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.

Procalcitonin detection in human plasma specimens using a fast version of proximity extension assay.

An exciting trend in clinical diagnostics is the development of easy-to-use, minimally invasive assays for screening and prevention of disease at the point of care. Proximity Extension Assay (PEA), an homogeneous, dual-recognition immunoassay, has proven to be sensitive, specific and convenient for detection or quantitation of one or multiple analytes in human plasma. In this paper, the PEA principle was applied to the detection of procalcitonin (PCT), a widely used biomarker for the identification of bacterial infection. A simple, short PEA protocol, with an assay time suitable for point-of-care diagnostics, is presented here as a proof of concept. Pairs of oligonucleotides and monoclonal antibodies were selected to generate tools specifically adapted to the development of an efficient PEA for PCT detection. The assay time was reduced by more than 13-fold compared to published versions of PEA, without significantly affecting assay performance. It was also demonstrated that T4 DNA polymerase could advantageously be replaced by other polymerases having strong 3'>5' exonuclease activity. The sensitivity of this improved assay was determined to be about 0.1 ng/mL of PCT in plasma specimen. The potential use of such an assay in an integrated system for the low-plex detection of biomarkers in human specimen at the point of care was discussed.

Panda Unit, a Mother-Baby Unit Nested in a Neonatal Care Service.

The PANDA unit is a full-time mother-baby hospitalization unit based on an original model of care for vulnerable dyads. It is located within a neonatal unit allowing tripartite care (perinatal psychiatry, neonatology and post-natal care). It thus differs from traditional mother-baby units in its close links with the other perinatal care actors, allowing comprehensive health and mental health care in the immediate post-partum period. Patients admitted to the Panda Unit may have been referred during the antenatal period or taken into care in an emergency if the mother's clinical condition requires it, in the aftermath of childbirth. During their stay, the dyads are evaluated daily by a perinatal psychiatrist. This includes assessment of maternal clinical state, the newborn's development and the quality of mother-infant interactions. During the first 6 months of use, 24 dyads have benefited from PANDA care. Three women among 5 were admitted during the antenatal period and almost one-third were aged under 21. The first primary diagnosis during the antepartum was major depressive disorder, two-fold that of personality disorder or bipolar disorder alone. At the end of PANDA stay, close to 3 women among 4 were back to their home with their child, and an out-of-home placement was mandated for 4 infants. PANDA unit is a step toward continuous and comprehensive integrative care. The mother and baby do not leave the maternity ward, and management of mother, baby, and their interactions can start immediately after birth. Considering the importance of the first months of life in the establishment of fundamental links and bonding, PANDA offers an innovative opportunity for what we hope will be both therapeutic and preventive for at-risk dyads. The detection, and ultimately prevention and management of risk of abuse and neglect is another major challenge that this unit hopes to address from the very beginning.

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

Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size largely retain good transportability performance when porting to different sites. The combination of routine laboratory test values at admission along with basic demographic features can predict mortality in patients hospitalized with COVID-19. Importantly, this potentially deployable model differs from prior work by demonstrating not only consistent performance but also reliable transportability across healthcare systems in the US and Europe, highlighting the generalizability of this model and the overall approach.

International electronic health record-derived post-acute sequelae profiles of COVID-19 patients.

The risk profiles of post-acute sequelae of COVID-19 (PASC) have not been well characterized in multi-national settings with appropriate controls. We leveraged electronic health record (EHR) data from 277 international hospitals representing 414,602 patients with COVID-19, 2.3 million control patients without COVID-19 in the inpatient and outpatient settings, and over 221 million diagnosis codes to systematically identify new-onset conditions enriched among patients with COVID-19 during the post-acute period. Compared to inpatient controls, inpatient COVID-19 cases were at significant risk for angina pectoris (RR 1.30, 95% CI 1.09-1.55), heart failure (RR 1.22, 95% CI 1.10-1.35), cognitive dysfunctions (RR 1.18, 95% CI 1.07-1.31), and fatigue (RR 1.18, 95% CI 1.07-1.30). Relative to outpatient controls, outpatient COVID-19 cases were at risk for pulmonary embolism (RR 2.10, 95% CI 1.58-2.76), venous embolism (RR 1.34, 95% CI 1.17-1.54), atrial fibrillation (RR 1.30, 95% CI 1.13-1.50), type 2 diabetes (RR 1.26, 95% CI 1.16-1.36) and vitamin D deficiency (RR 1.19, 95% CI 1.09-1.30). Outpatient COVID-19 cases were also at risk for loss of smell and taste (RR 2.42, 95% CI 1.90-3.06), inflammatory neuropathy (RR 1.66, 95% CI 1.21-2.27), and cognitive dysfunction (RR 1.18, 95% CI 1.04-1.33). The incidence of post-acute cardiovascular and pulmonary conditions decreased across time among inpatient cases while the incidence of cardiovascular, digestive, and metabolic conditions increased among outpatient cases. Our study, based on a federated international network, systematically identified robust conditions associated with PASC compared to control groups, underscoring the multifaceted cardiovascular and neurological phenotype profiles of PASC.

