Background Tuberculous meningitis (TBM) is the most lethal form of tuberculosis worldwide. Data on critically ill TBM patients in the intensive care unit (ICU) of China are lacking. We tried to identify prognostic factors of adult TBM patients admitted to ICU in China. Methods We conducted a retrospective study on adult TBM in ICU between January 2008 and April 2018. Factors associated with unfavorable outcomes at 28 days were identified by logistic regression. Factors associated with 1-year mortality were studied by Cox proportional hazards modeling. Results Eighty adult patients diagnosed with TBM (age 38.5 (18–79) years, 45 (56 %) males) were included in the study. An unfavorable outcome was observed in 39 (49 %) patients and were independently associated with Acute Physiology and Chronic Health Evaluation (APACHE) II > 23 (adjusted odds ratio (aOR) 5.57, 95 % confidence interval (CI) 1.55–19.97), Sequential Organ Failure Assessment (SOFA) > 8 (aOR 9.74, 95 % CI 1.46–64.88), and mechanical ventilation (aOR 18.33, 95 % CI 3.15–106.80). Multivariate Cox regression analysis identified two factors associated with 1-year mortality: APACHE II > 23 (adjusted hazard ratio (aHR) 4.83; 95 % CI 2.21–10.55), and mechanical ventilation (aHR 9.71; 95 % CI 2.31–40.87). Conclusions For the most severe adult TBM patients of Medical Research Council (MRC) stage III, common clinical factors aren’t effective enough to predict outcomes. Our study demonstrates that the widely used APACHE II and SOFA scores on admission can be used to predict short-term outcomes, while APACHE II could also be used to predict long-term outcomes of adult patients with TBM in ICU.
We propose an approach on two-step 3D warping with background filling for the multi-view autostereoscopic display. The method can obtain high quality virtual view images, and the multi-view autostereoscopic image is synthesized by using reference images and virtual view images. The experimental results show the effectiveness of the method for the autostereoscopic display.
We propose an elemental image array (EIA) generation method by using an optimized depth image‐based rendering (DIBR) algorithm. In this method, the EIA is synthesized by the reference and virtual viewpoint elemental images, and the virtual viewpoint elemental images at the given locations are generated by DIBR algorithm. We optimize the existing DIBR algorithm by adaptively repairing the warped depth images in the processing part and extend the generation dimension of the virtual viewpoint elemental images from one dimension to two dimensions. The optimized DIBR algorithm can effectively solve the problem: the low quality of virtual viewpoint elemental images caused by discontinuous depth values and disocclusion regions. We also implement the generations of virtual viewpoint elemental images and EIA in graph processing unit to reduce the time cost. Experimental results show that the proposed method can not only improve the quality of the virtual viewpoint images but also accelerate the generations of the virtual viewpoint elemental images and EIA.
Sepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. However, how this dysregulation occurs remains unclear. Herein, we used scRNA-seq to define the immune landscape of infection and found that when sepsis occurred, adaptive immunity was acutely and strongly suppressed, which manifested as a striking increase in the number of myeloid-derived suppressor cells (MDSCs), a decrease in the number of lymphocytes and drastic downregulation of the expression levels of B-cell-related genes and MHC class II molecules. Transcriptomic analysis showed that systemic immunosuppression occurred not only in the peripheral blood but also in all other immune compartments, including the spleen, lymph nodes, and bone marrow. Patients diagnosed with infection in the emergency department with extremely low expression levels of adaptive immunity-related genes had a high risk for developing sepsis. CD47 was identified as the key molecule that triggered this immunosuppression by inducing the production of amyloid-β precursor protein (App), which caused adaptive immunosuppression via B-cell suppression. Our study outlines a framework for how the dysregulated host response occurs and provides translational opportunities for sepsis immunotherapies.
Background Maternal mortality is still a major challenge for health systems, while severe maternal complications are the primary causes of maternal death. Our study aimed to determine whether severe maternal morbidity is effectively predicted by a newly proposed Modified Obstetric Early Warning Score (MOEWS) in the setting of an obstetric intensive care unit (ICU). Methods A retrospective study of pregnant women admitted in the ICU from August 2019 to August 2020 was conducted. MOEWS was calculated 24 h before and 24 h after admission in the ICU, and the highest score was taken as the final value. For women directly admitted from the emergency department, the worst value before admission was collected. The aggregate performance of MOEWS in predicting critical illness in pregnant women was evaluated and finally compared with that of the Acute Physiology and Chronic Health Evaluation II (APACHE II) score. Results A total of 352 pregnant women were enrolled; 290 women (82.4%) with severe maternal morbidity were identified and two of them died (0.6%). The MOEWSs of women with serious obstetric complications were significantly higher than those of women without serious obstetric complications [8(6, 10) vs. 4(2, 4.25), z = -10.347, P < 0.001]. MOEWSs of 24 h after ICU admission had higher sensitivity, specificity and AUROC than MOEWSs of 24 h before ICU admission. When combining the two MOEWSs, sensitivity of MOEWS was 99.3% (95% CI: 98–100), specificity 75.8% (95% CI: 63–86), positive predictive value (PPV) 95.1% (95% CI: 92–97) and negative predictive value (NPV) 95.9% (95% CI: 86–100). The areas under the receiver operator characteristic (ROC) curves of MOEWS were 0.92 (95% CI: 0.88–0.96) and 0.70 (95% CI: 0.63–0.76) of the APACHE II score. Conclusion The newly proposed MOEWS has an excellent ability to identify critically ill women early and is more effective than APACHE II. It will be a valuable tool for discriminating severe maternal morbidity and ultimately improve maternal health.
Background: Maternal mortality is still a major challenge to health systems, while the severe maternal morbidity is the primary reason. Our study was aimed to determine whether severe maternal morbidity is effectively predicted by a newly proposed Modified Obstetric Early Warning Score (MOEWS) in the setting of an obstetric intensive care unit (ICU).Methods: A retrospective study on pregnant women admitted to the ICU from August 2019 to August 2020 was conducted. The MOEWS was calculated 24 h before and 24 h after admission to the ICU, and the highest score was taken as the final value. The aggregate performance of the MOEWS in predicting critically ill patients was evaluated and finally compared with that of the Acute Physiology and Chronic Health Evaluation II (APACHE II) score.Results: A total of 352 pregnant women were enrolled; 290 cases (82.37%) with severe maternal morbidity were identified, and 2 of them died (0.57%). The MOEWSs of patients with serious obstetric complications were significantly higher than those of patients without serious obstetric complications [8(6, 10) vs. 4(2, 4.25), z = -10.347, P<0.001]. The sensitivity of the MOEWS was 99.31% (95% CI, 98-100), the specificity was 75.81% (95% CI, 63-86), the positive predictive value (PPV) was 95.05% (95% CI, 92-97) and the negative predictive value (NPV) was 95.92% (95% CI, 86-100). The areas under the receiver operator characteristic (ROC) curves of the MOEWS and APACHE II score were 0.917 (95% CI: 0.878-0.955, P<0.001) and 0.697 (95% CI: 0.629-0.764), respectively.Conclusion: The newly proposed MOEWS has an excellent ability to identify potentially at-risk patients early, and it is more effective than APACHE II. It will be a valuable tool for discriminating severe maternal morbidity and ultimately improve maternal safety.Trial registration: This is not a clinical trial rather a quality improvement project. It will be registered retrospectively.
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