Exploratory COVID-19 death risk score based on basic laboratory tests and physiological clinical measurements

doi:10.5847/wjem.j.1920-8642.2022.103

Abstract

Abstract:

BACKGROUND: In the event of a sudden shortage of medical resources, a rapid, simple, and accurate prediction model is essential for the 30-day mortality rate of patients with COVID-19.

METHODS: This retrospective study compared the characteristics of the survivals and non-survivals of 278 patients with COVID-19. Logistic regression analysis was performed to obtain the “COVID-19 death risk score” (CDRS) model. Using the area under the receiver operating characteristic (AUROC) curve and Hosmer-Lemeshow goodness-of-fit test, discrimination and calibration were assessed. Internal validation was conducted using a regular bootstrap method.

RESULTS: A total of 63 (22.66%) of 278 included patients died. The logistic regression analysis revealed that high-sensitivity C-reactive protein (hsCRP; odds ratio [OR]=1.018), D-dimer (OR=1.101), and respiratory rate (RR; OR=1.185) were independently associated with 30-day mortality. CDRS was calculated as follows: CDRS=-10.245+(0.022×hsCRP)+(0.172×D-dimer)+(0.203×RR). CDRS had the same predictive effect as the sequential organ failure assessment (SOFA) and “confusion, uremia, respiratory rate, blood pressure, and age over 65 years” (CURB-65) scores, with AUROCs of 0.984 for CDRS, 0.975 for SOFA, and 0.971 for CURB-65, respectively. And CDRS showed good calibration. The AUROC through internal validations was 0.980 (95% confidence interval [CI]: 0.965-0.995). Regarding the clinical value, the decision curve analysis of CDRS showed a net value similar to that of CURB-65 in this cohort.

CONCLUSION: CDRS is a novel, efficient and accurate prediction model for the early identification of COVID-19 patients with poor outcomes. Although it is not as advanced as the other models, CDRS had a similar performance to that of SOFA and CURB-65.

Key words: COVID-19, 30-day mortality, Prediction model

Gui-ying Dong, Fei-fei Jin, Qi Huang, Chun-bo Wu, Ji-hong Zhu, Tian-bing Wang. Exploratory COVID-19 death risk score based on basic laboratory tests and physiological clinical measurements[J]. World Journal of Emergency Medicine, 2022, 13(6): 453-458.

Figures/Tables 6

Figure 1.

