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Ackermann K, Baker J, Green M, Fullick M, Varinli H, Westbrook J, Li L. Computerized Clinical Decision Support Systems for the Early Detection of Sepsis Among Adult Inpatients: Scoping Review. J Med Internet Res 2022; 24:e31083. [PMID: 35195528 PMCID: PMC8908200 DOI: 10.2196/31083] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 09/23/2021] [Accepted: 10/29/2021] [Indexed: 12/21/2022] Open
Abstract
Background Sepsis is a significant cause of morbidity and mortality worldwide. Early detection of sepsis followed promptly by treatment initiation improves patient outcomes and saves lives. Hospitals are increasingly using computerized clinical decision support (CCDS) systems for the rapid identification of adult patients with sepsis. Objective This scoping review aims to systematically describe studies reporting on the use and evaluation of CCDS systems for the early detection of adult inpatients with sepsis. Methods The protocol for this scoping review was previously published. A total of 10 electronic databases (MEDLINE, Embase, CINAHL, the Cochrane database, LILACS [Latin American and Caribbean Health Sciences Literature], Scopus, Web of Science, OpenGrey, ClinicalTrials.gov, and PQDT [ProQuest Dissertations and Theses]) were comprehensively searched using terms for sepsis, CCDS, and detection to identify relevant studies. Title, abstract, and full-text screening were performed by 2 independent reviewers using predefined eligibility criteria. Data charting was performed by 1 reviewer with a second reviewer checking a random sample of studies. Any disagreements were discussed with input from a third reviewer. In this review, we present the results for adult inpatients, including studies that do not specify patient age. Results A search of the electronic databases retrieved 12,139 studies following duplicate removal. We identified 124 studies for inclusion after title, abstract, full-text screening, and hand searching were complete. Nearly all studies (121/124, 97.6%) were published after 2009. Half of the studies were journal articles (65/124, 52.4%), and the remainder were conference abstracts (54/124, 43.5%) and theses (5/124, 4%). Most studies used a single cohort (54/124, 43.5%) or before-after (42/124, 33.9%) approach. Across all 124 included studies, patient outcomes were the most frequently reported outcomes (107/124, 86.3%), followed by sepsis treatment and management (75/124, 60.5%), CCDS usability (14/124, 11.3%), and cost outcomes (9/124, 7.3%). For sepsis identification, the systemic inflammatory response syndrome criteria were the most commonly used, alone (50/124, 40.3%), combined with organ dysfunction (28/124, 22.6%), or combined with other criteria (23/124, 18.5%). Over half of the CCDS systems (68/124, 54.8%) were implemented alongside other sepsis-related interventions. Conclusions The current body of literature investigating the implementation of CCDS systems for the early detection of adult inpatients with sepsis is extremely diverse. There is substantial variability in study design, CCDS criteria and characteristics, and outcomes measured across the identified literature. Future research on CCDS system usability, cost, and impact on sepsis morbidity is needed. International Registered Report Identifier (IRRID) RR2-10.2196/24899
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Affiliation(s)
- Khalia Ackermann
- Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Australia
| | - Jannah Baker
- Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Australia
| | | | - Mary Fullick
- Clinical Excellence Commission, Sydney, Australia
| | | | - Johanna Westbrook
- Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Australia
| | - Ling Li
- Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Australia
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Sepsis in Complex Patients in the Emergency Department: Time to Recognition and Therapy in Pediatric Patients With High-Risk Conditions. Pediatr Emerg Care 2020; 36:63-65. [PMID: 31929394 DOI: 10.1097/pec.0000000000002038] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVES To compare timeliness of sepsis recognition and initial treatment in patients with and without high-risk comorbid conditions. METHODS This was a retrospective cohort study of patients presenting to a pediatric emergency department (ED) who triggered a vital sign-based electronic sepsis alert resulting in bedside "huddle" assessment per institutional practice. A positive sepsis alert was defined as age-specific tachycardia or hypotension, concern for infection, and at least 1 of the following: abnormal capillary refill, abnormal mental status, or a high-risk condition. High-risk conditions were derived from the American Academy of Pediatrics sepsis alert tool. Patients with a positive alert underwent bedside huddle resulting in a decision regarding initiation of sepsis protocol. Placement on the protocol and time to initiation of protocol and individual therapies were compared for patients with and without high-risk conditions. RESULTS During the 1-year study period, there were 1107 sepsis huddle alerts out of 96,427 ED visits. Of these, 713 (65%) had identified high-risk conditions, and 394 (35%) did not. Among patients with sepsis huddles, there was no difference in sepsis protocol initiation for patients with high-risk conditions compared with those without (24.8% vs 22.0%, P = 0.305). Between patients with high-risk conditions and those without, there were no differences in median time from triage to sepsis protocol activation, triage to initial intravenous antibiotic, triage to initial intravenous fluid therapy, or ED length of stay. CONCLUSIONS Timeliness of care initiation was no different in high-risk patients with sepsis when using an electronic sepsis alert and protocolized sepsis care.
