Clinical predictors of the estimated glomerular filtration rate 1 year after radical nephrectomy in Japanese patients

Predicting EGFR Status After Radical Nephrectomy or Partial Nephrectomy for Renal Cell Carcinoma on CT Using a Self-attention-based Model: Variable Vision Transformer (vViT)

OBJECTIVE

To assess the effectiveness of the vViT model for predicting postoperative renal function decline by leveraging clinical data, medical images, and image-derived features; and to identify the most dominant factor influencing this prediction.

MATERIALS AND METHODS

We developed two models, eGFR10 and eGFR20, to identify patients with a postoperative reduction in eGFR of more than 10 and more than 20, respectively, among renal cell carcinoma patients. The eGFR10 model was trained on 75 patients and tested on 27, while the eGFR20 model was trained on 77 patients and tested on 24. The vViT model inputs included class token, patient characteristics (age, sex, BMI), comorbidities (peripheral vascular disease, diabetes, liver disease), habits (smoking, alcohol), surgical details (ischemia time, blood loss, type and procedure of surgery, approach, operative time), radiomics, and tumor and kidney imaging. We used permutation feature importance to evaluate each sector's contribution. The performance of vViT was compared with CNN models, including VGG16, ResNet50, and DenseNet121, using McNemar and DeLong tests.

RESULTS

The eGFR10 model achieved an accuracy of 0.741 and an AUC-ROC of 0.692, while the eGFR20 model attained an accuracy of 0.792 and an AUC-ROC of 0.812. The surgical and radiomics sectors were the most influential in both models. The vViT had higher accuracy and AUC-ROC than VGG16 and ResNet50, and higher AUC-ROC than DenseNet121 (p < 0.05). Specifically, the vViT did not have a statistically different AUC-ROC compared to VGG16 (p = 1.0) and ResNet50 (p = 0.7) but had a statistically different AUC-ROC compared to DenseNet121 (p = 0.87) for the eGFR10 model. For the eGFR20 model, the vViT did not have a statistically different AUC-ROC compared to VGG16 (p = 0.72), ResNet50 (p = 0.88), and DenseNet121 (p = 0.64).

CONCLUSION

The vViT model, a transformer-based approach for multimodal data, shows promise for preoperative CT-based prediction of eGFR status in patients with renal cell carcinoma.

Estimating Postoperative Renal Function After Surgery for Nonmetastatic Renal Masses: A Systematic Review of Available Prediction Models

CONTEXT

A variety of models predicting postoperative renal function following surgery for nonmetastatic renal tumors have been reported, but their validity and clinical usefulness have not been formally assessed.

OBJECTIVE

To summarize prediction models available for estimation of mid- to long-term (>3 mo) postoperative renal function after partial nephrectomy (PN) or radical nephrectomy (RN) for nonmetastatic renal masses that include only preoperative or modifiable intraoperative variables.

EVIDENCE ACQUISITION

A systematic review of the English-language literature was conducted using the MEDLINE, Embase, and Web of Science databases from January 2000 to March 2022 according to the PRISMA guidelines (PROSPERO ID: CRD42022303492). Risk of bias was assessed according to the Prediction Model Study Risk of Bias Assessment Tool.

EVIDENCE SYNTHESIS

Overall, 21 prediction models from 18 studies were included (nine for PN only; eight for RN only; four for PN or RN). Most studies relied on retrospective patient cohorts and had a high risk of bias and high concern regarding the overall applicability of the proposed model. Patient-, kidney-, surgery-, tumor-, and provider-related factors were included among the predictors in 95%, 86%, 100%, 61%, and 0% of the models, respectively. All but one model included both patient age and preoperative renal function, while only a few took into account patient gender, race, comorbidities, tumor size/complexity, and surgical approach. There was significant heterogeneity in both the model building strategy and the performance metrics reported. Five studies reported external validation of six models, while three assessed their clinical usefulness using decision curve analysis.

CONCLUSIONS

Several models are available for predicting postoperative renal function after kidney cancer surgery. Most of these are not ready for routine clinical practice, while a few have been externally validated and might be of value for patients and clinicians.

PATIENT SUMMARY

We reviewed the tools available for predicting kidney function after partial or total surgical removal of a kidney for nonmetastatic cancer. Most of the models include patient and kidney characteristics such as age, comorbidities, and preoperative kidney function, and a few also include tumor characteristics and intraoperative variables. Some models have been validated by additional research groups and appear promising for improving counseling for patients with nonmetastatic cancer who are candidates for surgery.

Impact of Cigarette Smoking on Living Kidney Donors

BACKGROUND

Smoking is known to result in a decline in renal allograft function and survival of recipients; however, the effect of smoking on living kidney donors remains unknown. In this study we evaluated the impact of cigarette smoking on renal function of kidney donors.

METHODS

Among 1056 donors who underwent nephrectomy, 612 completed the 6-month follow-up protocol and were enrolled in the study. The association of smoking status, including pack-years smoking history, and postoperative renal function was evaluated.

RESULTS

Among donors, 68.1% had never smoked, 8% were former smokers, and 23.9% were current smokers. Donors who never smoked were older than former and current smokers (42.3 ± 11.8, 41.9 ± 11.1, and 38.3 ± 10.9 years, respectively; P < .001). There was no difference in preoperative renal function between groups; however, postoperative estimated glomerular filtration rate (eGFR) was lower in former and current smokers than in those who never smoked (64.6 ± 13.8, 64.7 ± 12.3, and 67.8 ± 13.1 mL/min/1.73 m², respectively; P = .023). In former and current smokers, pack-years smoking history was negatively associated with pre- and postoperative eGFR (r = -0.305 and -0.435, P < .001), and correlated with postoperative percent eGFR decline (r = 0.248, P < .001). Smoking history was associated with postoperative development of chronic kidney disease (CKD). Especially in former smokers, a smoking history of more than 12 pack-years was strongly associated with development of CKD (odds ratio = 7.5, P = .003).

CONCLUSION

Even if they no longer smoke, donors with a smoking history require close observation due to increased risk of CKD development after kidney donation. A detailed pack-years smoking history should be obtained, and smoking cessation strategies should be implemented in kidney donors.