{"title":"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。","authors":"Miki Ohta, Satoru Miyawaki, Shinichiroh Yokota, Makoto Yoshimoto, Tatsuya Maruyama, Daisuke Koide, Takashi Moritoyo, Nobuhito Saito","doi":"10.1007/s40801-023-00413-y","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>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.</p><p><strong>Objectives: </strong>This study aimed to explore the practical value of the ACAD-FCH when applying information available in clinical practice.</p><p><strong>Methods: </strong>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).</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":11282,"journal":{"name":"Drugs - Real World Outcomes","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11176114/pdf/","citationCount":"0","resultStr":"{\"title\":\"Causality Assessment Between Drugs and Fatal Cerebral Haemorrhage Using Electronic Medical Records: Comparative Evaluation of Disease-Specific and Conventional Methods.\",\"authors\":\"Miki Ohta, Satoru Miyawaki, Shinichiroh Yokota, Makoto Yoshimoto, Tatsuya Maruyama, Daisuke Koide, Takashi Moritoyo, Nobuhito Saito\",\"doi\":\"10.1007/s40801-023-00413-y\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>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.</p><p><strong>Objectives: </strong>This study aimed to explore the practical value of the ACAD-FCH when applying information available in clinical practice.</p><p><strong>Methods: </strong>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).</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>\",\"PeriodicalId\":11282,\"journal\":{\"name\":\"Drugs - Real World Outcomes\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2024-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11176114/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Drugs - Real World Outcomes\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/s40801-023-00413-y\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/2/6 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"PHARMACOLOGY & PHARMACY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Drugs - Real World Outcomes","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s40801-023-00413-y","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/2/6 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
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.
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.