Skip to main content
PMC home page
PLOS ONE logoLink to PLOS ONE
. 2020 Nov 18;15(11):e0241541. doi: 10.1371/journal.pone.0241541

Factors associated with disease severity and mortality among patients with COVID-19: A systematic review and meta-analysis

Vignesh Chidambaram 1, Nyan Lynn Tun 1, Waqas Z Haque 1, Marie Gilbert Majella 2, Ranjith Kumar Sivakumar 3, Amudha Kumar 4, Angela Ting-Wei Hsu 1, Izza A Ishak 1, Aqsha A Nur 1, Samuel K Ayeh 5, Emmanuella L Salia 6, Ahsan Zil-E-Ali 1, Muhammad A Saeed 7, Ayu P B Sarena 8, Bhavna Seth 9, Muzzammil Ahmadzada 7, Eman F Haque 10, Pranita Neupane 5, Kuang-Heng Wang 1, Tzu-Miao Pu 1, Syed M H Ali 11, Muhammad A Arshad 12, Lin Wang 1, Sheriza Baksh 1, Petros C Karakousis 5, Panagis Galiatsatos 9,*
Editor: Girish Chandra Bhatt13
PMCID: PMC7673562  PMID: 33206661

Abstract

Background

Understanding the factors associated with disease severity and mortality in Coronavirus disease (COVID-19) is imperative to effectively triage patients. We performed a systematic review to determine the demographic, clinical, laboratory and radiological factors associated with severity and mortality in COVID-19.

Methods

We searched PubMed, Embase and WHO database for English language articles from inception until May 8, 2020. We included Observational studies with direct comparison of clinical characteristics between a) patients who died and those who survived or b) patients with severe disease and those without severe disease. Data extraction and quality assessment were performed by two authors independently.

Results

Among 15680 articles from the literature search, 109 articles were included in the analysis. The risk of mortality was higher in patients with increasing age, male gender (RR 1.45, 95%CI 1.23–1.71), dyspnea (RR 2.55, 95%CI 1.88–2.46), diabetes (RR 1.59, 95%CI 1.41–1.78), hypertension (RR 1.90, 95%CI 1.69–2.15). Congestive heart failure (OR 4.76, 95%CI 1.34–16.97), hilar lymphadenopathy (OR 8.34, 95%CI 2.57–27.08), bilateral lung involvement (OR 4.86, 95%CI 3.19–7.39) and reticular pattern (OR 5.54, 95%CI 1.24–24.67) were associated with severe disease. Clinically relevant cut-offs for leukocytosis(>10.0 x109/L), lymphopenia(< 1.1 x109/L), elevated C-reactive protein(>100mg/L), LDH(>250U/L) and D-dimer(>1mg/L) had higher odds of severe disease and greater risk of mortality.

Conclusion

Knowledge of the factors associated of disease severity and mortality identified in our study may assist in clinical decision-making and critical-care resource allocation for patients with COVID-19.

Introduction

Since the first documented reports of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, the virus has had a global impact, affecting millions, which led the World Health Organization (WHO) to declare the outbreak a pandemic [1, 2]. Patients who develop Coronavirus Disease 2019 (COVID-19) may require hospitalization and intensive care unit admission [3–5].

With variable access to critical care resources across countries, recent guidelines for the COVID-19 pandemic have called for allocating life sustaining treatments based on a patient’s risk of mortality [6, 7]. Health systems preparedness requires a deeper understanding of how to effectively triage patients with COVID-19, in order to maximize the benefit of scarce intensive care unit resources while minimizing the potential harm of outpatient management of ill patients. While the guidelines warrant utilizing triage scores that have been previously validated for assessing organ failure/dysfunction and survival (e.g. sequential organ failure assessment, SOFA), these are non-specific in etiology [7, 8]. Thus, having an understanding of the predisposing conditions and disease-specific clinical, laboratory and radiological parameters, may lay the groundwork for developing a COVID-19 specific composite score at a later stage, which can predict unfavorable clinical outcomes.

With the massive influx of studies on COVID-19 in the recent months and their often conflicting or unclear findings, a systematic review of the factors associated with survival or disease severity in patients with COVID-19 that takes into consideration the inherent variability in study population, will be of great utility to clinicians, researchers and policy makers. In this systematic review and meta-analysis, we sought to better understand the clinical, laboratory and radiological parameters associated with mortality and disease severity among patients with COVID-19.

Methods

Search strategy and study selection

We followed the PRISMA guidelines for reporting in systematic reviews and meta-analyses [9]. We searched PubMed, Embase and the WHO COVID-19 database by using the search strategy included in the supplementary document in S1 Appendix (Section I). For PubMed and Embase, an initial search on March 26, 2020 was conducted, and updated multiple times with the final update performed on May 7, 2020. The WHO database was initially downloaded on March 27, 2020 and the final update was performed on May 8, 2020. Only articles published in the English language were included. We only included article published in peer-reviewed academic journals; we did not include articles uploaded in the preprint servers, as they are not peer reviewed and the findings may not be reliable [10].

We included observational studies that included patients with microbiologically confirmed SARS-CoV-2 infection, irrespective of the age of the participants. The study designs of the included studies were assessed and recorded independently by two authors (VC and MM) acting as arbiters. The differentiation between case series and cohort studies was made based on the criteria outlined by Dekkers et al [11]. Case reports, case series, and randomized control trials were excluded from this review. We included all studies that reported a direct comparison of clinical, laboratory or radiologic characteristics between a) patients who died and those who survived or b) patients with severe disease and those without severe disease. Only those studies which defined “severe disease” based on the American Thoracic Society guidelines for the treatment of Community-acquired Pneumonia [12] or the Chinese National Health Commission guidelines for the Treatment of Novel Coronavirus infection [13], were included in our analysis. Studies which only described the characteristics of patients who died or patients with severe disease were excluded if there was no comparison group.

We only included studies reporting primary hospital data on patients, while studies with centralized data from national health agencies and databases were excluded from our review. Efforts were made, as feasible, to minimize overlap of patients across studies by collecting information on the name of the hospital, date of hospital admission of the participants and the names of the investigators.

Literature screening

The COVIDENCE platform was used for conducting this systematic review [14]. After de-duplication, the titles and abstracts of the articles retrieved from the search strategy were screened independently by at least two of the following authors (VC, NT, WH, RS, AK, AH, II, AN, SA, ES, and MS) and conflicts were resolved by consensus between VC and NT. The full texts of the articles included after title and abstract screening were independently screened by at least two of the following authors (VC, NT, WH, RS, AK, AH, II, AN, SA, ES, and MS) and conflicts were resolved based on consensus between VC and NT. Specific reasons for study exclusion are listed in Fig 1.

Fig 1. Stages of the analysis process, assessing inclusion and exclusion criteria that amounted to the articles addressed in this review.

Fig 1

Open in a new tab

Data extraction and quality assessment

Data extraction was performed independently by at least two of the authors (VC, NT, WH, MM, RS, AK, AH, II, AN, SA, ES, MS, AS, KW, and TP) and conflicts were resolved by a consensus between two authors (MM and VC). The data extraction form for this review was created using the Qualtrics platform [15]. The primary outcomes were a) death of the patient and b) the presence of severe disease in the patient. There were no secondary outcomes. Data on the study characteristics, source of funding, demographic characteristics, comorbidities, clinical symptoms, in-hospital complications, laboratory, and radiological features of the study participants were extracted. Data on sex of the patient, smoking status, presence of comorbidities, clinical symptoms, in-hospital complications, and radiological features were extracted as binary variables, while age was extracted as continuous data. For the laboratory parameters, data were extracted as both continuous and categorical variables. Binary data for the laboratory variables were collected using all the cut-offs described in the included studies. The time points for lab measurements, radiological evaluation and the disease severity assessment were extracted for the included studies. Continuous data for the age and laboratory variables were documented as mean. The median values reported in studies were transformed into mean [16].

The risk assessment for bias for all the studies included in this review was performed using the Newcastle-Ottawa quality assessment scale (NOS) for observational and cohort studies [17]. The three major domains of quality of a study covered by this tool were selection of participants, comparability of cohorts and outcome assessment against a total score of 9. This was performed independently by at least two of the following authors (RS, MM, ES, SA). When conflicts related to bias arose, the final decision was taken by a consensus between VC and NT.

