Search Results (30)

Early prediction of weaning outcomes in mechanically ventilated patients has significant potential to influence the duration of treatment as well as associated morbidity and mortality. This study aimed to investigate the utility of signal analysis using electromyographic diaphragm (EMG) and electrocardiography (ECG) signals to classify the success or failure of weaning in mechanically ventilated patients. Electromyographic signals of 40 subjects were recorded using 5-channel surface electrodes placed around the diaphragm muscle, along with an ECG recording through a 3-lead Holter system during extubation. EMG and ECG signals were recorded from mechanically ventilated patients undergoing weaning trials. Linear and nonlinear signal analysis techniques were used to assess the interaction between diaphragm muscle activity and cardiac activity. Supervised machine learning algorithms were then used to classify the weaning outcomes. The study revealed clear differences in diaphragmatic and cardiac patterns between patients who succeeded and failed in the weaning trials. Successful weaning was characterised by a higher ECG-derived respiration amplitude, whereas failed weaning was characterised by an elevated EMG amplitude. Furthermore, successful weaning exhibited greater oscillations in diaphragmatic muscle activity. Spectral analysis and parameter extraction identified 320 parameters, of which 43 were significant predictors of weaning outcomes. Using seven of these parameters, the Naive Bayes classifier demonstrated high accuracy in classifying weaning outcomes. Surface electromyographic and electrocardiographic signal analyses can predict weaning outcomes in mechanically ventilated patients. This approach could facilitate the early identification of patients at risk of weaning failure, allowing for improved clinical management. Full article

Non-invasive ventilation (NIV) is a cornerstone in the management of acute and chronic respiratory failure, offering critical support without the risks of intubation. However, successful weaning from NIV remains a complex, high-stakes process. Poorly timed or improperly executed weaning significantly increases morbidity and mortality, yet current clinical practice often relies on subjective judgment rather than evidence-based protocols. This manuscript reviews the current landscape of NIV weaning, emphasizing structured approaches, objective monitoring, and predictors of weaning success or failure. It examines guideline-based indications, monitoring strategies, and various weaning techniques—gradual and abrupt—with evidence of their efficacy across different patient populations. Predictive tools such as the Rapid Shallow Breathing Index, Lung Ultrasound Score, Diaphragm Thickening Fraction, ROX index, and HACOR score are analyzed for their diagnostic value. Additionally, this review underscores the importance of care setting—ICU, step-down unit, or general ward—and how it influences outcomes. Finally, it highlights critical gaps in research, especially around weaning in non-ICU environments. By consolidating current evidence and identifying predictors and pitfalls, this article aims to support clinicians in making safe, timely, and patient-specific NIV weaning decisions. In the current literature, there are gaps regarding patient selection and lack of universal protocolization for initiation and de-escalation of NIV as the data has been scattered. This review aims to consolidate the relevant information to be utilized by clinicians throughout multiple levels of care in all hospital systems. Full article

Background/Objectives: Weaning failure remains a major challenge in patients requiring prolonged mechanical ventilation. This study aimed to describe outcomes in patients ventilated for >14 days and identify specific predictors of weaning success. Methods: A retrospective analysis of 88 patients from the Soroka University Medical Center database was conducted. Outcomes in the successful weaning (SW) group were compared to those in the failed weaning (FW) group. Predictors of weaning success were analyzed using multivariate logistic regression. Results: Forty patients (45%) were successfully weaned and discharged to rehabilitation or home. In-hospital mortality was 28%, with deaths occurring exclusively in the FW group (p < 0.001). One-month and one-year post-discharge all-cause mortality rates were 11% and 28%, respectively, with no group differences. Hypoalbuminemia and the Sequential Organ Failure Assessment (SOFA) score at admission significantly predicted weaning failure (odds ratio: 5.71 and 0.54, respectively). Demographics, comorbidities, ventilation indications, admission data, and diuretic use were not predictive. Conclusions: Hypoalbuminemia and the SOFA score at admission were key predictors of weaning success in patients ventilated for more than 2 weeks. Age and comorbidities were not significant. Prospective studies on albumin supplementation and high-protein diets are warranted to assess their impact on weaning outcomes. Full article

