Evaluation of Triple Whammy Prescriptions After the Implementation of a Drug Safety Algorithm.

IF 1.9 Q3 PHARMACOLOGY & PHARMACY Drugs - Real World Outcomes Pub Date : 2024-03-01 Epub Date: 2024-01-06 DOI:10.1007/s40801-023-00405-y
Hendrike Dahmke, Jana Schelshorn, Rico Fiumefreddo, Philipp Schuetz, Ali Reza Salili, Francisco Cabrera-Diaz, Carla Meyer-Massetti, Claudia Zaugg
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Abstract

Background and objective: The term triple whammy (TW) refers to the concomitant use of non-steroidal anti-inflammatory drugs, diuretics, and angiotensin system inhibitors; this combination significantly increases the risk of acute kidney injury (AKI). To prevent this serious complication, we developed an electronic algorithm that detects TW prescriptions in patients with additional risk factors such as old age and impaired kidney function. The algorithm alerts a clinical pharmacist who then evaluates and forwards the alert to the prescribing physician.

Methods: We evaluated the performance of this algorithm in a retrospective observational study of clinical data from all adult patients admitted to the Cantonal Hospital of Aarau in Switzerland in 2021. We identified all patients who received a TW prescription, had a TW alert, or developed AKI during TW therapy. Algorithm performance was evaluated by calculating the sensitivity and specificity as a primary endpoint and determining the acceptance rate among clinical pharmacists and physicians as a secondary endpoint.

Results: Among 21,332 hospitalized patients, 290 patients had a TW prescription, of which 12 patients experienced AKI. Overall, 216 patients were detected by the alert algorithm, including 11 of 12 patients with AKI; the algorithm sensitivity is 88.3% with a specificity of 99.7%. Physician acceptance was high (77.7%), but clinical pharmacists were reluctant to forward the alerts to prescribers in some cases.

Conclusion: The TW algorithm is highly sensitive and specific in identifying patients with TW therapy at risk for AKI. The algorithm may help to prevent AKI in TW patients in the future.

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药物安全算法实施后的三重打击处方评估。
背景和目的:三联疗法(TW)是指同时使用非甾体类抗炎药、利尿剂和血管紧张素系统抑制剂;这种组合会显著增加急性肾损伤(AKI)的风险。为了预防这种严重的并发症,我们开发了一种电子算法,用于检测具有高龄和肾功能受损等额外风险因素的患者的 TW 处方。该算法会向临床药剂师发出警报,临床药剂师会进行评估并将警报转发给处方医生:方法:我们在一项回顾性观察研究中评估了该算法的性能,该研究收集了 2021 年瑞士阿劳州医院收治的所有成年患者的临床数据。我们确定了所有收到 TW 处方、出现 TW 警报或在 TW 治疗期间发生 AKI 的患者。通过计算灵敏度和特异性(作为主要终点)以及确定临床药剂师和医生的接受率(作为次要终点)来评估算法性能:在 21,332 名住院患者中,290 名患者开具了 TW 处方,其中 12 名患者出现了 AKI。总体而言,警报算法检测到 216 名患者,其中 12 名患者中有 11 名出现了 AKI;算法灵敏度为 88.3%,特异度为 99.7%。医生的接受度很高(77.7%),但临床药剂师在某些情况下不愿将警报转发给处方医生:TW算法在识别有AKI风险的TW治疗患者方面具有高度敏感性和特异性。该算法未来可能有助于预防 TW 患者发生 AKI。
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来源期刊
Drugs - Real World Outcomes
Drugs - Real World Outcomes PHARMACOLOGY & PHARMACY-
CiteScore
3.60
自引率
5.00%
发文量
49
审稿时长
8 weeks
期刊介绍: Drugs - Real World Outcomes targets original research and definitive reviews regarding the use of real-world data to evaluate health outcomes and inform healthcare decision-making on drugs, devices and other interventions in clinical practice. The journal includes, but is not limited to, the following research areas: Using registries/databases/health records and other non-selected observational datasets to investigate: drug use and treatment outcomes prescription patterns drug safety signals adherence to treatment guidelines benefit : risk profiles comparative effectiveness economic analyses including cost-of-illness Data-driven research methodologies, including the capture, curation, search, sharing, analysis and interpretation of ‘big data’ Techniques and approaches to optimise real-world modelling.
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