ArticleNPJ digital medicine2023
Development and prospective validation of postoperative pain prediction from preoperative EHR data using attention-based set embeddings.
Article in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 20 citations in OpenAlex.
- Article
- Transforming perioperative care: The current landscape and future trajectory of artificial intelligence in anesthesia-A narrative review.The Journal of international medical research · 2026Review
- Dynamic prediction model for early postoperative pain based on generalized estimating equations: a multi-timepoint longitudinal study.American journal of translational research · 2026Article
- Comparison of the postoperative opioid-sparing effect between preemptive and preventive intravenous acetaminophen/ibuprofen fixed-dose combination in patients undergoing robot-assisted laparoscopic radical prostatectomy: a double-blind randomized controlled trial.The Korean journal of pain · 2026Article
- Association of peripheral nerve blocks with increased postoperative pain and opioid use in orthopaedic surgery: a single-centre retrospective cohort study.British journal of anaesthesia · 2025Article
- Predicting acute postsurgical pain in the postanesthesia care unit: risk tool development and internal validation.Pain reports · 2025Article
- Machine learning model for differentiating xanthogranulomatous cholecystitis and gallbladder cancer in multicenter largescale study.NPJ digital medicine · 2025Article
- Influence of Pre-Existing Pain on the Body's Response to External Pain Stimuli: Experimental Study.JMIR biomedical engineering · 2025Article
- Adequacy of Anesthesia Guidance for Combined General/Epidural Anesthesia in Patients Undergoing Open Abdominal Infrarenal Aortic Aneurysm Repair; Preliminary Report on Hemodynamic Stability and Pain Perception.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Predicting Postoperative Pain and Opioid Use with Machine Learning Applied to Longitudinal Electronic Health Record and Wearable Data.Applied clinical informatics · 2024Article
- Development and multicentre validation of the FLEX score: personalised preoperative surgical risk prediction using attention-based ICD-10 and Current Procedural Terminology set embeddings.British journal of anaesthesia · 2024Article
- The Adequacy of Anesthesia Guidance for Vitreoretinal Surgeries with Preemptive Paracetamol/Metamizole.Pharmaceuticals (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors at 2 institutions in 1 country.
Funding
Abstract
Preoperative knowledge of expected postoperative pain can help guide perioperative pain management and focus interventions on patients with the greatest risk of acute pain. However, current methods for predicting postoperative pain require patient and clinician input or laborious manual chart review and often do not achieve sufficient performance. We use routinely collected electronic health record data from a multicenter dataset of 234,274 adult non-cardiac surgical patients to develop a machine learning method which predicts maximum pain scores on the day of surgery and four subsequent days and validate this method in a prospective cohort. Our method, POPS, is fully automated and relies only on data available prior to surgery, allowing application in all patients scheduled for or considering surgery. Here we report that POPS achieves state-of-the-art performance and outperforms clinician predictions on all postoperative days when predicting maximum pain on the 0-10 NRS in prospective validation, though with degraded calibration. POPS is interpretable, identifying comorbidities that significantly contribute to postoperative pain based on patient-specific context, which can assist clinicians in mitigating cases of acute pain.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.