Ethnic disparities in cardiovascular and renal responses to canagliflozin between Asian and White patients with type 2 diabetes mellitus: A post hoc analysis of the CANVAS Program.

AIM: To assess the potential heterogeneity in cardiovascular (CV), renal and safety outcomes of canagliflozin between Whites and Asians, as well as these outcomes in each subgroup. MATERIALS AND METHODS: The CANVAS Program enrolled 10 142 patients with type 2 diabetes, comprising 78.34% Whites and 12.66% Asians. CV, renal and safety outcomes were comprehensively analysed using Cox regression models, while intermediate markers were assessed using time-varying mixed-effects models. Racial heterogeneity was evaluated by adding a treatment-race interacion term. RESULTS: Canagliflozin showed no significant racial disparities in the majority of the CV, renal and safety outcomes. The heterogeneity (p = .04) was observed on all-cause mortality, with reduced risk in Whites (hazard ratio 0.84; 95% confidence interval 0.71-0.99) and a statistically non-significant increased risk in Asians (hazard ratio 1.64; 95% confidence interval 0.94-2.90). There was a significant racial difference in acute kidney injury (p = .04) and a marginally significant racial heterogeneity for the composite of hospitalization for heart failure and CV death (p = .06) and serious renal-related adverse events (p = .07). CONCLUSION: Canagliflozin reduced CV and renal risks similarly in Whites and Asians; however, there was a significant racial discrepancy in all-cause mortality. This distinction may be attributed to the fact that Asian patients exhibited diminished CV protection effects and more renal adverse events with canagliflozin, potentially resulting from the smaller reductions in weight and uric acid. These findings highlight the importance of investigating the impact of race on treatment response to sodium-glucose cotransporter-2 inhibitors and provide more precise treatment strategies.

A generalizable and interpretable model for mortality risk stratification of sepsis patients in intensive care unit.

PURPOSE: This study aimed to construct a mortality model for the risk stratification of intensive care unit (ICU) patients with sepsis by applying a machine learning algorithm. METHODS: Adult patients who were diagnosed with sepsis during admission to ICU were extracted from MIMIC-III, MIMIC-IV, eICU, and Zigong databases. MIMIC-III was used for model development and internal validation. The other three databases were used for external validation. Our proposed model was developed based on the Extreme Gradient Boosting (XGBoost) algorithm. The generalizability, discrimination, and validation of our model were evaluated. The Shapley Additive Explanation values were used to interpret our model and analyze the contribution of individual features. RESULTS: A total of 16,741, 15,532, 22,617, and 1,198 sepsis patients were extracted from the MIMIC-III, MIMIC-IV, eICU, and Zigong databases, respectively. The proposed model had an area under the receiver operating characteristic curve (AUROC) of 0.84 in the internal validation, which outperformed all the traditional scoring systems. In the external validations, the AUROC was 0.87 in the MIMIC-IV database, better than all the traditional scoring systems; the AUROC was 0.83 in the eICU database, higher than the Simplified Acute Physiology Score III and Sequential Organ Failure Assessment (SOFA),equal to 0.83 of the Acute Physiology and Chronic Health Evaluation IV (APACHE-IV), and the AUROC was 0.68 in the Zigong database, higher than those from the systemic inflammatory response syndrome and SOFA. Furthermore, the proposed model showed the best discriminatory and calibrated capabilities and had the best net benefit in each validation. CONCLUSIONS: The proposed algorithm based on XGBoost and SHAP-value feature selection had high performance in predicting the mortality of sepsis patients within 24 h of ICU admission.

Assessing the impact of the 2018 tetanus guidelines on knowledge and practices of emergency physicians in trauma patients: a national survey study.

