A machine learning-based prediction model for gout in hyperuricemics: a nationwide cohort study.
Rheumatology (Oxford)
; 63(9): 2411-2417, 2024 Sep 01.
Article
in En
| MEDLINE
| ID: mdl-38895877
ABSTRACT
OBJECTIVE:
To develop a machine learning-based prediction model for identifying hyperuricemic participants at risk of developing gout.METHODS:
A retrospective nationwide Israeli cohort study used the Clalit Health Insurance database of 473 124 individuals to identify adults 18 years or older with at least two serum urate measurements exceeding 6.8 mg/dl between January 2007 and December 2022. Patients with a prior gout diagnosis or on gout medications were excluded. Patients' demographic characteristics, community and hospital diagnoses, routine medication prescriptions and laboratory results were used to train a risk prediction model. A machine learning model, XGBoost, was developed to predict the risk of gout. Feature selection methods were used to identify relevant variables. The model's performance was evaluated using the receiver operating characteristic area under the curve (ROC AUC) and precision-recall AUC. The primary outcome was the diagnosis of gout among hyperuricemic patients.RESULTS:
Among the 301 385 participants with hyperuricemia included in the analysis, 15 055 (5%) were diagnosed with gout. The XGBoost model had a ROC-AUC of 0.781 (95% CI 0.78-0.784) and precision-recall AUC of 0.208 (95% CI 0.195-0.22). The most significant variables associated with gout diagnosis were serum uric acid levels, age, hyperlipidemia, non-steroidal anti-inflammatory drugs and diuretic purchases. A compact model using only these five variables yielded a ROC-AUC of 0.714 (95% CI 0.706-0.723) and a negative predictive value (NPV) of 95%.CONCLUSIONS:
The findings of this cohort study suggest that a machine learning-based prediction model had relatively good performance and high NPV for identifying hyperuricemic participants at risk of developing gout.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Hyperuricemia
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Machine Learning
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Gout
Limits:
Adult
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Aged
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Female
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Humans
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Male
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Middle aged
Country/Region as subject:
Asia
Language:
En
Journal:
Rheumatology (Oxford)
Journal subject:
REUMATOLOGIA
Year:
2024
Document type:
Article
Affiliation country:
Country of publication: