Literature DB >> 32546725

Risk factors for death in 1859 subjects with COVID-19.

Lei Chen1, Jianming Yu1, Wenjuan He1, Li Chen2, Guolin Yuan3, Fang Dong4, Wenlan Chen1, Yulin Cao1, Jingyan Yang5, Liling Cai5, Di Wu1, Qijie Ran6, Lei Li7, Qiaomei Liu8, Wenxiang Ren1, Fei Gao1, Hongxiang Wang2, Zhichao Chen1, Robert Peter Gale9, Qiubai Li10, Yu Hu11.   

Abstract

We studied 1859 subjects with confirmed COVID-19 from seven centers in Wuhan 1651 of whom recovered and 208 died. We interrogated diverse covariates for correlations with risk of death from COVID-19. In multi-variable Cox regression analyses increased hazards of in-hospital death were associated with several admission covariates: (1) older age (HR = 1.04; 95% Confidence Interval [CI], 1.03, 1.06 per year increase; P < 0.001); (2) smoking (HR = 1.84 [1.17, 2.92]; P = 0.009); (3) admission temperature per °C increase (HR = 1.32 [1.07, 1.64]; P = 0.009); (4) Log10 neutrophil-to-lymphocyte ratio (NLR; HR = 3.30 [2.10, 5.19]; P < 0.001); (5) platelets per 10 E + 9/L decrease (HR = 0.996 [0.994, 0.998]; P = 0.001); (6) activated partial thromboplastin (aPTT) per second increase (HR = 1.04 [1.02, 1.05]; P < 0.001); (7) Log10 D-dimer per mg/l increase (HR = 3.00 [2.17, 4.16]; P < 0.001); and (8) Log10 serum creatinine per μmol/L increase (HR = 4.55 [2.72, 7.62]; P < 0.001). In piecewise linear regression analyses Log10NLR the interval from ≥0.4 to ≤1.0 was significantly associated with an increased risk of death. Our data identify covariates associated with risk of in hospital death in persons with COVID-19.

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Year:  2020        PMID: 32546725      PMCID: PMC7296516          DOI: 10.1038/s41375-020-0911-0

Source DB:  PubMed          Journal:  Leukemia        ISSN: 0887-6924            Impact factor:   12.883


Introduction

The SARS-CoV-2 (severe acute respiratory syndrome coronavirus-2) pandemic has caused many deaths from coronavirus disease 2019 (COVID-19) [1-8]. The outbreak began in December 2019 in Wuhan city, Hubei province, China [9-16]. There are several studies or risk factors for death from COVID-19 but most have relatively few subjects and come from 1 or 2 centers [17-26]. We analyzed prognostic covariates for death in 1859 subjects with confirmed COVID-19 from 7 centers in Wuhan city from January 20 to April 4, 2020. Curiously, we found COVID-19 progressed similarly until day 10 after admission but progressed more slowly in the cohort of subjects who died compared with those recovering, possibly because of therapy interventions [27, 28]. Subjects who died had a greater frequency of comorbidities before COVID-19 and complications after developing COVID-19. We were able to show a correlation between likelihood of death and several hematological and other laboratory covariates at diagnosis.

Methods

Subjects

From 20 January to 4 April 2020, all consecutive patients ≥18 years were enrolled from Union Hospital (main part, Union West Hospital and Union Tumor Hospital), Wuhan Central Hospital, General Hospital of Central Theater Command, PLA, Wuhan Third Hospital and Wuhan Jin-Yin-Tan Hospital. These hospitals were reconstructed and designated as COVID-19 treatment centers. Between February 4 and February 18, 2020 persons with clinical symptoms and a lung computed tomography (CT) scan consistent with COVID-19 were diagnosed as having COVID-19 without confirmation of SARS-CoV-2-infection by quantitative reverse transcript polymerase chain reaction (qRT-PCR). After hospitalization subjects were tested by qRT-PCR to confirm the diagnosis and monitor their course. Beginning 4 March, 2020, anti-SARS-CoV-2 IgM and/or IgG antibodies were assayed at Union Hospital and Wuhan Central Hospital by the centers to confirm the diagnosis and to evaluate suspected cases of COVID-19 which were qRT-PCR-negative [29]. Subjects in whom we could not confirm SARS-CoV-2-infection by a qRT-PCR, IgM/IgG assay, or either were excluded from the study. Subjects recovering from COVID-19 were discharged and transferred to designated hotels, Fangcang shelter hospitals [30] or Leishenshan Hospital for 2–4 weeks of isolation or further care if needed. The study was approved by the Ethics Committees of Union Hospital (2020-0095) and of Wuhan Central Hospital (2020-007). Written and orally informed consent from subjects was waived by the Ethics Committees.

Data collection

We obtained epidemiological, demographic, clinical, laboratory, radiological, therapy, and outcomes data from electronic medical records using a standardized data collection form. Therapies included antibiotics, anti-viral therapy, corticosteroids, and supportive care including supplemental oxygen, mechanical ventilation (with and without intubation), and extracorporeal membrane oxygenation (ECMO). Data were independently entered and cross validated by two physicians (WH and JY). A third researcher (QL) adjudicated discordances. Missing data were retrieved from the relevant hospital.

SARS-CoV-2 testing and laboratory covariants

Methods for diagnosis of SARS-CoV-2-infection by qRT-PCR are described [24]. Before January 11, 2020 testing was done by a few institutions such as the Chinese Center for Disease Control and Prevention. Beginning January 11, 2020, qRT-PCR testing was done at local Centres for Disease Control and Prevention and from January 27, 2020 in the study hospitals. Beginning March 4, 2020 IgM/IgG antibodies to SARS-CoV-2 were tested at Union Hospital and after March 5, 2020 in Wuhan Central Hospital. Nasopharyngeal swab specimens were obtained every other day if there was clinical improvement judged by clinical signs and symptoms and lung CT scan. Subjects recovering were discharged after ≥2 negative qRT-PCR tests >24 h apart. Studies on admission included a CBC and differential, biochemistry panel, coagulation profile, and tests of inflammation including C-reactive protein (CRP), procalcitonin, lactate dehydrogenase (LDH), and ferritin. All subjects had a lung CT scan.

