Chengsheng Mao

ICU readmissions remain a critical concern, carrying increased morbidity, mortality, and cost. We examined the epidemiology of unplanned ICU readmissions across 19 hospitals in the Common Longitudinal ICU data Format (CLIF) Consortium from January 2020 to December 2021 and the MIMIC-IV database. The cohort included 185,241 adult ICU admissions, excluding postoperative or post-interventional procedure readmissions. Overall, 8.6% of ICU discharges were readmitted during the same hospitalization. Unplanned readmissions occurred in 1.9% of cases within 24 hours, 3.4% within 48 hours, and 4.5% within 72 hours of discharge. Readmitted patients experienced markedly higher in-hospital mortality (20.6% vs. 2.1%; p < 0.001). Compared with initial ICU stays, readmissions were associated with greater use of respiratory support (42.3% vs. 35.3%) and vasopressors (26.1% vs. 23.1%). Hospitals with stepdown units demonstrated comparable unplanned ICU readmission rates. These findings demonstrate that ICU readmissions remain common, are associated with poor outcomes, and require greater organ support. Improved characterization of high-risk subphenotypes is needed to inform safer discharge processes.

The scarcity of health care resources, such as ventilators, often leads to the unavoidable consequence of rationing, particularly during public health emergencies or in resource-constrained settings like pandemics. The absence of a universally accepted standard for resource allocation protocols results in governments relying on varying criteria and heuristic-based approaches, often yielding suboptimal and inequitable outcomes. This study addresses the societal challenge of fair and effective critical care resource allocation by leveraging deep reinforcement learning to optimize policy decisions. We propose a transformer-based deep Q-network that integrates individual patient disease progression and interaction effects among patients to enhance allocation decisions. Our method aims to improve both fairness and overall patient outcomes. Experiments using metrics such as normalized survival rates and interracial allocation rate differences demonstrate that our approach significantly reduces excess deaths and achieves more equitable resource allocation compared to severity- and comorbidity-based protocols currently in use. Our findings highlight the potential of deep reinforcement learning to address critical health care challenges.

Spatial transcriptomics enables multiplex profiling of gene cellular expression and location within the tissue context. Although large volumes of spatial transcriptomics data have been generated, the lack of systematic curation and analysis limits biological discovery. We present Spatial transcriptOmics Analysis Resource (SOAR), a comprehensive spatial transcriptomics platform with 3461 uniformly processed samples across 13 species, 42 tissue types, and 19 different spatial transcriptomics technologies. Using SOAR, we found that CXCL16 / SPP1 macrophage polarity characterizes the coordination of immune cell polarity in the tumor microenvironment. SOAR’s integrative approach toward drug discovery revealed sirolimus and trichostatin A as potential anticancer agents targeting the phosphatidylinositol 3-kinase/Akt/mammalian target of rapamycin growth and proliferation pathway and identified Janus kinase/signal transducers and activators of transcription inhibitors for ulcerative colitis treatment. SOAR’s results demonstrate its broad application to data generated from diverse spatial technologies and pathological conditions. SOAR will support future benchmarking studies and method development, facilitating discoveries in molecular functions, disease mechanisms, and potential therapeutic targets.

Large AI models, connected to terminal devices via high-speed mobile communication networks, enable task collaboration and resource sharing, forming an intelligent framework for the Symbiotic Internet of Things (SIoT) paradigm in industry applications. Despite large language models hold significant potential for psychological intervention, their emotional interaction capabilities remain limited. This paper introduces a SIoT framework for psychological intervention and proposes an emotion-enhanced human-machine interaction architecture incorporating behavioral information captured by IoT devices. The system leverages ubiquitous sensing devices such as cameras and microphones, along with technologies like speech recognition and generation, as well as hyper-realistic digital humans, to create a natural interaction interface. Additionally, we introduce the end-of-utterance detection method and the behavior pattern control algorithm to facilitate smoother and more goal-oriented conversations. The proposed methods and prototype system have been validated through subjective and objective experiments, with results demonstrating their feasibility and suggesting that this approach could become one of the primary forms of humanmachine interaction for psychological intervention in the future.