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Amland RC, Burghart M, Overhage JM. Sepsis surveillance: an examination of parameter sensitivity and alert reliability. JAMIA Open 2020; 2:339-345. [PMID: 31984366 PMCID: PMC6951868 DOI: 10.1093/jamiaopen/ooz014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Revised: 03/18/2019] [Accepted: 04/26/2019] [Indexed: 12/02/2022] Open
Abstract
Objective To examine performance of a sepsis surveillance system in a simulated environment where modifications to parameters and settings for identification of at-risk patients can be explored in-depth. Materials and Methods This was a multiple center observational cohort study. The study population comprised 14 917 adults hospitalized in 2016. An expert-driven rules algorithm was applied against 15.1 million data points to simulate a system with binary notification of sepsis events. Three system scenarios were examined: a scenario as derived from the second version of the Consensus Definitions for Sepsis and Septic Shock (SEP-2), the same scenario but without systolic blood pressure (SBP) decrease criteria (near SEP-2), and a conservative scenario with limited parameters. Patients identified by scenarios as being at-risk for sepsis were assessed for suspected infection. Multivariate binary logistic regression models estimated mortality risk among patients with suspected infection. Results First, the SEP-2-based scenario had a hyperactive, unreliable parameter SBP decrease >40 mm Hg from baseline. Second, the near SEP-2 scenario demonstrated adequate reliability and sensitivity. Third, the conservative scenario had modestly higher reliability, but sensitivity degraded quickly. Parameters differed in predicting mortality risk and represented a substitution effect between scenarios. Discussion Configuration of parameters and alert criteria have implications for patient identification and predicted outcomes. Conclusion Performance of scenarios was associated with scenario design. A single hyperactive, unreliable parameter may negatively influence adoption of the system. A trade-off between modest improvements in alert reliability corresponded to a steep decline in condition sensitivity in scenarios explored.
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Affiliation(s)
- Robert C Amland
- Population Health, Cerner Corporation, Kansas City, Missouri, USA
| | - Mark Burghart
- Population Health, Cerner Corporation, Kansas City, Missouri, USA
| | - J Marc Overhage
- Population Health, Cerner Corporation, Kansas City, Missouri, USA
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Austrian JS, Jamin CT, Doty GR, Blecker S. Impact of an emergency department electronic sepsis surveillance system on patient mortality and length of stay. J Am Med Inform Assoc 2019; 25:523-529. [PMID: 29025165 DOI: 10.1093/jamia/ocx072] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2017] [Accepted: 07/06/2017] [Indexed: 12/29/2022] Open
Abstract
Objective The purpose of this study was to determine whether an electronic health record-based sepsis alert system could improve quality of care and clinical outcomes for patients with sepsis. Materials and Methods We performed a patient-level interrupted time series study of emergency department patients with severe sepsis or septic shock between January 2013 and April 2015. The intervention, introduced in February 2014, was a system of interruptive sepsis alerts triggered by abnormal vital signs or laboratory results. Primary outcomes were length of stay (LOS) and in-hospital mortality; other outcomes included time to first lactate and blood cultures prior to antibiotics. We also assessed sensitivity, positive predictive value (PPV), and clinician response to the alerts. Results Mean LOS for patients with sepsis decreased from 10.1 to 8.6 days (P < .001) following alert introduction. In adjusted time series analysis, the intervention was associated with a decreased LOS of 16% (95% CI, 5%-25%; P = .007, with significance of α = 0.006) and no change thereafter (0%; 95% CI, -2%, 2%). The sepsis alert system had no effect on mortality or other clinical or process measures. The intervention had a sensitivity of 80.4% and a PPV of 14.6%. Discussion Alerting based on simple laboratory and vital sign criteria was insufficient to improve sepsis outcomes. Alert fatigue due to the low PPV is likely the primary contributor to these results. Conclusion A more sophisticated algorithm for sepsis identification is needed to improve outcomes.
