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Abstract In recent years, large amounts of researches showed that pulmonary embolism (PE) has become a common disease, and PE remains a clinical challenge because of its high mortality, high disability, high missed and high misdiagnosed rates. To address this, we employed an artificial intelligence–based machine learning algorithm (MLA) to construct a robust predictive model for PE. We retrospectively analyzed 1480 suspected PE patients hospitalized in West China Hospital of Sichuan University between May 2015 and April 2020. 126 features were screened and diverse MLAs were utilized to craft predictive models for PE. Area under the receiver operating characteristic curves (AUC) were used to evaluate their performance and SHapley Additive exPlanation (SHAP) values were utilized to elucidate the prediction model. Regarding the efficacy of the single model that most accurately predicted the outcome, RF demonstrated the highest efficacy in predicting outcomes, with an AUC of 0.776 (95% CI 0.774–0.778). The SHAP summary plot delineated the positive and negative effects of features attributed to the RF prediction model, including D-dimer, activated partial thromboplastin time (APTT), fibrin and fibrinogen degradation products (FFDP), platelet count, albumin, cholesterol, and sodium. Furthermore, the SHAP dependence plot illustrated the impact of individual features on the RF prediction model. Finally, the MLA based PE predicting model was designed as a web page that can be applied to the platform of clinical management. In this study, PE prediction model was successfully established and designed as a web page, facilitating the optimization of early diagnosis and timely treatment strategies to enhance PE patient outcomes.

Systemic sclerosis (SSc) is a perplexing autoimmune disorder, characterized by mysterious causes, high mortality rates, and a lack of effective treatments. The role of abnormal glycosylation in the onset of autoimmune diseases has been recognized for some time. Nonetheless, the intricate details of intact glycopeptides in SSc remain elusive owing to challenges in their detection. In this study, we characterized plasma immunoglobulin G (IgG) intact N-glycopeptides from 30 SSc patients and 30 healthy controls (HCs) via our recently developed intact glycopeptide analysis method GlycoQuant. Through this approach, twelve differentially expressed intact N-glycopeptides were identified. The correlation of specific intact N-glycopeptides with the clinical features of SSc patients was analyzed. The results revealed a notable increase in the levels of 6 intact N-glycopeptides (IgG2-N3H3F1, IgG2-N3H4F1, IgG2-N4H4F1, IgG2-N4H5F1, IgG2-N5H4F1, and IgG2-N5H5F1) and a decrease in the levels of another set of 6 intact N-glycopeptides (IgG1-N4H3F1, IgG2-N3H6F1A1, IgG2-N4H4F1A1, IgG2-N5H3F1, IgG3-N4H3F1, and IgG3-N4H4F1). These changes in the levels of intact N-glycopeptides are associated with various aspects of SSc, including diffuse SSc (dSSc), interstitial lung disease (ILD), disease progression, cardiovascular involvement and C-reactive protein in the peripheral blood. In summary, this study offers a detailed overview of the intact N-glycopeptide profile in the peripheral blood of patients with SSc, providing valuable insights that could propel further research into SSc.

Abstract Background There is no individualized prediction model for intensive care unit (ICU) admission on patients with community-acquired pneumonia (CAP) and connective tissue disease (CTD) so far. In this study, we aimed to establish a machine learning-based model for predicting the need for ICU admission among those patients. Methods This was a retrospective study on patients admitted into a University Hospital in China between November 2008 and November 2021. Patients were included if they were diagnosed with CAP and CTD during admission and hospitalization. Data related to demographics, CTD types, comorbidities, vital signs and laboratory results during the first 24 h of hospitalization were collected. The baseline variables were screened to identify potential predictors via three methods, including univariate analysis, least absolute shrinkage and selection operator (Lasso) regression and Boruta algorithm. Nine supervised machine learning algorithms were used to build prediction models. We evaluated the performances of differentiation, calibration, and clinical utility of all models to determine the optimal model. The Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) techniques were performed to interpret the optimal model. Results The included patients were randomly divided into the training set (1070 patients) and the testing set (459 patients) at a ratio of 70:30. The intersection results of three feature selection approaches yielded 16 predictors. The eXtreme gradient boosting (XGBoost) model achieved the highest area under the receiver operating characteristic curve (AUC) (0.941) and accuracy (0.913) among various models. The calibration curve and decision curve analysis (DCA) both suggested that the XGBoost model outperformed other models. The SHAP summary plots illustrated the top 6 features with the greatest importance, including higher N-terminal pro-B-type natriuretic peptide (NT-proBNP) and C-reactive protein (CRP), lower level of CD4 + T cell, lymphocyte and serum sodium, and positive serum (1,3)-β-D-glucan test (G test). Conclusion We successfully developed, evaluated and explained a machine learning-based model for predicting ICU admission in patients with CAP and CTD. The XGBoost model could be clinical referenced after external validation and improvement.

Abstract Background Diaphragmatic dysfunction is known to be associated with difficulties weaning from invasive mechanical ventilation and is related to worse patient outcomes yet our understanding of how to prevent diaphragmatic dysfunction remains incomplete. We examined potentially modifiable risk factors for diaphragmatic dysfunction and attempted to estimate benefits attributable to altering these modifiable risk factors. Methods This prospective multicenter observational study was undertaken in the general ICUs of two tertiary care teaching hospitals. Critically ill adults expected to receive invasive mechanical ventilation for at least 48 h were enrolled. Diaphragm function was assessed by ultrasound each study day, with dysfunction defined as thickening fraction less than 20%. Results From January to December 2019, 856 patients were screened and 126 patients were enrolled. Overall, 40.5% (51/126) of patients experienced diaphragmatic dysfunction during invasive mechanical ventilation. Patients with diaphragmatic dysfunction were more likely to develop ventilator associated pneumonia (risk difference [RD] + 12.9%, 95% Confidence Interval [CI] 1.4 to 24.4%, P = 0.028), were more likely to experience extubation failure (RD + 8.5%, 95% CI 0.4 to 16.6%, P = 0.039) and required a longer duration of invasive mechanical ventilation (RD + 1.3 days, 95% CI 0.1 to 2.5 days, P = 0.035). They also required a longer hospital stay (RD + 1.2 days, 95% CI 0.04 to 2.4 days, P = 0.041) and were more likely to die before hospital discharge (RD + 18.1%, 95% CI 3.7 to 32.5%, P = 0.014). Multivariable analysis considered the impact of age, sex, pre-existing nutritional status, caloric intake, amino acid intake, acute disease severity, modes of mechanical ventilation, measures of respiratory status, sedation, pain control and baseline diaphragm thickness. Only SOFA score (P = 0.008) and early amino acid intake (P = 0.001) remained significant independent risk factors for the onset of diaphragmatic dysfunction. Causal path modeling suggested early amino acid intake may significantly reduce diaphragmatic dysfunction (RRR 29%, 95% CI 10% to 48%, P = 0.003) and may also reduce mortality (RRR 49%, 95% CI 25% to 73%, P

