Serial evaluation of the SOFA score to predict outcome in critically ill patients

Invasive Listeriosis in End-Stage Kidney Disease (ESKD) Patients Receiving Long-Term Dialysis: A 21-Year Case Series

Background

Listeriosis is caused by the facultative anaerobic bacterium Listeria monocytogenes. Infection from Listeria-contaminated food or water is the main etiology. If Listeria travels outside the intestines, it can cause invasive listeriosis, such as sepsis, meningitis, and meningoencephalitis. Invasive illness is especially dangerous for pregnant women and their newborns, elderly people, and people with compromised immune systems or medical conditions such as end-stage kidney disease (ESKD) patients receiving long-term dialysis.

Purpose

Describe the manifestations and hospital outcomes of invasive listeriosis and identify the risk factors for in-hospital and one-year mortality in ESKD patients receiving long-term dialysis.

Patients and Methods

This retrospective observational study examined hospitalized patient records at a Taiwanese tertiary medical center from August 1, 2000, to August 31, 2021. ESKD patients on chronic dialysis were identified with invasive listeriosis by blood culture and discharge diagnosis. Over 21 years, we accurately recorded 26 cases.

Results

ESKD patients on chronic dialysis with invasive listeriosis have a poor prognosis. Only 53.8% of chronic dialysis patients with invasive listeriosis survived their first hospital episode. 42.3% of hospitalized ESKD patients with invasive listeriosis survived one year later. In univariate analysis, shock, tachypnea (RR ≥ 22), respiratory failure, qSOFA score ≥ 2, and lower initial platelet count were linked to greater in-hospital mortality rates.

Conclusion

ESKD patients with invasive listeriosis have a grave prognosis. Our research reveals that an early blood sample for a bacterial culture may identify invasive listeriosis in chronic dialysis patients with fever, nausea or vomiting, confusion, and respiratory distress. This study is the first to identify a lower platelet count and qSOFA score ≥ 2 as markers of high-risk invasive listeriosis in ESKD patients.

Prognostic value of serial (1,3)-β-D-glucan measurements in ICU patients with invasive candidiasis

BACKGROUND

To determine whether a decrease in serum (1,3)-β-D-glucan (BDG) was associated with reduced mortality and to investigate the performance of BDG downslope in predicting clinical outcome in invasive candidiasis.

METHODS

Observational cohort study in ICU patients over a ten-year period (2012-2022) in Italy. Proven invasive candidiasis with at least 2 BDG determinations were considered.

RESULTS

In the study population of 103 patients (age 47 [35-62] years, SAPS II score 67 [52-77]) 68 bloodstream and 35 intrabdominal infections were recorded. Serial measurements showed that in 54 patients BDG decreased over time (BDG downslope group) while in 49 did not (N-BDG downslope group). Candida albicans was the pathogen most frequently isolated (61%) followed by C. parapsilosis (17%) and C. glabrata (12%), in absence of any inter-group difference. Invasive candidiasis related mortality was lower in BDG downslope than in N-BDG downslope group (17% vs 53%, p < 0.01). The multivariate Cox regression analysis showed the association of septic shock at infection occurrence and chronic liver disease with invasive candidiasis mortality (HR [95% CI] 3.24 [1.25-8.44] p = 0.02 and 7.27 [2.33-22.66] p < 0.01, respectively) while a BDG downslope was the only predictor of survival (HR [95% CI] 0.19 [0.09-0.43] p < 0.01). The area under the receiver operator characteristic curve for the performance of BDG downslope as predictor of good clinical outcome was 0.74 (p = 0.02) and our model showed that a BDG downslope > 70% predicted survival with both specificity and positive predictive value of 100%.

CONCLUSIONS

A decrease in serum BDG was associated with reduced mortality and a steep downslope predicted survival with high specificity in invasive candidiasis.

Observational study of missing SOFA score data frequency in RCTs relative to ICU length of stay

The Sequential Organ Failure Assessment, also known as SOFA score, was introduced to assess organ dysfunction of critical ill patients. However, understanding the impact of missing SOFA scores in randomized controlled trials and how this affect the validity and applicability of the SOFA score as a surrogate endpoint for predicting mortality has been a matter of interest. To address this, a secondary analysis of a systematic review was conducted to quantify the relationship between SOFA scores and the prediction of mortality in critically ill adults in randomized controlled trials (RCTs). The systematic review being referred to included 87 RCTs with a total of 12,064 critically ill patients. This analysis focused on missing SOFA score data in relation to the length of stay on the intensive care unit (ICU) and the methods used to handle missing data. SOFA score measurements from the included studies were categorized into three time frames: Early (t ≤ 4 days), Intermediate (t = 5-10 days) and Late (t > 10 days) measurement. Only one study reported a complete data set for calculating the SOFA score for an Early measurement. When considering all methods used to address missing data, 32% of studies still had missing data for Early measurements, and this percentage increased to 64% for Late measurements. These findings suggested that, over time, the number of studies with incomplete data sets has been increasing. The longer a patient is treated on the ICU, the higher the number of missing data which can impact the validity of SOFA score analyses. There was no clear trend towards a specific method for compensating missing data. An exemplary calculation demonstrated that ignoring missing data may lead to an underestimated variability of the treatment effect. This, in turn, could bias the interpretation of study results by policy- and clinical decision-makers. Overall, there are several limitations that need to be considered when using SOFA score as a surrogate endpoint for mortality. When employed as an outcome, the SOFA score is frequently missing and most studies do not adequately describe the amount or nature of missing data, or the methods used to handle missing data in the analysis.

Accuracy of Noninvasive Blood Pressure Monitoring in Critically Ill Adults

Background: Blood pressure (BP) is routinely invasively monitored by an arterial catheter in the intensive care unit (ICU). However, the available data comparing the accuracy of noninvasive methods to arterial catheters for measuring BP in the ICU are limited by small numbers and diverse methodologies. Purpose: To determine agreement between invasive arterial blood pressure monitoring (IABP) and noninvasive blood pressure (NIBP) in critically ill patients. Methods: This was a single center, observational study of critical ill adults in a tertiary care facility evaluating agreement (≤10% difference) between simultaneously measured IABP and NIBP. We measured clinical features at time of BP measurement inclusive of patient demographics, laboratory data, severity of illness, specific interventions (mechanical ventilation and dialysis), and vasopressor dose to identify particular clinical scenarios in which measurement agreement is more or less likely. Results: Of the 1852 critically ill adults with simultaneous IABP and NIBP readings, there was a median difference of 6 mm Hg in mean arterial pressure (MAP), interquartile range (1-12), P < .01. A logistic regression analysis identified 5 independent predictors of measurement discrepancy: increasing doses of norepinephrine (adjusted odds ratio [aOR] 1.10 [95% confidence interval, CI 1.08-1.12] P = .03 for every change in 5 µg/min), lower MAP value (aOR 0.98 [0.98-0.99] P < .01 for every change in 1 mm Hg), higher body mass index (aOR 1.04 [1.01-1.09] P = .01 for an increase in 1), increased patient age (aOR 1.31 [1.30-1.37] P < .01 for every 10 years), and radial arterial line location (aOR 1.74 [1.16-2.47] P = .04). Conclusions: There was broad agreement between IABP and NIBP in critically ill patients over a range of BPs and severity of illness. Several variables are associated with measurement discrepancy; however, their predictive capacity is modest. This may guide future study into which patients may specifically benefit from an arterial catheter.

Effect of a seven-strain probiotic on dietary intake, inflammatory markers, and T-cells in severe traumatic brain injury patients: A randomized, double-blind, placebo-controlled trial

BACKGROUND

Inflammatory processes are key factors in pathological events associated with severe traumatic brain injury (STBI). The aim of this trial was to determine the effect of probiotics on anthropometric measures, disease severity, inflammatory markers, and T cells in patients with STBI.

METHODS

Forty adult patients with STBI were enrolled in this parallel randomized, double-blind, placebo-controlled trial. Energy and protein status, Acute Physiology and Chronic Health Evaluation (APACHE II) score, Sequential Organ Failure Assessment (SOFA), interleukin 10 (IL-10), interleukin 1β (IL-1β), tumor necrosis factor-alpha (TNF-α), transforming growth factor beta (TGF-β), T-helper 17 (Th17), and T- Regulator (T-reg) cells were assessed at baseline (day 1), and week 2 (day 14) for each patient.

RESULTS

Probiotic supplementation led to a substantial reduction in the serum levels of TNF-α (from 10.15 ± 6.52 to 5.05 ± 3.27) (P = 0.034), IL-1β (from 11.84 ± 7.74 to 5.87 ± 3.77) (P < 0.001), and Th17 cells (from 5.19 ± 1.69 to 2.67 ± 1.89) (P < 0.001) and a substantial increase in the serum levels of IL-10 (from 3.35 ± 1.45 to 6.17 ± 2.04) (P = 0.038), TGF-β (from 30.5 ± 15.27 to 46.25 ± 21.05) (P < 0.001), and T-reg cells (from 2.83 ± 1.43 to 4.29 ± 1.89) (P < 0.001) compared with the placebo group. Furthermore, no notable changes were observed in energy and protein intake and also, terms of SOFA and APACHE II scores following probiotic treatment compared with the placebo.

CONCLUSIONS

Probiotics could reduce inflammation and improve cellular immunity and may be considered as an adjunctive therapy in STBI patients.

Ulinastatin shortens the length of ICU stay in critical patients with organ failure: A 7-year real-world study

BACKGROUND

Ulinastatin has been applied in a series of diseases associated with inflammation but its clinical effects remain somewhat elusive.

OBJECTIVE

We aimed to investigate the potential effects of ulinastatin on organ failure patients admitted to the intensive care unit (ICU).

METHODS

This is a single-center retrospective study on organ failure patients from 2013 to 2019. Patients were divided into two groups according to using ulinastatin or not during hospitalization. Propensity score matching was applied to reduce bias. The outcomes of interest were 28-day all-cause mortality, length of ICU stay, and mechanical ventilation duration.

