Urinary Tract Infection: Prescribing Pattern of Antibiotics at a Tertiary Care Hospital

Objectives: The purpose of Drug Utilization Review is to ensure drugs are used appropriately, safely and effectively to improve patient health status.  The use of drugs in appropriate, safe and effective manner would decrease the treatment cost for the patients. Urinary Tract infection is most common and can lead to secondary infections.  So continuous analysis of prescribing pattern in urinary tract infection is a vital one. To find out the various risk factors of urinary tract infection. To study the Prescribing pattern and rational use of Antibiotics in UTI Patients in a tertiary care hospital.Methods: A prospective observational study was carried out in 100 patients in a tertiary care teaching hospital, by collecting patient data from the medical records of patients from General Medicine Department.Results: Analysis of prescribed drugs revealed that use of antibiotics like Ceftriaxone (35%) and Amikacin (22%) were significantly higher than other drugs prescribed such as Ciprofloxacin (16%), Nitrofurantoin (15%), Ofloxacin (5%), Cefixime (3%), Moxifloxacin (2%) and Clarithromycin (2%). Culture sensitivity test was done only in 30% of the total cases (100) for which empirical treatment had to be applied rather than specific antibiotic treatment. The most common isolated organisms were E.coli (60%),Proteus (20%), Klebsiella (13.33%) and Psedomonas (6.66%).Conclusion: Cephalosporins, Aminoglycosides and Quinolones were the most commonly prescribed antibiotics in this study. Present findings together with previous ones are suggestive the need of periodic monitoring of antibiotic sensitivity pattern of the bacterial isolates to provide effective treatment.Â

Invasive Pulmonary Aspergillosis

Aspergillus spp. often colonize the respiratory tract of the critically ill patients in the intensive care units and subsequently cause invasive disease. The risk of developing invasive disease is more in immunocompromised patients. Here we report a case of fatal invasive pulmonary aspergillosis caused by Aspergillus versicolor in a post-operative patient on mechanical ventilation, who did not respond to intravenous itraconazole. We have discussed the challenges involved in accurate diagnosis of this condition and appropriate management

Aetiological agents of ventilator associated pneumonia and their resistance pattern

BackgroundVentilator-associated pneumonia (VAP) is a common type of nosocomial pneumonia encountered in intensive care units. There are several aetiological agents which make treatment challenging. Improper antibiotic treatment of ventilated patients may lead to the emergence of multidrug resistant (MDR) pathogens.MethodA prospective study was performed over a period of 20 months. Our study had two arms. The first, ‘Incidence and risk factors of VAP in a tertiary care hospital’ was the subject of an earlier publication. We present the second investigative arm in this work. The aetiological agents of patients on mechanical ventilation (MV) were identified by standard bacteriological method. The susceptibility pattern was evaluated by Kirby-Bauer disc diffusion method. Extended spectrum beta lactamase (ESBL) testing was performed by combination disc method, and metallo-beta lactamase (MBL) testing was performed by EDTA disk synergy test (EDS).ResultsLate-onset VAP was associated with Pseudomonas aeruginosa, Klebsiella pneumoniae and Escherichia coli, while early-onset VAP was commonly caused by members of Enterobacteriaceae, Candida albicans and Staphylococcus aureus. 72.2 per cent of VAP patients had monomicrobial and 27.8 per cent had polymicrobial infection. Out of the 24 isolates obtained from patients with VAP, seven (29.2 per cent) were MDR pathogens. ESBL and MBL production was detected in 40 per cent and 20 per cent of Klebsiella pneumoniae isolated in our study. Around 50 per cent of isolates associated with late-onset VAP were MDR, while 22.2 per cent isolates obtained from patients with early-onset VAP were MDR.ConclusionVAP is a nosocomial pneumonia that is common among ventilated patients. The aetiological agents vary from common organisms to MDR pathogens that are difficult to treat. A proper knowledge of MDR pathogens and early isolation followed by prevention of prolonged antibiotic therapy can reduce the mortality of late onset VAP

Incidence and Risk Factors of Ventilator Associated Pneumonia in a Tertiary Care Hospital

BackgroundVentilator associated pneumonia (VAP) is a type of nosocomial pneumonia associated with increased morbidity and mortality. Knowledge about the incidence and risk factors is necessary to implement preventive measures to reduce mortality in these patients.Method  A prospective study was conducted at a tertiary care teaching hospital for a period of 20 months from November 2009 to July 2011. Patients who were on mechanical ventilation (MV) for more than 48 hours were monitored at frequent intervals for development of VAP using clinical and microbiological criteria until discharge or death.ResultsOf the 76 patients, 18 (23.7%) developed VAP during their ICU stay. The incidence of VAP was 53.25 per 1,000 ventilator days. About 94% of VAP cases occurred within the first week of MV. Early-onset and late-onset VAP was observed in 72.2% and 27.8%, respectively. Univariate analysis showed chronic lung failure, H2 blockers usage, and supine head position were significant risk factors for VAP. Logistic regression revealed supine head position as an independent risk factor for VAP.ConclusionVAP occurred in a sizeable number of patients on MV. Chronic lung failure, H2 blockers usage, and supine head position were the risk factors associated with VAP. Awareness about these risk factors can be used to inform simple and effective preventive measures

