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1.
Front Cell Infect Microbiol ; 12: 819267, 2022.
Article in English | MEDLINE | ID: covidwho-1892612

ABSTRACT

Background and Aims: The aim of this study was to apply machine learning models and a nomogram to differentiate critically ill from non-critically ill COVID-19 pneumonia patients. Methods: Clinical symptoms and signs, laboratory parameters, cytokine profile, and immune cellular data of 63 COVID-19 pneumonia patients were retrospectively reviewed. Outcomes were followed up until Mar 12, 2020. A logistic regression function (LR model), Random Forest, and XGBoost models were developed. The performance of these models was measured by area under receiver operating characteristic curve (AUC) analysis. Results: Univariate analysis revealed that there was a difference between critically and non-critically ill patients with respect to levels of interleukin-6, interleukin-10, T cells, CD4+ T, and CD8+ T cells. Interleukin-10 with an AUC of 0.86 was most useful predictor of critically ill patients with COVID-19 pneumonia. Ten variables (respiratory rate, neutrophil counts, aspartate transaminase, albumin, serum procalcitonin, D-dimer and B-type natriuretic peptide, CD4+ T cells, interleukin-6 and interleukin-10) were used as candidate predictors for LR model, Random Forest (RF) and XGBoost model application. The coefficients from LR model were utilized to build a nomogram. RF and XGBoost methods suggested that Interleukin-10 and interleukin-6 were the most important variables for severity of illness prediction. The mean AUC for LR, RF, and XGBoost model were 0.91, 0.89, and 0.93 respectively (in two-fold cross-validation). Individualized prediction by XGBoost model was explained by local interpretable model-agnostic explanations (LIME) plot. Conclusions: XGBoost exhibited the highest discriminatory performance for prediction of critically ill patients with COVID-19 pneumonia. It is inferred that the nomogram and visualized interpretation with LIME plot could be useful in the clinical setting. Additionally, interleukin-10 could serve as a useful predictor of critically ill patients with COVID-19 pneumonia.


Subject(s)
COVID-19 , Interleukin-10 , CD8-Positive T-Lymphocytes , COVID-19/diagnosis , Critical Illness , Cytokines , Humans , Interleukin-6 , Nomograms , Patient Acuity , Retrospective Studies , Severity of Illness Index
2.
Frontiers in cellular and infection microbiology ; 12, 2022.
Article in English | EuropePMC | ID: covidwho-1812764

ABSTRACT

Background and Aims The aim of this study was to apply machine learning models and a nomogram to differentiate critically ill from non-critically ill COVID-19 pneumonia patients. Methods Clinical symptoms and signs, laboratory parameters, cytokine profile, and immune cellular data of 63 COVID-19 pneumonia patients were retrospectively reviewed. Outcomes were followed up until Mar 12, 2020. A logistic regression function (LR model), Random Forest, and XGBoost models were developed. The performance of these models was measured by area under receiver operating characteristic curve (AUC) analysis. Results Univariate analysis revealed that there was a difference between critically and non-critically ill patients with respect to levels of interleukin-6, interleukin-10, T cells, CD4+ T, and CD8+ T cells. Interleukin-10 with an AUC of 0.86 was most useful predictor of critically ill patients with COVID-19 pneumonia. Ten variables (respiratory rate, neutrophil counts, aspartate transaminase, albumin, serum procalcitonin, D-dimer and B-type natriuretic peptide, CD4+ T cells, interleukin-6 and interleukin-10) were used as candidate predictors for LR model, Random Forest (RF) and XGBoost model application. The coefficients from LR model were utilized to build a nomogram. RF and XGBoost methods suggested that Interleukin-10 and interleukin-6 were the most important variables for severity of illness prediction. The mean AUC for LR, RF, and XGBoost model were 0.91, 0.89, and 0.93 respectively (in two-fold cross-validation). Individualized prediction by XGBoost model was explained by local interpretable model-agnostic explanations (LIME) plot. Conclusions XGBoost exhibited the highest discriminatory performance for prediction of critically ill patients with COVID-19 pneumonia. It is inferred that the nomogram and visualized interpretation with LIME plot could be useful in the clinical setting. Additionally, interleukin-10 could serve as a useful predictor of critically ill patients with COVID-19 pneumonia.