RESULTS

Of the 841 patients who fulfilled the entry criteria, 247 received ulinastatin. A propensity-matched cohort of 608 patients was created. No significant differences in 28-day mortality between the two groups. Sequential organ failure assessment (SOFA) was identified as the independent risk factor associated with mortality. In the subgroup with SOFA ≤ 10, patients received ulinastatin experienced significantly shorter time in ICU (10.0 d [interquartile range, IQR: 7.0∼20.0] vs 15.0 d [IQR: 7.0∼25.0]; p = .004) and on mechanical ventilation (222 h [IQR:114∼349] vs 251 h [IQR: 123∼499]; P = .01), but the 28-day mortality revealed no obvious difference (10.5% vs 9.4%; p = .74).

CONCLUSION

Ulinastatin was beneficial in treating patients in ICU with organ failure, mainly by reducing the length of ICU stay and duration of mechanical ventilation.

Development and validation of risk prediction model for bacterial infections in acute liver failure patients

Infections significantly increase mortality in acute liver failure (ALF) patients, and there are no risk prediction models for early diagnosis and treatment of infections in ALF patients. This study aims to develop a risk prediction model for bacterial infections in ALF patients to guide rational antibiotic therapy. The data of ALF patients admitted to the Second Hospital of Hebei Medical University in China from January 2017 to January 2022 were retrospectively analyzed for training and internal validation. Patients were selected according to the updated 2011 American Association for the Study of Liver Diseases position paper on ALF. Serological indicators and model scores were collected within 24 h of admission. New models were developed using the multivariate logistic regression analysis. An optimal model was selected by receiver operating characteristic (ROC) analysis, Hosmer-Lemeshow test, the calibration curve, the Brier score, the bootstrap resampling, and the decision curve analysis. A nomogram was plotted to visualize the results. A total of 125 ALF patients were evaluated and 79 were included in the training set. The neutrophil-to-lymphocyte ratio and sequential organ failure assessment (SOFA) were integrated into the new model as independent predictive factors. The new SOFA-based model outperformed other models with an area under the ROC curve of 0.799 [95% confidence interval (CI): 0.652-0.926], the superior calibration and predictive performance in internal validation. High-risk individuals with a nomogram score ≥26 are recommended for antibiotic therapy. The new SOFA-based model demonstrates high accuracy and clinical utility in guiding antibiotic therapy in ALF patients.

Physiological network approach to prognosis in cirrhosis: A shifting paradigm

Decompensated liver disease is complicated by multi-organ failure and poor prognosis. The prognosis of patients with liver failure often dictates clinical management. Current prognostic models have focused on biomarkers considered as individual isolated units. Network physiology assesses the interactions among multiple physiological systems in health and disease irrespective of anatomical connectivity and defines the influence or dependence of one organ system on another. Indeed, recent applications of network mapping methods to patient data have shown improved prediction of response to therapy or prognosis in cirrhosis. Initially, different physical markers have been used to assess physiological coupling in cirrhosis including heart rate variability, heart rate turbulence, and skin temperature variability measures. Further, the parenclitic network analysis was recently applied showing that organ systems connectivity is impaired in patients with decompensated cirrhosis and can predict mortality in cirrhosis independent of current prognostic models while also providing valuable insights into the associated pathological pathways. Moreover, network mapping also predicts response to intravenous albumin in patients hospitalized with decompensated cirrhosis. Thus, this review highlights the importance of evaluating decompensated cirrhosis through the network physiologic prism. It emphasizes the limitations of current prognostic models and the values of network physiologic techniques in cirrhosis.

Predicting Organ Dysfunction in Septic and Critically Ill Patients: A Prospective Cohort Study Using Rapid Ex Vivo Immune Profiling

OBJECTIVES

While cytokine response patterns are pivotal in mediating immune responses, they are also often dysregulated in sepsis and critical illness. We hypothesized that these immunological deficits, quantifiable through ex vivo whole blood stimulation assays, may be indicative of subsequent organ dysfunction.

DESIGN

In a prospective observational study, adult septic patients and critically ill but nonseptic controls were identified within 48 hours of critical illness onset. Using a rapid, ex vivo assay based on responses to lipopolysaccharide (LPS), anti-CD3/anti-CD28 antibodies, and phorbol 12-myristate 13-acetate with ionomycin, cytokine responses to immune stimulants were quantified. The primary outcome was the relationship between early cytokine production and subsequent organ dysfunction, as measured by the Sequential Organ Failure Assessment score on day 3 of illness (SOFAd3).

SETTING

Patients were recruited in an academic medical center and data processing and analysis were done in an academic laboratory setting.

PATIENTS

Ninety-six adult septic and critically ill nonseptic patients were enrolled.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Elevated levels of tumor necrosis factor and interleukin-6 post-endotoxin challenge were inversely correlated with SOFAd3. Interferon-gamma production per lymphocyte was inversely related to organ dysfunction at day 3 and differed between septic and nonseptic patients. Clustering analysis revealed two distinct immune phenotypes, represented by differential responses to 18 hours of LPS stimulation and 4 hours of anti-CD3/anti-CD28 stimulation.

CONCLUSIONS

Our rapid immune profiling technique offers a promising tool for early prediction and management of organ dysfunction in critically ill patients. This information could be pivotal for early intervention and for preventing irreversible organ damage during the acute phase of critical illness.

Data-driven prediction of continuous renal replacement therapy survival

Continuous renal replacement therapy (CRRT) is a form of dialysis prescribed to severely ill patients who cannot tolerate regular hemodialysis. However, as the patients are typically very ill to begin with, there is always uncertainty whether they will survive during or after CRRT treatment. Because of outcome uncertainty, a large percentage of patients treated with CRRT do not survive, utilizing scarce resources and raising false hope in patients and their families. To address these issues, we present a machine learning-based algorithm to predict short-term survival in patients being initiated on CRRT. We use information extracted from electronic health records from patients who were placed on CRRT at multiple institutions to train a model that predicts CRRT survival outcome; on a held-out test set, the model achieves an area under the receiver operating curve of 0.848 (CI = 0.822-0.870). Feature importance, error, and subgroup analyses provide insight into bias and relevant features for model prediction. Overall, we demonstrate the potential for predictive machine learning models to assist clinicians in alleviating the uncertainty of CRRT patient survival outcomes, with opportunities for future improvement through further data collection and advanced modeling.

A simple electronic medical record-based predictors of illness severity in sepsis (sepsis) score

OBJECTIVE

Current scores for predicting sepsis outcomes are limited by generalizability, complexity, and electronic medical record (EMR) integration. Here, we validate a simple EMR-based score for sepsis outcomes in a large multi-centre cohort.

DESIGN

A simple electronic medical record-based predictor of illness severity in sepsis (SEPSIS) score was developed (4 additive lab-based predictors) using a population-based retrospective cohort study.

SETTING

Internal medicine services across four academic teaching hospitals in Toronto, Canada from April 2010-March 2015 (primary cohort) and 2015-2019 (secondary cohort).

PATIENTS

We identified patients admitted with sepsis based upon receipt of antibiotics and positive cultures.

MEASUREMENTS AND MAIN RESULTS

The primary outcome was in-hospital mortality and secondary outcomes were ICU admission at 72 hours, and hospital length of stay (LOS). We calculated the area under the receiver operating curve (AUROC) for the SEPSIS score, qSOFA, and NEWS2. We then evaluated the SEPSIS score in a secondary cohort (2015-2019) of hospitalized patients receiving antibiotics. Our primary cohort included 1,890 patients with a median age of 72 years (IQR: 56-83). 9% died during hospitalization, 18.6% were admitted to ICU, and mean LOS was 12.7 days (SD: 21.5). In the primary and secondary (2015-2019, 4811 patients) cohorts, the AUROCs of the SEPSIS score for predicting in-hospital mortality were 0.63 and 0.64 respectively, which were similar to NEWS2 (0.62 and 0.67) and qSOFA (0.62 and 0.68). AUROCs for predicting ICU admission at 72 hours, and length of stay > 14 days, were similar between scores, in the primary and secondary cohorts. All scores had comparable calibration for predicting mortality.

CONCLUSIONS

An EMR-based SEPSIS score shows a similar ability to predict important clinical outcomes compared with other validated scores (qSOFA and NEWS2). Because of the SEPSIS score's simplicity, it may prove a useful tool for clinical and research applications.

Machine learning for the prediction of in-hospital mortality in patients with spontaneous intracerebral hemorrhage in intensive care unit

This study aimed to develop a machine learning (ML)-based tool for early and accurate prediction of in-hospital mortality risk in patients with spontaneous intracerebral hemorrhage (sICH) in the intensive care unit (ICU). We did a retrospective study in our study and identified cases of sICH from the MIMIC IV (n = 1486) and Zhejiang Hospital databases (n = 110). The model was constructed using features selected through LASSO regression. Among five well-known models, the selection of the best model was based on the area under the curve (AUC) in the validation cohort. We further analyzed calibration and decision curves to assess prediction results and visualized the impact of each variable on the model through SHapley Additive exPlanations. To facilitate accessibility, we also created a visual online calculation page for the model. The XGBoost exhibited high accuracy in both internal validation (AUC = 0.907) and external validation (AUC = 0.787) sets. Calibration curve and decision curve analyses showed that the model had no significant bias as well as being useful for supporting clinical decisions. XGBoost is an effective algorithm for predicting in-hospital mortality in patients with sICH, indicating its potential significance in the development of early warning systems.

Association of social support before ICU admission with postdischarge mental health symptoms in ICU patients: a single-centre prospective cohort study in Japan

OBJECTIVES

Mental health problems after discharge from the intensive care unit (ICU) interfere with physical recovery and seriously affect daily life. Social support has been suggested to be associated with mental health but has not been sufficiently characterised. This study aimed to evaluate the association of social support before ICU admission with mental health after ICU discharge.

DESIGN

Prospective cohort study.

SETTING

Medical-surgical ICU of a hospital in Japan.

PARTICIPANTS

Patients admitted to the ICU for more than 48 hours were surveyed on social support prior to ICU admission, and 3 months after discharge from the ICU, mental health questionnaires were mailed to the patient.

