Search

The precise modulation of protein-carbohydrate interactions is critical in glycobiology, where multivalent binding governs key cellular processes. As such, synthetic glycopolymers are useful for probing these interactions. Herein, we developed precision glycopolymers (PGPs) with unambiguous local chemical composition and well-defined global structure and systematically evaluated the effect of polymer length, hydrophobicity, and backbone hybridization as well as glycan density and identity on the binding to both mammalian and plant lectins. Our studies identified glycan density as a critical factor, with PGPs below 50% grafting density showing significantly weaker lectin interactions. Coarse-grained molecular dynamics simulations suggest that the observed phenomena may be due to a decrease in carbohydrate-carbohydrate interactions in fully grafted PGPs, leading to improved solvent accessibility. In functional assays, these PGPs reduced the cell viability and migration in 4T1 breast cancer cells. Our findings establish a structure-activity relationship in glycopolymers, providing new strategies for designing synthetic glycomacromolecules for a myriad of applications.

Studies have shown that disrupting the formation of the ligand-RET-GFRα complex could be an effective way of treating pain and itch. Compared to traditional high-throughput screens, DNA encoded libraries (DELs) have distinguished themselves as a powerful technology for hit identification in recent years. The present work demonstrates the use of DEL technology identifying compound 16 as the first GFRa2/GFRa3 small molecule inhibitor (0.1/0.2 μM respectively) selective over RET. This molecule represents an opportunity to advance the development of small-molecule inhibitors targeting the GFRα-RET interface for the treatment of pain and itch., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

SNAP-tag is a single-turnover enzyme that has become a powerful tool, hence a popular choice, of targeted cellular protein labeling. Three SNAP-tag substrates that carry the copper-chelating 2-picolyl azide moiety are prepared, one of which has an unconventional 5-pyridylmethyl-substituted guanine structure, rather than the usual benzylguanine that is optimized to be accepted by SNAP-tag. All three substrates are effective in transferring a 2-picolyl azide moiety to SNAP-tag in live cells under conventional labeling conditions (30-minute incubation of cells with labeling reagents at 37 °C under 5% CO 2 ). Live cells that are decorated with chelating azido groups on the extracellular side of membranes undergo copper-catalyzed azide-alkyne cycloaddition (CuAAC) with an ethynyl-functionalized fluorophore to accomplish membrane protein labeling by a fluorescent dye. The chelation-assisted CuAAC labeling step is rapid (<1 minute) with a relatively low dose of the copper catalyst (20 μM), and consequently exerts no ill effect on the labeled cells. A SNAP-tag substrate that carries a non-chelating azide moiety, on the other hand, fails to produce satisfactory labeling under the same constraints. The rapid, live cell-compatible SNAP-tag/chelation-assisted CuAAC two-step method expands the utility of SNAP-tag in protein labeling applications.

The lysosomal cation channel TMEM175 is a Parkinson's disease-related protein and a promising drug target. Unlike whole-cell automated patch-clamp (APC), lysosomal patch-clamp (LPC) facilitates physiological conditions, but is not yet suitable for high-throughput screening (HTS) applications. Here, we apply solid supported membrane-based electrophysiology (SSME), which enables both direct access to lysosomes and high-throughput electrophysiological recordings. In SSME, ion translocation mediated by TMEM175 is stimulated using a concentration gradient at a resting potential of 0 mV. The concentration-dependent K + response exhibited an I/c curve with two distinct slopes, indicating the existence of two conducting states. We measured H + fluxes with a permeability ratio of P H /P K = 48,500, which matches literature findings from patch-clamp studies, validating the SSME approach. Additionally, TMEM175 displayed a high pH dependence. Decreasing cytosolic pH inhibited both K + and H + conductivity of TMEM175. Conversely, lysosomal pH and pH gradients did not have major effects on TMEM175. Finally, we developed HTS assays for drug screening and evaluated tool compounds (4-AP, Zn as inhibitors; DCPIB, arachidonic acid, SC-79 as enhancers) using SSME and APC. Additionally, we recorded EC 50 data for eight blinded TMEM175 enhancers and compared the results across all three assay technologies, including LPC, discussing their advantages and disadvantages.

Left ventricular ejection fraction (EF) is a key measure in the diagnosis and treatment of heart failure (HF) and many patients experience changes in EF overtime. Large-scale analysis of longitudinal changes in EF using electronic health records (EHRs) is limited. In a multi-site retrospective study using EHR data from three academic medical centers, we investigated longitudinal changes in EF measurements in patients diagnosed with HF. We observed significant variations in baseline characteristics and longitudinal EF change behavior of the HF cohorts from a previous study that is based on HF registry data. Data gathered from this longitudinal study were used to develop multiple machine learning models to predict changes in ejection fraction measurements in HF patients. Across all three sites, we observed higher performance in predicting EF increase over a 1-year duration, with similarly higher performance predicting an EF increase of 30% from baseline compared to lower percentage increases. In predicting EF decrease we found moderate to high performance with low confidence for various models. Among various machine learning models, XGBoost was the best performing model for predicting EF changes. Across the three sites, the XGBoost model had an F1-score of 87.2, 89.9, and 88.6 and AUC of 0.83, 0.87, and 0.90 in predicting a 30% increase in EF, and had an F1-score of 95.0, 90.6, 90.1 and AUC of 0.54, 0.56, 0.68 in predicting a 30% decrease in EF. Among features that contribute to predicting EF changes, baseline ejection fraction measurement, age, gender, and heart diseases were found to be statistically significant., (© 2023. The Author(s).)

Objectives: To develop and validate a standards-based phenotyping tool to author electronic health record (EHR)-based phenotype definitions and demonstrate execution of the definitions against heterogeneous clinical research data platforms., Materials and Methods: We developed an open-source, standards-compliant phenotyping tool known as the PhEMA Workbench that enables a phenotype representation using the Fast Healthcare Interoperability Resources (FHIR) and Clinical Quality Language (CQL) standards. We then demonstrated how this tool can be used to conduct EHR-based phenotyping, including phenotype authoring, execution, and validation. We validated the performance of the tool by executing a thrombotic event phenotype definition at 3 sites, Mayo Clinic (MC), Northwestern Medicine (NM), and Weill Cornell Medicine (WCM), and used manual review to determine precision and recall., Results: An initial version of the PhEMA Workbench has been released, which supports phenotype authoring, execution, and publishing to a shared phenotype definition repository. The resulting thrombotic event phenotype definition consisted of 11 CQL statements, and 24 value sets containing a total of 834 codes. Technical validation showed satisfactory performance (both NM and MC had 100% precision and recall and WCM had a precision of 95% and a recall of 84%)., Conclusions: We demonstrate that the PhEMA Workbench can facilitate EHR-driven phenotype definition, execution, and phenotype sharing in heterogeneous clinical research data environments. A phenotype definition that integrates with existing standards-compliant systems, and the use of a formal representation facilitates automation and can decrease potential for human error., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Elevations in initially obtained serum lactate levels are strong predictors of mortality in critically ill patients. Identifying patients whose serum lactate levels are more likely to increase can alert physicians to intensify care and guide them in the frequency of tending the blood test. We investigate whether machine learning models can predict subsequent serum lactate changes. We investigated serum lactate change prediction using the MIMIC-III and eICU-CRD datasets in internal as well as external validation of the eICU cohort on the MIMIC-III cohort. Three subgroups were defined based on the initial lactate levels: (i) normal group (< 2 mmol/L), (ii) mild group (2-4 mmol/L), and (iii) severe group (> 4 mmol/L). Outcomes were defined based on increase or decrease of serum lactate levels between the groups. We also performed sensitivity analysis by defining the outcome as lactate change of > 10% and furthermore investigated the influence of the time interval between subsequent lactate measurements on predictive performance. The LSTM models were able to predict deterioration of serum lactate values of MIMIC-III patients with an AUC of 0.77 (95% CI 0.762-0.771) for the normal group, 0.77 (95% CI 0.768-0.772) for the mild group, and 0.85 (95% CI 0.840-0.851) for the severe group, with only a slightly lower performance in the external validation. The LSTM demonstrated good discrimination of patients who had deterioration in serum lactate levels. Clinical studies are needed to evaluate whether utilization of a clinical decision support tool based on these results could positively impact decision-making and patient outcomes., (© 2021. The Author(s), under exclusive licence to Springer Nature B.V.)

