Quick Sequential [Sepsis-Related] Organ Failure Assessment (qSOFA) and St. John Sepsis Surveillance Agent to Detect Patients at Risk of Sepsis: An Observational Cohort Study

Amland, Robert C.; Sutariya, Bharat B.

doi:10.1177/1062860617692034

Cited by 33 publications

(23 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Health system leaders wanted to reimagine how data and technology could be effectively utilized to both detect sepsis and coordinate care to ensure the completion of recommended bundles. The team adopted computable sepsis criteria aligned with the CMS SEP-1 measure and quality improvement efforts at peer institutions, specified in Multimedia Appendix 1 [ 23 , 24 ]. Specific time windows to consider for each data element along with thresholds were decided upon by an interdisciplinary team of clinicians.…”

Section: Methodsmentioning

confidence: 99%

“…The newly published quick Sequential Organ Failure Assessment (qSOFA) was recommended to identify patients at risk of poor outcomes because of sepsis [ 31 ]. However, qSOFA was not adopted by CMS for the SEP-1 core measure and does not accurately identify patients at risk of developing sepsis [ 24 , 32 ]. Considering the lack of an available, validated model to predict sepsis in the ED at that time, an interdisciplinary team of clinicians proposed to develop a novel machine learning model using local data.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Real-World Integration of a Sepsis Deep Learning Technology Into Routine Clinical Care: Implementation Study

Sendak¹,

Ratliff²,

Sarro³

et al. 2020

JMIR Med Inform

117

104

View full text Add to dashboard Cite

Background Successful integrations of machine learning into routine clinical care are exceedingly rare, and barriers to its adoption are poorly characterized in the literature. Objective This study aims to report a quality improvement effort to integrate a deep learning sepsis detection and management platform, Sepsis Watch, into routine clinical care. Methods In 2016, a multidisciplinary team consisting of statisticians, data scientists, data engineers, and clinicians was assembled by the leadership of an academic health system to radically improve the detection and treatment of sepsis. This report of the quality improvement effort follows the learning health system framework to describe the problem assessment, design, development, implementation, and evaluation plan of Sepsis Watch. Results Sepsis Watch was successfully integrated into routine clinical care and reshaped how local machine learning projects are executed. Frontline clinical staff were highly engaged in the design and development of the workflow, machine learning model, and application. Novel machine learning methods were developed to detect sepsis early, and implementation of the model required robust infrastructure. Significant investment was required to align stakeholders, develop trusting relationships, define roles and responsibilities, and to train frontline staff, leading to the establishment of 3 partnerships with internal and external research groups to evaluate Sepsis Watch. Conclusions Machine learning models are commonly developed to enhance clinical decision making, but successful integrations of machine learning into routine clinical care are rare. Although there is no playbook for integrating deep learning into clinical care, learnings from the Sepsis Watch integration can inform efforts to develop machine learning technologies at other health care delivery systems.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Real-World Integration of a Sepsis Deep Learning Technology Into Routine Clinical Care: Implementation Study

Sendak¹,

Ratliff²,

Sarro³

et al. 2020

JMIR Med Inform

117

104

View full text Add to dashboard Cite

show abstract

“… 15 A similar sepsis definition has been used for model development efforts at peer institutions that developed at least 2 other published models, and this definition aligns with the CMS definition. 16 , 17 …”

Section: Methodsmentioning

confidence: 99%

Machine learning for early detection of sepsis: an internal and temporal validation study

