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

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

doi:10.1177/0049475519829600

Cited by 5 publications

(5 citation statements)

References 13 publications

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“…Finding a method to quantify the differences of body temperature curves among diseases can be a first step towards a new inexpensive diagnosing tool that fulfils the current and future needs of mass-analysis, patient isolation, and continuous and long-term monitoring. Recent efforts have been devoted successfully to accomplish this goal [ 17 , 18 , 19 , 20 ], although this is still a research field in its infancy.…”

Section: Discussionmentioning

confidence: 99%

“…These records have not been used in any other prior study yet, although they were acquired in the same clinical setting as the dataset used in [ 17 ]. The clinical contributors of this paper from Kasturba Medical College (India) are conducting an ongoing effort of recording body temperature data from a disparity of pathologies: dengue, malaria, tuberculosis, leptospirosis, thyroiditis, enteric fever, pyogenic sepsis, and others, and when a significant number of records of each type becomes available, we study their properties and the best methods to yield a differential diagnosis based solely on time series analysis, as was the case in [ 17 , 18 , 19 , 20 ] and in the present paper.…”

Section: Materials and Methodsmentioning

confidence: 99%

“…The classification accuracy achieved spanned from 0.60 up to 0.77, depending on the diseases under comparison. Other similar studies were devoted to find specific fever patterns in tuberculosis [ 18 ], or used a set of multiple features to increase classification accuracy of such patterns [ 19 ]. Using a training set, it is also possible to accomplish this task using tools such as neural networks [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fever Time Series Analysis Using Slope Entropy. Application to Early Unobtrusive Differential Diagnosis

Cuesta–Frau

Dakappa²,

Chakrapani

et al. 2020

Entropy

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Fever is a readily measurable physiological response that has been used in medicine for centuries. However, the information provided has been greatly limited by a plain thresholding approach, overlooking the additional information provided by temporal variations and temperature values below such threshold that are also representative of the subject status. In this paper, we propose to utilize continuous body temperature time series of patients that developed a fever, in order to apply a method capable of diagnosing the specific underlying fever cause only by means of a pattern relative frequency analysis. This analysis was based on a recently proposed measure, Slope Entropy, applied to a variety of records coming from dengue and malaria patients, among other fever diseases. After an input parameter customization, a classification analysis of malaria and dengue records took place, quantified by the Matthews Correlation Coefficient. This classification yielded a high accuracy, with more than 90% of the records correctly labelled in some cases, demonstrating the feasibility of the approach proposed. This approach, after further studies, or combined with more measures such as Sample Entropy, is certainly very promising in becoming an early diagnosis tool based solely on body temperature temporal patterns, which is of great interest in the current Covid-19 pandemic scenario.

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Section: Discussionmentioning

confidence: 99%

Section: Materials and Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fever Time Series Analysis Using Slope Entropy. Application to Early Unobtrusive Differential Diagnosis

Cuesta–Frau

Dakappa²,

Chakrapani

et al. 2020

Entropy

Self Cite

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“…Other works have used a combination of features; this is the case for the work described in [28], which used temperature temporal patterns to detect tuberculosis. In [29], the authors used a more sophisticated approach using the Fourier transform, entropy, energy, power, and a set of additional coefficients to train a quadratic support vector machine to carry out the classification of tuberculosis, intracellular bacterial infections, dengue, and inflammatory and neoplastic diseases temperature time series.…”

Section: Introductionmentioning

confidence: 99%

Discriminating Bacterial Infection from Other Causes of Fever Using Body Temperature Entropy Analysis

Vargas

Cuesta–Frau

González-López

et al. 2022

Entropy

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Body temperature is usually employed in clinical practice by strict binary thresholding, aiming to classify patients as having fever or not. In the last years, other approaches based on the continuous analysis of body temperature time series have emerged. These are not only based on absolute thresholds but also on patterns and temporal dynamics of these time series, thus providing promising tools for early diagnosis. The present study applies three time series entropy calculation methods (Slope Entropy, Approximate Entropy, and Sample Entropy) to body temperature records of patients with bacterial infections and other causes of fever in search of possible differences that could be exploited for automatic classification. In the comparative analysis, Slope Entropy proved to be a stable and robust method that could bring higher sensitivity to the realm of entropy tools applied in this context of clinical thermometry. This method was able to find statistically significant differences between the two classes analyzed in all experiments, with sensitivity and specificity above 70% in most cases.

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“…While classic definitions of fever patterns are based on intermittent temperature control, this is typically a small part of a continuous process and the degree to which this small part reflects the entire series is questionable (4). Studies that monitored continuous tympanic temperature in immune-competent patients have reported newer, more subtle trends that help to define the cause of fever (5). However, there are not many studies that have continuously recorded temperatures among HIV patients with fever.…”

Section: Introductionmentioning

confidence: 99%

Study of continuous temperature recording in various opportunistic infections in people with HIV

Pavan¹,

Hanji²,

Jeganathan³

2022

Biomedicine

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Introduction and Aim: Fever patterns in various infections can give important clues in arriving at an early diagnosis. As most recordings of temperature are not continuous the full utility of this parameter has never been given its due importance. The infections that occur in HIV individuals have varied presentations and hence leads to a delay in its diagnosis and necessitating many investigations. If 24 hours’ temperature monitoring could give reliable patterns of temperature in these various diseases, it would be an inexpensive tool that could be used in these patients as the initial diagnostic approach of these patients. Materials and Methods: In this study on patients with HIV and fever, continuous tympanic temperature monitoring was done. The visual characteristics patterns were compared with the various categories of infections that occurred in these HIV individuals. Results were expressed using mean (standard deviation), median (inter quartile range) and proportion. ANOVA was used to find the statistical difference between various opportunistic infections and a P value <0.05 was considered to be statistically significant. Results: A total of 65 HIV patients were included in the study. The type of jerky temperature pattern could differentiate between bacterial, intracellular, tuberculosis and parasitic infections (Pearson Chi-Square =16.4, P= 0.058). The type of plateau phase could also distinguish these categories of diseases. The Pearson Chi-Square value for plateau phase was 10.511 with p-value 0.015. Conclusion: When these temperature patterns were applied for diagnosing the various categories of diseases in HIV their accuracies were as follows: For bacterial diseases (62.5%); intra-cellular (50%); tuberculosis (82.1%); parasitic (64.3%). The overall accuracy was 71%, showing that the temperature patterns could be used as an important starting point in the diagnostic approach in these patients.

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

Cited by 5 publications

References 13 publications

Fever Time Series Analysis Using Slope Entropy. Application to Early Unobtrusive Differential Diagnosis

Fever Time Series Analysis Using Slope Entropy. Application to Early Unobtrusive Differential Diagnosis

Discriminating Bacterial Infection from Other Causes of Fever Using Body Temperature Entropy Analysis

Study of continuous temperature recording in various opportunistic infections in people with HIV

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