A nephrolithometric nomogram to predict treatment success of percutaneous nephrolithotomy.
OBJECTIVE
Imaging is routinely done preoperatively and postoperatively to assess patients treated with percutaneous nephrolithotomy. We developed a nomogram for percutaneous nephrolithotomy success.


METHODS
From November 2007 to December 2009 the CROES (Clinical Research Office of the Endourological Society) collected data on consecutive patients at 96 centers globally. Patients were evaluated for stone-free status using plain x-ray of the kidneys, ureters and bladder. Treatment success was defined as no visible stones or residual fragments less than 4 mm. Multivariate regression was used to model the relationship between preoperative descriptors and the stone-free rate. Variables included case load, prior treatment, body mass index, staghorn stones, renal anomalies, and stone burden, location and count. Bootstrapping techniques were used to validate the model. Adjusted chi-square statistic values were used to rank the prognostic value of variables. A nomogram was developed using significant predictors from the model. We assessed the predictive accuracy of the nomogram using the ROC curve AUC. The nomogram was calibrated.


RESULTS
Stone burden was the best predictor of the stone-free rate (chi-square = 30.27, p <0.001). Other factors associated with the stone-free rate were case volume (chi-square = 35.75, p <0.001), prior stone treatment (chi-square = 14.55, p <0.012), staghorn stone (adjusted chi-square = 4.73, p <0.029), stone location (chi-square = 14.74, p <0.001) and stone count (chi-square = 4.78, p <0.004). A nephrolithometric nomogram was developed with predictive accuracy (AUC 0.76).


CONCLUSIONS
The percutaneous nephrolithotomy stone-free rate can be predicted using preclinical data and radiological information. We present a nephrolithometric nomogram for percutaneous nephrolithotomy.
Department of Urology, Long Island Jewish Medical Center, New Hyde Park, New York, USA.
Arthur
A
Smith
Timothy D
TD
Averch
Khaled
K
Shahrour
Dedan
D
Opondo
Francisco P J
FP
Daels
Gaston
G
Labate
Burak
B
Turna
Jean J M C H
JJ
de la Rosette
D000328:Adult; D000368:Aged; D000704:Analysis of Variance; D015992:Body Mass Index; D015331:Cohort Studies; D016208:Databases, Factual; D005260:Female; D006801:Humans; D007669:Kidney Calculi / Q000175:diagnosis / Q000601:surgery*; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D019060:Minimally Invasive Surgical Procedures / Q000009:adverse effects / Q000379:methods; D015999:Multivariate Analysis; D009403:Nephrostomy, Percutaneous / Q000009:adverse effects / Q000379:methods*; D049451:Nomograms; D011237:Predictive Value of Tests; D011379:Prognosis; D012720:Severity of Illness Index; D016896:Treatment Outcome