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


Journal

BMC medical informatics and decision making
ISSN: 1472-6947
Titre abrégé: BMC Med Inform Decis Mak
Pays: England
ID NLM: 101088682

Informations de publication

Date de publication:
18 02 2021
Historique:
received: 11 12 2020
accepted: 03 02 2021
entrez: 19 2 2021
pubmed: 20 2 2021
medline: 24 4 2021
Statut: epublish

Résumé

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). 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. 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. 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. NCT03661450 (ClinicalTrials.gov); registered September 7, 2018.

Sections du résumé

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.

Identifiants

pubmed: 33602206
doi: 10.1186/s12911-021-01428-7
pii: 10.1186/s12911-021-01428-7
pmc: PMC7889709
doi:

Banques de données

ClinicalTrials.gov
['NCT03661450']

Types de publication

Journal Article Multicenter Study

Langues

eng

Sous-ensembles de citation

IM

Pagination

62

Références

Rebanta K. Chakraborty bracken burns. Systemic inflammatory response syndrome. Treasure Island: StatPearls Publishing; 2020 (PubMed PMID: 31613449).
Fleischmann-Struzek C, Goldfarb DM, Schlattmann P, et al. The global burden of paediatric and neonatal sepsis: a systematic review. Lancet Respir Med. 2018;6(3):223–30. https://doi.org/10.1016/S2213-2600(18)30063-8 (PubMed PMID: 29508706).
doi: 10.1016/S2213-2600(18)30063-8 pubmed: 29508706
Kissoon N, Reinhart K, Daniels R, et al. Sepsis in children: global implications of the world health assembly resolution on sepsis. Pediatr Crit Care Med. 2017;18(12):e625–7. https://doi.org/10.1097/PCC.0000000000001340 (PubMed PMID: 28914721).
doi: 10.1097/PCC.0000000000001340 pubmed: 28914721
Weiss SL, Fitzgerald JC, Pappachan J, et al. Global epidemiology of pediatric severe sepsis: the sepsis prevalence, outcomes, and therapies study. Am J Respir Crit Care Med. 2015;191(10):1147–57. https://doi.org/10.1164/rccm.201412-2323OC (PubMed PMID: 25734408).
doi: 10.1164/rccm.201412-2323OC pubmed: 25734408 pmcid: 4451622
Hartman ME, Linde-Zwirble WT, Angus DC, et al. Trends in the epidemiology of pediatric severe sepsis*. Pediatr Crit Care Med. 2013;14(7):686–93. https://doi.org/10.1097/PCC.0b013e3182917fad (PubMed PMID: 23897242).
doi: 10.1097/PCC.0b013e3182917fad pubmed: 23897242
Schlapbach LJ, Straney L, Alexander J, et al. Mortality related to invasive infections, sepsis, and septic shock in critically ill children in Australia and New Zealand, 2002–13: a multicentre retrospective cohort study. Lancet. 2015;15(1):46–54. https://doi.org/10.1016/S1473-3099(14)71003-5 .
doi: 10.1016/S1473-3099(14)71003-5
Goldstein B, Giroir B, Randolph A. International pediatric sepsis consensus conference: definitions for sepsis and organ dysfunction in pediatrics*. Pediatr Crit Care Med. 2005;6(1):2–8. https://doi.org/10.1097/01.PCC.0000149131.72248.E6 .
doi: 10.1097/01.PCC.0000149131.72248.E6 pubmed: 15636651
Shankar-Hari M, Phillips GS, Levy ML, et al. Developing a new definition and assessing new clinical criteria for septic shock: for the third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA. 2016;315(8):775–87. https://doi.org/10.1001/jama.2016.0289 (PubMed PMID: 26903336).
doi: 10.1001/jama.2016.0289 pubmed: 26903336 pmcid: 26903336
Singer M, Deutschman CS, Seymour CW, et al. The third international consensus definitions for sepsis and septic shock (sepsis-3). JAMA. 2016;315(8):801. https://doi.org/10.1001/jama.2016.0287 .
doi: 10.1001/jama.2016.0287 pubmed: 26903338 pmcid: 26903338
Proulx F, Fayon M, Farrell CA, et al. Epidemiology of sepsis and multiple organ dysfunction syndrome in children. Chest. 1996;109(4):1033–7. https://doi.org/10.1378/chest.109.4.1033 (PubMed PMID: 8635327).
doi: 10.1378/chest.109.4.1033 pubmed: 8635327