Data synthesis and analysis

We performed a meta-analysis with random effects model to obtain pooled effect sizes for the outcomes of interest. The associations between binary parameters and mortality were reported using pooled risk ratios (with 95%CI). Due to the lack of consistency in the time point of assessment of disease severity, odds ratios (with 95%CI) were used to determine the association between the various factors and the presence of severe disease. When the laboratory parameters were reported using different cut-offs, we reported the effect sizes for each cut-off taken separately in addition to the pooled effect sizes for all the cut-offs taken together. Statistical heterogeneity across the studies was assessed by forest plots, I2 and Tau2 statistics. When the I2 was more than 60%, we performed subgroup analyses based on the whether the studies included all patients with COVID-19 or only the patients who were critically ill. If the heterogeneity was still higher than 60%, we performed sensitivity analyses by excluding studies with a low quality (NOS ≤ 5). Sensitivity analyses for the laboratory and radiological parameters were also performed by excluding studies which did not report the time points of assessment. Publication bias was assessed by visual inspection of the funnel plot, and Egger’s test was performed for those exposures reported by at least 10 studies. For continuous variables, meta-regression was performed to assess the percentage change in mortality or the presence of severe disease with unit increase in the mean of laboratory parameter reported in the studies. For the binary exposures, unadjusted effect sizes were calculated from the summary data. Adjusted effect sizes were not used due to the lack of consistency in the parameters that they are adjusted for, among the included studies. All analyses were carried out using the meta package in Stata (StataCorp, version 16) [18].

Role of the funding source

This study was not supported by any funding source.

Results

We identified 15680 studies from three databases after removing duplicates, of which the full text was retrieved for 502 articles. All the articles included were in the English language. The reasons for exclusion of studies are outlined in Fig 1. A total of 109 studies were included for this review, of which 42 studies assessed mortality risks [19–60]; 72 studies determined association with severe disease [24, 34, 39, 41, 56, 58, 61–126], out of which 5 studies reported both the outcomes [24, 34, 39, 56, 58]. Of the total 109 studies in the review, 101 were retrospective cohorts, seven were prospective cohorts, and one of them was an ambispective cohort study. There were no cross-sectional or case control studies that satisfied the inclusion criteria. Of the studies that determined the risk of mortality, an aggregate of 20296 participants were assessed, with 32 studies from China, six from the United States, two from Spain, one from the United Kingdom, one from Italy, one from Iran and a multi-country study. Five of these studies only included patients who were critically ill or invasively ventilated [19, 23, 32, 35, 48]. Among the studies that assessed the association with severe disease, a total of 17992 participants were included with seventy-one studies from China and one study from Italy. The characteristics of the included studies, along with the time points for laboratory, radiological and disease severity assessment, are outlined in the S1 and S2 Tables (in S1 Appendix Section II).

Quality assessment was performed using the New-Castle Ottawa scale (NOS) as all of the studies used a cohort study design. This revealed that one of the studies (0.9%) had scored 9, 54 studies (49.5%) had scored 8, 39 studies (35.7%) scored 7, six studies (5.5%) scored 6. A total of nine studies were identified as low-quality studies (NOS ≤ 5) with six studies (5.5%) scoring 5, and the remaining three studies (2.7%) scoring 4 (S3 and S4 Tables—in S1 Appendix section III).

Table 1 and Fig 2 show the list of exposures and their relationship with mortality. Males (RR 1.45, 95%CI 1.23–1.71) and ever-smokers (RR 1.43, 95%CI: 1.09–1.87) had higher risk of mortality. Patients with diabetes, hypertension, cardiovascular diseases, chronic renal disease, chronic liver disease and malignancy were associated with an increased risk of mortality; while hepatitis B and HIV infections did not result in higher risk of mortality. Section V of the supplementary document in S1 Appendix illustrates the forest and funnel plots of these associations.

Table 1. Association of clinical characteristics with mortality and severe disease in patients with COVID–19.

Clinical characteristics Risk of Mortality Odds of Severe disease
No. of Studies No. of Patients Pooled RR [95% CI] Heterogeneity No. of Studies No. of Patients Pooled OR [95% CI] Heterogeneity
I2 T2 I2 T2
Baseline characteristics and co–morbidities
Male sex 26 16422 *1.45 [1.23–1.71] 49% 0.07 59 17063 1.38 [1.24–1.53] 31% 0.04
Ever smoker 7 10419 1.43 [1.09–1.87] 0 0 10 4511 1.51 [1.06–2.14] 62% 0.33
Diabetes 27 16263 *1.59 [1.41–1.78] 23% 0.02 36 7552 2.09 [1.66–2.64] 40% 0.16
Hypertension 26 15947 *1.90 [1.69–2.15] 28% 0.02 33 7002 2.63 [2.08–3.33] 64% 0.25
Cardiovascular diseases 25 16576 2.27 [1.88–2.79] 71% 0.13 31 6932 2.83 [2.21–3.63] 23% 0.09
Congestive Heart Failure 5 9910 2.08 [1.54–2.80] 0% 0 3 558 4.76 [1.34–16.97] 0% 0
Cerebrovascular Disease 15 2437 2.63 [1.97–3.51] 75% 0.20 13 4246 2.62 [1.76–3.90] 7% 0.04
COPD 15 9717 2.29 [1.90–2.75] 0% 0 19 4790 3.23 [1.97–5.31] 24% 0.27
Asthma 2 643 0.98 [0.42–2.32] 0% 0 ·· ·· ·· ·· ··
CKD 15 6556 2.24 [1.78–2.81] 39% 0.07 14 4442 2.62 [1.46–4.71] 27% 0.31
Chronic liver disease 6 3672 2.18 [1.40–3.40] 20% 0.07 17 8869 1.56 [1.12–2.17] 0% 0
Hepatitis B infection 2 822 1.14 [0.61–2.12] 0% 0 3 1945 0.54 [0.17–1.71] 0% 0
HIV 1 274 1.21 [0.17–8.64] ·· ·· 2 397 4.86 [0.50–47.22] 0% 0
Cancer 18 7008 1.52 [1.21–1.90] 0% 0 20 6026 2.90 [1.99–4.24] 4% 0.04
Immunodeficiency ·· ·· ·· ·· ·· 4 1838 2.51 [0.62–10.10] 16% 0.36
Endocrine diseases ·· ·· ·· ·· ·· 4 1378 2.45 [1.49–4.04] 0% 0
Clinical features
Fever 21 3551 0.82 [0.67–1.00] 28% 0.05 37 7501 1.75 [1.32–2.31] 56% 0.31
Sore throat 4 1256 0.79 [0.44–1.42] 0% 0 20 4721 0.79 [0.60–1.04] 0% 0
Cough 22 4098 1.00 [0.88–1.14] 16% 0.01 39 7746 1.22 [1.08–1.38] 5% 0.01
Expectoration 9 1977 1.19 [0.98–1.45] 55% 0.04 21 4960 1.55 [1.27–1.90] 32% 0.06
Vomiting 5 1644 0.86 [0.51–1.44] 0% 0 7 1919 1.02 [0.65–1.60] 0% 0
Diarrhea 14 3230 1.15 [0.85–1.57] 65% 0.17 33 6831 1.31 [1.00–1.71] 43% 0.19
Nausea 7 1725 1.00 [0.67–1.50] 1% 0 13 3809 1.01 [0.61–1.68] 49% 0.34
Myalgia 18 3270 0.91 [0.76–1.08] 8% 0.01 25 5831 1.22 [0.90–1.65] 62% 0.29
Headache 11 2645 0.95 [0.66–1.37] 21% 0.07 26 6340 1.44 [1.00–2.06] 53% 0.37
Anorexia 5 1119 1.04 [0.86–1.26] 0% 0 10 1157 2.72 [1.84–4.01] 0% 0
Chest pain 7 2026 1.16 [0.77–1.75] 43% 0.11 16 3558 2.70 [1.56–4.68] 75% 0.73
Dyspnea 20 3595 2.55 [1.88–3.46] 77% 0.30 34 7356 4.72 [3.18–7.01] 86% 0.90
Hemoptysis 6 1014 1.62 [1.25–2.11] 0% 0 5 2584 2.93 [1.47–5.83] 0% 0
Abdominal pain 5 1498 1.22[0.47–3.16] 7% 0.09 9 2506 2.86 [1.00–8.13] 69% 1.67
Palpitations 1 225 0.94 [0.59–1.49] ·· ·· 2 254 3.14 [0.88–11.19] 0% 0
Rhinorrhea 1 52 1.48 [0.96–2.29] ·· ·· 4 1096 0.94 [0.38–2.30] 0% 0
Anosmia 1 95 0.45 [0.03–6.40] ·· ·· ·· ·· ·· ·· ··
Complications
ARDS 14 2795 *20.19 [10.87–37.52] 79% 0.90 ·· ·· ·· ·· ··
Shock 9 1844 6.12 [3.59–10.45] 93% 0.54 ·· ·· ·· ·· ··
Sepsis 3 573 47.95 [11.81–194.72] 0% 0 ·· ·· ·· ·· ··
Bacteremia 6 1365 5.07 [2.02–12.69] 94% 1.18 ·· ·· ·· ·· ··
Acute cardiac injury 14 2860 5.42 [3.79–7.77] 86% 0.36 ·· ·· ·· ·· ··
Acute heart failure 3 495 3.10 [2.55–3.77] 0% 0 ·· ·· ·· ·· ··
DIC 4 1394 3.41 [2.00–5.81] 95% 0.28 ·· ·· ·· ·· ··
GI bleeding 4 1028 2.53 [1.42–4.49] 76% 0.26 ·· ·· ·· ·· ··
Acute Kidney Injury 15 5331 *4.65 [3.25–6.65] 95% 0.42 ·· ·· ·· ·· ··
Acute liver injury 10 4796 2.54 [1.77–3.66] 93% 0.28 ·· ·· ·· ·· ··
Hepatic encephalopathy 1 109 3.99 [2.71–5.87] ·· ·· ·· ·· ·· ·· ··
Ventilator associated pneumonia 1 52 0.51 [0.16–1.62] ·· ·· ·· ·· ·· ·· ··
CT features
Peripheral distribution 1 27 0.32 [0.05–2.08] ·· ·· 4 313 1.40 [0.36–5.50] 28% 0.57
Bilateral involvement 11 2067 1.35[1.07–1.69] 0 0 19 3515 4.86 [3.19–7.39] 47% 0.28
Consolidation 3 243 2.07 [1.35–3.16] 0 0 9 1084 3.01 [1.32–6.88] 76% 1.09
GGO 5 706 1.41 [0.87–2.28] 55% 0.15 14 2629 1.63 [1.22–2.17] 11% 0.03
Mixed GGO and consolidation ·· ·· ·· ·· ·· 3 263 1.63 [0.69–3.85] 0 0
Air bronchogram 1 27 3.56[1.37–9.29] ·· ·· 4 333 4.79[1.11–20.61] 85% 1.85
Nodular infiltrates 1 27 0.41 [0.03–5.41] ·· 0 4 356 1.03 [0.39–2.73] 46% 0.45
Hilar Lymphadenopathy ·· ·· ·· ·· ·· 5 397 8.34 [2.57–27.08] 0% 0
Tree in bud appearance ·· ·· ·· ·· ·· 1 120 4.30 [1.07–17.23] . 0
Unifocal involvement 2 463 0.31 [0.11–0.92] 44% 0.27 3 1237 1.13 [0.46–2.81] 66% 0.38
Pleural effusion 2 78 1.06 [0.27–4.21] ·· ·· 8 642 5.30 [2.74–10.26] 0% 0
Pleural thickening ·· ·· ·· ·· ·· 1 52 1.86[0.35–9.92] ·· 0
Inter–lobular septal thickening ·· ·· ·· ·· ·· 2 124 2.86 [1.06–7.72] 0% 0
Bronchiectasis ·· ·· ·· ·· ·· 2 221 5.62 [2.22–14.24] 0% 0
Linear infiltrates ·· ·· ·· ·· ·· 3 324 3.21[1.00–10.25] 72% 0.74
Crazy pavement sign ·· ·· ·· ·· ·· 5 650 4.52 [2.08–9.81] 64% 0.50
Reticular pattern ·· ·· ·· ·· ·· 4 425 5.54 [1.24–24.67] 72% 1.61
Open in a new tab

