Characteristics Of The Patients
A total of 360 records were collected in the dataset. 9 records were excluded, of which 6 were duplicate and 3 were lack of laboratory data. Finally, 351 patients with COVID-19 were eligible for this retrospective study. Of these, we randomly selected 246 subjects (70%) into the training dataset, and the remaining 105 subjects (30%) were assigned to the validation dataset. The clinical characteristics of the training and validation datasets are summarized in Table 1.
Table 1
Clinical characteristics of patients infected with COVID-19
Characteristics | Total(n = 351) | Training dataset (n = 246) | Validation dataset (n = 105) | P valuea |
Demographics | | | | |
Age (years), median (IQR) | 54(38–66) | 54(38-66.25) | 54(37-65.5) | 0.581 |
Female sex, no. (%) | 162(46.2) | 117(47.6) | 45(42.9) | 0.418 |
Smoking history, n (%) | 57(16.2) | 41(16.7) | 16(15.2) | 0.74 |
Comorbid conditions, n (%) | | | | |
Hypertension | 80(22.8) | 53(21.5) | 27(25.7) | 0.407 |
Diabetes mellitus | 41(11.7) | 27(11.0) | 14(13.3) | 0.529 |
Coronary heart disease | 20(5.7) | 13(5.3) | 7(6.7) | 0.609 |
Cerebrovascular diseases | 13(3.7) | 9(3.7) | 4(3.8) | 0.922 |
COPD | 9(2.6) | 7(2.8) | 2(1.9) | 0.73 |
Cancer | 9(2.6) | 9(3.7) | 0(0) | 0.062 |
Immunodeficiency | 1(0.3) | 1(0.4) | 0(0) | 1 |
Laboratory data | | | | |
White blood cell (× 109/L), median (IQR) | 6.3(5.2–8.4) | 6.35(5.2–8.5) | 6.2(5.25–8.05) | 0.725 |
Neutrophil (× 109/L), median (IQR) | 4.21(3.22–6.34) | 4.215(3.2325–6.6425) | 4.17(3.145–6.135) | 0.696 |
Monocyte (× 109/L), median (IQR) | 0.21(0.15–0.28) | 0.21(0.14–0.28) | 0.21(0.15–0.27) | 0.953 |
Lymphocyte (× 109/L), median (IQR) | 0.91(0.62–1.33) | 0.91(0.5975–1.3325) | 0.9(0.675–1.345) | 0.412 |
Distribution, no. (%) | | | | 0.85 |
< 0.8 (× 109/L) | 133(37.9) | 94(38.2) | 39(37.1) | |
≥0.8 (× 109/L) | 218(62.1) | 152(61.8) | 66(62.9) | |
Hemoglobin (g/L), median (IQR) | 108(98–121) | 107(97.75–120) | 110(100–122) | 0.365 |
Platelet (× 109/L), median (IQR) | 130(98–170) | 130(98-169.25) | 130.5(96.25–171.5) | 0.734 |
PT (s), median (IQR) | 10.9(10.5–11.3) | 10.9(10.5–11.3) | 10.8(10.4–11.2) | 0.157 |
APTT (s), median (IQR) | 29.7(26.3–33.6) | 29.85(26.8-33.575) | 29.25(25.425–33.8) | 0.284 |
Fibrinogen (g/L) | 2.664(2.026–3.645) | 2.664(2.026–3.542) | 2.975(2.081–3.959) | 0.192 |
D-Dimer (mg/L), median (IQR) | 0.6(0.51–1.365) | 0.62(0.52–1.518) | 0.57(0.51–1.048) | 0.052 |
TBIL (µmol/L), median (IQR) | 9.19(6.63–13.77) | 9.205(6.635–14.015) | 8.99(6.745–13.385) | 0.869 |
DBIL (µmol/L), median (IQR) | 2.39(1.585–3.57) | 2.41(1.6225–3.5375) | 2.35(1.56–3.845) | 0.866 |
Albumin (g/L), median (IQR) | 37.4(34-40.9) | 37.1(34-40.6) | 38.1(34-41.3) | 0.223 |
Globulin (g/L), median (IQR) | 26.4(23.825–28.8) | 26.4(23.725–28.875) | 26.25(23.925–28.75) | 0.907 |
ALT (U/L), median (IQR) | 21(13.75-34) | 20(14-34.5) | 21(13–33) | 0.69 |
AST (U/L), median (IQR) | 21(16–28) | 21(16–29) | 22(17–28) | 0.786 |
