全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

脓毒症预后评估的研究进展
Research Progress in the Prognostic Assessment of Sepsis

DOI: 10.12677/acm.2024.1461737, PP. 1-7

Keywords: 脓毒症,预后评估,预测模型,机器学习
Sepsis
, Prognostic Assessment, Predictive Modeling, Machine Learning

Full-Text   Cite this paper   Add to My Lib

Abstract:

脓毒症是威胁人类健康的急危重症之一,其发病后的死亡率均处于较高水平,早期发现、尽早评估、及时治疗脓毒症可以有效降低这一比例。因此,对脓毒症的早期评估已成为国际共识。目前,有很多临床手段及科学研究在脓毒症的预后评估方面进行了探索,并取得了相应的进展。该文常见的生物标志物、复合临床指标、传统临床评分、基于机器学习构建的临床预测模型等四个方面对脓毒症预后评估进行系统阐述,以期为临床医务工作者提供参考。
Sepsis is one of the acute and critical illnesses that threaten human health, and the mortality rate after its onset is at a high level. Early detection, early evaluation, and timely treatment of sepsis can effectively reduce this rate. Therefore, early assessment of sepsis has become an international consensus. Currently, there are many clinical tools and scientific studies exploring the prognostic assessment of sepsis and making progress accordingly. In this article, the common biomarkers, composite clinical indicators, traditional clinical scores, and clinical prediction models constructed based on machine learning are systematically described for the prognostic assessment of sepsis, in order to provide references for clinical medical workers.

