Acute kidney injury in patients with severe sepsis in Finnish Intensive Care Units.

BACKGROUND: Severe sepsis is one of the leading causes of acute kidney injury (AKI). Patients with sepsis-associated AKI demonstrate high-hospital mortality. We evaluated the incidence of severe sepsis-associated AKI and its association with outcome in intensive care units (ICUs) in Finland. METHODS: This was a predetermined sub-study of the prospective, observational, multicentre FINNAKI study conducted in 17 ICUs during 1 September 2011 and 1 February 2012. All emergency ICU admissions and elective admissions exceeding 24 hours in the ICU were screened for presence of severe sepsis and AKI up to 5 days in ICU. AKI was defined according to the Kidney Disease: Improving Global Outcomes (KDIGO) criteria and severe sepsis according to the American College of Chest Physicians/Society of Critical Care Medicine (ACCP/SCCM) criteria. RESULTS: Of the 2901 included patients, severe sepsis was diagnosed in 918 (31.6%, 95% confidence interval [CI] 29.9-33.4%) patients. Of these 918 patients, 488 (53.2% [95% CI 49.9-56.5%]) had AKI. The 90-day mortality rate was 38.1% (95% CI 33.7-42.5%) for severe sepsis patients with AKI and 24.7% (95% CI 20.5-28.8%) for those without AKI. After adjusting for covariates, KDIGO stage 3 AKI was associated with an increased risk for 90-day mortality with an adjusted odds ratio (OR) of 1.94 (95% CI 1.28-2.94), but stages 1 and 2 were not. CONCLUSIONS: More than half of the patients with severe sepsis had AKI according to the KDIGO classification, and AKI stage 3 was independently associated with 90-day mortality.

Association of automated data collection and data completeness with outcomes of intensive care. A new customised model for outcome prediction.

BACKGROUND: The Finnish Intensive Care Consortium coordinates a national intensive care benchmarking programme. Clinical information systems (CISs) that collect data automatically are widely used. The aim of this study was to explore whether the severity of illness-adjusted hospital mortality of Finnish intensive care unit (ICU) patients has changed in recent years and whether the changes reflect genuine improvements in the quality of care or are explained by changes in measuring severity of illness. METHODS: We retrospectively analysed data collected prospectively to the database of the Consortium. During the years 2001-2008, there were 116,065 admissions to the participating ICUs. We excluded readmissions, cardiac surgery patients, patients under 18 years of age and those discharged from an ICU to another hospital's ICU. The study population comprised 85,547 patients. The Simplified Acute Physiology Score II (SAPS II) was used to measure severity of illness and to calculate standardised mortality ratios (SMRs, the number of observed deaths divided by the number of expected deaths). RESULTS: The overall hospital mortality rate was 18.4%. The SAPS II-based SMRs were 0.74 in 2001-2004 and 0.64 in 2005-2008. The severity of illness-adjusted odds of death were 24% lower in 2005-2008 than in 2001-2004. One fifth of this computational difference could be explained by differences in data completeness and the automation of data collection with a CIS. CONCLUSION: The use of a CIS and improving data completeness do decrease severity-adjusted mortality rates. However, this explains only one fifth of the improvement in measured outcomes of intensive care in Finland.

Application of spectral entropy to EEG and facial EMG frequency bands for the assessment of level of sedation in ICU.

The applicability and performance of spectral entropy as a measure of the depth of sedation was studied by comparison to the Richmond sedation and agitation scale (RASS). A biopotential signal was measured from the forehead of eight ICU patients. From this biopotential four different frequency bands were defined using trend fitting to the low and high frequency limits of the pooled power spectra, two frequency bands representing EEG and the other two representing fEMG. The spectral entropy from the EEG bands correlated very well with the sedation levels of RASS. From levels 0 to -5 the decrease was almost linear (r=0.51 and r=0.53). A similar comparison for the spectral entropy of the fEMG bands did not produce any clear correlation (r=0.07 for both fEMG bands), however there was still some clear interaction at some levels. It seems that the RASS is dependent upon both EEG and fEMG effects. That is; RASS is related to both cortical and sub-cortical components of sedation.

Higuchi fractal dimension and spectral entropy as measures of depth of sedation in intensive care unit.

The ability of two easy-to-calculate nonlinear parameters, the Higuchi fractal dimension (HDf) and spectral entropy, to follow the depth of sedation in the intensive care unit is assessed. For comparison, the relative beta ratio is calculated. The results are evaluated using clinical assessment of the Ramsay score. The results show that the HD/sub f/ discriminates well between Ramsay scores 2-4 while beta ratio is superior for deeper levels of sedation. The value of the HD/sub f/ correlates highly with the cutoff frequency of the low-pass prefilter while spectral entropy is sensitive to the length of the analysis window.

On what are our end-of-life decisions based?

BACKGROUND: The incidence of withholding and withdrawing life support from the critically ill has increased in recent years. The aim of this study was to assess the degree of consistency between the weight assigned by intensivists to different determinants and their relation to end-of-life decisions, and to evaluate the current concepts in withholding or withdrawing intensive care in Nordic countries. METHODS: Forty-one intensivists from Nordic countries completed a questionnaire sent by e-mail: consistency between contributing factors and the decisions regarding 10 actual cases was evaluated by logistic regression analysis and by the classification (leave-one-out) method. Concepts in management after the withdrawal decision were also analyzed. RESULTS: The median (range) number of withdrawals per physician was four (range 0-10) out of 10 cases. No single factor was an independent covariant of all decisions made. The classification method revealed that approximately 70% only of decisions could be predicted correctly. Different actions taken after a decision to withdraw intensive care varied from 9.8% (discontinuing ventilator therapy) to 97.6% (informing relatives). CONCLUSIONS: No generally accepted grounds for end-of-life decisions could be detected among Nordic intensivists. In addition, the current concept of management after decision to withdraw therapy varies markedly. This study has implications in further assessment of the individual decision-making process and the uniformity of actions after withdrawal decisions.