TY - JOUR
T1 - A laparoscopic risk-adjusted model to predict major complications after primary debulking surgery in ovarian cancer: A single-institution assessment
AU - Vizzielli, Giuseppe
AU - Costantini, Barbara
AU - Tortorella, Lucia
AU - Pitruzzella, I.
AU - Gallotta, Valerio
AU - Fanfani, Francesco
AU - Gueli Alletti, Salvatore
AU - Cosentino, Francesco
AU - Nero, Camilla
AU - Scambia, Giovanni
AU - Fagotti, Anna
PY - 2016
Y1 - 2016
N2 - Objective To develop and validate a simple adjusted laparoscopic score to predict major postoperative complications after primary debulking surgery (PDS) in advanced epithelial ovarian cancer (AEOC). Methods From January 2006 to June 2015, preoperative, intraoperative, and post-operative outcome data from patients undergoing staging laparoscopy (S-LPS) before receiving PDS (n = 555) were prospectively collected in an electronic database and retrospectively analyzed. Major complications were defined as levels 3 to 5 of MSKCC classification. On the basis of a multivariate regression model, the score was developed using a random two-thirds of the population (n = 370) and was validated on the remaining one-third patients (n = 185). Results Major complication rate was 18.3% (102/555). Significant predictors included in the scoring system were: poor performance status, presence of ascites (> 500 cm3), CA125 serum level (> 1000 U/ml), and high laparoscopic tumor load (predictive index value, PIV ≥ 8). The mean risk of developing major postoperative complications was 3.7% in patients with score 0 to 2, 13.2% in patients with score 3 to 5, 37.1% in patients with score 6 to 8. In the validation population, the predicted risk of major complications was 17.8% (33/185) versus a 16.7% (31/185) observed risk (C-statistic index = 0.790). Conclusion This new score may accurately predict a patient's postoperative outcome. Early identification of high-risk patients could help the surgeon to adopt tailored strategies on individual basis.
AB - Objective To develop and validate a simple adjusted laparoscopic score to predict major postoperative complications after primary debulking surgery (PDS) in advanced epithelial ovarian cancer (AEOC). Methods From January 2006 to June 2015, preoperative, intraoperative, and post-operative outcome data from patients undergoing staging laparoscopy (S-LPS) before receiving PDS (n = 555) were prospectively collected in an electronic database and retrospectively analyzed. Major complications were defined as levels 3 to 5 of MSKCC classification. On the basis of a multivariate regression model, the score was developed using a random two-thirds of the population (n = 370) and was validated on the remaining one-third patients (n = 185). Results Major complication rate was 18.3% (102/555). Significant predictors included in the scoring system were: poor performance status, presence of ascites (> 500 cm3), CA125 serum level (> 1000 U/ml), and high laparoscopic tumor load (predictive index value, PIV ≥ 8). The mean risk of developing major postoperative complications was 3.7% in patients with score 0 to 2, 13.2% in patients with score 3 to 5, 37.1% in patients with score 6 to 8. In the validation population, the predicted risk of major complications was 17.8% (33/185) versus a 16.7% (31/185) observed risk (C-statistic index = 0.790). Conclusion This new score may accurately predict a patient's postoperative outcome. Early identification of high-risk patients could help the surgeon to adopt tailored strategies on individual basis.
KW - Laparoscopy
KW - Obstetrics and Gynecology
KW - Oncology
KW - Ovarian cancer
KW - Post-operative complications
KW - Predictive model
KW - Laparoscopy
KW - Obstetrics and Gynecology
KW - Oncology
KW - Ovarian cancer
KW - Post-operative complications
KW - Predictive model
UR - http://hdl.handle.net/10807/91615
UR - http://www.elsevier.com/inca/publications/store/6/2/2/8/4/0/index.htt
U2 - 10.1016/j.ygyno.2016.04.020
DO - 10.1016/j.ygyno.2016.04.020
M3 - Article
SN - 0090-8258
VL - 142
SP - 19
EP - 24
JO - Gynecologic Oncology
JF - Gynecologic Oncology
ER -