Pathophysiologic implications of elevated prevalence of hereditary alpha-tryptasemia in all mastocytosis subtypes.

BACKGROUND: Mastocytosis and monoclonal mast cell (MC) activation syndrome (MMAS) are heterogeneous conditions characterized by the accumulation of atypical MCs. Despite the recurrent involvement of KIT mutations, the pathophysiologic origin of mastocytosis and MMAS is unclear. Although hereditary α-tryptasemia (HαT, related to TPSAB1 gene duplication) is abnormally frequent in these diseases, it is not known whether the association is coincidental or causal. OBJECTIVE: We evaluated the prevalence of HαT in all mastocytosis subtypes and MMAS and assessed the pathophysiologic association with HαT. METHODS: Clinical data, laboratory data, KIT mutations, TPSAB1 duplication (assessed by droplet digital PCR), and HαT prevalence were retrospectively recorded for all patients with mastocytosis and MMAS registered in the French national referral center database and compared to a control cohort. To increase the power of our analysis for advanced systemic mastocytosis (advSM), we pooled our cohort with literature cases. RESULTS: We included 583 patients (27 with MMAS and 556 with mastocytosis). The prevalence of HαT in mastocytosis was 12.6%, significantly higher than in the general population (5.7%, P = .002) and lower than in MMAS (33.3%, P = .02). HαT+ patients were more likely to have anaphylactic reactions and less likely to have cutaneous lesions than HαT- patients (43.0% vs 24.4%, P = .006; 57.7% vs 75.6%, respectively, P = .006). In the pooled analysis, the prevalence of HαT was higher in advSM (11.5%) than in control cohorts (5.2%, P = .01). CONCLUSION: Here we confirm the increase incidence of anaphylaxis in HαT+ mastocytosis patients. The increased prevalence of HαT in all subtypes of systemic mastocytosis (including advSM) is suggestive of pathophysiologic involvement.

Natural history of rare diseases using natural language processing of narrative unstructured electronic health records: The example of Dravet syndrome.

OBJECTIVE: The increasing implementation of electronic health records allows the use of advanced text-mining methods for establishing new patient phenotypes and stratification, and for revealing outcome correlations. In this study, we aimed to explore the electronic narrative clinical reports of a cohort of patients with Dravet syndrome (DS) longitudinally followed at our center, to identify the capacity of this methodology to retrace natural history of DS during the early years. METHODS: We used a document-based clinical data warehouse employing natural language processing to recognize the phenotype concepts in the narrative medical reports. We included patients with DS who have a medical report produced before the age of 2 years and a follow-up after the age of 3 years ("DS cohort," 56 individuals). We selected two control populations, a "general control cohort" (275 individuals) and a "neurological control cohort" (281 individuals), with similar characteristics in terms of gender, number of reports, and age at last report. To find concepts specifically associated with DS, we performed a phenome-wide association study using Cox regression, comparing the reports of the three cohorts. We then performed a qualitative analysis of the surviving concepts based on their median age at first appearance. RESULTS: A total of 76 concepts were prevalent in the reports of children with DS. Concepts appearing during the first 2 years were mostly related with the epilepsy features at the onset of DS (convulsive and prolonged seizures triggered by fever, often requiring in-hospital care). Subsequently, concepts related to new types of seizures and to drug resistance appeared. A series of non-seizure-related concepts emerged after the age of 2-3 years, referring to the nonseizure comorbidities classically associated with DS. SIGNIFICANCE: The extraction of clinical terms by narrative reports of children with DS allows outlining the known natural history of this rare disease in early childhood. This original model of "longitudinal phenotyping" could be applied to other rare and very rare conditions with poor natural history description.

Histological characterization of liver involvement in systemic mastocytosis.

