Efficacy of the COVID-19 vaccination in patients with asymptomatic or mild illness during the Omicron epidemic in Guangzhou: a multi-centre retrospective cohort study.

BACKGROUND: Despite the widespread administration of coronavirus disease 2019 (COVID-19) vaccines, the impact on patients with asymptomatic to mild illness remains unclear. Here, we aimed to assess the efficacy of various vaccine doses and types on the duration of isolation duration and discharge rates, the viral shedding duration, and negative rates in asymptomatic to mild COVID-19 patients. METHODS: We included adult patients at the Fangcang isolation centres in Pazhou or Yongning between November and December 2022. We analysed data on basic demographics, admission details, laboratory indicators and vaccination information. RESULTS: A total of 6560 infected patients were included (3584 from Pazhou and 2976 from Yongning). Of these, 90.6% received inactivated vaccines, 3.66% received recombinant SARS-CoV-2 spike protein subunit vaccines and 0.91% received adenovirus vaccines. Among the 6173 vaccinated individuals, 71.9% received a booster dose. By day 9, the isolation rate reached 50% among vaccinated patients. On day 7.5, the positive rate among vaccinated individuals reached 50%. CONCLUSIONS: Full vaccination was effective, with heterologous vaccines showing greater efficacy than inactivated vaccines alone. However, there was no significant difference in the vaccine protective effect 12 months after vaccination.

Application Value of Cardiometabolic Index for the Screening of Obstructive Sleep Apnea with or Without Metabolic Syndrome.

Background: Obstructive sleep apnea (OSA) is a common chronic disease with various comorbidities. The cardiometabolic index (CMI) reflects visceral fat tissue distribution and function, assessing the risk of obesity-related conditions such as metabolic syndrome (MetS) and stroke, which are strongly connected to OSA. The relationship between CMI with OSA and OSA combined with MetS (OMS) remains unclear. This study aims to evaluate the screening value of CMI for OSA and OMS, compared to the lipid accumulation product (LAP). Methods: A total of 280 participants who underwent polysomnography were finally included, with the measurements of metabolic-related laboratory test results such as total cholesterol and triglyceride. Receiver operating curve (ROC) analysis and calculation of the area under the curve (AUC) were conducted to assess the screening potential of CMI, LAP, and the logistic regression models established based on them for OSA and OMS. The Youden index, sensitivity, and specificity were used to determine the optimal cutoff points. Results: ROC curve analysis revealed that the AUCs for CMI in screening OSA and OMS were 0.808 and 0.797, and the optimal cutoff values were 0.71 (sensitivity 0.797, specificity 0.776) and 0.89 (sensitivity 0.830, specificity 0.662), respectively, showing higher Youden index than LAP. The AUCs of screening models based on CMI for OSA and OMS were 0.887 and 0.824, respectively. Conclusion: CMI and LAP can effectively screen for OSA and OMS, while CMI has more practical cutoff values for identifying the diseased states. Screening models based on CMI demonstrate a high discriminatory ability for OSA and OMS, which needs verification in a large-scale population.

Analysis of Airway Thickening and Serum Cytokines in COPD Patients with Frequent Exacerbations: A Heart of the Matter.

