Risk factors for predicting mortality of COVID-19 patients: A systematic review and meta-analysis

Prognostic Factors for Predicting Post-COVID-19 Condition in Patients With COVID-19 in an Outpatient Setting

BACKGROUND

Although data on post-coronavirus disease 2019 (COVID-19) conditions are extensive, the prognostic factors affecting symptom duration in non-hospitalized patients with COVID-19 are currently not well known. We aimed to investigate the various prognostic factors affecting symptom duration among outpatients with COVID-19.

METHODS

Data were analyzed from 257 patients who were diagnosed with mild COVID-19 and visited the 'post-COVID-19 outpatient clinic' between April and December 2022 after a mandatory isolation period. The symptom duration was measured from diagnosis to symptom resolution. Laboratory and pulmonary function test results from their first visit were collected.

RESULTS

The mean age of patients was 55.7 years, and the median symptom duration was 57 days. The development of post-COVID-19 conditions (> 12 weeks) were significantly correlated with not using antiviral drugs, leukocytosis (white blood cell > 10,000/µL), lower 25(OH)D3 levels, forced vital capacity (FVC) < 90% predicted, and presence of dyspnea and anxiety/depression. Additionally, in multivariable Cox regression analysis, not using antiviral drugs, lower 25(OH)D3 levels, and having dyspnea were poor prognostic factors for longer symptom duration. Particularly, vitamin D deficiency (< 20 ng/mL) and not using antivirals during the acute phase were independent poor prognostic factors for both post-COVID-19 condition and longer symptom duration.

CONCLUSION

The non-use of antivirals, lower 25(OH)D3 levels, leukocytosis, FVC < 90% predicted, and the presence of dyspnea and anxiety/depression symptoms could be useful prognostic factors for predicting post-COVID-19 condition in outpatients with COVID-19. We suggest that the use of antiviral agents during the acute phase and vitamin D supplements might help reduce COVID-19 symptom duration.

Risk Factors Associated with Severity and Death from COVID-19 in Iran: A Systematic Review and Meta-Analysis Study

Objectives: This study aims to investigate the risk factors associated with severity and death from COVID-19 through a systematic review and meta-analysis of the published documents in Iran. Methods: A systematic search was performed based on all articles indexed in Scopus, Embase, Web of Science (WOS), PubMed, and Google Scholar in English and Scientific Information Database (SID) and Iranian Research Institute for Information Science and Technology (IRA)NDOC indexes in Persian. To assess quality, we used the Newcastle Ottawa Scale. Publication bias was assessed using Egger's tests. Forest plots were used for a graphical description of the results. We used HRs, and ORs reported for the association between risk factors and COVID-19 severity and death. Results: Sixty-nine studies were included in the meta-analysis, of which 62 and 13 had assessed risk factors for death and severity, respectively. The results showed a significant association between death from COVID-19 and age, male gender, diabetes, hypertension, cardiovascular disease (CVD), cerebrovascular disease, chronic kidney disease (CKD), Headache, and Dyspnea. We observed a significant association between increased white blood cell (WBC), decreased Lymphocyte, increased blood urea nitrogen (BUN), increased creatinine, vitamin D deficiency, and death from COVID-19. There was only a significant relationship between CVD and disease severity. Conclusion: It is recommended that the predictive risk factors of COVID-19 severity and death mentioned in this study to be used for therapeutic and health interventions, to update clinical guidelines and determine patients' prognoses.

Risk factors for death in hospitalized COVID-19 patients in Africa: A systematic review and meta-analysis

BACKGROUND

The coronavirus disease 2019 has quickly spread worldwide since it first appeared in Wuhan, China, in late 2019. The most affected country in Africa was South Africa. This study aimed to identify the risk factors for death in hospitalized COVID-19 patients in Africa.

METHODS

We conducted a systematic review following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. We searched articles from the following database: PubMed, Embase, Cochrane Library, Medline, and COVID-19 Research Database. We used Google Scholar for gray literature. The language used in this article was English. The last search was conducted on January 15, 2023. Pooled HRs, or ORs, and 95% confidence intervals, were calculated separately to identify the risk factors for death in hospitalized COVID-19 patients. Heterogeneity was assessed by Cochran's Q statistic and the I2 test. The Egger test was used to assess publication bias. Subgroup analysis was performed to determine the source of heterogeneity. Data analysis was performed using Stata version 17. A P value < .05 was considered significant.

RESULTS

A total of 16,600 articles were obtained from the database search; finally, 16 articles met the inclusion criteria and were eligible for data extraction. The analysis revealed that the pooled prevalence of mortality in hospitalized COVID-19 patients was 13.9%. Advanced age was a significant risk factor for death in hospitalized COVID-19 patients, with the pooled coronavirus mortality HR and OR being 3.73 (95% CI: 2.27-5.19) and 1.04 (95% CI: 1.02-1.06), respectively. In addition, male gender (pOR 1.23; 95% CI: 1.07-1.40), patients with diabetes mellitus (DM) (pOR 1.26; 95% CI: 1.01-1.51), hypertension (HTN) (pOR 1.56; 95% CI: 1.27-1.85), chronic kidney disease (CKD) (pHR 5.43; 95% CI: 0.18-10.67), severe or critical conditions (pOR 9.04; 95% CI: 3.14-14.94) had a significantly increased risk of coronavirus-related mortality. The main limitations of the present study stem from the predominant use of published studies, which could introduce publication bias.

CONCLUSION

According to this study, advanced age, male gender, hypertension, diabetes mellitus, chronic kidney disease, and severe or critical condition were clinical risk factors associated with death outcomes in hospitalized COVID-19 patients in Africa.

Hemodynamic, Oxygenation and Lymphocyte Parameters Predict COVID-19 Mortality

The mortality of COVID-19 patients has left the world devastated. Many scoring systems have been developed to predict the mortality of COVID-19 patients, but several scoring components cannot be carried out in limited health facilities. Herein, the authors attempted to create a new and easy scoring system involving mean arterial pressure (MAP), PF Ratio, or SF ratio-respiration rate (SF Ratio-R), and lymphocyte absolute, which were abbreviated as MPL or MSLR functioning, as a predictive scoring system for mortality within 30 days for COVID-19 patients. Of 132 patients with COVID-19 hospitalized between March and November 2021, we followed up on 96 patients. We present bivariate and multivariate analyses as well as the area under the curve (AUC) and Kaplan-Meier charts. From 96 patients, we obtained an MPL score of 3 points: MAP < 75 mmHg, PF Ratio < 200, and lymphocyte absolute < 1500/µL, whereas the MSLR score was 6 points: MAP < 75 mmHg, SF Ratio < 200, lymphocyte absolute < 1500/µL, and respiration rate 24/min. The MPL cut-off point is 2, while the MSLR is 4. MPL and MSLR have the same sensitivity (79.1%) and specificity (75.5%). The AUC value of MPL vs. MSLR was 0.802 vs. 0.807. The MPL ≥ 2 and MSLR ≥ 4 revealed similar predictions for survival within 30 days (p < 0.05). Conclusion: MPL and MSLR scores are potential predictors of mortality in COVID-19 patients within 30 days in a resource-limited country.

