Current Issue : July-September Volume : 2025 Issue Number : 3 Articles : 5 Articles
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....
The renal resistive index (RRI), a Doppler ultrasound-derived parameter measuring renal vascular resistance, has emerged as a promising non-invasive tool to evaluate renal hemodynamics in critically ill patients, particularly those with acute respiratory distress syndrome (ARDS) and heart failure (HF). This narrative review examines the current evidence for RRI measurement in these conditions, exploring its physiological bases, methodology, clinical applications, and limitations. In ARDS, RRI reflects the complex interactions between positive pressure ventilation, hypoxemia, and systemic inflammation, showing a role in predicting acute kidney injury and monitoring response to interventions. In HF, RRI is able to assess venous congestion and cardiorenal interactions and can also serve as a prognostic indicator. Many studies have shown RRI’s superiority or complementarity to traditional biomarkers in predicting renal dysfunction, although its interpretation requires consideration of multiple patient-related factors. Key challenges include operator dependency, lack of standardization, and complex interpretation in multi-organ dysfunction. Future research should focus on measurement standardization, development of automated techniques, investigation of novel applications like intraparenchymal renal resistive index variation, and validation of RRI-guided management strategies. Despite its limitations, RRI represents a valuable tool that offers bedside and real-time insights into renal hemodynamics and potential guidance for therapeutic interventions. Further research is needed to fully clarify its clinical potential and address current limitations, particularly in critical care settings involving multiple organ dysfunction....
In intensive care units (ICUs), serum lactate and methemoglobin (metHb) levels are considered significant biomarkers for predicting mortality in critically ill patients. This study investigates the relationship between lactate and metHb levels in blood gas analyses at admission and 24 h later, as well as their association with mortality in ICU patients. The study was conducted retrospectively between March and December 2022 at Adıyaman Training and Research Hospital, evaluating 114 patients, with statistical analyses performed on the collected data. The results indicated a statistically significant decrease in lactate levels between admission and 24 h after (p = 0.004). However, no significant change was found in metHb levels (p > 0.05). Lactate clearance was significantly lower in deceased patients compared to survivors (p = 0.037), whereas metHb clearance showed no statistically significant association with mortality. Lactate is highlighted as a key indicator of tissue hypoxia and plays a critical role in managing critically ill patients. Elevated lactate levels are associated with impaired oxygenation and worse prognoses. The literature consistently supports the association between high lactate levels and increased mortality in conditions such as sepsis and hemorrhagic shock. Similarly, this study confirms the prognostic value of lactate, particularly in the early phases of ICU admission. In contrast, metHb levels were not found to significantly impact mortality. Although some studies suggest a potential role of metHb as a biomarker for oxidative stress in inflammatory diseases, this relationship was not supported by the current findings. In conclusion, serum lactate levels serve as a crucial tool for mortality prediction and patient management in ICUs, while metHb levels have limited prognostic value. These findings suggest that greater emphasis should be placed on lactate monitoring in the management of critically ill patients....
Background/Objectives: Aerosolized medications are common practice for mechanically ventilated pediatric patients. Infants often receive nebulized medications via hand ventilation using an anesthesia bag, but evidence on optimal aerosol delivery with this method is limited. For this study, various configurations of the Mapleson breathing circuit were tested to optimize albuterol delivery to a simulated pediatric model. Methods: Using a simulated pediatric lung model (ASL 5000) with the semi-open Mapleson anesthesia circuit, 2.5 mg/3 mL of albuterol sulfate solution was nebulized to a viral/bacterial filter (Respiguard 202). Four models were compared with varying fresh gas flows (FGFs), smallvolume nebulizer (SVN) placements, and adjusting dead space. Five Registered Respiratory Therapists (RRTs) bagged the aerosol into a collection filter following defined ventilation parameters. Each model was tested in random order to avoid fatigue bias. Albuterol concentrations eluted from in-line filters were measured by spectrophotometry (absorbance at 276 nm). Results: No inter-user variability was observed among the RRTs. Significant differences in albuterol recovered were noted between models (One Way ANOVA, Tukey’s post hoc, n = 5). Model 4, with the nebulizer closest to the collecting filter, recovered 21.77 ± 1.89% of albuterol. The standard clinical model was the least effective, with only 0.10 ± 0.17% albuterol recovery. Conclusions: Modifying the anesthesia breathing circuit significantly improved aerosol drug delivery efficiency. Our findings suggest that current clinical practices for nebulized drug delivery are inefficient and can be markedly improved with simple adjustments in nebulizer positioning and gas flow within the circuit....
Background: Disorders of Consciousness (DoC) following acute brain injuries, such as intracerebral hemorrhage, present significant clinical challenges in intensive care and rehabilitation settings. Early multidisciplinary interventions, including physiatric care, are critical in optimizing recovery trajectories. However, evidence regarding the timing and intensity of rehabilitation interventions remains limited. This case report highlights the role of physiatrists in managing a critically ill patient with a DoC in an Intensive Care Unit (ICU), focusing on early rehabilitation strategies and individualized care planning. Case presentation: A 63-year-old male with a history of hypertension and cardiac disease presented with a left hemispheric hemorrhage and quadriventricular intraventricular hemorrhage. The patient was admitted to the ICU in a comatose state (Glasgow Coma Scale [GCS] 5). Initial physiatric evaluation revealed a critical condition precluding immediate initiation of an Individual Rehabilitation Project (IRP). Over subsequent weeks, clinical improvements were observed, including an increased GCS and Coma Recovery Scale-Revised (CRS-R) score. A tailored IRP was implemented, emphasizing passive mobilization to prevent complications such as muscle atrophy, joint contractures, and pressure ulcers. The patient demonstrated gradual progress, transitioning to a Minimally Conscious State (MCS) and achieving improved joint mobility and reduced peripheral edema. Discussion and Conclusions: This case underscores the pivotal role of physiatrists in ICU settings, particularly for patients with DoC. Early physiatric interventions, even in critically ill patients, can prevent secondary complications and facilitate functional recovery. Close collaboration with ICU teams and infectious disease specialists ensured the safe implementation of rehabilitation strategies despite the patient’s severe condition. The observed clinical improvements highlight the potential benefits of early mobilization and individualized care plans, both in terms of survival (quoad vitam) and quality of life (quoad valetudinem). This report emphasizes the need for further research to refine rehabilitation practices for patients with DoC, bridging gaps between acute care and neurorehabilitation....
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