A practical guide to chimerism analysis: Review of the literature and testing practices worldwide.

BACKGROUND AND PURPOSE: Currently there are no widely accepted guidelines for chimerism analysis testing in hematopoietic cell transplantation (HCT) patients. The objective of this review is to provide a practical guide to address key aspects of performing and utilizing chimerism testing results. In developing this guide, we conducted a survey of testing practices among laboratories that are accredited for performing engraftment monitoring/chimerism analysis by either the American Society for Histocompatibility & Immunogenetics (ASHI) and/or the European Federation of Immunogenetics (EFI). We interpreted the survey results in the light of pertinent literature as well as the experience in the laboratories of the authors. RECENT DEVELOPMENTS: In recent years there has been significant advances in high throughput molecular methods such as next generation sequencing (NGS) as well as growing access to these technologies in histocompatibility and immunogenetics laboratories. These methods have the potential to improve the performance of chimerism testing in terms of sensitivity, availability of informative genetic markers that distinguish donors from recipients as well as cost. SUMMARY: The results of the survey revealed a great deal of heterogeneity in chimerism testing practices among participating laboratories. The most consistent response indicated monitoring of engraftment within the first 30 days. These responses are reflective of published literature. Additional clinical indications included early detection of impending relapse as well as identification of cases of HLA-loss relapse.

Heart Failure Medications Detection and Prescription Status Classification in Clinical Narrative Documents.

Angiotensin Converting Enzyme Inhibitors (ACEI) and Angiotensin II Receptor Blockers (ARB) are two common medication classes used for heart failure treatment. The ADAHF (Automated Data Acquisition for Heart Failure) project aimed at automatically extracting heart failure treatment performance metrics from clinical narrative documents, and these medications are an important component of the performance metrics. We developed two different systems to detect these medications, rule-based and machine learning-based. The rule-based system used dictionary lookups with fuzzy string searching and showed successful performance even if our corpus contains various misspelled medications. The machine learning-based system uses lexical and morphological features and produced similar results. The best performance was achieved when combining the two methods, reaching 99.3% recall and 98.8% precision. To determine the prescription status of each medication (i.e., active, discontinued, or negative), we implemented a SVM classifier with lexical features and achieved good performance, reaching 95.49% accuracy, in a five-fold cross-validation evaluation.

Assisted annotation of medical free text using RapTAT.

OBJECTIVE: To determine whether assisted annotation using interactive training can reduce the time required to annotate a clinical document corpus without introducing bias. MATERIALS AND METHODS: A tool, RapTAT, was designed to assist annotation by iteratively pre-annotating probable phrases of interest within a document, presenting the annotations to a reviewer for correction, and then using the corrected annotations for further machine learning-based training before pre-annotating subsequent documents. Annotators reviewed 404 clinical notes either manually or using RapTAT assistance for concepts related to quality of care during heart failure treatment. Notes were divided into 20 batches of 19-21 documents for iterative annotation and training. RESULTS: The number of correct RapTAT pre-annotations increased significantly and annotation time per batch decreased by ~50% over the course of annotation. Annotation rate increased from batch to batch for assisted but not manual reviewers. Pre-annotation F-measure increased from 0.5 to 0.6 to >0.80 (relative to both assisted reviewer and reference annotations) over the first three batches and more slowly thereafter. Overall inter-annotator agreement was significantly higher between RapTAT-assisted reviewers (0.89) than between manual reviewers (0.85). DISCUSSION: The tool reduced workload by decreasing the number of annotations needing to be added and helping reviewers to annotate at an increased rate. Agreement between the pre-annotations and reference standard, and agreement between the pre-annotations and assisted annotations, were similar throughout the annotation process, which suggests that pre-annotation did not introduce bias. CONCLUSIONS: Pre-annotations generated by a tool capable of interactive training can reduce the time required to create an annotated document corpus by up to 50%.