开发并验证了心力衰竭患者 30 天内再入院预测工具（ToPP-HF）。
Development and validation of the Tool for Pharmacists to Predict 30-day hospital readmission in patients with Heart Failure (ToPP-HF).
机构信息
Department of Health Services, Policy, and Practice, Brown University School of Public Health, Providence, RI, USA.
Department of Pharmacy, Rhode Island Hospital, Providence, RI, USA.
出版信息
Am J Health Syst Pharm. 2021 Sep 7;78(18):1691-1700. doi: 10.1093/ajhp/zxab223.
PURPOSE
Pharmacists are well positioned to provide transitions of care (TOC) services to patients with heart failure (HF); however, hospitalizations for patients with HF likely exceed the capacity of a TOC pharmacist. We developed and validated a tool to help pharmacists efficiently identify high-risk patients with HF and maximize their potential impact by intervening on patients at the highest risk for 30-day all-cause readmission.
METHODS
We conducted a retrospective cohort study including adults with HF admitted to a health system between October 1, 2016, and October 31, 2019. We randomly divided the cohort into development (n = 2,114) and validation (n = 1,089) subcohorts. Nine models were applied to select the most important predictors of 30-day readmission. The final tool, called the Tool for Pharmacists to Predict 30-day hospital readmission in patients with Heart Failure (ToPP-HF) relied upon multivariable logistic regression. We assessed discriminative ability using the C statistic and calibration using the Hosmer-Lemeshow goodness-of-fit test.
RESULTS
The risk of 30-day all-cause readmission was 15.7% (n = 331) and 18.8% (n = 205) in the development and validation subcohorts, respectively. The ToPP-HF tool included 13 variables: number of hospital admissions in previous 6 months; admission diagnosis of HF; number of scheduled medications; chronic obstructive pulmonary disease diagnosis; number of comorbidities; estimated glomerular filtration rate; hospital length of stay; left ventricular ejection fraction; critical care requirement; renin-angiotensin-aldosterone system inhibitor use; antiarrhythmic use; hypokalemia; and serum sodium. Discriminatory performance (C statistic of 0.69; 95% confidence interval [CI], 0.65-0.73) and calibration (Hosmer-Lemeshow P = 0.28) were good.
CONCLUSIONS
The ToPP-HF performs well and can help pharmacists identify high-risk patients with HF most likely to benefit from TOC services.
目的
药剂师在为心力衰竭（HF）患者提供过渡护理（TOC）服务方面具有得天独厚的优势；然而，HF 患者的住院治疗可能超过 TOC 药剂师的能力。我们开发并验证了一种工具，以帮助药剂师有效地识别 HF 高危患者，并通过对 30 天全因再入院风险最高的患者进行干预，最大限度地发挥他们的潜在影响。
方法
我们进行了一项回顾性队列研究，纳入了 2016 年 10 月 1 日至 2019 年 10 月 31 日期间在一个医疗系统住院的 HF 成年患者。我们将队列随机分为开发（n = 2114）和验证（n = 1089）子队列。应用 9 种模型来选择 30 天再入院的最重要预测因素。最终的工具，称为药剂师预测心力衰竭患者 30 天住院再入院的工具（ToPP-HF），依赖于多变量逻辑回归。我们使用 C 统计量评估区分能力，使用 Hosmer-Lemeshow 拟合优度检验评估校准。
结果
在开发和验证子队列中，30 天全因再入院的风险分别为 15.7%（n = 331）和 18.8%（n = 205）。ToPP-HF 工具包括 13 个变量：过去 6 个月内的住院次数；HF 的入院诊断；计划使用药物的数量；慢性阻塞性肺疾病诊断；合并症数量；估计肾小球滤过率；住院时间；左心室射血分数；重症监护需求；肾素-血管紧张素-醛固酮系统抑制剂的使用；抗心律失常药物的使用；低钾血症；和血清钠。区分性能（C 统计量为 0.69；95%置信区间[CI]，0.65-0.73）和校准（Hosmer-Lemeshow P = 0.28）良好。
结论
ToPP-HF 性能良好，可帮助药剂师识别最有可能从 TOC 服务中受益的 HF 高危患者。
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