PRaG 3.0 therapy for human epidermal growth factor receptor 2-positive metastatic pancreatic ductal adenocarcinoma: A case report.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a highly fatal disease with limited effective treatment especially after first-line chemotherapy. The human epidermal growth factor receptor 2 (HER-2) immunohistochemistry (IHC) positive is associated with more aggressive clinical behavior and shorter overall survival in PDAC. CASE SUMMARY: We present a case of multiple metastatic PDAC with IHC mismatch repair proficient but HER-2 IHC weakly positive at diagnosis that didn't have tumor regression after first-line nab-paclitaxel plus gemcitabine and PD-1 inhibitor treatment. A novel combination therapy PRaG 3.0 of RC48 (HER2-antibody-drug conjugate), radiotherapy, PD-1 inhibitor, granulocyte-macrophage colony-stimulating factor and interleukin-2 was then applied as second-line therapy and the patient had confirmed good partial response with progress-free-survival of 6.5 months and overall survival of 14.2 month. She had not developed any grade 2 or above treatment-related adverse events at any point. Percentage of peripheral CD8+Temra and CD4+Temra were increased during first two activation cycles of PRaG 3.0 treatment containing radiotherapy but deceased to the baseline during the maintenance cycles containing no radiotherapy. CONCLUSION: PRaG 3.0 might be a novel strategy for HER2-positive metastatic PDAC patients who failed from previous first-line approach and even PD-1 immunotherapy but needs more data in prospective trials.

Uncertain prediction of deformable image registration on lung CT using multi-category features and supervised learning.

The assessment of deformable registration uncertainty is an important task for the safety and reliability of registration methods in clinical applications. However, it is typically done by a manual and time-consuming procedure. We propose a novel automatic method to predict registration uncertainty based on multi-category features and supervised learning. Three types of features, including deformation field statistical features, deformation field physiologically realistic features, and image similarity features, are introduced and calculated to train the random forest regressor for local registration uncertain prediction. Deformation field statistical features represent the numerical stability of registration optimization, which are correlated to the uncertainty of deformation fields; deformation field physiologically realistic features represent the biomechanical properties of organ motions, which mathematically reflect the physiological reality of deformation; image similarity features reflect the similarity between the warped image and fixed image. The multi-category features comprehensively reflect the registration uncertainty. The strategy of spatial adaptive random perturbations is also introduced to accurately simulate spatial distribution of registration uncertainty, which makes deformation field statistical features more discriminative to the uncertainty of deformation fields. Experiments were conducted on three publicly available thoracic CT image datasets. Seventeen randomly selected image pairs are used to train the random forest model, and 9 image pairs are used to evaluate the prediction model. The quantitative experiments on lung CT images show that the proposed method outperforms the baseline method for uncertain prediction of classical iterative optimization-based registration and deep learning-based registration with different registration qualities. The proposed method achieves good performance for registration uncertain prediction, which has great potential in improving the accuracy of registration uncertain prediction.

Preoperative prediction of microsatellite instability status in colorectal cancer based on a multiphasic enhanced CT radiomics nomogram model.

BACKGROUND: To investigate the value of a nomogram model based on the combination of clinical-CT features and multiphasic enhanced CT radiomics for the preoperative prediction of the microsatellite instability (MSI) status in colorectal cancer (CRC) patients. METHODS: A total of 347 patients with a pathological diagnosis of colorectal adenocarcinoma, including 276 microsatellite stabilized (MSS) patients and 71 MSI patients (243 training and 104 testing), were included. Univariate and multivariate regression analyses were used to identify the clinical-CT features of CRC patients linked with MSI status to build a clinical model. Radiomics features were extracted from arterial phase (AP), venous phase (VP), and delayed phase (DP) CT images. Different radiomics models for the single phase and multiphase (three-phase combination) were developed to determine the optimal phase. A nomogram model that combines clinical-CT features and the optimal phasic radscore was also created. RESULTS: Platelet (PLT), systemic immune inflammation index (SII), tumour location, enhancement pattern, and AP contrast ratio (ACR) were independent predictors of MSI status in CRC patients. Among the AP, VP, DP, and three-phase combination models, the three-phase combination model was selected as the best radiomics model. The best MSI prediction efficacy was demonstrated by the nomogram model built from the combination of clinical-CT features and the three-phase combination model, with AUCs of 0.894 and 0.839 in the training and testing datasets, respectively. CONCLUSION: The nomogram model based on the combination of clinical-CT features and three-phase combination radiomics features can be used as an auxiliary tool for the preoperative prediction of the MSI status in CRC patients.

