title: Early warning scores generated in developed healthcare settings are not sufficient at predicting early mortality in Blantyre, Malawi: a prospective cohort study.
abstract: OBJECTIVE
  Early 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.
  
  
  METHODS
  A 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.
  
  
  RESULTS
  Three-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).
  
  
  CONCLUSIONS
  Using 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.
authors.affiliation: College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom. indi_wheeler@hotmail.com
authors.forename: India
authors.initials: I
authors.lastname: Wheeler
authors.forename: Charlotte
authors.initials: C
authors.lastname: Price
authors.forename: Alice
authors.initials: A
authors.lastname: Sitch
authors.forename: Peter
authors.initials: P
authors.lastname: Banda
authors.forename: John
authors.initials: J
authors.lastname: Kellett
authors.forename: Mulinda
authors.initials: M
authors.lastname: Nyirenda
authors.forename: Jamie
authors.initials: J
authors.lastname: Rylance
mesh_terms: D000328:Adult; D000465:Algorithms; D001831:Body Temperature; D003661:Decision Support Techniques; D005260:Female; D006305:Health Status Indicators; D017052:Hospital Mortality; D006760:Hospitalization; D006801:Humans; D008295:Malawi; D008297:Male; D008875:Middle Aged; D016017:Odds Ratio; D010100:Oxygen / Q000378:metabolism; D011446:Prospective Studies; D012372:ROC Curve; D012044:Regression Analysis; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods*; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D059246:Tachypnea / Q000175:diagnosis; D016896:Treatment Outcome; D014218:Triage / Q000379:methods*; D014851:Wakefulness
chemical_list: D010100:Oxygen
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