Objective: The large-scale collection of observational data and digital technologies could help curb the COVID-19 pandemic. However, the coexistence of multiple Common Data Models (CDMs) and the lack of data extract, transform, and load (ETL) tool between different CDMs causes potential interoperability issue between different data systems. The objective of this study is to design, develop, and evaluate an ETL tool that transforms the PCORnet CDM format data into the OMOP CDM., Methods: We developed an open-source ETL tool to facilitate the data conversion from the PCORnet CDM and the OMOP CDM. The ETL tool was evaluated using a dataset with 1000 patients randomly selected from the PCORnet CDM at Mayo Clinic. Information loss, data mapping accuracy, and gap analysis approaches were conducted to assess the performance of the ETL tool. We designed an experiment to conduct a real-world COVID-19 surveillance task to assess the feasibility of the ETL tool. We also assessed the capacity of the ETL tool for the COVID-19 data surveillance using data collection criteria of the MN EHR Consortium COVID-19 project., Results: After the ETL process, all the records of 1000 patients from 18 PCORnet CDM tables were successfully transformed into 12 OMOP CDM tables. The information loss for all the concept mapping was less than 0.61%. The string mapping process for the unit concepts lost 2.84% records. Almost all the fields in the manual mapping process achieved 0% information loss, except the specialty concept mapping. Moreover, the mapping accuracy for all the fields were 100%. The COVID-19 surveillance task collected almost the same set of cases (99.3% overlaps) from the original PCORnet CDM and target OMOP CDM separately. Finally, all the data elements for MN EHR Consortium COVID-19 project could be captured from both the PCORnet CDM and the OMOP CDM., Conclusion: We demonstrated that our ETL tool could satisfy the data conversion requirements between the PCORnet CDM and the OMOP CDM. The outcome of the work would facilitate the data retrieval, communication, sharing, and analysis between different institutions for not only COVID-19 related project, but also other real-world evidence-based observational studies., (Copyright © 2022 Elsevier Inc. All rights reserved.)

BACKGROUNDRecently the α1 adrenergic receptor antagonist terazosin was shown to activate PGK1, a possible target for the mitochondrial deficits in Parkinson disease related to its function as the initial enzyme in ATP synthesis during glycolysis. An epidemiological study of terazosin users showed a lower incidence of Parkinson disease when compared with users of tamsulosin, an α1 adrenergic receptor antagonist of a different class that does not activate PGK1. However, prior research on tamsulosin has suggested that it may in fact potentiate neurodegeneration, raising the question of whether it is an appropriate control group.METHODSTo address this question, we undertook an epidemiological study on Parkinson disease occurrence rate in 113,450 individuals from the United States with 5 or more years of follow-up. Patients were classified as tamsulosin users (n = 45,380), terazosin/alfuzosin/doxazosin users (n = 22,690), or controls matched for age, sex, and Charlson comorbidity index score (n = 45,380).RESULTSIncidence of Parkinson disease in tamsulosin users was 1.53%, which was significantly higher than that in both terazosin/alfuzosin/doxazosin users (1.10%, P < 0.0001) and matched controls (1.01%, P < 0.0001). Terazosin/alfuzosin/doxazosin users did not differ in Parkinson disease risk from matched controls (P = 0.29).CONCLUSIONThese results suggest that zosins may not confer a protective effect against Parkinson disease, but rather that tamsulosin may in some way potentiate Parkinson disease progression.FUNDINGThis work was supported by Cerevel Therapeutics.

Background: Precision oncology has the potential to leverage clinical and genomic data in advancing disease prevention, diagnosis, and treatment. A key research area focuses on the early detection of primary cancers and potential prediction of cancers of unknown primary in order to facilitate optimal treatment decisions., Objective: This study presents a methodology to harmonize phenotypic and genetic data features to classify primary cancer types and predict cancers of unknown primaries., Methods: We extracted genetic data elements from oncology genetic reports of 1011 patients with cancer and their corresponding phenotypical data from Mayo Clinic's electronic health records. We modeled both genetic and electronic health record data with HL7 Fast Healthcare Interoperability Resources. The semantic web Resource Description Framework was employed to generate the network-based data representation (ie, patient-phenotypic-genetic network). Based on the Resource Description Framework data graph, Node2vec graph-embedding algorithm was applied to generate features. Multiple machine learning and deep learning backbone models were compared for cancer prediction performance., Results: With 6 machine learning tasks designed in the experiment, we demonstrated the proposed method achieved favorable results in classifying primary cancer types (area under the receiver operating characteristic curve [AUROC] 96.56% for all 9 cancer predictions on average based on the cross-validation) and predicting unknown primaries (AUROC 80.77% for all 8 cancer predictions on average for real-patient validation). To demonstrate the interpretability, 17 phenotypic and genetic features that contributed the most to the prediction of each cancer were identified and validated based on a literature review., Conclusions: Accurate prediction of cancer types can be achieved with existing electronic health record data with satisfactory precision. The integration of genetic reports improves prediction, illustrating the translational values of incorporating genetic tests early at the diagnosis stage for patients with cancer., (©Nansu Zong, Victoria Ngo, Daniel J Stone, Andrew Wen, Yiqing Zhao, Yue Yu, Sijia Liu, Ming Huang, Chen Wang, Guoqian Jiang. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 25.05.2021.)

