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CC BY 4.0 · ACI open 2022; 06(02): e76-e84
DOI: 10.1055/s-0042-1755373
Original Article

Variations among Electronic Health Record and Physiologic Streaming Vital Signs for Use in Predictive Algorithms in Pediatric Severe Sepsis

Adam C. Dziorny
1   Department of Pediatrics, University of Rochester School of Medicine, Rochester, New York, United States
2   Department of Biomedical Engineering, University of Rochester, Rochester, New York, United States
,
Robert B. Lindell
3   Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
4   Pediatric Sepsis Program, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
,
Julie C. Fitzgerald
3   Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
4   Pediatric Sepsis Program, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
,
Christopher P. Bonafide
5   Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
6   Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
› Author Affiliations Funding Dr. Fitzgerald is supported by a grant from the NIH National Institute of Diabetes and Digestive and Kidney Diseases (K23DK119463). The remaining authors (A.C.D., R.B.L., and C.P.B.) report no source of funding for this work.
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Abstract

Objective This study sought to describe the similarities and differences among physiologic streaming vital signs (PSVSs) and electronic health record (EHR)-documented vital signs (EVSs) in pediatric sepsis.

Methods In this retrospective cohort study, we identified sepsis patients admitted to the pediatric intensive care unit. We compared PSVS and EVS measures of heart rate (HR), respiratory rate, oxyhemoglobin saturation, and blood pressure (BP) across domains of completeness, concordance, plausibility, and currency.

Results We report 1,095 epochs comprising vital sign data from 541 unique patients. While counts of PSVS measurements per epoch were substantially higher, increased missingness was observed compared with EVS. Concordance was highest among HR and lowest among BP measurements, with bias present in all measures. Percent of time above or below defined plausibility cutoffs significantly differed by measure. All EVS measures demonstrated a mean delay from time recorded at the patient to EHR entry.

Conclusion We measured differences between vital sign sources across all data domains. Bias direction differed by measure, possibly related to bedside monitor measurement artifact. Plausibility differences may reflect the more granular nature of PSVS which can be critical in illness detection. Delays in EVS measure currency may impact real-time decision support systems. Technical limitations increased missingness in PSVS measures and reflect the importance of systems monitoring for data continuity. Both PSVS and EVS have advantages and disadvantages that must be weighed when making use of vital signs in decision support systems or as covariates in retrospective analyses.

Keywords

sepsis - vital signs - electronic health records - clinical decision support systems - physiologic

Protection of Human and Animal Subjects

This study was granted an exemption (# 19–016133, initial approval 5/24/2019, addendum approval 12/16/2020) by the Institutional Review Board at the Children's Hospital of Philadelphia.


Institution where this work was performed: The Children's Hospital of Philadelphia.


