Evaluation of SOFA-based models for predicting mortality in the ICU: A systematic review

Introduction To systematically review studies evaluating the performance of Sequential Organ Failure Assessment ( SOFA)based models for predicting mortality in patients in the intensive care unit (ICU). Methods Medline, EMBASE and other databases were searched for English-language articles with the major objective of evaluating the prognostic performance of SOFA-based models in predicting mortality in surgical and/or medical ICU admissions. The quality of each study was assessed based on a quality framework for prognostic models. Results Eighteen articles met all inclusion criteria. The studies differed widely in the SOFA derivatives used and in their methods of evaluation. Ten studies reported about developing a probabilistic prognostic model, only five of which used an independent validation data set. The other studies used the SOFA-based score directly to discriminate between survivors and non-survivors without fitting a probabilistic model. In five of the six studies, admission-based models ( Acute Physiology and Chronic Health Evaluation (APACHE) II/III) were reported to have a slightly better discrimination ability than SOFA-based models at admission ( the receiver operating characteristic curve (AUC) of SOFA-based models ranged between 0.61 and 0.88), and in one study a SOFA model had higher AUC than the Simplified Acute Physiology Score (SAPS) II model. Four of these studies used the Hosmer-Lemeshow tests for calibration, none of which reported a lack of fit for the SOFA models. Models based on sequential SOFA scores were described in 11 studies including maximum SOFA scores and maximum sum of individual components of the SOFA score ( AUC range: 0.69 to 0.92) and delta SOFA ( AUC range: 0.51 to 0.83). Studies comparing SOFA with other organ failure scores did not consistently show superiority of one scoring system to another. Four studies combined SOFA-based derivatives with admission severity of illness scores, and they all reported on improved predictions for the combination. Quality of studies ranged from 11.5 to 19.5 points on a 20-point scale. Conclusions Models based on SOFA scores at admission had only slightly worse performance than APACHE II/III and were competitive with SAPS II models in predicting mortality in patients in the general medical and/or surgical ICU. Models with sequential SOFA scores seem to have a comparable performance with other organ failure scores. The combination of sequential SOFA derivatives with APACHE II/III and SAPS II models clearly improved prognostic performance of either model alone. Due to the heterogeneity of the studies, it is impossible to draw general conclusions on the optimal mathematical model and optimal derivatives of SOFA scores. Future studies should use a standard evaluation methodology with a standard set of outcome measures covering discrimination, calibration and accurac

Rating organ failure via adverse events using data mining in the intensive care unit

The main intensive care unit (ICU) goal is to avoid or reverse the organ failure process by adopting a timely intervention. Within this context, early identification of organ impairment is a key issue. The sequential organ failure assessment (SOFA) is an expert-driven score that is widely used in European ICUs to quantify organ disorder. This work proposes a complementary data-driven approach based on adverse events, defined from commonly monitored biometrics. The aim is to 8. study the impact of these events when predicting the risk of ICU organ failure.FRICEBIOMED - projecto BMH4-CT96-0817, EURICUS IIFundação para a Ciência ea Tecnologia (FCT) - projecto PTDC/EIA/72819/2006

Improved diagnosis and management of sepsis and bloodstream infection

Sepsis is a severe organ dysfunction triggered by infections, and a leading cause of hospitalization and death. Concurrent bloodstream infection (BSI) is common and around one third of sepsis patients have positive blood cultures. Prompt diagnosis and treatment is crucial, but there is a trade-off between the negative effects of over diagnosis and failure to recognize sepsis in time. The emerging crisis of antimicrobial resistance has made bacterial infections more difficult to treat, especially gram-negative pathogens such as Pseudomonas aeruginosa. The overall aim with this thesis was to improve diagnosis, assess the influence of time to antimicrobial treatment and explore prognostic bacterial virulence markers in sepsis and BSI. The papers are based on observational data from 7 cohorts of more than 100 000 hospital episodes. In addition, whole genome sequencing has been performed on approximately 800 invasive P. aeruginosa isolates collected from centers in Europe and Australia. Paper I showed that automated surveillance of sepsis incidence using the Sepsis-3 criteria is feasible in the non-ICU setting, with examples of how implementing this model generates continuous epidemiological data down to the ward level. This information can be used for directing resources and evaluating quality-of-care interventions. In Paper II, evidence is provided for using peripheral oxygen saturation (SpO2) to diagnose respiratory dysfunction in sepsis, proposing the novel thresholds 94% and 90% to get 1 and 2 SOFA points, respectively. This has important implications for improving sepsis diagnosis, especially when conventional arterial blood gas measurements are unavailable. Paper III verified that sepsis surveillance data can be utilized to develop machine learning screening tools to improve early identification of sepsis. A Bayesian network algorithm trained on routine electronic health record data predicted sepsis onset within 48 hours with better discrimination and earlier than conventional NEWS2 outside the ICU. The results suggested that screening may primarily be suited for the early admission period, which have broader implications also for other sepsis screening tools. Paper IV demonstrated that delays in antimicrobial treatment with in vitro pathogen coverage in BSI were associated with increased mortality after 12 hours from blood culture collection, but not at 1, 3, and 6 hours. This indicates a time window where clinicians should focus on the diagnostic workup, and proposes a target for rapid diagnostics of blood cultures. Finally, Paper V showed that the virulence genotype had some influence on mortality and septic shock in P. aeruginosa BSI, however, it was not a major prognostic determinant. Together these studies contribute to better understanding of the sepsis and BSI populations, and provide several suggestions to improve diagnosis and timing of treatment, with implications for clinical practice. Future works should focus on the implementation of sepsis surveillance, clinical trials of time to antimicrobial treatment and evaluating the prognostic importance of bacterial genotype data in larger populations from diverse infection sources and pathogens