Background: Differences in lung function for Chronic Obstructive Pulmonary Disease (COPD) cause bias in the findings when identifying frequent exacerbator phenotype-related causes. The aim of this study was to determine whether computed tomographic (CT) biomarkers and circulating inflammatory biomarkers were associated with the COPD frequent exacerbator phenotype after eliminating the differences in lung function between a frequent exacerbator (FE) group and a non-frequent exacerbator (NFE) group. Methods: A total of 212 patients with stable COPD were divided into a FE group (n=106) and a NFE group (n=106) according to their exacerbation history. These patients were assessed by spirometry, quantitative CT measurements and blood sample measurements during their stable phase. Univariate and multivariate logistic regression were used to assess the association between airway thickening or serum cytokines and the COPD frequent exacerbator phenotype. Receiver operating characteristic (ROC) curves were calculated for Pi10, WA%, IL-1ß and IL-4 to identify frequent exacerbators. Results: Compared with NFE group, FE group had a greater inner perimeter wall thickness of a 10 mm diameter bronchiole (Pi10), a greater airway wall area percentage (WA%) and higher concentrations of IL-1ß and IL-4 (p<0.001). After adjusting for sex, age, BMI, FEV1%pred and smoking pack-years, Pi10, WA%, IL-ß and IL-4 were independently associated with a frequent exacerbator phenotype (p<0.001). Additionally, there was an increase in the odds ratio of the frequent exacerbator phenotype with increasing Pi10, WA%, IL-4, and IL-1ß (p for trend <0.001). The ROC curve demonstrated that IL-1ß had a significantly larger calculated area under the curve (p < 0.05) than Pi10, WA% and IL-4. Conclusion: Pi10, WA%, IL-4, and IL-1ß were independently associated with the frequent exacerbator phenotype among patients with stable COPD, suggesting that chronic airway and systemic inflammation contribute to the frequent exacerbator phenotype. Trial Registration: This trial was registered in Chinese Clinical Trial Registry (https://www.chictr.org.cn). Its registration number is ChiCTR2000038700, and date of registration is September 29, 2020.

Aetiological distribution of pulmonary hypertension and the value of transthoracic echocardiography screening in the respiratory department: A retrospective analysis from China.

METHODS: The aetiological composition and clinical characteristics of patients with pulmonary hypertension (PH) hospitalised in the respiratory department were retrospectively analysed, as well as the correlation between transthoracic echocardiography (TTE) and right heart catheterization (RHC) for evaluating pulmonary artery systolic pressure (PASP) and mean pulmonary artery pressure (mPAP). RESULTS: Of 731 patients, 544 (74.42%) were diagnosed with PH by RHC. Pulmonary arterial hypertension (PAH) was the most common type of PH, accounting for 30.10%; PH due to lung disease and/or hypoxia accounted for 20.79%, and PH due to pulmonary artery obstructions accounted for 19.29%. TTE has the highest specificity for diagnosing PH due to pulmonary artery obstructions. The specificity was 0.9375, the sensitivity was 0.7361 and the area under the ROC curve (AUC) was 0.836. PASP, and mPAP estimated by TTE were different for various types of PH. In terms of PASP, TTE overestimated PASP in PH due to lung disease and/or hypoxia, but there was no significant difference compared with RHC (P > 0.05). TTE underestimates PAH patients' PASP compared with RHC. In terms of mPAP, TTE underestimated the mPAP of all types of PH, as there was a significant difference in the TTE-estimated mPAP of patients with PAH compared with RHC but not on other types of PH. Pearson correlation analysis of TTE and RHC showed a moderate overall correlation (rPASP 0.598, P < 0.001; rmPAP 0.588, P < 0.001). CONCLUSIONS: Among the patients with PH in the respiratory department, patients with PAH accounted for the majority. TTE has high sensitivity and specificity for the diagnosis of PH due to pulmonary artery obstructions in the respiratory department.

Genome-wide association analyses identified novel susceptibility loci for pulmonary embolism among Han Chinese population.

