The state of the art of EGFR exon 20 insertions in non-small cell lung cancer: Diagnosis and future perspectives

Insertions in the epidermal growth factor receptor (EGFR) exon 20 (Ex20Ins) are the third most incident mutations in non-small cell lung cancer (NSCLC). The hypervariable nature of these driver mutations hinders their identification by traditional polymerase chain reaction (PCR)-based methods, requiring a comprehensive sequencing approach to detect all possible insertions. The prognosis of patients with EGFR Ex20Ins is similar to those with wild-type NSCLC, since no targeted drugs are approved in the first-line setting, and platinum-based chemotherapy is currently the front-line treatment. However, the new generation of drugs currently being tested in first and post-platinum settings will likely change the management of this entity. Here, we summarize the latest data on EGFR Ex20Ins molecular characteristics, patient profile, identification challenges, and emerging therapies to help lung clinicians face a growing treatment landscape.

Comparison of GLP-1 receptor agonists and other Glucose-Lowering agents on cardiovascular outcomes in individuals with type 2 diabetes and Obesity: A Spanish Real-World Population-Based study

AIMS

Assess the impact of glucagon-like peptide receptor agonists (GLP-1RA) compared to other glucose-lowering agents on cardiovascular outcomes in individuals with type 2 diabetes and obesity in a Spanish metropolitan area.

METHODS

A retrospective population-based type 2 diabetes cohort was identified from the Valencia Clinic-Malvarrosa Department electronic databases (2014-2019). Study groups included GLP-1RA, sodium-glucose co-transporter-2 inhibitors (SGLT2i), Insulin, and Miscellany (other glucose-lowering agents). 1:1:1:1 propensity score matching was conducted. The primary outcome was a composite of major adverse cardiovascular events (4-point MACE) comprising myocardial infarction, stroke, all-cause mortality, and heart failure. Secondary outcomes included individual 4-point MACE components. Hazard ratios were estimated using Cox regression analyses against the Miscellany group.

RESULTS

From 26,944 subjects, 1,848 adults were selected per group. GLP-1RA did not show a significant reduction in 4-point MACE risk (HR 1.05 [95%CI 0.82-1.34]). SGLT2i significantly reduced the risk of heart failure (HR 0.16 [95%CI 0.05-0.54]) and atrial fibrillation (HR 0.58, [95%CI 0.35-0.95]). The Insulin group exhibited a higher risk for 4-point MACE and most individual outcomes compared to GLP-1RA and SGLT2i.

CONCLUSIONS

Our findings do not provide evidence of a reduced cardiovascular risk, as assessed by 4-point MACE, with GLP-1RA. In contrast, SGLT2i demonstrated protective effects against heart failure and atrial fibrillation.

Real-World Evaluation of GLP-1 Receptor Agonist Therapy Persistence, Adherence and Therapeutic Inertia Among Obese Adults with Type 2 Diabetes

INTRODUCTION

In type 2 diabetes (T2D), key barriers to optimal glycaemic control include lack of persistence with treatment, reduced medication adherence and therapeutic inertia. This study aimed to assess the impact of these barriers in obese adults with type 2 diabetes treated with a GLP-1 receptor agonist (GLP-1RA) and compare them against other glucose-lowering agents in a real-world setting.

METHODS

A retrospective study was conducted using electronic medical records from 2014 to 2019 for adults with T2D at the Valencia Clínico-Malvarrosa Department of Health (Valencia, Spain). Four study groups were established: all GLP-1RA users, SGLT2i users, insulin users and other glucose-lowering agent users (miscellany group). To account for imbalance between groups, propensity score matching (PSM) including age, gender and pre-existing cardiovascular disease was performed. Chi-square tests were used for comparisons between groups. Time to first intensification was calculated using competing risk analysis.