Definitions

Exposure history was defined as exposure to persons with confirmed SARS-CoV-2-infection or visiting the Huanan Wholesale Seafood Market, possible origin site of the SARS-CoV-2 epidemic in Wuhan city. Smoking history was defined as current or former smoker (stopping >5 years ago) with exposure of ≥20 cigarettes per day for ≥1 year (1 pack year). Fever was defined as temperature ≥37.3 °C. Diagnosis of bacterial infection required ≥1 positive culture or a positive antigen detection test. Acute kidney injury, acute respiratory distress syndrome (ARDS), and acute cardiac injury were diagnosed according to guidelines or as reported [24, 31, 32]. Liver damage was defined as more than 2x upper limit of normal. Severity of COVID-19 was classified as; (1) mild; (2) moderate; (3) severe; or (4) critical according to the Chinese management guideline for COVID-19 (version 7) [29, 33]. Recovery was defined as complete resolution of clinical signs and symptoms, normalization of the lung CT scan (if abnormal) and ≥2 negative qRT-PCR tests for SARS-CoV-2. Subjects dying of unrelated causes were excluded from analyses of COVID-19-related deaths. Invasive and noninvasive mechanical ventilations were defined as mechanical ventilation with and without intubation.

Statistical analysis

Demographics and clinical covariates were presented using descriptive statistics with frequencies (percentage) for discrete variables and median (IQR) and range for continuous variables. Medians were compared using independent group t test and Mann–Whitney test for normally and abnormally distributed data. Proportions for categorical variables were compared by χ2 test. Missing data were ignored without multiple imputations. Covariates considered for correlations with death included age, sex, occupation, signs and symptoms, laboratory and radiological findings, smoking history, comorbidities including arterio-sclerotic cardio-vascular disease (ASCVD), arterial hypertension, diabetes mellitus, chronic obstructive pulmonary disease (COPD), cancer, ARDS, infection, septic shock, acute renal failure, myocardial infarction, liver injury, gastro-intestinal bleeding, disseminated vascular coagulation, and multiple organ failure. Neutrophil-to-lymphocyte ratio (NLR), D-dimer and serum creatine (Scr) were log10 transformed before the analyses because of non-normal distributions. Uni- and multi-variable Cox regression models were used to evaluate associations of covariants with risk of death. R version 3.5.2 was used for statistical analyses. For unadjusted comparisons, a two-sided alpha of <0.05 was considered significant. Analyses were not adjusted for multiple comparisons.

Results

Admission clinical covariates

From January 20 to April 4, 2020, 3559 subjects who died or had been discharged with clinical- or qRT-PCR-confirmed COVID-19 [34, 35] were enrolled. As shown in Fig. 1, 1859 subjects were included for analysis, with SARS-CoV-2-infection confirmed in 1790 subjects by qRT-PCR and by antibody testing in 69. Of the 1859 subjects, 208 died (11%), 157 (8%) recovered and discharged but remained hospitalized in other units for 2–4 weeks of recovery-related care, and 1494 (80%) discharged and transferred to COVID-19 designated hotels or Fangcang shelter hospitals [30] for 2–4 weeks of isolation.
Fig. 1

Study flow diagram.

Study flow diagram. Median age was 59 years (Interquartile Range [IQR] 45–68 years; Table 1). 806 (43%) were 60–79 years and 122 (7%) >80 years. 934 subjects were male (50%). 111 (6%) were current or former smokers, 71 (5%), health care provider, and 14 (1%), with pregnancy or puerperium. In total, 4 subjects were exposed at Huanan Seafood Wholesale Market and 78 (4%) had close contact with persons with confirmed SARS-CoV-2 infection. 579 (31%) had hypertension, 268 (14%), ASCVD, 262 (14%), diabetes, 98 (5%), gastro-intestinal disease, 69 (4%), cancer and 61 (3%), COPD. Most common signs and symptoms included fever (n = 1448, 78%), shortness of breath (n = 716, 39%), dry (n = 619, 43%) or wet cough (n = 715, 39%), fatigue (n = 695, 37%), chills (n = 281, 19%) and myalgia (n = 315, 17%). Bilateral pneumonia (n = 1570, 88%) and Ground-glass opacity (n = 1331, 75%) were two most findings in lung CT scan. 34 (2%) subjects had mild, 1170 (63%), moderate, 453 (24%), severe and 202 (11%) critical COVID-19.
Table 1

Demographic and clinical covariates.

