Intensive Care Medicine Experimental最新文献

Background: This systematic review aims to identify predictors of intradialytic hypotension (IDH) in critically ill patients undergoing kidney replacement therapy (KRT) for acute kidney injury (AKI).

Methods: A comprehensive search of PubMed was conducted from 2002 to April 2024. Studies included critically ill adults undergoing KRT for AKI, excluding pediatric patients, non-critically ill individuals, those with chronic kidney disease, and those not undergoing KRT. The primary outcome was identifying predictive tools for hypotensive episodes during KRT sessions.

Results: The review analyzed data from 8 studies involving 2873 patients. Various machine learning models were assessed for their predictive accuracy. The Extreme Gradient Boosting Machine (XGB) model was the top performer with an area under the receiver operating characteristic curve (AUROC) of 0.828 (95% CI 0.796-0.861), closely followed by the deep neural network (DNN) with an AUROC of 0.822 (95% CI 0.789-0.856). All machine learning models outperformed other predictors. The SOCRATE score, which includes cardiovascular SOFA score, index capillary refill, and lactate level, had an AUROC of 0.79 (95% CI 0.69-0.89, p < 0.0001). Peripheral perfusion index (PPI) and heart rate variability (HRV) showed AUROCs of 0.721 (95% CI 0.547-0.857) and 0.761 (95% CI 0.59-0.887), respectively. Pulmonary vascular permeability index (PVPI) and mechanical ventilation also demonstrated significant diagnostic performance. A PVPI ≥ 1.6 at the onset of intermittent hemodialysis (IHD) sessions predicted IDH associated with preload dependence with a sensitivity of 91% (95% CI 59-100%) and specificity of 53% (95% CI 42-63%).

Conclusion: This systematic review shows how combining predictive models with clinical indicators can forecast IDH in critically ill AKI patients undergoing KRT, with validation in diverse settings needed to improve accuracy and patient care strategies.

Background: Acute Respiratory Distress Syndrome (ARDS) has a high morbidity and mortality. One therapy that can decrease mortality is ventilation in the prone position (PP). Patients undergoing PP are amongst the sickest, and there is a need for early identification of patients at particularly high risk of death. These patients may benefit from an in-depth review of treatment or consideration of rescue therapies. We report the development of a machine learning model trained to predict early mortality in patients undergoing prone positioning as part of the management of their ARDS.

Methods: Prospectively collected clinical data were analysed retrospectively from a single tertiary ICU. The records of patients who underwent an initial session of prone positioning whilst receiving invasive mechanical ventilation were identified (n = 131). The decision to perform prone positioning was based on the criteria in the PROSEVA study. A C5.0 classifier algorithm with adaptive boosting was trained on data gathered before, during, and after initial proning. Data was split between training (85% of data) and testing (15% of data). Hyperparameter tuning was achieved through a grid-search using a maximal entropy configuration. Predictions for 7-day mortality after initial proning session were made on the training and testing data.

Results: The model demonstrated good performance in predicting 7-day mortality (AUROC: 0.89 training, 0.78 testing). Seven variables were used for prediction. Sensitivity was 0.80 and specificity was 0.67 on the testing data set. Patients predicted to survive had 13.3% mortality, while those predicted to die had 66.67% mortality. Among patients in whom the model predicted patient would survive to day 7 based on their response, mortality at day 7 was 13.3%. Conversely, if the model predicted the patient would not survive to day 7, mortality was 66.67%.

Conclusions: This proof-of-concept study shows that with a limited data set, a C5.0 classifier can predict 7-day mortality from a number of variables, including the response to initial proning, and identify a cohort at significantly higher risk of death. This can help identify patients failing conventional therapies who may benefit from a thorough review of their management, including consideration of rescue treatments, such as extracorporeal membrane oxygenation. This study shows the potential of a machine learning model to identify ARDS patients at high risk of early mortality following PP. This information can guide clinicians in tailoring treatment strategies and considering rescue therapies. Further validation in larger cohorts is needed.

Background: Enhancing venous return during cardiopulmonary resuscitation (CPR) can lead to better hemodynamics and improved outcome after cardiac arrest (CA). Peripheral Intravenous Analysis (PIVA) provides feedback on venous flow changes and may indicate an increase in venous return and cardiac output during CPR. We hypothesize PIVA can serve as an early indicator of increased venous return, preceding end-tidal CO₂ (etCO₂) increase, before the return of spontaneous circulation (ROSC) in a rat model of CA and CPR.

Results: Eight male Wistar rats were intubated and ventilated, and etCO₂ was measured. Vessels were cannulated in the tail vein, femoral vein, femoral artery, and central venous and connected to pressure transducers. Ventilation was discontinued to achieve asphyxial CA. After 8 min, CPR began with ventilation, epinephrine, and automated chest compressions 200 times per minute until mean arterial pressure increased to 120 mmHg. Waveforms were recorded and analyzed. PIVA was calculated using a Fourier transformation of venous waveforms. Data are mean ± SE. Maximum PIVA values occurred in the tail vein 34.7 ± 2.9 s before ROSC, with subsequent PIVA peaks in femoral vein and centrally at 30.9 ± 5.4 and 25.1 ± 5.0 s, respectively. All PIVA peaks preceded etCO₂ increase (21.5 ± 3.2 s before ROSC).