ALT peak (U/L), median (IQR) | 35(24–61) | 35(24–61) | 34(22-61.5) | 0.984 |
AST peak (U/L), median (IQR) | 26(20–39) | 26(20–42) | 25(20–38) | 0.432 |
Creatinine (µmol/L), median (IQR) | 67.35(54.1-79.825) | 67.25(54.65–80.35) | 68.2(53.65–79.5) | 0.57 |
Creatinine peak (µmol/L), median (IQR) | 73.4(58.25-87.925) | 73.1(58.2-87.75) | 74(58.25–88.65) | 0.993 |
LDH (U/L), median (IQR) | 207(164.75–263) | 203(164.25–269.5) | 211.5(166-260.75) | 0.882 |
LDH peak (U/L), median (IQR) | 224.5(175-305.25) | 225(175-311.5) | 220(177.5–292) | 0.762 |
Distribution, no. (%) | | | | 0.92 |
< 400 (U/L) | 286(81.5) | 200(81.3) | 86(81.9) | |
≥400 (U/L) | 40(11.4) | 29(11.8) | 11(10.5) | |
NA | 25(7.1) | 17(6.9) | 8(7.6) | |
CK (U/L), median (IQR) | 63(41-111.5) | 58.5(40-108.5) | 69.5(43-126.75) | 0.126 |
CK-MB (U/L), median (IQR) | 12.1(9.4–17.7) | 12.1(9.5–17.5) | 12.2(9.33–18.48) | 0.777 |
CRP (mg/L), median (IQR) | 21.2(4.65-51.625) | 20.7(4.35-52.475) | 21.55(6.3-48.475) | 0.764 |
PCT (ng/L), median (IQR) | 0.08(0.05–0.135) | 0.08(0.05-0.1375) | 0.08(0.06–0.135) | 0.665 |
Clinical classification, no. (%) | | | | 0.162 |
Mild | 6(1.7) | 2(0.8) | 4(3.8) | |
Moderate | 279(79.5) | 195(79.3) | 84(80) | |
Severe | 33(9.4) | 26(10.6) | 7(6.7) | |
Critical | 33(9.4) | 23(9.3) | 10(9.5) | |
Treatment, no. (%) | | | | |
Antiviral treatment | 349(99.4) | 244(99.2) | 105(100) | 1 |
Oseltamivir | 262(74.6) | 177(72.0) | 85(81) | 0.076 |
Lopinavir/ritonavir | 238(67.8) | 172(69.9) | 66(62.9) | 0.134 |
Ribavirin | 44(12.5) | 35(14.2) | 9(8.6) | 0.085 |
Arbidol | 165(47.0) | 116(47.2) | 49(46.7) | 0.412 |
Ganciclovir | 10(2.8) | 7(2.8) | 3(2.9) | 0.421 |
Interferon-alpha for nasal spray | 224(63.8) | 155(63.0) | 69(65.7) | 0.289 |
Antibacterial treatment | 328(93.4) | 229(93.1) | 99(94.3) | 0.678 |
Antifungal treatment | 20(5.7) | 13(5.3) | 7(6.7) | 0.33 |
Chinese patent medicine injection | | | | |
Reduning injection | 112(31.9) | 79(32.1) | 33(31.4) | 0.9 |
Xuebijing injection | 183(52.1) | 126(51.2) | 57(54.3) | 0.641 |
Tanreqing injection | 84(23.9) | 67(27.2) | 17(16.2) | 0.026b |
Glucocorticoids | 119(33.9) | 85(34.6) | 34(32.4) | 0.365 |
Intravenous immunoglobulin therapy | 66(18.8) | 46(18.7) | 20(19.0) | 0.39 |
CRRT | 5(1.4) | 3(1.2) | 2(1.9) | 0.024b |
NIVV or high-flow nasal cannula | 37(10.5) | 28(11.4) | 9(8.6) | 0.432 |
Invasive mechanical ventilation | 12(3.4) | 7(2.8) | 5(4.8) | 0.354 |
Aggravation, no. (%) | | | | 0.905 |
Mild to Moderate/Severe/Critical | 0(0) | 0(0) | 0(0) | |
Moderate to Severe | 19(5.4) | 14(5.7) | 5(4.8) | |
Moderate/Severe to Critical | 31(8.8) | 21(8.5) | 10(9.5) | |
No | 301(85.8) | 211(85.8) | 90(85.7) | |
Death, no. (%) | 14(4.0) | 9(3.7) | 5(4.8) | 0.766 |
Notes: IQR, interquartile; COPD, chronic obstructive pulmonary disease; PT, prothrombin time; APTT, activated partial thromboplastin time; TBIL, total bilirubin; DBIL, direct bilirubin; ALT, alanine aminotransferase; AST, aspartate Aminotransferase; LDH, lactate dehydrogenase; CK, creatine kinase; CKMB, Creatine kinase-MB; CRP, C-reactive protein; PCT, procalcitonin; CRRT, continuous renal replacement therapy; NIVV, non-invasive ventilation; NA, not available; aFor comparison between training dataset and validation dataset; bP<0.05. |
