Shikha Sharma, Shikha
Department of Biochemistry, Chacha Nehru Bal Chikitsalya Hospital, Associated to Maulana Azad Medical College, Delhi University, Geeta Colony, New Delhi – 110031.

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EVALUATION OF CSF FERRITIN AS AN EARLY MARKER FOR DIFFERENTIATING MENINGITIS IN PEDIATRIC PATIENTS Sharma, Shikha; Dabla, Pradeep Kumar
BALI MEDICAL JOURNAL Vol 3 No 2 (2014)
Publisher : BALI MEDICAL JOURNAL

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Abstract

Background and Objectives: - Bacterial meningitis is a medical emergency with a potential for serious neurological damage or even death. Rapid diagnosis is important and henceforth critical for the early intervention by antibiotic therapy to prevent complications. Therefore the aim of the present study was to evaluate CSF ferritin levels in children with different etiologies of meningitis. Materials and Methods: - 65 children (1-124 months) with suspected meningitis admitted at Chacha Nehru BalChikitsalya hospital were included in the study. CSF sample was analyzed for glucose, protein, cell count, ferritin, gram stain and culture. Results: - Based on the laboratory findings the 65 children were classified into 3 groups: 21 cases had bacterial meningitis, 18 had aseptic (viral) meningitis and 26 cases as the no-meningitis group. A significant relationship was observed between age and ferritin level in the non-meningitis group (p<0.05). CSF ferritin in bacterial meningitis group was 34.80 ±11.20 ng/mL and was significantly higher than the aseptic meningitis group. Cut off value of ferritin to differentiate meningitis vs. no-meningitis group was estimated at 18.2ng/ml with a sensitivity of 94.9% and specificity of 96.2 %. However on differentiating bacterial from aseptic meningitis cutoff value was 20.3 ng/mL with a sensitivity of 98% and specificity of 33.3%. Conclusion: - CSF ferritin levels were found to be significantly different between the meningitis and the no-meningitis groups. However due to low specificity it may not prove useful for the early differentiation of different types of meningitis. Further studies are required on a larger sample size before we can substantiate our findings.
Discovering Patterns of Cardiovascular Disease and Diabetes in Myocardial Infarction Patients Using Association Rule Mining Singh, Anju; Singh, Divakar; Sharma, Shikha; Upreti, Kamal; Maheshwari, Manish; Mehta, Vimal; Sharma, Jitender; Mehra, Pratishtha; Dabla, Pradeep Kumar
Folia Medica Indonesiana Vol. 58, No. 3
Publisher : Folia Medica Indonesiana

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Highlights: • Association Rule Mining tools predict the association of early-onset Myocardial Infarction with Hypertension and Diabetes Mellitus. • Association Rule Mining tools using clinical and biochemical attributes can predict the development of Hypertension and Diabetes Mellitus in Myocardial Infarction patients. Abstract: Cardiovascular diseases (CVDs) are a major cause of mortality in diabetic patients. Hypertensive patients are more likely to develop diabetes and hypertension contributes to the high prevalence of CVDs, in addition to dyslipidemia and smoking. This study was to find the different patterns and overall rules among CVD patients, including rules broken down by age, sex, cholesterol and triglyceride levels, smoking habits, myocardial infarction (MI) type on ECG, diabetes, and hypertension. The cross-sectional study was performed on 240 subjects (135 patients of ST-elevation MI below 45 years and 105 age matched controls). Association rule mining was used to detect new patterns for early-onset myocardial infarction. A hotspot algorithm was used to extract frequent patterns and various promising rules within real medical data. The experiment was carried out using "Weka'', a tool for extracting rules to find out the association between different stored real parameters. In this study, we found out various rules of hypertension like "Rule 6” says that if levels of BP Systolic > 131 mmHg, LpA2 > 43.2 ng/ml, hsCRP > 3.71 mg/L, initial creatinine > 0.5 mg/dl, and initial Hb ≤15 g/dl (antecedent), then the patient will have 88% chance of developing hypertension (consequent). Similarly for diabetes mellitus with finding their lift and confidence for different support like "Rule 6”, if MI type on ECG = 'Inferior Wall MI' with STATIN=No, and levels of Triglycerides ≤325 (antecedent), then the patient had a 67% chance of developing diabetes mellitus. We concluded that early-onset myocardial infarction is significantly associated with hypertension and diabetes mellitus.Using association rule mining, we can predict the development of hypertension and diabetes mellitus in MI patients.