Comparison of Sepsis Definitions as Automated Criteria

Yu, Sean; Betthauser, Kevin; Gupta, Aditi; Lyons, Patrick G.; Lai, Albert M.; Kollef, Marin H.; Payne, Philip R. O.; Michelson, Andrew P.

doi:10.1097/ccm.0000000000004875

Cited by 16 publications

(20 citation statements)

References 27 publications

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“…However, identification of sepsis onset time is not defined in the Sepsis-3 criteria and is prone to disagreement, which can significantly alter the results. 7 , 12 , 18 …”

Section: Discussionmentioning

confidence: 99%

“…Sepsis was defined according to the Sepsis-3 consensus statement as suspicion of infection (SOI; culture collection followed by antibiotics within 72 h or antibiotics followed by culture procurement within 24 h, Supplementary Appendix I ) accompanied by a qSOFA score ≥2. 12 Only the first sepsis event for each patient was evaluated. Time of onset was set as the time of SOI.…”

Section: Methodsmentioning

confidence: 99%

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Comparison of early warning scores for sepsis early identification and prediction in the general ward setting

Shivakumar

Betthauser

et al. 2021

JAMIA Open

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The objective of this study was to directly compare the ability of commonly used early warning scores (EWS) for early identification and prediction of sepsis in the general ward setting. For general ward patients at a large, academic medical center between early-2012 and mid-2018, common EWS and patient acuity scoring systems were calculated from electronic health records (EHR) data for patients that both met and did not meet Sepsis-3 criteria. For identification of sepsis at index time, National Early Warning Score 2 (NEWS 2) had the highest performance (area under the receiver operating characteristic curve: 0.803 [95% confidence interval [CI]: 0.795–0.811], area under the precision recall curves: 0.130 [95% CI: 0.121–0.140]) followed NEWS, Modified Early Warning Score, and quick Sequential Organ Failure Assessment (qSOFA). Using validated thresholds, NEWS 2 also had the highest recall (0.758 [95% CI: 0.736–0.778]) but qSOFA had the highest specificity (0.950 [95% CI: 0.948–0.952]), positive predictive value (0.184 [95% CI: 0.169–0.198]), and F1 score (0.236 [95% CI: 0.220–0.253]). While NEWS 2 outperformed all other compared EWS and patient acuity scores, due to the low prevalence of sepsis, all scoring systems were prone to false positives (low positive predictive value without drastic sacrifices in sensitivity), thus leaving room for more computationally advanced approaches.

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“…However, identification of sepsis onset time is not defined in the Sepsis-3 criteria and is prone to disagreement, which can significantly alter the results. 7 , 12 , 18 …”

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Comparison of early warning scores for sepsis early identification and prediction in the general ward setting

Shivakumar

Betthauser

et al. 2021

JAMIA Open

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“…As we look towards the future of AI/ML in sepsis care, there are many necessary barriers that need to be overcome before wide scale deployment is achieved. These include the need for larger, more integrated datasets, a harmonized definition of sepsis suitable for automated extraction, more robust explainability, and prospective algorithm validation with emphasis on end-user needs, expectations and clinical workflows [79][80][81]. Numerous clinical studies have demonstrated that delaying the administration of antibiotic therapy, even by an hour or two in septic shock, can be associated with greater risk of death 2.…”

Section: Using Ai/ml To Improve Sepsis Outcomesmentioning

confidence: 99%

Timing of antibiotic therapy in the ICU

et al. 2021

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Severe or life threatening infections are common among patients in the intensive care unit (ICU). Most infections in the ICU are bacterial or fungal in origin and require antimicrobial therapy for clinical resolution. Antibiotics are the cornerstone of therapy for infected critically ill patients. However, antibiotics are often not optimally administered resulting in less favorable patient outcomes including greater mortality. The timing of antibiotics in patients with life threatening infections including sepsis and septic shock is now recognized as one of the most important determinants of survival for this population. Individuals who have a delay in the administration of antibiotic therapy for serious infections can have a doubling or more in their mortality. Additionally, the timing of an appropriate antibiotic regimen, one that is active against the offending pathogens based on in vitro susceptibility, also influences survival. Thus not only is early empiric antibiotic administration important but the selection of those agents is crucial as well. The duration of antibiotic infusions, especially for β-lactams, can also influence antibiotic efficacy by increasing antimicrobial drug exposure for the offending pathogen. However, due to mounting antibiotic resistance, aggressive antimicrobial de-escalation based on microbiology results is necessary to counterbalance the pressures of early broad-spectrum antibiotic therapy. In this review, we examine time related variables impacting antibiotic optimization as it relates to the treatment of life threatening infections in the ICU. In addition to highlighting the importance of antibiotic timing in the ICU we hope to provide an approach to antimicrobials that also minimizes the unnecessary use of these agents. Such approaches will increasingly be linked to advances in molecular microbiology testing and artificial intelligence/machine learning. Such advances should help identify patients needing empiric antibiotic therapy at an earlier time point as well as the specific antibiotics required in order to avoid unnecessary administration of broad-spectrum antibiotics.

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“…A recent study that used different sepsis definitions to generate patient cohorts found significant heterogeneous characteristics and clinical outcomes between cohorts. 136 Similarly, previous work by Liu et al 137 demonstrated that using different infection criteria resulted in a different number of patients and slightly different outcomes. Similar to how changes in the definition and varying coding methods can affect sepsis mortality outcomes, 138 the sepsis definition and codes used in ML studies will likely change the outcome, results, and reporting methods.…”

Section: Discussionmentioning

confidence: 85%

Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review

Yan

Gustad

Nytrø

2021

Journal of the American Medical Informatics Association

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Objective To determine the effects of using unstructured clinical text in machine learning (ML) for prediction, early detection, and identification of sepsis. Materials and methods PubMed, Scopus, ACM DL, dblp, and IEEE Xplore databases were searched. Articles utilizing clinical text for ML or natural language processing (NLP) to detect, identify, recognize, diagnose, or predict the onset, development, progress, or prognosis of systemic inflammatory response syndrome, sepsis, severe sepsis, or septic shock were included. Sepsis definition, dataset, types of data, ML models, NLP techniques, and evaluation metrics were extracted. Results The clinical text used in models include narrative notes written by nurses, physicians, and specialists in varying situations. This is often combined with common structured data such as demographics, vital signs, laboratory data, and medications. Area under the receiver operating characteristic curve (AUC) comparison of ML methods showed that utilizing both text and structured data predicts sepsis earlier and more accurately than structured data alone. No meta-analysis was performed because of incomparable measurements among the 9 included studies. Discussion Studies focused on sepsis identification or early detection before onset; no studies used patient histories beyond the current episode of care to predict sepsis. Sepsis definition affects reporting methods, outcomes, and results. Many methods rely on continuous vital sign measurements in intensive care, making them not easily transferable to general ward units. Conclusions Approaches were heterogeneous, but studies showed that utilizing both unstructured text and structured data in ML can improve identification and early detection of sepsis.

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Comparison of Sepsis Definitions as Automated Criteria

Cited by 16 publications

References 27 publications

Comparison of early warning scores for sepsis early identification and prediction in the general ward setting

Comparison of early warning scores for sepsis early identification and prediction in the general ward setting

Timing of antibiotic therapy in the ICU

Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review

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