Effect of COVID-19 Pandemic on Patients Who Have Undergone Liver Transplantation: Retrospective Cohort Study

BACKGROUND

In liver transplant (LT) recipients, immunosuppressive therapy may potentially increase the risk of severe COVID-19 and may increase the mortality in patients. However, studies have shown conflicting results, with various studies reporting poor outcomes while the others show no difference between the LT recipients and healthy population. The aim of this study is to determine the impact of the COVID-19 pandemic on survival of LT recipients.

METHODS

This is a retrospective cohort study analyzing the data from 387 LT recipients diagnosed with COVID-19. LT recipients were divided into two groups: survival (n = 359) and non-survival (n = 28) groups. A logistic regression model was used to determine the independent risk factors for mortality. Machine learning models were used to analyze the contribution of independent variables to the mortality in LT recipients.

RESULTS

The COVID-19-related mortality rate in LT recipients was 7.2%. Multivariate analysis showed that everolimus use (p = 0.012; OR = 6.2), need for intubation (p = 0.001; OR = 38.4) and discontinuation of immunosuppressive therapy (p = 0.047; OR = 7.3) were independent risk factors for mortality. Furthermore, COVID-19 vaccination reduced the risk of mortality by 100 fold and was the single independent factor determining the survival of the LT recipients.

CONCLUSION

The effect of COVID-19 infection on LT recipients is slightly different from the effect of the disease on the general population. The COVID-19-related mortality is lower than the general population and vaccination for COVID-19 significantly reduces the risk of mortality.

Symptoms, Treatment, and Outcomes of COVID-19 Patients Coinfected with Clostridioides difficile: Single-Center Study from NE Romania during the COVID-19 Pandemic

The Coronavirus disease 2019 (COVID-19) pandemic has brought new challenges across medical disciplines, particularly in infectious disease medicine. In Romania, the incidence of SARS-CoV-2 (Severe acute respiratory syndrome coronavirus 2) infection increased dramatically since March 2020 until March 2022. Antibiotic administration for pulmonary superinfections in COVID-19 intensified and, consequently, increased rates of Clostridioides difficile infection (CDI) were hypothesized. We conducted a single-center, retrospective, observational study on patients from North-Eastern Romania to assess clinical characteristics and outcomes of COVID-19 and Clostridioides difficile (CD) coinfection, and to identify risk factors for CDI in COVID-19 patients. The study enrolled eighty-six CDI and COVID-19 coinfected patients admitted during March 2020-February 2021 (mean age 59.14 years, 53.49% men, 67.44% urban residents) and a group of eighty-six COVID-19 patients. On admission, symptoms were more severe in mono-infected patients, while coinfected patients associated a more intense acute inflammatory syndrome. The main risk factors for severe COVID-19 were smoking, diabetes mellitus, and antibiotic administration. Third generation cephalosporins (55%) and carbapenems (24%) were the main antibiotics used, and carbapenems were significantly associated with severe COVID-19 in patients coinfected with CD during hospitalization. Coinfection resulted in longer hospitalization and poorer outcomes. The extensive use of antibiotics in COVID-19, particularly carbapenems, contributed substantially to CD coinfection.

Early-stage predictors of deterioration among 3145 nonsevere SARS-CoV-2-infected people community-isolated in Wuhan, China: A combination of machine learning algorithms and competing risk survival analyses

OBJECTIVE

To determine which early-stage variables best predicted the deterioration of coronavirus disease 2019 (COVID-19) among community-isolated people infected with severe acute respiratory syndrome coronavirus 2 and to test the performance of prediction using only inexpensive-to-measure variables.

METHODS

Medical records of 3145 people isolated in two Fangcang shelter hospitals (large-scale community isolation centers) from February to March 2020 were accessed. Two complementary methods-machine learning algorithms and competing risk survival analyses-were used to test potential predictors, including age, gender, severity upon admission, symptoms (general symptoms, respiratory symptoms, and gastrointestinal symptoms), computed tomography (CT) signs, and comorbid chronic diseases. All variables were measured upon (or shortly after) admission. The outcome was deterioration versus recovery of COVID-19.

RESULTS

More than a quarter of the 3145 people did not present any symptoms, while one-third ended isolation due to deterioration. Machine learning models identified moderate severity upon admission, old age, and CT ground-glass opacity as the most important predictors of deterioration. Removing CT signs did not degrade the performance of models. Competing risk models identified age ≥ 35 years, male gender, moderate severity upon admission, cough, expectoration, CT patchy opacity, CT consolidation, comorbid diabetes, and comorbid cardiovascular or cerebrovascular diseases as significant predictors of deterioration, while a stuffy or runny nose as a predictor of recovery.

CONCLUSIONS

Early-stage prediction of COVID-19 deterioration can be made with inexpensive-to-measure variables, such as demographic characteristics, severity upon admission, observable symptoms, and self-reported comorbid diseases, among asymptomatic people and mildly to moderately symptomatic patients.

Predictors of death among severe COVID-19 patients admitted in Hawassa City, Sidama, Southern Ethiopia: Unmatched case-control study

INTRODUCTION

Since COVID-19 was announced as a worldwide pandemic, the world has been struggling with this disease. In Ethiopia, there is some information on the epidemiological characteristics of the disease and treatment outcomes of COVID-19 patients. But, there is limited evidence related to predictors of death in COVID-19 patients.

OBJECTIVE

To assess the predictor of death among severely ill COVID-19 patients admitted in Hawassa city COVID-19 treatment centers.

