الفهرس 
CRITICALLY ILL ELDERLY PATIENTSOnly 14 pages are availabe for public view
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Abstract
L
ife expectancy is rising worldwide and one of the key features of the Egyptian population over the last few decades is the gradual increase in the absolute and relative numbers of older people.
The presence of common geriatric syndromes such as frailty, cognitive decline, reduced activity of daily life, and several comorbid conditions makes this group particularly challenging as they represent a particular subgroup of ICU patients.
In view of the fact that resources for intensive care units (ICUs) are expensive and limited, There are many prognostic scoring systems and many new ones are being developed to achieve an objective and quantitative description of the degree of organ dysfunction to predict risk of mortality, evaluating outcome, prognosis and length of stay in critically ill patients which is important in modern medicine.
This study aimed to evaluate the predictive performance of three widely used severity of illness scoring systems. APACHE II, APACHE IV and SOFA scores were evaluated among 106 ill elderly Egyptian patients in our geriatric critical care unit of Ain Shams University hospitals. Since such models were constructed for general use in heterogeneous ICU populations and have not often been used to study risk prediction in elderly patients. Therefore, it was essential to assess their prediction accuracy among elderly patients specially in our country.
This study showed the following results:
• This study included 106 medical elderly patients; the mean age was 74.8±8.34 years. The actual patient mortality rate observed during the study period was 49.05%,
• On comparison between survivors and non-survivors as regard demographic data there were no significant differences.
• The number of non-survivors who had pulmonary diseases and previously ICU admission was significantly higher than survivors, while other comorbidities (DM, cardiac diseases, renal diseases, and dementia) showed non-significant difference between survivors and non survivors.
• Length of ICU stay was significantly longer in non-survivors than survivors (P =0.044).
• The results of our study showed that the most common comorbidity was Cardiac disease followed by Hypertension, Diabetes mellitus and Dementia, Neurological diseases, Pulmonary, Renal and Hepatic diseases.
• This study showed that the total number of Septic Shock was fivefold increase in non-survivors than in survivors.
• Calibration of the scoring systems was done using the Hosmer-Lemeshow statistics. SOFA Highest has the best calibration followed by APACHE II, APACHE IV and then SOFA Initial. Logistic regression analysis was also done to estimate odds of mortality using the different scores where they were highly significant for all (P<0.001).
• The results showed that APACHE II, APACHE IV and SOFA highest scores were good in predicting mortality as the study showed that the observed mortality is not statistically significantly different from the predicted mortality recorded by APACHE II, APACHE IV, and SOFA highest scores (P>0.05).
• Comparison between survivors and non-survivors as regards APACHE II, APACHE IV, Initial, highest scores of SOFA predictive mortality rates revealed highly statistically significant increase in the group of non-survivors (P<0.001).
• All scores (APACHE II, APACHE IV, SOFA(Initial, Highest and delta)) had a fair to good Discriminative power as their area under the curve (AUC) were 0.761, 0.777, 0.790, 0.879 and 0.788 respectively, (P<0.001 for all). SOFA Highest score gave the best AUC.
• Multivariable logistic regression model was used to explore the effects of demographics, comorbidities, vitals, and labs on predicting mortality. This study showed that age, smoking, stroke, CRP, albumin, respiratory rate, diastolic blood pressure, LOS, previous ICU admission and mechanical ventilation were found to be significant independent predictors for mortality.
from our patient cohort, we could conclude that APACHE II, APACHE IV and SOFA model Scores had a good discrimination and calibration and overall performance in predicting mortality in critically ill patients in our critical care unit.
These tools can inform mortality prediction and risk stratification, resource utilization, and optimization of patient outcomes and would be helpful to make clinical and therapeutic decisions in the future.