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1.
BMC Med Imaging ; 22(1): 55, 2022 03 26.
Article in English | MEDLINE | ID: covidwho-1765442

ABSTRACT

BACKGROUND: To identify effective factors and establish a model to distinguish COVID-19 patients from suspected cases. METHODS: The clinical characteristics, laboratory results and initial chest CT findings of suspected COVID-19 patients in 3 institutions were retrospectively reviewed. Univariate and multivariate logistic regression were performed to identify significant features. A nomogram was constructed, with calibration validated internally and externally. RESULTS: 239 patients from 2 institutions were enrolled in the primary cohort including 157 COVID-19 and 82 non-COVID-19 patients. 11 features were selected by LASSO selection, and 8 features were found significant using multivariate logistic regression analysis. We found that the COVID-19 group are more likely to have fever (OR 4.22), contact history (OR 284.73), lower WBC count (OR 0.63), left lower lobe involvement (OR 9.42), multifocal lesions (OR 8.98), pleural thickening (OR 5.59), peripheral distribution (OR 0.09), and less mediastinal lymphadenopathy (OR 0.037). The nomogram developed accordingly for clinical practice showed satisfactory internal and external validation. CONCLUSIONS: In conclusion, fever, contact history, decreased WBC count, left lower lobe involvement, pleural thickening, multifocal lesions, peripheral distribution, and absence of mediastinal lymphadenopathy are able to distinguish COVID-19 patients from other suspected patients. The corresponding nomogram is a useful tool in clinical practice.


Subject(s)
COVID-19 , COVID-19/diagnostic imaging , Humans , Logistic Models , Nomograms , Retrospective Studies , Tomography, X-Ray Computed
2.
BMC Gastroenterol ; 22(1): 113, 2022 Mar 09.
Article in English | MEDLINE | ID: covidwho-1736342

ABSTRACT

BACKGROUND: Most patients with coronavirus disease 2019 demonstrate liver function damage. In this study, the laboratory test data of patients with moderate coronavirus disease 2019 were used to establish and evaluate an early prediction model to assess the risk of liver function damage. METHODS: Clinical data and the first laboratory examination results of 101 patients with moderate coronavirus disease 2019 were collected from four hospitals' electronic medical record systems in Jilin Province, China. Data were randomly divided into training and validation sets. A logistic regression analysis was used to determine the independent factors related to liver function damage in patients in the training set to establish a prediction model. Model discrimination, calibration, and clinical usefulness were evaluated in the training and validation sets. RESULTS: The logistic regression analysis showed that plateletcrit, retinol-binding protein, and carbon dioxide combining power could predict liver function damage (P < 0.05 for all). The receiver operating characteristic curve showed high model discrimination (training set area under the curve: 0.899, validation set area under the curve: 0.800; P < 0.05). The calibration curve showed a good fit (training set: P = 0.59, validation set: P = 0.19; P > 0.05). A decision curve analysis confirmed the clinical usefulness of this model. CONCLUSIONS: In this study, the combined model assesses liver function damage in patients with moderate coronavirus disease 2019 performed well. Thus, it may be helpful as a reference for clinical differentiation of liver function damage. Trial registration retrospectively registered.


Subject(s)
COVID-19 , Humans , Liver , Nomograms , Retrospective Studies , Risk Factors , SARS-CoV-2
3.
Clin Lab ; 68(3)2022 Mar 01.
Article in English | MEDLINE | ID: covidwho-1732442

ABSTRACT

BACKGROUND: In the course of SARS-CoV-2 infection, early prognostic evaluation is important since clinical symptoms may worsen rapidly and may be fatal. Inflammation plays an important role in the pathogenesis of COVID-19 and can cause myocardial damage which is common in severe COVID-19 patients. Therefore, novel inflammatory indexes and myocardial damage may be predictive of prognosis in patients with COVID-19. The aim of the study was to evaluate the role of cardiac troponin I (cTnI), modified Glasgow prognostic score (mGPS), systemic immune inflammation index (SII), prognostic nutritional index (PNI), and CRP to albumin ratio (CAR) in the outcome estimation of COVID-19 and to develop a risk model predicting the survival probability of COVID-19 survivors during early post-discharge. METHODS: This was a single-center, observational, retrospective cohort study. Laboratory confirmed COVID-19 patients (n = 265) were included and grouped according to in-hospital mortality. ROC curve analysis was performed and Youden's J index was used to obtain optimal cutoff values for inflammatory indexes in discriminating survivors and non-survivors. Cox regression analysis was performed to assess the possible predictors of in-hospital mortality. A nomogram was constructed based on the Cox regression model, to calculate 7- and 14-day survival. RESULTS: The area under the ROC curve (AUC) of the variables ranged between 0.79 and 0.92 with the three highest AUC values for albumin, PNI, and cTnI (0.919, 0.918, and 0.911, respectively). Optimal threshold value for cTnI was 9.7 pg/mL. Univariate analysis showed that gender, albumin, CRP, CAR, PNI, SII, cTnI, and mGPS were significantly related to in-hospital mortality. The Cox regression analysis indicated that mGPS (p = 0.001), CRP (p = 0.026), and cTnI (p = 0.001) were significant prognostic factors. CONCLUSIONS: cTnI should not be considered merely as an indicator of myocardial damage. It also reflects the inflammatory phase and, along with other inflammatory markers, it should be included in risk models as a prognostic factor for COVID-19.


