Effectiveness of an Algorithmic Approach to Ventilator Withdrawal at the End of Life: A Stepped Wedge Cluster...

Background: The transition to spontaneous breathing puts patients who are undergoing ventilator withdrawal at high risk for developing respiratory distress. A patient-centered algorithmic approach could standardize this process and meet unique patient needs because a single approach (weaning vs. one-step extubation) does not capture the needs of a heterogenous population undergoing this palliative procedure. Objectives: (1) Demonstrate that the algorithmic approach can be effective to ensure greater patient respiratory comfort compared to usual care; (2) determine differences in opioid or benzodiazepine use; (3) predict factors associated with duration of survival. Design/Settings/Measures: A stepped-wedge cluster randomized design at five sites was used. Sites crossed over to the algorithm in random order after usual care data were obtained. Patient comfort was measured with the Respiratory Distress Observation Scale© (RDOS) at baseline, at ventilator off, and every 15-minutes for an hour. Parenteral morphine and lorazepam equivalents from the onset of the process until patient death were calculated. Results: Usual care data n = 120, algorithm data n = 48. Gender and race were evenly distributed. All patients in the usual care arm underwent a one-step ventilator cessation; 58% of patients in the algorithm arm were weaned over an average of 18 ± 27 minutes as prescribed in the algorithm. Patients had significantly less respiratory distress in the intervention arm (F = 10.41, p = 0.0013, effective size [es] = 0.49). More opioids (t = -2.30, p = 0.023) and benzodiazepines (t = -2.08, p = 0.040) were given in the control arm. Conclusions: The algorithm was effective in ensuring patient respiratory comfort. Surprisingly, more medication was given in the usual care arm; however, less may be needed when distress is objectively measured (RDOS), and treatment is initiated as soon as distress develops as in the algorithm. Clinical Trial Registration number: NCT03121391.