Background: Despite wide use of severity scoring systems for case-mix determination and benchmarking in the intensive care unit (ICU), the possibility of scoring bias across ethnicities has not been examined. Guidelines on the use of illness severity scores to inform triage decisions for allocation of scarce resources, such as mechanical ventilation, during the current COVID-19 pandemic warrant examination for possible bias in these models. We investigated the performance of the severity scoring systems Acute Physiology and Chronic Health Evaluation IVa (APACHE IVa), Oxford Acute Severity of Illness Score (OASIS), and Sequential Organ Failure Assessment (SOFA) across four ethnicities in two large ICU databases to identify possible ethnicity-based bias., Methods: Data from the electronic ICU Collaborative Research Database (eICU-CRD) and the Medical Information Mart for Intensive Care III (MIMIC-III) database, built from patient episodes in the USA from 2014-15 and 2001-12, respectively, were analysed for score performance in Asian, Black, Hispanic, and White people after appropriate exclusions. Hospital mortality was the outcome of interest. Discrimination and calibration were determined for all three scoring systems in all four groups, using area under receiver operating characteristic (AUROC) curve for different ethnicities to assess discrimination, and standardised mortality ratio (SMR) or proxy measures to assess calibration., Findings: We analysed 166 751 participants (122 919 eICU-CRD and 43 832 MIMIC-III). Although measurements of discrimination were significantly different among the groups (AUROC ranging from 0·86 to 0·89 [p=0·016] with APACHE IVa and from 0·75 to 0·77 [p=0·85] with OASIS), they did not display any discernible systematic patterns of bias. However, measurements of calibration indicated persistent, and in some cases statistically significant, patterns of difference between Hispanic people (SMR 0·73 with APACHE IVa and 0·64 with OASIS) and Black people (0·67 and 0·68) versus Asian people (0·77 and 0·95) and White people (0·76 and 0·81). Although calibrations were imperfect for all groups, the scores consistently showed a pattern of overpredicting mortality for Black people and Hispanic people. Similar results were seen using SOFA scores across the two databases., Interpretation: The systematic differences in calibration across ethnicities suggest that illness severity scores reflect statistical bias in their predictions of mortality., Funding: There was no specific funding for this study., (Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

The genetic basis of Lewy body dementia (LBD) is not well understood. Here, we performed whole-genome sequencing in large cohorts of LBD cases and neurologically healthy controls to study the genetic architecture of this understudied form of dementia, and to generate a resource for the scientific community. Genome-wide association analysis identified five independent risk loci, whereas genome-wide gene-aggregation tests implicated mutations in the gene GBA. Genetic risk scores demonstrate that LBD shares risk profiles and pathways with Alzheimer's disease and Parkinson's disease, providing a deeper molecular understanding of the complex genetic architecture of this age-related neurodegenerative condition.

This study developed and evaluated a JSON-LD 1.1 approach to automate the Resource Description Framework (RDF) serialization and deserialization of Fast Healthcare Interoperability Resources (FHIR) data, in preparation for updating the FHIR RDF standard. We first demonstrated that this JSON-LD 1.1 approach can produce the same output as the current FHIR RDF standard. We then used it to test, document and validate several proposed changes to the FHIR RDF specification, to address usability issues that were uncovered during trial use. This JSON-LD 1.1 approach was found to be effective and more declarative than the existing custom-code-based approach, in converting FHIR data from JSON to RDF and vice versa. This approach should enable future FHIR RDF servers to be implemented and maintained more easily., (©2020 AMIA - All rights reserved.)

Background and Objective: Identification and Standardization of data elements used in clinical trials may control and reduce the cost and errors during the operational process, and enable seamless data exchange between the electronic data capture (EDC) systems and Electronic Health Record (EHR) systems. This study presents a methodology to comprehensively capture the clinical trial data element needs., Materials and Methods: Case report forms (CRF) for clinical trial data collection were used to approximate the clinical information need, whereby these information needs were then mapped to a semantically equivalent field within an existing FHIR cancer profile. For items without a semantically equivalent field, we considered these items to be information needs that cannot be represented in current standards and proposed extensions to support these needs., Results: We successfully identified 62 discrete items from a preliminary survey of 43 base questions in four CRFs used in colorectal cancer clinical trials, in which 28 items are modeled with FHIR extensions and their associated responses for colorectal cancer. We achieved promising results in the data population of the CRFs with average Precision 98.5 %, Recall 96.2 %, and F-measure 96.8 % for all base questions. We also demonstrated the auto-filled answers in CRFs can be used to discover patient subgroups using a topic modeling approach., Conclusion: CRFs can be considered as a proxy for representing information needs for their respective cancer types. Mining the information needs can serve as a valuable resource for expanding existing standards to ensure they can comprehensively represent relevant clinical data without loss of granularity., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Competing Interests: Competing interests: None declared.

Machine learning (ML) is a discipline of computer science in which statistical methods are applied to data in order to classify, predict, or optimize, based on previously observed data. Pulmonary and critical care medicine have seen a surge in the application of this methodology, potentially delivering improvements in our ability to diagnose, treat, and better understand a multitude of disease states. Here we review the literature and provide a detailed overview of the recent advances in ML as applied to these areas of medicine. In addition, we discuss both the significant benefits of this work as well as the challenges in the implementation and acceptance of this non-traditional methodology for clinical purposes.

Background: Classical randomisation of clinical trial patients creates a source of genetic variance that may be contributing to the high failure rate seen in neurodegenerative disease trials. Our objective was to quantify genetic difference between randomised trial arms and determine how imbalance can affect trial outcomes., Methods: 5851 patients with Parkinson's disease of European ancestry data and two simulated virtual cohorts based on public data were used. Data were resampled at different sizes for 1000 iterations and randomly assigned to the two arms of a simulated trial. False-negative and false-positive rates were estimated using simulated clinical trials, and per cent difference in genetic risk score (GRS) and allele frequency was calculated to quantify variance between arms., Results: 5851 patients with Parkinson's disease (mean (SD) age, 61.02 (12.61) years; 2095 women (35.81%)) as well as simulated patients from virtually created cohorts were used in the study. Approximately 90% of the iterations had at least one statistically significant difference in individual risk SNPs between each trial arm. Approximately 5%-6% of iterations had a statistically significant difference between trial arms in mean GRS. For significant iterations, the average per cent difference for mean GRS between trial arms was 130.87%, 95% CI 120.89 to 140.85 (n=200). Glucocerebrocidase (GBA) gene-only simulations see an average 18.86%, 95% CI 18.01 to 19.71 difference in GRS scores between trial arms (n=50). When adding a drug effect of -0.5 points in MDS-UPDRS per year at n=50, 33.9% of trials resulted in false negatives., Conclusions: Our data support the hypothesis that within genetically unmatched clinical trials, genetic heterogeneity could confound true therapeutic effects as expected. Clinical trials should undergo pretrial genetic adjustment or, at the minimum, post-trial adjustment and analysis for failed trials., Competing Interests: Competing interests: ZG-O reports personal fees from Sanofi/Genzyme, Lysosomal Therapeutics, Idorsia, Denali, Prevail Therapeutics, Ventus, Deerfield and Allergan. ZG-O is also supported by the Fonds de recherche du Québec-Santé (FRQS) Chercheurs-boursiers award given in collaboration with Parkinson Quebec, and is a Parkinson Canada New Investigator awardee. AGD-W reports personal fees from Merck and Co and other from Biogen. DJS reports funding from Merck and Co., (© Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.)

