The importance of association of comorbidities on COVID-19 outcomes: a machine learning approach

Need for awareness and surveillance of long-term post-COVID neurodegenerative disorders. A position paper from the neuroCOVID-19 task force of the European Academy of Neurology

BACKGROUND

Neuropathological and clinical studies suggest that infection with SARS-CoV-2 may increase the long-term risk of neurodegeneration.

METHODS

We provide a narrative overview of pathological and clinical observations justifying the implementation of a surveillance program to monitor changes in the incidence of neurodegenerative disorders in the years after COVID-19.

RESULTS

Autopsy studies revealed diverse changes in the brain, including loss of vascular integrity, microthromboses, gliosis, demyelination, and neuronal- and glial injury and cell death, in both unvaccinated and vaccinated individuals irrespective of the severity of COVID-19. Recent data suggest that microglia play an important role in sustained COVID-19-related inflammation, which contributes to the etiology initiating a neurodegenerative cascade, to the worsening of pre-existing neurodegenerative disease or to the acceleration of neurodegenerative processes. Histopathological data have been supported by neuroimaging, and epidemiological studies also suggested a higher risk for neurodegenerative diseases after COVID-19.

CONCLUSIONS

Due to the high prevalence of COVID-19 during the pandemic, healthcare systems should be aware of, and be prepared for a potential increase in the incidence of neurodegenerative diseases in the upcoming years. Strategies may include follow-up of well-described cohorts, analyses of outcomes in COVID-19-registries, nationwide surveillance programs using record-linkage of ICD-10 diagnoses, and comparing the incidence of neurodegenerative disorders in the post-pandemic periods to values of the pre-pandemic years. Awareness and active surveillance are particularly needed, because diverse clinical manifestations due to earlier SARS-CoV-2 infections may no longer be quoted as post-COVID-19 symptoms, and hence, increasing incidence of neurodegenerative pathologies at the community level may remain unnoticed.

Machine learning applications in preventive healthcare: A systematic literature review on predictive analytics of disease comorbidity from multiple perspectives

Artificial intelligence is constantly revolutionizing biomedical research and healthcare management. Disease comorbidity is a major threat to the quality of life for susceptible groups, especially middle-aged and elderly patients. The presence of multiple chronic diseases makes precision diagnosis challenging to realize and imposes a heavy burden on the healthcare system and economy. Given an enormous amount of accumulated health data, machine learning techniques show their capability in handling this puzzle. The present study conducts a review to uncover current research efforts in applying these methods to understanding comorbidity mechanisms and making clinical predictions considering these complex patterns. A descriptive metadata analysis of 791 unique publications aims to capture the overall research progression between January 2012 and June 2023. To delve into comorbidity-focused research, 61 of these scientific papers are systematically assessed. Four predictive analytics of tasks are detected: disease comorbidity data extraction, clustering, network, and risk prediction. It is observed that some machine learning-driven applications address inherent data deficiencies in healthcare datasets and provide a model interpretation that identifies significant risk factors of comorbidity development. Based on insights, both technical and practical, gained from relevant literature, this study intends to guide future interests in comorbidity research and draw conclusions about chronic disease prevention and diagnosis with managerial implications.

An Interplay between Oxidative Stress (Lactate Dehydrogenase) and Inflammation (Anisocytosis) Mediates COVID-19 Severity Defined by Routine Clinical Markers

Viral infections activate the innate immune response and the secretion of inflammatory cytokines. They also alter oxidative stress markers, which potentially can have an involvement in the pathogenesis of the disease. The aim of this research was to study the role of the oxidative stress process assessed through lactate dehydrogenase (LDH) on the severity of COVID-19 measured by oxygen saturation (SaO2) and the putative interaction with inflammation. The investigation enrolled 1808 patients (mean age of 68 and 60% male) with COVID-19 from the HM Hospitals database. To explore interactions, a regression model and mediation analyses were performed. The patients with lower SaO2 presented lymphopenia and higher values of neutrophils-to-lymphocytes ratio and on the anisocytosis coefficient. The regression model showed an interaction between LDH and anisocytosis, suggesting that high levels of LDH (>544 U/L) and an anisocytosis coefficient higher than 10% can impact SaO2 in COVID-19 patients. Moreover, analysis revealed that LDH mediated 41% (p value = 0.001) of the effect of anisocytosis on SaO2 in this cohort. This investigation revealed that the oxidative stress marker LDH and the interaction with anisocytosis have an important role in the severity of COVID-19 infection and should be considered for the management and treatment of the oxidative phenomena concerning this within a precision medicine strategy.

HIV Patients’ Tracer for Clinical Assistance and Research during the COVID-19 Epidemic (INTERFACE): A Paradigm for Chronic Conditions

The health emergency linked to the SARS-CoV-2 pandemic has highlighted problems in the health management of chronic patients due to their risk of infection, suggesting the need of new methods to monitor patients. People living with HIV/AIDS (PLWHA) represent a paradigm of chronic patients where an e-health-based remote monitoring could have a significant impact in maintaining an adequate standard of care. The key objective of the study is to provide both an efficient operating model to “follow” the patient, capture the evolution of their disease, and establish proximity and relief through a remote collaborative model. These dimensions are collected through a dedicated mobile application that triggers questionnaires on the basis of decision-making algorithms, tagging patients and sending alerts to staff in order to tailor interventions. All outcomes and alerts are monitored and processed through an innovative e-Clinical platform. The processing of the collected data aims into learning and evaluating predictive models for the possible upcoming alerts on the basis of past data, using machine learning algorithms. The models will be clinically validated as the study collects more data, and, if successful, the resulting multidimensional vector of past attributes will act as a digital composite biomarker capable of predicting HIV-related alerts. Design: All PLWH > 18 sears old and stable disease followed at the outpatient services of a university hospital (n = 1500) will be enrolled in the interventional study. The study is ongoing, and patients are currently being recruited. Preliminary results are yielding monthly data to facilitate learning of predictive models for the alerts of interest. Such models are learnt for one or two months of history of the questionnaire data. In this manuscript, the protocol—including the rationale, detailed technical aspects underlying the study, and some preliminary results—are described. Conclusions: The management of HIV-infected patients in the pandemic era represents a challenge for future patient management beyond the pandemic period. The application of artificial intelligence and machine learning systems as described in this study could enable remote patient management that takes into account the real needs of the patient and the monitoring of the most relevant aspects of PLWH management today.