Evaluation of the efficacy and safety of a precise thymalfasin-regulated PRaG regimen for advanced refractory solid tumours: protocol for the open-label, prospective, multicentre study (PRaG5.0 study).

INTRODUCTION: The PRaG regimen, which consists of hypofractionated radiotherapy combined with a programmed cell death-1/programmed cell death ligand-1 (PD-1/PD-L1) inhibitor and granulocyte-macrophage colony stimulating factor (GM-CSF), has been demonstrated to have a survival benefit in patients with advanced solid tumours who have failed at least two lines of treatment. Nonetheless, lymphopenia poses an impediment to the enduring efficacy of PD-1/PD-L1 inhibitor therapy. Adequate lymphocyte reserves are essential for the efficacy of immunotherapy. Coupling the PRaG regimen with immunomodulatory agents that augment the number and functionality of lymphocytes may yield further survival benefits in this cohort of patients. OBJECTIVE: The aim of this study is to investigate the effectiveness and safety of a meticulously thymalfasin-controlled PRaG regimen in patients with advanced and chemotherapy-resistant solid tumours. METHODS AND ANALYSIS: The study has a prospective, single-arm, open-label, multicentre design and aims to recruit up to 60 patients with histologically confirmed advanced solid tumours that have relapsed or metastasised. All eligible patients will receive a minimum of two cycles of the PRaG regimen comprising thymalfasin followed by maintenance treatment with a PD-1/PD-L1 inhibitor and thymalfasin for 1 year or until disease progression. Patients will be monitored according to the predetermined protocol for a year or until disease progression after initiation of radiotherapy. ETHICS AND DISSEMINATION: The study protocol was approved by the Ethics Committee of the Second Affiliated Hospital of Soochow University, on 25 November 2022 (JD-LK-2022-151-01) and all other participating hospitals. Findings will be disseminated through national and international conferences. We also plan to publish our findings in high-impact peer-reviewed journal. TRIAL REGISTRATION NUMBER: NCT05790447.

The value of preoperative diagnosis of colorectal adenocarcinoma pathological T staging based on dual-layer spectral-detector computed tomography extracellular volume fraction: a preliminary study.

PURPOSE: To investigate the value of preoperative diagnosis of colorectal adenocarcinoma (CRAC) pathological T staging based on dual-layer spectral-detector computed tomography (DLCT) extracellular volume fraction (ECV) of CRAC lesions. METHODS: We prospectively collected clinical and DLCT imaging data from 165 patients with CRAC who attended two hospitals from June 2022 to April 2023. The enrolled patients were divided into a training group (n = 110, from Hospital 1) and an external validation group (n = 55, from Hospital 2). Measuring and calculating DLCT parameters of lesions, including CT values of 40 and 100 keV virtual mono-energetic images (VMI), iodine concentration (IC) and effective atomic number (Eff-Z) in the arterial phases (AP) and venous phases (VP), and ECV in the delayed phase (DP). The differences in clinical characteristics and DLCT parameters were compared between different pT subgroups. The correlation between DLCT parameters and pT stages were evaluated by Spearman correlation analysis. A multifactorial binary logistic stepwise forward regression analysis was performed to obtain independent influences associated with pT stage. Receiver operating characteristic curves (ROCs) were used to assess diagnostic efficacy and were expressed as area under the curve (AUC). RESULTS: Each DLCT parameter was higher in pT3 stage tumors than in pT1-2 stage tumors (all P < 0.05). The highest correlation was found between ECV and pT stage (r = 0.637). ECV were independent influences associated with pT stage. ECV had excellent diagnostic efficacy for CRAC pT staging in both the training and external validation groups (AUC = 0.919 and 0.892). CONCLUSION: ECV based on DLCT measurement can be used for preoperative noninvasive diagnosis of CRAC pT staging with excellent diagnostic efficacy. It can provide a new imaging marker for the preoperative evaluation of CRAC and help clinicians formulate individualized treatment earlier. However, it needs to be confirmed with a larger sample size.

