title: A simple prognostic index based on admission vital signs data among patients with sepsis in a resource-limited setting.
abstract: BACKGROUND
  In sub-Saharan Africa, vital signs are a feasible option for monitoring critically ill patients. We assessed how admission vital signs data predict in-hospital mortality among patients with sepsis. In particular, we assessed whether vital signs data can be incorporated into a prognostic index with reduced segmentation in the values of included variables.
  
  
  METHODS
  Subjects were patients with sepsis hospitalized in Uganda, who participated in two cohort studies. Using restricted cubic splines of admission vital signs data, we predicted probability of in-hospital death in the development cohort and used this information to construct a simple prognostic index. We assessed the performance of the index in a validation cohort and compared its performance to that of the Modified Early Warning Score (MEWS).
  
  
  RESULTS
  We included 317 patients (167 in the development cohort and 150 in the validation cohort). Based on how vital signs predicted mortality, we created a prognostic index giving a score of 1 for: respiratory rates ≥30 cycles/minute; pulse rates ≥100 beats/minute; mean arterial pressures ≥110/<70 mmHg; temperatures ≥38.6/<35.6°C; and presence of altered mental state defined as Glasgow coma score ≤14; 0 for all other values. The proposed index (maximum score = 5) predicted mortality comparably to MEWS. Patients scoring ≥3 on the index were 3.4-fold (95% confidence interval (CI) 1.6 to 7.3, P = 0.001) and 2.3-fold (95% CI 1.1 to 4.7, P = 0.031) as likely to die in hospital as those scoring 0 to 2 in the development and validation cohorts respectively; those scoring ≥5 on MEWS were 2.5-fold (95% CI 1.2 to 5.3, P = 0.017) and 1.8-fold (95% CI 0.74 to 4.2, P = 0.204) as likely to die as those scoring 0 to 4 in the development and validation cohorts respectively.
  
  
  CONCLUSIONS
  Among patients with sepsis, a prognostic index incorporating admission vital signs data with reduced segmentation in the values of included variables adequately predicted mortality. Such an index may be more easily implemented when triaging acutely-ill patients. Future studies using a similar approach may develop indexes that can be used to monitor treatment among acutely-ill patients, especially in resource-limited settings.
authors.affiliation: Department of Medicine, Mbarara Regional Referral Hospital, P.O. Box 40, Mbarara, Uganda. asiimwesteve@gmail.com.|Department of Epidemiology and Biostatistics, University of California San Francisco, 550 16th Street, Second Floor, Mission Hall: Global Health and Clinical Sciences Building, San Francisco, CA, 94158-2549, USA. asiimwesteve@gmail.com.
authors.forename: Stephen B
authors.initials: SB
authors.lastname: Asiimwe
authors.affiliation: Department of Medicine, Mbarara Regional Referral Hospital, P.O. Box 40, Mbarara, Uganda. dr.amir83@yahoo.co.uk.|Department of Medicine, Mbarara University of Science and Technology, P.O. Box 1410, Mbarara, Uganda. dr.amir83@yahoo.co.uk.
authors.forename: Amir
authors.initials: A
authors.lastname: Abdallah
authors.affiliation: Department of Medicine, College of Health Sciences, Makerere University, P.O. Box 7072, Kampala, Uganda. sekirchrd@yahoo.com.
authors.forename: Richard
authors.initials: R
authors.lastname: Ssekitoleko
mesh_terms: D000328:Adult; D016638:Critical Illness / Q000401:mortality; D005260:Female; D006295:Health Resources; D017052:Hospital Mortality; D006801:Humans; D008297:Male; D008875:Middle Aged; D008991:Monitoring, Physiologic / Q000379:methods*; D011379:Prognosis; D011446:Prospective Studies; D018805:Sepsis / Q000401:mortality*; D014454:Uganda; D055986:Vital Signs / Q000502:physiology*