To the Editor:
With the dramatic increase of confirmed cases of coronavirus disease (COVID-19) and the increasing death toll in China, timely and effective management of severely and critically ill patients appears to be particularly important. Previous studies on COVID-19 mainly described the general features of patients (1). However, little attention has been paid to clinical characteristics and outcomes of intensive care patients, data on whom are scarce but are of paramount importance to reduce mortality. Some of the results of these studies have been previously reported in the form of an abstract (2).
Methods
This study enrolled 344 severely and critically ill patients (intensive care patients) who were diagnosed with COVID-19 according to World Health Organization interim guidance by positive result of an RT-PCR assay of nasal and/or throat-swab specimens, and were hospitalized in eight intensive care wards (totaling approximately 330 beds) in Tongji hospital from January 25, 2020, through February 25, 2020. The intensive care wards staff intensivists and specialist nurses in intensive care and were equipped with continuous vital signs monitoring and respiratory support, including noninvasive and invasive ventilators, high-flow nasal cannula (HFNC) oxygen therapy, and extracorporeal membrane oxygenation. We collected demographic, clinical, laboratory, and radiologic findings, and treatment and outcome data from electronic medical records. The illness severity of COVID-19 was defined according to the Chinese management guideline for COVID-19 (version 6.0) (3). Cytokines were measured by a chemiluminescent immunometric assay (Immulite 1000; Diagnostic Products). Acute respiratory distress syndrome (ARDS) was diagnosed according to the Berlin definition, and septic shock was defined according to the 2016 Third International Consensus definition (4, 5). Disseminated intravascular coagulation was defined per the International Society of Thrombosis and Haemostasis, and acute kidney injury was diagnosed according to the Kidney Disease Improving Global Outcomes clinical practice guidelines (6). Myocardial damage was diagnosed according to the serum levels of cardiac biomarkers or new abnormalities in electrocardiography and echocardiography. Liver injury was diagnosed according to elevation of bilirubin and aminotransferase. Rhabdomyolysis was diagnosed on the basis of the serum level of creatine kinase and myoglobin. Survival endpoint was 28-day mortality after admission to the intensive care ward. The Ethics Commission of Tongji hospital approved this study, with a waiver of informed consent. Continuous variables are presented as median (interquartile range [IQR]), whereas categorical variables were expressed as frequencies (%). Statistical analyses were conducted with R3.6.2 (https://www.r-project.org/) using a Fisher’s exact test for categorical data and Mann-Whitney test for continuous data; Kaplan-Meier estimator and Cox regression were used for survival analysis. Correlations were measured by Spearman’s method (ρ). For unadjusted comparisons, a two-sided P less than 0.05 was considered statistically significant.
Results
Characteristics and treatment
Of the 344 intensive care patients (Table 1), nonsurvivors are generally older than survivors, with a higher proportion aged over 60 years, and every 10-year increase in age was associated with a 58% additional risk (hazard ratio [HR], 1.58; 95% confidence interval [CI], 1.38–1.81; P < 0.001). Dyspnea was more common in nonsurvivors, accompanied by a significantly higher respiratory rate and lower SpO 2/FiO 2 (S/F) ratio; S/F was negatively correlated (ρ = −0.68) with the incidence of ARDS, and every 10-U increase in S/F correlated with a 10% decrease in fatality (HR, 0.90; 95% CI, 0.88–0.92; P < 0.001). A total of 128 out of 145 (88.3%) patients who developed ARDS died at or before 28 days. Nonsurvivors were more likely to bear original comorbidities (all P ≤ 0.05). Lymphocytopenia occurred in 237 (69.5%) subjects and was predominant in nonsurvivors (91.6% vs. 55.7%; P < 0.001); higher lymphocyte count was significantly associated with decreasing mortality (HR, 0.1; 95% CI, 0.06–0.18; P < 0.001). A total of 283 (82.3%) patients received antiviral agents, and 266 (77.3%) received antibacterial agents. Other supportive treatments, including γ globulin (156 [45.3%]), muscle relaxant (38 [11.0%]), and glucocorticoids (225 [65.4%]), were given to the patients. Two (0.6%) patients were treated with extracorporeal membrane oxygenation, and nine (2.6%) with continuous renal replacement therapy.