PRIMARY OUTCOME MEASURES

Post-traumatic stress disorder (PTSD)-related symptoms were measured using the Impact of Event Scale-Revised, and anxiety and depressive symptoms were measured using the Hospital Anxiety and Depression Scale.

RESULTS

A total of 153 patients were enrolled; the prevalence of PTSD-related symptoms, anxiety and depressive symptoms 3 months after discharge from the ICU was 11.3%, 14.0% and 24.6%, respectively. Multivariate analysis using linear regression models adjusted for age, sex and years of education for PTSD-related symptoms, anxiety and depressive symptoms revealed that social support (β=-0.018, 95% CI: -0.029 to 0.006, p=0.002) and female sex (β=0.268, 95% CI: 0.005 to 0.531, p=0.046) were independent factors associated with the severity of depressive symptoms. In addition, sex differences were observed in the association between depressive symptoms and social support (p for interaction=0.056).

CONCLUSIONS

Higher social support before ICU admission was not associated with PTSD symptoms after ICU discharge, although it may be associated with a lower prevalence depressive symptoms after ICU discharge. Therefore, it is important to provide necessary social support when needed.

Nirmatrelvir and ritonavir for inpatients with severe or critical COVID-19 beyond five days of symptom onset: a propensity score-matched, multicenter, retrospective cohort study

BACKGROUND

There is an urgent need for therapeutic strategies for inpatients with severe or critical COVID-19. The evaluation of the clinical benefits of nirmatrelvir and ritonavir (Nmr/r) for these patients beyond five days of symptom onset is insufficient.

METHODS

A new propensity score-matched cohort was constructed by using multicenter data from 6695 adult inpatients with COVID-19 from December 2022 to February 2023 in China after the epidemic control measures were lifted across the country. The severity of disease of the inpatients was based on the tenth trial edition of the Guidelines on the Diagnosis and Treatment of COVID-19 in China. The symptom onset of 1870 enrolled severe or critical inpatients was beyond five days, and they received either Nmr/r plus standard treatment or only standard care. The ratio of patients whose SOFA score improved more than 2 points, crucial respiratory endpoints, changes in inflammatory markers, safety on the seventh day following the initiation of Nmr/r treatment, and length of hospital stay were evaluated.

RESULTS

In the Nmr/r group, on Day 7, the number of patients with an improvement in SOFA score ≥ 2 was much greater than that in the standard treatment group (P = 0.024) without a significant decrease in glomerular filtration rate (P = 0.815). Additionally, the rate of new intubation was lower (P = 0.004) and the no intubation days were higher (P = 0.003) in the first 7 days in the Nmr/r group. Other clinical benefits were limited.

CONCLUSIONS

Our study may provide new insight that inpatients with severe or critical COVID-19 beyond five days of symptom onset benefit from Nmr/r. Future studies, particularly randomized controlled trials, are necessary to verify the above findings.

Development of a Simple Scoring System for Predicting Discharge Safety from the Medical ICU to Low-Acuity Wards: The Role of the Sequential Organ Failure Assessment Score, Albumin, and Red Blood Cell Distribution Width

Despite advancements in artificial intelligence-based decision-making, transitioning patients from intensive care units (ICUs) to low-acuity wards is challenging, especially in resource-limited settings. This study aimed to develop a simple scoring system to predict ICU discharge safety. We retrospectively analyzed patients admitted to a tertiary hospital's medical ICU (MICU) between July 2016 and December 2021. This period was divided into two phases for model development and validation. We identified risk factors associated with unexpected death within 14 days of MICU discharge and developed a predictive scoring system that incorporated these factors. We verified the system's performance using validation data. In the development cohort, 522 patients were discharged from the MICU, and 42 (8.04%) died unexpectedly. In multivariate analysis, the Sequential Organ Failure Assessment (SOFA) score (odds ratio [OR] 1.26, 95% confidence interval [CI] 1.13-1.41), red blood cell distribution width (RDW) (OR 1.20, 95% CI 1.07-1.36), and albumin (OR 0.37, 95% CI 0.16-0.84) were predictors of unexpected death. Each variable was assigned a weighted point in the scoring system, and the area under the curve (AUC) was 0.788 (95% CI 0.714-0.855). The scoring system was performed using an AUC of 0.738 (95% CI 0.653-0.822) in the validation cohort of 343 patients with 9.62% of unexpected deaths. When a cut-off of 0.032 was applied, a sensitivity and a specificity of 81.8% and 55.2%, respectively, were achieved. This simple bedside predictive score for ICU discharge uses the SOFA score, albumin level, and RDW to aid in timely decision-making and optimize critical care facility allocation in resource-limited settings.

Explainable Deep Learning Model for Predicting Serious Adverse Events in Hospitalized Geriatric Patients Within 72 Hours

Background

The global aging population presents a significant challenge, with older adults experiencing declining physical and cognitive abilities and increased vulnerability to chronic diseases and adverse health outcomes. This study aims to develop an interpretable deep learning (DL) model to predict adverse events in geriatric patients within 72 hours of hospitalization.

Methods

The study used retrospective data (2017-2020) from a major medical center in Taiwan. It included non-trauma geriatric patients who visited the emergency department and were admitted to the general ward. Data preprocessing involved collecting prognostic factors like vital signs, lab results, medical history, and clinical management. A deep feedforward neural network was developed, and performance was evaluated using accuracy, sensitivity, specificity, positive predictive value (PPV), and area under the receiver operating characteristic curve (AUC). Model interpretation utilized the Shapley Additive Explanation (SHAP) technique.

Results

The analysis included 127,268 patients, with 2.6% experiencing imminent intensive care unit transfer, respiratory failure, or death during hospitalization. The DL model achieved AUCs of 0.86 and 0.84 in the validation and test sets, respectively, outperforming the Sequential Organ Failure Assessment (SOFA) score. Sensitivity and specificity values ranged from 0.79 to 0.81. The SHAP technique provided insights into feature importance and interactions.

Conclusion

The developed DL model demonstrated high accuracy in predicting serious adverse events in geriatric patients within 72 hours of hospitalization. It outperformed the SOFA score and provided valuable insights into the model's decision-making process.

Critical care specialists, the missing link in organ procurement for transplantation

The procurement process for organ donation begins with the identification of potential organ donors in emergency or critical care units (CCU), followed by their clinical evaluation, diagnostic procedures, and therapeutic interventions, mostly conducted in CCUs. It concludes with the request for organ donation and, if accepted, the retrieval of organs. Despite most interventions occurring in detection units, there has been a neglect of the strategic role played by critical care specialists (CCS) in managing and caring for brain-dead or near-brain-death patients. Questions arise: Are they willing to undertake this responsibility? Do they fully comprehend the nature of organ procurement? Are they aware of the specific interventions required to maintain possible organ donors in optimal physiological condition? Our objective is to examine the role of CCS in organ procurement and propose ways to enhance it, ultimately aiming to increase and enhance organ donation rates.

Early Enteral Nutrition Could Be Associated with Improved Survival Outcome in Cardiac Arrest

Background

Although the latest European and US guidelines recommend that early enteral nutrition (EN) be attempted in critically ill patients, there is still a lack of research on feeding strategies for patients after cardiac arrest (CA). Due to the unique pathophysiology following CA, it remains unknown whether evidence from other diseases can be applied in this condition.

Objective

We aimed to explore the relationship between the timing of EN (within 48 hours or after 48 hours) and clinical outcomes and safety in CA.

Method

From the MIMIC-IV (version 2.2) database, we conducted this retrospective cohort study. A 1 : 1 propensity score matching (PSM) analysis was also conducted to prevent potential interference from confounders. Moreover, adjusted proportional hazards model regression models were used to adjust for prehospital and hospitalization characteristics to verify the independence of the association between early EN initiation and patient outcomes.

Results

Of the initial 1286 patients, 670 were equally assigned to the early EN or delayed EN group after PSM. Patients in the early EN group had improved survival outcomes than those in the delayed EN group within 30 days (HR = 0.779, 95% confidence interval [CI] [0.611-0.994], p = 0.041). Similar results were shown at 90 and 180 days. However, there was no significant difference in neurological outcome between the two groups at 30 days (51% vs. 57%, odds ratio [OR] = 0.786, 95% CI [0.580-1.066], p = 0.070). Patients who underwent early EN had a lower risk of ileus than patients who underwent delayed EN (4% vs. 8%, OR = 0.461, 95% CI [0.233-0.909], p = 0.016). Moreover, patients who underwent early EN had shorter hospital stays.

Conclusion

Early EN could be associated with improved survival outcomes for patients after CA. Further studies are needed to verify it. However, at present, we might consider early EN to be a more suitable feeding strategy for CA.

Association of the TyG index with prognosis in surgical intensive care patients: data from the MIMIC-IV

BACKGROUND

The triglyceride-glucose (TyG) index, a tool for assessing insulin resistance, is increasingly recognized for its ability to predict cardiovascular and metabolic risks. However, its relationship with trauma and surgical patient prognosis is understudied. This study investigated the correlation between the TyG index and mortality risk in surgical/trauma ICU patients to identify high-risk individuals and improve prognostic strategies.

METHODS

This study identified patients requiring trauma/surgical ICU admission from the Medical Information Mart for Intensive Care (MIMIC-IV) database, and divided them into tertiles based on the TyG index. The outcomes included 28-day mortality and 180-day mortality for short-term and long-term prognosis. The associations between the TyG index and clinical outcomes in patients were elucidated using Cox proportional hazards regression analysis and RCS models.

RESULTS

A total of 2103 patients were enrolled. The 28-day mortality and 180-day mortality rates reached 18% and 24%, respectively. Multivariate Cox proportional hazards analysis revealed that an elevated TyG index was significantly related to 28-day and 180-day mortality after covariates adjusting. An elevated TyG index was significantly associated with 28-day mortality (adjusted hazard ratio, 1.19; 95% confidence interval 1.04-1.37) and 180-day mortality (adjusted hazard ratio, 1.24; 95% confidence interval 1.11-1.39). RCS models revealed that a progressively increasing risk of mortality was related to an elevated TyG index. According to our subgroup analysis, an elevated TyG index is associated with increased risk of 28-day and 180-day mortality in critically ill patients younger than 60 years old, as well as those with concomitant stroke or cardiovascular diseases. Additionally, in nondiabetic patients, an elevated TyG index is associated with 180-day mortality.

