Literature DB >> 30926557

LACE+ Index as Predictor of 30-Day Readmission in Brain Tumor Population.

Ian F Caplan1, Patricia Zadnik Sullivan1, David Kung1, Donald M O'Rourke1, Omar Choudhri1, Gregory Glauser1, Benjamin Osiemo2, Stephen Goodrich2, Scott D McClintock3, Neil R Malhotra4.   

Abstract

BACKGROUND: The LACE+ index (Length of stay, Acuity of admission, Charlson Comorbidity Index score, and Emergency department [ED] visits in the past 6 months) is a tool used to predict 30-day readmissions. We sought to examine this predictive tool in patients undergoing brain tumor surgery.
METHODS: Admissions and readmissions for patients undergoing craniotomy for supratentorial neoplasm at a single multihospital academic medical center were analyzed. All brain tumor cases for which the patient was alive at 30 days after surgery were included (n = 352). Simple logistic regression analyses were used to assess the ability of the LACE+ index and subsequent single variables to accurately predict the outcome measures of 30-day readmission, reoperation, and ED visit. Analysis of the model's or variable's discrimination was determined by the receiver operating characteristic curve as represented by the C-statistic.
RESULTS: The sample included admissions for craniotomy for supratentorial neoplasm (n = 352). Assessment of the LACE+ index demonstrates a 1.02× increased odds of 30-day readmission for every 1-unit increase in LACE+ score (P = 0.031, CI = 1.00-1.03). Despite this, analysis of the receiver operating characteristic curve indicates that LACE+ index has poor specificity in predicting 30-day readmission (C-statistic = 0.58). A 1-unit increase in LACE+ score also predicts a 0.98× reduction in odds of home discharge (P < 0.001, CI = 0.97-0.99, C-statistic = 0.70). But LACE+ index does not predict 30-day reoperation (P = 0.945) or 30-day ED visits (P = 0.218).
CONCLUSIONS: The results of this study demonstrate that the LACE+ index is not yet suitable as a prediction model for 30-day readmission in a brain tumor population.
Copyright © 2019 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Brain tumors; Discharge predictive tool; Hospital readmissions; LACE+ index

Year:  2019        PMID: 30926557     DOI: 10.1016/j.wneu.2019.03.169

Source DB:  PubMed          Journal:  World Neurosurg        ISSN: 1878-8750            Impact factor:   2.104


  4 in total

1.  Barriers to Implementing an Artificial Intelligence Model for Unplanned Readmissions.

Authors:  Sally L Baxter; Jeremy S Bass; Amy M Sitapati
Journal:  ACI open       Date:  2020-07

2.  Assess the Performance and Cost-Effectiveness of LACE and HOSPITAL Re-Admission Prediction Models as a Risk Management Tool for Home Care Patients: An Evaluation Study of a Medical Center Affiliated Home Care Unit in Taiwan.

Authors:  Mei-Chin Su; Yi-Jen Wang; Tzeng-Ji Chen; Shiao-Hui Chiu; Hsiao-Ting Chang; Mei-Shu Huang; Li-Hui Hu; Chu-Chuan Li; Su-Ju Yang; Jau-Ching Wu; Yu-Chun Chen
Journal:  Int J Environ Res Public Health       Date:  2020-02-02       Impact factor: 3.390

3.  The Promise for Reducing Healthcare Cost with Predictive Model: An Analysis with Quantized Evaluation Metric on Readmission.

Authors:  Kareen Teo; Ching Wai Yong; Farina Muhamad; Hamidreza Mohafez; Khairunnisa Hasikin; Kaijian Xia; Pengjiang Qian; Samiappan Dhanalakshmi; Nugraha Priya Utama; Khin Wee Lai
Journal:  J Healthc Eng       Date:  2021-11-02       Impact factor: 2.682

4.  External validation of EPIC's Risk of Unplanned Readmission model, the LACE+ index and SQLape as predictors of unplanned hospital readmissions: A monocentric, retrospective, diagnostic cohort study in Switzerland.

Authors:  Aljoscha Benjamin Hwang; Guido Schuepfer; Mario Pietrini; Stefan Boes
Journal:  PLoS One       Date:  2021-11-12       Impact factor: 3.240

  4 in total

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