International comparisons of laboratory values from the 4CE collaborative to predict COVID-19 mortality

Objectivizing issues in the diagnosis of complex rare diseases: lessons learned from testing existing diagnosis support systems on ciliopathies

BACKGROUND

There are approximately 8,000 different rare diseases that affect roughly 400 million people worldwide. Many of them suffer from delayed diagnosis. Ciliopathies are rare monogenic disorders characterized by a significant phenotypic and genetic heterogeneity that raises an important challenge for clinical diagnosis. Diagnosis support systems (DSS) applied to electronic health record (EHR) data may help identify undiagnosed patients, which is of paramount importance to improve patients' care. Our objective was to evaluate three online-accessible rare disease DSSs using phenotypes derived from EHRs for the diagnosis of ciliopathies.

METHODS

Two datasets of ciliopathy cases, either proven or suspected, and two datasets of controls were used to evaluate the DSSs. Patient phenotypes were automatically extracted from their EHRs and converted to Human Phenotype Ontology terms. We tested the ability of the DSSs to diagnose cases in contrast to controls based on Orphanet ontology.

RESULTS

A total of 79 cases and 38 controls were selected. Performances of the DSSs on ciliopathy real world data (best DSS with area under the ROC curve = 0.72) were not as good as published performances on the test set used in the DSS development phase. None of these systems obtained results which could be described as "expert-level". Patients with multisystemic symptoms were generally easier to diagnose than patients with isolated symptoms. Diseases easily confused with ciliopathy generally affected multiple organs and had overlapping phenotypes. Four challenges need to be considered to improve the performances: to make the DSSs interoperable with EHR systems, to validate the performances in real-life settings, to deal with data quality, and to leverage methods and resources for rare and complex diseases.

CONCLUSION

Our study provides insights into the complexities of diagnosing highly heterogenous rare diseases and offers lessons derived from evaluation existing DSSs in real-world settings. These insights are not only beneficial for ciliopathy diagnosis but also hold relevance for the enhancement of DSS for various complex rare disorders, by guiding the development of more clinically relevant rare disease DSSs, that could support early diagnosis and finally make more patients eligible for treatment.

Neurological diagnoses in hospitalized COVID-19 patients associated with adverse outcomes: A multinational cohort study

Few studies examining the patient outcomes of concurrent neurological manifestations during acute COVID-19 leveraged multinational cohorts of adults and children or distinguished between central and peripheral nervous system (CNS vs. PNS) involvement. Using a federated multinational network in which local clinicians and informatics experts curated the electronic health records data, we evaluated the risk of prolonged hospitalization and mortality in hospitalized COVID-19 patients from 21 healthcare systems across 7 countries. For adults, we used a federated learning approach whereby we ran Cox proportional hazard models locally at each healthcare system and performed a meta-analysis on the aggregated results to estimate the overall risk of adverse outcomes across our geographically diverse populations. For children, we reported descriptive statistics separately due to their low frequency of neurological involvement and poor outcomes. Among the 106,229 hospitalized COVID-19 patients (104,031 patients ≥18 years; 2,198 patients <18 years, January 2020-October 2021), 15,101 (14%) had at least one CNS diagnosis, while 2,788 (3%) had at least one PNS diagnosis. After controlling for demographics and pre-existing conditions, adults with CNS involvement had longer hospital stay (11 versus 6 days) and greater risk of (Hazard Ratio = 1.78) and faster time to death (12 versus 24 days) than patients with no neurological condition (NNC) during acute COVID-19 hospitalization. Adults with PNS involvement also had longer hospital stay but lower risk of mortality than the NNC group. Although children had a low frequency of neurological involvement during COVID-19 hospitalization, a substantially higher proportion of children with CNS involvement died compared to those with NNC (6% vs 1%). Overall, patients with concurrent CNS manifestation during acute COVID-19 hospitalization faced greater risks for adverse clinical outcomes than patients without any neurological diagnosis. Our global informatics framework using a federated approach (versus a centralized data collection approach) has utility for clinical discovery beyond COVID-19.

Short-term predictor for COVID-19 severity from a longitudinal multi-omics study for practical application in intensive care units

BACKGROUND

The COVID-19 pandemic challenged the management of technical and human resources in intensive care units (ICU) across the world. Several long-term predictors for COVID-19 disease progression have been discovered. However, predictors to support short-term planning of resources and medication that can be translated to future pandemics are still missing. A workflow was established to identify a predictor for short-term COVID-19 disease progression in the acute phase of intensive care patients to support clinical decision-making.

