Literature DB >> 32046199

Evaluation of Pain Management after Surgery: An Observational Study.

Regina Sierżantowicz1, Jolanta Lewko2, Dorota Bitiucka1, Karolina Lewko3, Bianka Misiak4, Jerzy Robert Ładny5.   

Abstract

Background and
Objectives: Choosing a pain management strategy is essential for improving recovery after surgery. Effective pain management reduces the stress response, facilitates mobilization, and improves the quality of the postoperative period. The aim of the study was to assess the effectiveness of pain management in patients after surgery. Materials and
Methods: The study included 216 patients operated on in the following surgical wards: the Department of Cardiosurgery and the Department of General and Endocrine Surgery. Patients were hospitalized on average for 6 ± 4.5 days. Patients were randomly selected for the study using a questionnaire technique with a numerical rating scale.
Results: Immediately after surgery, pre-emptive analgesia, multimodal analgesia, and analgosedation were used significantly more frequently than other methods (p < 0.001). In the subsequent postoperative days, the method of administering drugs on demand was used most often. Patients with confirmed complications during postoperative wound healing required significantly more frequent use of drugs from Steps 2 and 3 of the World Health Organization (WHO) analgesic ladder compared with patients without complications.
Conclusion: The mode of patient admission for surgery significantly affected the level of pain perception. Different pain management methods were used and not every method was effective.

Entities:  

Keywords:  pain; postoperative management; surgery

Year:  2020        PMID: 32046199     DOI: 10.3390/medicina56020065

Source DB:  PubMed          Journal:  Medicina (Kaunas)        ISSN: 1010-660X            Impact factor:   2.430


  2 in total

1.  Patients with gastroenteric tumor after upper abdominal surgery were more likely to require rescue analgesia than lower abdominal surgery.

Authors:  Ting-Ting Li; Fei Liu; Ting-Hua Wang; Quan-Yuan Chang; Liu-Lin Xiong; Yan-Jun Chen; Qi-Jun Li
Journal:  BMC Anesthesiol       Date:  2022-05-23       Impact factor: 2.376

2.  Design of a Diagnostic System for Patient Recovery Based on Deep Learning Image Processing: For the Prevention of Bedsores and Leg Rehabilitation.

Authors:  Donggyu Choi; Jongwook Jang
Journal:  Diagnostics (Basel)       Date:  2022-01-21
  2 in total

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