A nomogram prediction model based on clinicopathological combined radiological features for metachronous liver metastasis of colorectal cancer.

Objective: To establish a nomogram prediction model (based on clinicopathological and radiological features) for the development of metachronous liver metastasis (MLM) in patients with colorectal cancer (CRC). Methods: This retrospective study included patients with CRC who underwent surgery at Changshu No.1 People's Hospital and the Second Affiliated Hospital of Soochow University between January 2016 and December 2018. The clinical, pathological, and radiological features of each patient were investigated. Risk factors for MLM were identified by univariable and multivariable analyses. The predictive nomogram for MLM development was constructed. The predictive performance of the nomogram was estimated by the receiver operating characteristics curve, calibration curve, and decision curve analysis. Results: This study included 161 patients with CRC [median age: 66 (range, 33-87) years]. Fifty-nine developed MLM after a median of 12 (range, 2-52) months after surgery. The multivariable logistic regression analysis showed that age >66 years (OR=3.471, 95% CI: 1.272-9.473, P=0.015), N2 stage (OR=6.534, 95% CI: 1.456-29.317, P=0.014), positive vascular invasion (OR=2.995, 95% CI: 1.132-7.926, P=0.027), positive tumor deposit (OR=4.451, 95% CI: 1.153-17.179, P=0.030), and linear (OR=6.774, 95% CI: 1.306-35.135, P=0.023) and nodal pericolic fat infiltration patterns (OR=8.762, 95% CI: 1.521-50.457, P=0.015) were independently associated with MLM. These five factors were used to create a nomogram. The area under the receiver operating characteristics curve of the nomogram was 0.866 (95% CI: 0.803-0.914), indicating favorable prediction performance. The calibration curve of the nomogram showed a satisfactory agreement between the predicted and actual probabilities. Conclusions: A nomogram prediction model based on five clinicopathological and radiological features might have favorable prediction performance for MLM in patients who underwent surgery for CRC. Hence, the present study proposes a nomogram that can easily be used to predict MLM after CRC surgery based on readily available features.

An artificial intelligence-assisted diagnosis modeling software (AIMS) platform based on medical images and machine learning: a development and validation study.

Background: Supervised machine learning methods [both radiomics and convolutional neural network (CNN)-based deep learning] are usually employed to develop artificial intelligence models with medical images for computer-assisted diagnosis and prognosis of diseases. A classical machine learning-based modeling workflow involves a series of interconnected components and various algorithms, but this makes it challenging, tedious, and labor intensive for radiologists and researchers to build customized models for specific clinical applications if they lack expertise in machine learning methods. Methods: We developed a user-friendly artificial intelligence-assisted diagnosis modeling software (AIMS) platform, which supplies standardized machine learning-based modeling workflows for computer-assisted diagnosis and prognosis systems with medical images. In contrast to other existing software platforms, AIMS contains both radiomics and CNN-based deep learning workflows, making it an all-in-one software platform for machine learning-based medical image analysis. The modular design of AIMS allows users to build machine learning models easily, test models comprehensively, and fairly compare the performance of different models in a specific application. The graphical user interface (GUI) enables users to process large numbers of medical images without programming or script addition. Furthermore, AIMS also provides a flexible image processing toolkit (e.g., semiautomatic segmentation, registration, morphological operations) to rapidly create lesion labels for multiphase analysis, multiregion analysis of an individual tumor (e.g., tumor mass and peritumor), and multimodality analysis. Results: The functionality and efficiency of AIMS were demonstrated in 3 independent experiments in radiation oncology, where multiphase, multiregion, and multimodality analyses were performed, respectively. For clear cell renal cell carcinoma (ccRCC) Fuhrman grading with multiphase analysis (sample size =187), the area under the curve (AUC) value of the AIMS was 0.776; for ccRCC Fuhrman grading with multiregion analysis (sample size =177), the AUC value of the AIMS was 0.848; for prostate cancer Gleason grading with multimodality analysis (sample size =206), the AUC value of the AIMS was 0.980. Conclusions: AIMS provides a user-friendly infrastructure for radiologists and researchers, lowering the barrier to building customized machine learning-based computer-assisted diagnosis models for medical image analysis.

