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

Journal of Perinatology volume 41, pages 478–485 (2021)Cite this article

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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.

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

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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.

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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

Authors and Affiliations

  1. Division of Pediatric Critical Care, Department of Pediatrics, University of California at San Francisco, San Francisco, CA, USA

    Malini Mahendra & Martina Steurer-Muller

  2. Philip R. Lee Institute for Health Policy Studies, University of California at San Francisco, San Francisco, CA, USA

    Malini Mahendra & R. Adams Dudley

  3. Division of Neonatology, Department of Pediatrics, University of California at San Francisco, San Francisco, CA, USA

    Martina Steurer-Muller & Roberta L. Keller

  4. Vizient, Chicago, IL, USA

    Samuel F. Hohmann

  5. Department of Health Systems Management, Rush University, Chicago, IL, USA

    Samuel F. Hohmann

  6. Department of Industrial Engineering and Operations Research, University of California at Berkeley, Berkeley, CA, USA

    Anil Aswani

  7. The Center for Healthcare Value, University of California, San Francisco, CA, USA

    R. Adams Dudley

  8. Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Department of Medicine, University of Minnesota, Minneapolis, MN, USA

    R. Adams Dudley

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  1. Malini Mahendra
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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.

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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 41, 478–485 (2021). https://doi.org/10.1038/s41372-020-0723-0

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  • DOI: https://doi.org/10.1038/s41372-020-0723-0

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