BACKGROUND AND AIMS: Systemic mastocytosis (SM) is characterized by the accumulation of atypical mast cells (MCs) in organs. Liver histology of SM has been marginally described and accurate histological classification is critical, given the consequences of aggressive SM diagnosis. We aimed to describe the histological features associated with liver SM using updated tools. METHODS: Using the database of the French Reference Centre for Mastocytosis, we retrospectively identified patients with a liver biopsy (LB) and a diagnosis of SM. All LB procedures were performed according to the local physician in charge and centrally reviewed by an expert pathologist. RESULTS: A total of 28 patients were included: 6 had indolent SM, 9 had aggressive SM, and 13 had SM with an associated hematologic neoplasm. Twenty-five (89%) patients presented hepatomegaly, and 19 (68%) had portal hypertension. The LB frequently showed slight sinusoid dilatation (82%). Fibrosis was observed in 3/6 indolent SM and in almost all advanced SM cases (21/22), but none of them showed cirrhosis. A high MC burden (>50 MCs/high-power field) was correlated with elevated blood alkaline phosphatase levels (p = .030). The presence of portal hypertension was associated with a higher mean fibrosis grade (1.6 vs. 0.8 in its absence; p = .026). In advanced SM, the presence of nodular regenerative hyperplasia (NRH) was associated with decreased overall survival (9.5 vs. 46.3 months, p = .002). CONCLUSIONS: MC infiltration induced polymorphic hepatic lesions and the degree of fibrosis is associated with portal hypertension. NRH identifies a poor prognosis subgroup of patients with advanced SM. Assessing liver histology can aid in SM prognostic evaluation.

Adalimumab Therapy in Pediatric Crohn Disease: A 2-Year Follow-Up Comparing "Top-Down" and "Step-Up" Strategies.

OBJECTIVES: European Crohn's Colitis Organization (ECCO) and the European Society of Pediatric Gastroenterology, Hepatology and Nutrition (ESPGHAN) guidelines recommend the early use of anti-tumor necrosis factor (TNF) biologicals in pediatric Crohn disease (CD) patients with positive predictors for poor outcome. The objective of the present study was to compare early "Top-Down" use of adalimumab (ADA) immunomodulator/biologics-naive patients to conventional "Step-Up" management. METHODS: One hundred and twenty consecutive patients with a confirmed diagnosis of CD and treated with ADA between 2008 and 2019 were included and allocated to the ADA-Top Down (n = 59) or ADA-Step Up group (n = 61). The primary endpoint was prolonged steroid-/enteral nutrition-free clinical remission at 24 months, defined by a weighted Pediatric Crohn's Disease Activity Index (wPCDAI) < 12.5. Clinical and biological data were collected at 12 and 24 months. RESULTS: At start of ADA, disease activity was comparable between the ADA-Top Down group and the ADA-Step Up group (wPCDAI = 31 ± 16 vs 31.3 ± 15.2, respectively, P = 0.84). At 24 months, the remission rate was significantly higher in the ADA-Top Down group (73% vs 51%, P < 0.01). After propensity score, the Top-Down strategy is still more effective than the Step-Up strategy in maintaining remission at 24 months [hazard ratio (HR) = 0.36, 95% CI (0.15-0.87), P = 0.02]. Patients in the ADA-Top Down group were mainly on monotherapy compared to patients in the ADA-Step Up group (53/55 vs 28/55 respectively, P < 0.001). Serum levels of ADA were higher in the ADA-Top Down group than in the ADA-Step Up group (12.8 ± 4.3 vs 10.4 ± 3.9 µg/mL, respectively, P < 0.01). There were no serious adverse events. CONCLUSIONS: Early use of ADA appears to be more effective in maintaining relapse-free remission at 2 years, while using it as monotherapy. These findings further favor the recommendation of early anti-TNF use in high-risk CD patients.

Hyperventilation syndrome in children with asthma.

