Focus on sepsis

Møller, Morten Hylander; Alhazzani, Waleed; Shankar-Hari, Manu

doi:10.1007/s00134-019-05680-4

Cited by 12 publications

(9 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Clinically, accurate identification of sepsis and prediction of patients at risk of developing sepsis is essential to improve treatment [37]. Current approaches to identify septic patients have centered around biomarkers and (automated) clinical decision rules such as the SIRS and (q)SOFA criteria [38,39].…”

Section: Performance and Clinical Relevance Of Individual Modelsmentioning

confidence: 99%

Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy

et al. 2020

View full text Add to dashboard Cite

Purpose: Early clinical recognition of sepsis can be challenging. With the advancement of machine learning, promising real-time models to predict sepsis have emerged. We assessed their performance by carrying out a systematic review and meta-analysis. Methods: A systematic search was performed in PubMed, Embase.com and Scopus. Studies targeting sepsis, severe sepsis or septic shock in any hospital setting were eligible for inclusion. The index test was any supervised machine learning model for real-time prediction of these conditions. Quality of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology, with a tailored Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklist to evaluate risk of bias. Models with a reported area under the curve of the receiver operating characteristic (AUROC) metric were meta-analyzed to identify strongest contributors to model performance. Results: After screening, a total of 28 papers were eligible for synthesis, from which 130 models were extracted. The majority of papers were developed in the intensive care unit (ICU, n = 15; 54%), followed by hospital wards (n = 7; 25%), the emergency department (ED, n = 4; 14%) and all of these settings (n = 2; 7%). For the prediction of sepsis, diagnostic test accuracy assessed by the AUROC ranged from 0.68-0.99 in the ICU, to 0.96-0.98 in-hospital and 0.87 to 0.97 in the ED. Varying sepsis definitions limit pooling of the performance across studies. Only three papers clinically implemented models with mixed results. In the multivariate analysis, temperature, lab values, and model type contributed most to model performance. Conclusion: This systematic review and meta-analysis show that on retrospective data, individual machine learning models can accurately predict sepsis onset ahead of time. Although they present alternatives to traditional scoring systems, between-study heterogeneity limits the assessment of pooled results. Systematic reporting and clinical implementation studies are needed to bridge the gap between bytes and bedside.

show abstract

Section: Performance and Clinical Relevance Of Individual Modelsmentioning

confidence: 99%

Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy

et al. 2020

View full text Add to dashboard Cite

show abstract

“…ICU-ROX compared conservative oxygen therapy with usual oxygen therapy in 1000 mechanically ventilated ICU patients. Despite a growing body of sepsis-related research [10], there are no published studies evaluating oxygen regimens in patients with sepsis. Given this limited evidence base, we undertook a post hoc exploratory analysis to evaluate the effect of conservative vs. usual oxygen therapy on 90-day mortality and other patient-centered outcomes in the subset of patients with sepsis at the time of recruitment to ICU-ROX.…”

Section: Introductionmentioning

confidence: 99%

Conservative oxygen therapy for mechanically ventilated adults with sepsis: a post hoc analysis of data from the intensive care unit randomized trial comparing two approaches to oxygen therapy (ICU-ROX)

et al. 2019

View full text Add to dashboard Cite

Purpose: Sepsis is a common reason for intensive care unit (ICU) admission and mortality in ICU patients. Despite increasing interest in treatment strategies limiting oxygen exposure in ICU patients, no trials have compared conservative vs. usual oxygen in patients with sepsis. Methods: We undertook a post hoc analysis of the 251 patients with sepsis enrolled in a trial that compared conservative oxygen therapy with usual oxygen therapy in 1000 mechanically ventilated ICU patients. The primary end point for the current analysis was 90-day mortality. Key secondary outcomes were cause-specific mortality, ICU and hospital length of stay, ventilator-free days, vasopressor-free days, and the proportion of patients receiving renal replacement therapy in the ICU. Results: Patients with sepsis allocated to conservative oxygen therapy spent less time in the ICU with an SpO 2 ≥ 97% (23.5 h [interquartile range (IQR) 8-70] vs. 47 h [IQR 11-93], absolute difference, 23 h; 95% CI 8-38), and more time receiving an FiO 2 of 0.21 than patients allocated to usual oxygen therapy (20.5 h [IQR 1-79] vs. 0 h [IQR 0-10], absolute difference, 20 h; 95% CI 14-26). At 90-days, 47 of 130 patients (36.2%) assigned to conservative oxygen and 35 of 120 patients (29.2%) assigned to usual oxygen had died (absolute difference, 7 percentage points; 95% CI − 4.6 to 18.6% points; P = 0.24; interaction P = 0.35 for sepsis vs. non-sepsis). There were no statistically significant differences between groups for secondary outcomes but point estimates of treatment effects consistently favored usual oxygen therapy.

show abstract

“…All calculations are trivial for a computer, which may limit generalization of the results to other hospitals and hospital systems. Other arti cial intelligence systems such as random forest models may be a valuable tool to predict sepsis 6 .…”

Section: Discussionmentioning

confidence: 99%

“…Although there are many studies on the early predictions of sepsis, such as calcitonin, C-reactive protein, white blood cells, platelets, and lactic acid 4,5 . However, disappointingly, most studies are limited in clinical prediction 6 . Since sepsis is a complex clinical syndrome, it contains a wide range of multifaceted clinical and biological features; therefore, a single clinical index may not be a good re ection of the disease state 7 .…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Model for Accurate Prediction of Sepsis in ICU Patients in China

Wang¹,

Li²,

Sun³

et al. 2021

Preprint

View full text Add to dashboard Cite

Background: Although numerous studies are conducted every year on how to reduce the fatality rate associated with sepsis, it is still a major challenge faced by patients, clinicians, and medical systems worldwide. Early identification and prediction of patients at risk of sepsis and adverse outcomes associated with sepsis are critical. We aimed to develop an artificial intelligence algorithm that can predict sepsis early.Methods: This was a secondary analysis of an observational cohort study from the Intensive Care Unit of the First Affiliated Hospital of Zhengzhou University. A total of 4449 infected patients were randomly assigned to the development and validation data set at a ratio of 4:1. After extracting electronic medical record data, a set of 55 features (variables) was calculated and passed to the random forest algorithm to predict the onset of sepsis.Results: The pre-procedure clinical variables were used to build a prediction model from the training data set using the random forest machine learning method; a 5-fold cross-validation was used to evaluate the prediction accuracy of the model. Finally, we tested the model using the validation data set. The area obtained by the model under the receiver operating characteristic (ROC) curve (AUC) was 0.91, the sensitivity was 87%, and the specificity was 89%.Conclusions: The newly established model can accurately predict the onset of sepsis in ICU patients in clinical settings as early as possible. Prospective studies are necessary to determine the clinical utility of the proposed sepsis prediction model.

show abstract

Focus on sepsis

Cited by 12 publications

References 20 publications

Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy

Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy

Conservative oxygen therapy for mechanically ventilated adults with sepsis: a post hoc analysis of data from the intensive care unit randomized trial comparing two approaches to oxygen therapy (ICU-ROX)

A Machine Learning Model for Accurate Prediction of Sepsis in ICU Patients in China

Contact Info

Product

Resources

About