Home indoor air quality and cognitive function over one year for people working remotely during COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic triggered an increase in remote work-from-home for office workers. Given that many homes now function as offices despite not being designed to support office work, it is critical to research the impact of indoor air quality (IAQ) in homes on the cognitive performance of people working from home. In this study, we followed 206 office workers across the U.S. over one year under remote or hybrid-remote settings during 2021-2022. Participants placed two real-time, consumer-grade indoor environmental monitors in their home workstation area and bedroom. Using a custom smartphone application geofenced to their residential address, participants responded to surveys and periodic cognitive function tests, including the Stroop color-word interference test, Arithmetic two-digit addition/subtraction test, and Compound Remote Associates Task (cRAT). Exposures assessed included carbon dioxide (CO2) and thermal conditions (indoor heat index: a combination of temperature and relative humidity) averaged over 30 minutes prior to each cognitive test. In fully adjusted longitudinal mixed models (n≤121), we found that indoor thermal conditions at home were associated with cognitive function outcomes non-linearly (p<0.05), with poorer cognitive performance on the Stroop test and poorer creative problem-solving on the cRAT when conditions were either too warm or too cool. Most indoor CO2 levels were <640 ppm, but there was still a slight association between higher CO2 and poorer cognitive performance on Stroop (p=0.09). Our findings highlight the need to enhance home indoor environmental quality for optimal cognitive function during remote work, with benefits for both employees and employers.

Learning and depicting lobe-based radiomics feature for COPD Severity staging in low-dose CT images.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a prevalent and debilitating respiratory condition that imposes a significant healthcare burden worldwide. Accurate staging of COPD severity is crucial for patient management and treatment planning. METHODS: The retrospective study included 530 hospital patients. A lobe-based radiomics method was proposed to classify COPD severity using computed tomography (CT) images. First, we segmented the lung lobes with a convolutional neural network model. Secondly, the radiomic features of each lung lobe are extracted from CT images, the features of the five lung lobes are merged, and the selection of features is accomplished through the utilization of a variance threshold, t-Test, least absolute shrinkage and selection operator (LASSO). Finally, the COPD severity was classified by a support vector machine (SVM) classifier. RESULTS: 104 features were selected for staging COPD according to the Global initiative for chronic Obstructive Lung Disease (GOLD). The SVM classifier showed remarkable performance with an accuracy of 0.63. Moreover, an additional set of 132 features were selected to distinguish between milder (GOLD I + GOLD II) and more severe instances (GOLD III + GOLD IV) of COPD. The accuracy for SVM stood at 0.87. CONCLUSIONS: The proposed method proved that the novel lobe-based radiomics method can significantly contribute to the refinement of COPD severity staging. By combining radiomic features from each lung lobe, it can obtain a more comprehensive and rich set of features and better capture the CT radiomic features of the lung than simply observing the lung as a whole.

Material Removal Mechanism of SiC Ceramic by Porous Diamond Grinding Wheel Using Discrete Element Simulation.

SiC ceramics are typically hard and brittle materials. Serious surface/subsurface damage occurs during the grinding process due to the poor self-sharpening ability of monocrystalline diamond grits. Nevertheless, recent findings have demonstrated that porous diamond grits can achieve high-efficiency and low-damage machining. However, research on the removal mechanism of porous diamond grit while grinding SiC ceramic materials is still in the bottleneck stage. A discrete element simulation model of the porous diamond grit while grinding SiC ceramics was established to optimize the grinding parameters (e.g., grinding wheel speed, undeformed chip thickness) and pore parameters (e.g., cutting edge density) of the porous diamond grit. The influence of these above parameters on the removal and damage of SiC ceramics was explored from a microscopic perspective, comparing with monocrystalline diamond grit. The results show that porous diamond grits cause less damage to SiC ceramics and have better grinding performance than monocrystalline diamond grits. In addition, the optimal cutting edge density and undeformed chip thickness should be controlled at 1-3 and 1-2 um, respectively, and the grinding wheel speed should be greater than 80 m/s. The research results lay a scientific foundation for the efficient and low-damage grinding of hard and brittle materials represented by SiC ceramics, exhibiting theoretical significance and practical value.