Unsupervised registration for liver CT-MR images based on the multiscale integrated spatial-weight module and dual similarity guidance.

PURPOSE: Multimodal registration is a key task in medical image analysis. Due to the large differences of multimodal images in intensity scale and texture pattern, it is a great challenge to design distinctive similarity metrics to guide deep learning-based multimodal image registration. Besides, since the limitation of the small receptive field, existing deep learning-based methods are mainly suitable for small deformation, but helpless for large deformation. To address the above issues, we present an unsupervised multimodal image registration method based on the multiscale integrated spatial-weight module and dual similarity guidance. METHODS: In this method, a U-shape network with our multiscale integrated spatial-weight module is embedded into a multi-resolution image registration architecture to achieve end-to-end large deformation registration, where the spatial-weight module can effectively highlight the regions with large deformation and aggregate discriminative features, and the multi-resolution architecture further helps to solve the optimization problem of the network in a coarse-to-fine pattern. Furthermore, we introduce a special loss function based on dual similarity, which represents both global gray-scale similarity and local feature similarity, to optimize the unsupervised multimodal registration network. RESULTS: We verified the effectiveness of the proposed method on liver CT-MR images. Experimental results indicate that the proposed method achieves the optimal DSC value and TRE value of 92.70 ± 1.75(%) and 6.52 ± 2.94(mm), compared with other state-of-the-art registration algorithms. CONCLUSION: The proposed method can accurately estimate the large deformation field by aggregating multiscale features, and achieve higher registration accuracy and fast registration speed. Comparative experiments also demonstrate the effectiveness and generalization ability of the algorithm.

Lung Recruitment Assessed by Electrical Impedance Tomography (RECRUIT): A Multicenter Study of COVID-19 Acute Respiratory Distress Syndrome.

Rationale: Defining lung recruitability is needed for safe positive end-expiratory pressure (PEEP) selection in mechanically ventilated patients. However, there is no simple bedside method including both assessment of recruitability and risks of overdistension as well as personalized PEEP titration. Objectives: To describe the range of recruitability using electrical impedance tomography (EIT), effects of PEEP on recruitability, respiratory mechanics and gas exchange, and a method to select optimal EIT-based PEEP. Methods: This is the analysis of patients with coronavirus disease (COVID-19) from an ongoing multicenter prospective physiological study including patients with moderate-severe acute respiratory distress syndrome of different causes. EIT, ventilator data, hemodynamics, and arterial blood gases were obtained during PEEP titration maneuvers. EIT-based optimal PEEP was defined as the crossing point of the overdistension and collapse curves during a decremental PEEP trial. Recruitability was defined as the amount of modifiable collapse when increasing PEEP from 6 to 24 cm H2O (ΔCollapse24-6). Patients were classified as low, medium, or high recruiters on the basis of tertiles of ΔCollapse24-6. Measurements and Main Results: In 108 patients with COVID-19, recruitability varied from 0.3% to 66.9% and was unrelated to acute respiratory distress syndrome severity. Median EIT-based PEEP differed between groups: 10 versus 13.5 versus 15.5 cm H2O for low versus medium versus high recruitability (P < 0.05). This approach assigned a different PEEP level from the highest compliance approach in 81% of patients. The protocol was well tolerated; in four patients, the PEEP level did not reach 24 cm H2O because of hemodynamic instability. Conclusions: Recruitability varies widely among patients with COVID-19. EIT allows personalizing PEEP setting as a compromise between recruitability and overdistension. Clinical trial registered with www.clinicaltrials.gov (NCT04460859).

Ineffective Effort in Patients With Acute Brain Injury Undergoing Invasive Mechanical Ventilation.