METHODS

An institution-based unmatched case-control study was conducted at Hawassa city COVID-19 treatment centers from May 2021 to June 2021. All severe COVID-19-related deaths from May 2020 to May 2021 were included in the case group whereas randomly selected discharged severe COVID-19 patients were included in the control group. Extracted information was entered into Epi-data 4.6 and exported to SPSS 25 for analysis. Multivariable binary logistic regression was run to assess predictors. The result was presented as an adjusted odds ratio with a 95% confidence interval. Variables with a 95% confidence interval which not included one were considered statistically significant.

RESULT

A total of 372 (124 cases and 248 controls) patients were included in the study. Multivariable analysis revealed age ≥ 65 years (AOR = 2.62, 95% CI = 1.33-5.14), having shortness of breath (AOR = 1.87, 95% CI = 1.02-3.44), fatigue (AOR 1.78, 95% CI = 1.09-2.90), altered consciousness (AOR 3.02, 95% CI = 1.40, 6.49), diabetic Mellitus (AOR = 2.79, 95% CI = 1.16-6.73), chronic cerebrovascular disease (AOR = 2.1, 95% CI = 1.23, 3.88) were found to be predictors of death.

CONCLUSION

Older age, shortness of breath, fatigue, altered consciousness, and comorbidity were predictors of death in Severe COVID-19 patients.

Stenotrophomonas maltophilia pneumonia in critical COVID-19 patients

Stenotrophomonas maltophilia, an environmental aerobic non-fermentative Gram-negative bacilli, has gained attention in many nosocomial outbreaks. COVID-19 patients in intensive care unit have extended hospital stay and are severely immunosuppressed. This study aimed to determine the prevalence and risk factors of S. maltophilia pneumonia in critical COVID-19 patients. A total of 123 COVID-19 patients in ICU admitted between March 2020 and March 2021 were identified from the authors' institutional database and assessed for nosocomial pneumonia. Demographic data and factors predisposing to S. maltophilia pneumonia were collected and analyzed. The mean age was 66 ± 13 years and 74% were males. Median APACHE and SOFA scores were 13 (IQR = 8-19) and 4 (3-6), respectively. The Median NEWS2 score was 6 (Q1 = 5; Q3 = 8). The Median ICU stay was 12 (Q1 = 7; Q3 = 22) days. S. maltophilia was found in 16.3% of pneumonia patients, leading to a lengthier hospital stay (34 vs. 20 days; p < 0.001). Risk factors for S. maltophilia pneumonia included previous treatment with meropenem (p < 0.01), thrombopenia (p = 0.034), endotracheal intubation (p < 0.001), foley catheter (p = 0.009) and central venous catheter insertion (p = 0.016). S. maltophilia nosocomial pneumonia is frequent in critical COVID-19 patients. Many significant risk factors should be addressed to reduce its prevalence and negative impact on outcomes.

Admission eGFR predicts in-hospital mortality independently of admission glycemia and C-peptide in patients with type 2 diabetes mellitus and COVID-19

OBJECTIVE

Type 2 diabetes mellitus (T2DM) and impaired kidney function are associated with a higher risk of poor outcomes of COVID-19. We conducted a retrospective study in hospitalized T2DM patients with COVID-19 to assess the association between in-hospital mortality and admission values of different hematological/biochemical parameters, including estimated glomerular filtration rate (eGFR), plasma glucose and C-peptide (as a marker of beta-cell function).

METHODS

The study included T2DM patients with confirmed SARS-CoV-2 infection who were consecutively admitted to our Institution between October 1, 2020 and April 1, 2021.

RESULTS

Patients (n = 74) were categorized into survivors (n = 55) and non-survivors (n = 19). Non-survivors exhibited significantly higher median WBC count, D-dimer, neutrophil-to-lymphocyte ratio, hsCRP, and procalcitonin levels, as well as significantly lower median serum 25(OH)D levels compared to survivors. Non-survivors exhibited significantly higher median admission plasma glucose (APG) values compared to survivors (210 vs 166 mg/dL; p = 0.026). There was no statistically significant difference in median values of plasma C-peptide between non-survivors and survivors (3.55 vs 3.24 ng/mL; p = 0.906). A significantly higher percentage of patients with an eGFR < 60 mL/min/1.73 m² was observed in the non-survivor group as compared to the survivor group (57.9% vs 23.6%; p = 0.006). A multivariate analysis performed by a logistic regression model after adjusting for major confounders (age, sex, body mass index, major comorbidities) showed a significant inverse association between eGFR values and risk of in-hospital mortality (OR, 0.956; 95% CI, 0.931-0.983; p = 0.001). We also found a significant positive association between WBC count and risk of in-hospital mortality (OR, 1.210; 95% CI, 1.043-1.404; p = 0.011).

CONCLUSIONS

Admission eGFR and WBC count predict in-hospital COVID-19 mortality among T2DM patients, independently of traditional risk factors, APG and random plasma C-peptide. Hospitalized patients with COVID-19 and comorbid T2DM associated with impaired kidney function at admission should be considered at high risk for adverse outcomes and death.

Identification of Parameters Representative of Immune Dysfunction in Patients with Severe and Fatal COVID-19 Infection: a Systematic Review and Meta-analysis

Abnormal immunological indicators associated with disease severity and mortality in patients with COVID-19 have been reported in several observational studies. However, there are marked heterogeneities in patient characteristics and research methodologies in these studies. We aimed to provide an updated synthesis of the association between immune-related indicators and COVID-19 prognosis. We conducted an electronic search of PubMed, Scopus, Ovid, Willey, Web of Science, Cochrane library, and CNKI for studies reporting immunological and/or immune-related parameters, including hematological, inflammatory, coagulation, and biochemical variables, tested on hospital admission of COVID-19 patients with different severities and outcomes. A total of 145 studies were included in the current meta-analysis, with 26 immunological, 11 hematological, 5 inflammatory, 4 coagulation, and 10 biochemical variables reported. Of them, levels of cytokines, including IL-1β, IL-1Ra, IL-2R, IL-4, IL-6, IL-8, IL-10, IL-18, TNF-α, IFN-γ, IgA, IgG, and CD4⁺ T/CD8⁺ T cell ratio, WBC, neutrophil, platelet, ESR, CRP, ferritin, SAA, D-dimer, FIB, and LDH were significantly increased in severely ill patients or non-survivors. Moreover, non-severely ill patients or survivors presented significantly higher counts of lymphocytes, monocytes, lymphocyte/monocyte ratio, eosinophils, CD3⁺ T,CD4⁺T and CD8⁺T cells, B cells, and NK cells. The currently updated meta-analysis primarily identified a hypercytokinemia profile with the severity and mortality of COVID-19 containing IL-1β, IL-1Ra, IL-2R, IL-4, IL-6, IL-8, IL-10, IL-18, TNF-α, and IFN-γ. Impaired innate and adaptive immune responses, reflected by decreased eosinophils, lymphocytes, monocytes, B cells, NK cells, T cells, and their subtype CD4⁺ and CD8⁺ T cells, and augmented inflammation, coagulation dysfunction, and nonpulmonary organ injury, were marked features of patients with poor prognosis. Therefore, parameters of immune response dysfunction combined with inflammatory, coagulated, or nonpulmonary organ injury indicators may be more sensitive to predict severe patients and those non-survivors.

