Search NASA⌕ Search

Engineering topics

Young, Millennia

Publications and source records attributed to Young, Millennia.

14 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Food Acceptability, Menu Fatigue, and Aversion on ISS Missions

The acceptability of the spaceflight food system has been linked to caloric intake and associated nutritional benefits. The diets of the United States Operating Segment crewmembers during a mission are restricted to 200 processed and prepackaged standard menu items supplemented with personal preference foods. ISS crew members have noted in debriefs that they would prefer more food variety for the length of the missions and they tire of certain foods over six months. It is possible that menu fatigue leads to decreases in acceptability and increased aversion to available foods, potentially contributing to the body mass loss often experienced by ISS crew. However, the impact of repeat food consumption on acceptability within the current spaceflight food system has not yet been systematically investigated. Limited variety and crew preferences within food categories may have more severe physical and behavioral health and performance consequences as mission duration increases. Characterizing the relationship between food acceptability and mission duration will contribute to defining requirements for an acceptable food system that will support crew health and performance on long duration missions.

Douglas, Grace L.↗
NASA NTRS2018

Validation of the Nasa Integrated Medical Model: a Space Flight Medical Risk Prediction Tool

The Human Research Program funded the development of the Integrated Medical Model (IMM) to quantify the medical component of overall mission risk. The IMM uses Monte Carlo simulation methodology, incorporating space flight and ground medical data, to estimate the probability of mission medical outcomes and resource utilization. To determine the credibility of IMM output, the IMM project team completed two validation studies that compared IMM predicted output to observed medical events from a selection of Shuttle Transportation System (STS) and International Space Station (ISS) missions. The validation study results showed that the IMM underpredicted the occurrence of ~10% of the modeled medical conditions for the STS missions and overpredicted ~20% of the modeled medical conditions for the ISS missions. These findings imply that the strength of IMM predictions to inform decisions depends on simulated mission specifications including length. This discrepancy could result from medical recording differences between ISS and STS that possibly influence observed incidence rates, IMM combining all "mission type" data as constant occurrence rate or fixed proportion across both mission types, misspecification of symptoms to conditions, and gaps in the literature informing the model. Some of these issues will be alleviated by updating the IMM source data through incorporation of the observed validation data.

medical equipment↗
NASA NTRS2017

A Network-Based Algorithm for Clustering Multivariate Repeated Measures Data

The National Aeronautics and Space Administration (NASA) Astronaut Corps is a unique occupational cohort for which vast amounts of measures data have been collected repeatedly in research or operational studies pre-, in-, and post-flight, as well as during multiple clinical care visits. In exploratory analyses aimed at generating hypotheses regarding physiological changes associated with spaceflight exposure, such as impaired vision, it is of interest to identify anomalies and trends across these expansive datasets. Multivariate clustering algorithms for repeated measures data may help parse the data to identify homogeneous groups of astronauts that have higher risks for a particular physiological change. However, available clustering methods may not be able to accommodate the complex data structures found in NASA data, since the methods often rely on strict model assumptions, require equally-spaced and balanced assessment times, cannot accommodate missing data or differing time scales across variables, and cannot process continuous and discrete data simultaneously. To fill this gap, we propose a network-based, multivariate clustering algorithm for repeated measures data that can be tailored to fit various research settings. Using simulated data, we demonstrate how our method can be used to identify patterns in complex data structures found in practice.

Koslovsky, Matthew↗
NASA NTRS2017

Relationship Between Carbon Dioxide Levels and Reported Congestion and Headaches on the International Space Station

