Simple jQuery Dropdowns
Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorAbi Beaneen_US
dc.contributor.authorAmbepitiyawaduge Pubudu De Silvaen_US
dc.contributor.authorNirodha De Silvaen_US
dc.contributor.authorJayasingha A. Sujeewaen_US
dc.contributor.authorR. M.Dhanapala Rathnayakeen_US
dc.contributor.authorP. Chathurani Sigeraen_US
dc.contributor.authorPriyantha Lakmini Athapattuen_US
dc.contributor.authorPalitha G. Mahipalaen_US
dc.contributor.authorAasiyah Rashanen_US
dc.contributor.authorSithum Bandara Munasingheen_US
dc.contributor.authorKosala Saroj Amarasiri Jayasingheen_US
dc.contributor.authorArjen M. Dondorpen_US
dc.contributor.authorRashan Haniffaen_US
dc.contributor.otherMinistry of Health Colomboen_US
dc.contributor.otherUniversity of Colombo Faculty of Medicineen_US
dc.contributor.otherMahidol Universityen_US
dc.contributor.otherAmsterdam UMC - University of Amsterdamen_US
dc.contributor.otherIntensive Care National Audit and Research Centreen_US
dc.contributor.otherNetwork for Improving Critical Care Systems and Trainingen_US
dc.contributor.otherMonaragala District General Hospitalen_US
dc.contributor.otherNational Intensive Care Surveillanceen_US
dc.identifier.citationBMJ Open. Vol.8, No.4 (2018)en_US
dc.description.abstract© 2018 Article author(s). Objective: This study describes the availability of core parameters for Early Warning Scores (EWS), evaluates the ability of selected EWS to identify patients at risk of death or other adverse outcome and describes the burden of triggering that front-line staff would experience if implemented. Design: Longitudinal observational cohort study. Setting: District General Hospital Monaragala. Participants: All adult (age >17 years) admitted patients. Main outcome measures: Existing physiological parameters, adverse outcomes and survival status at hospital discharge were extracted daily from existing paper records for all patients over an 8-month period. Statistical analysis Discrimination for selected aggregate weighted track and trigger systems (AWTTS) was assessed by the area under the receiver operating characteristic (AUROC) curve. Performance of EWS are further evaluated at time points during admission and across diagnostic groups. The burden of trigger to correctly identify patients who died was evaluated using positive predictive value (PPV). Results: Of the 16 386 patients included, 502 (3.06%) had one or more adverse outcomes (cardiac arrests, unplanned intensive care unit admissions and transfers). Availability of physiological parameters on admission ranged from 90.97% (95% CI 90.52% to 91.40%) for heart rate to 23.94% (95% CI 23.29% to 24.60%) for oxygen saturation. Ability to discriminate death on admission was less than 0.81 (AUROC) for all selected EWS. Performance of the best performing of the EWS varied depending on admission diagnosis, and was diminished at 24 hours prior to event. PPV was low (10.44%). Conclusion: There is limited observation reporting in this setting. Indiscriminate application of EWS to all patients admitted to wards in this setting may result in an unnecessary burden of monitoring and may detract from clinician care of sicker patients. Physiological parameters in combination with diagnosis may have a place when applied on admission to help identify patients for whom increased vital sign monitoring may not be beneficial. Further research is required to understand the priorities and cues that influence monitoring of ward patients.en_US
dc.rightsMahidol Universityen_US
dc.titleEvaluation of the feasibility and performance of early warning scores to identify patients at risk of adverse outcomes in a low-middle income country settingen_US
Appears in Collections:Scopus 2018

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.