Acute Physiology Score Research Articles

Prioritizing Patient Selection in Clinical Trials: A Machine Learning Algorithm for Dynamic Prediction of In-Hospital Mortality for ICU Admitted Patients Using Repeated Measurement Data.

Background: A machine learning prognostic mortality scoring system was developed to address challenges in patient selection for clinical trials within the Intensive Care Unit (ICU) environment. The algorithm incorporates Red blood cell Distribution Width (RDW) data and other demographic characteristics to predict ICU mortality alongside existing ICU mortality scoring systems like Simplified Acute Physiology Score (SAPS). Methods: The developed algorithm, defined as a Mixed-effects logistic Random Forest for binary data (MixRFb), integrates a Random Forest (RF) classification with a mixed-effects model for binary outcomes, accounting for repeated measurement data. Performance comparisons were conducted with RF and the proposed MixRFb algorithms based solely on SAPS scoring, with additional evaluation using a descriptive receiver operating characteristic curve incorporating RDW's predictive mortality ability. Results: MixRFb, incorporating RDW and other covariates, outperforms the SAPS-based variant, achieving an area under the curve of 0.882 compared to 0.814. Age and RDW were identified as the most significant predictors of ICU mortality, as reported by the variable importance plot analysis. Conclusions: The MixRFb algorithm demonstrates superior efficacy in predicting in-hospital mortality and identifies age and RDW as primary predictors. Implementation of this algorithm could facilitate patient selection for clinical trials, thereby improving trial outcomes and strengthening ethical standards. Future research should focus on enriching algorithm robustness, expanding its applicability across diverse clinical settings and patient demographics, and integrating additional predictive markers to improve patient selection capabilities.

Patient Complexity, Social Factors, and Hospitalization Outcomes at Academic and Community Hospitals

There have been limited evaluations of the patients treated at academic and community hospitals. Understanding differences between academic and community hospitals has relevance for the design of clinical models of care, remuneration for clinical services, and health professional training programs. To evaluate differences in complexity and clinical outcomes between patients admitted to general medical wards at academic and community hospitals. This retrospective cohort study of patients admitted to general medicine at 28 hospitals in Ontario, Canada, was conducted between April 2015 and December 2021. All patients admitted to or discharged from general medicine during the study period who were older than 18 years were included. Data analysis occurred between February 2023 and June 2024. Patient admission to a general medicine inpatient service at an academic or community hospital. Demographic and clinical characteristics (age, sex, modified Laboratory-based Acute Physiology Score [mLAPS], discharge diagnosis, Charlson Comorbidity Index, frailty risk score, and disability), social factors (neighborhood-level markers of income, material deprivation, immigrant status, and racial and ethnic minority status) and clinical outcomes and processes (patient volume per physician, in-hospital mortality, length of stay, readmission rates, and intensive care unit [ICU] admission rates). There were 947 070 admissions, including 609 696 at 17 community hospitals (median [IQR] age, 73 [58-84] years) and 337 374 at 11 academic hospitals (median [IQR] age, 70 [56-82] years). Baseline clinical characteristics were similar at community and academic hospitals, including female sex (307 381 [50.4%] vs 168 033 [49.8%]; standardized mean difference [SMD] = 0.012), median (IQR) mLAPS (21 [11-36] vs 21 [10-34]; SMD = 0.001), and Charlson Comorbidity Index score of 2 or greater (182 171 [29.9%] vs 105 502 [31.3%]; SMD = 0.038). Social characteristics, including income, education, and neighborhood proportion of racial and ethnic minority and immigrant residents were also similar. The number of unique discharge diagnoses was similar at academic and community hospitals. Patient volumes per attending physician were higher at academic hospitals (median [IQR] daily census, 20 [19-22] vs 17 [15-19]; SMD = 1.086). After multivariable regression adjusting for baseline factors, mortality (adjusted odds ratio [aOR], 0.96; 95% CI, 0.78 to 1.17), ICU admission rate (aOR, 1.20; 95% CI, 0.80 to 1.79) and length of stay (β = -0.001; 95% CI, -0.10 to 0.10) were not significantly different, while 7-day readmission (aOR, 1.25; 95% CI, 1.10 to 1.43) and 30-day readmission (aOR, 1.25; 95% CI, 1.10 to 1.42) were significantly higher at academic hospitals than community hospitals. In this cohort study, patients admitted to general medicine at academic and community hospitals had similar baseline clinical characteristics and generally similar clinical outcomes, with greater readmission rates in academic hospitals. These findings suggest that the patient case mix in general internal medicine that trainees would be exposed to during their residency training at academic hospitals is largely representative of the case mix they would encounter at community hospitals, and has important implications for health services planning and funding.

