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Introduction: The Integrated Medical Model (IMM) Project represents one aspect of NASA's Human Research Program (HRP) to quantitatively assess medical risks to astronauts for existing operational missions as well as missions associated with future exploration and commercial space flight ventures. The IMM takes a probabilistic approach to assessing the likelihood and specific outcomes of one hundred medical conditions within the envelope of accepted space flight standards of care over a selectable range of mission capabilities. A specially developed Integrated Medical Evidence Database (iMED) maintains evidence-based, organizational knowledge across a variety of data sources. Since becoming operational in 2011, version 3.0 of the IMM, the supporting iMED, and the expertise of the IMM project team have contributed to a wide range of decision and informational processes for the space medical and human research community. This presentation provides an overview of the IMM conceptual architecture and range of application through examples of actual space flight community questions posed to the IMM project. Methods: Figure 1 [see document] illustrates the IMM modeling system and scenario process. As illustrated, the IMM computational architecture is based on Probabilistic Risk Assessment techniques. Nineteen assumptions and limitations define the IMM application domain. Scenario definitions include crew medical attributes and mission specific details. The IMM forecasts probabilities of loss of crew life (LOCL), evacuation (EVAC), quality time lost during the mission, number of medical resources utilized and the number and type of medical events by combining scenario information with in-flight, analog, and terrestrial medical information stored in the iMED. In addition, the metrics provide the integrated information necessary to estimate optimized in-flight medical kit contents under constraints of mass and volume or acceptable level of mission risk. Results and Conclusions: Historically, IMM simulations support Science and Technology planning, Exploration mission planning, and ISS program operations by supplying simulation support, iMED data information, and subject matter expertise to Crew Health and Safety and the HRP. Upcoming release of IMM version 4.0 seeks to provide enhanced functionality to increase the quality of risk decisions made using the IMM through a more accurate representation of the real world system.

The Integrated Medical Model (IMM) Project represents one aspect of NASA's Human Research Program (HRP) to quantitatively assess medical risks to astronauts for existing operational missions as well as missions associated with future exploration and commercial space flight ventures. The IMM takes a probabilistic approach to assessing the likelihood and specific outcomes of one hundred medical conditions within the envelope of accepted space flight standards of care over a selectable range of mission capabilities. A specially developed Integrated Medical Evidence Database (iMED) maintains evidence-based, organizational knowledge across a variety of data sources. Since becoming operational in 2011, version 3.0 of the IMM, the supporting iMED, and the expertise of the IMM project team have contributed to a wide range of decision and informational processes for the space medical and human research community. This presentation provides an overview of the IMM conceptual architecture and range of application through examples of actual space flight community questions posed to the IMM project.

Graphs are powerful tools for simplifying complex data. They are useful for quickly assessing patterns and relationships among one or more variables from a dataset. As the amount of data increases, it becomes more difficult to visualize potential associations. Lifetime Surveillance of Astronaut Health (LSAH) was charged with assessing its current visualization tools along with others on the market to determine whether new tools would be useful for supporting NASA's occupational surveillance effort. It was concluded by members of LSAH that the current tools hindered their ability to provide quick results to researchers working with the department. Due to the high volume of data requests and the many iterations of visualizations requested by researchers, software with a better ability to replicate graphs and edit quickly could improve LSAH's efficiency and lead to faster research results.

The emphasis of this research is on the Human Research Program (HRP) Exploration Medical Capabilities (ExMC) "Risk of Unacceptable Health and Mission Outcomes Due to Limitations of In-flight Medical Capabilities". Specifically, this project aims to contribute to the closure of gap ExMC 2.02: We do not know how the inclusion of a physician crew medical officer quantitatively impacts clinical outcomes during exploration missions. The experiments are specifically designed to address clinical outcome differences between physician and non-physician cohorts in both near-term and longer-term (mission impacting) outcomes.

