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Objective To determine the incremental detection rate of clinically significant prostate cancer (csPCa) provided by sequential cores during in-bore magnetic resonance imaging (MRI)-guided prostate biopsies. Methods Single-center, retrospective interpretation of prospectively acquired data in men without previous diagnosis of csPCa who underwent in-bore MRI-guided prostate biopsy between May 2017 and December 2019. Endpoints included detection of csPCa (grade group [GG] ≥ 2) and rate of GG upgrade provided by additional cores. Descriptive statistics presented as mean and standard deviation for the continuous variables, and frequency and percentage for the categorical variables. Results Four hundred and forty-three men with 747 lesions met eligibility criteria. Clinically significant prostate cancer was detected in 43.1% (322/747) of the biopsied lesions and GG 2 PCa or greater was identified by the first core in 78.3% (252/322) of them. On a per-core basis, cores 2, 3, 4, and 5 found new csPCa in 6% (42/744), 4% (26/719), 1% (2/137), and 0% (0/11) of the cases. Core biopsy 2, 3, 4, and 5 resulted in GG upgrade in 12% (91/744), 7% (49/719), 7% (9/137), and 0% (0/11) of the lesions, respectively. Each additional core was associated with a mean increase of 5 minutes in the duration of the biopsy. Conclusions In men undergoing in-bore MRI-guided prostate biopsies, 3 targeted cores per lesion provide an optimal trade-off between detection of clinically significant tumors and biopsy duration.

Objective To analyze the risk of megestrol, a glucocorticoid and progesterone receptor agonist used to enhance appetite, on the development of a new psychiatric diagnosis. Design and Participants Deidentified data of megestrol (n = 706) and propensity score-matched comparison (age, gender, and body mass index) patients (n = 2,118) from January 1, 2001 to June 30, 2018 were obtained from the UT Southwestern patient database. Data were analyzed using a series of conditional binary logistic regressions controlling for comorbidities, pre-existing psychiatric disorders, and number of patient encounters. Setting A large academic medical center database of megestrol-treated patients and matched comparison patients was used. Measurements and Results The regression model showed that megestrol was significantly associated with developing a new psychiatric diagnosis (B = 1.28, Wald χ21 = 83.12, odds ratio [OR] = 3.60, p Conclusions Patients taking megestrol were significantly more likely to develop a new psychiatric diagnosis than comparison patients. Highest risks were associated with the development of cognitive diagnoses. The findings suggest that megestrol, like other glucocorticoid agonists, is associated with an increased risk of developing a psychiatric disorder. This risk should be considered when determining the risk-to-benefit ratio of megestrol use in patients.

Risk prediction tools for assisting acute kidney injury (AKI) management have focused on AKI onset but have infrequently addressed kidney recovery. We developed clinical models for risk stratification of mortality and major adverse kidney events (MAKE) in critically ill patients with incident AKI.Multicenter cohort study.9,587 adult patients admitted to heterogeneous intensive care units (ICUs; March 2009 to February 2017) who experienced AKI within the first 3 days of their ICU stays.Multimodal clinical data consisting of 71 features collected in the first 3 days of ICU stay.(1) Hospital mortality and (2) MAKE, defined as the composite of death during hospitalization or within 120 days of discharge, receipt of kidney replacement therapy in the last 48 hours of hospital stay, initiation of maintenance kidney replacement therapy within 120 days, or a ≥50% decrease in estimated glomerular filtration rate from baseline to 120 days from hospital discharge.Four machine-learning algorithms (logistic regression, random forest, support vector machine, and extreme gradient boosting) and the SHAP (Shapley Additive Explanations) framework were used for feature selection and interpretation. Model performance was evaluated by 10-fold cross-validation and external validation.One developed model including 15 features outperformed the SOFA (Sequential Organ Failure Assessment) score for the prediction of hospital mortality, with areas under the curve of 0.79 (95% CI, 0.79-0.80) and 0.71 (95% CI, 0.71-0.71) in the development cohort and 0.74 (95% CI, 0.73-0.74) and 0.71 (95% CI, 0.71-0.71) in the validation cohort (P 0.001 for both). A second developed model including 14 features outperformed KDIGO (Kidney Disease: Improving Global Outcomes) AKI severity staging for the prediction of MAKE: 0.78 (95% CI, 0.78-0.78) versus 0.66 (95% CI, 0.66-0.66) in the development cohort and 0.73 (95% CI, 0.72-0.74) versus 0.67 (95% CI, 0.67-0.67) in the validation cohort (P 0.001 for both).The models are applicable only to critically ill adult patients with incident AKI within the first 3 days of an ICU stay.The reported clinical models exhibited better performance for mortality and kidney recovery prediction than standard scoring tools commonly used in critically ill patients with AKI in the ICU. Additional validation is needed to support the utility and implementation of these models.Acute kidney injury (AKI) occurs commonly in critically ill patients admitted to the intensive care unit (ICU) and is associated with high morbidity and mortality rates. Prediction of mortality and recovery after an episode of AKI may assist bedside decision making. In this report, we describe the development and validation of a clinical model using data from the first 3 days of an ICU stay to predict hospital mortality and major adverse kidney events occurring as long as 120 days after hospital discharge among critically ill adult patients who experienced AKI within the first 3 days of an ICU stay. The proposed clinical models exhibited good performance for outcome prediction and, if further validated, could enable risk stratification for timely interventions that promote kidney recovery.