Abbreviations: ARDS = Acute Respiratory Distress Syndrome. CKD = Chronic Kidney Disease. COPD = Chronic Obstructive Pulmonary Disease. CT = Computed Tomography. DIC = Disseminated Intravascular Coagulation. GGO = Ground Glass Opacity. GI Bleeding = Gastrointestinal Bleeding. HIV = Human Immunodeficiency Virus. OR = Odds ratio. RR = Risk ratio.

*denotes presence of publication bias by Egger’s test (p–value < 0·05).

Fig 2.

Fig 2

Open in a new tab

Meta-analysis to assess risk of mortality for (A) smoking status. (B) hypertension. (C) diabetes mellitus. (D) COPD. (E) acute cardiac injury. (F) acute kidney injury. (G) bilateral lung involvement. (H) Lung consolidation. COPD = chronic obstructive pulmonary disease. !Shenzhen Third People’s Hospital, Shenzhen, China. #Tongji Hospital, Wuhan, China. $Wuhan Jinyintan Hospital, Wuhan, China. *Zhongnan Hospital of Wuhan University, Wuhan, China. %Tianyou Hospital, Wuhan, China. &Taizhou Public Health Medical Center, Zhejiang, China. +Chongqing Three Gorges Central Hospital, Chongqing, China. @Renmin Hospital of Wuhan University, Wuhan, China. ^General Hospital of Central Theater Command of People’s Liberation Army, Wuhan, China.

Patients who had dyspnea (RR 2.55, 95%CI: 1.88–3.46) and hemoptysis (RR 1.62, 95%CI: 1.25–2.11) had a significantly higher risk of mortality while other clinical features like fever, sore throat, cough, expectoration, vomiting, diarrhea, nausea, myalgia, headache, anorexia, chest pain, abdominal pain, palpitations, and anosmia did not demonstrate any significant association with mortality. Acute respiratory distress syndrome (ARDS) was associated with an RR of 20.19 [95%CI: 10.87–37.52] with an I2 of 79%. Cardiac complications such as acute cardiac injury and acute cardiac failure had higher risk of mortality with RR of 5.42 [95%CI: 3.79–7.77] and 3.10 [95%CI: 2.55–3.77] respectively. Other complications with higher risk of mortality were sepsis, bacteremia, shock, disseminated intravascular coagulation (DIC), acute kidney injury and acute liver dysfunction. Association of laboratory parameters (based on specific cut-offs) with the risk of mortality are mentioned in the Table 2. Among the laboratory parameters that were assessed using binary cut-offs, increased total leucocyte count, increased neutrophil count, decreased lymphocyte count and reduced platelet count were associated with increased risk of death. Inflammatory parameters such as C-reactive protein and procalcitonin were associated with increased risk of death with RRs of 5.49 [95%CI: 1.72–17.51] and 3.09 [95%CI: 2.35–4.07] respectively. Abnormal renal and liver parameters, hypernatremia and hyperkalemia also were associated with increased risk of mortality.

Table 2. Association of laboratory parameters with mortality in patients with COVID–19.