Development of a fluorescent DNA sensor for dual detection of heavy metal ions utilising DAPI in distinct buffers.

The DNA-based biosensor utilises a thymine/guanine(T/G)-rich ODN-4 scaffold with 4',6-diamidino-2-phenylindole(DAPI) as a fluorescent emissary to monitor mercury/lead(Hg(II)/Pb(II)) ions simultaneously. Key to its bifocal detection capability is the twin unbound cytosine(C) bases strategically bridging the G-quadruplex and T-rich sequences, enabling their synergistic interplay. It facilitates the recognition of Hg(II)/Pb(II) ions, characterised by high specificity, and effectively mitigates interference from silver(Ag(I)). The G-quadruplex, guided by the C bases, induces a conformational transition in T-Hg(II)-T complexes, resulting in intense fluorescence. Pb(II) causes a spatial shift in the G-quadruplex, relaxing the T-Hg(II)-T base pairs and attenuating the fluorescence signal. The ODN-4 exhibits a robust, linear correlation with Hg(II) concentration (4.09 nmol/L to 1000 nmol/L) and Pb(II) concentration (3.22 nmol/L to 5 µmol/L). Recovery rates in milk, tap water, and rice water specimens with both ions validate method accuracy (Hg(II): 95.19% to 104.68%, Pb(II): 98.20% to 103.46%). It holds promising prospects for practical food analysis.

Defect scattering can lead to enhanced phonon transport at nanoscale.

Defect scattering is well known to suppress thermal transport. In this study, however, we perform both molecular dynamics and Boltzmann transport equation calculations, to demonstrate that introducing defect scattering in nanoscale heating zone could surprisingly enhance thermal conductance of the system by up to 75%. We further reveal that the heating zone without defects yields directional nonequilibrium with overpopulated oblique-propagating phonons which suppress thermal transport, while introducing defects redirect phonons randomly to restore directional equilibrium, thereby enhancing thermal conductance. We demonstrate that defect scattering can enable such thermal transport enhancement in a wide range of temperatures, materials, and sizes, and offer an unconventional strategy for enhancing thermal transport via the manipulation of phonon directional nonequilibrium.

Acute exacerbation prediction of COPD based on Auto-metric graph neural network with inspiratory and expiratory chest CT images.

Chronic obstructive pulmonary disease (COPD) is a widely prevalent disease with significant mortality and disability rates and has become the third leading cause of death globally. Patients with acute exacerbation of COPD (AECOPD) often substantially suffer deterioration and death. Therefore, COPD patients deserve special consideration regarding treatment in this fragile population for pre-clinical health management. Based on the above, this paper proposes an AECOPD prediction model based on the Auto-Metric Graph Neural Network (AMGNN) using inspiratory and expiratory chest low-dose CT images. This study was approved by the ethics committee in the First Affiliated Hospital of Guangzhou Medical University. Subsequently, 202 COPD patients with inspiratory and expiratory chest CT Images and their annual number of AECOPD were collected after the exclusion. First, the inspiratory and expiratory lung parenchyma images of the 202 COPD patients are extracted using a trained ResU-Net. Then, inspiratory and expiratory lung Radiomics and CNN features are extracted from the 202 inspiratory and expiratory lung parenchyma images by Pyradiomics and pre-trained Med3D (a heterogeneous 3D network), respectively. Last, Radiomics and CNN features are combined and then further selected by the Lasso algorithm and generalized linear model for determining node features and risk factors of AMGNN, and then the AECOPD prediction model is established. Compared to related models, the proposed model performs best, achieving an accuracy of 0.944, precision of 0.950, F1-score of 0.944, ad area under the curve of 0.965. Therefore, it is concluded that our model may become an effective tool for AECOPD prediction.

Construction and validation of an 18F-FDG-PET/CT-based prognostic model to predict progression-free survival in newly diagnosed multiple myeloma patients.

