Scott Hu

CXCR3 chemokine ligands during respiratory viral infections predict lung allograft dysfunction.

Community‐acquired respiratory viruses (CARV) can accelerate the development of lung allograft dysfunction, but the immunologic mechanisms are poorly understood. The chemokine receptor CXCR3 and its chemokine ligands, CXCL9, CXCL10 and CXCL11 have roles in the immune response to viruses and in the pathogenesis of bronchiolitis obliterans syndrome, the predominant manifestation of chronic lung allograft rejection. We explored the impact of CARV infection on CXCR3/ligand biology and explored the use of CXCR3 chemokines as biomarkers for subsequent lung allograft dysfunction. Seventeen lung transplant recipients with CARV infection had bronchoalveolar lavage fluid (BALF) available for analysis. For comparison, we included 34 BALF specimens (2 for each CARV case) that were negative for infection and collected at a duration posttransplant similar to a CARV case. The concentration of each CXCR3 chemokine was increased during CARV infection. Among CARV infected patients, a high BALF concentration of either CXCL10 or CXCL11 was predictive of a greater decline in forced expiratory volume in 1 s, 6 months later. CXCR3 chemokine concentrations provide prognostic information and this may have important implications for the development of novel treatment strategies to modify outcomes after CARV infection.

Protection against bronchiolitis obliterans syndrome is associated with allograft CCR7+ CD45RA- T regulatory cells.

Bronchiolitis obliterans syndrome (BOS) is the major obstacle to long-term survival after lung transplantation, yet markers for early detection and intervention are currently lacking. Given the role of regulatory T cells (Treg) in modulation of immunity, we hypothesized that frequencies of Treg in bronchoalveolar lavage fluid (BALF) after lung transplantation would predict subsequent development of BOS. Seventy BALF specimens obtained from 47 lung transplant recipients were analyzed for Treg lymphocyte subsets by flow cytometry, in parallel with ELISA measurements of chemokines. Allograft biopsy tissue was stained for chemokines of interest. Treg were essentially all CD45RA−, and total Treg frequency did not correlate to BOS outcome. The majority of Treg were CCR4+ and CD103− and neither of these subsets correlated to risk for BOS. In contrast, higher percentages of CCR7+ Treg correlated to reduced risk of BOS. Additionally, the CCR7 ligand CCL21 correlated with CCR7+ Treg frequency and inversely with BOS. Higher frequencies of CCR7+ CD3+CD4+CD25hiFoxp3+CD45RA− lymphocytes in lung allografts is associated with protection against subsequent development of BOS, suggesting that this subset of putative Treg may down-modulate alloimmunity. CCL21 may be pivotal for the recruitment of this distinct subset to the lung allograft and thereby decrease the risk for chronic rejection.

Personalized Risk Scoring for Critical Care Prognosis Using Mixtures of Gaussian Processes

Objective: In this paper, we develop a personalized real-time risk scoring algorithm that provides timely and granular assessments for the clinical acuity of ward patients based on their (temporal) lab tests and vital signs; the proposed risk scoring system ensures timely intensive care unit admissions for clinically deteriorating patients. Methods: The risk scoring system is based on the idea of sequential hypothesis testing under an uncertain time horizon. The system learns a set of latent patient subtypes from the offline electronic health record data, and trains a mixture of Gaussian Process experts, where each expert models the physiological data streams associated with a specific patient subtype. Transfer learning techniques are used to learn the relationship between a patients latent subtype and her static admission information (e.g., age, gender, transfer status, ICD-9 codes, etc). Results: Experiments conducted on data from a heterogeneous cohort of 6321 patients admitted to Ronald Reagan UCLA medical center show that our score significantly outperforms the currently deployed risk scores, such as the Rothman index, MEWS, APACHE, and SOFA scores, in terms of timeliness, true positive rate, and positive predictive value. Conclusion: Our results reflect the importance of adopting the concepts of personalized medicine in critical care settings; significant accuracy and timeliness gains can be achieved by accounting for the patients’ heterogeneity. Significance: The proposed risk scoring methodology can confer huge clinical and social benefits on a massive number of critically ill inpatients who exhibit adverse outcomes including, but not limited to, cardiac arrests, respiratory arrests, and septic shocks.

Prediction of Clinical Deterioration in Hospitalized Adult Patients with Hematologic Malignancies Using a Neural Network Model.

Introduction Clinical deterioration (ICU transfer and cardiac arrest) occurs during approximately 5–10% of hospital admissions. Existing prediction models have a high false positive rate, leading to multiple false alarms and alarm fatigue. We used routine vital signs and laboratory values obtained from the electronic medical record (EMR) along with a machine learning algorithm called a neural network to develop a prediction model that would increase the predictive accuracy and decrease false alarm rates. Design Retrospective cohort study. Setting The hematologic malignancy unit in an academic medical center in the United States. Patient Population Adult patients admitted to the hematologic malignancy unit from 2009 to 2010. Intervention None. Measurements and Main Results Vital signs and laboratory values were obtained from the electronic medical record system and then used as predictors (features). A neural network was used to build a model to predict clinical deterioration events (ICU transfer and cardiac arrest). The performance of the neural network model was compared to the VitalPac Early Warning Score (ViEWS). Five hundred sixty five consecutive total admissions were available with 43 admissions resulting in clinical deterioration. Using simulation, the neural network outperformed the ViEWS model with a positive predictive value of 82% compared to 24%, respectively. Conclusion We developed and tested a neural network-based prediction model for clinical deterioration in patients hospitalized in the hematologic malignancy unit. Our neural network model outperformed an existing model, substantially increasing the positive predictive value, allowing the clinician to be confident in the alarm raised. This system can be readily implemented in a real-time fashion in existing EMR systems.