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Affiliation(s)
- Jonathan S Austrian
- Department of Medicine, New York University Langone Medical Center, New York, NY, USA.,Medical Center Information Technology, New York University Langone Medical Center, New York, NY, USA
| | - Catherine T Jamin
- Department of Emergency Medicine, New York University Langone Medical Center, New York, NY, USA
| | - Glenn R Doty
- Medical Center Information Technology, New York University Langone Medical Center, New York, NY, USA
| | - Saul Blecker
- Department of Medicine, New York University Langone Medical Center, New York, NY, USA.,Department of Population Health, New York University School of Medicine, New York, NY, USA
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An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU. Crit Care Med 2019; 46:547-553. [PMID: 29286945 DOI: 10.1097/ccm.0000000000002936] [Citation(s) in RCA: 360] [Impact Index Per Article: 72.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
OBJECTIVES Sepsis is among the leading causes of morbidity, mortality, and cost overruns in critically ill patients. Early intervention with antibiotics improves survival in septic patients. However, no clinically validated system exists for real-time prediction of sepsis onset. We aimed to develop and validate an Artificial Intelligence Sepsis Expert algorithm for early prediction of sepsis. DESIGN Observational cohort study. SETTING Academic medical center from January 2013 to December 2015. PATIENTS Over 31,000 admissions to the ICUs at two Emory University hospitals (development cohort), in addition to over 52,000 ICU patients from the publicly available Medical Information Mart for Intensive Care-III ICU database (validation cohort). Patients who met the Third International Consensus Definitions for Sepsis (Sepsis-3) prior to or within 4 hours of their ICU admission were excluded, resulting in roughly 27,000 and 42,000 patients within our development and validation cohorts, respectively. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS High-resolution vital signs time series and electronic medical record data were extracted. A set of 65 features (variables) were calculated on hourly basis and passed to the Artificial Intelligence Sepsis Expert algorithm to predict onset of sepsis in the proceeding T hours (where T = 12, 8, 6, or 4). Artificial Intelligence Sepsis Expert was used to predict onset of sepsis in the proceeding T hours and to produce a list of the most significant contributing factors. For the 12-, 8-, 6-, and 4-hour ahead prediction of sepsis, Artificial Intelligence Sepsis Expert achieved area under the receiver operating characteristic in the range of 0.83-0.85. Performance of the Artificial Intelligence Sepsis Expert on the development and validation cohorts was indistinguishable. CONCLUSIONS Using data available in the ICU in real-time, Artificial Intelligence Sepsis Expert can accurately predict the onset of sepsis in an ICU patient 4-12 hours prior to clinical recognition. A prospective study is necessary to determine the clinical utility of the proposed sepsis prediction model.