BACKGROUND: Ineffective effort (IE) is a frequent patient-ventilator asynchrony in invasive mechanical ventilation. This study aimed to investigate the incidence of IE and to explore its relationship with respiratory drive in subjects with acute brain injury undergoing invasive mechanical ventilation. METHODS: We retrospectively analyzed a clinical database that assessed patient-ventilator asynchrony in subjects with acute brain injury. IE was identified based on airway pressure, flow, and esophageal pressure waveforms collected at 15-min intervals 4 times daily. At the end of each data set recording, airway-occlusion pressure (P0.1) was determined by the airway occlusion test. IE index was calculated to indicate the severity of IE. The incidence of IE in different types of brain injuries as well as its relationship with P0.1 was determined. RESULTS: We analyzed 852 data sets of 71 subjects with P0.1 measured and undergoing mechanical ventilation for at least 3 d after enrollment. IE was detected in 688 (80.8%) data sets, with a median index of 2.2% (interquartile range 0.4-13.1). Severe IE (IE index ≥ 10%) was detected in 246 (28.9%) data sets. The post craniotomy for brain tumor and the stroke groups had higher median IE index and lower P0.1 compared with the traumatic brain injury group (2.6% [0.7-9.7] vs 2.7% [0.3-21] vs 1.2% [0.1-8.5], P = .002; 1.4 [1-2] cm H2O vs 1.5 [1-2.2] cm H2O vs 1.8 [1.1-2.8] cm H2O, P = .001). Low respiratory drive (P0.1 < 1.14 cm H2O) was independently associated with severe IE in the expiratory phase (IEE) even after adjusting for confounding factors by logistic regression analysis (odds ratio 5.18 [95% CI 2.69-10], P < .001). CONCLUSIONS: IE was very common in subjects with acute brain injury. Low respiratory drive was independently associated with severe IEE.

Biparametric magnetic resonance imaging-based radiomics features for prediction of lymphovascular invasion in rectal cancer.

BACKGROUND: Preoperative assessment of lymphovascular invasion(LVI) of rectal cancer has very important clinical significance. However, accurate preoperative imaging evaluation of LVI is highly challenging because the resolution of MRI is still limited. Relatively few studies have focused on prediction of LVI of rectal cancer with the tool of radiomics, especially in patients with negative statue of MRI-based extramural vascular invasion (mrEMVI).The purpose of this study was to explore the preoperative predictive value of biparametric MRI-based radiomics features for LVI of rectal cancer in patients with the negative statue of mrEMVI. METHODS: The data of 146 cases of rectal adenocarcinoma confirmed by postoperative pathology were retrospectively collected. In the cases, 38 had positive status of LVI. All patients were examined by MRI before the operation. The biparametric MRI protocols included T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI). We used whole-volume three-dimensional method and two feature selection methods, minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO), to extract and select the features. Logistics regression was used to construct models. The area under the receiver operating characteristic curve (AUC) and DeLong's test were used to evaluate the diagnostic performance of the radiomics based on T2WI and DWI and the combined models. RESULTS: Radiomics models based on T2WI and DWI had good predictive performance for LVI of rectal cancer in both the training cohort and the validation cohort. The AUCs of the T2WI model were 0.87 and 0.87, and the AUCs of the DWI model were 0.94 and 0.92. The combined model was better than the T2WI model, with AUCs of 0.97 and 0.95. The predictive performance of the DWI model was comparable to that of the combined model. CONCLUSIONS: The radiomics model based on biparametric MRI, especially DWI, had good predictive value for LVI of rectal cancer. This model has the potential to facilitate the clinical recognition of LVI in rectal cancer preoperatively.

The value of diffusion kurtosis imaging and intravoxel incoherent motion quantitative parameters in predicting synchronous distant metastasis of rectal cancer.

