The lack of associations between alleles at the hypoxia-Inducible factor 1A C1772T loci and responses to acute hypoxia.

Objective The aim of this study was to investigate the associations between alleles of the hypoxia-inducible factor 1A (HIF1A) C1772T polymorphism and several physiological responses to hypoxia, including the hypoxic ventilatory response (HVR), and serum erythropoietin (EPO), arterial oxygen saturation (Sao2), and acute mountain sickness (AMS) responses during 8 hours of exposure to normobaric hypoxia. Methods A total of 76 males participated in the study; 52 participants completed an 8-hour exposure to 12.7% oxygen, during which time Sao2, EPO concentrations, and AMS scores were measured, while 62 individuals took part in an HVR trial (in total 38 individuals completed both protocols). DNA was obtained from leukocytes, and a 346-bp fragment of the HIF1A gene containing the C1772T polymorphism was amplified using polymerase chain reaction. Fragments were sequenced to reveal individual genotypes, and the associations between HIF1A genotype and EPO, Sao2, AMS responses to hypoxia and HVR were examined. Results The magnitude of the hypoxic responses was highly variable between individuals. The increase in participants' EPO responses ranged from 89% to 388% of baseline values following hypoxia, while Sao2 values during the exposure ranged from 71% to 89%. The HVR ranged from −0.04 to +2.18 L · min−1 · Sao2%−1 among participants. No significant differences in EPO, Sao2, AMS, or HVR results were observed between the HIF1A CC genotype and the combined CT/TT genotype group. Conclusion In this study, the HIF1A C1772T polymorphism does not appear to influence EPO, Sao2, or AMS responses during acute hypoxic exposure, or the magnitude of the HVR.

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 [rho] = 0.75) and were reliable in clinician-clinician (weighted kappa; [kappa] = 0.66) and clinician-researcher ([kappa] = 0.82) comparisons. The limb movement domain had fair correlation with Behavioral Pain Scale ([rho] = 0.24) and was reliable in clinician-clinician ([kappa] = 0.58) and clinician-researcher ([kappa] = 0.45) comparisons. Ventilator synchronization correlated with Behavioral Pain Scale ([rho] = 0.54), and reliability in clinician-clinician ([kappa] = 0.29) and clinician-researcher ([kappa] = 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