The use of high-flow nasal cannula (HFNC) as an oxygen therapy post-extubation has demonstrated varying success rates across different surgical populations. This study aimed to identify the predictive factors influencing HFNC weaning outcomes in patients with postoperative extubation hypoxemia. We conducted a retrospective analysis of patients in a surgical intensive care unit, categorized into three major postoperative groups: cardiothoracic surgery, upper abdominal surgery, and other surgeries. Our analysis examined pre-extubation weaning profiles, vital signs before and after HFNC initiation, and changes in physiological parameters during HFNC use. A total of 90 patients were included, divided into two groups based on HFNC weaning success or failure. Key parameters analyzed included maximal inspiratory pressure (MIP), PaO₂/FiO₂ (P/F) ratio, vital signs, SpO₂ levels, respiratory rate (RR), heart rate (HR), respiratory rate–oxygenation (ROX) index, and HFNC duration. The findings revealed that cardiothoracic and upper abdominal groups showed significantly higher HFNC weaning success rates (73.3% and 70.6%) compared to the other surgeries group (34.6%) (p = 0.004). Critical predictors of successful weaning included pre-HFNC SpO₂, P/F ratio, and changes in the ROX index, particularly in upper abdominal and other surgeries groups. In cardiothoracic surgery patients, higher maximal inspiratory pressure (MIP) (p = 0.031) was associated with improved outcomes, while prolonged HFNC use correlated with weaning success in this group (p = 0.047). These findings underscore the necessity of tailoring HFNC strategies to surgical characteristics and individual patient profiles. For cardiothoracic surgery patients, pre-extubation MIP, post-extubation RR, ΔROX, and ΔHR were identified as key predictive factors. In upper abdominal surgery, pre-extubation P/F ratio, post-extubation SpO₂, and ΔROX played crucial roles. For patients undergoing other types of surgeries, pre-extubation P/F ratio and ΔROX remained the most reliable predictors of HFNC weaning success. Full article

Background: Cardiogenic shock (CS), characterized by inadequate tissue perfusion due to cardiac dysfunction, has a high mortality rate despite advances in treatment. Systemic inflammation and organ failure exacerbate the severity of CS. Extracorporeal hemadsorption techniques such as CytoSorb^® have been introduced to control inflammation. However, evidence of their efficacy, particularly in patients on various mechanical circulatory support (MCS) systems, remains limited. Methods: This retrospective study analyzed data from 129 CS patients treated with CytoSorb^® at the University Hospital of Marburg between August 2019 and December 2023. Those patients receiving MCS were grouped according to MCS type: (1) Impella, (2) VA-ECMO, and (3) ECMELLA. The hemodynamic parameters of circulatory support (e.g., MCS flow rates and vasoactive inotropic score, VIS) and laboratory and ventilation parameters were assessed 24 h before start of CytoSorb^® therapy (T1) and 24 h after completion of CytoSorb^® therapy (T2). Results: Of 129 CS patients (mean age: 64.7 ± 13.1 years), 103 (79.8%) received MCS. Comparing T1 and T2, there was a significant reduction in VIS in the entire cohort (T1: 38.0, T2: 16.3; p = 0.002), with a concomitant significant reduction in the level of MCS support in all subgroups, indicating successful weaning. Analysis of laboratory parameters showed significant reductions in lactate (T1: 2.1, T2: 1.3 mmol/L; p = 0.014), myoglobin (T1: 1549.0, T2: 618.0 µg/L; p < 0.01), lactate dehydrogenase (T1: 872.0, T2: 632.0 U/L; p = 0.048), and procalcitonin (T1: 2.9, T2: 1.6 µg/L; p < 0.001). However, a significant decrease in platelets (T1: 140.0, T2: 54.0 tsd/µL; p < 0.001) and albumin (T1: 25.0, T2: 22.0 g/dL; p < 0.001) was also documented. The median SOFA score of the entire cohort was 15.0 (IQR 12.0–16.0), predicting a mortality rate of >80%, which could be reduced to 60.5% in the present study. Conclusions: During CytoSorb^® therapy in CS, a significant reduction in VIS was demonstrated, resulting in improved organ perfusion. Therefore, the results of this study underline that CytoSorb^® therapy can be considered a useful “component” in the complex management of CS, especially when combined with MCS. To refine and optimize treatment strategies in CS, prospective studies are needed to better define the role of hemadsorption. Full article

