Sepsis Screening: Combining Early Warning Scores and SIRS Criteria

Kangas, Corrie; Iverson, Lindsay; Pierce, Dustin

doi:10.1177/1054773818823334

Cited by 8 publications

(9 citation statements)

References 27 publications

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“…Considering the rapid progression from organ dysfunction A In-hospital mortality among those with suspected sepsis B ED-to-ICU admission among those with suspected sepsis C In-hospital mortality among those with an EHR-based sepsis diagnosis D ED-to-ICU admission among those with an EHR-based sepsis diagnosis to death in sepsis patients, as well as the difficulty establishing a sepsis diagnosis at triage, 23 providers must quickly identify patients at increased risk of poor outcomes when they present to the ED. Sepsis alerts often are built using SIRS criteria, 27 including the one used for sepsis surveillance at UCSF since 2012, 22 but the white blood cell count criterion is subject to a laboratory lag and could lead to a delay in identification. Implementation of a point-of-care bedside score alert that uses readily available clinical data could allow providers to identify patients at greatest risk of poor outcomes immediately at ED presentation and triage, which motivated us to explore the predictive performance of qSOFA, the Shock Index, and NEWS2.…”

Section: Discussionmentioning

confidence: 99%

Identifying the Sickest During Triage: Using Point‐of‐Care Severity Scores to Predict Prognosis in Emergency Department Patients With Suspected Sepsis

Prasad

Fang

Martínez

et al. 2021

Journal of Hospital Medicine

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BACKGROUND: Sepsis progresses rapidly and is associated with considerable morbidity and mortality. Bedside risk stratification scores can quickly identify patients at greatest risk of poor outcomes; however, there is lack of consensus on the best scale to use. OBJECTIVE: To compare the ability of quick Sequential Organ Failure Assessment (qSOFA), the National Early Warning System (NEWS2), and the Shock Index—which does not require mental status assessment—to predict poor outcomes among patients with suspected sepsis during triage. DESIGN, SETTING, AND PARTICIPANTS: Retrospective cohort study of adults presenting to an academic emergency department (ED) from June 2012 to December 2018 who had blood cultures and intravenous antibiotics within 24 hours. MAIN OUTCOMES AND MEASURES: Clinical data were collected from the electronic health record. Patients were considered positive at qSOFA ≥2, Shock Index >0.7, or NEWS2 ≥5 scores. We calculated test characteristics and area under the receiver operating characteristics curves (AUROCs) to predict in-hospital mortality and ED-to-intensive care unit (ICU) admission. RESULTS: We included 23,837 ED patients; 1,921(8.1%) were qSOFA-positive, 4,273 (17.9%) Shock Index-positive, and 11,832 (49.6%) NEWS2-positive. There were 1,427 (6.0%) deaths and 3,149 (13.2%) ED-to-ICU admissions in the sample. NEWS2 had the highest sensitivity for in-hospital mortality (76.0%) and ED-to-ICU admission (78.9%). qSOFA had the highest specificity for in-hospital mortality (93.4%) and ED-to-ICU admission (95.2%). Shock Index exhibited the highest AUROC for in-hospital mortality (0.648; 95 CI, 0.635-0.662) and ED-to-ICU admission (0.680; 95% CI, 0.617-0.689). Test characteristics were similar among those with sepsis. CONCLUSIONS: Institution priorities should drive score selection, balancing sensitivity and specificity. In our study, qSOFA was highly specific and NEWS2 was the most sensitive for ruling out patients at high risk. Performance of the Shock Index fell between qSOFA and NEWS2 and could be considered because it is easy to implement.

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Section: Discussionmentioning

confidence: 99%

Identifying the Sickest During Triage: Using Point‐of‐Care Severity Scores to Predict Prognosis in Emergency Department Patients With Suspected Sepsis

Prasad

Fang

Martínez

et al. 2021

Journal of Hospital Medicine

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“…20 MEWS cutoff scores of deterioration that trigger an evaluation vary within the literature based on patient population and clinical settings. 10,21,22…”