Background: Tetanus remains a significant public health issue in China, with the approach of anti-tetanus prophylaxis in the emergency department resulting in both overuse, particularly of human tetanus immune globulin (TIG), and underuse with the tetanus vaccine. This is largely due to the absence of updated guidelines on tetanus prophylaxis before 2018. Our study aimed to evaluate the effects of the 2018 Chinese tetanus guidelines on the knowledge and practices of emergency physicians about tetanus prevention in trauma patients. Methods: From November 2019 to April 2020, we conducted a web-based survey involving 499 emergency physicians. The survey included a questionnaire covering knowledge, attitudes, and practices related to tetanus. We assessed the influence of the 2018 tetanus guidelines on the knowledge and practices of emergency physicians related to tetanus prevention for patients with trauma using multiple regression analysis. Results: The survey results showed that only 45.3% of the participants had received formal training on tetanus immunization, despite 53.3% reporting the availability of tetanus vaccines at their institutions. Physicians typically prescribed tetanus antitoxin or human TIG instead of tetanus toxoid (TT) to treat injuries, regardless of the patient's TT vaccination history. Among the respondents, those who were aware of the 2018 tetanus guidelines had higher mean scores on the general knowledge, risk knowledge, and treatment knowledge scales, with increases of 6%, 13%, and 9%, respectively, compared to those who were unaware of the guidelines. Awareness of the 2018 tetanus guidelines was associated with a high level of knowledge, as indicated by the general knowledge score, recommendation knowledge score, and total knowledge score, after adjusting for the effects of all variables on the knowledge, attitudes, and practices of the participants. A high level of education was also associated with a high level of knowledge indicated by the recommendation knowledge score and total knowledge score. Conclusions: Our study highlights a substantial gap in the attitudes, knowledge, and practices of emergency physicians in China regarding tetanus immunization. The results suggest an urgent need to promote the Chinese Expert Consensus Guidelines on tetanus to improve emergency physicians' knowledge and competence in tetanus prophylaxis. The findings underscore the importance of enhancing physicians' awareness of the latest guidelines to ensure appropriate and effective treatment for patients with tetanus-prone injuries.

Fine scale hippocampus morphology variation cross 552 healthy subjects from age 20 to 80.

The cerebral cortex varies over the course of a person's life span: at birth, the surface is smooth, before becoming more bumpy (deeper sulci and thicker gyri) in middle age, and thinner in senior years. In this work, a similar phenomenon was observed on the hippocampus. It was previously believed the fine-scale morphology of the hippocampus could only be extracted only with high field scanners (7T, 9.4T); however, recent studies show that regular 3T MR scanners can be sufficient for this purpose. This finding opens the door for the study of fine hippocampal morphometry for a large amount of clinical data. In particular, a characteristic bumpy and subtle feature on the inferior aspect of the hippocampus, which we refer to as hippocampal dentation, presents a dramatic degree of variability between individuals from very smooth to highly dentated. In this report, we propose a combined method joining deep learning and sub-pixel level set evolution to efficiently obtain fine-scale hippocampal segmentation on 552 healthy subjects. Through non-linear dentation extraction and fitting, we reveal that the bumpiness of the inferior surface of the human hippocampus has a clear temporal trend. It is bumpiest between 40 and 50 years old. This observation should be aligned with neurodevelopmental and aging stages.

Construction of a Hantaan Virus Phage Antibody Library and Screening for Potential Neutralizing Activity.

China is one of the main epidemic areas for hemorrhagic fever with renal syndrome (HFRS). Currently, there is no human antibody specific to Hantaan virus (HTNV) for the emergency prevention and treatment of HFRS. To prepare human antibodies with neutralizing activity, we established an anti-HTNV phage antibody library using phage display technology by transforming peripheral blood mononuclear cells (PBMCs) of patients with HFRS into B lymphoblastoid cell lines (BLCLs) and extracting cDNA from BLCLs that secreted neutralizing antibodies. Based on the phage antibody library, we screened HTNV-specific Fab antibodies with neutralizing activities. Our study provides a potential way forward for the emergency prevention of HTNV and specific treatment of HFRS.

Identifying acute kidney injury subphenotypes using an outcome-driven deep-learning approach.

