Background: Healthcare-associated infections (HAIs) are a major global threat to patient safety. Systematic surveillance is crucial for understanding HAI rates and antimicrobial resistance trends and to guide infection prevention and control (IPC) activities based on local epidemiology. In India, no standardized national HAI surveillance system was in place before 2017. Methods: Public and private hospitals from across 21 states in India were recruited to participate in an HAI surveillance network. Baseline assessments followed by trainings ensured that basic microbiology and IPC implementation capacity existed at all sites. Standardized surveillance protocols for central-line–associated bloodstream infections (CLABSIs) and catheter-associated urinary tract infections (CAUTIs) were modified from the NHSN for the Indian context. IPC nurses were trained to implement surveillance protocols. Data were reported through a locally developed web portal. Standardized external data quality checks were performed to assure data quality. Results: Between May 2017 and April 2019, 109 ICUs from 37 hospitals (29 public and 8 private) enrolled in the network, of which 33 were teaching hospitals with >500 beds. The network recorded 679,109 patient days, 212,081 central-line days, and 387,092 urinary catheter days. Overall, 4,301 bloodstream infection (BSI) events and 1,402 urinary tract infection (UTI) events were reported. The network CLABSI rate was 9.4 per 1,000 central-line days and the CAUTI rate was 3.4 per 1,000 catheter days. The central-line utilization ratio was 0.31 and the urinary catheter utilization ratio was 0.57. Moreover, 3,542 (73%) of 4,742 pathogens reported from BSIs and 868 (53%) of 1,644 pathogens reported from UTIs were gram negative. Also, 1,680 (26.3%) of all 6,386 pathogens reported were Enterobacteriaceae. Of 1,486 Enterobacteriaceae with complete antibiotic susceptibility testing data reported, 832 (57%) were carbapenem resistant. Of 951 Enterobacteriaceae subjected to colistin broth microdilution testing, 62 (7%) were colistin resistant. The surveillance platform identified 2 separate hospital-level HAI outbreaks; one caused by colistin-resistant K. pneumoniae and another due to Burkholderia cepacia. Phased expansion of surveillance to additional hospitals continues. Conclusions: HAI surveillance was successfully implemented across a national network of diverse hospitals using modified NHSN protocols. Surveillance data are being used to understand HAI burden and trends at the facility and national levels, to inform public policy, and to direct efforts to implement effective hospital IPC activities. This network approach to HAI surveillance may provide lessons to other countries or contexts with limited surveillance capacity.

Funding: None

Disclosures: None

Background: The rising trend of antibiotic resistance imposes a heavy burden on healthcare both clinically and economically (US$55 billion), with 23,000 estimated annual deaths in the United States as well as increased length of stay and morbidity. Machine-learning–based methods have, of late, been used for leveraging patient’s clinical history and demographic information to predict antimicrobial resistance. We developed a machine-learning model ensemble that maximizes the accuracy of such a drug-sensitivity versus resistivity classification system compared to the existing best-practice methods. Methods: We first performed a comprehensive analysis of the association between infecting bacterial species and patient factors, including patient demographics, comorbidities, and certain healthcare-specific features. We leveraged the predictable nature of these complex associations to infer patient-specific antibiotic sensitivities. Various base-learners, including k-NN (k-nearest neighbors) and gradient boosting machine (GBM), were used to train an ensemble model for confident prediction of antimicrobial susceptibilities. Base learner selection and model performance evaluation was performed carefully using a variety of standard metrics, namely accuracy, precision, recall, F1 score, and Cohen κ. Results: For validating the performance on MIMIC-III database harboring deidentified clinical data of 53,423 distinct patient admissions between 2001 and 2012, in the intensive care units (ICUs) of the Beth Israel Deaconess Medical Center in Boston, Massachusetts. From ~11,000 positive cultures, we used 4 major specimen types namely urine, sputum, blood, and pus swab for evaluation of the model performance. Figure 1 shows the receiver operating characteristic (ROC) curves obtained for bloodstream infection cases upon model building and prediction on 70:30 split of the data. We received area under the curve (AUC) values of 0.88, 0.92, 0.92, and 0.94 for urine, sputum, blood, and pus swab samples, respectively. Figure 2 shows the comparative performance of our proposed method as well as some off-the-shelf classification algorithms. Conclusions: Highly accurate, patient-specific predictive antibiogram (PSPA) data can aid clinicians significantly in antibiotic recommendation in ICU, thereby accelerating patient recovery and curbing antimicrobial resistance.

Funding: This study was supported by Circle of Life Healthcare Pvt. Ltd.