Background Thyroid dysfunction is among the most common autoimmune diseases and immune checkpoint inhibitor (ICI)-induced immune-related adverse events (irAE). We determined the association between longitudinal thyroid function and clinical outcomes in patients treated with ICI. Methods We identified all patients treated with ICI at UT Southwestern Medical Center from January 1, 2011, through December 31, 2020. We defined normal thyroid stimulating hormone (TSH) and free thyroxine (FT4) levels according to institutional reference range. We defined clinical thyroid dysfunction using established criteria incorporating labs and treatment. We determined the association between thyroid function and overall survival (OS) using Kaplan–Meier curves, log-rank tests, and multivariate Cox proportional hazards model. Results A total of 1781 patients were included in analyses, of whom 381 (21%) had abnormal baseline TSH. Patients with abnormal baseline TSH were more likely to be female, have kidney cancer, and initiate levothyroxine after ICI initiation (all P P P P P = 0.008). Conclusions ICI-induced thyroid dysfunction is associated with improved survival, although abnormal TSH prior to ICI initiation is associated with inferior survival. Precis Thyroid abnormalities occur commonly in the general population and as immunotherapy toxicities. We found that immunotherapy-induced thyroid dysfunction is associated with better survival, but pre-existing thyroid abnormalities convey worse outcomes.

Clinical data networks that leverage large volumes of data in electronic health records (EHRs) are significant resources for research on coronavirus disease 2019 (COVID-19). Data harmonization is a key challenge in seamless use of multisite EHRs for COVID-19 research. We developed a COVID-19 application ontology in the national Accrual to Clinical Trials (ACT) network that enables harmonization of data elements that are critical to COVID-19 research. The ontology contains over 50 000 concepts in the domains of diagnosis, procedures, medications, and laboratory tests. In particular, it has computational phenotypes to characterize the course of illness and outcomes, derived terms, and harmonized value sets for severe acute respiratory syndrome coronavirus 2 laboratory tests. The ontology was deployed and validated on the ACT COVID-19 network that consists of 9 academic health centers with data on 14.5M patients. This ontology, which is freely available to the entire research community on GitHub at https://github.com/shyamvis/ACT-COVID-Ontology, will be useful for harmonizing EHRs for COVID-19 research beyond the ACT network.

Clinical data networks that leverage large volumes of data in electronic health records (EHRs) are significant resources for research on coronavirus disease 2019 (COVID-19). Data harmonization is a key challenge in seamless use of multisite EHRs for COVID-19 research. We developed a COVID-19 application ontology in the national Accrual to Clinical Trials (ACT) network that enables harmonization of data elements that that are critical to COVID-19 research. The ontology contains over 50,000 concepts in the domains of diagnosis, procedures, medications, and laboratory tests. In particular, it has computational phenotypes to characterize the course of illness and outcomes, derived terms, and harmonized value sets for SARS-CoV-2 laboratory tests. The ontology was deployed and validated on the ACT COVID-19 network that consists of nine academic health centers with data on 14.5M patients. This ontology, which is freely available to the entire research community on GitHub at https://github.com/shyamvis/ACT-COVID-Ontology, will be useful for harmonizing EHRs for COVID-19 research beyond the ACT network.

