The Effect of High-Flow Nasal Cannula Oxygen Therapy on Mortality and Intubation Rate in Acute Respiratory Failure: A Systematic Review and Meta-Analysis.

OBJECTIVE: High-flow nasal cannulae are used in adults with or at risk of acute respiratory failure. We conducted a systematic review and meta-analysis to evaluate the evidence for their use in this setting. DATA SOURCES: Ovid Medline, Embase, and Cochrane Database of Systematic Reviews. STUDY SELECTION: Databases were searched for randomized controlled trials comparing administration of high-flow nasal cannulae with usual care (i.e., conventional oxygen therapy or noninvasive ventilation) in adults with respiratory failure. The primary outcome was hospital mortality; the rate of intubation and assessment of delirium and comfort were secondary outcomes. DATA EXTRACTION: One hundred forty-seven nonduplicate citations were screened, 32 underwent full screening and data extraction, and 14 trials were eligible for inclusion in the review. Nine trials were used in the meta-analysis, including a total of 2,507 subjects. DATA SYNTHESIS: When high-flow nasal cannulae were compared with usual care, there was no difference in mortality (high-flow nasal cannulae, 60/1,006 [6%] vs usual care, 90/1,106 [8.1%]) (n = 2,112; p = 0.29; I, 25%; fixed effect model: odds ratio, 0.83; 95% CI, 0.58-1.17) or rate of intubation (high-flow nasal cannulae, 119/1,207 [9.9%] vs usual care, 204/1,300 [15.7%]) (n = 2,507; p = 0.08; I, 53%; random effect model: odds ratio, 0.63; 95% CI, 0.37-1.06). A qualitative analysis of 13 studies on tolerability and comfort suggested that high-flow nasal cannulae are associated with improved patient comfort and dyspnea scores. Trial sequential analyses on primary and secondary outcomes suggested that required information size was not reached. CONCLUSIONS: No difference in mortality or intubation was detected in patients with acute respiratory failure treated with high-flow nasal cannulae compared with usual care. High-flow nasal cannulae seem well tolerated by patients. Further large randomized controlled trials are required to evaluate their utility in this setting.

Staff education, regular sedation and analgesia quality feedback, and a sedation monitoring technology for improving sedation and analgesia quality for critically ill, mechanically ventilated patients: a cluster randomised trial.

BACKGROUND: Optimal sedation of patients in intensive care units (ICUs) requires the avoidance of pain, agitation, and unnecessary deep sedation, but these outcomes are challenging to achieve. Excessive sedation can prolong ICU stay, whereas light sedation can increase pain and frightening memories, which are commonly recalled by ICU survivors. We aimed to assess the effectiveness of three interventions to improve sedation and analgesia quality: an online education programme; regular feedback of sedation-analgesia quality data; and use of a novel sedation-monitoring technology (the Responsiveness Index [RI]). METHODS: We did a cluster randomised trial in eight ICUs, which were randomly allocated to receive education alone (two ICUs), education plus sedation-analgesia quality feedback (two ICUs), education plus RI monitoring technology (two ICUs), or all three interventions (two ICUs). Randomisation was done with computer-generated random permuted blocks, stratified according to recruitment start date. A 45 week baseline period was followed by a 45 week intervention period, separated by an 8 week implementation period in which the interventions were introduced. ICU and research staff were not masked to study group assignment during the intervention period. All mechanically ventilated patients were potentially eligible. We assessed patients' sedation-analgesia quality for each 12 h period of nursing care, and sedation-related adverse events daily. Our primary outcome was the proportion of care periods with optimal sedation-analgesia, defined as being free from excessive sedation, agitation, poor limb relaxation, and poor ventilator synchronisation. Analysis used multilevel generalised linear mixed modelling to explore intervention effects in a single model taking clustering and patient-level factors into account. A concurrent mixed-methods process evaluation was undertaken to help understand the trial findings. The trial is registered with ClinicalTrials.gov, number NCT01634451. FINDINGS: Between June 1, 2012, and Dec 31, 2014, we included 881 patients (9187 care periods) during the baseline period and 591 patients (6947 care periods) during the intervention period. During the baseline period, optimal sedation-analgesia was present for 5150 (56%) care periods. We found a significant improvement in optimal sedation-analgesia with RI monitoring (odds ratio [OR] 1·44 [95% CI 1·07-1·95]; p=0·017), which was mainly due to increased periods free from excessive sedation (OR 1·59 [1·09-2·31]) and poor ventilator synchronisation (OR 1·55 [1·05-2·30]). However, more patients experienced sedation-related adverse events (OR 1·91 [1·02-3·58]). We found no improvement in overall optimal sedation-analgesia with education (OR 1·13 [95% CI 0·86-1·48]), but fewer patients experienced sedation-related adverse events (OR 0·56 [0·32-0·99]). The sedation-analgesia quality data feedback did not improve quality (OR 0·74 [95% CI 0·54-1·00]) or sedation-related adverse events (OR 1·15 [0·61-2·15]). The process evaluation suggested many clinicians found the RI monitoring useful, but it was often not used for decision making as intended. Education was valued and considered useful by staff. By contrast, sedation-analgesia quality feedback was poorly understood and thought to lack relevance to bedside nursing practice. INTERPRETATION: Combination of RI monitoring and online education has the potential to improve sedation-analgesia quality and patient safety in mechanically ventilated ICU patients. The RI monitoring seemed to improve sedation-analgesia quality, but inconsistent adoption by bedside nurses limited its impact. The online education programme resulted in a clinically relevant improvement in patient safety and was valued by nurses, but any changes to behaviours did not seem to alter other measures of sedation-analgesia quality. Providing sedation-analgesia quality feedback to ICUs did not appear to improve any quality metrics, probably because staff did not think it relevant to bedside practice. FUNDING: Chief Scientist Office, Scotland; GE Healthcare.