Subject(s)
COVID-19 , Aftercare , Humans , Nomograms , Patient Discharge , Prognosis , Retrospective Studies , SARS-CoV-2 , Survival Rate
4.
Aging (Albany NY) ; 14(2): 544-556, 2022 01 17.
Article in English | MEDLINE | ID: covidwho-1626781

ABSTRACT

The wide spread of coronavirus disease 2019 is currently the most rigorous health threat, and the clinical outcomes of severe patients are extremely poor. In this study, we establish an early warning nomogram model related to severe versus common COVID-19. A total of 1059 COVID-19 patients were analyzed in the primary cohort and divided into common and severe according to the guidelines on the Diagnosis and Treatment of COVID-19 by the National Health Commission of China (7th version). The clinical data were collected for logistic regression analysis to assess the risk factors for severe versus common type. Furthermore, 123 COVID-19 patients were reviewed as the validation cohort to assess the performance of this model. Multivariate logistic analysis revealed that age, dyspnea, lymphocyte count, C-reactive protein and interleukin-6 were independent factors for prewarning the severe type occurrence. Then, the early warning nomogram model including these risk factors for inferring the severe disease occurrence out of common type of COVID-19 was constructed. The C-index of this nomogram in the primary cohort was 0.863, 95% confidence interval (CI) (0.836-0.889). Meanwhile, in the validation cohort, the C-index of this nomogram was 0.889, 95% CI (0.828-0.950). In both the primary cohort and validation cohorts, the calibration curve showed good agreement between prediction and actual probability. The early warning model shows that data at the very beginning including age, dyspnea, lymphocyte count, CRP, and IL-6 may prewarn the severe disease occurrence to some extent, which could help clinicians early and timely treatment.


Subject(s)
COVID-19/mortality , Clinical Decision Rules , Nomograms , Age Factors , COVID-19/pathology , China/epidemiology , Female , Humans , Logistic Models , Male , Multivariate Analysis , ROC Curve , Retrospective Studies , Risk Factors , Sex Factors
5.
Biomed Environ Sci ; 34(12): 984-991, 2021 Dec 20.
Article in English | MEDLINE | ID: covidwho-1608702

ABSTRACT

Objective: Early triage of patients with coronavirus disease 2019 (COVID-19) is pivotal in managing the disease. However, studies on the clinical risk score system of the risk factors for the development of severe disease are limited. Hence, we conducted a clinical risk score system for severe illness, which might optimize appropriate treatment strategies. Methods: We conducted a retrospective, single-center study at the JinYinTan Hospital from January 24, 2020 to March 31, 2020. We evaluated the demographic, clinical, and laboratory data and performed a 10-fold cross-validation to split the data into a training set and validation set. We then screened the prognostic factors for severe illness using the least absolute shrinkage and selection operator (LASSO) and logistic regression, and finally conducted a risk score to estimate the probability of severe illness in the training set. Data from the validation set were used to validate the score. Results: A total of 295 patients were included. From 49 potential risk factors, 3 variables were measured as the risk score: neutrophil to lymphocyte ratio ( OR, 1.27; 95% CI, 1.15-1.39), albumin ( OR, 0.76; 95% CI, 0.70-0.83), and chest computed tomography abnormalities ( OR, 2.01; 95% CI, 1.41-2.86) and the AUC of the validation cohort was 0.822 (95% CI, 0.7667-0.8776). Conclusion: This report may help define the potential of developing severe illness in patients with COVID-19 at an early stage, which might be related to the neutrophil to lymphocyte ratio, albumin, and chest computed tomography abnormalities.


