Declining Trend in Antimicrobial Sensitivity in Respiratory Tract Bacterial Pathogens against Commonly Used Drugs at a Tertiary Care Hospital.

OBJECTIVES: Antimicrobial resistance (AMR) is a major health issue. To determine trends in bacterial organisms in respiratory tract infections (RTIs) and their antibiotic sensitivity at a tertiary care center in India, we performed this study. METHODS: Successive samples received from January 2017 to December 2021 from the respiratory tract (sputum, endotracheal secretion, and bronchoalveolar lavage) from intensive care units and medical inpatients were processed for bacterial growth. The identification of isolates and antibiotic sensitivity patterns was performed using an automated VITEK-2 system. Descriptive statistics are reported. RESULTS: We received 7,204 respiratory samples. Significant bacterial growth was in 3,000 (41.6%), and 2,992 (41.5%) were gram-negative. Klebsiella pneumoniae was the most prevalent, followed by Pseudomonas aeruginosa, Acinetobacter baumannii, Escherichia coli, and Enterobacter aerogenes. Increasing secular trends were observed for Klebsiella and Pseudomonas and declining trends for Acinetobacter and Escherichia (p < 0.05). Antimicrobial sensitivity patterns showed that Klebsiella, Pseudomonas, Acinetobacter, E. coli, and Enterobacter had a high sensitivity with colistin and polymyxin (99-100%). Moderate sensitivity was observed with carbapenems (Acinetobacter: 47.5%, Enterobacter: 62.0%, Escherichia: 76.5%, Klebsiella: 72.3%, Pseudomonas: 66.7%) and tigecycline (Acinetobacter: 50.4%, Enterobacter: 68.0%, Escherichia: 81.1%, Klebsiella: 66.6%, Pseudomonas: 0%). Aminoglycosides had <50% sensitivity for various organisms, and <25% sensitivity was observed with third-generation cephalosporins and quinolones. Trend analysis showed persistent sensitivity of various pathogenic bacteria to colistin and polymyxin and declining pharmacological sensitivity in Acinetobacter (carbapenems and tigecycline), Escherichia (carbapenems, quinolones, and tigecycline), Klebsiella (carbapenems, quinolones, aminoglycosides, and tigecycline), and Pseudomonas (carbapenems and aminoglycosides) species (p < 0.05). CONCLUSION: Common respiratory tract gram-negative bacterial pathogens at a tertiary care hospital are K. pneumoniae, P. aeruginosa, A. baumannii, and E. coli. All these bacteria demonstrate high sensitivity only with colistin and polymyxin. Significant AMR is observed to carbapenems, tigecycline, aminoglycosides, and third-generation cephalosporins. Secular trends show declining antimicrobial sensitivity among various bacterial pathogens.

Influence of COVID-19 pandemic in India on coronary artery disease clinical presentation, angiography, interventions and in-hospital outcomes: a single centre prospective registry-based observational study.

OBJECTIVE: The study examined the influence of the COVID-19 pandemic in India on variation in clinical features, management and in-hospital outcomes in patients undergoing percutaneous coronary intervention (PCI). DESIGN: Prospective registry-based observational study. SETTING: A tertiary care hospital in India participant in the American College of Cardiology CathPCI Registry. PARTICIPANTS: 7089 successive patients who underwent PCI from April 2018 to March 2023 were enrolled (men 5627, women 1462). Details of risk factors, clinical presentation, coronary angiography, coronary interventions, clinical management and in-hospital outcomes were recorded. Annual data were classified into specific COVID-19 periods according to Government of India guidelines as pre-COVID-19 (April 2018 to March 2019, n=1563; April 2019 to March 2020, n=1594), COVID-19 (April 2020 to March 2020, n=1206; April 2021 to March 2022, n=1223) and post-COVID-19 (April 2022 to March 2023, n=1503). RESULTS: Compared with the patients in pre-COVID-19 and post-COVID-19 periods, during the first COVID-19 year, patients had more hypertension, non-ST elevation myocardial infarction (NSTEMI), lower left ventricular ejection fraction (LVEF) and multivessel coronary artery disease (CAD). In the second COVID-19 year, patients had more STEMI, lower LVEF, multivessel CAD, primary PCI, multiple stents and more vasopressor and mechanical support. There were 99 (1.4%) in-hospital deaths which in the successive years were 1.2%, 1.4%, 0.8%, 2.4% and 1.3%, respectively (p=0.019). Compared with the baseline year, deaths were slightly lower in the first COVID-19-year (age-sex adjusted OR 0.68, 95% CI 0.31 to 1.47) but significantly more in the second COVID-19-year (OR 1.97, 95% CI 1.10 to 3.54). This variation attenuated following adjustment for clinical presentation, extent of CAD, in-hospital treatment and duration of hospitalisation. CONCLUSIONS: In-hospital mortality among patients with CAD undergoing PCI was significantly higher in the second year of the COVID-19 pandemic in India and could be one of the reasons for excess deaths in the country. These patients had more severe CAD, lower LVEF, and more vasopressor and mechanical support and duration of hospitalisation.

