In-hospital Mortality and the Predictive Ability of the Modified Early Warning Score in Ghana: Single-Center, Retrospective Study

Background The modified early warning score (MEWS) is an objective measure of illness severity that promotes early recognition of clinical deterioration in critically ill patients. Its primary use is to facilitate faster intervention or increase the level of care. Despite its adoption in some African countries, MEWS is not standard of care in Ghana. In order to facilitate the use of such a tool, we assessed whether MEWS, or a combination of the more limited data that are routinely collected in current clinical practice, can be used predict to mortality among critically ill inpatients at the Korle-Bu Teaching Hospital in Accra, Ghana. Objective The aim of this study was to identify the predictive ability of MEWS for medical inpatients at risk of mortality and its comparability to a measure combining routinely measured physiologic parameters (limited MEWS [LMEWS]). Methods We conducted a retrospective study of medical inpatients, aged ≥13 years and admitted to the Korle-Bu Teaching Hospital from January 2017 to March 2019. Routine vital signs at 48 hours post admission were coded to obtain LMEWS values. The level of consciousness was imputed from medical records and combined with LMEWS to obtain the full MEWS value. A predictive model comparing mortality among patients with a significant MEWS value or LMEWS ≥4 versus a nonsignificant MEWS value or LMEWS <4 was designed using multiple logistic regression and internally validated for predictive accuracy, using the receiver operating characteristic (ROC) curve. Results A total of 112 patients were included in the study. The adjusted odds of death comparing patients with a significant MEWS to patients with a nonsignificant MEWS was 6.33 (95% CI 1.96-20.48). Similarly, the adjusted odds of death comparing patients with a significant versus nonsignificant LMEWS value was 8.22 (95% CI 2.45-27.56). The ROC curve for each analysis had a C-statistic of 0.83 and 0.84, respectively. Conclusions LMEWS is a good predictor of mortality and comparable to MEWS. Adoption of LMEWS can be implemented now using currently available data to identify medical inpatients at risk of death in order to improve care.

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In-hospital Mortality and the Predictive Ability of the Modified Early Warning Score in Ghana: Single-Center, Retrospective Study (Preprint)

10.2196/preprints.24645 ◽

2020 ◽

Author(s):

Enoch Joseph Abbey ◽

Jennifer S R Mammen ◽

Samara E Soghoian ◽

Maureen A F Cadorette ◽

Promise Ariyo

Keyword(s):

At Risk ◽

Retrospective Study ◽

Critically Ill ◽

Teaching Hospital ◽

Early Warning ◽

Roc Curve ◽

Predictive Ability ◽

Early Warning Score ◽

Risk Of Death ◽

Medical Inpatients

BACKGROUND The modified early warning score (MEWS) is an objective measure of illness severity that promotes early recognition of clinical deterioration in critically ill patients. Its primary use is to facilitate faster intervention or increase the level of care. Despite its adoption in some African countries, MEWS is not standard of care in Ghana. In order to facilitate the use of such a tool, we assessed whether MEWS, or a combination of the more limited data that are routinely collected in current clinical practice, can be used predict to mortality among critically ill inpatients at the Korle-Bu Teaching Hospital in Accra, Ghana. OBJECTIVE The aim of this study was to identify the predictive ability of MEWS for medical inpatients at risk of mortality and its comparability to a measure combining routinely measured physiologic parameters (limited MEWS [LMEWS]). METHODS We conducted a retrospective study of medical inpatients, aged ≥13 years and admitted to the Korle-Bu Teaching Hospital from January 2017 to March 2019. Routine vital signs at 48 hours post admission were coded to obtain LMEWS values. The level of consciousness was imputed from medical records and combined with LMEWS to obtain the full MEWS value. A predictive model comparing mortality among patients with a significant MEWS value or LMEWS ≥4 versus a nonsignificant MEWS value or LMEWS <4 was designed using multiple logistic regression and internally validated for predictive accuracy, using the receiver operating characteristic (ROC) curve. RESULTS A total of 112 patients were included in the study. The adjusted odds of death comparing patients with a significant MEWS to patients with a nonsignificant MEWS was 6.33 (95% CI 1.96-20.48). Similarly, the adjusted odds of death comparing patients with a significant versus nonsignificant LMEWS value was 8.22 (95% CI 2.45-27.56). The ROC curve for each analysis had a C-statistic of 0.83 and 0.84, respectively. CONCLUSIONS LMEWS is a good predictor of mortality and comparable to MEWS. Adoption of LMEWS can be implemented now using currently available data to identify medical inpatients at risk of death in order to improve care.