CONCLUSION

An increasing risk of mortality was related to an elevated TyG index. In critically ill patients younger than 60 years old, as well as those with concomitant stroke or cardiovascular diseases, an elevated TyG index is associated with adverse short-term and long-term outcomes. Furthermore, in non-diabetic patients, an elevated TyG index is associated with adverse long-term prognosis.

Intubation Decision Based on Illness Severity and Mortality in COVID-19: An International Study

OBJECTIVES

To evaluate the impact of intubation timing, guided by severity criteria, on mortality in critically ill COVID-19 patients, amidst existing uncertainties regarding optimal intubation practices.

DESIGN

Prospective, multicenter, observational study conducted from February 1, 2020, to November 1, 2022.

SETTING

Ten academic institutions in the United States and Europe.

PATIENTS

Adults (≥ 18 yr old) confirmed with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and hospitalized specifically for COVID-19, requiring intubation postadmission. Exclusion criteria included patients hospitalized for non-COVID-19 reasons despite a positive SARS-CoV-2 test.

INTERVENTIONS

Early invasive mechanical ventilation (EIMV) was defined as intubation in patients with less severe organ dysfunction (Sequential Organ Failure Assessment [SOFA] < 7 or Pa o2 /F io2 ratio > 250), whereas late invasive mechanical ventilation (LIMV) was defined as intubation in patients with SOFA greater than or equal to 7 and Pa o2 /F io2 ratio less than or equal to 250.

MEASUREMENTS AND MAIN RESULTS

The primary outcome was mortality within 30 days of hospital admission. Among 4464 patients, 854 (19.1%) required mechanical ventilation (mean age 60 yr, 61.7% male, 19.3% Black). Of those, 621 (72.7%) were categorized in the EIMV group and 233 (27.3%) in the LIMV group. Death within 30 days after admission occurred in 278 patients (42.2%) in the EIMV and 88 patients (46.6%) in the LIMV group ( p = 0.28). An inverse probability-of-treatment weighting analysis revealed a statistically significant association with mortality, with patients in the EIMV group being 32% less likely to die either within 30 days of admission (adjusted hazard ratio [HR] 0.68; 95% CI, 0.52-0.90; p = 0.008) or within 30 days after intubation irrespective of its timing from admission (adjusted HR 0.70; 95% CI, 0.51-0.90; p = 0.006).

CONCLUSIONS

In severe COVID-19 cases, an early intubation strategy, guided by specific severity criteria, is associated with a reduced risk of death. These findings underscore the importance of timely intervention based on objective severity assessments.

Using time-course as an essential factor to accurately predict sepsis-associated mortality among patients with suspected sepsis

BACKGROUND

Biomarker dynamics in different time-courses might be the primary reason why a static measurement of a single biomarker cannot accurately predict sepsis outcomes. Therefore, we conducted this prospective hospital-based cohort study to simultaneously evaluate the performance of several conventional and novel biomarkers of sepsis in predicting sepsis-associated mortality on different days of illness among patients with suspected sepsis.

METHODS

We evaluated the performance of 15 novel biomarkers including angiopoietin-2, pentraxin 3, sTREM-1, ICAM-1, VCAM-1, sCD14 and 163, E-selectin, P-selectin, TNF-alpha, interferon-gamma, CD64, IL-6, 8, and 10, along with few conventional markers for predicting sepsis-associated mortality. Patients were grouped into quartiles according to the number of days since symptom onset. Receiver operating characteristic curve (ROC) analysis was used to evaluate the biomarker performance.

RESULTS

From 2014 to 2017, 1483 patients were enrolled, of which 78% fulfilled the systemic inflammatory response syndrome criteria, 62% fulfilled the sepsis-3 criteria, 32% had septic shock, and 3.3% developed sepsis-associated mortality. IL-6, pentraxin 3, sCD163, and the blood gas profile demonstrated better performance in the early days of illness, both before and after adjusting for potential confounders (adjusted area under ROC curve [AUROC]:0.81-0.88). Notably, the Sequential Organ Failure Assessment (SOFA) score was relatively consistent throughout the course of illness (adjusted AUROC:0.70-0.91).

CONCLUSION

IL-6, pentraxin 3, sCD163, and the blood gas profile showed excellent predictive accuracy in the early days of illness. The SOFA score was consistently predictive of sepsis-associated mortality throughout the course of illness, with an acceptable performance.

Value of a secretomic approach for distinguishing patients with COVID-19 viral pneumonia among patients with respiratory distress admitted to intensive care unit

In intensive care units, COVID-19 viral pneumonia patients (VPP) present symptoms similar to those of other patients with Nonviral infection (NV-ICU). To better manage VPP, it is therefore interesting to better understand the molecular pathophysiology of viral pneumonia and to search for biomarkers that may clarify the diagnosis. The secretome being a set of proteins secreted by cells in response to stimuli represents an opportunity to discover new biomarkers. The objective of this study is to identify the secretomic signatures of VPP with those of NV-ICU. Plasma samples and clinical data from NV-ICU (n = 104), VPP (n = 30) or healthy donors (HD, n = 20) were collected at Nantes Hospital (France) upon admission. Samples were enriched for the low-abundant proteins and analyzed using nontarget mass spectrometry. Specifically deregulated proteins (DEP) in VPP versus NV-ICU were selected. Combinations of 2 to 4 DEPs were established. The differences in secretome profiles of the VPP and NV-ICU groups were highlighted. Forty-one DEPs were specifically identified in VPP compared to NV-ICU. We describe five of the best combinations of 3 proteins (complement component C9, Ficolin-3, Galectin-3-binding protein, Fibrinogen alpha, gamma and beta chain, Proteoglycan 4, Coagulation factor IX and Cdc42 effector protein 4) that show a characteristic receptor function curve with an area under the curve of 95.0%. This study identifies five combinations of candidate biomarkers in VPP compared to NV-ICU that may help distinguish the underlying causal molecular alterations.

Predicting in-hospital mortality in patients with acute pancreatitis in the ED: a direct, retrospective comparison of four clinical and radiological prognostic scores

BACKGROUND

Acute pancreatitis can be a severe disease that significantly impacts patients' quality of life and outcome. The clinical course is variable and predictive scoring systems have a debated role in early prognosis. This study aims to compare the prognostic accuracy of Balthazar, BISAP, HAPS and SOFA scores in the prediction of in-hospital mortality in patients with acute pancreatitis.

METHODS

This is a retrospective, single-center cohort study conducted in the Emergency Department of a third-level university hospital. Patients aged >18 years admitted from 1st January 2018 to 31st December 2021 for the first episode of acute pancreatitis were included.

RESULTS

A total of 385 patients (mean age of 65.4 years and 1.8% in-hospital mortality) were studied. Balthazar, BISAP and SOFA scores were significantly higher in patients with in-hospital mortality and AUROCs were equal to 0.95 (95% CI 0.91-0.99, P<0.001), 0.96 (95% CI 0.89-1, P=0.001), 0.91 (95% CI 0.81-1, P=0.001) with no differences among them and absence of in-hospital mortality in patients with HAPS=0.

CONCLUSIONS

Our data support the concept that clinical prediction scores can be useful for risk stratification in the Emergency Department. However, no single score has shown superiority in predicting acute pancreatitis-related in-hospital mortality among tested tools.

Acute kidney injury after CAR-T cell therapy: exploring clinical patterns, management, and outcomes

Background

Acute kidney injury (AKI) has been reported after CAR-T cells, but available data are limited. We sought to describe the incidence of AKI in a cohort of patients hospitalized in the intensive care unit (ICU) following CAR-T cell reinjection, identify the primary factors linked to the onset of AKI, and ascertain the key determinants associated with kidney outcomes and mortality.

Methods

We retrospectively analyzed 119 patients hospitalized in ICU after CAR-T cell therapy between 2017 and 2023. Factors associated with AKI, mortality, and kidney sequelae were identified using multivariate analyses.

Results

Of the 119 patients, 41 patients fulfilled diagnostic criteria of AKI (34%). By multivariate analysis, grade ≥3 cytokine release syndrome (CRS) [OR = 1.20 CI95% (1.01-1.43)] and elevated lactate dehydrogenase (LDH) levels at admission [OR = 1.44 CI95% (1.04-1.99)] were significantly associated with the occurrence of AKI during ICU stay. AKI KDIGO ≥2 was an independent risk factor for hospital mortality [OR = 1.50 (1.22-1.85), P < 0.001]. Nine out of 12 (75%) and 6/9 (67%) patients who had experienced AKI and survived had chronic kidney disease (CKD) at 6 months and 1 year, respectively. We did not identify any specific factor associated with kidney recovery.

Conclusion

AKI may occur in ICU patients receiving CAR-T cell therapy, especially those who experience CRS and exhibit elevated LDH levels. Early recognition of AKI is of utmost importance as it substantially compromises survival in these patients. Future studies should aim to elucidate the underlying pathophysiological mechanisms of AKI in this context and pinpoint predictive factors for long-term risks of CKD.