OBJECTIVE: To investigate the relationship between 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) related parameters and the prognosis of multiple myeloma and to establish and validate a prediction model regarding the progression-free survival (PFS) of multiple myeloma. METHODS: A retrospective analysis of 126 newly diagnosed multiple myeloma patients who attended Nanjing Drum Tower Hospital from 2014-2021. All patients underwent PET/CT before treatment and were divided into a training cohort (n = 75) and a validation cohort (n = 51). Multivariate Cox proportional hazard regression analysis incorporated PET/CT-related parameters and clinical indicators. A nomogram was established to individually predict PFS in MM patients. The model was evaluated by calculating the C-index and calibration curve. RESULTS: Here, 4.2 was used as the cut-off value of SUVmax to divide patients into high and low groups. PFS significantly differed between patients in the high-SUVmax group and low-SUVmax group, and SUVmax was an independent predictor of PFS in newly diagnosed multiple myeloma (NDMM) patients. Univariate and multivariate cox regression analysis suggested that lactate dehydrogenase (LDH), bone marrow plasma cell (BMPC), and SUVmax affected PFS. These factors were incorporated to construct a nomogram model for predicting PFS at 1 and 2 years in NDMM patients. The C-index and calibration curves of the nomogram exhibited good accuracy and consistency, and the DCA curves suggested that the model had good clinical utility. CONCLUSION: The PET/CT parameter SUVmax is closely related to the prognosis of myeloma patients. The nomogram constructed in this study based on PET/CT-related parameters and clinical indicators individually predicts the PFS rate of NDMM patients and enables further risk stratification of NDMM patients.

NatD epigenetically activates FOXA2 expression to promote breast cancer progression by facilitating MMP14 expression.

N-α-acetyltransferase D (NatD) mediates N-α-terminal acetylation of histone H4 (Nt-Ac-H4), but its role in breast cancer metastasis remains unknown. Here, we show that depletion of NatD directly represses the expression of FOXA2, and is accompanied by a significant reduction in Nt-Ac-H4 enrichment at the FOXA2 promoter. We show that NatD is commonly upregulated in primary breast cancer tissues, where its expression level correlates with FOXA2 expression, enhanced invasiveness, and poor clinical outcomes. Furthermore, we show that FOXA2 promotes the migration and invasion of breast cancer cells by activating MMP14 expression. MMP14 is also upregulated in breast cancer tissues, where its expression level correlates with FOXA2 expression and poor clinical prognosis. Our study shows that the NatD-FOXA2-MMP14 axis functions as a key signaling pathway to promote the migratory and invasive capabilities of breast cancer cells, suggesting that NatD is a critical epigenetic modulator of cell invasion during breast cancer progression.

COPD stage detection: leveraging the auto-metric graph neural network with inspiratory and expiratory chest CT images.

Chronic obstructive pulmonary disease (COPD) is a common lung disease that can lead to restricted airflow and respiratory problems, causing a significant health, economic, and social burden. Detecting the COPD stage can provide a timely warning for prompt intervention in COPD patients. However, existing methods based on inspiratory (IN) and expiratory (EX) chest CT images are not sufficiently accurate and efficient in COPD stage detection. The lung region images are autonomously segmented from IN and EX chest CT images to extract the 1 , 781 × 2 lung radiomics and 13 , 824 × 2 3D CNN features. Furthermore, a strategy for concatenating and selecting features was employed in COPD stage detection based on radiomics and 3D CNN features. Finally, we combine all the radiomics, 3D CNN features, and factor risks (age, gender, and smoking history) to detect the COPD stage based on the Auto-Metric Graph Neural Network (AMGNN). The AMGNN with radiomics and 3D CNN features achieves the best performance at 89.7 % of accuracy, 90.9 % of precision, 89.5 % of F1-score, and 95.8 % of AUC compared to six classic machine learning (ML) classifiers. Our proposed approach demonstrates high accuracy in detecting the stage of COPD using both IN and EX chest CT images. This method can potentially establish an efficient diagnostic tool for patients with COPD. Additionally, we have identified radiomics and 3D CNN as more appropriate biomarkers than Parametric Response Mapping (PRM). Moreover, our findings indicate that expiration yields better results than inspiration in detecting the stage of COPD.

A preliminary study of dynamic interactive simulation and computational CT scan of the ideal alveolus model.