Total n = 1859Alive n = 1651 (89)Died n = 208 (11)P value
Age, median (IQR), years59 (45, 68)57 (43, 66)70 (63, 78)<0.001
Age distribution<0.001
  <40 years342 (18)337 (20)5 (2)
  40–59 years589 (32)556 (34)33 (16)
  60–79 years806 (43)681 (41)125 (60)
  ≥80 years122 (7)77 (5)45 (22)
 Female sex925 (50)870 (53)55 (26)<0.001
 Smoking history111 (6)86 (5)25 (13)<0.001
  Former smoker66 (4)54 (3)12 (6)
  Current smoker45 (2)32 (2)13 (7)
 Health care provider71 (5)69 (6)2 (1)0.008
 Pregnancy/Puerperium14 (1)14 (1)0 (0)0.394
 Exposure history0.005
 Huanan Seafood Market4 (0.2)1 (0.1)3 (1)
 Close contact with patients78 (4)72 (4)6 (3)
 Comorbidity
  ASCVD268 (14)205 (12)63 (30)<0.001
  Hypertension579 (31)475 (29)104 (50)<0.001
  Diabetes262 (14)203 (12)59 (28)<0.001
  COPD61 (3)49 (3)12 (6)0.039
  Cancer69 (4)52 (3)17 (8)<0.001
  Chronic kidney disease45 (2)25 (2)20 (10)<0.001
  Gastro-intestinal disease98 (5)82 (5)16 (8)0.097
  Auto-immune disease10 (1)9 (1)1 (1)0.999
  Psychiatric disorders7 (0.5)6 (0.5)1 (1)0.586
Signs and symptoms
  Fevera1448 (78)1274 (77)174 (84)0.025
  Temperature (°C)b36.6 (36.4, 37.0)36.6 (36.4, 37.0)36.8 (36.5, 37.5)<0.001
  Shortness of breath716 (39)572 (35)144 (70)<0.001
  Dry cough619 (43)554 (43)65 (38)0.205
  Wet cough715 (39)609 (37)106 (51)<0.001
  Fatigue695 (37)595 (36)100 (48)<0.001
  Nausea or vomiting124 (9)114 (9)10 (6)0.186
  Diarrhea243 (13)213 (13)30 (14)0.522
  Chills281 (19)236 (18)45 (26)0.015
  Rhinorrhea33 (2)30 (2)3 (1)0.999
  Myalgia315(17)282 (17)33 (16)0.681
  Headache107 (6)101 (6)6 (3)0.061
Radiological features
  Bilateral pneumonia1570 (88)1402 (87)168 (96)<0.001
  Consolidation326 (18)266 (17)60 (35)<0.001
  Ground-glass opacity1331 (75)1213 (76)118 (69)0.042
  Patchy shadows736 (41)664 (41)72 (42)0.81
COVID-19 stage<0.001
  Mild34 (2)34 (2)0 (0)
  Moderate1170 (63)1162 (70)8 (4)
  Severe453 (24)427 (26)26 (13)
  Critical202 (11)28 (2)174 (84)

Data are median (IQR) or n (%).

ASCVD atherosclerotic cardio- and cerebro-vascular disease, COPD chronic obstructive pulmonary disease.

a≥1 temperature ≥37.3 °C from onset of symptoms to admission.

bAdmission temperature.

Demographic and clinical covariates. Data are median (IQR) or n (%). ASCVD atherosclerotic cardio- and cerebro-vascular disease, COPD chronic obstructive pulmonary disease. a≥1 temperature ≥37.3 °C from onset of symptoms to admission. bAdmission temperature.

Comparison of survivors and nonsurvivors by clinical covariates

Subjects who died were older (medians 70 versus 57 years; P < 0.001, Table 1), more likely male (74% versus 47%; P < 0.001), more likely smokers (13% versus 5%; P < 0.001) and less likely health care providers (1% versus 6%, P = 0.008). More subjects who died were exposure at the Huanan Seafood Wholesale Market (1% versus 0.1%, P = 0.005). Nonsurvivors more likely to have comorbidities of hypertension (50% versus 29%; P < 0.001), ASCVD (30% versus 12%; P < 0.001), diabetes (28% versus 12%; P < 0.001), COPD (6% versus 3%; P = 0.039), cancer (8% versus 3%; P < 0.001) and kidney failure (10% versus 2%; P < 0.001). Fever from illness onset to admission (84% versus 77%; P = 0.025), shortness of breath (70% versus 35%; P < 0.001), wet cough (51% versus 37%; P < 0.001), fatigue (48% versus 36%; P < 0.001), chills (26% versus 18%; P = 0.015), bilateral pneumonia (96% versus 87%; P < 0.001) and lung consolidation on CT scan (35% versus 17%; P < 0.001) were more common in subjects who died. Paradoxically, survivors were more likely to have ground-glass lung opacity on lung CT scan (76% versus 69%; P = 0.042). Subjects who died were more likely to have critical COVID-19 on admission (84% versus 2%; P < 0.001) and less likely to have moderate severity (4% versus 70%; P < 0.001).

Comparison of survivors and nonsurvivors by laboratory covariates

There were significant differences in admission laboratory covariates between survivors and nonsurvivors (Table 2). Subjects who died had higher median neutrophils (7 × 10 E + 9/L [IQR 4–10 × 10 E + 9/L] versus 3  × 10 E + 9/L [IQR 2–4 × 10 E + 9/L]; P < 0.001), lower median lymphocytes (0.6 × 10 E + 9/L [IQR 0.4–0.9 × 10 E + 9/L] versus median 1.2 × 10 E + 9/L [IQR 0.9–1.6 × 10 E + 9/L]; P < 0.001), lower median platelets (163 × 10 E + 9/L [IQR 113–223 × 10 E + 9/L] versus 207 10 E + 9/L [IQR 161–268 × 10 E + 9/L]; P < 0.001) and higher median neutrophil-to-lymphocyte ratios (NLR; 11 [IQR 6–20] versus 3 [IQR 2–4]; P < 0.001). Nonsurvivors had lower median proportions of CD3-positive cells (60% [IQR 52–70%] versus 72% [IQR 63–79%]; P < 0.001), median proportions of CD8-positive cells (16% [IQR 11–20%] versus 24% [IQR 18–30%]; P < 0.001), median proportions of NK-cells (8% [IQR 3–12%] versus 10%, [IQR 6–17%]; P = 0.011), and higher proportions of B-lymphocyte (15% [IQR 9–28%] versus 12% [IQR 9–17%]; P = 0.033) and higher median CD4/CD8 ratios (3 [IQR 2–4] versus 2 [IQR 1–3]; P < 0.001) compared with survivors.
Table 2

Laboratory covariates on admission.