Table 1.
Presenting Characteristics of Intensive Care Patients with COVID-19
| Characteristics | All Patients (N = 344)* | Survivors (n = 211) | Nonsurvivors (n = 133) | P Value |
|---|---|---|---|---|
| Age, yr | 64 (52–72) | 57 (47–69) | 70 (62–77) | <0.001 |
| Age range, yr | <0.001 | |||
| ≤60 | 150 (43.6) | 118 (55.9) | 32 (24.1) | |
| >60 | 194 (56.4) | 93 (44.1) | 101 (75.9) | |
| Sex, M | 179 (52.0) | 105 (49.8) | 74 (55.6) | 0.341 |
| Signs and symptoms | ||||
| Fever | 301 (87.5) | 186 (88.2) | 115 (86.5) | 0.770 |
| Dry cough | 233 (67.7) | 137 (64.9) | 96 (72.2) | 0.200 |
| Dyspnea | 208 (60.5) | 108 (51.2) | 100 (75.2) | <0.001 |
| Fatigue | 167 (48.5) | 102 (48.3) | 65 (48.9) | 1.000 |
| Expectoration | 135 (39.2) | 82 (38.9) | 53 (39.8) | 0.945 |
| Diarrhea | 92 (26.7) | 63 (29.9) | 29 (21.8) | 0.129 |
| Anorexia | 91 (26.5) | 57 (27.0) | 34 (25.6) | 0.864 |
| Original comorbidities or medication history | ||||
| Hypertension | 141 (41.0) | 72 (34.1) | 69 (52.3) | 0.001 |
| ACE inhibitors | 62 (18.0) | 32 (15.2) | 30 (22.6) | 0.083 |
| Diabetes | 64 (18.6) | 34 (16.1) | 30 (22.9) | 0.155 |
| Cardiovascular disease | 40 (11.6) | 18 (8.5) | 22 (16.5) | 0.030 |
| COPD | 16 (4.7) | 3 (1.4) | 13 (9.8) | 0.001 |
| Vital signs | ||||
| Respiratory rate, respirations/min | 21 (20–25) | 20 (20–22) | 24 (20–31) | <0.001 |
| Heart rate, beats/min | 90 (80–104) | 90 (80–104) | 93 (82–108) | 0.116 |
| SpO 2/FiO 2 | 279 (157–328) | 297 (272–448) | 114 (89–224) | <0.001 |
| Laboratory findings at admission | ||||
| Routine blood test | ||||
| White blood cells, ×109/L | 6.2 (4.5–8.9) | 5.3 (4.0–6.9) | 9.1 (6.1–13.3) | <0.001 |
| Lymphocytes, ×109/L | 0.9 (0.6–1.2) | 1.0 (0.8–1.4) | 0.6 (0.4–0.7) | <0.001 |
| Neutrophils, ×109/L | 4.7 (2.9–7.6) | 3.7 (2.5–5.3) | 8.0 (5.5–12.2) | <0.001 |
| Platelets, ×109/L | 189 (142–257) | 211 (161–290) | 159 (112–218) | <0.001 |
| Red cell distribution width | 12.4 (11.9–13.2) | 12.3 (11.8–13.0) | 12.7 (12.2–13.7) | <0.001 |
| Inflammatory marker | ||||
| hs-CRP, mg/L | 55 (14–106) | 28 (6–67) | 101 (61–153) | <0.001 |
| PCT, ng/ml | 0.09 (0.04–0.23) | 0.04 (0.03–0.09) | 0.21 (0.13–0.70) | <0.001 |
| IL-2R, U/ml | 811 (546–1154) | 716 (458–954) | 1098 (721–1512) | <0.001 |
| IL-6, pg/ml | 27.2 (5.9–60.1) | 10.8 (2.7–37.4) | 61.1 (29.9–132.2) | <0.001 |
| IL-8, pg/ml | 17.2 (9.1–34.0) | 12.5 (6.9–20.8) | 28.3 (14.7–59.1) | <0.001 |
| IL-10, pg/ml | 6.1 (2.5–10.6) | 2.5 (2.5–7.0) | 10.5 (5.9–18.5) | <0.001 |
| TNF-α, pg/ml | 8.8 (6.6–11.7) | 8.2 (6.1–10.2) | 10.7 (7.5–15.9) | <0.001 |
| Coagulation index | ||||
| Prothrombin time, s | 14.3 (13.5–15.4) | 13.9 (13.3–14.5) | 15.4 (14.3–17.4) | <0.001 |
| D-Dimer, μg/ml | 1.3 (0.5–5.0) | 0.7 (0.4–1.5) | 5.1 (1.7–31.5) | <0.001 |
| INR | 1.1 (1.0–1.2) | 1.1 (1.0–1.1) | 1.2 (1.1–1.4) | <0.001 |
| Cardiac biomarkers | ||||
| High-sensitivity cardiac troponin I, pg/ml | 9.7 (2.9–44.4) | 3.4 (1.4–8.7) | 46.7 (11.2–801.3) | <0.001 |
| Myo, ng/ml | 75 (32–199) | 31 (20–55) | 179 (103–367) | <0.001 |
| CK-MB, ng/ml | 1.5 (0.5–3.2) | 0.4 (0.3–1.2) | 2.5 (1.2–6.1) | <0.001 |