BACKGROUND: While the development of CT imaging technique has brought cognition of in vivo organs, the resolution of CT images and their static characteristics have gradually become barriers of microscopic tissue research. PURPOSE: Previous research used the finite element method to study the airflow and gas exchange in the alveolus and acinar to show the fate of inhaled aerosols and studied the diffusive, convective, and sedimentation mechanisms. Our study combines these techniques with CT scan simulation to study the mechanisms of respiratory movement and its imaging appearance. METHODS: We use 3D fluid-structure interaction simulation to study the movement of an ideal alveolus under regular and forced breathing situations and ill alveoli with different tissue elasticities. Additionally, we use the Monte Carlo algorithm within the OpenGATE platform to simulate the computational CT images of the dynamic process with different designated resolutions. The resolutions show the relationship between the kinematic model of the human alveolus and its imaging appearance. RESULTS: The results show that the alveolus and the wall thickness can be seen with an image resolution smaller than 15.6 µm. With ordinary CT resolution, the alveolus is expressed with four voxels. CONCLUSIONS: This is a preliminary study concerning the imaging appearance of the dynamic alveolus model. This technique will be used to study the imaging appearance of the dynamic bronchial tree and the lung lobe models in the future.

Exploring the Effectiveness of Multi-Objective Training for Organ Substructure Segmentation in Medical Imaging.

Chronic obstructive pulmonary disease (COPD) is closely related to the right ventricle and lung lobes. This study focuses on the segmentation of the right ventricle and lung lobes. We conducted experiments using the MMWHS and our lung lobe datasets and evaluated the segmentation using different training models. We observed that the multi-objective segmentation approach has advantages over single-objective segmentation in segmenting the right ventricle and lung lobes. For the segmentation of the right ventricle, the multi-objective segmentation approach yielded an improvement of 2.0% in the Dice coefficient and 2.5% in the Jaccard index compared to single-objective segmentation. For the segmentation of five lung lobes, the multi-objective segmentation outperformed the single-objective segmentation with Dice coefficient improvements of 1.4%, 1.0%, 1.5%, 0.7%, and 1.3%, respectively.

Current Advances and Future Strategies for BCL-2 Inhibitors: Potent Weapons against Cancers.

Targeting the intrinsic apoptotic pathway regulated by B-cell lymphoma-2 (BCL-2) antiapoptotic proteins can overcome the evasion of apoptosis in cancer cells. BCL-2 inhibitors have evolved into an important means of treating cancers by inducing tumor cell apoptosis. As the most extensively investigated BCL-2 inhibitor, venetoclax is highly selective for BCL-2 and can effectively inhibit tumor survival. Its emergence and development have significantly influenced the therapeutic landscape of hematological malignancies, especially in chronic lymphocytic leukemia and acute myeloid leukemia, in which it has been clearly incorporated into the recommended treatment regimens. In addition, the considerable efficacy of venetoclax in combination with other agents has been demonstrated in relapsed and refractory multiple myeloma and certain lymphomas. Although venetoclax plays a prominent antitumor role in preclinical experiments and clinical trials, large individual differences in treatment outcomes have been characterized in real-world patient populations, and reduced drug sensitivity will lead to disease recurrence or progression. The therapeutic efficacy may vary widely in patients with different molecular characteristics, and key genetic mutations potentially result in differential sensitivities to venetoclax. The identification and validation of more novel biomarkers are required to accurately predict the effectiveness of BCL-2 inhibition therapy. Furthermore, we summarize the recent research progress relating to the use of BCL-2 inhibitors in solid tumor treatment and demonstrate that a wealth of preclinical models have shown promising results through combination therapies. The applications of venetoclax in solid tumors warrant further clinical investigation to define its prospects.

GiftBTE: an efficient deterministic solver for non-gray phonon Boltzmann transport equation.

