Validation of Early Warning Scores at Two Long-Term Acute Care Hospitals

Churpek, Matthew M.; Carey, Kyle; Merced, Nino Dela; Prister, James; Brofman, John; Edelson, Dana P.

doi:10.1097/ccm.0000000000004026

Cited by 13 publications

(10 citation statements)

References 15 publications

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“…This finding was consistent with those of previous studies (Churpek et al, 2019;Redfern et al, 2018) but different from those of other studies (Pimentel et al, 2019). In this study, the AUC of NEWS2 was almost same as that of NEWS.…”

Section: Discussionsupporting

confidence: 90%

“…NEWS2 showed a good ability to predict the events of unanticipated in-hospital mortality, unplanned admission to ICUs and HDBs, and cardiac arrest within approximately 24 hr after assessment. The AUC in this study was higher than that in previous studies (Churpek et al, 2019;Pimentel et al, 2019). NEWS2 had the best performance, and NEWS, MEWS and qSOFA performed well in that order.…”

Section: Discussioncontrasting

confidence: 76%

“…However, there were arguments on the performance of NEWS2 regarding its predictive ability, including in-hospital mortality (Churpek et al, 2019;Pimentel et al, 2019). As such, a recent study reported that NEWS2 had an area under the receiver operating characteristic curve (AUC) of less than 0.7 in predicting acute hospital transfer and mortality in long-term care hospitals (Churpek et al, 2019). Another study found that NEWS2 had a slightly lower discriminating ability than NEWS in predicting in-hospital mortality (Pimentel et al, 2019).…”

Section: What Does This Paper Contribute To the Wider Global Clinicalmentioning

confidence: 99%

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Relationships between the National Early Warning Score 2, clinical worry and patient outcome at discharge: Retrospective observational study

Hwang

Chin

2020

Journal of Clinical Nursing

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Aims and objectives To examine the performance of the National Early Warning Score 2 and composite score for clinical worry in identifying patients at risk of clinical deterioration, and to determine relationships between National Early Warning Score 2, clinical worry score and patient outcome at discharge. Background The efficacy of early warning systems depends on patient population and care settings. Based on a theoretical framework on factors affecting clinical deterioration and patient outcomes, studies exploring the relationship between early warning systems and patient outcomes at discharge are sparse. Design Retrospective observational study. Methods A random sample of 732 medical records were reviewed. The area under the receiver operating characteristic curve was calculated to evaluate predictive abilities regarding the events of unanticipated in‐hospital mortality, unplanned intensive care unit/ higher dependency bed admission and cardiac arrest. Multiple logistic regression analyses were performed to determine relationships between National Early Warning Score 2, clinical worry score and patient outcome. Reporting followed the STROBE checklist. Results National Early Warning Score 2 and clinical worry score significantly predicted the events within 24 hr of the assessment. After controlling for other patient, treatment and organisational characteristics, National Early Warning Score 2 was a significant factor associated with patient outcome, but clinical worry score was not. Specifically, patients at high risk based on National Early Warning Score 2 were less likely to have improved outcome. Conclusions National Early Warning Score 2 and clinical worry score performed well for predicting deteriorating condition of patients. National Early Warning Score 2 was significantly associated with patient outcome. It can be used for efficient patient management for safe, quality care. Relevance to clinical practice National Early Warning Score 2 can be used for early assessment of not only clinical deterioration but also patient outcome and provide timely intervention, when coupled with clinical worry score.

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Section: Discussionsupporting

confidence: 90%

Section: Discussioncontrasting

confidence: 76%

Section: What Does This Paper Contribute To the Wider Global Clinicalmentioning

confidence: 99%

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Relationships between the National Early Warning Score 2, clinical worry and patient outcome at discharge: Retrospective observational study

Hwang

Chin

2020

Journal of Clinical Nursing

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“…Methods of identifying patients at high risk for or in the early stages of clinical deterioration have been actively researched for decades. The field has generated many severity-of-illness calculators, early warning scores, and, more recently, predictive analytic tools that use advanced machine learning and artificial intelligence [ 15 - 23 ]. Our goal was to derive a prediction model that estimates the risk of short-term (<48 hours) respiratory failure for patients with COVID-19 who were not initially placed on IMV.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Prediction Model of Respiratory Failure Within 48 Hours of Patient Admission for COVID-19: Model Development and Validation

