Application of Artificial Intelligence to Advance Individualized Diagnosis and Treatment in Emergency and Critical Care Medicine.

Emergency and critical illnesses refer to severe diseases or conditions characterized by rapid changes in health that may endanger life within a short period [...].

Application of radiomics in ischemic stroke.

In recent years, radiomics has emerged as a novel research methodology that plays a crucial role in the diagnosis and treatment of ischemic stroke. By integrating multimodal medical imaging techniques such as computed tomography and magnetic resonance imaging, radiomics offers in-depth insights into aspects such as the extent of brain tissue damage and hemodynamics. These data help physicians to accurately assess patient condition, select optimal treatment strategies, and predict recovery trajectories and long-term prognoses, thereby enhancing treatment efficacy and reducing the risk of complications. With the anticipated further advancements in radiomic technology, this methodology has great potential for expanded applications in the early detection, treatment, and prognosis of ischemic stroke. The present narrative review explores the burgeoning field of radiomics and its transformative impact on ischemic stroke.

Establishment of a headspace-thermal desorption and gas chromatography-mass spectrometry method (HS-TD-GC-MS) for simultaneous detection of 51 volatile organic compounds in human urine: Application in occupational exposure assessment.

Volatile organic compounds (VOCs) are a group of ubiquitous environment pollutants especially released into the workplace. Assessment of VOCs exposure in occupational populations is therefore a crucial issue for occupational health. However, simultaneous biomonitoring of a variety of VOCs is less studied. In this study, a simple and sensitive method was developed for the simultaneous determination of 51 prototype VOCs in urine by headspace-thermal desorption coupled to gas chromatography-mass spectrometry (HS-TD-GC-MS). The urinary sample was pretreated with only adding 0.50 g of sodium chloride to 2 mL of urine and 51 VOCs should be determined with limits of detection (LODs) between 13.6 ng/L and 24.5 ng/L. The method linearity ranged from 0.005 to 10 µg/L with correlation coefficients (r) of 0.991 to 0.999. The precision for intraday and inter-day, measured by the variation coefficient (CV) at three levels of concentration, was below 15 %, except for 4-isopropyl toluene, dichloromethane, and trichloromethane at low concentration. For medium and high levels, recoveries of all target VOCs were within the standard range, but 1,1-dichloropropene and styrene, which were slightly under 80 % at low levels. In addition, the proposed method has been used to determine urine samples collected in three times (before, during and after working) from 152 workers at four different factories. 41 types of prototype VOCs were detected in workers urine. Significant differences (Kruskal-Wallis chi-squared = 117.18, df = 1, P < 0.05) in the concentration levels of VOCs between the exposed and unexposed groups were observed, but not between the three sampling times (Kruskal-Wallis chi-squared = 3.39, df = 2, P = 0.183). The present study provides an alternative method for biomonitoring and assessing mixed exposures to VOCs in occupational populations.

Temporal patterns of organ dysfunction in COVID-19 patients hospitalized in the intensive care unit: A group-based multitrajectory modeling analysis.

BACKGROUND: The course of organ dysfunction (OD) in Corona Virus Disease 2019 (COVID-19) patients is unknown. Herein, we analyze the temporal patterns of OD in intensive care unit-admitted COVID-19 patients. METHODS: Sequential organ failure assessment scores were evaluated daily within 2 weeks of admission to determine the temporal trajectory of OD using group-based multitrajectory modeling (GBMTM). RESULTS: A total of 392 patients were enrolled with a 28-day mortality rate of 53.6%. GBMTM identified four distinct trajectories. Group 1 (mild OD, n = 64), with a median APACHE II score of 13 (IQR 9-21), had an early resolution of OD and a low mortality rate. Group 2 (moderate OD, n = 140), with a median APACHE II score of 18 (IQR 13-22), had a 28-day mortality rate of 30.0%. Group 3 (severe OD, n = 117), with a median APACHR II score of 20 (IQR 13-27), had a deterioration trend of respiratory dysfunction and a 28-day mortality rate of 69.2%. Group 4 (extremely severe OD, n = 71), with a median APACHE II score of 20 (IQR 17-27), had a significant and sustained OD affecting all organ systems and a 28-day mortality rate of 97.2%. CONCLUSIONS: Four distinct trajectories of OD were identified, and respiratory dysfunction trajectory could predict nonpulmonary OD trajectories and patient prognosis.