Clinical progress in MSC-based therapies for the management of severe COVID-19

Considering the high impact that severe Coronavirus disease 2019 (COVID-19) cases still pose on public health and their complex pharmacological management, the search for new therapeutic alternatives is essential. Mesenchymal stromal cells (MSCs) could be promising candidates as they present important immunomodulatory and anti-inflammatory properties that can combat the acute severe respiratory distress syndrome (ARDS) and the cytokine storm occurring in COVID-19, two processes that are mainly driven by an immunological misbalance. In this review, we provide a comprehensive overview of the intricate inflammatory process derived from the immune dysregulation that occurs in COVID-19, discussing the potential that the cytokines and growth factors that constitute the MSC-derived secretome present to treat the disease. Moreover, we revise the latest clinical progress made in the field, discussing the most important findings of the clinical trials conducted to date, which follow 2 different approaches: MSC-based cell therapy or the administration of the secretome by itself, as a cell-free therapy.

Homogeneous ensemble models for predicting infection levels and mortality of COVID-19 patients: Evidence from China

Background

Persistence of long-term COVID-19 pandemic is putting high pressure on healthcare services worldwide for several years. This article aims to establish models to predict infection levels and mortality of COVID-19 patients in China.

Methods

Machine learning models and deep learning models have been built based on the clinical features of COVID-19 patients. The best models are selected by area under the receiver operating characteristic curve (AUC) scores to construct two homogeneous ensemble models for predicting infection levels and mortality, respectively. The first-hand clinical data of 760 patients are collected from Zhongnan Hospital of Wuhan University between 3 January and 8 March 2020. We preprocess data with cleaning, imputation, and normalization.

Results

Our models obtain AUC = 0.7059 and Recall (Weighted avg) = 0.7248 in predicting infection level, while AUC=0.8436 and Recall (Weighted avg) = 0.8486 in predicting mortality ratio. This study also identifies two sets of essential clinical features. One is C-reactive protein (CRP) or high sensitivity C-reactive protein (hs-CRP) and the other is chest tightness, age, and pleural effusion.

Conclusions

Two homogeneous ensemble models are proposed to predict infection levels and mortality of COVID-19 patients in China. New findings of clinical features for benefiting the machine learning models are reported. The evaluation of an actual dataset collected from January 3 to March 8, 2020 demonstrates the effectiveness of the models by comparing them with state-of-the-art models in prediction.

Risk factors for mortality in COVID-19 patients in sub-Saharan Africa: A systematic review and meta-analysis

Aim

Mortality rates of coronavirus-2019 (COVID-19) disease continue to increase worldwide and in Africa. In this study, we aimed to summarize the available results on the association between sociodemographic, clinical, biological, and comorbidity factors and the risk of mortality due to COVID-19 in sub-Saharan Africa.

Methods

We followed the PRISMA checklist (S1 Checklist). We searched PubMed, Google Scholar, and European PMC between January 1, 2020, and September 23, 2021. We included observational studies with Subjects had to be laboratory-confirmed COVID-19 patients; had to report risk factors or predictors of mortality in COVID-19 patients, Studies had to be published in English, include multivariate analysis, and be conducted in the sub-Saharan region. Exclusion criteria included case reports, review articles, commentaries, errata, protocols, abstracts, reports, letters to the editor, and repeat studies. The methodological quality of the studies included in this meta-analysis was assessed using the methodological items for nonrandomized studies (MINORS). Pooled hazard ratios (HR) or odds ratios (OR) and 95% confidence intervals (CI) were calculated separately to identify mortality risk. In addition, publication bias and subgroup analysis were assessed.

Results and discussion

Twelve studies with a total of 43598 patients met the inclusion criteria. The outcomes of interest were mortality. The results of the analysis showed that the pooled prevalence of mortality in COVID-19 patients was 4.8%. Older people showed an increased risk of mortality from SARS-Cov-2. The pooled hazard ratio (pHR) and odds ratio (pOR) were 9.01 (95% CI; 6.30–11.71) and 1.04 (95% CI; 1.02–1.06), respectively. A significant association was found between COVID-19 mortality and men (pOR = 1.52; 95% CI 1.04–2). In addition, the risk of mortality in patients hospitalized with COVID-19 infection was strongly influenced by chronic kidney disease (CKD), hypertension, severe or critical infection on admission, cough, and dyspnea. The major limitations of the present study are that the data in the meta-analysis came mainly from studies that were published, which may lead to publication bias, and that the causal relationship between risk factors and poor outcome in patients with COVID-19 cannot be confirmed because of the inherent limitations of the observational study.

Conclusions

Advanced age, male sex, CKD, hypertension, severe or critical condition on admission, cough, and dyspnea are clinical risk factors for fatal outcomes associated with coronavirus. These findings could be used for research, control, and prevention of the disease and could help providers take appropriate measures and improve clinical outcomes in these patients.

Elevated lactate dehydrogenase predicts poor prognosis of acute ischemic stroke

Background

Lactate dehydrogenase (LDH) is associated with the prognosis of many diseases, but the relationship between LDH and the poor prognosis (recurrence and death) of acute ischemic stroke (AIS) has not been fully clarified. This study aimed to investigate the association between admission LDH level and poor prognosis in patients with AIS.

Methods

This retrospective study enrolled AIS patients treated in Taizhou Hospital of Zhejiang Province from July 2019 to December 2019. Poor prognosis included AIS recurrence and all-cause death at 3, 6, and 18 months. The correction between LDH and poor prognosis or all-cause death was assessed. Lasso Cox expression and multivariate Cox expression analyses were used to evaluate the association of LDH with the risk of poor prognosis and all-cause death, respectively. A nomogram was constructed to evaluate the predictive Values of LDH for the poor prognosis and all-cause death of AIS.

