Probabilistic Risk Assessment of Prophylactic Surgery Before Extended-Duration Spaceflight

• Published in: Surgical innovation p. 1553350620979809
• Main Authors: Reyes, David P, Carroll, Danielle J, Walton, Marlei E, Antonsen, Erik L, Kerstman, Eric L
• Format: Journal Article
• Language: English
• Published: United States 01.10.2021
• Subjects: spaceflight; appendicitis; cholecystitis; probabilistic risk assessment; small bowel obstruction; prophylactic surgery
• ISSN: 1553-3514
• DOI: 10.1177/1553350620979809

Prophylactic surgery before spaceflight may eliminate the risk of appendicitis and cholecystitis in astronauts on deep space missions. However, even minimally invasive surgery increases the risk of small bowel obstruction (SBO). Probabilistic risk assessment (PRA) is a method that can be used to estimate the benefits and risks of prophylactic surgery. Risks of appendicitis and cholecystitis during a 2.5-year Mars mission are compared to the risk of SBO after laparoscopic removal of the appendix, gallbladder, or both. A PRA model using Monte Carlo methodology was used to forecast the risks. Prophylactic appendectomy and cholecystectomy combined, conferred an increased probability of medical evacuation (pEVAC) due to SBO as compared to the no surgery group. A slightly higher probability for the loss of crew life (pLOCL) was found in the no surgery group when compared to the cases in which either prophylactic appendectomy alone, or appendectomy plus cholecystectomy are performed. The need for medical evacuation can be viewed as a potential risk for death in the context of a space mission where evacuation is not possible. Because of the higher pEVAC due to SBO and relatively small benefit in the reduction of pLOCL in the prophylactic surgery groups, this analysis does not support the prophylactic removal of appendix and/or gallbladder for spaceflight. Future advances in surgical or medical technique or mission medical capabilities may change these results. This work demonstrates the utility of PRA in providing quantitative answers to "what if" questions where limited information is available.