Current Utility of Sequential Organ Failure Assessment Score: A Literature Review and Future Directions

Platelet indices at admission and their performance associated with predicting all-cause mortality in the ICU: a large cross-sectional cohort study

Platelet indices at admission offer the most opportune time for clinical decision-making, as they provide earliest insights, unlike later assessments during the intensive care unit (ICU) stay. There is emerging evidence suggesting the utility of platelet indices in predicting mortality. The objective of this study was, for the first time as far as the literature indicates, to elucidate the utility of seven platelet indices at admission in a large ICU cohort using Sysmex XN-series analysers. This cross-sectional study enrolled 592 ICU patients. The association of platelet indices at admission with the in-ICU and 90-day mortality was evaluated using logistic regression and receiver operating characteristic curve analysis. Of the platelet indices studied, absolute-immature platelet fraction (A-IPF), and mean platelet volume (MPV) and percentage-immature platelet fraction (%-IPF) were shown to be independently associated with predicting the in-ICU and 90-day mortality, respectively. The A-IPF cut-off value for predicting the in-ICU mortality was >6.4 × 109/L (adjusted area under the curve (aAUC) 0.736, and adjusted Odds Ratio (aOR) 1.04), and the MPV and %-IPF cut-off values for predicting the 90-day mortality were >9.5 fL (aAUC 0.759, and aOR 1.26) and >6.3% (aAUC 0.762, and aOD 1.06), respectively (all p < 0.05). Admission A-IPF was the best predictor of in-ICU mortality, while admission MPV and %-IPF were the best predictors of 90-day mortality. These indices, all measured at admission, provide the earliest possible data relevant to mortality prediction. These are routinely available indices which deserve to be considered for new future ICU scoring systems.

Development of a Core Critical Care Data Dictionary With Common Data Elements to Characterize Critical Illness and Injuries Using a Modified Delphi Method

OBJECTIVES

To develop the first core Critical Care Data Dictionary (C2D2) with common data elements (CDEs) to characterize critical illness and injuries.

DESIGN

Group consensus process using modified Delphi approach.

SETTING

Electronic surveys and in-person meetings.

SUBJECTS

A multidisciplinary workgroup of clinicians and researchers with expertise in the care of the critically ill and injured.

INTERVENTIONS

The Delphi process was divided into domain and CDE portions with each composed of two item generation rounds and one item reduction/refinement rounds. Two in-person meetings augmented this process to facilitate review and consideration of the domains and by panel members. The final set of domains and CDEs was then reviewed by the group to meet the competing criteria of utility and feasibility, resulting in the core dataset.

MEASUREMENTS AND MAIN RESULTS

The 23-member Delphi panel was provided 1833 candidate variables for potential dataset inclusion. The final dataset includes 226 patient-level CDCs in nine domains, which include anthropometrics and demographics (8), chronic comorbid illnesses (18), advanced directives (1), ICU diagnoses (61), diagnostic tests (42), interventions (27), medications (38), objective assessments (26), and hospital course and outcomes (5). Upon final review, 91% of the panel endorsed the CDCs as meeting criteria for a minimum viable data dictionary. Data elements cross the lifespan of neonate through adult patients.

CONCLUSIONS

The resulting C2D2 provides a foundation to facilitate rapid collection, analyses, and dissemination of information necessary for research, quality improvement, and clinical practice to optimize critical care outcomes. Further work is needed to validate the effectiveness of the dataset in a variety of critical care settings.

Prediction of the need for surgery in patients with unruptured abdominal aortic aneurysm based on SOFA score

OBJECTIVE

This retrospective study aimed to explore the association and clinical value of sequential organ failure assessment (SOFA) score on the predictors of adverse events in patients with unruptured abdominal aortic aneurysms (AAA).

