Decreased Risk of Ventilator-Associated Pneumonia in Sepsis Due to Intra-Abdominal Infection

IKBIP might be a potential prognostic biomarker for glioblastoma multiforme

BACKGROUND

Due to the negative association between inhibitor of nuclear factor-kB kinase-interacting protein (IKBIP) and survival in gliomas, this study aimed to comprehensively analyze the potential function of IKBIP in glioblastoma multiforme (GBM).

METHODS

GBM samples were retrieved from The Cancer Genome Atlas and Chinese Glioma Genome Atlas as training and validation cohorts, respectively, and survival and Cox regression analyses were conducted. Based on clinical indicators and IKBIP, three prognostic models were established and then verified using the validation dataset. Infiltrating immune cell analysis and single-sample gene set enrichment analysis were also conducted to explore the underlying mechanisms. Finally, the key findings were validated through molecular biology experiments.

RESULTS

Patients in the high IKBIP score group had poorer survival. Based on Cox regression and subgroup analyses, IKBIP was identified as an independent prognostic factor. Among the three models constructed, the model combining the IKBIP signature and clinical features displayed good performance in terms of discrimination, calibration, and model improvement capability in the training cohort. This model was also successfully validated in an external cohort from the CGGA. Further analysis revealed that many immune cells and related pathways were involved in the high-risk group. In vitro experiments revealed that the knockdown of IKBIP inhibited cell invasion and proliferation, and promoted their senescence.

CONCLUSIONS

The prognostic value of IKBIP and its positive impact on the invasiveness of GBM were identified, indicating that IKBIP may serve as an underlying target for the treatment of GBM.

Defining persistent critical illness based on growth trajectories in patients with sepsis

BACKGROUND

Persistent critical illness is common in critically ill patients and is associated with vast medical resource use and poor clinical outcomes. This study aimed to define when patients with sepsis would be stabilized and transitioned to persistent critical illness, and whether such transition time varies between latent classes of patients.

METHODS

This was a retrospective cohort study involving sepsis patients in the eICU Collaborative Research Database. Persistent critical illness was defined at the time when acute physiological characteristics were no longer more predictive of in-hospital mortality (i.e., vital status at hospital discharge) than antecedent characteristics. Latent growth mixture modeling was used to identify distinct trajectory classes by using Sequential Organ Failure Assessment score measured during intensive care unit stay as the outcome, and persistent critical illness transition time was explored in each latent class.

RESULTS

The mortality was 16.7% (3828/22,868) in the study cohort. Acute physiological model was no longer more predictive of in-hospital mortality than antecedent characteristics at 15 days after intensive care unit admission in the overall population. Only a minority of the study subjects (n = 643, 2.8%) developed persistent critical illness, but they accounted for 19% (15,834/83,125) and 10% (19,975/198,833) of the total intensive care unit and hospital bed-days, respectively. Five latent classes were identified. Classes 1 and 2 showed increasing Sequential Organ Failure Assessment score over time and transition to persistent critical illness occurred at 16 and 27 days, respectively. The remaining classes showed a steady decline in Sequential Organ Failure Assessment scores and the transition to persistent critical illness occurred between 6 and 8 days. Elevated urea-to-creatinine ratio was a good biochemical signature of persistent critical illness.

CONCLUSIONS

While persistent critical illness occurred in a minority of patients with sepsis, it consumed vast medical resources. The transition time differs substantially across latent classes, indicating that the allocation of medical resources should be tailored to different classes of patients.