et al. 2020

View full text Add to dashboard Cite

Objective Determine if deep learning detects sepsis earlier and more accurately than other models. To evaluate model performance using implementation-oriented metrics that simulate clinical practice. Materials and Methods We trained internally and temporally validated a deep learning model (multi-output Gaussian process and recurrent neural network [MGP–RNN]) to detect sepsis using encounters from adult hospitalized patients at a large tertiary academic center. Sepsis was defined as the presence of 2 or more systemic inflammatory response syndrome (SIRS) criteria, a blood culture order, and at least one element of end-organ failure. The training dataset included demographics, comorbidities, vital signs, medication administrations, and labs from October 1, 2014 to December 1, 2015, while the temporal validation dataset was from March 1, 2018 to August 31, 2018. Comparisons were made to 3 machine learning methods, random forest (RF), Cox regression (CR), and penalized logistic regression (PLR), and 3 clinical scores used to detect sepsis, SIRS, quick Sequential Organ Failure Assessment (qSOFA), and National Early Warning Score (NEWS). Traditional discrimination statistics such as the C-statistic as well as metrics aligned with operational implementation were assessed. Results The training set and internal validation included 42 979 encounters, while the temporal validation set included 39 786 encounters. The C-statistic for predicting sepsis within 4 h of onset was 0.88 for the MGP–RNN compared to 0.836 for RF, 0.849 for CR, 0.822 for PLR, 0.756 for SIRS, 0.619 for NEWS, and 0.481 for qSOFA. MGP–RNN detected sepsis a median of 5 h in advance. Temporal validation assessment continued to show the MGP–RNN outperform all 7 clinical risk score and machine learning comparisons. Conclusions We developed and validated a novel deep learning model to detect sepsis. Using our data elements and feature set, our modeling approach outperformed other machine learning methods and clinical scores.

show abstract

“…For further investigation, the modeling approaches (multivariate logistic regression, decision tree [ 15 ], and naïve Bayes classifier [ 16 ]) were employed on SIRS and qSOFA variables. Following the literature, the performance matrices of SIRS and qSOFA were evaluated above the baseline risk [ 11 17 ]. The set of variables used for modeling purpose are summarized as follows:…”

Section: Methodsmentioning

confidence: 99%

Using Statistical and Machine Learning Methods to Evaluate the Prognostic Accuracy of SIRS and qSOFA

et al. 2018

View full text Add to dashboard Cite

ObjectivesThe objective of this study was to compare the performance of two popularly used early sepsis diagnostic criteria, systemic inflammatory response syndrome (SIRS) and quick Sepsis-related Organ Failure Assessment (qSOFA), using statistical and machine learning approaches.MethodsThis retrospective study examined patient visits in Emergency Department (ED) with sepsis related diagnosis. The outcome was 28-day in-hospital mortality. Using odds ratio (OR) and modeling methods (decision tree [DT], multivariate logistic regression [LR], and naïve Bayes [NB]), the relationships between diagnostic criteria and mortality were examined.ResultsOf 132,704 eligible patient visits, 14% died within 28 days of ED admission. The association of qSOFA ≥2 with mortality (OR = 3.06; 95% confidence interval [CI], 2.96–3.17) greater than the association of SIRS ≥2 with mortality (OR = 1.22; 95% CI, 1.18–1.26). The area under the ROC curve for qSOFA (AUROC = 0.70) was significantly greater than for SIRS (AUROC = 0.63). For qSOFA, the sensitivity and specificity were DT = 0.39, LR = 0.64, NB = 0.62 and DT = 0.89, LR = 0.63, NB = 0.66, respectively. For SIRS, the sensitivity and specificity were DT = 0.46, LR = 0.62, NB = 0.62 and DT = 0.70, LR = 0.59, NB = 0.58, respectively.ConclusionsThe evidences suggest that qSOFA is a better diagnostic criteria than SIRS. The low sensitivity of qSOFA can be improved by carefully selecting the threshold to translate the predicted probabilities into labels. These findings can guide healthcare providers in selecting risk-stratification measures for patients presenting to an ED with sepsis.

show abstract

Quick Sequential [Sepsis-Related] Organ Failure Assessment (qSOFA) and St. John Sepsis Surveillance Agent to Detect Patients at Risk of Sepsis: An Observational Cohort Study

Cited by 33 publications

References 28 publications

Real-World Integration of a Sepsis Deep Learning Technology Into Routine Clinical Care: Implementation Study

Real-World Integration of a Sepsis Deep Learning Technology Into Routine Clinical Care: Implementation Study

Machine learning for early detection of sepsis: an internal and temporal validation study

Using Statistical and Machine Learning Methods to Evaluate the Prognostic Accuracy of SIRS and qSOFA

Contact Info

Product

Resources

About