This paper focuses on the importance of early diagnosis of chronic kidney disease (CKD) before the development of prob lems that are used to diagnose the disease currently, especially in high-risk patients with diabetes mellitus, obesity, and hypertension. The main diagnosis of CKD depends on the glomerular filtration rate (GFR) values. However, GFR values remain insu%icient to detect diagnosis until the near-last stages of the disease because of the kidney’s intrinsic compensa tion mechanism. The kidney can normally not use nephrons at full power, but in such conditions as the initial stages of CKD, it can increase the power of filtration on nephrons to compensate for the body’s needs. However, this increase occurs at a single nephron level and does not reflect the GFR level until the near-last stages of CKD. With current technology, there is no usable method to calculate the single nephron filtration rate. The concept of kidney functional reserve (KFR) helps to understand the situation of the kidney at a single nephron. Also, the integration of cystatin C and creatinine for calcula tions increases precision. The proposed method mentioned in this paper can revolutionize the early detection of CKD and enhance the prognosis of the disease. Clinical trials are recommended to validate the reliability of KFR in CKD diagnosis and treatment strategies based on KFR assessments. This approach proposes a great potential for improving early diagnosis, prognosis, and life quality of individuals at risk of CKD. Cite this article as: Hatipo!lu A, Çopur S, Hasbal NB, Kanbay M. Revolutionizing early chronic kidney disease detection: the potential of kidney functional reserve and biomarker integration. Turk J Nephrol. 2025;34(2):86-91.

Background/Objectives: Clinical scores are recommended for excluding acute appendicitis without imaging, but their rule-out thresholds were derived in patients selected on suspicion of appendicitis. This study tested whether these thresholds retain their reported sensitivity in patients selected by CT performance, estimated the incremental diagnostic value of successive layers of clinical information, and, as an exploratory aim, compared machine learning models with the scores. Methods: This single centre retrospective cohort comprised 761 emergency department patients undergoing abdominal CT for acute abdominal pain. The Alvarado score, the Appendicitis Inflammatory Response (AIR) score and the Adult Appendicitis Score (AAS) were reconstructed from data documented before CT. The reference standard was histopathology in operated patients and the index CT report with the clinical course otherwise. Models were fitted to five cumulative information layers by cross-validation, with nested algorithm selection and a temporal split. Results: Appendicitis was confirmed in 178 patients (23.4%). Sensitivity at the recommended thresholds was 0.680 (95% CI 0.609 to 0.751), 0.848 (0.791 to 0.899) and 0.697 (0.628 to 0.764), against published values of 0.99, 0.95 and 0.959, leaving 57, 27 and 54 confirmed cases in the low probability strata, and the shortfall was present in both temporal periods. Discrimination was preserved, with areas under the curve of 0.848, 0.868 and 0.881. Model discrimination rose from 0.874 with symptoms to 0.918 with laboratory values, with each increment conditional on the preceding layers and the pattern similar with a single algorithm held fixed. With nested selection, the model was not distinguishable from the AAS, and in the later temporal period its deferral threshold attained a sensitivity of 0.925. Conclusions: In a population defined by imaging rather than by clinical suspicion, the recommended rule-out thresholds did not translate. Thresholds should be re-derived and verified in the population in which imaging is to be deferred. The model analysis was exploratory and was not externally validated.