Urinary proteomic marker COV50 accurately predicts adverse COVID-19 outcomes


Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19) with varying severity. Researchers have recently questioned whether disease progression can be predicted using specific noninvasive methods during the early stages of infection.

In a recent medRxiv* study, researchers provide an interim analysis of the prospective validation pilot study of a proteomics urine test for the early and accurate prognosis of critical complications in patients with SARS-CoV-2 infection (CRIT- U-Cov). To this end, the researchers described COV50, a urinary biomarker that can predict death and disease progression.

To study: Predictive performance and clinical application of COV50, a urinary proteomic biomarker in early COVID-19 infection: a cohort study. Image Credit: Kristini / Shutterstock.com

VOC50

Urine proteomic profiling (UPP) refers to the identification and generation of classifiers from a patient’s urine sample. In the case of SARS-CoV-2 infection, UPP is independent of viral strain and may also inform treatment decisions in patients with COVID-19.

CRIT-Cov-U discovered a new UPP biomarker, COV50, composed of 50 deregulated urinary peptides. COV50 can predict death and disease progression through World Health Organization (WHO) COVID-19 scores beyond risk factors and comorbidities.

The current study consolidates the findings of CRIT-Cov-U and offers potential applications of the COV50 biomarker in clinical practice.

CRIT-Cov-U study

The multicenter prospective cohort study was conducted in eight European countries, including Austria, France, Germany, Greece, North Macedonia, Poland, Spain and Sweden.

A total of 1,012 adults with reverse transcriptase polymerase chain reaction (RT-PCR) confirmed COVID-19 were enrolled in the study. According to the WHO eight-point clinical progression scale, all study participants were followed to death and progressed to recovery, hospital discharge, or death. Urine samples from the patients were analyzed and profiled by capillary electrophoresis coupled with mass spectrometry.

The researchers also calculated hospitalization costs based on several factors, including the number of hospital days and the cost rates of healthcare facilities. These hospitalization costs were standardized in the five countries.

Study results

Study participants had WHO scores of 1 to 3 in 44% of patients, 4 to 5 in 52.3% of patients, and six in 3.8% of patients. Of the 1,102 patients enrolled, 119 died and 271 progressed, all had a mean age of 62.3 years.

Of the study participants, 44.2% were female, 55% had hypertension, 15.2% had heart failure, 25.4% had diabetes, and 10.5% had cancer . The probabilities associated with COV50 were 1.67 times for death and 1.63 times for disease progression after adjusting for sex, age, body mass index, comorbidities and disease. WHO baseline score.

Performance of the COV50 urinary marker in addition to other baseline risk factors in the full data set to compare mortality versus survival (AC panels) and for progression versus non-progression in the score of WHO basis during follow-up (DF panels).  The basic model included sex, age, body mass index and the presence of comorbidities: hypertension, heart failure, diabetes or cancer.  In the following steps, the WHO base score was added and then the COV50 as a continuously distributed variable (panels B and E) or as a categorized variable based on an optimized threshold of 0.47 for the mortality (panel C) or 0.04 for a worsening of the WHO score (panel F).

Performance of the COV50 urinary marker in addition to other baseline risk factors in the full data set to compare mortality versus survival (AC panels) and for progression versus non-progression in the score of WHO basis during follow-up (DF panels). The basic model included sex, age, body mass index and the presence of comorbidities: hypertension, heart failure, diabetes or cancer. In the following steps, the WHO base score was added and then the COV50 as a continuously distributed variable (panels B and E) or as a categorized variable based on an optimized threshold of 0.47 for the mortality (panel C) or 0.04 for a worsening of the WHO score (panel F).

The prediction of the optimized COV50 0.47 threshold was 74.8% sensitive, 74.4% specific, and 74.4% accurate for mortality. Comparatively, the prediction of the 0.04 threshold was 67.5% sensitive, 67.3% specific, and 67.4% accurate for disease progression. Of the 196 outpatients, 194 did not reach the 0.04 threshold.

According to the current study, early intervention informed by the COV50 biomarker should reduce hospital days. As a result, the planned hospitalization cost reductions will range from 1,208 million euros (M€) to low risk in patients with a COV50 below the threshold of 0.04. Comparatively, cost reductions were estimated at €4,503 million in high-risk individuals defined as those with a COV50 threshold greater than 0.04 and aged over 65.

conclusion

The results of the current study demonstrate that the COV50 biomarker is a novel urinary marker that accurately predicts COVID-19 outcomes. Additionally, it is a non-invasive and relatively inexpensive biomarker to complete compared to other hospitalization costs. In fact, COV50 is registered in Germany and is available for clinical testing and research in the European Union.

The urine sample from patients can be stored for five days at room temperature without affecting the UPP and can be stored at -20°C for future research. This allows remote testing of non-hospitalized patients with COVID-19.

COV50 can predict disease outcomes in the initial stage of SARS-CoV-2 infection. At this stage, the progression of the disease is uncertain. A high-risk test result can help identify patients who require the early administration of effective treatments.

Even in mild to moderate SARS-CoV-2 infections confirmed by RT-PCR, the 0.04 COV50 threshold warrants earlier drug treatment, which may ultimately reduce hospital days and costs. Besides health care costs, cost-effectiveness can include non-monetary units such as quality-adjusted life years (QALYs), which were not discussed in this study.

Limits

The current study is observational and does not provide the optimal strategy for COVID-19 treatment guided by COV50 risk profiling. The health and economic implications of the COV50 score and treatment decisions need to be studied. Furthermore, the current study only studies white European individuals; therefore, there is no data available indicating whether UPP is affected by ethnicity.

Although COV50 risk profiling can aid in making informed decisions regarding therapeutic intervention, vaccination remains the most effective strategy to combat the COVID-19 pandemic.

*Important Notice

medRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be considered conclusive, guide clinical practice/health-related behaviors, or treated as established information.

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