Early Warning Scores Generated in Developed Healthcare Settings Are Not Sufficient at Predicting Early Mortality in Blantyre, Malawi: A Prospective Cohort Study

Abstract

AimEarly warning scores (EWS) are widely used in well-resourced healthcare settings to identify patients at risk of mortality. The Modified Early Warning Score (MEWS) is a well-known EWS used comprehensively in the United Kingdom. The HOTEL score (Hypotension, Oxygen saturation, Temperature, ECG abnormality, Loss of independence) was developed and tested in a European cohort; however, its validity is unknown in resource limited settings. This study compared the performance of both scores and suggested modifications to enhance accuracy.MethodsA prospective cohort study of adults (≥18 yrs) admitted to medical wards at a Malawian hospital. Primary outcome was mortality within three days. Performance of MEWS and HOTEL were assessed using ROC analysis. Logistic regression analysis identified important predictors of mortality and from this a new score was defined.ResultsThree-hundred-and-two patients were included. Fifty-one (16.9%) died within three days of admission. With a cut-point ≥2, the HOTEL score had sensitivity 70.6% (95% CI: 56.2 to 82.5) and specificity 59.4% (95% CI: 53.0 to 65.5), and was superior to MEWS (cut-point ≥5); sensitivity: 58.8% (95% CI: 44.2 to 72.4), specificity: 56.2% (95% CI: 49.8 to 62.4). The new score, dubbed TOTAL (Tachypnoea, Oxygen saturation, Temperature, Alert, Loss of independence), showed slight improvement with a cut-point ≥2; sensitivity 76.5% (95% CI: 62.5 to 87.2) and specificity 67.3% (95% CI: 61.1 to 73.1).ConclusionUsing an EWS generated in developed healthcare systems in resource limited settings results in loss of sensitivity and specificity. A score based on predictors of mortality specific to the Malawian population showed enhanced accuracy but not enough to warrant clinical use. Despite an assumption of common physiological responses, disease and population differences seem to strongly determine the performance of EWS. Local validation and impact assessment of these scores should precede their adoption in resource limited settings.

Strengths and limitations of early warning scores: A systematic review and narrative synthesis

“…The authors advised local validation and impact assessment before the adoption of early warning scores adoption in resource-limited settings. 62 …”

Global Critical Care: Moving Forward in Resource-Limited Settings

“…Region-specific equations or adaptations for resource-limited settings can improve performance and facilitate implementation [55][56][57][58][59]. Risk assessment tools, such as the Modified Early Warning Score, which might help triage critically patients and allocate resources [54] have shown conflicting results when applied in resource-limited settings and need additional modifications and validation [60,61].…”

“…In one ICU in Tanzania, an observational study showed that detection of vital sign derangements infrequently led to treatment modifications [80] and led the authors to conclude that nursing-based vital signs-directed clinical response protocols may not improve outcomes in resource-limited setting. Early warning systems have been shown to be predictive in some [54] but not all settings [60,61], underscoring the need for further development and validation in resource-limited settings.…”

Derivation and validation of a universal vital assessment (UVA) score: a tool for predicting mortality in adult hospitalised patients in sub-Saharan Africa

“…We converted alert, voice, pain, unresponsive (AVPU) and GCS scores to each other as needed. 15 To avoid negative numbers and outliers in our data that likely represented data entry errors or spurious values, we included clinical data points in the combined data set if they were positive, within the 1st and 99th percentile of a given variable, and within accepted limits for GCS score (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15) and oxygen saturation (≤100%). All patients included in the analysis had an outcome recorded for in-hospital mortality and greater than 50% of the following clinical variables: temperature, heart rate, respiratory rate, SBP, DBP, GCS and oxygen saturation.…”

“…12 There are few EWS derived from patients in SSA and they were performed in small populations at single sites with varying performance. 13 14 Therefore, we pooled data from hospital-based cohort studies conducted throughout SSA to (1) determine predictors of mortality, (2) create an accurate and easy-to-use universal vital assessment (UVA) score and 3) compare the performance of the UVA score in predicting mortality with that of MEWS and qSOFA.…”