Genetic correlations and causal relationships between cardio-metabolic traits and sepsis.

Cardio-metabolic traits have been reported to be associated with the development of sepsis. It is, however, unclear whether these co-morbidities reflect causal associations, shared genetic heritability, or are confounded by environmental factors. We performed three analyses to explore the relationships between cardio-metabolic traits and sepsis. Mendelian randomization (MR) study to evaluate the causal effects of multiple cardio-metabolic traits on sepsis. Global genetic correlation analysis to explore the correlations between cardio-metabolic traits and sepsis. Local genetic correlation (GC) analysis to explore shared genetic heritability between cardio-metabolic traits and sepsis. Some loci were further examined for related genes responsible for the causal relationships. Genetic associations were obtained from the UK Biobank data or published large-scale genome-wide association studies with sample sizes between 200,000 to 750,000. In MR, we found causality between BMI and sepsis (OR: 1.53 [1.4-1.67]; p < 0.001). Body mass index (BMI), which is confirmed by sensitivity analyses and multivariable MR adjusting for confounding factors. Global GC analysis showed a significant correlation between BMI and sepsis (rg = 0.55, p < 0.001). More cardio-metabolic traits were identified to be correlated to the sepsis onset such as CRP (rg = 0.37, p = 0.035), type 2 diabetes (rg = 0.33, p < 0.001), HDL (rg = - 0.41, p < 0.001), and coronary artery disease (rg = 0.43, p < 0.001). Local GC revealed some shared genetic loci responsible for the causality. The top locus 1126 was located at chromosome 7 and comprised genes HIBADH, JAZF1, and CREB5. The present study provides evidence for an independent causal effect of BMI on sepsis. Further detailed analysis of the shared genetic heritability between cardio-metabolic traits and sepsis provides the opportunity to improve the preventive strategies for sepsis.

Genomic surveillance indicates clonal replacement of hypervirulent Klebsiella pneumoniae ST881 and ST29 lineage strains in vivo.

The emergence of hypervirulent Klebsiella pneumoniae (hvKp) poses a significant public health threat, particularly regarding its carriage in the healthy population. However, the genomic epidemiological characteristics and population dynamics of hvKp within a single patient across distinct infection episodes remain largely unknown. This study aimed to investigate the clonal replacement of hvKp K2-ST881 and K54-ST29 lineage strains in a single patient experiencing multiple-site infections during two independent episodes. Two strains, designated EDhvKp-1 and EDhvKp-2, were obtained from blood and cerebrospinal fluid during the first admission, and the strain isolated from blood on the second admission was named EDhvKp-3. Whole-genome sequencing, utilizing both short-read Illumina and long-read Oxford Nanopore platforms, was conducted. In silico multilocus sequence typing (MLST), identification of antimicrobial resistance and virulence genes, and the phylogenetic relationship between our strains and other K. pneumoniae ST881 and ST29 genomes retrieved from the public database were performed. Virulence potentials were assessed through a mouse lethality assay. Our study indicated that the strains were highly susceptible to multiple antimicrobial agents. Plasmid sequence analysis confirmed that both virulence plasmids, pEDhvKp-1 (166,008 bp) and pEDhvKp-3 (210,948 bp), belonged to IncFIB type. Multiple virulence genes, including rmpA, rmpA2, rmpC, rmpD, iroBCDN, iucABCD, and iutA, were identified. EDhvKp-1 and EDhvKp-2 showed the closest relationship to strain 502 (differing by 51 SNPs), while EDhvKp-3 exhibited 69 SNPs differences compared to strain TAKPN-1, which all recovered from Chinese patients in 2020. In the mouse infection experiment, both ST881 EDhvKp-1 and ST29 EDhvKp-3 displayed similar virulence traits, causing 90 and 100% of the mice to die within 72 h after intraperitoneal infection, respectively. Our study expands the spectrum of hvKp lineages and highlights genomic alterations associated with clonal switching between two distinct lineages of hvKP that successively replaced each other in vivo. The development of novel strategies for the surveillance, diagnosis, and treatment of high-risk hvKp is urgently needed.

