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Jiaqi Pu,1,&ast; Qun Yi,1,2,&ast; Yuanming Luo,3 Hailong Wei,4 Huiqing Ge,5 Huiguo Liu,6 Xianhua Li,7 Jianchu Zhang,8 Pinhua Pan,9 Hui Zhou,10 Chen Zhou,11 Mengqiu Yi,12 Lina Cheng,12 Liang Liu,10 Jiarui Zhang,1 Lige Peng,1 Adila Aili,1 Yu Liu,1 Haixia Zhou1 On behalf of the MAGNET AECOPD Registry Investigators1Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, People’s Republic of China; 2Sichuan Cancer Hospital and Institution, Sichuan Cancer Center, Cancer Hospital Affiliate to School of Medicine, UESTC, Chengdu, People’s Republic of China; 3State Key Laboratory of Respiratory Disease, Guangzhou Medical University, Guangzhou, People’s Republic of China; 4Department of Respiratory and Critical Care Medicine, People’s Hospital of Leshan, Leshan, People’s Republic of China; 5Department of Respiratory and Critical Care Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, People’s Republic of China; 6Department of Respiratory and Critical Care Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China; 7Department of Respiratory and Critical Care Medicine, the First People’s Hospital of Neijiang City, Neijiang, People’s Republic of China; 8Department of Respiratory and Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China; 9Department of Respiratory and Critical Care Medicine, Xiangya Hospital, Central South University, Changsha, People’s Republic of China; 10Department of Respiratory and Critical Care Medicine, The Affiliated Hospital of Chengdu University, Chengdu, People’s Republic of China; 11West China School of Medicine, West China Hospital, Sichuan University, Chengdu, People’s Republic of China; 12Department of Emergency, First People’s Hospital of Jiujiang, Jiu Jiang, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Haixia Zhou, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Guo-Xue-Xiang 37&num;, Wuhou District, Chengdu, Sichuan Province, 610041, People’s Republic of China, Tel/Fax +86-28-85422571, Email zhouhaixia@wchscu.cnPurpose: The prognostic value of blood eosinophils in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) remains controversial. This study aimed to evaluate whether blood eosinophils could predict in-hospital mortality and other adverse outcomes in inpatients with AECOPD.Methods: The patients hospitalized for AECOPD were prospectively enrolled from ten medical centers in China. Peripheral blood eosinophils were detected on admission, and the patients were divided into eosinophilic and non-eosinophilic groups with 2% as the cutoff value. The primary outcome was all-cause in-hospital mortality.Results: A total of 12,831 AECOPD inpatients were included. The non-eosinophilic group was associated with higher in-hospital mortality than the eosinophilic group in the overall cohort (1.8% vs 0.7%, P < 0.001), the subgroup with pneumonia (2.3% vs 0.9%, P = 0.016) or with respiratory failure (2.2% vs 1.1%, P = 0.009), but not in the subgroup with ICU admission (8.4% vs 4.5%, P = 0.080). The lack of association still remained even after adjusting for confounding factors in subgroup with ICU admission. Being consistent across the overall cohort and all subgroups, non-eosinophilic AECOPD was also related to greater rates of invasive mechanical ventilation (4.3% vs 1.3%, P < 0.001), ICU admission (8.9% vs 4.2%, P < 0.001), and, unexpectedly, systemic corticosteroid usage (45.3% vs 31.7%, P < 0.001). Non-eosinophilic AECOPD was associated with longer hospital stay in the overall cohort and subgroup with respiratory failure (both P < 0.001) but not in those with pneumonia (P = 0.341) or ICU admission (P = 0.934).Conclusion: Peripheral blood eosinophils on admission may be used as an effective biomarker to predict in-hospital mortality in most AECOPD inpatients, but not in patients admitted into ICU. Eosinophil-guided corticosteroid therapy should be further studied to better guide the administration of corticosteroids in clinical practice.Keywords: acute exacerbation of chronic obstructive pulmonary disease, peripheral blood eosinophils, inpatients, in-hospital mortality, clinical outcomes

