Detection of 16S rRNA gene for rapid identification of bacterial pathogens causing peritonitis in patients on continuous ambulatory peritoneal dialysis.

PURPOSE: Peritonitis is the most important complication with high rate of morbidity and mortality in patients on continuous ambulatory peritoneal dialysis (CAPD) despite the success and advances. Rapid and accurate identification of pathogens causing peritonitis in a CAPD patient is essential for early targeted treatment. The aim of the study was to evaluate the role of 16S rRNA gene and ITS region PCR and sequencing in detecting bacterial and fungal pathogens from the dialysate of patients undergoing CAPD. METHODS: Fifty eight peritoneal dialysate from suspected cases of peritonitis on CAPD were subjected to conventional culture as per the ISPD guidelines and automated culture system. A conventional PCR was performed to detect the 16S rRNA gene and ITS region. Sequencing and analysis were performed to identify the etiological agent from the remaining dialysate. RESULTS: Among the 58 dialysate fluid, the etiological agents were identified in 8(14%) samples by conventional culture, 28(48%) by automated culture and 47(81%) by 16S rRNA sequencing and analysis. In 8 samples there was discordance in the results of the culture and 16S rRNA PCR. BLAST search of nine sequences obtained from 16S rRNA PCR revealed that these sequences matched best with uncultured bacterial clones. In eleven samples the sequence failed. CONCLUSION: The molecular tool 16S rRNA gene and ITS region PCR and sequencing cannot be used as a standalone test as it lacks sensitivity to identify some bacterial species due to high genetic similarity in some cases and inadequate database in GenBank. However, it could be used as a supplementary test to the culture method especially in the diagnosis of culture negative peritonitis.

Risk factors, bacteriological profile and outcome of surgical site infections following orthopaedic surgery.

BACKGROUND AND OBJECTIVES: Surgical site infection (SSI) is a challenge for the surgeon. Incidence of SSI reported in literature varies from 0.5% to 15%. Severity of SSI ranges from superficial skin infection to life-threatening condition like septicaemia. It is responsible for increased morbidity, mortality, and economic burden to the hospital in general, and the patient in particular. The aim of this study was to assess the risk factors, bacteriological profile, length of hospitalization, and cost due to orthopaedic SSI in patients admitted to a tertiary care hospital. MATERIALS AND METHODS: This was a prospective case control study. Cases were diagnosed based on CDC definition of nosocomial SSI. All cases were assessed preoperatively, intraoperatively and postoperatively, according to type of surgery, wound class, duration of operation, antimicrobial prophylaxis, use of drain, preoperative hospital stay, causative micro organism, total hospital stay, readmission rates and cost incurred. Age, sex and surgical procedure matched controls without SSI, were also assessed. Chi-square test and Fisher's exact test were used for analysis. P= <0.05 was considered significant. RESULTS: Out of 1023 patients, 47 cases had SSI, with a rate of 4.6%. Cigarette smoking was a risk factor for SSI (P = 0.0035). The most common etiologic agents were Acinetobacter baumannii and Staphylococcus aureus. Incidence of readmission among SSI cases was more compared to controls (P= 0.0001). Costs attributable to SSI (Indian Rupees) was Rs 32,542 (17,054 to 87,514) which was significantly more than those without SSI (P= <0.001). CONCLUSION: Despite latest surgical amenities, meticulous sterilization protocols and pre-operative antibiotic prophylaxis, SSI continues to be present in healthcare settings. The increase in duration of hospital stay due to SSI adds to additional burden to an already resource-constrained healthcare system.

Association of ABO blood groups with dengue fever and its complications in a tertiary care hospital.