Results

732 patients were included in the study. Multivariate analysis shows that admission LDH levels were significantly correlated with poor prognosis [odds ratio (OR),1.003; 95% confidence interval (95% CI), 1.001–1.005; P = 0.001] and all-cause death (OR, 1.005; 95% CI, 1.000–1.009; P = 0.031). The correlation analysis showed that admission LDH level was positively correlated with National Institutes of Health Stroke Scale (NIHSS) score and modified Rankin Scale (mRS) score. Time-dependent receiver operating characteristic (td-ROC) curves analysis showed that the AUC values of admission LDH level for predicting prognosis of AIS patients in 3-month, 6-month, 12-month and 18-month were 0.706 (95% CI, 0.604–0.810), 0.653 (95% CI, 0.583–0.723), 0.616 (95% CI, 0.556–60676) and 0.610 (95% CI, 0.552–0.680), respectively. And td-ROC also showed that the AUC values of admission LDH level for predicting all-cause death of AIS patients in 3-month, 6-month,12-month and 18-month were 0.861 (95% CI, 0.764–0.958), 0.824 (95% CI, 0.753–0.890), 0.726 (95% CI, 0.633–0.819) and 0.715 (95% CI, 0.622–0.807), respectively. The nomograms were constructed to create the predictive models of the poor prognosis and all-cause death of AIS.

Conclusion

Higher LDH levels are independently associated with poor prognosis and all-cause death of AIS.

Analysis of Mortality in Unvaccinated Patients with COVID-19 and Cardiovascular Risk

COVID-19 is a contagious disease that has spread globally, killing millions of people around the world. In order to reduce the likelihood of in-hospital death due to COVID-19, it is reasonable to select a group of patients with a low probability of survival and to implement measures in advance to minimize the risk of death. One way to do this is to establish cut-off values for the most commonly performed blood laboratory tests, above or below which the likelihood of death increases significantly. The aim of the study was to determine the basic laboratory parameters among unvaccinated patients hospitalized for COVID-19 with concomitant cardiovascular disease, which are the predictors of in-hospital death. Out of 1234 patients, 446 people who met the specific inclusion criteria were enrolled in the study. The multivariate regression analysis has shown that the independent predictors of death are: troponin levels of at least 0.033 μg/L (OR = 2.04 [1.10; 3.79]), creatinine of at least 1.88 mg/dL (OR = 2.88 [1.57; 5.30]), D-dimers of at least 0.97 g/L (OR = 2.04 [1.02; 4.07]), and C-reactive protein minimum of 0.89 mg/L (OR = 2.28 [1.24; 4.18]).

Factors Affecting the Self-Isolation Monitoring Program for COVID-19 Patients at the Universitas Indonesia Hospital

When the outbreak of the COVID-19 delta variant occurred in June 2021, there was a marked increase in Indonesia's number of self-isolated patients. The Universitas Indonesia Hospital provided a One-Stop Service (OSS) to monitor COVID-19 patients on self-isolation. This study was conducted to determine the effectiveness of the self-isolation monitoring performed by hospitals and the factors that determined the outcomes of patients on self-isolation. This study was conducted using a cross-sectional method based on secondary data from electronic medical records. Data analysis was performed by determining the relationship of patient risk factors and characteristics with COVID-19 outcomes. The study found that poorer symptoms, administration of antibiotics, absence of shortness of breath, and normal ALT levels significantly improved the outcome of OSS patients. The study also suggested that during monitoring of patients on COVID-19 self-isolation, chest/thorax radiography is necessary. The self-isolation monitoring program is essential to observe the patient's condition and evaluate the possibility of deteriorating conditions that could lead to admission decisions in the early or middle stages of the program. This will be beneficial during pandemic emergencies.

Predictors of Basic Activity in Daily Living and Length of Hospitalization in Patients with COVID-19

Background: Patients recovered from COVID-19 often suffer from the sequelae of the disease, which can hinder the patients’ activity in daily living. Early recognition of the patients at risk of prolonged hospitalization and impaired physical functioning is crucial for early intervention. We aim to identify the predictors of prolonged hospitalization and impaired activity in daily living in this study. Methods: COVID-19 patients hospitalized in a medical center were divided into two groups according to the Barthel index three months after discharge and the median length of hospital stay, respectively. Chi-square test and Mann–Whitney U test were performed to check the differences between the two groups in patient characteristics as well as hematology tests at the emergency department, the intensive care unit mobility scale (ICUMS), and the medical research council sum score (MRCSS). Logistic regression and the receiver operating characteristic curve analysis were further performed for the factors with significant differences between the two groups. Results: Both ICUMS and MRCSS showed significant differences between the groups. The ICUMS had an odds ratio of 0.61 and the MRCSS of 0.93 in predicting a Barthel index score less than 100 three months after discharge. The MRCSS had an odds ratio of 0.82 in predicting a prolonged length of hospital stay. Conclusion: Both ICUMS and MRCSS upon admission are predictive of a Barthel index score of less than 100 three months after discharge. On the other hand, only MRCSS has predictive value of a prolonged hospitalization.

Comprehensive Cytokine Profiling of Patients with COVID-19 Receiving Tocilizumab Therapy

Coronavirus disease 2019 (COVID-19) is characterized by immune activation in response to viral spread, in severe cases leading to the development of cytokine storm syndrome (CSS) and increased mortality. Despite its importance in prognosis, the pathophysiological mechanisms of CSS in COVID-19 remain to be defined. Towards this goal, we analyzed cytokine profiles and their interrelation in regard to anti-cytokine treatment with tocilizumab in 98 hospitalized patients with COVID-19. We performed a multiplex measurement of 41 circulating cytokines in the plasma of patients on admission and 3–5 days after, during the follow-up. Then we analyzed the patient groups separated in two ways: according to the clusterization of their blood cytokines and based on the administration of tocilizumab therapy. Patients with and without CSS formed distinct clusters according to their cytokine concentration changes. However, the tocilizumab therapy, administered based on the standard clinical and laboratory criteria, did not fully correspond to those clusters of CSS. Furthermore, among all cytokines, IL-6, IL-1RA, IL-10, and G-CSF demonstrated the most prominent differences between patients with and without clinical endpoints, while only IL-1RA was prognostically significant in both groups of patients with and without tocilizumab therapy, decreasing in the former and increasing in the latter during the follow-up period. Thus, CSS in COVID-19, characterized by a correlated release of multiple cytokines, does not fully correspond to the standard parameters of disease severity. Analysis of the cytokine signature, including the IL-1RA level in addition to standard clinical and laboratory parameters may be useful to define the onset of a cytokine storm in COVID-19 as well as the indications for anti-cytokine therapy.

