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.

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 Clı ´nico-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 preexisting 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,

GATA4 and GATA6 are transcription factors involved in the differentiation and development of PDAC. GATA6 expression is related to the classic molecular subtype, while its absence is related to the basal-like molecular subtype. The aim was to determine the clinical utility of IHC determination of GATA4 and GATA6 in a series of patients with resected PDAC. GATA4 and GATA6 expression was studied by IHC in TMA samples of normal tissue, PanIN, tumor tissue and lymph node metastases from a series of 89 patients with resected PDAC. Its relationship with clinicopathologic variables and the outcome was investigated. Seventy-two (81%) tumors were GATA6+ and 37 (42%) were GATA4+. While GATA4 expression was reduced during tumor progression, GATA6 expression remained highly conserved, except in lymph node metastases. All patients with early stages and well-differentiated tumors were GATA6+. The absence of GATA4 expression was related to smoking. Patients with GATA4+ or GATA6+ tumors had significantly lower Ca 19.9 levels. The expression of GATA4 and GATA6 was related to DFS, being more favorable in the GATA4+/GATA6+ group. The determination of the expression of GATA4 and GATA6 by IHC is feasible and provides complementary clinical and prognostic information that can help improve the stratification of patients with PDAC.

AimsTo assess the impact of anticoagulant treatment on risk for stroke and all-cause mortality of patients with atrial fibrillation using real-world data (RWD).MethodsPatients with prevalent or incident atrial fibrillation were selected throughout a study period of 5 years. Stroke, transitory ischemic attack, hemorrhagic stroke, and all-cause mortality were identified in the claims of the electronic health records (EHRs). Subjects were classified according to the anticoagulant treatment in four groups: untreated, vitamin K antagonists (VKAs), New Oral Anticoagulants (NOACs), and antiplatelet (AP). Risk of events and protection with anticoagulant therapy were calculated by Cox proportional hazard models adjusted by potential confounders.ResultsFrom a total population of 3,799,884 patients older than 18,123,227 patients with incident or prevalent atrial fibrillation (AF) were identified (mean age 75.2 ± 11.5 years old; 51.9% women). In a follow-up average of 3.2 years, 17,113 patients suffered from an ischemic stroke and transitory ischemic attack (TIA), 780 hemorrhagic stroke, and 42,558 all-cause death (incidence of 46, 8, 2, and 120 per 1,000 patients/year, respectively). Among CHA2DS2, VASc Score equal or >2, 11.7% of patients did not receive any anticoagulant therapy, and a large proportion of patients, 47%, shifted from one treatment to another. Although all kinds of anticoagulant treatments were significantly protective against the events and mortality, NOAC treatment offered significantly better protection compared to the other groups.ConclusionIn the real world, the use of anticoagulant treatments is far from guidelines recommendations and is characterized by variability in their use. NOACs offered better protection compared with VKAs.

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AimsThe objective of the present study is to assess the bidirectional association between heart failure (HF) and atrial fibrillation (AF) using real-world data. Methods and resultsFrom an electronic health recording with a population of 3 799 885 adult subjects, those with prevalent or incident HF were selected and followed throughout a study period of 5 years. Prevalence and incidence of AF, and their impact in the risk for acute HF hospitalization, worsening renal function, ischaemic and haemorrhagic stroke, and all-cause mortality were identified. We analysed all incident and prevalent patients with HF and AF, 128 086 patients (S1), and subsequently analysed a subset of patients with incident HF and AF, 57 354 patients (S2). We analysed all incident and prevalent patients with HF and AF, 128 086 patients (S1), and subsequently a subset of patients with incident HF and AF, 57 354 patients (S2). The prevalence of AF was 59 906 (46.7%) of the HF patients, while incidence in the S2 was 231/1000 patients/year. In both cohorts, S1 and S2, AF significantly increases the risk of acute heart failure hospitalization [incidence 79.1/1000 and 97.5/1000 patients/year; HR 1.53 (1.48-1.59 95% CI) and HR 1.32 (1.24-1.41 95% CI), respectively], risk of decreased renal function (eGFR reduced by >20%) [66.2/1000 and 94.0/1000 patients/year; HR 1.13 (1.09-1.18 95% CI) and HR 1.22 (1.14-1.31 95% CI), respectively] and all-cause mortality [203/1000 and 294/1000 patients/year; HR 1.62 (1.58-1.65 95% CI) and HR 1.65 (1.59-1.70 95% CI), respectively]. The number of episodes of hospitalization for acute heart failure was also significantly higher in the AF patients (27 623 vs. 10 036, P < 0.001). However, the risk for ischaemic stroke was reduced in the AF subjects [HR 0.66 (0.63-0.74 95% CI)], probably due to the anticoagulant treatment. Conclusions AF is associated with an increment in the risk of episodes of acute heart failure as well as decline of renal function and increment of all-cause mortality.

