Faculty Bibliography

BACKGROUND:Change in albuminuria as a surrogate endpoint for progression of chronic kidney disease is strongly supported by biological plausibility, but empirical evidence to support its validity in epidemiological studies is lacking. We aimed to assess the consistency of the association between change in albuminuria and risk of end-stage kidney disease in a large individual participant-level meta-analysis of observational studies. METHODS:In this meta-analysis, we collected individual-level data from eligible cohorts in the Chronic Kidney Disease Prognosis Consortium (CKD-PC) with data on serum creatinine and change in albuminuria and more than 50 events on outcomes of interest. Cohort data were eligible if participants were aged 18 years or older, they had a repeated measure of albuminuria during an elapsed period of 8 months to 4 years, subsequent end-stage kidney disease or mortality follow-up data, and the cohort was active during this consortium phase. We extracted participant-level data and quantified percentage change in albuminuria, measured as change in urine albumin-to-creatinine ratio (ACR) or urine protein-to-creatinine ratio (PCR), during baseline periods of 1, 2, and 3 years. Our primary outcome of interest was development of end-stage kidney disease after a baseline period of 2 years. We defined an end-stage kidney disease event as initiation of kidney replacement therapy. We quantified associations of percentage change in albuminuria with subsequent end-stage kidney disease using Cox regression in each cohort, followed by random-effects meta-analysis. We further adjusted for regression dilution to account for imprecision in the estimation of albuminuria at the participant level. We did multiple subgroup analyses, and also repeated our analyses using participant-level data from 14 clinical trials, including nine clinical trials not in CKD-PC. FINDINGS:<0·0001). In individuals with baseline ACR of 300 mg/g or higher, a 30% decrease in ACR over 2 years was estimated to confer a more than 1% absolute reduction in 10-year risk of end-stage kidney disease, even at early stages of chronic kidney disease. Results were generally similar when we used change in PCR and when study populations from clinical trials were assessed. INTERPRETATION:Change in albuminuria was consistently associated with subsequent risk of end-stage kidney disease across a range of cohorts, lending support to the use of change in albuminuria as a surrogate endpoint for end-stage kidney disease in clinical trials of progression of chronic kidney disease in the setting of increased albuminuria. FUNDING:US National Kidney Foundation and US National Institute of Diabetes and Digestive and Kidney Diseases.

BACKGROUND:Change in albuminuria has strong biological plausibility as a surrogate endpoint for progression of chronic kidney disease, but empirical evidence to support its validity is lacking. We aimed to determine the association between treatment effects on early changes in albuminuria and treatment effects on clinical endpoints and surrograte endpoints, to inform the use of albuminuria as a surrogate endpoint in future randomised controlled trials. METHODS:, or doubling of serum creatinine. We used a Bayesian mixed-effects meta-regression analysis to relate the treatment effects on albuminuria to those on the clinical endpoint across studies and developed a prediction model for the treatment effect on the clinical endpoint on the basis of the treatment effect on albuminuria. FINDINGS:0·72, 0·05-0·99]). For future trials, the model predicts that treatments that decrease the geometric mean albuminuria to 0·7 (ie, 30% decrease in albuminuria) relative to the control will provide an average hazard ratio (HR) for the clinical endpoint of 0·68, and 95% of sufficiently large studies would have HRs between 0·47 and 0·95. INTERPRETATION:Our results support a role for change in albuminuria as a surrogate endpoint for the progression of chronic kidney disease, particularly in patients with high baseline albuminuria; for patients with low baseline levels of albuminuria this association is less certain. FUNDING:US National Kidney Foundation.

