Using scores to identify patients at risk of short term mortality at arrival to the acute medical unit: A validation study of six existing scores

Brabrand, Mikkel; Hallas, Peter; Hansen, Søren Nygaard; Jensen, Kenneth; Posth, Stefan

doi:10.1016/j.ejim.2017.09.042

Cited by 20 publications

(19 citation statements)

References 15 publications

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“…Determining the baseline information provides insight into the weakness of current tools and the difficulty for the RN in determining patient deterioration based on an electronic alert within the EHR. Brabrand et al (2017) comment that a score perfectly identifying patients at risk will have inherent low specificity, so compromise must be achieved when designing a measuring tool. The results of this study are consistent with this concept.…”

Section: Discussionmentioning

confidence: 99%

“…Because of the positive predictive value of the NEWS scores to detect clinical deterioration, it was recommended that the NHS adopt this tool in conjunction with other efforts to assist in the detection of clinical deterioration of patients (Smith et al, 2013). Brabrand et al (2017) evaluated six different early warning scores—including NEWS—in an effort to validate the ability of these scores to determine patients at increased risk of mortality. NEWS succeeded in predicting good outcomes but did not have perfect discrimination or calibration (Brabrand et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Brabrand et al (2017) evaluated six different early warning scores—including NEWS—in an effort to validate the ability of these scores to determine patients at increased risk of mortality. NEWS succeeded in predicting good outcomes but did not have perfect discrimination or calibration (Brabrand et al, 2017). Although not specifically designed for sepsis screening, both MEWS and NEWS are adept at identifying patients who demonstrate signs of clinical deterioration (Brabrand et al, 2017; Smith et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…NEWS succeeded in predicting good outcomes but did not have perfect discrimination or calibration (Brabrand et al, 2017). Although not specifically designed for sepsis screening, both MEWS and NEWS are adept at identifying patients who demonstrate signs of clinical deterioration (Brabrand et al, 2017; Smith et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Sepsis Screening: Combining Early Warning Scores and SIRS Criteria

2019

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Providing effective screening tools to nurses is necessary to improve patient outcomes and health care quality. This research examines if the modification of two electronic health record sepsis screening tools using a combined systemic inflammatory response syndrome (SIRS), modified early warning score (MEWS), and national early warning score (NEWS) criteria improves the recognition of sepsis by nurses. Medical-surgical/telemetry units at a medical center in the Midwest were examined using a quasiexperimental design. Modifications of tool 1 captured 18% more correct classifications of sepsis (odds ratio [OR] = .82, 95% CI = [0.68, 0.98]), triggering for 10% fewer patients, t(1033) = 9.31, p < .001. 95% CI = [0.078, 0.119]. Modifications of tool 2 captured 3 times more correct alerts (OR = .29, 95% CI = [0.24, 0.35]), triggering for 46% fewer patients, t(1033) = 24.38, p < .001. 95% CI = [0.420, 0.493]. The updated criteria showed significant improvement toward correctly identifying sepsis and presents the opportunity to develop an effective tool that balances sensitivity with specificity.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sepsis Screening: Combining Early Warning Scores and SIRS Criteria

2019

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“…In one study, the MEWS had AUROC 0.68 for predicting ICU transfer and AUROC of 0.88 for predicting mortality within 24 h [ 19 ]. The NEWS has a reported AUROC of 0.65 for predicting in-hospital mortality at any time in sepsis while in another study it had an AUROC of 0.79 for in-hospital mortality and 0.83 for 24 h mortality among general medical patients [ 24 , 25 ]. The performance of the MEWS and NEWS in our cohorts falls within the range of these prior reports (AUROC 0.78–0.93 for MEWS and 0.83–0.91 for NEWS for in-hospital mortality in the 2013 and 2017 cohorts (Additional file 1 : Figure S1)).…”

Section: Discussionmentioning

confidence: 99%

Multicenter derivation and validation of an early warning score for acute respiratory failure or death in the hospital

et al. 2018

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BackgroundAcute respiratory failure occurs frequently in hospitalized patients and often starts before ICU admission. A risk stratification tool to predict mortality and risk for mechanical ventilation (MV) may allow for earlier evaluation and intervention. We developed and validated an automated electronic health record (EHR)-based model—Accurate Prediction of Prolonged Ventilation (APPROVE)—to identify patients at risk of death or respiratory failure requiring >= 48 h of MV.MethodsThis was an observational study of adults admitted to four hospitals in 2013 or a fifth hospital in 2017. Clinical data were extracted from the EHRs. The 2013 patients were randomly split 50:50 into a derivation/validation cohort. The qualifying event was death or intubation leading to MV >= 48 h. Random forest method was used in model derivation. APPROVE was calculated retrospectively whenever data were available in 2013, and prospectively every 4 h after hospital admission in 2017. The Modified Early Warning Score (MEWS) and National Early Warning Score (NEWS) were calculated at the same times as APPROVE. Clinicians were not alerted except for APPROVE in 2017cohort.ResultsThere were 68,775 admissions in 2013 and 2258 in 2017. APPROVE had an area under the receiver operator curve of 0.87 (95% CI 0.85–0.88) in 2013 and 0.90 (95% CI 0.84–0.95) in 2017, which is significantly better than the MEWS and NEWS in 2013 but similar to the MEWS and NEWS in 2017. At a threshold of > 0.25, APPROVE had similar sensitivity and positive predictive value (PPV) (sensitivity 63% and PPV 21% in 2013 vs 64% and 16%, respectively, in 2017). Compared to APPROVE in 2013, at a threshold to achieve comparable PPV (19% at MEWS > 4 and 22% at NEWS > 6), the MEWS and NEWS had lower sensitivity (16% for MEWS and NEWS). Similarly in 2017, at a comparable sensitivity threshold (64% for APPROVE > 0.25 and 67% for MEWS and NEWS > 4), more patients who triggered an alert developed the event with APPROVE (PPV 16%) while achieving a lower false positive rate (FPR 5%) compared to the MEWS (PPV 7%, FPR 14%) and NEWS (PPV 4%, FPR 25%).ConclusionsAn automated EHR model to identify patients at high risk of MV or death was validated retrospectively and prospectively, and was determined to be feasible for real-time risk identification.Trial registrationClinicalTrials.gov, NCT02488174. Registered on 18 March 2015.Electronic supplementary materialThe online version of this article (10.1186/s13054-018-2194-7) contains supplementary material, which is available to authorized users.

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