RESUMEN
To determine whether elevated intra-abdominal pressure (IAP) is associated with a higher rate of enteral nutrition-related gastrointestinal (GI) complications; to assess the value of IAP as a predictor of enteral nutrition (EN) intolerance. Intensive Care Unit (ICU) patients on mechanical ventilation requiring at least 5 days of EN were recruited for a prospective, observational, non-interventional, multicenter study. EN was performed and GI complications were managed with an established protocol. IAP was determined via a urinary catheter. Patients who developed any GI complications were considered as presenting EN intolerance. Variables related to EN, IAP and GI complications were monitored daily. Statistical analysis compared patients without GI complications (group A) vs. GI complications (group B). 247 patients were recruited from 28 participating ICUs (group A: 119, group B: 128). No differences between groups were recorded. Patients in group B (p < 0.001) spent more days on EN (8.1 ± 8.4 vs. 18.1 ± 13.7), on mechanical ventilation (8.0 ± 7.7 vs. 19.3 ± 14.9) and in the ICU (12.3 ± 11.4 vs. 24.8 ± 17.5). IAP prior to the GI complication was (14.3 ± 3.1 vs. 15.8 ± 4.8) (p < 0.003). The best IAP value identified for EN intolerance was 14 mmHg but it had low sensitivity and specificity. Although a higher IAP was associated with EN intolerance, IAP alone did not emerge as a good predictor of EN intolerance in critically ill patients.
Asunto(s)
Abdomen , Enfermedad Crítica/terapia , Nutrición Enteral/efectos adversos , Enfermedades Gastrointestinales/etiología , Presión , Anciano , Biomarcadores , Cuidados Críticos , Femenino , Humanos , Unidades de Cuidados Intensivos , Masculino , Persona de Mediana Edad , Estudios Prospectivos , Respiración ArtificialRESUMEN
Atrial fibrillation (AF) is a sustained cardiac arrhythmia associated with stroke, heart failure, and related health conditions. Though easily diagnosed upon presentation in a clinical setting, the transient and/or intermittent emergence of AF episodes present diagnostic and clinical monitoring challenges that would ideally be met with automated ambulatory monitoring and detection. Current approaches to address these needs, commonly available both in smartphone applications and dedicated technologies, combine electrocardiogram (ECG) sensors with predictive algorithms to detect AF. These methods typically require extensive preprocessing, preliminary signal analysis, and the integration of a wide and complex array of features for the detection of AF events, and are consequently vulnerable to over-fitting. In this paper, we introduce the application of symbolic recurrence quantification analysis (SRQA) for the study of ECG signals and detection of AF events, which requires minimal pre-processing and allows the construction of highly accurate predictive algorithms from relatively few features. In addition, this approach is robust against commonly-encountered signal processing challenges that are expected in ambulatory monitoring contexts, including noisy and non-stationary data. We demonstrate the application of this method to yield a highly accurate predictive algorithm, which at optimal threshold values is 97.9% sensitive, 97.6% specific, and 97.7% accurate in classifying AF signals. To confirm the robust generalizability of this approach, we further evaluated its performance in the implementation of a 10-fold cross-validation paradigm, yielding 97.4% accuracy. In sum, these findings emphasize the robust utility of SRQA for the analysis of ECG signals and detection of AF. To the best of our knowledge, the proposed model is the first to incorporate symbolic analysis for AF beat detection.