The QCOVID4 algorithm developed using data from the Omicron wave identifies those at highest risk of severe COVID-19 outcomes

A recent article published in the medRxiv* preprint server illustrated the derivation and validation of the QCOVID4 risk determination algorithm for predicting coronavirus disease 2019 (COVID-19) hospitalization and death risk in England.

Study: QCovid 4 - Predicting risk of death or hospitalisation from COVID-19 in adults testing positive for SARS-CoV-2 infection during the Omicron wave in England. Image Credit: Photo_imagery/Shutterstock
Study: QCovid 4 - Predicting risk of death or hospitalisation from COVID-19 in adults testing positive for SARS-CoV-2 infection during the Omicron wave in England. Image Credit: Photo_imagery/Shutterstock

Background

The QCOVID risk evaluation algorithm predicts the likelihood of COVID-19-linked mortality and hospitalization depending on individual traits. It has been employed to discover persons vulnerable to severe COVID-19 outcomes across England. This approach led to the inclusion of an extra 1.5 million individuals on the national shielded patient list in the United Kingdom (UK) and England for prioritizing people for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination. Of note, the QCOVID strategy has been updated following the second and third SARS-CoV-2 waves, resulting in QCOVID2 and QCOVID3.

Ethnic variations exist among severe COVID-19 consequences in the UK, mostly found during the initial 2020 SARS-CoV-2 pandemic wave. Besides, there are COVID-19 vaccines and therapeutics, such as antivirals and monoclonal antibodies, but these must be tailored to the people at risk of having detrimental effects.

About the study

The present work aimed to derive and verify the QCOVID4 risk prediction algorithm to determine the likelihood of SARS-CoV-2-associated hospitalization and mortality across UK adults with a COVID-19-positive test amid the SARS-CoV-2 Omicron pandemic surge. Further, the researchers sought to analyze the performance of QCOVID4 with earlier iterations of the algorithm created during preceding pandemic waves and the high-risk group recognized by National Health Service (NHS) Digital in England.

The team developed population-based cohort research utilizing the QResearch database connected to national information on SARS-CoV-2 vaccination, at-risk patients prioritized for SARS-CoV-2 medications, COVID-19 results, hospital admission, cancer database, systemic anticancer therapy, radiotherapy, and the national mortality registry. They recruited 1.3 million adults to the derivation group and 0.15 million adults to the validation group aged 18 to 100 years with a COVID-19-positive test between 11 December 2021 and 31 March 2022 with follow-up through 30 June 2022.

The primary study endpoint was SARS-CoV-2-linked mortality. Additionally, the co-primary research outcome was COVID-19-related hospitalization. With the help of various predictor factors, models were fitted to the derivation group to obtain risk equations. In addition, performance was assessed in a different validation cohort.

Results 

The study results showed that of the 1,297,984 adults in the derivation group with a COVID-19-positive test, 18,756, i.e., 1.45%, had a SARS-CoV-2-related hospital admission, and 3,878, i.e., 0.3%, died as a result of COVID-19 during follow-up. Further, there were 461, i.e., 0.3%, SARS-CoV-2-associated deaths and 2,124, i.e., 1.46%, COVID-19 hospitalizations out of 145,404 subjects in the validation group.

The SARS-CoV-2 death rate in males heightened with deprivation and age. The conditions with the highest hazard ratios (HRs) in the QCOVID4 model for men were dementia (1.62-fold elevation), Parkinson's disease (2.2-fold elevation), solid organ transplant ever (2.4-fold elevation), liver cirrhosis (2.5-fold elevation), radiotherapy (3.1-fold elevation), type 1 diabetes (3.4-fold elevation), grade A chemotherapy (3.8-fold elevation), Downs syndrome (4.9-fold elevation), grade B chemotherapy (5.8-fold elevation), kidney transplant (6.1-fold elevation), and grade C chemotherapy (10.9-fold elevation).

Stage 4 and 5 renal diseases, learning disability, respiratory cancer, blood cancer, oral steroids, immunosuppressants, coronary heart disease, chronic obstructive pulmonary disease, stroke, heart failure, atrial fibrillation, rheumatoid or systemic lupus erythematosus, bipolar disease, schizophrenia, type 2 diabetes, human immunodeficiency virus, and severe combined immunodeficiency were additional conditions linked to an enhanced COVID-19 mortality rate.

Notably, QCOVID4 results in the model for women were comparable. COVID-19 vaccinated subjects had lower SARS-CoV-2-related mortality risk relative to non-vaccinated participants, with evidence of an exposure-response association. The decreased death rates connected to previous COVID-19 were comparable in males and females. The QCOVID4 algorithm accounted for 76.6% of the difference in the time to SARS-CoV-2-linked death, i.e., R2, among women. 

Besides, the Harrells C statistic was 0.965, and the D statistic was 3.70. The analogous results for SARS-CoV-2-linked mortality among males were comparable with R2 76.0%, D statistic 3.65, and C statistic 0.970. QCOVID4 selectivity for death was only marginally higher than QCOVID2 and QCOVID1. Interestingly, QCOVID4 had much better calibration.

Conclusions 

The study findings demonstrated that the QCOVID4 risk algorithm, developed from data collected during the UK Omicron surge, now incorporates vaccination doses and COVID-19 history into account when predicting SARS-CoV-2 infection-related death in those who test positive. It performed excellently and may be utilized for tailored COVID-19 therapeutics and vaccinations.

Moreover, compared to NHS Digital's "conditions-based" strategy, dependent on the relative risk of a catalog of medical illnesses, QCOVID4 more precisely identifies people at the highest overall threat for individualized interventions. Although in the early pandemic waves, there were significant differences in the probabilities of severe SARS-CoV-2 outcomes across ethnic minorities, these differences were now much diminished, and there was no longer an elevated risk of death by ethnic population.

*Important notice

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

Journal reference:
Shanet Susan Alex

Written by

Shanet Susan Alex

Shanet Susan Alex, a medical writer, based in Kerala, India, is a Doctor of Pharmacy graduate from Kerala University of Health Sciences. Her academic background is in clinical pharmacy and research, and she is passionate about medical writing. Shanet has published papers in the International Journal of Medical Science and Current Research (IJMSCR), the International Journal of Pharmacy (IJP), and the International Journal of Medical Science and Applied Research (IJMSAR). Apart from work, she enjoys listening to music and watching movies.

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