Disclosures: None

Obesity indicators are known to predict the presence of type 2 diabetes mellitus (T2DM); however, evidence for which indicator best identifies undiagnosed T2DM in the Indian population is still very limited. In the present study we examined the utility of different obesity indicators to identify the presence of undiagnosed T2DM and determined their appropriate cut point for each obesity measure. Individuals were recruited from the large-scale population-based Kerala Diabetes Prevention Program. Oral glucose tolerance tests was performed to diagnose T2DM. Receiver operating characteristic (ROC) curve analyses were used to compare the association of different obesity indicators with T2DM and to determine the optimal cut points for identifying T2DM. A total of 357 new cases of T2DM and 1352 individuals without diabetes were identified. The mean age of the study participants was 46⋅4 (sd 7⋅4) years and 62 % were men. Waist circumference (WC), waist:hip ratio (WHR), waist:height ratio (WHtR), BMI, body fat percentage and fat per square of height were found to be significantly higher (P < 0⋅001) among those with diabetes compared with individuals without diabetes. In addition, ROC for WHR (0⋅67; 95 % 0⋅59, 0⋅75), WHtR (0⋅66; 95 % 0⋅57, 0⋅75) and WC (0⋅64; 95 % 0⋅55, 0⋅73) were shown to better identify patients with T2DM. The proposed cut points with an optimal sensitivity and specificity for WHR, WHtR and WC were 0⋅96, 0⋅56 and 86 cm for men and 0⋅88, 0⋅54 and 83 cm for women, respectively. The present study has shown that WHR, WHtR and WC are better than other anthropometric measures for detecting T2DM in the Indian population. Their utility in clinical practice may better stratify at-risk patients in this population than BMI, which is widely used at present.

Background: Canadian Stroke Guidelines recommend that Transient Ischemic Attack (TIA) patients at highest risk of stroke recurrence should undergo immediate vascular imaging. Computed tomography angiography (CTA) of the head and neck is recommended over carotid doppler because it allows for enhanced visualization of the intracranial and posterior circulation vasculature. Imaging while patients are in the emergency department (ED) is optimal for high-risk patients because the risk of stroke recurrence is highest in the first 48 hours. Aim Statement: At our hospital, a designated stroke centre, less than 5% of TIA patients meet national recommendations by undergoing CTA in the ED. We sought to increase the rate of CTA in high risk ED TIA patients from less than 5% to at least 80% in 10 months. Measures & Design: We used a multi-faceted approach to improve our adherence to guidelines including: 1) education for staff ED physicians; 2) agreements between ED and radiology to facilitate rapid access to CTA; 3) agreements between ED and neurology for consultations regarding patients with abnormal CTA; and 4) the creation of an electronic decision support tool to guide ED physicians as to which patients require CTA. We measured the rate of CTA in high risk patients biweekly using retrospective chart review of patients referred to the TIA clinic from the ED on a biweekly basis. As a balancing measure, we also measured the rate of CTA in non-high risk patients. Evaluation/Results: Data collection is ongoing. An interim run chart at 19 weeks shows a complete shift above the median after implementation, with CTA rates between 70 and 100%. At the time of submission, we had no downward trends below 80%, showing sustained improvement. The CTA rate in non-high risk patients did also increase. Disucssion/Impact: After 19 weeks of our intervention, 112 (78.9%) of high risk TIA patients had a CTA, compared to 10 (9.8%) in the 19 weeks prior to our intervention. On average, 10-15% of high risk patients will have an identifiable lesion on CTA, leading to immediate change in management (at minimum, an inpatient consultation with neurology). Our multi-faceted approach could be replicated in any ED with the engagement and availability of the same multi-disciplinary team (ED, radiology, and neurology), access to CTA, and electronic orders.

High body fat in apparently lean individuals is a commonly described phenotype in individuals of Asian descent, but very limited consolidated scientific literature is available on this topic. This phenotype is known as ‘normal-weight obesity’ and may explain the large disparity between the prevalence of obesity (as measured by BMI) and diabetes that occurs in these individuals. Routine use of obesity indicators that best predict body fat content would help to identify these individuals in clinical practice. In this debate, we would like to highlight that even though fat and BMI have a good correlation, as suggested by Kryst et al. (2019), clinicians, public health researchers and policymakers should consider the use of these indicators in conjunction with each other rather than individually. Future research is needed on pathogenic mechanisms, diagnostic modalities and therapeutic options in these individuals which will help to further characterize and manage these patients appropriately.

