Enhancement of the mechanical properties of organic-inorganic hybrid elastomers by introducing movable and reversible crosslinks.

Organic-inorganic materials have been widely utilized in various fields as multifunctional materials. Poly(dimethyl siloxane) (PDMS), a typical inorganic polymer, has industrially appealing functions, such as transparency, biocompatibility, and gas permeability; however, it has poor mechanical properties. We incorporated organic-inorganic hybrid elastomers (PDMS-γCD-AAl⊃P(EA-HEMA) (x)) with movable crosslinks, and we utilized hydrogen bonds as reversible crosslinks. The organic polymer poly ethyl acrylate-r-hydroxy ethyl methacrylate (P(EA-HEMA)) penetrated the cavity of triacetylated γ-cyclodextrin (γCD), which was introduced into the side chains of PDMS, and it compounded with PDMS at the nanoscale. Structural studies involving visual and X-ray scattering measurements revealed that movable crosslinks improved the compatibility levels of PDMS and acrylate copolymers. However, macroscopic phase separation occurred when the number of reversible crosslinks increased. Furthermore, studies on the mobility levels of acrylate copolymers and movable crosslinks indicated that the relaxation behaviour of PDMS-γCD-AAl⊃P(EA-HEMA) (x) changed with changing numbers of reversible crosslinks. Introducing reversible crosslinks improved the Young's modulus and toughness values. The movable and reversible crosslinks between the organic and inorganic polymers contributed to the high elongation properties. The design of PDMS-γCD-AAl⊃P(EA-HEMA) (x) incorporated cooperatively movable and reversible crosslinks to achieve high compatibility of immiscible polymers and to control the mechanical properties.

Explorative Clustering of the Nitrogen Balance Trajectory in Critically Ill Patients: A Preliminary post hoc Analysis of a Single-Center Prospective Observational Study.

BACKGROUND: The nitrogen balance estimates a protein net difference. However, since it has a number of limitations, it is important to consider the trajectory of the nitrogen balance in the clinical course of critically ill patients. OBJECTIVES: We herein exploratively classified the nitrogen balance trajectory using a machine learning method. METHOD: This is a post hoc analysis of a single-center prospective study for the patients admitted to our Emergency and Critical Center ICU. The nitrogen balance was evaluated with 24-h urine collection from ICU days 1-10 with 9 points. K-means clustering was performed to classify the nitrogen balance trajectory. We also evaluated factors associated with uncovered clusters. RESULTS: Seventy-six eligible patients were included in the present study. After clustering, the nitrogen balance trajectory was classified into 4 classes. Class 1 was trajected as a negative balance over 10 days (24 patients). Class 2 had a positive conversion on day 3 or 4 (8 patients). Class 3 had a positive conversion on day 8 or 9 (28 patients). Class 4 initially had a positive balance and then converted to a negative balance (16 patients). Sepsis complication and steroid use were associated with negative nitrogen balance trajectory. Class 2 was associated with lower length of hospital stay and femoral muscle volume loss, however, frequently had frailty and sarcopenia on admission. Active nutrition therapy intention was not correlated with positive trajectory. CONCLUSIONS: The nitrogen balance trajectory in critically ill patients may be classified into 4 classes for clinical practice. Among patients emergently admitted to the ICU, the positive conversion of the nitrogen balance might be delayed over 10 days.

Electrocardiogram monitoring as a predictor of neurological and survival outcomes in patients with out-of-hospital cardiac arrest: a single-center retrospective observational study.

Introduction: This study hypothesized that monitoring electrocardiogram (ECG) waveforms in patients with out-of-hospital cardiac arrest (OHCA) could have predictive value for survival or neurological outcomes. We aimed to establish a new prognostication model based on the single variable of monitoring ECG waveforms in patients with OHCA using machine learning (ML) techniques. Methods: This observational retrospective study included successfully resuscitated patients with OHCA aged ≥ 18 years admitted to an intensive care unit in Japan between April 2010 and April 2020. Waveforms from ECG monitoring for 1 h after admission were obtained from medical records and examined. Based on the open-access PTB-XL dataset, a large publicly available 12-lead ECG waveform dataset, we built an ML-supported premodel that transformed the II-lead waveforms of the monitoring ECG into diagnostic labels. The ECG diagnostic labels of the patients in this study were analyzed for prognosis using another model supported by ML. The endpoints were favorable neurological outcomes (cerebral performance category 1 or 2) and survival to hospital discharge. Results: In total, 590 patients with OHCA were included in this study and randomly divided into 3 groups (training set, n = 283; validation set, n = 70; and test set, n = 237). In the test set, our ML model predicted neurological and survival outcomes, with the highest areas under the receiver operating characteristic curves of 0.688 (95% CI: 0.682-0.694) and 0.684 (95% CI: 0.680-0.689), respectively. Conclusion: Our ML predictive model showed that monitoring ECG waveforms soon after resuscitation could predict neurological and survival outcomes in patients with OHCA.