Changes in laboratory value improvement and mortality rates over the course of the pandemic: an international retrospective cohort study of hospitalised patients infected with SARS-CoV-2.

OBJECTIVE: To assess changes in international mortality rates and laboratory recovery rates during hospitalisation for patients hospitalised with SARS-CoV-2 between the first wave (1 March to 30 June 2020) and the second wave (1 July 2020 to 31 January 2021) of the COVID-19 pandemic. DESIGN, SETTING AND PARTICIPANTS: This is a retrospective cohort study of 83 178 hospitalised patients admitted between 7 days before or 14 days after PCR-confirmed SARS-CoV-2 infection within the Consortium for Clinical Characterization of COVID-19 by Electronic Health Record, an international multihealthcare system collaborative of 288 hospitals in the USA and Europe. The laboratory recovery rates and mortality rates over time were compared between the two waves of the pandemic. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was all-cause mortality rate within 28 days after hospitalisation stratified by predicted low, medium and high mortality risk at baseline. The secondary outcome was the average rate of change in laboratory values during the first week of hospitalisation. RESULTS: Baseline Charlson Comorbidity Index and laboratory values at admission were not significantly different between the first and second waves. The improvement in laboratory values over time was faster in the second wave compared with the first. The average C reactive protein rate of change was -4.72 mg/dL vs -4.14 mg/dL per day (p=0.05). The mortality rates within each risk category significantly decreased over time, with the most substantial decrease in the high-risk group (42.3% in March-April 2020 vs 30.8% in November 2020 to January 2021, p<0.001) and a moderate decrease in the intermediate-risk group (21.5% in March-April 2020 vs 14.3% in November 2020 to January 2021, p<0.001). CONCLUSIONS: Admission profiles of patients hospitalised with SARS-CoV-2 infection did not differ greatly between the first and second waves of the pandemic, but there were notable differences in laboratory improvement rates during hospitalisation. Mortality risks among patients with similar risk profiles decreased over the course of the pandemic. The improvement in laboratory values and mortality risk was consistent across multiple countries.

An appraisal of the frequency and severity of noninfectious manifestations in primary immunodeficiencies: A study of a national retrospective cohort of 1375 patients over 10 years.

BACKGROUND: Noninfectious manifestations-allergy, autoimmunity/inflammation, lymphoproliferation, and malignancies-are known to exist in many primary immunodeficiency diseases (PID) and to participate in prognosis. OBJECTIVE: To obtain a global view on their occurrence, we retrieved data from a retrospective cohort of 1375 patients included in the French National Reference Center for Primary Immune Deficiencies (CEREDIH) for whom we had a 10-year follow-up since inclusion in the registry. METHODS: These patients were followed for 10 years (2009-2018) by specialized centers in university hospitals. This study showed that 20.1% of patients without prior curative therapy (n = 1163) developed at least 1 manifestation (event) encompassing 277 events. RESULTS: Autoimmune/inflammatory events (n = 138) and malignancies (n = 85) affected all age classes and virtually all PID diagnostic groups. They were associated with a risk of death that occurred in 195 patients (14.2%) and were found to be causal in 43% of cases. Malignancies (odds ratio, 5.62; 95% confidence interval, 3.66-8.62) and autoimmunity (odds ratio, 1.9; 95% confidence interval, 1.27-2.84) were clearly identified as risk factors for lethality. Patients who underwent curative therapy (mostly allogeneic hematopoietic stem cell transplantation, with a few cases of gene therapy or thymus transplantation) before the 10-year study period (n = 212) had comparatively reduced but still detectable clinical manifestations (n = 16) leading to death in 9.4% of them. CONCLUSION: This study points to the frequency and severity of noninfectious manifestations in various PID groups across all age groups. These results warrant further prospective analysis to better assess their consequences and to adapt therapy, notably indication of curative therapy.

