Flow and airway pressure analysis for detecting ineffective effort during mechanical ventilation in neuromuscular patients.
Cristina CiorbaJesus Gonzalez-BermejoMaria-Antonia Quera SalvaDjillali AnnaneDavid OrlikowskiFrédéric LofasoHélène PrigentPublished in: Chronic respiratory disease (2018)
Ineffective efforts (IEs) are among the most common types of patient-ventilator asynchrony. The objective of this study is to validate IE detection during expiration using pressure and flow signals, with respiratory effort detection by esophageal pressure (Pes) measurement as the reference, in patients with neuromuscular diseases (NMDs). We included 10 patients diagnosed with chronic respiratory failure related to NMD. Twenty-eight 5-minute recordings of daytime ventilation were studied for IE detection. Standard formulas were used to calculate sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of IE detection using pressure and flow signals compared to Pes measurement. Mean sensitivity and specificity of flow and pressure signal-based IE detection versus Pes measurement were 97.5% ± 5.3% and 91.4% ± 13.7%, respectively. NPV was 98.1% ± 8.2% and PPV was 67.6% ± 33.8%. Spearman's rank correlation coefficient indicated a moderately significant correlation between frequencies of IEs and controlled cycles ( ρ = 0.50 and p = 0.01). Among respiratory cycles, 311 (11.2%) were false-positive IEs overall. Separating false-positive IEs according to their mechanisms, we observed premature cycling in 1.2% of cycles, delayed ventilator triggering in 0.1%, cardiac contraction in 9.2%, and upper airway instability during expiration in 0.3%. Using flow and pressure signals to detect IEs is a simple and rapid method that produces adequate data to support clinical decisions.
Keyphrases
- mechanical ventilation
- loop mediated isothermal amplification
- respiratory failure
- end stage renal disease
- acute respiratory distress syndrome
- newly diagnosed
- ejection fraction
- intensive care unit
- label free
- real time pcr
- chronic kidney disease
- prognostic factors
- left ventricular
- physical activity
- heart failure
- big data
- magnetic resonance
- sensitive detection
- patient reported outcomes
- smooth muscle
- quality improvement
- obstructive sleep apnea
- machine learning
- contrast enhanced