Time-series analysis of multidimensional clinical-laboratory data by dynamic Bayesian networks reveals trajectories of COVID-19 outcomes. (June 2022)

• Record Type: Journal Article
• Authors: Longato, Enrico; Morieri, Mario Luca; Sparacino, Giovanni; Di Camillo, Barbara; Cattelan, Annamaria; Lo Menzo, Sara; Trevenzoli, Marco; Vianello, Andrea; Guarnieri, Gabriella; Lionello, Federico; Avogaro, Angelo; Fioretto, Paola; Vettor, Roberto; Fadini, Gian Paolo
• Is Part Of: Computer methods and programs in biomedicine. Volume 221(2022)
• Publication Date: 2022-06
• Subjects: COVID-19 -- Mortality -- Dynamic Bayesian network -- Dynamic time warping -- Graphical model; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.106873
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Dynamic Bayesian networks reveal trajectories to COVID-19 outcomes. We obtain conditional probability maps over time and visualise trajectories. Trajectories visualised via resampling, dynamic time warping, and prototyping. Kidney dysfunction and cardiac damage are crucial links to outcomes. Death was linked to elevated procalcitonin and D-dimer levels. Abstract: Background and Objective: COVID-19 severity spans an entire clinical spectrum from asymptomatic to fatal. Most patients who require in-hospital care are admitted to non-intensive wards, but their clinical conditions can deteriorate suddenly and some eventually die. Clinical data from patients' case series have identified pre-hospital and in-hospital risk factors for adverse COVID-19 outcomes. However, most prior studies used static variables or dynamic changes of a few selected variables of interest. In this study, we aimed at integrating the analysis of time-varying multidimensional clinical-laboratory data to describe the pathways leading to COVID-19 outcomes among patients initially hospitalised in a non-intensive care setting. Methods: We collected the longitudinal retrospective data of 394 patients admitted to non-intensive care units at the University Hospital of Padova (Padova, Italy) due to COVID-19. We trained a dynamic Bayesian network (DBN) to encode the conditional probability relationships over time between death and all available demographics, pre-existing conditions, and clinical laboratory variables. We applied resampling, dynamic time warping, and prototyping to describe the typical trajectories of patients who died vs. those who survived. Results: The DBN revealed that the trajectory linking demographics and pre-existing clinical conditions to death passed directly through kidney dysfunction or, more indirectly, through cardiac damage. As expected, admittance to the intensive care unit was linked to markers of respiratory function. Notably, death was linked to elevation in procalcitonin and D-dimer levels. Death was associated with persistently high levels of procalcitonin from admission and throughout the hospital stay, likely reflecting bacterial superinfection. A sudden raise in D-dimer levels 3–6 days after admission was also associated with subsequent death, possibly reflecting a worsening thrombotic microangiopathy. Conclusions: This innovative application of DBNs and prototyping to integrated data analysis enables visualising the patient's trajectories to COVID-19 outcomes and may instruct timely and appropriate clinical decisions.