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

Is China's fifth-largest inland lake to dry-up? Incorporated hydrological and satellite-based methods for forecasting Hulun Lake water levels

Hulun Lake, China’s fifth-largest inland lake, experienced severe declines in water level in the period of 2000-2010. This has prompted concerns whether the lake is drying up gradually. A multi-million US dollar engineering project to construct a water channel to transfer part of the river flow from a nearby river to maintain the water level was completed in August 2010. This study aimed to advance the understanding of the key processes controlling the lake water level variation over the last five decades, as well as investigate the impact of the river transfer engineering project on the water level. A water balance model was developed to investigate the lake water level variations over the last five decades, using hydrological and climatic data as well as satellite-based measurements and results from land surface modelling. The investigation reveals that the severe reduction of river discharge (- 364±64 mm/yr, ~70% of the five-decade average) into the lake was the key factor behind the decline of the lake water level between 2000 and 2010. The decline of river discharge was due to the reduction of total runoff from the lake watershed. This was a result of the reduction of soil moisture due to the decrease of precipitation (-49±45 mm/yr) over this period. The water budget calculation suggests that the groundwater component from the surrounding lake area as well as surface run off from the un-gauged area surrounding the lake contributed ~ net 210 Mm3/yr (equivalent to ~ 100 mm/yr) water inflows into the lake. The results also show that the water diversion project did prevent a further water level decline of over 0.5 m by the end of 2012. Overall, the monthly water balance model gave an excellent prediction of the lake water level fluctuation over the last five decades and can be a useful tool to manage lake water resources in the future.