Acute kidney injury (AKI) is a severe and prevalent syndrome, primarily observed in intensive care units (ICUs) and perioperative settings. The discovery of a new biomarker for kidney function and injury, capable of overcoming the limitations of traditional markers, has the potential to improve the diagnosis and management of AKI. Proenkephalin A 119–159 (PENK) has emerged as a novel biomarker for AKI and has been validated in various clinical settings. It has demonstrated a faster response to AKI compared to creatinine and has been shown to predict successful weaning from renal replacement therapy in the ICU. PENK has also shown promise as an AKI biomarker in perioperative patients. Additionally, PENK has been proven to be effective in estimating mortality and morbidity in patients undergoing cardiac surgery, and those with traumatic brain injury or ischemic stroke. Incorporating PENK into a novel estimation of the glomerular filtration rate, referred to as the PENK-Crea equation, has yielded promising results. Full article

Objective: Coronavirus disease 2019 (COVID-19) can cause intubation and ventilatory support due to respiratory failure, and extubation failure increases mortality risk. This study, therefore, aimed to explore the feasibility of using specific biochemical and ventilator parameters to predict survival status among COVID-19 patients by using machine learning. Methods: This study included COVID-19 patients from Taipei Medical University-affiliated hospitals from May 2021 to May 2022. Sequential data on specific biochemical and ventilator parameters from days 0–2, 3–5, and 6–7 were analyzed to explore differences between the surviving (successfully weaned off the ventilator) and non-surviving groups. These data were further used to establish separate survival prediction models using random forest (RF). Results: The surviving group exhibited significantly lower mean C-reactive protein (CRP) levels and mean potential of hydrogen ions levels (pH) levels on days 0–2 compared to the non-surviving group (CRP: non-surviving group: 13.16 ± 5.15 ng/mL, surviving group: 10.23 ± 5.15 ng/mL; pH: non-surviving group: 7.32 ± 0.07, survival group: 7.37 ± 0.07). Regarding the survival prediction performanace, the RF model trained solely with data from days 0–2 outperformed models trained with data from days 3–5 and 6–7. Subsequently, CRP, the partial pressure of carbon dioxide in arterial blood (PaCO₂), pH, and the arterial oxygen partial pressure to fractional inspired oxygen (P/F) ratio served as primary indicators in survival prediction in the day 0–2 model. Conclusions: The present developed models confirmed that early biochemical and ventilatory parameters—specifically, CRP levels, pH, PaCO₂, and P/F ratio—were key predictors of survival for COVID-19 patients. Assessed during the initial two days, these indicators effectively predicted the likelihood of successful weaning of from ventilators, emphasizing their importance in early management and improved outcomes in COVID-19-related respiratory failure. Full article

Background and Objectives: Extubation success in ICU patients is crucial for reducing ventilator-associated complications, morbidity, and mortality. The Rapid Shallow Breathing Index (RSBI) is a widely used predictor for weaning from mechanical ventilation. This study aims to determine the predictive value of serial RSBI measurements on extubation success in ICU patients on mechanical ventilation. Materials and Methods: This prospective observational study was conducted on 86 ICU patients at Hitit University between February 2024 and July 2024. Patients were divided into successful and unsuccessful extubation groups. RSBI values were compared between these groups. Results: This study included 86 patients (32 females, 54 males) with a mean age of 54.51 ± 12.1 years. Extubation was successful in 53 patients and unsuccessful in 33. There was no significant difference in age and intubation duration between the groups (p = 0.246, p = 0.210). Significant differences were found in RSBI-1a and RSBI-2 values (p = 0.013, p = 0.011). The median RSBI-2a was 80 in the successful group and 92 in the unsuccessful group (p = 0.001). The ΔRSBI was higher in the unsuccessful group (p = 0.022). ROC analysis identified optimal cut-off values: RSBI-2a ≤ 72 (AUC 0.715) and ΔRSBI ≤ −3 (AUC 0.648). RSBI-2a ≤ 72 increased the likelihood of successful extubation by 10.8 times, while ΔRSBI ≤ −3 increased it by 3.4 times. Using both criteria together increased the likelihood by 28.48 times. Conclusions: Serial RSBI measurement can be an effective tool for predicting extubation success in patients on IMV. These findings suggest that serially measured RSBI may serve as a potential indicator for extubation readiness. Full article