Subject(s)
COVID-19/diagnosis , Risk Assessment , Aged , Female , Humans , Male , Middle Aged , Nomograms , Retrospective Studies , Severity of Illness Index
6.
IEEE J Biomed Health Inform ; 25(11): 4110-4118, 2021 11.
Article in English | MEDLINE | ID: covidwho-1570200

ABSTRACT

Almost 25% of COVID-19 patients end up in ICU needing critical mechanical ventilation support. There is currently no validated objective way to predict which patients will end up needing ventilator support, when the disease is mild and not progressed. N = 869 patients from two sites (D1: N = 822, D2: N = 47) with baseline clinical characteristics and chest CT scans were considered for this study. The entire dataset was randomly divided into 70% training, D1train (N = 606) and 30% test-set (Dtest: D1test (N = 216) + D2 (N = 47)). An expert radiologist delineated ground-glass-opacities (GGOs) and consolidation regions on a subset of D1train, (D1train_sub, N = 88). These regions were automatically segmented and used along with their corresponding CT volumes to train an imaging AI predictor (AIP) on D1train to predict the need of mechanical ventilators for COVID-19 patients. Finally, top five prognostic clinical factors selected using univariate analysis were integrated with AIP to construct an integrated clinical and AI imaging nomogram (ClAIN). Univariate analysis identified lactate dehydrogenase, prothrombin time, aspartate aminotransferase, %lymphocytes, albumin as top five prognostic clinical features. AIP yielded an AUC of 0.81 on Dtest and was independently prognostic irrespective of other clinical parameters on multivariable analysis (p<0.001). ClAIN improved the performance over AIP yielding an AUC of 0.84 (p = 0.04) on Dtest. ClAIN outperformed AIP in predicting which COVID-19 patients ended up needing a ventilator. Our results across multiple sites suggest that ClAIN could help identify COVID-19 with severe disease more precisely and likely to end up on a life-saving mechanical ventilation.


Subject(s)
COVID-19 , Artificial Intelligence , Humans , Lung , Nomograms , Retrospective Studies , SARS-CoV-2 , Tomography, X-Ray Computed , Ventilators, Mechanical
7.
J Immunother Cancer ; 9(11)2021 11.
Article in English | MEDLINE | ID: covidwho-1541924

ABSTRACT

BACKGROUND: Patients with cancer on active immune checkpoint inhibitors therapy were recommended to seek prophylaxis from COVID-19 by vaccination. There have been few reports to date to discuss the impact of progression cell death-1 blockers (PD-1B) on immune or vaccine-related outcomes, and what risk factors that contribute to the serological status remains to be elucidated. The study aims to find the impact of PD-1B on vaccination outcome and investigate other potential risk factors associated with the risk of seroconversion failure. METHODS: Patients with active cancer treatment were retrospectively enrolled to investigate the interaction effects between PD-1B and vaccination. Through propensity score matching of demographic and clinical features, the seroconversion rates and immune/vaccination-related adverse events (irAE and vrAE) were compared in a head-to-head manner. Then, a nomogram predicting the failure risk was developed with variables significant in multivariate regression analysis and validated in an independent cohort. RESULTS: Patients (n=454) receiving either PD-1B or COVID-19 vaccination, or both, were matched into three cohorts (vac+/PD-1B+, vac+/PD-1B-, and vac-/PD-1B+, respectively), with a non-concer control group of 206 participants. 68.1% (94/138), 71.3% (117/164), and 80.5% (166/206) were seropositive in vac+/PD-1B+cohort, vac+/PD-1B- cohort, and non-cancer control group, respectively. None of irAE or vrAE was observed to be escalated in PD-1B treatment except for low-grade rash.The vaccinated patients with cancer had a significantly lower rate of seroconversion rates than healthy control. A nomogram was thus built that encompassed age, pathology, and chemotherapy status to predict the seroconversion failure risk, which was validated in an independent cancer cohort of 196 patients. CONCLUSION: Although patients with cancer had a generally decreased rate of seroconversion as compared with the healthy population, the COVID-19 vaccine was generally well tolerated, and seroconversion was not affected in patients receiving PD-1B. A nomogram predicting failure risk was developed, including age, chemotherapy status, pathology types, and rheumatic comorbidity.


Subject(s)
COVID-19 Vaccines/immunology , COVID-19/prevention & control , Immune Checkpoint Inhibitors/therapeutic use , Neoplasms/immunology , Seroconversion , Adult , China , Female , Humans , Male , Middle Aged , Nomograms , Propensity Score , Retrospective Studies , Vaccines, Inactivated/immunology
9.
BMC Infect Dis ; 21(1): 1004, 2021 Sep 25.
Article in English | MEDLINE | ID: covidwho-1438258

