Improving the Prognostic Evaluation Precision of Hospital Outcomes for Heart Failure Using Admission Notes and Clinical Tabular Data: Multimodal Deep Learning Model.

BACKGROUND: Clinical notes contain contextualized information beyond structured data related to patients' past and current health status. OBJECTIVE: This study aimed to design a multimodal deep learning approach to improve the evaluation precision of hospital outcomes for heart failure (HF) using admission clinical notes and easily collected tabular data. METHODS: Data for the development and validation of the multimodal model were retrospectively derived from 3 open-access US databases, including the Medical Information Mart for Intensive Care III v1.4 (MIMIC-III) and MIMIC-IV v1.0, collected from a teaching hospital from 2001 to 2019, and the eICU Collaborative Research Database v1.2, collected from 208 hospitals from 2014 to 2015. The study cohorts consisted of all patients with critical HF. The clinical notes, including chief complaint, history of present illness, physical examination, medical history, and admission medication, as well as clinical variables recorded in electronic health records, were analyzed. We developed a deep learning mortality prediction model for in-hospital patients, which underwent complete internal, prospective, and external evaluation. The Integrated Gradients and SHapley Additive exPlanations (SHAP) methods were used to analyze the importance of risk factors. RESULTS: The study included 9989 (16.4%) patients in the development set, 2497 (14.1%) patients in the internal validation set, 1896 (18.3%) in the prospective validation set, and 7432 (15%) patients in the external validation set. The area under the receiver operating characteristic curve of the models was 0.838 (95% CI 0.827-0.851), 0.849 (95% CI 0.841-0.856), and 0.767 (95% CI 0.762-0.772), for the internal, prospective, and external validation sets, respectively. The area under the receiver operating characteristic curve of the multimodal model outperformed that of the unimodal models in all test sets, and tabular data contributed to higher discrimination. The medical history and physical examination were more useful than other factors in early assessments. CONCLUSIONS: The multimodal deep learning model for combining admission notes and clinical tabular data showed promising efficacy as a potentially novel method in evaluating the risk of mortality in patients with HF, providing more accurate and timely decision support.

Linking preoperative and early intensive care unit data for prolonged intubation prediction.

Objectives: Prolonged intubation (PI) is a frequently encountered severe complication among patients following cardiac surgery (CS). Solely concentrating on preoperative data, devoid of sufficient consideration for the ongoing impact of surgical, anesthetic, and cardiopulmonary bypass procedures on subsequent respiratory system function, could potentially compromise the predictive accuracy of disease prognosis. In response to this challenge, we formulated and externally validated an intelligible prediction model tailored for CS patients, leveraging both preoperative information and early intensive care unit (ICU) data to facilitate early prophylaxis for PI. Methods: We conducted a retrospective cohort study, analyzing adult patients who underwent CS and utilizing data from two publicly available ICU databases, namely, the Medical Information Mart for Intensive Care and the eICU Collaborative Research Database. PI was defined as necessitating intubation for over 24 h. The predictive model was constructed using multivariable logistic regression. External validation of the model's predictive performance was conducted, and the findings were elucidated through visualization techniques. Results: The incidence rates of PI in the training, testing, and external validation cohorts were 11.8%, 12.1%, and 17.5%, respectively. We identified 11 predictive factors associated with PI following CS: plateau pressure [odds ratio (OR), 1.133; 95% confidence interval (CI), 1.111-1.157], lactate level (OR, 1.131; 95% CI, 1.067-1.2), Charlson Comorbidity Index (OR, 1.166; 95% CI, 1.115-1.219), Sequential Organ Failure Assessment score (OR, 1.096; 95% CI, 1.061-1.132), central venous pressure (OR, 1.052; 95% CI, 1.033-1.073), anion gap (OR, 1.075; 95% CI, 1.043-1.107), positive end-expiratory pressure (OR, 1.087; 95% CI, 1.047-1.129), vasopressor usage (OR, 1.521; 95% CI, 1.23-1.879), Visual Analog Scale score (OR, 0.928; 95% CI, 0.893-0.964), pH value (OR, 0.757; 95% CI, 0.629-0.913), and blood urea nitrogen level (OR, 1.011; 95% CI, 1.003-1.02). The model exhibited an area under the receiver operating characteristic curve (AUROC) of 0.853 (95% CI, 0.840-0.865) in the training cohort, 0.867 (95% CI, 0.853-0.882) in the testing cohort, and 0.704 (95% CI, 0.679-0.727) in the external validation cohort. Conclusions: Through multicenter internal and external validation, our model, which integrates early ICU data and preoperative information, exhibited outstanding discriminative capability. This integration allows for the accurate assessment of PI risk in the initial phases following CS, facilitating timely interventions to mitigate adverse outcomes.