RESULTS

Among the 26,944 adults with T2D, 7392 individuals were selected following PSM, with 1848 patients in each group. At 2 years, GLP-1RA users were less persistent than non-users (48.4% versus 72.7%, p < 0.0001) but more adherent (73.8% versus 68.9%, respectively, p < 0.0001). A greater proportion of persistent GLP-1RA users than non-persistent users exhibited reduced HbA1c (40.5% versus 18.6%, respectively, p < 0.0001), but no differences in cardiovascular outcomes and death were found. Overall, therapeutic inertia was observed in 38.0% of the study population. The large majority of GLP-1RA users received treatment intensification, whereas only 50.0% of GLP-1RA non-users were intensified.

CONCLUSION

Under real-life conditions, obese adults with T2D persistently treated with GLP-1RA showed improved glycaemic control. Despite benefits, persistence with GLP-1RA was limited after 2 years. Additionally, therapeutic inertia occurred in two out of three study participants. Strategies to facilitate medication adherence, persistence and treatment intensification in people with T2D should be made a priority in order to achieve and maintain glycaemic targets and improve outcomes in this population.

TRAIL REGISTRATION

Study registered in clinicaltrials.org with the identifier NCT05535322.

Development of a Minimally Invasive Screening Tool to Identify Obese Pediatric Population at Risk of Obstructive Sleep Apnea/Hypopnea Syndrome

Obstructive sleep apnea syndrome is a reduction of the airflow during sleep which not only produces a reduction in sleep quality but also has major health consequences. The prevalence in the obese pediatric population can surpass 50%, and polysomnography is the current gold standard method for its diagnosis. Unfortunately, it is expensive, disturbing and time-consuming for experienced professionals. The objective is to develop a patient-friendly screening tool for the obese pediatric population to identify those children at higher risk of suffering from this syndrome. Three supervised learning classifier algorithms (i.e., logistic regression, support vector machine and AdaBoost) common in the field of machine learning were trained and tested on two very different datasets where oxygen saturation raw signal was recorded. The first dataset was the Childhood Adenotonsillectomy Trial (CHAT) consisting of 453 individuals, with ages between 5 and 9 years old and one-third of the patients being obese. Cross-validation was performed on the second dataset from an obesity assessment consult at the Pediatric Department of the Hospital General Universitario of Valencia. A total of 27 patients were recruited between 5 and 17 years old; 42% were girls and 63% were obese. The performance of each algorithm was evaluated based on key performance indicators (e.g., area under the curve, accuracy, recall, specificity and positive predicted value). The logistic regression algorithm outperformed (accuracy = 0.79, specificity = 0.96, area under the curve = 0.9, recall = 0.62 and positive predictive value = 0.94) the support vector machine and the AdaBoost algorithm when trained with the CHAT datasets. Cross-validation tests, using the Hospital General de Valencia (HG) dataset, confirmed the higher performance of the logistic regression algorithm in comparison with the others. In addition, only a minor loss of performance (accuracy = 0.75, specificity = 0.88, area under the curve = 0.85, recall = 0.62 and positive predictive value = 0.83) was observed despite the differences between the datasets. The proposed minimally invasive screening tool has shown promising performance when it comes to identifying children at risk of suffering obstructive sleep apnea syndrome. Moreover, it is ideal to be implemented in an outpatient consult in primary and secondary care.

A receiver operating characteristic (ROC) curve analysis of a model of mental health services use by Puerto Rican poor

In this study, the contribution of four distinct domains of the Help Seeking-Decision Making model to predicting the use of mental health services is examined. Using a proposed methodology the authors assess the relevance of this model and its domains to mental services planning. The methodology combines logistic regression analysis and receiver operating characteristic (ROC) curves. Logistic regression analysis allows us to examine the individual variables of the model and generate predictions about use. ROC curves allow us to compare and interpret the relative contribution of a predisposing domain, a physical and mental health domain, an enabling-restrictive domain, and an organizational domain in correctly classifying users and nonusers of mental health services. The physical and mental health domain yielded a Somer's D-statistic of 0.7, which corresponds to an 85% correct classification of randomly selected pairs of users and nonusers. The study findings suggest that comparing ROC curves helps to describe and interpret the domains of the model that are relevant for making predictions about who will or will not use mental health services during a 1-year period.