Predictors of mortality and cost among surgical patients requiring rapid response team activation

Predictors of mortality and cost among surgical patients requiring rapid response team activation

Can J Surg 2020;63(6):E598-E605 | Full Text | PDF | Appendix

Alexandre Tran, MD, MSc; Shannon M. Fernando, MD, MSc; Daniel I. McIsaac, MD, MPH; Bram Rochwerg, MD, MSc; Garrick Mok, MD; Andrew J.E. Seely, MD, PhD; Dalibor Kubelik, MD; Kenji Inaba, MD; Dennis Y. Kim, MD; Peter M. Reardon, MD; Jennifer Shen; Peter Tanuseputro, MD, MHSc; Kednapa Thavorn, PhD; Kwadwo Kyeremanteng, MD, MHA

Abstract

Background: Prior studies of rapid response team (RRT) implementation for surgical patients have demonstrated mixed results with respect to reductions in poor outcomes. The aim of this study was to identify predictors of in-hospital mortality and hospital costs among surgical inpatients requiring RRT activation.

Methods: We analyzed data prospectively collected from May 2012 to May 2016 at The Ottawa Hospital. We included patients who were at least 18 years of age, who were admitted to hospital, who received either preoperative or postoperative care, and and who required RRT activation. We created a multivariable logistic regression model to describe mortality predictors and a multivariable generalized linear model to describe cost predictors.

Results: We included 1507 patients. The in-hospital mortality rate was 15.9%. The patient-related factors most strongly associated with mortality included an Elixhauser Comorbidity Index score of 20 or higher (odds ratio [OR] 3.60, 95% confidence interval [CI] 1.96–6.60) and care designations excluding admission to the intensive care unit and cardiopulmonary resuscitation (OR 3.52, 95% CI 2.25–5.52). The strongest surgical predictors included neurosurgical admission (OR 2.09, 95% CI 1.17–3.75), emergent surgery (OR 2.04, 95% CI 1.37–3.03) and occurrence of 2 or more operations (OR 1.73, 95% CI 1.21–2.46). Among RRT factors, occurrence of 2 or more RRT assessments (OR 2.01, 95% CI 1.44–2.80) conferred the highest mortality. Increased cost was strongly associated with admitting service, multiple surgeries, multiple RRT assessments and medical comorbidity.

Conclusion: RRT activation among surgical inpatients identifies a population at high risk of death. We identified several predictors of mortality and cost, which represent opportunities for future quality improvement and patient safety initiatives.

Résumé

Contexte : Les études sur la mobilisation d’équipes d’intervention rapide (EIR) auprès de patients en chirurgie ont donné des résultats mitigés quant à la réduction des issues négatives. La présente étude visait à déterminer les facteurs prédictifs de coûts pour les hôpitaux et de mortalité chez les patients en chirurgie nécessitant la mobilisation d’une EIR.

Méthodes : Nous avons analysé des données recueillies de manière prospective de mai 2012 à mai 2016 à l’Hôpital d’Ottawa. Nous avons inclus les patients hospitalisés de 18 ans et plus qui ont reçu des soins préopératoires ou postopératoires et qui ont nécessité l’intervention d’une EIR. Nous avons ensuite créé un modèle de régression logistique multivariée pour décrire les facteurs prédictifs de mortalité et un modèle linéaire généralisé multivarié pour décrire les facteurs prédictifs de coûts.

Résultats : Nous avons retenus 1507 patients. Le taux global de mortalité à l’hôpital était de 15,9 %. Les principaux facteurs de mortalité liés au patient étaient un indice de comorbidité d’Elixhauser supérieur ou égal à 20 (rapport de cotes [RC] 3,60, intervalle de confiance [IC] à 95 % 1,96–6,60) et des objectifs de soins excluant l’admission à l’unité des soins intensifs et la réanimation cardiorespiratoire (RC 3,52, IC à 95 % 2,25–5,52). Les principaux facteurs prédictifs liés aux interventions sont l’admission en neurochirurgie (RC 2,09, IC à 95 % 1,17–3,75), l’intervention chirurgicale d’urgence (RC 2,04, IC à 95 % 1,37–3,03) et le fait d’avoir subi au moins 2 opérations (RC 1,73, IC à 95 % 1,21–2,46). Parmi les facteurs liés aux EIR, la tenue d’au moins 2 évaluations par l’EIR s’accompagnait du mortalité le plus élevé (RC 2,01, IC à 95 % 1,44–2,80). L’augmentation des coûts était étroitement associée au service d’admission, aux interventions chirurgicales multiples, aux évaluations multiples par l’EIR et à la comorbidité médicale.

