The Probability of Lung Cancer in Patients With Incidentally Detected Pulmonary Nodules: Clinical Characteristics and Accuracy of Prediction Models.

BACKGROUND: The frequency of cancer and accuracy of prediction models have not been studied in large, population-based samples of patients with incidental pulmonary nodules measuring > 8 mm in diameter. RESEARCH QUESTIONS: How does the frequency of cancer vary by size and smoking history among patients with incidental nodules? How accurate are two widely used models for identifying cancer in these patients? STUDY DESIGN AND METHODS: We assembled a retrospective cohort of individuals with incidental nodules measuring > 8 mm in diameter identified by chest CT imaging between 2006 and 2016. We used a validated natural language processing algorithm to identify nodules and their characteristics by scanning the text of dictated radiology reports. We reported patient and nodule characteristics stratified by the presence or absence of a lung cancer diagnosis within 27 months of nodule identification and estimated the area under the receiver operating characteristic curve (AUC) to compare the accuracy of the Mayo Clinic and Brock models for identifying cancer. RESULTS: The sample included 23,780 individuals with a nodule measuring > 8 mm, including 2,356 patients (9.9%) with a lung cancer diagnosis within 27 months of nodule identification. Cancer was diagnosed in 5.4% of never smokers, 12.2% of former smokers, and 17.7% of current smokers. Cancer was diagnosed in 5.7% of patients with nodules measuring 9 to 15 mm, 12.1% of patients with nodules > 15 to 20 mm, and 18.4% of patients with nodules > 20 to 30 mm. In the full sample, the Mayo Clinic model (AUC, 0.747; 95% CI, 0.737-0.757) was more accurate than the Brock model (AUC, 0.713; 95% CI, 0.702-0.724; P < .0001). When restricted to ever smokers, the Mayo Clinic model was still more accurate. Both models overestimated the probability of cancer. INTERPRETATION: Almost 10% of patients with an incidental pulmonary nodule measuring > 8 mm in diameter will receive a lung cancer diagnosis. Existing prediction models have only fair accuracy and overestimate the probability of cancer.

Predictive models of infection in patients with systemic lupus erythematosus: A systematic literature review.

INTRODUCTION: Having reliable predictive models of prognosis/the risk of infection in systemic lupus erythematosus (SLE) patients would allow this problem to be addressed on an individual basis to study and implement possible preventive or therapeutic interventions. OBJECTIVE: To identify and analyze all predictive models of prognosis/the risk of infection in patients with SLE that exist in medical literature. METHODS: A structured search in PubMed, Embase, and LILACS databases was carried out until May 9, 2020. In addition, a search for abstracts in the American Congress of Rheumatology (ACR) and European League Against Rheumatism (EULAR) annual meetings' archives published over the past eight years was also conducted. Studies on developing, validating or updating predictive prognostic models carried out in patients with SLE, in which the outcome to be predicted is some type of infection, that were generated in any clinical context and with any time horizon were included. There were no restrictions on language, date, or status of the publication. To carry out the systematic review, the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) guideline recommendations were followed. The PROBAST tool (A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies) was used to assess the risk of bias and the applicability of each model. RESULTS: We identified four models of infection prognosis in patients with SLE. Mostly, there were very few events per candidate predictor. In addition, to construct the models, an initial selection was made based on univariate analyses with no contraction of the estimated coefficients being carried out. This suggests that the proposed models have a high probability of overfitting and being optimistic. CONCLUSIONS: To date, very few prognostic models have been published on the infection of SLE patients. These models are very heterogeneous and are rated as having a high risk of bias and methodological weaknesses. Despite the widespread recognition of the frequency and severity of infections in SLE patients, there is no reliable predictive prognostic model that facilitates the study and implementation of personalized preventive or therapeutic measures.Protocol registration number: PROSPERO CRD42020171638.

