A computational approach to early sepsis detection

Calvert, Jacob; Price, D A; Chettipally, Uli K.; Barton, Christopher; Feldman, Mitchell D.; Hoffman, Jana; Jay, Melissa; Das, Ritankar

doi:10.1016/j.compbiomed.2016.05.003

Cited by 210 publications

(173 citation statements)

References 24 publications

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“…The algodiagnostic assessed in this study has previously been examined in several retrospective studies, where it has been validated for detection of sepsis [14], severe sepsis [13], and septic shock [15]. The algodiagnostic has also been previously evaluated in prospective studies, including a randomized controlled trial where use of the MLA resulted in statistically significant decreases in in-hospital mortality and average length of stay [17].…”

Section: Discussionmentioning

confidence: 99%

“…West Virginia provider Cabell Huntington Hospital (CHH), a 303-bed facility, partnered with Dascena (Hayward, CA) to improve sepsis-related outcomes using a machine learning algodiagnostic (MLA). The Dascena MLA was validated for sepsis prediction and detection in several studies [13][14][15], demonstrating an area under the receiver operator characteristic (ROC) curve (AUROC) over 0.90 using only six vital signs, in a multicenter cohort study of over 650,000 encounters [16]. In a recent randomized clinical trial, mortality decreased by 12.4 percentage points with use of the MLA, a relative reduction of 58% [17].…”

Section: Introductionmentioning

confidence: 99%

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Evaluating a sepsis prediction machine learning algorithm in the emergency department and intensive care unit: a before and after comparative study

Burdick

Pino

Gabel-Comeau

et al. 2017

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluating a sepsis prediction machine learning algorithm in the emergency department and intensive care unit: a before and after comparative study

Burdick

Pino

Gabel-Comeau

et al. 2017

Preprint

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“…InSight applied to individual sepsis standards such as the SIRS standard for sepsis [21], severe sepsis [22], and septic shock [23], on the MIMIC retrospective datasets. We have also developed a related algorithm to detect patient stability [24] and predict mortality [25,26].…”

Section: Our Previous Studies Performed On Earlier Versions Of the Mmentioning

confidence: 99%

Multicenter validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU

Mao

Jay

Hoffman

et al. 2018

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Objectives: We validate a machine learning-based sepsis prediction algorithm (InSight) for detection and prediction of three sepsis-related gold standards, using only six vital signs. We evaluate robustness to missing data, customization to site-specific data using transfer learning, and generalizability to new settings. Design:A machine learning algorithm with gradient tree boosting. Features for prediction were created from combinations of only six vital sign measurements and their changes over time. Conclusions:InSight outperforms existing sepsis scoring systems in identifying and predicting sepsis, severe sepsis, and septic shock. This is the first sepsis screening system to exceed an AUROC of 0.90 using only vital sign inputs. InSight is robust to missing data, can be customized to novel hospital data using a small fraction of site data, and retained strong discrimination across all institutions. Strengths and limitations of this study •Machine learning is applied to the detection and prediction of three separate sepsis standards in the emergency department, general ward and intensive care settings.

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“…The latest state of the MLA was characterized in the retrospective analysis. Previous states of the algorithm have been studied retrospectively and prospectively [10][11][12][13][14][15][16]; however, this study was performed on significantly larger and more diverse datasets.…”

Section: Introductionmentioning

confidence: 99%

Effect of a Sepsis Prediction Algorithm on Patient Mortality, Length of Stay, and Readmission

Burdick

Pino

Gabel-Comeau

et al. 2018

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KEY POINTSQuestion: Is a machine learning algorithm capable of accurate severe sepsis prediction, and does its clinical implementation improve patient mortality rates, hospital length of stay, and 30-day readmission rates?Findings: In a retrospective analysis that included datasets containing a total of 585,644 patient encounters from 461 hospitals, the machine learning algorithm demonstrated an AUROC of 0.93 at time of severe sepsis onset, which exceeded those of MEWS (0.71), SOFA (0.74), and SIRS (0.62); and an AUROC of 0.77 for severe sepsis prediction 48 hours in advance of onset . In an analysis of real-world data from nine hospitals across 75,147 patient encounters, use of the machine learning algorithm was associated with a 39.5% reduction in in-hospital mortality, a 32.3% reduction in hospital length of stay, and a 22.7% reduction in 30-day readmission rate. Meaning:The accurate and predictive nature of this algorithm may encourage early recognition of patients trending toward severe sepsis, and therefore improve sepsis related outcomes. ABSTRACTObjective: To validate performance of a machine learning algorithm for severe sepsis determination up to 48 hours before onset, and to evaluate the effect of the algorithm on in-hospital mortality, hospital length of stay, and 30-day readmission.Setting: This cohort study includes a combined retrospective analysis and clinical outcomes evaluation: a dataset containing 510,497 patient encounters from 461 United States health centers for retrospective analysis, and a multiyear, multicenter clinical data set of real-world data containing 75,147 patient encounters from nine hospitals for clinical outcomes evaluation.Participants: For retrospective analysis, 270,438 adult patients with at least one documented measurement of five out of six vital sign measurements were included. For clinical outcomes analysis, 17,758 adult patients who met two or more Systemic Inflammatory Response Syndrome (SIRS) criteria at any point during their stay were included. Results:At severe sepsis onset, the MLA demonstrated an AUROC of 0.91 (95% CI 0.90, 0.92), which exceeded those of MEWS (0.71, P <.001), SOFA (0.74; P <.001), and SIRS (0.62; P <.001). For severe sepsis prediction 48 hours in advance of onset, the MLA achieved an AUROC of 0.77 (95% CI 0.73, 0.80). For the clinical outcomes study, when using the MLA, hospitals saw an average 39.5% reduction of in-hospital mortality, a 32.3% reduction in hospital length of stay, and a 22.7% reduction in 30-day readmission rate. Conclusions:The MLA accurately predicts severe sepsis onset up to 48 hours in advance using only readily available vital signs in retrospective validation. Reductions of in-hospital mortality, hospital length of stay, and 30-day readmissions were observed in real-world clinical use of the MLA. Results suggest this system may improve severe sepsis detection and patient outcomes over the use of rules-based sepsis detection systems.

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A computational approach to early sepsis detection

Cited by 210 publications

References 24 publications

Evaluating a sepsis prediction machine learning algorithm in the emergency department and intensive care unit: a before and after comparative study

Evaluating a sepsis prediction machine learning algorithm in the emergency department and intensive care unit: a before and after comparative study

Multicenter validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU

Effect of a Sepsis Prediction Algorithm on Patient Mortality, Length of Stay, and Readmission

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