Predict the Effects of Dolutegravir (DTG) Plus Lamivudine (3TC) on Immunological Responses in People Living with HIV (PLWHIV).

BACKGROUND: Immune recovery in people living with HIV (PLWHIV) is a residual aspect of antiretroviral treatment (ART) in most patients, but in a non-negligible proportion of them, the CD4+ lymphocytes count, or CD4/CD8 ratio remains suboptimal. METHODS: We performed a model of the immune response after 24 weeks of switching to a 2DR with DTG plus 3TC in a retrospective multicenter cohort of undetectable and experienced patients using significant predictor variables associated with the parameters or situations defined as success and failure. Clinical variables studied were CD4+ and CD8+ lymphocyte count, percentage of CD4, and CD4/CD8 ratio. These parameters were assessed at baseline and 24 weeks after the switch. Based on the evolution of each variable, four categories of immune response and four categories of non-immune response were defined. Immune response was defined as CD4+ count > 500 cells/mm3, %CD4 > 30%, CD8+ count < 1000 cells/mm3 and CD4/CD8 ratio ≥ 0.9. Non-response is just the opposite. RESULTS: In our different models of immunological response, the presence of stage of AIDS (p = 0.035, p = 0.065) and current age over 50 years (p = 0.045) are postulated as statistically significative limiting factors in achieving an improvement in CD4, %CD4, CD8, and CD4/CD8 ratio. Late HIV diagnosis (p = 0.156), without statistical significance, enhanced late the previous variables. In contrast, conditions where patients start with CD4 > 500 cells/mm3 (p = 0.054); CD4 > 30% (p = 0.054, p = 0.084); CD8 < 1000 cells/mm3 (p = 0.018), and CD4/CD8 ≥ 0.9 (p = 0.013, p = 0.09) are detected as stimulating or conducive to DTG plus 3TC treatment success. CONCLUSION: These models represent a proof of concept that could become a valuable tool for clinicians to predict the effects of DTG plus 3TC on immunological responses prior to the switch in undetectable pre-treated PLWHIV with immune dysfunction. The main predictors for immunological failure were late HIV diagnosis, stage of AIDS, and current age over 50 years. In contrast, starting with a normalized immune status was detected as stimulating or conducive to DTG plus 3TC treatment success.

Predicting In-Hospital Mortality in Severe COVID-19: A Systematic Review and External Validation of Clinical Prediction Rules.

Multiple prediction models for risk of in-hospital mortality from COVID-19 have been developed, but not applied, to patient cohorts different to those from which they were derived. The MEDLINE, EMBASE, Scopus, and Web of Science (WOS) databases were searched. Risk of bias and applicability were assessed with PROBAST. Nomograms, whose variables were available in a well-defined cohort of 444 patients from our site, were externally validated. Overall, 71 studies, which derived a clinical prediction rule for mortality outcome from COVID-19, were identified. Predictive variables consisted of combinations of patients' age, chronic conditions, dyspnea/taquipnea, radiographic chest alteration, and analytical values (LDH, CRP, lymphocytes, D-dimer); and markers of respiratory, renal, liver, and myocardial damage, which were mayor predictors in several nomograms. Twenty-five models could be externally validated. Areas under receiver operator curve (AUROC) in predicting mortality ranged from 0.71 to 1 in derivation cohorts; C-index values ranged from 0.823 to 0.970. Overall, 37/71 models provided very-good-to-outstanding test performance. Externally validated nomograms provided lower predictive performances for mortality in their respective derivation cohorts, with the AUROC being 0.654 to 0.806 (poor to acceptable performance). We can conclude that available nomograms were limited in predicting mortality when applied to different populations from which they were derived.

Performances of clinical characteristics and radiological findings in identifying COVID-19 from suspected cases.

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.

Heterogeneity Analysis of Chest CT Predict Individual Prognosis of COVID-19 Patients.