Chen Zhou,1,&ast; Yujie Guang,1,&ast; Yuanming Luo,2 Huiqing Ge,3 Hailong Wei,4 Huiguo Liu,5 Jianchu Zhang,6 Pinhua Pan,7 Jiarui Zhang,8 Lige Peng,8 Adila Aili,8 Yu Liu,8 Jiaqi Pu,8 Xia Zhong,1 Yixi Wang,1 Qun Yi,8,9 Haixia Zhou8 On behalf of the MAGNET AECOPD Registry Investigators1West China School of Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan Province, People’s Republic of China; 2State Key Laboratory of Respiratory Disease, Guangzhou Medical University, Guangzhou, Guangdong Province, People’s Republic of China; 3Department of Respiratory and Critical Care Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, People’s Republic of China; 4Department of Respiratory and Critical Care Medicine, People’s Hospital of Leshan, Leshan, Sichuan Province, People’s Republic of China; 5Department of Respiratory and Critical Care Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, People’s Republic of China; 6Department of Respiratory and Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, People’s Republic of China; 7Department of Respiratory and Critical Care Medicine, Xiangya Hospital, Central South University, Changsha, Hunan Province, People’s Republic of China; 8Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan Province, People’s Republic of China; 9Sichuan Cancer Hospital and Institution, Sichuan Cancer Center, Cancer Hospital Affiliate to School of Medicine, UESTC, Chengdu, Sichuan Province, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Haixia Zhou, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Guo-Xue-Xiang 37&num;, Wuhou District, Chengdu, Sichuan Province, 610041, People’s Republic of China, Tel +86-28-85422571, Fax +86-28-85422571, Email zhouhaixia@wchscu.cnBackground: The optimal tool for risk prediction of venous thromboembolism (VTE) in inpatients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is still unknown. This study aimed to evaluate whether D-dimer could predict the risk of VTE in inpatients with AECOPD compared to the Padua Prediction Score (PPS).Methods: Inpatients with AECOPD were prospectively enrolled from seven medical centers in China between December 2018 and June 2020. On admission, D-dimer was detected, PPS was calculated for each patient, and the incidence of 2-month VTE was investigated. The receiver operating characteristic (ROC) curve was used to evaluate the predictive value of D-dimer and PPS on VTE development, and the best cut-off value for both methods was evaluated through the Youden index.Results: Among the 4468 eligible patients with AECOPD, 90 patients (2.01%) developed VTE within 2 months after admission. The area under the receiver operating characteristic curves (AUCs) of D-dimer for predicting VTE were significantly higher than those of the PPS both in the overall cohort (0.724, 95% CI 0.672– 0.776 vs 0.620, 95% CI 0.562– 0.679; P< 0.05) and the subgroup of patients without thromboprophylaxis (0.747, 95% CI 0.695– 0.799 vs 0.640, 95% CI 0.582– 0.698; P< 0.05). By calculating the Youden Index, the best cut-off value of D-dimer was determined to be 0.96 mg/L with an AUC of 0.689, which was also significantly better than that of the PPS with the best cut-off value of 2 (AUC 0.581, P=0.007). After the combination of D-dimer with PPS, the AUC (0.621) failed to surpass D-dimer alone (P=0.104).Conclusion: D-dimer has a superior predictive value for VTE over PPS in inpatients with AECOPD, which might be a better choice to guide thromboprophylaxis in inpatients with AECOPD due to its effectiveness and convenience.Clinical Trial Registration: Chinese Clinical Trail Registry NO. ChiCTR2100044625; URL: http://www.chictr.org.cn/showproj.aspx?proj=121626.Keywords: acute exacerbation of chronic obstructive pulmonary disease, inpatients, D-dimer, Padua Prediction Score, venous thromboembolism

Despite the numerous studies on extubation readiness assessment for patients who are invasively ventilated in the intensive care unit, a 10-15% extubation failure rate persists. Although breathing variability has been proposed as a potential predictor of extubation failure, it is mainly assessed using simple statistical metrics applied to basic respiratory parameters. Therefore, the complex pattern of breathing variability conveyed by continuous ventilation waveforms may be underexplored.Here, we aimed to develop novel breathing variability indices to predict extubation failure among invasively ventilated patients. First, breath-to-breath basic and comprehensive respiratory parameters were computed from continuous ventilation waveforms 1 h before extubation. Subsequently, the basic and advanced variability methods were applied to the respiratory parameter sequences to derive comprehensive breathing variability indices, and their role in predicting extubation failure was assessed. Finally, after reducing the feature dimensionality using the forward search method, the combined effect of the indices was evaluated by inputting them into the machine learning models, including logistic regression, random forest, support vector machine, and eXtreme Gradient Boosting (XGBoost).The coefficient of variation of the dynamic mechanical power per breath (CV-MPThese results suggest that the proposed novel breathing variability indices can improve extubation failure prediction in invasively ventilated patients.

Background Sigh is a cyclic brief recruitment maneuver: previous physiologic studies showed that its use could be an interesting addition to pressure support ventilation to improve lung elastance, decrease regional heterogeneity, and increase release of surfactant. Research Question Is the clinical application of sigh during pressure support ventilation (PSV) feasible? Study Design and Methods We conducted a multicenter noninferiority randomized clinical trial on adult intubated patients with acute hypoxemic respiratory failure or ARDS undergoing PSV. Patients were randomized to the no-sigh group and treated by PSV alone, or to the sigh group, treated by PSV plus sigh (increase in airway pressure to 30 cm H2O for 3 s once per minute) until day 28 or death or successful spontaneous breathing trial. The primary end point of the study was feasibility, assessed as noninferiority (5% tolerance) in the proportion of patients failing assisted ventilation. Secondary outcomes included safety, physiologic parameters in the first week from randomization, 28-day mortality, and ventilator-free days. Results Two-hundred and fifty-eight patients (31% women; median age, 65 [54-75] years) were enrolled. In the sigh group, 23% of patients failed to remain on assisted ventilation vs 30% in the no-sigh group (absolute difference, –7%; 95% CI, –18% to 4%; P = .015 for noninferiority). Adverse events occurred in 12% vs 13% in the sigh vs no-sigh group (P = .852). Oxygenation was improved whereas tidal volume, respiratory rate, and corrected minute ventilation were lower over the first 7 days from randomization in the sigh vs no-sigh group. There was no significant difference in terms of mortality (16% vs 21%; P = .337) and ventilator-free days (22 [7-26] vs 22 [3-25] days; P = .300) for the sigh vs no-sigh group. Interpretation Among hypoxemic intubated ICU patients, application of sigh was feasible and without increased risk. Trial Registry ClinicalTrials.gov ; No.: NCT03201263 ; URL: www.clinicaltrials.gov