METHODS

A total of 322 patients from Medical Information Mart for Intensive Care IV database were enrolled. Logistic regression was conducted to explore the association between SOFA and primary outcome (need for surgery, NFS). Receiver operating characteristic (ROC) and nomogram analyses were used to assess its performance for predicting NFS. We also explored the association and clinical value of SOFA on secondary outcomes including hospital length of stay (LOS), ICU-LOS, and in-hospital mortality by linear and logistic regression analyses, generalized additive model, ROC, and decision curve analysis.

RESULTS

Totally 291 patients underwent the surgery. High SOFA score significantly correlated with NFS both in crude and adjusted models (all P<0.05). SOFA had a relatively favorable prediction performance on NFS (AUC = 0.701, 95%CI: 0.596-0.802). After adjusting for related diseases, its prediction performance was increased. When SOFA was combined with lactate and gender, the model showed an AUC of 0.888 (95%CI: 0.759-1.000) and 0.3-0.9 prediction possibility. Further, the SOFA also showed significant relationship with hospital-LOS, ICU-LOS, and in-hospital mortality (all P<0.05), and exerted some value in the prediction of 7-day hospital-LOS (AUC = 0.637, 95%CI: 0.575-0.686) and in-hospital mortality (AUC = 0.637, 95%CI: 0.680-0.845).

CONCLUSIONS

SOFA score was related to the NFS and can be regarded as a useful indicator for predicting the NFS in patients with AAA.

Machine Learning-Aided Decision-Making Model for the Discontinuation of Continuous Renal Replacement Therapy

INTRODUCTION

Continuous renal replacement therapy (CRRT) is a primary form of renal support for patients with acute kidney injury in an intensive care unit. Making an accurate decision of discontinuation is crucial for the prognosis of patients. Previous research has mostly focused on the univariate and multivariate analysis of factors in CRRT, without the capacity to capture the complexity of the decision-making process. The present study thus developed a dynamic, interpretable decision model for CRRT discontinuation.

METHOD

The study adopted a cohort of 1,234 adult patients admitted to an intensive care unit in the MIMIC-IV database. We used the eXtreme Gradient Boosting (XGBoost) machine learning algorithm to construct dynamic discontinuation decision models across 4 time points. SHapley Additive exPlanation (SHAP) analysis was conducted to exhibit the contributions of individual features to the model output.

RESULT

Of the 1,234 included patients with CRRT, 596 (48.3%) successfully discontinued CRRT. The dynamic prediction by the XGBoost model produced an area under the curve of 0.848, with accuracy, sensitivity, and specificity of 0.782, 0.786, and 0.776, respectively. The performance of the XGBoost model was far superior to other test models. SHAP demonstrated that the features that contributed most to the model results were the Sequential Organ Failure Assessment score, serum lactate level, and 24-h urine output.

CONCLUSION

Dynamic decision models supported by machine learning are capable of dealing with complex factors in CRRT and effectively predicting the outcome of discontinuation.

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.

21st century critical care medicine: An overview

Critical care medicine in the 21st century has witnessed remarkable advancements that have significantly improved patient outcomes in intensive care units (ICUs). This abstract provides a concise summary of the latest developments in critical care, highlighting key areas of innovation. Recent advancements in critical care include Precision Medicine: Tailoring treatments based on individual patient characteristics, genomics, and biomarkers to enhance the effectiveness of therapies. The objective is to describe the recent advancements in Critical Care Medicine. Telemedicine: The integration of telehealth technologies for remote patient monitoring and consultation, facilitating timely interventions. Artificial intelligence (AI): AI-driven tools for early disease detection, predictive analytics, and treatment optimization, enhancing clinical decision-making. Organ Support: Advanced life support systems, such as Extracorporeal Membrane Oxygenation and Continuous Renal Replacement Therapy provide better organ support. Infection Control: Innovative infection control measures to combat emerging pathogens and reduce healthcare-associated infections. Ventilation Strategies: Precision ventilation modes and lung-protective strategies to minimize ventilator-induced lung injury. Sepsis Management: Early recognition and aggressive management of sepsis with tailored interventions. Patient-Centered Care: A shift towards patient-centered care focusing on psychological and emotional well-being in addition to medical needs. We conducted a thorough literature search on PubMed, EMBASE, and Scopus using our tailored strategy, incorporating keywords such as critical care, telemedicine, and sepsis management. A total of 125 articles meeting our criteria were included for qualitative synthesis. To ensure reliability, we focused only on articles published in the English language within the last two decades, excluding animal studies, in vitro/molecular studies, and non-original data like editorials, letters, protocols, and conference abstracts. These advancements reflect a dynamic landscape in critical care medicine, where technology, research, and patient-centered approaches converge to improve the quality of care and save lives in ICUs. The future of critical care promises even more innovative solutions to meet the evolving challenges of modern medicine.

