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Use of an electronic medical record to optimize a neonatal sepsis score for mortality prediction

Abstract

Objective

Late-onset sepsis (LOS) is a significant cause of mortality in preterm infants. The neonatal sequential organ failure assessment (nSOFA) provides an objective assessment of sepsis risk but requires manual calculation. We developed an EMR pipeline to automate nSOFA calculation for more granular analysis of score performance and to identify optimal alerting thresholds.

Methods

Infants born <33 weeks of gestation with LOS were included. A SQL-based pipeline calculated hourly nSOFA scores 48 h before/after sepsis evaluation. Sensitivity analysis identified the optimal timing and threshold of nSOFA for LOS mortality.

Results

Eighty episodes of LOS were identified (67 survivors, 13 non-survivor). Non-survivors had persistently elevated nSOFA scores, markedly increasing 12 h prior to culture. At sepsis evaluation, the AUC for nSOFA >2 was 0.744 (p = 0.0047); thresholds of >3 and >4 produced lower AUCs.

Conclusions

nSOFA is persistently elevated for infants with LOS mortality compared to survivors with an optimal alert threshold >2.

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Fig. 1: Total nSOFA scores before and after sepsis evaluation.
Fig. 2: Mortality prediction AUC by threshold and time.

Data availability

The datasets generated during and/or analyzed during the current study are not publicly available due to patient privacy restrictions. A limited and de-identified dataset may be available from the corresponding author on reasonable request.

References

  1. Lawn JE, Wilczynska-Ketende K, Cousens SN. Estimating the causes of 4 million neonatal deaths in the year 2000. Int J Epidemiol. 2006;35:706–18.

    Article  PubMed  Google Scholar 

  2. Stoll BJ, Hansen NI, Bell EF, Walsh MC, Carlo WA, Shankaran S, et al. Trends in care practices, morbidity, and mortality of extremely preterm neonates, 1993–2012. JAMA. 2015;314:1039–51.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Stoll BJ, Hansen N. Infections in VLBW infants: studies from the NICHD Neonatal Research Network. Semin Perinatol. 2003;27:293–301.

    Article  PubMed  Google Scholar 

  4. Stoll BJ, Hansen N, Fanaroff AA, Wright LL, Carlo WA, Ehrenkranz RA, et al. Late-onset sepsis in very low birth weight neonates: the experience of the NICHD neonatal research network. Pediatrics. 2002;110:285–91.

    Article  PubMed  Google Scholar 

  5. Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M. et al.The third international consensus definitions for sepsis and septic shock (Sepsis-3).JAMA. 2016;315:801

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Vincent J-L, Moreno R, Takala J, Willatts S, De Mendonça A, Bruining H. et al.The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure: on behalf of the working group on sepsis-related problems of the European Society of Intensive Care Medicine (see contributors to the project in the appendix).Intensive Care Med.1996;22:707–10.

    Article  CAS  PubMed  Google Scholar 

  7. Vincent J-L, de Mendonca A, Cantraine F, Moreno R, Takala J, Suter PM, et al. Use of the SOFA score to assess the incidence of organ dysfunction/failure in intensive care units: results of a multicenter, prospective study. Crit Care Med. 1998;26:1793–800.

    Article  CAS  PubMed  Google Scholar 

  8. Matics TJ, Sanchez-Pinto LN. Adaptation and validation of a pediatric sequential organ failure assessment score and evaluation of the sepsis-3 definitions in critically Ill children. JAMA Pediatr. 2017;171:e172352.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Wynn JL, Wong HR, Shanley TP, Bizzarro MJ, Saiman L, Polin RA. Time for a neonatal-specific consensus definition for sepsis. Pediatr Crit Care Med. 2014;15:523–8.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Wynn JL, Polin RA. A neonatal sequential organ failure assessment score predicts mortality to late-onset sepsis in preterm very low birth weight infants. Pediatr Res. 2020;88:85–90.

    Article  PubMed  Google Scholar 

  11. Wynn J, Kelly M, Benjamin D, Clark R, Greenberg R, Benjamin D, et al. Timing of multiorgan dysfunction among hospitalized infants with fatal fulminant sepsis. Am J Perinatol. 2016;34:633–9.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Fleiss N, Coggins SA, Lewis AN, Zeigler A, Cooksey KE, Walker LA, et al. Evaluation of the neonatal sequential organ failure assessment and mortality risk in preterm infants with late-onset infection. JAMA Netw Open. 2021;4:e2036518.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Berka I, Korček P, Janota J, Straňák Z. Neonatal sequential organ failure assessment (nSOFA) score within 72 h after birth reliably predicts mortality and serious morbidity in very preterm infants. Diagnostics. 2022;12:1342.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Albert BL, Huesman L. Development of a modified early warning score using the electronic medical record. Dimens Crit Care Nurs. 2011;30:283–92.