References

[1]  Bone, R.C., Balk, R.A., Cerra, F.B., et al. (1992) Definitions for Sepsis and Organ Failure and Guidelines for the Use of Innovative Therapies in Sepsis. The ACCP/SCCM Consensus Conference Committee. American College of Chest Physicians/Society of Critical Care Medicine. Chest, 101, 1644-1655.
https://doi.org/10.1378/chest.101.6.1644
[2]  Rhodes, A., Evans, L.E., Alhazzani, W., et al. (2017) Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock: 2016. Intensive Care Medicine, 43, 304-377.
https://doi.org/10.1007/s00134-017-4683-6
[3]  Rudd, K.E., Johnson, S.C., Agesa, K.M., et al. (2020) Global, Regional, and National Sepsis Incidence and Mortality, 1990-2017: Analysis for the Global Burden of Disease Study. The Lancet, 395, 200-211.
https://doi.org/10.1016/S0140-6736(19)32989-7
[4]  Kumar, A., Hammond, N., Abbenbroek, B., et al. (2023) Sepsis-Coded Hospitalisations and Associated Costs in Australia: A Retrospective Analysis. BMC Health Services Research, 23, Article No. 1319.
https://doi.org/10.1186/s12913-023-10223-1
[5]  Dettmer, M., Holthaus, C.V. and Fuller, B.M. (2015) The Impact of Serial Lactate Monitoring on Emergency Department Resuscitation Interventions and Clinical Outcomes in Severe Sepsis and Septic Shock: An Observational Cohort Study. Shock, 43, 55-61.
https://doi.org/10.1097/SHK.0000000000000260
[6]  Pizzolato, E., Ulla, M., Galluzzo, C., et al. (2014) Role of Presepsin for the Evaluation of Sepsis in the Emergency Department. Clinical Chemistry and Laboratory Medicine, 52, 1395-1400.
https://doi.org/10.1515/cclm-2014-0199
[7]  Tan, M., Lu, Y., Jiang, H., et al. (2019) The Diagnostic Accuracy of Procalcitonin and C-Reactive Protein for Sepsis: A Systematic Review and Meta-Analysis. Journal of Cellular Biochemistry, 120, 5852-5859.
https://doi.org/10.1002/jcb.27870
[8]  Zhang, W., Wang, W., Hou, W., et al. (2022) The Diagnostic Utility of IL-10, IL-17, and PCT in Patients with Sepsis Infection. Frontiers in Public Health, 10, Article 923457.
https://doi.org/10.3389/fpubh.2022.923457
[9]  Nichol, A.D., Egi, M., Pettila, V., et al. (2010) Relative Hyperlactatemia and Hospital Mortality in Critically Ill Patients: A Retrospective Multi-Centre Study. Critical Care, 14, Article No. R25.
https://doi.org/10.1186/cc8888
[10]  Wardi, G., Brice, J., Correia, M., et al. (2020) Demystifying Lactate in the Emergency Department. Annals of Emergency Medicine, 75, 287-298.
https://doi.org/10.1016/j.annemergmed.2019.06.027
[11]  Innocenti, F., Meo, F., Giacomelli, I., et al. (2019) Prognostic Value of Serial Lactate Levels in Septic Patients with and without Shock. Internal and Emergency Medicine, 14, 1321-1330.
https://doi.org/10.1007/s11739-019-02196-z
[12]  Gong, C., Jiang, Y., Tang, Y., et al. (2022) Elevated Serum Lactic Acid Level Is an Independent Risk Factor for the Incidence and Mortality of Sepsis-Associated Acute Kidney Injury. Zhonghua Wei Zhong Bing Ji Jiu Yi Xue, 34, 714-720.
[13]  Guan, J., Wang, Z., Liu, X., et al. (2020) IL-6 and IL-10 Closely Correlate with Bacterial Bloodstream Infection. Iranian Journal of Immunology, 17, 185-203.
[14]  Huang, Z., Fu, Z., Huang, W., et al. (2020) Prognostic Value of Neutrophil-to-Lymphocyte Ratio in Sepsis: A Meta-Analysis. The American Journal of Emergency Medicine, 38, 641-647.
https://doi.org/10.1016/j.ajem.2019.10.023
[15]  Zheng, R., Shi, Y.Y., Pan, J.Y., et al. (2023) Decrease in the Platelet-to-Lymphocyte Ratio in Days after Admission for Sepsis Correlates with in-Hospital Mortality. Shock, 59, 553-559.
https://doi.org/10.1097/SHK.0000000000002087
[16]  Li, F., Ye, Z., Zhu, J., et al. (2023) Early Lactate/Albumin and Procalcitonin/Albumin Ratios as Predictors of 28-Day Mortality in ICU-Admitted Sepsis Patients: A Retrospective Cohort Study. Medical Science Monitor, 29, e940654.
https://doi.org/10.12659/MSM.940654
[17]  Liu, Y., Gao, Y., Liang, B., et al. (2023) The Prognostic Value of C-Reactive Protein to Albumin Ratio in Patients with Sepsis: A Systematic Review and Meta-Analysis. The Aging Male, 26, Article 2261540.
