SLC35A2â CDG: Functional characterization, expanded molecular, clinical, and biochemical phenotypes of 30 unreported Individuals

Pathogenic de novo variants in the Xâ linked gene SLC35A2 encoding the major Golgiâ localized UDPâ galactose transporter required for proper protein and lipid glycosylation cause a rare type of congenital disorder of glycosylation known as SLC35A2â congenital disorders of glycosylation (CDG; formerly CDGâ IIm). To date, 29 unique de novo variants from 32 unrelated individuals have been described in the literature. The majority of affected individuals are primarily characterized by varying degrees of neurological impairments with or without skeletal abnormalities. Surprisingly, most affected individuals do not show abnormalities in serum transferrin Nâ glycosylation, a common biomarker for most types of CDG. Here we present data characterizing 30 individuals and add 26 new variants, the single largest study involving SLC35A2â CDG. The great majority of these individuals had normal transferrin glycosylation. In addition, expanding the molecular and clinical spectrum of this rare disorder, we developed a robust and reliable biochemical assay to assess SLC35A2â dependent UDPâ galactose transport activity in primary fibroblasts. Finally, we show that transport activity is directly correlated to the ratio of wildâ type to mutant alleles in fibroblasts from affected individuals.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/150498/1/humu23731_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/150498/2/humu23731-sup-0001-Supp_Mat__2019.2.10_.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/150498/3/humu23731.pd

Scheduling multi-product flows in pipelines

Ph.D.H. Donald Ratlif

Improving the Accuracy of Predictive Models for Outcomes of Antidepressants by Using an Ontological Adjustment Approach

For patients with rare comorbidities, there are insufficient observations to accurately estimate the effectiveness of treatment. At the same time, all diagnosis, including rare diagnosis, are part of the International Classification of Disease (ICD). Grouping ICD into broader concepts (i.e., ontology adjustment) can not only increase accuracy of estimating antidepressant effectiveness for patients with rare conditions but also prevent overfitting in big data analysis. In this study, 3,678,082 depressed patients treated with antidepressants were obtained from OptumLabs® Data Warehouse (OLDW). For rare diagnoses, adjustments were made by using the likelihood ratio of the immediate broader concept in the ICD hierarchies. The accuracy of models in training (90%) and test (10%) sets was examined using the area under the receiver operating curves (AROC). The gap in training and test AROC shows how much random noise was modeled. If the gap is large, then the parameters of the model, including the reported effectiveness of the antidepressant for patients with rare conditions, are suspect. There was, on average, a 9.0% reduction in the AROC gap after using the ontological adjustment. Therefore, ontology adjustment can reduce model overfitting, leading to better parameter estimates from the training set

Deep neural network models for identifying incident dementia using claims and EHR datasets.

This study investigates the use of deep learning methods to improve the accuracy of a predictive model for dementia, and compares the performance to a traditional machine learning model. With sufficient accuracy the model can be deployed as a first round screening tool for clinical follow-up including neurological examination, neuropsychological testing, imaging and recruitment to clinical trials. Seven cohorts with two years of data, three to eight years prior to index date, and an incident cohort were created. Four trained models for each cohort, boosted trees, feed forward network, recurrent neural network and recurrent neural network with pre-trained weights, were constructed and their performance compared using validation and test data. The incident model had an AUC of 94.4% and F1 score of 54.1%. Eight years removed from index date the AUC and F1 scores were 80.7% and 25.6%, respectively. The results for the remaining cohorts were between these ranges. Deep learning models can result in significant improvement in performance but come at a cost in terms of run times and hardware requirements. The results of the model at index date indicate that this modeling can be effective at stratifying patients at risk of dementia. At this time, the inability to sustain this quality at longer lead times is more an issue of data availability and quality rather than one of algorithm choices

Identifying incident dementia by applying machine learning to a very large administrative claims dataset.