While natural language processing (NLP) of unstructured clinical narratives holds the potential for patient care and clinical research, portability of NLP approaches across multiple sites remains a major challenge. This study investigated the portability of an NLP system developed initially at the Department of Veterans Affairs (VA) to extract 27 key cardiac concepts from free-text or semi-structured echocardiograms from three academic edical centers: Weill Cornell Medicine, Mayo Clinic and Northwestern Medicine. While the NLP system showed high precision and recall easurements for four target concepts (aortic valve regurgitation, left atrium size at end systole, mitral valve regurgitation, tricuspid valve regurgitation) across all sites, we found moderate or poor results for the remaining concepts and the NLP system performance varied between individual sites., (©2019 AMIA - All rights reserved.)

Purpose: The Fast Healthcare Interoperability Resources (FHIR) is emerging as a next-generation standards framework developed by HL7 for exchanging electronic health care data. The modeling capability of FHIR in standardizing cancer data has been gaining increasing attention by the cancer research informatics community. However, few studies have been conducted to examine the capability of FHIR in electronic data capture (EDC) applications for effective cancer clinical trials. The objective of this study was to design, develop, and evaluate an FHIR-based method that enables the automation of the case report forms (CRFs) population for cancer clinical trials using real-world electronic health records (EHRs)., Materials and Methods: We developed an FHIR-based computational pipeline of EDC with a case study for modeling colorectal cancer trials. We first leveraged an existing FHIR-based cancer profile to represent EHR data of patients with colorectal cancer, and then we used the FHIR Questionnaire and QuestionnaireResponse resources to represent the CRFs and their data population. To test the accuracy of and overall quality of the computational pipeline, we used synoptic reports of 287 Mayo Clinic patients with colorectal cancer from 2013 to 2019 with standard measures of precision, recall, and F1 score., Results: Using the computational pipeline, a total of 1,037 synoptic reports were successfully converted as the instances of the FHIR-based cancer profile. The average accuracy for converting all data elements (excluding tumor perforation) of the cancer profile was 0.99, using 200 randomly selected records. The average F1 score for populating nine questions of the CRFs in a real-world colorectal cancer trial was 0.95, using 100 randomly selected records., Conclusion: We demonstrated that it is feasible to populate CRFs with EHR data in an automated manner with satisfactory performance. The outcome of the study provides helpful insight into future directions in implementing FHIR-based EDC applications for modern cancer clinical trials.

Dementia with Lewy bodies (DLB) is a clinically heterogeneous disorder with a substantial burden on healthcare. Despite this, the genetic basis of the disorder is not well defined and its boundaries with other neurodegenerative diseases are unclear. Here, we performed whole exome sequencing of a cohort of 1118 Caucasian DLB patients, and focused on genes causative of monogenic neurodegenerative diseases. We analyzed variants in 60 genes implicated in DLB, Alzheimer's disease, Parkinson's disease, frontotemporal dementia, and atypical parkinsonian or dementia disorders, in order to determine their frequency in DLB. We focused on variants that have previously been reported as pathogenic, and also describe variants reported as pathogenic which remain of unknown clinical significance, as well as variants associated with strong risk. Rare missense variants of unknown significance were found in APP, CHCHD2, DCTN1, GRN, MAPT, NOTCH3, SQSTM1, TBK1 and TIA1. Additionally, we identified a pathogenic GRN p.Arg493* mutation, potentially adding to the diversity of phenotypes associated with this mutation. The rarity of previously reported pathogenic mutations in this cohort suggests that the genetic overlap of other neurodegenerative diseases with DLB is not substantial. Since it is now clear that genetics plays a role in DLB, these data suggest that other genetic loci play a role in this disease.

Background: Standards-based clinical data normalization has become a key component of effective data integration and accurate phenotyping for secondary use of electronic healthcare records (EHR) data. HL7 Fast Healthcare Interoperability Resources (FHIR) is an emerging clinical data standard for exchanging electronic healthcare data and has been used in modeling and integrating both structured and unstructured EHR data for a variety of clinical research applications. The overall objective of this study is to develop and evaluate a FHIR-based EHR phenotyping framework for identification of patients with obesity and its multiple comorbidities from semi-structured discharge summaries leveraging a FHIR-based clinical data normalization pipeline (known as NLP2FHIR)., Methods: We implemented a multi-class and multi-label classification system based on the i2b2 Obesity Challenge task to evaluate the FHIR-based EHR phenotyping framework. Two core parts of the framework are: (a) the conversion of discharge summaries into corresponding FHIR resources - Composition, Condition, MedicationStatement, Procedure and FamilyMemberHistory using the NLP2FHIR pipeline, and (b) the implementation of four machine learning algorithms (logistic regression, support vector machine, decision tree, and random forest) to train classifiers to predict disease state of obesity and 15 comorbidities using features extracted from standard FHIR resources and terminology expansions. We used the macro- and micro-averaged precision (P), recall (R), and F1 score (F1) measures to evaluate the classifier performance. We validated the framework using a second obesity dataset extracted from the MIMIC-III database., Results: Using the NLP2FHIR pipeline, 1237 clinical discharge summaries from the 2008 i2b2 obesity challenge dataset were represented as the instances of the FHIR Composition resource consisting of 5677 records with 16 unique section types. After the NLP processing and FHIR modeling, a set of 244,438 FHIR clinical resource instances were generated. As the results of the four machine learning classifiers, the random forest algorithm performed the best with F1-micro(0.9466)/F1-macro(0.7887) and F1-micro(0.9536)/F1-macro(0.6524) for intuitive classification (reflecting medical professionals' judgments) and textual classification (reflecting the judgments based on explicitly reported information of diseases), respectively. The MIMIC-III obesity dataset was successfully integrated for prediction with minimal configuration of the NLP2FHIR pipeline and machine learning models., Conclusions: The study demonstrated that the FHIR-based EHR phenotyping approach could effectively identify the state of obesity and multiple comorbidities using semi-structured discharge summaries. Our FHIR-based phenotyping approach is a first concrete step towards improving the data aspect of phenotyping portability across EHR systems and enhancing interpretability of the machine learning-based phenotyping algorithms., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Multiple genome-wide association studies (GWAS) in Parkinson disease (PD) have identified a signal at chromosome 4p16.3; however, the causal variant has not been established for this locus. Deep investigation of the region resulted in one identified variant, the rs34311866 missense SNP (p.M393T) in TMEM175, which is 20 orders of magnitude more significant than any other SNP in the region. Because TMEM175 is a lysosomal gene that has been shown to influence α-synuclein phosphorylation and autophagy, the p.M393T variant is an attractive candidate, and we have examined its effect on TMEM175 protein and PD-related biology. After knocking down each of the genes located under the GWAS peak via multiple shRNAs, only TMEM175 was found to consistently influence accumulation of phosphorylated α-synuclein (p-α-syn). Examination of the p.M393T variant showed effects on TMEM175 function that were intermediate between the wild-type (WT) and knockout phenotypes, with reduced regulation of lysosomal pH in response to starvation and minor changes in clearance of autophagy substrates, reduced lysosomal localization, and increased accumulation of p-α-syn. Finally, overexpression of WT TMEM175 protein reduced p-α-syn, while overexpression of the p.M393T variant resulted in no change in α-synuclein phosphorylation. These results suggest that the main signal in the chromosome 4p16.3 PD risk locus is driven by the TMEM175 p.M393T variant. Modulation of TMEM175 may impact α-synuclein biology and therefore may be a rational therapeutic strategy for PD., (© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Competing Interests: We declare no competing interests. We thank Dr Mazyeh Ghassemi for inspiring this work during a debate at the Machine Learning for Health Unconference in Toronto in May 28, 2019.