Nucleosomes and neutrophil extracellular traps in septic and burn patients

NETosis is a host defense mechanism associated with inflammation and tissue damage. Experimental models show that platelets and von Willebrand factor (VWF) are key elements for intravascular NETosis. We determined NETosis in septic and burn patients at 1 and 4days post-admission (dpa). Nucleosomes were elevated in patients. In septics, they correlated with Human Neutrophil Elastase (HNE)-DNA complexes and SOFA score at 1dpa, and were associated with mortality. Patient´s neutrophils had spontaneous NETosis and were unresponsive to stimulation. Although platelet P-selectin and TNF-α were increased in both groups, higher platelet TLR4 expression, VWF levels and IL-6 were found in septics at 1dpa. Neither platelet activation markers nor cytokines correlated with nucleosomes or HNE-DNA. Nucleosomes could be indicators of organ damage and predictors of mortality in septic but not in burn patients. Platelet activation, VWF and cytokines do not appear to be key mediators of NETosis in these patient groups.Fil: Kaufman, Tomás. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Medicina Experimental. Academia Nacional de Medicina de Buenos Aires. Instituto de Medicina Experimental; ArgentinaFil: Magosevich, Débora. Clínica Sagrado Corazón; ArgentinaFil: Moreno, María Carolina. Clínica Bazterrica; ArgentinaFil: Guzman, María Alejandra. Gobiernos de la Ciudad de Buenos Aires. Hospital de Quemados Dr. Arturo Umberto Illia; ArgentinaFil: D'Atri, Lina Paola. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Medicina Experimental. Academia Nacional de Medicina de Buenos Aires. Instituto de Medicina Experimental; ArgentinaFil: Carestia, Agostina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Medicina Experimental. Academia Nacional de Medicina de Buenos Aires. Instituto de Medicina Experimental; ArgentinaFil: Fandiño, María Eugenia. Clínica Sagrado Corazón; ArgentinaFil: Fondevila, Carlos. Clínica Bazterrica; ArgentinaFil: Schattner, Mirta Ana. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Medicina Experimental. Academia Nacional de Medicina de Buenos Aires. Instituto de Medicina Experimental; Argentin

