Is it possible to implement a rare disease case-finding tool in primary care? A UK-based pilot study.

INTRODUCTION: This study implemented MendelScan, a primary care rare disease case-finding tool, into a UK National Health Service population. Rare disease diagnosis is challenging due to disease complexity and low physician awareness. The 2021 UK Rare Diseases Framework highlights as a key priority the need for faster diagnosis to improve clinical outcomes. METHODS AND RESULTS: A UK primary care locality with 68,705 patients was examined. MendelScan encodes diagnostic/screening criteria for multiple rare diseases, mapping clinical terms to appropriate SNOMED CT codes (UK primary care standardised clinical terminology) to create digital algorithms. These algorithms were applied to a pseudo-anonymised structured data extract of the electronic health records (EHR) in this locality to "flag" at-risk patients who may require further evaluation. All flagged patients then underwent internal clinical review (a doctor reviewing each EHR flagged by the algorithm, removing all cases with a clear diagnosis/diagnoses that explains the clinical features that led to the patient being flagged); for those that passed this review, a report was returned to their GP. 55 of 76 disease criteria flagged at least one patient. 227 (0.33%) of the total 68,705 of EHR were flagged; 18 EHR were already diagnosed with the disease (the highlighted EHR had a diagnostic code for the same RD it was screened for, e.g. Behcet's disease algorithm identifying an EHR with a SNOMED CT code Behcet's disease). 75/227 (33%) EHR passed our internal review. Thirty-six reports were returned to the GP. Feedback was available for 28/36 of the reports sent. GP categorised nine reports as "Reasonable possible diagnosis" (advance for investigation), six reports as "diagnosis has already been excluded", ten reports as "patient has a clear alternative aetiology", and three reports as "Other" (patient left study locality, unable to re-identify accurately). All the 9 cases considered as "reasonable possible diagnosis" had further evaluation. CONCLUSIONS: This pilot demonstrates that implementing such a tool is feasible at a population level. The case-finding tool identified credible cases which were subsequently referred for further investigation. Future work includes performance-based validation studies of diagnostic algorithms and the scalability of the tool.

Distribution of Iliac Artery Calcification on Unenhanced Computed Tomography Scans Performed on Potential Recipients Prior to Renal Transplantation.

PURPOSE: To investigate whether a significant difference exists between the calcification of the common iliac arteries (CIAs) and the external iliac arteries (EIAs) and test for associations between clinical factors and the distribution of calcification. METHODS: A retrospective review of renal transplant candidates who underwent a routine preoperative unenhanced computed tomography yielded 214 patients. Agatston scores for the patients' left CIA, left EIA, right CIA, and right EIA were assigned. A retrospective search of patient records screened for 5 clinical factors (diabetes, hypertension, coronary artery disease [CAD], smoking, and dialysis). Data were assessed using a 2-sided t test, odds ratio, and a multivariate linear regression calculated through generalized estimating equation (GEE). RESULTS: The log-transformed Agatston scores in the CIA were found to be significantly greater than that in the EIA (t = 9.57, P < .0001), with a mean difference of 1.5078 (95% confidence interval: 1.1962-1.8194), indicating relative EIA sparing. There were no significant differences in calcification between the right and left sides. Generalized estimating equation found that CAD and smoking demonstrated independent positive associations with EIA sparing (GEE = 2.6464 [P = .0197] and 1.9092 [P = .0470], respectively). Age was also significantly associated and indicated that EIA sparing remained relatively constant throughout patients' lives (GEE = 1.0711 [P < .0001]). CONCLUSION: This study has demonstrated statistically significant EIA sparing in end-stage renal disease patients and identified CAD and smoking as associated factors. This phenomenon warrants further investigation into its biological mechanisms and the impact of EIA sparing on outcomes following transplants.