ABSTRACT

BACKGROUND: Early identification of severe COVID-19 patients who will need intensive care unit (ICU) follow-up and providing rapid, aggressive supportive care may reduce mortality and provide optimal use of medical resources. We aimed to develop and validate a nomogram to predict severe COVID-19 cases that would need ICU follow-up based on available and accessible patient values. METHODS: Patients hospitalized with laboratory-confirmed COVID-19 between March 15, 2020, and June 15, 2020, were enrolled in this retrospective study with 35 variables obtained upon admission considered. Univariate and multivariable logistic regression models were constructed to select potential predictive parameters using 1000 bootstrap samples. Afterward, a nomogram was developed with 5 variables selected from multivariable analysis. The nomogram model was evaluated by Area Under the Curve (AUC) and bias-corrected Harrell's C-index with 95% confidence interval, Hosmer-Lemeshow Goodness-of-fit test, and calibration curve analysis. RESULTS: Out of a total of 1022 patients, 686 cases without missing data were used to construct the nomogram. Of the 686, 104 needed ICU follow-up. The final model includes oxygen saturation, CRP, PCT, LDH, troponin as independent factors for the prediction of need for ICU admission. The model has good predictive power with an AUC of 0.93 (0.902-0.950) and a bias-corrected Harrell's C-index of 0.91 (0.899-0.947). Hosmer-Lemeshow test p-value was 0.826 and the model is well-calibrated (p = 0.1703). CONCLUSION: We developed a simple, accessible, easy-to-use nomogram with good distinctive power for severe illness requiring ICU follow-up. Clinicians can easily predict the course of COVID-19 and decide the procedure and facility of further follow-up by using clinical and laboratory values of patients available upon admission.


Subject(s)
COVID-19 , Nomograms , Critical Care , Follow-Up Studies , Humans , Intensive Care Units , Retrospective Studies , SARS-CoV-2
10.
BMC Infect Dis ; 21(1): 760, 2021 Aug 05.
Article in English | MEDLINE | ID: covidwho-1403220

ABSTRACT

BACKGROUND: Coronavirus disease 2019 (COVID-19) has spread around the world. This retrospective study aims to analyze the clinical features of COVID-19 patients with cancer and identify death outcome related risk factors. METHODS: From February 10th to April 15th, 2020, 103 COVID-19 patients with cancer were enrolled. Difference analyses were performed between severe and non-severe patients. A propensity score matching (PSM) analysis was performed, including 103 COVID-19 patients with cancer and 206 matched non-cancer COVID-19 patients. Next, we identified death related risk factors and developed a nomogram for predicting the probability. RESULTS: In 103 COVID-19 patients with cancer, the main cancer categories were breast cancer, lung cancer and bladder cancer. Compared to non-severe patients, severe patients had a higher median age, and a higher proportion of smokers, diabetes, heart disease and dyspnea. In addition, most of the laboratory results between two groups were significantly different. PSM analysis found that the proportion of dyspnea was much higher in COVID-19 patients with cancer. The severity incidence in two groups were similar, while a much higher mortality was found in COVID-19 patients with cancer compared to that in COVID-19 patients without cancer (11.7% vs. 4.4%, P = 0.028). Furthermore, we found that neutrophil-to-lymphocyte ratio (NLR) and C-reactive protein (CRP) were related to death outcome. And a nomogram based on the factors was developed. CONCLUSION: In COVID-19 patients with cancer, the clinical features and laboratory results between severe group and non-severe group were significantly different. NLR and CRP were the risk factors that could predict death outcome.


Subject(s)
COVID-19 , Neoplasms , Adult , Aged , Aged, 80 and over , C-Reactive Protein/analysis , COVID-19/complications , COVID-19/mortality , Female , Humans , Lymphocytes/cytology , Male , Middle Aged , Neoplasms/complications , Neoplasms/mortality , Neutrophils/cytology , Nomograms , Retrospective Studies , Risk Factors , Young Adult
11.
J Korean Med Sci ; 36(35): e248, 2021 Sep 06.
Article in English | MEDLINE | ID: covidwho-1399125

ABSTRACT

BACKGROUND: Prediction of mortality in patients with coronavirus disease 2019 (COVID-19) is a key to improving the clinical outcomes, considering that the COVID-19 pandemic has led to the collapse of healthcare systems in many regions worldwide. This study aimed to identify the factors associated with COVID-19 mortality and to develop a nomogram for predicting mortality using clinical parameters and underlying diseases. METHODS: This study was performed in 5,626 patients with confirmed COVID-19 between February 1 and April 30, 2020 in South Korea. A Cox proportional hazards model and logistic regression model were used to construct a nomogram for predicting 30-day and 60-day survival probabilities and overall mortality, respectively in the train set. Calibration and discrimination were performed to validate the nomograms in the test set. RESULTS: Age ≥ 70 years, male, presence of fever and dyspnea at the time of COVID-19 diagnosis, and diabetes mellitus, cancer, or dementia as underling diseases were significantly related to 30-day and 60-day survival and mortality in COVID-19 patients. The nomogram showed good calibration for survival probabilities and mortality. In the train set, the areas under the curve (AUCs) for 30-day and 60-day survival was 0.914 and 0.954, respectively; the AUC for mortality of 0.959. In the test set, AUCs for 30-day and 60-day survival was 0.876 and 0.660, respectively, and that for mortality was 0.926. The online calculators can be found at https://koreastat.shinyapps.io/RiskofCOVID19/. CONCLUSION: The prediction model could accurately predict COVID-19-related mortality; thus, it would be helpful for identifying the risk of mortality and establishing medical policies during the pandemic to improve the clinical outcomes.


