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Evaluating a digital sepsis alert in a London multisite hospital network: a natural experiment using electronic health record data.

ABSTRACT: OBJECTIVE:The study sought to determine the impact of a digital sepsis alert on patient outcomes in a UK multisite hospital network. MATERIALS AND METHODS:A natural experiment utilizing the phased introduction (without randomization) of a digital sepsis alert into a multisite hospital network. Sepsis alerts were either visible to clinicians (patients in the intervention group) or running silently and not visible (the control group). Inverse probability of treatment-weighted multivariable logistic regression was used to estimate the effect of the intervention on individual patient outcomes. OUTCOMES:In-hospital 30-day mortality (all inpatients), prolonged hospital stay (?7 days) and timely antibiotics (?60 minutes of the alert) for patients who alerted in the emergency department. RESULTS:The introduction of the alert was associated with lower odds of death (odds ratio, 0.76; 95% confidence interval [CI], 0.70-0.84; n?=?21 183), lower odds of prolonged hospital stay ?7 days (OR, 0.93; 95% CI, 0.88-0.99; n?=?9988), and in patients who required antibiotics, an increased odds of receiving timely antibiotics (OR, 1.71; 95% CI, 1.57-1.87; n?=?4622). DISCUSSION:Current evidence that digital sepsis alerts are effective is mixed. In this large UK study, a digital sepsis alert has been shown to be associated with improved outcomes, including timely antibiotics. It is not known whether the presence of alerting is responsible for improved outcomes or whether the alert acted as a useful driver for quality improvement initiatives. CONCLUSIONS:These findings strongly suggest that the introduction of a network-wide digital sepsis alert is associated with improvements in patient outcomes, demonstrating that digital based interventions can be successfully introduced and readily evaluated.

SUBMITTER: Honeyford K

PROVIDER: S-EPMC7025344 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Evaluating a digital sepsis alert in a London multisite hospital network: a natural experiment using electronic health record data.

Honeyford Kate K Cooke Graham S GS Kinderlerer Anne A Williamson Elizabeth E Gilchrist Mark M Holmes Alison A Glampson Ben B Mulla Abdulrahim A Costelloe Ceire C

Journal of the American Medical Informatics Association : JAMIA 20200201 2

<h4>Objective</h4>The study sought to determine the impact of a digital sepsis alert on patient outcomes in a UK multisite hospital network.<h4>Materials and methods</h4>A natural experiment utilizing the phased introduction (without randomization) of a digital sepsis alert into a multisite hospital network. Sepsis alerts were either visible to clinicians (patients in the intervention group) or running silently and not visible (the control group). Inverse probability of treatment-weighted multiv ...[more]

PMID: 31743934

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Project description:This study aimed to assess the potential impact of implementing an electronic alert system (EAS) for systemic inflammatory syndrome (SIRS) and sepsis in pediatric patients mortality. This retrospective study had a pre and post design. We enrolled patients aged ≤ 14 years who were diagnosed with sepsis/severe sepsis upon admission to the pediatric intensive care unit (PICU) of our tertiary hospital from January 2014 to December 2018. We implemented an EAS for the patients with SIRS/sepsis. The patients who met the inclusion criteria pre-EAS implementation comprised the control group, and the group post-EAS implementation was the experimental group. Mortality was the primary outcome, while length of stay (LOS) and mechanical ventilation in the first hour were the secondary outcomes. Of the 308 enrolled patients, 147 were in the pre-EAS group and 161 in the post-EAS group. In terms of mortality, 44 patients in the pre-EAS group and 28 in the post-EAS group died (p 0.011). The average LOS in the PICU was 7.9 days for the pre-EAS group and 6.8 days for the post-EAS group (p 0.442). Considering the EAS initiation time as the "zero time", early recognition of SIRS and sepsis via the EAS led to faster treatment interventions in post-EAS group, which included fluid boluses with median (25th, 75th percentile) time of 107 (37, 218) min vs. 30 (11,112) min, p < 0.001) and time to initiate antimicrobial therapy median (25th, 75th percentile) of 170.5 (66,320) min vs. 131 (53,279) min, p 0.042). The difference in mechanical ventilation in the first hour of admission was not significant between the groups (25.17% vs. 24.22%, p 0.895). The implementation of the EAS resulted in a statistically significant reduction in the mortality rate among the patients admitted to the PICU in our study. An EAS can play an important role in saving lives and subsequent reduction in healthcare costs. Further enhancement of systematic screening is therefore highly recommended to improve the prognosis of pediatric SIRS and sepsis. The implementation of the EAS, warrants further validation in multicenter or national studies.

