Dataset Information

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.

SUBMITTER: Mahendra M

PROVIDER: S-EPMC7855290 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Publications

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

Mahendra Malini M Steurer-Muller Martina M Hohmann Samuel F SF Keller Roberta L RL Aswani Anil A Dudley R Adams RA

Journal of perinatology : official journal of the California Perinatal Association 20200716 3

<h4>Objective</h4>Describe NICU admission rate variation among hospitals in infants with birthweight ≥2500 g and low illness acuity, and describe factors that predict NICU admission.<h4>Study design</h4>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 hospi ...[more]

PMID: 32678315

Similar Datasets

Project description:BACKGROUND:Given system-level focus on avoidance of unnecessary hospitalizations, better understanding admission decision-making is of utility. Our study sought to identify factors associated with hospital admission versus discharge from the Emergency Department (ED) for a population of patients who were assessed as having low medical acuity at time of decision. METHODS:Using an institutional database, we identified ED admission requests received from March 1, 2018 to Feb 28, 2019 that were assessed by a physician at the time of request as potentially inappropriate based on lack of medical acuity. Focused chart review was performed to extract data related to patient demographics, socioeconomic information, measures of illness, and system-level factors such as previous healthcare utilization and day/time of presentation. A binary logistic regression model was constructed to correlate patient and system factors with disposition outcome of admission to the hospital versus discharge from the ED. Physician-reported contributors to admission decision-making and chief complaint/reason for admission were summarized. RESULTS:A total of 349 (77.2%) of 452 calls resulted in admission to the hospital and 103 (22.8%) resulted in discharge from the ED. Predictors of admission included age over 65 (OR 3.5 [95%CI 1.1-11.6], p?=?0.039), homelessness (OR 3.3 [95% CI 1.7-6.4], p=0.001), and night/weekend presentation (OR 2.0 [95%CI 1.1-3.5], p?=?0.020). The most common contributing factors to the decision to admit reported by the responding physician included: lack of outpatient social support (35.8% of admissions), homelessness (33.0% of admissions), and substance use disorder (23.5% of admissions). CONCLUSIONS:Physician medical decision-making regarding the need for hospitalization incorporates consideration of individual patient characteristics, social setting, and system-level barriers. Interventions aimed at reducing unnecessary hospitalizations, especially those involving patients with low medical acuity, should focus on underlying unmet needs and involve a broad set of perspectives.

Dataset Information

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

Objective

Study design

Results

Conclusion/relevance

Publications

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

Similar Datasets

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