Abstract
Acute physiology and chronic health evaluation II (APACHE-II) scoring system is used to classify disease severity of patients in the intensive care unit. However, several limitations render the scoring system inadequate in identifying risk factors associated with outcomes. Little is known about the association of platelet count patterns, and the timing of platelet count and other hematologic parameters in predicting mortality in patients with sepsis.This retrospective observational study included 205 septic shock patients, with an overall mortality of 47.8%, enrolled at a tertiary care hospital in Riyadh, Kingdom of Saudi Arabia between 2018 and 2020. Bivariate and multivariate regression analyses were used to identify hematologic risk factors associated with mortality. We used the bivariate Pearson Correlation test to determine correlations between the tested variables and APACHE-II score.Two platelet count patterns emerged: patients with a decline in platelet count after admission (group A pattern, 93.7%) and those with their lowest platelet count at admission (group B pattern, 6.3%). The lowest mean platelet count was significantly lower in nonsurvivors (105.62 ± 10.67 × 103/μL) than in survivors (185.52 ± 10.81 × 103/μL), P < .001. Bivariate Pearson correlation revealed that the lowest platelet count and platelet count decline were significantly correlated with APACHE-II score (r = -0.250, P < .01), (r = 0.326, P < .001), respectively. In multiple logistic regression analysis, the independent mortality risk factors were degree of platelet count decline in group A (odds ratio, 1.028 [95% confidence interval: 1.012-1.045], P = .001) and platelet pattern in group B (odds ratio, 6.901 [95% confidence interval: 1.446-32.932], P = .015). The patterns, values, subsets, and ratios of white blood cell count were not significantly associated with mortality.Nadir platelet count and timing, and degree of platelet count decline are useful markers to predict mortality in early septic shock. Therefore, platelet count patterns might enhance the performance of severity scoring systems in the intensive care unit.