Background: Candida auris infections are an emerging global threat with poor clinical outcome, high mortality rate, high transmission rate and outbreak potential. The objective of this work is to describe a multidisciplinary approach towards the investigation and containment of a Candida auris outbreak and the preventive measures adopted in a resource limited setting. Methods: This outbreak investigational study was conducted at a 1300-bedded tertiary care academic hospital in South India. The study included 15 adult inpatients with laboratory confirmed Candida auris isolates. The outbreak cluster was identified in adult patients admitted from September 2017 to 2019. The system response consisted of a critical alert system for laboratory confirmed Candida auris infection and multidisciplinary ‘Candida auris care team’ for patient management. The team implemented stringent Infection Prevention and Control (IPC) measures including patient cohorting, standardized therapy and decolonization, staff training, prospective surveillance and introduction of Candida auris specific care bundle. Results: Two outbreak clusters were identified; first cluster occurring between October and November 2017 and the second cluster in May 2018. The cohorts consisted of 7 and 8 Candida auris positive patients in the first and second waves of the outbreak respectively with a total survival rate of 93% (14/15). Deployment of containment measures led to gradual decline in the incidence of adult Candida auris positive cases and prevented further cluster formation. Conclusions: The sustained implementation of guideline and evidence-based IPC measures and training of healthcare workers for improving awareness on systematically following standardized protocols of Candida auris related IPC practices successfully contained Candida auris outbreaks at our hospital. This demonstrates the feasibility of establishing a multidisciplinary model and bundling of practices for preventing Candida auris outbreaks in a Low- and Middle-income country.

Background: Long COVID, or post-COVID-19 syndrome, is the persistence of signs and symptoms that develop during or after COVID-19 infection for more than 12 weeks and are not explained by an alternative diagnosis. In spite of health care recouping to prepandemic states, the post-COVID-19 state tends to be less recognized from low- and middle-income country settings and holistic therapeutic protocols do not exist. Owing to the syndemic nature of COVID-19, it is important to characterize post-COVID-19 syndrome. Objective: We aimed to determine the incidence of post-COVID-19 symptoms in a cohort of inpatients who recovered from COVID-19 from February to July 2021 at a tertiary-care center in South India. In addition, we aimed at comparing the prevalence of post-COVID-19 manifestations in intensive care unit (ICU) and non-ICU patients, assessing the persistence, severity, and characteristics of post-COVID-19 manifestations, and elucidating the risk factors associated with the presence of post-COVID-19 manifestations. Methods: A total of 120 adult patients admitted with COVID-19 in the specified time frame were recruited into the study after providing informed written consent. The cohort included 50 patients requiring intensive care and 70 patients without intensive care. The follow-up was conducted on the second and sixth weeks after discharge with a structured questionnaire. The questionnaire was filled in by the patient/family member of the patient during their visit to the hospital for follow-up at 2 weeks and through telephone follow-up at 6 weeks. Results: The mean age of the cohort was 55 years and 55% were men. Only 5% of the cohort had taken the first dose of COVID-19 vaccination. Among the 120 patients, 58.3% had mild COVID-19 and 41.7% had moderate to severe COVID-19 infection. In addition, 60.8% (n=73) of patients had at least one persistent symptom at the sixth week of discharge and 50 (41.7%) patients required intensive care during their inpatient stay. The presence of persistent symptoms at 6 weeks was not associated with severity of illness, age, or requirement for intensive care. Fatigue was the most common reported persistent symptom with a prevalence of 55.8%, followed by dyspnea (20%) and weight loss (16.7%). Female sex (odds ratio [OR] 2.4, 95% CI 1.03-5.58; P=.04) and steroid administration during hospital stay (OR 4.43, 95% CI 1.9-10.28; P=.001) were found to be significant risk factors for the presence of post-COVID-19 symptoms at 6 weeks as revealed by logistic regression analysis. Conclusions: Overall, 60.8% of inpatients treated for COVID-19 had post-COVID-19 symptoms at 6 weeks postdischarge from the hospital. The incidence of post-COVID-19 syndrome in the cohort did not significantly differ across the mild, moderate, and severe COVID-19 severity categories. Female sex and steroid administration during the hospital stay were identified as predictors of the persistence of post-COVID-19 symptoms at 6 weeks.

Background: In autoimmune inflammatory rheumatological diseases, routine cardiovascular risk assessment is becoming more important. As an increased cardiovascular disease (CVD) risk is recognized in patients with fibromyalgia (FM), a combination of traditional CVD risk assessment tool with Machine Learning (ML) predictive model could help to identify non-traditional CVD risk factors. Methods: This study was a retrospective case–control study conducted at a quaternary care center in India. Female patients diagnosed with FM as per 2016 modified American College of Rheumatology 2010/2011 diagnostic criteria were enrolled; healthy age and gender-matched controls were obtained from Non-communicable disease Initiatives and Research at AMrita (NIRAM) study database. Firstly, FM cases and healthy controls were age-stratified into three categories of 18–39 years, 40–59 years, and ≥ 60 years. A 10 year and lifetime CVD risk was calculated in both cases and controls using the ASCVD calculator. Pearson chi-square test and Fisher's exact were used to compare the ASCVD risk scores of FM patients and controls across the age categories. Secondly, ML predictive models of CVD risk in FM patients were developed. A random forest algorithm was used to develop the predictive models with ASCVD 10 years and lifetime risk as target measures. Model predictive accuracy of the ML models was assessed by accuracy, f1-score, and Area Under 'receiver operating Curve' (AUC). From the final predictive models, we assessed risk factors that had the highest weightage for CVD risk in FM. Results: A total of 139 FM cases and 1820 controls were enrolled in the study. FM patients in the age group 40–59 years had increased lifetime CVD risk compared to the control group (OR = 1.56, p = 0.043). However, CVD risk was not associated with FM disease severity and disease duration as per the conventional statistical analysis. ML model for 10-year ASCVD risk had an accuracy of 95% with an f1-score of 0.67 and AUC of 0.825. ML model for the lifetime ASCVD risk had an accuracy of 72% with an f1-score of 0.79 and AUC of 0.713. In addition to the traditional risk factors for CVD, FM disease severity parameters were important contributors in the ML predictive models. Conclusion: FM patients of the 40–59 years age group had increased lifetime CVD risk in our study. Although FM disease severity was not associated with high CVD risk as per the conventional statistical analysis of the data, it was among the highest contributor to ML predictive model for CVD risk in FM patients. This also highlights that ML can potentially help to bridge the gap of non-linear risk factor identification.