The use of machine learning (ML) in healthcare has enormous potential for improving disease detection, clinical decision support, and workflow efficiencies. In this commentary, we review published and potential applications for the use of ML for monitoring within the hospital environment. We present use cases as well as several questions regarding the application of ML to the analysis of the vast amount of complex data that clinicians must interpret in the realm of continuous physiological monitoring. ML, especially employed in bidirectional conjunction with electronic health record data, has the potential to extract much more useful information out of this currently under-analyzed data source from a population level. As a data driven entity, ML is dependent on copious, high quality input data so that error can be introduced by low quality data sources. At present, while ML is being studied in hybrid formulations along with static expert systems for monitoring applications, it is not yet actively incorporated in the formal artificial learning sense of an algorithm constantly learning and updating its rules without external intervention. Finally, innovations in monitoring, including those supported by ML, will pose regulatory and medico-legal challenges, as well as questions regarding precisely how to incorporate these features into clinical care and medical education. Rigorous evaluation of ML techniques compared to traditional methods or other AI methods will be required to validate the algorithms developed with consideration of database limitations and potential learning errors. Demonstration of value on processes and outcomes will be necessary to support the use of ML as a feature in monitoring system development: Future research is needed to evaluate all AI based programs before clinical implementation in non-research settings.

Illness severity scores are regularly employed for quality improvement and benchmarking in the intensive care unit, but poor generalization performance, particularly with respect to probability calibration, has limited their use for decision support. These models tend to perform worse in patients at a high risk for mortality. We hypothesized that a sequential modeling approach wherein an initial regression model assigns risk and all patients deemed high risk then have their risk quantified by a second, high-risk-specific, regression model would result in a model with superior calibration across the risk spectrum. We compared this approach to a logistic regression model and a sophisticated machine learning approach, the gradient boosting machine. The sequential approach did not have an effect on the receiver operating characteristic curve or the precision-recall curve but resulted in improved reliability curves. The gradient boosting machine achieved a small improvement in discrimination performance and was similarly calibrated to the sequential models., Competing Interests: Competing interestsO.B. is employed by Philips Healthcare. The other authors declare no competing interests.

Recent large-scale genetic studies have allowed for the first glimpse of the effects of common genetic variability in dementia with Lewy bodies (DLB), identifying risk variants with appreciable effect sizes. However, it is currently well established that a substantial portion of the genetic heritable component of complex traits is not captured by genome-wide significant SNPs. To overcome this issue, we have estimated the proportion of phenotypic variance explained by genetic variability (SNP heritability) in DLB using a method that is unbiased by allele frequency or linkage disequilibrium properties of the underlying variants. This shows that the heritability of DLB is nearly twice as high as previous estimates based on common variants only (31% vs 59.9%). We also determine the amount of phenotypic variance in DLB that can be explained by recent polygenic risk scores from either Parkinson's disease (PD) or Alzheimer's disease (AD), and show that, despite being highly significant, they explain a low amount of variance. Additionally, to identify pleiotropic events that might improve our understanding of the disease, we performed genetic correlation analyses of DLB with over 200 diseases and biomedically relevant traits. Our data shows that DLB has a positive correlation with education phenotypes, which is opposite to what occurs in AD. Overall, our data suggests that novel genetic risk factors for DLB should be identified by larger GWAS and these are likely to be independent from known AD and PD risk variants., (Copyright © 2019 Elsevier Inc. All rights reserved.)

As big data, machine learning , and artificial intelligence continue to penetrate into and transform many facets of our lives, we are witnessing the emergence of these powerful technologies within health care. The use and growth of these technologies has been contingent on the availability of reliable and usable data, a particularly robust resource in critical care medicine where continuous monitoring forms a key component of the infrastructure of care. The response to this opportunity has included the development of open databases for research and other purposes; the development of a collaborative form of clinical data science intended to fully leverage these data resources, and the creation of data-driven applications for purposes such as clinical decision support. Most recently, data levels have reached the thresholds required for the development of robust artificial intelligence features for clinical purposes. The systematic capture and analysis of clinical data in both individuals and populations allows us to begin to move toward precision medicine in the intensive care unit (ICU). In this perspective review, we examine the fundamental role of data as we present the current progress that has been made toward an artificial intelligence (AI)-supported, data-driven precision critical care medicine., Competing Interests: Declaration of conflicting interests:The author(s) received no financial support for the research, authorship, and/or publication of this article.

Objective: To explore the issue of counterintuitive data via analysis of a representative case in which the data obtained was unexpected and inconsistent with current knowledge. We then discuss the issue of counterintuitive data while developing a framework for approaching such findings., Design: The case study is a retrospective analysis of a cohort of coronary artery bypass graft (CABG) patients. Regression was used to examine the association between perceived pain in the intensive care unit (ICU) and selected outcomes., Setting: Medical Information Mart for Intensive Care-III, a publicly available, de-identified critical care patient database., Participants: 844 adult patients from the database who underwent CABG surgery and were extubated within 24 hours after ICU admission., Outcomes: 30 day mortality, 1 year mortality and hospital length of stay (LOS)., Results: Increased pain levels were found to be significantly associated with reduced mortality at 30 days and 1 year, and shorter hospital LOS. A one-point increase in mean pain level was found to be associated with a reduction in the odds of 30 day and 1 year mortality by a factor of 0.457 (95% CI 0.304 to 0.687, p<0.01) and 0.710 (95% CI 0.571 to 0.881, p<0.01) respectively, and a 0.916 (95% CI -1.159 to -0.673, p<0.01) day decrease in hospital LOS., Conclusion: The finding of an association between increased pain and improved outcomes was unexpected and clinically counterintuitive. In an increasingly digitised age of medical big data, such results are likely to become more common. The reliability of such counterintuitive results must be carefully examined. We suggest several issues to consider in this analytic process. If the data is determined to be valid, consideration must then be made towards alternative explanations for the counterintuitive results observed. Such results may in fact indicate that current clinical knowledge is incomplete or not have been firmly based on empirical evidence and function to inspire further research into the factors involved., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objective: To characterize the clinical and neuropathologic features of patients with amyotrophic lateral sclerosis (ALS) with the superoxide dismutase 1 ( SOD1 ) p.Ala90Val mutation, as well as the mutation frequency and the role of oligogenic mechanisms in disease penetrance., Methods: An index patient with autopsy-proven ALS was discovered to have the SOD1 p.Ala90Val mutation, which was screened in 2 Finnish ALS cohorts (n = 453). Additional contributing variants were analyzed from whole-genome or whole-exome sequencing data., Results: Seven screened patients (1.5%) were found to carry the SOD1 heterozygous mutation. Allele-sharing analysis suggested a common founder haplotype. Common clinical features included limb-onset, long disease course, and sensory symptoms. No TDP43 pathology was observed. All cases were apparently sporadic, and pedigree analysis demonstrated that the mutation has reduced penetrance. Analysis of other contributing genes revealed a unique set of additional variants in each patient. These included previously described rare ANG and SPG11 mutations. One patient was compound heterozygous for SOD1 p.Ala90Val and p.Asp91Ala., Conclusions: Our data suggest that the penetrance of SOD1 p.Ala90Val is modulated by other genes and indicates highly individual oligogenic basis of apparently sporadic ALS. Additional genetic variants likely contributing to disease penetrance were very heterogeneous, even among Finnish patients carrying the SOD1 founder mutation.