Age-dependent differences in the association between blood interleukin-6 levels and mortality in patients with sepsis: a retrospective observational study

BackgroundInterleukin-6 (IL-6) is a cytokine that predicts clinical outcomes in critically ill patients, including those with sepsis. Elderly patients have blunted and easily dysregulated host responses to infection, which may influence IL-6 kinetics and alter the association between IL-6 levels and clinical outcomes.MethodsThis retrospective observational study included patients aged ≥ 16 years who were admitted to the intensive care unit at Chiba University Hospital. The patients were categorized into two groups: non-elderly (< 70 years) and elderly (≥ 70 years). Associations between log-transformed blood IL-6 levels and 28-day in-hospital mortality (primary outcome) and multiple organ dysfunction (MOD) on days 3 and 7 (secondary outcomes) were examined.ResultsThe non-elderly and elderly groups included 272 and 247 patients, respectively. There were no significant differences in the Sequential Organ Failure Assessment score, components of the APACHE II score (Acute physiology score and Chronic health points), MOD at baseline, or any of the outcome measures between the groups. In the non-elderly group, univariate Cox regression analysis showed a significant association between IL-6 levels and mortality (hazard ratio [HR] 1.71, 95% confidence interval [CI] 1.25–2.37, P < 0.001). This association remained significant after adjusting for sex, body mass index, steroid use prior to sepsis onset, and number of chronic organ dysfunctions (HR 1.66, 95% CI 1.20–2.32, P = 0.002). However, no significant association was observed in the elderly group in either the univariate (P = 0.69) or multivariable analyses (P = 0.77). Multivariable logistic regression analysis of MOD on days 3 and 7 revealed significant associations between MOD and IL-6 levels in both groups.ConclusionsBlood IL-6 levels were significantly associated with mortality in non-elderly patients with sepsis, but not in elderly patients. IL-6 levels were associated with MOD in both groups. Therefore, IL-6 levels should be interpreted with caution when predicting mortality in elderly patients with sepsis.Trial registration: Not applicable.

Comparison between traditional logistic regression and machine learning for predicting mortality in adult sepsis patients.

Sepsis is a life-threatening disease associated with a high mortality rate, emphasizing the need for the exploration of novel models to predict the prognosis of this patient population. This study compared the performance of traditional logistic regression and machine learning models in predicting adult sepsis mortality. To develop an optimum model for predicting the mortality of adult sepsis patients based on comparing traditional logistic regression and machine learning methodology. Retrospective analysis was conducted on 606 adult sepsis inpatients at our medical center between January 2020 and December 2022, who were randomly divided into training and validation sets in a 7:3 ratio. Traditional logistic regression and machine learning methods were employed to assess the predictive ability of mortality in adult sepsis. Univariate analysis identified independent risk factors for the logistic regression model, while Least Absolute Shrinkage and Selection Operator (LASSO) regression facilitated variable shrinkage and selection for the machine learning model. Among various machine learning models, which included Bagged Tree, Boost Tree, Decision Tree, LightGBM, Naïve Bayes, Nearest Neighbors, Support Vector Machine (SVM), and Random Forest (RF), the one with the maximum area under the curve (AUC) was chosen for model construction. Model validation and comparison with the Sequential Organ Failure Assessment (SOFA) and the Acute Physiology and Chronic Health Evaluation (APACHE) scores were performed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) curves in the validation set. Univariate analysis was employed to assess 17 variables, namely gender, history of coronary heart disease (CHD), systolic pressure, white blood cell (WBC), neutrophil count (NEUT), lymphocyte count (LYMP), lactic acid, neutrophil-to-lymphocyte ratio (NLR), red blood cell distribution width (RDW), interleukin-6 (IL-6), prothrombin time (PT), international normalized ratio (INR), fibrinogen (FBI), D-dimer, aspartate aminotransferase (AST), total bilirubin (Tbil), and lung infection. Significant differences (p < 0.05) between the survival and non-survival groups were observed for these variables. Utilizing stepwise regression with the "backward" method, independent risk factors, including systolic pressure, lactic acid, NLR, RDW, IL-6, PT, and Tbil, were identified. These factors were then incorporated into a logistic regression model, chosen based on the minimum Akaike Information Criterion (AIC) value (98.65). Machine learning techniques were also applied, and the RF model, demonstrating the maximum Area Under the Curve (AUC) of 0.999, was selected. LASSO regression, employing the lambda.1SE criteria, identified systolic pressure, lactic acid, NEUT, RDW, IL6, INR, and Tbil as variables for constructing the RF model, validated through ten-fold cross-validation. For model validation and comparison with traditional logistic models, SOFA, and APACHE scoring. Based on deep machine learning principles, the RF model demonstrates advantages over traditional logistic regression models in predicting adult sepsis prognosis. The RF model holds significant potential for clinical surveillance and interventions to enhance outcomes for sepsis patients.

Outcome of critically ill patients receiving systemic chemotherapy on the intensive care unit.

Analyze the outcomes of critically ill patients who developed new-onset organ dysfunction and received systemic chemotherapy during their ICU stay. Retrospective cohort study. A tertiary medical center in Germany with an Intensive Care Medicine department consists of 11 intensive care units comprising 140 beds, serving all subspecialties of adult intensive care medicine. 167 patients receiving systemic oncological treatment from January 1st, 2015 to December 31st, 2021, with a data cut-off on December 31st, 2022. None. A total of 167 patients were included. The primary reasons for ICU admission were respiratory failure and shock/sepsis, each accounting for 34% of cases, while complications associated with oncological therapy accounted for less than 8%. The median age of hematological patients (n = 129) was 62 years (IQR 50-70), and for solid tumor patients (n = 38), it was 60 years (IQR 52-65). Predominant disease entities included lymphoma (43%) and acute myeloid leukemia (29%) among hematological patients, and lung cancer (47%) and gastrointestinal malignancies (17%) among solid tumor patients. Hematological patients had a significantly higher median Simplified Acute Physiology Score II (47 vs. 39 points; p=0.013), a higher need for invasive mechanical ventilation (59% vs. 50%; p=0.3), renal replacement therapy (54% vs. 24%; p < 0.001), and a higher 1-year mortality rate (64% vs. 53%; p=0.2) compared to solid tumor patients. The hazard ratio for 1 year survival for male sex was 2.34 (1.31-3.49), for mechanical ventilation 2.01 (1.33-3.04), for vasopressor therapy 1.98 (1.27-3.10), and for renal replacement therapy 1.51 (1.03-2.23), respectively. Administering intravenous chemotherapy in an ICU setting remains challenging, and the experience to establish an indication for systemic chemotherapy is still challenging. However, the study demonstrates that, after careful interdisciplinary decision-making, a substantial number of patients can benefit from it.