BACKGROUND: A large proportion of pulmonary embolism (PE) heritability remains unexplained, particularly among the East Asian (EAS) population. Our study aims to expand the genetic architecture of PE and reveal more genetic determinants in Han Chinese. METHODS: We conducted the first genome-wide association study (GWAS) of PE in Han Chinese, then performed the GWAS meta-analysis based on the discovery and replication stages. To validate the effect of the risk allele, qPCR and Western blotting experiments were used to investigate possible changes in gene expression. Mendelian randomization (MR) analysis was employed to implicate pathogenic mechanisms, and a polygenic risk score (PRS) for PE risk prediction was generated. RESULTS: After meta-analysis of the discovery dataset (622 cases, 8853 controls) and replication dataset (646 cases, 8810 controls), GWAS identified 3 independent loci associated with PE, including the reported loci FGG rs2066865 (p-value = 3.81 × 10-14), ABO rs582094 (p-value = 1.16 × 10-10) and newly reported locus FABP2 rs1799883 (p-value = 7.59 × 10-17). Previously reported 10 variants were successfully replicated in our cohort. Functional experiments confirmed that FABP2-A163G(rs1799883) promoted the transcription and protein expression of FABP2. Meanwhile, MR analysis revealed that high LDL-C and TC levels were associated with an increased risk of PE. Individuals with the top 10% of PRS had over a fivefold increased risk for PE compared to the general population. CONCLUSIONS: We identified FABP2, related to the transport of long-chain fatty acids, contributing to the risk of PE and provided more evidence for the essential role of metabolic pathways in PE development.

The relationship between obstructive sleep apnea and asthma severity and vice versa: a systematic review and meta-analysis.

BACKGROUND: There is a great association between the prevalence of obstructive sleep apnea (OSA) and asthma. Nonetheless, whether OSA impacts lung function, symptoms, and control in asthma and whether asthma increases the respiratory events in OSA are unknown. This meta-analysis aimed to examine the relationship between obstructive sleep apnea and asthma severity and vice versa. METHODS: We carried out a systematic search of PubMed, EMBASE, and Scopus from inception to September 2022. Primary outcomes were lung function, parameters of polysomnography, the risk of OSA in more severe or difficult-to-control asthmatic patients, and the risk of asthma in patients with more severe OSA. Heterogeneity was examined with the Q test and I2 statistics. We also performed subgroup analysis, Meta-regression, and Egger's test for bias analysis. RESULTS: 34 studies with 27,912 subjects were totally included. The results showed that the comorbidity of OSA aggravated lung function in asthmatic patients with a consequent decreased forced expiratory volume in one second %predicted (%FEV1) and the effect was particularly evident in children. %FEV1 tended to decrease in adult asthma patients complicated with OSA, but did not reach statistical significance. Interestingly, the risk of asthma seemed to be slightly lower in patients with more severe OSA (OR = 0.87, 95%CI 0.763-0.998). Asthma had no significant effect on polysomnography, but increased daytime sleepiness assessed by the Epworth Sleepiness Scale in OSA patients (WMD = 0.60, 95%CI 0.16-1.04). More severe asthma or difficult-to-control asthma was independently associated with OSA (odds ratio (OR) = 4.36, 95%CI 2.49-7.64). CONCLUSION: OSA was associated with more severe or difficult-to-control asthma with decreased %FEV1 in children. The effect of OSA on lung function in adult patients should be further confirmed. Asthma increased daytime sleepiness in OSA patients. More studies are warranted to investigate the effect of asthma on OSA severity and the impact of different OSA severity on the prevalence of asthma. It is strongly recommended that people with moderate-to-severe or difficult-to-control asthma screen for OSA and get the appropriate treatment.

Establishment and Application Evaluation of an Improved Obstructive Sleep Apnea Screening Questionnaire for Chinese Community: The CNCQ-OSA.