Artificial intelligence (AI) has found its way into clinical studies in the era of big data. Acute respiratory distress syndrome (ARDS) or acute lung injury (ALI) is a clinical syndrome that encompasses a heterogeneous population. Management of such heterogeneous patient population is a big challenge for clinicians. With accumulating ALI datasets being publicly available, more knowledge could be discovered with sophisticated analytics. We reviewed literatures with big data analytics to understand the role of AI for improving the caring of patients with ALI/ARDS. Many studies have utilized the electronic medical records (EMR) data for the identification and prognostication of ARDS patients. As increasing number of ARDS clinical trials data is open to public, secondary analysis on these combined datasets provide a powerful way of finding solution to clinical questions with a new perspective. AI techniques such as Classification and Regression Tree (CART) and artificial neural networks (ANN) have also been successfully used in the investigation of ARDS problems. Individualized treatment of ARDS could be implemented with a support from AI as we are now able to classify ARDS into many subphenotypes by unsupervised machine learning algorithms. Interestingly, these subphenotypes show different responses to a certain intervention. However, current analytics involving ARDS have not fully incorporated information from omics such as transcriptome, proteomics, daily activities and environmental conditions. AI technology is assisting us to interpret complex data of ARDS patients and enable us to further improve the management of ARDS patients in future with individual treatment plans.

Background: It remains controversial as how to set positive end-expiratory pressure (PEEP) for acute respiratory distress syndrome (ARDS) patients. This study aims to provide suggestions to the clinicians in selecting PEEP for ARDS patients receiving invasive mechanical ventilation based on artificial intelligence (AI).Methods: Invasively ventilated ARDS patients in MIMIC-IV and eICU databases were enrolled in the observational cohort study. An AI model trained by awarding survival for suggesting optimal PEEP was developed and tested on the MIMIC-IV database and externally validated on the eICU database. Three subgroups were defined in which the PEEP grades set by the AI model are lower, equal, and higher than that set by the clinicians (denoted as , , and , respectively). Intensive care unit (ICU) mortality and 28-day ventilation-free days are the primary and secondary outcomes.Results: 6839 (MIMIC-IV) and 2117 (eICU) ARDS admissions were included in the study. The ICU mortalities are 10.8% and 8.6% in the subgroup in the MIMIC-IV and eICU databases, respectively, and become higher in the and subgroups (MIMIC-IV: 25.6% and 23.7%, eICU: 26.9% and 27.6%). An explainable analysis reflects that the clinicians’ PEEP setting relates more to the oxygenation, respiratory mechanics, and ventilatory settings, while the RL model also pays attention to the more comprehensive parameters concerning patient characteristics such as Sequential Organ Failure Assessment (SOFA) and age.Conclusions: AI-based PEEP selection tends to consider clinical measurements and patient characteristics comprehensively, and is promising to improve the ICU outcomes for ARDS patients.

Background. The neutrophil–lymphocyte ratio (NLR) has been proposed as a surrogate marker of inflammation with prognostic value in various diseases. Our objective was to investigate the predictive value of the NLR as an indicator of persistent organ failure (POF) in patients with hypertriglyceridemic pancreatitis (HTGP). Methods. We retrospectively reviewed the data from patients with HTGP between 2016 and 2019. The NLR was obtained at admission. The diagnostic performance of the NLR for POF was evaluated by the area under the receiver operator characteristics curve (AUROC). Multivariate logistic regression determined whether elevated NLR was independently associated with POF. Results. Of the 446 patients enrolled, 89 (20.0%) developed POF. Patients with POF showed a significantly higher NLR than those without POF ( P < 0.001 ). A positive trend for the association across increasing NLR quartiles and the incidence of POF was observed ( P trend < 0.001 ). The AUROC of NLR to predict POF was 0.673 (95% confidence interval, 0.627-0.716). With a cut-off of NLR > 6.56 , the sensitivity and specificity were 73.0% and 55.7%, respectively. Multivariate analysis suggested that high NLR (>6.56) was independently associated with POF (odds ratio, 2.580; 95% confidence interval, 1.439-4.626; P = 0.001 ). Patients with a high NLR (>6.56) had a worse overall clinical course in HTGP. Conclusion. Elevated NLR was significantly associated with an increased risk of developing POF and could be an early independent predictor of POF in patients with HTGP.

The current American Joint Committee on Cancer (AJCC) M1a staging of non-small cell lung cancer (NSCLC) encompasses a wide disease spectrum, showing diverse prognosis.Patients who diagnosed in an earlier period formed the training cohort, and those who diagnosed thereafter formed the validation cohort. Kaplan-Meier analysis was performed for the training cohort by dividing the M1a stage into three subgroups: (I) malignant pleural effusion (MPE) or malignant pericardial effusion (MPCE); (II) separate tumor nodules in contralateral lung (STCL); and (III) pleural tumor nodules on the ipsilateral lung (PTIL). Gender, age, histologic, N stage, grade, surgery for primary site, lymphadenectomy, M1a groups, and chemotherapy were selected as independent prognostic factors using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. And a nomogram was constructed using Cox hazard regression analysis. Accuracy and clinical practicability were separately tested by Harrell's concordance index, the receiver operating characteristic (ROC) curve, calibration plots, residual plot, the integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA).The concordance index (0.661 for the training cohort and 0.688 for the validation cohort) and the area under the ROC curve (training cohort: 0.709 for 1-year and 0.727 for 2-year OS prediction; validation cohort: 0.737 for 1-year and 0.734 for 2-year OS prediction) indicated satisfactory discriminative ability of the nomogram. Calibration curve and DCA presented great prognostic accuracy, and clinical applicability. Its prognostic accuracy preceded the AJCC staging with evaluated NRI (1-year: 0.327; 2-year: 0.302) and IDI (1-year: 0.138; 2-year: 0.130).Our study established a nomogram for the prediction of 1- and 2-year OS in patients with NSCLC diagnosed with stage M1a, facilitating healthcare workers to accurately evaluate the individual survival of M1a NSCLC patients. The accuracy and clinical applicability of this nomogram were validated.

