Tracing the Progression of Sepsis in Critically Ill Children: Clinical Decision Support for Detection of Hematologic Dysfunction

Louisa Bode; Sven Schamer; Julia Böhnke; Oliver Johannes Bott; Michael Marschollek; Thomas Jack; Antje Wulff; ELISE Study Group

doi:10.1055/a-1950-9637

Applied Clinical Informatics, Table of Contents

CC BY 4.0 · Appl Clin Inform 2022; 13(05): 1002-1014
DOI: 10.1055/a-1950-9637

Research Article

Tracing the Progression of Sepsis in Critically Ill Children: Clinical Decision Support for Detection of Hematologic Dysfunction

Louisa Bode

¹Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Hannover, Germany

,

Sven Schamer

²Department of Pediatric Cardiology and Intensive Care Medicine, Hannover Medical School, Germany

,

Julia Böhnke

³Institute of Epidemiology and Social Medicine, University of Muenster, Muenster, Germany

,

Oliver Johannes Bott

⁴University of Applied Sciences and Arts Hannover, Faculty III – Media, Information and Design, Hannover, Germany

,

Michael Marschollek

¹Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Hannover, Germany

,

Thomas Jack

²Department of Pediatric Cardiology and Intensive Care Medicine, Hannover Medical School, Germany

,

Antje Wulff

¹Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Hannover, Germany

⁵Big Data in Medicine, Department of Health Services Research, School of Medicine and Health Sciences, Carl von Ossietzky University Oldenburg, Oldenburg, Germany

,

ELISE Study Group

› Author Affiliations

Abstract

Background One of the major challenges in pediatric intensive care is the detection of life-threatening health conditions under acute time constraints and performance pressure. This includes the assessment of pediatric organ dysfunction (OD) that demands extraordinary clinical expertise and the clinician's ability to derive a decision based on multiple information and data sources. Clinical decision support systems (CDSS) offer a solution to support medical staff in stressful routine work. Simultaneously, detection of OD by using computerized decision support approaches has been scarcely investigated, especially not in pediatrics.

Objectives The aim of the study is to enhance an existing, interoperable, and rule-based CDSS prototype for tracing the progression of sepsis in critically ill children by augmenting it with the capability to detect SIRS/sepsis-associated hematologic OD, and to determine its diagnostic accuracy.

Methods We reproduced an interoperable CDSS approach previously introduced by our working group: (1) a knowledge model was designed by following the commonKADS methodology, (2) routine care data was semantically standardized and harmonized using openEHR as clinical information standard, (3) rules were formulated and implemented in a business rule management system. Data from a prospective diagnostic study, including 168 patients, was used to estimate the diagnostic accuracy of the rule-based CDSS using the clinicians' diagnoses as reference.

Results We successfully enhanced an existing interoperable CDSS concept with the new task of detecting SIRS/sepsis-associated hematologic OD. We modeled openEHR templates, integrated and standardized routine data, developed a rule-based, interoperable model, and demonstrated its accuracy. The CDSS detected hematologic OD with a sensitivity of 0.821 (95% CI: 0.708–0.904) and a specificity of 0.970 (95% CI: 0.942–0.987).

Conclusion We could confirm our approach for designing an interoperable CDSS as reproducible and transferable to other critical diseases. Our findings are of direct practical relevance, as they present one of the first interoperable CDSS modules that detect pediatric SIRS/sepsis-associated hematologic OD.