Computational diagnostic methods on 2D photographs: A review of the literature.

Here we provide a literature review of all the methods reported to date for analyzing 2D pictures for diagnostic purposes. Pubmed was used to screen the MEDLINE database using MeSH (Medical Subject Heading) terms and keyworks. The different recognition steps and the main results were reported. All human studies involving 2D facial photographs used to diagnose one or several conditions in healthy populations or in patients were included. We included 1515 articles and 27 publications were finally retained. 67% of the articles aimed at diagnosing one particular syndrome versus healthy controls and 33% aimed at performing multi-class syndrome recognition. Data volume varied from 15 to 17,106 patient pictures. Manual or automatic landmarks were one of the most commonly used tools in order to extract morphological information from images, in 22/27 (81%) publications. Geometrical features were extracted from landmarks based on Procrustes superimposition in 4/27 (15%). Textural features were extracted in 19/27 (70%) publications. Features were then classified using machine learning methods in 89% of publications, while deep learning methods were used in 11%. Facial recognition tools were generally successful in identifying rare conditions in dysmorphic patients, with comparable or higher recognition accuracy than clinical experts.

International Comparisons of Harmonized Laboratory Value Trajectories to Predict Severe COVID-19: Leveraging the 4CE Collaborative Across 342 Hospitals and 6 Countries: A Retrospective Cohort Study.

Objectives: To perform an international comparison of the trajectory of laboratory values among hospitalized patients with COVID-19 who develop severe disease and identify optimal timing of laboratory value collection to predict severity across hospitals and regions. Design: Retrospective cohort study. Setting: The Consortium for Clinical Characterization of COVID-19 by EHR (4CE), an international multi-site data-sharing collaborative of 342 hospitals in the US and in Europe. Participants: Patients hospitalized with COVID-19, admitted before or after PCR-confirmed result for SARS-CoV-2. Primary and secondary outcome measures: Patients were categorized as "ever-severe" or "never-severe" using the validated 4CE severity criteria. Eighteen laboratory tests associated with poor COVID-19-related outcomes were evaluated for predictive accuracy by area under the curve (AUC), compared between the severity categories. Subgroup analysis was performed to validate a subset of laboratory values as predictive of severity against a published algorithm. A subset of laboratory values (CRP, albumin, LDH, neutrophil count, D-dimer, and procalcitonin) was compared between North American and European sites for severity prediction. Results: Of 36,447 patients with COVID-19, 19,953 (43.7%) were categorized as ever-severe. Most patients (78.7%) were 50 years of age or older and male (60.5%). Longitudinal trajectories of CRP, albumin, LDH, neutrophil count, D-dimer, and procalcitonin showed association with disease severity. Significant differences of laboratory values at admission were found between the two groups. With the exception of D-dimer, predictive discrimination of laboratory values did not improve after admission. Sub-group analysis using age, D-dimer, CRP, and lymphocyte count as predictive of severity at admission showed similar discrimination to a published algorithm (AUC=0.88 and 0.91, respectively). Both models deteriorated in predictive accuracy as the disease progressed. On average, no difference in severity prediction was found between North American and European sites. Conclusions: Laboratory test values at admission can be used to predict severity in patients with COVID-19. Prediction models show consistency across international sites highlighting the potential generalizability of these models.

International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium.

We leveraged the largely untapped resource of electronic health record data to address critical clinical and epidemiological questions about Coronavirus Disease 2019 (COVID-19). To do this, we formed an international consortium (4CE) of 96 hospitals across five countries (www.covidclinical.net). Contributors utilized the Informatics for Integrating Biology and the Bedside (i2b2) or Observational Medical Outcomes Partnership (OMOP) platforms to map to a common data model. The group focused on temporal changes in key laboratory test values. Harmonized data were analyzed locally and converted to a shared aggregate form for rapid analysis and visualization of regional differences and global commonalities. Data covered 27,584 COVID-19 cases with 187,802 laboratory tests. Case counts and laboratory trajectories were concordant with existing literature. Laboratory tests at the time of diagnosis showed hospital-level differences equivalent to country-level variation across the consortium partners. Despite the limitations of decentralized data generation, we established a framework to capture the trajectory of COVID-19 disease in patients and their response to interventions.