OBJECTIVE: Acute kidney injury (AKI), a common condition on the intensive-care unit (ICU), is characterized by an abrupt decrease in kidney function within a few hours or days, leading to kidney failure or damage. Although AKI is associated with poor outcomes, current guidelines overlook the heterogeneity among patients with this condition. Identification of AKI subphenotypes could enable targeted interventions and a deeper understanding of the injury's pathophysiology. While previous approaches based on unsupervised representation learning have been used to identify AKI subphenotypes, these methods cannot assess time series or disease severity. METHODS: In this study, we developed a data- and outcome-driven deep-learning (DL) approach to identify and analyze AKI subphenotypes with prognostic and therapeutic implications. Specifically, we developed a supervised long short-term memory (LSTM) autoencoder (AE) with the aim of extracting representation from time-series EHR data that were intricately correlated with mortality. Then, subphenotypes were identified via application of K-means. RESULTS: In two publicly available datasets, three distinct clusters were identified, characterized by mortality rates of 11.3%, 17.3%, and 96.2% in one dataset and 4.6%, 12.1%, and 54.6% in the other. Further analysis demonstrated that AKI subphenotypes identified by our proposed approach were statistically significant on several clinical characteristics and outcomes. CONCLUSION: In this study, our proposed approach could successfully cluster the AKI population in ICU settings into 3 distinct subphenotypes. Thus, such approach could potentially improve outcomes of AKI patients in the ICU, with better risk assessment and potentially better personalized treatment.

Identification of region-specific amino acid signatures for doxorubicin-induced chemo brain.

Doxorubicin (DOX) is a cornerstone of chemotherapy for solid tumors and leukemias. DOX-induced cognitive impairment, termed chemo brain, has been reported in cancer survivors, whereas its mechanism remains poorly understood. Here we initially evaluated the cognitive impairments of mice treated with clinically relevant, long-term, low-dosage of DOX. Using HILIC-MS/MS-based targeted metabolomics, we presented the changes of 21 amino acids across six anatomical brain regions of mice with DOX-induced chemo brain. By mapping the altered amino acids to the human metabolic network, we constructed an amino acid-based network module for each brain region. We identified phenylalanine, tyrosine, methionine, and γ-aminobutyric acid as putative signatures of three regions (hippocampus, prefrontal cortex, and neocortex) highly associated with cognition. Relying on the reported mouse brain metabolome atlas, we found that DOX might perturb the amino acid homeostasis in multiple brain regions, similar to the changes in the aging brain. Correlation analysis suggested the possible indirect neurotoxicity of DOX that altered the brain levels of phenylalanine, tyrosine, and methionine by causing metabolic disorders in the liver and kidney. In summary, we revealed the region-specific amino acid signatures as actionable targets for DOX-induced chemo brain, which might provide safer treatment and improve the quality of life among cancer survivors.

Three-Dimensional Digital Reconstruction of the Cerebellar Cortex: Lobule Thickness, Surface Area Measurements, and Layer Architecture.

The cerebellum is ontogenetically one of the first structures to develop in the central nervous system; nevertheless, it has been only recently reconsidered for its significant neurobiological, functional, and clinical relevance in humans. Thus, it has been a relatively under-studied compared to the cerebrum. Currently, non-invasive imaging modalities can barely reach the necessary resolution to unfold its entire, convoluted surface, while only histological analyses can reveal local information at the micrometer scale. Herein, we used the BigBrain dataset to generate area and point-wise thickness measurements for all layers of the cerebellar cortex and for each lobule in particular. We found that the overall surface area of the cerebellar granular layer (including Purkinje cells) was 1,732 cm2 and the molecular layer was 1,945 cm2. The average thickness of the granular layer is 0.88 mm (± 0.83) and that of the molecular layer is 0.32 mm (± 0.08). The cerebellum (both granular and molecular layers) is thicker at the depth of the sulci and thinner at the crowns of the gyri. Globally, the granular layer is thicker in the lateral-posterior-inferior region than the medial-superior regions. The characterization of individual layers in the cerebellum achieved herein represents a stepping-stone for investigations interrelating structural and functional connectivity with cerebellar architectonics using neuroimaging, which is a matter of considerable relevance in basic and clinical neuroscience. Furthermore, these data provide templates for the construction of cerebellar topographic maps and the precise localization of structural and functional alterations in diseases affecting the cerebellum.