BACKGROUND: Hyperventilation syndrome (HVS) may be associated with asthma. In the absence of a gold standard diagnosis for children, its impact on asthma has been rarely assessed. OBJECTIVE: To assess the impact of HVS on the symptoms and lung function of children with asthma and determine the diagnostic value of the Nijmegen questionnaire in comparison to a hyperventilation test (HVT). METHODS: Data from asthmatic children followed in the department of Pediatric Pulmonology of Necker Hospital and explored for HVS were retrospectively analyzed. HVS was diagnosed by a positive HVT. Asthma exacerbations, control and lung function were assessed in children with or without a positive HVT. The sensitivity and specificity of the Nijmegen questionnaire were determined relative to the positivity of a HVT. The Nijmegen questionnaire threshold was ≥23. RESULTS: Data from 112 asthmatic children, median age 13.9 years [11.6-16], were analyzed. Twenty-eight children (25%) had mild or moderate asthma and 84 (75%) severe asthma. The HVT was performed on 108 children and was negative for 34 (31.5%) and positive for 74 (68.5%). The number of asthma exacerbations in the past 12 months, Asthma Control Test (ACT) score, and lung function did not differ between children with a positive HVT and a negative HVT. The Nijmegen questionnaire was administered to 103 children. Its sensitivity was 56.3% and specificity 56.3%. CONCLUSION: The symptoms and lung function of adolescents with asthma are not affected by the presence of HVS. The sensitivity and specificity of the Nijmegen questionnaire are low.

Association between antidepressant use and reduced risk of intubation or death in hospitalized patients with COVID-19: results from an observational study.

A prior meta-analysis showed that antidepressant use in major depressive disorder was associated with reduced plasma levels of several pro-inflammatory mediators, which have been associated with severe COVID-19. Recent studies also suggest that several antidepressants may inhibit acid sphingomyelinase activity, which may prevent the infection of epithelial cells with SARS-CoV-2, and that the SSRI fluoxetine may exert in-vitro antiviral effects on SARS-CoV-2. We examined the potential usefulness of antidepressant use in patients hospitalized for COVID-19 in an observational multicenter retrospective cohort study conducted at AP-HP Greater Paris University hospitals. Of 7230 adults hospitalized for COVID-19, 345 patients (4.8%) received an antidepressant within 48 h of hospital admission. The primary endpoint was a composite of intubation or death. We compared this endpoint between patients who received antidepressants and those who did not in time-to-event analyses adjusted for patient characteristics, clinical and biological markers of disease severity, and other psychotropic medications. The primary analysis was a multivariable Cox model with inverse probability weighting. This analysis showed a significant association between antidepressant use and reduced risk of intubation or death (HR, 0.56; 95% CI, 0.43-0.73, p < 0.001). This association remained significant in multiple sensitivity analyses. Exploratory analyses suggest that this association was also significant for SSRI and non-SSRI antidepressants, and for fluoxetine, paroxetine, escitalopram, venlafaxine, and mirtazapine (all p < 0.05). These results suggest that antidepressant use could be associated with lower risk of death or intubation in patients hospitalized for COVID-19. Double-blind controlled randomized clinical trials of antidepressant medications for COVID-19 are needed.

Association of Antihypertensive Agents with the Risk of In-Hospital Death in Patients with Covid-19.

PURPOSE: The role of angiotensin receptor blockers (ARB), angiotensin-converting enzyme inhibitors (ACEi), or other antihypertensive agents in the case of Covid-19 remains controversial. We aimed to investigate the association between antihypertensive agent exposure and in-hospital mortality in patients with Covid-19. METHODS: We performed a retrospective multicenter cohort study on patients hospitalized between February 1 and May 15, 2020. All patients had been followed up for at least 30 days. RESULTS: Of the 8078 hospitalized patients for Covid-19, 3686 (45.6%) had hypertension and were included in the study. In this population, the median age was 75.4 (IQR, 21.5) years and 57.1% were male. Overall in-hospital 30-day mortality was 23.1%. The main antihypertensive pharmacological classes used were calcium channel blockers (CCB) (n=1624, 44.1%), beta-blockers (n=1389, 37.7%), ARB (n=1154, 31.3%), and ACEi (n=998, 27.1%). The risk of mortality was lower in CCB (aOR, 0.83 [0.70-0.99]) and beta-blockers (aOR, 0.80 [0.67-0.95]) users and non-significant in ARB (aOR, 0.88 [0.72-1.06]) and ACEi (aOR, 0.83 [0.68-1.02]) users, compared to non-users. These results remain consistent for patients receiving CCB, beta-blocker, or ARB as monotherapies. CONCLUSION: This large multicenter retrospective of Covid-19 patients with hypertension found a reduced mortality among CCB and beta-blockers users, suggesting a putative protective effect. Our findings did not show any association between the use of renin-angiotensin-aldosterone system inhibitors and the risk of in-hospital death. Although they need to be confirmed in further studies, these results support the continuation of antihypertensive agents in patients with Covid-19, in line with the current guidelines.