SOFA score performs worse than age for predicting mortality in patients with COVID-19

The use of the Sequential Organ Failure Assessment (SOFA) score, originally developed to describe disease morbidity, is commonly used to predict in-hospital mortality. During the COVID-19 pandemic, many protocols for crisis standards of care used the SOFA score to select patients to be deprioritized due to a low likelihood of survival. A prior study found that age outperformed the SOFA score for mortality prediction in patients with COVID-19, but was limited to a small cohort of intensive care unit (ICU) patients and did not address whether their findings were unique to patients with COVID-19. Moreover, it is not known how well these measures perform across races. In this retrospective study, we compare the performance of age and SOFA score in predicting in-hospital mortality across two cohorts: a cohort of 2,648 consecutive adult patients diagnosed with COVID-19 who were admitted to a large academic health system in the northeastern United States over a 4-month period in 2020 and a cohort of 75,601 patients admitted to one of 335 ICUs in the eICU database between 2014 and 2015. We used age and the maximum SOFA score as predictor variables in separate univariate logistic regression models for in-hospital mortality and calculated area under the receiver operator characteristic curves (AU-ROCs) and area under precision-recall curves (AU-PRCs) for each predictor in both cohorts. Among the COVID-19 cohort, age (AU-ROC 0.795, 95% CI 0.762, 0.828) had a significantly better discrimination than SOFA score (AU-ROC 0.679, 95% CI 0.638, 0.721) for mortality prediction. Conversely, age (AU-ROC 0.628 95% CI 0.608, 0.628) underperformed compared to SOFA score (AU-ROC 0.735, 95% CI 0.726, 0.745) in non-COVID-19 ICU patients in the eICU database. There was no difference between Black and White COVID-19 patients in performance of either age or SOFA Score. Our findings bring into question the utility of SOFA score-based resource allocation in COVID-19 crisis standards of care.

Association of Calf Circumference with Clinical and Biochemical Markers in Older Adults with COVID-19 Admitted at Intensive Care Unit: A Retrospective Cross-Sectional Study

BACKGROUND

COVID-19 is an infectious disease characterized by a severe catabolic and inflammatory state, leading to loss of muscle mass. The assessment of muscle mass can be useful to identify nutritional risk and assist in early management, especially in older adults who have high nutritional risks. The aim of this study was to evaluate the association of calf circumference (CC) with clinical and biochemical markers and mortality in older adults with COVID-19 admitted to the intensive care unit (ICU).

METHODS

A retrospective cross-sectional study was conducted in a public hospital. CC was adjusted for body mass index (BMI), reducing 3, 7, or 12 cm for a BMI of 25-29.9, 30-39.9, and ≥40 kg/m2, respectively, and classified as reduced when <33 cm for women and <34 cm for men. Pearson's correlation between BMI and CC was performed to assess the association between variables. Regression analysis was adjusted for sex, age, and BMI variables. Cox regression was used to assess survival related to CC.

RESULTS

A total of 208 older adults diagnosed with COVID-19 admitted to ICU were included, of which 84% (n = 176) were classified as having reduced CC. These patients were older, with lower BMI, higher nutritional risk, malnourished, and higher concentration of urea and urea-creatinine ratio (UCR) compared with the group with normal CC. There was an association between edematous patients at nutritional risk and malnourished with reduced CC in the Cox regression, either adjusted or not for confounding.

CONCLUSIONS

CC was not associated with severity, biochemical markers, or mortality in older adults with COVID-19 admitted to the ICU, but it was associated with moderately malnourished patients assessed by subjective global assessment (SGA).

The association of the Sequential Organ Failure Assessment score at intensive care unit discharge with intensive care unit readmission in the cardiac intensive care unit

AIMS

Unplanned intensive care unit (ICU) readmissions contribute to increased morbidity, mortality, and healthcare costs. The severity of patient illness at ICU discharge may predict early ICU readmission. Thus, in this study, we investigated the association of cardiac ICU (CICU) discharge Sequential Organ Failure Assessment (SOFA) score with unplanned CICU readmission in patients admitted to the CICU.

METHODS AND RESULTS

We retrospectively reviewed the hospital medical records of 4659 patients who were admitted to the CICU from 2012 to 18. Sequential Organ Failure Assessment scores at CICU admission and discharge were obtained. The predictive performance of organ failure scoring was evaluated by using area under the receiver operating characteristic (AUROC) curves. The primary outcome was unplanned CICU readmission. Of the 3949 patients successfully discharged from the CICU, 184 (4.7%) had an unplanned CICU readmission or they experienced a deteriorated condition but died without being readmitted to the CICU (readmission group). The readmission group had significantly higher rates of organ failure in all organ systems at both CICU admission and discharge than the non-readmission group. The AUROC of the discharge SOFA score for CICU readmission was 0.731, showing good predictive performance. The AUROC of the discharge SOFA score was significantly greater than that of either the initial SOFA score (P = 0.020) or the Acute Physiology and Chronic Health Evaluation II score (P < 0.001). In the multivariable regression analysis, SOFA score, overweight or obese status, history of heart failure, and acute heart failure as reasons for ICU admission were independent predictors of unplanned ICU readmission during the same hospital stay.

CONCLUSION

The discharge SOFA score may identify patients at a higher risk of unplanned CICU readmission, enabling targeted interventions to reduce readmission rates and improve patient outcomes.

The impact of cardiopulmonary bypass time on the Sequential Organ Failure Assessment score after cardiac surgery

OBJECTIVES

Postoperative organ dysfunction is common after cardiac surgery, particularly when cardiopulmonary bypass (CPB) is used. The Sequential Organ Failure Assessment (SOFA) score is validated to predict morbidity and mortality in cardiac surgery. However, the impact of CPB duration on postoperative SOFA remains unclear.

METHODS

This is a retrospective study. Categorical values are presented as percentages. The comparison of SOFA groups utilized the Kruskal-Wallis chi-squared test, complemented by ad hoc Dunn's test with Bonferroni correction. Multinomial logistics regressions were employed to evaluate the relationship between CPB time and SOFA.

RESULTS

A total of 1032 patients were included. CPB time was independently associated with higher postoperative SOFA scores at 24 h. CPB time was significantly higher in patients with SOFA 4-5 (**P = 0.0022) or higher (***P < 0.001) when compared to SOFA 0-1. The percentage of patients with no/mild dysfunction decreased with longer periods of CPB, down to 0% for CPB time >180min (50% of the patients with >180m in of CPB presented SOFA ≥ 10). The same trend is observed for each of the SOFA variables, with higher impact in the cardiovascular and renal systems. Severe dysfunction occurs especially >200 min of CPB (cardiovascular system >100 min; other systems mainly >200 min).

CONCLUSIONS

CPB time may predict the probability of postoperative SOFA categories. Patients with extended CPB durations exhibited higher SOFA scores (overall and for each variable) at 24 h, with higher proportion of moderate and severe dysfunction with increasing times of CPB.

Comparative Analysis of Artificial Intelligence (AI) Languages in Predicting Sequential Organ Failure Assessment (SOFA) Scores

PURPOSE

The Sequential Organ Failure Assessment (SOFA) score plays a crucial role in intensive care units (ICUs) by providing a reliable measure of a patient's organ function or extent of failure. However, the precise assessment is time-consuming, and daily assessment in clinical practice in the ICU can be challenging.

METHODS

Realistic scenarios in an ICU setting were created, and the data mining precision of ChatGPT 4.0 Plus, Bard, and Perplexity AI were assessed using Spearman's as well as the intraclass correlation coefficients regarding the accuracy in determining the SOFA score.

RESULTS

The strongest correlation was observed between the actual SOFA score and the score calculated by ChatGPT 4.0 Plus (r correlation coefficient 0.92) (p<0.001). In contrast, the correlation between the actual SOFA and that calculated by Bard was moderate (r=0.59, p=0.070), while the correlation with Perplexity AI was substantial, at 0.89, with a p<0.001. The interclass correlation coefficient analysis of SOFA with those of ChatGPT 4.0 Plus, Bard, and Perplexity AI was ICC=0.94.

CONCLUSION

Artificial intelligence (AI) tools, particularly ChatGPT 4.0 Plus, show significant promise in assisting with automated SOFA score calculations via AI data mining in ICU settings. They offer a pathway to reduce the manual workload and increase the efficiency of continuous patient monitoring and assessment. However, further development and validation are necessary to ensure accuracy and reliability in a critical care environment.

Robotic-Assisted In-Bed Mobilization in Ventilated ICU Patients With COVID-19: An Interventional, Randomized, Controlled Pilot Study (ROBEM II Study)

OBJECTIVES

The COVID-19 pandemic significantly impacted global healthcare systems, particularly in managing critically ill mechanically ventilated patients. This study aims to assess the feasibility of robotic-assisted mobilization in COVID-19 patients.

DESIGN

Randomized controlled pilot study.

SETTING

Four COVID-19 specialized ICUs at Charité-Universitätsmedizin Berlin (March 2021 to February 2022).

PATIENTS

Twenty critically ill COVID-19 patients expected to require greater than 24 hours of ventilation.

INTERVENTIONS

A 5-day intervention phase with bid robotic-assisted mobilization greater than or equal to 20 minutes and follow-up at day 180, compared with standard care.

MEASUREMENTS AND MAIN RESULTS

Intervention sessions were conducted in 98.9% according to protocol, with one session missing due to staff shortage. Primary outcome was the mobilization level measured with the ICU Mobility Scale (IMS) and Surgical ICU Optimal Mobilization Score (SOMS), assessed until day 5 or extubation. Safety events were recorded during mobilization. The median IMS and SOMS were 0 (0-0.16) and 1 (1-1.03) in the intervention group, and 0 (0-0.15) ( p = 0.77) and 0.8 (0.65-1.20) ( p = 0.08) in the standard care group, respectively. Significant secondary outcomes included average number of mobilization sessions (intervention: 8.5 [7.75-10] vs. standard care: 4.5 [3.5-5]; p = 0.001), total mobilization time (intervention: 232.5 min [187.25-266.5 min] vs. standard care: 147.5 min [107.5-167.5 min]; p = 0.011), and healthcare providers per session (intervention: 2 [2-2] vs. standard care: 1 [1-1.4]; p = 0.001) during intervention. Four safety events (hypertension and agitation, n = 2 each) in the intervention group and none in the standard care group were reported.

CONCLUSIONS

Robotic-assisted mobilization in mechanically ventilated COVID-19 patients appears to be safe and feasible.

