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Информативность основных клинико-лабораторных показателей для пациентов с тяжелой формой COVID-19
Станевич О.В., Бакин Е.А., Коршунова А.А., Гудкова А.Я., Афанасьев А.А., Шлык И.В., Лиознов Д.А., Полушин Ю.С., Куликов А.Н. Информативность основных клинико-лабораторных показателей для пациентов с тяжелой формой COVID-19. Терапевтический архив. 2022;94(11):1225–1233.
DOI: 10.26442/00403660.2022.11.201941
© ООО «КОНСИЛИУМ МЕДИКУМ», 2022 г.
DOI: 10.26442/00403660.2022.11.201941
© ООО «КОНСИЛИУМ МЕДИКУМ», 2022 г.
________________________________________________
Stanevich OV, Bakin EA, Korshunova AA, Gudkova AYa, Afanasev AA, Shlyk IV, Lioznov DA, Polushin YuS, Kulikov AN. Informativeness estimation for the main clinical and laboratory parameters in patients with severe COVID-19. Terapevticheskii Arkhiv (Ter. Arkh.). 2022;94(11):1225–1233. DOI: 10.26442/00403660.2022.11.201941
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Аннотация
Цель. Провести ретроспективную оценку клинико-лабораторных данных больных тяжелыми формами COVID-19, госпитализированных в отделение реанимации и интенсивной терапии (ОРИТ), с целью оценки вклада различных показателей в вероятность летального исхода.
Материалы и методы. Проведена ретроспективная оценка сведений о 224 пациентах с тяжелым течением COVID-19, госпитализированных в отделение интенсивной терапии. В анализ взяты данные биохимического, клинического анализов крови, коагулограммы, показатели воспалительного ответа. При переводе в ОРИТ фиксировались показатели формализованных шкал SOFA и APACHE. Отдельно выполнена выгрузка антропометрических и демографических данных.
Результаты. В ходе анализа наших данных оказалось, что лишь один демографический признак (возраст) и значительное количество лабораторных показателей могут служить в качестве возможных маркеров неблагоприятного прогноза. Выявлено 12 лабораторных признаков, наилучших с точки зрения прогнозирования: прокальцитонин, лимфоциты (абсолютное значение), натрий (КОС), креатинин, лактат (КОС), D-димер, индекс оксигенации, прямой билирубин, мочевина, гемоглобин, С-реактивный белок, возраст, лактатдегидрогеназа. Комбинация данных признаков позволяет обеспечить качество прогноза на уровне AUC=0,85, в то время как известные шкалы обеспечивают несколько меньшую результативность (APACHE: AUC=0,78, SOFA: AUC=0,74).
Заключение. Оценка прогноза течения COVID-19 у больных, находящихся в ОРИТ, актуальна не только с позиции адекватного распределения лечебных мероприятий, но и с точки зрения понимания патогенетических механизмов развития заболевания.
Ключевые слова: COVID-19, новая коронавирусная инфекция, отделение реанимации и интенсивной терапии, SOFA, APACHE
Материалы и методы. Проведена ретроспективная оценка сведений о 224 пациентах с тяжелым течением COVID-19, госпитализированных в отделение интенсивной терапии. В анализ взяты данные биохимического, клинического анализов крови, коагулограммы, показатели воспалительного ответа. При переводе в ОРИТ фиксировались показатели формализованных шкал SOFA и APACHE. Отдельно выполнена выгрузка антропометрических и демографических данных.
Результаты. В ходе анализа наших данных оказалось, что лишь один демографический признак (возраст) и значительное количество лабораторных показателей могут служить в качестве возможных маркеров неблагоприятного прогноза. Выявлено 12 лабораторных признаков, наилучших с точки зрения прогнозирования: прокальцитонин, лимфоциты (абсолютное значение), натрий (КОС), креатинин, лактат (КОС), D-димер, индекс оксигенации, прямой билирубин, мочевина, гемоглобин, С-реактивный белок, возраст, лактатдегидрогеназа. Комбинация данных признаков позволяет обеспечить качество прогноза на уровне AUC=0,85, в то время как известные шкалы обеспечивают несколько меньшую результативность (APACHE: AUC=0,78, SOFA: AUC=0,74).
Заключение. Оценка прогноза течения COVID-19 у больных, находящихся в ОРИТ, актуальна не только с позиции адекватного распределения лечебных мероприятий, но и с точки зрения понимания патогенетических механизмов развития заболевания.
