Nazanin Falconer , Ian A. Scott , Ahmad Abdel-Hafez , Neil Cottrell , Duncan Long , Christopher Morris , Centaine Snoswell , Ebtyhal Aziz , Jonathan Yong Jie Lam , Michael Barras
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引用次数: 0
Abstract
Background
Medication harm affects between 5 and 15% of hospitalised patients, with approximately half of the harm events considered preventable through timely intervention. The Adverse Inpatient Medication Event (AIME) risk prediction model was previously developed to guide a systematic approach to patient prioritisation for targeted clinician review, but frailty was not tested as a candidate predictor variable.
Aim
To evaluate the predictive performance of an updated AIME model, incorporating a measure of frailty, when applied to a new multisite cohort of hospitalised adult inpatients.
Methods
A retrospective cohort study was conducted at two tertiary Australian hospitals on patients discharged between 1st January and April 31, 2020. Data were extracted from electronic medical records (EMRs) and clinical coding databases. Medication harm was identified using ICD-10 Y-codes and confirmed by senior pharmacist review of medical records. The Hospital Frailty Risk Score (HFRS) was calculated for each patient. Logistic regression analysis was used to construct a modified AIME model. Candidate variables of the original AIME model, together with new variables including HFRS were tested. Performance of the final model was reported using area under the curve (AUC) and decision curve analysis (DCA).
Results
A total of 4089 patient admissions were included, with a mean age ± standard deviation (SD) of 64 years (±19 years), 2050 patients (50%) were males, and mean HFRS was 6.2 (±5.9). 184 patients (4.5%) experienced one or more medication harm events during hospitalisation. The new AIME-Frail risk model incorporated 5 of the original variables: length of stay (LOS), anti-psychotics, antiarrhythmics, immunosuppressants, and INR greater than 3, as well as 5 new variables: HFRS, anticoagulants, antibiotics, insulin, and opioid use. The AUC was 0.79 (95% CI: 0.76–0.83) which was superior to the original model (AUC = 0.70, 95% CI: 0.65–0.74) with a sensitivity of 69%, specificity of 81%, positive predictive value of 0.14 (95% CI: 0.10–0.17) and negative predictive value of 0.98 (95% CI: 0.97–0.99). The DCA identified the model as having potential clinical utility between the probability thresholds of 0.05–0.4.
Conclusion
The inclusion of a frailty measure improved the predictive performance of the AIME model. Screening inpatients using the AIME-Frail tool could identify more patients at high-risk of medication harm who warrant timely clinician review.
背景用药伤害影响着5%至15%的住院患者,其中约有一半的伤害事件是可以通过及时干预来预防的。Aim To evaluate the predictive performance of an updated AIME model, incorporating a measure of frailty, when applied to a new multisite cohort of hospitalised adult inpatients.Methods A retrospective cohort study was conducted at two tertiary Australian hospitals on patients discharge between 1st January and April 31, 2020.数据提取自电子病历(EMR)和临床编码数据库。用 ICD-10 Y 编码确定药物伤害,并由高级药剂师审查病历加以确认。计算每位患者的医院虚弱风险评分(HFRS)。使用逻辑回归分析构建修正的 AIME 模型。对原始 AIME 模型的候选变量以及包括 HFRS 在内的新变量进行了测试。结果 共纳入 4089 名住院患者,平均年龄(±标准差)为 64 岁(±19 岁),2050 名患者(50%)为男性,平均 HFRS 为 6.2(±5.9)。184名患者(4.5%)在住院期间发生过一次或多次药物伤害事件。新的 AIME-Frail 风险模型纳入了 5 个原始变量:住院时间 (LOS)、抗精神病药物、抗心律失常药物、免疫抑制剂和 INR 大于 3,以及 5 个新变量:HFRS、抗凝药物、抗生素、胰岛素和阿片类药物的使用。AUC为0.79(95% CI:0.76-0.83),优于原始模型(AUC = 0.70,95% CI:0.65-0.74),灵敏度为69%,特异性为81%,阳性预测值为0.14(95% CI:0.10-0.17),阴性预测值为0.98(95% CI:0.97-0.99)。DCA 认为该模型在概率阈值 0.05-0.4 之间具有潜在的临床实用性。使用 AIME-Frail 工具对住院患者进行筛查,可以发现更多需要临床医生及时复查的药物伤害高风险患者。
期刊介绍:
Research in Social and Administrative Pharmacy (RSAP) is a quarterly publication featuring original scientific reports and comprehensive review articles in the social and administrative pharmaceutical sciences. Topics of interest include outcomes evaluation of products, programs, or services; pharmacoepidemiology; medication adherence; direct-to-consumer advertising of prescription medications; disease state management; health systems reform; drug marketing; medication distribution systems such as e-prescribing; web-based pharmaceutical/medical services; drug commerce and re-importation; and health professions workforce issues.