Descriptive Data Analysis on the Effect of General Anesthesia Versus Epidural Anesthesia in Postoperative Patients Regarding Pain

Authors

  • Nabila Arif Post RN candidate The superior university Lahore.
  • Farah shafique Superior Nursing College Lahore Pakistan
  • Hafiza Ambreen Superior Nursing College Lahore Pakistan
  • Qurat ul ain Uroosa Nishter medical university Multan, Lahore

DOI:

https://doi.org/10.62019/abbdm.v4i3.219

Keywords:

Keywords: Pain Quality Scale, Visual Analog Scale, (GEA), anesthesia, psychological

Abstract

This study aims to investigate the impact of different anesthesia techniques, specifically regional anesthesia (RA) and general anesthesia (GEA), on postoperative pain, recovery, and overall experience in women undergoing cesarean sections. The primary objective is to compare pain levels and analgesic needs between RA and GEA, using tools like the Short-Form McGill Pain Questionnaire (SF–MPQ), Visual Analog Scale (VAS), and the Pain Quality Scale. Additionally, the study will assess recovery outcomes, including the time to first independent mobilization and the onset of lactation, alongside the emotional and psychological effects of each anesthesia method. A sample of 120-150 patients, selected via convenience sampling from private hospitals, will complete a questionnaire designed to collect both quantitative data (pain levels, mobilization time) and qualitative data (emotional experiences, satisfaction). The study hypothesizes that RA will result in lower pain levels and reduced analgesic consumption compared to GEA, as well as faster recovery, including quicker mobilization and lactation. The findings aim to provide valuable insights into optimizing postoperative care and anesthesia choices for cesarean deliveries, potentially improving patient outcomes and guiding clinical practices.

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Published

2024-09-12

How to Cite

Descriptive Data Analysis on the Effect of General Anesthesia Versus Epidural Anesthesia in Postoperative Patients Regarding Pain. (2024). The Asian Bulletin of Big Data Management , 4(3), . . https://doi.org/10.62019/abbdm.v4i3.219

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