Objective: Various features have been identified as predictors of relapse after complete resection of pheochromocytoma, but a comprehensive multivariable model for r ecurrence risk prediction is lacking. The aim of this study was to develop and internally validate an integra ted predictive model for post-surgical recurrence of pheochromocytoma. Methods: The present research retrospectively enrolled 177 patients affe cted by pheochromocytoma and submitted to radical surgery from 1990 to 2016, in nine referral centers for adrenal diseases. Cox regression analysis was adopted for model development, and a bootstrapping procedure was used f or internal validation. Results: Variables independently associated with recurrence were tumor size (hazard ratio (HR): 1.01, 95% CI: 1.00–1.02), positive genetic testing (HR: 5.14, 95% CI: 2.10–12 .55), age (HR: 0.97, 95% CI: 0.94–0.99), and Pheochromocytoma of the Adrenal Gland Scaled Score (PASS) (HR: 1.16, 95% CI: 1.04–1.29). The predictive performance of the overall model, evaluated by Somers’ D, was e qual to 0.594, and was significantly higher than the ones of any single predictor alone (P = 0.002 compared to tumor size; P = 0.004 compared to genetic testing; P = 0.048 compared to age; P = 0.006 compared to PASS). Internal validation by bootstrapping techniques estimated an optimistic bias of 6.3%, which reassured about a small tende ncy towards overfit. Conclusions: We proposed a multivariable model for the prediction of post-s urgical recurrence of pheochromocytoma, derived by the integration of genetic, histopathological, and c linical data. This predictive tool may be of value for a comprehensive tailoring of post-surgical follow-up in radically operated pheochromocytoma patients.

Development and internal validation of a predictive model for the estimation of pheochromocytoma recurrence risk after radical surgery

Parasiliti-Caprino, Mirko;Bioletto, Fabio;Lopez, Chiara;Maletta, Francesca;Caputo, Marina;Gasco, Valentina;Limone, Paolo;Volante, Marco;Papotti, Mauro;Terzolo, Massimo;Morino, Mario;Pasini, Barbara;Veglio, Franco;Ghigo, Ezio;Arvat, Emanuela;Maccario, Mauro
2022

Abstract

Objective: Various features have been identified as predictors of relapse after complete resection of pheochromocytoma, but a comprehensive multivariable model for r ecurrence risk prediction is lacking. The aim of this study was to develop and internally validate an integra ted predictive model for post-surgical recurrence of pheochromocytoma. Methods: The present research retrospectively enrolled 177 patients affe cted by pheochromocytoma and submitted to radical surgery from 1990 to 2016, in nine referral centers for adrenal diseases. Cox regression analysis was adopted for model development, and a bootstrapping procedure was used f or internal validation. Results: Variables independently associated with recurrence were tumor size (hazard ratio (HR): 1.01, 95% CI: 1.00–1.02), positive genetic testing (HR: 5.14, 95% CI: 2.10–12 .55), age (HR: 0.97, 95% CI: 0.94–0.99), and Pheochromocytoma of the Adrenal Gland Scaled Score (PASS) (HR: 1.16, 95% CI: 1.04–1.29). The predictive performance of the overall model, evaluated by Somers’ D, was e qual to 0.594, and was significantly higher than the ones of any single predictor alone (P = 0.002 compared to tumor size; P = 0.004 compared to genetic testing; P = 0.048 compared to age; P = 0.006 compared to PASS). Internal validation by bootstrapping techniques estimated an optimistic bias of 6.3%, which reassured about a small tende ncy towards overfit. Conclusions: We proposed a multivariable model for the prediction of post-s urgical recurrence of pheochromocytoma, derived by the integration of genetic, histopathological, and c linical data. This predictive tool may be of value for a comprehensive tailoring of post-surgical follow-up in radically operated pheochromocytoma patients.
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Parasiliti-Caprino, Mirko; Bioletto, Fabio; Lopez, Chiara; Maletta, Francesca; Caputo, Marina; Gasco, Valentina; La Grotta, Antonio; Limone, Paolo; Borretta, Giorgio; Volante, Marco; Papotti, Mauro; Terzolo, Massimo; Morino, Mario; Pasini, Barbara; Veglio, Franco; Ghigo, Ezio; Arvat, Emanuela; Maccario, Mauro
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2318/1843042
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