Congestion is commonly reported during spaceflight, and most crewmembers have reported using medications for congestion during International Space Station (ISS) missions. Although congestion has been attributed to fluid shifts during spaceflight, fluid status reaches equilibrium during the first week after launch while congestion continues to be reported throughout long duration missions. Congestion complaints have anecdotally been reported in relation to ISS CO2 levels; this evaluation was undertaken to determine whether or not an association exists. METHODS: Reported headaches, congestion symptoms, and CO2 levels were obtained for ISS expeditions 2-31, and time-weighted means and single-point maxima were determined for 24-hour (24hr) and 7-day (7d) periods prior to each weekly private medical conference. Multiple imputation addressed missing data, and logistic regression modeled the relationship between probability of reported event of congestion or headache and CO2 levels, adjusted for possible confounding covariates. The first seven days of spaceflight were not included to control for fluid shifts. Data were evaluated to determine the concentration of CO2 required to maintain the risk of congestion below 1% to allow for direct comparison with a previously published evaluation of CO2 concentrations and headache. RESULTS: This study confirmed a previously identified significant association between CO2 and headache and also found a significant association between CO2 and congestion. For each 1-mm Hg increase in CO2, the odds of a crew member reporting congestion doubled. The average 7-day CO2 would need to be maintained below 1.5 mmHg to keep the risk of congestion below 1%. The predicted probability curves of ISS headache and congestion curves appear parallel when plotted against ppCO2 levels with congestion occurring at approximately 1mmHg lower than a headache would be reported. DISCUSSION: While the cause of congestion is multifactorial, this study showed congestion is associated with CO2 levels on ISS. Data from additional expeditions could be incorporated to further assess this finding. CO2 levels are also associated with reports of headaches on ISS. While it may be expected for astronauts with congestion to also complain of headaches, these two symptoms are commonly mutually exclusive. Furthermore, it is unknown if a temporal CO2 relationship exists between congestion and headache on ISS. CO2 levels were time-weighted for 24hr and 7d, and thus the time course of congestion leading to headache was not assessed; however, congestion could be an early CO2-related symptom when compared to headache. Future studies evaluating the association of CO2-related congestion leading to headache would be difficult due to the relatively stable daily CO2 levels on ISS currently, but a systematic study could be implemented on-orbit if desired.

Cole, Robert↗
NASA NTRS2017

Carotid Intima Media Thickness in the Astronaut Corps: Association to Spacecraft

Background: Carotid Intima Media Thickness (CIMT) has been demonstrated to be predictive of future cardiovascular events. Within various populations, radiation exposure, stress, and physical confinement have all been linked to an increased CIMT. Recent research discovered CIMT was significantly increased in ten long duration astronauts from pre-flight to four days post flight. The relationship between spaceflight and CIMT is not understood and trends in CIMT within the larger astronaut population are unknown. Methods: In 2010, CIMT was offered as part of the astronaut annual exam at the JSC Flight Medicine Clinic using a standardized CIMT screening protocol and professional sonographers. Between 2010 and 2016, CIMT measurements were collected on 213 NASA astronauts and payload specialists. The values used in this retrospective chart review are the mean of the CIMT from the right and left. Spaceflight exposure was categorized based on the total number of days spent in space at the time of the ground-based ultrasound (0, 1-29, 30-100, 101-200, ≥200). Linear regression with generalized estimating equations were used to estimate the association between spaceflight exposures and CIMT. Results: 530 studies were completed among 213 astronauts with a mean of 2.5 studies (range 1-6) per astronaut over the six year period. As in other populations, CIMT was significantly associated with age; however, gender was not. While there was no significant direct correlation between total spaceflight exposure and CIMT found, astronauts with 30-100 spaceflight days and astronauts with greater than 100 spaceflight days had significantly increased CIMT over astronauts who had never flown (p=0.002 and p=<0.0001 respectively) after adjustment for age. Conclusion: Further work is needed to fully understand CIMT and its association to spaceflight. Current occupational surveillance activities are under way to study CIMT values in conjunction with other cardiovascular risk factors among astronauts as compared to the general population.

Suffredini, John↗
NASA NTRS2017

OCT Expanded Clinical Data Analysis

Vision changes identified in long duration space fliers has led to a more comprehensive clinical monitoring protocol. Optical Coherence Tomography (OCT) was recently implemented on board the International Space Station in 2013. NASA is collaborating with Heidelberg Engineering to expand our current OCT data analysis capability by implementing a volumetric approach. Volumetric maps will be created by combining the circle scan, the disc block scan, and the radial scan. This assessment may provide additional information about the optic nerve and further characterize changes related microgravity exposure. We will discuss challenges with collection and analysis of OCT data, present the results of this reanalysis and outline the potential benefits and limitations of the additional data.