The role of genetic variability in dementia with Lewy bodies (DLB) is now indisputable; however, data regarding copy number variation (CNV) in this disease has been lacking. Here, we used whole-genome genotyping of 1454 DLB cases and 1525 controls to assess copy number variability. We used 2 algorithms to confidently detect CNVs, performed a case-control association analysis, screened for candidate CNVs previously associated with DLB-related diseases, and performed a candidate gene approach to fully explore the data. We identified 5 CNV regions with a significant genome-wide association to DLB; 2 of these were only present in cases and absent from publicly available databases: one of the regions overlapped LAPTM4B, a known lysosomal protein, whereas the other overlapped the NME1 locus and SPAG9. We also identified DLB cases presenting rare CNVs in genes previously associated with DLB or related neurodegenerative diseases, such as SNCA, APP, and MAPT. To our knowledge, this is the first study reporting genome-wide CNVs in a large DLB cohort. These results provide preliminary evidence for the contribution of CNVs in DLB risk., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Background: Critically ill patients may die despite invasive intervention. In this study, we examine trends in the application of two such treatments over a decade, namely, endotracheal ventilation and vasopressors and inotropes administration, as well as the impact of these trends on survival durations in patients who die within a month of ICU admission., Methods: We considered observational data available from the MIMIC-III open-access ICU database and collected within a study period between year 2002 up to 2011. If a patient had multiple admissions to the ICU during the 30 days before death, only the first stay was analyzed, leading to a final set of 6,436 unique ICU admissions during the study period. We tested two hypotheses: (i) administration of invasive intervention during the ICU stay immediately preceding end-of-life would decrease over the study time period and (ii) time-to-death from ICU admission would also decrease, due to the decrease in invasive intervention administration. To investigate the latter hypothesis, we performed a subgroups analysis by considering patients with lowest and highest severity. To do so, we stratified the patients based on their SAPS I scores, and we considered patients within the first and the third tertiles of the score. We then assessed differences in trends within these groups between years 2002-05 vs. 2008-11., Results: Comparing the period 2002-2005 vs. 2008-2011, we found a reduction in endotracheal ventilation among patients who died within 30 days of ICU admission (120.8 vs. 68.5 hours for the lowest severity patients, p<0.001; 47.7 vs. 46.0 hours for the highest severity patients, p = 0.004). This is explained in part by an increase in the use of non-invasive ventilation. Comparing the period 2002-2005 vs. 2008-2011, we found a reduction in the use of vasopressors and inotropes among patients with the lowest severity who died within 30 days of ICU admission (41.8 vs. 36.2 hours, p<0.001) but not among those with the highest severity. Despite a reduction in the use of invasive interventions, we did not find a reduction in the time to death between 2002-2005 vs. 2008-2011 (7.8 days vs. 8.2 days for the lowest severity patients, p = 0.32; 2.1 days vs. 2.0 days for the highest severity patients, p = 0.74)., Conclusion: We found that the reduction in the use of invasive treatments over time in patients with very poor prognosis did not shorten the time-to-death. These findings may be useful for goals of care discussions., Competing Interests: The authors have declared that no competing interests exist.

Objectives: Although one third or more of critically ill patients in the United States are obese, obesity is not incorporated as a contributing factor in any of the commonly used severity of illness scores. We hypothesize that selected severity of illness scores would perform differently if body mass index categorization was incorporated and that the performance of these score models would improve after consideration of body mass index as an additional model feature., Design: Retrospective cohort analysis from a multicenter ICU database which contains deidentified data for more than 200,000 ICU admissions from 208 distinct ICUs across the United States between 2014 and 2015., Setting: First ICU admission of patients with documented height and weight., Patients: One-hundred eight-thousand four-hundred two patients from 189 different ICUs across United States were included in the analyses, of whom 4,661 (4%) were classified as underweight, 32,134 (30%) as normal weight, 32,278 (30%) as overweight, 30,259 (28%) as obese, and 9,070 (8%) as morbidly obese., Interventions: None., Measurements and Main Results: To assess the effect of adding body mass index as a risk adjustment element to the Acute Physiology and Chronic Health Evaluation IV and Oxford Acute Severity of Illness scoring systems, we examined the impact of this addition on both discrimination and calibration. We performed three assessments based upon 1) the original scoring systems, 2) a recalibrated version of the systems, and 3) a recalibrated version incorporating body mass index as a covariate. We also performed a subgroup analysis in groups defined using World Health Organization guidelines for obesity. Incorporating body mass index into the models provided a minor improvement in both discrimination and calibration. In a subgroup analysis, model discrimination was higher in groups with higher body mass index, but calibration worsened., Conclusions: The performance of ICU prognostic models utilizing body mass index category as a scoring element was inconsistent across body mass index categories. Overall, adding body mass index as a risk adjustment variable led only to a minor improvement in scoring system performance.

Importance: Laboratory data are frequently collected throughout the care of critically ill patients. Currently, these data are interpreted by comparison with values from healthy outpatient volunteers. Whether this is the most useful comparison has yet to be demonstrated., Objectives: To understand how the distribution of intensive care unit (ICU) laboratory values differs from the reference range, and how these distributions are related to patient outcomes., Design, Setting, and Participants: Cross-sectional study of a large critical care database, the Medical Information Mart for Intensive Care database, from January 1, 2001, to October 31, 2012. The database is collected from ICU data from a large tertiary medical center in Boston, Massachusetts. The data are collected from medical, cardiac, neurologic, and surgical ICUs. All patients in the database from all ICUs for 2001 to 2012 were included. Common laboratory measurements over the time window of interest were sampled. The analysis was conducted from March to June 2017., Main Outcomes and Measures: The overlapping coefficient and Cohen standardized mean difference between distributions were calculated, and kernel density estimate visualizations for the association between laboratory values and the probability of death or quartile of ICU length of stay were created., Results: Among 38 605 patients in the ICU (21 852 [56.6%] male; mean [SD] age, 74.5 [55.1] years), 8878 (23%) had the best outcome (ICU survival, shortest quartile length of stay) and 3090 (8%) had the worst outcome (ICU nonsurvival). Distribution curves based on ICU data differed significantly from the hospital standard range (mean [SD] overlapping coefficient, 0.51 [0.32-0.69]). All laboratory values for the best outcome group differed significantly from those in the worst outcome group. Both the best and worst outcome group curves revealed little overlap with and marked divergence from the reference range., Conclusions and Relevance: The standard reference ranges obtained from healthy volunteers differ from the analogous range generated from data from patients in intensive care. Laboratory data interpretation may benefit from greater consideration of clinically contextual and outcomes-related factors.