Boehne M, Sasse M, Karch A, et al. Systemic inflammatory response syndrome after pediatric congenital heart surgery: incidence, risk factors, and clinical outcome. J Card Surg. 2017;32(2):116–25. https://doi.org/10.1111/jocs.12879 (PubMed PMID: 27928843).
doi: 10.1111/jocs.12879 pubmed: 27928843
Nydert P, Vég A, Bastholm-Rahmner P, et al. Pediatricians’ understanding and experiences of an electronic clinical-decision-support-system. Online J Public Health Inform. 2017;9(3):e200. https://doi.org/10.5210/ojphi.v9i3.8149 (PubMed PMID: 29731956).
doi: 10.5210/ojphi.v9i3.8149 pubmed: 29731956 pmcid: 5931671
Berrouiguet S, Billot R, Larsen ME, et al. An approach for data mining of electronic health record data for suicide risk management: database analysis for clinical decision support. JMIR Ment Health. 2019;6(5):e9766. https://doi.org/10.2196/mental.9766 (PubMed PMID: 31066693).
doi: 10.2196/mental.9766 pubmed: 31066693 pmcid: 6707587
Wulff A, Montag S, Marschollek M, et al. Clinical decision-support systems for detection of systemic inflammatory response syndrome, sepsis and septic shock in critically-ill patients: a systematic review. Methods Inf Med. 2019;58(S02):243-e57. https://doi.org/10.1055/s-0039-1695717 (PubMed PMID: 31499571).
doi: 10.1055/s-0039-1695717
Dewan M, Vidrine R, Zackoff M, et al. Design, implementation, and validation of a pediatric icu sepsis prediction tool as clinical decision support. Appl Clin Inform. 2020;11(2):218–25. https://doi.org/10.1055/s-0040-1705107 (PubMed PMID: 32215893).
doi: 10.1055/s-0040-1705107 pubmed: 32215893 pmcid: 7096320
Scott HF, Colborn KL, Sevick CJ, et al. Development and validation of a predictive model of the risk of pediatric septic shock using data known at the time of hospital arrival. J Pediatr. 2020;217(145–151):e6. https://doi.org/10.1016/j.jpeds.2019.09.079 (PubMed PMID: 31733815).
doi: 10.1016/j.jpeds.2019.09.079
Vidrine R, Zackoff M, Paff Z, et al. Improving timely recognition and treatment of sepsis in the pediatric ICU. Jt Comm J Qual Patient Saf. 2020;46(5):299–307. https://doi.org/10.1016/j.jcjq.2020.02.005 (PubMed PMID: 32201121).
doi: 10.1016/j.jcjq.2020.02.005 pubmed: 32201121
Le S, Hoffman J, Barton C, et al. Pediatric severe sepsis prediction using machine learning. Front Pediatr. 2019;7:413. https://doi.org/10.3389/fped.2019.00413 (PubMed PMID: 31681711).
doi: 10.3389/fped.2019.00413 pubmed: 31681711 pmcid: 6798083
Sepanski RJ, Godambe SA, Mangum CD, et al. Designing a pediatric severe sepsis screening tool. Front Pediatr. 2014;2:56. https://doi.org/10.3389/fped.2014.00056 (PubMed PMID: 24982852).
doi: 10.3389/fped.2014.00056 pubmed: 24982852 pmcid: 4058838
Cruz AT, Williams EA, Graf JM, et al. Test characteristics of an automated age- and temperature-adjusted tachycardia alert in pediatric septic shock. Pediatr Emerg Care. 2012;28(9):889–94. https://doi.org/10.1097/PEC.0b013e318267a78a .
doi: 10.1097/PEC.0b013e318267a78a pubmed: 22929140
Eisenberg M, Madden K, Christianson JR, et al. Performance of an automated screening algorithm for early detection of pediatric severe sepsis. Pediatr Crit Care Med. 2019;20(12):e516–23. https://doi.org/10.1097/PCC.0000000000002101 (PubMed PMID: 31567896).
doi: 10.1097/PCC.0000000000002101 pubmed: 31567896
Wulff A, Haarbrandt B, Tute E, et al. An interoperable clinical decision-support system for early detection of SIRS in pediatric intensive care using openEHR. Artif Intell Med. 2018;89:10–23. https://doi.org/10.1016/j.artmed.2018.04.012 (PubMed PMID: 29753616).
doi: 10.1016/j.artmed.2018.04.012 pubmed: 29753616
Beale T. Archetypes: Constraint-based Domain Models for Future-proof Information Systems. In: Eleventh OOPSLA workshop on behavioral semantics 2002;16–32.
Cohen JF, Korevaar DA, Altman DG, et al. STARD 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration. BMJ Open. 2016;6(11):e012799. https://doi.org/10.1136/bmjopen-2016-012799 (PubMed PMID: 28137831).
doi: 10.1136/bmjopen-2016-012799 pubmed: 28137831 pmcid: 5128957
