下一代测序技术在关节假体周围感染诊断中的价值与争议
The Value and Controversy of Next-Generation Sequencing Technology in the Diagnosis of Periprosthetic Joint Infection
摘要: 假体周围感染(periprosthetic joint infection, PJI)是关节置换术后的灾难性并发症,也是当前骨科医生必须面对的重大挑战。对PJI的准确诊断是克服PJI问题的前提条件,但目前由于缺乏明确的诊断金标准,其诊断仍面临挑战。近年来,有关PJI的诊断方法涌现出大量的科学研究,特别是下一代测序技术(next-generation sequencing, NGS),因其快速、无偏向性地获取病原基因组信息的能力,已成为提高PJI诊断水平的重要工具,并在临床中得到广泛应用。但随着诊疗成功率的提高,如何采用更加精准且经济的检测方式进行诊断依旧值得我们深思。
Abstract: Periprosthetic joint infection (PJI) is a devastating complication following joint arthroplasty and remains a major challenge that orthopedic surgeons must confront. Accurate diagnosis of PJI is a prerequisite for addressing this issue; however, the absence of a definitive gold standard continues to pose diagnostic challenges. In recent years, a substantial body of scientific research has emerged on diagnostic methods for PJI, particularly next-generation sequencing (NGS). Due to its ability to rapidly and unbiasedly capture pathogen genomic information, NGS has become an important tool for improving the diagnostic accuracy of PJI and is now widely used in clinical practice. Nevertheless, despite improvements in diagnostic and treatment success rates, the pursuit of more precise and cost-effective diagnostic approaches remains a critical consideration.
文章引用:康润兴, 黄伟. 下一代测序技术在关节假体周围感染诊断中的价值与争议[J]. 临床医学进展, 2025, 15(11): 302-307. https://doi.org/10.12677/acm.2025.15113098

1. 引言

全关节置换术(Total Joint Arthroplasty, TJA)是治疗终末期骨关节病的有效手段。目前,这一手术技术已经非常成熟,成功率高,术后恢复期短,疼痛和关节畸形问题可以迅速得到解决。近些年来,全关节置换术的数量不断增长,预计到2030年,初次全髋关节置换术(Total Hip Arthroplasty, THA)和初次全膝关节置换术(Total Knee Arthroplasty, TKA)的数量将分别达到63.5万例和126万例[1]。假体周围感染(Periprosthetic Joint Infection, PJI)是关节置换术后严重的并发症,其发生率约为1.4%~2.5% [2]。PJI分别是全膝关节置换术后和全髋关节置换术后失败的第一和第三的原因[3] [4],给临床带来巨大负担。区分PJI和无菌性松动至关重要,因为PJI的治疗方案规定了特定的手术策略[5]。尽管培养是微生物鉴定的金标准,但PJI的培养阳性率较低[6] [7],且目前并无诊断PJI的统一金标准[8]。Klement等根据修正MSIS (Musculoskeletal Infection Society)主要标准或次要标准诊断PJI发现次要诊断标准培养阴性PJI的发生率可达到47% [9],故诊断PJI仍然存在挑战。

在过去的十几年间,随着国际共识会议和学术组织的不断努力,PJI的诊断标准经历了显著的演化和进步[10]-[14]。一些炎性标志物如血清ESR、CRP、D-二聚体;滑液白细胞计数(WBC)、中性粒细胞百分比(PMN%);白细胞酯酶(LE)和α-防御素已经被纳入诊断标准。但血清学标志物更多反映全身感染状态,难以反映局部感染状态,临床应用有限。传统的病因学检测仍然重要,但传统培养方法培养周期长,依赖于微生物的可培养性,且术前抗生素应用会影响培养结果。这催生了不依赖培养的分子诊断技术,其中就包括下一代测序技术(Next-Generation Sequencing, NGS)。

下一代测序技术也称高通量测序,是一种能够同时并行测序大量DNA或RNA序列的技术。在Sanger测序的基础上,下一代测序技术已经衍生出了第二代、第三代测序技术,未来也将不断发展[15]。下一代测序技术(NGS)不依赖于培养,可快速、无偏向性地从样本中获得全部病原的基因组信息,这一点在感染性疾病的诊断中尤为重要。例如,在北京大学国际医院感染疾病科举办的研讨会上,专家们分享了NGS技术在提高感染性疾病诊断效率和精准性方面的显著作用,这表明NGS技术已经逐渐成为提高包括假体关节感染(PJI)在内的多种疾病诊断水平的有力工具。本文旨在通过系统回顾现有文献,全面评估NGS技术在PJI诊断中的准确性、临床效用,存在的争议问题,并展望NGS在PJI诊断中的未来发展方向。