Prevalence of Candida co-infection in patients with pulmonary tuberculosis

BackgroundCandida species are emerging as a potentially pathogenic fungus in patients with broncho-pulmonary diseases. The synergistic growth promoting association of Candida and Mycobacterium tuberculosis has raised increased concern for studying the various Candida spp. and its significance in pulmonary tuberculosis patients during current years. AimsThis study was undertaken with the objective of discovering the prevalence of co-infection caused by different Candida species in patients with pulmonary tuberculosis.Method  A total of 75 patients with pulmonary tuberculosis diagnosed by sputum Ziehl-Neelsen staining were included in the study. Candida co-infection was confirmed using the Kahanpaa et al. criteria. Candida species were identified using gram stain morphology, germ tube formation, morphology on cornmeal agar with Tween-80, sugar fermentation tests and HiCrome Candida Agar.ResultsCandida co-infection was observed in 30 (40%) of patients with pulmonary tuberculosis. Candida albicans was the most common isolate observed in 50% of the patients with co-infection, followed by C. tropicalis (20%) and C. glabrata (20%). Candida co-infection was found in 62.5% of female patients, while it was observed in only 29.4% of the male patients (P value 0.0133). Mean ± SD age of the patients with C. glabrata infection was 65.83 ± 3.19, while the mean ± SD age of the patients with other Candida infections was 43.25 ± 20.44 (P value 0.0138).ConclusionMany patients with pulmonary tuberculosis have co-infection with Candida spp. The prevalence of non-albicans Candida species is increasing and may be associated with inadequate response to anti-tubercular drugs. C. glabrata infection has a strong association with old age.

Bacteriophage types of methicillin-resistant Staphylococcus aureus in a tertiary care hospital

BackgroundPhage typing had been utilised extensively to characterise methicillin-resistant Staphylococcus aureus (MRSA) outbreak strains in the past. It is an invaluable tool even today to monitor emergence & dissemination of MRSA strains.AimsThe aim of this study was to determine the prevalent phage types of MRSA in south India and the association between phage types, antibiotic resistance pattern and risk factors.Method  A total of 48 non-duplicate MRSA strains recovered from various clinical samples during January to December, 2010 were tested against a panel of anti-staphylococcal antibiotics. Phage typing was carried out at the National Staphylococcal Phage typing Centre, New Delhi. Out of 48, 32 hospitalised patients were followed up for risk factors and response to empirical and post sensitivity antibiotic therapy. The risk factors were compared with a control group of 30 patients with methicillin sensitive Staphylococcus aureus (MSSA) infection.ResultsAmongst the five prevalent phage types, 42E was most common (52%), followed by a non-typable variant (22.9%), 42E/47/54/75 (16.6%), 42E/47 (6.2%) and 47 (2%). Phage type 42E was the predominant strain in all wards and OPDs except in the ICU where 42E/47/54/75 was most common. Although not statistically significant, strain 42E/47/54/75 (n=8) showed higher resistance to all drugs, except ciprofloxacin and amikacin, and were mostly D-test positive (87.5%) compared to the 42E strain (32%). Duration of hospital stay, intravenous catheterisation and breach in skin were the most significant risk factors for MRSA infection.ConclusionWe found MRSA strain diversity in hospital wards with differences in their antibiotic susceptibility pattern. The findings may impact infection control and antibiotic policy significant

Ventilator-associated pneumonia

BackgroundVentilator-associated pneumonia (VAP) is a type of nosocomial pneumonia that occurs in patients who receive mechanical ventilation (MV). According to the International Nosocomial Infection Control Consortium (INICC), the overall rate of VAP is 13.6 per 1,000 ventilator days. The incidence varies according to the patient group and hospital setting. The incidence of VAP ranges from 13–51 per 1,000 ventilation days. Early diagnosis of VAP with appropriate antibiotic therapy can reduce the emergence of resistant organisms.MethodThe aim of this review was to provide an overview of the incidence, risk factors, aetiology, pathogenesis, treatment, and prevention of VAP. A literature search for VAP was done through the PUBMED/MEDLINE database. This review outlines VAP’s risk factors, diagnostic methods, associated organisms, and treatment modalities.ConclusionVAP is a common nosocomial infection associated with ventilated patients. The mortality associated with VAP is high. The organisms associated with VAP and their resistance pattern varies depending on the patient group and hospital setting. The diagnostic methods available for VAP are not universal; however, a proper infection control policy with appropriate antibiotic usage can reduce the mortality rate among ventilated patients

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Background Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. Methods The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model—a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates—with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality—which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. Findings The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2–100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1–290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1–211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4–48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3–37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7–9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. Interpretation Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere

On the relationship between corticomuscular (EEG-EMG) phase coupling and muscular fatigue

Contradictory results have been shown in studies measuring the effect of muscle fatigue on the level of synchrony between the oscillatory, cortical and muscular electrical activities (also known as corticomuscular coupling). In every study, the standard method (coherence) used to measure the level of synchrony takes into account both the amplitude and phase of the two signals. However, the use of the phase lock value (PLV) has been over looked as a method for determining the level of synchrony. While the PLV is modulated purely by the phase between the two signals, it is unaffected by any amplitude variation. This study aims to determine whether amplitude variations in electroencephalography (EEG) and electromyography (EMG) could have caused the contradictory results when comparing pre-,during and post-fatigue measures of corticomuscular coupling, which consequently affected the conclusions drawn regarding the monitoring of fatigue by the central nervous system. A determination will be made regarding the contradictions by directly comparing the two methods (coherence and PLV) on the same dataset of simultaneously measured EEG and EMG signals throughout an isometric pre-, during and post-fatigue task