Phenotypic similarity for rare disease: Ciliopathy diagnoses and subtyping.

Rare diseases are often hard and long to be diagnosed precisely, and most of them lack approved treatment. For some complex rare diseases, precision medicine approach is further required to stratify patients into homogeneous subgroups based on the clinical, biological or molecular features. In such situation, deep phenotyping of these patients and comparing their profiles based on subjacent similarities are thus essential to help fast and precise diagnoses and better understanding of pathophysiological processes in order to develop therapeutic solutions. In this article, we developed a new pipeline of using deep phenotyping to define patient similarity and applied it to ciliopathies, a group of rare and severe diseases caused by ciliary dysfunction. As a French national reference center for rare and undiagnosed diseases, the Necker-Enfants Malades Hospital (Necker Children's Hospital) hosts the Imagine Institute, a research institute focusing on genetic diseases. The clinical data warehouse contains on one hand EHR data, and on the other hand, clinical research data. The similarity metrics were computed on both data sources, and were evaluated with two tasks: diagnoses with EHRs and subtyping with ciliopathy specific research data. We obtained a precision of 0.767 in the top 30 most similar patients with diagnosed ciliopathies. Subtyping ciliopathy patients with phenotypic similarity showed concordances with expert knowledge. Similarity metrics applied to rare disease offer new perspectives in a translational context that may help to recruit patients for research, reduce the length of the diagnostic journey, and better understand the mechanisms of the disease.

Next generation phenotyping using narrative reports in a rare disease clinical data warehouse.

BACKGROUND: Secondary use of data collected in Electronic Health Records opens perspectives for increasing our knowledge of rare diseases. The clinical data warehouse (named Dr. Warehouse) at the Necker-Enfants Malades Children's Hospital contains data collected during normal care for thousands of patients. Dr. Warehouse is oriented toward the exploration of clinical narratives. In this study, we present our method to find phenotypes associated with diseases of interest. METHODS: We leveraged the frequency and TF-IDF to explore the association between clinical phenotypes and rare diseases. We applied our method in six use cases: phenotypes associated with the Rett, Lowe, Silver Russell, Bardet-Biedl syndromes, DOCK8 deficiency and Activated PI3-kinase Delta Syndrome (APDS). We asked domain experts to evaluate the relevance of the top-50 (for frequency and TF-IDF) phenotypes identified by Dr. Warehouse and computed the average precision and mean average precision. RESULTS: Experts concluded that between 16 and 39 phenotypes could be considered as relevant in the top-50 phenotypes ranked by descending frequency discovered by Dr. Warehouse (resp. between 11 and 41 for TF-IDF). Average precision ranges from 0.55 to 0.91 for frequency and 0.52 to 0.95 for TF-IDF. Mean average precision was 0.79. Our study suggests that phenotypes identified in clinical narratives stored in Electronic Health Record can provide rare disease specialists with candidate phenotypes that can be used in addition to the literature. CONCLUSIONS: Clinical Data Warehouses can be used to perform Next Generation Phenotyping, especially in the context of rare diseases. We have developed a method to detect phenotypes associated with a group of patients using medical concepts extracted from free-text clinical narratives.

A clinician friendly data warehouse oriented toward narrative reports: Dr. Warehouse.

INTRODUCTION: Clinical data warehouses are often oriented toward integration and exploration of coded data. However narrative reports are of crucial importance for translational research. This paper describes Dr. Warehouse®, an open source data warehouse oriented toward clinical narrative reports and designed to support clinicians' day-to-day use. METHOD: Dr. Warehouse relies on an original database model to focus on documents in addition to facts. Besides classical querying functionalities, the system provides an advanced search engine and Graphical User Interfaces adapted to the exploration of text. Dr. Warehouse is dedicated to translational research with cohort recruitment capabilities, high throughput phenotyping and patient centric views (including similarity metrics among patients). These features leverage Natural Language Processing based on the extraction of UMLS® concepts, as well as negation and family history detection. RESULTS: A survey conducted after 6 months of use at the Necker Children's Hospital shows a high rate of satisfaction among the users (96.6%). During this period, 122 users performed 2837 queries, accessed 4,267 patients' records and included 36,632 patients in 131 cohorts. The source code is available at this github link https://github.com/imagine-bdd/DRWH. A demonstration based on PubMed abstracts is available at https://imagine-plateforme-bdd.fr/dwh_pubmed/.

