Fig. 2: Comorbidity heatmaps for 239,332 CUIMC patients algorithmically placed on the A-by-G grid. | npj Digital Medicine

Fig. 2: Comorbidity heatmaps for 239,332 CUIMC patients algorithmically placed on the A-by-G grid.

From: Medical records-based chronic kidney disease phenotype for clinical care and “big data” observational and genetic studies

Fig. 2

The prevalence of a comorbidity within each cell is provided, with the shaded color scale varying from red (highest prevalence) to green (lowest prevalence). The arrows correspond to the direction of effect and P values the statistical tests of comorbidity gradients across the grid. The analysis excludes individuals with missing urine tests and those with ESRD on dialysis or after transplant. Models based on logistic regression was used for binary traits and Poisson regression for ICD counts. All models were adjusted for age and sex and P value <6.25 × 10−4 is considered as significant after Bonferroni correction. NS not significant.

Back to article page