Reliability, validity, and responsiveness of multidimensional pain assessment tools used in postoperative adult patients: a systematic review of measurement properties
Bibliographic record
Abstract
OBJECTIVE: The objective of this systematic review was to synthesize the best available evidence relating to the measurement properties of the multidimensional pain assessment tools used to assess postoperative pain in adults. INTRODUCTION: Pain is a common and poorly managed occurrence in patients during the postoperative period. Currently, postoperative pain is usually evaluated with assessment tools that measure one dimension of pain, namely pain intensity, resulting in inadequate management of postoperative pain. It is important to understand the complex nature of pain by considering all dimensions for optimal postoperative pain management. Systematic, robust evidence is lacking regarding the most psychometrically reliable and valid multidimensional pain assessment tool for adult postoperative patients. INCLUSION CRITERIA: This systematic review considered all study types for inclusion. Studies were considered if they assessed the measurement properties of a multidimensional pain assessment tool in adult postoperative patients within two weeks post-surgery. The outcomes included measurement of at least one of the psychometric properties, including reliability, validity, and responsiveness. METHODS: A three-step search strategy was undertaken, including a search of the MEDLINE, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), and CINAHL databases performed in October 2019. We also searched Dissertation Abstracts International, ProQuest Dissertations and Theses, MedNar, and ClinicalTrials.gov to identify unpublished studies. The title and abstracts of the studies were reviewed by two independent reviewers against the inclusion/exclusion criteria. The methodological quality of the potential studies was assessed independently by three reviewers using the COSMIN checklist. RESULTS: Seventeen studies involving five multidimensional postoperative pain assessment tools were included in the review: American Pain Society Pain Outcomes Questionnaire-Revised; Brief Pain Inventory; Houston Pain Outcome Instrument; McGill Pain Questionnaire; and the Quality Improvement in Postoperative Pain Management Postoperative Pain Questionnaire. The two most commonly used tools were the Brief Pain Inventory and the American Pain Society Pain Outcomes Questionnaire-Revised, which were assessed in six studies each. The included studies mainly reported internal consistency reliability, with four of the five identified tools demonstrating high Cronbach's alpha values ranging from 0.72 to 0.92. However, the Houston Pain Outcome Instrument demonstrated mixed findings, with eight of the nine subscales having moderate to high reliability while the expectations about pain subscale had poor reliability (α=0.003). CONCLUSIONS: This review provides much needed information about the current tools used in many clinical, educational, and research settings. Of the five tools included in this review, the Brief Pain Inventory demonstrated strong evidence of psychometric validity and is recommended for use in assessing postoperative pain. Further psychometric validation of multidimensional postoperative pain assessment tools with emphasis on responsiveness and measurement error is required in order to accurately assess the minimal clinically important difference in postoperative pain outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.049 | 0.230 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.014 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".