Establishment and verification of a nomogram that predicts the risk for coronary slow flow.

Background: Coronary slow flow (CSF) has gained significance as a chronic coronary artery disease, but few studies have integrated both biological and anatomical factors for CSF assessment. This study aimed to develop and validate a simple-to-use nomogram for predicting CSF risk by combining biological and anatomical factors. Methods: In this retrospective case-control study, 1042 patients (614 CSF cases and 428 controls) were randomly assigned to the development and validation cohorts at a 7:3 ratio. Potential predictive factors were identified using least absolute shrinkage and selection operator regression and subsequently utilized in multivariate logistic regression to construct the nomogram. Validation of the nomogram was assessed by discrimination and calibration. Results: N-terminal pro brain natriuretic peptide, high density lipoprotein cholesterol, hemoglobin, left anterior descending artery diameter, left circumflex artery diameter, and right coronary artery diameter were independent predictors of CSF. The model displayed high discrimination in the development and validation cohorts (C-index 0.771, 95% CI: 0.737-0.805 and 0.805, 95% CI: 0.757-0.853, respectively). The calibration curves for both cohorts showed close alignment between predicted and actual risk estimates, demonstrating improved model calibration. Decision curve analysis suggested high clinical utility for the predictive nomogram. Conclusion: The constructed nomogram accurately and individually predicts the risk of CSF for patients with suspected CSF and may be considered for use in clinical care.

A pilot study on a patient with refractory headache: Personalized deep brain stimulation through stereoelectroencephalography.

The integration of stereoelectroencephalography with therapeutic deep brain stimulation (DBS) holds immense promise as a viable approach for precise treatment of refractory disorders, yet it has not been explored in the domain of headache or pain management. Here, we implanted 14 electrodes in a patient with refractory migraine and integrated clinical assessment and electrophysiological data to investigate personalized targets for refractory headache treatment. Using statistical analyses and cross-validated machine-learning models, we identified high-frequency oscillations in the right nucleus accumbens as a critical headache-related biomarker. Through a systematic bipolar stimulation approach and blinded sham-controlled survey, combined with real-time electrophysiological data, we successfully identified the left dorsal anterior cingulate cortex as the optimal target for the best potential treatment. In this pilot study, the concept of the herein-proposed data-driven approach to optimizing precise and personalized treatment strategies for DBS may create a new frontier in the field of refractory headache and even pain disorders.

Evaluating Prognostic Bias of Critical Illness Severity Scores Based on Age, Sex, and Primary Language in the United States: A Retrospective Multicenter Study.

OBJECTIVES: Although illness severity scoring systems are widely used to support clinical decision-making and assess ICU performance, their potential bias across different age, sex, and primary language groups has not been well-studied. DESIGN SETTING AND PATIENTS: We aimed to identify potential bias of Sequential Organ Failure Assessment (SOFA) and Acute Physiology and Chronic Health Evaluation (APACHE) IVa scores via large ICU databases. SETTING/PATIENTS: This multicenter, retrospective study was conducted using data from the Medical Information Mart for Intensive Care (MIMIC) and eICU Collaborative Research Database. SOFA and APACHE IVa scores were obtained from ICU admission. Hospital mortality was the primary outcome. Discrimination (area under receiver operating characteristic [AUROC] curve) and calibration (standardized mortality ratio [SMR]) were assessed for all subgroups. INTERVENTIONS: Not applicable. MEASUREMENTS AND MAIN RESULTS: A total of 196,310 patient encounters were studied. Discrimination for both scores was worse in older patients compared with younger patients and female patients rather than male patients. In MIMIC, discrimination of SOFA in non-English primary language speakers patients was worse than that of English speakers (AUROC 0.726 vs. 0.783, p < 0.0001). Evaluating calibration via SMR showed statistically significant underestimations of mortality when compared with overall cohort in the oldest patients for both SOFA and APACHE IVa, female patients (1.09) for SOFA, and non-English primary language patients (1.38) for SOFA in MIMIC. CONCLUSIONS: Differences in discrimination and calibration of two scores across varying age, sex, and primary language groups suggest illness severity scores are prone to bias in mortality predictions. Caution must be taken when using them for quality benchmarking and decision-making among diverse real-world populations.