Background: There are limited published data on mortality trends in pulmonary hypertension (PH) worldwide. The objective of this study was to assess the PH-related mortality and time trends in the general population over the past 20 years. Material and methods: We used country-level PH mortality data from the World Health Organization (WHO) mortality database (2000–19), using the International Classification of Diseases, tenth revision (ICD-10) codes (I27.0, I27.2, I27.8, or I27.9). The average annual percentage changes (AAPCs) were calculated to describe mortality trends. Results: Fifty-four countries were included in this study. Between 2017 and 2019, the average age-standardized death rates (per 100,000) were 0.80 and 0.87 for males and females, respectively. Joinpoint analyses revealed a decreasing PH mortality trend for the overall population from 2000 to 2019 (AAPC -3.2 [95% confidence interval (CI) −4.1 to −2.4]), which was consistent between males and females (males: AAPC -5.3 [95% CI –6.2 to −4.4], females: AAPC -1.7 [95% CI –2.4 to −0.9]). When the estimates were stratified by etiology, we found that the mortality rates from idiopathic pulmonary arterial hypertension (I27.0) and pulmonary heart disease (unspecified, I27.9) had decreased significantly, while the mortality rates in other secondary PH (I27.2) and other specified pulmonary heart diseases (I27.8) had significantly increased. In addition, there were substantial differences in mortality rates and time trends across countries. Conclusion: Although an overall decrease in PH mortality trends over the past two decades, there were substantial differences across countries. For countries with high or rising mortality rates, more efforts are needed to reduce the mortality.

AbstractObjective This study aimed to determine whether the C-reactive protein-to-albumin ratio (CAR) can serve as a prognostic marker in patients with sepsis.Methods Chinese and English databases were searched to retrieve the included literature. The pooled sensitivity (SEN), specificity (SPE), positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC) of the summary receiver operating characteristic (SROC) with their 95% confidence interval (CI) were calculated using the bivariate model. Moreover, the hazard ratio (HR) and 95% CI were calculated using the random effect model.Results Nine articles comprising 3224 patients with sepsis were included in the meta-analysis. The pooled SEN was 0.73 (95% CI 0.65–0.80), the pooled SPE was 0.78 (95% CI 0.69–0.84), the pooled PLR was 3.29 (95% CI 2.15–5.03), the pooled NLR was 0.35 (95% CI 0.24–0.49), and the pooled DOR was 9.50 (95% CI 4.38–20.59). The AUC under the SROC was 0.82 (95% CI 0.78–0.85) for the prognostic meta-analysis. The pooled HR was 1.10 (95% CI 1.02–1.18).Conclusions This meta-analysis suggests that a high CAR level is associated with increased mortality and a poor prognosis.

Abstract Background No personalized prediction model or standardized algorithm exists to identify those at high risk of death among severe community-acquired pneumonia (SCAP) patients with chronic obstructive pulmonary disease (COPD). The aim of this study was to investigate the risk factors and to develop a useful nomogram for prediction of mortality in those patients. Methods We performed a retrospective, observational, cohort study in the intensive care unit (ICU) of West China Hospital, Sichuan University with all consecutive SCAP patients with COPD between December 2011 and December 2018. The clinical data within 24 h of admission to ICU were collected. The primary outcome was hospital mortality. We divided the patients into training and testing cohorts (70% versus 30%) randomly. In the training cohort, univariate and multivariate logistic regression analysis were used to identify independent risk factors applied to develop a nomogram. The prediction model was assessed in both training and testing cohorts. Results Finally, 873 SCAP patients with COPD were included, among which the hospital mortality was 41.4%. In training cohort, the independent risk factors for hospital mortality were increased age, diabetes, chronic renal diseases, decreased systolic blood pressure (SBP), and elevated fibrinogen, interleukin 6 (IL-6) and blood urea nitrogen (BUN). The C index was 0.840 (95% CI 0.809–0.872) in training cohort and 0.830 (95% CI 0.781–0.878) in testing cohort. Furthermore, the time-dependent AUC, calibration plots, DCA and clinical impact curves indicated the model had good predictive performance. Significant association of risk stratification based on nomogram with mortality was also found (P for trend

Abstract Background Emerging evidence shows that cardiovascular injuries and events in coronavirus disease 2019 (COVID-19) should be considered. The current study was conducted to develop an early prediction model for major adverse cardiovascular events (MACE) during hospitalizations of COVID-19 patients. Methods This was a retrospective, multicenter, observational study. Hospitalized COVID-19 patients from Wuhan city, Hubei Province and Sichuan Province, China, between January 14 and March 9, 2020, were randomly divided into a training set (70% of patients) and a testing set (30%). All baseline data were recorded at admission or within 24 h after admission to hospitals. The primary outcome was MACE during hospitalization, including nonfatal myocardial infarction, nonfatal stroke and cardiovascular death. The risk factors were selected by LASSO regression and multivariate logistic regression analysis. The nomogram was assessed by calibration curve and decision curve analysis (DCA). Results Ultimately, 1206 adult COVID-19 patients were included. In the training set, 48 (5.7%) patients eventually developed MACE. Six factors associated with MACE were included in the nomogram: age, PaO2/FiO2 under 300, unconsciousness, lymphocyte counts, neutrophil counts and blood urea nitrogen. The C indices were 0.93 (95% CI 0.90, 0.97) in the training set and 0.81 (95% CI 0.70, 0.93) in the testing set. The calibration curve and DCA demonstrated the good performance of the nomogram. Conclusions We developed and validated a nomogram to predict the development of MACE in hospitalized COVID-19 patients. More prospective multicenter studies are needed to confirm our results.