Advances in nanotechnology have facilitated the exploration of submicron thermal transport. At this scale, Fourier's law is no longer applicable, and the governing equation for thermal transport is the phonon Boltzmann transport equation (BTE). However, the availability of open-source solvers for the phonon BTE is limited, impeding progress in this field. This study introduces an open-source package, GiftBTE, for numerically solving the non-gray phonon BTE. GiftBTE employs deterministic solutions and provides both steady-state and transient solvers. For the steady-state solver, GiftBTE employs the implicit discrete ordinates method (DOM) with second-order spatial accuracy and the synthetic iterative scheme. For the transient solver, GiftBTE employs the explicit DOM with second-order spatial accuracy. This package demonstrates excellent computational efficiency, enabling realistic three-dimensional simulations of devices and materials. By interfacing with first-principles calculations, this solver enables parameter-free computation of submicron thermal transport. The application of GiftBTE includes, but is not limited to, computing the thermal conductivity of nanostructures, predicting temperature rises in transistors, and simulating laser heating processes.

Transformer-based 3D U-Net for pulmonary vessel segmentation and artery-vein separation from CT images.

Transformer-based methods have led to the revolutionizing of multiple computer vision tasks. Inspired by this, we propose a transformer-based network with a channel-enhanced attention module to explore contextual and spatial information in non-contrast (NC) and contrast-enhanced (CE) computed tomography (CT) images for pulmonary vessel segmentation and artery-vein separation. Our proposed network employs a 3D contextual transformer module in the encoder and decoder part and a double attention module in skip connection to effectively finish high-quality vessel and artery-vein segmentation. Extensive experiments are conducted on the in-house dataset and the ISICDM2021 challenge dataset. The in-house dataset includes 56 NC CT scans with vessel annotations and the challenge dataset consists of 14 NC and 14 CE CT scans with vessel and artery-vein annotations. For vessel segmentation, Dice is 0.840 for CE CT and 0.867 for NC CT. For artery-vein separation, the proposed method achieves a Dice of 0.758 of CE images and 0.602 of NC images. Quantitative and qualitative results demonstrated that the proposed method achieved high accuracy for pulmonary vessel segmentation and artery-vein separation. It provides useful support for further research associated with the vascular system in CT images. The code is available at https://github.com/wuyanan513/Pulmonary-Vessel-Segmentation-and-Artery-vein-Separation .

Tubular Structure Segmentation via Multi-Scale Reverse Attention Sparse Convolution.

Cerebrovascular and airway structures are tubular structures used for transporting blood and gases, respectively, providing essential support for the normal activities of the human body. Accurately segmenting these tubular structures is the basis of morphology research and pathological detection. Nevertheless, accurately segmenting these structures from images presents great challenges due to their complex morphological and topological characteristics. To address this challenge, this paper proposes a framework UARAI based on the U-Net multi-scale reverse attention network and sparse convolution network. The framework utilizes a multi-scale structure to effectively extract the global and deep detail features of vessels and airways. Further, it enhances the extraction ability of fine-edged features by a joint reverse attention module. In addition, the sparse convolution structure is introduced to improve the features' expression ability without increasing the model's complexity. Finally, the proposed training sample cropping strategy reduces the influence of block boundaries on the accuracy of tubular structure segmentation. The experimental findings demonstrate that the UARAI-based metrics, namely Dice and IoU, achieve impressive scores of 90.31% and 82.33% for cerebrovascular segmentation and 93.34% and 87.51% for airway segmentation, respectively. Compared to commonly employed segmentation techniques, the proposed method exhibits remarkable accuracy and robustness in delineating tubular structures such as cerebrovascular and airway structures. These results hold significant promise in facilitating medical image analysis and clinical diagnosis, offering invaluable support to healthcare professionals.

A meta-analysis of application of PD-1/PD-L1 inhibitor-based immunotherapy in unresectable locally advanced triple-negative breast cancer.

Aims: The purpose of this study was to explore the efficacy of immunotherapy for patients with triple-negative breast cancer (TNBC). Materials & methods: Randomized clinical trials comparing immunotherapy with chemotherapy for advanced TNBC patients were included. Results: A total of six articles (3183 patients) were eligible for this meta-analysis. PD-1/PD-L1 inhibitor-based immunotherapy combined with chemotherapy can significantly increase the progression-free survival (hazard ratio [HR] = 0.82; 95% CI = 0.76-1.14; p < 0.001) of unresectable locally advanced or metastatic TNBC patients without effect on overall survival, compared with chemotherapy. Conclusion: PD-1/PD-L1 inhibitors-based immunotherapy can safely improve progression-free survival in patients with unresectable locally advanced or metastatic TNBC, but has no effect on overall survival.

