Search Results (7)

Bone and bone marrow Metastasis (BBM) are life-threatening complications of advanced malignancies, frequently requiring intensive care and associated with high short-term mortality. However, prognostic tools specifically tailored to critically ill BBM patients are limited. This multicenter cohort study aimed to develop and validate deep learning models for predicting 30-day mortality using ICU data from MIMIC-IV, eICU-CRD, and the First Affiliated Hospital of Xinjiang Medical University. After univariate screening, XGBoost-Boruta and Lasso regression identified 11 key clinical features within 24 h of ICU admission. Thirteen deep learning models were trained using five-fold cross-validation, and their performance was evaluated through AUC, average precision, calibration, and decision curves. TabNet achieved the best internal performance (AUC 0.878; AP 0.940) and maintained strong discrimination in both same-region (eICU: AUC 0.840; AP 0.932) and cross-regional (Xinjiang: AUC 0.831; Accuracy 80.5%) validation. SHAP and attention-based interpretability analyses consistently identified SOFA, serum calcium, and albumin as dominant predictors. A TabNet-based online calculator was subsequently deployed to enable bedside mortality risk estimation. In conclusion, TabNet demonstrates potential as an accurate and interpretable tool for early mortality risk stratification in critically ill BBM patients, offering support for more timely and individualized decision-making in BBM-related critical care. Full article

Background: Brain injuries, including stroke and traumatic brain injury (TBI), pose a major healthcare challenge due to their severe consequences and complex recovery. While ischemic strokes are more common, hemorrhagic strokes have a worse prognosis. TBI often affects young adults and leads to long-term disability. A critical concern in these patients is the frequent development of chronic critical illness, compounded by metabolic disturbances and malnutrition that hinder recovery. Objective: This study aimed to compare changes in nutritional status parameters under standard enteral nutrition protocols and clinical outcomes in prolonged/chronic critically ill patients with TBI or stroke versus such a population of patients without TBI or stroke. Methods: This matched prospective–retrospective cohort study included intensive care unit (ICU) patients with TBI or stroke from the Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology and patients without these conditions from the eICU-CRD database. Inclusion criteria comprised age 18–74 years, ICU stay >5 days, and enteral nutrition. Patients with re-hospitalization, diabetes, acute organ failure, or incomplete data were excluded. Laboratory values and clinical outcomes were compared between the two groups. Propensity score matching (PSM) was used to balance baseline characteristics (age, sex, and body mass index). Results: After PSM, 29 patients with TBI or stroke and 121 without were included. Univariate analysis showed significant differences in 21 laboratory parameters and three hospitalization outcomes. On day 1, the TBI/stroke group had higher hemoglobin, hematocrit, lymphocytes, total protein, and albumin, but lower blood urea nitrogen (BUN), creatinine, and glucose. By day 20, they had statistically significantly lower calcium, BUN, creatinine, and glucose. This group also showed less change in lymphocytes, calcium, and direct bilirubin. Hospitalization outcomes showed longer mechanical ventilation duration (p = 0.030) and fewer cases of acute kidney injury (p = 0.0220) in the TBI/stroke group. Conclusions: TBI and stroke patients exhibit unique metabolic patterns during prolonged/chronic critical illness, differing significantly from other ICU populations in protein/glucose metabolism and complication rates. These findings underscore the necessity for specialized nutritional strategies in neurocritical care and warrant further investigation into targeted metabolic interventions. Full article