Abstract Background Researchers have linked cardiovascular disease (CVD) with advancing age; however, how it drives disease progression in elderly severe community acquired pneumonia (SCAP) patients is still unclear. This study aims to identify leading risk predictors of in-hospital mortality in elderly SCAP patients with CVD, and construct a comprehensive nomogram for providing personalized prediction. Patients and methods The study retrospectively enrolled 2365 elderly patients identified SCAP. Among them, 413 patients were found to have CVD. The LASSO regression and multivariate logistic regression analysis were utilized to select potential predictors of in-hospital mortality in elderly SCAP patients with CVD. By incorporating these features, a nomogram was then developed and subjected to internal validations. Discrimination, calibration, and clinical use of the nomogram were assessed via C-index, calibration curve analysis, and decision plot. Results Compared with patients without CVD, elderly SCAP patients with CVD had a significant poor outcome. Further analysis of the CVD population identified 7 independent risk factors for in-hospital mortality in elderly SCAP patients, including age, the use of vasopressor, numbers of primary symptoms, body temperature, monocyte, CRP and NLR. The nomogram model incorporated these 7 predictors showed sufficient predictive accuracy, with the C-index of 0.800 (95% CI 0.758–0.842). High C-index value of 0.781 was obtained in the internal validation via bootstrapping validation. Moreover, the calibration curve indicative a good consistency of risk prediction, and the decision curve manifested that the nomogram had good overall net benefits. Conclusion An integrated nomogram was developed to facilitate the personalized prediction of in-hospital mortality in elderly SCAP patients with CVD.

Abstract Because of immunotherapy failure in lung adenocarcinoma, we have tried to find new potential biomarkers for differentiating different tumor subtypes and predicting prognosis. We identified two subtypes based on tumor microenvironment-related genes in this study. We used seven methods to analyze the immune cell infiltration between subgroups. Further analysis of tumor mutation load and immune checkpoint expression among different subgroups was performed. The least absolute shrinkage and selection operator Cox regression was applied for further selection. The selected genes were used to construct a prognostic 14-gene signature for LUAD. Next, a survival analysis and time-dependent receiver operating characteristics were performed to verify and evaluate the model. Gene set enrichment analyses and immune analysis in risk groups was also performed. According to the expression of genes related to the tumor microenvironment, lung adenocarcinoma can be divided into cold tumors and hot tumors. The signature we built was able to predict prognosis more accurately than previously known models. The signature-based tumor microenvironment provides further insight into the prediction of lung adenocarcinoma prognosis and may guide individualized treatment.

Abstract Background The relation between depression and mortality in patients with asthma is not well known. This study aimed to assess the impact of depression on mortality in asthmatic patients in US adults. Methods This observational study used data from the 2005 to 2014 National Health and Nutrition Examination Survey (NHANES). Depression was measured using the Patient Health Questionnaire-9 (PHQ-9). We used survey-weight adjusted Cox proportional hazard models to estimate hazard ratios (HRs) with 95% confidence intervals (CIs) for the association between depression and all-cause mortality. Results A total of 1865 participants with asthma were included in this study. Among them, 264 (14.16%) had depressive symptoms. During 9970 person-years of follow-up, there were 24 (9.1%) deaths in 264 patients with depression compared with 100 (6.3%) deaths in 1601 patients without depression. For unadjusted analyses, depression was associated with an increased risk of all-cause mortality (HR, 2.22 [95% CI 1.32–3.73]). This association was persistent after adjustment for age, sex, race/ethnicity, and body mass index (HR, 2.71 [95% CI 1.58–4.66]). However, we did not observe a significant association between depression and mortality after controlling for extensive co-morbidities (HR, 1.92 [95% CI 0.82–4.45]). Subgroup analyses further revealed that depression was an independent risk factor for mortality only in the females (HR, 3.78 [95% CI 1.17, 12.26]) but not all asthmatic patients. Conclusions The present study suggested that depressive disorder was common in asthmatic patients and depression in asthmatic patients was associated with a higher mortality rate. Depression was an independent risk factor for mortality in female patients.

Background: The predictive ability of the ventilatory ratio (VR) for extubation failure risk in critically ill patients on mechanical ventilation is unclear. This study aims to examine the predictive ability of VR for extubation failure risk.Methods: This retrospective study was based on the MIMIC-IV database. The MIMIC-IV database consists of the clinical information of patients who were admitted to the intensive care unit at the Beth Israel Deaconess Medical Center between 2008 and 2019. With extubation failure as the primary outcome and in-hospital mortality as the secondary outcome, we assessed the predictive value of VR 4 hours before extubation using a multivariate logistic regression model.Results: Of 3,569 ventilated patients who were included, the rate of extubation-failure was 12.7% and the median Sequential Organ Failure Assessment (SOFA) score was 6 before extubation. Increased VR, elevated heart rate, greater positive end-expiratory pressure, higher blood urea nitrogen level, higher platelet count, greater SOFA score, decreased pH, decreased tidal volume, presence of chronic pulmonary disease, paraplegia, and metastatic solid tumor were independent predictors for extubation failure. A threshold of 1.595 of VR was associated with prolonged intensive care unit length of stay, higher risk of mortality and extubation failure. The area under the receiver operating characteristic curve (ROC) for VR was 0.669 [0.635–0.703], which was significantly larger than the rapid shallow breathing index [0.510 (0.476–0.545)] and the partial pressure of oxygen to the fraction of inspired oxygen [0.586 (0.551–0.621)].Conclusion: VR 4 hours before extubation was associated with extubation failure, mortality, and prolonged length of stay in the intensive care unit. VR provides good predictive performance for extubation failure (measured by ROC) than the rapid shallow breathing index. Further prospective studies are warranted to confirm these findings.

