Clinical Decision Support for Early Recognition of Sepsis.

Sepsis is an inflammatory response triggered by infection, with a high in-hospital mortality rate. Early recognition and treatment can reverse the inflammatory response, with evidence of improved patient outcomes. One challenge clinicians face is identifying the inflammatory syndrome against the background of the patient's infectious illness and comorbidities. An approach to this problem is implementation of computerized early warning tools for sepsis. This multicenter retrospective study sought to determine clinimetric performance of a cloud-based computerized sepsis clinical decision support system (CDS), understand the epidemiology of sepsis, and identify opportunities for quality improvement. Data encompassed 6200 adult hospitalizations from 2012 through 2013. Of 13% patients screened-in, 51% were already suspected to have an infection when the system activated. This study focused on a patient cohort screened-in before infection was suspected; median time from arrival to CDS activation was 3.5 hours, and system activation to diagnostic collect was another 8.6 hours.

Sepsis surveillance: an examination of parameter sensitivity and alert reliability.

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.

An investigation of sepsis surveillance and emergency treatment on patient mortality outcomes: An observational cohort study.

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.

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.

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.

A Multidisciplinary Sepsis Program Enabled by a Two-Stage Clinical Decision Support System: Factors That Influence Patient Outcomes.

Sepsis is an inflammatory response triggered by infection, with risk of in-hospital mortality fueled by disease progression. Early recognition and intervention by multidisciplinary sepsis programs may reverse the inflammatory response among at-risk patient populations, potentially improving outcomes. This retrospective study of a sepsis program enabled by a 2-stage sepsis Clinical Decision Support (CDS) system sought to evaluate the program's impact, identify early indicators that may influence outcomes, and uncover opportunities for quality improvement. Data encompassed 16 527 adult hospitalizations from 2014 and 2015. Of 2108 non-intensive care unit patients screened-in by sepsis CDS, 97% patients were stratified by 177 providers. Risk of adverse outcome improved 30% from baseline to year end, with gains materializing and stabilizing at month 7 after sepsis program go-live. Early indicators likely to influence outcomes include patient age, recent hospitalization, electrolyte abnormalities, hypovolemic shock, hypoxemia, patient location when sepsis CDS activated, and specific alert patterns.

Clinical Decision Support for Early Recognition of Sepsis.

Sepsis is an inflammatory response triggered by infection, with a high in-hospital mortality rate. Early recognition and treatment can reverse the inflammatory response, with evidence of improved patient outcomes. One challenge clinicians face is identifying the inflammatory syndrome against the background of the patient's infectious illness and comorbidities. An approach to this problem is implementation of computerized early warning tools for sepsis. This multicenter retrospective study sought to determine clinimetric performance of a cloud-based computerized sepsis clinical decision support system (CDS), understand the epidemiology of sepsis, and identify opportunities for quality improvement. Data encompassed 6200 adult hospitalizations from 2012 through 2013. Of 13% patients screened-in, 51% were already suspected to have an infection when the system activated. This study focused on a patient cohort screened-in before infection was suspected; median time from arrival to CDS activation was 3.5 hours, and system activation to diagnostic collect was another 8.6 hours.

A two-stage clinical decision support system for early recognition and stratification of patients with sepsis: an observational cohort study.

OBJECTIVE: To examine the diagnostic accuracy of a two-stage clinical decision support system for early recognition and stratification of patients with sepsis. DESIGN: Observational cohort study employing a two-stage sepsis clinical decision support to recognise and stratify patients with sepsis. The stage one component was comprised of a cloud-based clinical decision support with 24/7 surveillance to detect patients at risk of sepsis. The cloud-based clinical decision support delivered notifications to the patients' designated nurse, who then electronically contacted a provider. The second stage component comprised a sepsis screening and stratification form integrated into the patient electronic health record, essentially an evidence-based decision aid, used by providers to assess patients at bedside. SETTING: Urban, 284 acute bed community hospital in the USA; 16,000 hospitalisations annually. PARTICIPANTS: Data on 2620 adult patients were collected retrospectively in 2014 after the clinical decision support was implemented. MAIN OUTCOME MEASURE: 'Suspected infection' was the established gold standard to assess clinical decision support clinimetric performance. RESULTS: A sepsis alert activated on 417 (16%) of 2620 adult patients hospitalised. Applying 'suspected infection' as standard, the patient population characteristics showed 72% sensitivity and 73% positive predictive value. A postalert screening conducted by providers at bedside of 417 patients achieved 81% sensitivity and 94% positive predictive value. Providers documented against 89% patients with an alert activated by clinical decision support and completed 75% of bedside screening and stratification of patients with sepsis within one hour from notification. CONCLUSION: A clinical decision support binary alarm system with cross-checking functionality improves early recognition and facilitates stratification of patients with sepsis.

Computerized Clinical Decision Support to Prevent Venous Thromboembolism Among Hospitalized Patients: Proximal Outcomes from a Multiyear Quality Improvement Project.

Despite venous thromboembolism (VTE) policy initiatives, gaps exist between guidelines and practice. In response, hospitals implement clinical decision support (CDS) systems to improve VTE prophylaxis. To assess the impact of a VTE CDS on reducing incidence of VTE, this study used a pretest/posttest, longitudinal, cohort design incorporating electronic health record (EHR) data from one urban tertiary and level 1 trauma center, and one suburban hospital. VTE CDS was embedded into the EHR system. The study included 45,046 admissions; 171,753 patient days; and 110 VTE events. The VTE rate declined from 0.954 per 1,000 patient days to 0.434 comparing baseline to full VTE CDS. Compared to baseline, patients benefitting from VTE CDS were 35% less likely to have a VTE. VTE CDS utilization achieved 78.4% patients assessed within 24 hr from admission, 64.0% patients identified at risk, and 47.7% patients at risk for VTE with an initiated VTE interdisciplinary plan of care. CDS systems with embedded algorithms, alerts, and notification capabilities enable physicians at the point of care to utilize guidelines and make impactful decisions to prevent VTE. This study demonstrates a phased-in implementation of VTE CDS as an effective approach toward VTE prevention. Implications for future research and quality improvement are discussed as well.