人、患有蛛网膜下腔或脑出血、患有糖尿病、心脏病、高血压、慢性肾病、心律失常或抑郁症等合并症、从其他医院转来、或主要付款人为医疗保险患者，则出院至其他机构的几率更高。一个预测工具的区分能力在推导队列和验证队列中的曲线下面积估计值分别为0.737和0.724。
结论
我们的调查显示，田纳西州卒中患者的医院出院处置模式与选定人口统计学、临床指标和保险状态的关键患者特征相关。这些分析促成了一种易于使用的预测工具的开发，用于早期确定医院出院处置状态。
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