Purpose: There are an ever increasing number of social media platforms available for people to connect and build online communities. Pro-eating disorder communities, notably anorexia nervosa (AN), have developed a steady presence on Twitter. While these communities can be beneficial for individuals who are not yet ready or able to seek professional help, multiple studies have revealed the detrimental side effects these communities can have on users, such as normalization of maladaptive behaviors, encouragement of behaviors and sharing new ways to perpetuate behaviors. To date, few studies have investigated the ways in which available social media data from self-identified anorexic individuals could be used to better inform screening, treatment, and follow-up with these individuals. This study evaluated the ways in which natural language processing (NLP) and machine learning algorithms, coupled with qualitative methods, could collect, categorize, and inform clinician insights about pro-eating disorder communities. Method(s): Twitter crawling algorithms were developed and deployed through the Twitter API to find tweets based on key words such as: "ana" "proana" "thinspo" and "meanspo." This initial corpus of tweets containing the identified keywords was then qualitatively assessed to further refine the algorithmic process of identifying appropriate tweets and removing irrelevant tweets. This process resulted in a cleaned corpus of 970 unique tweets over a ten-day period. This cleaned dataset was then utilized for NLP to identify common words, phrases, and topics. Concurrently, data was hand coded in a thematic analysis process to identify deeper themes within the dataset. These themes could inform qualitative lines of inquiry as well as machine learning systems. Qualitative insights were utilized to improve sentiment analysis as well as classification of unstructured data though a semi-supervised machine learning process. Result(s): Analysis revealed that emotional restraint was not present and judgement of one's self on external standards was present within these Twitter communities. Additionally, users' frequently requested "meanspo," an extension of thinspo that serves as an inspiration for thinness by using aggressive and abrasive rhetoric to encourage users to aspire for thinness. Additionally, posts asking for an "ana buddy," a partner to help users hold each other accountable to their AN behaviors, were extremely common. Additional information about caloric restrictions, weekly weight loss goals, and a large number of individuals tweeting from residential treatment for ED about lying about wanting to get better as a means to be released, as well as genuine statements about wanting to change. Conclusion(s): AN is a complicated disease with multiple causes, side effects and comorbid illnesses. This pilot study offers a promising 'first-step' approach towards understanding the mindset, experiences, and potential gaps within current ED treatment approaches from a patient perspective. NLP and ML processes are now developed to scan, collect, and analyze this data in an ongoing way to develop new AI processes with the ultimate goal of identifying individuals with a higher likelihood of wanting to enter treatment. Overall, the present study highlights the benefits of using new available data streams to develop patient-informed comprehensive care models. Sources of Support: NYU CAMS Undergaduate Internship

Air pollution poses a great environmental risk to health. Outdoor fine particulate matter (PM_2.5) exposure is the fifth leading risk factor for death in the world, accounting for 4.2 million deaths and more than a hundred million disability-adjusted-life-years lost according to the Global Burden of Disease Report. The World Health Organization attributes 3.8 million additional deaths to Indoor air pollution. Air pollution can harm acutely, usually manifested by respiratory or cardiac symptoms, as well as chronically, potentially affecting every organ in the body. It can cause, complicate, or exacerbate many adverse health conditions. Tissue damage may result directly from pollutant toxicity, because fine and ultrafine particles can gain access to organs, or indirectly through systemic inflammatory processes. Susceptibility is partly under genetic and epigenetic regulation. Although air pollution affects people of all regions, ages, and social groups, it is likely to cause greater illness in those with heavy exposure and greater susceptibility. Persons are more vulnerable to air pollution if they have other illnesses or less social support. Harmful effects occur on a continuum of dosage and even at levels below air quality standards previously considered to be safe.

BACKGROUND:Blood eosinophils are used to determine eligibility for agents targeting IL-5 in patients with uncontrolled asthma. However, little is known about the variability of blood eosinophil measures in these patients before treatment initiation. OBJECTIVE:To characterize variability and patterns of variability of blood eosinophil levels in a real-world clinic for severe asthmatics. METHODS:Retrospective review of blood eosinophils measured over a 5-year period in patients enrolled in an urban clinic. Repeated measures of blood eosinophil levels in individuals were evaluated and cluster analysis was performed to characterize patients by eosinophil patterns. Clinical characteristics associated with eosinophil levels and patterns of variability were analyzed. RESULTS:Patients treated in the Bellevue Hospital Asthma Clinic within a 3-month period were identified (n = 219). Blood eosinophil measures were obtained over the previous 5 years. Only 6% (n= 13) of patients had levels that were consistently above 300 cells/μL. Nearly 50% (n = 104) had eosinophil levels that traversed the threshold of 300 cells/μL. In contrast, 102 (46%) had levels that never reached the threshold of 300 cells/μL. Cluster analyses revealed three clusters with differing patterns of levels and variability. There was a suggestion of decreased clinical control and increased atopy in the cluster with the greatest variability in blood eosinophil measures. CONCLUSION/CONCLUSIONS:In an urban clinic for patients referred for uncontrolled asthma, blood measures of eosinophils were variable and showed differing patterns of variability. These data reinforce the need to perform repeated eosinophil blood measures for appropriate designation for therapeutic intervention.