2. NGS的技术优势与诊断价值

目前培养阴性PJI的发生率仍非常高,可达7%~50% [16]。在一项研究中,研究者利用常规培养方法和基于NGS技术优化后的培养条件对术中采集的滑膜液、超声液和假体周围组织进行培养。通过比较分析两种方法产生的结果,旨在评估NGS技术在提高病原微生物检测效率和准确性方面的潜力。结果表明,与常规培养(60%、80.95%和67.86%)相比,优化条件后培养的敏感性、特异性和准确性(94.29%、76.19%和87.5%)显著提高[17]。因此,NGS可作为改进传统培养方法的手段,提升病原体的检出率。与PCR相比,PCR通过体外扩增特定DNA片段,利用DNA聚合酶在特定条件下对目标DNA进行多轮复制,从而实现对特定基因的高效扩增,且PCR技术需要特定引物。而NGS能够全面检测已知和未知的突变,提供详细的基因序列信息,其检测下限低,能发现极微量的微生物核酸。

微生物培养往往需要数天至数周,特定微生物甚至更久。2018年骨骼肌肉系统感染国际共识推荐培养应保持5~7天。怀疑低毒力微生物导致的PJI,或术前培养阴性,临床高度怀疑的PJI,培养应保持14~21天[18]。NGS检测平均所需时间为1.3天(从采样到报告) [19],这为快速诊断PJI提供了可能。NGS技术不仅能够从病原学角度为临床早期诊断PJI提供依据,而且在诊断培养阴性PJI方面具有显著优势,成为传统培养方法的重要补充。此外,NGS是一种高通量测序技术,可以同时对数百万条DNA分子进行测序,直接获得核酸序列信息,能够快速生成大量的序列数据。考虑到对整个基因组的检测速度更快,NGS是比PCR测序更好的替代技术[20]。因此,通过NGS可以早期诊断PJI,对PJI的针对性治疗具有重要意义。

对于培养阳性PJI,培养法最常检测到革兰氏阳性菌,其次是革兰氏阴性菌和真菌。在此基础上,NGS还可以检测出一些非常见病原体[19],如厌氧菌、结核分枝杆菌、支原体等。当培养阴性时,NGS可能为阳性。且传统培养易漏报混合感染,当培养提示单菌感染时,NGS可能提示多菌感染。NGS也可以检测出因既往使用抗生素而培养阴性的PJI。根据多篇文献报道,NGS技术在感染性疾病诊断中的应用显示了显著的优势,尤其是在病原微生物鉴定方面。例如,有研究指出,对于既往有抗生素使用史的PJI患者,NGS技术的检出率可达到74.05%至92.31%之间[21]。同时,一项多中心队列研究表明约三分之二的培养阴性PJI存在可识别的机会性致病生物,并且大多数感染为多重感染[22]。这显示了NGS在检出病原体方面的优势。

病原体的耐药性通常通过微生物培养结合肉汤微稀释或纸片扩散法进行药敏试验,这些被视为标准参考方法[23],但本质上它们都依赖于细菌培养。因此,基于PCR的方法越来越多地被用于靶向已知的抗菌耐药性标记基因[24]。NGS能够克服当前药敏试验和PCR技术的局限性[25],进行更深入的菌株分型及抗菌药物耐药性监测[26],为早期用药提供可靠依据。

3. NGS面临的争议与挑战

尽管大多数文献指出,NGS技术在诊断PJI时展现出比传统培养方法更高的敏感性、特异性和准确性,但也有研究指出,在某些情况下,下一代测序技术在PJI的诊断上并未显示出比传统培养方法更优的性能[27]。当然这是一个小样本研究,其可靠性仍待商榷。培养阳性的PJI都需要标本中有活菌存在,而NGS检测的是核酸,而非活菌。所以需要重视的一个问题是NGS技术的高灵敏度,有可能导致较高的假阳性结果,引起误诊[20]。除此之外,也有文章显示NGS诊断PJI的特异性低于培养[28]。对于定植菌来讲,如何确定阈值提高准确性也尚不明确。测序的DNA绝大多数是人类,尽管在实验室制备过程中努力减少污染,但仍不能排除。在实验室试剂中检测到正常人体菌群或污染生物可能导致生物体误识别,影响结果判定[29]。因此,如何利用NGS技术准确区分真正的感染,仍是当前需要攻克的难题。