Project description:ObjectivesTo describe the Children's Hospital Association's Improving Pediatric Sepsis Outcomes sepsis definitions and the identified patients; evaluate the definition using a published framework for evaluating sepsis definitions.DesignObservational cohort.SettingMulticenter quality improvement collaborative of 46 hospitals from January 2017 to December 2018, excluding neonatal ICUs.PatientsImproving Pediatric Sepsis Outcomes Sepsis was defined by electronic health record evidence of suspected infection and sepsis treatment or organ dysfunction. A more severely ill subgroup, Improving Pediatric Sepsis Outcomes Critical Sepsis, was defined, approximating septic shock.InterventionsParticipating hospitals identified patients, extracted data, and transferred de-identified data to a central data warehouse. The definitions were evaluated across domains of reliability, content validity, construct validity, criterion validity, measurement burden, and timeliness.Measurements and main resultsForty hospitals met data quality criteria across four electronic health record platforms. There were 23,976 cases of Improving Pediatric Sepsis Outcomes Sepsis, including 8,565 with Improving Pediatric Sepsis Outcomes Critical Sepsis. The median age was 5.9 years. There were 10,316 (43.0%) immunosuppressed or immunocompromised patients, 4,135 (20.3%) with central lines, and 2,352 (11.6%) chronically ventilated. Among Improving Pediatric Sepsis Outcomes Sepsis patients, 60.8% were admitted to intensive care, 26.4% had new positive-pressure ventilation, and 19.7% received vasopressors. Median hospital length of stay was 6.0 days (3.0-13.0 d). All-cause 30-day in-hospital mortality was 958 (4.0%) in Improving Pediatric Sepsis Outcomes Sepsis; 541 (6.3%) in Improving Pediatric Sepsis Outcomes Critical Sepsis. The Improving Pediatric Sepsis Outcomes Sepsis definitions demonstrated strengths in content validity, convergent construct validity, and criterion validity; weakness in reliability. Improving Pediatric Sepsis Outcomes Sepsis definitions had significant initial measurement burden (median time from case completion to submission: 15 mo [interquartile range, 13-18 mo]); timeliness improved once data capture was established (median, 26 d; interquartile range, 23-56 d).ConclusionsThe Improving Pediatric Sepsis Outcomes Sepsis definitions demonstrated feasibility for large-scale data abstraction. The patients identified provide important information about children treated for sepsis. When operationalized, these definitions enabled multicenter identification and data aggregation, indicating practical utility for quality improvement.

Project description:Background. While often first treated in the emergency department (ED), identification of sepsis is difficult. Electronic medical record (EMR) clinical decision tools offer a novel strategy for identifying patients with sepsis. The objective of this study was to test the accuracy of an EMR-based, automated sepsis identification system. Methods. We tested an EMR-based sepsis identification tool at a major academic, urban ED with 64,000 annual visits. The EMR system collected vital sign and laboratory test information on all ED patients, triggering a "sepsis alert" for those with ?2 SIRS (systemic inflammatory response syndrome) criteria (fever, tachycardia, tachypnea, leukocytosis) plus ?1 major organ dysfunction (SBP ? 90 mm Hg, lactic acid ?2.0 mg/dL). We confirmed the presence of sepsis through manual review of physician, nursing, and laboratory records. We also reviewed a random selection of ED cases that did not trigger a sepsis alert. We evaluated the diagnostic accuracy of the sepsis identification tool. Results. From January 1 through March 31, 2012, there were 795 automated sepsis alerts. We randomly selected 300 cases without a sepsis alert from the same period. The true prevalence of sepsis was 355/795 (44.7%) among alerts and 0/300 (0%) among non-alerts. The positive predictive value of the sepsis alert was 44.7% (95% CI [41.2-48.2%]). Pneumonia and respiratory infections (38%) and urinary tract infection (32.7%) were the most common infections among the 355 patients with true sepsis (true positives). Among false-positive sepsis alerts, the most common medical conditions were gastrointestinal (26.1%), traumatic (25.7%), and cardiovascular (20.0%) conditions. Rates of hospital admission were: true-positive sepsis alert 91.0%, false-positive alert 83.0%, no sepsis alert 5.7%. Conclusions. This ED EMR-based automated sepsis identification system was able to detect cases with sepsis. Automated EMR-based detection may provide a viable strategy for identifying sepsis in the ED.

Dataset Information

Evaluating a digital sepsis alert in a London multisite hospital network: a natural experiment using electronic health record data.

Publications

Evaluating a digital sepsis alert in a London multisite hospital network: a natural experiment using electronic health record data.

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