Objective: Obstructive sleep apnea (OSA) is a common sleep-disordered breathing disease. We aimed to establish an improved screening questionnaire without physical examinations for OSA named the CNCQ-OSA (Chinese community questionnaire for OSA). Methods: A total of 2585 participants who visited sleep medicine center and underwent overnight polysomnography were grouped into two independent cohorts: derivation (n = 2180) and validation (n = 405). The CNCQ-OSA was designed according to the baseline of patients in derivation cohort. We comprehensively analyzed the data to evaluate the predictive value of the CNCQ-OSA, compared to the GOAL questionnaire, STOP-Bang questionnaire (SBQ) and NoSAS questionnaire. Results: The CNCQ-OSA included seven variables: loud snoring, BMI ≥ 25 kg/m2, male gender, apnea, sleepiness, hypertension and age ≥30, with a total score ranging from 7 to 16.7 points (≥13.5 points indicating high risk of OSA, ≥14.5 points indicating extremely high risk). In the derivation and validation cohorts, the areas under the curve of the CNCQ-OSA were 0.761 and 0.767, respectively. In the validation cohort, the sensitivity and specificity of a CNCQ-OSA score ≥13.5 points for the apnea-hypopnea index (AHI) ≥5/h were 0.821 and 0.559, respectively (Youden index, 0.380), and the score ≥14.5 points were 0.494 and 0.887, respectively (Youden index, 0.375). The CNCQ-OSA had a better predictive value for AHI ≥ 5/h, AHI > 15/h and AHI > 30/h, with the highest Youden index, compared to the other questionnaires. Conclusion: The CNCQ-OSA can effectively identify the risk of OSA, which is appropriate for self-screening at home without physical examinations.

Association Between Smoking Behavior and Obstructive Sleep Apnea: A Systematic Review and Meta-Analysis.

INTRODUCTION: To systematically review the association between smoking behavior and obstructive sleep apnea (OSA). AIMS AND METHODS: PubMed, Medline, the Cochrane Library, EMBASE, and Scopus databases were used to conduct this review. The two researchers independently screened the literatures, conducted the quality assessment, and data extraction according to the inclusion and exclusion criteria. The RevMan 5.3 was used to analysis the apnea hypopnea index (AHI) index, min saturation of oxyhemoglobin (SaO2), Epworth Sleepiness Scale (ESS) score, and oxygen desaturation index (DOI) and publication bias analysis to assess the effect of smoking on OSA patients. Furthermore, we performed subgroup of the severity of OSA, different countries of sample origin (western countries or eastern countries), and pack-years (PYs < 10 or PYs ≥ 20) to analyze the heterogeneity. RESULTS: Thirteen studies were included in this analysis that conformed to inclusion criteria and exclusion criteria. Totally 3654 smokers and 9796 non-smokers have participated. The meta-analysis of 13 studies demonstrated that AHI levels were significantly higher in smoker group compared with non-smoker, ESS scores were also significantly higher in smoker group compared with non-smoker, min SaO2 levels were obviously lower in smoker group compared with non-smoker, however, DOI levels hadn't significantly different between two groups. The subgroup analysis showed that there was an association between severe OSA, eastern countries, pack-years, and smoking. CONCLUSIONS: Smoking behavior is a significant association with OSA. Heavy smokers with histories of more than 20 PYs were at a higher risk of OSA. Moreover, patient with severe OSA exhibited a significantly association with smoking compared with patients with mild or moderate OSA. IMPLICATIONS: The relationship between smoking and OSA was controversial, especially, whether smoking increase or aggravate the risk of OSA. In our review and meta-analysis, we demonstrated that smoking behavior is a significant association with OSA. Heavy smokers with histories of more than 20 PYs were at a higher risk of OSA. Moreover, patient with severe OSA exhibited a significant association with smoking compared with patients with mild or moderate OSA. More prospective long-term follow-up studies about effect of quit smoking on OSA are recommended to establish the further relationship.

Blood Cells and Venous Thromboembolism Risk: A Two-Sample Mendelian Randomization Study.

Background: Previous studies have shown that various cell indices are associated with a higher risk of venous thromboembolism (VTE), however, whether these findings reflect a causal relationship remains unclear. Therefore, we performed a two-sample Mendelian randomization (MR) analysis to assess the causal association of various blood cells with VTE risk. Study Design and Methods: Summary statistics of genetic instruments representing cell indices for erythrocytes, leukocytes, and platelets were extracted from genome-wide association studies of European ancestry, by Two-Sample Mendelian Randomization. Inverse variance weighting (IVW) was used as the primary analytical method for MR. Sensitivity analyses were performed to detect horizontal pleiotropy and heterogeneity. Results: Genetically predicted red blood cell distribution width, mean reticulocyte volume, and mean red blood cell volume were positively associated with VTE, with odds ratio (OR) of 1.002 [CI 1.000-1.003, P = 0.022), 1.003 (CI 1.001-1.004, P = 0.001, respectively)] and 1.001 (CI 1.000-1.002, P = 0.005). Genetically predicted monocyte count was negatively correlated with VTE, with OR = 0.998 (CI 0.996-0.999, P = 0.041). Conclusion: Genetically liability to high- red blood cell distribution width, mean reticulocyte volume, mean red blood cell volume, and low monocyte count are associated with the higher risk of VTE. Targeting these factors might be a potential strategy to prevent VTE.