The Value of Heparin Binding Protein in Early Identification of Sepsis-Induced Coagulopathy Disease and Prognosis.

The aim of this study was to explore the value of heparin-binding protein (HBP) in the early recognition of sepsis coagulopathy (SIC) and the prognosis of sepsis patients. A retrospective analysis was performed for 139 patients with sepsis admitted to the Intensive Care Unit (ICU) of Hefei Third People's Hospital from April 2022 through April 2024. The clinical baseline data, disease scores [sequential organ failure (SOFA) score, acute physiology and chronic health status (APACHE II) score, and SIC score], inflammatory markers [HBP, procalcitonin (PCT), and interleukin 6 (IL-6)], coagulation-related indexes [platelet count (PLT), prothrombin time (PT), prothrombin time international normalized ratio (PT-INR), activated partial thromboplastin time (APTT), fibrinogen (Fib), and D dimer (D-D)], and the survival time and 28-day prognosis of all patients were observed. The correlation between HBP and disease scores, inflammatory indexes, and coagulation-related indexes was analyzed. The receiver operating characteristic (ROC) curve was used to analyze the predictive value of HBP for SIC and the value of HBP and SIC score for the prognosis of sepsis, the risk stratification was carried out according to the optimal cutoff value of HBP, the differences in the occurrence of major clinical events under different HBP stratifications were compared, and the Kaplan-Meier survival curve was used to analyze the 28-day cumulative survival rate under different HBP stratifications. Among the 139 patients, 98 developed SIC, 41 did not, 73 died at 28 days, and 66 survived. The disease score, inflammation index, and coagulation-related indexes of the non-SIC group were better than those of the SIC group, and the disease scores, inflammation indexes, and coagulation-related indexes of the survival group were better than those of the death group. Correlation analysis showed that HBP was positively correlated with disease score and inflammation index. Coagulation-related index was positively correlated with PT, APTT, PT-INR, Fib, and D-D, and negatively correlated with PLT, among which HBP had the best correlation with disease score (HBP was best correlated with SIC, SOFA, and APACHE II scores; r = 0.818, 0.847, and 0.829, p < 0.001). ROC analysis showed that HBP had a high efficacy in identifying SIC (AUC = 0.934, sensitivity 96.9%, specificity 87.8%, p < 0.001), and the AUC of HBP and SIC score and their combination for 28-day death prediction were 0.802, 0.773, and 0.844 (p < 0.001), respectively. Compared with the HBP ≤ 118.25 ng/mL group (n = 52), the 28-day mortality rate, SIC incidence, APACHE II, and SOFA scores were higher in the HBP > 118.25 ng/mL group (n = 52) (p < 0.001). Kaplan-Meier survival curve analysis showed that the cumulative survival rate of the HBP > 118.25 ng/mL group was significantly lower than that of the HBP ≤ 118.25 ng/mL group (p < 0.001). HBP has a high predictive value in the early identification of SIC and the prognosis evaluation of sepsis, and patients with sepsis with an early HBP > 118.25 ng/mL in the ICU have a higher risk of SIC and death.

Significance of Relative Left Ventricle Wall Thickness in Predicting Acute Kidney Injury After Video-Assisted Thoracoscopic Surgery for Lung Cancer.

Aims/Background Given the strong association between relative wall thickness (RWT) and cardiovascular dysfunction, this study aims to explore RWT as a novel cardiovascular indicator to predict the risk of acute kidney injury (AKI) after lung cancer surgery and guide clinical interventions. Methods This study retrospectively analyzed 170 patients who underwent video-assisted thoracoscopic surgery (VATS) for lung cancer in Nanjing First Hospital, China, between January 2022 and December 2023. Patients were divided into AKI group (n = 52) and non-AKI group (n = 118) based on the occurrence of AKI. Univariate analysis was performed to identify factors affecting the development of AKI in patients undergoing VATS for lung cancer. Moreover, multivariate logistic regression analysis was conducted to determine influencing factors. Correlation analysis was used to analyze the relationships between variables, and receiver operating characteristic (ROC) curve analysis was conducted to assess predictive ability. Results There were no statistically significant differences in gender, comorbidities, smoking history, tumor location, Tumor-Node-Metastasis (TNM) Staging System, tumor differentiation, neutrophil count, red blood cell count, white blood cell count, creatinine, urea nitrogen, intraoperative blood loss, and operation time (p > 0.05). The comparison of age, Acute Physiology and Chronic Health Evaluation (APACHE II) score, mean arterial pressure, and RWT between the two groups showed statistically significant differences (p < 0.05). Multivariate logistic regression analysis indicated that age, APACHE II score, mean arterial pressure, and RWT significantly influenced the development of AKI in patients undergoing VATS for lung cancer (p < 0.05). RWT was negatively correlated with mean arterial pressure (r = -0.558, p < 0.05), and positively correlated with age and APACHE II Score (r = 0.573, 0.520, p < 0.05). Moreover, AKI showed a positive correlation with age, APACHE II Score, and RWT (r = 0.726, 0.685, 0.772, p < 0.05), and a negative correlation with mean arterial pressure (r = -0.724, p < 0.05). ROC analysis revealed that the area under the predicted curve for RWT was 0.864, and the standard error was 0.030 (95% confidence interval (CI): 0.805~0.923), with a Youden index of 0.55. At this time, the sensitivity was 87.29% and the specificity was 67.31%. Conclusion RWT shows excellent predictive value for postoperative AKI in patients undergoing VATS for lung cancer.

