Serum biomarkers for prediction of mortality in patients with COVID-19

Abstract

Background

There is limited information regarding the role of biomarker levels at predicting mortality in patients with the coronavirus disease (COVID-19) pandemic. The purpose of this study is to determine the differences in serum biomarker levels in adults with COVID-19 who survived hospitalization from those who did not.

Methods

A comprehensive search was completed on PubMed, EMBASE and Cochrane libraries to identify studies of interest. Endpoints of interest were blood counts, hepatic function test, acute phase reactants, cytokines and cardiac biomarkers.

Results

A total of 10 studies with 1584 patients were included in the pooled analyses. Biomarkers that were noted to be significantly higher in those who died from coronavirus disease included: white blood cell count, neutrophil count, C-reactive protein, high sensitivity C-reactive protein, procalcitonin, ferritin, D-dimer, interleukin-6, lactate dehydrogenase, creatine kinase, prothrombin time, aspartate aminotransferase, alanine aminotransferase, total bilirubin and creatinine. Lymphocyte count, platelet count and albumin were significantly lower in patients who died.

Conclusion

This pooled analysis of 10 studies including 1584 patients identified significant differences in biomarkers on admission in patients who survived from those who did not. Further research is needed to develop risk stratification models to help with judicious use of limited health-care resources.

Keywords

COVID coronavirus inflammation biomarkers mortality

Introduction

The world continues to battle the damaging effects of the coronavirus disease (COVID-19) pandemic. The epidemiologic data have shown a slowdown in virus transmission in certain parts of the world that were once deeply affected,¹ whereas other geographic areas are encountering a large increase of transmission, hospitalization rates and mortality.

More than 12 months into the pandemic with little glimpse of specific therapeutic regimens, front-line providers continue to be equipped only with the clinical presentation and basic imaging for the assessment of disease severity and prediction of mortality. Although there is limited information regarding the sensitivity and specificity of laboratory tests at determining illness severity in patients with COVID-19, biomarker levels provide an opportunity to quickly triage patients for disease severity to allow a more timely management and resource allocation. The primary objective of this study was to determine how serum biomarker values differ in adults hospitalized with COVID-19 who did not survive to discharge versus those who did, using previously published data.

Methods

Study identification

Studies of interest were searched for using PubMed, Embase and Cochrane databases. Hand searches of manuscripts referenced in studies identified by database search were also conducted. The following keywords were used in isolation and in various combinations to identify such studies: ‘2019-nCoV’, ‘SARS-CoV-2’, ‘COVID-19’, ‘COVID’, ‘novel coronavirus’, ‘coronavirus’, ‘biomarkers’, ‘inflammatory markers’ and ‘characteristics’.

The following inclusion criteria were set: (1) the study must have included COVID-19 patients; (2) the study must have included adult patients (over 18 years of age); (3) the study must have separated patients into those who survived to discharge and those who did not; (4) the study must have reported at least one serum biomarker; (5) serum biomarker levels must have been reported for each group, separately; (6) the study must have had biomarkers drawn at time of admission; (7) the study must have been published; (8) the study must have been in English.

Two authors (RL, JF) reviewed the resulting studies using the title and abstract. Studies meeting all the aforementioned inclusion criteria then had their full-text reviewed by the same two authors. Any discrepancies in studies deemed appropriate for inclusion were then reviewed by a third author (EV) and a consensus was reached by discussion. The last day of search was 4 May 2020.

Quality and bias review

Studies identified as meeting inclusion criteria after full-text review were then assessed for quality of the methods utilized by two authors (SA, GA). Any discrepancies were reviewed by third author (SF) and a consensus reached by discussion.

Endpoints

Studies deemed to be appropriate for inclusion after full-text review, quality, review and bias review then had all the endpoints reviewed to identify endpoints reported by multiple studies. Endpoints with data from three or more studies were deemed eligible for data extraction (Table 1).

Table 1.

Outcomes parameters pooled for analysis.

