PDF(Pubmed)
Abstract：
OBJECTIVE: The rate of readmission after hospitalisation for respiratory diseases has become a common and challenging clinical problem. Social and functional patient variables could help identify cases at high risk of readmission. The aim was to identify the nursing diagnoses that were associated with readmission after hospitalisation for respiratory disease in Spain.
METHODS: Case-control study within the cohort of patients admitted for respiratory disease during 2016-19 in a tertiary public hospital in Spain (n = 3781).
METHODS: Cases were patients who were readmitted within the first 30 days of discharge, and their controls were the remaining patients. All nursing diagnoses (n = 130) were collected from the electronic health record. They were then grouped into 29 informative diagnostic categories. Clinical confounder-adjusted odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated using logistic regression models.
RESULTS: The readmission rate was 13.1%. The nursing diagnoses categories \'knowledge deficit\' (OR: 1.61; 95%CI: 1.13-2.31), \'impaired skin integrity and risk of ulcer infection\' (OR: 1.45; 95%CI: 1.06-1.97) and \'activity intolerance associated with fatigue\' (OR: 1.56; 95%CI: 1.21-2.01) were associated with an increased risk of suffering an episode of hospital readmission rate at 30% after hospital discharge, and this was independent of sociodemographic background, care variables and comorbidity.
UNASSIGNED: The nursing diagnoses assigned as part of the care plan of patients during hospital admission may be useful for predicting readmissions.
METHODS: Case-control study within the cohort of patients admitted for respiratory disease during 2016-19 in a tertiary public hospital in Spain (n = 3781).
METHODS: Cases were patients who were readmitted within the first 30 days of discharge, and their controls were the remaining patients. All nursing diagnoses (n = 130) were collected from the electronic health record. They were then grouped into 29 informative diagnostic categories. Clinical confounder-adjusted odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated using logistic regression models.
RESULTS: The readmission rate was 13.1%. The nursing diagnoses categories \'knowledge deficit\' (OR: 1.61; 95%CI: 1.13-2.31), \'impaired skin integrity and risk of ulcer infection\' (OR: 1.45; 95%CI: 1.06-1.97) and \'activity intolerance associated with fatigue\' (OR: 1.56; 95%CI: 1.21-2.01) were associated with an increased risk of suffering an episode of hospital readmission rate at 30% after hospital discharge, and this was independent of sociodemographic background, care variables and comorbidity.
UNASSIGNED: The nursing diagnoses assigned as part of the care plan of patients during hospital admission may be useful for predicting readmissions.
摘要：
目的：呼吸系统疾病住院后的再入院率已成为一个常见且具有挑战性的临床问题。社会和功能患者变量可以帮助识别再入院风险高的病例。目的是确定与西班牙呼吸道疾病住院后再次入院相关的护理诊断。
方法：在西班牙一家三级公立医院（n=3781）的2016-19年期间因呼吸道疾病入院的患者队列中进行的病例对照研究。
方法：病例是指在出院前30天内再次入院的患者，他们的对照组是其余的病人.所有护理诊断（n=130）均从电子健康记录中收集。然后将它们分为29个信息诊断类别。使用逻辑回归模型计算临床混杂校正比值比（OR）和95％置信区间（95％CIs）。
结果：再入院率为13.1%。护理诊断类别“知识缺陷”(OR：1.61；95CI：1.13-2.31)，“皮肤完整性受损和溃疡感染风险”（OR：1.45;95CI：1.06-1.97）和“与疲劳相关的活动不耐受”（OR：1.56;95CI：1.21-2.01）与出院后30％再次入院的风险增加相关，这与社会人口统计学背景无关，护理变量和合并症。
■住院期间作为患者护理计划的一部分而指定的护理诊断可能有助于预测再入院。
方法：在西班牙一家三级公立医院（n=3781）的2016-19年期间因呼吸道疾病入院的患者队列中进行的病例对照研究。
方法：病例是指在出院前30天内再次入院的患者，他们的对照组是其余的病人.所有护理诊断（n=130）均从电子健康记录中收集。然后将它们分为29个信息诊断类别。使用逻辑回归模型计算临床混杂校正比值比（OR）和95％置信区间（95％CIs）。
结果：再入院率为13.1%。护理诊断类别“知识缺陷”(OR：1.61；95CI：1.13-2.31)，“皮肤完整性受损和溃疡感染风险”（OR：1.45;95CI：1.06-1.97）和“与疲劳相关的活动不耐受”（OR：1.56;95CI：1.21-2.01）与出院后30％再次入院的风险增加相关，这与社会人口统计学背景无关，护理变量和合并症。
■住院期间作为患者护理计划的一部分而指定的护理诊断可能有助于预测再入院。