BACKGROUND: Ground-glass Opacity (GGO) and Consolidation Opacity (CLO) are the common CT lung opacities, and their heterogeneity may have potential for prognosis ofcoronavirus disease-19 (COVID-19) patients. OBJECTIVE: This study aimed to estimate clinical outcomes in individual COVID-19 patients using histogram heterogeneity analysis based on CT opacities. METHODS: 71 COVID-19 cases' medical records were retrospectively reviewed from a designated hospital in Wuhan, China, from January 24th to February 28th at the early stage of the pandemic. Two characteristic lung abnormity opacities, GGO and CLO, were drawn on CT images to identify the heterogeneity using quantitative histogram analysis. The parameters (mean, mode, kurtosis, and skewness) were derived from histograms to evaluate the accuracy of clinical classification and outcome prediction. Nomograms were built to predict the risk of death and median length of hospital stays (LOS), respectively. RESULTS: A total of 57 COVID-19 cases were eligible for the study cohort after excluding 14 cases. The highest lung abnormalities were GGO mixed with CLO in both the survival populations (26 in 42, 61.9%) and died population (10 in 15, 66.7%). The best performance heterogeneity parameters to discriminate severe type from mild/moderate counterparts were as follows: GGO_skewness: specificity= 66.67%, sensitivity=78.12%, AUC=0.706; CLO_mean: specificity=70.00%, sensitivity= 76.92%, and AUC=0.746. Nomogram based on histogram parameters can predict the individual risk of death and the prolonged median LOS of COVID-19 patients. C-indexes were 0.763 and 0.888 for risk of death and prolonged median LOS, respectively. CONCLUSION: Histogram analysis method based on GGO and CLO has the ability for individual risk prediction in COVID-19 patients.

The usefulness of a combination of age, body mass index, and blood urea nitrogen as prognostic factors in predicting oxygen requirements in patients with coronavirus disease 2019.

INTRODUCTION: Risk factors for seriously ill coronavirus disease 19 (COVID-19) patients have been reported in several studies. However, to date, few studies have reported simple risk assessment tools for distinguishing patients becoming severely ill after initial diagnosis. Hence, this study aimed to develop a simple clinical risk nomogram predicting oxygenation risk in patients with COVID-19 at the first triage. METHODS: This retrospective study involved a chart review of the medical records of 84 patients diagnosed with COVID-19 between February 2020 and March 2021 at ten medical facilities. The patients were divided into requiring no oxygen therapy (non-severe group) and requiring oxygen therapy (severe group). Patient characteristics were compared between the two groups. We utilized univariate logistic regression analysis to confirm determinants of high risks of requiring oxygen therapy in patients with moderate COVID-19. RESULTS: Thirty-five patients ware in severe group and forty-nine patients were in non-severe group. In comparison with patients in the non-severe group, patients in the severe group were significantly older with higher body mass index (BMI), and had a history of hypertension and diabetes. Serum blood urea nitrogen (BUN), lactic acid dehydrogenase (LDH), and C-reactive protein (CRP) levels were significantly higher in the severe group. Multivariate analysis showed that older age, higher BMI, and higher BUN levels were significantly associated with oxygen requirements. CONCLUSIONS: This study demonstrated that age, BMI, and BUN were independent risk factors in the moderate-to-severe COVID-19 group. Elderly patients with higher BMI and BUN require close monitoring and early treatment initiation.

Clinical diagnosis of severe COVID-19: A derivation and validation of a prediction rule.

BACKGROUND: The widespread coronavirus disease 2019 (COVID-19) has led to high morbidity and mortality. Therefore, early risk identification of critically ill patients remains crucial. AIM: To develop predictive rules at the time of admission to identify COVID-19 patients who might require intensive care unit (ICU) care. METHODS: This retrospective study included a total of 361 patients with confirmed COVID-19 by reverse transcription-polymerase chain reaction between January 19, 2020, and March 14, 2020 in Shenzhen Third People's Hospital. Multivariate logistic regression was applied to develop the predictive model. The performance of the predictive model was externally validated and evaluated based on a dataset involving 126 patients from the Wuhan Asia General Hospital between December 2019 and March 2020, by area under the receiver operating curve (AUROC), goodness-of-fit and the performance matrix including the sensitivity, specificity, and precision. A nomogram was also used to visualize the model. RESULTS: Among the patients in the derivation and validation datasets, 38 and 9 participants (10.5% and 2.54%, respectively) developed severe COVID-19, respectively. In univariate analysis, 21 parameters such as age, sex (male), smoker, body mass index (BMI), time from onset to admission (> 5 d), asthenia, dry cough, expectoration, shortness of breath, asthenia, and Rox index < 18 (pulse oxygen saturation, SpO2)/(FiO2 × respiratory rate, RR) showed positive correlations with severe COVID-19. In multivariate logistic regression analysis, only six parameters including BMI [odds ratio (OR) 3.939; 95% confidence interval (CI): 1.409-11.015; P = 0.009], time from onset to admission (≥ 5 d) (OR 7.107; 95%CI: 1.449-34.849; P = 0.016), fever (OR 6.794; 95%CI: 1.401-32.951; P = 0.017), Charlson index (OR 2.917; 95%CI: 1.279-6.654; P = 0.011), PaO2/FiO2 ratio (OR 17.570; 95%CI: 1.117-276.383; P = 0.041), and neutrophil/lymphocyte ratio (OR 3.574; 95%CI: 1.048-12.191; P = 0.042) were found to be independent predictors of COVID-19. These factors were found to be significant risk factors for severe patients confirmed with COVID-19. The AUROC was 0.941 (95%CI: 0.901-0.981) and 0.936 (95%CI: 0.886-0.987) in both datasets. The calibration properties were good. CONCLUSION: The proposed predictive model had great potential in severity prediction of COVID-19 in the ICU. It assisted the ICU clinicians in making timely decisions for the target population.

