CADDIE2—evaluation of a clinical decision-support system for early detection of systemic inflammatory response syndrome in paediatric intensive care: study protocol for a diagnostic study

Wulff, Antje; Montag, Sara; Steiner, Bianca; Marschollek, Michael; Beerbaum, Philipp; Karch, André; Jack, Thomas

doi:10.1136/bmjopen-2019-028953

Cited by 13 publications

(16 citation statements)

References 27 publications

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“…The alert firing rate (n=12) 43 55 62 69 70 71 72 73 74 75 76 77 and alert acceptance rate (n=15) 27 29 43 58 61 62 69 74 78 79 80 81 82 83 84 were also common metrics used to understand alarm fatigue and acceptability. Although metrics of relevance such as sensitivity, specificity, and positive predictive value (PPV) were referenced several times 77 84 85 86 87 88 , only one study evaluated these metrics as pre-specified outcomes 49 . Tools for capturing clinician burnout, stress, or satisfaction after using a CDS tool were used in nine studies 26 44 78 80 82 89 90 91 92 .…”

Section: Resultsmentioning

confidence: 99%

Clinical Decision Support and Implications for the Clinician Burnout Crisis

Janković

Chen

2020

Yearb Med Inform

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Objectives: This survey aimed to review aspects of clinical decision support (CDS) that contribute to burnout and identify key themes for improving the acceptability of CDS to clinicians, with the goal of decreasing said burnout. Methods: We performed a survey of relevant articles from 2018-2019 addressing CDS and aspects of clinician burnout from PubMed and Web of Science™. Themes were manually extracted from publications that met inclusion criteria. Results: Eighty-nine articles met inclusion criteria, including 12 review articles. Review articles were either prescriptive, describing how CDS should work, or analytic, describing how current CDS tools are deployed. The non-review articles largely demonstrated poor relevance and acceptability of current tools, and few studies showed benefits in terms of efficiency or patient outcomes from implemented CDS. Encouragingly, multiple studies highlighted steps that succeeded in improving both acceptability and relevance of CDS. Conclusions: CDS can contribute to clinician frustration and burnout. Using the techniques of improving relevance, soliciting feedback, customization, measurement of outcomes and metrics, and iteration, the effects of CDS on burnout can be ameliorated.

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Section: Resultsmentioning

confidence: 99%

Clinical Decision Support and Implications for the Clinician Burnout Crisis

Janković

Chen

2020

Yearb Med Inform

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“…The study is reported in accordance with the Standards for Reporting of Diagnostic Accuracy Studies (STARD) (see Additional file 1 : Appendix 1) [ 24 ]. The study protocol has been approved by the Ethics Committee of Hannover Medical School and published [ 25 ].…”

Section: Methodsmentioning

confidence: 99%

“…This diagnostic study was designed to evaluate CDSS accuracy by using the reference standard defined by two experienced clinicians on the base of IPSCC SIRS criteria ( primary aim ). The secondary aim was to compare CDSS accuracy to the accuracy of assessments of clinicians working in clinical routine to assess SIRS awareness of clinicians during challenging routine work [ 25 ]. The study took place at the PICU of Hannover Medical School.…”

Section: Methodsmentioning

confidence: 99%

“…The patient level analysis summarizes the analysis on the level of patient-days in a conservative way so that e. g. individuals with SIRS can also contribute to the estimation of specificity (see below). Sensitivity (alternative hypothesis: 98%, null hypothesis: 90%) and specificity (alternative hypothesis: 90%, null hypothesis: 80%) were chosen as the co-primary endpoint [ 25 ]. A sample size of 97 patients with at least one SIRS episode, and 137 patients with or without a SIRS episode was calculated based on these assumptions (type I error = 0.05, power of 90%; chi square test) [ 25 ].…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Clinical evaluation of an interoperable clinical decision-support system for the detection of systemic inflammatory response syndrome in critically ill children

Wulff

Montag

Rübsamen³

et al. 2021

BMC Med Inform Decis Mak

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Background Systemic inflammatory response syndrome (SIRS) is defined as a non-specific inflammatory process in the absence of infection. SIRS increases susceptibility for organ dysfunction, and frequently affects the clinical outcome of affected patients. We evaluated a knowledge-based, interoperable clinical decision-support system (CDSS) for SIRS detection on a pediatric intensive care unit (PICU). Methods The CDSS developed retrieves routine data, previously transformed into an interoperable format, by using model-based queries and guideline- and knowledge-based rules. We evaluated the CDSS in a prospective diagnostic study from 08/2018–03/2019. 168 patients from a pediatric intensive care unit of a tertiary university hospital, aged 0 to 18 years, were assessed for SIRS by the CDSS and by physicians during clinical routine. Sensitivity and specificity (when compared to the reference standard) with 95% Wald confidence intervals (CI) were estimated on the level of patients and patient-days. Results Sensitivity and specificity was 91.7% (95% CI 85.5–95.4%) and 54.1% (95% CI 45.4–62.5%) on patient level, and 97.5% (95% CI 95.1–98.7%) and 91.5% (95% CI 89.3–93.3%) on the level of patient-days. Physicians’ SIRS recognition during clinical routine was considerably less accurate (sensitivity of 62.0% (95% CI 56.8–66.9%)/specificity of 83.3% (95% CI 80.4–85.9%)) when measurd on the level of patient-days. Evaluation revealed valuable insights for the general design of the CDSS as well as specific rule modifications. Despite a lower than expected specificity, diagnostic accuracy was higher than the one in daily routine ratings, thus, demonstrating high potentials of using our CDSS to help to detect SIRS in clinical routine. Conclusions We successfully evaluated an interoperable CDSS for SIRS detection in PICU. Our study demonstrated the general feasibility and potentials of the implemented algorithms but also some limitations. In the next step, the CDSS will be optimized to overcome these limitations and will be evaluated in a multi-center study. Trial registration: NCT03661450 (ClinicalTrials.gov); registered September 7, 2018.

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“…Currently, the interactive interphase with voice controls are designed to assist the health care professional to work more efficiently with time saving and cost effective clinical dental practice. 3…”

Section: Clinical Decision Support System Cdssmentioning

confidence: 99%