Disruption of the circadian rhythms is a frequent preclinical and clinical manifestation of Alzheimer's disease. Furthermore, it has been suggested that shift work is a risk factor for Alzheimer's disease. Previously, we have reported association of intolerance to shift work (job-related exhaustion in shift workers) with a variant rs12506228A, which is situated close to melatonin receptor type 1A gene (MTNR1A) and linked to MTNR1A brain expression levels. Here, we studied association of that variant with clinical and neuropathological Alzheimer's disease in a Finnish whole-population cohort Vantaa 85+ (n = 512, participants over 85 years) and two follow-up cohorts. Rs12506228A was associated with clinical Alzheimer's disease (p = 0.000073). Analysis of post-mortem brain tissues showed association with higher amount of neurofibrillary tangles (p = 0.0039) and amyloid beta plaques (p = 0.0041). We then followed up the associations in two independent replication samples. Replication for the association with clinical Alzheimer's disease was detected in Kuopio 75+ (p = 0.012, n = 574), but not in the younger case-control sample (n = 651 + 669). While melatonin has been established in regulation of circadian rhythms, an independent role has been also shown for neuroprotection and specifically for anti-amyloidogenic effects. Indeed, in vitro, RNAi mediated silencing of MTNR1A increased the amyloidogenic processing of amyloid precursor protein (APP) in neurons, whereas overexpression decreased it. Our findings suggest variation close to MTNR1A as a shared genetic risk factor for intolerance to shift work and Alzheimer's disease in old age. The genetic associations are likely to be mediated by differences in MTNR1A expression, which, in turn, modulate APP metabolism.

Purpose: While the use of transthoracic echocardiography (TTE) in the ICU is rapidly expanding, the contribution of TTE to altering patient outcomes among ICU patients with sepsis has not been examined. This study was designed to examine the association of TTE with 28-day mortality specifically in that population., Methods and Results: The MIMIC-III database was employed to identify patients with sepsis who had and had not received TTE. The statistical approaches utilized included multivariate regression, propensity score analysis, doubly robust estimation, the gradient boosted model, and an inverse probability-weighting model to ensure the robustness of our findings. Significant benefit in terms of 28-day mortality was observed among the TTE patients compared to the control (no TTE) group (odds ratio = 0.78, 95% CI 0.68-0.90, p < 0.001). The amount of fluid administered (2.5 vs. 2.1 L on day 1, p < 0.001), use of dobutamine (2% vs. 1%, p = 0.007), and the maximum dose of norepinephrine (1.4 vs. 1 mg/min, p = 0.001) were significantly higher for the TTE patients. Importantly, the TTE patients were weaned off vasopressors more quickly than those in the no TTE group (vasopressor-free days on day 28 of 21 vs. 19, p = 0.004)., Conclusion: In a general population of critically ill patients with sepsis, use of TTE is associated with an improvement in 28-day mortality.

Background: In Alzheimer's disease, there are striking changes in CSF composition that relate to altered choroid plexus (CP) function. Studying CP tissue gene expression at the blood-cerebrospinal fluid barrier could provide further insight into the epithelial and stromal responses to neurodegenerative disease states., Methods: Transcriptome-wide Affymetrix microarrays were used to determine disease-related changes in gene expression in human CP. RNA from post-mortem samples of the entire lateral ventricular choroid plexus was extracted from 6 healthy controls (Ctrl), 7 patients with advanced (Braak and Braak stage III-VI) Alzheimer's disease (AD), 4 with frontotemporal dementia (FTD) and 3 with Huntington's disease (HuD). Statistics and agglomerative clustering were accomplished with MathWorks, MatLab; and gene set annotations by comparing input sets to GeneGo ( http://www.genego.com ) and Ingenuity ( http://www.ingenuity.com ) pathway sets. Bonferroni-corrected hypergeometric p-values of < 0.1 were considered a significant overlap between sets., Results: Pronounced differences in gene expression occurred in CP of advanced AD patients vs. Ctrls. Metabolic and immune-related pathways including acute phase response, cytokine, cell adhesion, interferons, and JAK-STAT as well as mTOR were significantly enriched among the genes upregulated. Methionine degradation, claudin-5 and protein translation genes were downregulated. Many gene expression changes in AD patients were observed in FTD and HuD (e.g., claudin-5, tight junction downregulation), but there were significant differences between the disease groups. In AD and HuD (but not FTD), several neuroimmune-modulating interferons were significantly enriched (e.g., in AD: IFI-TM1, IFN-AR1, IFN-AR2, and IFN-GR2). AD-associated expression changes, but not those in HuD and FTD, were enriched for upregulation of VEGF signaling and immune response proteins, e.g., interleukins. HuD and FTD patients distinctively displayed upregulated cadherin-mediated adhesion., Conclusions: Our transcript data for human CP tissue provides genomic and mechanistic insight for differential expression in AD vs. FTD vs. HuD for stromal as well as epithelial components. These choroidal transcriptome characterizations elucidate immune activation, tissue functional resiliency, and CSF metabolic homeostasis. The BCSFB undergoes harmful, but also important functional and adaptive changes in neurodegenerative diseases; accordingly, the enriched JAK-STAT and mTOR pathways, respectively, likely help the CP in adaptive transcription and epithelial repair and/or replacement when harmed by neurodegeneration pathophysiology. We anticipate that these precise CP translational data will facilitate pharmacologic/transgenic therapies to alleviate dementia.

Rationale: Factors associated with one-year mortality after recovery from critical illness are not well understood. Clinicians generally lack information regarding post-hospital discharge outcomes of patients from the intensive care unit, which may be important when counseling patients and families., Objective: We sought to determine which factors among patients who survived for at least 30 days post-ICU admission are associated with one-year mortality., Methods: Single-center, longitudinal retrospective cohort study of all ICU patients admitted to a tertiary-care academic medical center from 2001-2012 who survived ≥30 days from ICU admission. Cox's proportional hazards model was used to identify the variables that are associated with one-year mortality. The primary outcome was one-year mortality., Results: 32,420 patients met the inclusion criteria and were included in the study. Among patients who survived to ≥30 days, 28,583 (88.2%) survived for greater than one year, whereas 3,837 (11.8%) did not. Variables associated with decreased one-year survival include: increased age, malignancy, number of hospital admissions within the prior year, duration of mechanical ventilation and vasoactive agent use, sepsis, history of congestive heart failure, end-stage renal disease, cirrhosis, chronic obstructive pulmonary disease, and the need for renal replacement therapy. Numerous effect modifications between these factors were found., Conclusion: Among survivors of critical illness, a significant number survive less than one year. More research is needed to help clinicians accurately identify those patients who, despite surviving their acute illness, are likely to suffer one-year mortality, and thereby to improve the quality of the decisions and care that impact this outcome.

