Comparison of nursing postoperative diagnoses in adults and elderly inpatients

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DOI:

https://doi.org/10.5216/ree.v13i4.15172

Keywords:

Aged, Nursing Process, Nursing Diagnosis, Geriatric Nursing, Perioperative Nursing

Abstract

The objective of this study was to compare the nursing diagnoses identified in adults and elderly in the postoperative period. A total of 20 adults and 20 elderly patients hospitalized in the surgical clinic of a general hospital in Niterói-RJ-Brazil were randomly recruited, from January to April 2009.The Protocol of Carpenito was selected as a data collection instrument. A statistical descriptive and inferential analysis was carried out. The following nursing diagnoses were identified in the elderly: impaired dentition (85%), constipation (75%), and self-care deficit for dressing and grooming (70%). In adults: anxiety (85%), self-care deficit for dressing and grooming (70%) and constipation (65%). 100% of risk of infection and harmed skin integrity for both groups was identified. The nursing diagnoses more frequently found in elderly were: impaired dentition (p=0.008) and risk of falls (p=0.000). On the other hand, higher proportion of sleep deprivation occurred among adults (p=0.028). Although both groups are undergoing post-operative phases, their needs for care are different, considering the changing aging process.

Descriptors: Aged; Nursing Process; Nursing Diagnosis; Geriatric Nursing; Perioperative Nursing

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Author Biographies

  • Graziele Ribeiro Bitencourt, Fluminense Federal University
    Enfermeira, Discente do Programa de Pós-Graduação Enfermagem, nível Mestrado, Universidade Federal Fluminense (UFF). Rio de Janeiro, RJ, E-mail: gra_uff@yahoo.com.br.
  • Rosimere Ferreira Santana, Fluminense Federal University
    Enfermeira, Doutora em Enfermagem. Professor Adjunto, UFF, Rio de Janeiro, RJ, E-mail: rosifesa@gmail.com.
  • Ana Carla Dantas Cavalcanti, Fluminense Federal University

    Enfermeira, Doutora em Enfermagem. Professor Adjunto, UFF, Rio de Janeiro, RJ, E-mail: ana_carladc@yahoo.com.br.

  • Keila Mara Cassiano, Fluminense Federal University
    Bacharel em Matemática, Mestre em Estatística, Discente do Programa de Pós-Graduação em Engenharia elétrica, nível Doutorado, Pontifícia Universidade Católica do Rio de Janeiro. Professor Assistente, UFF, Rio de Janeiro, RJ, E-mail: keilamath@hotmail.com.

Published

2011-12-31

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Section

Original Article