Application value of joint NoSAS score and Epworth Sleepiness Scale for assessment of obstructive sleep apnea hypopnea syndrome.

OBJECTIVE: By comparing the predictive value of the NoSAS (Neck circumference, Obesity, Snoring, Age and Sex) score combined with the Epworth Sleepiness Scale (ESS), STOP-Bang Questionnaire (STOP-Bang), STOP Questionnaire (STOP) and Berlin Questionnaire (Berlin), the application value of the NoSAS score combined with ESS in screening Obstructive sleep apnea hypopnea syndrome (OSAHS) in the population is evaluated. METHOD: 2560 suspected OSAHS patients visited the Sleep Medical Center of the First Hospital of Guangzhou Medical University between September 1, 2016 and October 31, 2020, and were monitored with a polysomnogram (PSG) after completing the NoSAS score, ESS, STOP-Bang, STOP and Berlin. The sensitivity, specificity, positive predictive value, negative predictive value and receiver operating characteristic (ROC) curve of each scale were calculated, and the accuracy in predicting OSAHS of the NoSAS score combined with ESS and each scale was analyzed. RESULTS: The areas under the ROC curve scored by Berlin were higher than those of the other four questionnaires with Apnea Hypopnea Index (AHI) cutoffs of ≥5 and ≥ 10 events/h, while the area under the ROC curve scored by the NoSAS score was the highest with AHI cutoffs of ≥15, ≥20, ≥25 and ≥ 30 events/h. Among the five scales, the diagnostic odds ratio (DOR) of the NoSAS score was the highest. When a NoSAS score of ≥7 was used as the cutoff point for diagnostic NoSAS, it had higher sensitivity and specificity with a NoSAS score of ≥8 as the cutoff point for diagnostic NoSAS. A NoSAS score of ≥7 combined with ESS significantly improved its specificity for predicted OSAHS patients. CONCLUSION: The NoSAS score is a simple and effective new tool for screening patients for OSAHS, while a NoSAS score of ≥7 combined with ESS can further improve its specificity. Thus, we suggest further screening with ESS after a NoSAS score of ≥7 in suspected populations.

Development and validation of a clinical model to predict hypertension in consecutive patients with obstructive sleep apnea hypopnea syndrome: a hospital-based study and nomogram analysis.

BACKGROUND: To screen for risk predictors of hypertension in patients with Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS) and develop and validate a clinical model for individualized prediction of hypertension in consecutive patients with OSAHS. METHODS: 114 consecutive patients with OSAHS confirmed by PSG monitoring participated in this study. Those individuals were divided into two sets at a ratio of 7:3, using computer-generated random numbers: 82 individuals were assigned to the training set and 32 to the validation set. Important risk predictors of hypertension in individuals with OSAHS were confirmed using the LASSO method and a clinical nomogram constructed. The predictive accuracy was assessed by unadjusted concordance index (C-index) and calibration plot. RESULTS: Univariate and multivariate regression analysis identified BMI, REM-AHI, REM-MSpO2 and T90% as predictive risk factors of OSAHS. Those risk factors were used to construct a clinical predictive nomogram. The calibration curves for hypertension in patients with OSAHS risk revealed excellent accuracy of the predictive nomogram model, internally and externally. The unadjusted concordance index (C-index) for the training and validation set was 0.897 [95% CI 0.795-0.912] and 0.894 [95% CI 0.788-0.820] respectively. The AUC of the training and validation set was 0.8175882 and 0.8031522, respectively. Decision curve analysis showed that the predictive model could be applied clinically when the threshold probability was 20 to 80%. CONCLUSION: We constructed and validated a clinical nomogram to individually predict the occurrence of hypertension in patients with OSAHS. We determined that BMI, REM-AHI, REM-MSpO2 and T90% were independent risk predictors for hypertension in patients with OSAHS. This practical prognostic nomogram may help improve clinical decision making.