3.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-315171

ABSTRACT

Objective: Sepsis is a life-threatening condition, and the mechanism of coagulation dysfunction in sepsis remains unknown. We aimed to investigate the mechanism of coagulation dysfunction in sepsis. Methods: . Standard methods were used to establish the sepsis models and generate gene expression profiles. Bioinformatics analysis was carried out by GO and KEGG enrichment analysis, construction of PPIs and screening of seed genes. Finally, seed genes were used to rebuild the disease-related pathways. Results: . Our experiments revealed an inflammatory response and coagulation dysfunction in both animal and cell models. After determining the DEGs, GO and KEGG functional analysis showed that there is a significant correlation between the inflammatory response and DNA damage. PPI network analysis screened 9 seed genes related to cell mitosis and platelet-derived growth factor receptor signaling pathways. Some of the seed genes were relevant to COVID-19. Conclusions: . This study explored the molecular mechanism of coagulation dysfunction in sepsis models by bioinformatics analysis. This may have guiding significance in reducing the risk of complications in patients with sepsis and improving the effectiveness of treatment.

4.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-307600

ABSTRACT

Background: The function of Extracorporeal membrane oxygenation (ECMO) is to maintain cardiopulmonary function in critical patients diagnosed with Coronavirus (COVID-19). Under the protection of ECMO, we recorded and analyzed the results of ventilator treatment following the adjustment of ventilator settings. Methods: This retrospective study enrolled six patients who received ECMO treatment. Clinical, laboratory and radiological characteristics, time of spontaneous respiration, and static lung compliance (CLst) were all recorded. Positive end-expiratory pressure (PEEP) and oxygen concentration (FIO2) were adjusted to record changes in oxygen saturation (SpO2), tidal volume (TV), peak airway pressure, and blood gas analysis. Results: During analysis, one patient died of COVID-19 within 28 days, and two patients were successfully weaned off mechanical ventilation and ECMO. Patients with an improved condition have a longer time of spontaneous respiration and better CLst than those who worsen. With an instantaneous increase in FIO2 alone or a combination of PEEP / FIO2, SpO2 and partial pressure of oxygen (PaO2) both increased, but no significant change was observed in PaCO2, PaO2/FIO2 and TV. With an instantaneous increase of PEEP alone, SpO2 , PaO2, PaCO2, PaO2/FIO2 and TV showed no significant change. Conclusions: ECMO can save some patients’ lives, but some patients will still suffer multiple organ failure and even death. The time of spontaneous respiration, CLst and TV may be a good choice for evaluating patients' lung situations. Increased PEEP may not significantly reduce lung exudation in COVID-19 patients supported by ECMO but further expand the over-expanded alveoli.

5.
Comput Biol Med ; 142: 105181, 2022 03.
Article in English | MEDLINE | ID: covidwho-1588026

ABSTRACT

The artificial bee colony algorithm (ABC) has been successfully applied to various optimization problems, but the algorithm still suffers from slow convergence and poor quality of optimal solutions in the optimization process. Therefore, in this paper, an improved ABC (CCABC) based on a horizontal search mechanism and a vertical search mechanism is proposed to improve the algorithm's performance. In addition, this paper also presents a multilevel thresholding image segmentation (MTIS) method based on CCABC to enhance the effectiveness of the multilevel thresholding image segmentation method. To verify the performance of the proposed CCABC algorithm and the performance of the improved image segmentation method. First, this paper demonstrates the performance of the CCABC algorithm itself by comparing CCABC with 15 algorithms of the same type using 30 benchmark functions. Then, this paper uses the improved multi-threshold segmentation method for the segmentation of COVID-19 X-ray images and compares it with other similar plans in detail. Finally, this paper confirms that the incorporation of CCABC in MTIS is very effective by analyzing appropriate evaluation criteria and affirms that the new MTIS method has a strong segmentation performance.


Subject(s)
COVID-19 , Image Processing, Computer-Assisted , Algorithms , Humans , SARS-CoV-2 , X-Rays
6.
Brief Bioinform ; 22(2): 1215-1224, 2021 03 22.
Article in English | MEDLINE | ID: covidwho-1343625