Laboratory Parameters Pooled Risk of Mortality Lab Cut-offs Risk of Mortality (based on cut-offs)
No. of Studies No. of Patients Pooled RR [95% CI] Heterogeneity No. of Studies No. of Patients Pooled OR [95% CI] Heterogeneity
I2 T2 I2 T2
Decreased TLC 8 1534 0.43[0.31–0.59] 0% 0 TLC < 3.5 x109/L 1 154 0.58[0.26–1.30] - -
TLC < 4.0 x109/L 7 1380 0.43 [0.29–0.58] 0% 0
Increased TLC 9 1708 3.85[2.56–5.78] 80.87% 0.06 TLC > 9.5 x109/L 3 353 3.60 [2.22–5.84] 31.88% 0.06
TLC >10.0 x109/L 6 1355 3.97[2.06–7.64] 90.80% 0.55
Decreased Neutrophil 2 727 0.33[0.03–3.15] 72.18% 1.93 N < 1.8 x109/L 1 179 0.94[0.24–3.72] - -
N < 2 x109/L 1 548 0.09[0.01–0.66] - -
Increased Neutrophil 3 1001 4.45 [3.20–6.18] 38.52% 0.03 N > 6.3 x109/L 2 453 3.83 [2.90–5.07] 0% 0
N > 6.5 x109/L 1 548 5.95[3.93–8.99] - -
Decreased Lymphocyte 8 1554 4.09 [1.69–9.91] 83.63% 1.24 L < 0.8 x109/L 2 299 4.45[2.70–7.32] 0% 0
L < 1.0 x109/L 2 329 2.20[0.35–14.07] 91.96% 1.65
L < 1.1 x109/L 4 378 5.97[0.98–36.29] 79.77% 2.68
Decreased Platelet count 6 1439 2.42 [1.78–3.30] 51.04% 0.07 Plt count < 100 x 109/L 3 354 2.45[1.30–4.62] 51.44% 0.16
Plt count < 125 x 109/L 2 537 2.45[1.26–4.75] 74.92% 0.17
Plt count < 150 x 109/L 1 548 2.08 [1.39–3.13] - -
Decreased Albumin 4 1031 3.30[2.61–4.18] 0% 0 Albumin < 32 g/L 1 274 3.52[2.61–4.74] - -
Albumin < 35 g/L 2 702 3.27[2.19–4.88] 0% 0
Albumin < 40 g/L 1 55 1.24[0.36–4.25] - -
Increased Globulin 2 603 1.65[1.15–2.37] 0% 0 Globulin >35 g/L 2 603 1.65[1.15–2.37] 0% 0
Increased T· Bilirubin 3 810 2.74[1.96–3.82] 0% 0 T. Bilirubin > 20 umol/L 3 810 2.74[1.96–3.82] 0% 0
Increased AST 5 1210 2.33[1.93–2.82] 0% 0 AST > 40 U/L 5 1210 2.33 [1.93–2.82] 0% 0
Increased ALT 6 1330 1.48[1.20–1.82] 8.88% 0.01 ALT > 40 U/L 3 976 1.30[1.04–1.64] 0% 0
ALT > 50 U/L 3 354 2.00 [1.39–2.87] 0% 0
Increased GGT ·· ·· ·· ·· ·· ·· ·· ·· ·· ·· ··
Increased ALP ·· ·· ·· ·· ·· ·· ·· ·· ·· ·· ··
Increased INR ·· ·· ·· ·· ·· ·· ·· ·· ·· ·· ··
Increased PT 2 345 1.97[1.41–2.76] 0% 0 PT > 16s 2 345 1.97 [1.41–2.76] 0% 0
Elevated CK–total 4 508 1.96[1.43–2.70] 9.52% 0.01 CK–total > 171 U/L 2 209 1.87[0.88–3.97] 54.89% 0.18
CK–total > 185 U/L 1 191 1.78[1.09–2.91] ·· ··
CK–total > 190 U/L 1 108 1.56[0.47–5.23] ·· ··
Elevated CK-MB 1 108 5.75[2.12–15.62] - - CK-MB > 25 U/L 1 108 5.75[2.12–15.62] ·· ··
Increased BUN 2 702 4.42[2.99–6.55] 45.89% 0.04 BUN ≥ 7.6 mmol/L 1 548 5.34 [3.63–7.85] ·· ··
BUN ≥ 8.3 mmol/L 1 154 3.58 [2.32–5.51] ·· ··
Increased Creatinine 6 1235 2.92[2.35–3.62] 0% 0 Creatinine > 85 umol/L 1 548 2.99[2.35–3.62] ·· ··
Creatinine > 104 umol/L 1 55 2.61[1.36–4.98] ·· ··
Creatinine > 115 umol/L 1 154 2.88[2.02–4.10] ·· ··
Creatinine > 133 umol/L 3 478 3.19 [1.62–6.31] 42.97% 0.15
Increased Na+ 2 428 2.70 [2.00–3.64] 63% 0.03 Na+ > 145 mmol/L 2 428 2.70 [2.00–3.64] 63% 0.03
Increased K+ 3 536 2.34[1.87–2.94] 9.45% 0.00 K+ > 5 mmol/L 2 428 2.36[1.85–3.01] 24.03% 0.01
K+ > 5.4 mmol/L 1 108 2.20[0.57–8.54] 9.45% 0.00
Decreased K+ 3 536 0.99[0.43–2.29] 60.38% 0.33 K+ < 3.5 mmol/L 2 428 0.70[0.22–2.27] 67.68% 0.52
K+ < 3.8 mmol/L 1 108 2.07 [0.69–6.23] ·· ··
Elevated LDH 5 1222 5.37 [2.10–13.74] 80.61% 0.84 LDH > 245 U/L 3 345 4.19[0.85–20.66] 83.47% 1.59
LDH > 250 U/L 1 548 13.10[3.26–52.66] ·· ··
LDH > 350 U/L 1 274 6.33[4.16–9.63] ·· ··
Elevated Myoglobin 1 179 5.30 [2.36–11.92] ·· 0 Myoglobin> 100 μg/L 1 179 5.30 [2.36–11.92] ·· 0
Increased Uric acid ·· ·· ·· ·· ·· ·· ·· ·· ·· ··
Elevated Cystatin C 1 108 1.75[0.41··7.50] ·· ·· Cystatin C >1.2mg/L 1 108 1.75[0.41··7.50] ·· ··
Elevated D–Dimer 9 2026 3.98[2.87–5.52] 65.13% 0.13 D–Dimer > 0.5mg/L 4 562 3.33[1.48–7.49] 63.45% ··
D–Dimer > 1mg/L 3 847 4.82[3.29–7.07] 0% 0
D–Dimer > 2mg/L 1 343 5.54[4.15–7.39] ·· ··
D–Dimer > 21mg/L 1 274 3.06[2.44–3.85] ·· ··
Increased CRP 6 1338 5.49[1.72–17.51] 93.21% 1.59 CRP > 10 mg/L 1 108 6.22[0.83–46.36] ·· ··
CRP > 100 mg/L 5 1175 5.72[1.40–23.41] 95.68% 2.12
Elevated ESR 2 603 0.96[0.64–1.44] 0% 0 ESR>15 mm/h 2 603 0.96[0.64–1.44] 0% 0
Elevated Procalcitonin 8 1555 3.09[2.35–4.07] 53.89% 0.07 Procalcitonin >0.05 μg/L 3 810 4.45[3.19–6.21] 0% 0
Procalcitonin >0.5 μg/L 4 690 2.21[1.13–4.33] 87.85% 0.35
Procalcitonin >1 μg/L 1 55 2.67[1.45–4.90] ·· ··
Increased Serum ferritin 1 191 5.6 [1.47–21.6] ·· ·· S. ferritin > 300 μg/L 1 191 5.6 [1.47–21.6] ·· ··
Increased SAA ·· ·· ·· ·· ·· ·· ·· ·· ·· ·· ··
Increased IL–1β 1 274 0.84 [0.38–1.83] ·· ·· IL–1β > 5 ng/L 1 274 0.84 [0.38–1.83] ·· ··
Increased IL–2 1 274 4.04 [2.18–7.49] ·· ·· IL–2 > 710 U/L 1 274 4.04 [2.18–7.49] ·· ··
Increased IL–6 2 503 22.59[3.19–160.03] 0% 0 IL–6 > 2.9 ng/L 1 229 12.66[0.79–202.89] ·· ··
IL–6 > 7 ng/L 1 274 40.13[2.53–636.24] ·· ··
Increased IL–8 1 274 2.29 [1.51–3.45] ·· ·· IL–8 > 62 ng/L 1 274 2.29 [1.51–3.45] ·· ··
Increased IL–10 1 274 4.19 [2.56–6.84] ·· ·· IL–10 > 9.2 ng/L 1 274 4.19 [2.56–6.84] ·· ··
Increased TNF–Alpha 1 274 2.57 [1.46–4.52] ·· ·· TNF–Alpha >8.1 ng/L 1 274 2.57 [1.46–4.52] ·· ··
Increased NT–ProBNP 2 822 5.48 [3.78–7.34] 0% 0 NT–ProBNP > 285 ng/L 1 275 5.87[3.43–10.03] ·· ··
NT–ProBNP > 500 ng/L 1 548 5.136[3.07–8.60] ·· ··
Elevated troponin 6 1006 4.49[3.74–5.38] 0% 0 hs Tn I > 15.6 ng/L 2 375 4.62[2.80–7.65] 52.63% 0.07
hs Tn I > 26.2 ng/L 1 107 6.01[2.80–7.65] ·· ··
hs Tn I > 28 ng/L 1 191 4.29[3.05–6.05] ·· ··
Tn I > 50 ng/L 1 179 5.47[2.44–12.28] ·· ··
hs Tn I > 100 ng/L 1 154 4.28[2.91–6.29] ·· ··
Open in a new tab

Abbreviations: ALP = Alkaline Phosphatase. ALT = Alanine Aminotransferase. AST = Aspartate Aminotransferase. BNP = Brain Natriuretic Peptide. BUN = Blood Urea Nitrogen. CK = Creatine Kinase. CRP = C–Reactive Protein. ESR = Erythrocyte Sedimentation Rate. GGT = Gamma–Glutamyl Transferase. IL = Interleukin. INR = International Normalized Ratio. K+ = Potassium. LDH = Lactate Dehydrogenase. N = Neutrophil count. Na+ = Sodium. NT–ProBNP = N–Terminal Fragment Brain Natriuretic Peptide. Plt count = Platelet count. OR = Odds ratio. PT = Prothrombin Time. RR = Risk ratio. T Bilirubin = Total Bilirubin. TLC = Total Leucocyte Count. TNF = Tumor Necrosis Factor.

