Effectiveness of Traditional Chinese Medicine on coronavirus disease 2019 in 92 patients: a retrospective study.

OBJECTIVE: To investigate the antipyretic effect of early treatment with Traditional Chinese Medicine (TCM) on coronavirus disease 2019 (COVID-19) patients. METHODS: We retrospectively analyzed 369 patients from January 26th, 2020 to April 15th, 2020, who had been diagnosed with COVID-19. Among 92 eligible cases, 45 cases were identified as treatment group Ⅰ ( 45) and 47 cases were identified as treatment group Ⅱ. Patients in the treatment group Ⅰ were treated with TCM herbal decoction within 5 d after admission. Patients in the treatment group Ⅱ were treated with TCM herbal decoction after the 6th admission day. The onset time of antipyretic effect, the antipyretic time, the time of negative oropharyngeal swab nucleic acid conversion, and the changes of cell count in blood routine test were compared. RESULTS: The treatment group I showed shorter average antipyretic duration (4 7 d; <0.05), and shorter average time for polymerase chain reaction (PCR) nucleic acid test results to turn negative (7 11 d; <0.05) than the treatment group II. For patients ( 54) with body temperature>38 ℃, patients in the treatment group I had shorter median onset time of antipyretic effect than those in the treatment group II (3 4 d; <0.05). The absolute lymphocyte (LYMPH) count and absolute eosinophil (EOS) count on the 3rd day after admission and the neutrophil/lymphocyte ratio on the 6th day after admission of patients in the treatment group I were notably different from those in the treatment group II at the same time point (0.05). Based on Spearman's rank correlation analysis, the change of body temperature on the 3rd day after admission was positively correlated with the increase of EOS count and the increase of EOS count and LYMPH counts on the 6th day after admission (0.01). CONCLUSIONS: Early TCM intervention within 5 d after hospital admission shortened the onset time of antipyretic effect and fever duration of COVID-19 patients, reduced the time required for PCR test results to turn negative. Moreover, early TCM intervention also improved the results of inflammatory markers for COVID-19 patients. LYMPH and EOS counts can be used as indicators of TCM antipyretic effect.

Development and validation of an online model to predict critical COVID-19 with immune-inflammatory parameters.

BACKGROUND: Immune and inflammatory dysfunction was reported to underpin critical COVID-19(coronavirus disease 2019). We aim to develop a machine learning model that enables accurate prediction of critical COVID-19 using immune-inflammatory features at admission. METHODS: We retrospectively collected 2076 consecutive COVID-19 patients with definite outcomes (discharge or death) between January 27, 2020 and March 30, 2020 from two hospitals in China. Critical illness was defined as admission to intensive care unit, receiving invasive ventilation, or death. Least Absolute Shrinkage and Selection Operator (LASSO) was applied for feature selection. Five machine learning algorithms, including Logistic Regression (LR), Support Vector Machine (SVM), Gradient Boosted Decision Tree (GBDT), K-Nearest Neighbor (KNN), and Neural Network (NN) were built in a training dataset, and assessed in an internal validation dataset and an external validation dataset. RESULTS: Six features (procalcitonin, [T + B + NK cell] count, interleukin 6, C reactive protein, interleukin 2 receptor, T-helper lymphocyte/T-suppressor lymphocyte) were finally used for model development. Five models displayed varying but all promising predictive performance. Notably, the ensemble model, SPMCIIP (severity prediction model for COVID-19 by immune-inflammatory parameters), derived from three contributive algorithms (SVM, GBDT, and NN) achieved the best performance with an area under the curve (AUC) of 0.991 (95% confidence interval [CI] 0.979-1.000) in internal validation cohort and 0.999 (95% CI 0.998-1.000) in external validation cohort to identify patients with critical COVID-19. SPMCIIP could accurately and expeditiously predict the occurrence of critical COVID-19 approximately 20 days in advance. CONCLUSIONS: The developed online prediction model SPMCIIP is hopeful to facilitate intensive monitoring and early intervention of high risk of critical illness in COVID-19 patients. TRIAL REGISTRATION: This study was retrospectively registered in the Chinese Clinical Trial Registry ( ChiCTR2000032161 ). vv.