ABSTRACT

The pandemic of coronavirus disease 2019 (COVID-19) urgently calls for more sensitive molecular diagnosis to improve sensitivity of current viral nuclear acid detection. We have developed an anchor primer (AP)-based assay to improve viral RNA stability by bioinformatics identification of RNase-binding site of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA and implementing AP dually targeting the N gene of SARS-CoV-2 RNA and RNase 1, 3, 6. The arbitrarily primed polymerase chain reaction (AP-PCR) improvement of viral RNA integrity was supported by (a) the AP increased resistance of the targeted gene (N gene) of SARS-CoV-2 RNA to RNase treatment; (b) the detection of SARS-CoV-2 RNA by AP-PCR with lower cycle threshold values (-2.7 cycles) compared to two commercially available assays; (c) improvement of the viral RNA stability of the ORF gene upon targeting of the N gene and RNase. Furthermore, the improved sensitivity by AP-PCR was demonstrated by detection of SARS-CoV-2 RNA in 70-80% of sputum, nasal, pharyngeal swabs and feces and 36% (4/11) of urine of the confirmed cases (n = 252), 7% convalescent cases (n = 54) and none of 300 negative cases. Lastly, AP-PCR analysis of 306 confirmed and convalescent cases revealed prolonged presence of viral loading for >20 days after the first positive diagnosis. Thus, the AP dually targeting SARS-CoV-2 RNA and RNase improves molecular detection by preserving SARS-CoV-2 RNA integrity and reveals the prolonged viral loading associated with older age and male gender in COVID-19 patients.


Subject(s)
COVID-19/virology , Polymerase Chain Reaction/methods , Ribonucleases/metabolism , SARS-CoV-2/metabolism , Aged , Binding Sites , Female , Humans , Male , RNA, Viral/genetics , SARS-CoV-2/genetics , SARS-CoV-2/isolation & purification , Viral Load
7.
Front Cell Infect Microbiol ; 11: 550456, 2021.
Article in English | MEDLINE | ID: covidwho-1334926

ABSTRACT

Objectives: The objective of this study was to investigate the clinical features and laboratory findings of patients with and without critical COVID-19 pneumonia and identify predictors for the critical form of the disease. Methods: Demographic, clinical, and laboratory data of 63 COVID-19 pneumonia patients were retrospectively reviewed. Laboratory parameters were also collected within 3-5 days, 7-9 days, and 11-14 days of hospitalization. Outcomes were followed up until March 12, 2020. Results: Twenty-two patients developed critically ill pneumonia; one of them died. Upon admission, older patients with critical illness were more likely to report cough and dyspnoea with higher respiration rates and had a greater possibility of abnormal laboratory parameters than patients without critical illness. When compared with the non-critically ill patients, patients with serious illness had a lower discharge rate and longer hospital stays, with a trend towards higher mortality. The interleukin-6 level in patients upon hospital admission was important in predicting disease severity and was associated with the length of hospitalization. Conclusions: Many differences in clinical features and laboratory findings were observed between patients exhibiting non-critically ill and critically ill COVID-19 pneumonia. Non-critically ill COVID-19 pneumonia also needs aggressive treatments. Interleukin-6 was a superior predictor of disease severity.


Subject(s)
COVID-19 , Critical Illness , Humans , Laboratories , Retrospective Studies , SARS-CoV-2
8.
Comput Biol Med ; 136: 104609, 2021 09.
Article in English | MEDLINE | ID: covidwho-1293682

ABSTRACT

This paper focuses on the study of multilevel COVID-19 X-ray image segmentation based on swarm intelligence optimization to improve the diagnostic level of COVID-19. We present a new ant colony optimization with the Cauchy mutation and the greedy Levy mutation, termed CLACO, for continuous domains. Specifically, the Cauchy mutation is applied to the end phase of ant foraging in CLACO to enhance its searchability and to boost its convergence rate. The greedy Levy mutation is applied to the optimal ant individuals to confer an improved ability to jump out of the local optimum. Furthermore, this paper develops a novel CLACO-based multilevel image segmentation method, termed CLACO-MIS. Using 2D Kapur's entropy as the CLACO fitness function based on 2D histograms consisting of non-local mean filtered images and grayscale images, CLACO-MIS was successfully applied to the segmentation of COVID-19 X-ray images. A comparison of CLACO with some relevant variants and other excellent peers on 30 benchmark functions from IEEE CEC2014 demonstrates the superior performance of CLACO in terms of search capability, and convergence speed as well as ability to jump out of the local optimum. Moreover, CLACO-MIS was shown to have a better segmentation effect and a stronger adaptability at different threshold levels than other methods in performing segmentation experiments of COVID-19 X-ray images. Therefore, CLACO-MIS has great potential to be used for improving the diagnostic level of COVID-19. This research will host a webservice for any question at https://aliasgharheidari.com.