IMPORTANCE: Current protocols to triage life support use scores that are biased and inaccurate. OBJECTIVES: To determine if adding age to triage protocols used in disaster scenarios improves the identification of critically ill patients likely to survive. DESIGN, SETTING, AND PARTICIPANTS: Observational cohort study from March 1, 2020, to March 1, 2022, at 22 hospitals in three networks, divided into derivation (12 hospitals) and validation cohorts (ten hospitals). Participants were critically ill adults (90% COVID-19 positive) who would have needed life support during an overwhelming case surge. Life support was defined as vasoactive medications for shock, invasive or noninvasive mechanical ventilation, or oxygen therapy with Pao ₂/Fio ₂ less than 200. MAIN OUTCOMES AND MEASURES: The primary outcome was death in the intensive care unit. We fit logistic regression models using a modified Sequential Organ Failure Assessment (SOFA) score with and without age in the derivation cohort and assessed predictive performance in the validation cohort using area under the receiver operating characteristic curves (AUCs) and compared observed and predicted mortality. RESULTS: The final analysis contained 7,660 patients with 16,711 life-support episodes. In the validation cohort, the AUC for age plus SOFA was significantly higher than the AUC for SOFA alone (0.66 vs. 0.54; p < 0.001). SOFA score substantially overpredicted mortality (13% predicted vs. 5% observed) for younger patients (< 40 yr) and underestimated mortality (14% predicted vs. 31% observed) for older patients (> 80 yr). In contrast, age plus SOFA had good calibration overall and across age groups. The addition of age improved but did not eliminate differences between observed and predicted mortality across racial-ethnic groups. CONCLUSIONS AND RELEVANCE: Age-inclusive triage better identifies ICU survivors than SOFA alone and is more equitable. Incorporating age into prioritization algorithms could save more lives in a crisis scenario.

Background: ICU readmissions are associated with increased morbidity, mortality, and healthcare costs. As ICU patient complexity increases and care practices evolve, the contemporary epidemiology of ICU readmissions remains unclear. We aimed to examine ICU readmission rates and timing across multiple health systems, focusing on unplanned readmissions occurring within 24, 48, and 72 hours after ICU discharge. Methods: We performed a retrospective cohort study using federated data from the Common Longitudinal ICU data Format (CLIF) Consortium, comprising nine healthcare systems between January 2020 and December 2021 and the MIMIC-IV database. The cohort included adult patients (≥18 years) discharged alive from the ICU. Readmissions following planned surgeries or interventional procedures were excluded. Data were analyzed locally at each site without centralizing patient-level data, and analyses focused on patient demographics, discharge disposition, readmission timing, and clinical interventions during ICU stays and readmissions. Statistical comparisons were performed using two-proportion z-tests and chi-squared tests. Results: Among 185,241 hospital admissions across 19 hospitals, 8.6% of ICU discharges were readmitted during the same hospitalization. Unplanned readmissions occurred within 24 hours in 1.9% of cases, 3.4% within 48 hours, and 4.5% within 72 hours. Readmitted patients experienced higher in-hospital mortality (20.6% vs. 2.1%, p<0.001). Compared to the initial ICU stay, ICU readmissions were associated with significantly increased respiratory (42.3% vs. 35.3%, p<0.001) and vasopressor support (26.1% vs. 23.1%, p<0.001). Conclusions: ICU readmissions remain common and are linked to worse outcomes. Readmissions require more respiratory and vasopressor support. Future work should focus on characterizing these subphenotypes and improving ICU discharge processes to reduce preventable readmissions.

