A predictive paradigm for COVID-19 prognosis based on the longitudinal measure of biomarkers

Abstract

Novel coronavirus disease 2019 (COVID-19) is an emerging, rapidly evolving crisis, and the ability to predict prognosis for individual COVID-19 patient is important for guiding treatment. Laboratory examinations were repeatedly measured during hospitalization for COVID-19 patients, which provide the possibility for the individualized early prediction of prognosis. However, previous studies mainly focused on risk prediction based on laboratory measurements at one time point, ignoring disease progression and changes of biomarkers over time. By using historical regression trees (HTREEs), a novel machine learning method, and joint modeling technique, we modeled the longitudinal trajectories of laboratory biomarkers and made dynamically predictions on individual prognosis for 1997 COVID-19 patients. In the discovery phase, based on 358 COVID-19 patients admitted between 10 January and 18 February 2020 from Tongji Hospital, HTREE model identified a set of important variables including 14 prognostic biomarkers. With the trajectories of those biomarkers through 5-day, 10-day and 15-day, the joint model had a good performance in discriminating the survived and deceased COVID-19 patients (mean AUCs of 88.81, 84.81 and 85.62% for the discovery set). The predictive model was successfully validated in two independent datasets (mean AUCs of 87.61, 87.55 and 87.03% for validation the first dataset including 112 patients, 94.97, 95.78 and 94.63% for the second validation dataset including 1527 patients, respectively). In conclusion, our study identified important biomarkers associated with the prognosis of COVID-19 patients, characterized the time-to-event process and obtained dynamic predictions at the individual level.

On-admission and dynamic trend of laboratory profiles as prognostic biomarkers in COVID-19 inpatients

“…Consistent with this study, other studies found the trend of CRP 9 , 10 , 18 , LDH 10 , 11 , 13 , 18 , PLT 11 , 13 , lymphocyte 8 , 13 , urea 11 , 13 , and Cr can discriminate COVID-19 outcome. Our results fairly confirm results of Burke et al study 13 using dynamic time warping analysis.…”

“…Monitoring and allocating inpatients during peaks of COVID-19 can be challenging. Studies tried to propose laboratory profile for monitoring a hospitalized patient using longitudinal weekly values 9 , comparing early and late results 10 , historical regression tree 11 , Wilcoxon sum rank test of daily averages trend 8 , Mann–Whitney test of daily results between mortality and survived 12 , and dynamic time wrapping analysis 13 . This study used the parallel pairwise comparison to investigate the trend of lab results among survivors and non-survivors.…”

Dynamics of circulating calprotectin accurately predict the outcome of moderate COVID-19 patients

“…For example, the trajectory of C-reactive protein concentration during the first week of hospital admission predicts bacterial co-infection and supports antimicrobial decision-making. 27 More sophisticated, a dynamic risk prediction model for COVID-19 outcome that includes 14 biomarkers was built by using a random forest-based machine learning method and a joint modeling technique 28 while the longitudinal analysis of differentially expressed serum proteins detected 40 proteins whose level increases or decreases with disease severity. 29 Finally, machine learning and plasma proteome analyses were combined to detect an early molecular host response that predicts COVID-19 progression according to age, i.e.…”

Factors associated to the duration of COVID-19 lockdowns in Chile

“…More studies using the joint modeling approach may be found in the literature. For instance, Chen et al 14 used a similar strategy for developing a dynamic risk prediction model for COVID-19 prognosis considering longitudinal measures. Lu et al 15 used a joint approach to study the association of oxygen saturation to the fraction of inspired oxygen ratio and time to death of patients diagnosed with COVID-19.…”