Subject(s)
COVID-19/mortality , Nomograms , SARS-CoV-2 , Adolescent , Adult , Aged , Aged, 80 and over , Child , Child, Preschool , Female , Humans , Infant , Infant, Newborn , Logistic Models , Male , Middle Aged , Probability , Proportional Hazards Models , Severity of Illness Index , Young Adult
12.
J Clin Lab Anal ; 35(8): e23871, 2021 Aug.
Article in English | MEDLINE | ID: covidwho-1361198

ABSTRACT

BACKGROUND: To verify the differential expression of miR-30c and miR-142-3p between tuberculosis patients and healthy controls and to investigate the performance of microRNA (miRNA) and subsequently models for the diagnosis of tuberculosis (TB). METHODS: We followed up 460 subjects suspected of TB, and finally enrolled 132 patients, including 60 TB patients, 24 non-TB disease controls (TB-DCs), and 48 healthy controls (HCs). The differential expression of miR-30c and miR-142-3p in serum samples of the TB patients, TB-DCs, and HCs were identified by reverse transcription-quantitative real-time PCR. Diagnostic models were developed by analyzing the characteristics of miRNA and electronic health records (EHRs). These models evaluated by the area under the curves (AUC) and calibration curves were presented as nomograms. RESULTS: There were differential expression of miR-30c and miR-142-3p between TB patients and HCs (p < 0.05). Individual miRNA has a limited diagnostic value for TB. However, diagnostic performance has been both significantly improved when we integrated miR-142-3p and ordinary EHRs to develop two models for the diagnosis of tuberculosis. The AUC of the model for distinguishing tuberculosis patients from healthy controls has increased from 0.75 (95% CI: 0.66-0.84) to 0.96 (95% CI: 0.92-0.99) and the model for distinguishing tuberculosis patients from non-TB disease controls has increased from 0.67 (95% CI: 0.55-0.79) to 0.94 (95% CI: 0.89-0.99). CONCLUSIONS: Integrating serum miR-142-3p and EHRs is a good strategy for improving TB diagnosis.


Subject(s)
Electronic Health Records , MicroRNAs/blood , Nomograms , Tuberculosis/diagnosis , Adult , Aged , Case-Control Studies , Female , Humans , Male , Middle Aged , ROC Curve
13.
J Med Virol ; 94(1): 131-140, 2022 01.
Article in English | MEDLINE | ID: covidwho-1359799

ABSTRACT

INTRODUCTION: The coronavirus disease 2019 (COVID-19) has quickly become a global threat to public health, and it is difficult to predict severe patients and their prognosis. Here, we intended developing effective models for the late identification of patients at disease progression and outcome. METHODS: A total of 197 patients were included with a 20-day median follow-up time. We first developed a nomogram for disease severity discrimination, then created a prognostic nomogram for severe patients. RESULTS: In total, 40.6% of patients were severe and 59.4% were non-severe. The multivariate logistic analysis indicated that IgG, neutrophil-to-lymphocyte ratio (NLR), lactate dehydrogenase, platelet, albumin, and blood urea nitrogen were significant factors associated with the severity of COVID-19. Using immune response phenotyping based on NLR and IgG level, the logistic model showed patients with the NLRhi IgGhi phenotype are most likely to have severe disease, especially compared to those with the NLRlo IgGlo phenotype. The C-indices of the two discriminative nomograms were 0.86 and 0.87, respectively, which indicated sufficient discriminative power. As for predicting clinical outcomes for severe patients, IgG, NLR, age, lactate dehydrogenase, platelet, monocytes, and procalcitonin were significant predictors. The prognosis of severe patients with the NLRhi IgGhi phenotype was significantly worse than the NLRlo IgGhi group. The two prognostic nomograms also showed good performance in estimating the risk of progression. CONCLUSIONS: The present nomogram models are useful to identify COVID-19 patients with disease progression based on individual characteristics and immune response-related indicators. Patients at high risk for severe illness and poor outcomes from COVID-19 should be managed with intensive supportive care and appropriate therapeutic strategies.