This study aims to investigate the reliability of radiomic features extracted from contrast-enhanced computer tomography (CT) by AX-Unet, a pancreas segmentation model, to analyse the recurrence of pancreatic ductal adenocarcinoma (PDAC) after radical surgery. In this study, we trained an AX-Unet model to extract the radiomic features from preoperative contrast-enhanced CT images on a training set of 205 PDAC patients. Then we evaluated the segmentation ability of AX-Unet and the relationship between radiomic features and clinical characteristics on an independent testing set of 64 patients with clear prognoses. The lasso regression analysis was used to screen for variables of interest affecting patients’ post-operative recurrence, and the Cox proportional risk model regression analysis was used to screen for risk factors and create a nomogram prediction model. The proposed model achieved an accuracy of 85.9% for pancreas segmentation, meeting the requirements of most clinical applications. Radiomic features were found to be significantly correlated with clinical characteristics such as lymph node metastasis, resectability status, and abnormally elevated serum carbohydrate antigen 19-9 (CA 19-9) levels. Specifically, variance and entropy were associated with the recurrence rate (p < 0.05). The AUC for the nomogram predicting whether the patient recurred after surgery was 0.92 (95% CI: 0.78–0.99) and the C index was 0.62 (95% CI: 0.48–0.78). The AX-Unet pancreas segmentation model shows promise in analysing recurrence risk factors after radical surgery for PDAC. Additionally, our findings suggest that a dynamic nomogram model based on AX-Unet can provide pancreatic oncologists with more accurate prognostic assessments for their patients.

Pupil dilation and eye movements are closely related to human emotional and cognitive processes. Visual stimulus, especially video clips, is widely used in computer-assisted experimental paradigms as emotional inducers. However, the level of attraction as a critical factor to such visual stimulus still needs comprehensive investigation. This article conducts a novel study of attraction assessment with pupil diameter and eye movements. We collected high temporal resolution ocular variation data from 50 subjects while they viewed a variety of emotional video stimuli. Besides, this article proposes two orthogonal-moment-based feature extraction methods for emotion classification, i.e., Legendre moment and Krawtchouk moment. The results of experiments show that our proposed feature sets achieve better classification performance compared with conventional time-or frequency-domain feature sets. The accuracy of predicting attraction level reached 87.4% and 91.0% when new features were used alone and combined with conventional features, respectively. Compared with the conventional features with an accuracy of 86.6%, our proposed features can improve the accuracy by 4.4%.This study conducts a ground investigation of quantitative attraction assessment for video clips, which might provide reference for paradigm design of affective computing research.

Background: Acute kidney injury (AKI) is a common clinical syndrome characterized by a sudden episode of kidney failure or kidney damage within a few hours or a few days. Accurate early prediction of AKI for patients in intensive care units (ICUs), who are more likely than others to develop AKI, can enable timely interventions and reduce complications. Much of the clinical information relevant to AKI is captured in unstructured clinical notes, requiring advanced natural language processing (NLP) techniques for effective information extraction. Methods: Pre-trained contextual language models, such as Bidirectional Encoder Representations from Transformers (BERT), have recently improved performance across various NLP tasks. This study explores the application of BERT in a disease-specific medical domain task: early prediction of AKI. A domain-specific pre-trained language model, AKI-BERT, was developed by training BERT on clinical notes from patients at risk for AKI. The model’s performance was evaluated using the Medical Information Mart for Intensive Care III (MIMIC-III) dataset. Results: The AKI-BERT model demonstrated improved performance in early prediction of AKI compared to general-domain models. These findings highlight the potential of pre-trained language models tailored to disease-specific medical domains. Conclusion: AKI-BERT expands the utility of the BERT model from general clinical domains to disease-specific applications, demonstrating its effectiveness in mining clinical notes for the early prediction of AKI. This approach has the potential to enhance clinical decision-making and improve patient outcomes in ICUs.

Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size largely retain good transportability performance when porting to different sites. The combination of routine laboratory test values at admission along with basic demographic features can predict mortality in patients hospitalized with COVID-19. Importantly, this potentially deployable model differs from prior work by demonstrating not only consistent performance but also reliable transportability across healthcare systems in the US and Europe, highlighting the generalizability of this model and the overall approach.