Breast cancer is a malignant tumor. It is most common in females. Triple-negative breast cancer is one type of malignant tumor that is not sensitive to treatment and is prone to recurrence. It can easily lead to death. Treatment mainly relies on chemotherapy. Immunotherapy is a new treatment method ad includes PD-1/L1 inhibitors. This research was conducted to assess its effects. Immunotherapy has good effects and can alleviate symptoms. It can improve prognosis and extend life. It has some side effects, mainly in the lungs and thyroid, but these side effects are controllable.

Integration of clinical features and sociodemographic factors: a simplified prognostic model for patients with multiple myeloma based on a double-center retrospective analysis.

This study aimed to develop and validate a prognostic nomogram that combines clinical and sociodemographic factors of patients with newly diagnosed multiple myeloma (MM). A total of 257 newly diagnosed patients with MM from two independent medical centers in China were included in this retrospective cohort study. Univariate and multivariate Cox regression models were used to identify independent risk factors and to construct the nomogram. The predictive ability of the nomogram was evaluated using the areas under the curve (AUCs) and calibration curves. K-fold cross-validation was employed for internal validation of the nomogram performance. Moreover, a stratification system to determine risk level was generated based on the nomogram. Hemoglobulin, creatinine, rurality, and marital status were significantly associated with overall survival (OS) and were incorporated into the nomogram for OS prediction. The prognostic nomogram showed good discrimination and accuracy, and its predictive capability was superior to the International Staging System. The AUC values predicting the 1-, 3-, and 5-year OS probabilities of the nomogram were 0.775, 0.755, and 0.754, respectively. Subsequently, patients were classified into high- and low-risk subgroups based on the median total points of the nomogram; this risk stratification clearly distinguished between high- and low-risk MM patients with significantly different clinical outcomes (median OS: 27 months vs. 84 months). We established a novel prognostic prediction model by comprehensively incorporating clinical and sociodemographic variables, which can effectively predict the survival outcomes in patients with MM.

Low human beta-defensin-2 levels in the sputum of COPD patients are associated with the risk of exacerbations.

RATIONALE: Chronic obstructive pulmonary disease (COPD) is a complicated chronic inflammatory disease. It is important to investigate the characteristics of acute exacerbation of COPD to develop new therapeutic strategies. OBJECTIVE: This study aimed to determine the relationship between the human beta-defensin-2 (hBD-2) levels and aggravation of COPD. METHODS: We detected the sputum hBD-2 level of 254 patients from Guangzhou, China, for 2 years. The study participants were categorized into the COPD group (n = 203, GOLD 0-4) and the control group (n = 51, 40-79 years old). At baseline, 12th month, and 24th month, we detected the sputum hBD-2 level and levels of cytokines, such as CXCL10, CXCL11, and IFN. RESULTS: At baseline, there were no significant differences in the sputum and serum hBD-2 levels between the patients and the controls. However, the sputum hBD-2 levels of patients who had at least one symptom aggravation over the next 2 years were significantly lower than those of patients without any exacerbations (1130.9 ± 858.4 pg/mL vs. 2103.7 ± 1294.2 pg/mL, respectively; p = 0.001). Nevertheless, there were no statistically significant differences in the sputum hBD-2 levels between patients (no aggravation history) and controls (2084.9 ± 1317.6 pg/mL vs. 2152.5 ± 1251.6 pg/mL, respectively; p = 0.626). We used a logistic regression model to assess the relationship between aggravation and sputum hBD-2 levels. Interestingly, we found that low hBD-2 level (< 1000 pg/mL) was significantly associated with exacerbations. Specifically, patients with low hBD-2 levels were more likely to experience exacerbations in the next 12 months (0.333 vs. 0.117; p = 0.001). Moreover, we compared the hBD-2 levels between controls and patients with GOLD 3-4 and found that participants with bacteria (+) and/or viruses (+) had an association between hBD-2 level and disease severity (p = 0.02). CONCLUSION: Patients at risk of exacerbations are more likely to have lower sputum hBD-2 levels. These results have important implications for future therapies for COPD.