Subject(s)
COVID-19/diagnosis , COVID-19/immunology , Aged , COVID-19/physiopathology , Disease Progression , Female , Humans , Immunoglobulin G/blood , Leukocyte Count , Lymphocytes , Male , Middle Aged , Neutrophils , Nomograms , Prognosis , Retrospective Studies , Severity of Illness Index
14.
Int J Med Inform ; 154: 104545, 2021 10.
Article in English | MEDLINE | ID: covidwho-1347660

ABSTRACT

BACKGROUND: This study utilized a comprehensive nomogram to evaluate the prognosis of patients with COVID-19 pneumonia. METHODS: COVID-19 pneumonia data was divided into training set (256 of 321, 80%), internal validation set (65 of 321, 20%) and independent external validation set (n = 188). After image processing, lesion segmentation, feature extraction and feature selection, radiomics signatures and clinical indicators were used to develop a radiomics model and a clinical model respectively. Combining radiomics signatures and clinical indicators, a radiomics nomogram was built. The performance of proposed models was evaluated by the receiver operating characteristic curve (AUC). Calibration curves and decision curve analysis were used to assess the performance of the radiomics nomogram. RESULTS: Two clinical indicators that were age and chronic lung disease or asthma and 21 radiomics features were selected to build the radiomics nomogram. The radiomics nomogram yielded an Area Under The Curve1 (AUC) of 0.88 and accuracy of 0.80 in the training set, an AUC of 0.85 and accuracy of 0.77 in internal testing validation set and an AUC of 0.84 and accuracy of 0.75 in independent external validation set. The performance of radiomics nomogram was better than clinical model (AUC = 0.77, p < 0.001) and radiomics model (AUC = 0.72, p = 0.025) in independent external validation set. CONCLUSIONS: The radiomics nomogram may be used to assess the deterioration of COVID-19 pneumonia.


Subject(s)
COVID-19 , Nomograms , Artificial Intelligence , Humans , Prognosis , Retrospective Studies , SARS-CoV-2 , Tomography, X-Ray Computed
15.
Am J Emerg Med ; 50: 218-223, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1347466

ABSTRACT

BACKGROUND: The use of accurate prediction tools and early intervention are important for addressing severe coronavirus disease 2019 (COVID-19). However, the prediction models for severe COVID-19 available to date are subject to various biases. This study aimed to construct a nomogram to provide accurate, personalized predictions of the risk of severe COVID-19. METHODS: This study was based on a large, multicenter retrospective derivation cohort and a validation cohort. The derivation cohort consisted of 496 patients from Jiangsu Province, China, between January 10, 2020, and March 15, 2020, and the validation cohort contained 105 patients from Huangshi, Hunan Province, China, between January 21, 2020, and February 29, 2020. A nomogram was developed with the selected predictors of severe COVID-19, which were identified by univariate and multivariate logistic regression analyses. We evaluated the discrimination of the nomogram with the area under the receiver operating characteristic curve (AUC) and the calibration of the nomogram with calibration plots and Hosmer-Lemeshow tests. RESULTS: Three predictors, namely, age, lymphocyte count, and pulmonary opacity score, were selected to develop the nomogram. The nomogram exhibited good discrimination (AUC 0.93, 95% confidence interval [CI] 0.90-0.96 in the derivation cohort; AUC 0.85, 95% CI 0.76-0.93 in the validation cohort) and satisfactory agreement. CONCLUSIONS: The nomogram was a reliable tool for assessing the probability of severe COVID-19 and may facilitate clinicians stratifying patients and providing early and optimal therapies.


Subject(s)
COVID-19/diagnosis , COVID-19/epidemiology , Nomograms , Adult , COVID-19/blood , China , Cohort Studies , Female , Humans , Logistic Models , Lymphocyte Count , Male , Middle Aged , Predictive Value of Tests , ROC Curve , Retrospective Studies
16.
Aging (Albany NY) ; 13(14): 17961-17977, 2021 07 19.
Article in English | MEDLINE | ID: covidwho-1318481

ABSTRACT

We intend to evaluate the differences of the clinical characteristics, cytokine profiles and immunological features in patients with different severity of COVID-19, and to develop novel nomograms based on inflammatory cytokines or lymphocyte subsets for the differential diagnostics for severe or critical and non-severe COVID-19 patients. We retrospectively studied 254 COVID-19 patients, 90 of whom were severe or critical patients and 164 were non-severe patients. Severe or critical patients had significantly higher levels of inflammatory cytokines than non-severe patients as well as lower levels of lymphocyte subsets. Significantly positive correlations between cytokine profiles were observed, while they were all significantly negatively correlated with lymphocyte subsets. Two effective nomograms were developed according to two multivariable logistic regression cox models based on inflammatory cytokine profiles and lymphocyte subsets separately. The areas under the receiver operating characteristics of two nomograms were 0.834 (95% CI: 0.779-0.888) and 0.841 (95% CI: 0.756-0.925). The bootstrapped-concordance indexes of two nomograms were 0.834 and 0.841 in training set, and 0.860 and 0.852 in validation set. Calibration curves and decision curve analyses demonstrated that the nomograms were well calibrated and had significantly more clinical net benefits. Our novel nomograms can accurately predict disease severity of COVID-19, which may facilitate the identification of severe or critical patients and assist physicians in making optimized treatment suggestions.


