Advertisement

Four early warning scores predict mortality in emergency surgical patients at University Teaching Hospital, Lusaka: a prospective observational study

  • Katie Ellen Foy
  • Janaki Pearson
  • Laura Kettley
  • Niharika Lal
  • Holly Blackwood
  • M. Dylan BouldEmail author
Reports of Original Investigations

Abstract

Purpose

The value of early warning scoring systems has been established in high-income countries. There is little evidence for their use in low-resource settings. We aimed to compare existing early warning scores to predict 30-day mortality.

Methods

University Teaching Hospital is a tertiary center in Lusaka, Zambia. Adult surgical patients, excluding obstetrics, admitted for > 24 hr were included in this prospective observational study. On days 1 to 3 of admission, we collected data on patient demographics, heart rate, blood pressure, oxygen saturation, oxygen administration, temperature, consciousness level, and mobility. Two-, three-, and 30-day mortality were recorded with their associated variables analyzed using area under receiver operating curves (AUROC) for the National Early Warning Score (NEWS); the Modified Early Warning Score (MEWS); a modified Hypotension, Oxygen Saturation, Temperature, ECG, Loss of Independence (mHOTEL) score; and the Tachypnea, Oxygen saturation, Temperature, Alertness, Loss of Independence (TOTAL) score.

Results

Data were available for 254 patients from March 2017 to July 2017. Eighteen (7.5%) patients died at 30 days. The four early warning scores were found to be predictive of 30-day mortality: MEWS (AUROC, 0.76; 95% confidence interval [CI], 0.63 to 0.88; P < 0.001), NEWS (AUROC 0.805; 95% CI, 0.688 to 0.92; P < 0.001), mHOTEL (AUROC 0.759; 95% CI, 0.63 to 0.89, P < 0.001), and TOTAL (AUROC 0.782; 95% CI, 0.66 to 0.90; P < 0.001).

Conclusions

We validated four scoring systems in predicting mortality in a Zambian surgical population. Further work is required to assess if implementation of these scoring systems can improve outcomes.

Quatre scores d’évaluation d’alerte précoce pour prédire la mortalité des patients chirurgicaux d’urgence au Centre hospitalier universitaire de Lusaka : une étude observationnelle prospective

Résumé

Objectif

L’utilité des scores d’évaluation d’alerte précoce a été établie dans les pays à revenu élevé. Il n’existe que peu de données probantes concernant leur utilisation dans les contextes de faibles ressources. Nous avons tenté de comparer les scores d’évaluation d’alerte précoce existants en fonction de leur capacité à prédire la mortalité à 30 jours.

Méthode

Le University Teaching Hospital est un centre de soins tertiaires à Lusaka, en Zambie. Les patients chirurgicaux adultes, y compris en obstétrique, admis pour plus de 24 h ont été inclus dans cette étude observationnelle prospective. Au cours des 3 premiers jours suivant l’admission, nous avons récolté des données concernant les patients, soit les données démographiques, leur fréquence cardiaque, leur tension artérielle, leur saturation en oxygène, l’administration d’oxygène, la température, le niveau d’éveil et la mobilité. La mortalité à deux, trois et 30 jours a été enregistrée accompagnée des variables associées, analysées à l’aide des surfaces sous la courbe de fonction d’efficacité de l’observateur (SSC-ROC) pour les scores d’évaluation suivants : NEWS (National Early Warning Score, soit Score national d’alerte précoce), MEWS (Modified Early Warning Score, soit Score modifié d’alerte précoce), mHOTEL (un score modifié évaluant l’hypotension, la saturation en oxygène, la température, l’ECG et la perte d’indépendance), et le score TOTAL (tachypnée, saturation en oxygène, température, vigilance et perte d’indépendance).

Résultats

Les données étaient disponibles pour 254 patients hospitalisés entre mars 2017 et juillet 2017. Dix-huit (7,5 %) patients sont décédés à 30 jours. Nous avons observé que les quatre scores d’évaluation d’alerte précoce permettaient de prédire la mortalité à 30 jours : MEWS (SSC-ROC, 0,76; intervalle de confiance [IC] 95 %, 0,63 à 0,88; P < 0,001), NEWS (SSC-ROC 0,805; IC 95 %, 0,688 à 0,92; P < 0,001), mHOTEL (SSC-ROC 0,759; IC 95 %, 0,63 à 0,89, P < 0,001), et TOTAL (SSC-ROC 0,782; IC 95 %, 0,66 à 0,90; P < 0,001).

Conclusion

Nous avons validé quatre scores d’évaluation pour prédire la mortalité dans une population chirurgicale zambienne. Des travaux supplémentaires sont nécessaires afin d’évaluer si la mise en œuvre de ces scores d’évaluation peut améliorer les devenirs.

Notes

Acknowledgements

We thank Dr. Hazel Mumphansha, Dr. Naomi Shamambo, and Dr. Zubair Rakhda for their input into the development of this study. Thank you to the administration of University Teaching Hospital, Zambia for supporting the work. Thank you to all research assistants for their contribution to data collection.

Conflicts of interest

None declared.

Editorial responsibility

This submission was handled by Dr. Hilary P. Grocott, Editor-in-Chief, Canadian Journal of Anesthesia.

Author contributions

Katie Ellen Foy contributed to study design, data acquisition, data analysis and wrote the manuscript. Janaki Pearson and Laura Kettley contributed to study conception and design, acquisition of ethics approval, data analysis and editing of the manuscript. Niharika Lal contributed to study design, data acquisition, data analysis and interpretation and editing of the manuscript. Holly Blackwood contributed to study conception and design. M. Dylan Bould contributed to study conception and design, data analysis, drafting and editing of the manuscript and was the supervising clinician. All authors gave final approval of the version to be published.