https://doi.org/10.1080/13685538.2023.2261540
[18]  Chen, Q., Zhang, L., Ge, S., et al. (2019) Prognosis Predictive Value of the Oxford Acute Severity of Illness Score for Sepsis: A Retrospective Cohort Study. PeerJ, 7, e7083.
https://doi.org/10.7717/peerj.7083
[19]  Wang, E.Y., Chen, M.K., Hsieh, M.Y., et al. (2022) Relationship between Preoperative Nutritional Status and Clinical Outcomes in Patients with Head and Neck Cancer. Nutrients, 14, Article 5331.
https://doi.org/10.3390/nu14245331
[20]  Nogueiro, J., Santos-Sousa, H., Pereira, A., et al. (2022) The Impact of the Prognostic Nutritional Index (PNI) in Gastric Cancer. Langenbecks Archives of Surgery, 407, 2703-2714.
https://doi.org/10.1007/s00423-022-02627-0
[21]  Wu, H., Zhou, C., Kong, W., et al. (2022) Prognostic Nutrition Index Is Associated with the All-Cause Mortality in Sepsis Patients: A Retrospective Cohort Study. Journal of Clinical Laboratory Analysis, 36, e24297.
https://doi.org/10.1002/jcla.24297
[22]  Li, T., Qi, M., Dong, G., et al. (2021) Clinical Value of Prognostic Nutritional Index in Prediction of the Presence and Severity of Neonatal Sepsis. Journal of Inflammation Research, 14, 7181-7190.
https://doi.org/10.2147/JIR.S343992
[23]  Guan, G., Lee, C.M.Y., Begg, S., et al. (2022) The Use of Early Warning System Scores in Prehospital and Emergency Department Settings to Predict Clinical Deterioration: A Systematic Review and Meta-Analysis. PLOS ONE, 17, e0265559.
https://doi.org/10.1371/journal.pone.0265559
[24]  Lan, L., Zhou, M., Chen, X., et al. (2023) Prognostic Accuracy of SOFA, MEWS, and SIRS Criteria in Predicting the Mortality Rate of Patients with Sepsis: A Meta-Analysis. Nursing in Critical Care.
https://doi.org/10.1111/nicc.13016
[25]  Singer, M., Deutschman, C.S., Seymour, C.W., et al. (2016) The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA, 315, 801-810.
https://doi.org/10.1001/jama.2016.0287
[26]  Liu, Z., Meng, Z., Li, Y., et al. (2019) Prognostic Accuracy of the Serum Lactate Level, the SOFA Score and the qSOFA Score for Mortality among Adults with Sepsis. Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine, 27, Article No. 51.
https://doi.org/10.1186/s13049-019-0609-3
[27]  Funk, D., Sebat, F. and Kumar, A. (2009) A Systems Approach to the Early Recognition and Rapid Administration of Best Practice Therapy in Sepsis and Septic Shock. Current Opinion in Critical Care, 15, 301-307.
https://doi.org/10.1097/MCC.0b013e32832e3825
[28]  Usman, O.A., Usman, A.A. and Ward, M.A. (2019) Comparison of SIRS, qSOFA, and NEWS for the Early Identification of Sepsis in the Emergency Department. The American Journal of Emergency Medicine, 37, 1490-1497.
https://doi.org/10.1016/j.ajem.2018.10.058
[29]  Qiu, X., Lei, Y.P. and Zhou, R.X. (2023) Sirs, Sofa, qSOFA, and NEWS in the Diagnosis of Sepsis and Prediction of Adverse Outcomes: A Systematic Review and Meta-Analysis. Expert Review of Anti-Infective Therapy, 21, 891-900.
https://doi.org/10.1080/14787210.2023.2237192
[30]  Seymour, C.W., Liu, V.X., Iwashyna, T.J., et al. (2016) Assessment of Clinical Criteria for Sepsis: For the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA, 315, 762-774.
https://doi.org/10.1001/jama.2016.0288
[31]  Evans, L., Rhodes, A., Alhazzani, W., et al. (2021) Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock 2021. Intensive Care Medicine, 47, 1181-1247.
https://doi.org/10.1007/s00134-021-06506-y
[32]  Knaus, W.A., Draper, E.A., Wagner, D.P., et al. (1985) APACHE II: A Severity of Disease Classification System. Critical Care Medicine, 13, 818-829.
https://doi.org/10.1097/00003246-198510000-00009
[33]  Nguyen, H.B., Van Ginkel, C., Batech, M., et al. (2012) Comparison of Predisposition, Insult/Infection, Response, and Organ Dysfunction, Acute Physiology and Chronic Health Evaluation II, and Mortality in Emergency Department Sepsis in Patients Meeting Criteria for Early Goal-Directed Therapy and the Severe Sepsis Resuscitation Bundle. Journal of Critical Care, 27, 362-369.