Subject(s)
COVID-19/diagnosis , Cytokines/blood , Decision Support Techniques , Inflammation Mediators/blood , Lymphocyte Subsets/immunology , Nomograms , Aged , Biomarkers/blood , COVID-19/blood , COVID-19/immunology , COVID-19/therapy , Clinical Decision-Making , Female , Humans , Lymphocyte Count , Male , Middle Aged , Predictive Value of Tests , Reproducibility of Results , Retrospective Studies , Risk Assessment , Risk Factors , Severity of Illness Index , Up-Regulation
17.
J Affect Disord ; 294: 128-136, 2021 Nov 01.
Article in English | MEDLINE | ID: covidwho-1317696

ABSTRACT

BACKGROUND: We aimed to explore the risk profiles attributable to psychosocial and behavioural problems during the coronavirus disease 2019 pandemic. To this end, we created a risk-prediction nomogram model. METHODS: A national multicentre study was conducted through an online questionnaire involving 12,186 children (6-11 years old) and adolescents (12-16 years old). Respondents' psychosocial and behavioural functioning were assessed using the Achenbach Child Behaviour Checklist (CBCL). Data were analysed using STATA software and R-language. RESULTS: The positive detection rate of psychological problems within Wuhan was greater than that outside Wuhan for schizoid (P = 0.005), and depression (P = 0.030) in children, and for somatic complaints (P = 0.048), immaturity (P = 0.023), and delinquent behaviour (P = 0.046) in adolescents. After graded multivariable adjustment, seven factors associated with psychological problems in children and adolescents outside Wuhan were parent-child conflict (odds ratio (OR): 4.94, 95% confidence interval (95% CI): 4.27-5.72), sleep problems (OR: 4.05, 95% CI: 3.77-4.36), online study time (OR: 0.41, 95% CI: 0.37-0.47), physical activity time (OR: 0.510, 95% CI: 0.44-0.59), number of close friends (OR: 0.51, 95% CI: 0.44-0.6), time spent playing videogames (OR: 2.26, 95% CI: 1.90-2.69) and eating disorders (OR: 2.71, 95% CI: 2.35-3.11) (all P < 0.001). Contrastingly, within Wuhan, only the first four factors, namely, parent-child conflict (5.95, 2.82-12.57), sleep problems (4.47, 3.06-6.54), online study time (0.37, 0.22-0.64), and physical activity time (0.42, 0.22-0.80) were identified (all P < 0.01). Accordingly, nomogram models were created with significant attributes and had decent prediction performance with C-indexes over 80%. LIMITATION: A cross-sectional study and self-reported measures. CONCLUSIONS: Besides the four significant risk factors within and outside Wuhan, the three additional factors outside Wuhan deserve special attention. The prediction nomogram models constructed in this study have important clinical and public health implications for psychosocial and behavioural assessment.


Subject(s)
COVID-19 , Problem Behavior , Adolescent , Child , Cross-Sectional Studies , Humans , Nomograms , Pandemics , Risk Factors , SARS-CoV-2
18.
Crit Care ; 25(1): 234, 2021 07 03.
Article in English | MEDLINE | ID: covidwho-1295477

ABSTRACT

BACKGROUND: The coronavirus disease 2019 (COVID-19) has induced a worldwide epidemiological event with a high infectivity and mortality. However, the predicting biomarkers and their potential mechanism in the progression of COVID-19 are not well known. OBJECTIVE: The aim of this study is to identify the candidate predictors of COVID-19 and investigate their underlying mechanism. METHODS: The retrospective study was conducted to identify the potential laboratory indicators with prognostic values of COVID-19 disease. Then, the prognostic nomogram was constructed to predict the overall survival of COVID-19 patients. Additionally, the scRNA-seq data of BALF and PBMCs from COVID-19 patients were downloaded to investigate the underlying mechanism of the most important prognostic indicators in lungs and peripherals, respectively. RESULTS: In total, 304 hospitalized adult COVID-19 patients in Wuhan Jinyintan Hospital were included in the retrospective study. CEA was the only laboratory indicator with significant difference in the univariate (P < 0.001) and multivariate analysis (P = 0.020). The scRNA-seq data of BALF and PBMCs from COVID-19 patients were downloaded to investigate the underlying mechanism of CEA in lungs and peripherals, respectively. The results revealed the potential roles of CEA were significantly distributed in type II pneumocytes of BALF and developing neutrophils of PBMCs, participating in the progression of COVID-19 by regulating the cell-cell communication. CONCLUSION: This study identifies the prognostic roles of CEA in COVID-19 patients and implies the potential roles of CEACAM8-CEACAM6 in the progression of COVID-19 by regulating the cell-cell communication of developing neutrophils and type II pneumocyte.


