A rampant increase in the number of coronavirus disease (COVID-19) cases has made it difficult for healthcare providers around the world to manage the overwhelming number of patients arriving at hospital outpatient departments.
While there have been multiple scoring systems that have been developed to analyze the risks of mortality among those severely ill and hospitalized, no such measure to monitor the severity of disease among outpatients has been developed thus far to assist in treatment and management decision-making processes.
Study: Simple risk scores to predict hospitalization or death in outpatients with COVID-19. Image Credit: janews / Shutterstock.com
This news article was a review of a preliminary scientific report that had not undergone peer-review at the time of publication. Since its initial publication, the scientific report has now been peer reviewed and accepted for publication in a Scientific Journal. Links to the preliminary and peer-reviewed reports are available in the Sources section at the bottom of this article. View Sources
Current COVID-19 scoring systems
Scoring systems like the 4C risk score, the ABCS risk score, and the COVID-GRAM risk score typically require laboratory tests and imaging. As a result, these types of facilities are not always available in every outpatient or telehealth setting.
The COVID-NoLab risk score for inpatient mortality is an exception that requires only oxygen saturation, age, and respiratory rate without any imaging or laboratory-based tests needed; however, this scoring system is not available for outpatients.
The OutCoV score predicts the likelihood of hospitalization among outpatients without needing laboratory testing and considers various factors including age, fever, dyspnea, hypertension, and chronic respiratory disease. Unfortunately, this scoring system has not been externally validated.
In an effort to overcome the limitations of current COVID-19 scoring systems, a group of researchers developed a risk-stratification system from existing scoring methods that are exclusively for outpatients. To this end, the researchers assembled a dataset with patients who had been diagnosed with COVID-19 in any Lehigh Valley Health Network (LVHN) outpatient primary care or ExpressCARE clinic and discuss their findings in a recent medRxiv* preprint study.
About the study
The researchers wanted to prospectively validate the OutCoV and COVID-NoLab risk scores for the prediction of hospitalization or death among outpatients. In doing so, they hoped to develop novel risk scores on the basis of simple measurable parameters without requiring imaging or laboratory-based tests.
Outpatient data were collected from electronic health records from primary care and ExpressCARE outpatient clinics in the LVHN who had tested positive for COVID-19 by polymerase chain reaction (RT-PCR) between March 13, 2020, and September 30, 2021.
A total of 13,418 outpatients diagnosed with COVID-19 were considered for analysis and, after excluding patients under the age of 12 and patients with missing data, the final dataset consisted of 9,649 outpatients with COVID-19.
The early cohort with data obtained before March 1, 2021, included 5,843 patients, while the late cohort included data obtained after March 1, 2021, and comprised a total of 3,806 patients. These cohorts were also referred to as the early and late cohorts, respectively.
Study findings
A total of 89 of the 641 (13.9%) patients were hospitalized on the same day as their outpatient visit. Taken together, a total of 641 patients were hospitalized, of which 55 died. No non-hospitalized patients died following a diagnosis of COVID-19.
The overall likelihood of hospitalization or death was lower in the late cohort than in the early cohort at 5.5% and 7.4%, respectively. Increasing age, increasing respiratory rate, lower oxygen saturation, a complaint of dyspnea, and all comorbidities were associated with an increased likelihood of hospitalization.
The researchers used three regression models that assumed either oxygen levels, respiratory levels, or both may be absent among the five major collected data points. The accuracy of these three data points was then assessed, which subsequently led to the identification that the AUC was between 0.772 to 0.785, thereby indicating good accuracy. The researchers also found hospitalization rates of 12/1,199 (1.0%), 23/519 (4.4%), 32/259 (12.4%), and 15/49 (30.6%) in the very low, low, moderate, and high-risk groups, respectively, in the late cohort.
The OutCoV and COVID-NoLab risk scores in the late cohort identified more patients in the low-risk groups. This group also had higher rates of hospitalization at 3.8% to 4.0% in the early cohort and 2.8% in the late cohort as compared to the novel models developed by the researchers.
Implications
In the validation group, the novel risk scores developed by researchers identified a very low-risk group that comprised 53% to 57% of the entire population with a lower likelihood of hospitalization of 1.7% or less. This implied that such patients could potentially be managed initially as outpatients with guidance to contact their primary care physician in the event of worsening symptoms.
The low risk had a 5.2% to 5.9% likelihood of hospitalization, which is similar to that for the population as a whole. About one in six patients were classified in the moderate risk category with 14.7% to 15.6% chances of hospitalization or in high-risk groups with 32.0% to 34.2% chances of hospitalization. These patients required guidance on monitoring oxygen regularly if managed as outpatients rather than in the hospital setting.
Such models, if implemented in practical scenarios, would help stratify the risk among patients and reduce the unnecessary burden in hospitals. This could lead to more efficient management of the healthcare system and allow for these services to remain available for emergencies only.
This news article was a review of a preliminary scientific report that had not undergone peer-review at the time of publication. Since its initial publication, the scientific report has now been peer reviewed and accepted for publication in a Scientific Journal. Links to the preliminary and peer-reviewed reports are available in the Sources section at the bottom of this article. View Sources
Article Revisions
- Jun 10 2023 - The preprint preliminary research paper that this article was based upon was accepted for publication in a peer-reviewed Scientific Journal. This article was edited accordingly to include a link to the final peer-reviewed paper, now shown in the sources section.
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