The prognostic value of skin temperature variability analysis in clinical settings: a systematic review of current evidence.

Objective.While core body temperature in humans remains relatively stable within a narrow range, peripheral skin temperature fluctuates dynamically. This variability reflects complex interactions between heat loss (e.g. via vasodilation) and heat gain (e.g. through metabolic activity), all modulated by an integrated thermoregulatory network. Emerging evidence suggests that skin temperature variability (STV) may offer insight into the integrity of thermoregulatory, vascular, and autonomic control systems. This systematic review evaluates the prognostic utility of STV as a dynamic physiological marker across diverse clinical settings.Method.The review followed PRISMA guidelines, with comprehensive searches conducted in Ovid MEDLINE, EMBASE, and AMED up to June 2026. Studies examining STV in relation to prognosis were eligible, and methodological quality was assessed using the QualSyst and QUIPS tools. This study used a narrative evidence synthesis guided by a logic model.Results.Of 41 papers screened, 10 met the inclusion criteria. The included studies identified a range of analytic methods used to quantify STV. Overall, the prognostic value of STV appeared to depend on both the timescale of temperature fluctuations and the underlying disease context. In life-threatening conditions such as sepsis, multiple organ failure and decompensated cirrhosis, reduced short-term variability or loss of complexity predicted higher mortality, potentially reflecting severe autonomic or microvascular regulation. At longer timescales, attenuated 24-hour circadian amplitude of temperature time-series in healthy individuals predicted future cardiometabolic diseases, indicating circadian dysregulation associated with increased all-cause mortality risk.Discussion.STV shows promise as a non-invasive physiological marker of prognosis, but current evidence is limited by methodological heterogeneity, small clinical cohorts, and inconsistent confounder control. Standardised measurement protocols, agreed analytical metrics, multicentre validation, and large-scale comparison with established risk scores are needed to enable clinical implementation.