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References 30

1	Du B, Wang CT, Singer M. Learning for the next pandemic: the Wuhan experience of managing critically ill people. BMJ. 2021; 375:e066090.
2	National Health Commission of the People’s Republic of China. Guideline for the diagnosis and treatment of COVID-19 infections (version1-7). 2020. Available at: http://www.nhc.gov.cn/yzygj/zcwj2/new_zcwj.shtml
3	Smilowitz NR, Nguy V, Aphinyanaphongs Y, Newman JD, Xia YH, Reynolds HR, et al. Multiple biomarker approach to risk stratification in COVID-19. Circulation. 2021; 143(13): 1338-40. doi: 10.1161/CIRCULATIONAHA.120.053311
4	Zakeri R, Pickles A, Carr E, Bean DM, O’Gallagher K, Kraljewic Z, et al. Biological responses to COVID-19: insights from physiological and blood biomarker profiles. Curr Res Transl Med. 2021; 69(2):103276. doi: 10.1016/j.retram.2021.103276
5	Oldoni E, van Gool A, García Bermejo L, Scherer A, Mayrhofer MT, Florindi F, et al. Biomarker research and development for coronavirus disease 2019 (COVID-19): European medical research infrastructures call for global coordination. Clin Infect Dis. 2021; 72(10):1838-42. doi: 10.1093/cid/ciaa1250 pmid: 33091118
6	Bivona G, Agnello L, Ciaccio M. Biomarkers for prognosis and treatment response in COVID-19 patients. Ann Lab Med. 2021; 41(6):540-8. doi: 10.3343/alm.2021.41.6.540 pmid: 34108281
7	Kamran F, Tang SP, Otles E, McEvoy DS, Saleh SN, Gong J, et al. Early identification of patients admitted to hospital for covid-19 at risk of clinical deterioration: model development and multisite external validation study. BMJ. 2022; 376:e068576.
8	Gupta RK, Harrison EM, Ho A, Docherty AB, Knight SR, van Smeden M, et al. Development and validation of the ISARIC 4C deterioration model for adults hospitalised with COVID-19: a prospective cohort study. Lancet Respir Med. 2021; 9(4):349-59. doi: 10.1016/S2213-2600(20)30559-2 pmid: 33444539
9	Berenguer J, Borobia AM, Ryan P, Rodríguez-Baño J, Bellón JM, Jarrín I, et al. Development and validation of a prediction model for 30-day mortality in hospitalised patients with COVID-19: the COVID-19 SEIMC score. Thorax. 2021;76(9): 920-9.
10	Heber S, Pereyra D, Schrottmaier WC, Kammerer K, Santol J, Rumpf B, et al. A model predicting mortality of hospitalized covid-19 patients four days after admission: development, internal and temporal-external validation. Front Cell Infect Microbiol. 2022; 11:795026. doi: 10.3389/fcimb.2021.795026
11	Bellou V, Tzoulaki I, van Smeden M, Moons KGM, Evangelou E, Belbasis L. Prognostic factors for adverse outcomes in patients with COVID-19: a field-wide systematic review and meta-analysis. Eur Respir J. 2022; 59(2):2002964. doi: 10.1183/13993003.02964-2020
12	Signorini SG, Brugnoni D, Levaggi R, Garrafa E. Less is more: an ecological and economic point of view on appropriate use of lab testing for COVID-19 patients. Bioanalysis. 2021; 13(24):1781-3. doi: 10.4155/bio-2021-0064 pmid: 34355575
13	von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP, et al. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. BMJ. 2007; 335(7624):806-8. doi: 10.1136/bmj.39335.541782.AD
14	Vincent JL, Moreno R, Takala J, Willatts S, Bruining H, et al. The SOFA (sepsis-related organ failure assessment) score to describe organ dysfunction/failure. On behalf of the working group on sepsis-related problems of the European society of intensive care medicine. Intensive Care Med. 1996; 22(7):707-10. doi: 10.1007/BF01709751
15	Mandell LA, Wunderink RG, Anzueto A, Bartlett JG, Campbell GD, Dean NC, et al. Infectious Diseases Society of America/American Thoracic Society consensus guidelines on the management of community-acquired pneumonia in adults. Clin Infect Dis. 2007 ;44(Suppl 2):S27-S72.
16	Mayer TA, Walker ML. Pediatric head injury: the critical role of the emergency physician. Ann Emerg Med. 1985; 14(12):1178-84. pmid: 4061990
17	Chen W, Janz DR, Shaver CM, Bernard GR, Bastarache JA, Ware LB. Clinical characteristics and outcomes are similar in ARDS diagnosed by oxygen saturation//FiO₂ ratio compared with PaO₂/FiO₂ ratio. Chest. 2015; 48(6):1477-83.
18	Dong GY, Du Z, Zhu JH, Guo Y, Gao WB, Guo W, et al. The clinical characteristics and prognosis of COVID-19 patients with comorbidities: a retrospective analysis of the infection peak in Wuhan. Ann Transl Med. 2021; 9(4):280. doi: 10.21037/atm-20-4052 pmid: 33708907
19	Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement. Ann Intern Med. 2015; 162(1):55-63. doi: 10.7326/M14-0697 pmid: 25560714
20	Shi L, Wang Y, Wang YD, Duan GC, Yang HY. Dyspnea rather than fever is a risk factor for predicting mortality in patients with COVID-19. J Infect. 2020; 81(4):647-79. doi: S0163-4453(20)30288-7 pmid: 32417316
21	Ko JY, Danielson ML, Town M, Derado G, Greenlund KJ, Kirley PD, et al. Risk factors for coronavirus disease 2019 (COVID-19)-associated hospitalization: COVID-19-associated hospitalization surveillance network and behavioral risk factor surveillance system. Clin Infect Dis. 2021; 72(11):e695-e703. doi: 10.1093/cid/ciaa1419 pmid: 32945846
22	Rothman KJ. Epidemiology:an Introduction. New York: Oxford University Press. 2002.
23	Raschke RA, Agarwal S, Rangan P, Heise CW, Curry SC. Discriminant accuracy of the SOFA score for determining the probable mortality of patients with COVID-19 pneumonia requiring mechanical ventilation. JAMA. 2021; 325(14):1469-70. doi: 10.1001/jama.2021.1545 pmid: 33595630
24	Elmoheen A, Abdelhafez I, Salem W, Bahgat M, Elkandow A, Tarig A, et al. External validation and recalibration of the CURB-65 and PSI for predicting 30-day mortality and critical care intervention in multiethnic patients with COVID-19. Int J Infect Dis. 2021; 111:108-16. doi: 10.1016/j.ijid.2021.08.027 pmid: 34416403
25	World Health Organization. Ageing: preventing and managing the global epidemic:report on a WHO consultation. Geneva: World Health Organization; 2000. (WHO Technical Report Series 894).
26	Hong Kong Health Organization. Coronavirus disease (COVID-2019) situation report. Available at: https://www.chp.gov.hk/en/index.html
27	Karim S, Karim QA. Omicron SARS-CoV-2 variant: a new chapter in the COVID-19 pandemic. Lancet. 2021; 398(10317): 2126-8. doi: 10.1016/S0140-6736(21)02758-6 pmid: 34871545
28	Martínez-Lacalzada M, Viteri-Noël A, Manzano L, Fabregate M, Rubio-Rivas M, Luis García S, et al. Predicting critical illness on initial diagnosis of COVID-19 based on easily obtained clinical variables: development and validation of the PRIORITY model. Clin Microbiol Infect. 2021; 27(12):1838-44. doi: 10.1016/j.cmi.2021.07.006
29	Soltan AAS, Kouchaki S, Zhu TT, Kiyasseh D, Taylor T, Hussain ZB, et al. Rapid triage for COVID-19 using routine clinical data for patients attending hospital: development and prospective validation of an artificial intelligence screening test. Lancet Digit Health. 2021 ;3(2):e78-e87.
30	He F, Page JH, Weinberg KR, Mishra A. The development and validation of simplified machine learning algorithms to predict prognosis of hospitalized patients with COVID-19: multicenter, retrospective study. J Med Internet Res. 2022; 24(1):e31549. doi: 10.2196/31549