Major Adverse Cardiovascular Events and Prognosis in Patients With Coronary Slow Flow.

This study aimed to investigate its clinical implications, risk factors, prognosis, and overall long-term outcomes. Demographic profiles, various clinical characteristics, and clinical outcomes were compared between 614 patients with coronary slow flow (CSF) and 428 patients with normal coronary artery. The incidence of CSF was found to be 2.65%. Significant differences were observed between patients with CSF and control subjects in terms of sex, chest tightness, hyperlipidemia, smoking history, alcohol consumption, age, height, weight, body mass index, diastolic blood pressure, heart rate, and body surface area (P < 0.05). CSF (hazard ratio: 1.531; 95% confidence interval: 1.064-2.202; p = 0.022) proved to be independent prognostic predictors of major adverse cardiovascular events (MACEs). Kaplan-Meier survival evaluations for MACEs presented a worser outcome for patients with CSF. Patients with CSF are at high risk for cardiovascular events and experience generally poor clinical outcomes.

[Wearable devices: Perspectives on assessing and monitoring human physiological status].

This review article aims to explore the major challenges that the healthcare system is currently facing and propose a new paradigm shift that harnesses the potential of wearable devices and novel theoretical frameworks on health and disease. Lifestyle-induced diseases currently account for a significant portion of all healthcare spending, with this proportion projected to increase with population aging. Wearable devices have emerged as a key technology for implementing large-scale healthcare systems focused on disease prevention and management. Advancements in miniaturized sensors, system integration, the Internet of Things, artificial intelligence, 5G, and other technologies have enabled wearable devices to perform high-quality measurements comparable to medical devices. Through various physical, chemical, and biological sensors, wearable devices can continuously monitor physiological status information in a non-invasive or minimally invasive way, including electrocardiography, electroencephalography, respiration, blood oxygen, blood pressure, blood glucose, activity, and more. Furthermore, by combining concepts and methods from complex systems and nonlinear dynamics, we developed a novel theory of continuous dynamic physiological signal analysis-dynamical complexity. The results of dynamic signal analyses can provide crucial information for disease prevention, diagnosis, treatment, and management. Wearable devices can also serve as an important bridge connecting doctors and patients by tracking, storing, and sharing patient data with medical institutions, enabling remote or real-time health assessments of patients, and providing a basis for precision medicine and personalized treatment. Wearable devices have a promising future in the healthcare field and will be an important driving force for the transformation of the healthcare system, while also improving the health experience for individuals.

[Development of intelligent monitoring system based on Internet of Things and wearable technology and exploration of its clinical application mode].

Wearable monitoring, which has the advantages of continuous monitoring for a long time with low physiological and psychological load, represents a future development direction of monitoring technology. Based on wearable physiological monitoring technology, combined with Internet of Things (IoT) and artificial intelligence technology, this paper has developed an intelligent monitoring system, including wearable hardware, ward Internet of Things platform, continuous physiological data analysis algorithm and software. We explored the clinical value of continuous physiological data using this system through a lot of clinical practices. And four value points were given, namely, real-time monitoring, disease assessment, prediction and early warning, and rehabilitation training. Depending on the real clinical environment, we explored the mode of applying wearable technology in general ward monitoring, cardiopulmonary rehabilitation, and integrated monitoring inside and outside the hospital. The research results show that this monitoring system can be effectively used for monitoring of patients in hospital, evaluation and training of patients' cardiopulmonary function, and management of patients outside hospital.

[Exploratory study on quantitative analysis of nocturnal breathing patterns in patients with acute heart failure based on wearable devices].

