Effects of Trigger Algorithms on Trigger Performance and Patient–Ventilator Synchrony

Background: Patient-ventilator synchrony is essential for successful patient-triggered ventilation. This study compared the ability of a trigger algorithm, based on detailed analysis of flow changes (IntelliSync+, Hamilton Medical), to trigger patient breaths with conventional algorithms. Methods: Three models with different lung mechanics (normal, ARDS, and COPD) at 3 severities were simulated with a lung model ventilated in pressure control continuous mandatory ventilation or pressure control continuous spontaneous ventilation (PC-CSV). Inspiratory pressure above PEEP was set at 15 cm H₂O and PEEP at 5 cm H₂O. Inspiratory trigger was selected from IntelliSync+ (IS+insp), flow trigger (1- 5 L/min), or pressure trigger (-1 to -5 cm H₂O). In PC-CSV, expiratory trigger was set at IntelliSync+ (IS+exp) or cycling criteria (5%, 25%, and 40% for ARDS, normal, and COPD, respectively). Measurements were performed with and without leak (50% inspiratory tidal volume). Five breaths per condition were collected to calculate trigger delay time and asynchronous events. Results: For pressure trigger, none of the conditions resulted in 3 successfully triggered consecutive breaths. Overall trigger delay time was significantly longer with flow trigger than with IS+insp in normal (99 vs 81 ms without leak, P < .001; 98 vs 80 ms with leak, P < .001) and ARDS models (334 vs 223 ms without leak, P < .001; 320 vs 236 ms with leak, P = .02). Across all conditions, ineffective efforts occurred more frequently with flow trigger than with IS+insp (7.3% vs 1.5% without leak, P = .01; 10.8% vs 3.0% with leak, P = .01). In PC-CSV, overall cycling delay time with IS+exp was equivalent or longer compared with cycling criteria. Conclusions: In this lung model study, IS+insp demonstrated similar trigger time and fewer ineffective efforts compared with flow trigger even in simulated respiratory conditions, whereas cycling delay time was unaffected by IS+exp because of large variations between conditions.