TRIPOD checklist: Prediction model development.

<div> Background Understanding whether symptoms suggestive of chronic kidney disease (CKD) are reported to primary care before diagnosis may provide opportunities for earlier detection, thus supporting strategies to prevent progression and improve long-term outcomes. Our aim was to determine whether symptoms/signs or consultation frequency recorded in primary care could predict a subsequent diagnosis of chronic kidney disease in children. Methods We undertook a case-control study within Clinical Practice Research Datalink. Cases were children <21 years with an incident code for severe CKD during the study period (January 2000-September 2018). Controls were matched on age (+/-3 years), sex, and practice-level kidney function testing rate. Conditional logistic regression modelling was used to identify symptoms predictive of severe CKD and differences in consultation frequency in 24- and 6-month timeframes before the index date. Results Symptoms predictive of severe CKD in the 24 months before the index date included growth concerns (OR 7.4, 95% CI 3.5, 15.4), oedema (OR 5.7, 95% CI 2.9, 11.2) and urinary tract infection (OR 3.3, 95% CI 2.1, 5.4); within 6 months of the index date, effect estimates and specificity strengthened although sensitivity decreased. Overall, positive predictive value of symptoms was low. Cases consulted more frequently than controls in both timeframes. In combination, symptoms and consultation frequency demonstrated modest discrimination for CKD (c-statistic after bootstrapping 0.70, 95% CI 0.66, 0.73). Conclusion Despite increased consultation frequency and several symptoms being associated with severe chronic kidney disease, the positive predictive value of symptoms is low given disease rarity making earlier diagnosis challenging. </div>