Objectives: To evaluate the relative validity of criteria for the identification of sepsis in an ICU database., Design: Retrospective cohort study of adult ICU admissions from 2008 to 2012., Setting: Tertiary teaching hospital in Boston, MA., Patients: Initial admission of all adult patients to noncardiac surgical ICUs., Interventions: Comparison of five different algorithms for retrospectively identifying sepsis, including the Sepsis-3 criteria., Measurements and Main Results: 11,791 of 23,620 ICU admissions (49.9%) met criteria for the study. Within this subgroup, 59.9% were suspected of infection on ICU admission, 75.2% of admissions had Sequential Organ Failure Assessment greater than or equal to 2, and 49.1% had both suspicion of infection and Sequential Organ Failure Assessment greater than or equal to 2 thereby meeting the Sepsis-3 criteria. The area under the receiver operator characteristic of Sequential Organ Failure Assessment (0.74) for hospital mortality was consistent with previous studies of the Sepsis-3 criteria. The Centers for Disease Control and Prevention, Angus, Martin, Centers for Medicare & Medicaid Services, and explicit coding methods for identifying sepsis revealed respective sepsis incidences of 31.9%, 28.6%, 14.7%, 11.0%, and 9.0%. In-hospital mortality increased with decreasing cohort size, ranging from 30.1% (explicit codes) to 14.5% (Sepsis-3 criteria). Agreement among the criteria was acceptable (Cronbach's alpha, 0.40-0.62)., Conclusions: The new organ dysfunction-based Sepsis-3 criteria have been proposed as a clinical method for identifying sepsis. These criteria identified a larger, less severely ill cohort than that identified by previously used administrative definitions. The Sepsis-3 criteria have several advantages over prior methods, including less susceptibility to coding practices changes, provision of temporal context, and possession of high construct validity. However, the Sepsis-3 criteria also present new challenges, especially when calculated retrospectively. Future studies on sepsis should recognize the differences in outcome incidence among identification methods and contextualize their findings according to the different cohorts identified.

To identify novel genes associated with ALS, we undertook two lines of investigation. We carried out a genome-wide association study comparing 20,806 ALS cases and 59,804 controls. Independently, we performed a rare variant burden analysis comparing 1,138 index familial ALS cases and 19,494 controls. Through both approaches, we identified kinesin family member 5A (KIF5A) as a novel gene associated with ALS. Interestingly, mutations predominantly in the N-terminal motor domain of KIF5A are causative for two neurodegenerative diseases: hereditary spastic paraplegia (SPG10) and Charcot-Marie-Tooth type 2 (CMT2). In contrast, ALS-associated mutations are primarily located at the C-terminal cargo-binding tail domain and patients harboring loss-of-function mutations displayed an extended survival relative to typical ALS cases. Taken together, these results broaden the phenotype spectrum resulting from mutations in KIF5A and strengthen the role of cytoskeletal defects in the pathogenesis of ALS., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Objective: Severity of illness scores rest on the assumption that patients have normal physiologic values at baseline and that patients with similar severity of illness scores have the same degree of deviation from their usual state. Prior studies have reported differences in baseline physiology, including laboratory markers, between obese and normal weight individuals, but these differences have not been analyzed in the ICU. We compared deviation from baseline of pertinent ICU laboratory test results between obese and normal weight patients, adjusted for the severity of illness., Design: Retrospective cohort study in a large ICU database., Setting: Tertiary teaching hospital., Patients: Obese and normal weight patients who had laboratory results documented between 3 days and 1 year prior to hospital admission., Interventions: None., Measurements and Main Results: Seven hundred sixty-nine normal weight patients were compared with 1,258 obese patients. After adjusting for the severity of illness score, age, comorbidity index, baseline laboratory result, and ICU type, the following deviations were found to be statistically significant: WBC 0.80 (95% CI, 0.27-1.33) × 10/L; p = 0.003; log (blood urea nitrogen) 0.01 (95% CI, 0.00-0.02); p = 0.014; log (creatinine) 0.03 (95% CI, 0.02-0.05), p < 0.001; with all deviations higher in obese patients. A logistic regression analysis suggested that after adjusting for age and severity of illness at least one of these deviations had a statistically significant effect on hospital mortality (p = 0.009)., Conclusions: Among patients with the same severity of illness score, we detected clinically small but significant deviations in WBC, creatinine, and blood urea nitrogen from baseline in obese compared with normal weight patients. These small deviations are likely to be increasingly important as bigger data are analyzed in increasingly precise ways. Recognition of the extent to which all critically ill patients may deviate from their own baseline may improve the objectivity, precision, and generalizability of ICU mortality prediction and severity adjustment models.

Objective: To test the association of distinct neuropathologic features of Alzheimer disease (AD) with risk loci identified in genome-wide association studies., Methods: Vantaa 85+ is a population-based study that includes 601 participants aged ≥85 years, of which 256 were neuropathologically examined. We analyzed 29 AD risk loci in addition to APOE ε4, which was studied separately and used as a covariate. Genotyping was performed using a single nucleotide polymorphism (SNP) array (341 variants) and imputation (6,038 variants). Participants with Consortium to Establish a Registry for Alzheimer Disease (CERAD) (neuritic Aβ plaques) scores 0 (n = 65) vs score M + F (n = 171) and Braak (neurofibrillary tangle pathology) stages 0-II (n = 74) vs stages IV-VI (n = 119), and with capillary Aβ (CapAβ, n = 77) vs without (n = 179) were compared. Cerebral amyloid angiopathy (CAA) percentage was analyzed as a continuous variable., Results: Altogether, 24 of the 29 loci were associated (at p < 0.05) with one or more AD-related neuropathologic features in either SNP array or imputation data. Fifteen loci associated with CERAD score, smallest p = 0.0002122, odds ratio (OR) 2.67 (1.58-4.49) at MEF2C locus. Fifteen loci associated with Braak stage, smallest p = 0.004372, OR 0.31 (0.14-0.69) at GAB2 locus. Twenty loci associated with CAA, smallest p = 7.17E-07, β 14.4 (8.88-20) at CR1 locus. Fifteen loci associated with CapAβ smallest p = 0.002594, OR 0.54 (0.37-0.81) at HLA-DRB1 locus. Certain loci associated with specific neuropathologic features. CASS4 , CLU , and ZCWPW1 associated only with CAA, while TREM2 and HLA-DRB5 associated only with CapAβ., Conclusions: AD risk loci differ in their association with neuropathologic features, and we show for the first time distinct risk loci for CAA and CapAβ.