The clinical data of 76 severe illness patients with novel coronavirus SARS-CoV-2 from July to August, 2020 admitted to the ICU Intensive Care Unit ward in a hospital in Urumqi were collected in the paper. By using the Laplace approximation parameter estimation method based on maximum likelihood estimation, the generalized linear mixed effect model (GLMM) was established to analyze the characteristics of clinical indicators in critical patients, and to screen the main influencing factors of COVID-19 critical patients' inability to be transferred out of the ICU in a short time: age, C-reactive protein, serum creatinine and lactate dehydrogenase.
Citation: Zemin Luan, Zhaoxia Yu, Ting Zeng, Rui Wang, Maozai Tian, Kai Wang. A study on the factors influencing the transfer of COVID-19 severe illness patients out of the ICU based on generalized linear mixed effect model[J]. Mathematical Biosciences and Engineering, 2022, 19(10): 10602-10617. doi: 10.3934/mbe.2022495
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[4] | Mingzhu Qu, Chunrui Zhang, Xingjian Wang . Analysis of dynamic properties on forest restoration-population pressure model. Mathematical Biosciences and Engineering, 2020, 17(4): 3567-3581. doi: 10.3934/mbe.2020201 |
[5] | Wanxiao Xu, Ping Jiang, Hongying Shu, Shanshan Tong . Modeling the fear effect in the predator-prey dynamics with an age structure in the predators. Mathematical Biosciences and Engineering, 2023, 20(7): 12625-12648. doi: 10.3934/mbe.2023562 |
[6] | Elvira Barbera, Giancarlo Consolo, Giovanna Valenti . A two or three compartments hyperbolic reaction-diffusion model for the aquatic food chain. Mathematical Biosciences and Engineering, 2015, 12(3): 451-472. doi: 10.3934/mbe.2015.12.451 |
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The clinical data of 76 severe illness patients with novel coronavirus SARS-CoV-2 from July to August, 2020 admitted to the ICU Intensive Care Unit ward in a hospital in Urumqi were collected in the paper. By using the Laplace approximation parameter estimation method based on maximum likelihood estimation, the generalized linear mixed effect model (GLMM) was established to analyze the characteristics of clinical indicators in critical patients, and to screen the main influencing factors of COVID-19 critical patients' inability to be transferred out of the ICU in a short time: age, C-reactive protein, serum creatinine and lactate dehydrogenase.
[1] |
C. Huang, Y. Wang, X. Li, L. Ren, J. Zhao, Y. Hu, et al., Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China, Lancet, 395 (2020), 497–506. https://doi.org/10.1016/S0140-6736(20)30183-5 doi: 10.1016/S0140-6736(20)30183-5
![]() |
[2] |
CDC COVID-19 Response Team, Severe outcomes among patients with coronavirus disease 2019 (COVID-19)–-United States, February 12– March 16, 2020, MMWR Morb Mortal Wkly Rep, 69 (2020), 343–346. https://doi.org/10.15585/mmwr.mm6912e2 doi: 10.15585/mmwr.mm6912e2
![]() |
[3] |
K. Liu, Y. Y. Fang, Y. Deng, W. Liu, M. F. Wang, J. P. Ma, et al., Clinical characteristics of novel coronavirus cases in tertiary hospitals in Hubei Province, Chin. Med. J. (Engl.), 33 (2020), 1025–1031. https://doi.org/10.1097/CM9.0000000000000744 doi: 10.1097/CM9.0000000000000744
![]() |
[4] | H. A. Rothan, S. N. Byrareddy, The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak, J. Autoimmun., (2020), 102433. https://doi.org/10.1016/j.jaut.2020.102433 |
[5] | National Health Commission of the People's Republic of China, Announcement of the National Health Commission of the People's Republic of China (No. 1, 2020). (2020-01-20), Available from: http://www.nhc.gov.cn/xcs/zhengcwj/202001/44a3b8245e8049d2837a4f27529cd386.shtml. Accessed date: April 1, 2020. |
[6] |
J. F. Chan, S. Yuan, K. H. Kok, K. To, H. Chu, J, Yang, et al., A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster, Lancet, 395 (2020), 514–523. https://doi.org/10.1016/S0140-6736(20)30154-9 doi: 10.1016/S0140-6736(20)30154-9