Heart transplant (HT) recipients are at higher risk of varicella zoster virus (VZV) reactivation. Risk factors for VZV reactivation are currently not well defined, impeding the ability to design and implement strategies to minimize the burden of this illness in this population. Automated data extraction tools were used to retrieve data from the electronic health record (EHR) of all adult HT recipients at our center between 2010 and 2016. Information from the Organ Procurement and Transplantation Network Standard Analysis and Research Files was merged with the extracted data. Potential cases were manually reviewed and adjudicated using consensus definitions. Cumulative incidence and risk factors for VZV reactivation in HT recipients were assessed by the Kaplan-Meier method and Cox modeling, respectively. In 203 HT recipients, the cumulative incidence of VZV reactivation at 8-years post-transplantation was 26.4% (95% CI: 17.8-38.0). The median time to VZV reactivation was 2.1 years (IQR, 1.5-4.1). Half (14/28) of the cases experienced post-herpetic neuralgia (PHN). Post-transplant CMV infection (HR 9.05 [95% CI: 3.76-21.77) and post-transplant pulse-dose steroids (HR 3.19 [95% CI: 1.05-9.68]) were independently associated with a higher risk of VZV reactivation in multivariable modeling. Identification of risk factors will aid in the development of targeted preventive strategies.

BACKGROUND: We created an electronic health record-based registry using automated data extraction tools to study the epidemiology of bloodstream infections (BSI) in solid organ transplant recipients. The overarching goal was to determine the usefulness of an electronic health record-based registry using data extraction tools for clinical research in solid organ transplantation. METHODS: We performed a retrospective single-center cohort study of adult solid organ transplant recipients from 2010 to 2015. Extraction tools were used to retrieve data from the electronic health record, which was integrated with national data sources. Electronic health records of subjects with positive blood cultures were manually adjudicated using consensus definitions. One-year cumulative incidence, risk factors for BSI acquisition, and 1-year mortality were analyzed by Kaplan-Meier method and Cox modeling, and 30-day mortality with logistic regression. RESULTS: In 917 solid organ transplant recipients the cumulative incidence of BSI was 8.4% (95% confidence interval 6.8–10.4) with central line-associated BSI as the most common source. The proportion of multidrug-resistant isolates increased from 0% in 2010 to 47% in 2015 (p=0.03). BSI was the strongest risk factor for 1-year mortality (HR=8.44; 4.99–14.27; p

PURPOSE Mobile devices provide individuals with rapid and frequent access to electronic patient portals. We investigated how oncology patients use this technology to review test results and communicate with providers. PATIENTS AND METHODS We performed a retrospective study of patients enrolled in the MyChart electronic health portal associated with the Epic electronic medical record at the Harold C. Simmons Comprehensive Cancer Center from 2012 to 2017. We recorded type of portal access according to year and patient characteristics. Associations among patient characteristics and types of portal access were tested using Mann-Whitney U test, χ2 test, and linear Gaussian regression models. RESULTS Since the availability of a mobile device application in 2012, 2,524 patients with cancer accessed MyChart from a mobile device at least once, which accounted for 291,526 mobile log-ins. The number of patients with MyChart mobile application log-ins increased from 4% in 2012 to 13% in 2017 ( P = .004). Among these patients, the median proportion of log-ins that occurred through mobile device use increased from 22% to 72% during this time period ( P < .001). Mobile access occurred more frequently among younger ( P < .001), black ( P = .002), and Hispanic ( P = .004) patients. Since 2012, total portal log-in frequency increased approximately 110% among patients who used the mobile application compared with 25% among those who did not use the mobile application ( P < .001). CONCLUSION Mobile access to electronic health portals has increased patient portal use, particularly among traditionally underserved populations. How this widely and immediately available technology affects patient expectations and experiences warrants additional study.