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Li E, Nash D. The Gift of Fine China: An Appropriate 20th Anniversary Look Back. Am J Med Qual 2019; 34:425-429. [DOI: 10.1177/1062860619865143] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Erica Li
- Thomas Jefferson University Hospital, Philadelphia, PA
| | - David Nash
- Jefferson College of Population Health, Philadelphia, PA
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Umberger R, Indranoi CY, Simpson M, Jensen R, Shamiyeh J, Yende S. Enhanced Screening and Research Data Collection via Automated EHR Data Capture and Early Identification of Sepsis. SAGE Open Nurs 2019; 5:2377960819850972. [PMID: 33415243 PMCID: PMC7774418 DOI: 10.1177/2377960819850972] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2018] [Revised: 03/01/2019] [Accepted: 04/20/2019] [Indexed: 11/16/2022] Open
Abstract
Clinical research in sepsis patients often requires gathering large amounts of longitudinal information. The electronic health record can be used to identify patients with sepsis, improve participant study recruitment, and extract data. The process of extracting data in a reliable and usable format is challenging, despite standard programming language. The aims of this project were to explore infrastructures for capturing electronic health record data and to apply criteria for identifying patients with sepsis. We conducted a prospective feasibility study to locate and capture/abstract electronic health record data for future sepsis studies. We located parameters as displayed to providers within the system and then captured data transmitted in Health Level Seven® interfaces between electronic health record systems into a prototype database. We evaluated our ability to successfully identify patients admitted with sepsis in the target intensive care unit (ICU) at two cross-sectional time points and then over a 2-month period. A majority of the selected parameters were accessible using an iterative process to locate and abstract them to the prototype database. We successfully identified patients admitted to a 20-bed ICU with sepsis using four data interfaces. Retrospectively applying similar criteria to data captured for 319 patients admitted to ICU over a 2-month period was less sensitive in identifying patients admitted directly to the ICU with sepsis. Classification into three admission categories (sepsis, no-sepsis, and other) was fair (Kappa .39) when compared with manual chart review. This project confirms reported barriers in data extraction. Data can be abstracted for future research, although more work is needed to refine and create customizable reports. We recommend that researchers engage their information technology department to electronically apply research criteria for improved research screening at the point of ICU admission. Using clinical electronic health records data to classify patients with sepsis over time is complex and challenging.
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Affiliation(s)
- Reba Umberger
- Department of Acute and Tertiary Care, College of Nursing, The University of Tennessee Health Science Center, Memphis, TN, USA
| | - Chayawat Yo Indranoi
- University Health System, The University of Tennessee Medical Center, Knoxville, TN, USA
| | - Melanie Simpson
- University Health System, The University of Tennessee Medical Center, Knoxville, TN, USA
| | - Rose Jensen
- University Health System, The University of Tennessee Medical Center, Knoxville, TN, USA
| | - James Shamiyeh
- University Health System, The University of Tennessee Medical Center, Knoxville, TN, USA
| | - Sachin Yende
- Department of Critical Care Medicine, University of Pittsburgh, PA, USA
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Clinical Decision Support Systems in the Emergency Department: Opportunities to Improve Triage Accuracy. J Emerg Nurs 2019; 45:220-222. [DOI: 10.1016/j.jen.2018.12.016] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
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Amland RC, Sutariya BB. An investigation of sepsis surveillance and emergency treatment on patient mortality outcomes: An observational cohort study. JAMIA Open 2018; 1:107-114. [PMID: 31984322 PMCID: PMC6951936 DOI: 10.1093/jamiaopen/ooy013] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2017] [Revised: 03/02/2018] [Accepted: 04/20/2018] [Indexed: 01/20/2023] Open
Abstract
Objective To determine the prevalence of initiating the sepsis 3-h bundle of care and estimate effects of bundle completion on risk-adjusted mortality among emergency department (ED) patients screened-in by electronic surveillance. Materials and Methods This was a multiple center observational cohort study conducted in 2016. The study population was comprised of patients screened-in by St. John Sepsis Surveillance Agent within 4 h of ED arrival, had a sepsis bundle initiated, and admitted to hospital. We built multivariable logistic regression models to estimate impact of a 3-h bundle completed within 3 h of arrival on mortality outcomes. Results Approximately 3% ED patients were screened-in by electronic surveillance within 4 h of arrival and admitted to hospital. Nearly 7 in 10 (69%) patients had a bundle initiated, with most bundles completed within 3 h of arrival. The fully-adjusted risk model achieved good discrimination on mortality outcomes [area under the receiver operating characteristic 0.82, 95% confidence interval (CI) 0.79-0.85] and estimated 34% reduced mortality risk among patients with a bundle completed within 3 h of arrival compared to non-completers. Discussion The sepsis bundle is an effective intervention for many vulnerable patients, and likely to be completed within 3 h after arrival when electronic surveillance with reliable alert notifications are integrated into clinical workflow. Beginning at triage, the platform and sepsis program enables identification and management of patients with greater precision, and increases the odds of good outcomes. Conclusion Sepsis surveillance and clinical decision support accelerate accurate recognition and stratification of patients, and facilitate timely delivery of health care.
