Immune age and biological age as determinants of vaccine responsiveness among elderly populations: the Human Immunomics Initiative research program.

The Human Immunomics Initiative (HII), a joint project between the Harvard T.H. Chan School of Public Health and the Human Vaccines Project (HVP), focuses on studying immunity and the predictability of immuneresponsiveness to vaccines in aging populations. This paper describes the hypotheses and methodological approaches of this new collaborative initiative. Central to our thinking is the idea that predictors of age-related non-communicable diseases are the same as predictors for infectious diseases like COVID-19 and influenza. Fundamental to our approach is to differentiate between chronological, biological and immune age, and to use existing large-scale population cohorts. The latter provide well-typed phenotypic data on individuals' health status over time, readouts of routine clinical biochemical biomarkers to determine biological age, and bio-banked plasma samples to deep phenotype humoral immune responses as biomarkers of immune age. The first phase of the program involves 1. the exploration of biological age, humoral biomarkers of immune age, and genetics in a large multigenerational cohort, and 2. the subsequent development of models of immunity in relation to health status in a second, prospective cohort of an aging population. In the second phase, vaccine responses and efficacy of licensed COVID-19 vaccines in the presence and absence of influenza-, pneumococcal- and pertussis vaccines routinely offered to elderly, will be studied in older aged participants of prospective population-based cohorts in different geographical locations who will be selected for representing distinct biological and immune ages. The HII research program is aimed at relating vaccine responsiveness to biological and immune age, and identifying aging-related pathways crucial to enhance vaccine effectiveness in aging populations.

Let technology do the work: Improving prediction of massive transfusion with the aid of a smartphone application.

BACKGROUND: The use of massive transfusion protocols (MTPs) is now common in civilian trauma settings, and early activation of MTP has been shown to increase survival of MTP recipients. Numerous MTP prediction tools have been developed; however, they are often cumbersome to use efficiently or have traded predictive power for ease of use. We hypothesized that a highly accurate predictor of massive transfusion could be created and incorporated into a smartphone application that would provide an additional tool for clinicians to use in directing the resuscitation of critically injured patients. METHODS: Data from all trauma admissions since the inception of MTP were put in place at Grady Memorial Hospital in Atlanta, Georgia, were collected. A predictive model was developed using the least absolute shrinkage and selection operator (LASSO) and 10-fold cross validation. Data were resampled over 500 iterations, each using a unique and random subset of 80% of the data for model training and 20% for validation. RESULTS: The trauma registry contained 13,961 cases between 2007 and November 2011, of which 10,900 were complete and 394 received MTP. Of 44 input terms, only the mechanism of injury, heart rate, systolic blood pressure, and base deficit were found to be important predictors of massive transfusion. Our model has an area under the receiver operating curve of 0.96 (against data not used during model training) and accurately predicted MTP status for 97% of all patients. The model accurately discriminated full MTPs from MTP activations that did not meet criteria for massive transfusion. While complex to calculate by hand, our model has been packaged into a mobile application, allowing for efficient use while minimizing potential for user error. CONCLUSION: We have developed a highly accurate model for the prediction of massive transfusion that has potential to be easily accessed and used within a simple and efficient mobile application for smartphones. LEVEL OF EVIDENCE: Prognostic/epidemiologic study, level III.