Alzheimer's disease and related dementias (ADRD) are highly prevalent conditions, and prior efforts to develop predictive models have relied on demographic and clinical risk factors using traditional logistical regression methods. We hypothesized that machine-learning algorithms using administrative claims data may represent a novel approach to predicting ADRD. Using a national de-identified dataset of more than 125 million patients including over 10,000 clinical, pharmaceutical, and demographic variables, we developed a cohort to train a machine learning model to predict ADRD 4-5 years in advance. The Lasso algorithm selected a 50-variable model with an area under the curve (AUC) of 0.693. Top diagnosis codes in the model were memory loss (780.93), Parkinson's disease (332.0), mild cognitive impairment (331.83) and bipolar disorder (296.80), and top pharmacy codes were psychoactive drugs. Machine learning algorithms can rapidly develop predictive models for ADRD with massive datasets, without requiring hypothesis-driven feature engineering

Assessment of potentially inappropriate prescribing of opioid analgesics requiring prior opioid tolerance.

Importance: Opioid-tolerant only (OTO) medications, such as transmucosal immediate-release fentanyl products and certain extended-release opioid analgesics, require prior opioid tolerance for safe use, as patients without tolerance may be at increased risk of overdose. Studies using insurance claims have found that many patients initiating these medications do not appear to be opioid tolerant. Objectives: To measure prevalence of opioid tolerance in patients initiating OTO medications and to determine whether linked electronic health record (EHR) data contribute evidence of opioid tolerance not found in insurance claims data. Design, Setting, and Participants: This retrospective cohort study used a national database of deidentified longitudinal health information, including medical and pharmacy claims, insurance enrollment, and EHR data, from January 1, 2007, to December 31, 2016. Data included 131 756 US residents with at least 183 days of continuous enrollment in commercial or Medicare Advantage insurance (including medical and pharmacy benefits) who had received an OTO medication and who had no inpatient stays in the 30 days prior to starting an OTO medication; of these, 20 044 individuals had linked EHR data within the prior 183 days. Data were analyzed from July 1, 2017, to August 31, 2018. Exposures: Initiating an OTO medication. Main Outcomes and Measures: Prior opioid tolerance demonstrated through pharmacy fills or EHR data on prescriptions written. Results: Among 153 385 OTO use episodes identified, 89 029 (58.0%) occurred among women, 62 900 (41.0%) occurred among patients with Medicare Advantage insurance, 39 394 (25.7%) occurred in the Midwest, 17 366 (11.3%) occurred in the Northeast, 73 316 (47.8%) occurred in the South, and 23 309 (15.2%) occurred in the West. Less than half of use episodes (73 117 episodes [47.7%]) involved patients with evidence in claims data of opioid tolerance prior to initiating therapy with an OTO medication, including 31 392 of 101 676 episodes (30.9%) involving transdermal fentanyl, 1561 of 2440 episodes (64.0%) involving transmucosal fentanyl, 36 596 of 43 559 episodes (84.0%) involving extended-release oxycodone, and 3568 of 5710 episodes (62.5%) involving extended-release hydromorphone. Among 20 044 OTO use episodes with linked EHR and claims data, less than 1% of OTO episodes identified in claims had evidence of opioid tolerance in structured EHR data that was not present in claims data (108 episodes [0.5%]). After limiting the sample to OTO episodes identified in claims with a matching OTO prescription within 14 days in the structured EHR data, only 40 of 939 episodes (4.0%) occurred among patients with evidence of tolerance that was not present in claims data. Conclusions and Relevance: This cohort study found that most patients initiating OTO medications did not have evidence of prior opioid tolerance, suggesting they were at increased risk of opioid-related harms, including fatal overdose. Data from EHRs did not contribute substantial additional evidence of opioid tolerance beyond the data found in prescription claims. Future research is needed to understand the clinical rationale behind these observed prescribing patterns and to quantify the risk of harm to patients associated with potentially inappropriate prescribing