Informativeness estimation for the main clinical and laboratory parameters in patients with severe COVID-19
- 作者: Stanevich O.1, Bakin E.1, Korshunova A.1, Gudkova A.1, Afanasev A.1, Shlyk I.1, Lioznov D.1, Polushin Y.1, Kulikov A.1
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隶属关系:
- Pavlov First Saint Petersburg State Medical University
- 期: 卷 94, 编号 11 (2022)
- 页面: 1225-1233
- 栏目: Editorial
- URL: https://journals.rcsi.science/0040-3660/article/view/232332
- DOI: https://doi.org/10.26442/00403660.2022.11.201941
- ID: 232332
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Aim. To conduct a retrospective assessment of the clinical and laboratory data of patients with severe forms of COVID-19 hospitalized in the intensive care and intensive care unit, in order to assess the contribution of various indicators to the likelihood of death.
Materials and methods. A retrospective assessment of data on 224 patients with severe COVID-19 admitted to the intensive care unit was carried out. The analysis included the data of biochemical, clinical blood tests, coagulograms, indicators of the inflammatory response. When transferring to the intensive care units (ICU), the indicators of the formalized SOFA and APACHE scales were recorded. Anthropometric and demographic data were downloaded separately.
Results. Analysis of obtained data, showed that only one demographic feature (age) and a fairly large number of laboratory parameters can serve as possible markers of an unfavorable prognosis. We identified 12 laboratory features the best in terms of prediction: procalcitonin, lymphocytes (absolute value), sodium (ABS), creatinine, lactate (ABS), D-dimer, oxygenation index, direct bilirubin, urea, hemoglobin, C-reactive protein, age, LDH. The combination of these features allows to provide the quality of the forecast at the level of AUC=0.85, while the known scales provided less efficiency (APACHE: AUC=0.78, SOFA: AUC=0.74).
Conclusion. Forecasting the outcome of the course of COVID-19 in patients in ICU is relevant not only from the position of adequate distribution of treatment measures, but also from the point of view of understanding the pathogenetic mechanisms of the development of the disease.
作者简介
Oksana Stanevich
Pavlov First Saint Petersburg State Medical University
Email: oksana.stanevich@gmail.com
ORCID iD: 0000-0002-6894-6121
врач-инфекционист отд. эпидемиологии
俄罗斯联邦, Saint PetersburgEvgeny Bakin
Pavlov First Saint Petersburg State Medical University
Email: eugene.bakin@gmail.com
ORCID iD: 0000-0002-5694-4348
PhD, RM Gorbacheva Research Institute senior researcher
俄罗斯联邦, Saint PetersburgAleksandra Korshunova
Pavlov First Saint Petersburg State Medical University
编辑信件的主要联系方式.
Email: aftotrof@gmail.com
ORCID iD: 0000-0002-7419-7227
MD, Emergency Deprtment physician
俄罗斯联邦, Saint PetersburgAlexandra Gudkova
Pavlov First Saint Petersburg State Medical University
Email: alexagood-1954@mail.ru
ORCID iD: 0000-0003-0156-8821
д-р мед. наук, проф. каф. факультетской терапии, зав. лаб. кардиомиопатий Научно-исследовательского института сердечно-сосудистых заболеваний НКИЦ
俄罗斯联邦, Saint PetersburgAleksey Afanasev
Pavlov First Saint Petersburg State Medical University
Email: alex-txf@mail.ru
ORCID iD: 0000-0003-0277-3456
SPIN 代码: 4389-6271
MD, Cand. Sci. (Med.), Assistant Lecturer
俄罗斯联邦, Saint PetersburgIrina Shlyk
Pavlov First Saint Petersburg State Medical University