BackgroundThe physiological effects of HFNC devices are closely related to temperature and humidity. HFNC devices from different manufacturers may have varied performances. It is unclear whether there are differences in the humidification performance of different HFNC devices and the degree of differences.MethodsFour integrated HFNC devices (AIRVO 2, Fisher & Paykel Healthcare, Auckland, New Zealand; TNI softFlow 50, TNI Medical AG, Würzburg, Germany; HUMID-BH, RESPIRACARE, Shenyang, China; OH-70C, Micomme, Hunan, China) and a ventilator with an HFNC module (bellavista 1000, Imtmedical, Buchs, Switzerland) were evaluated using their matching circuits. The dew point temperature was set at 31, 34, and 37°C (set-DP). In MR850, it was set to non-invasive mode (34°C/−3°C) and invasive mode (40°C/−3°C), respectively. At each level of set-DP, the flow was set from 20 L/min up to its maximum set limit at a gradient of 5 L/min or 10 L/min. After stabilization, the dew point temperature, temperature, relative humidity, and flow rate of the delivered gas from the cannulas were recorded.ResultsThere were significant differences in actual-DP among these devices at any set-DP (p

Objective: This meta-analysis aims to determine whether ocular surface alterations are associated with disease severity in patients with obstructive sleep apnea–hypopnea syndrome (OSAHS).Methods: The protocol for this systematic review and meta-analysis was registered in PROSPERO. We conducted the search in six electronic databases (China National Knowledge Infrastructure, EMBASE, Cochrane Library, Web of Science, Wanfang, and PubMed) from since the construction of the databases to 30 December 2022. The standard mean difference (SMD) and correlation coefficients are reported as measures of the effect size in the presence of retrieved data. In addition, the random effects model or fixed effects model was used in a combined analysis. Stata 11.0 and R 3.6.1 were used for statistical analyses of the data.Results: A total of 15 studies satisfied the inclusion criteria for this study. The prevalence of floppy eyelid syndrome (FES) and dry eye syndrome in patients with obstructive sleep apnea–hypopnea syndrome was 40 and 48%, respectively. In addition, the Schirmer 1 value and tear break-up time (TBUT) were remarkably reduced in patients with OSAHS when compared to that of the controls. The ocular surface disease index (OSDI) scores, Oxford corneal staining scores, and the rates of loss in the meibomian glands were elevated in patients with obstructive sleep apnea–hypopnea syndrome when compared to that of the controls, especially those with severe disease. Moreover, the Schirmer 1 value and tear break-up time exhibited a negative correlation with the apnea–hypopnea index (AHI), and the OSDI showed a positive association with the apnea–hypopnea index.Conclusion: Patients with OSAHS had a greater prevalence of FES than the healthy controls. They also showed lower Schirmer 1 value and tear break-up time but had a higher OSDI, Oxford corneal staining scores, and rates of loss in the meibomian glands than the healthy controls.Clinical Trial Registration: (https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=392527).

BackgroundThe risk factors for depression in asthma are still unclear. The objective of this study was to identify the risk factors associated with depression in asthmatic individuals.MethodsWe used data from the 2005-2018 National Health and Nutrition Examination Survey (NHANES). Univariate analysis and multivariate logistic regression analyses were used to identify risk factors for depression and calculate unadjusted and adjusted odds ratios (ORs) and 95% confidence intervals (CIs).ResultsA total of 5,379 asthmatic participants were included. Of these subjects, 767 individuals had depression, and 4,612 individuals had no depression. Univariate analysis and multivariate analyses suggested that asthmatic individuals with smoking (OR 1.98, 95% CI 1.19-3.29), hypertension (OR 2.73, 95% CI 1.48-5.04), and arthritis (OR 2.83, 95% CI 1.53-5.22) were more likely to have depression. Asthmatic individuals who had more than a high school education had lower depression risk than those with less than a high school education (OR 0.55, 95% CI 0.30-0.99). Increasing age was also associated with decreased depression risk (OR 0.97, 95% CI 0.95-0.99).ConclusionsDepression was more likely in asthmatic individuals with smoking, hypertension, and arthritis and less likely in individuals with higher education and increasing age. These findings could improve the identification of target populations for effective interventions to improve the mental health of asthmatic individuals.

Abstract Background The present study was performed to investigate the impacts of type 2 diabetes mellitus (T2DM) on severe community-acquired pneumonia (SCAP) and to develop a novel prediction model for mortality in SCAP patients with T2DM. Methods This was a retrospective observational study conducted in consecutive adult patients with SCAP admitted to the intensive care unit (ICU) of West China Hospital, Sichuan University, China, between September 2011 and September 2019. The primary outcome was hospital mortality. A propensity score matching (PSM) analysis model with a 1:2 ratio was used for the comparisons of clinical characteristics and outcomes between T2DM and nondiabetic patients. The independent risk factors were identified via univariate and then multivariable logistic regression analysis and were then used to establish a nomogram. Results In total, 1262 SCAP patients with T2DM and 2524 matched patients without T2DM were included after PSM. Patients with T2DM had longer ICU length of stay (LOS) (13 vs. 12 days, P = 0.016) and higher 14-day mortality (15% vs. 10.8%, P