Predictive Value of Red Cell Distribution Width in the Diagnosis of Peripherally Inserted Central Catheter (PICC)-Related Thrombosis Among Cancer Patients.

Purpose: This study aimed to investigate the predictive value of red cell distribution width (RDW) in patients with peripherally inserted central catheter (PICC)-related thrombosis. Patients and methods: This was a case‒control study. A total of 98 cancer patients undergoing PICC placement from January 2017 to December 2019 at the ICU Venous Access Center of Peking University Cancer Hospital were retrospectively analyzed. These included 47 cases in the thrombosis group and 51 cases in the nonthrombosis group according to the occurrence of PICC-related thrombosis within 90 days after the PICC procedure. Results: a) RDW was significantly higher in the thrombosis group (15.98±1.93) than in the nonthrombosis group (13.98±1.09). Additionally, the number of smoking histories in the thrombosis group (18, 38.30%) was significantly higher than that in the nonthrombosis group (5, 9.80%). b) Logistic regression analysis indicated that RDW and smoking history were independent risk factors for PICC-related thrombosis (OR: 3.028, P<0.001; OR: 5.640, P=0.023). c) The area under the receiver operating characteristic (ROC) curve of the combination of RDW and smoking history was 86.8%, and the sensitivity and specificity for predicting thrombosis after PICC were 85.1% and 80%, respectively. d) Cox regression analysis revealed that RDW and smoking history were independent risk factors for PICC-related thrombosis (p<0.001). e) Results from Kaplan‒Meier analysis showed that incidence of PICC-related thrombosis in patients with RDW less than 14.75% is significantly lower than those with RDW 14.75% and above (p<0.001). e) Baseline RDW was not different between the two groups (P>0.05), ΔRDW was significantly different between the two groups (p<0.001). Conclusion: The combined use of RDW and smoking history has early evaluation and predictive value in the diagnosis of PICC-related thrombosis.

Efficacy and safety of rivaroxaban for the treatment of PICC-related upper extremity deep vein thrombosis in cancer patients: a retrospective study.

BACKGROUND: The optimal duration and choice of anticoagulant for the treatment of Peripherally inserted central catheters (PICC)-related upper extremity deep vein thrombosis (UEDVT) in cancer patients are still undetermined. OBJECTIVES: The aim of this study was to assess the efficacy and safety of rivaroxaban for the treatment of PICC-related UEDVT in cancer patients. METHODS: We conducted a retrospective cohort study including consecutive cancer patients for the management of acute symptomatic PICC-related UEDVT. The efficacy outcome of the study was the 180-day recurrence of any venous thromboembolism (VTE), while the safety outcome was the 180-day incidence of all bleeding events. The Kaplan‒Meier method was used to estimate the overall incidence. Hazard ratios (HRs) were obtained with a Cox proportional hazards model to estimate the risk of the outcome events. RESULTS: A total of 217 patients were included in the final analysis with a median age of 56 years old, 41.5% of whom had metastases. After the initial 3-5 days of nadroparin, patients received sequential anticoagulation, either with nadroparin (118 patients) or with rivaroxaban (99 patients). Four patients with recurrent VTE were observed (nadroparin, n = 2; rivaroxaban, n = 2). The 180-day cumulative VTE recurrence rates were 1.7% and 2.0% (p = 0.777) in patients receiving nadroparin and rivaroxaban, respectively. The overall bleeding rate at 180 days was 8.8%. Although no major bleeding events were observed, nineteen patients with clinically relevant nonmajor bleeding (CRNMB) were observed. The 180-day cumulative rate of CRNMB was 5.1% for nadroparin and 13.1% for rivaroxaban (HR = 3.303, 95% CI 1.149-9.497, p = 0.027). CONCLUSION: Our study supported the efficacy of rivaroxaban for treating PICC-related UEDVT in cancer patients. However, data on anticoagulation therapy for PICC-related UEDVT presented with a low risk of VTE recurrence and a relatively high risk of CRNMB bleeding events. Considering the risk-benefit ratio, further well-designed trials are required to optimize the drug selection and duration for the treatment of PICC-related UEDVT in cancer patients.