*denotes presence of publication bias by Egger’s test (p–value < 0·05).

Presence of bilateral lung infiltrates (RR 1.35, 95%CI 1.07–1.69), consolidation (RR 2.07, 95%CI 1.35–3.16) and air-bronchogram (RR 3.56, 95%CI 1.37–9.29) on CT were associated with increased RR for mortality. The presence of unifocal involvement on CT had lower risk of death (RR 0.31, 95%CI 0.11–0.92). The presence of ground glass opacities or pleural effusion on CT demonstrated no association with mortality.

The odds ratios (OR) of severe disease in patients with various clinical characteristics are shown in Table 1 and Fig 3. The odds of severe disease were high in patients with diabetes, hypertension, cardiovascular diseases, chronic kidney disease, chronic liver disease and chronic obstructive pulmonary disease. HIV and hepatitis B infections were not associated with severe disease. Odds ratios for severe disease were higher in patients with fever, cough, expectoration, anorexia, chest pain, dyspnea, and hemoptysis. The OR for severe disease in patients who had dyspnea was 4.72 [95% CI: 3.18–7.01] among 34 studies. Gastrointestinal symptoms nausea, vomiting and diarrhea did not show association with disease severity.

Fig 3. Meta-analysis to assess odds of severe disease for (A) smoking status (B) hypertension (C) diabetes mellitus (D) COPD E. bilateral lung involvement (F) Lung consolidation.

Fig 3

Open in a new tab

COPD = chronic obstructive pulmonary disease. !Shenzhen Third People’s Hospital, Shenzhen, China. # Tongji Hospital, Wuhan, China. $Wuhan Jinyintan Hospital, Wuhan, China. *Zhongnan Hospital of Wuhan University, Wuhan, China. %Tianyou Hospital, Wuhan, China. &Central hospital of Wuhan, Wuhan. +Chongqing Three Gorges Central Hospital, Chongqing, China. @Renmin Hospital of Wuhan University, Wuhan, China. ^General Hospital of Central Theater Command of People’s Liberation Army, Wuhan, China.

Among the laboratory parameters that were assessed for association with severe disease using binary cut-offs (Table 3), decreased lymphocyte count (OR 2.64, 95%CI 1.12–6.23), decreased serum albumin (OR 5.63, 95%CI 1.45–21.87), increased aspartate transaminase (OR 4.33, 95%CI 2.17–8.63) demonstrated increased odds of severe disease. Inflammatory parameters namely, elevated CRP (OR 3.32, 95%CI 1.92–5.71), elevated procalcitonin (OR 5.50, 95%CI 3.38–8.93) and increased ESR (OR 1.68, 95%CI 1.13–2.49) had high ORs for severe disease. Bilateral lung involvement on CT was associated with an OR of 4.86[95%CI: 3.19–7.39] among 19 studies. Other radiological features on CT with higher odds of severe disease were lung consolidation, ground glass opacities, air bronchogram, hilar lymphadenopathy, pleural effusion, crazy-pavement pattern, reticular pattern, tree-in bud appearance, inter-lobar septal thickening, and bronchiectasis. However, the OR of severe disease were not increased with peripheral distribution of infiltrates, nodular infiltrates, linear infiltrates, unifocal involvement or pleural thickening.

Table 3. Association of laboratory parameters with disease severity in patients with COVID–19.