Supplementary Material



Publication History

Received: 22 April 2021

Accepted: 15 June 2022

Article published online:
18 September 2022

© 2022. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Watson RS, Carcillo JA. Scope and epidemiology of pediatric sepsis. Pediatr Crit Care Med 2005; 6 (3, Suppl): S3-S5
    CrossrefPubMedGoogle Scholar
  • 2 Weiss SL, Fitzgerald JC, Pappachan J. et al; Sepsis Prevalence, Outcomes, and Therapies (SPROUT) Study Investigators and Pediatric Acute Lung Injury and Sepsis Investigators (PALISI) Network. Global epidemiology of pediatric severe sepsis: the sepsis prevalence, outcomes, and therapies study. Am J Respir Crit Care Med 2015; 191 (10) 1147-1157
    Google Scholar
  • 3 Weiss SL, Fitzgerald JC, Balamuth F. et al. Delayed antimicrobial therapy increases mortality and organ dysfunction duration in pediatric sepsis. Crit Care Med 2014; 42 (11) 2409-2417
    Google Scholar
  • 4 Reyna MA, Josef CS, Jeter R. et al. Early prediction of sepsis from clinical data: the PhysioNet/Computing in Cardiology Challenge 2019. Crit Care Med 2020; 48 (02) 210-217
    CrossrefPubMedGoogle Scholar
  • 5 Masino AJ, Harris MC, Forsyth D. et al. Machine learning models for early sepsis recognition in the neonatal intensive care unit using readily available electronic health record data. PLoS One 2019; 14 (02) e0212665
    CrossrefPubMedGoogle Scholar
  • 6 Nemati S, Holder A, Razmi F, Stanley MD, Clifford GD, Buchman TG. An interpretable machine learning model for accurate prediction of sepsis in the ICU. Crit Care Med 2018; 46 (04) 547-553
    CrossrefPubMedGoogle Scholar
  • 7 Umscheid CA, Betesh J, VanZandbergen C. et al. Development, implementation, and impact of an automated early warning and response system for sepsis. J Hosp Med 2015; 10 (01) 26-31
    CrossrefPubMedGoogle Scholar
  • 8 Hsu HE, Abanyie F, Agus MSD. et al. A national approach to pediatric sepsis surveillance. Pediatrics 2019; 144 (06) e20191790
    CrossrefPubMedGoogle Scholar
  • 9 Eisenberg M, Madden K, Christianson JR, Melendez E, Harper MB. Performance of an automated screening algorithm for early detection of pediatric severe sepsis. Pediatr Crit Care Med 2019; 20 (12) e516-e523
    PubMedGoogle Scholar
  • 10 Weiss SL, Balamuth F, Chilutti M. et al. Identification of pediatric sepsis for epidemiologic surveillance using electronic clinical data. Pediatr Crit Care Med 2020; 21 (02) 113-121
    CrossrefPubMedGoogle Scholar
  • 11 Martin B, Bennett TD. Sepsis computable phenotypes in the service of observational research. Crit Care Med 2019; 47 (02) 303-305
    CrossrefPubMedGoogle Scholar
  • 12 Wager KA, Schaffner MJ, Foulois B, Swanson Kazley A, Parker C, Walo H. Comparison of the quality and timeliness of vital signs data using three different data-entry devices. Comput Inform Nurs 2010; 28 (04) 205-212
    CrossrefPubMedGoogle Scholar
  • 13 Stevenson JE, Israelsson J, Nilsson GC, Petersson GI, Bath PA. Recording signs of deterioration in acute patients: the documentation of vital signs within electronic health records in patients who suffered in-hospital cardiac arrest. Health Informatics J 2016; 22 (01) 21-33
    CrossrefPubMedGoogle Scholar
  • 14 Skyttberg N, Chen R, Blomqvist H, Koch S. Exploring vital sign data quality in electronic health records with focus on emergency care warning scores. Appl Clin Inform 2017; 8 (03) 880-892
    Article in Thieme ConnectPubMedGoogle Scholar
  • 15 Kallioinen N, Hill A, Christofidis MJ, Horswill MS, Watson MO. Quantitative systematic review: sources of inaccuracy in manually measured adult respiratory rate data. J Adv Nurs 2021; 77 (01) 98-124
    CrossrefPubMedGoogle Scholar
  • 16 De Georgia MA, Kaffashi F, Jacono FJ, Loparo KA. Information technology in critical care: review of monitoring and data acquisition systems for patient care and research. Scientific World Journal 2015; 2015: 727694
    CrossrefPubMedGoogle Scholar
  • 17 Goodwin AJ, Eytan D, Greer RW. et al. A practical approach to storage and retrieval of high-frequency physiological signals. Physiol Meas 2020; 41 (03) 035008
    CrossrefPubMedGoogle Scholar