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Glucose as an additional parameter to National Early Warning Score (NEWS) in prehospital setting enhances identification of patients at risk of death: an observational cohort study

Emergency Medicine Journal ◽

10.1136/emermed-2018-208309 ◽

2020 ◽

pp. emermed-2018-208309

Author(s):

Hanna Vihonen ◽

Mitja Lääperi ◽

Markku Kuisma ◽

Jussi Pirneskoski ◽

Jouni Nurmi

Keyword(s):

At Risk ◽

Blood Glucose ◽

Regression Model ◽

Early Warning ◽

Prehospital Setting ◽

Early Warning Score ◽

Calibration Model ◽

Risk Of Death ◽

Patients At Risk ◽

National Early Warning Score

BackgroundTo determine if prehospital blood glucose could be added to National Early Warning Score (NEWS) for improved identification of risk of short-term mortality.MethodsRetrospective observational study (2008–2015) of adult patients seen by emergency medical services in Helsinki metropolitan area for whom all variables for calculation of NEWS and a blood glucose value were available. Survival of 24 hours and 30 days were determined. The NEWS parameters and glucose were tested by multivariate logistic regression model. Based on ORs we formed NEWSgluc model with hypoglycaemia (≤3.0 mmol/L) 3, normoglycaemia 0 and hyperglycaemia (≥11.1 mmol/L) 1 points. The scores from NEWS and NEWSgluc were compared using discrimination (area under the curve), calibration (Hosmer-Lemeshow test), likelihood ratio tests and reclassification (continuous net reclassification index (cNRI)).ResultsData of 27 141 patients were included in the study. Multivariable regression model for NEWSgluc parameters revealed a strong association with glucose disturbances and 24-hour and 30-day mortality. Likelihood ratios (LRs) for mortality at 24 hours using a cut-off point of 15 were for NEWSgluc: LR+ 17.78 and LR− 0.96 and for NEWS: LR+ 13.50 and LR− 0.92. Results were similar at 30 days. Risks per score point estimation and calibration model showed glucose added benefit to NEWS at 24 hours and at 30 days. Although areas under the curve were similar, reclassification test (cNRI) showed overall improvement of classification of survivors and non-survivors at 24 days and 30 days with NEWSgluc.ConclusionsIncluding glucose in NEWS in the prehospital setting seems to improve identification of patients at risk of death.

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Peer Review of “In-hospital Mortality and the Predictive Ability of the Modified Early Warning Score in Ghana: Single-Center, Retrospective Study” (Preprint)

10.2196/preprints.30785 ◽

2021 ◽

Author(s):

John Mogaka

Keyword(s):

Retrospective Study ◽

Hospital Mortality ◽

Peer Review ◽

Early Warning ◽

Predictive Ability ◽

Early Warning Score ◽

Single Center

UNSTRUCTURED This is a peer review report.

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Authors’ Responses to Peer Reviews of “In-hospital Mortality and the Predictive Ability of the Modified Early Warning Score in Ghana: Single-Center, Retrospective Study” (Preprint)

10.2196/preprints.30790 ◽

2021 ◽

Author(s):

Enoch Joseph Abbey ◽

Jennifer S R Mammen ◽

Samara E Soghoian ◽

Maureen A F Cadorette ◽

Promise Ariyo

Keyword(s):

Retrospective Study ◽

Hospital Mortality ◽

Peer Review ◽

Early Warning ◽

Predictive Ability ◽

Early Warning Score ◽

Single Center ◽

Peer Reviews

UNSTRUCTURED These are authors responses to peer review.

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IN-HOSPITAL MORTALITY AND THE PREDICTIVE ABILITY OF THE MODIFIED EARLY WARNING SCORE IN GHANA

10.1101/2020.08.05.20169219 ◽

2020 ◽

Author(s):

Enoch J Abbey ◽

Jennifer S. Mammen ◽

Samara E. Soghoian ◽

Maureen Cadorette ◽

Promise Ariyo

Keyword(s):

Early Warning ◽

Roc Curve ◽

Predictive Accuracy ◽

Early Recognition ◽

Vital Signs ◽

Objective Measure ◽

Predictive Ability ◽

Clinical Deterioration ◽

Early Warning Score ◽

African Countries

ABSTRACT BACKGROUND The modified early warning score (MEWS) is an objective measure of illness severity that promotes early recognition of clinical deterioration in critically ill patients. Its primary use is to; facilitate faster intervention or increase the level of care. Despite, its adoption in some African countries, MEWS is not standard of care in Ghana. We assessed the validity of MEWS as a predictor of mortality, among medically ill inpatients at the Korle Bu Teaching Hospital (KBTH), Accra, Ghana. We sought to identify the predictive ability of MEWS in detecting clinical deterioration among medical in-patients and its comparability to the routinely measured vital signs. METHOD This was a retrospective study of medical inpatients, aged >=13 years and admitted at KBTH from January 2017 to March 2019. Vital signs at 48 hours after admission were coded using MEWS criteria, to obtain a limited MEWS score (LMEWS) and the level of consciousness imputed to obtain a full MEWS score (MEWS). A predictive model comparing mortality among patients with significant MEWS (L/MEWS >=4) and non-significant MEWS (L/MEWS <4) scores was designed using multiple logistic regression. Internally validated for predictive accuracy, using the Receiver Operating Characteristic (ROC) curve. RESULTS 112 patients were included in the study. The adjusted odds of death comparing patients with a significant MEWS to patients with non-significant MEWS was 6.33(95% CI 1.96 to 20.48). Similarly, the adjusted odds of death comparing patients with significant versus non-significant LMEWS was 8.22(95% CI 2.45 to 27.56). The ROC curve for each analysis had a C static of 0.83 and 0.84 respectively. CONCLUSION LMEWS is a good predictor of mortality and comparable to MEWS. Adoption of LMEWS can identify medical in-patients at risk of deterioration and death.

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