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自动疼痛评估在常规临床应用中的可行性与应用专家共识

Expert consensus on feasibility and application of automatic pain assessment in routine clinical use.

作者信息

Cascella Marco, Ponsiglione Alfonso Maria, Santoriello Vittorio, Romano Maria, Cerrone Valentina, Esposito Dalila, Montedoro Mario, Pellecchia Roberta, Savoia Gennaro, Lo Bianco Giuliano, Innamorato Massimo, Natoli Silvia, Montomoli Jonathan, Semeraro Federico, Bignami Elena Giovanna, Bellini Valentina, Leoni Matteo Luigi Giuseppe, Occhigrossi Felice, Vittori Alessandro, Pace Maria Caterina, Buonanno Pasquale, Forte Mauro, Chinè Elisabetta, Carpenedo Roberta, De Cassai Alessandro, Papa Alfonso, Marchesini Maurizio, Terranova Gaetano, Micheli Fabrizio, Demartini Laura, Marinangeli Franco, Raffaeli William, Coluzzi Flaminia, Tinnirello Andrea, Arcioni Roberto, Marra Angelo, Shariff Mohammed Naveed, Monaco Federica, Finco Gabriele, Bramanti Alessia, Piazza Ornella

机构信息

Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", Anesthesia and Pain Medicine, University of Salerno, Baronissi, Italy.

Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy.

出版信息

J Anesth Analg Crit Care. 2025 Jun 2;5(1):29. doi: 10.1186/s44158-025-00249-8.

Abstract

BACKGROUND

Pain is often difficult to assess, particularly in non-communicative patients. While artificial intelligence (AI)-based objective Automatic Pain Assessment (APA) systems are a promising solution, their clinical implementation raises essential questions, primarily regarding clinician acceptance.

METHODS

We conducted a survey-to-consensus investigation on the feasibility and application of APA for clinical use. Firstly, the steering committee implemented the CHERRIES guidelines and designed a questionnaire for healthcare professionals. Given the survey results, 26 experts in pain medicine were asked to participate in a two-round consensus by rating 10 statements through a 7-point Likert scale. Consensus was defined as ≥ 75% agreement ("agree" or "completely agree"). For both phases, data was collected through online questionnaires and analyzed quantitatively.

RESULTS

For the survey, we collected responses from 628 healthcare professionals. The output highlighted excellent acceptance of the technology and a preference for multidimensional techniques. After two rounds, consensus was achieved on 8 out of 10 statements. Experts agreed on APA utility in supporting healthcare professionals and real-time pain monitoring. A strong consensus (96.2%) supported the need to inform patients about the use and limitations of AI systems. Adequate staff training is mandatory. Moreover, 92.3% agreed on the importance of implementing risk management, data quality control, and AI governance throughout the APA lifecycle. The experts stressed the need for internal and external validation processes and periodic updates, even for research purposes. Consensus was also reached about the importance of involving interdisciplinary stakeholders and addressing regulatory, ethical, and social implications. Multimodal inputs (e.g., physiological signals, facial expressions, speech, and clinical data) in APA systems are recommended. Additionally, APA systems should be capable of grading pain levels (e.g., via NRS), not just detecting the presence of pain. On the other hand, two statements did not reach consensus: the applicability of APA systems for acute and chronic pain conditions and their potential to improve therapeutic strategies.

CONCLUSION

APA is viewed as a promising and potentially feasible technology for clinical pain assessment, particularly in vulnerable populations. Further research is needed to validate the dedicated tools, define applications in different clinical conditions (e.g., acute and chronic pain), and demonstrate their impact on routine clinical practice for pain management.

摘要

背景

疼痛往往难以评估,尤其是对于无法进行沟通的患者。虽然基于人工智能(AI)的客观自动疼痛评估(APA)系统是一个很有前景的解决方案,但其临床应用引发了一些关键问题,主要涉及临床医生的接受度。

方法

我们针对APA在临床应用的可行性和应用情况进行了一项从调查到达成共识的研究。首先,指导委员会实施了CHERRIES指南,并为医疗保健专业人员设计了一份问卷。根据调查结果,邀请了26位疼痛医学专家通过7点李克特量表对10条陈述进行评分,参与两轮共识达成过程。共识定义为≥75%的同意率(“同意”或“完全同意”)。对于两个阶段,数据均通过在线问卷收集并进行定量分析。

结果

在调查中,我们收集了628名医疗保健专业人员的回复。结果显示对该技术的接受度很高,且倾向于多维技术。经过两轮讨论,10条陈述中有8条达成了共识。专家们认同APA在支持医疗保健专业人员和实时疼痛监测方面的效用。强烈共识(96.2%)支持有必要告知患者关于AI系统的使用和局限性。必须进行充分的人员培训。此外,92.3%的人认同在APA的整个生命周期中实施风险管理、数据质量控制和AI治理的重要性。专家们强调,即使是出于研究目的,也需要进行内部和外部验证过程以及定期更新。对于跨学科利益相关者的参与以及应对监管、伦理和社会影响的重要性也达成了共识。建议在APA系统中采用多模态输入(例如生理信号、面部表情、语音和临床数据)。此外,APA系统应能够对疼痛程度进行分级(例如通过数字评分量表),而不仅仅是检测疼痛的存在。另一方面,有两条陈述未达成共识:APA系统在急性和慢性疼痛状况中的适用性以及其改善治疗策略的潜力。

结论

APA被视为一种有前景且可能可行的临床疼痛评估技术,尤其在弱势群体中。需要进一步研究以验证专用工具,确定在不同临床状况(例如急性和慢性疼痛)中的应用,并证明其对疼痛管理常规临床实践的影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2874/12131339/049377aa3fa4/44158_2025_249_Fig1_HTML.jpg

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