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Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Oct 3, 2023
Open Peer Review Period: Oct 3, 2023 - Oct 19, 2023
Date Accepted: Feb 27, 2024
Date Submitted to PubMed: Feb 28, 2024
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

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

Sung S, Kim Y, Kim SH, Jung H

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

J Med Internet Res 2024;26:e53343

DOI: 10.2196/53343

PMID: 38414056

PMCID: 10984341

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Identification of Predictors for Clinical Deterioration in Patients With Coronavirus Disease 2019 via Electronic Nursing Records: Retrospective Observational Study

  • Sumi Sung; 
  • Youlim Kim; 
  • Su Hwan Kim; 
  • Hyesil Jung

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:

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 55,646 nursing statements from 221 patients with COVID-19 were analyzed. Among these, 43,940 statements were from 202 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. 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.


 Citation

Please cite as:

Sung S, Kim Y, Kim SH, Jung H

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

J Med Internet Res 2024;26:e53343

DOI: 10.2196/53343

PMID: 38414056

PMCID: 10984341

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