Background Inpatients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) are at increased risk for venous thromboembolism (VTE); however, the prophylaxis for VTE is largely underused in China. Identifying high-risk patients requiring thromboprophylaxis is critical to reduce the mortality and morbidity associated with VTE. This study aimed to evaluate and compare the validities of the Padua Prediction Score and Caprini risk assessment model (RAM) in predicting the risk of VTE in inpatients with AECOPD in China. Methods The inpatients with AECOPD were prospectively enrolled from seven medical centers of China between September 2017 and January 2020. Caprini and Padua scores were calculated on admission, and the incidence of 3-month VTE was investigated. Results Among the 3,277 eligible patients with AECOPD, 128 patients (3.9%) developed VTE within 3 months after admission. The distribution of the study population by the Caprini risk level was as follows: high, 53.6%; moderate, 43.0%; and low, 3.5%. The incidence of VTE increased by risk level as high, 6.1%; moderate, 1.5%; and low, 0%. According to the Padua RAM, only 10.9% of the study population was classified as high risk and 89.1% as low risk, with the corresponding incidence of VTE of 7.9 and 3.4%, respectively. The Caprini RAM had higher area under curve compared with the Padua RAM (0.713 ± 0.021 vs. 0.644 ± 0.023, p = 0.029). Conclusion The Caprini RAM was superior to the Padua RAM in predicting the risk of VTE in inpatients with AECOPD and might better guide thromboprophylaxis in these patients.

Cigarette smoking causes skeletal muscle dysfunction and worse prognosis for patients with diverse systemic diseases. Benzo[a]pyrene (BaP), one major constituent that is inhaled during smoking, is particularly known for its ability to impair neurodevelopment, impede reproductivity, or reduce birth weight. Here, we found that BaP exposure led to the inhibition of C2C12 myoblasts differentiation in a dose-dependent manner and reduced the expression of both early and late myogenic differentiation markers. BaP exposure significantly decreased the expression of p38 mitogen-activated protein kinase (p38MAPK), but not AKT, which are both critical during myogenic differentiation. Mechanistically, BaP downregulated the expression levels of MAPK-activated protein kinase 2 (MK2) and heat shock protein 70 (Hsp70), both of which stabilize p38MAPK. Interestingly, treatment of proteasome inhibitor MG132 was able to reverse BaP-induced degradation of Hsp70/ MK2 and p38MAPK in myoblasts, implying BaP-mediated p38MAPK degradation is proteasome-dependent. Overexpression of p38MAPK also rescued the defective differentiation phenotype of C2C12 induced by BaP. Taken together, we suggest that BaP exposure induces MK2/Hsp70/p38MAPK complex degradation in C2C12 myoblasts and impairs myogenic differentiation by proteasomal-dependent mechanisms. As application of the proteasome inhibitor MG132 or overexpression of p38MAPK could reverse impaired differentiation of myoblasts induced by BaP, this may suggest potential related strategies for preventing tobacco-related skeletal muscle diseases or for respiratory rehabilitation.

Background Coronavirus disease 2019 (COVID-19) is a declared global pandemic, causing a lot of death. How to quickly screen risk population for severe patients is essential for decreasing the mortality. Many of the predictors might not be available in all hospitals, so it is necessary to develop a simpler screening tool with predictors which can be easily obtained for wide wise. Methods This retrospective study included all the 813 confirmed cases diagnosed with COVID-19 before March 2nd, 2020 in a city of Hubei Province in China. Data of the COVID-19 patients including clinical and epidemiological features were collected through Chinese Disease Control and Prevention Information System. Predictors were selected by logistic regression, and then categorized to four different level risk factors. A screening tool for severe patient with COVID-19 was developed and tested by ROC curve. Results Seven early predictors for severe patients with COVID-19 were selected, including chronic kidney disease (OR 14.7), age above 60 (OR 5.6), lymphocyte count less than 9 per L (OR 2.5), Neutrophil to Lymphocyte Ratio larger than 4.7 (OR 2.2), high fever with temperature ≥ 38.5℃ (OR 2.2), male (OR 2.2), cardiovascular related diseases (OR 2.0). The Area Under the ROC Curve of the screening tool developed by above seven predictors was 0.798 (95% CI 0.747–0.849), and its best cut-off value is > 4.5, with sensitivity 72.0% and specificity 75.3%. Conclusions This newly developed screening tool can be a good choice for early prediction and alert for severe case especially in the condition of overload health service.

Background. Accurate interpretation of lung function tests requires appropriate spirometry reference values derived from large-scale population-specific epidemiological surveys. The aim of this cross-sectional study was to establish normal spirometric values for the population of healthy, nonsmoking Han Chinese adults residing in Zhejiang province, China. Methods. We measured lung function parameters such as forced expiratory volume in 1 s, forced vital capacity, peak expiratory flow, maximal midexpiratory flow, and diffusion capacity for carbon monoxide and considered age, height, and weight as independent factors that may modify these parameters. The clinical data were divided into the study arm and validation group. The study arms were used to construct predictive equations using stepwise multiple linear regression, and data from the validation group were used to assess the robustness of the equations. Results. The 3866 participants were randomized into a study arm (n = 1,949) and a validation arm (n = 1,917). Lung function parameters had a negative association with age and a positive association with height. Data from the two groups were similar. Predictive equations were constructed from the study arm, and the validation group was used to test the feasibility of the reference equations. Conclusions. The reference values we derived can be used to evaluate lung function in this cohort in both epidemiological studies and clinical practice.