Covariates (normal range)NTotalAliveDiedP value
CBC
 Neutrophils ×10E + 9/L (1.8–6.3)18163 (2, 5)3 (2, 4)7 (4, 10)<0.001
 Lymphocytes ×10 E + 9/L (1.1–3.2)18471.0 (0.8, 1.6)1.2 (0.9, 1.6)0.6 (0.4, 0.9)<0.001
 Monocytes ×10 E + 9/L (0.1–0.6)18050.4 (0.3, 0.5)0.4 (0.3, 0.5)0.3 (0.2, 0.5)<0.001
 Hemoglobin, g/L (115–150)1433128 (117, 139)128 (117, 139)129 (117, 140)0.539
 Platelets ×10 E + 9/L (125–350)1814203 (155, 264)207 (161, 268)163 (113, 223)<0.001
 NLR18143 (2, 5)3 (2, 4)11 (6, 20)<0.001
Inflammation covariates
 hCRP, mg/L (<4)9954 (1, 10)3 (1, 10)10 (10, 80)<0.001
 Procalcitonin, ng/ml (< 0.5)16430.06 (0.05, 0.1)0.05 (0.04, 0.1)0.3 (0.1, 0.6)<0.001
 LDH, U/L (109–245)1729212 (170, 292)201 (165, 261)412 (306, 561)<0.001
 Ferritin, ng/ml (4.6–204)308567 (246, 1218)470 (197, 940)1579 (1206, 2000)<0.001
Coagulation covariates
 aPTT, s (28–43.5)135634 (30, 38)34 (30, 38)37 (31, 42)<0.001
 Fibrinogen, g/L (2–4)13233.7 (2.9, 4.6)3.7 (2.9, 4.6)4.3 (3.2, 5.1)<0.001
 D-dimer, mg/L (<0.5)16020.4 (0.2, 1.1)0.4 (0.2, 0.8)2.5 (0.7, 8)<0.001
Biochemical covariates
 ALT, U/L (5–35)183238 (22, 67)36 (21, 63)58 (30, 139)<0.001
 AST, U/L (8–40)183032 (22, 49)30 (22, 44)64 (40, 140)<0.001
 Total bilirubin, μmol/L (5.1–19)158614 (10, 19)13 (10, 18)24 (15, 36)<0.001
 Creatine kinase, U/L (26–140)149388 (54, 165)81 (52, 135)262 (135, 636)<0.001
 BNP, pg/ml (< 100)83861 (18, 242)47 (15, 140)467 (121, 1467)<0.001
 Myoglobin, ng/ml (<140)97235 (21, 73)30 (21, 51)576 (175, 1013)<0.001
 Troponin I, ng/L (<26.2)10834 (1, 14)3 (1, 8)161 (46, 712)<0.001
 BUN, mmol/L (2.9–8.2)18155 (4, 7)5 (4, 6)15 (9, 27)<0.001
 Scr, μmol/L (44–106)181371 (59, 85)69 (58, 81)108 (76, 256)<0.001
Lymphocyte subsets
 CD3+, (58–84%)75971 (62, 78)72 (63, 79)60 (52, 70)<0.001
 CD4+, (25–51%)75941 (32, 48)41 (33, 48)37 (28, 46)0.094
 CD8+, (14–39%)75923 (17, 30)24 (18, 30)16 (11, 20)<0.001
 NK cell, (3–30%)56110 (6, 16)10 (6, 17)8 (3, 12)0.011
 B lymphocyte, (4–18%)56113 (9, 18)12 (9, 17)15 (9, 28)0.033
 CD4 + /CD8 + Ratio (0.41–2.72)7552 (1, 3)2 (1, 3)3 (2, 4)<0.001
Cytokines
 IL-4, pg/ml (0.1–3.2)5053 (2, 4)3 (2, 4)2 (2, 3)0.21
 IL-6, pg/ml (0.1–2.9)85710 (4, 42)9 (4, 32)79 (23, 525)<0.001
 IL-10, pg/ml (0.1–5)5054 (3, 6)4 (3, 5)10 (5, 22)<0.001
 TNF-α, pg/ml (0.1–23)5053.3 (2.2, 5.4)3.5 (2.3, 5.6)2.7 (1.9, 3.6)0.009
 IFN-γ, pg/ml (0.1–18)5053.1 (2.0, 4.1)3.1 (2.0, 4.1)2.7 (1.8, 4.2)0.313

Data are median (IQR).

NLR neutrophil-to-lymphocyte ratio, hCRP high-sensitivity c-reactive protein, LDH lactate dehydrogenase, aPTT activated partial thromboplastin time, ALT alanine aminotransferase, AST aspartate aminotransferase, BNP B-type natriuretic peptide, BUN blood urea nitrogen, Scr serum creatinine, IL interleukin, TNF tumor necrosis factor, IFN interferon.