Pediatric Sequential Organ Assessment Score: A Comprehensive Review of the Prognostic Marker in the Pediatric Intensive Care Unit

Critically ill children admitted to the pediatric intensive care unit (PICU) face a substantial risk of morbidity and mortality, regardless of whether they are in developed or developing countries. To aid in treatment planning, various prognostic scoring systems have been developed to predict the likelihood of morbidity and death in these young patients. While the sequential organ failure assessment (SOFA) score has been validated as an independent risk predictor for adult mortality in cases of confirmed or suspected sepsis, it is not suitable for use in children due to its lack of age normalization. Children in critical condition often exhibit significant deviations from the normal physiological balance of their bodies. These deviations from the typical range of physiological variables can be leveraged to estimate the extent of these variations and create scoring systems. In this context, the pediatric SOFA (pSOFA) score was developed by modifying the original SOFA score and incorporating age-adjusted cutoffs for various bodily systems. The objective of this review is to assess the effectiveness of the pSOFA score in predicting sepsis-related mortality in pediatric patients within the PICU setting.

Predicting the mortality of patients with cardiogenic shock after coronary artery bypass grafting

INTRODUCTION

Cardiogenic shock (CS) is a critical condition and the leading cause of mortality after coronary artery bypass grafting (CABG). To define the risk factors for CS in patients who undergo CABG and create a risk-predictive model is crucial.

METHODS

In this observational study, we retrospectively evaluated consecutive patients who underwent CABG between January 2018 and October 2022 at Beijing Anzhen Hospital. A total of 496 patients were enrolled and categorized into the training (396 cases) and internal test (100 cases) sets. The variables significantly associated with mortality (p < 0.05) were analyzed using logistic regression analyses.

RESULTS

The E/A ratio at admission, postoperative brain natriuretic peptide, postoperative arterial lactate, two or more arrhythmias at the same time after CABG, and carotid artery stenosis at admission were identified as independent prognostic factors for in-hospital mortality after multivariate logistic regression analysis. The CS after CABG score (ACCS) was established and three classes of ACCS, named classes I (ACCS, <20), II (ACCS, 20-30), and III (ACCS, >30), made up the risk model. The ACCS showed better discrimination with an AUROC of 0.937 (95% confidence interval, 0.982-0.892) and calibration with the Hosmer-Lemeshow test (X2 = 5.854 with 8 df; p = 0.664). In addition, tenfold cross-validation demonstrated that the mean misdiagnosis rate was 5.56% and the lowest misdiagnosis rate was 6.38%.

CONCLUSION

The ACCS score represents a risk-predictive model for in-hospital mortality of patients with CS after CABG in acute care settings. Patients identified as class III may have a worse prognosis.

Improve the prediction of liver transplant mortality based on pre-transplant factors: A multi-center study from China: Mortality prediction of LT

AIMS

To designed a new model using pre-transplant data to predict post-transplant mortality for Chinese population and compared its performance to that of existing models.

METHODS

In this multicenter study, 544 recipients of liver transplants for non-tumor indications were enrolled in the training group and 276 patients in the validation group. The new Simplified Mortality Prediction Scores (SMOPS) model was compared to the MELD and four existing models using the C-statistic.

RESULTS

SMOPS model used 6 independent pre-transplantation risk factors screened from the training group (chronic liver failure/organ failure scores, fever > 37.6 ℃, ABO blood-type compatibility, arterial lactate level, leukocyte count and re-transplantation). The SMOPS accurately predicted patients' 30-day, 90-day and 365-day mortality following liver transplantation, and its' scores were more accurate than those of the other models. The SMOPS generated four levels of risk: low risk (<10 points), moderate risk (11-20 points), high risk (21-25 points) and futile risk (≥26 points). The survival within all risk levels was not different between MELD=40 and MELD<40. The survival within moderate-, high- or extreme-risk ALF was not different between ALF and non-ALF.

CONCLUSION

The SMOPS model uses pre-transplant risk factors to stratify post-transplant survival and is superior to current models for Chinese population, and has the potential to contribute to improvements in organ-allocation policies.

ALTA: a simple nutritional prognostic score for patients with hepatitis B virus-related acute-on-chronic liver failure

Background

Malnutrition, despite being a common complication, is often neglected in patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF). The objective of this study was to develop a simplified nutritional prognostic score to accurately predict mortality in HBV-ACLF patients.

Methods

In this multicenter retrospective study, clinical data from 530 HBV-ACLF patients were used to create a new prognostic score, which was then validated in two external cohorts (n = 229 and 248).

Results

Four independent factors were significantly associated with 28-day mortality in HBV-ACLF patients, forming a novel prognostic score (ALTA score = 0.187 × age-0.849 × lymphocyte count-2.033 × total cholesterol-0.148 × albumin-0.971). Notably, the AUROC of ALTA score for 28/90-day mortality (0.950/0.967) were significantly higher than those of three other ACLF prognostic scores (COSSH-ACLF II, 0.864/0.734; MELD, 0.525/0.488; MELD-Na, 0.546/0.517; all P < 0.001), and three known nutritional scores (CONUT, 0.739/0.861; OPNI, 0.279/0.157; NRS-2002, 0.322/0.286; all P < 0.001). The prediction error rates of ALTA score for 28-day mortality were significantly lower than COSSH-ACLF II (7.3%), MELD (14.4%), MELD-Na (12.7%), CONUT (9.0%), OPNI (30.6%), and NRS2002 (34.1%) scores. Further classifying ALTA score into two strata, the hazard ratios of mortality at 28/90 days were notably increased in the high-risk groups compared to the low-risk group (15.959 and 5.740). These results were then validated in two external cohorts.

Conclusion

ALTA, as a simplified nutritional prognostic score for HBV-ACLF, demonstrates superiority over the COSSH-ACLF II and other scores in predicting short-term mortality among HBV-ACLF patients. Therefore, it may be used to guide clinical management, particularly in primary care settings.

Lower vs Higher Oxygenation Target and Days Alive Without Life Support in COVID-19: The HOT-COVID Randomized Clinical Trial

Importance

Supplemental oxygen is ubiquitously used in patients with COVID-19 and severe hypoxemia, but a lower dose may be beneficial.

Objective

To assess the effects of targeting a Pao2 of 60 mm Hg vs 90 mm Hg in patients with COVID-19 and severe hypoxemia in the intensive care unit (ICU).

Design, Setting, and Participants

Multicenter randomized clinical trial including 726 adults with COVID-19 receiving at least 10 L/min of oxygen or mechanical ventilation in 11 ICUs in Europe from August 2020 to March 2023. The trial was prematurely stopped prior to outcome assessment due to slow enrollment. End of 90-day follow-up was June 1, 2023.

Interventions

Patients were randomized 1:1 to a Pao2 of 60 mm Hg (lower oxygenation group; n = 365) or 90 mm Hg (higher oxygenation group; n = 361) for up to 90 days in the ICU.

Main Outcomes and Measures

The primary outcome was the number of days alive without life support (mechanical ventilation, circulatory support, or kidney replacement therapy) at 90 days. Secondary outcomes included mortality, proportion of patients with serious adverse events, and number of days alive and out of hospital, all at 90 days.

Results

Of 726 randomized patients, primary outcome data were available for 697 (351 in the lower oxygenation group and 346 in the higher oxygenation group). Median age was 66 years, and 495 patients (68%) were male. At 90 days, the median number of days alive without life support was 80.0 days (IQR, 9.0-89.0 days) in the lower oxygenation group and 72.0 days (IQR, 2.0-88.0 days) in the higher oxygenation group (P = .009 by van Elteren test; supplemental bootstrapped adjusted mean difference, 5.8 days [95% CI, 0.2-11.5 days]; P = .04). Mortality at 90 days was 30.2% in the lower oxygenation group and 34.7% in the higher oxygenation group (risk ratio, 0.86 [98.6% CI, 0.66-1.13]; P = .18). There were no statistically significant differences in proportion of patients with serious adverse events or in number of days alive and out of hospital.

Conclusion and Relevance

In adult ICU patients with COVID-19 and severe hypoxemia, targeting a Pao2 of 60 mm Hg resulted in more days alive without life support in 90 days than targeting a Pao2 of 90 mm Hg.

Trial Registration

ClinicalTrials.gov Identifier: NCT04425031.

Sleep in the intensive and intermediate care units: Exploring related factors of delirium, benzodiazepine use and mortality

AIM OF THE STUDY

The primary purpose was to examine sleep difficulties and delirium in the Intensive and Intermediate Care Unit. Secondarily, factors impacting night-time sleep duration and quality, mortality, and the impact of benzodiazepine use on sleep outcomes were investigated.

MATERIALS AND METHODS

This retrospective study encompassed data from 323 intensive and intermediate care unit admissions collected in the Netherlands, spanning from November 2018 to May 2020. Sleep quality was measured using the Richards-Campbell Sleep Questionnaire. Night-time sleep duration was nurse-reported. We investigated associations of these sleep outcomes with age, sex, length-of-stay, natural daylight, disease severity, mechanical ventilation, benzodiazepine use, and delirium using Generalized Estimating Equations models. Associations with one-year post-discharge mortality were analyzed using Cox regression.

RESULTS

Night-time sleep duration was short (median 4.5 hours) and sleep quality poor (mean score 4.9/10). Benzodiazepine use was common (24 % of included nights) and was negatively associated with night-time sleep duration and quality (B = -0.558 and -0.533, p <.001). Delirium and overnight transfers were negatively associated with sleep quality (B = -0.716 and -1.831, p <.05). The day-to-night sleep ratio was higher in the three days before delirium onset than in non-delirious individuals (p <.05). Age, disease severity and female sex were associated with increased one-year mortality. Sleep quality was negatively, but not-significantly, associated with mortality (p =.070).

CONCLUSIONS

Night-time sleep in the critical care environment has a short duration and poor quality. Benzodiazepine use was not associated with improved sleep. Sleep patterns change ahead of delirium onset.

IMPLICATIONS FOR CLINICAL PRACTICE

Consistent sleep monitoring should be part of routine nursing practice, using a validated instrument like the Richards-Campbell Sleep Questionnaire. Given the lack of proven efficacy of benzodiazepines in promoting sleep in critical care settings, it is vital to develop more effective sleep treatments that include non-benzodiazepine medication and sleep hygiene strategies.

