Impact of a national collaborative project to improve the care of mechanically ventilated patients

Abstract

This prospective quasi-experimental study from the NASAM (National Approach to Standardize and Improve Mechanical Ventilation) collaborative assessed the impact of evidence-based practices including subglottic suctioning, daily assessment for spontaneous awakening trial (SAT), spontaneous breathing trial (SBT), head of bed elevation, and avoidance of neuromuscular blockers unless otherwise indicated. The study outcomes included VAE (primary) and intensive care unit (ICU) mortality. Changes in daily care process measures and outcomes were evaluated using repeated measures mixed modeling. The results were reported as incident rate ratio (IRR) for each additional month with 95% confidence interval (CI). A comprehensive program that included education on evidence-based practices for optimal care of mechanically ventilated patients with real-time benchmarking of daily care process measures to drive improvement in forty-two ICUs from 26 hospitals in Saudi Arabia (>27,000 days of observation). Compliance with subglottic suctioning, SAT and SBT increased monthly during the project by 3.5%, 2.1% and 1.9%, respectively (IRR 1.035, 95%CI 1.007–1.064, p = 0.0148; 1.021, 95% CI 1.010–1.032, p = 0.0003; and 1.019, 95%CI 1.009–1.029, p = 0.0001, respectively). The use of neuromuscular blockers decreased monthly by 2.5% (IRR 0.975, 95%CI 0.953–0.998, p = 0.0341). The compliance with head of bed elevation was high at baseline and did not change over time. Based on data for 83153 ventilator days, VAE rate was 15.2/1000 ventilator day (95%CI 12.6–18.1) at baseline and did not change during the project (IRR 1.019, 95%CI 0.985–1.053, p = 0.2812). Based on data for 8523 patients; the mortality was 30.4% (95%CI 27.4–33.6) at baseline, and decreased monthly during the project by 1.6% (IRR 0.984, 95%CI 0.973–0.996, p = 0.0067). A national quality improvement collaborative was associated with improvements in daily care processes. These changes were associated with a reduction in mortality but not VAEs. Registration The study is registered in clinicaltrials.gov (NCT03790150).

Quality Indicators in Adult Critical Care Medicine

“…[ 50 ] The multicenter prospective quasi-experimental National Approach to Standardize and Improve Mechanical Ventilation collaborative assessed the impact of evidence-based practices (subglottic suctioning, daily assessment for spontaneous awakening trial, spontaneous breathing trial, head-of-bed elevation, and avoidance of neuromuscular blockers) in 42 ICUs from 26 hospitals in Saudi Arabia, using the CUSP model and interventions that included online educational activities and real-time benchmarking of daily care process measures to drive improvement. [ 51 ] The collaborative was associated with improvements in daily care processes and with a reduction in mortality. [ 51 ]…”

“…[ 51 ] The collaborative was associated with improvements in daily care processes and with a reduction in mortality. [ 51 ]…”

“…Sustaining quality of care requires continuous or periodic auditing of quality indicators, and so significant resources, which may not be available for many ICUs. Electronic health records may have the feature of automated extraction of data including quality indicators,[ 51 , 69 ] as well as the real-/near-real-time display and share of data in different formats including control charts and dashboards. [ 12 , 51 ] This may also allow for detecting variation in process or outcome measures and identifying data points (i.e., a significant decline in a certain measure) that should be investigated,[ 12 ] thus possibly contributing to the performance and sustainability of quality projects.…”

“…Electronic health records may have the feature of automated extraction of data including quality indicators,[ 51 , 69 ] as well as the real-/near-real-time display and share of data in different formats including control charts and dashboards. [ 12 , 51 ] This may also allow for detecting variation in process or outcome measures and identifying data points (i.e., a significant decline in a certain measure) that should be investigated,[ 12 ] thus possibly contributing to the performance and sustainability of quality projects. In practice, automated data extraction is currently limited by the variable capabilities of the different electronic health records and by the validity of entered data.…”