Patients with acute heart failure (AHF) often experience dyspnea, and monitoring and quantifying their breathing patterns can provide reference information for disease and prognosis assessment. In this study, 39 AHF patients and 24 healthy subjects were included. Nighttime chest-abdominal respiratory signals were collected using wearable devices, and the differences in nocturnal breathing patterns between the two groups were quantitatively analyzed. Compared with the healthy group, the AHF group showed a higher mean breathing rate (BR_mean) [(21.03 ± 3.84) beat/min vs. (15.95 ± 3.08) beat/min, P < 0.001], and larger R_RSBI_cv [70.96% (54.34%-104.28)% vs. 58.48% (45.34%-65.95)%, P = 0.005], greater AB_ratio_cv [(22.52 ± 7.14)% vs. (17.10 ± 6.83)%, P = 0.004], and smaller SampEn (0.67 ± 0.37 vs. 1.01 ± 0.29, P < 0.001). Additionally, the mean inspiratory time (TI_mean) and expiration time (TE_mean) were shorter, TI_cv and TE_cv were greater. Furthermore, the LBI_cv was greater, while SD1 and SD2 on the Poincare plot were larger in the AHF group, all of which showed statistically significant differences. Logistic regression calibration revealed that the TI_mean reduction was a risk factor for AHF. The BR_ mean demonstrated the strongest ability to distinguish between the two groups, with an area under the curve (AUC) of 0.846. Parameters such as breathing period, amplitude, coordination, and nonlinear parameters effectively quantify abnormal breathing patterns in AHF patients. Specifically, the reduction in TI_mean serves as a risk factor for AHF, while the BR_mean distinguishes between the two groups. These findings have the potential to provide new information for the assessment of AHF patients.

[A wearable six-minute walk-based system to predict postoperative pulmonary complications after cardiac valve surgery: an exploratory study].

In recent years, wearable devices have seen a booming development, and the integration of wearable devices with clinical settings is an important direction in the development of wearable devices. The purpose of this study is to establish a prediction model for postoperative pulmonary complications (PPCs) by continuously monitoring respiratory physiological parameters of cardiac valve surgery patients during the preoperative 6-Minute Walk Test (6MWT) with a wearable device. By enrolling 53 patients with cardiac valve diseases in the Department of Cardiovascular Surgery, West China Hospital, Sichuan University, the grouping was based on the presence or absence of PPCs in the postoperative period. The 6MWT continuous respiratory physiological parameters collected by the SensEcho wearable device were analyzed, and the group differences in respiratory parameters and oxygen saturation parameters were calculated, and a prediction model was constructed. The results showed that continuous monitoring of respiratory physiological parameters in 6MWT using a wearable device had a better predictive trend for PPCs in cardiac valve surgery patients, providing a novel reference model for integrating wearable devices with the clinic.

Illness severity assessment of older adults in critical illness using machine learning (ELDER-ICU): an international multicentre study with subgroup bias evaluation.

BACKGROUND: Comorbidity, frailty, and decreased cognitive function lead to a higher risk of death in elderly patients (more than 65 years of age) during acute medical events. Early and accurate illness severity assessment can support appropriate decision making for clinicians caring for these patients. We aimed to develop ELDER-ICU, a machine learning model to assess the illness severity of older adults admitted to the intensive care unit (ICU) with cohort-specific calibration and evaluation for potential model bias. METHODS: In this retrospective, international multicentre study, the ELDER-ICU model was developed using data from 14 US hospitals, and validated in 171 hospitals from the USA and Netherlands. Data were extracted from the Medical Information Mart for Intensive Care database, electronic ICU Collaborative Research Database, and Amsterdam University Medical Centers Database. We used six categories of data as predictors, including demographics and comorbidities, physical frailty, laboratory tests, vital signs, treatments, and urine output. Patient data from the first day of ICU stay were used to predict in-hospital mortality. We used the eXtreme Gradient Boosting algorithm (XGBoost) to develop models and the SHapley Additive exPlanations method to explain model prediction. The trained model was calibrated before internal, external, and temporal validation. The final XGBoost model was compared against three other machine learning algorithms and five clinical scores. We performed subgroup analysis based on age, sex, and race. We assessed the discrimination and calibration of models using the area under receiver operating characteristic (AUROC) and standardised mortality ratio (SMR) with 95% CIs. FINDINGS: Using the development dataset (n=50 366) and predictive model building process, the XGBoost algorithm performed the best in all types of validations compared with other machine learning algorithms and clinical scores (internal validation with 5037 patients from 14 US hospitals, AUROC=0·866 [95% CI 0·851-0·880]; external validation in the US population with 20 541 patients from 169 hospitals, AUROC=0·838 [0·829-0·847]; external validation in European population with 2411 patients from one hospital, AUROC=0·833 [0·812-0·853]; temporal validation with 4311 patients from one hospital, AUROC=0·884 [0·869-0·897]). In the external validation set (US population), the median AUROCs of bias evaluations covering eight subgroups were above 0·81, and the overall SMR was 0·99 (0·96-1·03). The top ten risk predictors were the minimum Glasgow Coma Scale score, total urine output, average respiratory rate, mechanical ventilation use, best state of activity, Charlson Comorbidity Index score, geriatric nutritional risk index, code status, age, and maximum blood urea nitrogen. A simplified model containing only the top 20 features (ELDER-ICU-20) had similar predictive performance to the full model. INTERPRETATION: The ELDER-ICU model reliably predicts the risk of in-hospital mortality using routinely collected clinical features. The predictions could inform clinicians about patients who are at elevated risk of deterioration. Prospective validation of this model in clinical practice and a process for continuous performance monitoring and model recalibration are needed. FUNDING: National Institutes of Health, National Natural Science Foundation of China, National Special Health Science Program, Health Science and Technology Plan of Zhejiang Province, Fundamental Research Funds for the Central Universities, Drug Clinical Evaluate Research of Chinese Pharmaceutical Association, and National Key R&D Program of China.