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Affiliation(s)
- Robert C Amland
- Population Health, Cerner Corporation, Kansas City, Missouri, USA
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Amland RC, Sutariya BB. Quick Sequential [Sepsis-Related] Organ Failure Assessment (qSOFA) and St. John Sepsis Surveillance Agent to Detect Patients at Risk of Sepsis: An Observational Cohort Study. Am J Med Qual 2017; 33:50-57. [PMID: 28693336 PMCID: PMC5774614 DOI: 10.1177/1062860617692034] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The 2016 Sepsis-3 guidelines included the Quick Sequential [Sepsis-related] Organ Failure Assessment (qSOFA) tool to identify patients at risk of sepsis. The objective was to compare the utility of qSOFA to the St. John Sepsis Surveillance Agent among patients with suspected infection. The primary outcomes were in-hospital mortality or admission to the intensive care unit. A multiple center observational cohort study design was used. The study population comprised 17 044 hospitalized patients between January and March 2016. For the primary analysis, receiver operator characteristic curves were constructed for patient outcomes using qSOFA and the St. John Sepsis Surveillance Agent, and the areas under the curve were compared against a baseline risk model. Time-to-event clinical process modeling also was applied. The St. John Sepsis Surveillance Agent, when compared to qSOFA, activated earlier and was more accurate in predicting patient outcomes; in this regard, qSOFA fell far behind on both objectives.
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Bittman J, Tam P, Little C, Khan N. Who to handover: a case-control study of a novel scoring system to prioritise handover of internal medicine inpatients. Postgrad Med J 2016; 93:313-318. [PMID: 27655897 DOI: 10.1136/postgradmedj-2016-133999] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2016] [Revised: 07/17/2016] [Accepted: 08/30/2016] [Indexed: 01/08/2023]
Abstract
BACKGROUND Handover of patients between care providers is a critical event in patient care. There is, however, little evidence to guide the handover process, including determining which patients to handover. AIM Compare the ability of gestalt-based handover with two structured scores, the modified early warning score (MEWS) and our novel iHAND clinical decision support system, to predict which patients will be assessed by a physician overnight. METHODS This case-control study included 90 inpatients, comprising 32 patients assessed overnight (cases) and 58 patients not assessed overnight (controls) at a teaching hospital in British Columbia, Canada (May 2012). Gestalt, MEWS and iHAND scores were analysed against patients seen overnight using logistic regression and receiver-operating characteristic (ROC) curves. RESULTS Neither current gestalt-based handover practice (odds ratio (OR) 1.50, 95% CI 0.89 to 3.83) nor MEWS (OR 0.96, 95% CI 0.75 to 1.24, area under the ROC curve (AUC) 0.61, 95% CI 0.49 to 0.73) were significantly associated with need to be seen overnight. The iHAND score was associated with need to be seen (OR 1.93, 95% CI 1.24 to 3.02, AUC 0.70, 95% CI 0.60 to 0.81). CONCLUSIONS The iHAND score had moderate ability to predict which patients required assessment overnight, while MEWS score and current gestalt approach correlated poorly, suggesting the iHAND score may help prioritisation of patients likely to be seen overnight for handover.
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Affiliation(s)
- Jesse Bittman
- Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Penny Tam
- Division of General Internal Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Chris Little
- Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Nadia Khan
- Division of General Internal Medicine, University of British Columbia, Vancouver, British Columbia, Canada
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