SurvMaximin: Robust federated approach to transporting survival risk prediction models.

OBJECTIVE: For multi-center heterogeneous Real-World Data (RWD) with time-to-event outcomes and high-dimensional features, we propose the SurvMaximin algorithm to estimate Cox model feature coefficients for a target population by borrowing summary information from a set of health care centers without sharing patient-level information. MATERIALS AND METHODS: For each of the centers from which we want to borrow information to improve the prediction performance for the target population, a penalized Cox model is fitted to estimate feature coefficients for the center. Using estimated feature coefficients and the covariance matrix of the target population, we then obtain a SurvMaximin estimated set of feature coefficients for the target population. The target population can be an entire cohort comprised of all centers, corresponding to federated learning, or a single center, corresponding to transfer learning. RESULTS: Simulation studies and a real-world international electronic health records application study, with 15 participating health care centers across three countries (France, Germany, and the U.S.), show that the proposed SurvMaximin algorithm achieves comparable or higher accuracy compared with the estimator using only the information of the target site and other existing methods. The SurvMaximin estimator is robust to variations in sample sizes and estimated feature coefficients between centers, which amounts to significantly improved estimates for target sites with fewer observations. CONCLUSIONS: The SurvMaximin method is well suited for both federated and transfer learning in the high-dimensional survival analysis setting. SurvMaximin only requires a one-time summary information exchange from participating centers. Estimated regression vectors can be very heterogeneous. SurvMaximin provides robust Cox feature coefficient estimates without outcome information in the target population and is privacy-preserving.

Dexamethasone use and mortality in hospitalized patients with coronavirus disease 2019: A multicentre retrospective observational study.

AIMS: To examine the association between dexamethasone use and mortality among patients hospitalized for COVID-19. METHODS: We examined the association between dexamethasone use and mortality at AP-HP Greater Paris University hospitals. Study baseline was defined as the date of hospital admission. The primary endpoint was time to death. We compared this endpoint between patients who received dexamethasone and those who did not in time-to-event analyses adjusted for patient characteristics (such as age, sex and comorbidity) and clinical and biological markers of clinical severity of COVID-19, and stratified by the need for respiratory support, i.e. mechanical ventilation or oxygen. The primary analysis was a multivariable Cox regression model. RESULTS: Of 12 217 adult patients hospitalized with a positive COVID-19 reverse transcriptase-polymerase chain reaction test, 171 (1.4%) received dexamethasone orally or by intravenous perfusion during the visit. Among patients who required respiratory support, the end-point occurred in 10/63 (15.9%) patients who received dexamethasone and 298/1129 (26.4%) patients who did not. In this group, there was a significant association between dexamethasone use and reduced mortality in the primary analysis (hazard ratio, 0.46; 95% confidence interval 0.22-0.96, P = .039). Among patients who did not require respiratory support, there was no significant association between dexamethasone use and the endpoint. CONCLUSIONS: In this multicentre observational study, dexamethasone use administered either orally or by intravenous injection at a cumulative dose between 60 mg and 150 mg was associated with reduced mortality among patients with COVID-19 requiring respiratory support.

The Epidemiology of Patients' Email Addresses in a French University Hospital: Case-Control Study.

