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Predicting Acute Pain after Cesarean Delivery Using Three Simple Questions
Open accessAbstract
BACKGROUND: Interindividual variability in postoperative pain presents a clinical challenge. Preoperative quantitative sensory testing is useful but time consuming in predicting postoperative pain intensity. The current study was conducted to develop and validate a predictive model of acute postcesarean pain using a simple three-item preoperative questionnaire. METHODS: A total of 200 women scheduled for elective cesarean delivery under subarachnoid anesthesia were enrolled (192 subjects analyzed). Patients were asked to rate the intensity of loudness of audio tones, their level of anxiety and anticipated pain, and analgesic need from surgery. Postoperatively, patients reported the intensity of evoked pain. Regression analysis was performed to generate a predictive model for pain from these measures. A validation cohort of 151 women was enrolled to test the reliability of the model (131 subjects analyzed). RESULTS: Responses from each of the three preoperative questions correlated moderately with 24-h evoked pain intensity (r = 0.24-0.33, P < 0.001). Audio tone rating added uniquely, but minimally, to the model and was not included in the predictive model. The multiple regression analysis yielded a statistically significant model (R = 0.20, P < 0.001), whereas the validation cohort showed reliably a very similar regression line (R = 0.18). In predicting the upper 20th percentile of evoked pain scores, the optimal cut point was 46.9 (z =0.24) such that sensitivity of 0.68 and specificity of 0.67 were as balanced as possible. CONCLUSIONS: This simple three-item questionnaire is useful to help predict postcesarean evoked pain intensity, and could be applied to further research and clinical application to tailor analgesic therapy to those who need it most.
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“A single-question approach has previously shown high predictive validity after cesarean delivery [85].”
From Pre- and post-operative psychological interventions to prevent pain and fatigue after breast cancer surgery (PREVENT): Protocol for a randomized controlled trial (2022) · cites this paper as [85]
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Metadata
- Authors
- Pan, P. H., Tonidandel, A. M., Aschenbrenner, C. A., Houle, T. T., Harris, L. C., & Eisenach, J. C.
- Affiliations
- Professor
- Assistant Professor
- Biostatistician
- Associate Professor
- Research Coordinator
- Wake Forest University Health Sciences (US)
- Venue
- Anesthesiology
- Published
- 2013-05
- Volume
- 118
- Issue
- 5
- Pages
- 1170-1179
- ISSN
- 0003-3022 (ISSN-L)
- PMC
- PMC3951732
- OpenAlex
- W2033813026
- Cited by
- 113
- References
- 29
- Citations by year
- 2026: 7 · 2025: 10 · 2024: 6 · 2023: 9 · 2022: 9 · 2021: 21 · 2020: 9 · 2019: 9 · 2018: 11 · 2017: 4 · 2016: 7 · 2015: 8 · 2014: 2
- FWCI
- 5.58
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Anesthesiology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of General Medical Sciences (R01GM048085, R37GM048085, GM 48085)
- National Center for Research Resources (M01RR007122)
- Topics
- Health Sciences › Medicine › Anesthesiology and Pain Medicine › Anesthesia and Pain Management
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Pediatric Pain Management Techniques
- Health Sciences › Medicine › Obstetrics and Gynecology › Maternal and Perinatal Health Interventions
- Keywords
- postoperative pain, cesarean section, pain prediction, pain anticipation, anxiety, analgesic requirement, spinal anesthesia, prediction model
- MeSH terms
- Acoustic Stimulation, Adult, Anesthesia, Obstetrical, Anxiety, Cesarean Section, Female, Humans, Intraoperative Care, Postoperative Pain, Predictive Value of Tests, Pregnancy, Preoperative Care, Regression Analysis, Reproducibility of Results, Models, Statistical, Cohort Studies, Linear Models, Treatment Outcome, Young Adult, Acute Pain
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