Using machine learning to identify diabetes patients with canagliflozin prescriptions at high-risk of lower extremity amputation using real-world data.
Pharmacoepidemiol Drug Saf
; 30(5): 644-651, 2021 05.
Article
en En
| MEDLINE
| ID: mdl-33606340
ABSTRACT
AIMS:
Canagliflozin, a sodium-glucose cotransporter 2 inhibitor indicated for lowering glucose, has been increasingly used in diabetes patients because of its beneficial effects on cardiovascular and renal outcomes. However, clinical trials have documented an increased risk of lower extremity amputations (LEA) associated with canagliflozin. We applied machine learning methods to predict LEA among diabetes patients treated with canagliflozin.METHODS:
Using claims data from a 5% random sample of Medicare beneficiaries, we identified 13 904 diabetes individuals initiating canagliflozin between April 2013 and December 2016. The samples were randomly and equally split into training and testing sets. We identified 41 predictor candidates using information from the year prior to canagliflozin initiation, and applied four machine learning approaches (elastic net, least absolute shrinkage and selection operator [LASSO], gradient boosting machine and random forests) to predict LEA risk after canagliflozin initiation.RESULTS:
The incidence rate of LEA was 0.57% over a median 1.5 years follow-up. LASSO produced the best prediction, yielding a C-statistic of 0.81 (95% CI 0.76, 0.86). Among individuals categorized in the top 5% of the risk score, the actual incidence rate of LEA was 3.74%. Among the 16 factors selected by LASSO, history of LEA [adjusted odds ratio (aOR) 33.6 (13.8, 81.9)] and loop diuretic use [aOR 3.6 (1.8,7.3)] had the strongest associations with LEA incidence.CONCLUSIONS:
Our machine learning model efficiently predicted the risk of LEA among diabetes patients undergoing canagliflozin treatment. The risk score may support optimized treatment decisions and thus improve health outcomes of diabetes patients.Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Diabetes Mellitus
/
Diabetes Mellitus Tipo 2
Tipo de estudio:
Diagnostic_studies
/
Etiology_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Aged
/
Humans
País/Región como asunto:
America do norte
Idioma:
En
Revista:
Pharmacoepidemiol Drug Saf
Asunto de la revista:
EPIDEMIOLOGIA
/
TERAPIA POR MEDICAMENTOS
Año:
2021
Tipo del documento:
Article
País de afiliación:
Estados Unidos