Evaluation of the Charlson Comorbidity Index and Laboratory Parameters as Independent Early Mortality Predictors in Covid 19 Patients

Purpose

Patients and methods

Results

Conclusion

Molecular Hydrogen Positively Affects Physical and Respiratory Function in Acute Post-COVID-19 Patients: A New Perspective in Rehabilitation

Molecular hydrogen (H₂) is potentially a novel therapeutic gas for acute post-coronavirus disease 2019 (COVID-19) patients because it has antioxidative, anti-inflammatory, anti-apoptosis, and antifatigue properties. The aim of this study was to determine the effect of 14 days of H₂ inhalation on the respiratory and physical fitness status of acute post-COVID-19 patients. This randomized, single-blind, placebo-controlled study included 26 males (44 ± 17 years) and 24 females (38 ± 12 years), who performed a 6-min walking test (6 MWT) and pulmonary function test, specifically forced vital capacity (FVC) and expiratory volume in the first second (FEV1). Symptomatic participants were recruited between 21 and 33 days after a positive polymerase chain reaction test. The experiment consisted of H₂/placebo inhalation, 2 × 60 min/day for 14 days. Results showed that H₂ therapy, compared with placebo, significantly increased 6 MWT distance by 64 ± 39 m, FVC by 0.19 ± 0.24 L, and, in FEV1, by 0.11 ± 0.28 L (all p ≤ 0.025). In conclusion, H₂ inhalation had beneficial health effects in terms of improved physical and respiratory function in acute post-COVID-19 patients. Therefore, H₂ inhalation may represent a safe, effective approach for accelerating early function restoration in post-COVID-19 patients.

Utility of Age-adjusted Charlson Comorbidity Index as a Predictor of Need for Invasive Mechanical Ventilation, Length of Hospital Stay, and Survival in COVID-19 Patients

Background

Multiple parameters may be used to prognosticate coronavirus disease-2019 (COVID-19) patients, which are often expensive laboratory or radiological investigations. We evaluated the utility of age-adjusted Charlson comorbidity index (CCI) as a predictor of outcome in COVID-19 patients treated with remdesivir.

Materials and methods

This was a single-center, retrospective study on 126 COVID-19 patients treated with remdesivir. The age-adjusted CCI, length of hospital stay (LOS), need for invasive mechanical ventilation (IMV), and survival were recorded.

Results

The mean and standard deviation (SD) of age-adjusted CCI were 3.37 and 2.186, respectively. Eighty-six patients (70.5%) had age-adjusted CCI ≤4, and 36 (29.5%) had age-adjusted CCI >4. Among patients with age-adjusted CCI ≤4, 20 (23.3%) required IMV, whereas in those with age-adjusted CCI >4, 19 (52.8%) required IMV (p <0.05, Pearson's chi-square test). In those with age-adjusted CCI ≤4, the mortality was 18.6%, whereas it was 41.7% in patients with age-adjusted CCI >4 (p <0.05, Pearson's chi-square test). The receiver operating curve (ROC) of age-adjusted CCI for predicting the mortality had an area under the curve (AUC) of 0.709, p = 0.001, and sensitivity 68%, specificity 62%, and 95% confidence interval (CI) [0.608, 0.810], for a cutoff score >4. The ROC for age-adjusted CCI for predicting the need for IMV had an AUC of 0.696, p = 0.001, and sensitivity 67%, specificity 63%, and 95% CI [0.594, 0.797], for a cutoff score >4. ROC for age-adjusted CCI as a predictor of prolonged LOS (≥14 days) was insignificant.

Conclusion

In COVID-19 patients, the age-adjusted CCI is an independent predictor of the need for IMV (score >4) and mortality (score >4) but is not useful to predict LOS (CTRI/2020/11/029266).

How to cite this article

Shanbhag V, Arjun NR, Chaudhuri S, Pandey AK. Utility of Age-adjusted Charlson Comorbidity Index as a Predictor of Need for Invasive Mechanical Ventilation, Length of Hospital Stay, and Survival in COVID-19 Patients. Indian J Crit Care Med 2021;25(9):987-991.

Prediction of in-hospital mortality with machine learning for COVID-19 patients treated with steroid and remdesivir

We aimed to create the prediction model of in-hospital mortality using machine learning methods for patients with coronavirus disease 2019 (COVID-19) treated with steroid and remdesivir. We reviewed 1571 hospitalized patients with laboratory confirmed COVID-19 from the Mount Sinai Health System treated with both steroids and remdesivir. The important variables associated with in-hospital mortality were identified using LASSO (least absolute shrinkage and selection operator) and SHAP (SHapley Additive exPlanations) through the light gradient boosting model (GBM). The data before February 17th, 2021 (N = 769) was randomly split into training and testing datasets; 80% versus 20%, respectively. Light GBM models were created with train data and area under the curves (AUCs) were calculated. Additionally, we calculated AUC with the data between February 17th, 2021 and March 30th, 2021 (N = 802). Of the 1571 patients admitted due to COVID-19, 331 (21.1%) died during hospitalization. Through LASSO and SHAP, we selected six important variables; age, hypertension, oxygen saturation, blood urea nitrogen, intensive care unit admission, and endotracheal intubation. AUCs using training and testing datasets derived from the data before February 17th, 2021 were 0.871/0.911. Additionally, the light GBM model has high predictability for the latest data (AUC: 0.881) (https://risk-model.herokuapp.com/covid). A high-value prediction model was created to estimate in-hospital mortality for COVID-19 patients treated with steroid and remdesivir.

Centenarians exposed to the Spanish flu in their early life better survived to COVID-19

Background: Although it is known that mortality due to COVID-19 increases progressively with age, the probability of dying from this serious infection among the oldest-old population is little known, and controversial data are found in literature.

Methods: We examine the mortality by year and month of birth of Belgians who had turned 100 during the current COVID-19 pandemic and whose birth fell on the years around the end the First World War and the outbreak of the H1N1 "Spanish flu" pandemic.

Findings: The COVID-19 mortality of the "older" centenarians is significantly lower than that of "younger" centenarians, and this difference between the two groups reaches a maximum on August 1, 1918 as the discriminating cut-off date of birth. Having excluded the plausible impact of the end of WWI it becomes clear that this date corresponds to the time of reporting the first victims of the Spanish flu pandemic in Belgium.