Mechanical ventilation is an essential life-support treatment for patients who cannot breathe independently. Patient–ventilator asynchrony (PVA) occurs when ventilatory support does not match the needs of the patient and is associated with a series of adverse clinical outcomes. Deep learning methods have shown a strong discriminative ability for PVA detection, but they require a large number of annotated data for model training, which hampers their application to this task. We developed a transfer learning architecture based on pretrained convolutional neural networks (CNN) and used it for PVA recognition based on small datasets. The one-dimensional signal was converted to a two-dimensional image, and features were extracted by the CNN using pretrained weights for classification. A partial dropping cross-validation technique was developed to evaluate model performance on small datasets. When using large datasets, the performance of the proposed method was similar to that of non-transfer learning methods. However, when the amount of data was reduced to 1%, the accuracy of transfer learning was approximately 90%, whereas the accuracy of the non-transfer learning was less than 80%. The findings suggest that the proposed transfer learning method can obtain satisfactory accuracies for PVA detection when using small datasets. Such a method can promote the application of deep learning to detect more types of PVA under various ventilation modes.

Airway pressure release ventilation (APRV) maintains a sustained airway pressure over a large proportion of the respiratory cycle, and has a long inspiratory time at high pressure. The purpose of this study was to determine the influence of the APRV with and without spontaneous breathing on albuterol aerosol delivery with a continuous vibrating-mesh nebulizer (VMN) placed at different positions on an adult lung model of invasive mechanical ventilation.An adult lung model was assembled by connecting a ventilator with a dual-limb circuit to an 8-mm inner diameter endotracheal tube (ETT) and collecting filter attached to a test lung with set compliance of 0.1 L/cmHAlbuterol (in micrograms, mean ± standard deviation) delivered was higher with VMN placed at the gas inlet of the humidifier with each mode of ventilation (p 0.01). APRVs has the highest albuterol delivery followed by PCV, PCVSpontaneous breathing increased the albuterol delivery during APRV, compared with APRV alone and PCV modes. Placing the nebulizer proximal to the ventilator was more efficient for all modes tested.

ImportancePostoperative pulmonary complications and cardiovascular complications are major causes of morbidity, mortality, and resource utilization in cardiac surgery patients.ObjectivesTo investigate the effects of airway pressure release ventilation (APRV) on respiration and hemodynamics in post cardiac surgery patients.Main Outcomes and MeasuresA single-center randomized control trial was performed. In total, 138 patients undergoing cardiopulmonary bypass were prospectively screened. Ultimately 39 patients met the inclusion criteria and were randomized into two groups: 19 patients were managed with pressure control ventilation (PCV) and 20 patients were managed with APRV. Respiratory mechanics after 4 h, hemodynamics within the first day, and Chest radiograph score (CRS) and blood gasses within the first three days were recorded and compared.ResultsA higher cardiac index (3.1 ± 0.7 vs. 2.8 ± 0.8 L⋅min–1⋅m2; p < 0.05), and shock volume index (35.4 ± 9.2 vs. 33.1 ± 9.7 ml m–2; p < 0.05) were also observed in the APRV group after 4 h as well as within the first day (p < 0.05). Compared to the PCV group, the PaO2/FiO2 was significantly higher after 4 h in patients of APRV group (340 ± 97 vs. 301 ± 82, p < 0.05) and within the first three days (p < 0.05) in the APRV group. CRS revealed less overall lung injury in the APRV group (p < 0.001). The duration of mechanical ventilation and ICU length of stay were not significantly (p = 0.248 and 0.424, respectively).Conclusions and RelevanceCompared to PCV, APRV may be associated with increased cardiac output improved oxygenation, and decreased lung injury in postoperative cardiac surgery patients.

Objective. The measurement of the static compliance of the respiratory system (Cstat) during mechanical ventilation requires zero end-inspiratory flow. An inspiratory pause maneuver is needed if the zero end-inspiratory flow condition cannot be satisfied under normal ventilation.Approach. We propose a method to measure the quasi-static respiratory compliance (Cqstat) under pressure control ventilation mode without the inspiratory pause maneuver. First, a screening strategy was applied to filter out breaths affected strongly by spontaneous breathing efforts or artifacts. Then, we performed a virtual extrapolation of the flow-time waveform when the end-inspiratory flow was not zero, to allow for the calculation ofCqstatfor each kept cycle. Finally, the outputCqstatwas obtained as the average of the smallest 40Cqstatmeasurements. The proposed method was validated against the gold standardCstatmeasured from real clinical settings and compared with two reported algorithms. The gold standardCstatwas obtained by applying an end-inspiratory pause maneuver in the volume-control ventilation mode.Main results. Sixty-nine measurements from 36 patients were analyzed. The Bland-Altman analysis showed that the bias of agreement forCqstatversus the gold standard measurement was -0.267 ml/cmH2O (95% limits of agreement was -4.279 to 4.844 ml/cmH2O). The linear regression analysis indicated a strong correlation (R2 = 0.90) between theCqstatand gold standard.Significance. The results showed that theCqstatcan be accurately estimated from continuous ventilator waveforms, including spontaneous breathing without an inspiratory pause maneuver. This method promises to provide continuous measurements compliant with mechanical ventilation.