Laboratory Parameters Pooled Odds of Disease Severity Lab Cut-offs Pooled Odds of Disease Severity (based on cut-offs)
No. of Studies No. of Patients Pooled RR [95% CI] Heterogeneity No. of Studies No. of Patients Pooled OR [95% CI] Heterogeneity
I2 T2 I2 T2
Decreased TLC 11 3074 0.84[0.52–1.37] 78.58% 0.46 TLC < 3.5 x109/L 4 548 0.70[0.37–1.33] 29.06% ··
TLC < 4.0 x109/L 6 2482 0.89[0.43–1.82] 88.86% 0.67
TLC < 5.0 x109/L 1 44 1.21[0.36–4.08] ·· ··
Increased TLC 11 3353 3.70[2.31–5.93] 49.71% 0.27 TLC > 9.5 x109/L 4 548 2.94[2.31–5.93] 47.12% 0.47
TLC >10.0 x109/L 6 1706 4.65[2.17–9.98] 63.09% 0.49
TLC >11.0 x109/L 1 1099 2.54[1.43–4.52] ·· ··
Decreased Neutrophil 2 689 0.33[0.19–0.58] 0% 0 N < 1.8 x109/L 1 141 0.95[0.10–8.97] ·· ··
N < 2 x109/L 1 548 0.31[0.17–0.55] ·· ··
Increased Neutrophil 3 554 2.00[0.93–4.31] 52.14% 0.25 N > 6.3 x109/L 1 141 4.95[1.67–14.68] ·· ··
N > 6.5 x109/L 1 90 1.19[0.32–4.42] ·· ··
N > 7.5 x109/L 1 323 1.52[0.93–2.47] ·· ··
Decreased Lymphocyte 7 2922 2.64[1.12–6.23] 87.02% 1.04 L < 0.8 x109/L 2 342 3.10[0.49–19.69] 83.84% 1.49
L < 1.0 x109/L 1 476 4.04[2.59–6.31] ·· ··
L < 1.1 x109/L 2 638 1.77[0.09–33.23] 94.51% 4.23
L < 1.5 x109/L 1 1143 8.56[0.44–165.38] ·· ··
L < 2.0 x109/L 1 323 1.84[1.12–6.23] ·· ··
Decreased Platelet count 9 2691 2.39[1.72–3.34] 34.31% 0.13 Plt count < 100 x 109/L 3 592 2.27[1.08–4.78] 0% 0
Plt count < 125 x 109/L 3 408 3.09[1.37–6.99] 45.21% 0.23
Plt count < 150 x 109/L 3 1691 2.22[1.32–3.73] 67.23% 0.13
Decreased Albumin 2 663 5.63[1.45–21.87] 77.32% 0.77 Albumin < 35 g/L 1 548 3.19[2.22–4.57] ·· ··
Albumin < 40 g/L 1 115 13.07[3.68–46.40] ·· ··
Increased Globulin 2 663 1.98[1.44–2.72] 0% 0 Globulin >30 g/L 1 115 1.67[0.77–3.64] 0% 0
Globulin >35 g/L 1 548 2.05[1.44–2.90] 0% 0
Increased T· Bilirubin 5 2031 2.23[1.34–3.71] 23.14% 0.08 T. Bilirubin > 20 umol/L 5 2031 2.23[1.34–3.71] 23.14% 0.08
Increased AST 7 2624 4.85[2.52–9.34] 86.47% 0.62 AST > 40 U/L 7 2624 4.85[2.52–9.34] 86.47% 0.62
Increased ALT 7 2920 2.40[1.09–5.29] 89.03% 0.97 ALT > 40 U/L 4 2607 2.36[0.81–6.89] 97.33% 2.41
ALT > 50 U/L 3 313 2.33[0.85–6.38] 0% 0
Increased GGT 1 115 1.42[0.44–4.55] ·· ·· GGT > 57 U/L 1 115 1.42[0.44–4.55] ·· ··
Increased ALP 1 115 6.07[1.05–35.03] ·· ·· ALP > 120 U/L 1 115 6.07[1.05–35.03] ·· ··
Increased INR 1 115 1.38[0.60–3.18] ·· ·· INR > 1.15 1 115 1.38[0.60–3.18] ·· ··
Increased PT 2 413 2.35[1.26–4.41] 0% 0 PT > 12.8s 1 90 1.65[0.58–4.67] ·· ··
PT > 14s 1 323 2.88[1.31–6.32] ·· ··
Elevated CK–total 6 1733 3.11[1.74–5.55] 41.86% 0.21 CK–total > 190 U/L 3 359 3.73[1.97–7.07] 2.42% 0.01
CK–total > 190 U/L 3 1374 2.87[1.03–8.00] 49.25% 0.42
Elevated CK-MB 4 794 1.41[0.42–4.67] 72.23% 1.02 CK-MB > 5 U/L 2 596 0.73[0.14–3.69] 77.79% 1.08
CK-MB > 25 U/L 2 198 2.83[1.03–7.82] 0% 0
Increased BUN 2 871 4.06[2.71–6.07] 0% 0 BUN ≥ 7.6 mmol/L 1 548 4.77[2.75–8.29] ·· ··
BUN ≥ 8 mmol/L 1 323 3.37[1.87–6.09] ·· ··
Increased Creatinine 8 2508 2.49[1.41–4.41] 21.59% 0.14 Creatinine > 87 umol/L 2 709 1.66[1.14–2.43] 0% 0
Creatinine > 97 umol/L 2 225 5.88[0.63–54.73] 47.51% 1.33
Creatinine > 133 umol/L 3 1251 3.81[1.4–10.11] 0% 0
Creatinine > 144 umol/L 1 323 2.18[0.42–11.41] ·· ··
Increased Na+ ·· ·· ·· ·· ·· ·· ·· ·· ·· ·· ··
Increased K+ 1 108 0.94[0.18–4.86] ·· ·· K+ > 5.1 mmol/L 1 108 0.94[0.18–4.86] ·· ··
Decreased K+ 1 108 7.59 [2.67–21.60] ·· ·· K+ < 3.8 mmol/L 1 108 7.59 [2.67–21.60] ·· ··
Elevated LDH 7 2425 3.77[1.95–7.30] 82.69% 0.61 LDH > 225 U/L 1 161 4.70[2.02–10.94] ·· ··
LDH > 243 U/L 1 115 5.90[2.33–14.95] ·· ··
LDH > 250 U/L 4 2105 2.92[0.95–8.93] 92.70% 1.18
LDH > 300 U/L 1 44 6.29[1.60–24.73] ·· ··
Elevated Myoglobin 1 273 4.57[2.05–10.17] ·· ·· Myoglobin> 100 μg/L 1 273 4.57[2.05–10.17] ·· ··
Increased Uric acid 1 90 3.28[0.52–20.77] ·· ·· Uric acid >417 umol/L 1 90 3.28[0.52–20.77] ·· ··
Increased Cystatin C 1 108 2.14[0.66–6.88] ·· ·· Cystatin C >1.2mg/L 1 108 2.14[0.66–6.88] ·· ··
Elevated D–Dimer 5 1985 2.75[1.92–3.93] 32.40% 0.05 D–Dimer > 0.25mg/L 2 230 3.44[1.48–8.02] 0% 0
D–Dimer > 0.5mg/L 1 1099 1.94[1.27–2.97] ·· ··
D–Dimer > 1mg/L 2 656 3.33[2.37–4.69] 0% 0
Increased CRP 12 3375 3.32[1.92–5.71] 73.74% 0.55 CRP > 30 mg/L 3 595 1.82[0.63–5.24] 50.77% 0.45
CRP > 50 mg/L 1 108 9.25[2.05–41.80]] ·· ··
CRP > 80 mg/L 2 301 14.43[3.89–53.50] 0% 0
CRP > 100 mg/L 6 2371 2.92[1.55–5.51] 77.52% 0.43
Elevated ESR 1 548 1.68[1.13–2.49] ·· ·· ESR>20 mm/h 1 548 1.68[1.13–2.49] ·· ··
Elevated Procalcitonin 8 2392 5.50[3.38–8.93] 38.70% 0.18 Procalcitonin >0.05 μg/L 2 240 3.45[1.61–7.37] 0% 0
Procalcitonin >0.1 μg/L 2 275 6.34[1.58–25.38] 78.13% 0.79
Procalcitonin >0.5 μg/L 4 1877 6.97[3.12–15.58] 39.61% 0.26
Increased Serum ferritin 1 548 3.57 [2.12–6.01] ·· ·· S. ferritin > 500 μg/L 1 548 3.57 [2.12–6.01] ·· ··
Increased SAA 5 830 1.54[0.84–2.82] 19.73% 0.10 SAA > 1mg/L 1 121 0.92[0.43–2.01] ·· ··
SAA > 10mg/L 4 709 2.39[0.92–6.22] 27.08% 0.28
Increased IL–1β 1 548 0.62[0.37–1.02] ·· ·· IL–1β > 5 ng/L 1 548 0.62[0.37–1.02] ·· ··
Increased IL–2 1 548 2.60[1.63–4.14] ·· ·· IL–2 receptor > 710 U/L 1 548 2.60[1.63–4.14] ·· ··
Increased IL–6 3 778 3.30[0.73–14.93] 82.0% 1.38 IL–6 > 7 ng/L 3 778 3.30[0.73–14.93] 82.0% 1.38
Increased IL–8 1 548 1.83[0.79–4.27] ·· ·· IL–8 > 62 ng/L 1 548 1.83[0.79–4.27] ·· ··
Increased IL–10 1 548 1.62[0.98–2.67] ·· ·· IL–10 > 9.2 ng/L 1 548 1.62[0.98–2.67] ·· ··
Increased TNF–Alpha 1 548 1.90[1.20–3.03] ·· ·· TNF–Alpha >8.1 ng/L 1 548 1.90[1.20–3.03] ·· ··
Increased NT–Pro BNP 2 821 4.43[2.80–7.02] 0% 0 NT–Pro BNP > 500 ng/L 1 548 4.23[2.36–7.59] ·· ··
NT–Pro BNP > 900 ng/L 1 283 4.78[2.27–10.08] ·· ··
Elevated troponin 2 596 3.04 [1.03–8.97] 77.67% 0.48 hs Tn I > 40 ng/L 2 596 3.04 [1.03–8.97] 77.67% 0.48
Open in a new tab

Abbreviations: ALP = Alkaline Phosphatase. ALT = Alanine Aminotransferase. AST = Aspartate Aminotransferase. BNP = Brain Natriuretic Peptide. BUN = Blood Urea Nitrogen. CK = Creatine Kinase. CRP = C–Reactive Protein. ESR = Erythrocyte Sedimentation Rate. GGT = Gamma–Glutamyl Transferase. IL = Interleukin. INR = International Normalized Ratio. K+ = Potassium. LDH = Lactate Dehydrogenase. N = Neutrophil count. Na+ = Sodium. NT–ProBNP = N–Terminal Fragment Brain Natriuretic Peptide. Plt count = Platelet count. OR = Odds ratio. PT = Prothrombin Time. RR = Risk ratio. T Bilirubin = Total Bilirubin. TLC = Total Leucocyte Count. TNF = Tumor Necrosis Factor.

*denotes presence of publication bias by Egger’s test (p–value < 0·05).

Meta-regression of continuous variables (Table 4) revealed that with every ten-year increase in the mean age of the patients, there was a 7.6% and 11.2% increase in the mortality (p-value = 0.02) and disease severity (p-value <0.001) respectively. Increase in the mean total leukocyte count (p-value = 0.04) and a decrease in the mean lymphocyte count (p-value = 0.02) were significantly associated with higher mortality. Higher levels of mean C-reactive protein and mean D-dimer during admission were linked to higher proportion of both severe disease as well as mortality. Increasing mean lactate dehydrogenase and creatine kinase among the included studies were associated with higher mortality but not severe disease. Higher serum creatinine levels and lower serum albumin levels correlated with disease severity but was not significantly associated with mortality, though the direction of the relationship was consistent. Unit increase in the mean blood urea nitrogen resulted in 5.3% increase in the mortality (p-value = 0.030). Our analysis did not reveal a significant association between mortality or disease severity and other laboratory parameters, such as platelet count, hemoglobin, prothrombin time, activated partial thromboplastin time (aPTT), procalcitonin, aspartate transaminase (AST), alanine transaminase (ALT), erythrocyte sedimentation rate, total bilirubin and interleukin-6 levels. Bubble plots assessing the linear relationship between the variables and the outcomes (proportion with severe disease and proportion who died) are shown in Figs 4 and 5 and Supplementary Section VI in S1 Appendix.

Table 4. Association of clinical parameters expressed as continuous variables with mortality and disease severity in patients with COVID–19 by meta-regression.