Clinical Criteria for Persistent Inflammation, Immunosuppression, and Catabolism Syndrome: An Exploratory Analysis of Optimal Cut-Off Values for Biomarkers.

Background: While clinical criteria have been proposed for persistent inflammation, immunosuppression, and catabolism syndrome (PICS) using C-reactive protein (CRP), albumin, and lymphocyte count, there is no substantial basis for their optimal cut-off values. We herein aimed to develop and externally validate clinical criteria for PICS by investigating the optimal cut-off values for these biomarkers using machine-learning approaches and confirmed it with external validation. Methods: To develop criteria, we included ICU patients treated at a tertiary care hospital in Japan between 2018 and 2021 (derivation cohort). We introduced CRP, albumin and lymphocyte counts at around day 14 into six machine-learning models to predict PICS, defined as the compound outcome of the Barthel index (BI) < 70 at hospital discharge and in-hospital death. We incorporated the results of these models to assess the optimal cut-off values for biomarkers. We then developed and externally validated criteria for PICS using a nationwide claims database in Japan (validation cohort). Results: In the derivation cohort, 291 out of 441 patients had BI < 70 or in-hospital death. Based on machine-learning models, the optimal cut-off values for biomarkers to predict them were a CRP of 2.0 mg/dL, albumin of 3.0 g/dL, and a lymphocyte count of 800/µL, with an AUROC of 0.67. In the external validation cohort, 4492 out of 15,302 patients had BI < 70 or in-hospital death. The AUROC of the criteria was 0.71, with sensitivity of 0.71 and specificity of 0.68 to predict PICS. Conclusions: We herein provide a fundamental basis for PICS clinical criteria with CRP >2.0 mg/dL, albumin <3.0 g/dL, and a lymphocyte count <800/µL on day 14. The criteria developed will identify patients with PICS whose long-term mortality and activity of daily living may be poor.

Acceptance, Barriers, and Facilitators to Implementing Artificial Intelligence-Based Decision Support Systems in Emergency Departments: Quantitative and Qualitative Evaluation.

BACKGROUND: Despite the increasing availability of clinical decision support systems (CDSSs) and rising expectation for CDSSs based on artificial intelligence (AI), little is known about the acceptance of AI-based CDSS by physicians and its barriers and facilitators in emergency care settings. OBJECTIVE: We aimed to evaluate the acceptance, barriers, and facilitators to implementing AI-based CDSSs in the emergency care setting through the opinions of physicians on our newly developed, real-time AI-based CDSS, which alerts ED physicians by predicting aortic dissection based on numeric and text information from medical charts, by using the Unified Theory of Acceptance and Use of Technology (UTAUT; for quantitative evaluation) and the Consolidated Framework for Implementation Research (CFIR; for qualitative evaluation) frameworks. METHODS: This mixed methods study was performed from March to April 2021. Transitional year residents (n=6), emergency medicine residents (n=5), and emergency physicians (n=3) from two community, tertiary care hospitals in Japan were included. We first developed a real-time CDSS for predicting aortic dissection based on numeric and text information from medical charts (eg, chief complaints, medical history, vital signs) with natural language processing. This system was deployed on the internet, and the participants used the system with clinical vignettes of model cases. Participants were then involved in a mixed methods evaluation consisting of a UTAUT-based questionnaire with a 5-point Likert scale (quantitative) and a CFIR-based semistructured interview (qualitative). Cronbach α was calculated as a reliability estimate for UTAUT subconstructs. Interviews were sampled, transcribed, and analyzed using the MaxQDA software. The framework analysis approach was used during the study to determine the relevance of the CFIR constructs. RESULTS: All 14 participants completed the questionnaires and interviews. Quantitative analysis revealed generally positive responses for user acceptance with all scores above the neutral score of 3.0. In addition, the mixed methods analysis identified two significant barriers (System Performance, Compatibility) and two major facilitators (Evidence Strength, Design Quality) for implementation of AI-based CDSSs in emergency care settings. CONCLUSIONS: Our mixed methods evaluation based on theoretically grounded frameworks revealed the acceptance, barriers, and facilitators of implementation of AI-based CDSS. Although the concern of system failure and overtrusting of the system could be barriers to implementation, the locality of the system and designing an intuitive user interface could likely facilitate the use of optimal AI-based CDSS. Alleviating and resolving these factors should be key to achieving good user acceptance of AI-based CDSS.