Funding

This work was funded by the Zambia Anesthesia Development Program who in turn have received funding from the Tropical Health and Education Trust and the UK Department for International Development.

References

  1. 1.
    Meara JG, Leather AJ, Hagander L, et al. Global Surgery 2030: evidence and solutions for achieving health, welfare, and economic development. Lancet 2015; 386: 569-624.CrossRefGoogle Scholar
  2. 2.
    United Nations. Sustainable Development Goals, Goal 3 Good Health and Wellbeing. Available from URL: http://www.un.org/sustainabledevelopment/health/ (accessed August 2019).
  3. 3.
    Biccard BM, Madiba TE, Kluyts HL, et al. Perioperative Patient outcomes in African Surgical Outcomes Study: a 7-day prospective observational cohort study. Lancet 2018; 391: 1589-98.CrossRefGoogle Scholar
  4. 4.
    Bainbridge D, Martin J, Arango M, Cheng D; Evidence-based Peri-operative Clinical Outcomes Research (EPICOR) Group. Perioperative and anaesthetic-related mortality in developed and developing countries: a systematic review and meta-analysis. Lancet 2012; 380: 1075-81.CrossRefGoogle Scholar
  5. 5.
    Lillie EM, Holmes CJ, O’Donohoe EA, et al. Avoidable perioperative mortality at the University Teaching Hospital, Lusaka, Zambia: a retrospective cohort study. Can J Anesth 2015; 62: 1259-67.CrossRefGoogle Scholar
  6. 6.
    Alarn N, Hobbelink EL, Van Tienhoven AJ, van de Ven PM, Jansma EP, Nanayakkara PW. The impact of the use of the Early Warning Score (EWS) on patient outcomes: a systemic review. Resuscitation 2014; 85: 587-94.CrossRefGoogle Scholar
  7. 7.
    Kruisselbrink R, Kwizera A, Crowther M, et al. Modified Early Warning Score (MEWS) identifies critical illness among ward patients in a resource restricted setting in Kampala, Uganda: a prospective observational study. Plos One 2016; DOI:  https://doi.org/10.1371/journal.pone.0151408.CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Bickler SN, Weiser TG, Kassebaum N, et al. Global burden of surgical conditions. In: Debas H, Donkor P, Gawande A, Jamison DT, Kruk M, Mock CN, editors. Essential Surgery - Disease Control Priorities (Third Edition), vol. 1. Washington, DC: World Bank; 2015. p. 2015.Google Scholar
  9. 9.
    Esquivel MM, Uribe-Leitz T, Makasa E, et al. Mapping disparities in access to safe, timely, and essential surgical care in Zambia. JAMA Surg 2016; 151: 1064-9.CrossRefGoogle Scholar
  10. 10.
    Jochberger S, Ismailova F, Lederer W, et al. Anesthesia and its allied disciplines in the developing world: a nationwide survey of the Republic of Zambia. Anesth Analg 2008; 106: 942-8.CrossRefGoogle Scholar
  11. 11.
    Bowman KG, Jovic G, Rangel S, Berry WR, Gawande AA. Pediatric emergency and essential surgical care in Zambian hospitals: a nationwide study. J Pediatr Surg 2013; 48: 1363-70.CrossRefGoogle Scholar
  12. 12.
    Dart PJ, Kinnear J, Bould MD, Mwansa SL, Rakhda Z, Snell D. An evaluation of inpatient morbidity and critical care provision in Zambia. Anaesthesia 2017; 72: 172-80.CrossRefGoogle Scholar
  13. 13.
    Bock P, Cox H. Acute care – an important component of the continuum of care for HIV and tuberculosis in developing countries. Anaesthesia 2017; 72: 147-50.CrossRefGoogle Scholar
  14. 14.
    Wheeler I, Price C, Stitch A, et al. Early warning scores generated in developed healthcare settings are not sufficient at predicting early mortality in Blantyre, Malawi: a prospective cohort study. PloS One 2013; DOI:  https://doi.org/10.1371/journal.pone.0059830.CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD Statement. Ann Intern Med 2015; 162: 55-63.CrossRefGoogle Scholar
  16. 16.
    Friedman AM, Campbell ML, Kline CR, Wiesner S, D’Alton ME, Shields LE. Implementing obstetric early warning systems. AJP Rep 2018; 8: e79-84.CrossRefGoogle Scholar
  17. 17.
    McNarry AF, Goldhill DR. Simple bedside assessment of level of consciousness: comparison of two simple assessment scales with the Glasgow Coma Scale. Anaesthesia 2004; 59: 34-7.CrossRefGoogle Scholar
  18. 18.
    Rylance J, Baker T, Mushi E, Mashaga D. Use of an early warning score and ability to walk predicts mortality in medical patients admitted to hospital in Tanzania. Trans R Soc Top Med Hyg 2009; 103: 790-4.CrossRefGoogle Scholar
  19. 19.
    Baker T, Blixt J, Lugazia E, et al. Single deranged physiological parameters are associated with mortality in a low-income country. Crit Care Med 2016; 43: 2171-9.CrossRefGoogle Scholar

Copyright information

© Canadian Anesthesiologists' Society 2019

Authors and Affiliations

  1. 1.Department of AnaesthesiaBristol Royal InfirmaryBristolUK
  2. 2.Department of AnaesthesiaSunderland Royal HospitalSunderlandUK
  3. 3.Department of AnaesthesiaRoyal Alexandra HospitalPaisleyUK
  4. 4.Department of PediatricsPinderfields HospitalWakefieldUK
  5. 5.Department of Anesthesiology and Pain MedicineChildren’s Hospital of Eastern OntarioOttawaCanada

Personalised recommendations