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.

RNAseq analysis of heart tissue from mice treated with atenolol and isoproterenol reveals a reciprocal transcriptional response.

BACKGROUND: The transcriptional response to many widely used drugs and its modulation by genetic variability is poorly understood. Here we present an analysis of RNAseq profiles from heart tissue of 18 inbred mouse strains treated with the ß-blocker atenolol (ATE) and the ß-agonist isoproterenol (ISO). RESULTS: Differential expression analyses revealed a large set of genes responding to ISO (n = 1770 at FDR = 0.0001) and a comparatively small one responding to ATE (n = 23 at FDR = 0.0001). At a less stringent definition of differential expression, the transcriptional responses to these two antagonistic drugs are reciprocal for many genes, with an overall anti-correlation of r = -0.3. This trend is also observed at the level of most individual strains even though the power to detect differential expression is significantly reduced. The inversely expressed gene sets are enriched with genes annotated for heart-related functions. Modular analysis revealed gene sets that exhibit coherent transcription profiles across some strains and/or treatments. Correlations between these modules and a broad spectrum of cardiovascular traits are stronger than expected by chance. This provides evidence for the overall importance of transcriptional regulation for these organismal responses and explicits links between co-expressed genes and the traits they are associated with. Gene set enrichment analysis of differentially expressed groups of genes pointed to pathways related to heart development and functionality. CONCLUSIONS: Our study provides new insights into the transcriptional response of the heart to perturbations of the ß-adrenergic system, implicating several new genes that had not been associated to this system previously.

Sox4 participates in the modulation of Schwann cell myelination.

In order to identify new regulators of Schwann cell myelination potentially playing a role in peripheral nervous system (PNS) pathologies, we analysed gene expression profiling data from three mouse models of demyelinating neuropathies and from the developing PNS. This analysis revealed that Sox4, which encodes a member of the Sry-related high-mobility group box protein family, was consistently upregulated in all three analysed models of neuropathy. Moreover, Sox4 showed a peak in its expression during development that corresponded with the onset of myelination. To gain further insights into the role of Sox4 in PNS development, we generated a transgenic mouse that specifically overexpresses Sox4 in Schwann cells. Sox4 overexpression led to a temporary delay in PNS myelination without affecting axonal sorting. Importantly, we observed that, whereas Sox4 mRNA could be efficiently overexpressed, Sox4 protein expression in Schwann cells was strictly regulated. Finally, our data showed that enforced expression of Sox4 in the mouse model for Charcot-Marie-Tooth 4C aggravated its neuropathic phenotype. Together, these observations reveal that Sox4 contributes to the regulation of Schwann cell myelination, and also indicates its involvement in the pathophysiology of peripheral neuropathies.

Integrating row level security in i2b2: segregation of medical records into data marts without data replication and synchronization.

Objective: i2b2 offers the possibility to store biomedical data of different projects in subject oriented data marts of the data warehouse, which potentially requires data replication between different projects and also data synchronization in case of data changes. We present an approach that can save this effort and assess its query performance in a case study that reflects real-world scenarios. Material and Methods: For data segregation, we used PostgreSQL's row level security (RLS) feature, the unit test framework pgTAP for validation and testing as well as the i2b2 application. No change of the i2b2 code was required. Instead, to leverage orchestration and deployment, we additionally implemented a command line interface (CLI). We evaluated performance using 3 different queries generated by i2b2, which we performed on an enlarged Harvard demo dataset. Results: We introduce the open source Python CLI i2b2rls, which orchestrates and manages security roles to implement data marts so that they do not need to be replicated and synchronized as different i2b2 projects. Our evaluation showed that our approach is on average 3.55 and on median 2.71 times slower compared to classic i2b2 data marts, but has more flexibility and easier setup. Conclusion: The RLS-based approach is particularly useful in a scenario with many projects, where data is constantly updated, user and group requirements change frequently or complex user authorization requirements have to be defined. The approach applies to both the i2b2 interface and direct database access.

Concurrent analysis of hospital stay durations and mortality of emerging severe acute respiratory coronavirus virus 2 (SARS-CoV-2) variants using real-time electronic health record data at a large German university hospital.