Development and multicenter validation of a CT-based radiomics signature for predicting severe COVID-19 pneumonia.

OBJECTIVES: To develop and validate a radiomics nomogram for timely predicting severe COVID-19 pneumonia. MATERIALS AND METHODS: Three hundred and sixteen COVID-19 patients (246 non-severe and 70 severe) were retrospectively collected from two institutions and allocated to training, validation, and testing cohorts. Radiomics features were extracted from chest CT images. Radiomics signature was constructed based on reproducible features using the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm with 5-fold cross-validation. Logistic regression modeling was employed to build different models based on quantitative CT features, radiomics signature, clinical factors, and/or the former combined features. Nomogram performance for severe COVID-19 prediction was assessed with respect to calibration, discrimination, and clinical usefulness. RESULTS: Sixteen selected features were used to build the radiomics signature. The CT-based radiomics model showed good calibration and discrimination in the training cohort (AUC, 0.9; 95% CI, 0.843-0.942), the validation cohort (AUC, 0.878; 95% CI, 0.796-0.958), and the testing cohort (AUC, 0.842; 95% CI, 0.761-0.922). The CT-based radiomics model showed better discrimination capability (all p < 0.05) compared with the clinical factors joint quantitative CT model (AUC, 0.781; 95% CI, 0.708-0.843) in the training cohort, the validation cohort (AUC, 0.814; 95% CI, 0.703-0.897), and the testing cohort (AUC, 0.696; 95% CI, 0.581-0.796). Decision curve analysis demonstrated that in terms of clinical usefulness, the radiomics model outperformed the clinical factors model and quantitative CT model alone. CONCLUSIONS: The CT-based radiomics signature shows favorable predictive efficacy for severe COVID-19, which might assist clinicians in tailoring precise therapy. KEY POINTS: • Radiomics can be applied in CT images of COVID-19 and radiomics signature was an independent predictor of severe COVID-19. • CT-based radiomics model can predict severe COVID-19 with satisfactory accuracy compared with subjective CT findings and clinical factors. • Radiomics nomogram integrated with the radiomics signature, subjective CT findings, and clinical factors can achieve better severity prediction with improved diagnostic performance.

Radiomics nomogram for the prediction of 2019 novel coronavirus pneumonia caused by SARS-CoV-2.

OBJECTIVES: To develop and validate a radiomics model for predicting 2019 novel coronavirus (COVID-19) pneumonia. METHODS: For this retrospective study, a radiomics model was developed on the basis of a training set consisting of 136 patients with COVID-19 pneumonia and 103 patients with other types of viral pneumonia. Radiomics features were extracted from the lung parenchyma window. A radiomics signature was built on the basis of reproducible features, using the least absolute shrinkage and selection operator method (LASSO). Multivariable logistic regression model was adopted to establish a radiomics nomogram. Nomogram performance was determined by its discrimination, calibration, and clinical usefulness. The model was validated in 90 consecutive patients, of which 56 patients had COVID-19 pneumonia and 34 patients had other types of viral pneumonia. RESULTS: The radiomics signature, consisting of 3 selected features, was significantly associated with COVID-19 pneumonia (p < 0.05) in both training and validation sets. The multivariable logistic regression model included the radiomics signature and distribution; maximum lesion, hilar, and mediastinal lymph node enlargement; and pleural effusion. The individualized prediction nomogram showed good discrimination in the training sample (area under the receiver operating characteristic curve [AUC], 0.959; 95% confidence interval [CI], 0.933-0.985) and in the validation sample (AUC, 0.955; 95% CI, 0.899-0.995) and good calibration. The mixed model achieved better predictive efficacy than the clinical model. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful. CONCLUSIONS: The radiomics model derived has good performance for predicting COVID-19 pneumonia and may help in clinical decision-making. KEY POINTS: • A radiomics model showed good performance for prediction 2019 novel coronavirus pneumonia and favorable discrimination for other types of pneumonia on CT images. • A central or peripheral distribution, a maximum lesion range > 10 cm, the involvement of all five lobes, hilar and mediastinal lymph node enlargement, and no pleural effusion is associated with an increased risk of 2019 novel coronavirus pneumonia. • A radiomics model was superior to a clinical model in predicting 2019 novel coronavirus pneumonia.