A Simple Nomogram for Predicting Hospital Mortality of Patients Over 80 Years in ICU: An International Multicenter Retrospective Study.

OBJECTIVES: This study aimed to develop and validate an easy-to-use intensive care unit (ICU) illness scoring system to evaluate the in-hospital mortality for very old patients (VOPs, over 80 years old). METHODS: We performed a multicenter retrospective study based on the electronic ICU (eICU) Collaborative Research Database (eICU-CRD), Medical Information Mart for Intensive Care Database (MIMIC-III CareVue and MIMIC-IV), and the Amsterdam University Medical Centers Database (AmsterdamUMCdb). Least Absolute Shrinkage and Selection Operator regression was applied to variables selection. The logistic regression algorithm was used to develop the risk score and a nomogram was further generated to explain the score. RESULTS: We analyzed 23 704 VOPs, including 3 726 deaths (10 183 [13.5% mortality] from eICU-CRD [development set], 12 703 [17.2%] from the MIMIC, and 818 [20.8%] from the AmsterdamUMC [external validation sets]). Thirty-four variables were extracted on the first day of ICU admission, and 10 variables were finally chosen including Glasgow Coma Scale, shock index, respiratory rate, partial pressure of carbon dioxide, lactate, mechanical ventilation (yes vs no), oxygen saturation, Charlson Comorbidity Index, blood urea nitrogen, and urine output. The nomogram was developed based on the 10 variables (area under the receiver operating characteristic curve: training of 0.792, testing of 0.788, MIMIC of 0.764, and AmsterdamUMC of 0.808 [external validating]), which consistently outperformed the Sequential Organ Failure Assessment, acute physiology score III, and simplified acute physiology score II. CONCLUSIONS: We developed and externally validated a nomogram for predicting mortality in VOPs based on 10 commonly measured variables on the first day of ICU admission. It could be a useful tool for clinicians to identify potentially high risks of VOPs.

Subphenotyping heterogeneous patients with chronic critical illness to guide individualised fluid balance treatment using machine learning: a retrospective cohort study.