Machine Learning-Based Model for Predicting Prolonged Mechanical Ventilation in Patients with Congestive Heart Failure

BACKGROUND

Mechanical ventilation (MV) is widely used to relieve respiratory failure in patients with congestive heart failure (CHF). Prolonged MV (PMV) is associated with a poor prognosis. We aimed to establish a prediction model based on machine learning (ML) algorithms for the early identification of patients with CHF requiring PMV.

METHODS

Twelve commonly used ML algorithms were used to build the prediction model. The least absolute shrinkage and selection operator (LASSO) regression was employed to select the key features. We examined the area under the curve (AUC) statistics to evaluate the prediction performance. Data from another database were used to conduct external validation.

RESULTS

We screened out 10 key features from the initial 65 variables via LASSO regression to improve the practicability of the model. The CatBoost model showed the best performance for predicting PMV among the 12 commonly used ML algorithms, with favorable discrimination (AUC = 0.790) and calibration (Brier score = 0.154). Moreover, hospital mortality could be accurately predicted using the CatBoost model as well (AUC = 0.844). In the external validation, the CatBoost model also showed satisfactory prediction performance (AUC = 0.780), suggesting certain generalizability of the model. Finally, a nomogram with risk classification of PMV was shown in this study.

CONCLUSION

The present study developed and validated a CatBoost model, which could accurately predict PMV in mechanically ventilated patients with CHF. Moreover, this model has a favorable performance in predicting hospital mortality in these patients.

Time-Limited Trials for Patients With Critical Illness: A Review of the Literature

TOPIC IMPORTANCE

Since the 1990s, time-limited trials have been described as an approach to navigate uncertain benefits and limits of life-sustaining therapies in patients with critical illness. In this review, we aim to synthesize the evidence on time-limited trials in critical care, establish what is known, and highlight important knowledge gaps.

REVIEW FINDINGS

We identified 18 empirical studies and 15 ethical analyses about time-limited trials in patients with critical illness. Observational studies suggest time-limited trials are part of current practice in ICUs in the United States, but their use varies according to unit and physician factors. Some ICU physicians are familiar with, endorse, and have participated in time-limited trials, and some older adults appear to favor time-limited trial strategies over indefinite life-sustaining therapy or care immediately focused on comfort. When time-limited trials are used, they are often implemented incompletely and challenged by systematic barriers (eg, continually rotating ICU staff). Predictive modeling studies support prevailing clinical wisdom that prognostic uncertainty decreases over time in the ICU for some patients. One study prospectively comparing usual ICU care with an intervention designed to support time-limited trials yielded promising preliminary results. Ethical analyses describe time-limited trials as a pragmatic approach within the longstanding discussion about withholding and withdrawing life-sustaining therapies.

SUMMARY

Time-limited trials are endorsed by physicians, align with the priorities of some older adults, and are part of current practice. Substantial efforts are needed to test their impact on patient-centered outcomes, improve their implementation, and maximize their potential benefit.

Clinical Presentation and Outcome of Critically Ill Patients with Inflammatory Bowel Disease

Introduction

Admission to the intensive care unit severely affects inflammatory bowel disease (IBD) patients. This study aimed to determine factors associated with mortality in IBD patients admitted to the intensive care unit.

Methods

A retrospective cohort study was performed, analyzing data of all IBD patients admitted to the Department of Intensive Care Medicine at the University Medical Center Hamburg-Eppendorf between 2013 and 2022. Bivariate comparisons and multivariate regression analyses were performed to identify factors associated with mortality.

Results

Overall, 439 IBD patients were admitted to the intensive care unit, representing 0.56% of total admissions. In 98 of these patients, IBD-associated complications were accountable for admission (22.3%). In detail, 39 (40.8%) patients were admitted after IBD-related surgery, 36 (35.7%) due to infections, and 23 (23.5%) due to medical conditions such as bleeding or electrolyte derangement. A total of 16 (16.3%) of these patients died within 90 days after admission. Parameters associated with increased mortality were age (p < 0.001), later age at diagnosis (p 0.026), catecholamine therapy (p 0.003), mechanical ventilation (p < 0.001), renal replacement therapy (p < 0.001), and parenteral nutrition (p 0.002). Prior treatment with anti-TNF therapy was associated with a higher chance of survival (p 0.018). There was no association between prior immunosuppressant therapy and admission because of infections (p 0.294).

Conclusions

16.3% of IBD patients admitted to the intensive care unit died within 90 days after admission. Prior treatment with anti-TNF therapy was associated with a higher chance of survival.

Position Paper on the Reporting of Norepinephrine Formulations in Critical Care from the Society of Critical Care Medicine and European Society of Intensive Care Medicine Joint Task Force

OBJECTIVES

To provide guidance on the reporting of norepinephrine formulation labeling, reporting in publications, and use in clinical practice.

DESIGN

Review and task force position statements with necessary guidance.

SETTING

A series of group conference calls were conducted from August 2023 to October 2023, along with a review of the available evidence and scope of the problem.

SUBJECTS

A task force of multinational and multidisciplinary critical care experts assembled by the Society of Critical Care Medicine and the European Society of Intensive Care Medicine.

INTERVENTIONS

The implications of a variation in norepinephrine labeled as conjugated salt (i.e., bitartrate or tartrate) or base drug in terms of effective concentration of norepinephrine were examined, and guidance was provided.

MEASUREMENTS AND MAIN RESULTS

There were significant implications for clinical care, dose calculations for enrollment in clinical trials, and results of datasets reporting maximal norepinephrine equivalents. These differences were especially important in the setting of collaborative efforts across countries with reported differences.

CONCLUSIONS

A joint task force position statement was created outlining the scope of norepinephrine-dose formulation variations, and implications for research, patient safety, and clinical care. The task force advocated for a uniform norepinephrine-base formulation for global use, and offered advice aimed at appropriate stakeholders.

Early prediction of mortality at sepsis diagnosis time in critically ill patients by using interpretable machine learning

This study applied machine learning for the early prediction of 30-day mortality at sepsis diagnosis time in critically ill patients. Retrospective study using data collected from the Medical Information Mart for Intensive Care IV database. The data of the patient cohort was divided on the basis of the year of hospitalization, into training (2008-2013), validation (2014-2016), and testing (2017-2019) datasets. 24,377 patients with the sepsis diagnosis time < 24 h after intensive care unit (ICU) admission were included. A gradient boosting tree-based algorithm (XGBoost) was used for training the machine learning model to predict 30-day mortality at sepsis diagnosis time in critically ill patients. Model performance was measured in both discrimination and calibration aspects. The model was interpreted using the SHapley Additive exPlanations (SHAP) module. The 30-day mortality rate of the testing dataset was 17.9%, and 39 features were selected for the machine learning model. Model performance on the testing dataset achieved an area under the receiver operating characteristic curve (AUROC) of 0.853 (95% CI 0.837-0.868) and an area under the precision-recall curves of 0.581 (95% CI 0.541-0.619). The calibration plot for the model revealed a slope of 1.03 (95% CI 0.94-1.12) and intercept of 0.14 (95% CI 0.04-0.25). The SHAP revealed the top three most significant features, namely age, increased red blood cell distribution width, and respiratory rate. Our study demonstrated the feasibility of using the interpretable machine learning model to predict mortality at sepsis diagnosis time.

Exposure levels and target attainment of piperacillin/tazobactam in adult patients admitted to the intensive care unit: a prospective observational study

PURPOSE

The objective of this study was to evaluate the exposure and the pharmacodynamic target attainment of piperacillin/tazobactam (PTZ) in adult critically ill patients.

METHODS

We conducted a prospective observational study in the intensive care unit (ICU) of the Hôpital du Sacré-Cœur de Montréal (a Level I trauma centre in Montreal, QC, Canada) between January 2021 and June 2022. We included patients aged 18 yr or older admitted to the ICU who received PTZ by intravenous administration. Demographic and clinical characteristics were collected, and clinical scores were calculated. On study day 1 of antimicrobial therapy, three blood samples were collected at the following timepoints: one hour after PTZ dose administration and at the middle and at the end of the dosing interval. The sampling schedule was repeated on days 4 and 7 of therapy if possible. Samples were analyzed by ultra-high performance liquid chromatography with diode array detector to determine the total piperacillin concentration. Middle- and end-of-interval concentrations were used for target attainment analyses, and were defined as a concentration above the minimal inhibitory concentration of 16 mg·L-1, corresponding to the breakpoint of Enterobacteriaceae and Pseudomonas aeruginosa.

RESULTS

Forty-three patients were recruited and 202 blood samples were analyzed. The most prevalent dose was 3/0.375 g every six hours (n = 50/73 doses administered, 68%) with a 30-min infusion. We observed marked variability over the three sampling timepoints, and the median [interquartile range] piperacillin concentrations at peak, middle of interval, and end of interval were 109.4 [74.0-152.3], 59.3 [21.1-74.4], and 25.3 [6.8-44.6] mg·L-1, respectively. When assessing target attainment, 37% of patients did not reach the efficacy target of a trough concentration of 16 mg·L-1. The majority of patients who were underexposed were patients with normal to augmented renal clearance.

CONCLUSION

In this prospective observational study of adult ICU patients receiving intravenous PTZ, a large proportion had subtherapeutic concentrations of piperacillin. This was most notable in patients with normal to augmented renal clearance. More aggressive dosage regimens may be required for this subpopulation to ensure attainment of efficacy targets.

Is it Time to Develop an Indian Sepsis-related Mortality Prediction Score?

How to cite this article: Dedeepiya VD. Is it Time to Develop an Indian Sepsis-related Mortality Prediction Score? Indian J Crit Care Med 2024;28(4):320-322.