Background: Sepsis is a heterogenous syndrome and individualized management strategy is the key to successful treatment. Genome wide expression profiling has been utilized for identifying subclasses of sepsis, but the clinical utility of these subclasses was limited because of the classification instability, and the lack of a robust class prediction model with extensive external validation. The study aimed to develop a parsimonious class model for the prediction of class membership and validate the model for its prognostic and predictive capability in external datasets. Methods: The Gene Expression Omnibus (GEO) and ArrayExpress databases were searched from inception to April 2020. Datasets containing whole blood gene expression profiling in adult sepsis patients were included. Autoencoder was used to extract representative features for k-means clustering. Genetic algorithms (GA) were employed to derive a parsimonious 5-gene class prediction model. The class model was then applied to external datasets (n = 780) to evaluate its prognostic and predictive performance. Findings: A total of 12 datasets involving 1613 patients were included. Two classes were identified in the discovery cohort (n = 685). Class 1 was characterized by immunosuppression with higher mortality than class 2 (21.8% [70/321] vs. 12.1% [44/364]; p < 0.01 for Chi-square test). A 5-gene class model (C14orf159, AKNA, PILRA, STOM and USP4) was developed with GA. In external validation cohorts, the 5-gene class model (AUC: 0.707; 95% CI: 0.664 – 0.750) performed better in predicting mortality than sepsis response signature (SRS) endotypes (AUC: 0.610; 95% CI: 0.521 – 0.700), and performed equivalently to the APACHE II score (AUC: 0.681; 95% CI: 0.595 – 0.767). In the dataset E-MTAB-7581, the use of hydrocortisone was associated with increased risk of mortality (OR: 3.15 [1.13, 8.82]; p = 0.029) in class 2. The effect was not statistically significant in class 1 (OR: 1.88 [0.70, 5.09]; p = 0.211). Interpretation: Our study identified two classes of sepsis that showed different mortality rates and responses to hydrocortisone therapy. Class 1 was characterized by immunosuppression with higher mortality rate than class 2. We further developed a 5-gene class model to predict class membership. Funding: The study was funded by the National Natural Science Foundation of China (Grant No. 81,901,929).

Background and objectives: The timing of invasive mechanical ventilation (IMV) is controversial in COVID-19 patients with acute respiratory hypoxemia. The study aimed to develop a novel biomarker called cumulative oxygen deficit (COD) for the initiation of IMV.Methods: The study was conducted in four designated hospitals for treating COVID-19 patients in Jingmen, Wuhan, from January to March 2020. COD was defined to account for both the magnitude and duration of hypoxemia. A higher value of COD indicated more oxygen deficit. The predictive performance of COD was calculated in multivariable Cox regression models. Time-dependent propensity score matching was performed to explore the effectiveness of IMV versus other non-invasive respiratory supports on survival outcome.Results: A number of 111 patients including 80 in the non-IMV group and 31 in the IMV group were included. Patients with IMV had significantly lower PaO2 (62 (49, 89) vs. 90.5 (68, 125.25) mmHg; p < 0.001), and higher COD (-6.87 (-29.36, 52.38) vs. -231.68 (-1040.78, 119.83)) than patients without IMV. As compared to patients with COD < 0, patients with COD > 30 had higher risk of fatality (HR: 3.79, 95% CI: 2.57 to 16.93; p = 0.037) , and those with COD > 50 were 10 times more likely to die (HR: 10.45, 95% CI: 1.28 to 85.37; p = 0.029). The Cox regression model performed in the time-dependent propensity score matched cohort showed that IMV was associated with half of the hazard of death than those without IMV (HR: 0.56; 95% CI: 0.16 to 1.93; p = 0.358).Conclusions: The study developed a novel biomarker COD which considered both magnitude and duration of hypoxemia, to assist the timing of IMV in patients with COVID-19. We suggest IMV should be the preferred ventilatory support once the COD reaches 30.

Background Health education emerged as an important intervention for improving the awareness and self-management abilities of chronic disease patients. The development of information technologies has changed the form of patient educational materials from traditional paper materials to electronic materials. To date, the amount of patient educational materials on the internet is tremendous, with variable quality, which makes it hard to identify the most valuable materials by individuals lacking medical backgrounds. Objective The aim of this study was to develop a health recommender system to provide appropriate educational materials for chronic disease patients in China and evaluate the effect of this system. Methods A knowledge-based recommender system was implemented using ontology and several natural language processing (NLP) techniques. The development process was divided into 3 stages. In stage 1, an ontology was constructed to describe patient characteristics contained in the data. In stage 2, an algorithm was designed and implemented to generate recommendations based on the ontology. Patient data and educational materials were mapped to the ontology and converted into vectors of the same length, and then recommendations were generated according to similarity between these vectors. In stage 3, the ontology and algorithm were incorporated into an mHealth system for practical use. Keyword extraction algorithms and pretrained word embeddings were used to preprocess educational materials. Three strategies were proposed to improve the performance of keyword extraction. System evaluation was based on a manually assembled test collection for 50 patients and 100 educational documents. Recommendation performance was assessed using the macro precision of top-ranked documents and the overall mean average precision (MAP). Results The constructed ontology contained 40 classes, 31 object properties, 67 data properties, and 32 individuals. A total of 80 SWRL rules were defined to implement the semantic logic of mapping patient original data to the ontology vector space. The recommender system was implemented as a separate Web service connected with patients' smartphones. According to the evaluation results, our system can achieve a macro precision up to 0.970 for the top 1 recommendation and an overall MAP score up to 0.628. Conclusions This study demonstrated that a knowledge-based health recommender system has the potential to accurately recommend educational materials to chronic disease patients. Traditional NLP techniques combined with improvement strategies for specific language and domain proved to be effective for improving system performance. One direction for future work is to explore the effect of such systems from the perspective of patients in a practical setting.