Abstract Background Examinations based on lung tissue specimen can play a significant role in the diagnosis for critically ill and intubated patients with lung infiltration. However, severe complications including tension pneumothorax and intrabronchial hemorrhage limit the application of needle biopsy. Methods A refined needle biopsy technique, named bronchus-blocked ultrasound-guided percutaneous transthoracic needle biopsy (BUS-PTNB), was performed on four intubated patients between August 2020 and April 2021. BUS-PTNB was done at bedside, following an EPUBNOW (evaluation, preparation, ultrasound location, bronchus blocking, needle biopsy, observation, and withdrawal of blocker) workflow. Parameters including procedure feasibility, sample acquisition, perioperative conditions, and complications were observed. Tissue specimens were sent to pathological examinations and microbial tests. Results Adequate specimens were successfully obtained from four patients. Diagnosis and treatment were correspondingly refined based on pathological and microbial tests. Intrabronchial hemorrhage occurred in patient 1 but was stopped by endobronchial blocker. Mild pneumothorax happened in patient 4 due to little air leakage, and closed thoracic drainage was placed. During the procedure, peripheral capillary hemoglobin oxygen saturation (SPO2), blood pressure, and heart rate of patient 4 fluctuated but recovered quickly. Vital signs were stable for patient 1–3. Conclusions BUS-PTNB provides a promising, practical and feasible method in acquiring tissue specimen for critically ill patients under intratracheal intubation. It may facilitate the pathological diagnosis or other tissue-based tests for intubated patients and improve clinical outcomes.

Background: Savolitinib, a selective MET inhibitor, showed efficacy in patients with non-small cell lung cancer (NSCLC), including pulmonary sarcomatoid carcinoma (PSC), harbouring MET exon 14 skipping alteration ( MET ex14). Objective: To analyse post hoc , the association between circulating tumour DNA (ctDNA) biomarkers and clinical outcomes, including resistance, with savolitinib. Design: A multicentre, single-arm, open-label phase 2 study. Methods: All enrolled patients with baseline plasma samples were included. Outcomes were objective response rate (ORR), progression-free survival (PFS) and overall survival (OS) by baseline MET ex14 and post-treatment clearance, coexisting gene alterations at baseline and disease progression. Results: Among 66 patients with baseline ctDNA sequencing, 46 (70%) had detectable MET ex14. Frequent coexisting baseline gene alterations included TP53 and POT1 mutations. Patients with detectable baseline MET ex14 exhibited worse PFS [hazard ratio (HR), 1.77; 95% confidence interval (CI), 0.88–3.57; p = 0.108] and OS (HR, 3.26; 95% CI, 1.35–7.89; p = 0.006) than those without, despite showing a numerically higher ORR. Among 24 patients with baseline detectable MET ex14 and evaluable postbaseline samples, 13 achieved MET ex14 clearance post-treatment. Median time to first clearance was 1.3 months (range, 0.7–1.5). MET ex14 post-treatment clearance was associated with better ORR (92.3%; 95% CI, 64.0–99.8 versus 36.4%; 95% CI, 10.9–69.2; p = 0.0078), PFS (HR, 0.44; 95% CI, 0.2–1.3; p = 0.1225) and OS (HR, 0.31; 95% CI, 0.1–1.0; p = 0.0397) versus non-clearance. Among 22 patients with disease progression, 10 acquired pathway alterations (e.g. in RAS/RAF and PI3K/PTEN) alone or with secondary MET mutations (D1228H/N and Y1230C/H/S). Conclusion: ctDNA biomarkers may allow for longitudinal monitoring of clinical outcomes with savolitinib in patients with MET ex14-positive PSC and other NSCLC subtypes. Specifically, undetectable baseline MET ex14 or post-treatment clearance may predict favourable clinical outcomes, while secondary MET mutations and other acquired gene alterations may explain resistance to savolitinib. Registration: The trial was registered with ClinicalTrials.gov (NCT02897479) on 13 September 2016.

Introduction: Savolitinib has been found to have encouraging antitumor activity and a favorable safety profile in Chinese patients with pulmonary sarcomatoid carcinoma and other NSCLCs with MET exon 14 skipping alterations (MET ex14 positive) at the primary analysis of a phase 2 study. Here, we present the long-term efficacy and safety data of savolitinib, including subgroup analyses. Methods: This multicenter, single-arm, open-label, phase 2 study in the People’s Republic of China enrolled MET inhibitor-naive adults with locally advanced or metastatic METex14-positive NSCLC (NCT02897479). Oral savolitinib at a dose of 400 or 600 mg was administered once daily (body weight dependent). The primary objectives of the final analysis were long-term overall survival (OS) and subgroup analyses by previous systemic treatment, NSCLC subtypes, and brain metastases. Results: At the final analysis cutoff date (June 28, 2021), a total of 70 patients were enrolled and receiving savolitinib, and median follow-up was 28.4 (interquartile range: 26.2–36.3) months. The median OS was 12.5 months (95% confidence interval [CI]: 10.5–21.4 [18- and 24-mo OS rates, 42.1% and 31.5%, respectively]). Median OS in pretreated or treatment-naive patients was 19.4 (95% CI: 10.5–31.3) and 10.9 (95% CI: 7.5–14.0) months, respectively; it was 10.6 months (95% CI: 4.6–14.0) in patients with pulmonary sarcomatoid carcinoma, 17.3 months (95% CI: 10.6–23.6) in other NSCLC subtypes, and 17.7 months (95% CI: 10.5–not evaluable) in patients with brain metastases. No new safety signals emerged with prolonged follow-up and exposure. Conclusions: The updated results further confirm the favorable benefit and acceptable safety of savolitinib in Chinese patients with METex14-positive NSCLC.