3043 Background: Baseline circulating white blood cell differential counts have been proposed as possible markers of response to Immune therapy (ICI) and immune toxicity (irAE), but have not been validated for clinical practice. Methods: 214 patients with various cancers receiving ICI had clinical lab results and overall/organ specific irAE analyzed. Absolute lymphocyte (ALC), eosinophil (AEC) and neutrophil (ANC) counts

e15126 Background: The mechanisms by which immune checkpoint inhibitors (ICI) cause immune related adverse events (irAE) are not clear. No current blood tests predict which patients will suffer irAEs. Methods: 70 cancer patients had blood drawn prior to ICI and then 4-6 weeks later. 63 had lung cancer; median age was 69 yrs; 21 were female and median ICI treatment duration was 142 days. Patient blood samples were analyzed using flow cytometry for over 50 lymphoid and myeloid markers to determine levels of circulating white blood cells, as well as their changes over time. Medical records were reviewed for irAE’s grades in 8 groups: rash, colitis, hypothyroidism, hyperthyroidism, hypophysitis, hepatitis, adrenal insufficiency, and pneumonitis. To determine association between toxicity and cell counts we used logistic regression and random forest for the univariate and multivariate analysis. We included a random permutation of the baseline age (which is associated with higher toxicity rates) as the analytical control in the random forest model to help identify the threshold of association strength of a random noise and to identify only those cell subsets that were statistically significant (reported below). Results: Patients with elevated baseline levels of specific subsets of T-cells were more likely to develop irAE’s, and elderly patients were more likely to develop Grade 2 irAE’s. T-cell populations associated with any toxicity included CD3+CD8+PD1+; CD3+CD8+; CD25+CD127lowFoxP3+ and CD3+CD8+Ki-67+. Also, patients who developed any irAE’s demonstrated changes in the following cell types after 2 doses of immune therapy compared to their baseline readings. These included changes in the percentage of circulating cells that were CD3+CD8+PD1+; CD3+CD8+HLA-DR+; CD3+CD4+HLA-DR+; CD3+CD8+CCR7-; CD56+; CD3-CD19-; CD3+CD8+; CD16+CD56dim. Patients with grade ≥2 toxicities, or organ specific toxicities demonstrated statistically significant differing levels of T-cells subsets, though the number of patients in each group was limited. Patients with > median baseline expression of CD3+ CD8+ PD1+ cells had greater survival compared with those that had < median levels. Conclusions: Flow cytometry analysis of patients receiving immune therapy at baseline and 6 weeks after initiating therapy may predict which patients are at greater likelihood of any/organ specific irAEs, potentially leading to an improved understanding their mechanisms. Responding to leukocyte changes by substitution of drugs or shortening of treatment duration may reduce irAEs.

e15103 Background: Immune checkpoint inhibitors (ICI) frequently cause thyroid dysfunction. We performed a longitudinal analysis of thyroid function tests in a large, single-center cohort of patients with multiple cancer types receiving ICI. Methods: We performed a retrospective medical records review of consecutive patients treated with ICI from 1/1/2005 to 12/31/2018. We collected demographic and clinical data, including serial thyroid function tests. We compared overall survival between patients with normal and abnormal thyroid stimulating hormone (TSH) at baseline and after ICI initiation using Kaplan-Meier curves, log-rank tests, and multivariate Cox proportional hazards model. Results: A total of 910 patients were included: 63% male, 82% white, median age 67. The most common cancer types were lung (26%), kidney (18%), and melanoma (17%). ICI types were anti-PD1/L1 (78%), anti-CTLA-4 (7%), and combination ICI (15%). Normal baseline TSH and abnormal post-treatment TSH was associated with longer overall survival (median survival 26 months) compared to all other TSH permutations (median survival < 10 months) ( P< 0.001). This finding persisted after multivariate Cox regression adjustment for age, gender and cancer type (P < 0. 001), and also after sensitivity analysis censoring patients who died within 2 months after starting ICI. Conversely, abnormal TSH at baseline was associated with lower overall survival (median 8 months) compared to normal TSH at baseline (median 18 months) ( P< 0.001), which also persisted in multivariate analysis ( P< 0.001). Kidney and head and neck cancers (71% and 69%) were associated with increased development of thyroid dysfunction compared to melanoma, lung and other urological cancers (52%, 50% and 35%) ( P< 0.01). Conclusions: Although abnormal thyroid function after ICI initiation was associated with improved overall survival, pre-treatment thyroid abnormalities were associated with worse overall survival. Given the prevalence of thyroid abnormalities in the general population, further research into these observations is warranted.