BACKGROUND: The incidence and mortality rate of rectal cancer are still high, the metastasis of rectal cancer are main causes of death. The control of the distant metastasis is one of the main concerns in the treatment of locally advanced rectal cancer, but there are few studies on predicting synchronous distant metastasis (SDM) of rectal cancer. METHOD: The data of patients with rectal adenocarcinoma confirmed by endoscopic biopsy or postoperative pathology from September 2015 to May 2020 in hospital A (center 1) and hospital B (center 2) were analyzed retrospectively, including age, sex, carcinoembryonic antigen, carbohydrate antigen 19-9, tumor location, tumor length, image staging and characteristics. The average age of the 169 patients consisting of 105 males and 64 females in study is 61.2 years. All patients underwent rectal routine rectal MRI, DKI and IVIM examinations on a 3.0-T scanner. Two radiologists sketched regions of interest (ROIs) on b = 1000 s/mm2 DKI and IVIM images to obtain quantitative parameters with FireVoxel manually. We evaluated the difference of histogram analysis, clinical and image data between SDM group and non-SDM group, and evaluated the efficacy of each index in predicting SDM of rectal cancer. RESULTS: The 90th percentile of f values in the SDM group is lower than that in the non-SDM group (29.4 ± 8.4% vs. 35 ± 17.8%, P = 0.005). CA19-9 in the SDM group is higher than that in the non-SDM group (P = 0.003). Low and high rectal cancer are more likely to develop SDM than middle rectal cancer (P = 0.05 and P = 0.047). The combination of these three indexes has a greater area under the curve (AUC) than any one index (0.801 vs. 0.685 (f (90th percentile)) and 0.627 (CA19-9), P = 0.0075 and 0.0058, respectively), and its specificity and sensitivity are 80.0% and 71.6%, respectively. When this combination is incorporated into the predictive nomogram model, the c-index is 0.801 (95% confidence interval (CI): 0.730-0.871). CONCLUSIONS: IVIM quantitative parameters combine with CA19-9 and tumor location can better predict the risk of SDM of rectal cancer.

PD-1 Inhibitor Combined With Radiotherapy and GM-CSF (PRaG) in Patients With Metastatic Solid Tumors: An Open-Label Phase II Study.

Patients with metastatic cancer refractory to standard systemic therapies have a poor prognosis and few therapeutic options. Radiotherapy can shape the tumor microenvironment (TME) by inducing immunogenic cell death and promoting tumor recognition by natural killer cells and T lymphocytes. Granulocyte macrophage-colony stimulating factor (GM-CSF) was known to promote dendric cell maturation and function, and might also induce the macrophage polarization with anti-tumor capabilities. A phase II trial (ChiCTR1900026175) was conducted to assess the clinical efficacy and safety of radiotherapy, PD-1 inhibitor and GM-CSF (PRaG regimen). This trial was registered at http://www.chictr.org.cn/index.aspx. A PRaG cycle consisted of 3 fractions of 5 or 8 Gy delivered for one metastatic lesion from day 1, followed by 200 µg subcutaneous injection of GM-CSF once daily for 2 weeks, and intravenous infusion of PD-1 inhibitor once within one week after completion of radiotherapy. The PRaG regimen was repeated every 21 days for at least two cycles. Once the PRaG therapy was completed, the patient continued PD-1 inhibitor monotherapy until confirmed disease progression or unacceptable toxicity. The primary endpoint was objective response rate (ORR). A total of 54 patients were enrolled with a median follow-up time of 16.4 months. The ORR was 16.7%, and the disease control rate was 46.3% in intent-to-treat patients. Median progression-free survival was 4.0 months (95% confidence interval [CI], 3.3 to 4.8), and median overall survival was 10.5 months (95% CI, 8.7 to 12.2). Grade 3 treatment-related adverse events occurred in five patients (10.0%) and grade 4 in one patient (2.0%). Therefore, the PRaG regimen was well tolerated with acceptable toxicity and may represent a promising salvage treatment for patients with chemotherapy-refractory solid tumors. It is likely that PRaG acts via heating upthe TME with radiotherapy and GM-CSF, which was further boosted by PD-1 inhibitors.

Incidence, Risk Factors and Outcomes of Sepsis in Critically Ill Post-craniotomy Patients: A Single-Center Prospective Cohort Study.