Keywords

decision support systems, clinical - hematology - openEHR - organ failure - intensive care units, pediatric - diagnostic accuracy

Full Text

References

References
1 Zavala AM, Day GE, Plummer D, Bamford-Wade A. Decision-making under pressure: medical errors in uncertain and dynamic environments. Aust Health Rev 2018; 42 (04) 395-402
2 Bone RC, Balk RA, Cerra FB. et al; The ACCP/SCCM Consensus Conference Committee. American College of Chest Physicians/Society of Critical Care Medicine. Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. Chest 1992; 101 (06) 1644-1655
3 Goldstein B, Giroir B, Randolph A. International Consensus Conference on Pediatric Sepsis. International pediatric sepsis consensus conference: definitions for sepsis and organ dysfunction in pediatrics. Pediatr Crit Care Med 2005; 6 (01) 2-8
4 Marshall JC. The multiple organ dysfunction syndrome. In: Marshall JC. ed. Surgical Treatment: Evidence-Based and Problem-Oriented. Zuckschwerdt; 2001
5 Williamson DR, Lesur O, Tétrault J-P, Nault V, Pilon D. Thrombocytopenia in the critically ill: prevalence, incidence, risk factors, and clinical outcomes. Can J Anaesth 2013; 60 (07) 641-651
6 Denekamp Y. Clinical decision support systems for addressing information needs of physicians. Isr Med Assoc J 2007; 9 (11) 771-776
7 Abdel-Rahman SM, Gill H, Carpenter SL. et al. Design and usability of an electronic health record-integrated, point-of-care, clinical decision support tool for modeling and simulation of antihemophilic factors. Appl Clin Inform 2020; 11 (02) 253-264
8 Ikoma S, Furukawa M, Busuttil A. et al. Optimizing inpatient blood utilization using real-time clinical decision support. Appl Clin Inform 2021; 12 (01) 49-56
9 Mack EH, Wheeler DS, Embi PJ. Clinical decision support systems in the pediatric intensive care unit. Pediatr Crit Care Med 2009; 10 (01) 23-28
10 Robert L, Rousseliere C, Beuscart J-B. et al. Integration of explicit criteria in a clinical decision support system through evaluation of acute kidney injury events. Stud Health Technol Inform 2021; 281: 640-644
11 Williams CN, Bratton SL, Hirshberg EL. Computerized decision support in adult and pediatric critical care. World J Crit Care Med 2013; 2 (04) 21-28
12 Berner ES, Osheroff JA. Tamblyn Robyn. Clinical Decision Support Systems: State of the Art. AHRQ Publication No. 09–0069-EF; 2009
13 Miller RA. Medical diagnostic decision support systems–past, present, and future: a threaded bibliography and brief commentary. J Am Med Inform Assoc 1994; 1 (01) 8-27
14 Pearson S-A, Moxey A, Robertson J. et al. Do computerised clinical decision support systems for prescribing change practice? A systematic review of the literature (1990-2007). BMC Health Serv Res 2009; 9: 154
15 Spreckelsen C, Spitzer K. Wissensbasen und Expertensysteme in der Medizin: Kl-Ansätze zwischen klinischer Entscheidungsunterstützung und medizinischem Wissensmanagement. Vieweg+Teubner Verlag/GWV Fachverlage GmbH Wiesbaden; 2009
16 Wilkinson MD, Dumontier M, Aalbersberg IJJ. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 2016; 3: 160018
17 Beale T. Archetypes: Constraint-based Domain Models for Future-proof Information Systems. Paper presented at: Eleventh OOPSLA workshop on behavioral semantics; 2002:16–32
18 HL7 International. CDA® Release 2: HL7 Standards Product Brief. Published October 14, 2021. Accessed October 14, 2021 at: http://www.hl7.org/implement/standards/product_brief.cfm?product_id=7
19 HL7 International. . Welcome to FHIR v4.0.1. Published November 25, 2019. Accessed October 14, 2021 at: http://hl7.org/fhir/index.html
20 Wulff A, Haarbrandt B, Tute E, Marschollek M, Beerbaum P, Jack T. An interoperable clinical decision-support system for early detection of SIRS in pediatric intensive care using openEHR. Artif Intell Med 2018; 89: 10-23