e19359 Background: EMR generated QTc prolongation alerts are ubiquitous in cancer patients. Their significance and impact on mortality is unknown. Methods: From 2006-2018, 19,223 adult cancer patients at UT-Southwestern hospitals had EMR generated QTc prolongation drug alerts. We collected information on cancer site, demographics and alert details. Deaths ≤10 days from a first QTc alert (acute mortality) were considered more attributable to QTc prolongation. We analyzed overall patient survival through multivariable Kaplan-Meier analysis and Cox regression with the two-outcomes (dead/alive) using a ≤10 days cutoff and followed patients for 365 days from their 1st alert. Results: Analysis of mortality from 1st QTc alert demonstrated statistically significant (p = < 0.05) higher risk of ≤10-day mortality in: patients age > 70 yrs, hazard ratio (HR) = 2.32; black, HR = 1.26 or other/unknown race, HR = 1.29; and increasing number of QTc alerts, particularly patients with 6-10, HR = 1.67 or > 11 HR = 1.88 alerts. Females had lower mortality compared to males, HR = 0.83. Compared to a head and neck cancer patient baseline, significantly increased ≤10-day mortality was seen in: GI, HR = 2.16; lung, HR = 1.94; blood, HR = 1.46; soft tissue, HR = 2.26; and female genital, HR = 1.55 cancers. Male genital, HR = 0.39; breast, HR = 0.57; and endocrine, HR = 0.48 cancers had significantly decreased ≤10-day mortality. In patients with EKG’s recorded: 34 males who died ≤10-days of their 1st alert had significantly longer QTc intervals compared the 5465 who survived to day 11 (469 vs 450 msec, p = < 0.0001). There was no QTc prolongation in the 24 females who died ≤10-days compared to the 4392 who survived to Day 11. Deaths ≤10 days after a QTc alert were rare (total 149/19,223; range 0.01 for male genital to 2.5% for GI), while the majority of deaths within 1 year of a patient’s 1st alert occurred between 11-180 days (range 1.03% for endocrine to 11.05% for lung). Most patients were alive > 1 year after their 1st alert: 63.77% (lung)-95.9% (endocrine). Conclusions: Patients with EKG-proven prolongation of QTc intervals after QTc alerts likely represent a segment of the population where intervention may reduce acute mortality. It is unclear whether the QTc interval prolongation is the actual cause of death or if the EMR alerts identify patients with co-morbidities associated with high risk of acute mortality.

