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Impact of Pharmacist Involvement in Heart Failure Transition of Care.药师参与心力衰竭过渡期护理的影响。
Ann Pharmacother. 2020 Mar;54(3):239-246. doi: 10.1177/1060028019882685. Epub 2019 Oct 11.
2
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Clin Drug Investig. 2019 Aug;39(8):703-712. doi: 10.1007/s40261-019-00797-2.
3
Machine learning-based prediction of heart failure readmission or death: implications of choosing the right model and the right metrics.基于机器学习的心力衰竭再入院或死亡预测:选择正确模型和指标的意义。
ESC Heart Fail. 2019 Apr;6(2):428-435. doi: 10.1002/ehf2.12419. Epub 2019 Feb 27.
4
Heart Failure Postdischarge Clinic: A Pharmacist-led Approach to Reduce Readmissions.出院后心力衰竭诊所:一种由药剂师主导的减少再入院的方法。
Curr Probl Cardiol. 2019 Oct;44(10):100407. doi: 10.1016/j.cpcardiol.2018.12.004. Epub 2019 Jan 5.
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Predictive models for identifying risk of readmission after index hospitalization for heart failure: A systematic review.用于识别心力衰竭首次住院后再入院风险的预测模型:一项系统综述。
Eur J Cardiovasc Nurs. 2018 Dec;17(8):675-689. doi: 10.1177/1474515118799059. Epub 2018 Sep 7.
6
The Effect of Clinical Pharmacists on Readmission Rates of Heart Failure Patients in the Accountable Care Environment.临床药师在问责制医疗环境下对心力衰竭患者再入院率的影响。
J Manag Care Spec Pharm. 2018 Aug;24(8):795-799. doi: 10.18553/jmcp.2018.24.8.795.
7
A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data.机器学习模型预测心力衰竭患者 30 天再入院风险:电子病历数据的回顾性分析。
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Predicting Risk of 30-Day Readmissions Using Two Emerging Machine Learning Methods.使用两种新兴机器学习方法预测30天再入院风险。
Stud Health Technol Inform. 2018;250:250-255.
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Impact of a pharmacy team-led intervention program on the readmission rate of elderly patients with heart failure.药剂师团队主导的干预项目对老年心力衰竭患者再入院率的影响。
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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.

DOI:10.1093/ajhp/zxab223
PMID:34048528
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8522353/
Abstract

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.

摘要

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。