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

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.

Construction, internal validation and implementation in a mobile application of a scoring system to predict nonadherence to proton pump inhibitors

BACKGROUND

Other studies have assessed nonadherence to proton pump inhibitors (PPIs), but none has developed a screening test for its detection.

OBJECTIVES

To construct and internally validate a predictive model for nonadherence to PPIs.

METHODS

This prospective observational study with a one-month follow-up was carried out in 2013 in Spain, and included 302 patients with a prescription for PPIs. The primary variable was nonadherence to PPIs (pill count). Secondary variables were gender, age, antidepressants, type of PPI, non-guideline-recommended prescription (NGRP) of PPIs, and total number of drugs. With the secondary variables, a binary logistic regression model to predict nonadherence was constructed and adapted to a points system. The ROC curve, with its area (AUC), was calculated and the optimal cut-off point was established. The points system was internally validated through 1,000 bootstrap samples and implemented in a mobile application (Android).

RESULTS

The points system had three prognostic variables: total number of drugs, NGRP of PPIs, and antidepressants. The AUC was 0.87 (95% CI [0.83-0.91], p < 0.001). The test yielded a sensitivity of 0.80 (95% CI [0.70-0.87]) and a specificity of 0.82 (95% CI [0.76-0.87]). The three parameters were very similar in the bootstrap validation.

CONCLUSIONS

A points system to predict nonadherence to PPIs has been constructed, internally validated and implemented in a mobile application. Provided similar results are obtained in external validation studies, we will have a screening tool to detect nonadherence to PPIs.

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

BACKGROUND

The existing models to predict mortality in intensive care units (ICU) present difficulties in clinical practice.

OBJECTIVES

The aim of this study was to develop and internally validate a points system to predict mortality in the ICU, which can be applied instantly and with high discriminating power.

METHODS

This cohort study comprised all patients admitted to the ICU in a Spanish region between January 2013 and April 2014, followed from admission to death or discharge (N=1113). Primary variable: ICU mortality. Secondary variables at admission: gender, Fried criteria for frailty, function scale, medical admission, cardiac arrest, cardiology admission, sepsis, mechanical ventilation, inotropic support, age, frailty index and clinical frailty scale. The sample was divided randomly into two groups (80% and 20%): construction (n=844) and internal validation (n=269). Construction: A logistic regression model was implemented and adapted to the points system.

VALIDATION

the area under the ROC curve (AUC) of the model was calculated and the risk quintiles were created to determine whether differences existed between observed and expected deaths.

RESULTS

The points system included: function scale, medical admission, cardiology admission, sepsis, mechanical ventilation and inotropic support. The validation showed: (i) AUC=0.95 (95% CI: 0.91-0.99, p<.001); (ii) No differences between observed and expected deaths (p=.799).

CONCLUSIONS

A predictive model of mortality in the ICU has been constructed and internally validated. This model improves on the previous models through its simplicity, its discriminating power and free use. External validation studies are needed in other geographical areas.

Construction and Validation of a 14-Year Cardiovascular Risk Score for Use in the General Population: The Puras-GEVA Chart

The current cardiovascular risk tables are based on a 10-year period and therefore, do not allow for predictions in the short or medium term. Thus, we are unable to take more aggressive therapeutic decisions when this risk is very high.To develop and validate a predictive model of cardiovascular disease (CVD), to enable calculation of risk in the short, medium and long term in the general population.Cohort study with 14 years of follow-up (1992-2006) was obtained through random sampling of 342,667 inhabitants in a Spanish region.

MAIN OUTCOME

time-to-CVD. The sample was randomly divided into 2 parts [823 (80%), construction; 227 (20%), validation]. A stepwise Cox model was constructed to determine which variables at baseline (age, sex, blood pressure, etc) were associated with CVD. The model was adapted to a points system and risk groups based on epidemiological criteria (sensitivity and specificity) were established. The risk associated with each score was calculated every 2 years up to a maximum of 14. The estimated model was validated by calculating the C-statistic and comparison between observed and expected events.In the construction sample, 76 patients experienced a CVD during the follow-up (82 cases per 10,000 person-years). Factors in the model included sex, diabetes, left ventricular hypertrophy, occupational physical activity, age, systolic blood pressure × heart rate, number of cigarettes, and total cholesterol. Validation yielded a C-statistic of 0.886 and the comparison between expected and observed events was not significant (P: 0.49-0.75).We constructed and validated a scoring system able to determine, with a very high discriminating power, which patients will develop a CVD in the short, medium, and long term (maximum 14 years). Validation studies are needed for the model constructed.