Model-Based Estimation of Inspiratory Effort Using Surface EMG.

OBJECTIVE: The quantification of inspiratory patient effort in assisted mechanical ventilation is essential for the adjustment of ventilatory assistance and for assessing patient-ventilator interaction. The inspiratory effort is usually measured via the respiratory muscle pressure (P mus) derived from esophageal pressure (P es) measurements. As yet, no reliable non-invasive and unobtrusive alternatives exist to continuously quantify P mus. METHODS: We propose a model-based approach to estimate P mus non-invasively during assisted ventilation using surface electromyographic (sEMG) measurements. The method combines the sEMG and ventilator signals to determine the lung elastance and resistance as well as the neuromechanical coupling of the respiratory muscles via a novel regression technique. Using the equation of motion, an estimate for P mus can then be calculated directly from the lung mechanical parameters and the pneumatic ventilator signals. RESULTS: The method was applied to data recorded from a total of 43 ventilated patients and validated against P es-derived P mus. Patient effort was quantified via the P mus pressure-time-product (PTP). The sEMG-derived PTP estimated using the proposed method was highly correlated to P es-derived PTP ([Formula: see text]), and the breath-wise deviation between the two quantities was [Formula: see text]. CONCLUSION: The estimated, sEMG-derived P mus is closely related to the P es-based reference and allows to reliably quantify inspiratory effort. SIGNIFICANCE: The proposed technique provides a valuable tool for physicians to assess patients undergoing assisted mechanical ventilation and, thus, may support clinical decision making.

Mental disorders at the beginning of adolescence: Prevalence estimates in a sample aged 11-14 years.

Objectives: This study aims to provide a deeper insight into mental disorders in early adolescence. We report prevalence rates (mental health problems, depressive symptoms, eating disorders, NSSI, STBs) to be used in future studies and clinical ventures. We also expected to find gender differences, with girls being be more affected than boys are. Study design: 877 adolescents (M = 12.43, SD = 0.65) from seven German high schools completed a series of questionnaires assessing their mental health (SDQ, PHQ-9, SEED, DSHI-9, Paykel Suicide Scale, FAS III). Methods: We calculated cut-off-based prevalence estimates for mental health issues for the whole sample and compared estimates between genders. Results: 12.5% of the sample reported general mental health problems. The estimated prevalence of depressive symptoms lay at of 11.5%. Additionally, 12.1% and 1.3% of the participants displayed relevant symptoms of anorexia or bulimia nervosa, respectively. A total of 10.8% reported engaging in non-suicidal self-injury (NSSI) at least once in their lifetime, of whom 5.6% reported repetitive NSSI. 30.1% of the participants described suicidal thoughts, 9.9% suicide plans, and 3.5% at least one suicide attempt. Girls were generally more affected than boys, except for bulimia nervosa, suicidal behavior, and partly NSSI. Conclusion: Our findings corroborate the established relevance of early adolescence for the development of mental health problems and suggest that a substantial proportion of young adolescents suffer from such problems early on. Considering the ongoing COVID-19 pandemic and reported negative mental health consequences, the current findings underline the importance of preventive interventions to avoid the manifestation of mental disorders during adolescence.

Automatic Estimation of Respiratory Effort using Esophageal Pressure.

Esophageal pressure is currently seen as the gold standard to quantify the respiratory effort during assisted spontaneous ventilation. Yet, the assessment of waveforms at the bedside is often complicated due to heavy interference by cardiac artifacts and due to the unknown dependency on the lung volume. We propose an algorithm that automatically removes artifacts and gives an estimate for the respiratory effort of a patient. The estimator is based on fitting a respiratory system model to the Campbell diagram and, thus, also gives insight into important patient parameters like the chest wall elastance. The feasibility of our approach is demonstrated using clinical datasets of patients on pressure support ventilation. The algorithm facilitates the interpretation of ventilatory waveforms and may support the overall assessment of patients.