Predicting NICU admissions in near-term and term infants with low illness acuity

Abstract

Objective

Describe NICU admission rate variation among hospitals in infants with birthweight ≥2500 g and low illness acuity, and describe factors that predict NICU admission.

Study design

Retrospective study from the Vizient Clinical Data Base/Resource Manager®. Support vector machine methodology was used to develop statistical models using (1) patient characteristics (2) only the indicator for the inborn hospital and (3) patient characteristics plus indicator for the inborn hospital.

Results

NICU admission rates of 427,449 infants from 154 hospitals ranged from 0 to 28.6%. C-statistics for the patient characteristics model: 0.64 (Confidence Interval (CI) 0.62–0.65), hospital only model: 0.81 (CI, 0.81–0.82), and patient characteristic plus hospital variable model: 0.84 (CI, 0.83–0.84).

Conclusion/relevance

There is wide variation in NICU admission rates in infants with low acuity diagnoses. In all cohorts, birth hospital better predicted NICU admission than patient characteristics alone.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: The inclusion and exclusion criteria are described by a flowchart.
Fig. 2: ROC Curves.

Code availability

Data was directly downloaded from the Vizient website and was cleaned and analyzed using Rstudio version 1.1.463 and Python-Spyder version 2.7. Computer code used in this study may be available from the authors after approval by the University of California, San Francisco IRB.

References

  1. 1.

    Harrison W, Goodman D. Epidemiologic trends in neonatal intensive care, 2007-2012. JAMA Pediatr. 2015;169:855–62. https://doi.org/10.1001/jamapediatrics.2015.1305

    Article  PubMed  Google Scholar 

  2. 2.

    Schulman J, Braun D, Lee HC, Profit J, Duenas G, Bennett MV, et al. Association between neonatal intensive care unit admission rates and illness acuity. JAMA Pediatr. 2018;172:17–23. https://doi.org/10.1001/jamapediatrics.2017.3913

    Article  PubMed  Google Scholar 

  3. 3.

    Ziegler KA, Paul DA, Hoffman M, Locke R. Variation in NICU admission rates without identifiable cause. Hosp Pediatr. 2016;6:255–60. https://doi.org/10.1542/hpeds.2015-0058

    Article  PubMed  Google Scholar 

  4. 4.

    Edwards EM, Horbar JD. Variation in Use by NICU Types in the United States. Pediatrics 2018;142:e20180457 https://doi.org/10.1542/peds.2018-0457

    Article  PubMed  Google Scholar 

  5. 5.

    Freedman S. Capacity and utilization in health care: the effect of empty beds on neonatal intensive care admission. Am Econ J Econ Policy 2016;8:154–85. https://doi.org/10.1257/pol.20120393.Capacity

    Article  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Harrison WN, Wasserman JR, Goodman DC. Regional variation in neonatal intensive care admissions and the relationship to bed supply. J Pediatr. 2018;192:73–79.e4. https://doi.org/10.1016/j.jpeds.2017.08.028

    Article  PubMed  Google Scholar 

  7. 7.

    Carroll AE. The concern for supply-sensitive neonatal intensive care unit care if you build them, they will come. 2015:11–12. https://doi.org/10.1377/hlthaff.w4

  8. 8.

    Loehrer AP, Chang DC, Scott JW, Hutter MM, Patel VI, Lee JE, et al. Association of the affordable care act medicaid expansion with access to and quality of care for surgical conditions. JAMA Surg. 2018;153. https://doi.org/10.1001/jamasurg.2017.5568

  9. 9.

    McHugh KE, Hillman DG, Gurka MJ, Gutgesell HP. Three-stage palliation of hypoplastic left heart syndrome in the university healthSystem consortium. Congenit Heart Dis. 2010;5:8–15. https://doi.org/10.1111/j.1747-0803.2009.00367.x

    Article  PubMed  Google Scholar 

  10. 10.

    Dean PN, Hillman DG, McHugh KE, Gutgesell HP. Inpatient costs and charges for surgical treatment of hypoplastic left heart syndrome. Pediatrics . 2011;128:e1181–e1186. https://doi.org/10.1542/peds.2010-3742

    Article  PubMed  Google Scholar 

  11. 11.

    Basu SK, Fernandez ID, Fisher SG, Asselin BL, Lyman GH. Length of stay and mortality associated with febrile neutropenia among children with cancer. J Clin Oncol. 2005;23:7958–66. https://doi.org/10.1200/JCO.2005.01.6378

    Article  PubMed  Google Scholar 

  12. 12.

    Kane JM, Harbert J, Hohmann S, Pillai S, Behal R, selip DJT. Case volume and outcomes of congenital diaphragmatic hernia surgery in academic medical centers. Am J Perinatol. 2015;32:845–52. https://doi.org/10.1055/s-0034-1543980

    Article  PubMed  Google Scholar 

  13. 13.

    Kane JM, Scalcucci J, Hohmann SF, Johnson T, Behal R. Using administrative data for mortality risk adjustment in pediatric congenital cardiac surgery. Pediatr Crit Care Med. 2013;14:491–8. https://doi.org/10.1097/pcc.0b013e31828a87ea

    Article  PubMed  Google Scholar 

  14. 14.

    Wang ML, Dorer DJ, Fleming MP, Catlin EA. Clinical outcomes of near-term infants. Pediatrics 2004;114:372–6. https://doi.org/10.1542/peds.114.2.372

    Article  PubMed  Google Scholar 

  15. 15.