Background: Diaphragmatic dysfunction correlates with weaning failure, highlighting the need to independently assess the diaphragm’s effects on weaning. We modified the rapid shallow breathing index (RSBI), a predictor of successful weaning, by incorporating temporal variables into existing ultrasound-derived diaphragm index to create a simpler index closer to tidal volume. Methods: We conducted a prospective observational study of patients who underwent a spontaneous breathing trial in the medical intensive care unit (ICU) at Severance Hospital between October 2022 and June 2023. Diaphragmatic displacement (DD) and diaphragm inspiratory time (Ti) were measured using lung ultrasonography. The modified RSBI was defined as follows: respiratory rate (RR) divided by DD was defined as D-RSBI, and RR divided by the sum of the products of DD and Ti on both sides was defined as DTi-RSBI. Results: Among the sonographic indices, DTi-RSBI had the highest area under the receiver operating characteristic (ROC) curve of 0.774 in ROC analysis, and a correlation was found between increased DTi-RSBI and unsuccessful extubation in a multivariable logistic regression analysis (adjusted odds ratio 0.02, 95% confidence interval 0.00–0.97). Conclusions: The DTi-RSBI is beneficial in predicting successful weaning in medical ICU patients. Full article

Background: It is unclear whether other cardiac biomarkers than NT-proBNP can be useful in the risk stratification of patients weaning from mechanical ventilation. The aim of this study is to summarize the role of ischemic cardiac biomarkers in predicting spontaneous breathing trial (SBT) or extubation failure. Methods: We systematically searched Embase, MEDLINE, Web of Science, and Cochrane Central for studies published before January 2024 that reported the association between ischemic cardiac biomarkers and SBT or extubation failure. Data were extracted using a standardized form and methodological assessment was performed using the QUIPS tool. Results: Seven observational studies investigating four ischemic cardiac biomarkers (Troponin-T, Troponin-I, CK-MB, Myoglobin) were included. One study reported a higher peak Troponin-I in patients with extubation failure compared to extubation success (50 ng/L [IQR, 20–215] versus 30 ng/L [IQR, 10–86], p = 0.01). A second study found that Troponin-I measured before the SBT was higher in patients with SBT failure in comparison to patients with SBT success (100 ± 80 ng/L versus 70 ± 130 ng/L, p = 0.03). A third study reported a higher CK-MB measured at the end of the SBT in patients with weaning failure (SBT or extubation failure) in comparison to weaning success (8.77 ± 20.5 ng/mL versus 1.52 ± 1.42 ng/mL, p = 0.047). Troponin-T and Myoglobin as well as Troponin-I and CK-MB measured at other time points were not found to be related to SBT or extubation failure. However, most studies were underpowered and with high risk of bias. Conclusions: The association with SBT or extubation failure is limited for Troponin-I and CK-MB and appears absent for Troponin-T and Myoglobin, but available studies are hampered by significant methodological drawbacks. To more definitively determine the role of ischemic cardiac biomarkers, future studies should prioritize larger sample sizes, including patients at risk of cardiac disease, using stringent SBTs and structured timing of laboratory measurements before and after SBT. Full article

Background: Mechanical ventilation significantly improves patient survival but is associated with complications, increasing healthcare costs and morbidity. Identifying optimal weaning times is paramount to minimize these risks, yet current methods rely heavily on clinical judgment, lacking specificity. Methods: This study introduces a novel multiparametric predictive score, the MUSVIP (MUltiparametric Score for Ventilation discontinuation in Intensive care Patients), aimed at accurately predicting successful extubation. Conducted at Santo Stefano Hospital’s ICU, this single-center, observational, prospective cohort study will span over 12 months, enrolling adult patients undergoing invasive mechanical ventilation. The MUSVIP integrates variables measured before and during a spontaneous breathing trial (SBT) to formulate a predictive score. Results: Preliminary analyses suggest an Area Under the Curve (AUC) of 0.815 for the MUSVIP, indicating high predictive capacity. By systematically applying this score, we anticipate identifying patients likely to succeed in weaning earlier, potentially reducing ICU length of stay and associated healthcare costs. Conclusion: This study’s findings could significantly influence clinical practices, offering a robust, easy-to-use tool for optimizing weaning processes in ICUs. Full article

The management of mechanical ventilation (MV) remains a challenge in intensive care units (ICUs). The digitalization of healthcare and the implementation of artificial intelligence (AI) and machine learning (ML) has significantly influenced medical decision-making capabilities, potentially enhancing patient outcomes. Acute respiratory distress syndrome, an overwhelming inflammatory lung disease, is common in ICUs. Most patients require MV. Prolonged MV is associated with an increased length of stay, morbidity, and mortality. Shortening the MV duration has both clinical and economic benefits and emphasizes the need for better MV weaning management. AI and ML models can assist the physician in weaning patients from MV by providing predictive tools based on big data. Many ML models have been developed in recent years, dealing with this unmet need. Such models provide an important prediction regarding the success of the individual patient’s MV weaning. Some AI models have shown a notable impact on clinical outcomes. However, there are challenges in integrating AI models into clinical practice due to the unfamiliar nature of AI for many physicians and the complexity of some AI models. Our review explores the evolution of weaning methods up to and including AI and ML as weaning aids. Full article