Acute pancreatitis: A review of diagnosis, severity prediction and prognosis assessment from imaging technology, scoring system and artificial intelligence

Acute pancreatitis (AP) is a potentially life-threatening inflammatory disease of the pancreas, with clinical management determined by the severity of the disease. Diagnosis, severity prediction, and prognosis assessment of AP typically involve the use of imaging technologies, such as computed tomography, magnetic resonance imaging, and ultrasound, and scoring systems, including Ranson, Acute Physiology and Chronic Health Evaluation II, and Bedside Index for Severity in AP scores. Computed tomography is considered the gold standard imaging modality for AP due to its high sensitivity and specificity, while magnetic resonance imaging and ultrasound can provide additional information on biliary obstruction and vascular complications. Scoring systems utilize clinical and laboratory parameters to classify AP patients into mild, moderate, or severe categories, guiding treatment decisions, such as intensive care unit admission, early enteral feeding, and antibiotic use. Despite the central role of imaging technologies and scoring systems in AP management, these methods have limitations in terms of accuracy, reproducibility, practicality and economics. Recent advancements of artificial intelligence (AI) provide new opportunities to enhance their performance by analyzing vast amounts of clinical and imaging data. AI algorithms can analyze large amounts of clinical and imaging data, identify scoring system patterns, and predict the clinical course of disease. AI-based models have shown promising results in predicting the severity and mortality of AP, but further validation and standardization are required before widespread clinical application. In addition, understanding the correlation between these three technologies will aid in developing new methods that can accurately, sensitively, and specifically be used in the diagnosis, severity prediction, and prognosis assessment of AP through complementary advantages.

Predicting Survival Outcomes in Post-Cardiac Arrest Syndrome: The Impact of Combined Sequential Organ Failure Assessment Score and Serum Lactate Measurement

BACKGROUND Post-cardiac arrest syndrome (PCAS) is a major concern and shares pathophysiology with sepsis. Sequential organ failure assessment (SOFA) scores and serum lactate levels, as suggested in the Survival Sepsis Guidelines, have shown significant predictive value for prognosis in patients with sepsis. This retrospective study aimed to evaluate combined use of the SOFA score and serum lactate measurement on survival prognosis in PCAS. MATERIAL AND METHODS Our study included patients with return of spontaneous circulation after cardiac arrest who were age >18 years and underwent targeted temperature management. The 438 patients were allocated to a surviving group and a deceased group at discharge. Multivariable regression models were used to evaluate any association with SOFA scores, serum lactate levels, and survival. To evaluate the predictive value of regression models, the area under the receiver operating characteristic curve (AUROC) was assessed. RESULTS Lower SOFA score and serum lactate level were associated with better survival rates in the post-cardiac arrest patients (SOFA score: odds ratio (OR), 0.77; 95% confidence interval (CI), 0.67-0.88; P<0.001; lactate level: OR, 0.85; 95% CI, 0.81-0.94; P<0.001). The combined model of the SOFA score and serum lactate level was superior to models including either SOFA score or serum lactate level alone in predicting survival (AUROC, 0.86 vs 0.83, P=0.028, 0.86 vs 0.81, P=0.004). CONCLUSIONS Because of the superiority of the combined model of SOFA score and serum lactate level, combining these 2 factors could improve prediction of prognosis and survival outcomes in PCAS.

