Abnormalities in the 24-hour rhythm of skin temperature in cirrhosis: Sleep-wake and general clinical implications.

BACKGROUND & AIMS: Sleep preparation/onset are associated with peripheral vasodilatation and a decrease in body temperature. The hyperdynamic syndrome exhibited by patients with cirrhosis may impinge on sleep preparation, thus contributing to their difficulties falling asleep. The aim of this study was the assessment of skin temperature, in relation to sleep-wake patterns, in patients with cirrhosis. METHODS: Fifty-three subjects were initially recruited, and 46 completed the study. Of the final 46, 12 were outpatients with cirrhosis, 13 inpatients with cirrhosis, 11 inpatients without cirrhosis and 10 healthy volunteers. All underwent baseline sleep-wake evaluation and blood sampling for inflammatory markers and morning melatonin levels. Distal/proximal skin temperature and their gradient (DPG) were recorded for 24 hours by a wireless device. Over this period subjects kept a sleep-wake diary. RESULTS: Inpatients with cirrhosis slept significantly less well than the other groups. Inpatients and outpatients with cirrhosis had higher proximal temperature and blunted rhythmicity compared to the other groups. Inpatients with/without cirrhosis had higher distal temperature values and blunted rhythmicity compared to the other groups. Inpatients and outpatients with cirrhosis had significantly lower DPG values compared to the other groups, and DPG reached near-zero values several hours later. Significant correlations were observed between temperature and sleep-wake variables and inflammatory markers. CONCLUSIONS: Alterations of distal/proximal skin temperature, their gradient and their time-course were observed in patients with cirrhosis, which may contribute to their sleep disturbances.

Expected accuracy of proximal and distal temperature estimated by wireless sensors, in relation to their number and position on the skin.

A popular method to estimate proximal/distal temperature (TPROX and TDIST) consists in calculating a weighted average of nine wireless sensors placed on pre-defined skin locations. Specifically, TPROX is derived from five sensors placed on the infra-clavicular and mid-thigh area (left and right) and abdomen, and TDIST from four sensors located on the hands and feet. In clinical practice, the loss/removal of one or more sensors is a common occurrence, but limited information is available on how this affects the accuracy of temperature estimates. The aim of this study was to determine the accuracy of temperature estimates in relation to number/position of sensors removed. Thirteen healthy subjects wore all nine sensors for 24 hours and reference TPROX and TDIST time-courses were calculated using all sensors. Then, all possible combinations of reduced subsets of sensors were simulated and suitable weights for each sensor calculated. The accuracy of TPROX and TDIST estimates resulting from the reduced subsets of sensors, compared to reference values, was assessed by the mean squared error, the mean absolute error (MAE), the cross-validation error and the 25th and 75th percentiles of the reconstruction error. Tables of the accuracy and sensor weights for all possible combinations of sensors are provided. For instance, in relation to TPROX, a subset of three sensors placed in any combination of three non-homologous areas (abdominal, right or left infra-clavicular, right or left mid-thigh) produced an error of 0.13°C MAE, while the loss/removal of the abdominal sensor resulted in an error of 0.25°C MAE, with the greater impact on the quality of the reconstruction. This information may help researchers/clinicians: i) evaluate the expected goodness of their TPROX and TDIST estimates based on the number of available sensors; ii) select the most appropriate subset of sensors, depending on goals and operational constraints.