| Biochemistry† | ||||
| ALT (≤41), U/L | 24 (15–38) | 21 (14–36) | 29 (19–42) | 0.001 |
| AST (≤41), U/L | 31 (22–47) | 27 (20–37) | 43 (28–66) | <0.001 |
| Albumin (35–52), g/L | 34 (30–37) | 36 (33–39) | 31 (28–34) | <0.001 |
| TBIL (≤26.0), μmol/L | 10.2 (7.3–14.2) | 8.5 (6.3–11.3) | 12.9 (9.8–19.2) | <0.001 |
| Cr (59–104), μmol/L | 74 (58–93) | 66 (56.3–86) | 86 (67–111) | <0.001 |
| BUN (3.6–9.5), mmol/L | 5.3 (3.8–8.3) | 4.3 (3.2–5.8) | 8.3 (5.9–12.1) | <0.001 |
| CK (≤170), U/L | 109 (60–242) | 81 (39–139) | 168 (96–387) | <0.001 |
| LDH (135–225), U/L | 338 (237–491) | 271 (205–347) | 525 (419–676) | <0.001 |
| e-GFR (>90), ml/min/1.73 m2 | 87 (70–101) | 93 (78–107) | 74 (55–91) | <0.001 |
| Glu (3.9–6.1), mmol/L | 6.8 (5.7–9.0) | 6.1 (5.2–7.8) | 8.2 (6.6–11.4) | <0.001 |
| Radiologic manifestation | <0.001 | |||
| GGO | 164 (47.7) | 101 (50.8) | 63 (54.8) | |
| Local patchy opacities | 38 (11.0) | 35 (17.6) | 3 (2.6) | |
| Bilateral patchy opacities | 110 (32.0) | 61 (30.7) | 49 (42.6) | |
| Organ function injury | ||||
| ARDS | 145 (42.2) | 17 (8.1) | 128 (97.0) | <0.001 |
| Septic shock | 114 (33.1) | 2 (0.9) | 112 (84.2) | <0.001 |
| DIC | 71 (20.6) | 1 (0.5) | 70 (52.6) | <0.001 |
| AKI | 86 (25.0) | 6 (2.8) | 80 (60.2) | <0.001 |
| Myocardial damage | 111 (32.3) | 4 (1.9) | 107 (80.5) | <0.001 |
| Liver injury | 54 (15.7) | 9 (4.3) | 45 (33.8) | <0.001 |
| Rhabdomyolysis | 9 (2.6) | 0 (0) | 9 (6.9) | <0.001 |
| Ventilatory support throughout the course | ||||
| HFNC oxygen therapy | 35 (10.2) | 7 (3.3) | 28 (21.1) | <0.001 |
| NIV | 34 (9.9) | 7 (3.3) | 27 (20.3) | <0.001 |
| IV | 100 (29.1) | 3 (1.4) | 97 (72.9) | <0.001 |
Definition of abbreviations: ACE = angiotensin-converting enzyme; AKI = acute kidney injury; ALT = alanine transaminase; ARDS = acute respiratory distress syndrome; AST = aspartate transaminase; BUN = blood urea nitrogen; CK = creatine kinase; CK-MB = creatine kinase, MB form; COPD = chronic obstructive pulmonary disease; COVID-19 = coronavirus disease; Cr = serum creatinine; DIC = disseminated intravascular coagulation; e-GFR = estimated glomerular filtration rate; GGO = ground-glass opacity; Glu = fasting blood glucose; HFNC = high-flow nasal cannula; hs-CRP = high-sensitivity C-reactive protein; INR = international normalized ratio; IV = invasive ventilation; LDH = lactate dehydrogenase; Myo = myoglobin; NIV = noninvasive ventilation; PCT = procalcitonin; SpO 2 = oxygen saturation as measured by pulse oximetry; TBIL = total bilirubin; TNF-α = tumor necrosis factor α.
All records were measured at admission to intensive care wards unless otherwise indicated. Data are shown as n (%) or median (interquartile range).
The percentages represent the frequency divided by the total cohort size (N = 344), whereas percentages in subgroups were calculated according to contingency table, with missing data removed first.
Normal ranges of listed biochemical parameters are indicated in parentheses.
Ventilatory support
