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Accuracy of Defining Characteristics for Nursing Diagnoses Related to Patients with Respiratory Deterioration
Author(s) -
Vieira Laura F.,
Fernandes Vivian R.,
Papathanassoglou Elizabeth,
Azzolin Karina de O.
Publication year - 2020
Publication title -
international journal of nursing knowledge
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.545
H-Index - 23
eISSN - 2047-3095
pISSN - 2047-3087
DOI - 10.1111/2047-3095.12272
Subject(s) - hypercapnia , medical diagnosis , medicine , cluster (spacecraft) , respiratory system , ventilation (architecture) , logistic regression , medical record , retrospective cohort study , anesthesia , intensive care medicine , pathology , computer science , mechanical engineering , engineering , programming language
Purpose To evaluate accuracy of defining characteristics (DCs) for impaired gas exchange (IGE), impaired spontaneous ventilation (ISV), and ineffective breathing pattern (IBP) in respiratory deterioration. Methods This study is a retrospective analysis of medical records. The accuracy and predictive ability of DC or of clusters are calculated. Findings In this study, 391 records were evaluated. For IGE, DCs or clusters with higher efficiency were “hypercapnia” (78%), “somnolence” (74.4%), and “hypercapnia + tachycardia” (88%); for ISV, the cluster with higher efficiency was “increased heart rate ± decrease in cooperation” (70.1%); and for IBP, no DC or cluster exceeded 70% efficiency. These were confirmed by logistic regression. Conclusion Few DCs had adequate efficiency for respiratory nursing diagnoses, while in some cases clusters accounted for higher efficiency. Implications for Nursing Practice Clusters of DC may be relevant for respiratory diagnoses.