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非临床因素能否改善再入院风险预测?:远程心力衰竭(Tele-HF)研究的结果

Do Non-Clinical Factors Improve Prediction of Readmission Risk?: Results From the Tele-HF Study.

作者信息

Krumholz Harlan M, Chaudhry Sarwat I, Spertus John A, Mattera Jennifer A, Hodshon Beth, Herrin Jeph

机构信息

Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut; Robert Wood Johnson Foundation Clinical Scholars Program, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut; Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, Connecticut; Department of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut.

Section of General Internal Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut.

出版信息

JACC Heart Fail. 2016 Jan;4(1):12-20. doi: 10.1016/j.jchf.2015.07.017. Epub 2015 Dec 2.

Abstract

OBJECTIVES

This study sought to determine whether a model that included self-reported socioeconomic, health status, and psychosocial characteristics obtained from patients recently discharged from hospitalizations for heart failure substantially improved 30-day readmission risk prediction compared with a model that incorporated only clinical and demographic factors.

BACKGROUND

Existing readmission risk models have poor discrimination and it is unknown whether they would be markedly improved by the inclusion of patient-reported information.

METHODS

As part of the Tele-HF (Telemonitoring to Improve Heart Failure Outcomes) trial, we conducted medical record abstraction and telephone interviews in a sample of 1,004 patients recently hospitalized for heart failure to obtain clinical, functional, and psychosocial information within 2 weeks of discharge. Candidate risk factors included 110 variables divided into 2 groups: demographic and clinical variables generally available from the medical record; and socioeconomic, health status, adherence, and psychosocial variables from patient interview.

RESULTS

The 30-day readmission rate was 17.1%. Using the 3-level risk score derived from the restricted medical record variables, patients with a score of 0 (no risk factors) had a readmission rate of 10.9% (95% confidence interval [CI]: 8.2% to 14.2%), and patients with a score of 2 (all risk factors) had a readmission rate of 32.1% (95% CI: 22.4% to 43.2%), a C-statistic of 0.62. Using the 5-level risk score derived from all variables, patients with a score of 0 (no risk factors) had a readmission rate of 9.6% (95% CI: 6.1% to 14.2%), and patients with a score of 4 (all risk factors) had a readmission rate of 55.0% (95% CI: 31.5% to 76.9%), a C-statistic of 0.65.

CONCLUSIONS

Self-reported socioeconomic, health status, adherence, and psychosocial variables are not dominant factors in predicting readmission risk for patients with heart failure. Patient-reported information improved model discrimination and extended the predicted ranges of readmission rates, but the model performance remained poor. (Telemonitoring to Improve Heart Failure Outcomes [Tele-HF]; NCT00303212).

摘要

目的

本研究旨在确定,与仅纳入临床和人口统计学因素的模型相比,纳入从因心力衰竭住院后近期出院的患者处获得的自我报告的社会经济、健康状况及心理社会特征的模型,是否能显著改善30天再入院风险预测。

背景

现有的再入院风险模型辨别能力较差,尚不清楚纳入患者报告信息是否会显著改善这些模型。

方法

作为远程心力衰竭监测(Tele-HF,Telemonitoring to Improve Heart Failure Outcomes)试验的一部分,我们对1004例近期因心力衰竭住院的患者进行了病历摘要提取及电话访谈,以在出院后2周内获取临床、功能及心理社会信息。候选风险因素包括110个变量,分为两组:通常可从病历中获取的人口统计学和临床变量;以及通过患者访谈获得的社会经济、健康状况、依从性及心理社会变量。

结果

30天再入院率为17.1%。使用从受限病历变量得出的3级风险评分,评分为0(无风险因素)的患者再入院率为10.9%(95%置信区间[CI]:8.2%至14.2%),评分为2(所有风险因素)的患者再入院率为32.1%(95%CI:22.4%至43.2%),C统计量为0.62。使用从所有变量得出的5级风险评分,评分为0(无风险因素)的患者再入院率为9.6%(95%CI:6.1%至14.2%),评分为4(所有风险因素)的患者再入院率为55.0%(95%CI:31.5%至76.9%),C统计量为0.65。

结论

自我报告的社会经济、健康状况、依从性及心理社会变量并非预测心力衰竭患者再入院风险的主要因素。患者报告信息改善了模型辨别能力,并扩大了再入院率的预测范围,但模型表现仍然较差。(远程心力衰竭监测[Tele-HF];NCT00303212)

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