Laboratory covariates on admission. Data are median (IQR). NLR neutrophil-to-lymphocyte ratio, hCRP high-sensitivity c-reactive protein, LDH lactate dehydrogenase, aPTT activated partial thromboplastin time, ALT alanine aminotransferase, AST aspartate aminotransferase, BNP B-type natriuretic peptide, BUN blood urea nitrogen, Scr serum creatinine, IL interleukin, TNF tumor necrosis factor, IFN interferon. Subjects who died had longer median aPTT (37 s [IQR 31–42 s] versus 34 s [IQR 30–38 s]; P < 0.001) and higher median concentrations of fibrinogen (4.3 g/L [IQR 3.2–5.1 g/L] versus 3.7 g/L [IQR 2.9–4.6 g/L]; P < 0.001) and median D-dimer concentrations (2.5 mg/L [IQR 0.7–8 mg/L] versus 0.4 mg/L [IQR 0.2–0.8 mg/L]; P < 0.001). Subjects who died also had higher median hCRP concentrations (10 mg/L [IQR 10–80 mg/L] versus 3 mg/L [IQR 1–10 mg/L]; P < 0.001), median procalcitonin concentrations (0.3 ng/ml [IQR 0.1–0.6 ng/mL] versus 0.05 ng/ml [IQR 0.04–0.1 ng/mL]; P < 0.001), median LDH activities (412 U/L [IQR 306–561 U/L] versus 201 U/L [IQR 165–261 U/L]; P < 0.001), median ferritin concentrations (1579 ng/mL [IQR 1206-2000 ng/mL] versus 470 ng/mL [IQR 197–940 ng/mL]; P < 0.001), median IL-6 concentrations (79 pg/mL [IQR 23–525 pg/mL] versus 9 pg/mL [IQR 4–32 pg/mL]; P < 0.001), median IL-10 concentrations (10 pg/mL [IQR 5–22 pg/mL] versus 4 pg/mL[IQR 3–5 pg/mL]; P < 0.001) and lower median TNF-α concentrations (2.7 pg/mL [IQR 1.9–3.6 pg/mL] versus 3.5 pg/mL [IQR 2.3–5.6 pg/mL]; P = 0.009). Subjects who died also had higher median activities of alanine aminotransferase (ALT; 58 U/L [IQR 30–139 U/L] versus 36 U/L [IQR 21–63 U/L]; P < 0.001), aspartate aminotransferase (AST; 64 U/L [IQR 40–140 U/L] versus 30 U/L [IQR 22–44 U/L]; P < 0.001), creatine kinase (262 U/L [IQR 135–636 U/L] versus 81 U/L [IQR 52–135 U/L]; P < 0.001) and median concentrations of total bilirubin (24 μmol/L [IQR 15–36 μmol/L] versus 13 μmol/L [IQR 10–18 μmol/L]; P < 0.001), b-type natriuretic peptide (BNP; 467 pg/ml [IQR 121–1467 pg/ml] versus 47 pg/ml, [IQR 15–140 pg/ml]; P < 0.001), myoglobin (576 ng/ml [IQR 175–1013 ng/ml] versus 30 ng/ml [IQR 21–51 ng/ml]; P < 0.001) and troponin I (161 ng/L [IQR 46–712 ng/L] versus 3 ng/L [IQR 1–8 ng/L]; P < 0.001), blood urea nitrogen (BUN; 15 mmol/L [IQR 9–27 mmol/L] versus 5 mmol/L [IQR 4–6 mmol/L]; P < 0.001) and Scr (108 μmol/L [IQR 76–256 μmol/L] versus 69 μmol/L [IQR 58–81 μmol/L]; P < 0.001).

Complications and treatments for survivors and non-survivors

Subjects who died were more likely to have complications (207 [99.5%] versus 1039 [63%]; P < 0.001) including ARDS (174 [84%] versus 53 [3%]; P < 0.001), bacterial infection (180 [87%] versus 383 [23%]; P < 0.001) and liver damage (102 [50%] versus 354 [22%]; P < 0.001; Table 3). Nonsurvivors also had higher incidence of heart injury (130 [63%] versus 99 [6%]; P < 0.001), multiple organ failure (126 [61%] versus 3 [0.2%]; P < 0.001), acute kidney injury (82 [39%] versus 17 [1%]; P < 0.001), septic shock (63 [37%] versus 1 [0.1%]; P < 0.001), abnormal coagulation parameters (47 [23%] versus 2 [0.1%]; P < 0.001) and gastro-intestinal bleeding (29 [14%] versus 5 [0.3%]; P < 0.001).
Table 3

Complications and therapy.

Total n = 1859Alive n = 1651 (89)Died n = 208 (11)P value
Complications
 ARDS227 (12)53 (3)174 (84)<0.001
 Bacterial infections563 (30)383 (23)180 (87)<0.001
 Septic shock64 (4)1 (0.1)63 (37)<0.001
 Acute kidney injury99 (5)17 (1)82 (39)<0.001
 Cardiac injury229 (12)99 (6)130 (63)<0.001
 Abnormal LFT456 (25)354 (22)102 (50)<0.001
 Gastro-intestinal bleeding34 (2)5 (0.3)29 (14)<0.001
 Coagulopathy49 (3)2 (0.1)47 (23)<0.001
 Multiple organ failure129 (7)3 (0.2)126 (61)<0.001
Therapy
 Antibiotics1559 (85)1356 (84)203 (98)<0.001
 Antifungal drugs71 (4)32 (2)39 (20)<0.001
 Oseltamivir757 (41)688 (42)69 (33)0.021
 Umifenovir1386 (75)1226 (74)160 (77)0.351
 Lopinavir/Ritonavir339 (23)280 (22)59 (35)<0.001
 Interferon387 (21)344 (21)43 (21)0.957
 Corticosteroids753 (41)588 (36)165 (80)<0.001
 IVIG506 (29)401 (26)105 (52)<0.001
 High-flow nasal cannula oxygen therapy233 (16)81 (6)152 (89)<0.001
 Noninvasive mechanical ventilation145 (8)27 (2)118 (57)<0.001
 Invasive mechanical ventilation85 (5)12 (1)73 (35)<0.001
 ECMO4 (0.2)1 (0.1)3 (1)0.005
 CRRT23 (2)4 (0.3)19 (11)<0.001
Outcomes
 ICU admission106 (6)36 (2)70 (34)<0.001
 Time from illness onset to ICU admission, median (IQR), days14 (10, 20)14 (10, 21)14 (10, 20)0.962
 ICU length of stay, median (IQR), days10 (4, 17)10 (5, 18)10 (4, 16)0.676
 Time from illness onset to repeated negative SARS-CoV-2 tests, median (IQR), days22 (17, 28)22 (17, 28)21 (15, 27)0.284
 Time from illness onset to admission, median (IQR), days10 (7, 15)10 (7, 16)9 (6, 12)<0.001
 Time from illness onset to progression, median (IQR), days10 (7, 15)10 (6, 14)12 (9, 18)<0.001
 Time from illness onset to outcome, median (IQR), days30 (23, 37)31 (24, 38)21 (14, 28)<0.001
 Time from diagnosis to outcome, median (IQR), days19 (13, 27)20 (14, 28)11 (5, 17)<0.001
 Time from admission to outcome, median (IQR), days18 (12, 23)18 (14, 23)10 (6, 19)<0.001

LFT liver function test, ARDS acute respiratory distress syndrome, IVIG intravenous immunoglobin, ECMO extra-corporeal membrane oxygenation, CRRT continuous renal replacement therapy, ICU intensive care unit.

