Development and validation of an integrated nomogram to predict personalized new baseline functional outcomes after partial nephrectomy.

Background: The prediction of new baseline renal function after partial nephrectomy (PN) has important clinical implications. This study aimed to establish a precise personalized nomogram integrating pre-, intra- and post-operative variables to predict new baseline function after PN.

Methods: This nomogram was constructed based on 213 consecutive PN cases at a large-volume institution from 2014 to 2017 and externally validated by a prospective cohort from January to December 2018 at the same institution. Multivariate cox regression and logistic least absolute shrinkage and selection operator (LASSO) regression were used to select predictors. The performance of the nomogram was assessed by the concordance index (C-index), calibration plot, decision curve analysis and Kaplan-Meier plot.

Results: The average drop percent of the estimated glomerular filtration rate (eGFR) was -8.6% (-12.3%, -7.2%). Multivariate Cox regression analysis and LASSO regression revealed that age, baseline eGFR, RENAL nephrometry score, ischemia time, and AKI were independent predictive factors. These five factors were subsequently incorporated to establish an integrated nomogram, with a C-index of 0.910, excellent calibration plot and net clinical benefit. An external validation of 67 patients showed a C-index of 0.801, excellent calibration and clinical net benefit.

Conclusions: Our proposed nomogram based on pre-, intra- and post-operative outcomes accurately predicts personalized new baseline eGFR after PN. The successful personalized prediction of at-risk individuals at an early stage can provide multi-professional consideration and timely management.