Latent transition analysis of cardiac arrest patients treated in the intensive care unit

BACKGROUND AND OBJECTIVE

Post-cardiac arrest (CA) syndrome is heterogenous in their clinical presentations and outcomes. This study aimed to explore the transition and stability of subphenotypes (profiles) of CA treated in the intensive care unit (ICU).

PATIENTS AND METHODS

Clinical features of CA patients on day 1 and 3 after ICU admission were modeled by latent transition analysis (LTA) to explore the transition between subphenotypes over time. The association between different transition patterns and mortality outcome was explored using multivariable logistic regression.

RESULTS

We identified 848 eligible patients from the database. The LPA identified three distinct subphenotypes: Profile 1 accounted for the largest proportion (73%) and was considered as the baseline subphenotype. Profile 2 (13%) was characterized by brain injury and profile 3 (14%) was characterized by multiple organ dysfunctions. The same three subphenotypes were identified on day 3. The LTA showed consistent subphenotypes. A majority of patients in profile 2 (72%) and 3 (82%) on day 1 switched to profile 1 on day 3. In the logistic regression model, patients in profile 1 on day 1 transitioned to profile 3 had worse survival outcome than those continue to remain in profile 1 (OR: 20.64; 95% CI: 6.01 to 70.94; p < 0.001) and transitioned to profile 2 (OR: 8.42; 95% CI: 2.22 to 31.97; p = 0.002) on day 3.

CONCLUSION

The study identified three subphenotypes of CA, which was consistent on day 1 and 3 after ICU admission. Patients who transitioned to profile 3 on day 3 had significantly worse survival outcome than those remained in profile 1 or 2.

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.

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.

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.

Enhancing Patient Selection in Sepsis Clinical Trials Design Through an AI Enrichment Strategy: Algorithm Development and Validation

BACKGROUND

Sepsis is a heterogeneous syndrome, and enrollment of more homogeneous patients is essential to improve the efficiency of clinical trials. Artificial intelligence (AI) has facilitated the identification of homogeneous subgroups, but how to estimate the uncertainty of the model outputs when applying AI to clinical decision-making remains unknown.

OBJECTIVE

We aimed to design an AI-based model for purposeful patient enrollment, ensuring that a patient with sepsis recruited into a trial would still be persistently ill by the time the proposed therapy could impact patient outcome. We also expected that the model could provide interpretable factors and estimate the uncertainty of the model outputs at a customized confidence level.

METHODS

In this retrospective study, 9135 patients with sepsis requiring vasopressor treatment within 24 hours after sepsis onset were enrolled from Beth Israel Deaconess Medical Center. This cohort was used for model development, and 10-fold cross-validation with 50 repeats was used for internal validation. In total, 3743 patients with sepsis from the eICU Collaborative Research Database were used as the external validation cohort. All included patients with sepsis were stratified based on disease progression trajectories: rapid death, recovery, and persistent ill. A total of 148 variables were selected for predicting the 3 trajectories. Four machine learning algorithms with 3 different setups were used. We estimated the uncertainty of the model outputs using conformal prediction (CP). The Shapley Additive Explanations method was used to explain the model.

RESULTS

The multiclass gradient boosting machine was identified as the best-performing model with good discrimination and calibration performance in both validation cohorts. The mean area under the receiver operating characteristic curve with SD was 0.906 (0.018) for rapid death, 0.843 (0.008) for recovery, and 0.807 (0.010) for persistent ill in the internal validation cohort. In the external validation cohort, the mean area under the receiver operating characteristic curve (SD) was 0.878 (0.003) for rapid death, 0.764 (0.008) for recovery, and 0.696 (0.007) for persistent ill. The maximum norepinephrine equivalence, total urine output, Acute Physiology Score III, mean systolic blood pressure, and the coefficient of variation of oxygen saturation contributed the most. Compared to the model without CP, using the model with CP at a mixed confidence approach reduced overall prediction errors by 27.6% (n=62) and 30.7% (n=412) in the internal and external validation cohorts, respectively, as well as enabled the identification of more potentially persistent ill patients.

CONCLUSIONS

The implementation of our model has the potential to reduce heterogeneity and enroll more homogeneous patients in sepsis clinical trials. The use of CP for estimating the uncertainty of the model outputs allows for a more comprehensive understanding of the model's reliability and assists in making informed decisions based on the predicted outcomes.

Critical care studies using large language models based on electronic healthcare records: A technical note

The integration of large language models (LLMs) in clinical medicine, particularly in critical care, has introduced transformative capabilities for analyzing and managing complex medical information. This technical note explores the application of LLMs, such as generative pretrained transformer 4 (GPT-4) and Qwen-Chat, in interpreting electronic healthcare records to assist with rapid patient condition assessments, predict sepsis, and automate the generation of discharge summaries. The note emphasizes the significance of LLMs in processing unstructured data from electronic health records (EHRs), extracting meaningful insights, and supporting personalized medicine through nuanced understanding of patient histories. Despite the technical complexity of deploying LLMs in clinical settings, this document provides a comprehensive guide to facilitate the effective integration of LLMs into clinical workflows, focusing on the use of DashScope's application programming interface (API) services for judgment on patient prognosis and organ support recommendations based on natural language in EHRs. By illustrating practical steps and best practices, this work aims to lower the technical barriers for clinicians and researchers, enabling broader adoption of LLMs in clinical research and practice to enhance patient care and outcomes.