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Related Experiment Videos

Outcomes analysis in cranial base surgery - preliminary results

B Holmes1, L Sekhar, S Sofaer

  • 1Department of Surgery and Health Care Sciences, The George Washington University Medical Center, Washington, DC, USA.

Acta Neurochirurgica
|January 1, 1995
PubMed
Summary

Predictors of cranial base surgery outcomes were analyzed. Severe brainstem compression, large tumors, and major artery encasement correlate with incomplete resection, while age and low KPS increase stroke risk and hospital stay.

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Area of Science:

  • Neurosurgery
  • Oncology
  • Medical Informatics

Background:

  • A comprehensive outcomes analysis system for Cranial Base Surgery is currently lacking in published literature.
  • This study addresses the need for data on predictors influencing management outcomes in Cranial Base Surgery.

Purpose of the Study:

  • To analyze predictors of management outcomes in Cranial Base Surgery.
  • To identify factors influencing tumor resection completeness, complications, and patient recovery.
  • To evaluate preoperative features impacting treatment costs and patient return to work.

Main Methods:

  • Prospective analysis of 79 consecutive patients undergoing Cranial Base Surgery (excluding pituitary microadenoma).
  • Outcomes assessed included completeness of resection, neurological morbidity, and return to work/independent living.

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  • Preoperative features analyzed for impact on surgical procedures, hospital/ICU stay, and return-to-work time.

    • Main Results:

    • Severe brainstem compression, large tumor size (>3cm), high cavernous sinus grade, and major cerebral artery encasement are linked to incomplete tumor resection (p < 0.05).
    • Patient age >65, preoperative Karnofsky Performance Score (KPS) <80, and severe brainstem compression increase stroke risk (p < 0.05).
    • Age >65 and preoperative KPS <80 correlate with longer hospital stays (p < 0.05).

    Conclusions:

    • Identified key predictors for outcomes in Cranial Base Surgery, including tumor characteristics and patient factors.
    • Proposed a model for outcomes analysis using a database, incorporating non-operated patients and long-term quality of life measures.
    • Highlights the importance of preoperative assessment for predicting surgical outcomes and resource utilization.