NGS技术在不断进步,目前全球尚未形成统一标准[29]。从样本采集、核酸提取、引物选择、测序平台到生物信息学分析流程(去噪、嵌合体去除、数据库比对)再到结果解读,每个环节都面临着技术更新和标准化的挑战。检测的每一步都可能会影响最后结果的判读,实验室环境中的样品、试剂和微生物的检测都会影响结果的准确性[30],不同公司可能有不同的数据库,这使得不同研究之间的结果难以比较。同时,阳性报告的形成过程主观性较强,需要具备深厚生物信息学分析专业知识的人员进行解读。

对于临床应用来讲,成本效益也相对重要。正常NGS的费用显著高于传统培养方案,但从整个诊疗过程来讲,是否应用NGS于临床仍需讨论。NGS常规应用于所有疑似PJI患者,还是仅作为培养阴性、疑难病例的补充手段更具性价比?

Michael等人通过建立模型对NGS和培养诊断假体周围感染的成本效益进行分析比较。研究发现,NGS技术在PJI诊断中的成本效益受到PJI预测概率和NGS技术性能特征(如敏感性和特异性)的影响[31]。不同样本处理方法的效能比较也显示了在检测病原体时的敏感性差异,这可能影响到诊断方法的成本效益。因此,有必要进一步开展研究,以明确哪些人群能够从NGS技术中获益。同时,耐药基因预测并不能完全等同于药敏试验。单纯依靠NGS技术检测致病菌,无法同步开展药物敏感性试验。NGS技术虽能识别细菌,却无法判定所检测到的细菌是否为耐药菌。药物敏感性分析需基于阳性细菌培养结果展开,如此方能更有效地指导临床用药[32]。或许,在细菌培养的基础上,有针对性地应用NGS技术作为PJI的诊断手段,不失为一种更优的选择。

4. 总结与展望

NGS是一项革命性技术,可能颠覆PJI的诊断模式,在提升PJI诊断率方面具有巨大潜力。然而,需指出的是,受标准化流程不统一、假阳性率偏高、成本高昂等多种因素影响,NGS技术的常规临床应用面临诸多障碍。未来,有必要开发针对PJI的标准化、自动化NGS检测盒,同时优化宿主DNA去除技术,并推动定量NGS技术的发展。当前,大数据模型备受关注,通过构建大型数据库,并借助人工智能(AI)辅助结果解读,可确立可靠的阳性判断阈值。从临床实践角度出发,需设计大规模、前瞻性、多中心的临床研究,以评估患者预后,从而将NGS技术转化为具有临床实用价值的检测方法。NGS或将成为疑难PJI诊断的金标准,并最终实现感染病原体的快速、精准诊断,指导个体化治疗。