Outcomes	Units	Total, n	Death, n	Survival, n	Chen T	Deng Y	Tang N	Wu C	Yang X	Zhou F	Ruan Q	Xu Bo	Yao Q	Chen TL	Number of studies per outcome
WBC	10⁹/L	1349	447	902	X	X		X		X	X	X	X	X	8
Neutrophils	10⁹/L	783	216	567	X			X				X	X	X	5
Lymphocytes	10⁹/L	1401	479	922	X	X		X	X	X	X	X	X	X	9
Platelets	10⁹/L	1031	342	689	X			X	X	X	X		X	X	7
Haemoglobin	g/dL	775	279	496	X				X	X	X		X	X	5
AST	U/L	1050	381	669	X	X		X			X	X		X	6
ALT	U/L	850	225	625	X	X		X		X	X	X	X	X	8
Total bilirubin	mg/dL	765	269	516	X			X	X		X		X		5
Creatinine	mg/dL	1210	425	785	X	X		X	X		X	X	X	X	8
CRP	mg/L	575	224	351		X					X	X		X	4
Hs-CRP	mg/L	583	169	414	X			X					X		3
PCT	ng/mL	773	226	547	X					X		X	X	X	5
ESR	mm/h	380	176	204	X			X						X	3
Ferritin	ng/mL	816	279	537	X			X		X	X				4
D-dimer	mg/L	1157	291	866	X		X	X		X		X	X	X	7
IL-6	pg/ml	877	304	573	X			X		X	X	X		X	6
LDH	U/L	871	298	573	X			X		X	X			X	5
CK	U/L	923	294	629	X					X	X	X	X	X	6
CK-MB	U/L	454	84	370				X				X		X	3
Albumin	g/dL	979	310	669	X			X		X	X		X	X	6
PT	s	1046	292	754	X		X	X	X	X		X			6
PTT	s	700	266	434	X		X	X							3

WBC: white blood cells; AST: aspartate aminotransferase; ALT: alanine aminotransferase; CRP: C-reactive protein; Hs-CRP: high sensitivity C-reactive protein; PCT: procalcitonin; ESR: erythrocyte sedimentation rate; IL-6: Interleukin-6; LDH: lactate dehydrogenase; CK: creatine kinase; CK-MB: creatine kinase myocardial band; PT: prothrombin time; PTT: partial thromboplastin time.

The following endpoints were identified for the pooled analyses: white blood cell count (×10⁹/L), neutrophil count (×10⁹/L), lymphocyte count (×10⁹/L), platelet count (×10⁹/L), haemoglobin (g/dL), C-reactive protein (CRP) (mg/L), high sensitivity CRP (mg/L), procalcitonin (ng/mL), erythrocyte sedimentation rate (ESR) (mm/h), ferritin (µg/L), D-dimer (µg/mL), interleukin-6 (IL-6) (pg/mL), lactate dehydrogenase (LDH) (U/L), creatine kinase (CK) (U/L), creatine kinase-myocardial band (CK-Mb)(U/L), prothrombin time (PT) (s), partial thromboplastin time (PTT) (s), aspartate aminotransferase (AST) (U/L), alanine aminotransferase (ALT) (U/L), total bilirubin (mg/dL), creatinine (mg/dL), albumin (g/dL).

Data extraction

Data were then extracted independently by two authors (RL, JF) using the same electronic data collection tool that was developed specifically for this review protocol. No previously existing data collection tool or protocol was utilized. The extracted data were then reviewed by a third author (EV) to identify any discrepancies. Discrepancies were reviewed as a group and a group consensus was achieved. Only data available from the published manuscripts were included in the pooled analyses. Authors were not contacted for clarification of published data or for unpublished data.

