Diagnostic accuracy of clinical outcome prediction using nursing data in intensive care patients: A systematic review

Abstract

Background: Nursing data consist of observations of patients' conditions and information on nurses' clinical judgment based on critically ill patients' behavior and physiological signs. Nursing data in electronic health records were recently emphasized as important predictors of patients' deterioration but have not been systematically reviewed. Objective: We conducted a systematic review of prediction models using nursing data for clinical outcomes, such as prolonged hospital stay, readmission, and mortality in intensive care patients, compared to physiological data only. In addition, the type of nursing data used in prediction model developments was investigated. Design: A systematic review. Methods: PubMed, CINAHL, Cochrane CENTRAL, EMBASE, IEEE Xplore Digital Library, Web of Science, and Scopus were searched. Clinical outcome prediction models using nursing data for intensive care patients were included. Clinical outcomes were prolonged hospital stay, readmission, and mortality. Data were extracted from selected studies such as study design, data source, outcome definition, sample size, predictors, reference test, model development, model performance, and evaluation. The risk of bias and applicability was assessed using the Prediction model Risk of Bias Assessment Tool checklist. Descriptive summaries were produced based on paired forest plots and summary receiver operating characteristic curves. Results: Sixteen studies were included in the systematic review. The data types of predictors used in prediction models were categorized as physiological data, nursing data, and clinical notes. The types of nursing data consisted of nursing notes, assessments, documentation frequency, and flowsheet comments. The studies using physiological data as a reference test showed higher predictive performance in combined data or nursing data than in physiological data. The overall risk of bias indicated that most of the included studies have a high risk. Conclusions: This study was conducted to identify and review the diagnostic accuracy of clinical outcome prediction using nursing data in intensive care patients. Most of the included studies developed models using nursing notes, and other studies used nursing assessments, documentation frequency, and flowsheet comments. Although the findings need careful interpretation due to the high risk of bias, the area under the curve scores of nursing data and combined data were higher than physiological data alone. It is necessary to establish a strategy in prediction modeling to utilize nursing data, clinical notes, and physiological data as predictors, considering the clinical context rather than physiological data alone. Registration: The protocol for this study is registered with PROSPERO (registration number: CRD42021273319). © 2022 The Authors