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Identification of Predictors for Clinical Deterioration in Patients With Coronavirus Disease 2019 via Electronic Nursing Records: A Retrospective Observational Study.
Journal of Medical Internet Research 2024 Februrary 28
BACKGROUND: Few studies have used standardized nursing records with Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) to identify predictors of clinical deterioration.
OBJECTIVE: To standardize the nursing documentation records of patients with coronavirus disease 2019 (COVID-19) using SNOMED CT and identify predictive factors of clinical deterioration in patients with COVID-19 via the standardized nursing records.
METHODS: In this study 57,558 nursing statements from 226 patients with COVID-19 were analyzed. Among these, 45,852 statements were from 207 patients in the stable (control) group and 11,706 from 19 patients in the exacerbated (case) group who were transferred to intensive care unit within seven days. The data were collected between December 2019 and June 2022. These nursing statements were standardized using the SNOMED CT International edition released on November 30, 2022. The 260 unique nursing statements that accounted for the top 90% of 57,558 statements were selected as the mapping source and mapped into SNOMED CT concepts based on its meaning by two experts with more than five years of SNOMED CT mapping experience. To identify the main features of nursing statements associated with the exacerbation of patient condition, random forest algorithms were used, and optimal hyperparameters were selected for nursing problems or outcomes and nursing procedure-related statements. Additionally, logistic regression analysis was conducted to identify features that determine clinical deterioration in patients with COVID-19.
RESULTS: All nursing statements were semantically mapped to SNOMED CT concepts for clinical finding, situation with explicit context, and procedure hierarchies. The inter-rater reliability of the mapping results was 87.7%. The most important features calculated by random forest were "oxygen saturation below reference range," "dyspnea," "tachypnea," and "cough" in clinical finding, and "oxygen therapy," "pulse oximetry monitoring," "temperature taking," "notification of physician," and "education about isolation for infection control" in procedure. Among these, "dyspnea" and "inadequate food diet" in clinical finding increased clinical deterioration risk (dyspnea: Odds ratio [OR] 5.99, 95% confidence interval [CI] 2.25-20.29; in adequate food diet: OR 10.0, 95% CI 2.71-40.84), and "oxygen therapy" and "notification of physician" in procedure also increased the risk of clinical deterioration in patients with COVID-19 (oxygen therapy: OR 1.89, 95% CI 1.25-3.05; notification of physician: OR 1.72, 95% CI 1.02-2.97).
CONCLUSIONS: The study employed SNOMED CT to express and standardize nursing statements. Further, it revealed the importance of standardized nursing records as predictive variables for clinical deterioration in patients.
OBJECTIVE: To standardize the nursing documentation records of patients with coronavirus disease 2019 (COVID-19) using SNOMED CT and identify predictive factors of clinical deterioration in patients with COVID-19 via the standardized nursing records.
METHODS: In this study 57,558 nursing statements from 226 patients with COVID-19 were analyzed. Among these, 45,852 statements were from 207 patients in the stable (control) group and 11,706 from 19 patients in the exacerbated (case) group who were transferred to intensive care unit within seven days. The data were collected between December 2019 and June 2022. These nursing statements were standardized using the SNOMED CT International edition released on November 30, 2022. The 260 unique nursing statements that accounted for the top 90% of 57,558 statements were selected as the mapping source and mapped into SNOMED CT concepts based on its meaning by two experts with more than five years of SNOMED CT mapping experience. To identify the main features of nursing statements associated with the exacerbation of patient condition, random forest algorithms were used, and optimal hyperparameters were selected for nursing problems or outcomes and nursing procedure-related statements. Additionally, logistic regression analysis was conducted to identify features that determine clinical deterioration in patients with COVID-19.
RESULTS: All nursing statements were semantically mapped to SNOMED CT concepts for clinical finding, situation with explicit context, and procedure hierarchies. The inter-rater reliability of the mapping results was 87.7%. The most important features calculated by random forest were "oxygen saturation below reference range," "dyspnea," "tachypnea," and "cough" in clinical finding, and "oxygen therapy," "pulse oximetry monitoring," "temperature taking," "notification of physician," and "education about isolation for infection control" in procedure. Among these, "dyspnea" and "inadequate food diet" in clinical finding increased clinical deterioration risk (dyspnea: Odds ratio [OR] 5.99, 95% confidence interval [CI] 2.25-20.29; in adequate food diet: OR 10.0, 95% CI 2.71-40.84), and "oxygen therapy" and "notification of physician" in procedure also increased the risk of clinical deterioration in patients with COVID-19 (oxygen therapy: OR 1.89, 95% CI 1.25-3.05; notification of physician: OR 1.72, 95% CI 1.02-2.97).
CONCLUSIONS: The study employed SNOMED CT to express and standardize nursing statements. Further, it revealed the importance of standardized nursing records as predictive variables for clinical deterioration in patients.
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