Expectation-maximization algorithm leads to domain adaptation for a perineural invasion and nerve extraction task in whole slide digital pathology images.

In addition to lymphatic and vascular channels, tumor cells can also spread via nerves, i.e., perineural invasion (PNI). PNI serves as an independent prognostic indicator in many malignancies. As a result, identifying and determining the extent of PNI is an important yet extremely tedious task in surgical pathology. In this work, we present a computational approach to extract nerves and PNI from whole slide histopathology images. We make manual annotations on selected prostate cancer slides once but then apply the trained model for nerve segmentation to both prostate cancer slides and head and neck cancer slides. For the purpose of multi-domain learning/prediction and investigation on the generalization capability of deep neural network, an expectation-maximization (EM)-based domain adaptation approach is proposed to improve the segmentation performance, in particular for the head and neck cancer slides. Experiments are conducted to demonstrate the segmentation performances. The average Dice coefficient for prostate cancer slides is 0.82 and 0.79 for head and neck cancer slides. Comparisons are then made for segmentations with and without the proposed EM-based domain adaptation on prostate cancer and head and neck cancer whole slide histopathology images from The Cancer Genome Atlas (TCGA) database and significant improvements are observed.

Plasma Pharmacokinetics and Tissue Distribution of Doxorubicin in Rats following Treatment with Astragali Radix.

Doxorubicin (DOX) is an essential component in chemotherapy, and Astragali Radix (AR) is a widely used tonic herbal medicine. The combination of DOX and AR offers widespread, well-documented advantages in treating cancer, e.g., reducing the risk of adverse effects. This study mainly aims to uncover the impact of AR on DOX disposition in vivo. Rats received a single intravenous dose of 5 mg/kg DOX following a single-dose co-treatment or multiple-dose pre-treatment of AR (10 g/kg × 1 or × 10). The concentrations of DOX in rat plasma and six tissues, including heart, liver, lung, kidney, spleen, and skeletal muscle, were determined by a fully validated LC-MS/MS method. A network-based approach was further employed to quantify the relationships between enzymes that metabolize and transport DOX and the targets of nine representative AR components in the human protein−protein interactome. We found that short-term (≤10 d) AR administration was ineffective in changing the plasma pharmacokinetics of DOX in terms of the area under the concentration−time curve (AUC, 1303.35 ± 271.74 µg/L*h versus 1208.74 ± 145.35 µg/L*h, p > 0.46), peak concentrations (Cmax, 1351.21 ± 364.86 µg/L versus 1411.01 ± 368.38 µg/L, p > 0.78), and half-life (t1/2, 31.79 ± 5.12 h versus 32.05 ± 6.95 h, p > 0.94), etc. Compared to the isotype control group, DOX concentrations in six tissues slightly decreased under AR pre-administration but only showed statistical significance (p < 0.05) in the liver. Using network analysis, we showed that five of the nine representative AR components were not localized to the vicinity of the DOX disposition-associated module. These findings suggest that AR may mitigate DOX-induced toxicity by affecting drug targets rather than drug disposition.

Open-source algorithm and software for computed tomography-based virtual pancreatoscopy and other applications.

Pancreatoscopy plays a significant role in the diagnosis and treatment of pancreatic diseases. However, the risk of pancreatoscopy is remarkably greater than that of other endoscopic procedures, such as gastroscopy and bronchoscopy, owing to its severe invasiveness. In comparison, virtual pancreatoscopy (VP) has shown notable advantages. However, because of the low resolution of current computed tomography (CT) technology and the small diameter of the pancreatic duct, VP has limited clinical use. In this study, an optimal path algorithm and super-resolution technique are investigated for the development of an open-source software platform for VP based on 3D Slicer. The proposed segmentation of the pancreatic duct from the abdominal CT images reached an average Dice coefficient of 0.85 with a standard deviation of 0.04. Owing to the excellent segmentation performance, a fly-through visualization of both the inside and outside of the duct was successfully reconstructed, thereby demonstrating the feasibility of VP. In addition, a quantitative analysis of the wall thickness and topology of the duct provides more insight into pancreatic diseases than a fly-through visualization. The entire VP system developed in this study is available at https://github.com/gaoyi/VirtualEndoscopy.git .

