Evaluating the impact of a computerized surveillance algorithm and decision support system on sepsis mortality

Objective: We created a system using a triad of change management, electronic surveillance, and algorithms to detect sepsis and deliver highly sensitive and specific decision support to the point of care using a mobile application. The investigators hypothesized that this system would result in a reduction in sepsis mortality. Methods: A before-and-after model was used to study the impact of the interventions on sepsis-related mortality. All patients admitted to the study units were screened per the Institute for Healthcare Improvement Surviving Sepsis Guidelines using real-time electronic surveillance. Sepsis surveillance algorithms that adjusted clinical parameters based on comorbid medical conditions were deployed for improved sensitivity and specificity. Nurses received mobile alerts for all positive sepsis screenings as well as severe sepsis and shock alerts over a period of 10 months. Advice was given for early goal-directed therapy. Sepsis mortality during a control period from January 1, 2011 to September 30, 2013 was used as baseline for comparison. Results: The primary outcome, sepsis mortality, decreased by 53% (P = 0.03; 95% CI, 1.06-5.25). The 30-day readmission rate reduced from 19.08% during the control period to 13.21% during the study period (P = 0.05; 95% CI, 0.97-2.52). No significant change in length of hospital stay was noted. The system had observed sensitivity of 95% and specificity of 82% for detecting sepsis compared to gold-standard physician chart review. Conclusion: A program consisting of change management and electronic surveillance with highly sensitive and specific decision support delivered to the point of care resulted in significant reduction in deaths from sepsis.

[1]  Dylan S. Small,et al.  Post-Acute Care Use and Hospital Readmission after Sepsis. , 2015, Annals of the American Thoracic Society.

[2]  E. Ivers,et al.  Early Goal-Directed Therapy in the Treatment of Severe Sepsis and Septic Shock , 2001 .

[3]  V. Herasevich,et al.  Automated Sepsis Detection, Alert, and Clinical Decision Support: Act on It or Silence the Alarm? , 2015, Critical care medicine.

[4]  C. Steiner,et al.  Comorbidity measures for use with administrative data. , 1998, Medical care.

[5]  P. Lamont,et al.  Sepsis and the systemic inflammatory response syndrome. , 2001, Journal of the Royal College of Surgeons of Edinburgh.

[6]  Michael Bailey,et al.  Bedside electronic capture of clinical observations and automated clinical alerts to improve compliance with an Early Warning Score protocol. , 2011, Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine.

[7]  A. Viera,et al.  Understanding interobserver agreement: the kappa statistic. , 2005, Family medicine.

[8]  Joshua A. Doherty,et al.  Hospital-wide impact of a standardized order set for the management of bacteremic severe sepsis* , 2009, Critical care medicine.

[9]  MS Dr. Michael P. Young MD,et al.  Inpatient transfers to the intensive care unit , 2007, Journal of General Internal Medicine.

[10]  Adil Rafiq Rather,et al.  The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) , 2015 .

[11]  S. Singer,et al.  Using the integrated nurse leadership program to reduce sepsis mortality. , 2015, Joint Commission journal on quality and patient safety.

[12]  P. Pronovost,et al.  A targeted real-time early warning score (TREWScore) for septic shock , 2015, Science Translational Medicine.

[13]  R. Wenzel,et al.  Septic shock: an analysis of outcomes for patients with onset on hospital wards versus intensive care units. , 1998, Critical care medicine.

[14]  B. Carr,et al.  Benchmarking the Incidence and Mortality of Severe Sepsis in the United States* , 2013, Critical care medicine.

[15]  Valerie J. Gooder,et al.  Inpatient transfers to the intensive care unit , 2003 .

[16]  Developing an Early Sepsis Alert Program , 2014, Journal of nursing care quality.

[17]  F. Gao,et al.  The impact of compliance with 6-hour and 24-hour sepsis bundles on hospital mortality in patients with severe sepsis: a prospective observational study , 2005, Critical care.

[18]  W. Knaus,et al.  Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. The ACCP/SCCM Consensus Conference Committee. American College of Chest Physicians/Society of Critical Care Medicine. , 1992, Chest.

[19]  C. Sprung,et al.  Surviving Sepsis Campaign: International Guidelines for Management of Severe Sepsis and Septic Shock, 2012 , 2013, Intensive Care Medicine.

[20]  Kevin M. Heard,et al.  A trial of a real-time alert for clinical deterioration in patients hospitalized on general medical wards. , 2013, Journal of hospital medicine.

[21]  P. Austin,et al.  A Modification of the Elixhauser Comorbidity Measures Into a Point System for Hospital Death Using Administrative Data , 2009, Medical care.

[22]  Ferdinand T. Velasco,et al.  Improving Outcomes with Clinical Decision Support: An Implementer's Guide , 2012 .

[23]  S. Simpson,et al.  Identifying Severe Sepsis via Electronic Surveillance , 2015, American journal of medical quality : the official journal of the American College of Medical Quality.

[24]  C. Torio,et al.  National Inpatient Hospital Costs: The Most Expensive Conditions by Payer, 2011 , 2013 .

[25]  W. Knaus,et al.  Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. The ACCP/SCCM Consensus Conference Committee. American College of Chest Physicians/Society of Critical Care Medicine. 1992. , 2009, Chest.

[26]  Steen Andreassen,et al.  Prediction of bacteremia using TREAT, a computerized decision-support system. , 2006, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[27]  S. Lemeshow,et al.  Surviving Sepsis Campaign: association between performance metrics and outcomes in a 7.5-year study , 2014, Intensive Care Medicine.

[28]  Mitchell M. Levy,et al.  2001 SCCM/ESICM/ACCP/ATS/SIS International Sepsis Definitions Conference , 2003, Intensive Care Medicine.

[29]  Todd W. Rice,et al.  An Electronic Tool for the Evaluation and Treatment of Sepsis in the ICU: A Randomized Controlled Trial* , 2015, Critical care medicine.

[30]  Benjamin French,et al.  Development, implementation, and impact of an automated early warning and response system for sepsis. , 2015, Journal of hospital medicine.

[31]  G. Clermont,et al.  Epidemiology of severe sepsis in the United States: Analysis of incidence, outcome, and associated costs of care , 2001, Critical care medicine.

[32]  J. Younger,et al.  Prospective trial of real-time electronic surveillance to expedite early care of severe sepsis. , 2011, Annals of emergency medicine.

[33]  Theodore J Iwashyna,et al.  Identifying Patients With Severe Sepsis Using Administrative Claims: Patient-Level Validation of the Angus Implementation of the International Consensus Conference Definition of Severe Sepsis , 2014, Medical care.

[34]  S. Lapinsky,et al.  Early combination antibiotic therapy yields improved survival compared with monotherapy in septic shock: A propensity-matched analysis* , 2010, Critical care medicine.

[35]  John P. Donnelly,et al.  Automated electronic medical record sepsis detection in the emergency department , 2014, PeerJ.