Dataset Information

Identification of Predictors for Clinical Deterioration in Patients With COVID-19 via Electronic Nursing Records: Retrospective Observational Study.

ABSTRACT:

Background

Few studies have used standardized nursing records with Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) to identify predictors of clinical deterioration.

Objective

This study aims to standardize the nursing documentation records of patients with COVID-19 using SNOMED CT and identify predictive factors of clinical deterioration in patients with COVID-19 via 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 the intensive care unit within 7 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 their meaning by 2 experts with more than 5 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 interrater 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% CI 2.25-20.29; inadequate 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 used 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.

SUBMITTER: Sung S

PROVIDER: S-EPMC10984341 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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Publications

Identification of Predictors for Clinical Deterioration in Patients With COVID-19 via Electronic Nursing Records: Retrospective Observational Study.

Sung Sumi S Kim Youlim Y Kim Su Hwan SH Jung Hyesil H

Journal of medical Internet research 20240329

<h4>Background</h4>Few studies have used standardized nursing records with Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) to identify predictors of clinical deterioration.<h4>Objective</h4>This study aims to standardize the nursing documentation records of patients with COVID-19 using SNOMED CT and identify predictive factors of clinical deterioration in patients with COVID-19 via standardized nursing records.<h4>Methods</h4>In this study, 57,558 nursing statements from 226 pat ...[more]

PMID: 38414056

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Project description:ObjectiveCoronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains pandemic with considerable morbidity and mortality around the world. The aim of this study was to identify the predictors for clinical deterioration in patients with COVID-19 who did not show clinical deterioration upon hospital admission.MethodsTwo hundred fifty-seven patients with confirmed COVID-19 pneumonia admitted to Guangzhou Eighth People's Hospital between 23 January and 21 March 2020 were retrospectively enrolled. Demographic data, symptoms, laboratory values, comorbidities and treatments were all collected. The study endpoint was clinical deterioration within 20 days from hospital admission. Univariate and multivariable logistic regression methods were used to explore the risk factors associated with clinical deterioration.ResultsA total of 49 (19%) patients showed clinical deterioration after admission. Compared with patients that did not experience clinical deterioration, clinically deteriorated patients had more dyspnea, cough and myalgia (65.3% versus 29.3%) symptoms and more had comorbidities (89.8% versus 36.1%). Clinical and laboratory characteristics at admission that were associated with clinical deterioration included senior age, diabetes, hypertension, myalgia, higher temperature, systolic blood pressure, C-reactive protein (CRP), procalcitonin, activated partial thromboplastin time, aspartate aminotransferase, alanine transaminase, direct bilirubin, plasma creatinine, lymphocytopenia, thrombocytopenia, decreased albumin and bicarbonate concentration. Medical history of angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, calcium channel blockers and metformin were also risk factors.ConclusionThe four best predictors for clinical deterioration were CRP, procalcitonin, age and albumin. A "best" multivariable prediction model, resulting from using a variable selection procedure, included senior age, presentation with myalgia, and higher level of CRP and serum creatinine (bias-corrected c-statistic = 0.909). Sensitivity and specificity corresponding to a cut point of CRP ≥18.45 mg/L for predicting clinical deterioration were 85% and 74%, respectively.

Dataset Information

Identification of Predictors for Clinical Deterioration in Patients With COVID-19 via Electronic Nursing Records: Retrospective Observational Study.

Background

Objective

Methods

Results

Conclusions

Publications

Identification of Predictors for Clinical Deterioration in Patients With COVID-19 via Electronic Nursing Records: Retrospective Observational Study.

Similar Datasets

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