Mean core to peripheral temperature difference and mean lactate levels in first 6 hours of hospitalisation as two indicators of prognosis: an observational cohort study

Abstract

Background: To study mean core to peripheral temperature difference (CPTD) and the mean lactate levels over the first 6 h of admission to hospital, as indicators of prognosis in critically ill children. Methods: A prospective observational study in a tertiary level Pediatrics ICU in Delhi, India. Seventy eight paediatric patients from 1 month to 12 years were studied. Children with physical trauma, post-surgical patients and patients with peripheral vascular disease were excluded. Core temperature (skin over temporal artery) to peripheral temperature (big toe) difference was measured repeatedly every minute over 6 h and mean of temperature difference was calculated. Pediatric Risk of Mortality (PRISM) II, lactate clearance and mean lactate levels during that time were also studied. In-hospital mortality was used as the outcome measure. Results: Mean temperature difference During the first 6 h after admission the mean temperature difference was 9.37 ± 2 °C in those who died and 3.71 ± 2.27 °C in those who survived (p < 0.0001). The area under the receiver operating curve (AUROC) was 0.953 (p < 0.0001). The comparable AUROC of PRISM II was 0.999 (p < 0.0001). Mean Lactate Mean lactate level in the first 6 h was 7.1 ± 2.02 mg/dl in those who died compared to 2.86 ± 0.87 mg/dl in those who survived (p < 0.0001). The AUROC curve for mean lactate was 0.989 (95% CI = 0.933 to 0.999; p < 0.0001). AUROC for the lactate clearance was 0.682 (p = 0.0214). Conclusions: The mean core to peripheral temperature difference over the first 6 h is an easy-to-use and non-invasive method that is useful to predict mortality in children admitted to the Pediatric ICU. The mean lactate during the first 6 h of Pediatric ICU admission is a better index of prognosis than the lactate clearance over the same time period. They may be used as components of a scoring system to predict mortality.