Literature DB >> 31102908

The LACE+ index fails to predict 30-90 day readmission for supratentorial craniotomy patients: A retrospective series of 238 surgical procedures.

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

Abstract

OBJECTIVE: The LACE + index (Length of stay, Acuity of admission, Charlson Comorbidity Index (CCI) score, and Emergency department visits in the past 6 months) is a tool utilized to predict 30-90 day readmission and other secondary outcomes. We sought to examine the effectiveness of this predictive tool in patients undergoing brain tumor surgery. PATIENTS AND METHODS: Admissions and readmissions for patients undergoing craniotomy for supratentorial neoplasm at a single, multi-hospital, academic medical center, were analyzed. Key data was prospectively collected with the Neurosurgery Quality Improvement Initiative (NQII)-EpiLog tool. This included all supratentorial craniotomy cases for which the patient was alive at 90 days after surgery (n = 238). 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 30-90 day readmission, 30-90 day emergency department (ED) visit, and 30-90 day reoperation. 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 = 238) from 227 patients, of which 50.00% were female (n = 119). The average LACE + index score was 53.48 ± 16.69 (Range 9-83). The LACE + index did not accurately predict 30-90 day readmissions (P = 0.127), 30-90 day ED visits (P = 0.308), nor reoperations (P = 0.644). ROC confirmed that the LACE + index was little better than random chance at predicting these events in this population (C-statistic = 0.51-0.58). However, a single unit increase in LACE + leads to a 0.97 times reduction in the odds of being discharged home with fair predictive accuracy (P < 0.001, CI = 0.96-0.98, C-statistic = 0.69).
CONCLUSION: The results of this study show that the LACE + index is ill-equipped to predict 30-90 day readmissions in the brain tumor population and further analysis of significant covariates or other prediction tools should be undertaken.
Copyright © 2019 Elsevier B.V. All rights reserved.

Entities:  

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

Year:  2019        PMID: 31102908     DOI: 10.1016/j.clineuro.2019.04.026

Source DB:  PubMed          Journal:  Clin Neurol Neurosurg        ISSN: 0303-8467            Impact factor:   1.876


  6 in total

Review 1.  LACE Index to Predict the High Risk of 30-Day Readmission: A Systematic Review and Meta-Analysis.

Authors:  Vasuki Rajaguru; Whiejong Han; Tae Hyun Kim; Jaeyong Shin; Sang Gyu Lee
Journal:  J Pers Med       Date:  2022-03-30

2.  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

3.  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

4.  Factors associated with unplanned readmissions and costs following resection of brain metastases in the United States.

Authors:  Raees Tonse; Alexandra Townsend; Muni Rubens; Vitaly Siomin; Michael W McDermott; Martin C Tom; Matthew D Hall; Yazmin Odia; Manmeet S Ahluwalia; Minesh P Mehta; Rupesh Kotecha
Journal:  Sci Rep       Date:  2021-11-12       Impact factor: 4.379

5.  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

6.  Derivation and Validation of the Cancer READMIT Score: A Readmission Risk Scoring System for Patients With Solid Tumor Malignancies.

Authors:  Joanna-Grace M Manzano; Heather Lin; Hui Zhao; Josiah Halm; Maria E Suarez-Almazor
Journal:  JCO Oncol Pract       Date:  2021-08-06
  6 in total

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