Kidney Week

Abstract: FR-PO314

Development and Validation of a Model to Predict a Positive CKD Screening among Patients with Type 2 Diabetes

Session Information

• Diabetic Kidney Disease: Clinical Modeling, Diagnosis, Education, and More
  October 25, 2024 | Location: Exhibit Hall, Convention Center
  Abstract Time: 10:00 AM - 12:00 PM

Category: Diabetic Kidney Disease

• 702 Diabetic Kidney Disease: Clinical

Authors

• Obrador, Gregorio T., Universidad Panamericana School of Medicine, Ciudad de México, CDMX, Mexico

Gregorio T. Obrador, MD, MPH

• Freyria, Ana, Universidad Panamericana School of Medicine, Ciudad de México, CDMX, Mexico

Ana Freyria

• Sotelo, Alma, Universidad Panamericana School of Nursing, Ciudad de México, CDMX, Mexico

Alma Sotelo, MSc

• Chevaile, Alejandro, Universidad Autonoma de San Luis Potosi, San Luis Potosi, SLP, Mexico

Alejandro Chevaile, MD

• Valdez-Ortiz, Rafael, Hospital General de Mexico Dr Eduardo Liceaga, Ciudad de Mexico, CDMX, Mexico

Rafael Valdez-Ortiz, MD

• Karuzin, Nikita, Universidad Panamericana School of Medicine, Ciudad de México, CDMX, Mexico

Nikita Karuzin

• Brenner Muslera, Eduardo, Universidad Panamericana School of Medicine, Ciudad de México, CDMX, Mexico

Eduardo Brenner Muslera, MD

• Olvera, Nadia, Universidad Panamericana, Ciudad de Mexico, Ciudad de México, Mexico

Nadia Olvera

Background

Cost barriers limit chronic kidney disease (CKD) screening in low- and middle-income countries. This study aims to provide a formula for identifying patients with type 2 diabetes mellitus (T2DM) who will be screened positive for CKD to optimize available resources.

Methods

The development cohort consisted of adult patients with T2DM who participated in a CKD screening program in three Mexican states. Demographic and clinical data were collected. Standardized serum creatinine was measured, and the glomerular filtration rate was estimated with the 2021 CKD-EPI equation. The albumin/creatinine ratio was assessed using Siemens’ Clinitek Status. Univariate and multivariate logistic regression models were developed, and odds ratios with 95% confidence intervals, receiver operator characteristic curves (ROC), and area under the curve (AUC) were calculated. The validation cohort consisted of adult patients with T2DM who were screened for CKD in three clinics of Mexico City’s Secretariat of Health. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) are reported in each cohort.

Results

Overall, 602 patients with T2DM underwent screening in the development cohort. The mean age was 57 years, and 73% were female. The multivariate model included the following variables (ORs): age (1.02), female gender (0.82), fasting glucose (1.0008), years with T2DM (1.01), and family income (0.99). AUC from the ROC was 71.8% (Fig. 1). After setting the threshold to 0.2 to reduce false negatives, the sensitivity was 80.7%, specificity 46.3%, PPV 15.2%, and NPV 95.2%. The validation cohort consisted of 449 patients with T2DM. The mean age was 60 years, and 66% were female. With the same threshold of 0.2, sensitivity reached 80.0%, specificity 38.5%, PPV 4%, and NPV 98.3%.

Conclusion

The formula's accuracy, high NPV, and easy availability of the included variables make it a valuable tool, mainly when CKD screening resources for patients with T2DM are limited.

Funding

• Commercial Support – AstraZeneca