Reducing patient mortality, length of stay and readmissions through machine learning-based sepsis prediction in the emergency department, intensive care unit and hospital floor units

McCoy, Andrea J.; Das, Ritankar

doi:10.1136/bmjoq-2017-000158

Cited by 119 publications

(119 citation statements)

References 26 publications

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“…A recent trial across two surgical ICUs with 142 patients (75 controls) reported a 2.7-day reduction in the length of stay (P ϭ 0.04) and a 12% reduction in in-hospital mortality (P ϭ 0.02). Another study, reporting a 4-month experience in a 242-bed acute-care hospital, demonstrated a reduction in the length of stay of 0.43 days, along with a reduction in the mortality rate by 60.24% and a reduction in the rate of sepsis-related 30-day readmission, by 50%, postimplementation (215). Across these two studies, the algorithm score had a sensitivity and a specificity of 0.83 and 0.96, respectively, for sepsis.…”

Section: Beyond Rule-based Decision Support: Power Of Electronic Medimentioning

confidence: 99%

Emerging Technologies for Molecular Diagnosis of Sepsis

et al. 2018

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Rapid and accurate profiling of infection-causing pathogens remains a significant challenge in modern health care. Despite advances in molecular diagnostic techniques, blood culture analysis remains the gold standard for diagnosing sepsis. However, this method is too slow and cumbersome to significantly influence the initial management of patients. The swift initiation of precise and targeted antibiotic therapies depends on the ability of a sepsis diagnostic test to capture clinically relevant organisms along with antimicrobial resistance within 1 to 3 h. The administration of appropriate, narrow-spectrum antibiotics demands that such a test be extremely sensitive with a high negative predictive value. In addition, it should utilize small sample volumes and detect polymicrobial infections and contaminants. All of this must be accomplished with a platform that is easily integrated into the clinical workflow. In this review, we outline the limitations of routine blood culture testing and discuss how emerging sepsis technologies are converging on the characteristics of the ideal sepsis diagnostic test. We include seven molecular technologies that have been validated on clinical blood specimens or mock samples using human blood. In addition, we discuss advances in machine learning technologies that use electronic medical record data to provide contextual evaluation support for clinical decision-making.

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Section: Beyond Rule-based Decision Support: Power Of Electronic Medimentioning

confidence: 99%

Emerging Technologies for Molecular Diagnosis of Sepsis

et al. 2018

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“…One clinical validation study in the ED showed the machine learning model outperformed manual scoring by nurses and the SIRS criteria when identifying severe sepsis and septic shock [28], the other study made no comparison [29]. The interventional studies included two pre-post implementation studies (in-hospital) [30,31] and one ICU randomized controlled trial [32]. All looked at mortality and hospital length of stay, but results are mixed as shown in Table 1.…”

Section: Study Characteristicsmentioning

confidence: 99%

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

et al. 2020

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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.

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“…Common methods include linear regression (n ¼ 2), 8,9 logistic regression (n ¼ 5), 10-14 support vector machines (n ¼ 4), [15][16][17][18] Markov models (n ¼ 4), [19][20][21][22] and Bayesian networks (n ¼ 2). 23,24 Additionally, a few studies (n ¼ 6), [25][26][27][28][29][30] used an industry created tool, InSight (Dascena Inc.), to validate performance compared with the more commonly used methods. In particular, Mao et al, used InSight to test the predictive abilities of the industrycreated sepsis detection algorithm on open source and local data sets, determining the transferability of the algorithm across varying data sets.…”

Section: Variability In Machine Learning or Modeling Techniquesmentioning

confidence: 99%

“…Many studies used publicly available data sets, such as Medical Information Mart for Intensive Care (MIMIC) (n ¼ 8), 8,12,19,[25][26][27][28]32 or the less commonly used Medical Data Warehousing and Analysis (MEDAN) project 18,34 These data sets are extensive and provide researchers with real, de-identified data that can be used as testing, training, or validation sets when using predictive analytics. Additionally, many studies (n ¼ 22) used ICU data (either local 14,17,[20][21][22][23][29][30][31][35][36][37] or MIMIC), while nine studies used ED [9][10][11]13,15,16,24,38,39 data. While local data varied greatly in size, ranging from 24 to 198,833, some used MIMIC in addition to their local data sets, which created a potentially more generalizable set of data to increase statistical significance and to increase the transfer of learning.…”

Section: Variability In Data Sample Selection and Sizementioning

confidence: 99%

A Review of Predictive Analytics Solutions for Sepsis Patients

Teng

Wilcox

2020

Appl Clin Inform

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Background Early detection and efficient management of sepsis are important for improving health care quality, effectiveness, and costs. Due to its high cost and prevalence, sepsis is a major focus area across institutions and many studies have emerged over the past years with different models or novel machine learning techniques in early detection of sepsis or potential mortality associated with sepsis. Objective To understand predictive analytics solutions for sepsis patients, either in early detection of onset or mortality. Methods and Results We performed a systematized narrative review and identified common and unique characteristics between their approaches and results in studies that used predictive analytics solutions for sepsis patients. After reviewing 148 retrieved papers, a total of 31 qualifying papers were analyzed with variances in model, including linear regression (n = 2), logistic regression (n = 5), support vector machines (n = 4), and Markov models (n = 4), as well as population (range: 24–198,833) and feature size (range: 2–285). Many of the studies used local data sets of varying sizes and locations while others used the publicly available Medical Information Mart for Intensive Care data. Additionally, vital signs or laboratory test results were commonly used as features for training and testing purposes; however, a few used more unique features including gene expression data from blood plasma and unstructured text and data from clinician notes. Conclusion Overall, we found variation in the domain of predictive analytics tools for septic patients, from feature and population size to choice of method or algorithm. There are still limitations in transferability and generalizability of the algorithms or methods used. However, it is evident that implementing predictive analytics tools are beneficial in the early detection of sepsis or death related to sepsis. Since most of these studies were retrospective, the translational value in the real-world setting in different wards should be further investigated.

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Reducing patient mortality, length of stay and readmissions through machine learning-based sepsis prediction in the emergency department, intensive care unit and hospital floor units

Cited by 119 publications

References 26 publications

Emerging Technologies for Molecular Diagnosis of Sepsis

Emerging Technologies for Molecular Diagnosis of Sepsis

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

A Review of Predictive Analytics Solutions for Sepsis Patients

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