A total of 35 (10.2%) patients were treated with HFNC, of whom 23 (65.7%) also received invasive ventilation. Of the 12 patients who received HFNC only, 7 (58.3%) died at or before 28 days. A total of 134 (40.6%) patients were treated with mechanical ventilation (either noninvasive or invasive), of whom 34 received treatment of noninvasive ventilation only, and 27 (79.4%) died at or before 28 days, whereas invasive ventilation was given to 100 patients, with 97 (97%) deaths at or before 28 days. Median duration from admission to invasive ventilation was 5 (IQR, 1–8) days, and median duration of invasive ventilation was 4 (IQR, 3–8) days. Of the 145 patients who developed ARDS, 100 (69.0%) were treated with invasive ventilation.
Clinical course and outcomes
A total of 133 (38.7%) patients died at or before 28 days, with a median survival of 25 days (Figure 1). For nonsurvivors, median duration from admission to death was 10 (IQR, 6–15) days. Of the 211 survivors, 185 (87.7%) were discharged. Median duration from onset of symptoms to laboratory confirmation of infection by RT-PCR was 8 (IQR, 5–11) days. In survivors, median duration from positive to negative RT-PCR result was 12 (IQR, 9–15) days, whereas, in nonsurvivors, median duration from infection confirmation to death was 15 (IQR, 10–19) days (Figure 1).
Figure 1.
(Left panel) Kaplan-Meier curve showing a 28-day median survival of 312 intensive care patients in this cohort (32 out of 344 patients lack records of survival time). (Right panel) Timeline showing the time span from symptoms onset (median) to three important events. IQR = interquartile range.
Discussion
This report, to our knowledge, is the largest case series of patients with COVID-19 in intensive care, with informative laboratory characteristics, detailed clinical course, and outcome.
In our cohort, nonsurvivors were older than survivors, which is consistent with an earlier study (7). We did not observe survival differences in regard to sex, but this is inconsistent with the results of a previous study (8). Compared with survivors, nonsurvivors presented more commonly with dyspnea and a higher respiratory rate, indicating that more attention should be paid to changes in vital signs with respect to respiratory rate for intensive care patients. A previous study revealed that original comorbidities were potential risk factors (8), and we observed that hypertension is significantly differentially distributed between nonsurvivors (69 [52.3%]) and survivors (72 [34.1%]), and 62 out of 141 (44.0%) patients with hypertension had a medication history of taking ACE (angiotensin-converting enzyme) inhibitors. Given that ACE2 plays a dual role of vasopeptidase and severe acute respiratory syndrome (SARS) virus receptor, we speculated that patients with hypertension with COVID-19 might be more likely to become critically ill (9). In addition, S/F may be a useful and noninvasive predictive marker, which was defined by the Kigali modification of the Berlin definition and had good correlation with the diagnosis of ARDS (10). Given a large patient flow during epidemic conditions, this indicator could be flexibly used for screening and monitoring.
