Comparison of Conventional Mercury Thermometer and Continuous TherCom® Temperature Recording in Hospitalized Patients

Dakappa, Pradeepa H.; Bhat, Ganesh; Ganaraja, B; Rao, Satish; Adappa, Sushma; Chakrapani, M

doi:10.7860/jcdr/2016/21617.8586

Cited by 13 publications

(18 citation statements)

References 13 publications

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“…For many years, temperature recordings in standard clinical practice have been limited to scarce measurements (once per day or once per shift), which provides very little information about the processes underlying body temperature regulation [ 18 , 19 ]. For these reasons, physicians are only capable of distinguishing between febrile patients and afebrile patients.…”

Section: Introductionmentioning

confidence: 99%

Model Selection for Body Temperature Signal Classification Using Both Amplitude and Ordinality-Based Entropy Measures

Cuesta–Frau

Miró-Martínez

Oltra-Crespo

et al. 2018

Entropy

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Many entropy-related methods for signal classification have been proposed and exploited successfully in the last several decades. However, it is sometimes difficult to find the optimal measure and the optimal parameter configuration for a specific purpose or context. Suboptimal settings may therefore produce subpar results and not even reach the desired level of significance. In order to increase the signal classification accuracy in these suboptimal situations, this paper proposes statistical models created with uncorrelated measures that exploit the possible synergies between them. The methods employed are permutation entropy (PE), approximate entropy (ApEn), and sample entropy (SampEn). Since PE is based on subpattern ordinal differences, whereas ApEn and SampEn are based on subpattern amplitude differences, we hypothesized that a combination of PE with another method would enhance the individual performance of any of them. The dataset was composed of body temperature records, for which we did not obtain a classification accuracy above 80% with a single measure, in this study or even in previous studies. The results confirmed that the classification accuracy rose up to 90% when combining PE and ApEn with a logistic model.

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Section: Introductionmentioning

confidence: 99%

Model Selection for Body Temperature Signal Classification Using Both Amplitude and Ordinality-Based Entropy Measures

Cuesta–Frau

Miró-Martínez

Oltra-Crespo

et al. 2018

Entropy

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show abstract

“…In addition, the 24-hour continuous temperature recording also helps us in identifying the undetected fever spikes in conventional monitoring method. Two scientific studies were reported on the significance of continuous temperature monitoring over conventional temperature monitoring method [ 4 , 24 ]. Varela et al studied in 62 patients presenting with fever and found that continuous temperature recording method detected mean of 0.7 (95% CI, 0.27–1.33) peaks of fever unnoticed by conventional care [ 4 ].…”

Section: Discussionmentioning

confidence: 99%

“…Anthropometric parameters like age, blood pressure, pulse rate, and BMI of each subject were noted. The continuous 24-hour tympanic temperature was recorded by using TherCom® temperature monitoring device [ 23 , 24 ]. The final diagnosis of each patient was noted.…”

Section: Methodsmentioning

confidence: 99%

A Predictive Model to Classify Undifferentiated Fever Cases Based on Twenty-Four-Hour Continuous Tympanic Temperature Recording

Dakappa

Prasad

Rao

et al. 2017

Journal of Healthcare Engineering

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Diagnosis of undifferentiated fever is a major challenging task to the physician which often remains undiagnosed and delays the treatment. The aim of the study was to record and analyze a 24-hour continuous tympanic temperature and evaluate its utility in the diagnosis of undifferentiated fevers. This was an observational study conducted in the Kasturba Medical College and Hospitals, Mangaluru, India. A total of ninety-six (n = 96) patients were presented with undifferentiated fever. Their tympanic temperature was recorded continuously for 24 hours. Temperature data were preprocessed and various signal characteristic features were extracted and trained in classification machine learning algorithms using MATLAB software. The quadratic support vector machine algorithm yielded an overall accuracy of 71.9% in differentiating the fevers into four major categories, namely, tuberculosis, intracellular bacterial infections, dengue fever, and noninfectious diseases. The area under ROC curve for tuberculosis, intracellular bacterial infections, dengue fever, and noninfectious diseases was found to be 0.961, 0.801, 0.815, and 0.818, respectively. Good agreement was observed [kappa = 0.618 (p < 0.001, 95% CI (0.498–0.737))] between the actual diagnosis of cases and the quadratic support vector machine learning algorithm. The 24-hour continuous tympanic temperature recording with supervised machine learning algorithm appears to be a promising noninvasive and reliable diagnostic tool.

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“…Conventional fever recordings every 6-8 h may regularly miss important fluctuations. 7,8 A century ago, Woodhead et al reported on the prognosis and diagnosis of quasi-continuous temperature recordings in TB patients. 9 Rectal probes were used and were therefore cumbersome and inconvenient for the patient; the lack of advanced hardware probably contributed to further studies not being done.…”

Section: Introductionmentioning

confidence: 99%

“…The presence of an erythematous or maculopapular rash, found in 25-39% of patients with neuro-chikungunya, may be helpful because they are not features of Japanese encephalitis or herpes simplex encephalitis. [5][6][7] Dengue encephalitis may be confused with CHIK because of the presence of a rash as well as reports of concurrent outbreaks of CHIK and dengue encephalitis. However, arthralgia is more common, while leukopenia and petechial rash…”

Section: Introductionmentioning

confidence: 99%

Unique temperature patterns in 24-h continuous tympanic temperature in tuberculosis

Dakappa¹,

Rao

Ganaraja

et al. 2019

Trop Doct

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Body temperature monitoring in most healthcare institutions is limited to checking the presence or absence of fever. Our present study evaluated the 24h continuous tympanic temperature pattern in patients with fever in order to detect typical patterns seen in tuberculosis (TB). This observational study was conducted on 81 undifferentiated fever patients whose recordings were stored using the TherCom device. Unique temperature patterns were analysed and compared. TB patients exhibited a unique temperature pattern, namely a slow temperature elevation followed by slow temperature fall seen in 78.5% (22/28) compared to 24.52% (13/53) of non-TB patients. Recognition of this pattern may therefore be useful as a valuable diagnostic aid in the early diagnosis of TB.

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Comparison of Conventional Mercury Thermometer and Continuous TherCom® Temperature Recording in Hospitalized Patients

Cited by 13 publications

References 13 publications

Model Selection for Body Temperature Signal Classification Using Both Amplitude and Ordinality-Based Entropy Measures

Model Selection for Body Temperature Signal Classification Using Both Amplitude and Ordinality-Based Entropy Measures

A Predictive Model to Classify Undifferentiated Fever Cases Based on Twenty-Four-Hour Continuous Tympanic Temperature Recording

Unique temperature patterns in 24-h continuous tympanic temperature in tuberculosis

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