Email: irina_shlyk@mail.ru
ORCID iD: 0000-0003-0977-8081
SPIN 代码: 1715-1770
MD, Dr. Sci. (Med.), Professor
俄罗斯联邦, Saint PetersburgDmitry Lioznov
Pavlov First Saint Petersburg State Medical University
Email: dlioznov@yandex.ru
ORCID iD: 0000-0003-3643-7354
д-р мед. наук, зав. каф. инфекционных болезней и эпидемиологии
俄罗斯联邦, Saint PetersburgYury Polushin
Pavlov First Saint Petersburg State Medical University
Email: polushinyus@1spbgmu.ru
ORCID iD: 0000-0002-6313-5856
SPIN 代码: 2006-1194
MD, Dr. Sci. (Med.), Professor, Academician of the RAS
俄罗斯联邦, Saint PetersburgAlexandr Kulikov
Pavlov First Saint Petersburg State Medical University
Email: ankulikov2005@yandex.ru
ORCID iD: 0000-0002-4544-2967
SPIN 代码: 3851-6072
MD, Dr. Sci. (Med.), Professor
俄罗斯联邦, Saint Petersburg参考
- Xu J, Yang X, Yang L, et al. Clinical course and predictors of 60-day mortality in 239 critically ill patients with COVID-19: a multicenter retrospective study from Wuhan, China. Crit Care. 2020;24(1):394. doi: 10.1186/s13054-020-03098-9
- Izquierdo JL, Ancochea J, Savana COVID-19 Research Group, Soriano JB. Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing. J Med Internet Res. 2020;22(10):e21801. doi: 10.2196/21801
- Abate SM, Ahmed Ali S, Mantfardo B, Basu B. Rate of Intensive Care Unit admission and outcomes among patients with coronavirus: A systematic review and Meta-analysis. PLOS One. 2020;15(7):e0235653. doi: 10.1371/journal.pone.0235653
- Wendel Garcia PD, Fumeaux T, Guerci P, et al; RISC-19-ICU Investigators. Prognostic factors associated with mortality risk and disease progression in 639 critically ill patients with COVID-19 in Europe: Initial report of the international RISC-19-ICU prospective observational cohort. EClinicalMedicine. 2020;25:100449. doi: 10.1016/j.eclinm.2020.100449
- Core Team R. A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing, 2019. Available at: https://www.R-project.org/ Accessed: 22.06.2021.
- Mendenhall WM, Sincich T. Statistics for engineering and the sciences, Sixth edition. Boca Raton: CRC Press, Taylor & Francis Group, 2016.
- Fay MP, Proschan MA. Wilcoxon-Mann-Whitney or t-test? On assumptions for hypothesis tests and multiple interpretations of decision rules. Stat Surv. 2010;4:1-39. doi: 10.1214/09-SS051
- Farrar DE, Glauber RR. Multicollinearity in Regression Analysis: The Problem Revisited. Rev Econ Stat. 1967;49(1):92. doi: 10.2307/1937887
- Yul Lee K, Weissfeld LA. A multicollinearity diagnostic for the cox model with time dependent covariates. Commun Stat – Simul Comput. 1996;25(1)41-60. doi: 10.1080/03610919608813297
- Maalouf M. Logistic regression in data analysis: an overview. Int J Data Anal Tech Strateg. 2011;3(3):281. doi: 10.1504/IJDATS.2011.041335
- Breiman L. Random Forests. Mach Learn. 2001;45(1):5-32. doi: 10.1023/A:1010933404324
- Hastie T, Tibshirani R, Friedman JH. The elements of statistical learning: data mining, inference, and prediction, 2nd ed. New York, NY: Springer, 2009.
- Stone M. Cross-Validatory Choice and Assessment of Statistical Predictions. J R Stat Soc Ser B Methodol. 1974;36(2):111-33. doi: 10.1111/j.2517-6161.1974.tb00994.x
- Kuhn M. Caret: Classification and Regression Training. 2020. Available at: https://CRAN.R-project.org/package=caret. Accessed: 22.06.2021.
- Wickham H. Ggplot2: elegant graphics for data analysis, Second edition. Cham: Springer, 2016.