Development and validation of nomogram for predicting the probability of progression of patients with COVID-19
Based on univariate logistic analysis of the training cohort, we identified 24 variables significantly associated with risk of progression (Table 2). Of the relevant variables, 4 predictive factors (including WBC, CRP, whether lymphocyte ≥ 0.8 × 109/L, and whether LDH ≥ 400 U/L) identified by LASSO regression were shown as a nomogram (Fig. 1a). In the training cohort, the AUC of our nomogram was 0.945 (95%CI: 0.91–0.98) (Fig. 1b). The calibration curve of our nomogram for the probability of exacerbation in the training dataset demonstrated good agreement between the predicted and observed risks (Fig. 1c). The Hosmer-Lemeshow test yielded a nonsignificant statistic (Chi-square = 7.951, P value = 0.539). We performed DCA to assess the clinical value of the nomogram (Fig. 1d). The DCA curve showed that, if the threshold probability of 30–80%, using this nomogram to identify patients who might aggravate would be more benefit than either “treat-all” or “treat-no” schemes.
Table 2
Univariate logistic regression of progression factors in patients with COVID-19
Variables | OR | 95CI% | Estimate | S. E | z value | P value |
Age (years) | 1.339 | 1.191–1.498 | 0.041 | 0.012 | 3.399 | 0.001b |
Sex | | | | | | |
Male | 1 | - | - | - | - | - |
Female | 0.699 | 0.337–1.448 | -0.358 | 0.372 | -0.964 | 0.335 |
Smoking | 1.598 | 0.668–3.823 | 0.469 | 0.445 | 1.054 | 0.292 |
Hypertension | 3.96 | 1.864–8.415 | 1.376 | 0.385 | 3.579 | 3.452E-04b |
Diabetes mellitus | 5.586 | 2.324–13.426 | 1.720 | 0.447 | 3.845 | 1.207E-04b |
Coronary heart disease | 8.542 | 2.678–27.24 | 2.145 | 0.592 | 3.625 | 2.889E-04b |
Cerebrovascular diseases | 5.29 | 1.347–20.777 | 1.646 | 0.698 | 2.358 | 0.018b |
COPD | 2.497 | 0.465–13.404 | 0.915 | 0.857 | 1.067 | 0.286 |
Cancer | 8.625 | 2.193–33.922 | 2.155 | 0.699 | 3.084 | 0.002b |
Immunodeficiency | 1.00E + 10 | 0 | 16.392 | 882.743 | 0.019 | 0.985 |
White blood cell | 1.339 | 1.197–1.498 | 0.292 | 0.057 | 5.116 | 3.120E-07b |
Neutrophil | 1.225 | 1.12–1.34 | 0.203 | 0.046 | 4.423 | 9.740E-06b |
Monocyte | 0 | 0-0.027 | -7.771 | 2.122 | -3.662 | 2.507E-04b |
Lymphocyte | | | | | | |
Lymphocyte < 0.8(× 109/L) | 1 | - | - | - | - | |
Lymphocyte ≥ 0.8(× 109/L) | 0.012 | 0.002–0.087 | -4.449 | 1.026 | -4.337 | 1.440E-05b |
Hemoglobin (g/L) | 0.942 | 0.92–0.965 | -0.060 | 0.012 | -4.969 | 6.740E-07b |
Platelet (× 109/L) | 0.984 | 0.976–0.993 | -0.016 | 0.004 | -3.627 | 2.868E-04b |
PT (s) | 1.785 | 1.335–2.387 | 0.580 | 0.148 | 3.912 | 9.140E-05b |
APTT (s) | 1.05 | 0.994–1.108 | 0.049 | 0.028 | 1.747 | 0.081 |
Fibrinogen (g/L) | 0.542 | 0.363–0.81 | -0.612 | 0.205 | -2.990 | 0.003b |