Background/Objectives: Surgical procedures in chronic obstructive pulmonary disease (COPD) patients carry a high risk of postoperative respiratory failure, often causing the need for mechanical ventilation and prolonged intensive care unit (ICU) stays. Accompanying COPD with heart failure further increases the risk of complications. This study aimed to identify predictors of mortality, prolonged ICU and hospital stays, the need for mechanical ventilation, and vasoactive drug usage in ICU patients with moderate to severe COPD undergoing elective non-cardiac surgery. Methods: This retrospective cohort study analyzed eICU-CRD data, including adult patients with moderate to severe COPD admitted to the ICU from the operating room following elective non-cardiac surgery. Spearman’s correlation analysis was performed to assess associations between intraoperative ventilation parameters and ICU/hospital length of stay, postoperative laboratory parameters, and their perioperative dynamics. Results: This study included 680 patients (21% with severe COPD). Hospital and ICU mortality were 8.6% and 4.4%, respectively. Median ICU and hospital stays were 1.9 and 6.6 days, respectively. Intraoperative tidal volume, expired minute ventilation, positive end-expiratory pressure, mean airway pressure, peak inspiratory pressure, and compliance had no statistically significant association with mortality, postoperative mechanical ventilation, its duration, or the use of vasopressors/inotropes. Tidal volume correlated positively with changes in monocyte count (R = 0.611; p = 0.016), postoperative lymphocytes (R = 0.327; p = 0.017), and neutrophil count (R = 0.332; p = 0.02). Plateau pressure showed a strong positive association with the neutrophil-to-lymphocyte ratio (R = 0.708; p = 0.001). Conclusions: Intraoperative ventilation modes and parameters in COPD patients appear to have no significant impact on the outcomes or laboratory markers, except possibly for the neutrophil-to-lymphocyte ratio, although its elevation cause remains unclear. Full article

Background: Artificial intelligence (AI) has been increasingly employed in healthcare across diverse domains, including medical imaging, personalized diagnostics, therapeutic interventions, and predictive analytics using electronic health records. Its integration is particularly impactful in critical care, where AI has demonstrated the potential to enhance patient outcomes. This systematic review critically evaluates the current applications of AI within the domain of critical care nursing. Methods: This systematic review is registered with PROSPERO (CRD42024545955) and was conducted in accordance with PRISMA guidelines. Comprehensive searches were performed across MEDLINE/PubMed, SCOPUS, CINAHL, and Web of Science. Results: The initial review identified 1364 articles, of which 24 studies met the inclusion criteria. These studies employed diverse AI techniques, including classical models (e.g., logistic regression), machine learning approaches (e.g., support vector machines, random forests), deep learning architectures (e.g., neural networks), and generative AI tools (e.g., ChatGPT). The analyzed health outcomes encompassed postoperative complications, ICU admissions and discharges, triage assessments, pressure injuries, sepsis, delirium, and predictions of adverse events or critical vital signs. Most studies relied on structured data from electronic medical records, such as vital signs and laboratory results, supplemented by unstructured data, including nursing notes and patient histories; two studies also integrated audio data. Conclusion: AI demonstrates significant potential in nursing, facilitating the use of clinical practice data for research and decision-making. The choice of AI techniques varies based on the specific objectives and requirements of the model. However, the heterogeneity of the studies included in this review limits the ability to draw definitive conclusions about the effectiveness of AI applications in critical care nursing. Future research should focus on more robust, interventional studies to assess the impact of AI on nursing-sensitive outcomes. Additionally, exploring a broader range of health outcomes and AI applications in critical care will be crucial for advancing AI integration in nursing practices. Full article