Background: The great heterogeneity of patients with chronic critical illness (CCI) leads to difficulty for intensive care unit (ICU) management. Identifying subphenotypes could assist in individualized care, which has not yet been explored. In this study, we aim to identify the subphenotypes of patients with CCI and reveal the heterogeneous treatment effect of fluid balance for them. Methods: In this retrospective study, we defined CCI as an ICU length of stay over 14 days and coexists with persistent organ dysfunction (cardiovascular Sequential Organ Failure Assessment (SOFA) score ≥1 or score in any other organ system ≥2) at Day 14. Data from five electronic healthcare record datasets covering geographically distinct populations (the US, Europe, and China) were studied. These five datasets include (1) subset of Derivation (MIMIC-IV v1.0, US) cohort (2008-2019); (2) subset Derivation (MIMIC-III v1.4 'CareVue', US) cohort (2001-2008); (3) Validation I (eICU-CRD, US) cohort (2014-2015); (4) Validation II (AmsterdamUMCdb/AUMC, Euro) cohort (2003-2016); (5) Validation III (Jinling, CN) cohort (2017-2021). Patients who meet the criteria of CCI in their first ICU admission period were included in this study. Patients with age over 89 or under 18 years old were excluded. Three unsupervised clustering algorithms were employed independently for phenotypes derivation and validation. Extreme Gradient Boosting (XGBoost) was used for phenotype classifier construction. A parametric G-formula model was applied to estimate the cumulative risk under different daily fluid management strategies in different subphenotypes of ICU mortality. Findings: We identified four subphenotypes as Phenotype A, B, C, and D in a total of 8145 patients from three countries. Phenotype A is the mildest and youngest subgroup; Phenotype B is the most common group, of whom patients showed the oldest age, significant acid-base abnormality, and low white blood cell count; Patients with Phenotype C have hypernatremia, hyperchloremia, and hypercatabolic status; and in Phenotype D, patients accompany with the most severe multiple organ failure. An easy-to-use classifier showed good effectiveness. Phenotype characteristics showed robustness across all cohorts. The beneficial fluid balance threshold intervals of subphenotypes were different. Interpretation: We identified four novel phenotypes that revealed the different patterns and significant heterogeneous treatment effects of fluid therapy within patients with CCI. A prospective study is needed to validate our findings, which could inform clinical practice and guide future research on individualized care. Funding: This study was funded by 333 High Level Talents Training Project of Jiangsu Province (BRA2019011), General Program of Medical Research from the Jiangsu Commission of Health (M2020052), and Key Research and Development Program of Jiangsu Province (BE2022823).

[Design and implementation of Internet of Things for emergency medical devices based on cloud-edge-device architecture].

Internet of Things (IoT) technology plays an important role in smart healthcare. This paper discusses IoT solution for emergency medical devices in hospitals. Based on the cloud-edge-device architecture, different medical devices were connected; Streaming data were parsed, distributed, and computed at the edge nodes; Data were stored, analyzed and visualized in the cloud nodes. The IoT system has been working steadily for nearly 20 months since it run in the emergency department in January 2021. Through preliminary analysis with collected data, IoT performance testing and development of early warning model, the feasibility and reliability of the in-hospital emergency medical devices IoT was verified, which can collect data for a long time on a large scale and support the development and deployment of machine learning models. The paper ends with an outlook on medical device data exchange and wireless transmission in the IoT of emergency medical devices, the connection of emergency equipment inside and outside the hospital, and the next step of analyzing IoT data to develop emergency intelligent IoT applications.

Clinically Interpretable Machine Learning Models for Early Prediction of Mortality in Older Patients with Multiple Organ Dysfunction Syndrome: An International Multicenter Retrospective Study.

BACKGROUND: Multiple organ dysfunction syndrome (MODS) is associated with a high risk of mortality among older patients. Current severity scores are limited in their ability to assist clinicians with triage and management decisions. We aim to develop mortality prediction models for older patients with MODS admitted to the ICU. METHODS: The study analyzed older patients from 197 hospitals in the United States and 1 hospital in the Netherlands. The cohort was divided into the young-old (65-80 years) and old-old (≥80 years), which were separately used to develop and evaluate models including internal, external, and temporal validation. Demographic characteristics, comorbidities, vital signs, laboratory measurements, and treatments were used as predictors. We used the XGBoost algorithm to train models, and the SHapley Additive exPlanations (SHAP) method to interpret predictions. RESULTS: Thirty-four thousand four hundred and ninety-seven young-old (11.3% mortality) and 21 330 old-old (15.7% mortality) patients were analyzed. Discrimination AUROC of internal validation models in 9 046 U.S. patients was as follows: 0.87 and 0.82, respectively; discrimination of external validation models in 1 905 EUR patients was as follows: 0.86 and 0.85, respectively; and discrimination of temporal validation models in 8 690 U.S. patients: 0.85 and 0.78, respectively. These models outperformed standard clinical scores like Sequential Organ Failure Assessment and Acute Physiology Score III. The Glasgow Coma Scale, Charlson Comorbidity Index, and Code Status emerged as top predictors of mortality. CONCLUSIONS: Our models integrate data spanning physiologic and geriatric-relevant variables that outperform existing scores used in older adults with MODS, which represents a proof of concept of how machine learning can streamline data analysis for busy ICU clinicians to potentially optimize prognostication and decision making.