Around 10% of critically ill patients suffer acute kidney injury (AKI) requiring kidney replacement therapy (KRT), with a mortality rate approaching 50%. Although most survivors achieve sufficient renal recovery to be weaned from KRT, there are no recognized guidelines on the optimal period for weaning from KRT. A systematic review was conducted using a peer-reviewed strategy, combining themes of KRT (intermittent hemodialysis, CKRT: continuous veno-venous hemo/dialysis/filtration/diafiltration, sustained low-efficiency dialysis/filtration), factors predictive of successful weaning (defined as a prolonged period without new KRT) and patient outcomes. Our research resulted in studies, all observational, describing clinical and biological parameters predictive of successful weaning from KRT. Urine output prior to KRT cessation is the most studied variable and the most widely used in practice. Other predictive factors, such as urinary urea and creatinine and new urinary and serum renal biomarkers, including cystatin C and neutrophil gelatinase-associated lipocalin (NGAL), were also analyzed in the light of recent studies. This review presents the rationale for early weaning from KRT, the parameters that can guide it, and its practical modalities. Once the patient’s clinical condition has stabilized and volume status optimized, a diuresis greater than 500 mL/day should prompt the intensivist to consider weaning. Urinary parameters could be useful in predicting weaning success but have yet to be validated. Full article

Long-term mechanical circulatory support by a left ventricular assist device (LVAD), with or without an additional temporary or long-term right ventricular (RV) support, is a life-saving therapy for advanced heart failure (HF) refractory to pharmacological treatment, as well as for both device and surgical optimization therapies. In patients with chronic non-ischemic cardiomyopathy (NICM), timely prediction of HF’s transition into its end stage, necessitating life-saving heart transplantation or long-term VAD support (as a bridge-to-transplantation or destination therapy), remains particularly challenging, given the wide range of possible etiologies, pathophysiological features, and clinical presentations of NICM. Decision-making between the necessity of an LVAD or a biventricular assist device (BVAD) is crucial because both unnecessary use of a BVAD and irreversible right ventricular (RV) failure after LVAD implantation can seriously impair patient outcomes. The pre-operative or, at the latest, intraoperative prediction of RV function after LVAD implantation is reliably possible, but necessitates integrative evaluations of many different echocardiographic, hemodynamic, clinical, and laboratory parameters. VADs create favorable conditions for the reversal of structural and functional cardiac alterations not only in acute forms of HF, but also in chronic HF. Although full cardiac recovery is rather unusual in VAD recipients with pre-implant chronic HF, the search for myocardial reverse remodelling and functional improvement is worthwhile because, for sufficiently recovered patients, weaning from VADs has proved to be feasible and capable of providing survival benefits and better quality of life even if recovery remains incomplete. This review article aimed to provide an updated theoretical and practical background for those engaged in this highly demanding and still current topic due to the continuous technical progress in the optimization of long-term VADs, as well as due to the new challenges which have emerged in conjunction with the proof of a possible myocardial recovery during long-term ventricular support up to levels which allow successful device explantation. Full article

Discontinuing mechanical ventilation remains challenging. We developed a machine learning model to predict weaning outcomes using only continuous monitoring parameters obtained from ventilators during spontaneous breathing trials (SBTs). Patients who received mechanical ventilation in the medical intensive care unit at a tertiary university hospital from 2019–2021 were included in this study. During the SBTs, three waveforms and 25 numerical data were collected as input variables. The proposed convolutional neural network (CNN)-based weaning prediction model extracts features from input data with diverse lengths. Among 138 enrolled patients, 35 (25.4%) experienced weaning failure. The dataset was randomly divided into training and test sets (8:2 ratio). The area under the receiver operating characteristic curve for weaning success by the prediction model was 0.912 (95% confidence interval [CI], 0.795–1.000), with an area under the precision-recall curve of 0.767 (95% CI, 0.434–0.983). Furthermore, we used gradient-weighted class activation mapping technology to provide visual explanations of the model’s prediction, highlighting influential features. This tool can assist medical staff by providing intuitive information regarding readiness for extubation without requiring any additional data collection other than SBT data. The proposed predictive model can assist clinicians in making ventilator weaning decisions in real time, thereby improving patient outcomes. Full article