Artificial Intelligence is creating a paradigm shift in health care, with phenotyping patients through clustering techniques being one of the areas of interest. Objective: To develop a predictive model to classify heart failure (HF) patients according to their left ventricular ejection fraction (LVEF), by using available data from Electronic Health Records (EHR). Subjects and methods: 2854 subjects over 25 years old with a diagnosis of HF and LVEF, measured by echocardiography, were selected to develop an algorithm to predict patients with reduced EF using supervised analysis. The performance of the developed algorithm was tested in heart failure patients from Primary Care. To select the most influentual variables, the LASSO algorithm setting was used, and to tackle the issue of one class exceeding the other one by a large amount, we used the Synthetic Minority Oversampling Technique (SMOTE). Finally, Random Forest (RF) and XGBoost models were constructed. Results: The full XGBoost model obtained the maximum accuracy, a high negative predictive value, and the highest positive predictive value. Gender, age, unstable angina, atrial fibrillation and acute myocardial infarct are the variables that most influence EF value. Applied in the EHR dataset, with a total of 25,594 patients with an ICD-code of HF and no regular follow-up in cardiology clinics, 6170 (21.1%) were identified as pertaining to the reduced EF group. Conclusion: The obtained algorithm was able to identify a number of HF patients with reduced ejection fraction, who could benefit from a protocol with a strong possibility of success. Furthermore, the methodology can be used for studies using data extracted from the Electronic Health Records.

The objective is to assess the impact of anticoagulant treatment in non-valvular atrial fibrillation (AF) and different categories of renal dysfunction in real world. Electronic Health recordings of patients with diagnosis of AF and renal function collected throughout 5 years and classified according to KDIGO categories. Stroke, transitory ischemic attack (TIA), intracranial hemorrhage and all-cause mortality were identified. Anticoagulant treatments during the study period were classified in untreated (never received therapy), VKA, NOAC and Aspirin. The risk of events was calculated by Cox-proportional hazard models adjusted by confounders. A total of 65,734 patients with AF, mean age 73.3 ± 10.49 years old and 47% females and follow-up of 3.2 years were included. KDIGO classification were: G1 33,903 (51.6%), G2 17,456 (26.6%), G3 8024 (12.2%) and G4 6351 (9.7%). There were 8592 cases of stroke and TIA, 437 intracranial hemorrhage, and 9603 all-cause deaths (incidence 36, 2 and 38 per 103 person/year, respectively). 4.1% of patients with CHA2DS2-VASc Score 2 or higher did not receive anticoagulant therapy. Risk of stroke, TIA, and all-cause mortality increased from G1 to G4 groups. Anticoagulant treatments reduced the risk of events in the four categories, but NOAC seemed to offer significantly better protection. Renal dysfunction increases the risk of events in AF and anticoagulant treatments reduced the risk of stroke and all-cause mortality, although NOAC were better than VKA. Efforts should be done to reduce the variability in the use of anticoagulants even in this high risk group.