Development of a prediction models for chemotherapy-induced adverse drug reactions: A retrospective observational study using electronic health records

On, Jeongah; Park, Hyeoun-Ae; Yoo, Sooyoung

doi:10.1016/j.ejon.2021.102066

Cited by 19 publications

(15 citation statements)

References 31 publications

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“… 30 , 40 , 51–53 While 17 studies (68%) investigated a single ADE, 31 , 33–41 , 43–47 , 49 , 50 8 studies (32%) investigated multiple ADEs. 30 , 32 , 42 , 47 , 51–54 Only 1 study (4%) reported the involvement of key stakeholders before model development. 47 This study was guided by a technical expert panel (including clinical pharmacy specialists, health information technology experts, and researchers in quality measurement and epidemiology) and performed a stakeholder evaluation to identify relevant preventable ADEs before model development.…”

Section: Resultsmentioning

confidence: 99%

Electronic health record-based prediction models for in-hospital adverse drug event diagnosis or prognosis: a systematic review

Kom

Dongelmans

Keizer

et al. 2023

Journal of the American Medical Informatics Association

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Objective We conducted a systematic review to characterize and critically appraise developed prediction models based on structured electronic health record (EHR) data for adverse drug event (ADE) diagnosis and prognosis in adult hospitalized patients. Materials and Methods We searched the Embase and Medline databases (from January 1, 1999, to July 4, 2022) for articles utilizing structured EHR data to develop ADE prediction models for adult inpatients. For our systematic evidence synthesis and critical appraisal, we applied the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS). Results Twenty-five articles were included. Studies often did not report crucial information such as patient characteristics or the method for handling missing data. In addition, studies frequently applied inappropriate methods, such as univariable screening for predictor selection. Furthermore, the majority of the studies utilized ADE labels that only described an adverse symptom while not assessing causality or utilizing a causal model. None of the models were externally validated. Conclusions Several challenges should be addressed before the models can be widely implemented, including the adherence to reporting standards and the adoption of best practice methods for model development and validation. In addition, we propose a reorientation of the ADE prediction modeling domain to include causality as a fundamental challenge that needs to be addressed in future studies, either through acquiring ADE labels via formal causality assessments or the usage of adverse event labels in combination with causal prediction modeling.

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Section: Resultsmentioning

confidence: 99%

Electronic health record-based prediction models for in-hospital adverse drug event diagnosis or prognosis: a systematic review

Kom

Dongelmans

Keizer

et al. 2023

Journal of the American Medical Informatics Association

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“…43 studies showed current landscape and applied scenario of AI used in cancer survivorship domain. Most of the studies were conducted in Europe ( n = 16) 10 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , Asia ( n = 12) 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , USA ( n = 12) 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 followed by Middle East ( n = 2) 53 , 54 , and UK ( n = 1) 55 Supplement A presents an overview of these studies’ characteristics. Based on the various clinical application and potential functions, the studies can be divided into following categories, they are 1.…”

Section: Resultsmentioning

confidence: 99%

“…The study 53 from Turkey was published in 2016, relative early, in which researchers used data generated from cancer cell lines to provide a testbed for machine learning algorithms and to predict the response of cancer cells to different agents, thereby improving the prediction of drug response of cancer cell lines. Korean 29 researchers similarly developed the prediction models for various chemotherapy regimen-related adverse reactions (ARDs).…”

Section: Resultsmentioning

confidence: 99%

Artificial intelligence empowered digital health technologies in cancer survivorship care: A scoping review

Pan

Zhang

et al. 2022

Asia-Pacific Journal of Oncology Nursing

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“…Sabe-se que a especialização torna a assistência mais qualificada, pois permite que o profissional desenvolva novas habilidades e competências, tornando-o mais capaz para enfrentar as demandas diárias. (Kitajima et al, 2020;On et al, 2022).…”

Section: Discussionunclassified

Percepção dos Enfermeiros a respeito das dificuldades vivenciadas em um Centro Oncológico de Alta Complexidade

Santos

Alves

Santos

et al. 2022

RSD

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Objetivo: Analisar as percepções dos enfermeiros a respeito de suas rotinas e dos desafios vivenciados no cotidiano do cuidado às pessoas que vivem com câncer. Metodologia: Estudo qualitativo com enfermeiros que atuam na assistência direta ao paciente oncológico no Centro de Alta Complexidade em Oncologia, do Hospital Universitário professor Alberto Antunes, conduzido mediante entrevista semiestruturada, com uso da técnica de análise de conteúdo de Bardin. Resultados: Participaram 14 profissionais; após análise, emergiram duas categorias: “A consulta de enfermagem como instrumento qualificador do cuidado e o impacto da pandemia da COVID-19 em um centro oncológico”; e “Situações difíceis no cuidado às pessoas que vivem com câncer”. Identificaram múltiplos fatores influenciadores de adoecimento físico e psíquico relacionados ao cotidiano da assistência oncológica. Conclusão: Os profissionais apresentaram agentes influenciadores de desgastes físicos e emocionais e fizeram reflexões importantes a respeito da temática. Aponta-se que esses profissionais precisam de medidas de prevenção para auxiliar no enfrentamento dos problemas cotidianos.

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Development of a prediction models for chemotherapy-induced adverse drug reactions: A retrospective observational study using electronic health records

Cited by 19 publications

References 31 publications

Electronic health record-based prediction models for in-hospital adverse drug event diagnosis or prognosis: a systematic review

Electronic health record-based prediction models for in-hospital adverse drug event diagnosis or prognosis: a systematic review

Artificial intelligence empowered digital health technologies in cancer survivorship care: A scoping review

Percepção dos Enfermeiros a respeito das dificuldades vivenciadas em um Centro Oncológico de Alta Complexidade

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