Abstract Background The possible benefits associated with corticosteroid treatment in acute respiratory distress syndrome (ARDS) patients are not fully known. We conducted an updated meta-analysis to assess the effect of corticosteroids in the treatment of patients with ARDS. Methods We systematically searched MEDLINE, Embase, and the Cochrane Library from inception to January 2021 via Ovid to identify randomized controlled trials evaluating the efficacy of glucocorticoids in the treatment of patients with ARDS. The primary outcome was hospital mortality. Secondary outcomes included the number of ventilator-free days at day 28, oxygenation improvement (PaO2/FIO2 ratios), and adverse events. Results Nine studies with 1371 participants were analyzed. The pooled analysis revealed that glucocorticoid use was associated with reduced mortality [relative risk (RR), 0.83; 95% confidence interval (CI) 0.74–0.93; P

Abstract Background Crazy-paving patterns are rarely reported as radiological manifestations of pulmonary cryptococcosis. Case presentation Herein, we presented a very rare case of a crazy-paving pattern as a radiological manifestation of pulmonary cryptococcosis in a patient with primary ciliary dyskinesia. The diagnosis of pulmonary cryptococcosis and primary ciliary dyskinesia was ultimately confirmed by bronchoscopic biopsy, fungus culture, whole exome sequencing of blood, etc. The patient received flucytosine (PO, 5 g per day) and amphotericin B (IV, 70 mg per day) during hospitalization and sequential therapy with voriconazole (PO, 200 mg twice a day) after discharge. He recovered during follow-up. Conclusions We concluded that pulmonary cryptococcosis should be considered a possible cause of crazy-paving patterns in chest CT scans.

Abstract Background We conducted this study to explore a novel risk score to predict cardiovascular complications in patients with coronavirus disease 2019 (COVID‐19). Methods The current study was a retrospective, multicenter, observational study. The clinical data of COVID‐19 patients at admission were collected. Patients were randomly divided into training set and testing set (70% vs. 30% of patients). Independent risk factors were identified via logistic regression analysis. Results Finally, 1207 patients were included. Ten independent risk factors associated with cardiovascular complications were identified in training set: male (odds ratio [OR]: 1.84; 95% confidence interval [CI]: 1.18, 2.85), age ≥ 60 years old (OR: 2.01; 95% CI: 1.3, 3.2), cough (OR: 1.86; 95% CI: 1.16, 3), chronic heart disease (OR: 2.3; 95% CI: 1.19, 4.46), lymphocyte count ≤1.1 × 109/L at admission (OR: 1.60; 95% CI: 1.03, 2.47), blood urea nitrogen ≥7 mmol/L at admission (OR: 2.14; 95% CI: 1.27, 3.62), estimated glomerular filtration rate ≤90 ml/min/1.73 m2 at admission (OR: 2.08; 95% CI: 1.13, 3.83), activated partial thromboplastin time ≥37 s (OR: 3.07; 95% CI: 1.37, 6.86), D‐dimer ≥ 0.5 mg/L (OR: 2.12; 95% CI: 1.33, 3.36) and procalcitonin ≥0.5 μg/L (OR: 3.58; 95% CI: 1.40, 9.14). The area under curve of ROC curve was 0.773 (95% CI: 0.723, 0.822; p

Abstract Background Novel coronavirus disease 2019 (COVID-19) is a global public health emergency. Here, we developed and validated a practical model based on the data from a multi-center cohort in China for early identification and prediction of which patients will be admitted to the intensive care unit (ICU). Methods Data of 1087 patients with laboratory-confirmed COVID-19 were collected from 49 sites between January 2 and February 28, 2020, in Sichuan and Wuhan. Patients were randomly categorized into the training and validation cohorts (7:3). The least absolute shrinkage and selection operator and logistic regression analyzes were used to develop the nomogram. The performance of the nomogram was evaluated for the C-index, calibration, discrimination, and clinical usefulness. Further, the nomogram was externally validated in a different cohort. Results The individualized prediction nomogram included 6 predictors: age, respiratory rate, systolic blood pressure, smoking status, fever, and chronic kidney disease. The model demonstrated a high discriminative ability in the training cohort (C-index = 0.829), which was confirmed in the external validation cohort (C-index = 0.776). In addition, the calibration plots confirmed good concordance for predicting the risk of ICU admission. Decision curve analysis revealed that the prediction nomogram was clinically useful. Conclusion We established an early prediction model incorporating clinical characteristics that could be quickly obtained on hospital admission, even in community health centers. This model can be conveniently used to predict the individual risk for ICU admission of patients with COVID-19 and optimize the use of limited resources.

Abstract Coronavirus disease (COVID-19) is an emerging viral infection that is rapidly spreading across the globe. SARS-CoV-2 belongs to the same coronavirus class that caused respiratory illnesses such as severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS). During the SARS and MERS outbreaks, many frontline healthcare workers were infected when performing high-risk aerosol-generating medical procedures as well as when providing basic patient care. Similarly, COVID-19 disease has been reported to infect healthcare workers at a rate of ~ 3% of cases treated in the USA. In this review, we conducted an extensive literature search to develop practical strategies that can be implemented when providing respiratory treatments to COVID-19 patients, with the aim to help prevent nosocomial transmission to the frontline workers.

BackgroundAlthough probiotic intake had beneficial effects on several specific disorders, limited evidence was available about the benefits of probiotic intake in the general population. This study aimed to evaluate the relationship between yogurt (as a natural probiotic source) and dietary supplements containing probiotic consumption and mortality in US adults.MethodsWe conducted an observational cohort study comprised of a nationally representative sample of adults who were enrolled in the National Health and Nutrition Examination Survey (NHANES) between 1999 and 2014. Individuals were linked to the US National Death Index.ResultsWe included 32,625 adults in our study. Of the study cohort, 3,539 participants had yogurt consumption, 213 had dietary supplements containing probiotic consumption, and the remaining participants (28,873) did not have yogurt and/or dietary supplements containing probiotic consumption. During 266,432 person-years of follow-up, 3,881 deaths from any cause were ascertained, of which 651 were due to cardiovascular disorders and 863 were due to cancer. Weighted Cox proportional hazards models suggested that yogurt consumption was inversely associated with all-cause mortality (adjusted hazard ratio (HR), 0.83 [95% confidence interval (CI), 0.71–0.98]) but not cardiovascular mortality (adjusted HR, 0.68 [95%CI, 0.43–1.08]) and cancer mortality (adjusted HR, 1.00 [95%CI, 0.72–1.38]). However, dietary supplements containing probiotic were not associated with decreased all-cause and cause-specific mortality.ConclusionsThe present study suggested that yogurt consumption was associated with a lower risk of all-cause mortality among U.S. adults. Yogurt consumption in diet might be a sensible strategy for reducing the risk of death.