Conclusion : La mobilisation d’EIR auprès de patients en chirurgie permet de mettre en évidence une population à risque élevé de décès. Nous avons découvert plusieurs facteurs prédictifs de mortalité et de coûts, dont on pourra se servir pour améliorer la qualité des soins et la sécurité des patients.


Accepted Feb. 19, 2020

Affiliations: From the Department of Surgery, University of Ottawa, Ottawa, Ont. (Tran, Seely, Kubelik); the Division of Critical Care Medicine, Department of Medicine, University of Ottawa, Ottawa, Ont. (Fernando, Seely, Kubelik, Reardon, Kyeremanteng); the Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ont. (Tran, McIsaac, Seely, Shen, Tanuseputro, Thavorn, Kyeremanteng); the Department of Emergency Medicine, University of Ottawa, Ottawa, Ont. (Fernando, Mok, Reardon); the Department of Anesthesiology and Pain Medicine, University of Ottawa, Ottawa, Ont. (McIsaac); the School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ont. (McIsaac, Seely, Tanuseputro, Thavorn); the Department of Medicine, Division of Critical Care, McMaster University, Hamilton, Ont. (Rochwerg); the Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ont. (Rochwerg); the Division of Acute Care Surgery, Department of Surgery, University of Southern California, Los Angeles, Calif. (Inaba); the Department of Surgery, University of California, Los Angeles, Calif. (Kim); the Division of Palliative Care, Department of Medicine, University of Ottawa, Ottawa, Ont. (Tanuseputro, Kyeremanteng); and the Institut du Savoir Montfort, Ottawa, Ont. (Kyeremanteng).

Funding: This study was funded by a grant from the Canadian Institutes of Health Research (MOP-142237) and the Bruyère Research Center for Individualized Health, which is supported by the Bruyère Foundation. This study was also supported by ICES, which is funded by an annual grant from the Ontario Ministry of Health and Long-Term Care (MOHLTC). The opinions, results and conclusions reported in this article are those of the authors and are independent from the funding sources. No endorsement by ICES or the Ontario MOHLTC is intentded or should be inferred.

Competing interests: Andrew Seely holds patents related to multiorgan variability analysis and is the founder and CEO of Therapeutic Monitoring Systems Inc. No other competing interests were declared.

Contributors: A. Tran, S. Fernando, D. Kim and D. McIsaac conceived the study. A. Tran, S. Fernando, D. McIsaac, G. Mok, D. Kim and P. Reardon acquired the data, which A. Tran, S. Fernando, D. McIsaac, B. Rochwerg, A. Seely, D. Kubelik, K. Inaba, D. Kim, P. Reardon, J. Shen, P. Tanuseputro, K. Thavorn and K. Kyeremanteng analyzed. A. Tran, S. Fernando, D. McIsaac, A. Seely, D. Kim, P. Reardon, J. Shen, K. Thavorn and K. Kyeremanteng wrote the manuscript, which A. Tran, S. Fernando, D. McIsaac, B. Rochwerg, G. Mok, A. Seely, D. Kubelik, K. Inaba, D. Kim, P. Reardon, P. Tanuseputro and K. Thavorn critically revised. All authors gave final approval of the version to be published.

DOI: 10.1503/cjs.017319

Correspondence to: A. Tran, Department of Surgery, The Ottawa Hospital, Civic Campus, 1053 Carling Ave, Ottawa ON K1Y 4E9, aletran@toh.ca