Point-of-Care Blood Coagulation Assay Based on Dynamic Monitoring of Blood Viscosity Using Droplet Microfluidics.

Monitoring of the coagulation function has applications in many clinical settings. Routine coagulation assays in the clinic are sample-consuming and slow in turnaround. Microfluidics provides the opportunity to develop coagulation assays that are applicable in point-of-care settings, but reported works required bulky sample pumping units or costly data acquisition instruments. In this work, we developed a microfluidic coagulation assay with a simple setup and easy operation. The device continuously generated droplets of blood sample and buffer mixture and reported the temporal development of blood viscosity during coagulation based on the color appearance of the resultant droplets. We characterized the relationship between blood viscosity and color appearance of the droplets and performed experiments to validate the assay results. In addition, we developed a prototype analyzer equipped with simple fluid pumping and economical imaging module and obtained similar assay measurements. This assay showed great potential to be developed into a point-of-care coagulation test with practical impact.

Acute kidney injury in traumatic brain injury intensive care unit patients.

BACKGROUND: The exact definition of Acute kidney injury (AKI) for patients with traumatic brain injury (TBI) is unknown. AIM: To compare the power of the "Risk, Injury, Failure, Loss of kidney function, and End-stage kidney disease" (RIFLE), Acute Kidney Injury Network (AKIN), Creatinine kinetics (CK), and Kidney Disease Improving Global Outcomes (KDIGO) to determine AKI incidence/stage and their association with the in-hospital mortality rate of patients with TBI. METHODS: This retrospective study collected the data of patients admitted to the intensive care unit for neurotrauma from 2001 to 2012, and 1648 patients were included. The subjects in this study were assessed for the presence and stage of AKI using RIFLE, AKIN, CK, and KDIGO. In addition, the propensity score matching method was used. RESULTS: Among the 1648 patients, 291 (17.7%) had AKI, according to KDIGO. The highest incidence of AKI was found by KDIGO (17.7%), followed by AKIN (17.1%), RIFLE (12.7%), and CK (11.5%) (P = 0.97). Concordance between KDIGO and RIFLE/AKIN/CK was 99.3%/99.1%/99.3% for stage 0, 36.0%/91.5%/44.5% for stage 1, 35.9%/90.6%/11.3% for stage 2, and 47.4%/89.5%/36.8% for stage 3. The in-hospital mortality rates increased with the AKI stage in all four definitions. The severity of AKI by all definitions and stages was not associated with in-hospital mortality in the multivariable analyses (all P > 0.05). CONCLUSION: Differences are seen in AKI diagnosis and in-hospital mortality among the four AKI definitions or stages. This study revealed that KDIGO is the best method to define AKI in patients with TBI.

Validated tool for early prediction of intensive care unit admission in COVID-19 patients.

BACKGROUND: The novel coronavirus disease 2019 (COVID-19) pandemic is a global threat caused by the severe acute respiratory syndrome coronavirus-2. AIM: To develop and validate a risk stratification tool for the early prediction of intensive care unit (ICU) admission among COVID-19 patients at hospital admission. METHODS: The training cohort included COVID-19 patients admitted to the Wuhan Third Hospital. We selected 13 of 65 baseline laboratory results to assess ICU admission risk, which were used to develop a risk prediction model with the random forest (RF) algorithm. A nomogram for the logistic regression model was built based on six selected variables. The predicted models were carefully calibrated, and the predictive performance was evaluated and compared with two previously published models. RESULTS: There were 681 and 296 patients in the training and validation cohorts, respectively. The patients in the training cohort were older than those in the validation cohort (median age: 63.0 vs 49.0 years, P < 0.001), and the percentages of male gender were similar (49.6% vs 49.3%, P = 0.958). The top predictors selected in the RF model were neutrophil-to-lymphocyte ratio, age, lactate dehydrogenase, C-reactive protein, creatinine, D-dimer, albumin, procalcitonin, glucose, platelet, total bilirubin, lactate and creatine kinase. The accuracy, sensitivity and specificity for the RF model were 91%, 88% and 93%, respectively, higher than those for the logistic regression model. The area under the receiver operating characteristic curve of our model was much better than those of two other published methods (0.90 vs 0.82 and 0.75). Model A underestimated risk of ICU admission in patients with a predicted risk less than 30%, whereas the RF risk score demonstrated excellent ability to categorize patients into different risk strata. Our predictive model provided a larger standardized net benefit across the major high-risk range compared with model A. CONCLUSION: Our model can identify ICU admission risk in COVID-19 patients at admission, who can then receive prompt care, thus improving medical resource allocation.