Bolourani¹,

Brenner²,

Wang³

et al. 2021

J Med Internet Res

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Background Predicting early respiratory failure due to COVID-19 can help triage patients to higher levels of care, allocate scarce resources, and reduce morbidity and mortality by appropriately monitoring and treating the patients at greatest risk for deterioration. Given the complexity of COVID-19, machine learning approaches may support clinical decision making for patients with this disease. Objective Our objective is to derive a machine learning model that predicts respiratory failure within 48 hours of admission based on data from the emergency department. Methods Data were collected from patients with COVID-19 who were admitted to Northwell Health acute care hospitals and were discharged, died, or spent a minimum of 48 hours in the hospital between March 1 and May 11, 2020. Of 11,525 patients, 933 (8.1%) were placed on invasive mechanical ventilation within 48 hours of admission. Variables used by the models included clinical and laboratory data commonly collected in the emergency department. We trained and validated three predictive models (two based on XGBoost and one that used logistic regression) using cross-hospital validation. We compared model performance among all three models as well as an established early warning score (Modified Early Warning Score) using receiver operating characteristic curves, precision-recall curves, and other metrics. Results The XGBoost model had the highest mean accuracy (0.919; area under the curve=0.77), outperforming the other two models as well as the Modified Early Warning Score. Important predictor variables included the type of oxygen delivery used in the emergency department, patient age, Emergency Severity Index level, respiratory rate, serum lactate, and demographic characteristics. Conclusions The XGBoost model had high predictive accuracy, outperforming other early warning scores. The clinical plausibility and predictive ability of XGBoost suggest that the model could be used to predict 48-hour respiratory failure in admitted patients with COVID-19.

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“…Among these scoring systems, MEWS can generally be obtained within minutes after the patient is admitted, providing a rapid assessment result for clinicians and enabling timely treatment to high risk patients (Churpek et al, 2019;Salottolo et al, 2017;Yu et al, 2019). Although MEWS has been found to be useful for assessment of pneumonia deterioration, (Jo et al, 2016), its utility has not been examined in predicting outcome of the older adults with COVID-19.…”

Section: Introductionmentioning

confidence: 99%

The utility of MEWS for predicting the mortality in the elderly adults with COVID-19: a retrospective cohort study with comparison to other predictive clinical scores

Wang

Zhang

et al. 2020

PeerJ

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Background Older adults have been reported to be a population with high-risk of death in the COVID-19 outbreak. Rapid detection of high-risk patients is crucial to reduce mortality in this population. The aim of this study was to evaluate the prognositc accuracy of the Modified Early Warning Score (MEWS) for in-hospital mortality in older adults with COVID-19. Methods A retrospective cohort study was conducted in Wuhan Hankou Hospital in China from 1 January 2020 to 29 February 2020. Receiver operating characteristic (ROC) analysis was used to evaluate the predictive value of MEWS, Acute Physiology and Chronic Health Evaluation II (APACHE II), Sequential Organ Function Assessment (SOFA), quick Sequential Organ Function Assessment (qSOFA), Pneumonia Severity Index (PSI), Combination of Confusion, Urea, Respiratory Rate, Blood Pressure, and Age ≥65 (CURB-65), and the Systemic Inflammatory Response Syndrome Criteria (SIRS) for in-hospital mortality. Logistic regression models were performed to detect the high-risk older adults with COVID-19. Results Among the 235 patients included in this study, 37 (15.74%) died and 131 (55.74%) were male, with an average age of 70.61 years (SD 8.02). ROC analysis suggested that the capacity of MEWS in predicting in-hospital mortality was as good as the APACHE II, SOFA, PSI and qSOFA (Difference in AUROC: MEWS vs. APACHE II, −0.025 (95% CI [−0.075 to 0.026]); MEWS vs. SOFA, −0.013 (95% CI [−0.049 to 0.024]); MEWS vs. PSI, −0.015 (95% CI [−0.065 to 0.035]); MEWS vs. qSOFA, 0.024 (95% CI [−0.029 to 0.076]), all P > 0.05), but was significantly higher than SIRS and CURB-65 (Difference in AUROC: MEWS vs. SIRS, 0.218 (95% CI [0.156–0.279]); MEWS vs. CURB-65, 0.064 (95% CI [0.002–0.125]), all P < 0.05). Logistic regression models implied that the male patients (≥75 years) had higher risk of death than the other older adults (estimated coefficients: 1.16, P = 0.044). Our analysis further suggests that the cut-off points of the MEWS score for the male patients (≥75 years) subpopulation and the other elderly patients should be 2.5 and 3.5, respectively. Conclusions MEWS is an efficient tool for rapid assessment of elderly COVID-19 patients. MEWS has promising performance in predicting in-hospital mortality and identifying the high-risk group in elderly patients with COVID-19.

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Validation of Early Warning Scores at Two Long-Term Acute Care Hospitals

Cited by 13 publications

References 15 publications

Relationships between the National Early Warning Score 2, clinical worry and patient outcome at discharge: Retrospective observational study

Relationships between the National Early Warning Score 2, clinical worry and patient outcome at discharge: Retrospective observational study

A Machine Learning Prediction Model of Respiratory Failure Within 48 Hours of Patient Admission for COVID-19: Model Development and Validation

The utility of MEWS for predicting the mortality in the elderly adults with COVID-19: a retrospective cohort study with comparison to other predictive clinical scores

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