  • 18 Matam BR, Duncan H. Technical challenges related to implementation of a formula one real time data acquisition and analysis system in a paediatric intensive care unit. J Clin Monit Comput 2018; 32 (03) 559-569
    CrossrefPubMedGoogle Scholar
  • 19 Ghassemi M, Celi LA, Stone DJ. State of the art review: the data revolution in critical care. Crit Care 2015; 19: 118
    CrossrefPubMedGoogle Scholar
  • 20 Sun Y, Guo F, Kaffashi F, Jacono FJ, DeGeorgia M, Loparo KA. INSMA: an integrated system for multimodal data acquisition and analysis in the intensive care unit. J Biomed Inform 2020; 106: 103434
    CrossrefPubMedGoogle Scholar
  • 21 Lindell RB, Nishisaki A, Weiss SL. et al. Comparison of methods for identification of pediatric severe sepsis and septic shock in the virtual pediatric systems database. Crit Care Med 2019; 47 (02) e129-e135
    CrossrefPubMedGoogle Scholar
  • 22 Weiskopf NG, Weng C. Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. J Am Med Inform Assoc 2013; 20 (01) 144-151
    Google Scholar
  • 23 Chiotos K, Fitzgerald J, Weiss S. et al. Suspected sepsis clinical pathway. Published 2019. Accessed February 20, 2020 at: https://www.chop.edu/clinical-pathway/sepsis-inpatient-care-clinical-pathway
    PubMedGoogle Scholar
  • 24 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
    CrossrefPubMedGoogle Scholar
  • 25 Team Rs. RStudio: Integrated Development Environment for R. Published online 2016. Accessed July 3, 2022 at: http://www.rstudio.com/
    PubMedGoogle Scholar
  • 26 Hester J, Wickham H. odbc: Connect to ODBC Compatible Databases (using the DBI Interface). Published online 2020. Accessed July 3, 2022 at: https://cran.r-project.org/package=odbc
    PubMedGoogle Scholar
  • 27 Wickham H, Ruiz E. dbplyr: A “dplyr” Back End for Databases. Published online 2020. Accessed July 3, 2022 at: https://cran.r-project.org/package=dbplyr
    PubMedGoogle Scholar
  • 28 Wickham H, Francois R, Henry L, Müller K. dplyr: A Grammar of Data Manipulation. Published online 2017. Accessed July 3, 2022 at: https://cran.r-project.org/package=dplyr
    PubMedGoogle Scholar
  • 29 Wickham H. Ggplot2: Elegant Graphics for Data Analysis. New York, NY: Springer-Verlag; 2016
    CrossrefGoogle Scholar
  • 30 Wilke CO. cowplot: Streamlined Plot Theme and Plot Annotations for “ggplot2.” Published online 2017. Accessed July 3, 2022 at: https://cran.r-project.org/package=cowplot
    PubMedGoogle Scholar
  • 31 Xie Y. knitr: A General-Purpose Package for Dynamic Report Generation in R. Published online 2020. Accessed July 3, 2022 at: https://yihui.org/knitr/
    PubMedGoogle Scholar
  • 32 Sapo M, Wu S, Asgari S. et al. A comparison of vital signs charted by nurses with automated acquired values using waveform quality indices. J Clin Monit Comput 2009; 23 (05) 263-271
    CrossrefPubMedGoogle Scholar
  • 33 Feldman K, Rohan AJ, Chawla NV. Discrete heart rate values or continuous streams? Representation, variability, and meaningful use of vital sign data. Comput Inform Nurs 2021; 39 (11) 793-803
    CrossrefPubMedGoogle Scholar
  • 34 Laverriere EK, Polansky M, French B, Nadkarni VM, Berg RA, Topjian AA. Association of duration of hypotension with survival after pediatric cardiac arrest. Pediatr Crit Care Med 2020; 21 (02) 143-149
    CrossrefPubMedGoogle Scholar
  • 35 Maslove DM, Dubin JA, Shrivats A, Lee J. Errors, omissions, and outliers in hourly vital signs measurements in intensive care. Crit Care Med 2016; 44 (11) e1021-e1030
    CrossrefPubMedGoogle Scholar
  • 36 Skyttberg N, Vicente J, Chen R, Blomqvist H, Koch S. How to improve vital sign data quality for use in clinical decision support systems? A qualitative study in nine Swedish emergency departments. BMC Med Inform Decis Mak 2016; 16: 61
    CrossrefPubMedGoogle Scholar
  • 37 Orenstein EW, Muthu N, Weitkamp AO. et al. Towards a maturity model for clinical decision support operations. Appl Clin Inform 2019; 10 (05) 810-819
    Article in Thieme ConnectPubMedGoogle Scholar
  • 38 Pollack MM, Patel KM, Ruttimann UE. PRISM III: an updated pediatric risk of mortality score. Crit Care Med 1996; 24 (05) 743-752
    CrossrefPubMedGoogle Scholar