    Sarici SÜ, Serdar MA, Korkmaz A, Erdem G, Oran O, Tekinalp G, et al. Incidence, course, and prediction of hyperbilirubinemia in near-term and term newborns. Pediatrics. 2004;113:775–80. https://doi.org/10.1542/peds.113.4.775

    Article  PubMed  Google Scholar 

  16. 16.

    World Health Organization. Global Nutrition Targets 2025: Low birth weight policy brief. Report No. WHO/NMH/NHD/14.5.

  17. 17.

    Klugman D, Berger JT, Sable CA, He J, Khandelwal SG, Slonim AD. Pediatric patients hospitalized with myocarditis: a multi-institutional analysis. Pediatr Cardiol. 2010;31:222–8. https://doi.org/10.1007/s00246-009-9589-9

    Article  PubMed  Google Scholar 

  18. 18.

    McCormick PJ, Lin HM, Deiner SGLM. Validation of the All Patient Refined Diagnosis Related Group (APR-DRG) risk of mortality and severity of illness modifiers as a measure of perioperative risk. J Med Syst. 2018;22. https://doi.org/10.1007/s10916-018-0936-3.

  19. 19.

    Bratton SL, Odetola FO, McCollegan J, Cabana MD, Levy FHKH. Regional variation in ICU care for pediatric patients with asthma. J Pediatr. 2005;147:355–61.

    Article  Google Scholar 

  20. 20.

    Muldoon JH. MEASUREMENT structure and performance of different DRG classification systems for neonatal medicine. Pediatrics 1999;103(January):302–18.

    CAS  PubMed  Google Scholar 

  21. 21.

    Clinical Classifications Software (CCS) for ICD-9-CM. https://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp

  22. 22.

    Horgan MJ. Management of the Late Preterm Infant. Pediatr Clin North Am. 2015;62:439–51. https://doi.org/10.1016/j.pcl.2014.11.007

    Article  PubMed  Google Scholar 

  23. 23.

    Bhutani VK, Johnson LH, Maisels MJ, Newman TB, Phibbs C, Stark AR, et al. Kernicterus: epidemiological strategies for its prevention through systems-based approaches. J Perinatol. 2004;24:650–62. https://doi.org/10.1038/sj.jp.7211152

    Article  PubMed  Google Scholar 

  24. 24.

    Benitz WE. Hospital stay for healthy term newborn infants. Pediatrics 2015;135:948–53. https://doi.org/10.1542/peds.2015-0699

    Article  PubMed  Google Scholar 

  25. 25.

    Hastie T, Tibshirani RFJ. The elements of statistical learning data mining, inference, and prediction. 2nd ed. New York, NY: Springer; 2016.

    Google Scholar 

  26. 26.

    Platt J. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv Large Margin Classif. 1999;10:61–74.

    Google Scholar 

  27. 27.

    Chen CL, Lin GA, Bardach NS, Clay TH, Boscardin WJ, Gelb AW, et al. Preoperative medical testing in medicare patients undergoing cataract surgery. N. Engl J Med. 2015;372:1530–8. https://doi.org/10.1056/NEJMsa1410846

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Larsen K, Merlo J. Appropriate assessment of neighborhood effects on individual health: integrating random and fixed effects in multilevel logistic regression. Am J Epidemiol. 2005;161:81–8.

    Article  Google Scholar 

  29. 29.

    Merlo J, Chaix B, Ohlsson H, Beckman A, Johnell K, Hjerpe P, et al. A brief conceptual tutorial of multilevel analysis in social epidemiology: using measures of clustering in multilevel logistic regression to investigate contextual phenomena. J Epidemiol Community Health. 2006;60:290–7.

    Article  Google Scholar 

  30. 30.

    Maisels MJ, Bhutani VK, Bogen D, Newman TB, Stark AR, Watchko JF. Hyperbilirubinemia in the Newborn Infant >=35 Weeks’ Gestation: an update with clarifications. Pediatrics 2009;124:1193–8. https://doi.org/10.1542/peds.2009-0329

    Article  PubMed  Google Scholar 

  31. 31.

    Wickremasinghe AC, Kuzniewicz MW, McCulloch CE, Newman TB. Efficacy of subthreshold newborn phototherapy during the birth hospitalization in preventing readmission for phototherapy. JAMA Pediatr. 2018;172:378–85. https://doi.org/10.1001/jamapediatrics.2017.5630

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Funding

Support for this study was received from an NIH T32 award (HD049303-11). This work was performed at Benioff Children’s Hospital, San Francisco and the University of California, San Francisco. Dr MM work was supported by an NIH T32 award (HD049303-11).

Author information

Affiliations

Authors

Contributions

Dr MM made substantial contributions to designing the study, analyzing the data, and interpreting the results, and writing the paper. Drs MSM, RLK, and RAD made substantial contributions to designing the study, interpreting the data, and editing the paper. Dr AA made substantial contributions to designing the study, providing expertize on statistical methodologies, interpreting the data, and editing the paper. Dr SFH made substantial contributions to designing the study and providing expertize in analysis of the data set. All authors reviewed and approved of the final version of the paper.

Corresponding author

Correspondence to Malini Mahendra.

Ethics declarations

Conflict of interest

The authors declare they have no conflict of interest

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Mahendra, M., Steurer-Muller, M., Hohmann, S.F. et al. Predicting NICU admissions in near-term and term infants with low illness acuity. J Perinatol (2020). https://doi.org/10.1038/s41372-020-0723-0

Download citation

Search