https://doi.org/10.1016/j.jcrc.2011.08.013
[34]  Zhou, S., Lu, Z., Liu, Y., et al. (2024) Interpretable Machine Learning Model for Early Prediction of 28-Day Mortality in ICU Patients with Sepsis-Induced Coagulopathy: Development and Validation. European Journal of Medical Research, 29, Article No. 14.
https://doi.org/10.1186/s40001-023-01593-7
[35]  Liu, Y., Bu, L., Chao, Y., et al. (2022) Combined Serum NGAL and Fetuin A to Predict 28-Day Mortality in Patients with Sepsis and Risk Prediction Model Construction. Cellular and Molecular Biology, 68, 47-52.
https://doi.org/10.14715/cmb/2022.68.11.9
[36]  Yang, L., Yang, J., Zhang, X., et al. (2024) Predictive Value of Soluble CD40L Combined with APACHE II Score in Elderly Patients with Sepsis in the Emergency Department. BMC Anesthesiology, 24, Article No. 32.
https://doi.org/10.1186/s12871-023-02381-w
[37]  Deo, R.C. (2015) Machine Learning in Medicine. Circulation, 132, 1920-1930.
https://doi.org/10.1161/CIRCULATIONAHA.115.001593
[38]  Haug, C.J. and Drazen, J.M. (2023) Artificial Intelligence and Machine Learning in Clinical Medicine, 2023. The New England Journal of Medicine, 388, 1201-1208.
https://doi.org/10.1056/NEJMra2302038
[39]  Thomas-Rueddel, D.O., Poidinger, B., Weiss, M., et al. (2015) Hyperlactatemia Is an Independent Predictor of Mortality and Denotes Distinct Subtypes of Severe Sepsis and Septic Shock. Journal of Critical Care, 30, 439.E1-E6.
https://doi.org/10.1016/j.jcrc.2014.10.027
[40]  Zhang, L., Huang, T., Xu, F., et al. (2022) Prediction of Prognosis in Elderly Patients with Sepsis Based on Machine Learning (Random Survival Forest). BMC Emergency Medicine, 22, Article No. 26.
https://doi.org/10.1186/s12873-022-00582-z
[41]  Baniasadi, A., Rezaeirad, S., Zare, H., et al. (2021) Two-Step Imputation and AdaBoost-Based Classification for Early Prediction of Sepsis on Imbalanced Clinical Data. Critical Care Medicine, 49, e91-e97.
https://doi.org/10.1097/CCM.0000000000004705
[42]  Eskandari, M.A., Moridani, M.K. and Mohammadi, S. (2023) Detection of Sepsis Using Biomarkers Based on Machine Learning. Bratislavské lekárske listy, 124, 239-250.
https://doi.org/10.4149/BLL_2023_037
[43]  Delahanty, R.J., Alvarez, J., Flynn, L.M., et al. (2019) Development and Evaluation of a Machine Learning Model for the Early Identification of Patients at Risk for Sepsis. Annals of Emergency Medicine, 73, 334-344.
https://doi.org/10.1016/j.annemergmed.2018.11.036
[44]  Chen, Q., Li, R., Lin, C., et al. (2022) Transferability and Interpretability of the Sepsis Prediction Models in the Intensive Care Unit. BMC Medical Informatics and Decision Making, 22, Article No. 343.
https://doi.org/10.1186/s12911-022-02090-3
[45]  Hou, N., Li, M., He, L., et al. (2020) Predicting 30-Days Mortality for MIMIC-III Patients with Sepsis-3: A Machine Learning Approach Using XGboost. Journal of Translational Medicine, 18, Article No. 462.
https://doi.org/10.1186/s12967-020-02620-5
[46]  García-Gallo, J.E., Fonseca-Ruiz, N.J., Celi, L.A., et al. (2020) A Machine Learning-Based Model for 1-Year Mortality Prediction in Patients Admitted to an Intensive Care Unit with a Diagnosis of Sepsis. Medicina Intensiva, 44, 160-170.
https://doi.org/10.1016/j.medin.2018.07.016
[47]  Bouza, C., Lopez-Cuadrado, T. and Amate-Blanco, J.M. (2016) Use of Explicit ICD9-CM Codes to Identify Adult Severe Sepsis: Impacts on Epidemiological Estimates. Critical Care, 20, Article No. 313.
https://doi.org/10.1186/s13054-016-1497-9
[48]  Zhang, G., Shao, F., Yuan, W., et al. (2024) Predicting Sepsis in-Hospital Mortality with Machine Learning: A Multi-Center Study Using Clinical and Inflammatory Biomarkers. European Journal of Medical Research, 29, Article No. 156.
https://doi.org/10.1186/s40001-023-01606-5
[49]  Wang, Z., Zhang, L., Chao, Y., et al. (2023) Development of a Machine Learning Model for Predicting 28-Day Mortality of Septic Patients with Atrial Fibrillation. Shock, 59, 400-408.
https://doi.org/10.1097/SHK.0000000000002078

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133