    Article  PubMed  Google Scholar 

  15. Escobar GJ, LaGuardia JC, Turk BJ, Ragins A, Kipnis P, Draper D. Early detection of impending physiologic deterioration among patients who are not in intensive care: development of predictive models using data from an automated electronic medical record. J Hosp Med. 2012;7:388–95.

    Article  PubMed  Google Scholar 

  16. Kipnis P, Turk BJ, Wulf DA, LaGuardia JC, Liu V, Churpek MM, et al. Development and validation of an electronic medical record-based alert score for detection of inpatient deterioration outside the ICU. J Biomed Inform. 2016;64:10–9.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Lee J, Maslove DM. Customization of a severity of illness score using local electronic medical record data. J Intensive Care Med. 2017;32:38–47.

    Article  PubMed  Google Scholar 

  18. Wickham H. Ggplot2: elegant graphics for data analysis. New York: Springer; 2009.

  19. Janett RS, Yeracaris PP. Electronic medical records in the American Health System: challenges and lessons learned. Ciênc Saúde Coletiva. 2020;25:1293–304.

    Article  Google Scholar 

  20. Campanella P, Lovato E, Marone C, Fallacara L, Mancuso A, Ricciardi W, et al. The impact of electronic health records on healthcare quality: a systematic review and meta-analysis. Eur J Public Health. 2016;26:60–4.

    Article  PubMed  Google Scholar 

  21. Kruse CS, Stein A, Thomas H, Kaur H. The use of electronic health records to support population health: a systematic review of the literature. J Med Syst. 2018;42:214.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Diamond CC, Mostashari F, Shirky C. Collecting and sharing data for population health: a new paradigm. Health Aff. 2009;28:454–66.

    Article  Google Scholar 

  23. Paul MM, Greene CM, Newton-Dame R, Thorpe LE, Perlman SE, McVeigh KH, et al. The state of population health surveillance using electronic health records: a narrative review. Popul Health Manag. 2015;18:209–16.

    Article  PubMed  Google Scholar 

  24. Parry G, Tucker J, Tarnow-Mordi W. CRIB II: an update of the clinical risk index for babies score. Lancet. 2003;361:1789–91.

    Article  PubMed  Google Scholar 

  25. Richardson DK, Corcoran JD, Escobar GJ, Lee SK. SNAP-II and SNAPPE-II: simplified newborn illness severity and mortality risk scores. J Pediatr. 2001;138:92–100.

    Article  CAS  PubMed  Google Scholar 

  26. Zeigler AC, Ainsworth JE, Fairchild KD, Wynn JL, Sullivan BA. Sepsis and mortality prediction in very low birth weight infants: analysis of HeRO and nSOFA. Am J Perinatol. 2021:s-0041-1728829.

  27. Escobar GJ, Turk BJ, Ragins A, Ha J, Hoberman B, LeVine SM, et al. Piloting electronic medical record–based early detection of inpatient deterioration in community hospitals. J Hosp Med. 2016;11:S18–24.

    Article  PubMed  Google Scholar 

  28. Chen D, Wang Z, Chen K, Zeng Q, Wang L, Xu X, et al. Classification of unlabeled cells using lensless digital holographic images and deep neural networks. Quant Imaging Med Surg. 2021;11:4137–48.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Beck AH, Sangoi AR, Leung S, Marinelli RJ, Nielsen TO, van de Vijver MJ, et al. Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Sci Transl Med. 2011;3:108ra113–108ra113.

    Article  PubMed  Google Scholar 

  30. Shah SJ, Katz DH, Selvaraj S, Burke MA, Yancy CW, Gheorghiade M, et al. Phenomapping for novel classification of heart failure with preserved ejection fraction. Circulation. 2015;131:269–79.

    Article  PubMed  Google Scholar 

  31. Benjamens S, Dhunnoo P, Meskó B. The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. NPJ Digit Med. 2020;3:118.

    Article  PubMed  PubMed Central  Google Scholar 

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Funding

This project was supported by the following grants. NIH/NCATS UL1 TR002345, NIH/NINDS K23 NS111086.

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Authors

Contributions

AH and ZAV conceived the project; AH and EE acquired the data; AH, EE, and ZAV contributed to the analysis of the data; AH wrote the initial draft of the manuscript; EE and ZAV critically revised the manuscript. All authors have reviewed and approved the final version to be published. The authors agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Corresponding author

Correspondence to Zachary A. Vesoulis.

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Competing interests

No authors have no financial ties or potential/perceived competing financial interests in relation to this work.

Ethics approval

This study was reviewed and approved under a waiver of consent per 45 CFR 46.104 by the Washington University Institutional Review Board.

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Husain, A.N., Eiden, E. & Vesoulis, Z.A. Use of an electronic medical record to optimize a neonatal sepsis score for mortality prediction. J Perinatol (2022). https://doi.org/10.1038/s41372-022-01573-5

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