Upregulated LncRNA-LINC00659 expression by H. pylori infection promoted the progression of gastritis to cancer by regulating PTBP1 expression.

CONTEXT: Helicobacter pylori (H. pylori), a spiral-shaped bacterium, is closely associated with chronic, progressive gastric mucosal damage, gastric atrophy, and even gastric cancer (GC). An increasing number of studies have addressed the correlation between long noncoding RNAs (lncRNAs) and H. pylori pathogenicity in GC. OBJECTIVE: In this study, we found that the expression level of LINC00659 gradually increased in the progression from atrophic gastritis, intestinal metaplasia, and dysplasia to GC in H. pylori-infected patients. Thus, we aimed to further explore the function of LINC00659 in the progression of gastritis to cancer under H. pylori infection. MATERIALS AND METHODS: StarBase predictions, ribonucleic acid (RNA)-binding protein immunoprecipitation assays, and gene ontology functional annotation (GO)/Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis were performed to identify the RNA-binding proteins of LINC00659; moreover, qRT[FIGURE DASH]PCR, western blotting, RNA interference, and immunofluorescence assays were used to investigate the function of LINC00659. RESULTS: LINC00659 bound directly to the RNA-binding protein polypyrimidine tract-binding protein (PTBP1). Importantly, qRT[FIGURE DASH]PCR and western blot assays demonstrated that PTBP1 expression increased in the progression from inflammation to cancer in the stomach of H. pylori-infected patients and H. pylori-infected GES-1 cells. However, LINC00659 knockdown downregulated PTBP1 expression and inhibited PTBP1 binding under H. pylori infection. Finally, LINC00659 knockdown significantly reduced H. pylori-induced human gastric epithelial cell senescence and suppressed interleukin (IL)-6 and IL-8 secretion by reducing the phosphorylation level of NF-κB p65. CONCLUSIONS: This study indicated that LINC00659 may have the potential to be a novel promising prognostic and therapeutic marker for H. pylori-associated gastric diseases.

Therapeutic Effect of Curcumol on Chronic Atrophic Gastritis (CAG) and Gastric Cancer Is Achieved by Downregulating SDF-1α/CXCR4/VEGF Expression.

CAG is an essential procession of the transformation from gastritis into gastric cancer. A series of timely moves of diagnosis, treatment, and monitoring towards CAG to anticipate the potential population at risk of gastric cancer is an effective means to prevent gastric cancer occurrence. The main active monomer in Fuzheng Huowei Decoction is Curcumol, which is an indispensable ingredient in the treatment to CAG and gastric cancer. In this study, the CAG model, in vitro cultured gastric cancer cells, and participating nude mice were treated with Curcumol, and alterations in SDF-1α/CXCR4/VEGF expression were estimated using the assays of immunohistochemistry and Western blot. MTT, flow cytometry, transwell, HE staining, and tumor volume determination were applied for the verification of the regulatory effects of Curcumol on CAG and gastric cancer cells. The results showed that the expressions of VEGF, SDF-1α, CXCR4, and CD34 decreased in our CAG model with Curcumol treatment. Curcumol is in procession of an inhibitory effect toward the activity, migration, and invasion of gastric cancer cells, and it would also result in gastric cancer cells' apoptosis. We subsequently added SDF-1α overexpressing lentivirus to the Curcumol-treated group and found that the expressions of SDF-1α, CXCR4, and VEGF protein increased, and the inhibitory effect of Curcumol on gastric cancer cells was withdrawn. Our nude mouse experiment showed that Curcumol + SDF-1α group ended up with the largest tumor volume, while Fuzheng Huowei + NC group was with the smallest tumor volume. In conclusion, Curcumol is able to effectively protect the gastric tissue and suppress gastric cancer cells' viability. Curcumol functions as a therapeutic factor in chronic atrophic gastritis and gastric cancer by downregulating SDF-1α/CXCR4/VEGF expression.

Enhancing instantaneous oxygen uptake estimation by non-linear model using cardio-pulmonary physiological and motion signals.

