Clinical evaluation of an interoperable clinical decision-support system for the detection of systemic inflammatory response syndrome in critically ill children.
Clinical decision support systems
Diagnostic study
Pediatric intensive care units
Systemic inflammatory response syndrome
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
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
62Références
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.