A call to action: MTHFR polymorphisms should not be a part of inherited thrombophilia testing

Testing for polymorphisms in the methylenetetrahydrofolate reductase (MTHFR) gene is still a standard part of thrombophilia testing in many laboratories. However, it is clear that these polymorphisms are not risk factors for arterial or venous thrombosis and therefore should not be part of thrombophilia testing. Eliminating MTHFR from thrombophilia testing will reduce patient concerns and health care costs

A call to action: MTHFR polymorphisms should not be a part of inherited thrombophilia testing

Abstract Testing for polymorphisms in the methylenetetrahydrofolate reductase (MTHFR) gene is still a standard part of thrombophilia testing in many laboratories. However, it is clear that these polymorphisms are not risk factors for arterial or venous thrombosis and therefore should not be part of thrombophilia testing. Eliminating MTHFR from thrombophilia testing will reduce patient concerns and health care costs

Chronic liver disease, thrombocytopenia and procedural bleeding risk; are novel thrombopoietin mimetics the solution?

Chronic liver disease (CLD) alters normal hemostatic and thrombotic systems via multiple mechanisms including reduced platelet function and number, leading to challenging peri-operative planning. Hepatic thrombopoietin (TPO) synthesis is reduced in CLD, leading to several recent randomized, placebo-controlled trials examining the utility of TPO-mimetics to increase platelet counts prior to surgery. While these trials do suggest that TPO-mimetics are efficacious at increasing platelet counts in patients with CLD and have led to several recent drug approvals in this space by the U.S. Food & Drug Administration, it remains unclear whether these results translate to the relevant clinical endpoint of reduced perioperative bleeding rate and severity. In this article, we review several recently-published, phase 3 trials on the TPO-mimetics eltrombopag, avatrombopag and lusutrombopag, and discuss the clinical significance of their results

Correction: Detecting rare diseases in electronic health records using machine learning and knowledge engineering: Case study of acute hepatic porphyria.

[This corrects the article DOI: 10.1371/journal.pone.0235574.]

Detecting rare diseases in electronic health records using machine learning and knowledge engineering: Case study of acute hepatic porphyria.

BACKGROUND:With the growing adoption of the electronic health record (EHR) worldwide over the last decade, new opportunities exist for leveraging EHR data for detection of rare diseases. Rare diseases are often not diagnosed or delayed in diagnosis by clinicians who encounter them infrequently. One such rare disease that may be amenable to EHR-based detection is acute hepatic porphyria (AHP). AHP consists of a family of rare, metabolic diseases characterized by potentially life-threatening acute attacks and chronic debilitating symptoms. The goal of this study was to apply machine learning and knowledge engineering to a large extract of EHR data to determine whether they could be effective in identifying patients not previously tested for AHP who should receive a proper diagnostic workup for AHP. METHODS AND FINDINGS:We used an extract of the complete EHR data of 200,000 patients from an academic medical center and enriched it with records from an additional 5,571 patients containing any mention of porphyria in the record. After manually reviewing the records of all 47 unique patients with the ICD-10-CM code E80.21 (Acute intermittent [hepatic] porphyria), we identified 30 patients who were positive cases for our machine learning models, with the rest of the patients used as negative cases. We parsed the record into features, which were scored by frequency of appearance and filtered using univariate feature analysis. We manually choose features not directly tied to provider attributes or suspicion of the patient having AHP. We trained on the full dataset, with the best cross-validation performance coming from support vector machine (SVM) algorithm using a radial basis function (RBF) kernel. The trained model was applied back to the full data set and patients were ranked by margin distance. The top 100 ranked negative cases were manually reviewed for symptom complexes similar to AHP, finding four patients where AHP diagnostic testing was likely indicated and 18 patients where AHP diagnostic testing was possibly indicated. From the top 100 ranked cases of patients with mention of porphyria in their record, we identified four patients for whom AHP diagnostic testing was possibly indicated and had not been previously performed. Based solely on the reported prevalence of AHP, we would have expected only 0.002 cases out of the 200 patients manually reviewed. CONCLUSIONS:The application of machine learning and knowledge engineering to EHR data may facilitate the diagnosis of rare diseases such as AHP. Further work will recommend clinical investigation to identified patients' clinicians, evaluate more patients, assess additional feature selection and machine learning algorithms, and apply this methodology to other rare diseases. This work provides strong evidence that population-level informatics can be applied to rare diseases, greatly improving our ability to identify undiagnosed patients, and in the future improve the care of these patients and our ability study these diseases. The next step is to learn how best to apply these EHR-based machine learning approaches to benefit individual patients with a clinical study that provides diagnostic testing and clinical follow up for those identified as possibly having undiagnosed AHP