Background Antimicrobial usage is the most important driver of antimicrobial resistance. Despite compelling reasons to use antimicrobials judiciously, it has been challenging to implement antimicrobial stewardship programs (ASP) in the solid-organ transplant (SOT) population. The objective of our study is to assess the impact of multidrug-resistant bacterial infections (MDRBI) on the 1-year post-transplant survival in SOT recipients. Methods In this retrospective cohort study, we included all patients with a first SOT from January 1, 2010–December 31, 2016 at our institution. Patients were followed for a year. Data extraction tools retrieved information from the electronic health record (EHR) and merged it with data from the Social Security Death Index (SSDI) and Standard Transplant Analysis and Research (STAR) files. Charts of subjects with positive cultures were manually reviewed and adjudicated using CDC/ECDC and CDC/NHSN criteria. The 1-year MDRBI cumulative incidence and survival were estimated using the Kaplan–Meier method and compared using the Log-rank test. A Cox proportional hazards model was used to identify predictors of 1-year mortality. Cytomegalovirus (CMV) Infection, renal replacement therapy (RRT), and post-transplant extra-corporeal membrane oxygenation (ECMO) were analyzed as a time-dependent covariate. Results 1,112 SOT recipients met inclusion criteria. Patient characteristics are shown in Table 1. 105 patients had at least one MDRBI. The cumulative incidence of MDRBI was 9.7% (95% CI 14.6–5.9) (Figure 1). The most common MDR pathogens were Vancomycin-resistant Enterococci and E. coli (Figure 2A), and the most common sites of infection were urinary tract infection and pneumonia (Figure 2B). The 1-year post-SOT survival in patients with MDR infection was 75.3% (95% CI 82.8–65.2) (Figure 2C). In multivariable analysis, MDRBI (HR = 6.2 [3.5–10.9]) and post-SOT RRT (HR = 17.8 [10.3–30.6]) were associated with an increased risk of 1-year mortality (Table 2). Conclusion MDRBI significantly impacts the 1-year survival of SOT recipients. Our results highlight the need to strengthen ASP measures in SOT. Additionally, this study illustrates the versatility of EHR-based registries and data extraction tools in the field of transplantation. Disclosures All authors: No reported disclosures.

Background We created a retrospective database of solid-organ transplant (SOT) recipients using innovative data mining tools. This study describing the epidemiology of Varicella Zoster Virus (VZV) reactivation in SOT serves as a proof of concept of such techniques in clinical research. Methods The study design was a retrospective single-center cohort study. Using data mining tools, information was extracted from the electronic medical record and merged with data from the Scientific Registry of Transplant Recipients. First SOT from January 1, 2010–December 31, 2016 were included. Charts of subjects with ICD9/10 codes related to VZV/Herpes infections; positive VZV PCR, DFA or cultures; and recipients of acyclovir, valacyclovir or famciclovir were manually reviewed. The cumulative incidence was calculated using the Kaplan–Meier method. Cox proportional hazards models were used to identify risk factors for VZV reactivation among heart transplant (HT) recipients. Results A total of 1,076 SOT recipients met inclusion criteria (203 heart, 395 lung, 280 kidney, 198 liver). Forty-nine patients experienced at least one episode of VZV reactivation; median time post-transplant was 2.25 years (IQR 1.44–4.20 years). The cumulative incidence was 11.9% at 8 years post-transplant. Heart transplant (HT) recipients were at highest risk (Figure 1), with an 8-year cumulative incidence of 26.3% (Figure 2). Thirty-nine of 49 (80%) patients presented with localized disease and 4/49 (8%) with disseminated disease. In multivariable analysis (Figure 3), the risk of VZV reactivation in HT recipients after 12 months (47 patients) was associated with CMV infection before 12 months (HR [95% CI] = 4.74 [1.67–13.47]). Postherpetic neuralgia (PHN) occurred in 23/49 (47%), recurrence in 3/49 (6%), and other complications in 11/49 (22%). In univariable analysis, no risk factors for PHN were identified. Figure 1 Figure 2 Figure 3 Conclusion HT recipients are at highest risk for VZV reactivation. CMV infection before 1 year is associated with increased risk of VZV reactivation after 1 year in HT. This information may help design clinical trials of the recombinant zoster vaccine. Disclosures All authors: No reported disclosures.

235 Background: Patients with cancer receive many prescriptions associated with QTc interval prolongation. Prognostic implications of drug-induced QTc interactions are unknown. EMR's regularly generate QTc-alerts but their significance or impact is not known. Methods: Using the UTSW tumor registry we identified 14,731 adult cancer patients from 2005-2018 for whom the EMR had generated 16,797 unique alerts for potential QTc drug interactions. We collected patient cancer details, demographics and specific drugs prescribed. After a QTc alert, timing of patient death within a < 365 day period was recorded and analyzed. Prior studies have defined death within 10 days as acute mortality that may be related to QTc prolongation-associated factors. We compared actual deaths that occurred ≤10 days from a QTc alert to expected deaths based on the null hypothesis that deaths ≤10 days were evenly distributed by cancer primary site and would be proportionate to the number of patients in each group; data were analyzed by chi-squared goodness of fit testing. Results: The number of ACTUAL deaths ≤10 days compared to those EXPECTED by the null model varied significantly by cancer site (Table). No mortality differences were noted based on patient demographics, age at first alert, ethnicity, or race. Conclusions: There is a significant association between EMR QTc alerts and higher than expected acute mortality for some cancers, but not universally. In lung, breast, and GI cancer patients who died 15% died within 10 days of a QTc alert. EMR generated QTc alerts may identify cancer patients who are at imminent risk of death in some cancers but may represent distractions in other types of cancers. [Table: see text]