Interpretation: In this study, the striking temporal coincidence between the outbreak of the Spanish flu epidemic and the birth of the cohorts characterized by greater fragility towards COVID-19 in 2020 strongly suggests a link between exposure to 1918 H1N1 pandemic influenza and resistance towards 2020 SARS-Cov-2. It can be speculated that the lifetime persistence of cross-reactive immune mechanisms has enabled centenarians exposed to the Spanish flu to overcome the threat of COVID-19 a century later.

Procalcitonin for individualizing antibiotic treatment: an update with a focus on COVID-19

Procalcitonin (PCT) is useful for differentiating between viral and bacterial infections and for reducing the unnecessary use of antibiotics. As the rise of antimicrobial resistance reaches “alarming” levels according to the World Health Organization, the importance of using biomarkers, such as PCT to limit unnecessary antibiotic exposure has further increased. Randomized trials in patients with respiratory tract infections have shown that PCT has prognostic implications and its use, embedded in stewardship protocols, leads to reductions in the use of antibiotics in different clinical settings without compromising clinical outcomes. However, available data are heterogeneous and recent trials found no significant benefit. Still, from these trials, we have learned several key considerations for the optimal use of PCT, which depend on the clinical setting, severity of presentation, and pretest probability for bacterial infection. For patients with respiratory infections and sepsis, PCT can be used to determine whether to initiate antimicrobial therapy in low-risk settings and, together with clinical data, whether to discontinue antimicrobial therapy in certain high-risk settings. There is also increasing evidence regarding PCT-guided therapy in patients with coronavirus disease 2019 (COVID-19). This review provides an up-to-date overview of the use of PCT in different clinical settings and diseases, including a discussion about its potential to improve the care of patients with COVID-19.

Machine-learning-based COVID-19 mortality prediction model and identification of patients at low and high risk of dying

Background

The coronavirus disease 2019 (COVID-19) pandemic caused by the SARS-Cov2 virus has become the greatest health and controversial issue for worldwide nations. It is associated with different clinical manifestations and a high mortality rate. Predicting mortality and identifying outcome predictors are crucial for COVID patients who are critically ill. Multivariate and machine learning methods may be used for developing prediction models and reduce the complexity of clinical phenotypes.

Methods

Multivariate predictive analysis was applied to 108 out of 250 clinical features, comorbidities, and blood markers captured at the admission time from a hospitalized cohort of patients (N = 250) with COVID-19. Inspired modification of partial least square (SIMPLS)-based model was developed to predict hospital mortality. Prediction accuracy was randomly assigned to training and validation sets. Predictive partition analysis was performed to obtain cutting value for either continuous or categorical variables. Latent class analysis (LCA) was carried to cluster the patients with COVID-19 to identify low- and high-risk patients. Principal component analysis and LCA were used to find a subgroup of survivors that tends to die.

Results

SIMPLS-based model was able to predict hospital mortality in patients with COVID-19 with moderate predictive power (Q² = 0.24) and high accuracy (AUC > 0.85) through separating non-survivors from survivors developed using training and validation sets. This model was obtained by the 18 clinical and comorbidities predictors and 3 blood biochemical markers. Coronary artery disease, diabetes, Altered Mental Status, age > 65, and dementia were the topmost differentiating mortality predictors. CRP, prothrombin, and lactate were the most differentiating biochemical markers in the mortality prediction model. Clustering analysis identified high- and low-risk patients among COVID-19 survivors.

Conclusions

An accurate COVID-19 mortality prediction model among hospitalized patients based on the clinical features and comorbidities may play a beneficial role in the clinical setting to better management of patients with COVID-19. The current study revealed the application of machine-learning-based approaches to predict hospital mortality in patients with COVID-19 and identification of most important predictors from clinical, comorbidities and blood biochemical variables as well as recognizing high- and low-risk COVID-19 survivors.

Mortality in critically ill patients with coronavirus disease 2019-associated pulmonary aspergillosis: A systematic review and meta-analysis

Reports of COVID-19 associated pulmonary aspergillosis (CAPA) are rising, but the associated mortality and factors affecting it are not well-characterised. We performed a systematic review including 20 peer-reviewed English language studies reporting mortality in CAPA published till 18 February 2021from PubMed, Ovid SP, Web of Science, Embase and CINHAL. The pooled mortality in CAPA was 51.2% (95% CI: 43.1-61.1, I2  = 38%). The leave one out sensitivity analysis and influential case diagnostics revealed one outlier and its exclusion resulted in a mortality estimate of 54% (95% CI: 45-62). Higher odds of mortality: 2.83 (95% CI: 1.8-4.5) were seen in CAPA compared to controls. No significant difference in various subgroups according to the country of study, the continent of study, income category of country and quality of the included study was seen. None of the host risk factors, mycological test results, therapy for COVID-19 and antifungal therapy affected mortality. Thus, patients with CAPA have a high probability of mortality and early diagnosis with prompt therapy must be ensured to optimally manage these patients. However, more prospective studies with global and multi-centre coordination may help to address CAPA in a better way.

Specific Risk Factors for Fatal Outcome in Critically Ill COVID-19 Patients: Results from a European Multicenter Study

(1) Background: The aim of our study was to identify specific risk factors for fatal outcome in critically ill COVID-19 patients. (2) Methods: Our data set consisted of 840 patients enclosed in the LEOSS registry. Using lasso regression for variable selection, a multifactorial logistic regression model was fitted to the response variable survival. Specific risk factors and their odds ratios were derived. A nomogram was developed as a graphical representation of the model. (3) Results: 14 variables were identified as independent factors contributing to the risk of death for critically ill COVID-19 patients: age (OR 1.08, CI 1.06–1.10), cardiovascular disease (OR 1.64, CI 1.06–2.55), pulmonary disease (OR 1.87, CI 1.16–3.03), baseline Statin treatment (0.54, CI 0.33–0.87), oxygen saturation (unit = 1%, OR 0.94, CI 0.92–0.96), leukocytes (unit 1000/μL, OR 1.04, CI 1.01–1.07), lymphocytes (unit 100/μL, OR 0.96, CI 0.94–0.99), platelets (unit 100,000/μL, OR 0.70, CI 0.62–0.80), procalcitonin (unit ng/mL, OR 1.11, CI 1.05–1.18), kidney failure (OR 1.68, CI 1.05–2.70), congestive heart failure (OR 2.62, CI 1.11–6.21), severe liver failure (OR 4.93, CI 1.94–12.52), and a quick SOFA score of 3 (OR 1.78, CI 1.14–2.78). The nomogram graphically displays the importance of these 14 factors for mortality. (4) Conclusions: There are risk factors that are specific to the subpopulation of critically ill COVID-19 patients.