Background. High-flow nasal cannula (HFNC) oxygen therapy has been recommended for use in coronavirus disease 2019 (COVID-19) patients with acute respiratory failure and many other clinical conditions. HFNC devices produced by different manufacturers may have varied performance. Whether there is a difference in these devices and the extent of the differences in performance remain unknown. Methods. Four HFNC devices (AIRVO 2, TNI softFlow 50, HUMID-BH, and OH-70C) and a ventilator with an HFNC module (bellavista 1000) were evaluated. The flow was set at 20, 25, 30, 35, 40, 45, 50, 60, 70, and 80 L/min, and the FiO2 was set at 21%, 26%, 30%, 35%, 40%, 45%, 50%, 60%, 70%, 80%, and 90%. Then, one side of the cannulas was clipped to simulate the compression, bending, or blocking of the nasal cannulas. The flow and FiO2 of the delivered gas were recorded and compared among settings and devices. Results. The actual-flow and actual-FiO2 delivered by different settings and devices varied. AIRVO 2 had superior performance in flow and FiO2 accuracy. bellavista 1000 and OH-70C had good performance in the accuracy of actual-flows and actual-FiO2, respectively. bellavista 1000 and HUMID-BH had a larger flow range from 10 to 80 L/min, but only bellavista 1000 could provide a stable flow with an excessive resistance up to 60 L/min. TNI softFlow 50 had the best flow compensation and could provide sufficient flow with excessive resistance at 20–50 L/min. Conclusions. The variation in flow, FiO2 settings, and devices could influence the actual-flow and actual-FiO2 delivered. AIRVO 2 and OH-70C showed better FiO2 accuracy. TNI softFlow 50, bellavista 1000, and HUMID-BH could lower the risk of insufficient flow support due to accidental compression or blocking of the cannulas. In addition, ventilators with HFNC modules provided comparable flow and FiO2 and could be an alternative to standalone HFNC devices.

Venous thromboembolism (VTE) is a common complication for critically ill patients. Intermittent pneumatic compression (IPC) is recommended for patients with high risk of bleeding. We aim to evaluate the effectiveness of IPC for thromboprophylaxis in critically ill patients. We searched PubMed, Embase, and ClinicalTrials for randomized controlled trials (RCTs) and observational studies that evaluated IPC in critically ill patients. RevMan 5.3 software was used for the meta-analysis. A total of 10 studies were included. The IPC group significantly reduced the VTE incidence compared with no thromboprophylaxis group (risk ratio [RR]: 0.35, confidence interval [CI]: 0.18-0.68, P = .002) and the IPC group also showed lower VTE incidence than the graduated compression stockings (GCS) group (RR: 0.47, CI: 0.24-0.91, P = .03). There were no significant differences between using IPC and low-molecular-weight heparin (LMWH) for VTE incidence (RR: 1.26, CI: 0.72-2.22, P = .41), but LMWH showed significantly more bleeding events. Intermittent pneumatic compression as an adjunctive treatment did not further reduce VTE incidence (RR: 0.55, CI: 0.24-1.27, P = .16). Intermittent pneumatic compression can reduce the incidence of VTE for critically ill patients, which is better than GCS and similar to LMWH, but it has no significant advantage as an adjunct therapy for thromboprophylaxis.

BackgroundAlthough expression of long non-coding RNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT-1) in tumor tissues has been assessed in several malignancies. However, the association between lncRNA MALAT-1 expression and prognosis or clinicopathological feature remains controversial. Therefore, we conducted a meta-analysis to verify whether lncRNA MALAT-1 expression was associated with prognosis or clinicopathological features in patients with non-small cell lung cancer (NSCLC).MethodsWe searched Embase, PubMed, Web of Science, Cochrane library, The Chinese National Knowledge Infrastructure, and Wanfang databases from inception to March, 1, 2020. The language restrictions were Chinese and English. The published literature on lncRNA MALAT-l expression and prognosis or clinicopathological characteristics of NSCLC patients was statistically analyzed. Combined hazard ratios (HRs), odds ratios (OR), and 95% confidence intervals (95% CIs) were used to evaluate the effects of lncRNA MALAT-l on the prognosis and clinicopathological features of NSCLC.ResultsFifteen studies with 1477 NSCLC patients were enrolled. The results showed that the elevated expression of lncRNA MALAT-l in tumor tissues was associated with shorter overall survival (OS) (HR: 2.20, 95% CI: 1.53-3.16; P = 0.000). Additionally, high lncRNA MALAT-l expression was also significantly associated with gender (OR: 0.69, 95% CI: 0.51-0.93; P = 0.014), tumor size (OR: 1.87, 95% CI:1.13-3.09; P = 0.016), lymph node metastasis (LNM) (OR: 2.87, 95% CI:1.05-7.83, P = 0.04), tumor differentiation (OR: 1.60, 95% CI:1.17-2.20; P = 0.003), and tumor-node-metastasis (TNM) stage (OR: 0.42, 95% CI: 0.25-0.70; P = 0.001). There was no significant relationship between lncRNA MALAT-l expression and other clinicopathological features including age (OR: 1.03, 95% CI: 0.79-1.34; P = 0.830), number of tumors (OR: 1.02, 95% CI: 0.63-1.64; P = 0.943), vascular invasion (OR: 1.23, 95% CI: 0.50-3.05; P = 0.652), and recurrence (OR: 1.98, 95% CI: 0.67-5.85; P = 0.214).ConclusionThe overexpression of lncRNA MALAT-l in NSCLC tissues was correlated with OS, gender, tumor size, LNM, tumor differentiation, and TNM stage. Thus, lncRNA MALAT-l may serve as a prognostic factor for NSCLC.