Background: Data concerning the epidemiology of sepsis in critically ill post-craniotomy patients are scarce. This study aimed to assess the incidence, risk factors, and outcomes of sepsis in this population. Methods: This was a single-center prospective cohort study. Post-craniotomy patients admitted to the intensive care unit (ICU) were screened daily for the presence of infection and sepsis. Results: Of the 900 included patients, 300 developed sepsis. The cumulative incidence of sepsis was 33.3% [95% confidence interval (CI), 30.2-36.4%]. Advanced age, male, hypertension, trauma, postoperative intracranial complications, and lower Glasgow Coma Scale (GCS) on the first postoperative day were independent risk factors of sepsis. Septic patients had higher hospital mortality (13.7 vs. 8.3%, P = 0.012), longer ICU length of stay (LOS) (14 vs. 4 days, P < 0.001), longer hospital LOS (31 vs. 19 days, P < 0.001), and higher total medical cost (CNY 138,394 vs. 75,918, P < 0.001) than patients without sepsis. Conclusion: Sepsis is a frequent complication in critically ill post-craniotomy patients. Advanced age, male, hypertension, trauma, postoperative intracranial complications, and lower GCS on the first postoperative day were independent risk factors of sepsis.

Partition of respiratory mechanics in patients with acute respiratory distress syndrome and association with outcome: a multicentre clinical study.

PURPOSE: In acute respiratory distress syndrome (ARDS), physiological parameters associated with outcome may help defining targets for mechanical ventilation. This study aimed to address whether transpulmonary pressures (PL), including transpulmonary driving pressure (DPL), elastance-derived plateau PL, and directly-measured end-expiratory PL, are better associated with 60-day outcome than airway driving pressure (DPaw). We also tested the combination of oxygenation and stretch index [PaO2/(FiO2*DPaw)]. METHODS: Prospective, observational, multicentre registry of ARDS patients. Respiratory mechanics were measured early after intubation at 6 kg/ml tidal volume. We compared the predictive power of the parameters for mortality at day-60 through receiver operating characteristic (ROC) and assessed their association with 60-day mortality through unadjusted and adjusted Cox regressions. Finally, each parameter was dichotomized, and Kaplan-Meier survival curves were compared. RESULTS: 385 patients were enrolled 2 [1-4] days from intubation (esophageal pressure and arterial blood gases in 302 and 318 patients). As continuous variables, DPaw, DPL, and oxygenation stretch index were associated with 60-day mortality after adjustment for age and Sequential Organ Failure Assessment, whereas elastance-derived plateau PL was not. DPaw and DPL performed equally in ROC analysis (P = 0.0835). DPaw had the best-fit Cox regression model. When dichotomizing the variables, DPaw ≥ 15, DPL ≥ 12, plateau PL ≥ 24, and oxygenation stretch index < 10 exhibited lower 60-day survival probability. Directly measured end-expiratory PL ≥ 0 was associated with better outcome in obese patients. CONCLUSION: DPL was equivalent predictor of outcome than DPaw. Our study supports the soundness of limiting lung and airway driving pressure and maintaining positive end-expiratory PL in obese patients.

A U-shaped association of tracheostomy timing with all-cause mortality in mechanically ventilated patients admitted to the intensive care unit: A retrospective cohort study.

Objectives: To evaluate the association of tracheostomy timing with all-cause mortality in patients with mechanical ventilation (MV). Method: It's a retrospective cohort study. Adult patients undergoing invasive MV who received tracheostomy during the same hospitalization based on the Medical Information Mart for Intensive Care-III (MIMIC-III) database, were selected. The primary outcome was the relationship between tracheostomy timing and 90-day all-cause mortality. A restricted cubic spline was used to analyze the potential non-linear correlation between tracheostomy timing and 90-day all-cause mortality. The secondary outcomes included free days of MV, incidence of ventilator-associated pneumonia (VAP), free days of analgesia/sedation in the intensive care unit (ICU), length of stay (LOS) in the ICU, LOS in hospital, in-ICU mortality, and 30-day all-cause mortality. Results: A total of 1,209 patients were included in this study, of these, 163 (13.5%) patients underwent tracheostomy within 4 days after intubation, while 647 (53.5%) patients underwent tracheostomy more than 11 days after intubation. The tracheotomy timing showed a U-shaped relationship with all-cause mortality, patients who underwent tracheostomy between 5 and 10 days had the lowest 90-day mortality rate compared with patients who underwent tracheostomy within 4 days and after 11 days [84 (21.1%) vs. 40 (24.5%) and 206 (31.8%), P < 0.001]. Conclusion: The tracheotomy timing showed a U-shaped relationship with all-cause mortality, and the risk of mortality was lowest on day 8, but a causal relationship has not been demonstrated.

