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

JMIRx Med ◽  
10.2196/30785 ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. e30785
Author(s):  
John Mogaka
Keyword(s):  
Retrospective Study ◽  
Hospital Mortality ◽  
Peer Review ◽  
Early Warning ◽  
Predictive Ability ◽  
Early Warning Score ◽  
Single Center

scholarly journals 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)

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.

scholarly journals 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”

JMIRx Med ◽  
10.2196/30790 ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. e30790
Author(s):  
Enoch Joseph Abbey ◽  
Jennifer S R Mammen ◽  
Samara E Soghoian ◽  
Maureen A F Cadorette ◽  
Promise Ariyo
Keyword(s):  
Retrospective Study ◽  
Hospital Mortality ◽  
Early Warning ◽  
Predictive Ability ◽  
Early Warning Score ◽  
Single Center ◽  
Peer Reviews

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

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.

Sign in / Sign up

Export Citation Format

Share Document