BackgroundSince December 2019, coronavirus disease 2019 (COVID-19) emerged in Wuhan and spread across the globe. The objective of this study is to build and validate a practical nomogram for estimating the risk of severe COVID-19.MethodsA cohort of 366 patients with laboratory-confirmed COVID-19 was used to develop a prediction model using data collected from 47 locations in Sichuan province from January 2020 to February 2020. The primary outcome was the development of severe COVID-19 during hospitalization. The least absolute shrinkage and selection operator (LASSO) regression model was used to reduce data size and select relevant features. Multivariable logistic regression analysis was applied to build a prediction model incorporating the selected features. The performance of the nomogram regarding the C-index, calibration, discrimination, and clinical usefulness was assessed. Internal validation was assessed by bootstrapping.ResultsThe median age of the cohort was 43 years. Severe patients were older than mild patients by a median of 6 years. Fever, cough, and dyspnea were more common in severe patients. The individualized prediction nomogram included seven predictors: body temperature at admission, cough, dyspnea, hypertension, cardiovascular disease, chronic liver disease, and chronic kidney disease. The model had good discrimination with an area under the curve of 0.862, C-index of 0.863 (95% confidence interval, 0.801-0.925), and good calibration. A high C-index value of 0.839 was reached in the interval validation. Decision curve analysis showed that the prediction nomogram was clinically useful.ConclusionWe established an early warning model incorporating clinical characteristics that could be quickly obtained on admission. This model can be used to help predict severe COVID-19 and identify patients at risk of developing severe disease.

Background and objectiveAerosol therapies are widely used for mechanically ventilated patients. However, the practice pattern of aerosol therapy in mainland China remains unknown. This study aimed to determine the current practice of aerosol therapy in mainland China.MethodsA web-based survey was conducted by the China Union of Respiratory Care (CURC) from August 2018 to January 2019. The survey was disseminated via Email or WeChat to members of CURC. A questionnaire comprising 16 questions related to hospital information and 12 questions related to the practice of aerosol therapy. Latent class analysis was employed to identify the distinct classes of aerosol therapy practice.Main resultsA total of 693 valid questionnaires were returned by respiratory care practitioners from 447 hospitals. Most of the practitioners used aerosol therapy for both invasive mechanical ventilation (90.8%) and non-invasive mechanical ventilation (91.3%). Practitioners from tertiary care centers were more likely to use aerosol therapy compared with those from non-tertiary care centers (91.9% vs. 85.4%, respectively; p = 0.035). The most commonly used drugs for aerosol therapy were bronchodilators (64.8%) followed by mucolytic agents (44.2%), topical corticosteroids (43.4%) and antibiotics (16.5%). The ultrasonic nebulizer (48.3%) was the most commonly used followed by the jet nebulizer (39.2%), the metered dose inhaler (15.4%) and the vibrating mesh nebulizer (14.6%). Six latent classes were identified via latent class analysis. Class 1 was characterized by the aggressive use of aerosol therapy without a standard protocol, while class 3 was characterized by the absence of aerosol therapy.ConclusionsSubstantial heterogeneity among institutions with regard to the use of aerosol therapy was noted. The implementation of aerosol therapy during mechanical ventilation was inconsistent in light of recent practice guidelines. Additional efforts by the CURC to improve the implementation of aerosol therapy in mainland China are warranted.

Smear-negative pulmonary tuberculosis (PTB) is common and difficult to diagnose. In this study, we investigated the diagnostic value of nucleic acid amplification testing and sequencing combined with acid-fast bacteria (AFB) staining of needle biopsy lung tissues for patients with suspected smear-negative PTB.Patients with suspected smear-negative PTB who underwent percutaneous transthoracic needle biopsy between May 1, 2012, and June 30, 2015, were enrolled in this retrospective study. Patients with AFB in sputum smears were excluded. All lung biopsy specimens were fixed in formalin, embedded in paraffin, and subjected to acid-fast staining and tuberculous polymerase chain reaction (TB-PCR). For patients with positive AFB and negative TB-PCR results in lung tissues, probe assays and 16S rRNA sequencing were used for identification of nontuberculous mycobacteria (NTM). The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy of PCR and AFB staining were calculated separately and in combination.Among the 220 eligible patients, 133 were diagnosed with TB (men/women: 76/57; age range: 17-80 years, confirmed TB: 9, probable TB: 124). Forty-eight patients who were diagnosed with other specific diseases were assigned as negative controls, and 39 patients with indeterminate final diagnosis were excluded from statistical analysis. The sensitivity, specificity, PPV, NPV, and accuracy of histological AFB (HAFB) for the diagnosis of smear-negative were 61.7% (82/133), 100% (48/48), 100% (82/82), 48.5% (48/181), and 71.8% (130/181), respectively. The sensitivity, specificity, PPV, and NPV of histological PCR were 89.5% (119/133), 95.8% (46/48), 98.3% (119/121), and 76.7% (46/60), respectively, demonstrating that histological PCR had significantly higher accuracy (91.2% [165/181]) than histological acid-fast staining (71.8% [130/181]), P < 0.001. Parallel testing of histological AFB staining and PCR showed the sensitivity, specificity, PPV, NPV, and accuracy to be 94.0% (125/133), 95.8% (46/48), 98.4% (125/127), 85.2% (46/54), and 94.5% (171/181), respectively. Among patients with positive AFB and negative PCR results in lung tissue specimens, two were diagnosed with NTM infections (Mycobacterium avium-intracellulare complex and Mycobacterium kansasii).Nucleic acid amplification testing combined with acid-fast staining in lung biopsy tissues can lead to early and accurate diagnosis in patients with smear-negative pulmonary tuberculosis. For patients with positive histological AFB and negative tuberculous PCR results in lung tissue, NTM infection should be suspected and could be identified by specific probe assays or 16S rRNA sequencing.