Conclusion: PIVA consistently detected venous pressure changes prior to changes in etCO₂. This suggests that PIVA has the potential to serve as an important indicator of venous return and cardiac output during CPR, and also as a predictor of ROSC.

Background: Sepsis remains a major global health concern due to its high prevalence and mortality. Changes in body temperature (Tb), such as hypothermia or fever, are diagnostic indicators and play a crucial role in the pathophysiology of sepsis. This study aims to characterize the thermoregulatory mechanisms during sepsis using the cecal ligation and puncture (CLP) model and explore how sepsis severity and ambient temperature (Ta) influence Tb regulation and mortality. Rats were subjected to mild or severe sepsis by CLP while housed at thermoneutral (28 °C) or subthermoneutral (22 °C) Ta, and their Tb was monitored for 12 h. Blood and hypothalamus were collected for cytokines and prostaglandin E₂ (PGE₂) analysis.

Results: At 28 °C, febrile response magnitude correlated with sepsis severity and inflammatory response, with tail vasoconstriction as the primary heat retention mechanism. At 22 °C, Tb was maintained during mild sepsis but dropped during severe sepsis, linked to reduced UCP1 expression in brown adipose tissue and less effective vasoconstriction. Despite differences in thermoregulatory responses, both Ta conditions induced a persistent inflammatory response and increased hypothalamic PGE₂ production. Notably, mortality in severe sepsis was significantly higher at 28 °C (80%) compared to 22 °C (0%).

Conclusions: Our findings reveal that ambient temperature and the inflammatory burden critically influence thermoregulation and survival during early sepsis. These results emphasize the importance of considering environmental factors in preclinical sepsis studies. Although rodents in experimental settings are often adapted to cold environments, these conditions may not fully translate to human sepsis, where cold adaptation is rare. Thus, researchers should carefully consider these variables when designing experiments and interpreting translational implications.

Background: Post-intensive care syndrome (PICS) poses a notable public health concern, with survivors of critical illness experiencing long-term physical, psychological, and cognitive challenges. Mitochondrial dysfunction has gained attention for its potential involvement in PICS. However, the long-term impact of mitochondrial status on patient recovery remains uncertain. A single-centre retrospective analysis was conducted in Leeuwarden, the Netherlands, between May and November 2019, within a mixed ICU survivor cohort. Patients were assessed for mitochondrial markers (mtDNA damage represented by the presence of mtDNA fragmentation and mitochondrial DNA levels evaluated by the ratio of mtDNA and nuclear DNA), clinical factors, and long-term outcomes measured by the physical functioning (PF) domain of health-related quality of life.

Results: A total of 43 patients were included in this study divided into recovery and non-recovery groups based on age-adjusted PF scores at 12 months post-ICU. Nineteen patients scored below these thresholds. No significant differences in mitochondrial markers between groups were identified. Furthermore, no significant correlations were found between mtDNA levels and mtDNA damage at baseline and 12 months with PF scores. However, mtDNA levels decreased over time in the recovery (p-value <  < 0.01) and non-recovery groups (p-value < 0.01).

Conclusion: No significant correlation was found between mitochondrial markers and physical functioning scores. This study underscores the multifactorial nature of PICS and the need for a comprehensive understanding of its metabolic and cellular components. While mitochondrial markers may play a role in PICS, they operate within a framework influenced by various factors. This exploratory study serves as a foundation for future investigations aimed at developing targeted interventions to enhance the quality of life for ICU survivors grappling with PICS.

Background: Septic encephalopathy is frequent but its pathophysiology is enigmatic. We studied expression of neurotransmitters, inflammation and integrity of the blood-brain barrier (BBB) in several brain regions during abdominal sepsis. We compared mice with either lethal or surviving phenotype in the first 4 sepsis days. Mature CD-1 females underwent cecal ligation and puncture (CLP). Body temperature (BT) was measured daily and predicted-to-die (within 24 h) mice (for P-DIE; BT < 28 °C) were sacrificed together (1:1 ratio) with mice predicted-to-survive (P-SUR; BT > 35 °C), and healthy controls (CON). Brains were dissected into neocortex, cerebellum, midbrain, medulla, striatum, hypothalamus and hippocampus.

Results: CLP mice showed an up to threefold rise of serotonin in the hippocampus, 5-hydroxyindoleacetic and homovanillic acid (HVA) in nearly all regions vs. CON. Compared to P-SUR, P-DIE mice showed a 1.7 to twofold rise of HVA (386 ng/g of tissue), dopamine (265 ng/g) and 3,4-Dihydroxyphenylacetic acid (DOPAC; 140 ng/g) in the hippocampus, hypothalamus and medulla (174, 156, 82 ng/g of tissue, respectively). CLP increased expression of TNFα, IL-1β and IL-6 mRNA by several folds in the midbrain, cerebellum and hippocampus versus CON. The same cytokines were further elevated in P-DIE vs P-SUR in the midbrain and cerebellum. Activation of astrocytes and microglia was robust across regions but remained typically phenotype independent. There was a similar influx of sodium fluorescein across the BBB in both P-DIE and P-SUR mice.

Conclusions: Compared to survivors, the lethal phenotype induced a stronger deregulation of amine metabolism and cytokine expression in selected brain regions, but the BBB permeability remained similar regardless of the predicted outcome.