- Kassambara A, Kosinski M, Biecek P. Survminer: Drawing Survival Curves using „ggplot2“. 2019. Available at: https://CRAN.R-project.org/package=survminer. Accessed: 22.06.2021.
- Raivo Kolde. Pheatmap: Pretty Heatmaps. 2019. Available at: https://CRAN.R-project.org/package=pheatmap. Accessed: 22.06.2021.
- Gupta S, Hayek SS, Wang W, et al. Factors Associated With Death in Critically Ill Patients With Coronavirus Disease 2019 in the US. JAMA Intern Med. 2020;180(11):1436. doi: 10.1001/jamainternmed.2020.3596
- Guillamet MCV, Guillamet RV, Kramer AA, et al. Toward a COVID-19 score-risk assessments and registry. Int Care Crit Care Med. 2020. doi: 10.1101/2020.04.15.20066860
- Wynants L, Van Calster B, Collins GS, et al. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ. 2020:m1328. doi: 10.1136/bmj.m1328
- Zhang H, Shi T, Wu X, et al. Risk prediction for poor outcome and death in hospital in-patients with COVID-19: derivation in Wuhan, China and external validation in London, UK. Public and Global Health. 2020. doi: 10.1101/2020.04.28.20082222
- Levy TJ, Richardson S, Coppa K, et al. A predictive model to estimate survival of hospitalized COVID-19 patients from admission data. Health Informatics. 2020. doi: 10.1101/2020.04.22.20075416
- Han Y, Zhang H, Mu S, et al. Lactate dehydrogenase, an independent risk factor of severe COVID-19 patients: a retrospective and observational study. Aging. 2020;12(12):11245-58. doi: 10.18632/aging.103372
- Chen Z, Hu J, Liu L, et al. Clinical Characteristics of Patients with Severe and Critical COVID-19 in Wuhan: A Single-Center, Retrospective Study. Infect Dis Ther. 2021;10(1):421-38. doi: 10.1007/s40121-020-00379-2
- Hu C, Liu Z, Jiang Y, et al. Early prediction of mortality risk among patients with severe COVID-19, using machine learning. Int J Epidemiol. 2021;49(6):1918-29. doi: 10.1093/ije/dyaa171
- Rod JE, Oviedo-Trespalacios O, Cortes-Ramirez J. A brief-review of the risk factors for covid-19 severity. Rev Saúde Pública. 2020;54:60. doi: 10.11606/s1518-8787.2020054002481
- Tan L, Wang Q, Zhang D, et al. Lymphopenia predicts disease severity of COVID-19: a descriptive and predictive study. Signal Transduct Target Ther. 2020;5(1):33. doi: 10.1038/s41392-020-0148-4
- Carfora V, Spiniello G, Ricciolino R, et al. Anticoagulant treatment in COVID-19: a narrative review. J Thromb Thrombolysis. 2021;51(3):642-8. doi: 10.1007/s11239-020-02242-0
- Pourhomayoun M, Shakibi M. Predicting mortality risk in patients with COVID-19 using machine learning to help medical decision-making. Smart Health. 2021;20:100178. doi: 10.1016/j.smhl.2020.100178
- Singh K, Valley TS, Tang S, et al. Evaluating a Widely Implemented Proprietary Deterioration Index Model among Hospitalized COVID-19 Patients. Ann Am Thorac Soc. 2021;18(7):1129-37. doi: 10.1513/AnnalsATS.202006-698OC
- Hu H, Yao N, Qiu Y. Comparing Rapid Scoring Systems in Mortality Prediction of Critically Ill Patients With Novel Coronavirus Disease. Acad Emerg Med. 2020;27(6):461-8. doi: 10.1111/acem.13992
- Williamson EJ, Walker AJ, Bhaskaran K, et al. Factors associated with COVID-19-related death using OpenSAFELY. Nature. 2020;584(7821):430-6. doi: 10.1038/s41586-020-2521-4