Ensemble Support Vector Recurrent Neural Network for Brain Signal Detection.

The brain-computer interface (BCI) P300 speller analyzes the P300 signals from the brain to achieve direct communication between humans and machines, which can assist patients with severe disabilities to control external machines or robots to complete expected tasks. Therefore, the classification method of P300 signals plays an important role in the development of BCI systems and technologies. In this article, a novel ensemble support vector recurrent neural network (E-SVRNN) framework is proposed and developed to acquire more accurate and efficient electroencephalogram (EEG) signal classification results. First, we construct a support vector machine (SVM) to formulate EEG signals recognizing model. Second, the SVM formulation is transformed into a standard convex quadratic programming (QP) problem. Third, the convex QP problem is solved by combining a varying parameter recurrent neural network (VPRNN) with a penalty function. Experimental results on BCI competition II and BCI competition III datasets demonstrate that the proposed E-SVRNN framework can achieve accuracy rates as high as 100% and 99%, respectively. In addition, the results of comparison experiments verify that the proposed E-SVRNN possesses the best recognition accuracy and information transfer rate (ITR) compared with most of the state-of-the-art algorithms.

Development of an early prediction model for postoperative delirium in neurosurgical patients admitted to the ICU after elective craniotomy (E-PREPOD-NS): A secondary analysis of a prospective cohort study.

Postoperative delirium (POD) is a significant clinical problem in neurosurgical patients after intracranial surgery. Identification of high-risk patients may optimize perioperative management, but an adequate risk model for use at early phase after operation has not been developed. In the secondary analysis of a prospective cohort study, 800 adult patients admitted to the ICU after elective intracranial surgeries were included. The POD was diagnosed as Confusion Assessment Method for the ICU positive on postoperative day 1 to 3. Multivariate logistic regression analysis was used to develop early prediction model (E-PREPOD-NS) and the final model was validated with 200 bootstrap samples. The incidence of POD in this cohort was19.6%. We identified nine variables independently associated with POD in the final model: advanced age (OR 3.336, CI 1.765-6.305, 1 point), low education level (OR 2.528, 1.446-4.419, 1), smoking history (OR 2.582, 1.611-4.140, 1), diabetes (OR 2.541, 1.201-5.377, 1), supra-tentorial lesions (OR 3.424, 2.021-5.802, 1), anesthesia duration > 360 min (OR 1.686, 1.062-2.674, 0.5), GCS < 9 at ICU admission (OR 6.059, 3.789-9.690, 1.5), metabolic acidosis (OR 13.903, 6.248-30.938, 2.5), and neurosurgical drainage tube (OR 1.924, 1.132-3.269, 0.5). The area under the receiver operator curve (AUROC) of the risk score for prediction of POD was 0.865 (95% CI 0.835-0.895). The AUROC was 0.851 after internal validation (95% CI 0.791-0.912). The model showed good calibration. The E-PREPOD-NS model can predict POD in patients admitted to the ICU after elective intracranial surgery with good accuracy. External validation is needed in the future.

Features of CT and EUS in mesenteric plexiform neurofibroma with Neurofibromatosis type I: A case report.

Plexiform neurofibroma(PNF) is a rare benign tumor of the peripheral nerve, belonging to a subtype of neurofibroma. PNF is common in the head, neck and trunk. It is uncommonly observed in the mesentery. We report a case of mesenteric PNF in a 64-year-old man history of neurofibromatosis type I(NF1), which caused abdomen pain. In addition, the computer tomography(CT) and endoscopic ultrasonography(EUS) manifestations of mesenteric PNF were analyzed. The imaging appearance of a mesenteric plexiform neurofibroma is that many low-density (CT) /mixed echo (EUS) soft tissue masses surrounding the superior mesenteric artery, but not surrounding the superior mesenteric vein. Our case adds to the limited literature regarding NF1 presenting with mesenteric PNF. The computer tomography and endoscopic ultrasonography may facilitate confirma diagnosis of mesenteric PNF.