Characterizing Utilization and Outcomes of Digoxin Immune Fab for Digoxin Toxicity.

IF 1.9 Q3 PHARMACOLOGY & PHARMACY Drugs - Real World Outcomes Pub Date : 2024-09-01 Epub Date: 2024-06-05 DOI:10.1007/s40801-024-00435-0
Sophia Sheikh, Taylor Munson, Gerard Garvan, Claire Layton, Dawn Sollee, Colleen Cowdery, Alexa Peterson, Lindsay Schaack Rothstein, Morgan Henson, Hayley Gartner, Michael Ujhelyi
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Abstract

Background: Digoxin is a widely prescribed drug for congestive heart failure and atrial fibrillation. Digoxin has a narrow therapeutic index and toxicity can develop quite easily. Digoxin immune fab (DIF) is an effective treatment for toxicity, however there are limited studies characterizing its impact on clinical outcomes in real-world clinical practice.

Objectives: The aim of this study was to identify factors and healthcare outcomes associated with digoxin immune fab (DIF) treatment in patients with confirmed/suspected digoxin toxicity.

Methods: An IRB-approved retrospective chart review of digoxin toxic patients (2011-2020) presenting at an academic healthcare system was conducted. Demographic and clinical data were collected. Patients were stratified by DIF treatment versus non-DIF treatment. DIF utilization patterns (appropriate, use when not indicated, or underutilized) were determined using pre-defined criteria. Severe digoxin toxicity was defined as having one or more of the following: mental status disturbances, antiarrhythmic therapy, acute renal impairment or dehydration, serum digoxin concentration (SDC) > 4 ng/mL, or serum K+ > 5 mEq/mL. Logistic multivariable regression analysis evaluated factors associated with DIF use. All statistical analyses were performed in R version 4.1.

Results: Data from 96 patients (non-DIF treated group = 49; DIF treated group = 47) were analyzed. DIF was used appropriately in 70 patients (73%), underutilized in 19 (20%), and administered to 7 (7%) patients when it was not indicated. Several clinical parameters differentiated the DIF from the non-DIF group (p < 0.05) including higher mean SDC (3.41 ± 1.63 vs 2.87 ± 1.17), higher mean potassium (5.33 ± 1.48 vs 4.55 ± 0.87), more toxicity severity (85% vs 49%), and more likely to require cardiac pacing (26% vs 4%). Digoxin toxicity resolved sooner in the DIF group (coefficient - 0.702, 95% CI - 1.137 to - 0.267) (p < 0.01) and they had shorter intensive care unit lengths of stay (12.4 ± 20.3 vs 24.4 ± 28.7 days; p = 0.018). The all-cause mortality rate in patients appropriately managed with DIF therapy versus those patients where DIF was underutilized was 11% and 21%, respectively.

Conclusions: Based on our study population, DIF therapy appears to be beneficial in limiting duration of toxicity and intensive care unit lengths of stay in digoxin toxic patients. Although DIF was appropriately utilized in most cases, there was a relatively high proportion of cases in which DIF treatment was either underutilized or not indicated.

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地高辛免疫球蛋白治疗地高辛中毒的使用情况和结果。
背景:地高辛是一种广泛用于治疗充血性心力衰竭和心房颤动的处方药。地高辛的治疗指数较窄,很容易产生毒性。地高辛免疫疗法(DIF)是治疗毒性的一种有效方法,但在实际临床实践中,有关其对临床疗效影响的研究十分有限:本研究旨在确定确诊/疑似地高辛毒性患者接受地高辛免疫疗法(DIF)治疗的相关因素和医疗效果:方法:对在一家学术医疗系统就诊的地高辛中毒患者(2011-2020 年)进行了一项经 IRB 批准的回顾性病历审查。收集了人口统计学和临床数据。根据 DIF 治疗与非 DIF 治疗对患者进行分层。使用预定义标准确定 DIF 的使用模式(适当使用、无指征使用或使用不足)。严重的地高辛毒性定义为以下一项或多项:精神状态紊乱、抗心律失常治疗、急性肾功能损害或脱水、血清地高辛浓度 (SDC) > 4 ng/mL,或血清 K+ > 5 mEq/mL。逻辑多变量回归分析评估了与使用 DIF 相关的因素。所有统计分析均在 R 4.1 版本中进行:分析了 96 例患者的数据(未接受 DIF 治疗组 = 49 例;接受 DIF 治疗组 = 47 例)。70名患者(73%)适当使用了DIF,19名患者(20%)未充分利用DIF,7名患者(7%)在无指征的情况下使用了DIF。有几项临床参数将 DIF 组和非 DIF 组区分开来(P 结论:DIF 是一种有效的治疗方法:根据我们的研究对象,DIF疗法似乎有利于限制地高辛中毒患者的毒性持续时间和重症监护室的住院时间。虽然在大多数病例中 DIF 得到了适当使用,但也有相当高比例的病例未充分利用 DIF 治疗或不适用 DIF 治疗。
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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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