21 Wulff A, Montag S, Steiner B. et al. 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. BMJ Open 2019; 9 (06) e028953
22 Leslie H.. openEHR-the World's Record. 2015.
23 Garde S, Knaup P, Hovenga E, Heard S. Towards semantic interoperability for electronic health records. Methods Inf Med 2007; 46 (03) 332-343
24 Wulff A, Sommer KK, Ballout S, Haarbrandt B, Gietzelt M. HiGHmed Consortium. A report on archetype modelling in a nationwide data infrastructure project. Stud Health Technol Inform 2019; 258: 146-150
25 Beale T. Archetype Technology Overview. Published May 14, 2022. Accessed June 3, 2022 at: https://specifications.openehr.org/releases/AM/latest/Overview.html#_archetype_technology_overview
26 Garde S. Ocean Health Systems. Clinical Knowledge Manager: ELISE Laboratory Report. Published October 21, 2021. Accessed June 3, 2022 at: https://ckm.highmed.org/ckm/templates/1246.169.1671
27 SNOMED International. The value of SNOMED CT. Accessed October 14, 2021 at: https://www.snomed.org/snomed-ct/why-snomed-ct
28 Regenstrief Institute. LOINC: The international standard for identifying health measurements, observations, and documents. Accessed October 14, 2021 at: https://loinc.org/
29 openEHR Foundation. Terminology in openEHR. Accessed November 09, 2020 at: https://specifications.openehr.org/releases/1.0.2/html/architecture/overview/Output/terminology.html
30 Garde S. Clinical Knowledge Manager. Accessed April 03, 2021 at: https://ckm.openehr.org/ckm/
31 Better Deutschland Gmb H. Better care: Helping medical teams improve patient care. Accessed March 24, 2022 at: https://www.better.care
32 openEHR Foundation. Archetype Query Language (AQL). Accessed October 12, 2020 at: https://specifications.openehr.org/releases/QUERY/latest/AQL.html#_overview
33 Schreiber G. Knowledge Engineering and Management: The CommonKADS Methodology. 3. printing. MIT Press; 2002
34 The JBoss Drools team. Drools Expert User Guide: Community Documentation. Accessed October 15, 2020 at: https://docs.jboss.org/drools/release/5.4.0.Final/drools-expert-docs/html_single/#d0e128
35 Oracle. JavaFX Overview (Release 8). Accessed October 15, 2020 at: https://docs.oracle.com/javase/8/javafx/get-started-tutorial/jfx-overview.htm#JFXST784
36 Brunner E, Zapf A. Nonparametric ROC analysis for diagnostic trials. In: Balakrishnan N. ed. Methods and Applications of Statistics in Clinical Trials: Volume 2–Planning, Analysis, and Inferential Methods. Wiley; 2014: 483-495
37 Lange K. . Nichtparametrische Analyse Diagnostischer Gütemaße Bei Clusterdaten. [dissertation]: University of Goettingen; 2011. Accessed January 20, 2022 at: https://ediss.uni-goettingen.de/bitstream/handle/11858/00-1735-0000-000D-F1D1-B/lange.pdf?sequence=1
38 Rooney D. Covariate Adjusted Nonparametric Estimation of Sensitivity and Specificity in Clustered Data. [Master thesis]: Heidelberg University Hospital; 2017
39 Bundesinstitut für Arzneimittel und Medizinprodukte. ICD-10-GM: Internationale statistische Klassifikation der Krankheiten und verwandter Gesundheitsprobleme, German Modification. Accessed March 24, 2022 at: https://www.bfarm.de/DE/Kodiersysteme/Klassifikationen/ICD/ICD-10-GM/_node.html
40 Wulff A, Mast M, Bode L. et al. ELISE - an Open Pediatric Intensive Care Data Set. [Data Set]. 2022
41 Mast M, Bode L, Wulff A. . ELISE · GitLab: Open demonstrator. Published June 3, 2022. Accessed June 3, 2022 at: https://gitlab.com/marcelmast/elise
42 Brooke J. SUS: A “Quick and Dirty” Usability Scale. In: Jordan PW, Thomas B, McClelland IL, Weerdmeester B. eds. Usability Evaluation In Industry. CRC Press; 1996: 207-212

Supplementary Material

Supplementary Material