Complications and therapy. LFT liver function test, ARDS acute respiratory distress syndrome, IVIG intravenous immunoglobin, ECMO extra-corporeal membrane oxygenation, CRRT continuous renal replacement therapy, ICU intensive care unit. Also, subjects who died were more likely to receive antibiotics (203 [98%] versus 1356 [84%]; P < 0.001), antifungal drugs (39 [20%] versus 32 [2%]; P < 0.001), lopinavir and ritonavir (59 [35%] versus 280 [22%]; P < 0.001), corticosteroids (165 [80%] versus 588 [36%]; P < 0.001), intravenous immunoglobin (IVIG; 105 [52%] versus 401 [26%]; P < 0.001), high-flow nasal cannula oxygen therapy (152 [89%] versus 81 [6%]; P < 0.001), noninvasive mechanical ventilation (118 [57%] versus 27 [2%]; P < 0.001), invasive mechanical ventilation (73 [35%] versus 12 [1%]; P < 0.001), ECMO (3 [1%] versus 1 [0.1%]; P = 0.005) and continuous renal replacement therapy (CRRT) (19 [11%] versus 4 [0.3%]; P < 0.001). Nonsurvivors had briefer median intervals from onset of symptoms to admission (9 d [IQR 6–12 d] versus 10 d [IQR 7–16 d]; P < 0.001) and median intervals from onset of symptoms to death or discharge (21 d [IQR 14–28] versus 31 d [IQR 24–38 d]; P < 0.001), and from admission to death or discharge (10 d [IQR] 6–19 d versus 18 d [IQR 14–23 d]; P < 0.001) but longer median intervals from onset of symptoms to progression (median 12 d [IQR 9–18 d] versus 10 d [QR 6–14 d]; P < 0.001). There were no differences between survivors and nonsurvivors in median intervals from symptoms onset to ICU admission (14 d [IQR 10–20 d] versus 14 d [IQR 10–21 d]; P = 0.962) or median intervals to negative SARS-CoV-2 testing (21 d [IQR 15–27 d] versus 22 d [IQR 17–28 d]; P = 0.284). In total, 178 of the 208 subjects who died (86%) had a positive qRT-PCR test until death.

Risk factors for death

In total, 33 covariants had significant associations with risk of death in uni-variable analyses, 8 of which remained significant in multi-variable analyses including age (HR = 1.04 [1.03, 1.06]; P < 0.001), smoking history (HR = 1.84 [1.17, 2.92]; P = 0.009), temperature value (°C) at admission (HR = 1.32 [1.07–1.64]; P = 0.009), log10 NLR (HR = 3.30 [2.10, 5.19]; P < 0.001), admission platelet concentration (HR = 0.996; [0.994–0.998]; P = 0.001), aPTT on admission (HR = 1.04 [1.02, 1.05]; P < 0.001), Log10 D-dimer (HR = 3.00 [2.17, 4.16]; P < 0.001), and Log10 Cr (HR = 4.55 [2.72, 7.62]; P < 0.001; Table 4).
Table 4

Risk factors for death.

Uni-variable HR (95% CI)P valueMultivariable HR (95% CI)P value
Clinical covariates
 Age, years1.07 (1.06–1.08)<0.0011.04 (1.03–1.06)<0.001
 Female sex (vs male)0.35 (0.26–0.48)<0.001....
 Smoking history (vs nonsmoking)2.43 (1.59–3.73)<0.0011.84 (1.17–2.92)0.009
 Health care provider (vs non health care provider)0.24 (0.06–0.96)0.044....
Comorbidity (Yes/No)
 ASCVD2.56 (1.90–3.45)<0.001....
 Diabetes2.47 (1.82–3.34)<0.001....
 Hypertension2.21 (1.68–2.90)<0.001....
 Cancer2.59 (1.58–4.26)<0.001....
Symptoms and complications (Y/N)
 Dyspnea6.26 (4.76–8.24)<0.001....
 Wet cough1.63 (1.24–2.14)0.001....
 ARDS54.21 (37.13–79.14)<0.001....
 Bacterial infections18.36 (12.16–27.72)<0.001....
 Temperature at admission (°C)1.50 (1.28–1.75)<0.0011.32 (1.07–1.64)0.009
Laboratory covariates
 Neutrophils ×10E + 9/L1.23 (1.20–1.26)<0.001....
 Lymphocytes x10E + 9/L0.07 (0.05–0.10)<0.001....
 Log10 NLR1.06 (1.06–1.07)<0.0013.30 (2.10–5.19)<0.001
 Platelets x10E + 9/L0.99 (0.99–1.00)<0.0010.996 (0.994–0.998)0.001
 hCRP, mg/L1.02 (1.02–1.02)<0.001....
 Procalcitonin, ng/ml1.23 (1.18–1.28)<0.001....
 LDH, U/L1.00 (1.00–1.00)<0.001....
 Ferritin, ng/ml1.00 (1.00–1.00)<0.001....
 aPTT, s1.06 (1.04–1.08)<0.0011.04 (1.02–1.05)<0.001
 Log10 D-dimer, mg/L1.09 (1.08–1.11)<0.0013.00 (2.17–4.16)<0.001
 Total bilirubin, μmol/L1.03 (1.03–1.04)<0.001....
 Creatine kinase, U/L1.00 (1.00–1.00)<0.001....
 Troponin I, ng/L1.00 (1.00–1.00)<0.001....
 BUN, mmol/L1.06 (1.05–1.07)<0.001....
 Log10 Scr, μmol/L1.00 (1.00–1.00)<0.0014.55 (2.72–7.62)<0.001
 IL-61.00 (1.00–1.00)<0.001....
 IL-101.00 (1.00–1.00)0.004....
 CD3-positive, %0.95 (0.93–0.97)<0.001....
 CD8-positive, %0.91 (0.88–0.94)<0.001....
 CD4/CD8 ratio1.27 (1.18–1.37)<0.001....