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(6):e028953. https://doi.org/10.1136/bmjopen-2019-028953 (PubMed PMID: 31221891).
doi: 10.1136/bmjopen-2019-028953 pubmed: 31221891 pmcid: 6588987
Genders TSS, Spronk S, Stijnen T, et al. Methods for calculating sensitivity and specificity of clustered data: a tutorial. Radiology. 2012;265(3):910–6. https://doi.org/10.1148/radiol.12120509 (PubMed PMID: 23093680).
doi: 10.1148/radiol.12120509 pubmed: 23093680
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org . Accessed 21 August 2020
Halekoh U, Højsgaard S, Yan J. The R package geepack for generalized estimating equations. J Stat Soft. 2006. https://doi.org/10.18637/jss.v015.i02 .
doi: 10.18637/jss.v015.i02
Jack T, Boehne M, Brent BE, et al. In-line filtration reduces severe complications and length of stay on pediatric intensive care unit: a prospective, randomized, controlled trial. Intensive Care Med. 2012;38(6):1008–16. https://doi.org/10.1007/s00134-012-2539-7 (PubMed PMID: 22527062).
doi: 10.1007/s00134-012-2539-7 pubmed: 22527062 pmcid: 3351606
Sasse M, Dziuba F, Jack T, et al. In-line filtration decreases systemic inflammatory response syndrome, renal and hematologic dysfunction in pediatric cardiac intensive care patients. Pediatr Cardiol. 2015;36(6):1270–8. https://doi.org/10.1007/s00246-015-1157-x (PMID: 25845941).
doi: 10.1007/s00246-015-1157-x pubmed: 25845941 pmcid: 4495711
Boehne M, Jack T, Köditz H, et al. In-line filtration minimizes organ dysfunction: new aspects from a prospective, randomized, controlled trial. BMC Pediatr. 2013;6(13):21. https://doi.org/10.1186/1471-2431-13-21 (PMID: 23384207).
doi: 10.1186/1471-2431-13-21
Komorowski M. Artificial intelligence in intensive care: are we there yet? Intensive Care Med. 2019;45(9):1298–300. https://doi.org/10.1007/s00134-019-05662-6 (PubMed PMID: 31236638).
doi: 10.1007/s00134-019-05662-6 pubmed: 31236638
Lamping F, Jack T, Rubsamen N, et al. Development and validation of a diagnostic model for early differentiation of sepsis and non-infectious SIRS in critically ill children - a data-driven approach using machine-learning algorithms. BMC Pediatr. 2018;18(1):112. https://doi.org/10.1186/s12887-018-1082-2 (PubMed PMID: 29544449).
doi: 10.1186/s12887-018-1082-2 pubmed: 29544449 pmcid: 5853156
Sargeant A, von Landesberger T, Baier C, et al. Early detection of infection chains & outbreaks: use case infection control. Stud Health Technol Inform. 2019;258:245–6. https://doi.org/10.3233/978-1-61499-959-1-245 .
doi: 10.3233/978-1-61499-959-1-245 pubmed: 30942759
Gesundheitsforschung-bmbf.de. SmICS: Smarte Software gegen SARS-CoV-2. Bundesministerium für Bildung und Forschung. https://www.gesundheitsforschung-bmbf.de/de/smics-smarte-softwaregegen-sars-cov-2-11471.php . Accessed 12 January 2021. German.

Auteurs

Antje Wulff (A)

Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Karl-Wiechert-Allee 3, 30625, Hannover, Germany. antje.wulff@plri.de.

Sara Montag (S)

Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Karl-Wiechert-Allee 3, 30625, Hannover, Germany. sara.montag@elisabethgruppe.de.

Nicole Rübsamen (N)

Institute of Epidemiology and Social Medicine, University of Muenster, Domagkstr. 3, 48149, Muenster, Germany.

Friederike Dziuba (F)

Department of Pediatric Cardiology and Intensive Care Medicine, Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany.

Michael Marschollek (M)

Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Karl-Wiechert-Allee 3, 30625, Hannover, Germany.

Philipp Beerbaum (P)

Department of Pediatric Cardiology and Intensive Care Medicine, Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany.

André Karch (A)

Institute of Epidemiology and Social Medicine, University of Muenster, Domagkstr. 3, 48149, Muenster, Germany.

Thomas Jack (T)

Department of Pediatric Cardiology and Intensive Care Medicine, Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany.

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