BACKGROUND Health education emerged as an important intervention for improving the awareness and self-management abilities of chronic disease patients. The development of information technologies has changed the form of patient educational materials from traditional paper materials to electronic materials. To date, the amount of patient educational materials on the internet is tremendous, with variable quality, which makes it hard to identify the most valuable materials by individuals lacking medical backgrounds. OBJECTIVE The aim of this study was to develop a health recommender system to provide appropriate educational materials for chronic disease patients in China and evaluate the effect of this system. METHODS A knowledge-based recommender system was implemented using ontology and several natural language processing (NLP) techniques. The development process was divided into 3 stages. In stage 1, an ontology was constructed to describe patient characteristics contained in the data. In stage 2, an algorithm was designed and implemented to generate recommendations based on the ontology. Patient data and educational materials were mapped to the ontology and converted into vectors of the same length, and then recommendations were generated according to similarity between these vectors. In stage 3, the ontology and algorithm were incorporated into an mHealth system for practical use. Keyword extraction algorithms and pretrained word embeddings were used to preprocess educational materials. Three strategies were proposed to improve the performance of keyword extraction. System evaluation was based on a manually assembled test collection for 50 patients and 100 educational documents. Recommendation performance was assessed using the macro precision of top-ranked documents and the overall mean average precision (MAP). RESULTS The constructed ontology contained 40 classes, 31 object properties, 67 data properties, and 32 individuals. A total of 80 SWRL rules were defined to implement the semantic logic of mapping patient original data to the ontology vector space. The recommender system was implemented as a separate Web service connected with patients' smartphones. According to the evaluation results, our system can achieve a macro precision up to 0.970 for the top 1 recommendation and an overall MAP score up to 0.628. CONCLUSIONS This study demonstrated that a knowledge-based health recommender system has the potential to accurately recommend educational materials to chronic disease patients. Traditional NLP techniques combined with improvement strategies for specific language and domain proved to be effective for improving system performance. One direction for future work is to explore the effect of such systems from the perspective of patients in a practical setting.

Background Enhanced recovery after surgery (ERAS) has shown sufficient superiority in terms of cutting down hospital stay and costs, and reducing complications in patients undergoing laparoscopic hepatectomy (LH). However, the benefit of ERAS in elderly patients undergoing LH remains unclear, and clinical studies on this topic are still limited. Methods In total, 177 elderly patients (aged over 65 and underwent LH) were divided into two groups. The 107 patients in the control group received standard care, while the 70 patients in the ERAS group underwent the ERAS program after hepatectomy. The primary endpoint was the postoperative hospital stay. The secondary endpoints were resumption of oral intake, readmission rate and complications. Results ERAS had a positive effect on reducing length of hospital stay {6 [4–8] vs. 9 [7–14] days; P

Protective mechanical ventilation based on multiple ventilator parameters such as tidal volume, plateau pressure, and driving pressure has been widely used in acute respiratory distress syndrome (ARDS). More recently, mechanical power (MP) was found to be associated with mortality. The study aimed to investigate whether MP normalized to predicted body weight (norMP) was superior to other ventilator variables and to prove that the discrimination power cannot be further improved with a sophisticated machine learning method. The study included individual patient data from eight randomized controlled trials conducted by the ARDSNet. The data was split 3:1 into training and testing subsamples. The discrimination of each ventilator variable was calculated in the testing subsample using the area under receiver operating characteristic curve. The gradient boosting machine was used to examine whether the discrimination could be further improved. A total of 5159 patients with acute onset ARDS were included for analysis. The discrimination of norMP in predicting mortality was significantly better than the absolute MP (p = 0.011 for DeLong’s test). The gradient boosting machine was not able to improve the discrimination as compared to norMP (p = 0.913 for DeLong’s test). The multivariable regression model showed a significant interaction between norMP and ARDS severity (p

Background: The effects of different modes of mechanical ventilation in the same ventilatory support level on ventilator-induced diaphragm dysfunction onset were assessed in healthy rabbits. Methods: Twenty New Zealand rabbits were randomly assigned to 4 groups (n = 5 in each group). Group 1: no mechanical ventilation; group 2: controlled mechanical ventilation (CMV) for 24 hours; group 3: assist/control ventilation (A/C) mode for 24 hours; group 4: high-level pressure support ventilation (PSV) mode for 24 hours. Heart rate, mean arterial blood pressure, PH, partial pressure of arterial oxygen/fraction of inspired oxygen and partial pressure of arterial carbon dioxide were monitored and diaphragm electrical activity was analyzed in the 4 groups. Caspase-3 was evaluated by protein analysis and diaphragm ultra structure was assessed by electron microscopy. Results: The centroid frequency and the ratio of high frequency to low frequency were significantly reduced in the CMV, A/C and PSV groups (P < 0.001). The percent change in centroid frequency was significantly lower in the PSV group than in the CMV and A/C groups (P = 0.001 and P = 0.028, respectively). Electromyography of diaphragm integral amplitude decreased by 90% ± 1.48%, 67.8% ± 3.13% and 70.2% ± 4.72% in the CMV, A/C and PSV groups, respectively (P < 0.001). Caspase-3 protein activation was attenuated in the PSV group compared with the CMV and A/C groups (P = 0.035 and P = 0.033, respectively). Irregular swelling of mitochondria along with fractured and fuzzy cristae was observed in the CMV group, whereas mitochondrial cristae were dense and rich in the PSV group. The mitochondrial injury scores (Flameng scores) in the PSV group were the lowest among the 3 ventilatory groups (0.93 ± 0.09 in PSV versus 2.69 ± 0.05 in the CMV [P < 0.01] and PSV versus A/C groups [2.02 ± 0.08, P < 0.01]). Conclusions: The diaphragm myoelectric activity was reduced in the PSV group, although excessive oxidative stress and ultra-structural changes of diaphragm were found. However, partial diaphragm electrical activity was retained and diaphragm injury was minimized using the PSV mode.