CI confidence interval, ASCVD atherosclerotic cardio- and cerebro-vascular disease, ARDS acute respiratory distress syndrome, NLR neutrophil-to-lymphocyte ratio, hCRP high-sensitive c-reactive protein, LDH lactate dehydrogenase, aPTT activated partial thromboplastin time, BUN blood urea nitrogen, Scr serum creatinine.

Risk factors for death. CI confidence interval, ASCVD atherosclerotic cardio- and cerebro-vascular disease, ARDS acute respiratory distress syndrome, NLR neutrophil-to-lymphocyte ratio, hCRP high-sensitive c-reactive protein, LDH lactate dehydrogenase, aPTT activated partial thromboplastin time, BUN blood urea nitrogen, Scr serum creatinine. We further showed the linear relationship between these covariants except age, smoking history, and temperature at admission and risk of death (Fig. 2). Based on the steep curve of Log10 NLR we conducted a further piecewise linear regression analysis of NLR and death. The results indicate a Log10 NLR value of ≥0.4 to ≤1.0 is significantly associated with risk of death (Table 5).
Fig. 2

The linear relationship between admission covariants and risk of death.

(a) log10NLR, (b) platelet (x 10 E+9/L), (c) log10D-dimer (mg/L), (d) aPTT (s) and (e) log10Scr (μmol/L).

Table 5

Piecewise linear regression analysis of the effect of Neutrophil-to-lymphocyte ratio (NLR) on risk of death.

Hazard ratio (95% CI)P value
Log10 NLR3.30 (2.10–5.19)<0.001
Log10 NLR < 0.40.44 (0.02–10.09)0.608
Log10 NLR ≥ 0.4, ≤ 1.014.06 (3.23–61.21)<0.001
Log10 NLR > 1.00.49 (0.17–1.44)0.195

The linear relationship between admission covariants and risk of death.

(a) log10NLR, (b) platelet (x 10 E+9/L), (c) log10D-dimer (mg/L), (d) aPTT (s) and (e) log10Scr (μmol/L). Piecewise linear regression analysis of the effect of Neutrophil-to-lymphocyte ratio (NLR) on risk of death.

Discussion

We identified eight hospital admission covariates which are independent risk factors for death in almost 2000 persons with COVID-19 including older age, smoking history, higher body temperature (°C), and levels of D-dimer, aPTT, Scr, platelet, and NLR on admission. Several are reported by others; however, we were unable to confirm other risk factors reported in smaller datasets [15, 18–21, 23, 24, 26, 36]. We identified Log10 NLR as an independent risk factor for death with an HR = 14.1 (3.2, 61.2) with Log10 values ≥0.4 to ≤1.0 but not otherwise (Table 5). Although higher neutrophil and lower lymphocyte concentrations and higher NLR were previously reported [23, 26, 36–38], Log10NLR has not. There are important limitations to our study. First, not all covariates were available in all subjects including body mass index and SOFA score (a factor for death identified by logistic regression analysis) [24]. Also, BNP and TNI were reported in different units and were therefore not included for multi-variable Cox regression analyses. Second, at the data lock 65 (1.8%) subjects remained hospitalized and are excluded from our analyses. Third, some covariates such as bacterial coinfection and BUN could not be accurately analysed and interpreted together with other covariants for interactions. Our conclusions although based on a large dataset require confirmation. Nevertheless, they may be useful in predicting outcomes in persons with COVID-19.
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Authors:  Graziano Onder; Giovanni Rezza; Silvio Brusaferro
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2.  Preparing for the Most Critically Ill Patients With COVID-19: The Potential Role of Extracorporeal Membrane Oxygenation.

Authors:  Graeme MacLaren; Dale Fisher; Daniel Brodie
Journal:  JAMA       Date:  2020-04-07       Impact factor: 56.272

3.  Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China.

Authors:  Dawei Wang; Bo Hu; Chang Hu; Fangfang Zhu; Xing Liu; Jing Zhang; Binbin Wang; Hui Xiang; Zhenshun Cheng; Yong Xiong; Yan Zhao; Yirong Li; Xinghuan Wang; Zhiyong Peng
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Review 4.  Acute kidney injury: an increasing global concern.

Authors:  Norbert H Lameire; Arvind Bagga; Dinna Cruz; Jan De Maeseneer; Zoltan Endre; John A Kellum; Kathleen D Liu; Ravindra L Mehta; Neesh Pannu; Wim Van Biesen; Raymond Vanholder
Journal:  Lancet       Date:  2013-05-31       Impact factor: 79.321

5.  Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia.

Authors:  Qun Li; Xuhua Guan; Peng Wu; Xiaoye Wang; Lei Zhou; Yeqing Tong; Ruiqi Ren; Kathy S M Leung; Eric H Y Lau; Jessica Y Wong; Xuesen Xing; Nijuan Xiang; Yang Wu; Chao Li; Qi Chen; Dan Li; Tian Liu; Jing Zhao; Man Liu; Wenxiao Tu; Chuding Chen; Lianmei Jin; Rui Yang; Qi Wang; Suhua Zhou; Rui Wang; Hui Liu; Yinbo Luo; Yuan Liu; Ge Shao; Huan Li; Zhongfa Tao; Yang Yang; Zhiqiang Deng; Boxi Liu; Zhitao Ma; Yanping Zhang; Guoqing Shi; Tommy T Y Lam; Joseph T Wu; George F Gao; Benjamin J Cowling; Bo Yang; Gabriel M Leung; Zijian Feng
Journal:  N Engl J Med       Date:  2020-01-29       Impact factor: 176.079

6.  A novel coronavirus outbreak of global health concern.

Authors:  Chen Wang; Peter W Horby; Frederick G Hayden; George F Gao
Journal:  Lancet       Date:  2020-01-24       Impact factor: 79.321

Review 7.  Fangcang shelter hospitals: a novel concept for responding to public health emergencies.