BACKGROUND: Health care professionals are caught between the wish of patients to speed up health-related communication via emails and the need for protecting health information. OBJECTIVE: We aimed to analyze the demographic characteristics of patients providing an email, and study the distribution of emails' domain names. METHODS: We used the information system of the European Hospital Georges Pompidou (HEGP) to identify patients who provided an email address. We used a 1:1 matching strategy to study the demographic characteristics of the patients associated with the presence of an email, and described the characteristics of the emails used (in terms of types of emails-free, business, and personal). RESULTS: Overall, 4.22% (41,004/971,822) of the total population of patients provided an email address. The year of last contact with the patient is the strongest driver of the presence of an email address (odds ratio [OR] 20.8, 95% CI 18.9-22.9). Patients more likely to provide an email address were treated for chronic conditions and were more likely born between 1950 and 1969 (taking patients born before 1950 as reference [OR 1.60, 95% CI 1.54-1.67], and compared to those born after 1990 [OR 0.56, 95% CI 0.53-0.59]). Of the 41,004 email addresses collected, 37,779 were associated with known email providers, 31,005 email addresses were associated with Google, Microsoft, Orange, and Yahoo!, 2878 with business emails addresses, and 347 email addresses with personalized domain names. CONCLUSIONS: Emails have been collected only recently in our institution. The importance of the year of last contact probably reflects this recent change in contact information collection policy. The demographic characteristics and especially the age distribution are likely the result of a population bias in the hospital: patients providing email are more likely to be treated for chronic diseases. A risk analysis of the use of email revealed several situations that could constitute a breach of privacy that is both likely and with major consequences. Patients treated for chronic diseases are more likely to provide an email address, and are also more at risk in case of privacy breach. Several common situations could expose their private information. We recommend a very restrictive use of the emails for health communication.

Authorship Correction: International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study.

[This corrects the article DOI: 10.2196/31400.].

International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study.

BACKGROUND: Many countries have experienced 2 predominant waves of COVID-19-related hospitalizations. Comparing the clinical trajectories of patients hospitalized in separate waves of the pandemic enables further understanding of the evolving epidemiology, pathophysiology, and health care dynamics of the COVID-19 pandemic. OBJECTIVE: In this retrospective cohort study, we analyzed electronic health record (EHR) data from patients with SARS-CoV-2 infections hospitalized in participating health care systems representing 315 hospitals across 6 countries. We compared hospitalization rates, severe COVID-19 risk, and mean laboratory values between patients hospitalized during the first and second waves of the pandemic. METHODS: Using a federated approach, each participating health care system extracted patient-level clinical data on their first and second wave cohorts and submitted aggregated data to the central site. Data quality control steps were adopted at the central site to correct for implausible values and harmonize units. Statistical analyses were performed by computing individual health care system effect sizes and synthesizing these using random effect meta-analyses to account for heterogeneity. We focused the laboratory analysis on C-reactive protein (CRP), ferritin, fibrinogen, procalcitonin, D-dimer, and creatinine based on their reported associations with severe COVID-19. RESULTS: Data were available for 79,613 patients, of which 32,467 were hospitalized in the first wave and 47,146 in the second wave. The prevalence of male patients and patients aged 50 to 69 years decreased significantly between the first and second waves. Patients hospitalized in the second wave had a 9.9% reduction in the risk of severe COVID-19 compared to patients hospitalized in the first wave (95% CI 8.5%-11.3%). Demographic subgroup analyses indicated that patients aged 26 to 49 years and 50 to 69 years; male and female patients; and black patients had significantly lower risk for severe disease in the second wave than in the first wave. At admission, the mean values of CRP were significantly lower in the second wave than in the first wave. On the seventh hospital day, the mean values of CRP, ferritin, fibrinogen, and procalcitonin were significantly lower in the second wave than in the first wave. In general, countries exhibited variable changes in laboratory testing rates from the first to the second wave. At admission, there was a significantly higher testing rate for D-dimer in France, Germany, and Spain. CONCLUSIONS: Patients hospitalized in the second wave were at significantly lower risk for severe COVID-19. This corresponded to mean laboratory values in the second wave that were more likely to be in typical physiological ranges on the seventh hospital day compared to the first wave. Our federated approach demonstrated the feasibility and power of harmonizing heterogeneous EHR data from multiple international health care systems to rapidly conduct large-scale studies to characterize how COVID-19 clinical trajectories evolve.

What Every Reader Should Know About Studies Using Electronic Health Record Data but May Be Afraid to Ask.

