Evaluation of illness severity scoring systems and risk prediction in vascular intensive care admissions

Low interferon-gamma release in response to phytohemagglutinin predicts the high severity of diseases

A clinically useful immune biomarker could potentially assist clinicians in their decision making. We stimulated T-cell proliferation to secret interferon gamma (IFN-γ) by phytohemagglutinin, and then measured the production of IFN-γ (mitogen value [M value]). We aimed to determine the relationship between the M value, clinical severity, and outcomes of diseases.In all, 484 patients admitted to intensive care units were enrolled in this retrospective study. The Acute Physiology and Chronic Health Evaluation II (APACHE II) scores were collected within the first 24 hours. M value, C-reaction protein (CRP), procalcitonin (PCT), erythrocyte sedimentation rate (ESR), and routine blood tests were analyzed and collected during the study.When APACHE II scores were greater than 15 and M values were less than 6, the hospital mortality rose in a straight line. There was an inverse correlation between APACHE II score and M value (rs = -0.212, P < .001). There was a positive correlation between M value and lymphocyte numbers (b' = 0.249, P < .001); however, there was an inverse correlation between M value and WBC (b' = -0.230, P < .001), and ESR (b' = -0.100, P = .029). Neurological diseases had the greatest influence on APACHE II scores (b' = 10.356, P < .001), whereas respiratory diseases had the greatest influence on M value (b' = 1.933, P < .001). Furthermore, in the respiratory system, severe pneumonia had a greater influence on M value. Taking the APACHE II score as the gold standard, the area under the curve of M was 0.632 (95% confidence interval [CI] 0.575-0.690, P < .001), PCT was 0.647 (95% CI 0.589-0.705, P < .001), CRP was 0.570 (95% CI 0.511-0.629, P = .022), and ESR was 0.553 (95% CI 0.494-0.612, P = .078). Divided by M value = 5, the positive predictive value of the M value is 37.22% (115/309) and negative predictive value is 75.43% (132/175).The results show that the M values, PCT, and CRP were better than ESR to predict the severity of diseases. The number and proportion of lymphocytes also affected the result of the M value. To a certain extent, the M value may be a clinically useful immune biomarker, which may help clinicians objectively evaluate the severity of diseases, especially in the respiratory system.

Years of Life Lost After Complications of Coronary Artery Bypass Operations

BACKGROUND

We currently have an incomplete understanding of which postoperative complications after coronary artery bypass grafting (CABG) are associated with long-term death. The purpose of this study was to find the associations between complications and attributable death.

METHODS

Prospectively collected data on patient characteristics, risk factors, and complications of patients undergoing isolated CABG with 20-year follow-up were analyzed with a Cox regression model to calculate the overall hazard of dying associated with each postoperative complication. An individual's age and hazard of dying from each complication were then used to calculate years of life lost to each complication.

RESULTS

The postoperative mortality rate was 0.79% (69 of 8,773) at 30 days, 2.85% (250 of 8,773) at 180 days, and 6.38% (560 of 8,773) at 2 years. At a median follow-up of 9.8 years, 1,891 patients (21.6%) had died. Postoperative complications occurred in 3,438 patients (39.2%). Cardiac arrest (hazard ratio, 2.153), reoperation (hazard ratio, 1.679), and new dialysis (hazard ratio, 1.64) were the complications with the greatest hazard of death. After adjusting for complication incidence and patient age, cardiac arrest (703 years), reoperation (544 years), atrial fibrillation (470 years), and prolonged mechanical ventilation (371 years) were associated with the greatest number of years of life lost.

CONCLUSIONS

Acute cardiac arrest, reoperation for other cardiac reasons, new dialysis, atrial fibrillation, and prolonged mechanical ventilation are associated with the largest increase in attributable deaths. Prevention and treatment of these complications may improve mortality rates after cardiac operations.

To develop a regional ICU mortality prediction model during the first 24 h of ICU admission utilizing MODS and NEMS with six other independent variables from the Critical Care Information System (CCIS) Ontario, Canada

Background

Intensive care unit (ICU) scoring systems or prediction models evolved to meet the desire of clinical and administrative leaders to assess the quality of care provided by their ICUs. The Critical Care Information System (CCIS) is province-wide data information for all Ontario, Canada level 3 and level 2 ICUs collected for this purpose. With the dataset, we developed a multivariable logistic regression ICU mortality prediction model during the first 24 h of ICU admission utilizing the explanatory variables including the two validated scores, Multiple Organs Dysfunctional Score (MODS) and Nine Equivalents Nursing Manpower Use Score (NEMS) followed by the variables age, sex, readmission to the ICU during the same hospital stay, admission diagnosis, source of admission, and the modified Charlson Co-morbidity Index (CCI) collected through the hospital health records.

Methods

This study is a single-center retrospective cohort review of 8822 records from the Critical Care Trauma Centre (CCTC) and Medical-Surgical Intensive Care Unit (MSICU) of London Health Sciences Centre (LHSC), Ontario, Canada between 1 Jan 2009 to 30 Nov 2012. Multivariable logistic regression on training dataset (n = 4321) was used to develop the model and validate by bootstrapping method on the testing dataset (n = 4501). Discrimination, calibration, and overall model performance were also assessed.

Results

The predictors significantly associated with ICU mortality included: age (p < 0.001), source of admission (p < 0.0001), ICU admitting diagnosis (p < 0.0001), MODS (p < 0.0001), and NEMS (p < 0.0001). The variables sex and modified CCI were not significantly associated with ICU mortality. The training dataset for the developed model has good discriminating ability between patients with high risk and those with low risk of mortality (c-statistic 0.787). The Hosmer and Lemeshow goodness-of-fit test has a strong correlation between the observed and expected ICU mortality (χ² = 5.48; p > 0.31). The overall optimism of the estimation between the training and testing data set ΔAUC = 0.003, indicating a stable prediction model.

Conclusions

This study demonstrates that CCIS data available after the first 24 h of ICU admission at LHSC can be used to create a robust mortality prediction model with acceptable fit statistic and internal validity for valid benchmarking and monitoring ICU performance.

Electronic supplementary material

The online version of this article (doi:10.1186/s40560-016-0143-6) contains supplementary material, which is available to authorized users.