利用电子病历评估药物与致命脑出血之间的因果关系:疾病特异性方法与传统方法的比较评估。

IF 1.9 Q3 PHARMACOLOGY & PHARMACY
Drugs - Real World Outcomes Pub Date : 2024-06-01 Epub Date: 2024-02-06 DOI:10.1007/s40801-023-00413-y
Miki Ohta, Satoru Miyawaki, Shinichiroh Yokota, Makoto Yoshimoto, Tatsuya Maruyama, Daisuke Koide, Takashi Moritoyo, Nobuhito Saito
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引用次数: 0

摘要

简介2021 年发布了药物与致命性脑出血因果关系评估的新算法(ACAD-FCH)。然而,该算法在临床实践中的应用尚未得到验证:本研究旨在探讨 ACAD-FCH 在临床实践中应用现有信息的实用价值:方法:对东京大学医院 2020 年死亡患者的病历进行审查,并筛选出颅内出血病例。两名评估员使用三种方法(ACAD-FCH、Naranjo 算法和 WHO-UMC 量表)对这些病例进行独立评估。两名评估员对每个问题的 "是"、"否 "和 "无信息/不知道 "回答数量相加并进行比较。使用一致率和卡帕系数以及 95% 的置信区间 (CI) 对每种方法的评分者之间的可靠性进行评估:在 316 例死亡病例中,对 24 例颅内出血病例进行了评估。对每个问题的 "无信息/不知道 "回答比例,ACAD-FCH 为 35.6%(95% CI 31.4-40.6%),Naranjo 算法为 66.9%(95% CI 62.5-71.1%)。ACAD-FCH的吻合率和卡帕系数分别为0.917(0.798-1.00)和0.867(0.675-1.00),纳兰霍算法的吻合率和卡帕系数分别为0.708(0.512-0.904)和0.139(-0.236-0.513),WHO-UMC量表的吻合率和卡帕系数分别为0.50(0.284-0.716)和0.326(0.110-0.541):我们的研究结果表明,ACAD-FCH 在评估颅内出血死亡病例时具有实用性。然而,为了进一步验证这一算法,需要进行包括评分者内部评估在内的更大规模的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Causality Assessment Between Drugs and Fatal Cerebral Haemorrhage Using Electronic Medical Records: Comparative Evaluation of Disease-Specific and Conventional Methods.

Introduction: A new algorithm for causality assessment of drugs and fatal cerebral haemorrhage (ACAD-FCH) was published in 2021. However, its use in clinical practice has not been verified.

Objectives: This study aimed to explore the practical value of the ACAD-FCH when applying information available in clinical practice.

Methods: The medical records of patients who died at the University of Tokyo Hospital in 2020 were reviewed, and cases with intracranial haemorrhage were selected. Two evaluators independently assessed these cases using three methods (the ACAD-FCH, Naranjo algorithm, and WHO-UMC scale). The number of 'Yes', 'No', and 'No information/Do not know' responses to each question by both evaluators were summed and compared. Inter-rater reliability was evaluated for each method using agreement rates and kappa coefficients with 95% confidence intervals (CI).

Results: Among 316 deaths, 24 cases with intracranial haemorrhage were evaluated. The proportion of ‛No information/Do not know' responses for each question was 35.6% (95% CI 31.4-40.6%) for the ACAD-FCH and 66.9% (95% CI 62.5-71.1%) for the Naranjo algorithm. The respective agreement rates and kappa coefficients were 0.917 (0.798-1.00) and 0.867 (0.675-1.00) for the ACAD-FCH, 0.708 (0.512-0.904) and 0.139 (-0.236 to 0.513) for the Naranjo algorithm, and 0.50 (0.284-0.716) and 0.326 (0.110-0.541) for the WHO-UMC scale, respectively.

Conclusion: Our findings suggest the utility of the ACAD-FCH when assessing death cases with intracranial haemorrhage. However, larger studies including intra-rater assessments are warranted for further validation of this algorithm.

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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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