medwireNews: Primary care electronic health record (EHR) codes for rickets, genu varum or low levels of phosphate could help flag a potential diagnosis of X-linked hypophosphataemia (XLH), suggest study findings published in the Journal of Clinical Endocrinology & Metabolism.
“[T]his study provides evidence regarding XLH clinical feature recording in routine primary care records – these data may support better understanding of the epidemiology of this rare condition or inform new approaches to early case finding”, say Ashley Clift (Mendelian, London, UK) and co-authors.
“The latter is of importance as early initiation of treatment in children with XLH is associated with better outcomes, including improved height and reduced skeletal deformities”, they write.
The team collated information from the Optimum Patient Care Research Database, which covers more than 1000 general practices and is “broadly representative of the UK population in terms of age, sex, ethnicity and socioeconomic status, and the clinical data is representative of routine primary care”, say the researchers.
They used Systematized Nomenclature of Medicine Clinical Terms and CTV3 coding systems of clinical events in the EHRs to identify 261 patients with a diagnosis of XHL, familial XHL or vitamin D-refractory rickets, at a prevalence of 1.16 cases per 100,000.
The majority (67%) of patients were female and the median age at diagnosis was 4 years but ranged from birth to 88 years; 99 patients who were diagnosed before the age of 20 years and after January 2000 were included in the study analysis and age-matched to 100 individuals without XHL.
Overall, 84 of the cohort had one or more of 17 specified clinical features of XHL mentioned in their EHRs, and the researchers note that three of the 15 patients without XHL-related codes had “very sparse” EHRs prior to diagnosis.
The most common XHL symptom recorded was rickets, but this affected less than 48% of patients, say Clift et al. The next most common features were low phosphate, genu varum and short stature, followed by fractures, joint or bone pain, sensorineural hearing loss, nephrocalcinosis, tooth problems, genu valgum and craniosynostosis. By contrast, none of the patients had recorded codes for arthritis, talar dome flattening, elevated bone mineral density, insufficiency fracture or periodontal disease.
Looking at the timing of symptom records, the researchers found that there was “some clustering” of the first mention of codes for rickets, low phosphate, short stature and genu varum around a median of 1 year before XHL diagnosis.
But less than 20% of patients were diagnosed with rickets before XHL diagnosis and 36% of patients received their diagnosis of rickets and XHL on the same day. Fractures, joint or bone pain, craniosynostosis, low phosphate and sensorineural hearing impairment were also commonly only coded for the first time after XHL diagnosis.
EHRs for nine patients had codes indicating referrals to a clinical genetics team or another possible route to genetic testing for an XHL genotype, and 50 patients had codes for a referral to an orthopaedic service.
Compared with controls, patients with XHL were significantly more likely to have EHR codes for rickets, genu varum, low phosphate, nephrocalcinosis, short stature, or a delayed ability to stand. But there was no significant difference between the groups with regard to codes for tooth problems, fracture, craniosynostosis, or joint and bone pain.
Of note, the controls were significantly more likely to have had sensorineural hearing impairment than the patients, which the authors say “likely partly reflects other causes of sensorineural hearing loss being more common in the wider paediatric population than XLH-associated hearing loss in conjunction with primary care recording behaviours.”
While acknowledging that “some archetypal XHL features such as rickets are only recorded in primary EHR data prior to XHL diagnosis in under 20% of individuals”, the team concludes that “several features might be discriminatory between cases and controls, with potential to inform approaches to case finding algorithms.”
By Lynda Williams
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