Low body mass index is associated with adverse cardiovascular outcomes following PCI in India: ACC-NCDR registry.

Objective: Registry-based prospective study was conducted to evaluate association of body mass index (BMI) with major adverse coronary events (MACE) following percutaneous coronary intervention (PCI). Methods: Successive patients undergoing PCI were enrolled from April'19 to March'22 and classified into five BMI categories (<23.0,23.0-24.9,25.0-26.9,27.0-29.9, and ≥30.0 kg/m2). Clinical, angiographic features, interventions and outcomes were obtained by in-person or telephonic follow-up. Primary endpoints were (a) MACE(cardiovascular deaths, acute coronary syndrome or stroke, revascularization, hospitalization and all-cause deaths) and (b)cardiovascular deaths. Cox-proportionate hazard ratios(HR) and 95 % confidence intervals(CI) were calculated. Results: The cohort included 4045 patients. Mean age was 60.3 ± 11y, 3233(79.7 %) were men. There was high prevalence of cardiometabolic risk factors. 90 % patients had acute coronary syndrome(STEMI 39.6 %, NSTEMI/unstable angina 60.3 %), 60.0 % had impaired ejection fraction(EF) and multivessel CAD. Lower BMI groups (<23.0 kg/m2) had higher prevalence of tobacco use, reduced ejection fraction(EF), multivessel CAD, stents, and less primary PCI for STEMI. There was no difference in discharge medications and in-hospital deaths. Median follow-up was 24 months (IQR 12-36), available in 3602(89.0 %). In increasing BMI categories, respectively, MACE was in 10.9,8.9,9.5,9.1 and 6.8 % (R2 = 0.73) and CVD deaths in 5.1,4.5,4.4,5.1 and 3.5 % (R2 = 0.39). Compared to lowest BMI category, age-sex adjusted HR in successive groups for MACE were 0.89,0.87,0.79,0.69 and CVD deaths 0.98,0.87,0.95,0.75 with overlapping CI. HR attenuated following multivariate adjustments. Conclusions: Low BMI patients have higher incidence of major adverse cardiovascular events following PCI in India. These patients are older, with greater tobacco use, lower EF, multivessel CAD, delayed STEMI-PCI, and longer hospitalization.

Biomarkers and outcomes in hospitalised patients with COVID-19: a prospective registry.