Background. Sepsis presents significant diagnostic and prognostic challenges, and traditional scoring systems, such as SOFA and APACHE, show limitations in predictive accuracy. Machine learning (ML)-based predictive survival models can support risk assessment and treatment decision-making in the intensive care unit (ICU) by accounting for the numerous and complex factors that influence the outcome in the septic patient. Methods. A systematic literature review of studies published from 2014 to 2024 was conducted using the PubMed database. Eligible studies investigated the development of ML models incorporating commonly available laboratory and clinical data for predicting survival outcomes in adult ICU patients with sepsis. Study selection followed the PRISMA guidelines and relied on predefined inclusion criteria. All records were independently assessed by two reviewers, with conflicts resolved by a third senior reviewer. Data related to study design, methodology, results, and interpretation of the results were extracted in a predefined grid. Results. Overall, 19 studies were identified, encompassing primarily logistic regression, random forests, and neural networks. Most used datasets were US-based (MIMIC-III, MIMIC-IV, and eICU-CRD). The most common variables used in model development were age, albumin levels, lactate levels, and ventilator. ML models demonstrated superior performance metrics compared to conventional methods and traditional scoring systems. The best-performing model was a gradient boosting decision tree, with an area under curve of 0.992, an accuracy of 0.954, and a sensitivity of 0.917. However, several critical limitations should be carefully considered when interpreting the results, such as population selection bias (i.e., single center studies), small sample sizes, limited external validation, and model interpretability. Conclusions. Through real-time integration of routine laboratory and clinical data, ML-based tools can assist clinical decision-making and enhance the consistency and quality of sepsis management across various healthcare contexts, including ICUs with limited resources. Full article

Background: The prevailing model for understanding chronic critical illness is a biphasic model, suggesting phases of acute and chronic critical conditions. A major challenge within this model is the difficulty in determining the timing of the process chronicity. It is likely that the triad of symptoms (inflammation, catabolism, and immunosuppression [ICIS]) could be associated with this particular point. We aimed to explore the impact of the symptom triad (inflammation, catabolism, immunosuppression) on the outcomes of patients hospitalized in intensive care units (ICUs). Methods: The eICU-CRD database with 200,859 ICU admissions was analyzed. Adult patients with the ICIS triad, identified by elevated CRP (>20 mg/L), reduced albumin (<30 g/L), and low lymphocyte counts (<0.8 × 10⁹/L), were included. The cumulative risk of developing ICIS was assessed using the Nelson–Aalen estimator. Results: This retrospective cohort study included 894 patients (485 males, 54%), with 60 (6.7%) developing ICIS. The cumulative risk of ICIS by day 21 was 22.5%, with incidence peaks on days 2–3 and 10–12 after ICU admission. Patients with the ICIS triad had a 2.5-fold higher mortality risk (p = 0.009) and double the likelihood of using vasopressors (p = 0.008). The triad onset day did not significantly affect mortality (p = 0.104). Patients with ICIS also experienced extended hospital (p = 0.041) and ICU stays (p < 0.001). Conclusions: The symptom triad (inflammation, catabolism, immunosuppression) during hospitalization increases mortality risk by 2.5 times (p = 0.009) and reflects the chronicity of the critical condition. Identifying two incidence peaks allows the proposal of a new Tri-steps model of chronic critical illness with acute, extended, and chronic phases. Full article

Venous thromboembolism (VTE) in patients with COVID-19 in intensive care units (ICU) is frequent, but risk factors (RF) remain unidentified. In this meta-analysis (CRD42020188764) we searched for observational studies from ICUs reporting the association between VTE and RF in Medline/Embase up to 15 April 2021. Reviewers independently extracted data in duplicate and assessed the certainty of the evidence using the GRADE approach. Analyses were conducted using the random-effects model and produced a non-adjusted odds ratio (OR). We analysed 83 RF from 21 studies (5296 patients). We found moderate-certainty evidence for an association between VTE and the D-dimer peak (OR 5.83, 95%CI 3.18–10.70), and length of hospitalization (OR 7.09, 95%CI 3.41–14.73) and intubation (OR 2.61, 95%CI 1.94–3.51). We identified low-certainty evidence for an association between VTE and CRP (OR 1.83, 95% CI 1.32–2.53), D-dimer (OR 4.58, 95% CI 2.52–8.50), troponin T (OR 8.64, 95% CI 3.25–22.97), and the requirement for inotropic drugs (OR 1.67, 95% CI 1.15–2.43). Traditional VTE RF (i.e., history of cancer, previous VTE events, obesity) were not found to be associated to VTE in COVID-19. Anticoagulation was not associated with a decreased VTE risk. VTE RF in severe COVID-19 correspond to individual illness severity, and inflammatory and coagulation parameters. Full article