INTRODUCTION/BACKGROUND:According to current National Comprehensive Cancer Network guidelines, routine imagining for staging low-risk prostate cancer is not recommended. However, extensive overuse of guideline-discordant imaging continues to persist. Incidental findings are common on imaging and little is known about the optimal management. Rates of incidental findings vs. false positive diagnosis from inappropriate imaging are poorly understood and have yet to be quantified for low- and intermediate-risk prostate cancer patients. OBJECTIVE:To determine the frequency of positive radiologic findings in patients with low- and intermediate-risk prostate cancer during initial staging at VA New York Harbor Healthcare System. METHODS:We retrospectively reviewed all low- and intermediate-risk prostate cancer patients' medical records from the VA New York Harbor Healthcare System for diagnosis from 2005 to 2015. We reviewed each individual's prebiopsy prostate specific antigen (PSA), Gleason score, and clinical stage. We also determined if imaging obtained yielded a false positive, incidental finding, or if metastatic disease occurred within the 6 months following initial diagnosis. RESULTS:There were 414 men, who were classified as low- to intermediate-risk prostate cancer and underwent inappropriate staging imaging of 4,306 men diagnosed with prostate cancer. Of these 414 men, 178 (43%) had additional follow-up imaging for positive findings. We calculated an incidental finding rate of 10% and a false positive rate of 38% for patients. Five (1%) patients had metastatic disease. CONCLUSION/CONCLUSIONS:Despite guideline recommendations, imaging overuse remains an issue for low-intermediate-risk prostate cancer patients. The false positive rate found in this analysis is alarmingly high at 38%. This use of scans is burdensome to the healthcare system and patient. This study highlights the frequency of inappropriate imaging and its negative consequences.

BACKGROUND:Federal regulations (42 CFR Part 2) provide special privacy protections for persons seeking treatment for substance use disorders. Primary care providers, hospitals, and health care organizations have struggled to balance best practices for medical care with adherence to 42 CFR Part 2, but little formal research has examined this issue. The aim of this study was to explore institutional variability in the interpretation and implementation of 42 CFR Part 2 regulations related to health systems data privacy practices, policies, and information technology architecture. METHODS:This was a cross-sectional qualitative study using purposive sampling to conduct interviews with privacy/legal officers (n = 17) and information technology specialists (n = 10) from 15 integrated healthcare organizations affiliated with three research nodes of the National Institute on Drug Abuse (NIDA) National Drug Abuse Treatment Clinical Trials Network (CTN). Trained staff completed a short survey and digitally recorded semi-structured qualitative interview with each participant. Interviews were transcribed and coded within Atlas.ti. Framework analysis was used to identify and organize key themes across selected codes. RESULTS:Participants voiced concern over balancing patient safety with 42 CFR Part 2 privacy protections. Although similar standards of protection regarding release of information outside of the health system was described, numerous workarounds were used to manage intra-institutional communication and care coordination. To align 42 CFR Part 2 restrictions with electronic health records, health systems used sensitive note designation, "break the glass" technology, limited role-based access for providers, and ad hoc solutions (e.g., provider messaging). CONCLUSIONS:In contemporary integrated care systems, substance-related EHR records (e.g., patient visit history, medication logs) are often accessible internally without specific consent for sharing despite the intent of 42 CFR Part 2. Recent amendments to 42 CFR Part 2 have not addressed information sharing needs within integrated care settings.

Purpose: In 2016 there were roughly 77,000 PrEP users in the United States, while over 1.2 million Americans were identified as "high-risk" for HIV infection. The reasons for this discrepancy are vast; however, potential reasons that have been identified are stigma, ineffective or poorly targeted marketing, access, and cost, amongst other factors. This pilot project seeks to understand the ways in which people and companies talk about PrEP on social media to glean deeper insights on methods to increase PrEP use. The increased use of social media gives researchers, clinicians, policymakers, and health organizations the opportunity to have access to real time data and potentially influence awareness of PrEP. This inductive exploratory study uses natural language processing (NLP) and content analysis to examine the ways in which people are using social media to talk about PrEP. Method(s): An R script was utilized to crawl Twitter the Twitter API based on keywords related to PrEP and HIV, plus all lemmatized variations related to the word pair. Data cleaning was then performed to remove tweets that were not in English, tweets that had been retweeted, as well as removing any identifying information. The resulting data frame was then used both qualitatively and quantitatively for analysis. Qualitative analysis involved a comprehensive reading of tweets, development of a category dictionary, and identification of themes that would help to train an algorithm to automatically process and count tweets based on its category. The quantitative process involved further cleaning and removing of stop-words to develop a Ngram frequency cloud as well as development of a process to automatically categorize the different types of tweets based on the type of tweet (advertisement, question about PrEP, comment on cost or availability, criticism of manufacturer, etc.). Result(s): This processes resulted in identification of 587 unique HIV related PrEP tweets. Qualitative insights from this reduced dataset indicated that there are preventative concerns related to access and cost which may be preventing high-risk individuals from getting PrEP. Algorithmic sorting and categorization processes also identified concerns about targeted marketing, specifically the lack of campaigns focusing on transgender, female, and minority communities. Our bootstrap method of training and testing resulted in a process that had an 80% likelihood of identifying, analyzing, and classifying HIV related PrEP tweets. Once classified, 40% of tweets were advertising and messaging, the rest were concerns about cost (31%), requests for info/ways to pay for PrEP (20%), as well as other non-classified comments. Conclusion(s): There are a number of different conversations about HIV/PrEP awareness happening on Twitter. However, access and cost were consistently the most common themes being discussed. Currently, a 30-day supply of PrEP costs between 0-$1600, in the US, which may be creating a substantial barrier to further reducing HIV rates. Additionally, Improving online marketing strategies of PrEP could increase awareness and use by offering targeted information as well as identification of local resources to those interested or in need. Sources of Support: NYU CAMS Undergradaute Internship

