Scoring System for Mortality in Patients Diagnosed with and Treated Surgically for Differentiated Thyroid Carcinoma with a 20-Year Follow-Up

Abstract

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.

A scoring system to predict recurrence in patients with differentiated thyroid cancer

“…As with other scoring systems, we have to note that the results must be validated in different samples of patients, both the discriminatory capacity of our points system and the calibration . Thus, if we obtain a satisfactory C‐statistic and there are no differences between the expected and the observed risks, we shall be able to state that our model has been validated for use in general clinical practice . This validation process should be undertaken in other geographical areas and in different populations, as there might be differences with the study population used here.…”

A scoring system to predict 5-year mortality in patients diagnosed with laryngeal glottic cancer

“…Although it might appear that our model is not entirely correct, since it includes factors that do not reach statistical significance, it should be noted that the factors were not evaluated independently, but rather they were assessed together to verify whether the model, as a whole, successfully predicted the patient's prognosis (López-Bru, Palazón-Bru, Folgado-de la Rosa, & Gil-Guillén, 2015). This assessment was satisfactory, as can be seen in the discrimination and calibration results.…”

Construction and internal validation of a new mortality risk score for patients admitted to the intensive care unit

“…The combination providing a greater AUC was selected, i.e. that combination which best determined which patients died in the ICU . Having estimated the model with the best possible combination, this was adapted to a points system using the Framingham Heart Study methodology.…”

“…that combination which best determined which patients died in the ICU. 17,23 Having estimated the model with the best possible combination, this was adapted to a points system using the Framingham Heart Study methodology. This methodology classifies the factors of the model into categories and weighs their coefficients to give each category a score with a given probability, so that it is easy to use in routine clinical practice.…”

“…The main strength of our work lies in the construction and internal validation of a predictive model that is very simple to use, free and has we must consider that we are not assessing each variable separately, but all together, that is, we are evaluating the goodness-of-fit of the global model, 23 which was very satisfactory (χ 2 =296.82, p<.001). Finally, we have not directly introduced the comorbidities of each patient (preexisting medical conditions).…”