ORIGINAL ARTICLES

Mutation prediction models in Lynch syndrome: evaluation in a clinical genetic setting

D Ramsoekh^1,2, M E van Leerdam¹, A Wagner³, E J Kuipers^1,4, E W Steyerberg²

¹ Department of Gastroenterology and Hepatology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
² Department of Public Health, Erasmus MC University Medical Center, Rotterdam, The Netherlands
³ Department of Clinical Genetics, Erasmus MC University Medical Center, Rotterdam, The Netherlands
⁴ Department of Internal Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands

^{Correspondence to} Dr D Ramsoekh, Department of Gastroenterology and Hepatology & Department of Public Health, Erasmus MC University Medical Center, ‘s Gravendijkwal 230, 3015 CE Rotterdam, The Netherlands; d.ramsoekh{at}erasmusmc.nl

Background/aims: The identification of Lynch syndrome is hampered by the absence of specific diagnostic features and underutilisation of genetic testing. Prediction models have therefore been developed, but they have not been validated for a clinical genetic setting. The aim of the present study was to evaluate the usefulness of currently available prediction models.

Methods: The authors collected data of 321 index probands who were referred to the department of clinical genetics of the Erasmus Medical Center because of a family history of colorectal cancer. These data were used as input for five previously published models. External validity was assessed by discriminative ability (AUC: area under the receiver operating characteristic curve) and calibration. For further insight, predicted probabilities were categorised with cut-offs of 5%, 10%, 20% and 40%. Furthermore, costs of different testing strategies were related to the number of extra detected mutation carriers.

Results: Of the 321 index probands, 66 harboured a germline mutation. All models discriminated well between high risk and low risk index probands (AUC 0.82–0.84). Calibration was well for the Premm_1,2 and Edinburgh model, but poor for the other models. Cut-offs could be found for the prediction models where costs could be saved while missing only few mutations.

Conclusions: The Edinburgh and Premm_1,2 model were the models with the best performance for an intermediate to high risk setting. These models may well be of use in clinical practice to select patients for further testing of mismatch repair gene mutations.

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