Subject(s)
COVID-19 , Image Processing, Computer-Assisted , Algorithms , COVID-19/diagnostic imaging , Humans , Mutation , SARS-CoV-2 , X-Rays
9.
Front Public Health ; 9: 663965, 2021.
Article in English | MEDLINE | ID: covidwho-1295721

ABSTRACT

Objectives: To develop and validate a radiomics model for distinguishing coronavirus disease 2019 (COVID-19) pneumonia from influenza virus pneumonia. Materials and Methods: A radiomics model was developed on the basis of 56 patients with COVID-19 pneumonia and 90 patients with influenza virus pneumonia in this retrospective study. Radiomics features were extracted from CT images. The radiomics features were reduced by the Max-Relevance and Min-Redundancy algorithm and the least absolute shrinkage and selection operator method. The radiomics model was built using the multivariate backward stepwise logistic regression. A nomogram of the radiomics model was established, and the decision curve showed the clinical usefulness of the radiomics nomogram. Results: The radiomics features, consisting of nine selected features, were significantly different between COVID-19 pneumonia and influenza virus pneumonia in both training and validation data sets. The receiver operator characteristic curve of the radiomics model showed good discrimination in the training sample [area under the receiver operating characteristic curve (AUC), 0.909; 95% confidence interval (CI), 0.859-0.958] and in the validation sample (AUC, 0.911; 95% CI, 0.753-1.000). The nomogram was established and had good calibration. Decision curve analysis showed that the radiomics nomogram was clinically useful. Conclusions: The radiomics model has good performance for distinguishing COVID-19 pneumonia from influenza virus pneumonia and may aid in the diagnosis of COVID-19 pneumonia.


Subject(s)
COVID-19 , Orthomyxoviridae , Humans , Retrospective Studies , SARS-CoV-2 , Tomography, X-Ray Computed
10.
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue ; 33(2): 145-149, 2021 Feb.
Article in Chinese | MEDLINE | ID: covidwho-1138768

ABSTRACT

OBJECTIVE: To explore the correlation between early inflammation indicators and the severity of coronavirus disease 2019 (COVID-19). METHODS: A retrospective study was conducted. Patients with COVID-19 admitted to Wenzhou Central Hospital from January 17 to February 14, 2020 were enrolled. The general information, chest CT before admission, the first laboratory parameters and chest CT within 24 hours after admission were collected. Patients were followed up for 30 days after the first onset of dyspnea or pulmonary imaging showed that the lesions progressed more than 50% within 24 to 48 hours (according to the criteria for severe cases) as the study endpoint. According to the endpoint, the patients were divided into two groups: mild type/common type group and severe/critical group, and the differences in general information and inflammation index of the two groups were compared. Logistic regression was used to analyze the inflammation index and the severity of COVID-19. Receiver operating characteristic (ROC) curve was draw to evaluate the predictive value of early inflammation indicators for severe/critical in patients with COVID-19. RESULTS: A total of 140 patients with COVID-19 were included, 74 males and 66 females; the average age was (45±14) years old; 6 cases (4.3%) of mild type, 107 cases (76.4%) of common type, and 22 cases (15.7%) of severe type, 5 cases (3.6%) were critical. There were significantly differences in ages (years old: 43±13 vs. 57±13), the proportion of patients with one chronic disease (17.7% vs. 55.6%), C-reactive protein [CRP (mg/L): 7.3 (2.3, 21.0) vs. 40.1 (18.8, 62.6)], lymphocyte count [LYM (×109/L): 1.3 (1.0, 1.8) vs. 0.8 (0.7, 1.1)], the neutrophil/lymphocyte ratio [NLR: 2.1 (1.6, 3.0) vs. 3.1 (2.2, 8.8)] and multilobularinltration, hypo-lymphocytosis, bacterial coinfection, smoking history, hyper-tension and age [MuLBSTA score: 5.0 (3.0, 5.0) vs. 5.0 (5.0, 7.0)] between mild/common group and severe/critical group (all P < 0.05). Univariate Logistic regression analysis showed that CRP, NLR, MuLBSTA score, age, and whether chronic diseases were associated with the severity of COVID-19 [odds ratio (OR) and 95% confidence interval (95%CI) were 1.037 (1.020-1.055), 1.374 (1.123-1.680), 1.574 (1.296-1.911), 1.082 (1.042-1.125), 6.393 (2.551-16.023), respectively, all P < 0.01]. Further multivariate Logistic regression analysis showed that CRP and MuLBSTA score were risk factors for the development of COVID-19 to severe/critical cases [OR and 95%CI were 1.024 (1.002-1.048) and 1.321 (1.027-1.699) respectively, both P < 0.05]. ROC curve analysis showed that the area under the curve for CRP and MuLBSTA score to predict severe/critical cases were both 0.818, and the best cut-off points were 27.4 mg/L and 6.0 points, respectively. CONCLUSIONS: CRP and MuLBSTA score are related to the severity of COVID-19, and may have good independent predictive ability for the development of severe/critical illness.