Effectiveness of Copeptin, MR-proADM and MR-proANP in Predicting Adverse Outcomes, Alone and in Combination with Traditional Severity Scores, a Secondary Analysis in COVID-19 Patients Requiring Intensive Care Admission

Objective: To investigate whether copeptin, MR-proADM and MR-proANP, alone or integrated with the SOFA, MuLBSTA and SAPS II scores, are capable of early recognition of COVID-19 ICU patients at increased risk of adverse outcomes. Methods: For this predefined secondary analysis of a larger cohort previously described, all consecutive COVID-19 adult patients admitted between March and December 2020 to the ICU of a referral, university hospital in Northern Italy were screened, and clinical severity scores were calculated upon admission. A blood sample for copeptin, MR-proADM and MR-proANP was collected within 48 h (T1), on day 3 (T3) and 7 (T7). Outcomes considered were ICU and in-hospital mortality, bacterial superinfection, recourse to renal replacement therapy (RRT) or veno-venous extracorporeal membrane oxygenation, need for invasive mechanical ventilation (IMV) and pronation. Results: Sixty-eight patients were enrolled, and in-hospital mortality was 69.1%. ICU mortality was predicted by MR-proANP measured at T1 (HR 1.005, 95% CI 1.001-1.010, p = 0.049), although significance was lost if the analysis was adjusted for procalcitonin and steroid treatment (p = 0.056). Non-survivors showed higher MR-proADM levels than survivors at all time points, and an increase in the ratio between values at baseline and at T7 > 4.9% resulted in a more than four-fold greater risk of in-hospital mortality (HR 4.417, p < 0.001). Finally, when considering patients with any reduction in glomerular filtration, an early copeptin level > 23.4 pmol/L correlated with a more than five-fold higher risk of requiring RRT during hospitalization (HR 5.305, p = 0.044). Conclusion: Timely evaluation of MR-proADM, MR-proANP and copeptin, as well as changes in the former over time, might predict mortality and other adverse outcomes in ICU patients suffering from severe COVID-19.

The Association between the rs3747406 Polymorphism in the Glucocorticoid-Induced Leucine Zipper Gene and Sepsis Survivals Depends on the SOFA Score

The variability in mortality in sepsis could be a consequence of genetic variability. The glucocorticoid system and the intermediate TSC22D3 gene product-glucocorticoid-induced leucine zipper-are clinically relevant in sepsis, which is why this study aimed to clarify whether TSC22D3 gene polymorphisms contribute to the variance in sepsis mortality. Blood samples for DNA extraction were obtained from 455 patients with a sepsis diagnosis according to the Sepsis-III criteria and from 73 control subjects. A SNP TaqMan assay was used to detect single-nucleotide polymorphisms (SNPs) in the TSC22D3 gene. Statistical and graphical analyses were performed using the SPSS Statistics and GraphPad Prism software. C-allele carriers of rs3747406 have a 2.07-fold higher mortality rate when the sequential organ failure assessment (SOFA) score is higher than eight. In a multivariate COX regression model, the SNP rs3747406 with a SOFA score ≥ 8 was found to be an independent risk factor for 30-day survival in sepsis. The HR was calculated to be 2.12, with a p-value of 0.011. The wild-type allele was present in four out of six SNPs in our cohort. The promoter of TSC22D3 was found to be highly conserved. However, we discovered that the C-allele of rs3747406 poses a risk for sepsis mortality for SOFA Scores higher than 6.

Assessing SOFA score trajectories in sepsis using machine learning: A pragmatic approach to improve the accuracy of mortality prediction

INTRODUCTION

An increasing amount of longitudinal health data is available on critically ill septic patients in the age of digital medicine, including daily sequential organ failure assessment (SOFA) score measurements. Thus, the assessment in sepsis focuses increasingly on the evaluation of the individual disease's trajectory. Machine learning (ML) algorithms may provide a promising approach here to improve the evaluation of daily SOFA score dynamics. We tested whether ML algorithms can outperform the conventional ΔSOFA score regarding the accuracy of 30-day mortality prediction.

METHODS

We used the multicentric SepsisDataNet.NRW study cohort that prospectively enrolled 252 sepsis patients between 03/2018 and 09/2019 for training ML algorithms, i.e. support vector machine (SVM) with polynomial kernel and artificial neural network (aNN). We used the Amsterdam UMC database covering 1,790 sepsis patients for external and independent validation.

RESULTS

Both SVM (AUC 0.84; 95% CI: 0.71-0.96) and aNN (AUC 0.82; 95% CI: 0.69-0.95) assessing the SOFA scores of the first seven days led to a more accurate prognosis of 30-day mortality compared to the ΔSOFA score between day 1 and 7 (AUC 0.73; 95% CI: 0.65-0.80; p = 0.02 and p = 0.05, respectively). These differences were even more prominent the shorter the time interval considered. Using the SOFA scores of day 1 to 3 SVM (AUC 0.82; 95% CI: 0.68 0.95) and aNN (AUC 0.80; 95% CI: 0.660.93) led to a more accurate prognosis of 30-day mortality compared to the ΔSOFA score (AUC 0.66; 95% CI: 0.58-0.74; p < 0.01 and p < 0.01, respectively). Strikingly, all these findings could be confirmed in the independent external validation cohort.

CONCLUSIONS

The ML-based algorithms using daily SOFA scores markedly improved the accuracy of mortality compared to the conventional ΔSOFA score. Therefore, this approach could provide a promising and automated approach to assess the individual disease trajectory in sepsis. These findings reflect the potential of incorporating ML algorithms as robust and generalizable support tools on intensive care units.

Immune Dysfunction-Associated Elevated RDW, APACHE-II, and SOFA Scores Were a Possible Cause of 28-Day Mortality in Sepsis Patients

Objective

To explore the early predictors and their predicting value of 28-day mortality in sepsis patients and to investigate the possible causes of death.

Methods

127 sepsis patients were included, including 79 cases in the survival group and 48 cases in the death group. The results of all patients on admission were recorded. After screening the risk factors of 28-day mortality, the receiver operating characteristic curve (ROC) was used to determine their predictive value for the 28-day mortality rate on admission, and the Kaplan-Meier curve was drawn to compare the 28-day mortality rate between groups. Finally, patients with cytokine and lymphocyte subsets results were included for investigating the possible causes of death through correlation analysis.

Results

APACHE II (acute physiology and chronic health evaluation II), SOFA (Sequential Organ Failure Assessment) and red blood cell distribution width (RDW) were the risk factors for 28-day mortality in sepsis patients (OR: 1.130 vs.1.160 vs.1.530, P < 0.05). The area under the curve (AUC), sensitivity and specificity of APACHE II, SOFA and RDW in predicting the mortality rate at 28 days after admission in sepsis patients were 0.763 vs 0.806 vs 0.723, 79.2% vs 68.8% vs 75.0%, 65.8% vs 89.9% vs 68.4%. The combined predicted AUC was 0.873, the sensitivity was 89.6%, and the specificity was 82.3%. The Kaplan-Meier survival curve showed that the 28-day mortality rates of sepsis patients with APACHE II≥18.5, SOFA≥11.5 and RDW≥13.8 were 58.5%, 80.5% and 59.0%, respectively. In the death group, APACHE II was positively correlated with SOFA, IL-2, and IL-10, and RDW was positively correlated with PLT, TNF-α, CD3+ lymphocyte count, and CD8+ lymphocyte count.

Conclusion

Sepsis patients with high APACHE II, SOFA and RDW levels at admission have an increased 28-day mortality rate. The elevation of these indicators in dead patients are related to immune dysfunction.

Comparative Impact of an Optimized PK/PD Target Attainment of Piperacillin-Tazobactam vs. Meropenem on the Trend over Time of SOFA Score and Inflammatory Biomarkers in Critically Ill Patients Receiving Continuous Infusion Monotherapy for Treating Documented Gram-Negative BSIs and/or VAP

(1) Background: The advantage of using carbapenems over beta-lactam/beta-lactamase inhibitor combinations in critically ill septic patients still remains a debated issue. We aimed to assess the comparative impact of an optimized pharmacokinetic/pharmacodynamic (PK/PD) target attainment of piperacillin-tazobactam vs. meropenem on the trend over time of both Sequential Organ Failure Assessment (SOFA) score and inflammatory biomarkers in critically ill patients receiving continuous infusion (CI) monotherapy with piperacillin-tazobactam or meropenem for treating documented Gram-negative bloodstream infections (BSI) and/or ventilator-associated pneumonia (VAP). (2) Methods: We performed a retrospective observational study comparing critically ill patients receiving targeted treatment with CI meropenem monotherapy for documented Gram-negative BSIs or VAP with a historical cohort of critical patients receiving CI piperacillin-tazobactam monotherapy. Patients included in the two groups were admitted to the general and post-transplant intensive care unit in the period July 2021-September 2023 and fulfilled the same inclusion criteria. The delta values of the SOFA score between the baseline of meropenem or piperacillin-tazobactam treatment and those at 48-h (delta 48-h SOFA score) or at 7-days (delta 7-days SOFA) were selected as primary outcomes. Delta 48-h and 7-days C-reactive protein (CRP) and procalcitonin (PCT), microbiological eradication, resistance occurrence, clinical cure, multi-drug resistant colonization at 90-day, ICU, and 30-day mortality rate were selected as secondary outcomes. Univariate analysis comparing primary and secondary outcomes between critically ill patients receiving CI monotherapy with piperacillin-tazobactam vs. meropenem was carried out. (3) Results: Overall, 32 critically ill patients receiving CI meropenem monotherapy were compared with a historical cohort of 43 cases receiving CI piperacillin-tazobactam monotherapy. No significant differences in terms of demographics and clinical features emerged at baseline between the two groups. Optimal PK/PD target was attained in 83.7% and 100.0% of patients receiving piperacillin-tazobactam and meropenem, respectively. No significant differences were observed between groups in terms of median values of delta 48-h SOFA (0 points vs. 1 point; p = 0.89) and median delta 7-days SOFA (2 points vs. 1 point; p = 0.43). Similarly, no significant differences were found between patients receiving piperacillin-tazobactam vs. meropenem for any of the secondary outcomes. (4) Conclusion: Our findings may support the contention that in critically ill patients with documented Gram-negative BSIs and/or VAP, the decreases in the SOFA score and in the inflammatory biomarkers serum levels achievable with CI piperacillin-tazobactam monotherapy at 48-h and at 7-days may be of similar extent and as effective as to those achievable with CI meropenem monotherapy provided that optimization on real-time by means of a TDM-based expert clinical pharmacological advice program is granted.