Variables	All (n=278)	Survival (n=215)	Non-survival (n=63)	P-value
Demographics
Male	146 (52.52)	107 (49.77)	39 (61.90)	0.090
Age, years	64 (52-71)	61 (49-69)	70 (62-80)	<0.001*
Youth (18-65 years)	156 (56.12)	136 (63.26)	20 (31.75)	<0.001*
Middle-aged (66-79 years)	92 (33.09)	66 (30.70)	26 (41.27)	0.185
Elderly (80-99 years)	30 (10.79)	13 (6.05)	17 (26.98)	<0.001*
Comorbidity
Hypertension	113 (40.65)	86 (40.00)	27 (42.86)	0.685
Diabetes mellitus	57 (20.50)	39 (18.14)	18 (28.57)	0.071
Cardiovascular disease	53 (19.06)	34 (15.81)	19 (30.16)	0.011*
Chronic pulmonary disease	25 (8.99)	14 (6.51)	11 (17.46)	0.008*
Chronic kidney disease	16 (5.76)	15 (6.98)	1 (1.59)	0.102
Malignancy tumor	10 (3.60)	6 (2.79)	4 (6.35)	0.182
Chronic liver disease	7 (2.52)	4 (1.86)	5 (7.94)	0.017*
Intervention during hospital
Mechanical ventilation	71 (25.54)	11 (5.11)	60 (95.24)	<0.001*
Vasoactive drug therapy	42 (15.11)	4 (1.86)	42 (66.67)	<0.001*

Risk factors	Univariate regression analysis		Multivariate regression analysis
Risk factors	OR (95%CI)	P-value	OR (95%CI)	P-value
Age	1.066 (1.040-1.094)	<0.001*	1.080 (0.979-1.193)	0.126
RR	1.292 (1.212-1.376)	<0.001*	1.185 (1.032-1.361 )	0.016*
WBC	1.505 (1.351-1.678)	<0.001*	1.131 (0.943-1.358)	0.185
D-dimer	1.264 (1.199-1.334)	<0.001*	1.101 (0.986-1.229)	0.089*
hsCRP	1.026 (1.020-1.033)	<0.001*	1.018 (1.003-1.033)	0.017*
IL-6	1.024 (1.017-1.031)	<0.001*	1.003 (0.992-1.014)	0.628
Ferritin	1.001 (1.001-1.002)	<0.001*	1.001 (1.000-1.002)	0.207
hsTnI	1.002 (1.001-1.003)	0.001*	1.000 (1.000-1.000)	0.606
NT-proBNP	1.000 (1.000-1.000)	<0.001*	1.000 (1.000-1.000)	0.272
Scr	1.003 (1.002-1.005)	<0.001*	1.002 (0.999-1.006)	0.192
ALT	1.013 (1.006-1.019)	<0.001*	1.015 (0.987-1.042)	0.298

Scores	All (n=278)	Survival (n=215)	Non-survival (n=63)	P-value	AUC (95% CI)	Cut-off
CDRS	-2.80 (-5.72 - -1.01)	-4.71 (-6.05 - -3.96)	3.71 (1.12-6.54)	<0.001*	0.984 (0.969-0.998)	-1.14
SOFA	2 (0-4)	1 (0-2)	12 (8-15)	<0.001*	0.975 (0.945-1.000)	5
CURB-65	1 (0-2)	1 (0-1)	4 (3-5)	<0.001*	0.971 (0.949-0.994)	3

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