Subject(s)
COVID-19/metabolism , Carcinoembryonic Antigen/metabolism , Pneumonia, Viral/metabolism , Adult , Aged , Biomarkers/metabolism , Bronchoalveolar Lavage Fluid/chemistry , COVID-19/mortality , Cell Communication , China/epidemiology , Disease Progression , Hospitalization , Humans , Male , Middle Aged , Neutrophils/metabolism , Nomograms , Pneumonia, Viral/mortality , Pneumonia, Viral/virology , Predictive Value of Tests , Prognosis , Retrospective Studies , SARS-CoV-2 , Survival Analysis
19.
BMC Infect Dis ; 21(1): 608, 2021 Jun 25.
Article in English | MEDLINE | ID: covidwho-1282243

ABSTRACT

BACKGROUND: Convenient and precise assessment of the severity in coronavirus disease 2019 (COVID-19) contributes to the timely patient treatment and prognosis improvement. We aimed to evaluate the ability of CT-based radiomics nomogram in discriminating the severity of patients with COVID-19 Pneumonia. METHODS: A total of 150 patients (training cohort n = 105; test cohort n = 45) with COVID-19 confirmed by reverse transcription polymerase chain reaction (RT-PCR) test were enrolled. Two feature selection methods, Max-Relevance and Min-Redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO), were used to extract features from CT images and construct model. A total of 30 radiomic features were finally retained. Rad-score was calculated by summing the selected features weighted by their coefficients. The radiomics nomogram incorporating clinical-radiological features was eventually constructed by multivariate regression analysis. Nomogram, calibration, and decision-curve analysis were all assessed. RESULTS: In both cohorts, 40 patients with COVID-19 pneumonia were severe and 110 patients were non-severe. By combining the 30 radiomic features extracted from CT images, the radiomics signature showed high discrimination between severe and non-severe patients in the training set [Area Under the Curve (AUC), 0.857; 95% confidence interval (CI), 0.775-0.918] and the test set (AUC, 0.867; 95% CI, 0.732-949). The final combined model that integrated age, comorbidity, CT scores, number of lesions, ground glass opacity (GGO) with consolidation, and radiomics signature, improved the AUC to 0.952 in the training cohort and 0.98 in the test cohort. The nomogram based on the combined model similarly exhibited excellent discrimination performance in both training and test cohorts. CONCLUSIONS: The developed model based on a radiomics signature derived from CT images can be a reliable marker for discriminating the severity of COVID-19 pneumonia.


Subject(s)
COVID-19/diagnostic imaging , COVID-19/diagnosis , Nomograms , Tomography, X-Ray Computed/methods , Adult , Female , Humans , Male , Middle Aged , Multivariate Analysis , Prognosis , SARS-CoV-2/pathogenicity
20.
Dis Markers ; 2021: 5598824, 2021.
Article in English | MEDLINE | ID: covidwho-1262420

ABSTRACT

Assessing the length of hospital stay (LOS) in patients with coronavirus disease 2019 (COVID-19) pneumonia is helpful in optimizing the use efficiency of hospital beds and medical resources and relieving medical resource shortages. This retrospective cohort study of 97 patients was conducted at Beijing You'An Hospital between January 21, 2020, and March 21, 2020. A multivariate Cox proportional hazards regression based on the smallest Akaike information criterion value was used to select demographic and clinical variables to construct a nomogram. Discrimination, area under the receiver operating characteristic curve (AUC), calibration, and Kaplan-Meier curves with the log-rank test were used to assess the nomogram model. The median LOS was 13 days (interquartile range [IQR]: 10-18). Age, alanine aminotransferase, pneumonia, platelet count, and PF ratio (PaO2/FiO2) were included in the final model. The C-index of the nomogram was 0.76 (95%confidence interval [CI] = 0.69-0.83), and the AUC was 0.88 (95%CI = 0.82-0.95). The adjusted C-index was 0.75 (95%CI = 0.67-0.82) and adjusted AUC 0.86 (95%CI = 0.73-0.95), both after 1000 bootstrap cross internal validations. A Brier score of 0.11 (95%CI = 0.07-0.15) and adjusted Brier score of 0.130 (95%CI = 0.07-0.20) for the calibration curve showed good agreement. The AUC values for the nomogram at LOS of 10, 20, and 30 days were 0.79 (95%CI = 0.69-0.89), 0.89 (95%CI = 0.83-0.96), and 0.96 (95%CI = 0.92-1.00), respectively, and the high fit score of the nomogram model indicated a high probability of hospital stay. These results confirmed that the nomogram model accurately predicted the LOS of patients with COVID-19. We developed and validated a nomogram that incorporated five independent predictors of LOS. If validated in a future large cohort study, the model may help to optimize discharge strategies and, thus, shorten LOS in patients with COVID-19.


Subject(s)
COVID-19/therapy , Length of Stay , Nomograms , SARS-CoV-2 , Adult , Aged , Female , Humans , Male , Middle Aged , Proportional Hazards Models , Retrospective Studies
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