Association between statin use and 30-day mortality among patients with sepsis-associated encephalopathy: a retrospective cohort study.

Sepsis-associated encephalopathy (SAE) is a common and serious complication of sepsis with poor prognosis. Statin was used in SAE patients, whereas its effects on these patients remain unknown. This study is aimed at investigating the impact of statins on the 30-day mortality of patients with SAE. In this retrospective cohort study, data from SAE patients were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV). Statins include atorvastatin, pravastatin, rosuvastatin, and simvastatin. The outcome was 30-day mortality of SAE patients starting 24 h after the first intensive care unit (ICU) admission and at the first time after hospitalization. Potential covariates (sociodemographic characteristics, vital signs, score indexes, laboratory parameters, comorbidities, and treatment intervention methods) were selected using univariate Cox proportional hazard analysis. Associations between statin use and statin type and 30-day mortality were explored using univariate and multivariate Cox proportional hazard models with hazard ratios (HRs) and 95% confidence intervals (CIs). Associations were further explored in different age groups, sex, sequential organ failure assessment (SOFA), simplified acute physiology score II (SAPS II), and systemic inflammatory response syndrome (SIRS) populations. A total of 2,729 SAE patients were included in the study, and 786 (28.8%) died within 30 days. Statin use was associated with lower odds of 30-day mortality (HR = 0.77, 95%CI: 0.66-0.90) in all SAE patients. Patients who took simvastatin treatments were associated with lower odds of 30-day mortality (HR = 0.58, 95%CI: 0.43-0.78). Rosuvastatin treatments had a higher 30-day mortality risk (HR = 1.88, 95%CI: 1.29-2.75). Statin use was also associated with lower 30-day mortality among patients of different ages, sex, sequential organ failure assessment (SOFA), SAPS II, and SIRS. Patients who were treated with simvastatin were associated with lower odds of 30-day mortality in SAE patients. Caution should be paid to statin use in SAE patients, particularly in patients treated with rosuvastatin or pravastatin.

Independent prognostic importance of the albumin-corrected anion gap in critically ill patients with congestive heart failure: a retrospective study from MIMIC-IV database

BackgroundThe associations between the albumin-corrected anion gap (ACAG) and all-cause mortality in patients with congestive heart failure in the intensive care unit remain uncertain. This study aimed to investigate this unknown.MethodsThe MIMIC-IV (version 3.0) database was used to analyze critically ill patients with congestive heart failure. Patients were grouped into tertiles (T1-T3) on the basis of the ACAG. The association between ACAG levels and 1-year all-cause mortality was assessed using Kaplan-Meier survival analyses, multivariate adjusted Cox regression models, and restricted cubic spline curves. An analysis of subgroups was performed to evaluate ACAG’s prognostic impact across diverse populations. Mediation analysis was conducted to identify and elucidate potential causal pathways linking ACAG to all-cause mortality.ResultsA cohort of 7787 patients was analyzed. On the basis of Kaplan-Meier curves, Cox regression, restricted cubic spline curves and subgroup analysis, T2 (hazard ratio 1.09, 95% confidence interval 1.02 ~ 1.16) and T3 (hazard ratio 1.25, 95% confidence interval 1.17 ~ 1.33) individuals presented a greater mortality risk compared to T1 individuals (p for linear trend < 0.001), and most subgroups consistently observed this relationship, except for those with different levels of left ventricular ejection fraction. Mediation analysis indicated that the red cell distribution width, stage of acute kidney injury, chloride and acute physiology score III partially mediated the relationship between ACAG and mortality, accounting for 12.4%, 7.0%, 12.9%, and 31.2% of the mediating effect, respectively.ConclusionsThe ACAG was associated with higher 1-year all-cause mortality in critically ill patients with congestive heart failure, with stronger impact in those with lower left ventricular ejection fractions. The ACAG may serve as an indicator in high-risk groups.Clinical trial numberNot applicable.

Can early diaphragm dysfunction in critically ill ventilated patients predict clinical outcomes? A pilot study.