Correlation between early intracranial pressure and cerebral perfusion pressure with 28-day intensive care unit mortality in patients with hemorrhagic stroke.

INTRODUCTION: Hemorrhagic stroke may cause changes in intracranial pressure (ICP) and cerebral perfusion pressure (CPP), which may influence the prognosis of patients. The aim of this study was to investigate the relationship between early ICP, CPP, and 28-day mortality in the intensive care unit (ICU) of patients with hemorrhagic stroke. PATIENTS AND METHODS: A retrospective study was performed using the Medical Information Mart for Intensive Care (MIMIC-IV) and the eICU Collaborative Research Database (eICU-CRD), including hemorrhagic stroke patients in the ICU with recorded ICP monitoring. The median values of ICP and CPP were collected for the first 24 h of the patient's monitoring. The primary outcome was 28-day ICU mortality. Multivariable Cox proportional hazards models were used to analyze the relationship between ICP, CPP, and 28-day ICU mortality. Restricted cubic regression splines were used to analyze nonlinear relationships. RESULTS: The study included 837 patients with a 28-day ICU mortality rate of 19.4%. Multivariable analysis revealed a significant correlation between early ICP and 28-day ICU mortality (HR 1.08, 95% CI 1.04-1.12, p < 0.01), whereas early CPP showed no correlation with 28-day ICU mortality (HR 1.00, 95% CI 0.98-1.01, p = 0.57), with a correlation only evident when CPP < 60 mmHg (HR 1.99, 95% CI 1.14-3.48, p = 0.01). The study also identified an early ICP threshold of 16.5 mmHg. DISCUSSION AND CONCLUSION: Early ICP shows a correlation with 28-day mortality in hemorrhagic stroke patients, with a potential intervention threshold of 16.5 mmHg. In contrast, early CPP showed no correlation with patient prognosis.

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.

Exploring disease axes as an alternative to distinct clusters for characterizing sepsis heterogeneity.

PURPOSE: Various studies have analyzed sepsis subtypes, yet the reproducibility of such results remains unclear. This study aimed to determine the reproducibility of sepsis subtypes across multiple cohorts. METHODS: The study examined 63,547 sepsis patients from six distinct cohorts who had similar sepsis-related characteristics (vital signs, lactate, sequential organ failure assessment score, bilirubin, serum, urine output, and Glasgow coma scale). Identical cluster analysis techniques were used, employing 27 clustering schemes, and normalized mutual information (NMI), a metric ranging from 0 to 1 with higher values indicating better concordance, was employed to quantify the clustering solutions' reproducibility. Principal component analysis (PCA) was utilized to obtain the disease axis, and its uniformity across cohorts was evaluated through patterns of feature loading and correlation. RESULTS: The reproducibility of sepsis clustering subtypes across the various studies was modest (median NMI ranging from 0.08 to 0.54). The top-down transfer learning method (model trained on cohorts with greater severity was transferred to cohorts with lower severity score) had a higher NMI value than the bottom-up approach (median [Q1, Q3]: 0.64 [0.49, 0.78] vs. 0.23 [0.2, 0.31], p < 0.001). The reproducibility was greater when the transfer solution was performed within United States (US) cohorts. The PCA analysis revealed that the correlation pattern between variables was consistent across all cohorts, and the first two disease axes were the "shock axis" and "systemic inflammatory response syndrome (SIRS) axis." CONCLUSIONS: Cluster analysis of sepsis patients across various cohorts showed modest reproducibility. Sepsis heterogeneity is better characterized through continuous disease axes that coexist to varying degrees within the same individual instead of mutually exclusive subtypes.

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.

Development of a machine learning-based prediction model for sepsis-associated delirium in the intensive care unit.