Multistate methodology proves effective in analyzing hospitalized coronavirus disease 2019 (COVID-19) patients with emerging variants in real time. An analysis of 2,548 admissions in Freiburg, Germany, showed reduced severity over time in terms of shorter hospital stays and higher discharge rates when comparing more recent phases with earlier phases of the pandemic.

Validation of Multiple Path Translation for SNOMED CT Localisation.

The MTP (multiple translation paths) approach supports human translators in clinical terminology localization. It exploits the results of web-based machine translation tools and generates, for a chosen target language, a scored output of translation candidates for each input terminology code. We present first results of a validation, using four SNOMED CT benchmarks and three translation engines. For German as target language, there was a significant advantage of MTP as a generator of plausible translation candidate lists, and a moderate advantage of the top-ranked MTP translation candidate over single best performing direct-translation approaches.

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.

Automatic Generation of German Translation Candidates for SNOMED CT Textual Descriptions.

We present an approach called MTP (multiple translation paths) aiming at assisting human translation in SNOMED CT localisation projects based on free, web-based machine translation tools. For a chosen target language, MTP generates a scored output of translation candidates (TCs) for each input concept. This paper describes the basic idea of MTP, the distribution of its output TCs and discusses typical examples with German as target language. The MTP approach capitalises on combinatorial growth by the combination of input languages, support languages, and translation engines. We applied MTP on the SNOMED CT Starter Set, using Google Translator, DeepL and Systran, together with the four source languages English, Spanish, Swedish and French, and Danish, Dutch, Norwegian, Italian, Portuguese, Polish and Russian as support languages. The descriptive assessment of TC variety, together with an analysis of typical results is the focus of this paper. MTP defines, for each input concept, TPs by the combination of input languages, support languages and translation engines, resulting in 91 translation results with various degrees of co-incidence (cardinality). The most configurations produce an average number of TCs indicating that the same TC is often derived via different translation paths. Combinations of translation engines result in distributions with a higher number of distinct TCs per concept. We present work in progress on using machine translation (MT) for terminology translation, by leveraging several free MT tools fed by different languages and language combinations. A first qualitative analysis was promising and supports our hypothesis that a majority voting applied to many translation candidates yields higher quality results than from one single engine and input language.

Mineralocorticoid receptor antagonism limits experimental choroidal neovascularization and structural changes associated with neovascular age-related macular degeneration.

Choroidal neovascularization (CNV) is a major cause of visual impairment in patients suffering from wet age-related macular degeneration (AMD), particularly when refractory to intraocular anti-VEGF injections. Here we report that treatment with the oral mineralocorticoid receptor (MR) antagonist spironolactone reduces signs of CNV in patients refractory to anti-VEGF treatment. In animal models of wet AMD, pharmacological inhibition of the MR pathway or endothelial-specific deletion of MR inhibits CNV through VEGF-independent mechanisms, in part through upregulation of the extracellular matrix protein decorin. Intravitreal injections of spironolactone-loaded microspheres and systemic delivery lead to similar reductions in CNV. Together, our work suggests MR inhibition as a novel therapeutic option for wet AMD patients unresponsive to anti-VEGF drugs.

A frequent variant in the Japanese population determines quasi-Mendelian inheritance of rare retinal ciliopathy.

Hereditary retinal degenerations (HRDs) are Mendelian diseases characterized by progressive blindness and caused by ultra-rare mutations. In a genomic screen of 331 unrelated Japanese patients, we identify a disruptive Alu insertion and a nonsense variant (p.Arg1933*) in the ciliary gene RP1, neither of which are rare alleles in Japan. p.Arg1933* is almost polymorphic (frequency = 0.6%, amongst 12,000 individuals), does not cause disease in homozygosis or heterozygosis, and yet is significantly enriched in HRD patients (frequency = 2.1%, i.e., a 3.5-fold enrichment; p-value = 9.2 × 10-5). Familial co-segregation and association analyses show that p.Arg1933* can act as a Mendelian mutation in trans with the Alu insertion, but might also associate with disease in combination with two alleles in the EYS gene in a non-Mendelian pattern of heredity. Our results suggest that rare conditions such as HRDs can be paradoxically determined by relatively common variants, following a quasi-Mendelian model linking monogenic and complex inheritance.