The Secondary Use of Longitudinal Critical Care Data

Aims To examine the strengths and limitations of a novel United Kingdom (UK) critical care data resource that repurposes routinely collected physiological data for research. Exemplar clinical research studies will be developed to explore the unique longitudinal nature of the resource. Objectives - To evaluate the suitability of the National Institute for Health Research (NIHR) Critical Care theme of the Health Informatics Collaborative (CCHIC) data model as a representation of the Electronic Health Record (EHR) for secondary research use. - To conduct a data quality evaluation of data stored within the CC-HIC research database. - To use the CC-HIC research database to conduct two clinical research studies that make use of the longitudinal data supported by the CC-HIC: - The association between cumulative exposure to excess oxygen and outcomes in the critically ill. - The association between different morphologies of longitudinal physiology—in particular organ dysfunction—and outcomes in sepsis. The CC-HIC The EHR is now routinely used for the delivery of patient care throughout the United Kingdom (UK). This has presented the opportunity to learn from a large volume of routinely collected data. The CC-HIC data model represents 255 distinct clinical concepts including demographics, outcomes and granular longitudinal physiology. This model is used to harmonise EHR data of 12 contributing Intensive Care Units (ICUs). This thesis evaluates the suitability of the CC-HIC data model in this role and the quality of data within. While representing an important first step in this field, the CC-HIC data model lacks the necessary normalisation and semantic expressivity to excel in this role. The quality of the CC-HIC research database was variable between contributing sites. High levels of missing data, missing meta-data, non-standardised units and temporal drop out of submitted data are amongst the most challenging features to tackle. It is the principal finding of this thesis that the CC-HIC should transition towards implementing internationally agreed standards for interoperability. Exemplar Clinical Studies Two exemplar studies are presented, each designed to make use of the longitudinal data made available by the CC-HIC and address domains that are both contemporaneous and of importance to the critical care community. Exposure to Excess Oxygen Longitudinal data from the CC-HIC cohort were used to explore the association between the cumulative exposure to excess oxygen and outcomes in the critically ill. A small (likely less than 1% absolute risk reduction) dose-independent association was found between exposure to excess oxygen and mortality. The lack of dosedependency challenges a causal interpretation of these findings. Physiological Morphologies in Sepsis The joint modelling paradigm was applied to explore the different longitudinal profiles of organ failure in sepsis, while accounting for informative censoring from patient death. The rate of change of organ failure was found to play a more significan't role in outcomes than the absolute value of organ failure at a given moment. This has important implications for how the critical care community views the evolution of physiology in sepsis. DECOVID The Decoding COVID-19 (DECOVID) project is presented as future work. DECOVID is a collaborative data sharing project that pools clinical data from two large NHS trusts in England. Many of the lessons learnt from the prior work with the CC-HIC fed into the development of the DECOVID data model and its quality evaluation

Renal recovery

Acute kidney injury (AKI) research in the past decade has mostly focused upon development of a standard AKI definition, validation of early novel biomarkers to predict AKI prior to serum creatinine rise and predict AKI severity, and assessment of aspects of renal replacement therapies and their impact on survival. Given the independent association between AKI and mortality in the acute phase, such focus makes imminent sense. More recently, the recognition that AKI is associated with subsequent development of chronic kidney disease and end-stage renal disease, with the attendant increase in mortality, has led to interest in the clinical epidemiology and the mechanistic understanding of renal recovery after an AKI episode in critically ill patients. We review the current knowledge surrounding renal recovery after an AKI episode, including renal replacement therapy initiation timing and modality impact, biomarker assessment and mechanistic targets to guide potential future clinical trials

A novel time series analysis approach for prediction of dialysis in critically ill patients using echo-state networks

Background: Echo-state networks (ESN) are part of a group of reservoir computing methods and are basically a form of recurrent artificial neural networks (ANN). These methods can perform classification tasks on time series data. The recurrent ANN of an echo-state network has an 'echo-state' characteristic. This 'echo-state' functions as a fading memory: samples that have been introduced into the network in a further past, are faded away. The echostate approach for the training of recurrent neural networks was first described by Jaeger H. et al. In clinical medicine, until this moment, no original research articles have been published to examine the use of echo-state networks. Methods: This study examines the possibility of using an echo-state network for prediction of dialysis in the ICU. Therefore, diuresis values and creatinine levels of the first three days after ICU admission were collected from 830 patients admitted to the intensive care unit (ICU) between May 31th 2003 and November 17th 2007. The outcome parameter was the performance by the echo-state network in predicting the need for dialysis between day 5 and day 10 of ICU admission. Patients with an ICU length of stay < 10 days or patients that received dialysis in the first five days of ICU admission were excluded. Performance by the echo-state network was then compared by means of the area under the receiver operating characteristic curve (AUC) with results obtained by two other time series analysis methods by means of a support vector machine (SVM) and a naive Bayes algorithm (NB). Results: The AUC's in the three developed echo-state networks were 0.822, 0.818, and 0.817. These results were comparable to the results obtained by the SVM and the NB algorithm. Conclusions: This proof of concept study is the first to evaluate the performance of echo-state networks in an ICU environment. This echo-state network predicted the need for dialysis in ICU patients. The AUC's of the echo-state networks were good and comparable to the performance of other classification algorithms. Moreover, the echostate network was more easily configured than other time series modeling technologies

Review of a New Biomarker in Sepsis

Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.Sepsis is a life-threatening syndrome characterized by a dysregulated host response to an infection that may evolve rapidly into septic shock and multiple organ failure. Management of sepsis relies on the early recognition and diagnosis of infection and the providing of adequate and prompt antibiotic therapy and organ support. A novel protein biomarker, the pancreatic stone protein (PSP), has recently been studied as a biomarker of sepsis and the available evidence suggests that it has a higher diagnostic performance for the identification of infection than the most used available biomarkers and adds prognostic value. This review summarizes the clinical evidence available for PSP in the diagnosis and prognosis of sepsis.publishersversionpublishe