The use of predictive models is becoming widespread. However, these models should be developed appropriately (CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modeling Studies [CHARMS] and Prediction model Risk Of Bias ASsessment Tool [PROBAST] statements). Concerning mortality/recurrence in oropharyngeal cancer, we are not aware of any systematic reviews of the predictive models. We carried out a systematic review of the MEDLINE/EMBASE databases of those predictive models. In these models, we analyzed the 11 domains of the CHARMS statement and the risk of bias and applicability, using the PROBAST tool. Six papers were finally included in the systematic review and all of them presented high risk of bias and several limitations in the statistical analysis. The applicability was satisfactory in five out of six studies. None of the models could be considered ready for use in clinical practice.

Introduction Predictive models must meet clinical/methodological standards to be used in clinical practice. However, no critique of those models relating to mortality/recurrence in tongue cancer has been done bearing in mind the accepted standards. Methods We conducted a systematic review evaluating the methodology and clinical applicability of predictive models for mortality/recurrence in tongue cancer published in MEDLINE and Scopus. For each model, we analysed (domains of CHARMS, Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) the following: source of data, participants, outcome to be predicted, candidate predictors, sample size, missing data, model development, model performance, model evaluation, results and interpretation and discussion. Results We found two papers that included eight prediction models, neither of which adhered to the CHARMS recommendations. Conclusion Given the quality of tongue cancer models, new studies following current consensus are needed to develop predictive tools applicable in clinical practice.

BackgroundDifferentiated thyroid carcinoma (DTC) is associated with an increased mortality. Few studies have constructed predictive models of all-cause mortality with a high discriminating power for patients with this disease that would enable us to determine which patients are more likely to die.ObjectiveTo construct a predictive model of all-cause mortality at 5, 10, 15 and 20 years for patients diagnosed with and treated surgically for DTC for use as a mobile application.DesignWe undertook a retrospective cohort study using data from 1984 to 2013.SettingAll patients diagnosed with and treated surgically for DTC at a general university hospital covering a population of around 200,000 inhabitants in Spain.ParticipantsThe study involved 201 patients diagnosed with and treated surgically for DTC (174, papillary; 27, follicular).ExposuresAge, gender, town, family history, type of surgery, type of cancer, histological subtype, microcarcinoma, multicentricity, TNM staging system, diagnostic stage, permanent post-operative complications, local and regional tumor persistence, distant metastasis, and radioiodine therapy.Main outcome measureAll-cause mortality.MethodsA Cox multivariate regression model was constructed to determine which variables at diagnosis were associated with mortality. Using the model a risk table was constructed based on the sum of all points to estimate the likelihood of death. This was then incorporated into a mobile application.ResultsThe mean follow-up was 8.8±6.7 years. All-cause mortality was 12.9% (95% confidence interval [CI]: 8.3–17.6%). Predictive variables: older age, local tumor persistence and distant metastasis. The area under the ROC curve was 0.81 (95% CI: 0.72–0.91, p<0.001).ConclusionThis study provides a practical clinical tool giving a simple and rapid indication (via a mobile application) of which patients with DTC are at risk of dying in 5, 10, 15 or 20 years. Nonetheless, caution should be exercised until validation studies have corroborated our results.

This study provides an instrument able to predict rapidly and very simply which patients with differentiated thyroid carcinoma have a greater risk of recurrence.