![]() |
[7] |
Q. Li, X. Guan, P. Wu, X. Wang, L. Zhou, Y. Tong, et al., Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia, N. Engl. J. Med., 382 (2020), 1199–1207. https://doi.org/10.1056/NEJMoa2001316 doi: 10.1056/NEJMoa2001316
![]() |
[8] |
J. Li, G. Xu, H. Yu, X. Peng, Y. Luo, C. Cao, Clinical characteristics and outcomes of 74 patients with severe or critical COVID-19, Am. J. Med. Sci., 360 (2020), 229–235. https://doi.org/10.1016/j.amjms.2020.05.040 doi: 10.1016/j.amjms.2020.05.040
![]() |
[9] |
W. Yang, Q. Cao, L. Qin, X. Wang, Z. Cheng, A. Pan, et al., Clinical characteristics and imaging manifestations of the 2019 novel coronavirus disease (COVID-19): a multi-center study in Wenzhou city, Zhejiang, China, J. Infect., 80 (2020), 388–393. https://doi.org/10.1016/j.jinf.2020.02.016 doi: 10.1016/j.jinf.2020.02.016
![]() |
[10] |
Y. Xu, J. Dong, W. An, X. Lv, X. Yin, J. Zhang, et al., Clinical and computed tomographic imaging features of novel coronavirus pneumonia caused by SARS-CoV-2, J. Infect., 80 (2020), 394–400. https://doi.org/10.1016/j.jinf.2020.02.017 doi: 10.1016/j.jinf.2020.02.017
![]() |
[11] |
Z. Chen, J. Hu, L. Liu, Y. Zhang, D. Liu, M. Xiong, et al., Clinical characteristics of patients with severe and critical COVID-19 in Wuhan: A single-center, retrospective study, Infect. Dis. Ther., 10 (2021), 1–18. https://doi.org/10.1007/s40121-020-00379-2 doi: 10.1007/s40121-020-00379-2
![]() |
[12] |
F. Zhou, T. Yu, R. Du, G. Fan, Y. Liu, Z. Liu, et al., Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study, Lancet, 395 (2020), 1054–1062. https://doi.org/10.1016/S0140-6736(20)30566-3 doi: 10.1016/S0140-6736(20)30566-3
![]() |
[13] |
J. Tian, X. Yuan, J. Xiao, Q. Zhong, C. Yang, B. Liu, et al., Clinical characteristics and risk factors associated with COVID-19 disease severity in patients with cancer in Wuhan, China: a multicentre, retrospective, cohort study, Lancet Oncol., 21 (2020), 893–903. https://doi.org/10.1016/S1470-2045(20)30309-0 doi: 10.1016/S1470-2045(20)30309-0
![]() |
[14] |
G. Grasselli, A. Zangrillo, A. Zanella, M. Antonelli, L. Cabrini, A. Castelli, et al., Baseline characteristics and outcomes of 1591 patients infected with SARS-CoV-2 admitted to ICUs of the Lombardy Region Italy, JAMA, 323 (2020), 1574–1581. https://doi.org/10.1001/jama.2020.5394 doi: 10.1001/jama.2020.5394
![]() |
[15] |
M. G. Argenziano, S. L. Bruce, C. L. Slater, J. R. Tiao, M. R. Baldwin, R. G. Barr, et al., Characterization and clinical course of 1000 patients with coronavirus disease 2019 in New York: retrospective case series, BMJ, 369 (2020), m1996. https://doi.org/10.1136/bmj.m1996 doi: 10.1136/bmj.m1996
![]() |
[16] |
X. Yang, Y. Yu, J. Xu, H. Shu, J. Xia, H. Liu, et al., Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study, Lancet Respir. Med., 8 (2020), 475–481. https://doi.org/10.1016/S2213-2600(20)30079-5 doi: 10.1016/S2213-2600(20)30079-5
![]() |
[17] | National Health Committee of the People's Republic of China, The Diagnostic Criteria of COVID-19 Diagnosis and Treatment Protocol (Trial Edition 8). [2020-08-19], Available from: http://www.nhc.gov.cn/yzygj/s7653p/202008/0a7bdf12bd4b46e5bd28ca7f9a7f5e5a.shtml. |
[18] |
R. Krishnapuram, J. M. Keller, The possibilistic C-means algorithm: insights and recommendations, IEEE Trans. Fuzzy Syst., 4 (2002), 385–393. https://doi.org/10.1109/91.531779 doi: 10.1109/91.531779
![]() |
[19] |
R. Krishnapuram, J. M. Keller, A possibilistic approach to clustering, IEEE Trans. Fuzzy Syst., 1 (2002), 98–110. https://doi.org/10.1109/91.227387 doi: 10.1109/91.227387
![]() |
[20] |
F. Carvalho, C. P. Tenorio, N. Junior, Partitional fuzzy clustering methods based on adaptive quadratic distances, Fuzzy Sets Syst., 157 (2006), 2833–2857. https://doi.org/10.1016/j.fss.2006.06.004 doi: 10.1016/j.fss.2006.06.004
![]() |
