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Table 5 Positive predictive value, sensitivity and specificity for simplified scoring systems when applying to the threshold that was proposed in the development study and best threshold on our dataset, calculated using the Youden’s method [43]

From: An external validation of models to predict the onset of chronic kidney disease using population-based electronic health records from Salford, UK

Study Threshold (SD) PPV (SD) Sensitivity (SD) Specificity (SD)
Bang et al. [54] Proposed 4 0.146 (0.002) 0.865 (0.004) 0.805 (0.001)
Best 4 0.146 (0.002) 0.865 (0.004) 0.805 (0.001)
Chien et al. [51] Proposed 7 0.106 (0.001) 0.916 (0.003) 0.701 (0.001)
Best 8 0.133 (0.002) 0.863 (0.004) 0.783 (0.001)
QKidney® [36] Proposed NR NA NA NA
Best 0.017 (0.002) 0.147 (0.006) 0.870 (0.012) 0.805 (0.012)
Kshirsagar et al. [53] Proposed 3 0.143 (0.002) 0.872 (0.004) 0.799 (0.001)
Best 3 0.143 (0.002) 0.872 (0.004) 0.799 (0.001)
Kwon et al. [55] Proposed 4 0.147 (0.002) 0.862 (0.004) 0.807 (0.001)
Best 4 0.147 (0.002) 0.862 (0.004) 0.807 (0.001)
O’Seaghdha et al. [52] Proposed NA NA NA NA
Best 0.086 (0.010) 0.138 (0.007) 0.885 (0.015) 0.786 (0.015)
Thakkinstian et al. [56] Proposed 5 0.071 (0.001) 0.936 (0.003) 0.529 (0.001)
Best 6 0.140 (0.002) 0.861 (0.004) 0.796 (0.001)
  1. PPV, positive predictive value; NR, Not reported; NA, not applicable; SD, standard deviation
  2. Note: As QKidney® does not have any associated score in the original publication, we reported results for the full model. O’Seaghdha et al. [52] reported a simplified score system; however, this could not be used in our population because of missing predictors. Therefore, we calculated performance for the full model instead
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