Institution-Specific Machine Learning Models for Prehospital Assessment to Predict Hospital Admission: Prediction Model Development Study.

BACKGROUND: Although multiple prediction models have been developed to predict hospital admission to emergency departments (EDs) to address overcrowding and patient safety, only a few studies have examined prediction models for prehospital use. Development of institution-specific prediction models is feasible in this age of data science, provided that predictor-related information is readily collectable. OBJECTIVE: We aimed to develop a hospital admission prediction model based on patient information that is commonly available during ambulance transport before hospitalization. METHODS: Patients transported by ambulance to our ED from April 2018 through March 2019 were enrolled. Candidate predictors were age, sex, chief complaint, vital signs, and patient medical history, all of which were recorded by emergency medical teams during ambulance transport. Patients were divided into two cohorts for derivation (3601/5145, 70.0%) and validation (1544/5145, 30.0%). For statistical models, logistic regression, logistic lasso, random forest, and gradient boosting machine were used. Prediction models were developed in the derivation cohort. Model performance was assessed by area under the receiver operating characteristic curve (AUROC) and association measures in the validation cohort. RESULTS: Of 5145 patients transported by ambulance, including deaths in the ED and hospital transfers, 2699 (52.5%) required hospital admission. Prediction performance was higher with the addition of predictive factors, attaining the best performance with an AUROC of 0.818 (95% CI 0.792-0.839) with a machine learning model and predictive factors of age, sex, chief complaint, and vital signs. Sensitivity and specificity of this model were 0.744 (95% CI 0.716-0.773) and 0.745 (95% CI 0.709-0.776), respectively. CONCLUSIONS: For patients transferred to EDs, we developed a well-performing hospital admission prediction model based on routinely collected prehospital information including chief complaints.

Disseminated Intravascular Coagulopathy Is Associated with the Outcome of Persistent Inflammation, Immunosuppression and Catabolism Syndrome.

Persistent inflammation, immunosuppression and catabolism syndrome (PIICS) often occur after critical care. Disseminated intravascular coagulation (DIC) is expected to be associated independently with PIICS development. We retrospectively analyzed 5397 patients admitted to the Hitachi General Hospital emergency and critical care center during four years. We classified PIICS as C-reactive protein > 3.0 mg/dL or albumin < 3.0 g/dL or lymphocyte count < 800/µL on day 14. Prolonged hospital stay (>14 days) without PIICS and early recovery (discharged alive within 14 days) were assigned as non-PIICS. Early death (death within 14 days) was identified. We analyzed the association between the International Society on Thrombosis and Haemostasis overt DIC and PIICS outcomes. Results revealed 488 PIICS, 416 early death and 4493 non-PIICS cases. Analyses showed DIC as associated significantly with mortality, the Barthel index at discharge and PIICS development. Multivariate regression analysis and a generalized structural equation model identified DIC on admission as an independent risk factor for PIICS in surviving patients.

C-reactive protein clustering to clarify persistent inflammation, immunosuppression and catabolism syndrome.

PURPOSE: Among patients surviving treatment in intensive care units (ICU), some cases exist for which inflammation persisted with prolonged hospital stays, referred as persistent inflammatory, immunosuppressed, catabolic syndrome (PIICS). C reactive protein (CRP) is regarded as the most important marker for PIICS. Nevertheless, the applicable cut-off of CRP for PIICS has never been described in the literature. METHODS: Data of patients admitted to the ICU/Emergency ward from May 2015 through June 2019 were analyzed retrospectively. Using K-means clustering, a 14-day CRP transition dataset was analyzed and categorized finally into 7 classes: 4 PIICS classes and 3 non-PIICS classes. Outcomes and the other PIICS characteristics were evaluated. RESULTS: From all 5513 admitted patients, this study examined data of 539 patients who had been admitted for more than 14 days, and for whom 14 day CRP transition analysis could be performed. By the CRP transitions of 7 categorized classes, the CRP cut-off for PIICS was regarded as 3.0 mg/dl on day 14. The Barthel Index at discharge, albumin, and total lymphocyte counts on day 14 were significantly lower in PIICS classes than those of non-PIICS classes. Creatinine kinase, antithrombin activity and thrombomodulin on admission were regarded as independent risk factors for PIICS. CONCLUSIONS: Among patients with prolonged hospital stay, the PIICS population had elevated CRP, but lower Barthel Index, albumin, and total lymphocyte counts. The criterion of day 14 CRP for PIICS should be 3.0 mg/dl.