METHODS

Thirty-two patients with SARS-CoV-2 infection were recruited on admission to the ICU and clinical data collected. During their hospitalization, plasma samples were acquired from each patient on multiple occasions, excepting one patient for which only one time point was possible, and the proteome (Inflammation, Immune Response and Organ Damage panels from Olink® Target 96), metabolome and lipidome (flow injection analysis and liquid chromatography-mass spectrometry) analyzed for each sample. Patient visits were grouped according to changes in disease severity based on their respiratory and organ function, and evaluated using a combination of statistical analysis and machine learning. The resulting short-term predictor from this multi-omics approach was compared to the human assessment of disease progression. Furthermore, the potential markers were compared to the baseline levels of 50 healthy subjects with no known SARS-CoV-2 or other viral infections.

RESULTS

A total of 124 clinical parameters, 271 proteins and 782 unique metabolites and lipids were assessed. The dimensionality of the dataset was reduced, selecting 47 from the 1177 parameters available following down-selection, to build the machine learning model. Subsequently, two proteins (C-C motif chemokine 7 (CCL7) and carbonic anhydrase 14 (CA14)) and one lipid (hexosylceramide 18:2; O2/20:0) were linked to disease progression in the studied SARS-CoV-2 infections. Thus, a predictor delivering the prognosis of an upcoming worsening of the patient's condition up to five days in advance with a reasonable accuracy (79 % three days prior to event, 84 % four to five days prior to event) was found. Interestingly, the predictor's performance was complementary to the clinicians' capabilities to foresee a worsening of a patient.

CONCLUSION

This study presents a workflow to identify omics-based biomarkers to support clinical decision-making and resource management in the ICU. This was successfully applied to develop a short-term predictor for aggravation of COVID-19 symptoms. The applied methods can be adapted for future small cohort studies.

Federated and distributed learning applications for electronic health records and structured medical data: a scoping review

OBJECTIVES

Federated learning (FL) has gained popularity in clinical research in recent years to facilitate privacy-preserving collaboration. Structured data, one of the most prevalent forms of clinical data, has experienced significant growth in volume concurrently, notably with the widespread adoption of electronic health records in clinical practice. This review examines FL applications on structured medical data, identifies contemporary limitations, and discusses potential innovations.

MATERIALS AND METHODS

We searched 5 databases, SCOPUS, MEDLINE, Web of Science, Embase, and CINAHL, to identify articles that applied FL to structured medical data and reported results following the PRISMA guidelines. Each selected publication was evaluated from 3 primary perspectives, including data quality, modeling strategies, and FL frameworks.

RESULTS

Out of the 1193 papers screened, 34 met the inclusion criteria, with each article consisting of one or more studies that used FL to handle structured clinical/medical data. Of these, 24 utilized data acquired from electronic health records, with clinical predictions and association studies being the most common clinical research tasks that FL was applied to. Only one article exclusively explored the vertical FL setting, while the remaining 33 explored the horizontal FL setting, with only 14 discussing comparisons between single-site (local) and FL (global) analysis.

CONCLUSIONS

The existing FL applications on structured medical data lack sufficient evaluations of clinically meaningful benefits, particularly when compared to single-site analyses. Therefore, it is crucial for future FL applications to prioritize clinical motivations and develop designs and methodologies that can effectively support and aid clinical practice and research.

Prediction models for in-hospital deaths of patients with COVID-19 using electronic healthcare data

OBJECTIVE

Many models for predicting various disease prognoses have achieved high performance without laboratory test results. However, whether laboratory test results can improve performance remains unclear. This study aimed to investigate whether laboratory test results improve the model performance for coronavirus disease 2019 (COVID-19).

METHODS

Prediction models were developed using data from the electronic healthcare record database in Japan. Patients aged ≥18 years hospitalized for COVID-19 after February 11, 2020, were included. Their age, sex, comorbidities, laboratory test results, and number of days from February 11, 2020, were collected. We developed a logistic regression, XGBOOST, random forest, and neural network analysis and compared the performance with and without laboratory test results. The performance of predicting in-hospital death was evaluated using the area under the curve (AUC).

RESULTS

Data from 8,288 hospitalized patients (females, 46.5%) were analyzed. The median patient age was 71 years. A total of 6,630 patients were included in the training dataset, and 312 (4.7%) died. In the logistic regression model, the area under the curve was 0.88 (95% confidence interval [CI] = 0.83-0.93) and 0.75 (95% CI = 0.68-0.81) with and without laboratory test results, respectively. The performance was not fundamentally different between the model types, and the laboratory test results improved the performance in all cases. The variables useful for prediction were blood urea nitrogen, albumin, and lactate dehydrogenase.