Diaphragm dysfunction (DD) is a keystone factor in difficult weaning from invasive mechanical ventilation (IMV). Diaphragm ultrasound (DUS) is the preferred method for the evaluation of diaphragm function in the Intensive Care Unit (ICU) setting, namely through the diaphragm thickening fraction (DTF). However, its potential role in the decision-making process of mechanical ventilation weaning is yet to be established. We aimed to assess the incidence of early DD and its role as a predictor of prolonged IMV. We conducted a prospective, non-interventional study in a university hospital ICU. Non-consecutive adult patients subject to at least 48h of IMV were enrolled. Exclusion criteria was a prior period of IMV in the past three months. DUS was performed at 48h of IMV. End-inspiratory and end-expiratory diaphragm thickness were measured using M-mode, with a high-frequency linear probe placed at the zone of apposition of the diaphragm. The mean values of three measurements were used to calculate DTF. Interobserver measurement variability was not evaluated. Forty-five patients were included. Thirty-eight percent were female, average age was 62.3 years. Mean Simplified Acute Physiology Score (SAPS) II and Sequential Organ Failure Assessment (SOFA) at admission were 50.9 and 9.02, respectively. Mean DTF was 23.46%±17.15. Average IMV duration was 9.36±7.66 days. Half of patients had DD at 48h of IMV. A weak negative correlation was observed between DTF, days of endotracheal intubation (Sp -0.27; p=0.07) and days of IMV (Sp -0.25; p=0.09). Using DTF cut-off values of 20% and 30%, DTF at 48h of IMV was not associated with prolonged IMV (p-values 0.17 and 0.58, respectively). In our study, there was a high prevalence of DD at 48h of IMV, as suggested in previous literature. Diaphragm dysfunction at 48h when measured through DTF did not seem to predict prolonged IMV. Late VAP incidence was associated with DD. Diaphragm ultrasound is well-established for diaphragm functional assessment, but further research regarding its trajectory during critical illness is needed to clarify its application in clinical practice.

Evaluating the feasibility of using whole blood gene expression profiles to identify novel targets in canine septic peritonitis.

To assess gene expression profiles in canine whole blood with and without septic peritonitis to assess workflow feasibility and identify potential blood biomarkers that could be further investigated in future studies. This study enrolled 6 dogs with cytologically confirmed septic peritonitis of any cause and 6 healthy dogs. All dogs had a CBC and biochemistry performed. The dogs with septic peritonitis also had point-of-care lactate and blood oxygen saturation measured for acute patient physiologic and laboratory evaluation score calculation. All dogs then had 2.5 mL of whole blood collected and placed into an RNA stabilization tube, which was processed using a commercial assay based on the hybridization of fluorescent probes for transcript quantification. Quality control, normalization, and data visualization were performed. Raw counts were exported, and differential expression was performed. The evaluation of canine whole blood expression profiles was confirmed to be feasible. Differential expression analysis of septic and nonseptic dogs demonstrated distinct gene expression profile signatures. Five genes of interest were upregulated in septic whole blood including matrix metallopeptidase 9, IL-1 receptor type 2, proliferating cell nuclear antigen, phosphatidylinositol 3-kinase catalytic-γ, and cluster of differentiation 55. The study and associated workflow were feasible and can be scaled in confirmatory studies. Future studies are now proposed to further validate the increased expression of putative biomarkers in a larger cohort of canine septic peritonitis patients with more relevant comparator control cohorts.

Urinary Output as a Predictor of Mortality in Cardiogenic Shock: An Explorative Retrospective Analysis.

Background/Objectives: Cardiogenic shock (CS) remains a critical condition with high mortality rates despite advances in treatment. This study aimed to evaluate the prognostic significance of urinary output at various time intervals during CS and its effectiveness as a predictor of 30-day mortality, particularly in comparison to the Simplified Acute Physiology Score 3 (SAPS 3). Methods: We conducted a retrospective analysis of 96 patients diagnosed with CS, assessing urinary output at different intervals (0-6 h, 6-12 h, 12-24 h, and 0-24 h) as potential predictors of 30-day mortality. SAPS 3 was calculated for all patients, and its predictive value was compared to that of urinary output using both univariate and multivariate analyses. Additional analyses included ROC curve assessment and Kaplan-Meier survival analysis. Results: Urinary output at 6-12 h was significantly associated with 30-day mortality in univariate analysis. Area under the receiver operating characteristic curves (AUROCs) for urinary output at 0-6 h, 6-12 h, and 12-24 h was 0.61 (p = 0.07), 0.63 (p = 0.04), and 0.61 (p = 0.08), respectively. These AUROCs did not differ significantly between the three urinary output parameters. Regarding the cumulative urinary output of 0-24 h, the most pronounced impact was observed in patients producing less than 0.5 mL/kg/h. In multivariate analysis, when combined with SAPS 3, the predictive power of urinary output diminished. SAPS 3 alone demonstrated significant predictive value with an AUROC of 0.77 (p < 0.001). Conclusions: While early urinary output is a valuable predictor of 30-day mortality in patients with CS, its prognostic strength is limited when considered alongside comprehensive risk assessments like SAPS 3. These findings suggest that a multifaceted approach, incorporating both early and comprehensive indicators, is essential for accurately predicting outcomes in CS patients.

Portal vein pulsatility is associated with the cumulative fluid balance: A post hoc longitudinal analysis of a prospective, general intensive care unit cohort.

