Chakrapani Mahabala

Factors correlating with severity of coronary artery disease in type 2 diabetic patients on treatment for more than 5 years.

Aims: To evaluate the correlation between insulin resistance and other conventional risk nfactors with respect to severity of coronary artery disease (CAD) in patients with more nthan 5 years of treatment for type 2 diabetes mellitus. nStudy Design: Cross-sectional study. nPlace and Duration of Study: Department of Medicine and Department of Cardiology, nKasturba Medical College, Hospital Mangalore, between February 2013 and December n2013. nMethodology: 61 people with more than 5 years of type 2 diabetes who underwent coronary angiogram for the evaluation of CAD were recruited in this study. Insulin nresistance (HOMA-IR), anthropometric and biochemical parameters were determined, nand was correlated with severity of CAD which was assessed by syntax score. nResults: There was significant positive linear correlation between log HOMA-IR and nsyntax score in people with more than 5 years of type 2 diabetes [r=0.605 (95%CI n0.417–0.744), P<0.001]. The correlation of syntax score with other known risk factors of nCAD was not significant. Further multivariate analysis after adjusting for conventional risk nfactors showed a significant association of Log-IR with severity of CAD in people with ntype 2 diabetes mellitus of more than 5 years of duration (β=0.667, P<0.001) nConclusion: In type 2 diabetes mellitus with treatment more than 5 years of duration, nhigh HOMA-IR appears to be a good indicator of severity of CAD in Type 2 diabetes nmellitus and might be a marker of severity of disease, thus helping us in identifying high nrisk type 2 diabetes mellitus subjects.

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

INTRODUCTIONnDetection 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.nnnAIMnTo compare the conventional and continuous body temperature recordings in hospitalized patients.nnnMATERIALS AND METHODSnThis 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.nnnRESULTSnThe 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.nnnCONCLUSIONnThe 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.

Manifestation of malaria in Mangaluru, southern India

BackgroundSevere and fatal vivax malaria is increasingly reported from India. In Mangaluru, southern India, malaria is focused in urban areas and associated with importation by migrant workers. In Wenlock Hospital, the largest governmental hospital, the clinical, parasitological and biochemical characteristics of malaria patients were assessed.MethodsDuring the peak malaria season in 2015 (June to December), outpatients were interviewed and clinically assessed. Malaria was ascertained by microscopy and PCR assays, concentrations of haemoglobin, creatinine and bilirubin, as well as thrombocyte count, were determined, and severe malaria was defined according to WHO criteria.ResultsAmong 909 malaria patients, the vast majority was male (93%), adult (median, 26xa0years) and of low socio-economic status. Roughly half of them were migrants from beyond the local Karnataka state, mostly from northern and northeastern states. Vivax malaria (69.6%) predominated over mixed Plasmodium vivax–Plasmodium falciparum infection (21.3%) and falciparum malaria (9.0%). The geometric mean parasite density was 3412/µL. As compared to vivax malaria, patients with falciparum malaria had higher parasite density and more frequently showed impaired general condition, affected consciousness and splenomegaly. Also, they tended to more commonly have anaemia and increased creatinine levels, and to be hospitalized (7.3%). Mixed-species infections largely assumed an interim position. Severe malaria (3.5%) was not associated with parasite species. No fatality occurred.ConclusionIn this study, uncomplicated cases of malaria predominated, with P. falciparum causing slightly more intense manifestation. Severe malaria was infrequent and fatalities absent. This contrasts with the reported pattern of manifestation in other parts of India, which requires the analysis of underlying causes.

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.

Correlation between serum ferritin and bone marrow iron stores

Bone marrow aspirate examination is a gold standard to assess bone marrow iron stores. The correlation between serum ferritin and bone marrow iron has not been established in detail, as the cutoff value for iron stores have not been uniformly established. Ours was a cross-sectional study. Perl’s Prussian blue stain was used to stain bone marrow, assessed by Gale’s grading. Receiver operating characteristic curve analysis and Spearman’s correlation coefficient calculated. Gale’s grading revealed iron store deficiency in 26 and sufficiency in 13. Spearman’s correlation coefficient of 0.90 showed a significant relation between serum ferritin and bone marrow iron stores. A serum ferritin of 228u2009pmol/L had high sensitivity and specificity for iron deficiency; our study suggests that this level be taken as the cutoff value to predict iron store deficiency in bone marrow.

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 [kappau2009=u20090.618 (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.

Analysis of Long-Term Temperature Variations in the Human Body.

Body temperature is a continuous physiological variable. In normal healthy adults, oral temperature is estimated to vary between 36.1°C and 37.2°C. Fever is a complex host response to many external and internal agents and is a potential contributor to many clinical conditions. Despite being one of the foremost vital signs, temperature and its analysis and variations during many pathological conditions has yet to be examined in detail using mathematical techniques. Classical fever patterns based on recordings obtained every 8-12 h have been developed. However, such patterns do not provide meaningful information in diagnosing diseases. Because fever is a host response, it is likely that there could be a unique response to specific etiologies. Continuous long-term temperature monitoring and pattern analysis using specific analytical methods developed in engineering and physics could aid in revealing unique fever responses of hosts and in different clinical conditions. Furthermore, such analysis can potentially be used as a novel diagnostic tool and to study the effect of pharmaceutical agents and other therapeutic protocols. Thus, the goal of our article is to present a comprehensive review of the recent relevant literature and analyze the current state of research regarding temperature variations in the human body.