CONCLUSIONS

Laboratory test results, such as blood urea nitrogen, albumin, and lactate dehydrogenase levels, along with background information, helped estimate the prognosis of patients hospitalized for COVID-19.

Characterization of long COVID temporal sub-phenotypes by distributed representation learning from electronic health record data: a cohort study

Background

Characterizing Post-Acute Sequelae of COVID (SARS-CoV-2 Infection), or PASC has been challenging due to the multitude of sub-phenotypes, temporal attributes, and definitions. Scalable characterization of PASC sub-phenotypes can enhance screening capacities, disease management, and treatment planning.

Methods

We conducted a retrospective multi-centre observational cohort study, leveraging longitudinal electronic health record (EHR) data of 30,422 patients from three healthcare systems in the Consortium for the Clinical Characterization of COVID-19 by EHR (4CE). From the total cohort, we applied a deductive approach on 12,424 individuals with follow-up data and developed a distributed representation learning process for providing augmented definitions for PASC sub-phenotypes.

Findings

Our framework characterized seven PASC sub-phenotypes. We estimated that on average 15.7% of the hospitalized COVID-19 patients were likely to suffer from at least one PASC symptom and almost 5.98%, on average, had multiple symptoms. Joint pain and dyspnea had the highest prevalence, with an average prevalence of 5.45% and 4.53%, respectively.

Interpretation

We provided a scalable framework to every participating healthcare system for estimating PASC sub-phenotypes prevalence and temporal attributes, thus developing a unified model that characterizes augmented sub-phenotypes across the different systems.

Funding

Authors are supported by National Institute of Allergy and Infectious Diseases, National Institute on Aging, National Center for Advancing Translational Sciences, National Medical Research Council, National Institute of Neurological Disorders and Stroke, European Union, National Institutes of Health, National Center for Advancing Translational Sciences.

[Risk Factors for COVID-19 Mortality]

The COVID-19 pandemic has changed the world significantly within the last two years and has put a major burden on health care systems worldwide. Due to the imbalance between the number of patients requiring treatment and the shortage of necessary healthcare resources, a new mode of triage had to be established. The allocation of resources and definition of treatment priorities could be supported by taking the actual short-term mortality risk of patients with COVID-19 into account. We therefore analyzed the current literature for criteria to predict mortality in COVID-19.

Risk Factors for Longer-Term Mortality in Discharged Patients with Dementia and SARS-CoV-2 Infection: A Matched Case-Control Study

BACKGROUND

Persisting symptoms and increased mortality after SARS-CoV-2 infection has been described in COVID-19 survivors.

OBJECTIVE

We examined longer-term mortality in patients with dementia and SARS-CoV-2 infection.

METHODS

A retrospective matched case-control study of 165 patients with dementia who survived an acute hospital admission with COVID-19 infection, and 1325 patients with dementia who survived a hospital admission but without SARS-CoV-2 infection. Potential risk factors investigated included socio-demographic factors, clinical features, and results of investigations. Data were fitted using a Cox proportional hazard model.

RESULTS

Compared to patients with dementia but without SARS-CoV-2 infection, people with dementia and SARS-CoV-2 infection had a 4.4-fold risk of death (adjusted hazard ratio [aHR] = 4.44, 95% confidence interval [CI] 3.13-6.30) even beyond the acute phase of infection. This excess mortality could be seen up to 125 days after initial recovery but was not elevated beyond this time. Risk factors for COVID-19-associated mortality included prescription of antipsychotics (aHR = 3.06, 95% CI 1.40-6.69) and benzodiazepines (aHR = 3.00, 95% CI 1.28-7.03). Abnormalities on investigation associated with increased mortality included high white cell count (aHR = 1.21, 95% CI 1.04-1.39), higher absolute neutrophil count (aHR = 1.28, 95% CI 1.12-1.46), higher C-reactive protein (aHR = 1.01, 95% CI 1.00-1.02), higher serum sodium (aHR = 1.09, 95% CI 1.01-1.19), and higher ionized calcium (aHR = 1.03, 95% CI 1.00-1.06). The post-acute COVID mortality could be modeled for the first 120 days after recovery with a balanced accuracy of 87.2%.

CONCLUSION

We found an increased mortality in patients with dementia beyond the acute phase of illness. We identified several investigation results associated with increased mortality, and increased mortality in patients prescribed antipsychotics or benzodiazepines.

Bioinformatics and High-Performance Computing Methods for Deciphering and Fighting COVID-19-Editorial

The COVID-19 disease (Coronavirus Disease 19), caused by the SARS-CoV-2 virus (Severe Acute Respiratory Syndrome Coronavirus 2), has posed many challenges worldwide at various levels, with special focus to the biological, medical, and epidemiological ones [...].