Predicting Postoperative Outcomes in Brain Tumor Patients With a 5-Factor Modified Frailty Index

  • Sakibul Huq
    Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland
  • Adham M Khalafallah
    Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland
  • Adrian E Jimenez
    Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland
  • Abhishek Gami
    Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland
  • Shravika Lam
    Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland
  • Miguel A Ruiz-Cardozo
    Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland
  • Leonardo A P Oliveira
    Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland
  • Debraj Mukherjee
    Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland

書誌事項

公開日
2020-08-17
権利情報
  • https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model
DOI
  • 10.1093/neuros/nyaa335
公開者
Ovid Technologies (Wolters Kluwer Health)

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説明

<jats:title>Abstract</jats:title> <jats:sec> <jats:title>BACKGROUND</jats:title> <jats:p>Frailty indices may represent useful decision support tools to optimize modifiable drivers of quality and cost in neurosurgical care. However, classic indices are cumbersome to calculate and frequently require unavailable data. Recently, a more lean 5-factor modified frailty index (mFI-5) was introduced, but it has not yet been rigorously applied to brain tumor patients.</jats:p> </jats:sec> <jats:sec> <jats:title>OBJECTIVE</jats:title> <jats:p>To investigate the predictive value of the mFI-5 on length of stay (LOS), complications, and charges in surgical brain tumor patients.</jats:p> </jats:sec> <jats:sec> <jats:title>METHODS</jats:title> <jats:p>We retrospectively reviewed data for brain tumor patients who underwent primary surgery from 2017 to 2018. Bivariate (ANOVA) and multivariate (logistic and linear regression) analyses assessed the predictive power of the mFI-5 on postoperative outcomes.</jats:p> </jats:sec> <jats:sec> <jats:title>RESULTS</jats:title> <jats:p>Our cohort included 1692 patients with a mean age of 55.5 yr and mFI-5 of 0.80. Mean intensive care unit (ICU) and total LOS were 1.69 and 5.24 d, respectively. Mean pulmonary embolism (PE)/deep vein thrombosis (DVT), physiological/metabolic derangement, respiratory failure, and sepsis rates were 7.2%, 1.1%, 1.6%, and 1.7%, respectively. Mean total charges were $42 331. On multivariate analysis, each additional point on the mFI-5 was associated with a 0.32- and 1.38-d increase in ICU and total LOS, respectively; increased odds of PE/DVT (odds ratio (OR): 1.50), physiological/metabolic derangement (OR: 3.66), respiratory failure (OR: 1.55), and sepsis (OR: 2.12); and an increase in total charges of $5846.</jats:p> </jats:sec> <jats:sec> <jats:title>CONCLUSION</jats:title> <jats:p>The mFI-5 is a pragmatic and actionable tool which predicts LOS, complications, and charges in brain tumor patients. It may guide future efforts to risk-stratify patients with subsequent impact on postoperative outcomes.</jats:p> </jats:sec>

収録刊行物

  • Neurosurgery

    Neurosurgery 88 (1), 147-154, 2020-08-17

    Ovid Technologies (Wolters Kluwer Health)

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