POS0367 FILLING GAPS IN FEMALE GOUT: A CROSS-SECTIONAL STUDY OF 192,000 PATIENTS HOSPITALIZED WITH GOUT FROM 2005 TO 2015 - Rodríguez-Sosa, E.; De Miguel, E.; Borrás, F.; Andrés, M. (Annals of the rheumatic diseases, 2023)
BackgroundPatients with gout show several comorbidities, especially cardiovascular, renal and metabolic diseases. The odds of developing new comorbidities increase progressively since the first flare, with a higher risk than general population. Female gout has received little attention in the published literature, although clinical experience and some studies suggest different clinical picture with higher mortality.ObjectivesTo compare the distribution of comorbidities by sex in hospitalized patients with gout in Spain.MethodsRetrospective, nation-based cohort study. The Minimal Basic Data Set from all Spanish hospitalizations including gout as either primary or secondary diagnosis (ICD-9 coding) from 2005 to 2015 was collected. The comorbidities of interest were obesity, dyslipidemia, chronic kidney disease, diabetes mellitus, coronary heart disease, chronic heart failure, peripheral vascular disease, arrhythmia, venous thromboembolism, cerebrovascular disease, dementia, urinary tract infection, pneumonia, sepsis, obstructive pulmonary disease, liver disease and rheumatological disease. Arterial hypertension and urinary lithiasis were not analyzed since they were underrepresented in the dataset. Comparative analyses were performed by sex. Later, stratification by age was performed and the population was divided by sextets (≤70years; 71-80years; 81-85years; 86-90years; 91-95years; >95years). A multiple logistic regression model was built to discern the strength of association of comorbidities with each sex, for the entire population and stratifying by age (≤60years and >60years). Additionally, a decision tree was constructed to have sex predicting algorithms based on the presence of comorbidities.Results192,037 admissions were analyzed, 5.47% (n=10,512) with gout as primary diagnosis. 158,646 cases occurred in men (82.6%), significantly younger than women (64.0±14.4 vs 73.9±13.7 years; p<0.001). Female predominant comorbidities were obesity (16.3% vs 10.8%), dyslipidemia (31.8% vs 30.7%), chronic kidney disease (33.8% vs 25.1%), diabetes (36.2% vs 25.9%), heart failure (31.8% vs 16.6%), dementia (2.1% vs 1.2%), urinary tract infection (12.0% vs 5.4%) and concurrent rheumatic disease (2.6% vs 1.4%). Otherwise, male predominant comorbidities were coronary heart disease (21.8% vs 16.9%), peripheral artery disease (3.8% vs 1.2%), liver disease (2.2% vs 1.3%), pneumonia (4.5% vs 3.8%) and obstructive pulmonary disease (8.9% vs 2.3%). No differences were seen for others. After age stratification, differences in dementia were no longer significant. Multiple logistic regression, with an accuracy score of 68.5%, confirmed a differential comorbidity profile between men and women (Figure 1). After age stratification, for <60years renal and rheumatic diseases were closely linked to women with gout, whilst in >60years heart failure was the leading. For men, coronary heart disease and obstructive pulmonary disease were the chief associations. A decision tree algorithm was built [1], with an accuracy of 74.4%.Figure 1.CHF: chronic heart failure; UTI: urinary tract infection; DM: diabetes mellitus; Rheuma: rheumatological disease; CKD: chronic kidney disease; VTE: venous thromboembolism; CVD: cerebrovascular disease; PVD: peripheral vascular disease; CHD: coronary heart disease; OPD: obstructive pulmonary disease. Positive coefficients associate with women with gout, negative ones otherwise.ConclusionA nation-wide analysis of 11years of hospitalizations with gout confirm a different comorbidity profile between men and women. Women with gout were significantly older and more likely to suffer from heart failure, obesity, urinary infection and diabetes. Association between sex and certain comorbidities is intense enough for sex prediction by an algorithm with considerable accuracy. A different approach for female gout is thus needed, to reduce gender blindness.Reference[1]“Decision Tree Algorithm - Sex and Comorbidities in gout”, Mendeley Data, V1, doi: 10.17632/j4vf42jhkw.1Acknowledgements:NIL.Disclosure of InterestsNone Declared.