Development of Process Control Methodology for Tracking the Quality and Safety of Pain, Agitation, and Sedation Management in Critical Care Units.

OBJECTIVE: To develop sedation, pain, and agitation quality measures using process control methodology and evaluate their properties in clinical practice. DESIGN: A Sedation Quality Assessment Tool was developed and validated to capture data for 12-hour periods of nursing care. Domains included pain/discomfort and sedation-agitation behaviors; sedative, analgesic, and neuromuscular blocking drug administration; ventilation status; and conditions potentially justifying deep sedation. Predefined sedation-related adverse events were recorded daily. Using an iterative process, algorithms were developed to describe the proportion of care periods with poor limb relaxation, poor ventilator synchronization, unnecessary deep sedation, agitation, and an overall optimum sedation metric. Proportion charts described processes over time (2 monthly intervals) for each ICU. The numbers of patients treated between sedation-related adverse events were described with G charts. Automated algorithms generated charts for 12 months of sequential data. Mean values for each process were calculated, and variation within and between ICUs explored qualitatively. SETTING: Eight Scottish ICUs over a 12-month period. PATIENTS: Mechanically ventilated patients. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The Sedation Quality Assessment Tool agitation-sedation domains correlated with the Richmond Sedation Agitation Scale score (Spearman ρ = 0.75) and were reliable in clinician-clinician (weighted kappa; κ = 0.66) and clinician-researcher (κ = 0.82) comparisons. The limb movement domain had fair correlation with Behavioral Pain Scale (ρ = 0.24) and was reliable in clinician-clinician (κ = 0.58) and clinician-researcher (κ = 0.45) comparisons. Ventilator synchronization correlated with Behavioral Pain Scale (ρ = 0.54), and reliability in clinician-clinician (κ = 0.29) and clinician-researcher (κ = 0.42) comparisons was fair-moderate. Eight hundred twenty-five patients were enrolled (range, 59-235 across ICUs), providing 12,385 care periods for evaluation (range 655-3,481 across ICUs). The mean proportion of care periods with each quality metric varied between ICUs: excessive sedation 12-38%; agitation 4-17%; poor relaxation 13-21%; poor ventilator synchronization 8-17%; and overall optimum sedation 45-70%. Mean adverse event intervals ranged from 1.5 to 10.3 patients treated. The quality measures appeared relatively stable during the observation period. CONCLUSIONS: Process control methodology can be used to simultaneously monitor multiple aspects of pain-sedation-agitation management within ICUs. Variation within and between ICUs could be used as triggers to explore practice variation, improve quality, and monitor this over time.