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1.
Trop Doct ; 49(2): 75-79, 2019 Apr.
Artículo en Inglés | MEDLINE | ID: mdl-30782109

RESUMEN

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.


Asunto(s)
Temperatura Corporal , Monitoreo Fisiológico , Tuberculosis/diagnóstico , Adolescente , Adulto , Anciano , Fiebre/diagnóstico , Fiebre/patología , Humanos , Persona de Mediana Edad , Monitoreo Fisiológico/instrumentación , Tuberculosis/patología , Adulto Joven
2.
Crit Rev Biomed Eng ; 46(2): 173-183, 2018.
Artículo en Inglés | MEDLINE | ID: mdl-30055533

RESUMEN

Fever is one of the major clinical symptoms of undifferentiated fever cases. Early diagnosis of undifferentiated fever is a challenging task for the physician. The aim of this study was to classify infectious and noninfectious diseases from 24-hour continuous tympanic temperature recordings of patients with undifferentiated fever using a machine learning algorithm (artificial neural network). This was an observational study conducted in 103 patients who presented with undifferentiated fever. Twenty-four-hour continuous tympanic temperature was recorded from each patient. Features were extracted from temperature signals and classified into infectious and noninfectious diseases using an artificial neural network (ANN). The ANN classifier provided the highest accuracy at 91.3% for differentiating infectious and noninfectious diseases from undifferentiated fever cases. Significant kappa agreement (κ = 0.777) was found between the final diagnosis as determined by the physician and the classification obtained using an ANN classifier. Based on our results, we conclude that the continuous 24-hour temperature monitoring and application of an ANN classifier provides a simple noninvasive and inexpensive supplementary diagnostic method to differentiate infectious and noninfectious diseases.


Asunto(s)
Algoritmos , Temperatura Corporal , Enfermedades Transmisibles/clasificación , Fiebre/diagnóstico , Monitoreo Fisiológico/métodos , Redes Neurales de la Computación , Enfermedades no Transmisibles/clasificación , Adulto , Ritmo Circadiano , Enfermedades Transmisibles/diagnóstico , Diagnóstico Diferencial , Oído Medio , Femenino , Registros de Salud Personal , Humanos , Aprendizaje Automático , Masculino , Persona de Mediana Edad
3.
Trop Doct ; 48(2): 93-97, 2018 Apr.
Artículo en Inglés | MEDLINE | ID: mdl-29419375

RESUMEN

Dengue fever is an acute febrile illness with a duration of 2-12 days. Our observational study observed the 24-h continuous tympanic temperature pattern of 15 patients with dengue fever and compared this with 26 others with fever due to a non-dengue aetiology. A tri-phasic fever pattern was seen among two-thirds of dengue fever patients, but in only one with an inflammatory disease. One-third of dengue fever patients exhibited a single peak temperature. Continuous temperature monitoring and temperature pattern analysis in clinical settings can aid in the early differentiation of dengue fever from non-dengue aetiology.


Asunto(s)
Dengue/fisiopatología , Fiebre/fisiopatología , Adulto , Presión Sanguínea/fisiología , Femenino , Humanos , Masculino , Persona de Mediana Edad
4.
J Healthc Eng ; 2017: 5707162, 2017.
Artículo en Inglés | MEDLINE | ID: mdl-29359037

RESUMEN

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.


Asunto(s)
Oído Medio , Fiebre/clasificación , Valor Predictivo de las Pruebas , Adulto , Anciano , Algoritmos , Dengue , Diagnóstico Diferencial , Humanos , India , Persona de Mediana Edad , Enfermedades no Transmisibles , Curva ROC , Máquina de Vectores de Soporte , Adulto Joven
5.
J Clin Diagn Res ; 10(9): OC43-OC46, 2016 Sep.
Artículo en Inglés | MEDLINE | ID: mdl-27790493

RESUMEN

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.

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