Data regarding the studies themselves was collected including study design and date of publication. Data regarding characteristics of the included patient cohort were also included. Data for the serum biomarkers were also collected. Since all the variables of interest were continuous variables, the data collected from the source studies were recorded as mean and standard deviation. If data were presented as median and range in the source study, they were converted to mean and standard deviation using the methods proposed by Wan et al.²

Statistical analyses

The data extracted for each endpoint were assessed for heterogeneity. This was done for each endpoint separately. The Q-statistic and I²-statistic were greater than 50%. If the Q-statistic and I²-statistic resulted in different assessment of heterogeneity, the endpoint was classified as having significant heterogeneity. For endpoints without significant heterogeneity, pooled analyses were conducted using a fixed effects model, while for endpoints with significant heterogeneity, pooled analyses were conducted using a random effects model. Pooled values are presented as mean different and 95% confidence interval using the units previously mentioned for each specific endpoint. Sensitivity analyses were conducted to help qualitatively determine the impact of age and comorbidities on the endpoints. Meta-regression was not done, as some of the endpoints did not have a robust number of studies to do a well-powered meta-regression. A results table was created to depict the results. Pooled analyses were conducted using Comprehensive Meta-Analysis Version 3.0 (Biostat Inc., Englewood, NJ). A P-value of less than 0.05 was considered statistically significant.

Results

Study characteristics

A total of 10 studies with 1,584 patients were included in the pooled analyses (Figure 1).^3–12 Of these patients, 500 (31.5%) did not survive to discharge. Average age of the patients was 55.8 years and 58.6% of these patients were male. A comorbidity was present in 41.4% of patients. Hypertension was present in 23.1%, diabetes in 11.5%, cardiovascular disease in 6.0% and chronic lung disease in 4.4% (Table 2).

Figure 1.

Study identification diagram.

Table 2.

Study characteristics describing sample size, gender and comorbidities.

Studies	n	Study weight (%)	Death, n	Survival, n	Mean age (years)	Male (%)	Any comorbidity (%)	Diabetes (%)	Hypertension (%)	Cardiovascular disease (%)	Chronic obstructive pulmonary disease (%)
Chen, T	274	17	113	161	58.66	62	49	17	34	8	7
Deng, Y	225	14	109	116	55.42	55	56	11.5	25.7	7.5	11.1
Tang, N	183	12	21	162	54.1	53.5	41	–	–	–	–
Wu, C	201	13	44	157	51.3	63.7	–	10.9	19.4	4	2.5
Yang, X	52	3	32	20	51.9	70	25	19	–	10	10
Zhou, F	191	12	54	137	56.3	62	48	19	30	8	3
Ruan, Q	150	9	68	82	56.51	68	51.3	16.6	34.6	8.6	2
Xu, Bo	145	9	28	117	58.42	52.4	53.1	–	21.3	–	1.3
Yao, Q	108	7	12	96	49	39.8	23.1	4.6	14.8	3.7	2.8
Chen, TL	55	3	19	36	74	61.8	67.3	21.8	38.2	20	3.6

Heterogeneity

Significant heterogeneity was noted in the following endpoints: white blood cell count, neutrophil count, lymphocyte count, platelet count, high sensitivity CRP, procalcitonin, D-dimer, IL-6, LDH, CK, CK-Mb, PT, PPT, AST, ALT, creatinine and albumin.

Publication bias

Egger’s tests demonstrated no significant publication bias in any of the endpoints.

Comparison of serum biomarkers between non-survivors and survivors

Due to the large number of endpoints, it is impractical to report the detailed results for each endpoint within the text. These data can be found in Table 3. The following biomarkers were statistically significantly higher in non-survivors: white blood cell count, neutrophil count, CRP, high sensitivity CRP, procalcitonin, ferritin, D-dimer, IL-6, LDH, CK, PT, AST, ALT, total bilirubin and creatinine. The following biomarkers were statistically significantly lower in non-survivors: lymphocyte count, platelet count and albumin. Sensitivity analyses demonstrated a qualitatively greater magnitude in the mean difference with greater age for ferritin.

Table 3.

Table comparing biomarkers in COVID-19 patients who died versus those who survived.

