Pressure-flow breath representation eases asynchrony identification in mechanically ventilated patients.

Breathing asynchronies are mismatches between the requests of mechanically ventilated subjects and the support provided by mechanical ventilators. The most widespread technique in identifying these pathological conditions is the visual analysis of the intra-tracheal pressure and flow time-trends. This work considers a recently introduced pressure-flow representation technique and investigates whether it can help nurses in the early detection of anomalies that can represent asynchronies. Twenty subjects-ten Intensive Care Unit (ICU) nurses and ten persons inexperienced in medical practice-were asked to find asynchronies in 200 breaths pre-labeled by three experts. The new representation increases significantly the detection capability of the subjects-average sensitivity soared from 0.622 to 0.905-while decreasing the classification time-from 1107.0 to 567.1 s on average-at the price of a not statistically significant rise in the number of wrong identifications-specificity average descended from 0.589 to 0.52. Moreover, the differences in experience between the nurse group and the inexperienced group do not affect the sensitivity, specificity, or classification times. The pressure-flow diagram significantly increases sensitivity and decreases the response time of early asynchrony detection performed by nurses. Moreover, the data suggest that operator experience does not affect the identification results. This outcome leads us to believe that, in emergency contexts with a shortage of nurses, intensive care nurses can be supplemented, for the sole identification of possible respiratory asynchronies, by inexperienced staff.

An effective pressure-flow characterization of respiratory asynchronies in mechanical ventilation.

Ineffective effort during expiration (IEE) occurs when there is a mismatch between the demand of a mechanically ventilated patient and the support delivered by a Mechanical ventilator during the expiration. This work presents a pressure-flow characterization for respiratory asynchronies and validates a machine-learning method, based on the presented characterization, to identify IEEs. 1500 breaths produced by 8 mechanically-ventilated patients were considered: 500 of them were included into the training set and the remaining 1000 into the test set. Each of them was evaluated by 3 experts and classified as either normal, artefact, or containing inspiratory, expiratory, or cycling-off asynchronies. A software implementing the proposed method was trained by using the experts' evaluations of the training set and used to identify IEEs in the test set. The outcomes were compared with a consensus of three expert evaluations. The software classified IEEs with sensitivity 0.904, specificity 0.995, accuracy 0.983, positive and negative predictive value 0.963 and 0.986, respectively. The Cohen's kappa is 0.983 (with 95% confidence interval (CI): [0.884, 0.962]). The pressure-flow characterization of respiratory cycles and the monitoring technique proposed in this work automatically identified IEEs in real-time in close agreement with the experts.

Neck circumference as reliable predictor of mechanical ventilation support in adult inpatients with COVID-19: A multicentric prospective evaluation.

AIMS: COVID-19 is especially severe for elderly subjects with cardiometabolic and respiratory comorbidities. Neck circumference (NC) has been shown to be strongly related to cardiometabolic and respiratory illnesses even after adjustment for body mass index (BMI). We performed a prospective study to investigate the potential of NC to predict the need for invasive mechanical ventilation (IMV) in adult COVID-19 inpatients. MATERIALS AND METHODS: We prospectively and consecutively enrolled COVID-19 adult patients admitted to dedicated medical wards of two Italian hospitals from 25 March to 7 April 2020. On admission, clinical, biochemical and anthropometric data, including BMI and NC were collected. As primary outcome measure, the maximum respiratory support received was evaluated. Follow-up time was 30 days from hospital admission. RESULTS: We enrolled 132 subjects (55.0-75.8 years, 32% female). During the study period, 26 (19.7%) patients underwent IMV. In multivariable logistic regression analyses, after adjusting for age, sex, diabetes, hypertension and COPD, NC resulted independently and significantly associated with IMV risk (adjusted OR 1.260-per 1 cm increase 95% CI:1.120-1.417; P < .001), with a stronger association in the subgroup with BMI ≤30 Kg/m2 (adjusted OR 1.526; 95% CI:1.243-1.874; P < .001). NC showed a good discrimination power in predicting patients requiring IMV (AUC 0.783; 95% CI:0.684-0.882; P < .001). In particular, NC > 40.5 cm (>37.5 for females and >42.5 for males) showed a higher and earlier IMV risk compared to subjects with lower NC (Log-rank test: P < .001). CONCLUSIONS: NC is an easy to measure parameter able to predict the need for IMV in adult COVID-19 inpatients.