Evidence map›Paper›PMID 41642508›Full record

Observational studyClinical rheumatology2026

Development of a non-genetic risk prediction model for allopurinol-induced severe cutaneous adverse drug reactions: a multicenter retrospective observational study.

Suppachai Lawanaskol, Wichittra Tassaneeyakul, Chonlaphat Sukasem, Niwat Saksit, Parinya Konyoung, Nontaya Nakkam, Warayuwadee Amornpinyo, Ticha Rerkpattanapipat, Jettanong Klaewsongkram, Pawinee Rerknimitr and 7 more

Abstract readObservational StudyMulticenter Study
PubMed Publisher
In one paragraph

Observational study in Clinical rheumatology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Suppachai LawanaskolChaiprakarn Hospital, Chiang Mai, Thailand.
Wichittra TassaneeyakulDepartment of Pharmacology, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand.
Chonlaphat SukasemDivision of Pharmacogenomics and Personalized Medicine, Department of Pathology Faculty of Medicine, and Laboratory for Pharmacogenomics, Somdech Phra Debaratana Medical Center (SDMC), Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.
Niwat SaksitUnit of Excellence on Pharmacogenomic Pharmacokinetic and Pharmacotherapeutic Researches (UPPER), School of Pharmaceutical Sciences, University of Phayao, Phayao, Thailand.
Parinya KonyoungPharmacy Unit, Udon Thani Hospital, Udon Thani, Thailand.
Nontaya NakkamDepartment of Pharmacology, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand.
Warayuwadee AmornpinyoDivision of Dermatology, Department of Internal Medicine, Khon Kaen Hospital, Khon Kaen, Thailand.
Ticha RerkpattanapipatDivision of Allergy Immunology and Rheumatology, Department of Medicine, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.
Jettanong KlaewsongkramCenter of Excellence for Skin and Allergy Research, Division of Allergy and Clinical Immunology, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
Pawinee RerknimitrCenter of Excellence for Skin and Allergy Research, Division of Dermatology, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
Thawinee JantararoungtongDivision of Pharmacogenomics and Personalized Medicine, Department of Pathology Faculty of Medicine, and Laboratory for Pharmacogenomics, Somdech Phra Debaratana Medical Center (SDMC), Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.
Duangkamon PoolpunDepartment of Pharmacy, Buddhachinaraj Hospital, Phitsanulok, Thailand.
Kittiphan ChalomChaiprakarn Hospital, Chiang Mai, Thailand.
Apichat TantraworasinDepartment of Surgery, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand.
Jayanton PatumanondClinical Epidemiology and Clinical Statistics Unit, Naresuan University, Phitsanulok, Thailand.
Worawit LouthrenooDivision of Rheumatology, Department of Internal Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand.
Patapong TowiwatDepartment of Medicine, Faculty of Medicine, Naresuan University, Phitsanulok, Thailand. puidulian@gmail.com.ORCID http://orcid.org/0000-0002-1956-7651

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveIn addition to the Human Leukocyte Antigen B*58:01(HLA-B*58:01), several non-genetic factors have been associated with allopurinol hypersensitivity syndrome, particularly severe cutaneous adverse drug reactions (SCAR), which are clinically serious. However, the magnitude of the impact of these non-genetic factors on the development of SCAR remains unclear. This study aimed to develop a non-genetic risk prediction model for predicting allopurinol-induced SCAR.

methodsThis retrospective observational study was performed during the same time period. SCAR cases were collected from tertiary care hospital centers, while the non-SCAR cases were collected from primary and tertiary care hospital centers. Non-genetic factors including sex, age, renal function, concomitant use of diuretics, starting dose of allopurinol, and serum urate (SU) were used for the development of the prediction models.

resultsOf the 23,294 cases, 209 were SCAR and 23,085 were non-SCAR cases. Three risk stratification models were developed. Models 1A and 1B were applied for patients who did not have and had SU level at the time of starting allopurinol, respectively. Model 2 was applied for patients who had all non-genetic risk factors, started allopurinol within 60 days, but had not yet developed SCAR. The area under the receiver operating characteristic curve for Models 1A, 1B, and 2 was 0.73 (95% CI 0.68-0.77), 0.81 (95% CI 0.75-0.87), and 0.83 (95% CI 0.80-0.86), respectively, indicating good discriminative performance.

conclusionsThe developed non-genetic prediction model demonstrated good discriminative performance. This prediction score could assist physicians' decisions in prescribing allopurinol in the areas where HLA-B*58:01 is limited or unavailable. External validation in future studies is warranted. Key Points • A non-genetic risk prediction model for allopurinol-induced Severe Cutaneous Adverse Drug Reactions, NoG-ASCAR Score, is simple to access and apply in real clinical practice. • The performance of the area under the receiver operating characteristic curve for predicting SCAR of each model was good and had an impact on clinical utility. • It could provide confidence to physicians in prescribing allopurinol in the situation where HLA-B*58:01 assessment is limited or unavailable. • The NoG-ASCAR Score should be considered by policymakers when conducting cost-effectiveness analysis prior to recommending routine HLA-B*58:01 testing before allopurinol initiation.

Indexed as

AllopurinolDrug EruptionsGout SuppressantsAdultAgedFemaleHLA-B AntigensHumansMaleMiddle AgedRetrospective StudiesRisk AssessmentRisk FactorsROC CurveAllopurinolGout SuppressantsHLA-B*58:01 antigenHLA-B AntigensAllopurinolDrug hypersensitivityRisk factorsRisk prediction modelSevere cutaneous adverse reactions

Identifiers

PMID41642508

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.