D-Dimer (mg/L) | 1.114 | 1.059–1.171 | 0.108 | 0.026 | 4.201 | 2.650E-05b |
TBIL (µmol/L) | 1.018 | 0.963–1.075 | 0.017 | 0.028 | 0.624 | 0.533 |
DBIL (µmol/L) | 1.023 | 0.983–1.065 | 0.023 | 0.021 | 1.129 | 0.259 |
Albumin (g/L) | 0.876 | 0.819–0.938 | -0.132 | 0.035 | -3.814 | 1.369E-04b |
Globulin (g/L) | 1.016 | 0.932–1.108 | 0.016 | 0.044 | 0.362 | 0.717 |
ALT (U/L) | 1.011 | 0.997–1.025 | 0.011 | 0.007 | 1.553 | 0.120b |
AST (U/L) | 1.032 | 1.011–1.054 | 0.032 | 0.011 | 2.954 | 0.003b |
ALT peak (U/L) | 1.005 | 1.001–1.009 | 0.005 | 0.002 | 2.400 | 0.016b |
AST peak (U/L) | 1.007 | 1-1.013 | 0.007 | 0.003 | 1.968 | 0.049b |
Creatinine (µmol/L) | 1.007 | 1-1.013 | 0.007 | 0.003 | 2.057 | 0.040b |
Creatinine peak (µmol/L) | 1.01 | 1.003–1.016 | 0.010 | 0.003 | 2.953 | 0.003b |
CK (U/L) | 1.001 | 1-1.003 | 0.001 | 0.001 | 1.445 | 0.148 |
CKMB (U/L) | 1.005 | 0.999–1.012 | 0.005 | 0.003 | 1.621 | 0.105 |
LDH peak (U/L) | 1.005 | 1.003–1.006 | 0.004 | 0.001 | 4.610 | 4.03E-06b |
LDH < 400 U/L | 1 | - | - | - | - | - |
LDH ≥ 400 U/L | 17.472 | 7.053–43.284 | 2.861 | 0.463 | 6.180 | 6.390E-10b |
CRP (mg/L) | 1.029 | 1.02–1.039 | 0.029 | 0.004 | 6.468 | 9.960E-11b |
PCT (ng/L) | 1.107 | 1.031–1.189 | 0.102 | 0.036 | 2.793 | 0.005b |
Notes: OR, odds ratio; CI: confidence interval; S. E, standard error; COPD, chronic obstructive pulmonary disease; PT, prothrombin time; APTT, activated partial thromboplastin time; TBIL, total bilirubin; DBIL, direct bilirubin; ALT, alanine aminotransferase; AST, aspartate Aminotransferase; LDH, lactate dehydrogenase; CK, creatine kinase; CKMB, Creatine kinase-MB; CRP, C-reactive protein; PCT, procalcitonin; NA, not available; bP<0.05. |
The validation of nomogram for predicting the probability of the exacerbation of patients with COVID-19 was provided in the Supplementary material and Fig. S1.
Nomogram For Predicting The Survival Of Patients With Covid-19
According to the nomogram developed in the training cohort, the total point of each of the 322 patients in the total cohort was calculated, and another 29 cases were excluded due to lack of information. Based on a total pointof 160 on the nomogram, corresponding to a 50%probability of disease progression, it was defined as the cut-off value. Patients in the total cohort were stratified into low-risk group (total point < 160, n = 289) and high-risk group (total point ≥ 160, n = 33). Median follow-up was 56 days. Kaplan-Meier analysis demonstrated a significant difference in the overall survival (OS) rates between high-risk group and low-risk group. The 8-week survival rate was 71.41% in the high-risk group, while all patients in low-risk group survived (Log-rank P < 0.0001, Fig. 1e). Time-dependent ROC curve analysis using timeROC package in R software showed that the nomogram achieved an AUC value of 0.96 (95%CI: 0.931–0.989) at 8-week of OS (Fig. 1f), demonstrating excellent performance of this nomogram for predicting survival of patients with COVID-19.