Ключевые слова: COVID-19, новая коронавирусная инфекция, отделение реанимации и интенсивной терапии, SOFA, APACHE
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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.
Keywords: COVID-19, new coronavirus disease, intensive care unit, SOFA, APACHE
Полный текст
Список литературы
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32. 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
2. 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
3. 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
4. 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
5. 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.
6. Mendenhall WM, Sincich T. Statistics for engineering and the sciences, Sixth edition. Boca Raton: CRC Press, Taylor & Francis Group, 2016.
7. 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
8. Farrar DE, Glauber RR. Multicollinearity in Regression Analysis: The Problem Revisited. Rev Econ Stat. 1967;49(1):92. DOI:10.2307/1937887
9. 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
10. 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
11. Breiman L. Random Forests. Mach Learn. 2001;45(1):5-32. DOI:10.1023/A:1010933404324
12. Hastie T, Tibshirani R, Friedman JH. The elements of statistical learning: data mining, inference, and prediction, 2nd ed. New York, NY: Springer, 2009.
13. 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
14. Kuhn M. Caret: Classification and Regression Training. 2020. Available at: https://CRAN.R-project.org/package=caret. Accessed: 22.06.2021.
15. Wickham H. Ggplot2: elegant graphics for data analysis, Second edition. Cham: Springer, 2016.
16. 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.
17. Raivo Kolde. Pheatmap: Pretty Heatmaps. 2019. Available at: https://CRAN.R-project.org/package=pheatmap. Accessed: 22.06.2021.
18. 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
19. 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
20. 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
21. 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
22. 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
23. 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
24. 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
25. 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
26. 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
27. 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
28. 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
29. 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
30. 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
31. 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
32. 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
________________________________________________
1. 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
2. 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
3. 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
4. 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
5. 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.
6. Mendenhall WM, Sincich T. Statistics for engineering and the sciences, Sixth edition. Boca Raton: CRC Press, Taylor & Francis Group, 2016.
7. 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
8. Farrar DE, Glauber RR. Multicollinearity in Regression Analysis: The Problem Revisited. Rev Econ Stat. 1967;49(1):92. DOI:10.2307/1937887
9. 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
10. 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
11. Breiman L. Random Forests. Mach Learn. 2001;45(1):5-32. DOI:10.1023/A:1010933404324
12. Hastie T, Tibshirani R, Friedman JH. The elements of statistical learning: data mining, inference, and prediction, 2nd ed. New York, NY: Springer, 2009.
13. 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
14. Kuhn M. Caret: Classification and Regression Training. 2020. Available at: https://CRAN.R-project.org/package=caret. Accessed: 22.06.2021.
15. Wickham H. Ggplot2: elegant graphics for data analysis, Second edition. Cham: Springer, 2016.
16. 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.
17. Raivo Kolde. Pheatmap: Pretty Heatmaps. 2019. Available at: https://CRAN.R-project.org/package=pheatmap. Accessed: 22.06.2021.
18. 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
19. 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
20. 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
21. 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
22. 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
23. 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
24. 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
25. 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
26. 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
27. 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
28. 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
29. 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
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Статья поступила в редакцию / The article received: 22.06.2021
Авторы
О.В. Станевич, Е.А. Бакин, А.А. Коршунова*, А.Я. Гудкова, А.А. Афанасьев, И.В. Шлык, Д.А. Лиознов, Ю.С. Полушин, А.Н. Куликов
ФГБОУ ВО «Первый Санкт-Петербургский государственный медицинский университет им. акад. И.П. Павлова» Минздрава России, Санкт-Петербург, Россия
*aftotrof@gmail.com
ФГБОУ ВО «Первый Санкт-Петербургский государственный медицинский университет им. акад. И.П. Павлова» Минздрава России, Санкт-Петербург, Россия
*aftotrof@gmail.com
________________________________________________
Oksana V. Stanevich, Evgeny A. Bakin, Aleksandra A. Korshunova*, Alexandra Ya. Gudkova, Aleksey A. Afanasev, Irina V. Shlyk, Dmitry A. Lioznov, Yury S. Polushin, Alexandr N. Kulikov
Pavlov First Saint Petersburg State Medical University, Saint Petersburg, Russia
*aftotrof@gmail.com
Цель портала OmniDoctor – предоставление профессиональной информации врачам, провизорам и фармацевтам.