Section: Mews Guidementioning

confidence: 99%

“…Various clinical decision support tools (CDSTs) and existing early warning tools are used for the early identification of sepsis 9-12. Despite the availability of CDSTs, initial signs and symptoms of sepsis can be challenging to recognize.…”

mentioning

confidence: 99%

Clinical decision support tools useful for identifying sepsis risk

Heineman,

Orrick,

Phan

et al. 2024

Nursing

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Purpose: Evaluate the effectiveness of the clinical decision support tools (CDSTs), POC Advisor (POCA), and Modified Early Warning System (MEWS) in identifying sepsis risk and influencing time to treatment for inpatients, comparing their respective alert mechanisms. Methods: This study was conducted at two academic university medical center hospitals. Data from adult inpatients in medical-surgical and telemetry units were analyzed from January 1, 2020, to December 31, 2020. Criteria included sepsis-related ICD-10 codes, antibiotic administration, and ordered sepsis labs. Subsequent statistical analyses utilized Fisher's exact test and Wilcoxon Rank Sum test, focusing on mortality differences by age, sex, and race/ethnicity. Results: Among 744 patients, 143 sepsis events were identified, with 83% already receiving treatment upon CDST alert. Group 1 (POCA alert) showed reduced response time compared with MEWS, while Group 3 (MEWS) experienced longer time to treatment. Group 4 included sepsis events missed by both systems. Mortality differences were not significant among the groups. Conclusion: While CDSTs play a role, nursing assessment and clinical judgment are crucial. This study recognized the potential for alarm fatigue due to a high number of CDST-driven alerts, while emphasizing the importance of a collaborative approach for prompt sepsis treatment and potential reduction in sepsis-related mortality.

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“…Our results support a correlation between inflammatory biomarkers and the mEWS but highlight the easier implementation of the mEWS into clinical decision-making. Kangas et al [26] found that combining the systemic inflammatory response syndrome (SIRS) criteria with the mEWS resulted in correctly capturing three times more sepsis diagnoses while triggering 46% fewer falsepositive alerts. Often biomarkers are used for evaluation of sepsis prior to culture results or in settings without clear symptoms.…”

Section: Predictive Value Of the Mewsmentioning

confidence: 99%

A Retrospective Cohort Study Evaluating the Use of the Modified Early Warning Score to Improve Outcome Prediction in Neurosurgical Patients

Karsy¹,

Hunsaker²,

Hamrick³

et al. 2022

Cureus

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The modified early warning score (mEWS) has been used to identify decompensating patients in critical care settings, potentially leading to better outcomes and safer, more cost-effective patient care. We examined whether the admission or maximum mEWS of neurosurgical patients was associated with outcomes and total patient costs across neurosurgical procedures. MethodsThis retrospective cohort study included all patients hospitalized at a quaternary care hospital for neurosurgery procedures during 2019. mEWS were automatically generated during a patient's hospitalization from data available in the electronic medical record. Primary and secondary outcome measures were the first mEWS at admission, maximum mEWS during hospitalization, length of stay (LOS), discharge disposition, mortality, cost of hospitalization, and patient biomarkers (i.e., white blood cell count, erythrocyte sedimentation rate, C-reactive protein, and procalcitonin). ResultsIn 1,408 patients evaluated, a mean first mEWS of 0.5 ± 0.9 (median: 0) and maximum mEWS of 2.6 ± 1.4 (median: 2) were observed. The maximum mEWS was achieved on average one day (median = 0 days) after admission and correlated with other biomarkers (p < 0.0001). Scores correlated with continuous outcomes (i.e., LOS and cost) distinctly based on disease types. Multivariate analysis showed that the maximum mEWS was associated with longer stay (OR = 1.8; 95% CI = 1.6-1.96, p = 0.0001), worse disposition (OR = 0.82, 95% CI = 0.71-0.95, p = 0.0001), higher mortality (OR = 1.7; 95% CI = 1.3-2.1, p = 0.0001), and greater cost (OR = 1.2, 95% CI = 1.1-1.3, p = 0.001). Machine learning algorithms suggested that logistic regression, naïve Bayes, and neural networks were most predictive of outcomes. ConclusionmEWS was associated with outcomes in neurosurgical patients and may be clinically useful. The composite score could be integrated with other clinical factors and was associated with LOS, discharge disposition, mortality, and patient cost. mEWS also could be used early during a patient's admission to stratify risk. Increase in mEWS scores correlated with the outcome to a different degree in distinct patient/disease types. These results show the potential of the mEWS to predict outcomes in neurosurgical patients and suggest that it could be incorporated into clinical decision-making and/or monitoring of neurosurgical patients during admission. However, further studies and refinement of mEWS are needed to better integrate it into patient care.

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Sepsis Screening: Combining Early Warning Scores and SIRS Criteria

Cited by 8 publications

References 27 publications

Identifying the Sickest During Triage: Using Point‐of‐Care Severity Scores to Predict Prognosis in Emergency Department Patients With Suspected Sepsis

Identifying the Sickest During Triage: Using Point‐of‐Care Severity Scores to Predict Prognosis in Emergency Department Patients With Suspected Sepsis

Clinical decision support tools useful for identifying sepsis risk

A Retrospective Cohort Study Evaluating the Use of the Modified Early Warning Score to Improve Outcome Prediction in Neurosurgical Patients

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