Lymphocytopenia occurred in almost 70% and was predominant in nonsurvivors, which contradicts a previous study with a relatively small sample size (8). Lymphocytopenia is a prominent feature of critically ill patients with SARS (11) and Middle East respiratory syndrome, which is the result of apoptosis of lymphocytes (12); thus, lymphocyte depletion could be harmful, and lymphocyte count might serve as another prognostic factor for SARS–coronavirus 2 (SARS-CoV-2). In addition, we observed a higher level of hs-CRP (high-sensitivity C-reactive protein), along with other inflammatory markers, which is consistent with relevant reports of SARS and Middle East respiratory syndrome (13). Unexpectedly, however, nonsurvivors showed a higher level of IL-2R. Highly expressed IL-2R initiates autoreactive cytotoxic CD8+ T-cell–mediated autoimmunity. Meanwhile, IL-2 stimulates the proliferation of natural killer cells that highly express IL-2R, promoting the release of cytokines, further inducing the lethal “cytokine storm” (14). We also observed that factors, such as red blood cell distribution width, lactate dehydrogenase, and coagulation index, were upregulated in nonsurvivors, which was probably due to their active participation in inflammatory response. It has been reported that chest computed tomography imaging can be more sensitive diagnostically compared with RT-PCR (15), and we reasoned that computed tomography might even show guiding significance in the critical stage of COVID-19. The high mortality rate of patients who received mechanical ventilation may have been due, in part, to the centralized admission of a large number of intensive care patients in February and the fact that patients were sometimes transferred late to the hospital. These conditions made us question the effectiveness of noninvasive ventilation treatment or HFNC in the first line, and whether the early use of invasive ventilation would improve prognosis. Both questions may be worth further study in a larger cohort.
In summary, in this single-center case series study, older patients with comorbidities are at dramatically increased risk of mortality. Real-time monitoring of S/F and regular measurements of lymphocyte count and inflammatory markers may be essential to disease management.
Supplementary Material
Acknowledgments
Acknowledgment
The authors thank all the hospital staff for their efforts in collecting the information that was used in this study, all the patients who consented to donate their data for analysis, and the medical staff who are on the frontlines of caring for patients.
Footnotes
Supported by National Key R&D Program of China grant 2019YFC1711000, National Natural Science Foundation of China grant 81973145, “Double First-Class” University project grant CPU2018GY09, China Postdoctoral Science Foundation grant 2019M651805, Science Foundation of Jiangsu Commission of Health grant H2018117, Emergency Project for the Prevention and Control of the Novel Coronavirus Outbreak in Suzhou grant SYS2020012, Fundamental Research Funds for the Central Universities (HUST: 2017KFYXJJ113; 2020-021414380462), and Wuhan Municipal Science and Technology Bureau (2017060201010173).
Author Contributions: Y.L. and J.W. had full access to all of the data in the study; conceptualization—Y.W., X.L., and T.C.; acquisition, analysis, or interpretation of data—Y.W., X.L., T.C., Y.L., and J.W.; statistical analysis—X.L. and F.Y.; investigation—X.L., H.C., T.C., N.S., F.H., J.Z., and B.Z.; drafting and editing of the manuscript—Y.W., X.L., and T.C.; funding acquisition—Y.W., F.Y., and J.W.; supervision—F.Y. and J.W.
Originally Published in Press as DOI: 10.1164/rccm.202003-0736LE on April 8, 2020
Author disclosures are available with the text of this letter at www.atsjournals.org.
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