Obesity indicators are useful clinical tools in the measurement of obesity, but it is important for clinicians to appropriately interpret their values in individuals with different ethnicities. Future research is needed to identify optimal cut-offs that can predict the occurrence of cardio-metabolic comorbidities in individuals of different ethnic descent. Assessment of more recently developed indicators like the Edmonton Obesity Staging System and visceral adipose tissue are able to appropriately identify metabolically at-risk individuals.

OBJECTIVES/SPECIFIC AIMS: The first aim of the study is to evaluate the accuracy of serum biomarkers of acute GVHD measured after four weeks of corticosteroid therapy to predict 6 month NRM. The second aim of this study is to compare the accuracy of the biomarker algorithm to that of clinical response to corticosteroids after four weeks. The third aim of the study is to develop a novel regression model that uses weekly biomarker measurements over the first month of corticosteroid therapy to predict 6 month NRM. METHODS/STUDY POPULATION:. Patients who received HCT at one of 22 IRB-approved centers and provided blood samples to the Mount Sinai Acute GVHD International Consortium (MAGIC) biorepository and developed GVHD between January 2008 to May 2018 are included in this study. Patients were divided by time into a training set (Jan 2008-Dec 2015, n=233) for model development and a validation set (Jan 2015-May 2018, n=357) to evaluate the predictive performance of the model. The later time of the validation set was chosen deliberately to model contemporaneous GVHD treatment practices. The size of each group was designed so that there would be roughly equal numbers of deaths in both groups. RESULTS/ANTICIPATED RESULTS:. Serum concentrations of GVHD biomarkers after one month of corticosteroid therapy were measured in the validation set, and the predicted probability of NRM ( $\hat{\rm p}$ ) was computed according to the previously published algorithm: $\log[-\log(1 - \hat{\rm p})]=-11.263 + 1.844({\rm logST}2)+ 0.577({\rm logREG}3\alpha)$ . The performance of the biomarker algorithm was evaluated by creating receiver operating characteristic (ROC) curves and calculating the area under the curve (AUC) in the validation set. The AUC of the biomarker algorithm was a significantly better predictor of 6 month NRM than clinical response to treatment after four weeks of corticosteroids (0.84 vs. 0.64, p<0.001), which is a clinically relevant improvement in accuracy. To evaluate serial biomarker monitoring, serum biomarker concentrations will be measured weekly at five time points from treatment initiation to one month after corticosteroid therapy. We will use these values in the training set to develop a regression model for 6 month NRM that accounts for repeated biomarker measurements. The performance of this model will be tested in the validation set and the accuracy of the serial biomarker measurements will be compared to the accuracy of measuring biomarkers at the single time point after four weeks of corticosteroid therapy. An AUC improvement of 0.05 would be considered clinically significant. DISCUSSION/SIGNIFICANCE OF IMPACT: Clinical response to treatment after four weeks has been the standard endpoint in GVHD interventional trials for decades. If biomarkers measured at the same time more accurately predict long term mortality, this study would provide the basis for a novel endpoint in GVHD trials and enable more accurate determination of effect size of experimental interventions. An accurate biomarker algorithm will prove useful in guiding immunosuppressive treatment decisions for patients with GVHD. Patients identified by the algorithm as low-risk may benefit from reduced-dose corticosteroid therapy, potentially reducing lethal opportunistic infections. Patients identified as high-risk will be candidates for more intensive immunosuppression or investigational therapies. This precision medicine approach tailors therapy to the individual patient’s biology.

Rural communities in developing countries extract provisioning ecosystem services from the natural environment to meet their subsistence needs, generate cash income and create employment opportunities. Caterpillar fungus Ophiocordyceps sinensis, known as yartsagunbu in Tibet and in the literature on this subject, is a medicinal resource extracted by the mountain communities of the Himalaya. Studies of the contribution of the fungus to local livelihoods in the Indian Himalaya are scarce. We investigated trade and harvest and analysed the contribution of caterpillar fungus to household economies in 32 villages in Dhauliganga Valley, Nanda Devi Biosphere Reserve, in the Western Himalaya. Caterpillar fungus harvesting has become an integral part of local livelihoods in the study area, and on average contributes c. 74% of household cash income. The majority of harvesters perceived that harvesting had become more difficult during 2010–2015 as a result of competition and a decline in abundance of the species. The mean annual per capita harvest declined by 54 pieces during 2011–2015. Increasing harvesting and trade, coupled with the dependency of local communities on the fungus, may result in greater extraction of the resource for short-term economic benefits, and could eventually lead to depletion and ecological damage. Harvesting of the fungus has already created environmental, legal and social challenges, although it has become a lucrative livelihood opportunity. The ongoing decline of the fungus threatens local livelihoods. Good governance and livelihood security should be integrated with biodiversity conservation when devising government policies and plans for sustainable management of the caterpillar fungus.