NOTES

*通讯作者。

参考文献

[1] Sloan, M., Premkumar, A. and Sheth, N.P. (2018) Projected Volume of Primary Total Joint Arthroplasty in the U.S., 2014 to 2030. Journal of Bone and Joint Surgery, 100, 1455-1460. [Google Scholar] [CrossRef] [PubMed]
[2] Zardi, E.M. and Franceschi, F. (2020) Prosthetic Joint Infection. a Relevant Public Health Issue. Journal of Infection and Public Health, 13, 1888-1891. [Google Scholar] [CrossRef] [PubMed]
[3] Koh, C.K., Zeng, I., Ravi, S., Zhu, M., Vince, K.G. and Young, S.W. (2017) Periprosthetic Joint Infection Is the Main Cause of Failure for Modern Knee Arthroplasty: An Analysis of 11,134 Knees. Clinical Orthopaedics & Related Research, 475, 2194-2201. [Google Scholar] [CrossRef] [PubMed]
[4] Kamath, A.F., Ong, K.L., Lau, E., Chan, V., Vail, T.P., Rubash, H.E., et al. (2015) Quantifying the Burden of Revision Total Joint Arthroplasty for Periprosthetic Infection. The Journal of Arthroplasty, 30, 1492-1497. [Google Scholar] [CrossRef] [PubMed]
[5] Shahi, A. and Parvizi, J. (2017) The Role of Biomarkers in the Diagnosis of Periprosthetic Joint Infection. EFORT Open Reviews, 1, 275-278. [Google Scholar] [CrossRef] [PubMed]
[6] Peng, H., Zhou, Z., Wang, F., Yan, S., Xu, P., Shang, X., et al. (2021) Microbiology of Periprosthetic Hip and Knee Infections in Surgically Revised Cases from 34 Centers in Mainland China. Infection and Drug Resistance, 14, 2411-2418. [Google Scholar] [CrossRef] [PubMed]
[7] Li, F., Qiao, Y., Zhang, H., Cao, G. and Zhou, S. (2023) Comparable Clinical Outcomes of Culture-Negative and Culture-Positive Periprosthetic Joint Infections: A Systematic Review and Meta-Analysis. Journal of Orthopaedic Surgery and Research, 18, Article No. 210. [Google Scholar] [CrossRef] [PubMed]
[8] Gollwitzer, H., Dombrowski, Y., Prodinger, P.M., Peric, M., Summer, B., Hapfelmeier, A., et al. (2013) Antimicrobial Peptides and Proinflammatory Cytokines in Periprosthetic Joint Infection. Journal of Bone and Joint Surgery, 95, 644-651. [Google Scholar] [CrossRef] [PubMed]
[9] Klement, M.R., Siddiqi, A., Rock, J.M., Seyler, T.M., Parvizi, J. and Chen, A.F. (2018) Are All Periprosthetic Joint Infections the Same? Evaluating Major vs Minor Criteria. The Journal of Arthroplasty, 33, 1515-1519. [Google Scholar] [CrossRef] [PubMed]
[10] Workgroup Convened by the Musculoskeletal Infection Society (2011) New Definition for Periprosthetic Joint Infection. The Journal of Arthroplasty, 26, 1136-1138.
[11] Osmon, D.R., Berbari, E.F., Berendt, A.R., Lew, D., Zimmerli, W., Steckelberg, J.M., et al. (2013) Diagnosis and Management of Prosthetic Joint Infection: Clinical Practice Guidelines by the Infectious Diseases Society of America. Clinical Infectious Diseases, 56, e1-e25. [Google Scholar] [CrossRef] [PubMed]
[12] Parvizi, J. and Gehrke, T. (2014) Definition of Periprosthetic Joint Infection. The Journal of Arthroplasty, 29, Article 1331. [Google Scholar] [CrossRef] [PubMed]
[13] Parvizi, J., Tan, T.L., Goswami, K., Higuera, C., Della Valle, C., Chen, A.F., et al. (2018) The 2018 Definition of Periprosthetic Hip and Knee Infection: An Evidence-Based and Validated Criteria. The Journal of Arthroplasty, 33, 1309-1314.e2. [Google Scholar] [CrossRef] [PubMed]
[14] McNally, M., Sousa, R., Wouthuyzen-Bakker, M., Chen, A.F., Soriano, A., Vogely, H.C., et al. (2021) The EBJIS Definition of Periprosthetic Joint Infection. The Bone & Joint Journal, 103, 18-25. [Google Scholar] [CrossRef] [PubMed]
[15] Hu, T., Chitnis, N., Monos, D. and Dinh, A. (2021) Next-Generation Sequencing Technologies: An Overview. Human Immunology, 82, 801-811. [Google Scholar] [CrossRef] [PubMed]
[16] Goswami, K. and Parvizi, J. (2020) Culture-Negative Periprosthetic Joint Infection: Is There a Diagnostic Role for Next-Generation Sequencing? Expert Review of Molecular Diagnostics, 20, 269-272. [Google Scholar] [CrossRef] [PubMed]
[17] Gamie, Z., Karthikappallil, D., Gamie, E., Stamiris, S., Kenanidis, E. and Tsiridis, E. (2022) Molecular Sequencing Technologies in the Diagnosis and Management of Prosthetic Joint Infections. Expert Review of Molecular Diagnostics, 22, 603-624. [Google Scholar] [CrossRef] [PubMed]