Modelo de predicción del riesgo a corto plazo en pacientes con síndrome coronario agudo sin elevación del segmento ST / Prediction model of short-term risk in patients with acute coronary syndrome without ST segment elevation

Introducción: teniendo en cuenta los diversos factores relacionados con el pronóstico adverso en pacientes con síndrome coronario agudo sin elevación del segmento ST, es importante determinarlos que se relacionan con aumento en la tasa de eventos. Objetivo: desarrollar un modelo de predicción a corto plazo en pacientes con síndrome coronario agudo sin elevación del segmento ST, con base en las escalas TIMI y GRACE, que incluya otras variables predictoras. Metodología: estudio observacional, analítico, de cohorte prospectiva, de desarrollo de un modelo de regresión logística, en pacientes mayores de 18 años con diagnóstico de síndrome coronario agudo sin elevación del segmento ST, que ingresan a dos centros con unidad de cuidados coronarios. Se construyeron modelos de predicción con las escalas de riesgo GRACE y TIMI como modelos independientes (modelo nulo), y comparados con un modelo de dos o tres variables formado por cada una de las escalas asociado a la creatinina y la fracción de eyección (Modelo completo). El desenlace evaluado fue el compuesto de muerte, reinfarto, ACV y sangrado. Resultados: se recolectaron datos de 422 pacientes que ingresaron con impresión diagnóstica de SCA sin elevación del segmento ST y tuvieron seguimiento al menos durante el primer mes posterior el evento. El promedio de edad fue de 64.36 ± 11.9 años, el 54.1% fueron hombres y la mayoría ingresaron con diagnóstico de infarto sin elevación del segmento ST (52.7%). La mayoría de los pacientes ingresaron en Killip I (90.8%) y el acceso vascular para el cateterismo fue radial en el 57.7%. La discriminación de los dos modelos es adecuada con estadístico C de 0.65 para TIMI y 0.69 para GRACE. La comparación de las dos curvas ROC no demuestra diferencias estadísticamente significativas (p=0.39). Los modelos completos demuestran mejor poder predictivo; sin embargo la diferencia no es significativa. Los dos modelos finales muestran adecuada calibración (Hosmer Lemershow p=0.96 para la escala TIMI y 0.86 para la escala GRACE). Conclusión: en pacientes con síndrome coronario agudo sin elevación del segmento ST los modelos basados en las escalas TIMI y GRACE predicen adecuadamente el riesgo de eventos a corto plazo. (Acta Med Colomb 2015; 40: 109-117).

Introduction: considering the various factors associated with adverse prognosis in patients with acute coronary syndrome without ST-segment elevation, is important to identify those factors associated with an increase in the rate of events. Objective: to develop a prediction model of short-term risk in patients with acute coronary syndrome without ST segment elevation, based on the TIMI and GRACE scales, including other predictor variables. Methodology: observational, analytical, prospective cohort study of development of a logistic regression model, in patients older than 18 years diagnosed with acute coronary syndrome without ST segment elevation, entering two coronary care unit centers. Prediction models were constructed with risk scales GRACE and TIMI as independent models (null model), and compared with a modelof 2 or 3 variables formed by each of the scales associated with creatinine and ejection fraction (full Model ) .The outcome evaluated was the composite of death, re-infarction, stroke and bleeding.Results: data from 422 patients admitted with diagnostic impression of ACS without ST-segment elevation who were followed for at least the first month after the event, were collected. The average age was 64.36 ± 11.9 years. 54.1% were men and most were admitted with diagnosis of myocardial infarction without ST segment elevation (52.7%). Most patients were admitted in Killip I (90.8%) and vascular access for catheterization was radial at 57.7%. Discrimination of the two models is adequate with C statistic of 0.65 for TIMI and 0.69 for GRACE. Comparison of the two ROC curves shows no statistically significant difference (p = 0.39). Complete models show better predictive power, but the difference is not significant. The final two models show proper calibration (p = 0.96 HosmerLemershow for the TIMI scale and 0.86 for the GRACE scale). Conclusion: in patients with acute coronary syndrome without ST-segment elevation models based on the TIMI and GRACE scales adequately predict the risk of short-term events. (Acta MedColomb 2015; 40: 109-117).