Finding patients using similarity measures in a rare diseases-oriented clinical data warehouse: Dr. Warehouse and the needle in the needle stack.

OBJECTIVE: In the context of rare diseases, it may be helpful to detect patients with similar medical histories, diagnoses and outcomes from a large number of cases with automated methods. To reduce the time to find new cases, we developed a method to find similar patients given an index case leveraging data from the electronic health records. MATERIALS AND METHODS: We used the clinical data warehouse of a children academic hospital in Paris, France (Necker-Enfants Malades), containing about 400,000 patients. Our model was based on a vector space model (VSM) to compute the similarity distance between an index patient and all the patients of the data warehouse. The dimensions of the VSM were built upon Unified Medical Language System concepts extracted from clinical narratives stored in the clinical data warehouse. The VSM was enhanced using three parameters: a pertinence score (TF-IDF of the concepts), the polarity of the concept (negated/not negated) and the minimum number of concepts in common. We evaluated this model by displaying the most similar patients for five different rare diseases: Lowe Syndrome (LOWE), Dystrophic Epidermolysis Bullosa (DEB), Activated PI3K delta Syndrome (APDS), Rett Syndrome (RETT) and Dowling Meara (EBS-DM), from the clinical data warehouse representing 18, 103, 21, 84 and 7 patients respectively. RESULTS: The percentages of index patients returning at least one true positive similar patient in the Top30 similar patients were 94% for LOWE, 97% for DEB, 86% for APDS, 71% for EBS-DM and 99% for RETT. The mean number of patients with the exact same genetic diseases among the 30 returned patients was 51%. CONCLUSION: This tool offers new perspectives in a translational context to identify patients for genetic research. Moreover, when new molecular bases are discovered, our strategy will help to identify additional eligible patients for genetic screening.

Improving a full-text search engine: the importance of negation detection and family history context to identify cases in a biomedical data warehouse.

OBJECTIVE: The repurposing of electronic health records (EHRs) can improve clinical and genetic research for rare diseases. However, significant information in rare disease EHRs is embedded in the narrative reports, which contain many negated clinical signs and family medical history. This paper presents a method to detect family history and negation in narrative reports and evaluates its impact on selecting populations from a clinical data warehouse (CDW). MATERIALS AND METHODS: We developed a pipeline to process 1.6 million reports from multiple sources. This pipeline is part of the load process of the Necker Hospital CDW. RESULTS: We identified patients with "Lupus and diarrhea," "Crohn's and diabetes," and "NPHP1" from the CDW. The overall precision, recall, specificity, and F-measure were 0.85, 0.98, 0.93, and 0.91, respectively. CONCLUSION: The proposed method generates a highly accurate identification of cases from a CDW of rare disease EHRs.

PPIDD: an extraction and visualisation method of biological protein-protein interfaces.

Today, the information for generating reliable protein-protein complex datasets is not directly accessible from PDB structures. Moreover, in X-ray protein structures, different types of contacts can be observed between proteins: contacts in homodimers or inside heterocomplexes considered to be specific, and contacts induced by crystallogenesis processes, considered to be non-specific. However, none of the databases giving access to protein-protein complexes allows the crystallographic interfaces to be distinguished from the biological interfaces. For this reason we developed PPIDD (Protein-Protein Interface Description Database), an innovative tool, which allows the extraction and visualisation of biological protein-protein interfaces from an annotated subset of crystallographic structures of proteins. This tool is focused on the description of protein-protein interfaces corresponding to well-identified classes of protein assemblies. It permits the representation of any of these protein-protein assemblies (duplex) and their interfaces as well as the export of the corresponding molecular structures under a flexible format, which is an extension of the PDBML. Moreover, PPIDD facilitates the construction of subsets of interfaces presenting user-specified common characteristics, to enhance the understanding of the determinants of specific protein-protein interactions.