Transforming growth factor βs (TGF-βs) regulate key aspects of embryonic development and major human diseases. Although Smad2, Smad3, and extracellular signal-regulated kinase (ERK) mitogen-activated protein kinases (MAPKs) have been proposed as key mediators in TGF-β signaling, their functional specificities and interactivity in controlling transcriptional programs in different cell types and (patho)physiological contexts are not known. We investigated expression profiles of genes controlled by TGF-β in fibroblasts with ablations of Smad2, Smad3, and ERK MAPK. Our results suggest that Smad3 is the essential mediator of TGF-β signaling and directly activates genes encoding regulators of transcription and signal transducers through Smad3/Smad4 DNA-binding motif repeats that are characteristic for immediate-early target genes of TGF-β but absent in intermediate target genes. In contrast, Smad2 and ERK predominantly transmodulated regulation of both immediate-early and intermediate genes by TGF-β/Smad3. These results suggest a previously uncharacterized hierarchical model of gene regulation by TGF-β in which TGF-β causes direct activation by Smad3 of cascades of regulators of transcription and signaling that are transmodulated by Smad2 and/or ERK.

Background We created a retrospective and prospective database of SOT recipients using innovative data mining tools. This study describing the epidemiology of BSI in SOT serves as a proof of concept of such techniques in clinical research. Methods The design of the study was a retrospective, single-center, cohort study. Data mining tools were used to extract information from the electronic medical record and merged it with data from the SRTR (Figure 1). First SOT from January 1, 2010 to December 31, 2015 were included. Charts of subjects with positive blood cultures were manually reviewed and adjudicated using CDC/NHSN and SCCM/ESICM criteria. The 1-year cumulative incidence was calculated using the Kaplan–Meier method. Cox proportional hazards models were used to identify risk factors for BSI and 1-year mortality. BSI was analyzed as a time-dependent covariate in the mortality model. Fisher’s exact test and chi-square were used to identify risk factors for 30-day mortality and MDRO. Results A total of 917 SOT recipients met inclusion criteria. Seventy-five patients experienced at least one BSI. The cumulative incidence was 8.4% (95% CI 6.8–10.4) (Figure 2). The onset of the first BSI episode was: 30 episodes (40%) 6 months. The most common pathogens were Klebsiella sp. (16%), Vancomycin-resistant E. faecium (12%), E. coli (12%), CoNS (12%), and Candida sp. (9.3%). Nineteen isolates (25%) were identified as MDRO; the risk of MDRO was highest 6 months (44.8 vs. 12.1 vs. 16.7; P = 0.01). The most common source of BSI was CLABSI (29%) (Figure 3). In multivariable analysis, the risk of BSI was associated with organ type (HR [95% CI] = Multiorgan 3.5 [1.1–11.6], liver 2.5 [1.1–5.4], heart 2.4 [1.1–5.1]) and acquisition of a BSI was associated with a higher 1-year mortality (HR = 8.7 [5.1–14.7]). In univariable analysis, a polymicrobial BSI (14.7 vs. 57.1%; P = 0.02), qSOFA ≥ 2 (0.0 vs. 25.5%; P = 0.02) and septic shock (3.9 vs. 52.2%; P Conclusion A BSI significantly affects the 1-year survival of SOT recipients. A qSOFA ≥ 2 can be used to identify patients at risk for death. Additionally, this study illustrates the potential of data mining tools to study infectious complications. Disclosures All authors: No reported disclosures.