Coincident with the tsunami of COVID-19-related publications, there has been a surge of studies using real-world data, including those obtained from the electronic health record (EHR). Unfortunately, several of these high-profile publications were retracted because of concerns regarding the soundness and quality of the studies and the EHR data they purported to analyze. These retractions highlight that although a small community of EHR informatics experts can readily identify strengths and flaws in EHR-derived studies, many medical editorial teams and otherwise sophisticated medical readers lack the framework to fully critically appraise these studies. In addition, conventional statistical analyses cannot overcome the need for an understanding of the opportunities and limitations of EHR-derived studies. We distill here from the broader informatics literature six key considerations that are crucial for appraising studies utilizing EHR data: data completeness, data collection and handling (eg, transformation), data type (ie, codified, textual), robustness of methods against EHR variability (within and across institutions, countries, and time), transparency of data and analytic code, and the multidisciplinary approach. These considerations will inform researchers, clinicians, and other stakeholders as to the recommended best practices in reviewing manuscripts, grants, and other outputs from EHR-data derived studies, and thereby promote and foster rigor, quality, and reliability of this rapidly growing field.

Lessons Learned from the Usability Evaluation of a Simulated Patient Dialogue System.

Simulated consultations through virtual patients allow medical students to practice history-taking skills. Ideally, applications should provide interactions in natural language and be multi-case, multi-specialty. Nevertheless, few systems handle or are tested on a large variety of cases. We present a virtual patient dialogue system in which a medical trainer types new cases and these are processed without human intervention. To develop it, we designed a patient record model, a knowledge model for the history-taking task, and a termino-ontological model for term variation and out-of-vocabulary words. We evaluated whether this system provided quality dialogue across medical specialities (n = 18), and with unseen cases (n = 29) compared to the cases used for development (n = 6). Medical evaluators (students, residents, practitioners, and researchers) conducted simulated history-taking with the system and assessed its performance through Likert-scale questionnaires. We analysed interaction logs and evaluated system correctness. The mean user evaluation score for the 29 unseen cases was 4.06 out of 5 (very good). The evaluation of correctness determined that, on average, 74.3% (sd = 9.5) of replies were correct, 14.9% (sd = 6.3) incorrect, and in 10.7% the system behaved cautiously by deferring a reply. In the user evaluation, all aspects scored higher in the 29 unseen cases than in the 6 seen cases. Although such a multi-case system has its limits, the evaluation showed that creating it is feasible; that it performs adequately; and that it is judged usable. We discuss some lessons learned and pivotal design choices affecting its performance and the end-users, who are primarily medical students.

Electronic health records for the diagnosis of rare diseases.

With the emergence of electronic health records, the reuse of clinical data offers new perspectives for the diagnosis and management of patients with rare diseases. However, there are many obstacles to the repurposing of clinical data. The development of decision support systems depends on the ability to recruit patients, extract and integrate the patients' data, mine and stratify these data, and integrate the decision support algorithm into patient care. This last step requires an adaptability of the electronic health records to integrate learning health system tools. In this literature review, we examine the research that provides solutions to unlock these barriers and accelerate translational research: structured electronic health records and free-text search engines to find patients, data warehouses and natural language processing to extract phenotypes, machine learning algorithms to classify patients, and similarity metrics to diagnose patients. Medical informatics is experiencing an impellent request to develop decision support systems, and this requires ethical considerations for clinicians and patients to ensure appropriate use of health data.

Association of blood bicarbonate and pH with mineral metabolism disturbance and outcome after kidney transplantation.

In kidney transplant recipients (KTRs), scarce evidence has associated low blood bicarbonate levels with mineral metabolic disturbance and reduced allograft survival. However, the contribution of the blood pH to these observations remains unassessed. Equally, little is known about the influence of the blood provenance (arteriovenous fistula vs peripheral vein) on bicarbonate values. We analyzed blood gas parameters in a single-center cohort of 1260 stable KTRs, 3 months after transplantation. Inspection of pO2 distribution allowed the unambiguous identification of the arterial (N = 914) or venous (N = 346) origin of the samples. In patients with arterial blood samples, 435 (46%) had bicarbonate levels below 22 mmol/L. Among them, 196 (40%) were acidemic (blood pH <7.38). In multivariate analysis, low arterial blood pH was associated with increased blood ionized calcium and phosphate and reduced fibroblast growth factor 23 and calcitriol, but not with outcome. In contrast, low bicarbonate concentration predicted allograft loss independently of measured glomerular filtration rate and other potential confounders (hazard ratio [HR] 1.70; 95% confidence interval [CI] 1.04-2.80). In KTRs, reduced arterial blood bicarbonate levels predict outcome while acidemia is associated with altered mineral metabolism.