Simple prognostic factors and change of inflammatory markers in patients with severe coronavirus disease 2019: a single-center observational study

Aim

The aim of this study was to investigate the prognostic factors and evaluate the change in inflammatory markers of patients with coronavirus disease 2019 (COVID‐19) requiring mechanical ventilation.

Methods

This retrospective observational study conducted from April 1, 2020, to February 18, 2021, included 97 adult patients who required mechanical ventilation for severe COVID‐19 pneumonia and excluded nonintubated patients with a positive COVID‐19 polymerase chain reaction test and those who had any obvious bacterial infection on admission. All patients were followed up to discharge or death. We obtained clinical information and laboratory data including levels of presepsin, interleukin‐6, procalcitonin, and severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) antibody every day. Poor outcome was defined as death or receiving a tracheostomy during hospitalization, and favorable outcome was defined as discharge after extubation.

Results

Differences (median [interquartile range]) were detected in age (76 [70–82] versus 66 [55–74] years), day from the onset of first symptoms to admission for mechanical ventilation (5 [3–7] versus 10 [8–12] days), and P/F ratio (i.e., ratio of arterial oxygen concentration to the fraction of inspired oxygen) after intubation (186 [149–251] versus 236 [180–296]) in patients with poor outcome versus those with favorable outcome on admission. Serum SARS‐CoV‐2 antibody levels had already increased on admission in patients with favorable outcome. We determined the day from the onset of first symptoms to admission for mechanical ventilation to be one of the independent prognostic factors of patients with COVID‐19 (adjusted odds ratio 0.69, confidence interval 0.56–0.85).

Conclusion

These results may contribute to understanding the mechanism of progression in severe COVID‐19 and may be helpful in devising an effective therapeutic strategy.

Dietary Supplementation with Selenium and Coenzyme Q10 Prevents Increase in Plasma D-Dimer While Lowering Cardiovascular Mortality in an Elderly Swedish Population

A low intake of selenium is associated with increased cardiovascular mortality. This could be reduced by supplementation with selenium and coenzyme Q₁₀. D-dimer, a fragment of fibrin mirroring fibrinolysis, is a biomarker of thromboembolism, increased inflammation, endothelial dysfunction and is associated with cardiovascular mortality in ischemic heart disease. The objective was to examine the impact of selenium and coenzyme Q₁₀ on the level of D-dimer, and its relationship to cardiovascular mortality. D-dimer was measured in 213 individuals at the start and after 48 months of a randomised double-blind placebo-controlled trial with selenium yeast (200 µg/day) and coenzyme Q₁₀ (200 mg/day) (n = 106) or placebo (n = 107). The follow-up time was 4.9 years. All included individuals were low in selenium (mean 67 μg/L, SD 16.8). The differences in D-dimer concentration were evaluated by the use of T-tests, repeated measures of variance and ANCOVA analyses. At the end, a significantly lower D-dimer concentration was observed in the active treatment group in comparison with those on placebo (p = 0.006). Although D-dimer values at baseline were weakly associated with high-sensitive CRP, while being more strongly associated with soluble tumour necrosis factor receptor 1 and sP-selectin, controlling for these in the analysis there was an independent effect on D-dimer. In participants with a D-dimer level above median at baseline, the supplementation resulted in significantly lower cardiovascular mortality compared to those on placebo (p = 0.014). All results were validated with a persisting significant difference between the two groups. Therefore, supplementation with selenium and coenzyme Q₁₀ in a group of elderly low in selenium and coenzyme Q₁₀ prevented an increase in D-dimer and reduced the risk of cardiovascular mortality in comparison with the placebo group. The obtained results also illustrate important associations between inflammation, endothelial function and cardiovascular risk.

Potential mechanisms of action of convalescent plasma in COVID-19

The COVID-19 pandemic will be remembered as one of the worst catastrophic events in human history. Unfortunately, no universally recognized effective therapeutic agents are currently available for the treatment of severe SARS-CoV-2 infection. In this context, the use of convalescent plasma from recovered COVID-19 patients has gained increasing interest thanks to the initially positive clinical reports. A number of mechanisms of action have been proposed for convalescent plasma, including direct neutralization and suppression of viremia, anti-inflammatory and immunomodulation effects and mitigation of the COVID-19-associated hypercoagulable state. These immune and non-immune mechanisms will be critically discussed in this narrative review.

D-dimer daily continuous tendency predicts the short-term prognosis for COVID-19 independently: A retrospective study from Northeast China

BACKGROUND AND OBJECTIVE

To study whether D-dimer daily continuous tendency could predict the short-term prognosis of COVID-19.

PATIENTS AND METHODES

According to the short-term prognosis, 81 COVID-19 patients were divided into two groups, one of worse prognosis (Group W) and the other of better prognosis (Group B). The slope of D-dimer linear regression during hospitalization (SLOPE) was calculated as an indicator of D-dimer daily continuous tendency. The SLOPE difference between Group W and Group B was compared. The difference between the discharge results and the 3-month follow-up results was also compared. COX regression analysis was used to analyze the relationship between SLOPE and short-term prognosis of COVID-19.

RESULTS

There were 16 patients in Group W and 65 patients in Group B. Group W had more critical proportion (p <  0.0001), indicating that the symptoms of its patients were more severe during hospitalization. ARDS, the most visible cause of worse prognosis, accounted for up to 68.75%, and many symptoms merged and resulted in worse prognosis. The D-dimer levels of Group W not only were significantly higher (p <  0.0001), but also showed an increasing trend. In addition, the D-dimer levels at discharge were significantly higher than those at follow-up (p = 0.0261), and the mean difference was as high as 0.7474. SLOPE significantly correlated with the short-term prognosis of COVID-19 independently (RR: 1.687, 95% CI: 1.345-2.116, P <  0.0001). The worst prognosis occurred most likely during the first month after COVID-19 diagnosis.

CONCLUSION

Our study found that D-dimer daily continuous tendency independently correlates with worse prognosis and can be used as an independent predictor of the short-term prognosis for COVID-19.