Clinical profile and comparison of causality assessment tools in cutaneous adverse drug reactions

Beniwal, Ranjana; Gupta, Lalit; Khare, Ashok Kumar; Mittal, Asit; Mehta, Sharad; Balai, Manisha

doi:10.4103/idoj.idoj_207_18

Cited by 7 publications

(1 citation statement)

References 27 publications

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“…[ 15 ]. Adverse drug reactions (ADRs) frequently result in patient non-compliance with their prescribed medication regimen, particularly when the causative factor remains unrecognized until later stages [ 16 ]. In the case of a suspected ACDR, even if symptoms are mild, the early recognition and identification of the underlying pharmacotherapeutic agent are key to alleviate the dermatological symptoms and ensure patient compliance [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Novel Strategy in the Detection of Adverse Cutaneous Drug Reactions: A Case Series Study

Bán,

Lechsner,

Dho-Nagy

et al. 2024

Diagnostics

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With multimorbidity on the rise, adverse cutaneous drug reactions are becoming a daily challenge in clinical practice. The objective evaluation of the skin lesion is crucial but hardly realized due to missing technology and guidelines. In this study, the novel Dermus SkinScanner-U, an optically guided high-frequency ultrasound imaging device, was evaluated regarding its comparability with the Dermatology Life Quality Index (DLQI) and the pharmacological analysis of the patients’ drug therapy. A total of 40 adult patients were evaluated, all with chronic medication use and skin lesions that led to non-compliance toward the pharmacotherapy. With the ongoing aim of further improving the methodology, the first results, with two detailed patient cases, are presented here. It was concluded that in the cases evaluated, there was a significant correlation between the characteristics of the lesions observed on the optical and ultrasound image, the DLQI score, and the pharmacological analysis. The next steps include increasing the scale of the study to ultimately develop a quality-assured methodology for the correct diagnosis of skin-related adverse drug reactions and to prepare a database with the most frequently observed events.

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Section: Introductionmentioning

confidence: 99%

Novel Strategy in the Detection of Adverse Cutaneous Drug Reactions: A Case Series Study

Bán,

Lechsner,

Dho-Nagy

et al. 2024

Diagnostics

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Factors associated with the etiological uncertainty of drug reactions: a cross-sectional study in southern Brazil

Oliveira

Chiamenti

Horn

et al. 2021

Anais Brasileiros de Dermatologia

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Adverse drug event presentation and tracking (ADEPT): semiautomated, high throughput pharmacovigilance using real-world data

et al. 2020

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Objective To advance use of real-world data (RWD) for pharmacovigilance, we sought to integrate a high-sensitivity natural language processing (NLP) pipeline for detecting potential adverse drug events (ADEs) with easily interpretable output for high-efficiency human review and adjudication of true ADEs. Materials and methods The adverse drug event presentation and tracking (ADEPT) system employs an open source NLP pipeline to identify in clinical notes mentions of medications and signs and symptoms potentially indicative of ADEs. ADEPT presents the output to human reviewers by highlighting these drug-event pairs within the context of the clinical note. To measure incidence of seizures associated with sildenafil, we applied ADEPT to 149 029 notes for 982 patients with pediatric pulmonary hypertension. Results Of 416 patients identified as taking sildenafil, NLP found 72 [17%, 95% confidence interval (CI) 14–21] with seizures as a potential ADE. Upon human review and adjudication, only 4 (0.96%, 95% CI 0.37–2.4) patients with seizures were determined to have true ADEs. Reviewers using ADEPT required a median of 89 s (interquartile range 57–142 s) per patient to review potential ADEs. Discussion ADEPT combines high throughput NLP to increase sensitivity of ADE detection and human review, to increase specificity by differentiating true ADEs from signs and symptoms related to comorbidities, effects of other medications, or other confounders. Conclusion ADEPT is a promising tool for creating gold standard, patient-level labels for advancing NLP-based pharmacovigilance. ADEPT is a potentially time savings platform for computer-assisted pharmacovigilance based on RWD.

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Clinical profile and comparison of causality assessment tools in cutaneous adverse drug reactions

Cited by 7 publications

References 27 publications

Novel Strategy in the Detection of Adverse Cutaneous Drug Reactions: A Case Series Study

Novel Strategy in the Detection of Adverse Cutaneous Drug Reactions: A Case Series Study

Factors associated with the etiological uncertainty of drug reactions: a cross-sectional study in southern Brazil

Adverse drug event presentation and tracking (ADEPT): semiautomated, high throughput pharmacovigilance using real-world data

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