Subject(s)
COVID-19 , Adult , Female , Humans , Inflammation , Male , Middle Aged , Prognosis , ROC Curve , Retrospective Studies , SARS-CoV-2
11.
Int J Infect Dis ; 103: 507-513, 2021 Feb.
Article in English | MEDLINE | ID: covidwho-974112

ABSTRACT

OBJECTIVES: The aim was to evaluate the safety and effectiveness of thalidomide, an immunomodulatory agent, in combination with glucocorticoid, for the treatment of COVID-19 patients with life-threatening symptoms. METHODS: A nonrandomized comparative case series study was performed. Six patients received thalidomide 100 mg per day (with therapy lasting for ≥7 days) plus low-dose short-term dexamethasone, and 6 control patients matched with patients in the thalidomide group, received low-dose short-term treatment with dexamethasone alone. The main outcomes were: the duration of SARS-CoV-2 negative conversion from admission; length of hospital stay; and changes in inflammatory cytokine concentrations and lymphocyte subsets. RESULTS: The median thalidomide treatment time was 12.0 days. The median duration of SARS-CoV-2 negative conversion from admission and hospital stay length were briefer in the thalidomide group compared to the control group (respectively, 11.0 vs 23.0 days, P = 0.043; 18.5 vs 30.0 days, P = 0.043). The mean reduction rates at 7-10 days after treatment for serum interleukin-6 and interferon-γ concentrations were greater in the thalidomide group compared to the control group. Alterations in lymphocyte numbers in the subsets between the 2 groups were similar. CONCLUSIONS: Thalidomide plus short-term glucocorticoid therapy is an effective and safe regimen for the treatment of severely ill COVID-19 patients. The mechanism of action is most likely inhibition of inflammatory cytokine production.


Subject(s)
COVID-19/drug therapy , Dexamethasone/administration & dosage , SARS-CoV-2 , Thalidomide/administration & dosage , Aged , Aged, 80 and over , Drug Therapy, Combination , Female , Humans , Male , Middle Aged
12.
SSRN; 2020.
Preprint | SSRN | ID: ppcovidwho-848

ABSTRACT

Background: As a very serious city outside HubeProvince, China, Wenzhou had a rapid increase in the number of COVID-19, but clinical characteristics of critic

13.
Front Med (Lausanne) ; 7: 552002, 2020.
Article in English | MEDLINE | ID: covidwho-797119

ABSTRACT

Information about severe cases of 2019 novel coronavirus disease (COVID-19) infection is scarce. The aim of this study was to report the clinical characteristics and outcomes of severe and critical patients with confirmed COVID-19 infection in Wenzhou city. In this single-centered, retrospective cohort study, we consecutively enrolled 37 RT-PCR confirmed positive severe or critical patients from January 28 to February 16, 2020 in a tertiary hospital. Outcomes were followed up until 28-day mortality. Fifteen severe and 22 critical adult patients with the COVID-19 infection were included. Twenty-six (68.4%) were men. Echocardiography data results suggest that normal or increased cardiac output and diastolic dysfunction are the most common manifestations. Compared with severe patients, critical patients were older, more likely to exhibit low platelet counts and high blood urea nitrogen, and were in hospital for longer. Most patients had organ dysfunction during hospitalization, including 11 (29.7%) with ARDS, 8 (21.6%) with acute kidney injury, 17 (45.9%) with acute cardiac injury, and 33 (89.2%) with acute liver dysfunction. Eighteen (48.6%) patients were treated with high-flow ventilation, 9 (13.8%) with non-invasive ventilation, 10 (15.4%) with invasive mechanical ventilation, 7 (18.9%) with prone position ventilation, 6 (16.2%) with extracorporeal membrane oxygenation (ECMO), and 3 (8.1%) with renal replacement therapy. Only 1 (2.7%) patient had died in the 28-day follow up in our study. All patients had bilateral infiltrates on their chest CT scan. Twenty-one (32.3%) patients presented ground glass opacity (GGO) with critical patients more localized in the periphery and the center. The mortality of critical patients with the COVID-19 infection is low in our study. Cardiac function was enhanced in the early stage and less likely to develop into acute cardiac injury, but most patients suffered with acute liver injury. The CT imaging presentations of COVID-19 in critical patients were more likely with consolidation and bilateral lung involvement.