[21] | N. E. Breslow, D. G. Clayton, Approximate inference in generalized linear mixed models, J. Am. Stat. Assoc., 88 (1993), 9–25. https://doi.org/10.2307/2290687 |
[22] |
N. E. Breslow, X. Lin, Bias correction in generalised linear mixed models with a single component of dispersion, Biometrika, 82 (1995), 81–91. https://doi.org/10.1093/biomet/82.1.81 doi: 10.1093/biomet/82.1.81
![]() |
[23] |
S. W. Raudenbush, M. Yang, M. Yosef, Maximum likelihood for generalized linear models with nested random effects via high-Order, multivariate laplace approximation, J. Comput. Graphical Stat., 9 (2000), 141–157. https://doi.org/10.2307/1390617 doi: 10.2307/1390617
![]() |
[24] |
D. Bates, M. Maechler, B. Bolker, S. Walker, Fitting linear mixed-effects models using lme4, J. Stat. Software, 67 (2015), 1–48. https://doi.org/10.18637/jss.v067.i01 doi: 10.18637/jss.v067.i01
![]() |
[25] |
M. E. Brooks, K. Kristensen, K. J. van Benthem, A. Magnusson, C. W. Berg, A. Nielsen, et al., glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling, R J., 9 (2017), 378–400. https://doi.org/10.32614/rj-2017-066 doi: 10.32614/rj-2017-066
![]() |
[26] |
P. E. Shrout, J. L. Fleiss, Intraclass correlations: uses in assessing rater reliability, Psychol. Bull., 86 (1979), 420–428. https://doi.org/10.1037/0033-2909.86.2.420 doi: 10.1037/0033-2909.86.2.420
![]() |
[27] | J. Twisk, Applied Multilevel Analysis, Cambridge University Press, 2006. https://doi.org/10.1017/cbo9780511610806 |
[28] |
P. K. Andersen, R. D. Gill, Cox's regression model for counting pro- cesses: A large sample study, Ann. Stat., 10 (1982), 1100–1120. https://doi.org/10.1214/aos/1176345976 doi: 10.1214/aos/1176345976
![]() |
[29] | T. Fleming, D. Harrington, Counting Processes and Survival Analysis, Wiley, New York, (1991), 343–346. |
[30] | P. Andersen, O. Borgan, R. Gill, N. Keiding, Statistical Models Based on Counting Processes, Springer-Verlag, New York, 1993. https://doi.org/10.1007/978-1-4612-4348-9 |
[31] |
D. Ludecke, M. Ben-Shachar, I. Patil, P. Waggoner, D. Makowski, Performance: An R package for assessment, comparison and testing of statistical Models, J. Open Source Software, 6 (2021), 3139. https://doi.org/10.21105/joss.03139 doi: 10.21105/joss.03139
![]() |
[32] |
H. Akaike, A new look at the statistical model identification, IEEE Trans. Autom. Control, 19 (1974), 716–723. https://doi.org/10.1109/tac.1974.1100705 doi: 10.1109/tac.1974.1100705
![]() |
[33] | A. Gelman, Y. Su, arm: Data analysis using regression and multilevel/hierarchical models, R package version 1.12-2, 2021. |
[34] | J. Fox, S. Weisberg, An R Companion to Applied Regression, 3rd edition, Thousand Oaks CA: Sage, 2019. |
[35] | T. M. Therneau, P. M. Grambsch, Modeling Survival Data: Extending the Cox Model, Springer, New York, 2000. |
[36] |
Y. Liu, B. Mao, S. Liang, J. Yang, H. Lu, Y. Chai, et al., Association between age and clinical characteristics and outcomes of COVID-19, Eur. Respir. J., 55 (2020), 2001112. https://doi.org/10.1183/13993003.01112-2020 doi: 10.1183/13993003.01112-2020
![]() |
[37] |
L. Wang, W. He, X. Yu, D. Hu, M. Bao, H. Liu, et al., Coronavirus disease 2019 in elderly patients: characteristics andprognostic factors based on 4-week follow-up, J. Infect., 80 (2020), 639–645. https://doi.org/10.1016/j.jinf.2020.03.019 doi: 10.1016/j.jinf.2020.03.019
![]() |
[38] |
J. Lian, X. Jin, S. Hao, H. Cai, S. Zhang, L. Zheng, et al., Analysis of epidemiological and clinical features in older patients with Coronavirus disease 2019 (COVID-19) outside Wuhan, Clin. Infect. Dis., 71 (2020), 740–747. https://doi.org/10.1093/cid/ciaa242 doi: 10.1093/cid/ciaa242
![]() |
[39] |
G. Ye, Z. Pan, Y. Pan, Q. Deng, L. Chen, J. Li, et al., Clinical characteristics of severe acute respiratory syndrome coronavirus 2 reactivation, J. Infect., 80 (2020), e14–17. https://doi.org/10.1016/j.jinf.2020.03.001 doi: 10.1016/j.jinf.2020.03.001
![]() |
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