Van Baalen, Mary↗
NASA NTRS2016

Quantitative Validation of the Integrated Medical Model (IMM) for ISS Missions

Lifetime Surveillance of Astronaut Health (LSAH) provided observed medical event data on 33 ISS and 111 STS person-missions for use in further improving and validating the Integrated Medical Model (IMM). Using only the crew characteristics from these observed missions, the newest development version, IMM v4.0, will simulate these missions to predict medical events and outcomes. Comparing IMM predictions to the actual observed medical event counts will provide external validation and identify areas of possible improvement. In an effort to improve the power of detecting differences in this validation study, the total over each program ISS and STS will serve as the main quantitative comparison objective, specifically the following parameters: total medical events (TME), probability of loss of crew life (LOCL), and probability of evacuation (EVAC). Scatter plots of observed versus median predicted TMEs (with error bars reflecting the simulation intervals) will graphically display comparisons while linear regression will serve as the statistical test of agreement. Two scatter plots will be analyzed 1) where each point reflects a mission and 2) where each point reflects a condition-specific total number of occurrences. The coefficient of determination (R2) resulting from a linear regression with no intercept bias (intercept fixed at zero) will serve as an overall metric of agreement between IMM and the real world system (RWS). In an effort to identify as many possible discrepancies as possible for further inspection, the -level for all statistical tests comparing IMM predictions to observed data will be set to 0.1. This less stringent criterion, along with the multiple testing being conducted, should detect all perceived differences including many false positive signals resulting from random variation. The results of these analyses will reveal areas of the model requiring adjustment to improve overall IMM output, which will thereby provide better decision support for mission critical applications.

validation↗
NASA NTRS2015

The Integrated Medical Model: A Probabilistic Simulation Model Predicting In-Flight Medical Risks

The Integrated Medical Model (IMM) is a probabilistic model that uses simulation to predict mission medical risk. Given a specific mission and crew scenario, medical events are simulated using Monte Carlo methodology to provide estimates of resource utilization, probability of evacuation, probability of loss of crew, and the amount of mission time lost due to illness. Mission and crew scenarios are defined by mission length, extravehicular activity (EVA) schedule, and crew characteristics including: sex, coronary artery calcium score, contacts, dental crowns, history of abdominal surgery, and EVA eligibility. The Integrated Medical Evidence Database (iMED) houses the model inputs for one hundred medical conditions using in-flight, analog, and terrestrial medical data. Inputs include incidence, event durations, resource utilization, and crew functional impairment. Severity of conditions is addressed by defining statistical distributions on the dichotomized best and worst-case scenarios for each condition. The outcome distributions for conditions are bounded by the treatment extremes of the fully treated scenario in which all required resources are available and the untreated scenario in which no required resources are available. Upon occurrence of a simulated medical event, treatment availability is assessed, and outcomes are generated depending on the status of the affected crewmember at the time of onset, including any pre-existing functional impairments or ongoing treatment of concurrent conditions. The main IMM outcomes, including probability of evacuation and loss of crew life, time lost due to medical events, and resource utilization, are useful in informing mission planning decisions. To date, the IMM has been used to assess mission-specific risks with and without certain crewmember characteristics, to determine the impact of eliminating certain resources from the mission medical kit, and to design medical kits that maximally benefit crew health while meeting mass and volume constraints.

Risk Analysis↗
NASA NTRS2015

The Integrated Medical Model: A Probabilistic Simulation Model for Predicting In-Flight Medical Risks

The Integrated Medical Model (IMM) is a probabilistic model that uses simulation to predict mission medical risk. Given a specific mission and crew scenario, medical events are simulated using Monte Carlo methodology to provide estimates of resource utilization, probability of evacuation, probability of loss of crew, and the amount of mission time lost due to illness. Mission and crew scenarios are defined by mission length, extravehicular activity (EVA) schedule, and crew characteristics including: sex, coronary artery calcium score, contacts, dental crowns, history of abdominal surgery, and EVA eligibility. The Integrated Medical Evidence Database (iMED) houses the model inputs for one hundred medical conditions using in-flight, analog, and terrestrial medical data. Inputs include incidence, event durations, resource utilization, and crew functional impairment. Severity of conditions is addressed by defining statistical distributions on the dichotomized best and worst-case scenarios for each condition. The outcome distributions for conditions are bounded by the treatment extremes of the fully treated scenario in which all required resources are available and the untreated scenario in which no required resources are available. Upon occurrence of a simulated medical event, treatment availability is assessed, and outcomes are generated depending on the status of the affected crewmember at the time of onset, including any pre-existing functional impairments or ongoing treatment of concurrent conditions. The main IMM outcomes, including probability of evacuation and loss of crew life, time lost due to medical events, and resource utilization, are useful in informing mission planning decisions. To date, the IMM has been used to assess mission-specific risks with and without certain crewmember characteristics, to determine the impact of eliminating certain resources from the mission medical kit, and to design medical kits that maximally benefit crew health while meeting mass and volume constraints.

Risk Analysis↗