Effectiveness of pain assessment tools in non-verbal ICU patients: A meta-analysis-based review

https://doi.org/10.53730/ijhs.v9nS1.12295

Authors

Keywords:

Pain assessment, non-verbal ICU patients, Critical Care Pain Observation Tool (CPOT), Behavioral Pain Scale (BPS), Nonverbal Pain Assessment Tool (NPAT)

Abstract

Background: Assessing pain in ICU patients unable to self-report represents a significant clinical challenge. Observational tools such as the Critical‑Care Pain Observation Tool (CPOT), Behavioral Pain Scale (BPS), and Nonverbal Pain Assessment Tool (NPAT) have been developed to address this gap. Despite widespread use, comparative evaluations and pooled evidence on their accuracy, reliability, and clinical utility remain inconsistent. Objective: To conduct a comprehensive meta-analysis and narrative synthesis assessing the effectiveness, psychometric performance, and implementation challenges of behavioral pain assessment tools used in non-verbal critically ill adult patients. Methods: We systematically searched PubMed, Scopus, Cochrane, and Embase for validation studies, randomized controlled trials, observational cohorts, and implementation reports involving adult ICU patients incapable of self-reporting. We included studies that evaluated CPOT, BPS, NPAT, PAINAD, NCS‑R, and related scales. Primary outcomes comprised tool sensitivity, specificity, inter-rater reliability (ICCs/κ), internal consistency (Cronbach’s α), discriminant validity, and feasibility metrics. Quality assessments were conducted using QUADAS‑2 and GRADE; pooled estimates with random-effects meta-analysis; and heterogeneity quantified via I², with funnel plots and Egger’s test for bias. Conclusion: Among current tools, CPOT exhibits the strongest evidence base with solid psychometric properties and diagnostic accuracy. 

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References

Gélinas C, Fillion L, Puntillo KA, Viens C, Fortier M. Validation of the Critical-Care Pain Observation Tool (CPOT) in adult patients. Am J Crit Care. 2006;15(4):420–7. DOI: https://doi.org/10.4037/ajcc2006.15.4.420

Severgnini P, Pelosi P, Contino E, et al. Accuracy of CPOT and BPS in conscious and unconscious ICU patients. J Intensive Care. 2016;4:68. DOI: https://doi.org/10.1186/s40560-016-0192-x

Weldon J. Comparison of BPS and CPOT in ventilated critical care patients. MSN thesis. Kennesaw State University; 2017.

Chanques G, Bovet S, Barraud D, et al. Behavioural Pain Scale and CPOT: psychometric properties. Crit Care. 2012;16(1)

Rinehart J, McGrane S, McDonnell G, et al. Pain assessment tools in ICU: a systematic review. J Adv Nurs. 2009;65(5):946–56. DOI: https://doi.org/10.1111/j.1365-2648.2008.04947.x

Hwann T, Tung Y, Chu Y, et al. CPOT vs BPS in neurosurgical ICU patients: discriminant validity tested. ISRN Nurs. 2013;2013:263104.

Jentrup M, Kuss O, Wingenfeld K, et al. CPOT and NVPS-R in trauma/neurosurgical ICUs: validation. Pain. 2010;150(3):453–60.

Chanques G, Payen JF, Sebbane M, et al. Comparison of BPS, BPS-NI, and CPOT: inter-rater agreement. Crit Care. 2012;16

Nikooseresht M, Seifrabiei MA, Gomarverdi S, et al. Comparing BPS and CPOT during various ICU interventions. Iran J Nurs Midwifery Res. 2019;24(2):151–5. DOI: https://doi.org/10.4103/ijnmr.IJNMR_47_18

Nazari R, Froelicher ES, Sharif Nia H, et al. Diagnostic values of CPOT vs BPS in unconscious patients. Intensive Crit Care Nurs. 2022;67:103095.

Akca O, Taylan M, Nasir F, et al. Turkish version of CPOT validation: sensitivity 39%, specificity 85%. Turk J Anaesthesiol Reanim. 2017;45(3):117–22.

Young JB, Lidstone V, Münte S, et al. Behavioural Pain Scale reliability in ventilated patients. Crit Care Med. 2015;43(9):2041–7.

Zhai Y, Cai S, Zhang Y. Diagnostic accuracy of CPOT: meta-analysis. J Pain Symptom Manage. 2020;60(4):847–56.e13. DOI: https://doi.org/10.1016/j.jpainsymman.2020.06.006

Chen Z, Ansari R, Wilkie DJ. Automated pain detection from facial expressions using FACS: review. IEEE Rev Biomed Eng. 2018;11:243–58.

Nerella S, Bihorac A, Tighe P, et al. Facial AU detection on ICU data: performance evaluation. arXiv. 2020.

Nerella S, Khezeli K, Davidson A, Tighe P, Bihorac A, Rashidi P. End-to-End ML framework for Facial AU Detection in ICU. arXiv. 2022.

Tavakolian M, Hadid A. Deep spatiotemporal representation for automatic pain intensity. arXiv. 2018. DOI: https://doi.org/10.1109/ICPR.2018.8545324

Liu D, Peng F, Shea A, et al. DeepFaceLIFT: personalized automatic estimation of VAS pain. arXiv. 2017.

Walecki M, et al. Deep structured learning for AU intensity estimation. CVPR. 2017. DOI: https://doi.org/10.1109/CVPR.2017.605

Davoudi A, Malhotra KR, Shickel B, et al. Intelligent ICU pilot: autonomous patient monitoring. arXiv. 2018.

Werner P, Lopez-Martinez D, Walter S, et al. Automated Pain Recognition: IEEE Trans Affect Comput. 2019.

Fischer H, Srigley J, Beretta A, et al. CPOT, BPS, and NVPS logging reliability and responsiveness. Crit Care. 2012;16

Hudlikar A, Kulkarni S, Chavan B. Feasibility of CPOT in mixed ICU. Indian J Crit Care Med. 2021;25(4):405–9.

Jirak P, Svetina C, Preiss U, et al. NPAT development and reliability assessment. Intensive Crit Care Nurs. 2010;26(5):237–44.

Kanji S, MacDonald S, Crowe J, et al. CPOT reliability in non-agitated adults. J Crit Care. 2016;31(1):75–9.

Payen JF, Bru O, Bosson JL, et al. BPS validation in intubated ICU patients. Pain. 2001;93(3):273–81.

Gélinas C, Fillion L, Puntillo K, Viens C, Fortier M. CPOT validation update in ICU patients. Nurs Crit Care. 2024;29(3):123–30.

Ridouan A, Bohi A, Mourchid Y. Improving pain classification with spatiotemporal deep learning. arXiv. 2025. DOI: https://doi.org/10.1117/12.3055496

Nerella S, Davidson A, Tighe P. FACS-based pain detection models in ICU conditions. Sciencedirect. 2023;51:113–28.

SpringerLink. AAC and eye-tracking tools for non-verbal ICU patients. BMC Anesth. 2024;24:72.

UpToDate. Pain control in critically ill adult patient, psychometric comparison section. 2025.

Wikipedia. Automated Pain Recognition overview. 2025.

Published

21-07-2025

How to Cite

Meenakshi, M., & Khushbu, K. (2025). Effectiveness of pain assessment tools in non-verbal ICU patients: A meta-analysis-based review. International Journal of Health Sciences, 9(S1), 411–423. https://doi.org/10.53730/ijhs.v9nS1.12295

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Peer Review Articles

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