Does lactate enhance the prognostic accuracy of the quick Sequential Organ Failure Assessment for adult patients with sepsis? A systematic review

OBJECTIVES

To investigate whether adding lactate to the quick Sequential (sepsis-related) Organ Failure Assessment (qSOFA) improves the prediction of mortality in adult hospital patients, compared with qSOFA alone.

DESIGN

Systematic review in accordance with Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies guidelines.

DATA SOURCES

Embase, Medline, PubMed, SCOPUS, Web of Science, CINAHL and Open Grey databases were searched in November 2020.

ELIGIBILITY CRITERIA

Original research studies published after 2016 comparing qSOFA in combination with lactate (LqSOFA) with qSOFA alone in adult patients with sepsis in hospital. The language was restricted to English.

DATA EXTRACTION AND SYNTHESIS

Title and abstract screening, full-text screening, data extraction and quality assessment (using Quality Assessment of Diagnostic Accuracy Studies-2) were conducted independently by two reviewers. Extracted data were collected into tables and diagnostic test accuracy was compared between the two tests.

RESULTS

We identified 1621 studies, of which 11 met our inclusion criteria. Overall, there was a low risk of bias across all studies. The area under the receiver operating characteristic (AUROC) curve for qSOFA was improved by the addition of lactate in 9 of the 10 studies reporting it. Sensitivity was increased in three of seven studies that reported it. Specificity was increased in four of seven studies that reported it. Of the six studies set exclusively within the emergency department, five published AUROCs, all of which reported an increase following the addition of lactate. Sensitivity and specificity results varied throughout the included studies. Due to insufficient data and heterogeneity of studies, a meta-analysis was not performed.

CONCLUSIONS

LqSOFA is an effective tool for identifying mortality risk both in adult inpatients with sepsis and those in the emergency department. LqSOFA increases AUROC over qSOFA alone, particularly within the emergency department. However, further original research is required to provide a stronger base of evidence in lactate measurement timing, as well as prospective trials to strengthen evidence and reduce bias.

PROSPERO REGISTRATION NUMBER

CRD42020207648.

Impact of intraabdominal hypertension on kidney failure in critically ill patients: A post-hoc database analysis

PURPOSE

To assess whether intraabdominal hypertension (IAH) may influence kidney failure as well as mortality.

METHODS

This post-hoc analysis of two databases (IROI and iSOFA study) tested the independent association between IAH and kidney failure. Mortality was assessed using four prespecified groups (IAH present, kidney failure present, IAH and kidney failure present and no IAH or kidney failure present).

RESULTS

Of 825 critically ill patients, 302 (36.6%) developed kidney failure and 192 (23.7%) died during the first 90 days. Only 'Cumulative days with IAH grade II or more' was significantly associated with kidney failure (OR 1.29 (1.08-1.55), p = 0.003) while 'cumulative days with IAH grade I or more' (p = 0.135) or highest daily IAP (p = 0.062) was not. IAH combined with kidney failure was independently associated with 90-day mortality (OR 2.20 (1.20-4.05), p = 0.011), which was confirmed for higher grades of IAH (grade II or more) alone (OR 2.14 (1.07-4.30), p = 0.032) and combined with kidney failure (OR 3.25 (1.72-6.12), p < 0.001).

CONCLUSIONS

This study suggest that duration as well as higher grades of IAH are associated with kidney failure and may increase mortality.

Ideal scoring system for acute pancreatitis: Quest for the Holy Grail

Clinical scoring systems are required to predict complications, severity, need for intensive care unit admission, and mortality in patients with acute pancreatitis. Over the years, many scores have been developed, tested, and compared for their efficacy and accuracy. An ideal score should be rapid, reliable, and validated in different patient populations and geographical areas and should not lose relevance over time. A combination of scores or serial monitoring of a single score may increase their efficacy.