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.

Classification of Infectious and Noninfectious Diseases Using Artificial Neural Networks from 24-Hour Continuous Tympanic Temperature Data of Patients with Undifferentiated Fever.

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.

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.

Healthcare-Associated Methicillin-Resistant Staphylococcus aureus: Clinical characteristics and antibiotic resistance profile with emphasis on macrolide-lincosamide-streptogramin B resistance.

OBJECTIVES: Healthcare-associated methicillin-resistant Staphylococcus aureus (MRSA) is a common pathogen worldwide and its multidrug resistance is a major concern. This study aimed to determine the clinical characteristics and antibiotic susceptibility profile of healthcare-associated MRSA with emphasis on resistance to macrolide-lincosamide-streptogramin B (MLSB) phenotypes and vancomycin. METHODS: This cross-sectional study was carried out between February 2014 and February 2015 across four tertiary care hospitals in Mangalore, South India. Healthcare-associated infections among 291 inpatients at these hospitals were identified according to the Centers for Disease Control and Prevention guidelines. Clinical specimens were collected based on infection type. S. aureus and MRSA isolates were identified and antibiotic susceptibility tests performed using the Kirby-Bauer disk diffusion method. The minimum inhibitory concentration of vancomycin was determined using the Agar dilution method and inducible clindamycin resistance was detected with a double-disk diffusion test (D-test). RESULTS: Out of 291 healthcare-associated S. aureus cases, 88 were MRSA (30.2%). Of these, 54.6% were skin and soft tissue infections. All of the isolates were susceptible to teicoplanin and linezolid. Four MRSA isolates exhibited intermediate resistance to vancomycin (4.6%). Of the MRSA strains, 10 (11.4%) were constitutive MLSB phenotypes, 31 (35.2%) were inducible MLSB phenotypes and 14 (15.9%) were macrolide-streptogramin B phenotypes. CONCLUSION: Healthcare-associated MRSA multidrug resistance was alarmingly high. In routine antibiotic susceptibility testing, a D-test should always be performed if an isolate is resistant to erythromycin but susceptible to clindamycin. Determination of the minimum inhibitory concentration of vancomycin is necessary when treating patients with MRSA infections.

Urinary tract infection due to Enterobacter sakazakii.

Enterobacter sakazakii is a rare but important cause of necrotizing enterocolitis, bloodstream infection and central nervous system infections in humans, with mortality rates of 40-80%. It has not been reported to cause urinary tract infection. We report a case of urinary tract infection due to E. sakazakii in a 63-year-old lady with chronic renal failure.