Natural Language Processing for Rapid Response to Emergent Diseases: Case Study of Calcium Channel Blockers and Hypertension in the COVID-19 Pandemic.

BACKGROUND: A novel disease poses special challenges for informatics solutions. Biomedical informatics relies for the most part on structured data, which require a preexisting data or knowledge model; however, novel diseases do not have preexisting knowledge models. In an emergent epidemic, language processing can enable rapid conversion of unstructured text to a novel knowledge model. However, although this idea has often been suggested, no opportunity has arisen to actually test it in real time. The current coronavirus disease (COVID-19) pandemic presents such an opportunity. OBJECTIVE: The aim of this study was to evaluate the added value of information from clinical text in response to emergent diseases using natural language processing (NLP). METHODS: We explored the effects of long-term treatment by calcium channel blockers on the outcomes of COVID-19 infection in patients with high blood pressure during in-patient hospital stays using two sources of information: data available strictly from structured electronic health records (EHRs) and data available through structured EHRs and text mining. RESULTS: In this multicenter study involving 39 hospitals, text mining increased the statistical power sufficiently to change a negative result for an adjusted hazard ratio to a positive one. Compared to the baseline structured data, the number of patients available for inclusion in the study increased by 2.95 times, the amount of available information on medications increased by 7.2 times, and the amount of additional phenotypic information increased by 11.9 times. CONCLUSIONS: In our study, use of calcium channel blockers was associated with decreased in-hospital mortality in patients with COVID-19 infection. This finding was obtained by quickly adapting an NLP pipeline to the domain of the novel disease; the adapted pipeline still performed sufficiently to extract useful information. When that information was used to supplement existing structured data, the sample size could be increased sufficiently to see treatment effects that were not previously statistically detectable.

Osmoregulation Performance and Kidney Transplant Outcome.

BACKGROUND: Kidney transplant recipients have an impaired ability to dilute urine but seldom develop baseline hyponatremia before ESRD. Although hyponatremia is a risk factor for adverse events in CKD and in kidney transplant recipients, it remains unclear whether subtler alterations in osmoregulation performance are associated with outcome. METHODS: We studied a single-center prospective cohort of 1258 kidney transplant recipients who underwent a water-loading test 3 months after transplant to determine osmoregulation performance. Measured GFR (mGFR) was performed at the same visit. A group of 164 healthy candidates for kidney donation served as controls. We further evaluated the association of osmoregulation performance with transplantation outcomes and subsequent kidney function. RESULTS: Unlike controls, most kidney transplant recipients failed to maintain plasma sodium during water loading (plasma sodium slope of -0.6±0.4 mmol/L per hour in transplant recipients versus -0.12±0.3 mmol/L per hour in controls; P<0.001). Steeper plasma sodium reduction during the test independently associated with the composite outcome of all-cause mortality and allograft loss (hazard ratio [HR], 1.73 per 1 mmol/L per hour decrease in plasma sodium; 95% confidence interval [95% CI], 1.23 to 2.45; P=0.002) and allograft loss alone (HR, 2.04 per 1 mmol/L per hour decrease in plasma sodium; 95% CI, 1.19 to 3.51; P=0.01). The association remained significant in a prespecified sensitivity analysis excluding patients with hyperglycemia. In addition, a steeper plasma sodium slope 3 months after transplantation independently correlated with lower mGFR at 12 months (ß=1.93; 95% CI, 0.46 to 3.41; P=0.01). CONCLUSIONS: Reduced osmoregulation performance occurs frequently in kidney transplant recipients and is an independent predictor of renal outcome.

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.

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/.