Previous studies have explored tools for evaluating the effects of positive fluid balance, with recent emphasis, and controversies, on venous ultrasound parameters and composite scores. The portal vein pulsatility index and the renal venous pattern have emerged as the most promising indicators of volume-induced venous congestion. But in the general intensive care unit (ICU), numerous factors influence cardiovascular homeostasis, affecting venous function. This study aimed to evaluate the factors associated with portal vein pulsatility index in general ICU patients. Secondary objectives were to examine the correlations between pulsatility index and additional markers of congestion. This exploratory study was a post hoc analysis of a prospective, multicentric, observational database. The data collection was performed in four ICUs in university-affiliated or tertiary hospitals. This study included adult patients within 24 h of general ICU admission with an expected ICU length of stay of more than 2 days. Patients underwent clinical, biological, and echocardiographic assessments at several times: ICU admission, day 1, day 2, day 5 and the last day of ICU. The study primary endpoint was the portal vein pulsatility index during the course of the patients' stay on the ICU. One hundred forty-five patients and 514 haemodynamic evaluations were analysed. The mean age of the patients was 64 ± 15 years, 41% were women, with a median [IQR] admission simplified acute physiology score II of 46 [37 to 59]. The univariable followed by multivariable mixed-effects linear regression analyses demonstrated an association between portal vein pulsatility index, heart rate [estimate -0.002 (95% CI, -0.003 to -0.001), P < 0.001] and the cumulative fluid balance [estimate 0.0007 (95% CI, 0.00007 to 0.001), P = 0.024]. Portal vein pulsatility index showed no agreement with CVP of at least 12 mmHg (kappa correlation -0.008, P = 0.811), negative passive leg raising (kappa correlation -0.036, P = 0.430), mean inferior vena caval (IVC) diameter greater than 2 cm (kappa correlation -0.090, P = 0.025), maximal IVC diameter greater than 2 cm (kappa correlation -0.010, P = 0.835), hepatic vein systolic/diastolic ratio less than 1 (kappa correlation 0.043, P = 0.276), or renal vein pulsatile pattern (kappa correlation -0.243, P < 0.001). The study findings emphasise the unique sensitivity of portal vein pulsatility index in assessing fluid balance in general ICU patients. The lack of correlation between portal vein pulsatility index and other parameters of venous congestion underscores its potential to provide distinctive insights into venous congestion.

A stacked ensemble deep learning model for predicting the intensive care unit patient mortality

Accurate mortality prediction in intensive care units (ICUs) is essential for optimizing patient treatment, nursing care, and resource allocation. Traditional models, such as Acute Physiology and Chronic Health Evaluation and Simplified Acute Physiology Score, have been very important in clinical practice, but they frequently have issues with prediction accuracy and adaptability, especially when dealing with complex and evolving patient data. These issues can be resolved, and the accuracy of mortality prediction increased due to recent developments in machine learning, especially deep learning. The present study introduces a new deep learning ensemble model that achieves a significant improvement over existing methods. Using stacked ensemble learning, our approach combines the advantages of one Random Forests model and two CatBoost models. We achieved a notable performance in mortality prediction by carefully training and optimizing this ensemble using the electronic ICU Collaborative Research Database. Our model boasts an accuracy of 94.19%, precision of 94.097%, recall of 94.29%, and F1-score of 94.191%, demonstrating a substantial improvement over conventional approaches. The prediction of ICU mortality has been significantly improved using ensemble learning, which helps medical and nursing staff to better treat patients individually, allocate resources efficiently, and enhance patient outcomes. This approach gives healthcare experts the ability to make data-driven decisions, leading to more effective and efficient care within the ICU.

Ig-M and Ig-A Enriched Ig-G Infusion as Adjuvant Therapy in the Critically ill Patients Experiencing SARS-CoV-2 Severe Infection.

SARS-CoV-2 in patients who need Intensive Care (ICU) is associated with a mortality rate ranging from 10 to 40%-45%, with an increase in morbidity and mortality in presence of sepsis. We assumed that immunoglobulin (Ig) M and IgA enriched IgG (IGAM) therapy may support SARS COV-2 sepsis-related phase improving patient outcome. We conducted a retrospective case-control study on all the patients admitted to our ICU during the three pandemic waves between February 2020 and April 2021. Upon ICU admission, patients received anticoagulants with the standard supportive treatment (ST) ± IGAM therapy. After matching for the baseline characteristics and treatments, the patients receiving IGAM therapy too (group A), were compared with those undergoing ST (group B) only. 85 patients were enrolled in group A, whereas 111 in group B. The mortality resulted lower in group A [37.6% versus 55.8%, OR: 0.7 (02-08), P = .01)]. A logistic regression analysis identified IGAM treatment as a survival predictor [OR: 0.35 (95%CI, 0.2-0.8)], whereas experiencing a super-infection [OR: 1.88 (95%CI, 1.5-4.9)] and a septic shock [OR: 1.92 (95%CI, 1.4-4.3)] as predictors of death. On day 7, the probability of dying was 3 times higher in patients treated with ST only. Variable life adjustment display (VLAD) was equal to 2.4 in group A, while - 2.2 group B (in terms of lives saved in relation with those expected, in according with Simplified Acute Physiology Score II (SAPS II) score. The treatment based on IGAM infusion seems to give an advantage chance of survival in SARS-CoV-2 severe infection. Further prospective studies are warranted.

Mechanical Power Is Associated With Mortality in Pressure-Controlled Ventilated Patients: A Dutch, Single-Center Cohort Study.