Septic patients in the intensive care unit (ICU) often develop sepsis-associated delirium (SAD), which is strongly associated with poor prognosis. The aim of this study is to develop a machine learning-based model for the early prediction of SAD. Patient data were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and the eICU Collaborative Research Database (eICU-CRD). The MIMIC-IV data were divided into a training set and an internal validation set, while the eICU-CRD data served as an external validation set. Feature variables were selected using least absolute shrinkage and selection operator regression, and prediction models were built using logistic regression, support vector machines, decision trees, random forests, extreme gradient boosting (XGBoost), k-nearest neighbors and naive Bayes methods. The performance of the models was evaluated in the validation set. The model was also applied to a group of patients who were not assessed or could not be assessed for delirium. The MIMIC-IV and eICU-CRD databases included 14,620 and 1723 patients, respectively, with a median time to diagnosis of SAD of 24 and 30 h. Compared with Non-SAD patients, SAD patients had higher 28-days ICU mortality rates and longer ICU stays. Among the models compared, the XGBoost model had the best performance and was selected as the final model (internal validation area under the receiver operating characteristic curves (AUROC) = 0.793, external validation AUROC = 0.701). The XGBoost model outperformed other models in predicting SAD. The establishment of this predictive model allows for earlier prediction of SAD compared to traditional delirium assessments and is applicable to patients who are difficult to assess with traditional methods.

Significant delay and decreased chance of treatment for acute ischemic stroke patients on remote outer islets of China compared with the main island: the PUTUO Study.

INTRODUCTION: Data from acute ischemic stroke patients throughout 2021 from one district of an archipelago city of China were collected and analyzed retrospectively to determine the management difference due to time lags from onset of symptoms to the arrival at the stroke center (FMCT) of two regions: main island (MI) and outer islets (OIs). METHODS: All patients information from 1 January to 31 December 2021 was retrieved through the electronic medical records system of the only stroke center in MI. After screening and exclusion, each patient's medical record was reviewed by two neurologists separately. Before OI patients were allocated to a group, their residential addresses at onset of the stroke were confirmed by telephone. Comparisons were analyzed between the two regions for gender, age, pre-stroke risk factors and peri-admission management parameters. RESULTS: A total of 326 patients met the inclusion criteria: 300 from the MI group and 26 for the OI group. Intergroup comparisons for gender, age and most of the risk factors showed no significant difference. FMCT were shown to be significantly distinct (p<0.001). Hospitalization expenses also showed significant difference. The odds ratio of the definite treatment IV thrombolysis was 0.131 (OI group to MI group range: 0.017-0.987, p=0.021). CONCLUSION: The diagnosis and treatment of acute ischemic stroke patients from OIs was significantly postponed compared to those from MI. Therefore, new effective and efficient solutions are urgently needed.

Establishment of a Chinese critical care database from electronic healthcare records in a tertiary care medical center.

The medical specialty of critical care, or intensive care, provides emergency medical care to patients suffering from life-threatening complications and injuries. The medical specialty is featured by the generation of a huge amount of high-granularity data in routine practice. Currently, these data are well archived in the hospital information system for the primary purpose of routine clinical practice. However, data scientists have noticed that in-depth mining of such big data may provide insights into the pathophysiology of underlying diseases and healthcare practices. There have been several openly accessible critical care databases being established, which have generated hundreds of scientific outputs published in scientific journals. However, such work is still in its infancy in China. China is a large country with a huge patient population, contributing to the generation of large healthcare databases in hospitals. In this data descriptor article, we report the establishment of an openly accessible critical care database generated from the hospital information system.

The application of artificial intelligence in the management of sepsis.

Sepsis is a complex and heterogeneous syndrome that remains a serious challenge to healthcare worldwide. Patients afflicted by severe sepsis or septic shock are customarily placed under intensive care unit (ICU) supervision, where a multitude of apparatus is poised to produce high-granularity data. This reservoir of high-quality data forms the cornerstone for the integration of AI into clinical practice. However, existing reviews currently lack the inclusion of the latest advancements. This review examines the evolving integration of artificial intelligence (AI) in sepsis management. Applications of artificial intelligence include early detection, subtyping analysis, precise treatment and prognosis assessment. AI-driven early warning systems provide enhanced recognition and intervention capabilities, while profiling analyzes elucidate distinct sepsis manifestations for targeted therapy. Precision medicine harnesses the potential of artificial intelligence for pathogen identification, antibiotic selection, and fluid optimization. In conclusion, the seamless amalgamation of artificial intelligence into the domain of sepsis management heralds a transformative shift, ushering in novel prospects to elevate diagnostic precision, therapeutic efficacy, and prognostic acumen. As AI technologies develop, their impact on shaping the future of sepsis care warrants ongoing research and thoughtful implementation.