[18] Garrigues, G.E., Zmistowski, B., Cooper, A.M. and Green, A. (2019) Proceedings from the 2018 International Consensus Meeting on Orthopedic Infections: Rationale and Methods of the Shoulder Subgroup. Journal of Shoulder and Elbow Surgery, 28, S4-S7. [Google Scholar] [CrossRef] [PubMed]
[19] Hao, L., Wen, P., Song, W., Zhang, B., Wu, Y., Zhang, Y., et al. (2023) Direct Detection and Identification of Periprosthetic Joint Infection Pathogens by Metagenomic Next-Generation Sequencing. Scientific Reports, 13, Article No. 7897. [Google Scholar] [CrossRef] [PubMed]
[20] Haddad, F.S. (2018) Next Generation Sequencing: Is This the Moment? The Bone & Joint Journal, 100, 125-126. [Google Scholar] [CrossRef] [PubMed]
[21] Tang, Y., Zhao, D., Wang, S., Yi, Q., Xia, Y. and Geng, B. (2022) Diagnostic Value of Next-Generation Sequencing in Periprosthetic Joint Infection: A Systematic Review. Orthopaedic Surgery, 14, 190-198. [Google Scholar] [CrossRef] [PubMed]
[22] Goswami, K., Clarkson, S., Phillips, C.D., Dennis, D.A., Klatt, B.A., O’Malley, M.J., et al. (2022) An Enhanced Understanding of Culture-Negative Periprosthetic Joint Infection with Next-Generation Sequencing. Journal of Bone and Joint Surgery, 104, 1523-1529. [Google Scholar] [CrossRef] [PubMed]
[23] Jorgensen, J.H. and Ferraro, M.J. (2009) Antimicrobial Susceptibility Testing: A Review of General Principles and Contemporary Practices. Clinical Infectious Diseases, 49, 1749-1755. [Google Scholar] [CrossRef] [PubMed]
[24] Corona, P.S., Goswami, K., Kobayashi, N., Li, W., Llinás, A., Marín-Peña, Ó., et al. (2019) General Assembly, Diagnosis, Pathogen Isolation: Proceedings of International Consensus on Orthopedic Infections. The Journal of Arthroplasty, 34, S207-S214. [Google Scholar] [CrossRef] [PubMed]
[25] Motro, Y. and Moran-Gilad, J. (2017) Next-Generation Sequencing Applications in Clinical Bacteriology. Biomolecular Detection and Quantification, 14, 1-6. [Google Scholar] [CrossRef] [PubMed]
[26] Lüftinger, L., Ferreira, I., Frank, B.J.H., Beisken, S., Weinberger, J., von Haeseler, A., et al. (2021) Predictive Antibiotic Susceptibility Testing by Next-Generation Sequencing for Periprosthetic Joint Infections: Potential and Limitations. Biomedicines, 9, Article 910. [Google Scholar] [CrossRef] [PubMed]
[27] Kildow, B.J., Ryan, S.P., Danilkowicz, R., Lazarides, A.L., Penrose, C., Bolognesi, M.P., et al. (2021) Next-Generation Sequencing Not Superior to Culture in Periprosthetic Joint Infection Diagnosis. The Bone & Joint Journal, 103, 26-31. [Google Scholar] [CrossRef] [PubMed]
[28] Tarabichi, M., Shohat, N., Goswami, K., Alvand, A., Silibovsky, R., Belden, K., et al. (2018) Diagnosis of Periprosthetic Joint Infection: The Potential of Next-Generation Sequencing. Journal of Bone and Joint Surgery, 100, 147-154. [Google Scholar] [CrossRef] [PubMed]
[29] Indelli, P.F., Ghirardelli, S., Violante, B. and Amanatullah, D.F. (2021) Next Generation Sequencing for Pathogen Detection in Periprosthetic Joint Infections. EFORT Open Reviews, 6, 236-244. [Google Scholar] [CrossRef] [PubMed]
[30] Bukowska-Ośko, I., Perlejewski, K., Nakamura, S., Motooka, D., Stokowy, T., Kosińska, J., et al. (2016) Sensitivity of Next-Generation Sequencing Metagenomic Analysis for Detection of RNA and DNA Viruses in Cerebrospinal Fluid: The Confounding Effect of Background Contamination. In: Pokorski, M., Ed., Advances in Experimental Medicine and Biology, Springer US, 53-62. [Google Scholar] [CrossRef
[31] Torchia, M.T., Austin, D.C., Kunkel, S.T., Dwyer, K.W. and Moschetti, W.E. (2019) Next-Generation Sequencing vs Culture-Based Methods for Diagnosing Periprosthetic Joint Infection after Total Knee Arthroplasty: A Cost-Effectiveness Analysis. The Journal of Arthroplasty, 34, 1333-1341. [Google Scholar] [CrossRef] [PubMed]
[32] Chang, Y., Jiang, K., Zhang, L., Yang, F. and Huang, J. (2023) Application of Next-Generation Sequencing Technology in the Detection of Pathogenic Bacteria of the Periprosthetic Joint Infection after Arthroplasty. International Wound Journal, 20, 2121-2128. [Google Scholar] [CrossRef] [PubMed]