Mortality prediction for patients with acute respiratory distress syndrome based on machine learning: a population-based study.

BACKGROUND: Traditional scoring systems for patients' outcome prediction in intensive care units such as Oxygenation Saturation Index (OSI) and Oxygenation Index (OI) may not reliably predict the clinical prognosis of patients with acute respiratory distress syndrome (ARDS). Thus, none of them have been widely accepted for mortality prediction in ARDS. This study aimed to develop and validate a mortality prediction method for patients with ARDS based on machine learning using the Medical Information Mart for Intensive Care (MIMIC-III) and Telehealth Intensive Care Unit (eICU) Collaborative Research Database (eICU-CRD) databases. METHODS: Patients with ARDS were selected based on the Berlin definition in MIMIC-III and eICU-CRD databases. The APPS score (using age, PaO2/FiO2, and plateau pressure), Simplified Acute Physiology Score II (SAPS-II), Sepsis-related Organ Failure Assessment (SOFA), OSI, and OI were calculated. With MIMIC-III data, a mortality prediction model was built based on the random forest (RF) algorithm, and the performance was compared to those of existing scoring systems based on logistic regression. The performance of the proposed RF method was also validated with the combined MIMIC-III and eICU-CRD data. The performance of mortality prediction was evaluated by using the area under the receiver operating characteristics curve (AUROC) and performing calibration using the Hosmer-Lemeshow test. RESULTS: With the MIMIC-III dataset (308 patients, for comparisons with the existing scoring systems), the RF model predicted the in-hospital mortality, 30-day mortality, and 1-year mortality with an AUROC of 0.891, 0.883, and 0.892, respectively, which were significantly higher than those of the SAPS-II, APPS, OSI, and OI (all P<0.001). In the multi-source validation (the combined dataset of 2,235 patients in MIMIC-III and 331 patients in eICU-CRD), the RF model achieved an AUROC of 0.905 and 0.736 for predicting in-hospital mortality for the MIMIC-III and eICU-CRD datasets, respectively. The calibration plots suggested good fits for our RF model and these scoring systems for predicting mortality. The platelet count and lactate level were the strongest predictive variables for predicting in-hospital mortality. CONCLUSIONS: Compared to the existing scoring systems, machine learning significantly improved performance for predicting ARDS mortality. Validation with multi-source datasets showed a relatively robust generalisation ability of our prediction model.

Clinical diagnosis of severe COVID-19: A derivation and validation of a prediction rule.