OBJECTIVES: To determine association of biomarkers-high-sensitivity C reactive protein (hsCRP), D-dimer, interleukin-6 (IL-6), lactic dehydrogenase (LDH), ferritin and neutrophil-lymphocyte ratio (NLR)-at hospitalisation with outcomes in COVID-19. DESIGN AND SETTING: Tertiary-care hospital based prospective registry. PARTICIPANTS: Successive virologically confirmed patients with COVID-19 hospitalised from April 2020 to July 2021 were prospectively recruited. Details of clinical presentation, investigations, management and outcomes were obtained. PRIMARY AND SECONDARY OUTCOME MEASURES: All biomarkers were divided into tertiles to determine associations with clinical features and outcomes. Primary outcome was all-cause deaths and secondary outcome was oxygen requirement, non-invasive and invasive ventilation, dialysis, duration of stay in ICU and hospital. Numerical data are presented in median and interquartile range (IQR 25-75). Univariate and multivariate (age, sex, risk factors, comorbidities, treatments) ORs and 95% CIs were calculated. RESULTS: 3036 virologically confirmed patients with COVID-19 were detected and 1251 hospitalised. Men were 70.0%, aged >60 years 44.8%, hypertension 44.1%, diabetes 39.6% and cardiovascular disease 18.9%. Median symptom duration was 5 days (IQR 4-7) and oxygen saturation 95% (90%-97%). Total white cell count was 6.9×109/L (5.0-9.8), neutrophils 79.2% (68.1%-88.2%), lymphocytes 15.8% (8.7%-25.5%) and creatinine 0.93 mg/dL (0.78-1.22). Median (IQR) for biomarkers were hsCRP 6.9 mg/dL (2.2-18.9), D-dimer 464 ng/dL (201-982), IL-6 20.1 ng/dL (6.5-60.4), LDH 284 mg/dL (220-396) and ferritin 351 mg/dL (159-676). Oxygen support at admission was in 38.6%, subsequent non-invasive or invasive ventilatory support in 11.0% and 11.6%, and haemodialysis in 38 (3.1%). 173 (13.9%) patients died and 15 (1.2%) transferred to hospice care. For each biomarker, compared with the first, those in the second and third tertiles had more clinical and laboratory abnormalities, and oxygen, ventilatory and dialysis support. Multivariate-adjusted ORs (95% CI) for deaths in second and third versus first tertiles, respectively, were hsCRP 2.24 (1.11 to 4.50) and 12.56 (6.76 to 23.35); D-dimer 3.44 (1.59 to 7.44) and 14.42 (7.09 to 29.30); IL-6 2.56 (1.13 to 5.10) and 10.85 (5.82 to 20.22); ferritin 2.88 (1.49 to 5.58) and 8.19 (4.41 to 15.20); LDH 1.75 (0.81 to 3.75) and 9.29 (4.75 to 18.14); and NLR 3.47 (1.68 to 7.14) and 17.71 (9.12 to 34.39) (p<0.001). CONCLUSION: High levels of biomarkers-hsCRP, D-dimer, IL-6, LDH, ferritin and NLR-in COVID-19 are associated with more severe illness and higher in-hospital mortality. NLR, a widely available investigation, provides information similar to more expensive biomarkers.

Epidemiological study of prevalence, determinants, and outcomes of infections in medical ICU at a tertiary care hospital in India.

OBJECTIVES: To determine the prevalence of infections, risk factors, and outcomes in a medical intensive care unit (ICU), we performed a hospital-based study. MATERIALS AND METHODS: Consecutive patients were enrolled and details of risk factors and bacteriological data were obtained. Outcomes were death/transfer to palliative care or recovery. Statistical analyses were performed. RESULTS: Four hundred and eighty-seven patients were admitted during the study period (age 55.6 ± 19 yr, men 68%). Diseases responsible were respiratory (37%), gastrointestinal/liver (22%), neurological (20%), renal (8%), and trauma (6%) related. Majority of admissions were direct (45%) or transfers from other hospitals (41%). Most important comorbidities were hypertension (41%), diabetes (31%), and chronic obstructive pulmonary disease (15%). Median APACHE-2 score was 13.0 (IQR 1-25). Antibiotics were administered in 98%. Bacteriological cultures were positive in 28% (n = 623). Respiratory infections were the most common (45.5%) followed by blood (23.3%) and urinary (16.1%). Gram-negative bacteria were common-Acinetobacter baumannii (20.9%), Klebsiella pneumoniae (19.7%), Escherichia coli (18.3%), and Pseudomonas aeruginosa (14.0%). There a high prevalence of resistance to common antibiotics. Patients with positive cultures were older, transferees (46 vs 37%, P = 0.07), with respiratory disease (48 vs. 33%, P = 0.003), with more than two comorbidities (33 vs 21%, P = 0.009), and higher APACHE-2 score (17.7 ± 8 vs. 13.3 ± 8, P = 0.07). Three hundred and fifty-two (72.3%) recovered, 68 (13.9%) died, and 67 (13.8%) were transferred to palliative care. Survival was associated with younger age, lower APACHE-2 score, negative cultures, and shorter duration in ICU (P < 0.05). Mortality was greater in patients with Acinetobacter (OR 2.36, 1.17-4.73), Klebsiella (OR 2.81, 1.33-5.92), Pseudomonas (OR 8.03, 2.83-22.76), or Enterobacter (OR 6.73, 1.29-35.12) infection. CONCLUSIONS: There is high prevalence of infections in patients in a medical ICU in India. Gram-negative bacteria are the most prevalent and resistance to antibiotics is high. Risk factors are age, hospital transfers, APACHE-2 score, and multiple comorbidities.