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

Dover, Mary; Tawfick, Wael; Hynes, Niamh; Sultan, Sherif

doi:10.1177/1708538115604089

Cited by 3 publications

(2 citation statements)

References 23 publications

(21 reference statements)

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“…Other studies have shown that these complications are associated with an increase in operative deaths and other short-term measures of death, but most have treated these complications in isolation and even fewer have studied long-term outcomes. Similar to other studies, we found that cardiac arrest, need for reoperation in the postoperative period, new need for dialysis, and multisystem organ dysfunction were associated with increased death [19][20][21][22][23][24][25][26]. However, our study expands on this knowledge by showing the relative contributions of different complications to years of life lost and adjusts for the fact that patients can have multiple complications but only one death.…”

Section: Commentsupporting

confidence: 86%

Years of Life Lost After Complications of Coronary Artery Bypass Operations

Freundlich

Maile

Hajjar

et al. 2017

The Annals of Thoracic Surgery

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Section: Commentsupporting

confidence: 86%

Years of Life Lost After Complications of Coronary Artery Bypass Operations

Freundlich

Maile

Hajjar

et al. 2017

The Annals of Thoracic Surgery

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“…There are many scoring systems such as Acute Physiology and Chronic Health Evaluation (APACHE) II, III and IV, Simplified Acute Physiology Score (SAPS), Mortality Prediction Model (MPM), Multiple Organ Dysfunction Score (MODS), Sepsis-related Organ Failure Assessment Score (SOFA), Logistic Organ Dysfunction Score (LODS), and so on. [4,5] APACHE II includes an acute physiology score, age points, and chronic health points. [6] Within the first 24 hours of patient admittance, the worst value for each physiological variable is calculated into an integer score ranging from 0 to 71.…”

Section: Introductionmentioning

confidence: 99%

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

Liu

et al. 2019

Medicine

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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 ( r s = −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.

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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

2016

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

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

Abstract: The good discrimination of these scoring systems indicates their value as an adjunct to clinical assessment but should not be used on an individual basis as a clinical decision-making tool.

Cited by 3 publications

References 23 publications

Years of Life Lost After Complications of Coronary Artery Bypass Operations

Years of Life Lost After Complications of Coronary Artery Bypass Operations

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

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

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