Variable Mean increase of the variable No· of studies No· of patients Percentage change in mortality [95% CI] p–value No· of studies No· of patients Percentage change in severe disease [95% CI] p–value
Age 10 years 41 20296 7.6 [1.0, 14.2] 0.02 70 17799 11.3 [6.5, 16.2] <0.001
Hb 1g/L 12 2519 1.1 [–1.4, 3.6] 0.41 13 3013 –1.1 [–1.6, –0.5] 0.002
TLC 1x10^9/L 20 9797 5.4 [0.2, 10.6] 0.04 28 5370 15.2 [6.23, 24.18] <0.001
Lymphocyte count 0·1x10^9/L 24 10097 –4.1 [–7.4, –0.8] 0.02 31 5696 –17.3 [– 35.9, 1.3] 0.07
Neutrophil count 1x10^9/L 14 8930 10.5 [0.5, 20.6] 0.04 22 3586 9.8 [0.5, 19.1] 0.04
Platelet count 50 x 10^9/L 14 3120 –18.8 [–41.6, 3.85] 0.10 15 3190 0.1 [–0.4, 0.51] 0.82
Prothrombin time 1 s 16 3277 3.3 [–4.2, 10.8] 0.39 ·· ·· ·· ··
aPTT 1 s 12 2651 1.0 [–0.9, 3.0] 0.24 ·· ·· ·· ··
CRP 100 mg/L 19 3974 6.1 [3.3, 8.9] <0.001 27 4517 5.0 [2.9, 7.0] <0.001
D–dimer 1mg/L 15 3621 21.6 [7.0, 36.4] <0.01 21 3387 2.9 [0.1, 5.8] 0.04
Pro–calcitonin 0·1 mg/L 10 2349 4.5 [–19.3, 28.5] 0.71 19 2725 8.5 [–1.5, 18.4] 0.09
Serum LDH 100 U/L 13 2682 9.1 [2.4, 15.8] 0.01 12 1666 –0.02 [–0.1, 0.1] 0.52
Serum creatinine 10 μmol/L 19 3817 0.2 [–0.4, 0.7] 0.51 18 3062 9 [0.7, 17.5] 0.03
BUN 1 mmol/L 14 2979 5.3 [0.5, 10.2] 0.03 ·· ·· ·· ··
Total Bilirubin 1 μmol/L 12 2191 2.6 [–0.2, 5.5] 0.07 13 8040 –2.0 [–6.8, 2.7] 0.41
Albumin 10 g/L 12 1986 –4.1 [––22.9, 14.6] 0.67 12 7789 –5.0 [–7.2, –2.8] <0.001
AST 10 U/L 15 8927 1.7 [–0.9, 4.4] 0.19 18 8395 –1.9 [–14.6, 10.8] 0.77
ALT 10 U/L 19 9664 2.6 [–1.2, 6.4] 0.18 20 8836 7.4 [– 7.6, 22.5] 0.33
CK–total 10 U/L 10 2051 1.6 [0.02, 3.1] 0.04 12 2331 –0.3 [–0.7, 0.2] 0.31
IL–6 10 ng/L ·· ·· ·· ·· 13 2404 1 [–0.2, 3] 0.09
Open in a new tab

Abbreviations: ALT = Alanine Aminotransferase. aPTT = activated Partial Thromboplastin Time. AST = Aspartate Aminotransferase. BUN = Blood Urea Nitrogen. CK = Creatinine Kinase. CRP = C–Reactive Protein. Hb = Hemoglobin. IL = Interleukin. LDH = Lactate Dehydrogenase. TLC = Total Leucocyte Count.

Fig 4.

Fig 4

Open in a new tab

Meta-regression plot showing the proportion increase in mortality among COVID-19 patients regressed against (A) mean age. (B) mean leukocyte count. (C) mean lymphocyte count. (D) mean serum albumin. (E) mean C-reactive protein. (F) mean D-dimer.

Fig 5.

Fig 5

Open in a new tab

Meta-regression plot shows the proportion increase in severity among COVID-19 patients regressed against (A) mean age. (B) mean leukocyte count. (C) mean lymphocyte count. (D) mean serum albumin. (E) mean C-reactive protein. (F) mean D-dimer.

Subgroup analyses based on the restriction of inclusion to critically ill participants are shown in S5 Table and in the Supplementary Figures in S1 Appendix Section IV. Subgroup analysis did not significantly reduce heterogeneity between studies except for the association of elevated procalcitonin levels and presence of diarrhea with mortality. The magnitude of the effect size changed significantly in the subgroups however the direction of the effect remained consistent for multiple parameters, including dyspnea, cardiovascular disease, cerebrovascular disease, diabetes mellitus, bacteremia, and gastrointestinal bleeding. In studies with critically ill patients, the direction of the effect was reversed for gender and elevated procalcitonin levels, but the association was not statistically significant in the subgroups. Sensitivity analysis by excluding low-quality studies (NOS≤5) did not significantly reduce heterogeneity or alter the pooled effect sizes for the exposures with I2>50% for mortality. Sensitivity analysis by excluding low-quality studies reporting COVID-19 severity yielded a decrease in the heterogeneity for various exposures, such as smoking, hypertension, myalgia, and air-bronchogram in CT. Nevertheless, heterogeneity was substantially high for exposures, such as dyspnea, chest pain, abdominal pain, unifocal involvement in CT and certain laboratory parameters. Sensitivity analysis for the laboratory and radiological variables by excluding studies with an unclear timepoint for the assessment of the parameters did not result in change in the direction or significance of the association. In our analysis, bias due to small study effect could not be ruled out while assessing risk of mortality for the following exposures, such as gender, diabetes mellitus, hypertension, ARDS and acute kidney injury and hence the results for these factors should be interpreted with caution.

Discussion

A total of 109 articles were deemed suitable for data synthesis and identification of variables associated with severe COVID-19 disease and mortality. Specific determinants were identified from a array of clinical parameters such as symptoms, co-morbidities, laboratory, and radiological data. Our findings have potential implications for clinical decision-making, as well as allocation of scarce critical care resources for patients with COVID-19.

The presence of various comorbidities was reported to be associated with severe disease and/or death in patients with COVID-19 in prior studies [127]. Although the direction of association was consistent with previous reports, the risks of death in patients with diabetes and hypertension were lower in our study with an RR of 1.59 [95% CI: 1.41–1.78] and 1.90 [95%CI: 1.69–2.15] respectively for mortality. The levels of control of diabetes and hypertension in these patients, as well as pharmacotherapy for these conditions were not taken into consideration in our review, which might account for the clinical heterogeneity. There was a significant association between pre-existing cardiovascular diseases and COVID-19 attributable mortality, with an RR of 2.27 [95%CI: 1.88–2.79]. This is similar to the association of cardiovascular diseases with mortality seen in patients with other viral infections [128, 129]. Chronic kidney disease and chronic liver disease were also associated with higher mortality, but because of the lack of data in the available studies, distinction could not be made with respect to the stage of the kidney and liver dysfunction. Pre-existing diseases of the lung were also associated with adverse outcomes in our study.

The identification of COVID-19-related symptoms associated with mortality and severe disease is especially important since this is among the most readily accessible information during the initial evaluation of patients. Our finding that dyspnea was associated with higher RR of mortality and higher OR for severe disease is consistent with data reported in other retrospective studies on ICU admission and the development of ARDS in patients with COVID-19 [23, 130]. Despite the common occurrence of gastrointestinal symptoms (nausea, vomiting and diarrhea) in patients with COVID-19, no association was found between the presence of these symptoms and the presence of severe disease or mortality in our study.

We found that ARDS had a RR of 20.19 [95%CI: 10.87–37.52] for mortality, which is consistent with a previous study reporting a 28-day survival of 50% among COVID-19 patients with severe ARDS [20]. In contrast to the relatively transient cardiac involvement in SARS-CoV infection [131], we found that cardiac complications such as acute heart failure and acute cardiac injury were associated with a high RR of death in COVID-19 in our study. While the presence of underlying cardiovascular disease increases the risk of developing cardiac complications, Chen et al reported that COVID-19 related cardiac complications were also frequent among those without pre-existing cardiovascular diseases [20]. A previous report from 2009 suggested direct cardiac muscle damage in patients with SARS-CoV infection [132]. The cardiac injury seen in patients with COVID-19, might be due to a similar mechanism. Elevated troponin levels were associated with an OR of 3.04 [95% CI: 1.03–8.97] for severe disease in our study. Consistent with our findings, patients with underlying cardiovascular diseases and non-elevated levels of troponin were shown to have lower death rates compared to those without cardiovascular disease but with elevated troponin [133].

Patients with leukocytosis and lymphopenia had higher OR for severe disease and greater RR for mortality. The occurrence of lymphopenia in severe disease may be due to apoptosis of lymphocytes as a result of increased levels of cytokines in the blood in patients with severe disease [134–136]. Hypoalbuminemia was associated with an increased RR of mortality (RR 3.30, 95%CI 2.61–4.18), and is likely related to the systemic inflammatory response in severe COVID-19 [74]. Increased levels of acute phase reactants, such as CRP and ferritin in patients with severe disease, also documented in our review, further supports the inflammatory nature of the disease [85]. Elevated levels of procalcitonin may indicate a secondary bacterial sepsis in patients with severe COVID-19, which was associated with a high RR of mortality in our meta-analysis [137]. COVID-19 has been hypothesized to be a prothrombotic state due to endothelial dysfunction and increased hypoxia-inducible transcription factor in patients with severe pneumonia, and plasminogen activation inhibition in patients who develop sepsis [138–140]. DIC, which is common in patients succumbing to COVID-19, is typically accompanied by elevated D-dimer levels [21]. The levels of D-dimer were found in our meta-analysis to be significantly associated with mortality (RR 3.98, 95%CI 2.87–5.52) and severe disease (OR 2.75, 95%CI 1.92–3.93). Although their association with mortality has not been fully investigated, thromboembolic complications, such as pulmonary embolism and acute stroke, have been noted with increasing frequency in patients with COVID-19 [141, 142].