14.
Cell Commun Signal ; 18(1):104-104, 2020.
Article in English | MEDLINE | ID: covidwho-662379

ABSTRACT

BACKGROUND: Sepsis is an infection-induced aggressive and life-threatening organ dysfunction with high morbidity and mortality worldwide. Infection-associated inflammation and coagulation promote the progression of adverse outcomes in sepsis. Here, we report that phospho-Tyr705 of STAT3 (pY-STAT3), not total STAT3, contributes to systemic inflammation and coagulopathy in sepsis. METHODS: Cecal ligation and puncture (CLP)-induced septic mice were treated with BP-1-102, Napabucasin, or vehicle control respectively and then assessed for systemic inflammation, coagulation response, lung function and survival. Human pulmonary microvascular endothelial cells (HPMECs) and Raw264.7 cells were exposed to lipopolysaccharide (LPS) with pharmacological or genetic inhibition of pY-STAT3. Cells were assessed for inflammatory and coagulant factor expression, cell function and signaling. RESULTS: Pharmacological inhibition of pY-STAT3 expression by BP-1-102 reduced the proinflammatory factors, suppressed coagulation activation, attenuated lung injury, alleviated vascular leakage and improved the survival rate in septic mice. Pharmacological or genetic inhibition of pY-STAT3 diminished LPS-induced cytokine production in macrophages and protected pulmonary endothelial cells via the IL-6/JAK2/STAT3, NF-κB and MAPK signaling pathways. Moreover, the increase in procoagulant indicators induced by sepsis such as tissue factor (TF), the thrombin-antithrombin complex (TAT) and D-Dimer were down-regulated by pY-STAT3 inhibition. CONCLUSIONS: Our results revealed a therapeutic role of pY-STAT3 in modulating the inflammatory response and defective coagulation during sepsis. Video Abstract.

15.
Int J Med Sci ; 17(15): 2257-2263, 2020.
Article in English | MEDLINE | ID: covidwho-761081

ABSTRACT

Background: Corona Virus Disease 2019 (COVID-19) has become a global pandemic. This study established prognostic scoring models based on comorbidities and other clinical information for severe and critical patients with COVID-19. Material and Methods: We retrospectively collected data from 51 patients diagnosed as severe or critical COVID-19 who were admitted between January 29, 2020, and February 18, 2020. The Charlson (CCI), Elixhauser (ECI), and age- and smoking-adjusted Charlson (ASCCI) and Elixhauser (ASECI) comorbidity indices were used to evaluate the patient outcomes. Results: The mean hospital length of stay (LOS) of the COVID-19 patients was 22.82 ± 12.32 days; 19 patients (37.3%) were hospitalized for more than 24 days. Multivariate analysis identified older age (OR 1.064, P = 0.018, 95%CI 1.011-1.121) and smoking (OR 3.696, P = 0.080, 95%CI 0.856-15.955) as positive predictors of a long LOS. There were significant trends for increasing hospital LOS with increasing CCI, ASCCI, and ASECI scores (OR 57.500, P = 0.001, 95%CI 5.687-581.399; OR 71.500, P = 0.001, 95%CI 5.689-898.642; and OR 19.556, P = 0.001, 95%CI 3.315-115.372, respectively). The result was similar for the outcome of critical illness (OR 21.333, P = 0.001, 95%CI 3.565-127.672; OR 13.000, P = 0.009, 95%CI 1.921-87.990; OR 11.333, P = 0.008, 95%CI 1.859-69.080, respectively). Conclusions: This study established prognostic scoring models based on comorbidities and clinical information, which may help with the graded management of patients according to prognosis score and remind physicians to pay more attention to patients with high scores.


Subject(s)
Comorbidity , Coronavirus Infections/mortality , Critical Illness/mortality , Models, Statistical , Pneumonia, Viral/mortality , Severity of Illness Index , Adult , Aged , Aged, 80 and over , Betacoronavirus/isolation & purification , Betacoronavirus/pathogenicity , COVID-19 , Clinical Decision-Making , Coronavirus Infections/diagnosis , Coronavirus Infections/therapy , Coronavirus Infections/virology , Female , Hospital Mortality , Humans , Length of Stay/statistics & numerical data , Male , Middle Aged , Pandemics , Pneumonia, Viral/diagnosis , Pneumonia, Viral/therapy , Pneumonia, Viral/virology , Prognosis , Retrospective Studies , Risk Assessment/methods , SARS-CoV-2
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