Oxygen uptake (VO2) is an important parameter in sports medicine, health assessment and clinical treatment. At present, more and more wearable devices are used in daily life, clinical treatment and health care. The parameters obtained by wearables have great research potential and application prospect. In this paper, an instantaneous VO2 estimation model based on XGBoost was proposed and verified by using data obtained from a medical-grade wearable device (Beijing SensEcho) at different posture and activity levels. Furthermore, physiological characteristics extracted from single-lead electrocardiogram, thoracic and abdominal respiration signal and tri-axial acceleration signal were studied to optimize the model. There were 29 healthy volunteers recruited for the study to collect data while stationary (lying, sitting, standing), walking, Bruce treadmill test and recuperating with SensEcho and the gas analyzer (Metalyzer 3B). The results show that the VO2 values estimated by the proposed model are in good agreement with the true values measured by the gas analyzer (R2 = 0.94 ± 0.03, n = 72,235), and the mean absolute error (MAE) is 1.83 ± 0.59 ml/kg/min. Compared with the estimation method using a separate heart rate as input, our method reduced MAE by 54.70%. At the same time, other factors affecting the performance of the model were studied, including the influence of different input signals, gender and movement intensity, which provided more enlightenment for the estimation of VO2. The results show that the proposed model based on cardio-pulmonary physiological signals as inputs can effectively improve the accuracy of instantaneous VO2 estimation in various scenarios of activities and was robust between different motion modes and state. The VO2 estimation method proposed in this paper has the potential to be used in daily life covering the scenario of stationary, walking and maximal exercise.

Curcumol Undermines SDF-1α/CXCR4/NF-κB Signaling Pathway to Suppress the Progression of Chronic Atrophic Gastritis (CAG) and Gastric Cancer.

CAG is the most common precancerous disease of gastric cancer, which belongs to a kind of chronic gastritis. CAG is in close association with gastric cancer, which makes itself a critical node clinically in cancer prevention and treatment. Curcumol is a main active monomer in Fuzheng Huowei decoction, which has the properties of antioxidant, antiviral, and antitumor. In this study, the expression of SDF-1α/CXCR4/NF-κB was detected by in vivo and in vitro methods. Then, we found that the expressions of NF-κB, SDF-1α, CXCR4, and p-NF-κB were decreased in the curcumol treatment group. Curcumol inhibited gastric cancer cells' viability, migration, and invasion and induced their apoptosis. After adding the lentivirus overexpressing SDF-1α to the curcumol treatment group, it was found that SDF-1α, CXCR4, NF-κB, and p-NF-κB protein expressions were all increased, and the effect of curcumol on gastric cancer cells was reversed. In the nude mouse experiment, the tumor volume in the curcumol + SDF-1α group was the largest, and the tumor volume in the Fuzheng Huowei decoction + NC group was the smallest. In conclusion, curcumol effectively protects gastric tissue and inhibits the viability of gastric cancer cells, and curcumol regulates SDF-1α/CXCR4/NF-κB to play a therapeutic role in chronic atrophic gastritis and gastric cancer.

Predicting Adverse Events During Six-Minute Walk Test Using Continuous Physiological Signals.

Background and Objective: The 6-min walk test (6MWT) is a common functional assessment test, but adverse events during the test can be potentially dangerous and can lead to serious consequences and low quality of life. This study aimed to predict the occurrence of adverse events during 6MWT, using continuous physiological parameters combined with demographic variables. Methods: 578 patients with respiratory disease who had performed standardized 6MWT with wearable devices from three hospitals were included in this study. Adverse events occurred in 73 patients (12.6%). ECG, respiratory signal, tri-axial acceleration signals, oxygen saturation, demographic variables and scales assessment were obtained. Feature extraction and selection of physiological signals were performed during 2-min resting and 1-min movement phases. 5-fold cross-validation was used to assess the machine learning models. The predictive ability of different models and scales was compared. Results: Of the 16 features selected by the recursive feature elimination method, those related to blood oxygen were the most important and those related to heart rate were the most numerous. Light Gradient Boosting Machine (LightGBM) had the highest AUC of 0.874 ± 0.063 and the AUC of Logistic Regression was AUC of 0.869 ± 0.067. The mMRC (Modified Medical Research Council) scale and Borg scale had the lowest performance, with an AUC of 0.733 and 0.656 respectively. Conclusion: It is feasible to predict the occurrence of adverse event during 6MWT using continuous physiological parameters combined with demographic variables. Wearable sensors/systems can be used for continuous physiological monitoring and provide additional tools for patient safety during 6MWT.