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

OBJECTIVES: Though predictive models have been constructed to determine the risk of recurrence in differentiated thyroid carcinoma, various aspects of these models are inadequate. Therefore, we aimed to construct, internally validate and implement on a mobile application a scoring system to determine this risk within 10 years. DESIGN: A retrospective cohort study in 1984-2016. SETTING: A Spanish region. PARTICIPANTS: We enrolled 200 patients with differentiated thyroid carcinoma without distant metastasis at diagnosis. MAIN OUTCOME MEASURES: Time-to-recurrence. A risk table was constructed based on the sum of points to estimate the likelihood of recurrence. The model was internally validated and implemented as a mobile application for Android. RESULTS: Predictive factors were follicular histology, T, N and multifocality. This risk table had a C-statistic of 0.723. The calibration was satisfactory. CONCLUSIONS: This study provides an instrument able to predict rapidly and very simply which patients with differentiated thyroid carcinoma have a greater risk of recurrence.

Screening Tool to Determine Risk of Having Muscle Dysmorphia Symptoms in Men Who Engage in Weight Training at a Gym.

OBJECTIVE: Although 2 screening tests exist for having a high risk of muscle dysmorphia (MD) symptoms, they both require a long time to apply. Accordingly, we proposed the construction, validation, and implementation of such a test in a mobile application using easy-to-measure factors associated with MD. DESIGN: Cross-sectional observational study. SETTING: Gyms in Alicante (Spain) during 2013 to 2014. PARTICIPANTS: One hundred forty-one men who engaged in weight training. ASSESSMENT OF RISK FACTORS: The variables are as follows: age, educational level, income, buys own food, physical activity per week, daily meals, importance of nutrition, special nutrition, guilt about dietary nonadherence, supplements, and body mass index (BMI). A points system was constructed through a binary logistic regression model to predict a high risk of MD symptoms by testing all possible combinations of secondary variables (5035). The system was validated using bootstrapping and implemented in a mobile application. MAIN OUTCOME MEASURES: High risk of having MD symptoms (Muscle Appearance Satisfaction Scale). RESULTS: Of the 141 participants, 45 had a high risk of MD symptoms [31.9%, 95% confidence interval (CI), 24.2%-39.6%]. The logistic regression model combination providing the largest area under the receiver operating characteristic curve (0.76) included the following: age [odds ratio (OR) = 0.90; 95% CI, 0.84-0.97, P = 0.007], guilt about dietary nonadherence (OR = 2.46; 95% CI, 1.06-5.73, P = 0.037), energy supplements (OR = 3.60; 95% CI, 1.54-8.44, P = 0.003), and BMI (OR = 1.33, 95% CI, 1.12-1.57, P < 0.001). The points system was validated through 1000 bootstrap samples. CONCLUSIONS: A quick, easy-to-use, 4-factor test that could serve as a screening tool for a high risk of MD symptoms has been constructed, validated, and implemented in a mobile application.

A critical appraisal of the clinical applicability and risk of bias of the predictive models for mortality and recurrence in patients with oropharyngeal cancer: Systematic review.

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.

A cardiovascular risk score for hypertensive patients previously admitted to hospital.

BACKGROUND: Cardiovascular risk scales in hypertensive populations have limitations for clinical practice. AIMS: To develop and internally validate a predictive model to estimate one-year cardiovascular risk for hypertensive patients admitted to hospital. METHODS: Cohort study of 303 hypertensive patients admitted through the Emergency Department in a Spanish region in 2015-2017. The main variable was the onset of cardiovascular disease during follow-up. The secondary variables were: gender, age, educational level, family history of cardiovascular disease, Charlson score and its individual conditions, living alone, quality of life, smoking, blood pressure, physical activity and adherence to the Mediterranean diet. A Cox regression model was constructed to predict cardiovascular disease one year after admission. This was then adapted to a points system, externally validated by bootstrapping (discrimination and calibration) and implemented in a mobile application for Android. RESULTS: A total of 93 patients developed cardiovascular disease (30.7%) over a mean period of 1.68 years. The predictors in the points system were: gender, age, myocardial infarction, heart failure, peripheral arterial disease and daily activity (quality of life). The internal validation by bootstrapping was satisfactory. CONCLUSION: A novel points system was developed to predict short-term cardiovascular disease in hypertensive patients after hospital admission. External validation studies are needed to corroborate the results obtained.