Development and validation of a simple clinical nomogram for predicting obstructive sleep apnea.

Obstructive sleep apnea is the most common type of sleep breathing disorder. Therefore, the purpose of our research is to construct and verify an objective and easy-to-use nomogram that can accurately predict a patient's risk of obstructive sleep apnea. In this study, we retrospectively collected the data of patients undergoing polysomnography at the Sleep Medicine Center of the First Affiliated Hospital of Guangzhou Medical University. Participants were randomly assigned to a training cohort (50%) and a validation cohort (50%). Logistic regression and Lasso regression models were used to reduce data dimensions, select factors and construct the nomogram. C-index, calibration curve, decision curve analysis and clinical impact curve analysis were used to evaluate the identification, calibration and clinical effectiveness of the nomogram. Nomograph validation was performed in the validation cohort. The study included 1035 people in the training cohort and 1078 people in the validation cohort. Logistic and Lasso regression analysis identified age, gender, diastolic blood pressure, body mass index, neck circumference and Epworth Sleepiness Scale as the predictive factors included in the nomogram. The training cohort (C-index = 0.741) and validation cohort (C-index = 0.745) had better identification and calibration effects. The areas under the curve of the nomogram and STOP-Bang were 0.741 (0.713-0.767) and 0.728 (0.700-0.755), respectively. Decision curve analysis and clinical impact curve analysis showed that the nomogram is clinically useful. We have established a concise and practical nomogram that will help doctors better determine the priority of patients referred to the sleep centre.

Artificial intelligence for stepwise diagnosis and monitoring of COVID-19.

BACKGROUND: Main challenges for COVID-19 include the lack of a rapid diagnostic test, a suitable tool to monitor and predict a patient's clinical course and an efficient way for data sharing among multicenters. We thus developed a novel artificial intelligence system based on deep learning (DL) and federated learning (FL) for the diagnosis, monitoring, and prediction of a patient's clinical course. METHODS: CT imaging derived from 6 different multicenter cohorts were used for stepwise diagnostic algorithm to diagnose COVID-19, with or without clinical data. Patients with more than 3 consecutive CT images were trained for the monitoring algorithm. FL has been applied for decentralized refinement of independently built DL models. RESULTS: A total of 1,552,988 CT slices from 4804 patients were used. The model can diagnose COVID-19 based on CT alone with the AUC being 0.98 (95% CI 0.97-0.99), and outperforms the radiologist's assessment. We have also successfully tested the incorporation of the DL diagnostic model with the FL framework. Its auto-segmentation analyses co-related well with those by radiologists and achieved a high Dice's coefficient of 0.77. It can produce a predictive curve of a patient's clinical course if serial CT assessments are available. INTERPRETATION: The system has high consistency in diagnosing COVID-19 based on CT, with or without clinical data. Alternatively, it can be implemented on a FL platform, which would potentially encourage the data sharing in the future. It also can produce an objective predictive curve of a patient's clinical course for visualization. KEY POINTS: • CoviDet could diagnose COVID-19 based on chest CT with high consistency; this outperformed the radiologist's assessment. Its auto-segmentation analyses co-related well with those by radiologists and could potentially monitor and predict a patient's clinical course if serial CT assessments are available. It can be integrated into the federated learning framework. • CoviDet can be used as an adjunct to aid clinicians with the CT diagnosis of COVID-19 and can potentially be used for disease monitoring; federated learning can potentially open opportunities for global collaboration.