Immune-Inflammatory Parameters in COVID-19 Cases: A Systematic Review and Meta-Analysis.

Background: The recent outbreak of coronavirus disease 2019 (COVID-19) has been rapidly spreading on a global scale and poses a great threat to human health. Acute respiratory distress syndrome, characterized by a rapid onset of generalized inflammation, is the leading cause of mortality in patients with COVID-19. We thus aimed to explore the effect of risk factors on the severity of the disease, focusing on immune-inflammatory parameters, which represent the immune status of patients. Methods: A comprehensive systematic search for relevant studies published up to April 2020 was performed by using the PubMed, Web of Science, EMBASE, and China National Knowledge Internet (CNKI) databases. After extracting all available data of immune-inflammatory indicators, we statistically analyzed the risk factors of severe and non-severe COVID-19 patients with a meta-analysis. Results: A total of 4,911 patients from 29 studies were included in the final meta-analysis. The results demonstrated that severe patients tend to present with increased white blood cell (WBC) and neutrophil counts, neutrophil-lymphocyte ratio (NLR), procalcitonin (PCT), C-reaction protein (CRP), erythrocyte sedimentation rate (ESR), and Interleukin-6 (IL-6) and a decreased number of total lymphocyte and lymphocyte subtypes, such as CD4+ T lymphocyte and CD8+ T lymphocyte, compared to the non-severe patients. In addition, the WBC count>10 × 109/L, lymphocyte count<1 × 109/L, PCT>0.5 ng/mL, and CRP>10 mg/L were risk factors for disease progression in patients with COVID-19 (WBC count>10 × 109/L: OR = 2.92, 95% CI: 1.96-4.35; lymphocyte count<1 × 109/L: OR = 4.97, 95% CI: 3.53-6.99; PCT>0.5 ng/mL: OR = 6.33, 95% CI: 3.97-10.10; CRP>10 mg/L: OR = 3.51, 95% CI: 2.38-5.16). Furthermore, we found that NLR, as a novel marker of systemic inflammatory response, can also help predict clinical severity in patients with COVID-19 (OR = 2.50, 95% CI: 2.04-3.06). Conclusions: Immune-inflammatory parameters, such as WBC, lymphocyte, PCT, CRP, and NLR, could imply the progression of COVID-19. NLR has taken both the levels of neutrophil and lymphocyte into account, indicating a more complete, accurate, and reliable inspection efficiency; surveillance of NLR may help clinicians identify high-risk COVID-19 patients at an early stage.

Clinical value of immune-inflammatory parameters to assess the severity of coronavirus disease 2019.

OBJECTIVE: To explore the clinical value of immune-inflammatory markers to assess the severity of coronavirus disease 2019 (COVID-19). METHODS: 127 consecutive hospitalized patients with confirmed COVID-19 were enrolled in this study, and classified into non-severe and severe groups. Demographics, symptoms, underlying diseases and laboratory data were collected and assessed for predictive value. RESULTS: Of 127 COVID-19 patients, 16 cases (12.60%) were classified into the severe group. High level of interleukin-6 (IL-6), C-reaction protein (CRP) and hypertension were independent risk factors for the severity of COVID-19. The risk model based on IL-6, CRP and hypertension had the highest area under the receiver operator characteristic curve (AUROC). Additionally, the baseline IL-6 was positively correlated with other immune-inflammatory parameters and the dynamic change of IL-6 in the severe cases were parallel to the amelioration of the disease. CONCLUSION: Our study showed that high level of IL-6, CRP and hypertension were independent risk factors for assessing the severity of COVID-19. The risk model established upon IL-6, CRP and hypertension had the highest predictability in this study. Besides, IL-6 played a pivotal role in the severity of COVID-19 and had a potential value for monitoring the process of severe cases.