Management of candidemia in developing countries like India encounters laxity in appropriate clinical management and challenges in terms of healthcare capacity, despite its association with high morbidity and mortality. Our study aims to evaluate the impact of a comprehensive candidemia care bundle implementation on appropriateness of therapy and major clinical outcomes. The single-center, quasi-experimental study conducted at a south Indian tertiary care center included adult patients diagnosed with candidemia. Following a retrospective review of candidemia patients of the pre-implementation period (January 2013–December 2015), the hospital antifungal stewardship team instituted a clinical pharmacist driven comprehensive candidemia care bundle for candidemia patients during the post-implementation period (October 2017–2019) and its impact on appropriateness of antifungal prescriptions and inpatient mortality was evaluated. The study included 175 patients with candidemia, comprising of 103 patients in the pre-implementation period and 72 patients in the post-implementation period. Appropriateness of antifungal prescriptions rose to 65% during post-implementation period from 30% observed in pre-implementation phase (P = .0005). The inhospital mortality rate reduced from 40% in the pre-implementation phase to 36% in the post-implementation phase, recording a 10% reduction over 2 years post-implementation (P = .26). No significant difference was observed in terms length of stay (P = .17). Our study demonstrates the successful implementation of an antifungal stewardship led comprehensive care bundle in a low middle income countries setting. The results of our study will have profound implications in improving the appropriateness of management of candidemia and feasibility of scaling up to wider settings could be explored.

The study aims to characterize community-acquired sepsis patients admitted to our 1300-bedded tertiary care hospital in South India from the Surviving Sepsis Campaign (SSC) guideline-compliant e-sepsis registry stratified by focus of infection. The prospective observational study recruited 1009 adult sepsis patients presenting to the emergency department at the center based on Sepsis-2 criteria for a period of three years. Of the patients, 41% were between 61 and 80 years with a mean age of 57.37 ± 13.5%. A total of 13.5% (136) was under septic shock and in-hospital mortality for the study cohort was 25%. The 3 h and 6 h bundle compliance rates observed were 37% and 49%, respectively, without significant survival benefits. Predictors of mortality among patients with bloodstream infections were septic shock (p = 0.01, OR 2.4, 95% CI 1.23–4.79) and neutrophil-to-lymphocyte ratio (p = 0.008, OR 1.01, 95% CI 1.009–1.066). The presence of Acinetobacter (p = 0.005, OR 4.07, 95% CI 1.37–12.09), Candida non-albicans (p = 0.001, OR16.02, 95% CI 3.0–84.2) and septic shock (p = 0.071, OR 2.5, 95% CI 0.97–6.6) were significant predictors of mortality in patients with community-acquired pneumonia. The registry has proven to be a key data source detailing regional microbial etiology and clinical outcomes of adult sepsis patients, enabling comprehensive evaluation of regional community-acquired sepsis to tailor institutional sepsis treatment protocols.

Background: Sepsis, a life-threatening condition characterized by a dysregulated immune response to infection, remains a significant clinical challenge globally. This study aims to enhance the predictive accuracy of existing sepsis severity scores by developing augmented versions of the SOFA and SAPS-III models, termed Pro-SOFA and Pro-SAPS, through the integration of biomarkers procalcitonin (PCT), neutrophil-tolymphocyte ratio (NLR), and C-reactive protein (CRP). Methods: This prospective observational study was conducted in the medical ICU of a tertiary care hospital in southern India from August 2022 to December 2023. A total of 301 adult patients suspected or confirmed to have sepsis were assessed for eligibility, with 171 patients completing the study. Demographic and clinical data were collected; SOFA and SAPS-III scores were calculated and augmented with PCT, NLR, and CRP to develop Pro-SOFA and Pro-SAPS models. The performance of these models was evaluated using Brier scores, AUC, and net reclassification index (NRI). Results: The augmented Pro-SOFA and Pro-SAPS models demonstrated superior predictive accuracy compared to their original counterparts. The Brier scores for Pro-SOFA and Pro-SAPS were 0.181 and 0.165, respectively, indicating better calibration than the original scores. The Pro- SAPS showed significant improvement over the original SAPS-III score (NRI = 0.50, SE = 0.14, p < 0.01). Similarly, Pro-SOFA outperformed the original SOFA (NRI = 0.49, SE = 0.13, p < 0.01). Conclusion and clinical significance: Integrating PCT, CRP, and NLR with SOFA and SAPS-III scores to develop Pro-SOFA and Pro-SAPS significantly improves the predictive accuracy for sepsis mortality and can thus potentially improve sepsis outcomes.