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Analysis of factors associated with extended recovery time after colonoscopy
- Source :
- PLoS ONE, PLoS ONE, Vol 13, Iss 6, p e0199246 (2018)
- Publication Year :
- 2017
-
Abstract
- Background & aims A common limiting factor in the throughput of gastrointestinal endoscopy units is the availability of space for patients to recover post-procedure. This study sought to identify predictors of abnormally long recovery time after colonoscopy performed with procedural sedation. In clinical research, this type of study would be performed using only one regression modeling approach. A goal of this study was to apply various "machine learning" techniques to see if better prediction could be achieved. Methods Procedural data for 31,442 colonoscopies performed on 29,905 adult patients at Massachusetts General Hospital from 2011 to 2015 were analyzed to identify potential predictors of long recovery times. These data included the identities of hospital personnel, and the initial statistical analysis focused on the impact of these personnel on recovery time via multivariate logistic regression. Secondary analyses included more information on patient vitals both to identify secondary predictors and to predict long recoveries using more complex techniques. Results In univariate analysis, the endoscopist, procedure room nurse, recovery room nurse, and surgical technician all showed a statistically significant relationship to long recovery times, with p-value below 0.0001 in all cases. In the multivariate logistic regression, the most significant predictor of a long recovery time was the identity of the recovery room nurse, with the endoscopist also showing a statistically significant relationship with a weaker effect. Complex techniques led to a negligible improvement over simple techniques in prediction of long recovery periods. Conclusion The hospital personnel involved in performing a colonoscopy show a strong association with the likelihood of a patient spending an abnormally long time recovering from the procedure, with the most pronounced effect for the nurse in the recovery room. The application of more advanced approaches to improve prediction in this clinical data set only yielded modest improvements.
- Subjects :
- Male
Science and Technology Workforce
Multivariate analysis
Time Factors
Decision Analysis
Health Care Providers
Colonoscopy
lcsh:Medicine
Nurses
Logistic regression
Careers in Research
Machine Learning
0302 clinical medicine
Mathematical and Statistical Techniques
Medicine and Health Sciences
Medicine
030212 general & internal medicine
Medical Personnel
lcsh:Science
Gastrointestinal endoscopy
Univariate analysis
Multidisciplinary
medicine.diagnostic_test
Technician
Regression analysis
Middle Aged
Fentanyl
Professions
Physical Sciences
Regression Analysis
Engineering and Technology
030211 gastroenterology & hepatology
Female
medicine.symptom
Management Engineering
Statistics (Mathematics)
Research Article
medicine.medical_specialty
Computer and Information Sciences
Meperidine
Neural Networks
Science Policy
Sedation
Surgical and Invasive Medical Procedures
Research and Analysis Methods
03 medical and health sciences
Digestive System Procedures
Artificial Intelligence
Medical Staff, Hospital
Humans
Statistical Methods
Aged
business.industry
lcsh:R
Decision Trees
Biology and Life Sciences
Endoscopy
Recovery of Function
Models, Theoretical
Technicians
Health Care
Emergency medicine
Multivariate Analysis
People and Places
lcsh:Q
Population Groupings
business
Factor Analysis, Statistical
Mathematics
Forecasting
Neuroscience
Subjects
Details
- ISSN :
- 19326203
- Volume :
- 13
- Issue :
- 6
- Database :
- OpenAIRE
- Journal :
- PloS one
- Accession number :
- edsair.doi.dedup.....020bd8c0a87e74bbc0cdcc5037d099be