Mathematical modeling of thermal and circulatory effects during hemodialysis

Recent Advances and Future Perspectives in the Use of Machine Learning and Mathematical Models in Nephrology

We reviewed some of the latest advancements in the use of mathematical models in nephrology. We looked over 2 distinct categories of mathematical models that are widely used in biological research and pointed out some of their strengths and weaknesses when applied to health care, especially in the context of nephrology. A mechanistic dynamical system allows the representation of causal relations among the system variables but with a more complex and longer development/implementation phase. Artificial intelligence/machine learning provides predictive tools that allow identifying correlative patterns in large data sets, but they are usually harder-to-interpret black boxes. Chronic kidney disease (CKD), a major worldwide health problem, generates copious quantities of data that can be leveraged by choice of the appropriate model; also, there is a large number of dialysis parameters that need to be determined at every treatment session that can benefit from predictive mechanistic models. Following important steps in the use of mathematical methods in medical science might be in the intersection of seemingly antagonistic frameworks, by leveraging the strength of each to provide better care.

Separate and combined effects of cold dialysis and intradialytic exercise on the thermoregulatory responses of hemodialysis patients: a randomized-cross-over study

Background

The separate and combined effects of intradialytic exercise training (IET) and cold dialysis (CD) on patient thermoregulation remain unknown. This study assessed the thermoregulatory responses of hemodialysis patients under four different hemodialysis protocols: a) one typical dialysis (TD) protocol (dialysate temperature at 37 °C), b) one cold dialysis (CD) protocol (dialysate temperature at 35 °C), c) one typical dialysis protocol which included a single exercise bout (TD + E), d) one cold dialysis protocol which included a single exercise bout (CD + E).

Methods

Ten hemodialysis patients (57.2 ± 14.9 years) participated in this randomized, cross-over study. Core and skin temperatures were measured using an ingestible telemetric pill and by four wireless iButtons attached on the skin, respectively. Body heat storage (S) calculated using the thermometric method proposed by Burton.

Results

The TD and TD + E protocols were associated with increased S leading to moderate effect size increases in core body temperature (as high as 0.4 °C). The low temperature of the dialysate during the CD and the CD + E protocols prevented the rise in S and core temperature (p > 0.05), even during the period that IET took place.

Conclusions

TD and IET are accompanied by a moderate level of hyperthermia, which can be offset by CD. We recommended that CD or with IET can prevent the excessive rise of S.

Trial registration

Clinical Trial Registry number: NCT03905551 (clinicaltrials.gov), DOR: 05/04/2019,

Effect of local skin blood flow during light and medium activities on local skin temperature predictions

The quality of local skin temperature prediction by thermophysiological models depends on the local skin blood flow (SBF) control functions. These equations were derived for low activity levels (0.8-1met) and mostly in sitting or supine position. This study validates and discusses the prediction of foot SBF during activities of 1-3met in male and females, and the effect on the foot skin temperature prediction (ΔT_skin,foot) using the thermophysiological simulation model ThermoSEM. The SBF at the foot was measured for ten male and ten female human subjects at baseline and during three activities (sitting, walking at 1km/h, preferred walking around 3km/h). Additional measurements included the energy expenditure, local skin temperatures (T_skin,loc), environmental conditions and body composition. Measured, normalized foot SBF is 2-8 times higher than the simulated SBF during walking sessions. Also, SBF increases are significantly higher in females vs. males (preferred walking: 4.8±1.5 versus 2.7±1.4, P < 0.05). The quality of ΔT_skin,foot using the simulated foot SBF is poor (median deviation is -4.8°C, maximumumdeviationis-6°C). Using the measured SBF in ThermoSEM results in an improved local skin temperature prediction (new maximum deviation is -3.3°C). From these data a new SBF model was developed that includes the walking activity level and gender, and improves SBF prediction and ΔT_skin,foot of the thermophysiological model. Accurate SBF and local skin temperature predictions are beneficial in optimizing thermal comfort simulations in the built environment, and might also be applied in sport science or patient's temperature management.