	Significant heterogeneity	Non-survivor mean	Survivor mean	Mean difference (95% confidence interval)
White blood cell count (10⁹/L)	Yes	9.32	5.47	3.67 (2.86 to 4.48), P< 0.01
Neutrophil count (10⁹/L)	Yes	8.26	3.75	3.82 (2.19 to 5.46), P < 0.01
Lymphocyte count (10⁹/L)	Yes	0.65	1.07	–0.43 (–0.50 to –0.35), P < 0.01
Platelet count (10⁹/L)	Yes	167.30	202.85	–31.47 (–49.72 to –13.22), P < 0.01
Haemoglobin (g/dl)	No	12.78	12.77	–0.03 (–0.29 to 0.23), P = 0.82
C-reactive protein (mg/L)	No	112.92	26.08	83.95 (71.33 to 96.58), P < 0.01
High sensitivity C-reactive protein (mg/L)	Yes	106.47	29.79	56.71 (13.07 to 100.34), P = 0.01
Procalcitonin (ng/ml)	Yes	0.67	0.10	0.29 (0.13 to 0.44), P < 0.01
Erythrocyte sedimentation rate (mm/h)	No	45.75	49.97	6.77 (–0.13 to 13.66), P = 0.05
Ferritin (µg/L)	No	1393.96	577.80	836.69 (707.12 to 966.26), P > 0.01
D-dimer (µg/mL)	Yes	8.62	1.02	5.24 (2.42 to 8.05), P < 0.01
Interleukin-6 (pg/mL)	Yes	43.37	17.41	12.45 (6.50 to 18.39), P < 0.01
Lactate dehydrogenase (U/L)	Yes	615.75	282.81	242.39 (169.85 to 314.92), P < 0.01
Creatinine kinase (U/L)	Yes	214.80	93.74	63.71 (28.33 to 99.10), P < 0.01
Creatinine kinase- myocardial band (U/L)	Yes	18.89	15.14	4.41 (–0.33 to 9.15), P = 0.07
Prothrombin time (s)	Yes	13.96	12.48	1.33 (0.84 to 1.81), P < 0.01
Partial thromboplastin time (s)	Yes	40.12	36.61	0.14 (–5.06 to 5.34), P = 0.96
AST (U/L)	Yes	87.90	32.91	13.31 (7.00 to 19.62), P < 0.01
ALT (U/L)	Yes	52.39	30.45	4.99 (0.82 to 9.16), P = 0.02
Total bilirubin (mg/dL)	No	0.99	0.73	0.28 (0.22 to 0.34), P < 0.01
Creatinine (mg/dL)	Yes	1.05	0.90	0.13 (0.05 to 0.21), P < 0.01
Albumin (g/dL)	Yes	2.96	3.48	–0.50 (–0.62 to –0.38), P < 0.01

Discussion

These pooled analyses demonstrate serum biomarkers on admission that are associated with inpatient mortality in COVID-19 admissions. Our results are in accordance with some of the recently published studies evaluating biomarkers with severity illness and mortality in coronavirus patients.^13,14 These biomarkers hold significance in assessing patient’s prognosis on admission and guiding treatment strategies, while we are still learning about the disease’s pathogenesis. Stratifying patients as per their biomarkers would also help in allocating the resources judiciously to sicker patients. An excessive inflammatory response to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been shown to be a major cause of severe disease and has likewise been associated with rise in inflammatory markers like IL-6, ESR and CRP. CRP has been particularly associated as an early marker for the development of severe COVID-19 infection.¹⁵

Haematologic biomarkers including white blood cells, lymphocytes, neutrophils, platelet count and haemoglobin can be used in identifying sicker patients, as Yang et al. reported lymphopaenia in critically ill COVID-19 patients.⁷ Henry et al. also through a systematic review showed that increased WBC with lower lymphocyte counts are more prevalent in patients with severe and fatal disease.¹³ Lower platelet counts have also shown to be associated with more severe infection and higher COVID-19 mortality.¹⁶