Effects of antiviral drugs on the survival of COVID-19 patients with high-risk
As all patients with low-risk were alive, we selected the high-risk patients (n = 33) to analyze the effects of drugs on the survival of patients with COVID-19. Of these patients, 9.1%, 24.2% and 66.7% were Mild type, Severe type and Critical type, respectively. Mean age was 66.8 years and median follow-up was 58 days.
As in shown Fig. 2a, kaplan-Meier analysis indicated that addition of antivirals may significantly improve the OS of COVID-19 patients with high-risk. In order to clarify which antiviral drugs can affect survival, we analyzed six commonly used antiviral drugs, including oseltamivir, lopinavir/ritonavir, ribavirin, arbidol, ganciclovir, and interferon-alpha (IFN-α) for nasal spray. Kaplan-Meier analysis showed that both oseltamivir and lopinavir/ritonavir significantly prolonged the OS of patients with COVID-19 (8-week survival rate: 79.58% vs. 38.1%, Log-rank P < 0.05; 85.56% vs. 41.56%, Log-rank P < 0.01; respectively; Fig. 2b-c), but ribavirin shorten that of patients (7-week survival rate: 50.0% vs. 89.66%, Log-rank P < 0.05; Fig. 2d). There were no effects of arbidol, ganciclovir and IFN-α on the survival of patients (Fig. S2a-c).
Effects of Chinese patent medicine injections on the survival of COVID-19 patients with high-risk
In China, three Chinese patent medicine injections (including Reduning injection, Xuebijing injection, and Tanreqing injection) have been used in the treatment of patients with COVID-19. The 8-week survival rate was higher in the Reduning group than the no Reduning group (100% vs. 59.14%, Log-rank P < 0.01, Fig. 3a), while lower in the Xuebijing group than the no Xuebijing group (59.7% vs. 100%, Log-rank P < 0.01; Fig. 3b). There was no difference in OS between patients treated with and without Tanreqing injection (Log-rank P = 0.17; Fig. 3c).
Effects of other drugs on the survival of COVID-19 patients with high-risk
As COVID-19 patients are seriously ill, various drugs may be always applied simultaneously. We found that OS at 8-week was shorter in the antifugal therapy group compared tothe no antifungal therapy group (48.61% vs. 84.71%, Log-rank P < 0.01; Fig. S3a). There were no significant differences in OS of COVID-19 patients whether they received with glucocorticoids, thymalfasin, intravenous immunoglobulin (IVIG), or ambroxol (Fig. S3b-e). Because all high-risk patients were treated with antibacterials, we cannot analyze the impact of antibacterials on survival.
Effects of the different combinations of oseltamivir, lopinavir/ritonavir and Reduning injection on the survival of COVID-19 patients with high-risk
Based on the above results, we further analyzed the effects of the different combinations of oseltamivir, lopinavir/ritonavir and Reduning injection on the survival of COVID-19 patients with high-risk.
As shown in Fig. 4a, patients treated with the combination of oseltamivir and lopinavir/ritonavir had longer OS than those who treated without oseltamivir and lopinavir/ritonavir (8-week survival rate: 84.38% vs. 20.0%, Log-rank P < 0.01), while those with oseltamivir alone or with lopinavir/ritonavir alone did not have longer OS (Log-rank P = 0.15 and P = 0.23, respectively). Patients treated with the combination of oseltamivir and Reduning or with oseltamivir alone exhibited better OS than those without oseltamivir and Reduning (8-week survival rate: 100% vs. 26.67%, Log-rank P < 0.001; 77.92% vs. 26.67%, Log-rank P < 0.01; respectively), while those with Reduning alone did not exhibit better OS (Log-rank P = 0.073) (Fig. 4b). The 8-week survival rates of patients treated with the combination of lopinavir/ritonavir and Reduning or with lopinavir/ritonavir alone were longer than those without lopinavir/ritonavir and Reduning (100% vs. 26.67%, Log-rank P < 0.01; 70.1% vs. 26.67%, Log-rank P < 0.01; respectively), while that of patients treated with Reduning alone was not longer than that of those without lopinavir/ritonavir and Reduning (Log-rank P = 0.073)(Fig. 4c). As shown in the Fig. 4d, patients treated with the combination of these three drugs exhibited better OS than those without these three drugs or with single drug (7-week survival rate: 100% vs. 25.0%, Log-rank P < 0.001; 8-week survival rate: 100% vs. 66.67%, Log-rank P = 0.048; respectively), but not better than that of two drugs (8-week survival rate: 100% vs. 78.79%, Log-rank P = 0.13).