BACKGROUND: The widespread coronavirus disease 2019 (COVID-19) has led to high morbidity and mortality. Therefore, early risk identification of critically ill patients remains crucial. AIM: To develop predictive rules at the time of admission to identify COVID-19 patients who might require intensive care unit (ICU) care. METHODS: This retrospective study included a total of 361 patients with confirmed COVID-19 by reverse transcription-polymerase chain reaction between January 19, 2020, and March 14, 2020 in Shenzhen Third People's Hospital. Multivariate logistic regression was applied to develop the predictive model. The performance of the predictive model was externally validated and evaluated based on a dataset involving 126 patients from the Wuhan Asia General Hospital between December 2019 and March 2020, by area under the receiver operating curve (AUROC), goodness-of-fit and the performance matrix including the sensitivity, specificity, and precision. A nomogram was also used to visualize the model. RESULTS: Among the patients in the derivation and validation datasets, 38 and 9 participants (10.5% and 2.54%, respectively) developed severe COVID-19, respectively. In univariate analysis, 21 parameters such as age, sex (male), smoker, body mass index (BMI), time from onset to admission (> 5 d), asthenia, dry cough, expectoration, shortness of breath, asthenia, and Rox index < 18 (pulse oxygen saturation, SpO2)/(FiO2 × respiratory rate, RR) showed positive correlations with severe COVID-19. In multivariate logistic regression analysis, only six parameters including BMI [odds ratio (OR) 3.939; 95% confidence interval (CI): 1.409-11.015; P = 0.009], time from onset to admission (≥ 5 d) (OR 7.107; 95%CI: 1.449-34.849; P = 0.016), fever (OR 6.794; 95%CI: 1.401-32.951; P = 0.017), Charlson index (OR 2.917; 95%CI: 1.279-6.654; P = 0.011), PaO2/FiO2 ratio (OR 17.570; 95%CI: 1.117-276.383; P = 0.041), and neutrophil/lymphocyte ratio (OR 3.574; 95%CI: 1.048-12.191; P = 0.042) were found to be independent predictors of COVID-19. These factors were found to be significant risk factors for severe patients confirmed with COVID-19. The AUROC was 0.941 (95%CI: 0.901-0.981) and 0.936 (95%CI: 0.886-0.987) in both datasets. The calibration properties were good. CONCLUSION: The proposed predictive model had great potential in severity prediction of COVID-19 in the ICU. It assisted the ICU clinicians in making timely decisions for the target population.

Development and validation of a risk stratification model for predicting the mortality of acute kidney injury in critical care patients.

BACKGROUND: This study aimed to develop and validate a model for mortality risk stratification of intensive care unit (ICU) patients with acute kidney injury (AKI) using the machine learning technique. METHODS: Eligible data were extracted from the Medical Information Mart for Intensive Care (MIMIC-III) database. Calibration, discrimination, and risk classification for mortality prediction were evaluated using conventional scoring systems and the new algorithm. A 10-fold cross-validation was performed. The predictive models were externally validated using the eICU database and also patients treated at the Second People's Hospital of Shenzhen between January 2015 to October 2018. RESULTS: For the new model, the areas under the receiver operating characteristic curves (AUROCs) for mortality during hospitalization and at 28 and 90 days after discharge were 0.91, 0.87, and 0.87, respectively, which were higher than for the Simplified Acute Physiology Score (SAPS II) and Sequential Organ Failure Assessment (SOFA). For external validation, the AUROC was 0.82 for in-hospital mortality, higher than SOFA, SAPS II, and Acute Physiology and Chronic Health Evaluation (APACHE) IV in the eICU database, but for the 28- and 90-day mortality, the new model had AUROCs (0.79 and 0.80, respectively) similar to that of SAPS II in the SZ2 database. The reclassification indexes were superior for the new model compared with the conventional scoring systems. CONCLUSIONS: The new risk stratification model shows high performance in predicting mortality in ICU patients with AKI.

Microengineered Flexural Post Rings for Effective Blood Sample Fencing and High-Throughput Measurement of Clot Retraction Force.

During blood clotting, clot retraction alters its mechanical properties and critically affects hemostasis. Despite that, existing clot retraction assays hold limitations such as large footprint and low throughput. In this work, we report the design of flexural post rings for a miniaturized assay of clot retraction force (CRF) with high throughput. Leveraging surface tensions, the post rings hold blood samples in a highly reproducible fashion while simultaneously serving as cantilever beams to measure the CRF. We investigated the effect on the device performance of major parameters, namely, surface hydrophobicity, post number, and post stiffness. We then tested the devices using 14 patient samples and revealed the correlation between CRF and fibrinogen levels. We further implemented an automated liquid handler and developed a high-throughput platform for clot retraction assay. The device's small sample consumption, simple operation, and good compatibility with existing automation facilities make it a promising high-throughput clot retraction assay.