B-136 From Result to Response: The Development of Laboratory-Based Scoring Models to Predict COVID-19 Patient Outcomes
Authors: Scott M S , Vishnyakova O , Elliot L , Morgan G , Casalino S , Frangione E , Lapadula E , Haller S , Thakur S , Khan Z , Wong I , Nomigolzar R , MacDonald G , Arnoldo S , Bearss E , Binnie A , Borgundvaag B , Devine L , Richardson D , Stern S , Taher A , Lerner-Ellis J , Taher J 
Published: October 2024

Abstract

Background
Patient characteristics relating to increased risk for COVID-19 severity are well-documented, including older age and pre-existing health concerns; however, methods to accurately predict patients’ acute reaction to SARS-CoV-2 infections remain lacking. Identification of specific laboratory tests informative of patient outcomes could effectively screen incoming patients and better inform physicians regarding optimal treatment plans. The aim of this study was to examine associations between frequently ordered laboratory markers and COVID-19 patient mortality to develop an objective laboratory-based scoring system that estimates patients’ risk of mortality.

Methods
Study participants included inpatients from 3 hospitals recruited into the GENCOV project based in Ontario, Canada. Participants (n=325) were at least 18 years of age, provided consent, and were hospitalized within 1 month of having a PCR confirmed COVID-19 infection between January 2020 and February 2022. Extensive clinical data including patient demographics, laboratory results, and treatment outcomes were extracted from patient’s electronic medical records (EMR). Results for 32 biochemical and hematological tests including complete blood cell counts, coagulation (activated partial thromboplastin time, D-dimer, fibrinogen, prothrombin time), general chemistry (albumin, blood gases, creatine kinase, electrolytes, glucose, triglycerides), inflammatory (lactate dehydrogenase, ferritin, C-reactive protein), liver (alanine aminotransferase, aspartate aminotransferase, total bilirubin), renal (creatinine, urea) and cardiac (troponin, NT-proBNP, BNP) markers were collected from each chart. Univariable logistic regression with nested likelihood ratio tests were used to determine significant associations between laboratory results and patient outcomes. Significant markers were incorporated into multivariable models and variable risk values were assigned. Total calculated risk scores for each participant were compared using univariable and multivariable risk values. Validation of each scoring method was performed on a 20% subset of inpatients. Receiver operating characteristic (ROC) curves evaluated model performance.

Results
Six laboratory markers were associated with COVID-19 patient mortality when controlling for age and sex. Univariable regression showed that elevated creatinine, elevated lactate, elevated white blood cell, low base excess, low bicarbonate, or low pH results upon admission were significantly associated with increased odds of mortality, in comparison to test results within the reference range for these same markers. Multivariable regression revealed fewer markers (only low bicarbonate and low base excess) showing significant associations with COVID-19 mortality. Area under the ROC curves (AUC) determined that risk scores derived from univariable values performed similarly to multivariable values in validation (0.800 vs 0.802) and development cohorts (0.821 vs 0.829). Total risk scores calculated from the univariable vs multivariable models suggest higher sensitivity (90% vs 85%) than specificity (58% vs 67%) in the validation cohort, whereas the development cohort had higher sensitivity (88%) than specificity (66%) with univariable scores, and higher specificity (81%) than sensitivity (74%) with multivariable scores.

Conclusions
Laboratory results associated with COVID-19 mortality following both univariable and multivariable analyses suggest hospitalized patients infected with SARS-CoV-2 may present with acidosis. Total risk scores derived from univariable and multivariable values performed similarly in predicting mortality; however, differences in marker significance between models indicate discrepancies with risk interpretation. Further comparison of the created risk score assessments with equivalent models in other populations is warranted.