CONTEXT: Dengue fever (DF) has been steadily increasing in India with outbreaks in certain areas taking the proportion of epidemics. Along with secondary dengue, several risk factors predispose to dengue hemorrhagic fever and dengue shock syndrome. Very few studies associating the relationship between dengue and its severity with ABO blood group have been documented. AIMS: The aim of this study was to determine the association between distribution of ABO Rh blood groups and DF and DF with thrombocytopenia. SETTINGS AND DESIGN: This was a retrospective descriptive study conducted at the clinical laboratory of the department of microbiology. MATERIALS AND METHODS: Dengue patients whose case record contained information on blood group were screened for details of blood group and confirmed dengue diagnosis. Randomly 384 case records were selected. These were divided into two groups; Group 1 included DF cases (platelet count >20,000) and Group 2 included DF cases with thrombocytopenia (platelet count <20,000). Control group consisted of patients other than dengue, whose blood grouping had been done; randomly 390 were selected and analyzed. STATISTICAL ANALYSIS USED: P value was calculated using the Chi-square test. Odds ratio were calculated using the Fisher's exact test. RESULTS: DF was higher in 23% of individuals with AB blood group as compared to 8.5% of controls (P = 0.0004), whereas patients with blood group O were significantly less affected with DF (P = 0.0048). Disease severity was not associated with any of the blood groups. CONCLUSIONS: Individuals with AB blood group are more prone to DF, whereas individuals with blood group O are less prone.

Agreement between Framingham Risk Score and United Kingdom Prospective Diabetes Study Risk Engine in Identifying High Coronary Heart Disease Risk in North Indian Population.

BACKGROUND: The aim of the study is to evaluate the concurrence between Framingham Risk score (FRS) and United Kingdom Prospective Diabetes Study (UKPDS) risk engine in identifying coronary heart disease (CHD) risk in newly detected diabetes mellitus patients and to explore the characteristics associated with the discrepancy between them. METHODS: A cross-sectional study involving 489 subjects newly diagnosed with type 2 diabetes mellitus was conducted. Agreement between FRS and UKPDS in classifying patients as high risk was calculated using kappa statistic. Subjects with discrepant scores between two algorithms were identified and associated variables were determined. RESULTS: The FRS identified 20.9% subjects (range, 17.5 to 24.7) as high-risk while UKPDS identified 21.75% (range, 18.3 to 25.5) as high-risk. Discrepancy was observed in 17.9% (range, 14.7 to 21.7) subjects. About 9.4% had high risk by UKPDS but not FRS, and 8.6% had high risk by FRS but not UKPDS. The best agreement was observed at high-risk threshold of 20% for both (κ=0.463). Analysis showed that subjects having high risk on FRS but not UKPDS were elderly females having raised systolic and diastolic blood pressure. Patients with high risk on UKPDS but not FRS were males and have high glycosylated hemoglobin. CONCLUSION: The FRS and UKPDS (threshold 20%) identified different populations as being at high risk, though the agreement between them was fairly good. The concurrence of a number of factors (e.g., male sex, low high density lipoprotein cholesterol, and smoking) in both algorithms should be regarded as increasing the CHD risk. However, longitudinal follow-up is required to form firm conclusions.

Agreement between Framingham Risk Score and United Kingdom Prospective Diabetes Study Risk Engine in Identifying High Coronary Heart Disease Risk in North Indian Population

BACKGROUND: The aim of the study is to evaluate the concurrence between Framingham Risk score (FRS) and United Kingdom Prospective Diabetes Study (UKPDS) risk engine in identifying coronary heart disease (CHD) risk in newly detected diabetes mellitus patients and to explore the characteristics associated with the discrepancy between them. METHODS: A cross-sectional study involving 489 subjects newly diagnosed with type 2 diabetes mellitus was conducted. Agreement between FRS and UKPDS in classifying patients as high risk was calculated using kappa statistic. Subjects with discrepant scores between two algorithms were identified and associated variables were determined. RESULTS: The FRS identified 20.9% subjects (range, 17.5 to 24.7) as high-risk while UKPDS identified 21.75% (range, 18.3 to 25.5) as high-risk. Discrepancy was observed in 17.9% (range, 14.7 to 21.7) subjects. About 9.4% had high risk by UKPDS but not FRS, and 8.6% had high risk by FRS but not UKPDS. The best agreement was observed at high-risk threshold of 20% for both (kappa=0.463). Analysis showed that subjects having high risk on FRS but not UKPDS were elderly females having raised systolic and diastolic blood pressure. Patients with high risk on UKPDS but not FRS were males and have high glycosylated hemoglobin. CONCLUSION: The FRS and UKPDS (threshold 20%) identified different populations as being at high risk, though the agreement between them was fairly good. The concurrence of a number of factors (e.g., male sex, low high density lipoprotein cholesterol, and smoking) in both algorithms should be regarded as increasing the CHD risk. However, longitudinal follow-up is required to form firm conclusions.