{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T09:54:11Z","timestamp":1791194051727,"version":"4.1.0"},"reference-count":20,"publisher":"BMJ","issue":"10","content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["Reg Anesth Pain Med"],"accepted":{"date-parts":[[2025,10,6]]},"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Chronic postsurgical pain after cesarean delivery affects 15%\u201317% of women and impairs maternal recovery, yet existing prediction models predominantly rely on demographic or procedural factors, with acute pain intensity a key but limited predictor. Symptom cluster research in chronic pain populations has consistently identified co-occurring domains in sleep, pain, affect, cognition, and energy (SPACE) that may represent a modifiable risk phenotype.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The SPACE-Postpartum framework was conceptualized through triangulation of qualitative interviews with postpartum individuals experiencing persistent pain, exploratory analyses from a prospective cohort study, and existing literature on symptom clusters in chronic pain. The resulting multidomain model was operationalized into a prototype tool and subsequently evaluated for proof-of-concept feasibility within the same cohort of women following cesarean delivery.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Qualitative data confirmed the salience of all five SPACE domains in women\u2019s recovery narratives. In the cohort, exploratory analyses supported the feasibility of applying the prototype tool for early multidomain risk stratification. Early symptom profiles consistent with SPACE domains were also identified using latent class methods and were associated with chronic pain intensity and interference, reinforcing the framework\u2019s coherence.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>SPACE-Postpartum represents an early operationalization of a multidomain, symptom-based biopsychosocial framework for postpartum recovery after cesarean delivery. Developed from converging qualitative, observational, and literature evidence and tested at the proof-of-concept level, it provides a patient-centered foundation for future validation studies. Larger and more diverse cohorts are needed to establish predictive performance and clinical utility.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/rapm-2025-107149","type":"journal-article","created":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T07:30:26Z","timestamp":1761291026000},"page":"1197-1202","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":3,"title":["SPACE-Postpartum: a multidomain biopsychosocial framework for predicting chronic pain and impaired recovery after cesarean delivery \u2013 proof-of-concept report"],"prefix":"10.1136","volume":"51","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5906-1797","authenticated-orcid":false,"given":"Sarah","family":"Ciechanowicz","sequence":"first","affiliation":[{"name":"Division of Anaesthetics, Pain Management and Intensive Care, Imperial College London, London, UK"},{"name":"Department of Anaesthesia and Perioperative Medicine, University College London Hospital, London, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daqing","family":"Ma","sequence":"additional","affiliation":[{"name":"Division of Anaesthetics, Pain Management and Intensive Care, Imperial College London, London, UK"},{"name":"Children's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"239","published-online":{"date-parts":[[2025,10,23]]},"reference":[{"key":"2026100502151092000_51.10.1197.1","doi-asserted-by":"crossref","first-page":"111832","DOI":"10.1016\/j.jclinane.2025.111832","article-title":"Incidence, Severity, and Interference of Chronic Postsurgical Pain After Cesarean Delivery: A Systematic Review and Meta-analysis","volume":"104","author":"Ciechanowicz","year":"2025","journal-title":"J Clin Anesth"},{"key":"2026100502151092000_51.10.1197.2","doi-asserted-by":"crossref","first-page":"104331","DOI":"10.1016\/j.ijoa.2025.104331","article-title":"Chronic pain after cesarean delivery: what do we know today? A narrative review","volume":"62","author":"Langenaeken","year":"2025","journal-title":"Int J Obstet Anesth"},{"key":"2026100502151092000_51.10.1197.3","doi-asserted-by":"crossref","unstructured":"Liu F , Li T , Zhou D , et al . A machine learning-based framework for predicting postpartum chronic pain: a retrospective study. 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Development and Validation of the Stanford Obstetric Recovery Checklist (STORK): A Delphi Consensus and Multicenter Clinical Validation Study. 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