Background and objectives The timing of invasive mechanical ventilation (IMV) is controversial in COVID-19 patients with acute respiratory hypoxemia. The study aimed to develop a novel predictor called cumulative oxygen deficit (COD) for the risk stratification. Methods The study was conducted in four designated hospitals for treating COVID-19 patients in Jingmen, Wuhan, from January to March 2020. COD was defined to account for both the magnitude and duration of hypoxemia. A higher value of COD indicated more oxygen deficit. The predictive performance of COD was calculated in multivariable Cox regression models. Results A number of 111 patients including 80 in the non-IMV group and 31 in the IMV group were included. Patients with IMV had substantially lower PaO2 (62 (49, 89) vs. 90.5 (68, 125.25) mmHg; p < 0.001), and higher COD (−6.87 (−29.36, 52.38) vs. −231.68 (−1040.78, 119.83) mmHg·day) than patients without IMV. As compared to patients with COD < 0, patients with COD > 30 mmHg·day had higher risk of fatality (HR: 3.79, 95% CI [2.57–16.93]; p = 0.037), and those with COD > 50 mmHg·day were 10 times more likely to die (HR: 10.45, 95% CI [1.28–85.37]; p = 0.029). Conclusions The study developed a novel predictor COD which considered both magnitude and duration of hypoxemia, to assist risk stratification of COVID-19 patients with acute respiratory distress.

Background and objective Mismatch between invasive mechanical ventilation and the requirements of patients results in patient-ventilator asynchrony (PVA), which is associated with a series of adverse clinical outcomes. Although the efficiency of the available approaches for automatically detecting various types of PVA from the ventilator waveforms is unsatisfactory, the feasibility of powerful deep learning approaches in addressing this problem has not been investigated. Methods We propose a 2-layer long short-term memory (LSTM) network to detect two most frequently encountered types of PVA, namely, double triggering (DT) and ineffective inspiratory effort during expiration (IEE), on two datasets. The performance of the networks is evaluated first using cross-validation on the combined dataset, and then using a cross testing scheme, in which the LSTM networks are established on one dataset and tested on the other. Results Compared with the reported rule-based algorithms and the machine learning models, the proposed 2-layer LSTM network exhibits the best overall performance, with the F1 scores of 0.983 and 0.979 for DT and IEE detection, respectively, on the combined dataset. Furthermore, it outperforms the other approaches in cross testing. Conclusions The findings suggest that LSTM is an excellent technique for accurate recognition of PVA in clinics. Such a technique can help detect and correct PVA for a better patient ventilator interaction.

Background Postoperative hypoxemia is quite common in patients with acute aortic dissection (AAD) and is associated with poor clinical outcomes. However, there is no method to predict this potentially life-threatening complication. The study aimed to develop a regression model in patients with AAD to predict postoperative hypoxemia, and to validate it in an independent dataset. Methods All patients diagnosed with AAD from December 2012 to December 2017 were retrospectively screened for potential eligibility. Preoperative and intraoperative variables were included for analysis. Logistic regression model was fit by using purposeful selection procedure. The original dataset was split into training and validating datasets by 4:1 ratio. Discrimination and calibration of the model was assessed in the validating dataset. A nomogram was drawn for clinical utility. Results A total of 211 patients, involving 168 in non-hypoxemia and 43 in hypoxemia group, were included during the study period (incidence: 20.4%). Duration of mechanical ventilation (MV) was significantly longer in the hypoxemia than non-hypoxemia group (41(10.5140) vs. 12(3.75,70.25) hours; p = 0.002). There was no difference in the hospital mortality rate between the two groups. The purposeful selection procedure identified 8 variables including hematocrit (odds ratio [OR]: 0.89, 95% confidence interval [CI]: 0.80 to 0.98, p = 0.011), PaO2/FiO2 ratio (OR: 0.99, 95% CI: 0.99 to 1.00, p = 0.011), white blood cell count (OR: 1.21, 95% CI: 1.06 to 1.40, p = 0.008), body mass index (OR: 1.32, 95% CI: 1.15 to 1.54; p = 0.000), Stanford type (OR: 0.22, 95% CI: 0.06 to 0.66; p = 0.011), pH (OR: 0.0002, 95% CI: 2*10− 8 to 0.74; p = 0.048), cardiopulmonary bypass time (OR: 0.99, 95% CI: 0.98 to 1.00; p = 0.031) and age (OR: 1.03, 95% CI: 0.99 to 1.08; p = 0.128) to be included in the model. In an independent dataset, the area under curve (AUC) of the prediction model was 0.869 (95% CI: 0.802 to 0.936). The calibration was good by visual inspection. Conclusions The study developed a model for the prediction of postoperative hypoxemia in patients undergoing operation for AAD. The model showed good discrimination and calibration in an independent dataset that was not used for model training.