Authors:  Simiao Chen; Zongjiu Zhang; Juntao Yang; Jian Wang; Xiaohui Zhai; Till Bärnighausen; Chen Wang
Journal:  Lancet       Date:  2020-04-02       Impact factor: 79.321

8.  Analysis of clinical characteristics and laboratory findings of 95 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a retrospective analysis.

Authors:  Gemin Zhang; Jie Zhang; Bowen Wang; Xionglin Zhu; Qiang Wang; Shiming Qiu
Journal:  Respir Res       Date:  2020-03-26

9.  Clinical characteristics of 113 deceased patients with coronavirus disease 2019: retrospective study.

Authors:  Tao Chen; Di Wu; Huilong Chen; Weiming Yan; Danlei Yang; Guang Chen; Ke Ma; Dong Xu; Haijing Yu; Hongwu Wang; Tao Wang; Wei Guo; Jia Chen; Chen Ding; Xiaoping Zhang; Jiaquan Huang; Meifang Han; Shusheng Li; Xiaoping Luo; Jianping Zhao; Qin Ning
Journal:  BMJ       Date:  2020-03-26

10.  Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention.

Authors:  Zunyou Wu; Jennifer M McGoogan
Journal:  JAMA       Date:  2020-04-07       Impact factor: 56.272

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1.  Clinical Features Associated with COVID-19 Outcome in MM: First Results from International Myeloma Society Dataset.

Authors:  Ajai Chari; Mehmet Kemal Samur; Joaquin Martinez-Lopez; Gordon Cook; Noa Biran; Kwee L Yong; Vania Tietsche de Moraes Hungria; Monika Engelhardt; Francesca Gay; Ana Garcia-Feria; Stefania Oliva; Rimke Oostvogels; Alessandro Gozzetti; Cara A Rosenbaum; Shaji K Kumar; Edward Stadtmauer; Hermann Einsele; Meral Beksac; Katja C Weisel; Kenneth C Anderson; Maria-Victoria Mateos; Philippe Moreau; Jesús San Miguel; Nikhil C Munshi; Hervé Avet-Loiseau
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2.  Adjuvant Therapy System of COVID-19 Patient: Integrating Warning, Therapy, Post-Therapy Psychological Intervention.

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Journal:  IEEE Trans Netw Sci Eng       Date:  2021-05-04

Review 3.  Venous and arterial thrombosis in COVID-19: An updated narrative review.

Authors:  Zainab Al Duhailib; Simon Oczkowski; Kamil Polok; Jakub Fronczek; Wojciech Szczeklik; Joshua Piticaru; Manoj J Mammen; Fayez Alshamsi; John Eikelboom; Emilie Belley-Cote; Waleed Alhazzani
Journal:  J Infect Public Health       Date:  2022-05-14       Impact factor: 7.537

4.  Correlates of In-Hospital COVID-19 Deaths: A Competing Risks Survival Time Analysis of Retrospective Mortality Data.

Authors:  Ashish Goel; Alpana Raizada; Ananya Agrawal; Kamakshi Bansal; Saurabh Uniyal; Pratima Prasad; Anil Yadav; Asha Tyagi; R S Rautela
Journal:  Disaster Med Public Health Prep       Date:  2021-03-25       Impact factor: 1.385

5.  Association Between Air Pollution in Lima and the High Incidence of COVID-19: Findings from a Post Hoc Analysis.

Authors:  Vanessa Vasquez-Apestegui; Enrique Parras-Garrido; Vilma Tapia; Valeria M Paz-Aparicio; Jhojan P Rojas; Odón R Sánchez-Ccoyllo; Gustavo F Gonzales
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6.  A variant in TMPRSS2 is associated with decreased disease severity in COVID-19.

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7.  B-Type Natriuretic Peptide Concentrations, COVID-19 Severity, and Mortality: A Systematic Review and Meta-Analysis With Meta-Regression.

Authors:  Angelo Zinellu; Salvatore Sotgia; Ciriaco Carru; Arduino A Mangoni
Journal:  Front Cardiovasc Med       Date:  2021-06-24

8.  Association between air pollution in Lima and the high incidence of COVID-19: findings from a post hoc analysis.

Authors:  Bertha V Vasquez-Apestegui; Enrique Parras-Garrido; Vilma Tapia; Valeria M Paz-Aparicio; Jhojan P Rojas; Odón R Sanchez-Ccoyllo; Gustavo F Gonzales
Journal:  BMC Public Health       Date:  2021-06-16       Impact factor: 4.135

Review 9.  Neutrophils and COVID-19: Active Participants and Rational Therapeutic Targets.

Authors:  Jon Hazeldine; Janet M Lord
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10.  Chronic lung diseases are associated with gene expression programs favoring SARS-CoV-2 entry and severity.

Authors:  Linh T Bui; Nichelle I Winters; Mei-I Chung; Chitra Joseph; Austin J Gutierrez; Arun C Habermann; Taylor S Adams; Jonas C Schupp; Sergio Poli; Lance M Peter; Chase J Taylor; Jessica B Blackburn; Bradley W Richmond; Andrew G Nicholson; Doris Rassl; William A Wallace; Ivan O Rosas; R Gisli Jenkins; Naftali Kaminski; Jonathan A Kropski; Nicholas E Banovich
Journal:  Nat Commun       Date:  2021-07-14       Impact factor: 14.919

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