Stanford Advisors


All Publications


  • A data-driven health index for neonatal morbidities. iScience De Francesco, D., Blumenfeld, Y. J., Maric, I., Mayo, J. A., Chang, A. L., Fallahzadeh, R., Phongpreecha, T., Butwick, A. J., Xenochristou, M., Phibbs, C. S., Bidoki, N. H., Becker, M., Culos, A., Espinosa, C., Liu, Q., Sylvester, K. G., Gaudilliere, B., Angst, M. S., Stevenson, D. K., Shaw, G. M., Aghaeepour, N. 2022; 25 (4): 104143

    Abstract

    Whereas prematurity is a major cause of neonatal mortality, morbidity, and lifelong impairment, the degree of prematurity is usually defined by the gestational age (GA) at delivery rather than by neonatal morbidity. Here we propose a multi-task deep neural network model that simultaneously predicts twelve neonatal morbidities, as the basis for a new data-driven approach to define prematurity. Maternal demographics, medical history, obstetrical complications, and prenatal fetal findings were obtained from linked birth certificates and maternal/infant hospitalization records for 11,594,786 livebirths in California from 1991 to 2012. Overall, our model outperformed traditional models to assess prematurity which are based on GA and/or birthweight (area under the precision-recall curve was 0.326 for our model, 0.229 for GA, and 0.156 for small for GA). These findings highlight the potential of using machine learning techniques to predict multiple prematurity phenotypes and inform clinical decisions to prevent, diagnose and treat neonatal morbidities.

    View details for DOI 10.1016/j.isci.2022.104143

    View details for PubMedID 35402862

  • Data-Driven Modeling of Pregnancy-Related Complications. Trends in molecular medicine Espinosa, C. n., Becker, M. n., Marić, I. n., Wong, R. J., Shaw, G. M., Gaudilliere, B. n., Aghaeepour, N. n., Stevenson, D. K. 2021

    Abstract

    A healthy pregnancy depends on complex interrelated biological adaptations involving placentation, maternal immune responses, and hormonal homeostasis. Recent advances in high-throughput technologies have provided access to multiomics biological data that, combined with clinical and social data, can provide a deeper understanding of normal and abnormal pregnancies. Integration of these heterogeneous datasets using state-of-the-art machine-learning methods can enable the prediction of short- and long-term health trajectories for a mother and offspring and the development of treatments to prevent or minimize complications. We review advanced machine-learning methods that could: provide deeper biological insights into a pregnancy not yet unveiled by current methodologies; clarify the etiologies and heterogeneity of pathologies that affect a pregnancy; and suggest the best approaches to address disparities in outcomes affecting vulnerable populations.

    View details for DOI 10.1016/j.molmed.2021.01.007

    View details for PubMedID 33573911

  • Objective Activity Parameters Track Patient-Specific Physical Recovery Trajectories After Surgery and Link With Individual Preoperative Immune States. Annals of surgery Fallahzadeh, R., Verdonk, F., Ganio, E., Culos, A., Stanley, N., Marić, I., Chang, A. L., Becker, M., Phongpreecha, T., Xenochristou, M., De Francesco, D., Espinosa, C., Gao, X., Tsai, A., Sultan, P., Tingle, M., Amanatullah, D. F., Huddleston, J. I., Goodman, S. B., Gaudilliere, B., Angst, M. S., Aghaeepour, N. 2021

    Abstract

    The longitudinal assessment of physical function with high temporal resolution at a scalable and objective level in patients recovering from surgery is highly desirable to understand the biological and clinical factors that drive the clinical outcome. However, physical recovery from surgery itself remains poorly defined and the utility of wearable technologies to study recovery after surgery has not been established.Prolonged postoperative recovery is often associated with long-lasting impairment of physical, mental, and social functions. While phenotypical and clinical patient characteristics account for some variation of individual recovery trajectories, biological differences likely play a major role. Specifically, patient-specific immune states have been linked to prolonged physical impairment after surgery. However, current methods of quantifying physical recovery lack patient specificity and objectivity.Here, a combined high-fidelity accelerometry and state-of-the-art deep immune profiling approach was studied in patients undergoing major joint replacement surgery. The aim was to determine whether objective physical parameters derived from accelerometry data can accurately track patient-specific physical recovery profiles (suggestive of a 'clock of postoperative recovery'), compare the performance of derived parameters with benchmark metrics including step count, and link individual recovery profiles with patients' preoperative immune state.The results of our models indicate that patient-specific temporal patterns of physical function can be derived with a precision superior to benchmark metrics. Notably, six distinct domains of physical function and sleep are identified to represent the objective temporal patterns: "activity capacity" and "moderate and overall activity" (declined immediately after surgery); "sleep disruption and sedentary activity" (increased after surgery); "overall sleep", "sleep onset", and "light activity" (no clear changes were observed after surgery). These patterns can be linked to individual patients' preoperative immune state using cross-validated canonical-correlation analysis. Importantly, the pSTAT3 signal activity in M-MDSCs predicted a slower recovery.Accelerometry-based recovery trajectories are scalable and objective outcomes to study patient-specific factors that drive physical recovery.

    View details for DOI 10.1097/SLA.0000000000005250

    View details for PubMedID 35129529