Opportunities for an early recognition of spinal muscular atrophy in primary care: a nationwide, population-based, study in Italy. (11th August 2022)

• Record Type: Journal Article
• Authors: Maggi, Lorenzo; Vita, Gianluca; Marconi, Ettore; Taddeo, Daiana; Davì, Michele; Lovato, Valeria; Cricelli, Claudio; Lapi, Francesco
• Is Part Of: Family practice. Volume 40:Number 2(2023)
• Page Start: 308
• Page End: 313
• Publication Date: 2022-08-11
• Subjects: nested case–control study -- primary care -- rare disease -- spinal muscular atrophy; Primary care (Medicine) -- Periodicals; Clinical medicine -- Periodicals; 616.005
• DOI: 10.1093/fampra/cmac091
• Languages: English
• ISSNs: 0263-2136

Abstract

Abstract: Background: Spinal muscular atrophy (SMA) is a rare genetic disease with a broad spectrum of severity. Although an early diagnosis of SMA is crucial to allow proper management of patients, the diagnostic delay is still an issue. Therefore, this study aimed to investigate the clinical correlates of SMA among primary care patients. Methods: The Health Search Database (HSD) was adopted. To estimate the prevalence and incidence rate of SMA, a cohort study was conducted on the population (aged ≥6 years) being registered in HSD from 1 January 2000 up to 31 December 2019. To investigate the clinical correlates of SMA, a nested case–control study was performed. SMA cases have been classified according to a clinically based iterative process as "certain", "probable" or "possible". To test the association between clinical correlates and SMA cases a multivariate conditional logistic regression model was estimated. Results: The SMA prevalence combining "certain", "probable" and "possible" cases was 5.1 per 100, 000 in 2019 (i.e. 1.12 per 100, 000 when limited to "certain" cases), while the yearly incidence rate ranged from 0.12 to 0.56 cases per 100, 000. Comparing "certain" cases with matched controls, the presence of neurology visits (OR = 6.5; 95% CI: 1.6–25.6) and prescription of electromyography (OR = 4.6; 95% CI: 1.1–18.7) were associated with higher odds of SMA diagnosis. Conclusions: Our findings suggest that primary care databases may be used to enhance the early identification of SMA. Additional efforts are needed to exploit the electronic health records of general practitioners to allow early recognition of SMA.