COVID-19 can manifest in a variety of radiographic patterns on chest CT scan, most of which are consistent with viral pneumonia. We found that bilateral lung involvement and consolidation were associated with higher RR of mortality and higher OR of severe disease. Consistent with our findings, the occurrence of bilateral lung involvement was shown to increase with disease progression, and is more commonly observed in the late phases of COVID-19 [143]. The ground-glass opacities seen in a large proportion of COVID-19 patients may be due to the thickening of the alveolar septa following inflammation or the incomplete filling of the alveoli, as seen in Influenza A [144]. As noted for other respiratory viruses, our data revealed that consolidation was associated with a higher RR of mortality and OR of severe disease compared to ground glass opacities in patients with COVID-19 [145, 146]. Hilar lymphadenopathy, though rare in COVID-19 patients, could be due to infiltration of the hilar lymph nodes by lymphocytes and macrophages, and appears to be associated with severe disease in our meta-analysis [147–149].

Although this systematic review is able to delineate important parameters associated with disease severity and mortality in COVID-19, our study has a few limitations. First, we included current published articles related to the highly dynamic information available on COVID-19. As this pandemic has not impacted all regions within the same time frame, there is a potential timing bias, whereby the majority of patients described are from early-hit regions, which may not be representative of other patient populations, based on sociodemographic characteristics and pre-existing conditions. Although a pooled analysis of effect sizes adjusted for potential confounders would have been desirable, most studies did not uniformly report estimates adjusted for the same parameters. Time points of evaluation of laboratory, radiological and disease severity assessment were not clearly defined in many of the studies, which precluded the calculation of risk ratio of severe disease. We did not take into account the critical care interventions and strategies that potentially impact the course of the disease and/or survival, as such interventions (e.g. mechanical ventilation, extracorporeal membrane oxygenation, convalescent plasma) vary across different regions. Further investigation is warranted to evaluate which interventions impact the morbidity and mortality of patients with COVID-19. Future studies may investigate if individual and contextual-level sociodemographic factors are associated with morbidity and survival.

In summary, this study comprehensively examined the effect of several demographic, clinical, laboratory and radiological risk factors associated with mortality and severe disease among patients with COVID-19. Knowledge of these risk factors may help health care professionals to develop improved clinical management plans based on risk stratification. Policy makers can utilize these findings to develop triage protocols and effectively allocate resources in resource-limited settings.

Supporting information

S1 Appendix

(PDF)

Click here for additional data file. (6MB, pdf)
S1 PRISMA Checklist

(DOC)

Click here for additional data file. (65.5KB, doc)
S1 File

(DOCX)

Click here for additional data file. (40.9KB, docx)

Acknowledgments

We thank Lori Rosman (Lead informationist, Welch Medical Library, Johns Hopkins University) for her advice on the literature search strategy.

Data Availability

All relevant data are within the manuscript and its Supporting information files.

Funding Statement

The authors received no specific funding for this work.

References

Decision Letter 0

Girish Chandra Bhatt

7 Oct 2020

PONE-D-20-24192

Factors Associated with Disease Severity and Mortality among Patients with Coronavirus Disease 2019: A Systematic Review and Meta-Analysis

PLOS ONE

Dear Dr. Galiatsatos,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Nov 21 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

  • A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

  • A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

  • An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

We look forward to receiving your revised manuscript.

Kind regards,

Girish Chandra Bhatt, MD, FASN

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. PLOS requires an ORCID iD for the corresponding author in Editorial Manager on papers submitted after December 6th, 2016. Please ensure that you have an ORCID iD and that it is validated in Editorial Manager. To do this, go to ‘Update my Information’ (in the upper left-hand corner of the main menu), and click on the Fetch/Validate link next to the ORCID field. This will take you to the ORCID site and allow you to create a new iD or authenticate a pre-existing iD in Editorial Manager. Please see the following video for instructions on linking an ORCID iD to your Editorial Manager account: https://www.youtube.com/watch?v=_xcclfuvtxQ

3. Please include a separate caption for each figure in your manuscript.

4. Please include your tables as part of your main manuscript and remove the individual files.

Please note that supplementary tables should be uploaded as separate "supporting information" files

5. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Comments

A very interesting systematic review article to read and it highlights some important findings. Some concerns that need to addressed.

1. “We did not include articles uploaded in the preprint servers, as they are not peer reviewed and the findings may not be reliable”

The pre-prints serve as a source of unpublished data and if the authors have planned to not include unpublished data, they should state it

2. “Data extraction was performed independently by at least two of the authors (VC, NT, WH, MM, RS, AK, AH, II, AN, SA, ES, MS, AS, KW, and TP) and conflicts were resolved by a consensus between two authors (MM and VC)” Were these two authors acting as arbitors ? If so please mention it.

3. “The risk assessment for bias for all the studies included in this review was performed using the Newcastle-Ottawa quality assessment scale (NOS) for observational studies” Please mention that you used the NOS for cohort studies

4. The protocol registration number is missing. If it was registered apriori?

5. As regarding the heterogeneity, the authors have been trying to adjust for heterogeneity but haven’t really looked in for the reasons of the same. Highlighting the reasons of heterogeneity will be important for the readers and users of this systematic review.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2020 Nov 18;15(11):e0241541. doi: 10.1371/journal.pone.0241541.r002

Author response to Decision Letter 0

11 Oct 2020

To the Editor:

1) We have added the corresponding author’s ORCID ID.

2) Tables were added to the manuscript.

3) We have added captions for the figures, and are embedded in the manuscript.

4) All supplemental material in the attached PDF have tables and figures with titles.

To Reviewer #1:

A very interesting systematic review article to read and it highlights some important findings. Some concerns that need to addressed.

1. “We did not include articles uploaded in the preprint servers, as they are not peer reviewed and the findings may not be reliable”

The pre-prints serve as a source of unpublished data and if the authors have planned to not include unpublished data, they should state it

Response: We have re-worded the statement as such:

We only included article published in peer-reviewed academic journals; we did not include articles uploaded in the preprint servers, as they are not peer reviewed and the findings may not be reliable(10).

2. “Data extraction was performed independently by at least two of the authors (VC, NT, WH, MM, RS, AK, AH, II, AN, SA, ES, MS, AS, KW, and TP) and conflicts were resolved by a consensus between two authors (MM and VC)”

Were these two authors acting as arbitors ? If so please mention it.

Response: An important question to address; these authors were acting as arbitors and mentioned as such in the manuscript.

3. “The risk assessment for bias for all the studies included in this review was performed using the Newcastle-Ottawa quality assessment scale (NOS) for observational studies”

Please mention that you used the NOS for cohort studies

Response: We have added that language into the manuscript.

4. The protocol registration number is missing. If it was registered apriori?

Response: The protocol was not registered.

Attachment

Submitted filename: A. Response Letter for submission.docx

Click here for additional data file. (38KB, docx)

Decision Letter 1

Girish Chandra Bhatt

19 Oct 2020

Factors Associated with Disease Severity and Mortality among Patients with Coronavirus Disease 2019: A Systematic Review and Meta-Analysis

PONE-D-20-24192R1

Dear Dr. Galiatsatos,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Girish Chandra Bhatt, MD, FASN

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Acceptance letter

Girish Chandra Bhatt

10 Nov 2020

PONE-D-20-24192R1

Factors Associated with Disease Severity and Mortality among Patients with COVID-19: A Systematic Review and Meta-Analysis

Dear Dr. Galiatsatos:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Girish Chandra Bhatt

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Appendix

    (PDF)

    Click here for additional data file. (6MB, pdf)
    S1 PRISMA Checklist

    (DOC)

    Click here for additional data file. (65.5KB, doc)
    S1 File

    (DOCX)

    Click here for additional data file. (40.9KB, docx)
    Attachment

    Submitted filename: A. Response Letter for submission.docx

    Click here for additional data file. (38KB, docx)

    Data Availability Statement

    All relevant data are within the manuscript and its Supporting information files.

    Articles from PLoS ONE are provided here courtesy of PLOS

    RESOURCES