Biochemical markers like total bilirubin, CK, ferritin and IL-6 have also been associated with severe as well as more fatal COVID-19 outcomes.^8,9 ALT, AST, creatinine, CK and LDH were also seen at higher levels in patients who died because of COVID-19 in an observational study done by Chen et al.³ Cardiac biomarkers like troponin-I also correlate with severe COVID-19 illness likely from a relative oxygen supply and demand mismatch, possible viral myocarditis like pathology or an element of stress-cardiomyopathy.¹⁷ Hypercoagulable states leading to venous or arterial thrombosis are most relevant to increased D-dimer levels. Similarly, severe infections and sepsis are associated with disseminated intravascular coagulation (DIC), a condition characterized by the activation of coagulation and organ dysfunction that leads to platelet depletion, consumption of coagulation factors and elevated fibrin degradation products, such as D-dimer.¹⁸ This could explain our findings of lower platelets and higher D-dimer levels in patients who died. Also, hypoalbuminaemia has also been observed in severe COVID-19 patients.¹⁹ Increased capillary permeability, reduced protein synthesis, shorter half-life of serum albumin, with increased volume of distribution are the likely factors causing the low albumin concentrations.²⁰

Of note, using the means in the non-survivor group, those in the non-survivor group meet criteria set forth for macrophage activation syndrome. It is important to note that the only criteria with an emphasis on serum biomarkers arise from the paediatric juvenile idiopathic arthritis population. These criteria are a ferritin level of greater than 684 ng/mL along with any two of the following: platelet count of less than or equal to 180 × 10⁹/L, triglycerides of greater than 1.76 mmol/L, fibrinogen of less than 3.6 g/L, AST greater than 48. Our pooled analyses did not capture triglycerides or fibrinogen but, despite this, the means of the non-survivor group met these criteria with the available biomarker data.

Macrophage activation syndrome is characterized by a failure of natural killer cells to clear infected cells and cytotoxic T cells.^21,22 Regulatory T cells become overwhelmed and are also unable to clear infected cells. Cytotoxic T cells begin to proliferate and lead to activation and proliferation of histiocytes. The resulting haemophagocytosis then results in a cytokine storm with upregulation of several interleukins which further propagates the cycle.²³ Many with macrophage activation syndrome will require mechanical ventilation and vasoactive support.²⁴ Neurologic dysfunction, acute kidney injury, acute respiratory distress and coagulation dysfunction are all often noted in the setting of macrophage activation syndrome. In adults, the mortality for macrophage activation syndrome has been reported to be as high as 40%.²⁵ Viral infections are among the most common triggers of macrophage activation syndrome in adults and it appears that the systemic cytokine profiles seen with severe COVID-19 has similarities to those observed in cytokine release syndromes, such as macrophage activation syndrome.²⁶

In different setting and scenarios, biomarkers are used to help guide physicians in the diagnosis risk stratification and treatment of such as diagnosing pulmonary embolism, ruling out non-ST-elevation myocardial infarction, guiding antibiotic therapy and others clinical scenarios.^27–29 In particularly, algorithms have been proposed to rule out sepsis from non-infection systemic inflammatory response both in emergency department and medical intensive care unit.^30,31 Although ideally, biomarkers should always be used together with the clinical presentation and image studies; they should never replace the clinical judgement of the physician.

Strengths and limitations

The strengths of our study include the large sample size and inclusion of multiple haematological, biochemical and inflammatory biomarkers. Although a similar meta-analysis has been performed earlier, it was done with far less studies and patients that compared biomarkers in patients who survived to those who did not survived.¹³ We performed our analysis using recommended approach and statistical methods. However, our study also has limitations. Our analysis includes observational studies which suffer from confounding. However, there are not many randomized trials for us to be able to conduct a robust meta-analysis of only randomized studies. Many of the endpoints in our analysis demonstrated significant heterogeneity. There was also significant amount of bias noted in studies. Hence, our findings should be interpreted with caution.

Conclusions

Footnotes

Acknowledgements

In memoriam: We dedicate this paper to the memory of Luis Carlos Gamboa Chavez (1974–2020), devoted father, son, and friend.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Ethical approval

Not applicable.

Guarantor

RL.

Contributorship

All authors contributed to design and execution of the study. All authors reviewed and edited the article and approved the final version of the article.

ORCID iD

Juan S Farias

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