American journal of surgery · 2020
A risk score model to predict incidental gallbladder cancer in patients scheduled for cholecystectomy.
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Muszynska Carolina, Nilsson Johan, Lundgren Linda, Lindell Gert, Andersson Roland, Sandström Per, Andersson Bodil
Аннотация
BACKGROUND: Gallbladder cancer (GBC) has a poor prognosis. The aim was to develop and validate a preoperative risk score for incidental gallbladder cancer (IGBC) in patients scheduled for cholecystectomy.
METHODS: Data registered in the nationwide Swedish Registry for Gallstone Surgery (GallRiks) was analyzed, including the derivation cohort (n = 28915, 2007-2014) and the validation cohort (n = 7851, 2014-2016). An additive risk score model based on odds ratio was created.
RESULTS: The scoring model to predict IGBC includes age, female gender, previous cholecystitis, and either jaundice or acute cholecystitis. The calibration by HL test and discrimination by AUROC was 8.27 (P = 0.291) and 0.76 in the derivation cohort (214 IGBC) and 14.28 (P = 0.027) and 0.79 in the validation cohort (35 IGBC). The scoring system was applied to three risk-groups, based on the risk of having IGBC, eg. the high-risk group (>8 points) included 7878 patients, with 154 observed and 148 expected IGBC cases.
CONCLUSION: We present the first risk score model to predict IGBC. The model estimates the expected risk for the individual patient and may help to optimize treatment strategies.
METHODS: Data registered in the nationwide Swedish Registry for Gallstone Surgery (GallRiks) was analyzed, including the derivation cohort (n = 28915, 2007-2014) and the validation cohort (n = 7851, 2014-2016). An additive risk score model based on odds ratio was created.
RESULTS: The scoring model to predict IGBC includes age, female gender, previous cholecystitis, and either jaundice or acute cholecystitis. The calibration by HL test and discrimination by AUROC was 8.27 (P = 0.291) and 0.76 in the derivation cohort (214 IGBC) and 14.28 (P = 0.027) and 0.79 in the validation cohort (35 IGBC). The scoring system was applied to three risk-groups, based on the risk of having IGBC, eg. the high-risk group (>8 points) included 7878 patients, with 154 observed and 148 expected IGBC cases.
CONCLUSION: We present the first risk score model to predict IGBC. The model estimates the expected risk for the individual patient and may help to optimize treatment strategies.