A Novel Model to Predict Cutaneous Finger Blood Flow via Finger and Rectal Temperatures ANDRES E. CARRILLO,* STEPHEN S. CHEUNG,   AND ANDREAS D. FLOURIS* ,à *FAME Laboratory, Institute of Human Performance and Rehabilitation, Centre for Research and Technology Thessaly, Trikala, Greece;   Department of Kinesiology, Brock University, St. Catharines, Ontario, Canada; à Department of Research and Technology Development, Biomnic Ltd., Trikala, Greece Address for correspondence: Andreas D. Flouris, FAME Laboratory, Institute of Human Performance and Rehabilitation, Centre for Research and Technology Thessaly, Karies, Trikala 42100, Greece. E-mail: aflouris@cereteth.gr Received 8 June 2011; accepted 19 September 2011. 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 ( lag T) 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 lag T (independent variables) obtained from the model group to predict BF f (dependent variable). Results: Mean BF f (109.5 ± 158.2 PU) and predicted BF f (P-BF f ) (111.4 ± 136.7 PU) in the model group calculated using the strongest (R 2 = 0.766, p < 0.001) prediction model [P-BF f = T f · 19.930 + lag4 T f · 74.766 + lag4 T re · 124.255 – 447.474] were similar (p = 0.6) and correlated (r = 0.880, p < 0.001). Auto- regressive 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. Key words: finger perfusion, skin temperature, core temperature, forearm temperature, cold-induced vasodilation Abbreviations used : ARIMA, auto-regressive integrative moving average; BFf, fingertip blood flow; P-BFf, predicted fingertip blood flow; PU, perfusion units; R 2 , coefficient of variation; T f , fingertip temperature; T F or - f , forearm temperature minus fingertip temperature; lagT, temperature lag; T re , rectal temperature. Please cite this paper as: Carrillo, Cheung, and Flouris (2011). A Novel Model to Predict Cutaneous Finger Blood Flow Via finger and Rectal Temperatures. Microcirculation 18(8), 670–676. INTRODUCTION Thermoregulatory measurements are widely important in both clinical and field settings [4,12], and are necessary components for various research and bioengineering appli- cations [6,7]. However, the high equipment cost and the extensive requirements from participants necessary for accurate measurements introduce complications that may prevent practical use. In this light, measurements from practical and inexpensive techniques have been used to estimate data recorded from direct techniques. For exam- ple, measurements of skin-surface temperature gradients (forearm temperature minus fingertip temperature [T For-f ]) have been associated with fingertip blood flow (BF f ) in steady-state conditions [17]. To our knowledge, however, no studies have attempted to predict BF f during dynamic fluctuations in thermal balance, which may introduce com- plications with acquiring accurate blood flow predictions. It is well known that the measured fingertip temperature (T f ) is a slow indicator of what occurs deeper in the tissue [2]. Hence, problems arise when T f measurements are solely used to represent BF f changes within the microcircu- latory vessels of the finger. Interpretation of the results may be inaccurate particularly when a strong thermal stimulus is introduced that induces an immediate change in BF f , which is associated with a delayed T f response. For example, cold-induced vasodilation is commonly reported as a cyclical increase in BF f during exposure to cold, and has been assessed often by our lab [8] and others [3,13,16] through T f data. Given the delayed T f response, DOI:10.1111/j.1549-8719.2011.00136.x Original Article 670 ª 2011 John Wiley & Sons Ltd, Microcirculation, 18, 670–676