Introduction: Early team experiences can influence the professional trajectories of early-career investigators profoundly, yet they remain underexplored in the team science literature, which has focused primarily on large, multisite teams led by established researchers. To better understand the unique challenges of teams led by early-career investigators, we conducted a qualitative pilot study.

Methods: Interviews were conducted with the principal investigator and members of 5 teams led by KL2 and K12 scholars at the University of Pittsburgh. A code book was developed and thematic analysis was conducted.

Results: Seven distinct themes emerged. Interview subjects reported a high level of trust and strong communication patterns on their teams; however, the data also suggested underlying tensions that have the potential to escalate into larger problems if unaddressed.

Conclusions: This study yields a deeper understanding of teams led by early-career investigators, which can help us provide appropriately targeted training and support.

OBJECTIVES/SPECIFIC AIMS: The need to diversify the biomedical research workforce is well documented. The Career Education and Enhancement for Health Care Research Diversity (CEED) program at the University of Pittsburgh Institute for Clinical Research Education (ICRE) promotes success and helps seal the “leaky pipeline” for under-represented background (URB) biomedical researchers with a purposefully designed program consisting of a monthly seminar series, multilevel mentoring, targeted coursework, and networking. METHODS/STUDY POPULATION: Over 10 program years, we collected survey data on characteristics of CEED Scholars, such as race, ethnicity, and current position. We created a matched set of URB trainees not enrolled in CEED during that time using propensity score matching in a 1:1 ratio. RESULTS/ANTICIPATED RESULTS: Since 2007, CEED has graduated 45 Scholars. Seventy-six percent have been women, 78% have been non-White, and 33% have been Hispanic/Latino. Scholars include 20 M.D.s and 25 Ph.D.s. Twenty-eight CEED Scholars were matched to non-CEED URB students. Compared with matched URB students, CEED graduates had a higher mean number of peer-reviewed publications (9.25 vs. 5.89; p<0.0001) were more likely to hold an assistant professor position (54% vs. 14%; p=0.004) and be in the tenure stream (32% vs. 7%; p=0.04), respectively. There were no differences in Career Development Awards (p=0.42) or Research Project Grants (p=0.24). DISCUSSION/SIGNIFICANCE OF IMPACT: Programs that support URB researchers can help expand and diversify the biomedical research workforce. CEED has been successful despite the challenges of a small demographic pool. Further efforts are needed to assist URB researchers to obtain grant awards.

We estimate the causal effect of independent candidates on voter turnout and election outcomes in India. To do this, we exploit exogenous changes in the entry deposit candidates pay for their participation in the political process, changes that disproportionately excluded candidates with no affiliation to established political parties. A one standard deviation increase in the number of independent candidates increases voter turnout by more than 6 percentage points, as some voters choose to vote rather than stay home. The vote share of independent candidates increases by more than 10 percentage points, as some existing voters switch who they vote for. Thus, independents allow winning candidates to win with less vote share, decrease the probability of electing a candidate from the governing coalition by about 31 percentage points, and ultimately increase the probability of electing an ethnic-party candidate. Altogether, the results imply that the price of participation by independents is constituency representation in government.

Mycobacterium marinum, a bacterium found in freshwater and saltwater, can infect persons with direct exposure to fish or aquariums. During December 2013, the New York City Department of Health and Mental Hygiene learned of four suspected or confirmed M. marinum skin or soft tissue infections (SSTIs) among persons who purchased whole fish from Chinese markets. Ninety-eight case-patients with non-tuberculous mycobacteria (NTM) SSTIs were identified with onset June 2013–March 2014. Of these, 77 (79%) were female. The median age was 62 years (range 30–91). Whole genome sequencing of clinical isolates revealed two main clusters and marked genetic diversity. Environmental samples from distributors yielded NTM though not M. marinum. We compared 56 case-patients with 185 control subjects who shopped in Chinese markets, frequency-matched by age group and sex. Risk factors for infection included skin injury to the finger or hand (odds ratio [OR]: 15·5; 95% confidence interval [CI]: 6·9–37·3), hand injury while preparing fish or seafood (OR 8·3; 95% CI 3·8–19·1), and purchasing tilapia (OR 3·6; 95% CI 1·1–13·9) or whiting (OR 2·7; 95% CI 1·1–6·6). A definitive environmental outbreak source was not identified.