Predicting survival in critical patients by use of body temperature regularity measurement based on approximate entropy

Cuesta, David; Varela, Manuel; Miró, Pau; Galdos, Pedro; Abásolo, Daniel; Hornero, Roberto

doi:10.1007/s11517-007-0200-3

Cited by 30 publications

(30 citation statements)

References 30 publications

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“…However, our study consisted of visual assessment of relatively infrequent temperature measurements. Furthermore, previous investigations of body temperature variability focused on predicting disease severity in patients with existing multiple organ failure [31,32] or in patients who were already suspected of being infected [33]. None of the patients in our study, however, were clinically suspected of being septic during the time period of the temperature measurements.…”

Section: Discussionmentioning

confidence: 86%

Body temperature patterns as a predictor of hospital-acquired sepsis in afebrile adult intensive care unit patients: a case-control study

et al. 2013

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IntroductionEarly treatment of sepsis improves survival, but early diagnosis of hospital-acquired sepsis, especially in critically ill patients, is challenging. Evidence suggests that subtle changes in body temperature patterns may be an early indicator of sepsis, but data is limited. The aim of this study was to examine whether abnormal body temperature patterns, as identified by visual examination, could predict the subsequent diagnosis of sepsis in afebrile critically ill patients.MethodsRetrospective case-control study of 32 septic and 29 non-septic patients in an adult medical and surgical ICU. Temperature curves for the period starting 72 hours and ending 8 hours prior to the clinical suspicion of sepsis (for septic patients) and for the 72-hour period prior to discharge from the ICU (for non-septic patients) were rated as normal or abnormal by seven blinded physicians. Multivariable logistic regression was used to compare groups in regard to maximum temperature, minimum temperature, greatest change in temperature in any 24-hour period, and whether the majority of evaluators rated the curve to be abnormal.ResultsBaseline characteristics of the groups were similar except the septic group had more trauma patients (31.3% vs. 6.9%, p = .02) and more patients requiring mechanical ventilation (75.0% vs. 41.4%, p = .008). Multivariable logistic regression to control for baseline differences demonstrated that septic patients had significantly larger temperature deviations in any 24-hour period compared to control patients (1.5°C vs. 1.1°C, p = .02). An abnormal temperature pattern was noted by a majority of the evaluators in 22 (68.8%) septic patients and 7 (24.1%) control patients (adjusted OR 4.43, p = .017). This resulted in a sensitivity of 0.69 (95% CI [confidence interval] 0.50, 0.83) and specificity of 0.76 (95% CI 0.56, 0.89) of abnormal temperature curves to predict sepsis. The median time from the temperature plot to the first culture was 9.40 hours (IQR [inter-quartile range] 8.00, 18.20) and to the first dose of antibiotics was 16.90 hours (IQR 8.35, 34.20).ConclusionsAbnormal body temperature curves were predictive of the diagnosis of sepsis in afebrile critically ill patients. Analysis of temperature patterns, rather than absolute values, may facilitate decreased time to antimicrobial therapy.

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Section: Discussionmentioning

confidence: 86%

Body temperature patterns as a predictor of hospital-acquired sepsis in afebrile adult intensive care unit patients: a case-control study

et al. 2013

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“…These signal complexity calculations contribute to capture the dynamic aspects of temperature regulation. Some results in this regard have already been published, see [12]. …”

Section: Device Descriptionmentioning

confidence: 99%

“…New analytical techniques derived from complex time-series analysis (approximate entropy [6, 7], sample entropy [8], Lempel Ziv algorithm [9, 10], detrended fluctuation analysis [11]) have been shown useful to study and classify temperature, even in the absence of fever. For instance, it has been shown that there is a good correlation between physiological status and ApEn of the temperature profile in critically ill patients, and that ApEn is a prognostic marker as good as conventional scoring systems, with the advantage of being non-invasive and a continuous, rather than episodic, evaluation [12]. …”

Section: Introductionmentioning

confidence: 99%

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Description of a PortableWireless Device for High-Frequency Body Temperature Acquisition and Analysis

Cuesta–Frau

Varela

Miró-Martínez

2009

Sensors

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We describe a device for dual channel body temperature monitoring. The device can operate as a real time monitor or as a data logger, and has Bluetooth capabilities to enable for wireless data download to the computer used for data analysis. The proposed device is capable of sampling temperature at a rate of 1 sample per minute with a resolution of 0.01 °C . The internal memory allows for stand-alone data logging of up to 10 days. The device has a battery life of 50 hours in continuous real-time mode. In addition to describing the proposed device in detail, we report the results of a statistical analysis conducted to assess its accuracy and reproducibility.

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“…Using body temperature entropy estimation, in a similar way as in Sec.3.2, we found a correlation between a patient condition, and their body temperature regularity [18,19]. There was a statistically significant difference between the regularity of signals from patients that survived and patients that did not (72% classifier accuracy).…”

Section: Predicting Survival In Critical Patientsmentioning

confidence: 54%

Development of a Novel Scheme for Long-Term Body Temperature Monitoring: A Review of Benefits and Applications

Cuesta–Frau

Varela-Entrecanales

Valor-Perez³

et al. 2015

J Med Syst

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Body temperature is a health or disease marker that has been in clinical use for centuries. The threshold currently applied to define fever, with small variations, is 38 • C. However, current approaches do not provide a full picture of the thermoregulation process and its correlation with disease.This paper describes a new non-invasive body temperature device that improves the understanding of the pathophysiology of diseases by integrating a variety of temperature data from different body locations. This device enables to gain a deeper insight into fever, endogenous rhythms, subject activity and ambient temperature to provide anticipatory and more efficient treatments. Its clinical use would be a big step in the overcoming of the anachronistic febrile/afebrile dichotomy and walking towards a system medicine approach to certain diseases.This device has already been used in some clinical applications successfully. Other possible applications based on the device features and clinical requirements are also described in this paper.

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Predicting survival in critical patients by use of body temperature regularity measurement based on approximate entropy

Cited by 30 publications

References 30 publications

Body temperature patterns as a predictor of hospital-acquired sepsis in afebrile adult intensive care unit patients: a case-control study

Body temperature patterns as a predictor of hospital-acquired sepsis in afebrile adult intensive care unit patients: a case-control study

Description of a PortableWireless Device for High-Frequency Body Temperature Acquisition and Analysis

Development of a Novel Scheme for Long-Term Body Temperature Monitoring: A Review of Benefits and Applications

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