Mechanical power (MP) could serve as a valuable parameter in clinical practice to estimate the likelihood of adverse outcomes. However, the safety thresholds for MP in mechanical ventilation remain underexplored and contentious. This study aims to investigate the association between MP and hospital mortality across varying degrees of lung disease severity, classified by Pao2/Fio2 ratios. This is a retrospective cohort study using automatically extracted data. Patients admitted to the ICU of a tertiary referral hospital in The Netherlands between 2018 and 2024 and ventilated in pressure-controlled mode were included. Logistic regression, adjusted for age, sex, Acute Physiology and Chronic Health Evaluation-IV score, and Pao2/Fio2 ratio, was used to calculate the odds ratio (OR) for all-cause in-hospital mortality. A total of 2184 patients were analyzed, with a mean age of 62.5 ± 13.8 years, of whom 1508 (70.2%) were male. The mean MP was highest in patients with the lowest Pao2/Fio2 ratios (21.5 ± 6.5 J/min) compared with those with the highest ratios (12.0 ± 3.8 J/min; p < 0.001). Adjusted analyses revealed that increased MP was associated with higher mortality (OR, 1.06; 95% CI, 1.03-1.09 per J/min increase). Similarly, MP normalized for body weight showed a stronger association with mortality (OR, 1.004; 95% CI, 1.002-1.006 per J/min/kg increase). An increase in mortality was seen when MP exceeded 16-18 J/min. Our findings demonstrate a significant association between MP and hospital mortality, even after adjusting for key confounders. Mortality increases notably when MP exceeds 16-18 J/min. Normalized MP presents an even stronger association with mortality. These results underscore the need for further research into ventilation strategies that consider MP adjustments.

Markers of Endothelial Injury in Extracorporeal Membrane Oxygenation: A New Risk Assessment Method

IntroductionExtracorporeal membrane oxygenation (ECMO) has become more widely used in recent years. However, ECMO remains a resource-intensive modality, and identifying patients most likely to benefit from it can be a complex task. Few methods exist to help risk stratify potential ECMO patients. Syndecan-1 (SDC-1) and soluble thrombomodulin (sTM) are markers of endothelial dysfunction and are used as a sign of disease severity in various forms of trauma. Our study aims to evaluate the association between precannulation levels of SDC-1 and sTM with mortality, current scoring systems, and their ability to predict mortality on ECMO. MethodsPatients initiated on venoarterial ECMO were retrospectively analyzed. Clinical data were collected, and precannulation Acute Physiology and Chronic Health Evaluation scores were calculated. Blood samples from precannulation collection were assayed for SDC-1 and sTM by enzyme linked immunosorbent assay. The primary outcome was mortality on ECMO. ResultsThirty-four patients were included in the analysis. Most were male (76.5%), with a median age of 61.5 y and body mass index of 28.2. Overall mortality was 61.7%. sTM was significantly higher in patients who died on venoarterial ECMO compared to those who lived. Pre–SDC-1 level of ≥951 ng/mL is marginally predictive of a higher mortality risk (area under the receiver operating characteristic curve 0.70; P = 0.070). Pre-sTM levels of ≥5348 pg/mL predicted mortality (area under the receiver operating characteristic curve 0.89; P = 0.003). ConclusionsSDC-1 and sTM are associated with a higher mortality risk in patients on ECMO. These biomarkers may be a valuable addition to current scoring systems. Furthermore, more work should focus on characterizing the effects of cardiogenic shock on the endothelium.

Effect of metagenomic next-generation sequencing on clinical outcomes in adults with severe pneumonia post-cardiac surgery: a single-center retrospective study

Reports on the application of metagenomic next-generation sequencing (mNGS) in adult patients with severe pneumonia after cardiac surgery remain limited. This study aimed to evaluate the clinical outcomes of mNGS analysis of bronchoalveolar lavage fluid (BALF) in such patients.A retrospective cohort study was conducted on adult patients with severe pneumonia after cardiac surgery. Samples were collected from patients in the surgical intensive care unit (SICU) of Sichuan Provincial People’s Hospital between January 2019 and March 2024. Upon diagnosis of severe pneumonia, bronchoalveolar lavage fluid was obtained via bronchoscopy within 24 h. The mNGS group was composed of patients tested using mNGS and conventional microbiological tests. BALF was detected only by the conventional microbiological test (CMT) method in the CMT group, which involved examining bacterial and fungal smears and cultures at least. We reviewed a total of 4,064 cardiac surgeries, and based on the inclusion criteria, a total of 113 adult patients with severe pneumonia after cardiac surgery were included in this study. The overall positive rate detected by mNGS was significantly higher than that of the culture method (98% vs. 58%, P<0.0001). After receipt of the microbiological results, the mNGS group exhibited a higher incidence of antibiotic adjustments in comparison to the CMT group (P = 0.0021). After adjusting the treatment plan based on microbial testing results, the mNGS group showed an improvement in ventilator-free days within 28 days (P = 0.0475), with a shorter duration of invasive ventilation compared to the CMT group (P = 0.0208). The detection of mNGS can significantly improve the Acute Physiology and Chronic Health Evaluation-II (APACHE II) score (P = 0.0161) and Sequential Organ Failure Assessment (SOFA) score (P = 0.0076) on the 7th day after admission to the SICU. In this study, the mNGS group showed signs of having a positive impact on the length of stay in ICU (median: 9 days, IQR: 7–10 days vs. median: 10 days, IQR: 8-13.75 days, P = 0.0538), length of stay in Hospital (median: 20 days, IQR: 17–28 days vs. median: 25 days, IQR: 18–29 days, P = 0.1558), mortality in 28 days (19% vs. 20%, P = 0.8794), in-hospital mortality (19% vs. 22%, P = 0.7123); however, statistical analysis did not confirm these differences to be significant. mNGS could serve as a valuable complement to conventional diagnostic approaches in adult patients with severe pneumonia after cardiac surgery, potentially improving diagnostic accuracy and leading to more precise and timely interventions, with significant potential to inform clinical decision-making and enhance patient outcomes.

