A novel method for measuring sublingual temperature using conventional non-contact forehead thermometer.

Background: Sublingual temperature measurement is a quick and accurate representation of oral temperature and corresponds closely with core temperature. Sub-lingual temperature measurement using non-contact infrared thermometers has not been studied for this purpose and if accurate they would be a reliable and convenient way of recording temperature of a patient very quickly. The aim of the study was to evaluate the utility of recording sublingual temperature using an infrared non-contact thermometer and establish its accuracy by comparing the readings with tympanic thermometer recordings. Methods: This cross-sectional study was carried out in 29 patients (328 paired recordings from sublingual and tympanic sites simultaneously). Subjects were requested to keep their mouth closed for five minutes before recording the temperature. Sublingual recordings were performed for each patient at different times of the day using an infrared thermometer. The infrared thermometer was quickly brought 1cm away from the sublingual part of the tongue and the recordings were then done immediately. Readings were compared with the corresponding tympanic temperature. Results: The non-contact sublingual temperature correlated very closely with tympanic temperature (r=0.86, p<0.001). The mean difference between the infrared sublingual and tympanic temperature was 0.21°C (standard deviation [SD]:0.48°C, 95% confidence interval [CI] of 0.16-0.27). The intra-class correlation co-efficient (ICC) between core and sublingual temperatures was 0.830 (95% CI: 0.794 to 0.861) p<0.001. The sensitivity of sublingual IR (infrared) temperature of 37.65°C was 90% and specificity was 89% for core temperature >38°C. Conclusions: This innovative modification of using the forehead infrared thermometer to measure the sublingual temperature offers an accurate, rapid and non-contact estimation of core temperature.

Severe headache presenting as an early sign of retroperitoneal para aortic paraganglioma.

BACKGROUND: Paraganglioma is a rare type of neuroendocrine tumor with the ability to secrete neuropeptide and catecholamines in excess. Sympathetic hyperactivity, severe persistent headache and hypertension is the most common clinical presentation of paraganglioma similar to pheocromocytoma. Case report We reported a case of 19 year old girl with severe headache and hypertension, from past 6 month. On further imaging evaluation for the headache, the computed tomography of the abdomen plus pelvis was suggestive of left pre para aortic paraganglioma measuring of 3.4 cm in diameter. Surgical excision of mass was done. Histopathological examination of surgical specimen was consistent with the diagnosis of paraganglioma. Patient is on regular follow up without any subjective or objective evidence of the disease. CONCLUSION: Retroperitoneal paraganglioma may be one of the causes for commonly occurring symptomatic headache, which is benign in nature, but possibility of transformation of malignant paraganglioma can occur. The surgical excision of mass is the treatment of choice.

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

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.

Classification of fever patterns using a single extracted entropy feature: A feasibility study based on Sample Entropy.

Fever is a common symptom of many diseases. Fever temporal patterns can be different depending on the specific pathology. Differentiation of diseases based on multiple mathematical features and visual observations has been recently studied in the scientific literature. However, the classification of diseases using a single mathematical feature has not been tried yet. The aim of the present study is to assess the feasibility of classifying diseases based on fever patterns using a single mathematical feature, specifically an entropy measure, Sample Entropy. This was an observational study. Analysis was carried out using 103 patients, 24 hour continuous tympanic temperature data. Sample Entropy feature was extracted from temperature data of patients. Grouping of diseases (infectious, tuberculosis, non-tuberculosis, and dengue fever) was made based on physicians diagnosis and laboratory findings. The quantitative results confirm the feasibility of the approach proposed, with an overall classification accuracy close to 70%, and the capability of finding significant differences for all the classes studied.

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

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.

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

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.

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

INTRODUCTION: Detection of accurate body temperature fluctu-ations in hospitalized patients is crucial for appropriate clinical decision-making. The accuracy and reliability of body temperature assessment may significantly affect the proper treatment. AIM: To compare the conventional and continuous body temperature recordings in hospitalized patients. MATERIALS AND METHODS: This cross-sectional study was carried out at a tertiary care centre and study included 55 patients aged between 18-65 years with a history of fever admitted to a tertiary care hospital. A noninvasive continuous temperature recording was done using TherCom® device through tympanic temperature probe at tympanic site at one-minute intervals for 24 hours. The conventional temperatures were recorded in the axilla using mercury thermometer at specific time intervals at 12:00 noon, 8:00 PM and 5:00 AM. Peak temperature differences between continuous and conventional methods were compared by applying Independent sample t-test. Intra class Correlation Coefficient (ICC) test was performed to assess the reliability between two temperature-monitoring methods. A p<0.05 was considered as significant. RESULTS: The average peak temperature by non-invasive continuous recording method was 39.07°C ±0.76°C while it was 37.55°C ±0.62°C by the conventional method. A significant temperature difference of 1.52°C [p<0.001;95% CI(1.26-1.78)] was observed between continuous and conventional temperature methods. Intra class Correlation Coefficient (ICC) between continuous and conventional temperature readings at 12:00 noon was α= 0.540, which had moderate reliability. The corresponding coefficients at 8:00 PM and 5:00 AM were α=0.425 and 0.435, respectively, which had poor reliability. CONCLUSION: The conventional recording of temperature is routinely practiced and does not reflect the true temperature fluctuations. However, the continuous non-invasive temperature recording is simple, inexpensive and a better tool for recording the actual temperature changes.