A Novel Model to Predict Cutaneous Finger Blood Flow via Finger and Rectal Temperatures

Abstract

Objectives: To generate a model that predicts fingertip blood flow (BF f) and to cross-validate it in another group of subjects. Methods: We used fingertip temperature (T f), forearm temperature minus T f (T For-f), rectal temperature (T re), and their changes across time ( lagT) to estimate BF f. Ten participants (six male, four female) were randomly divided into "model" and "validation" groups. We employed a passive hot-cold water immersion protocol during which each participant's core temperature increased and decreased by 0.5°C above/below baseline during hot/cold conditions, respectively. A hierarchical multiple linear regression analysis was introduced to generate models using temperature indicators and lagT (independent variables) obtained from the model group to predict BF f (dependent variable). Results: Mean BF f (109.5±158.2PU) and predicted BF f (P-BF f) (111.4±136.7PU) in the model group calculated using the strongest (R 2=0.766, p<0.001) prediction model [P-BF f =T f× 19.930+ lag4T f×74.766+ lag4T re×124.255-447.474] were similar (p=0.6) and correlated (r=0.880, p<0.001). Autoregressive integrated moving average time-series analyses demonstrated a significant association between P-BF f and BF f (R 2=0.381; Ljung-Box statistic=8.097; p<0.001) in the validation group. Conclusions: We provide a model that predicts BF f via two practical temperature indicators that can be implemented in both clinical and field settings. © 2011 John Wiley & Sons Ltd.