Medical Operations, Toxicology, and the Lifetime Surveillance of Astronaut Health collaborated to assess the association of CO2 levels on board the International Space Station and USOS crew reported symptoms inflight, i.e. headache and vision changes. Private Medical Conference (PMC) documents and the weekly Space Medicine Operations Team (SMOT) Notes were used to provide a robust data set of inflight medical events. All events and non‐events were documented independent of CO2 levels and other potential contributors. Average (arithmetic mean) and single point maximum ppCO2 was calculated for the 24 hours and 7 days prior to the PMC or SMOT date and time provided by LSAH. Observations falling within the first 7 days of flight (147) were removed from the datasets analyzed to avoid confounding with Space Adaptation Syndrome. The final analysis was based on 1716 observations. For headache, 46 headaches were observed. CO2 level, age at launch, time inflight, and data source were all significantly associated with headache. In particular, for each 1 mmHg increase in CO2, the odds of a crewmember reporting a headache doubled. For vision changes, 29 reports of vision changes were observed. These observations were not found to be statistically associated with CO2 levels as analyzed. While the incidence of headache has was not high (~3%), headaches may be an indicator of underlying increases in intracranial pressure, which may result likely from the synergy between CO2‐induced cerebral vasodilatation and decreased venous drainage in microgravity. Vision changes were inconsistently reported and as a result did not align appropriately with the CO2 levels. Further analysis is needed. Our results support ongoing efforts to lower the CO2 exposure limits in spacecraft.

The default method of dealing with missing data in statistical analyses is to only use the complete observations (complete case analysis), which can lead to unexpected bias when data do not meet the assumption of missing completely at random (MCAR). For the assumption of MCAR to be met, missingness cannot be related to either the observed or unobserved variables. A less stringent assumption, missing at random (MAR), requires that missingness not be associated with the value of the missing variable itself, but can be associated with the other observed variables. When data are truly MAR as opposed to MCAR, the default complete case analysis method can lead to biased results. There are statistical options available to adjust for data that are MAR, including multiple imputation (MI) which is consistent and efficient at estimating effects. Multiple imputation uses informing variables to determine statistical distributions for each piece of missing data. Then multiple datasets are created by randomly drawing on the distributions for each piece of missing data. Since MI is efficient, only a limited number, usually less than 20, of imputed datasets are required to get stable estimates. Each imputed dataset is analyzed using standard statistical techniques, and then results are combined to get overall estimates of effect. A simulation study will be demonstrated to show the results of using the default complete case analysis, and MI in a linear regression of MCAR and MAR simulated data. Further, MI was successfully applied to the association study of CO2 levels and headaches when initial analysis showed there may be an underlying association between missing CO2 levels and reported headaches. Through MI, we were able to show that there is a strong association between average CO2 levels and the risk of headaches. Each unit increase in CO2 (mmHg) resulted in a doubling in the odds of reported headaches.

Understanding the nuances in clinical data is critical in developing a successful data analysis plan. Carbon dioxide (CO2) data are collected on board the International Space Station (ISS) in a continuous stream. Clinical data on ISS are primarily collected via conversations between individual crewmembers and NASA Flight Surgeons during weekly Private Medical Conferences (PMC). Law, et.al, 20141 demonstrated a statistically significant association between weekly average CO2 levels on ISS and self-reported headaches over the reporting period from March 14, 2001 to May 31, 2012. The purpose of this analysis is to describe the evaluation of a possible association between visual changes and CO2 levels on ISS and to discuss challenges in developing an appropriate analysis plan. METHODS & PRELIMINARY RESULTS: A first analysis was conducted following the same study design as the published work on CO2 and self-reported headaches1; substituting self-reported changes in visual acuity in place of self-reported headaches. The analysis demonstrated no statistically significant association between visual impairment characterized by vision symptoms self-reported during PMCs and ISS average CO2 levels over ISS missions. Closer review of the PMC records showed that vision outcomes are not well-documented in terms of clinical severity, timing of onset, or timing of resolution, perhaps due to the incipient nature of vision changes. Vision has been monitored in ISS crewmembers, pre- and post-flight, using standard optometry evaluations. In-flight visual assessments were limited early in the ISS program, primarily consisting of self-perceived changes reported by crewmembers. Recently, on-orbit capabilities have greatly improved. Vision data ranges from self-reported post-flight changes in visual acuity, pre- to postflight changes identified during fundoscopic examination, and in-flight progression measured by advanced on-orbit clinical imaging capabilities at predetermined testing intervals. In contrast, CO2 data are recorded in a continuous stream over time; however, for the initial analysis this data was categorized into weekly averages.