INTRODUCTION/BACKGROUND:This study assesses preventable hospitalization rates among New York City residents living in public housing developments compared with all New York City residents and residents in low-income areas. Additionally, preventable hospitalization rates by development (one or multiple buildings in close proximity and served by the same management office) were determined. METHODS:The 2010-2014 New York City hospital discharge data were geocoded and linked with New York City Housing Authority records using building-level identifiers. Preventable hospitalizations resulting from ambulatory care-sensitive conditions were identified for public housing residents, citywide, and residents of low-income areas. Age-adjusted overall and ambulatory care-sensitive, condition-specific preventable hospitalization rates (11 outcomes) were determined and compared across groups to assess potential disparities. Additionally, rates were ranked and compared among public housing developments by quartiles. The analysis was conducted in 2016 and 2017. RESULTS:The age-adjusted rate of preventable hospitalization was significantly higher among public housing residents than citywide (rate ratio [RR]=2.67, 95% CI=2.65, 2.69), with the greatest disparities in hospitalizations related to diabetes (RR=3.12, 95% CI=3.07, 3.18) and asthma (RR=4.14, 95% CI=4.07, 4.21). The preventable hospitalization rate was also higher among residents of public housing than low-income areas (RR=1.33, 95% CI=1.31, 1.35). There were large differences between developments ranked in the top and bottom quartiles of preventable hospitalization (RR=1.81, 95% CI=1.76, 1.85) with the largest difference related to chronic obstructive pulmonary disease (RR=3.38, 95% CI=3.08, 3.70). CONCLUSIONS:Preventable hospitalization rates are high among public housing residents, and vary significantly by development and condition. By providing geographically granular information, geocoded hospital discharge data can serve as a valuable tool for health assessment and engagement of the healthcare sector and other stakeholders in interventions that address health inequities.

A timely determination of the risk of post-traumatic stress disorder (PTSD) is a prerequisite for efficient service delivery and prevention. We provide a risk estimate tool allowing a calculation of individuals' PTSD likelihood from early predictors. Members of the International Consortium to Predict PTSD (ICPP) shared individual participants' item-level data from ten longitudinal studies of civilian trauma survivors admitted to acute care centers in six countries. Eligible participants (N=2,473) completed an initial clinical assessment within 60 days of trauma exposure, and at least one follow-up assessment 4-15 months later. The Clinician-Administered PTSD Scale for DSM-IV (CAPS) evaluated PTSD symptom severity and diagnostic status at each assessment. Participants' education, prior lifetime trauma exposure, marital status and socio-economic status were assessed and harmonized across studies. The study's main outcome was the likelihood of a follow-up PTSD given early predictors. The prevalence of follow-up PTSD was 11.8% (9.2% for male participants and 16.4% for females). A logistic model using early PTSD symptom severity (initial CAPS total score) as a predictor produced remarkably accurate estimates of follow-up PTSD (predicted vs. raw probabilities: r=0.976). Adding respondents' female gender, lower education, and exposure to prior interpersonal trauma to the model yielded higher PTSD likelihood estimates, with similar model accuracy (predicted vs. raw probabilities: r=0.941). The current model could be adjusted for other traumatic circumstances and accommodate risk factors not captured by the ICPP (e.g., biological, social). In line with their use in general medicine, risk estimate models can inform clinical choices in psychiatry. It is hoped that quantifying individuals' PTSD risk will be a first step towards systematic prevention of the disorder.