The association between the use of angiotensin-converting enzyme inhibitors /angiotensin receptor blockers and the development of ventilator-associated pneumonia in the intensive care unit: a retrospective cohort study

BackgroundThis study was to examine the association between treatment with angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin receptor blockers (ARBs) and the risk of developing ventilator-associated pneumonia (VAP) among patients receiving mechanical ventilation (MV) in the intensive care unit (ICU).MethodsUtilizing a retrospective cohort approach, the data were extracted from the Medical Information Mart for Intensive Care IV database. VAP diagnoses were ascertained through the international classification of disease codes recorded in the database. Both univariate and multivariable logistic regression analyses were conducted to assess the association between ACEI or ARB use and VAP. Subgroup analyses were performed to evaluate the impact of comorbidities (AKI, renal failure, diabetes, hypertension, and sepsis), simplified acute physiology score II (SAPS II), as well as the use of vasopressors and antibiotics on this association. Odds ratios (ORs) with 95% confidence intervals (CIs) were used as the evaluation metrics.ResultsThe study comprised 8,888 patients, with 897 (10.09%) experiencing VAP. The analysis revealed that patients on ACEI or ARB therapy had a lower risk of developing VAP (OR: 0.79, 95% CI: 0.62–0.99, P = 0.047). Subgroup analyses revealed that the protective effect was observed in patients with AKI (OR: 0.70, 95% CI: 0.52–0.94, P = 0.020), renal failure (OR: 0.14, 95% CI: 0.02–0.84, P = 0.032), and diabetes (OR: 0.64, 95% CI: 0.43–0.94, P = 0.024), as well as in those receiving vasopressors (OR: 0.67, 95% CI: 0.49–0.92, P = 0.012), and antibiotics (OR: 0.74, 95% CI: 0.57–0.96, P = 0.021). No significant difference in VAP development was observed between patients treated with ACEI versus ARB (OR: 0.84, 95% CI: 0.49–1.47, P = 0.547).ConclusionThis study’s findings suggest a substantial association between the use of ACEIs or ARBs and reduced development of VAP, particularly among patients with specific comorbidities and those on vasopressor and antibiotic therapy. This study may educate the ICU team on the potential benefits of ACEIs and ARBs in preventing VAP, emphasizing the importance of considering these medications in the overall treatment plan.

Correlation of the triglyceride-glucose index and heart rate with 28-day all-cause mortality in severely ill patients: analysis of the MIMIC-IV database

BackgroundResearch has identified a link between the triglyceride-glucose index (TyG-i) and the risk of mortality in severely ill patients. However, it remains uncertain if the TyG-i affects mortality by influencing heart rate (HR).MethodsThis study enrolled 3,509 severely ill participants from the Medical Information Mart for Intensive Care (MIMIC-IV) database who had triglyceride, glucose, and HR data upon entering the ICU. Cox regression models were applied to estimate the effect of the TyG-i and HR on 28-day all-cause mortality (ACM) and 28-day in-hospital mortality (IHM). Additionally, Kaplan-Meier (K-M) survival analysis was employed to explore outcome variations among different patient groups. The association of the TyG-i with HR, Sequential Organ Failure Assessment (SOFA) score, and Simplified Acute Physiology Score (SAPS) II was explored through linear regression analysis. Subgroup analysis explored potential interactions among patient characteristics, while sensitivity analysis gauged the robustness of the findings. Additionally, mediation analysis was conducted to assess whether elevated HR acts as an intermediary factor linking the TyG-i to both 28-day ACM and 28-day IHM.ResultsDuring the 28-day follow-up, 586 cases (16.7%) died from all causes, and 439 cases (12.5%) died during hospitalisation. Cox results showed that individuals with a heightened TyG-i and elevated HR had the highest 28-day ACM (Hazard Ratio 1.70, P-value below 0.001) and 28-day IHM (Hazard Ratio 1.72, P-value below 0.001) compared to those with a reduced TyG-i and HR. The K-M curves showed that individuals with low TyG-i and low HR had the lowest incidence of 28-day ACM and 28-day IHM. The linear analysis results evidenced that the TyG-i was independently connected to HR (beta = 3.05, P-value below 0.001), and the TyG-i was also independently associated with SOFA score (beta = 0.39, P-value below 0.001) and SAPS II (beta = 1.79, P-value below 0.001). Subgroup analysis revealed a significant association in participants without hypertension, the interaction of an elevated TyG-i and HR strongly correlated with a higher 28-day death risk (interaction P-value below 0.05). Furthermore, HR mediated 29.5% of the connection between the TyG-i and 28-day ACM (P-value = 0.002), as well as 20.4% of the connection between the TyG-i and 28-day IHM (P-value = 0.002).ConclusionFor severely ill patients, the TyG-i is distinctly correlated with HR, and elevated levels of both are strongly connected to greater 28-day ACM and 28-day IHM risks, especially in patients without hypertension. Furthermore, elevated HR mediates the connection between the TyG-i and 28-day mortality.