Pathology Informatics and Robotics Strategies for Improving Efficiency of COVID-19 Pooled Testing

Automated HL7v2 LRI informatics framework for streamlining genomics-EHR data integration

While VCF formatted files are the lingua franca of next-generation sequencing, most EHRs do not provide native VCF support. As a result, labs often must send non-structured PDF reports to the EHR. On the other hand, while FHIR adoption is growing, most EHRs support HL7 interoperability standards, particularly those based on the HL7 Version 2 (HL7v2) standard. The HL7 Version 2 genomics component of the HL7 Laboratory Results Interface (HL7v2 LRI) standard specifies a formalism for the structured communication of genomic data from lab to EHR. We previously described an open-source tool (vcf2fhir) that converts VCF files into HL7 FHIR format. In this report, we describe how the utility has been extended to output HL7v2 LRI data that contains both variants and variant annotations (e.g., predicted phenotypes and therapeutic implications). Using this HL7v2 converter, we implemented an automated pipeline for moving structured genomic data from the clinical laboratory to EHR. We developed an open source hl7v2GenomicsExtractor that converts genomic interpretation report files into a series of HL7v2 observations conformant to HL7v2 LRI. We further enhanced the converter to produce output conformant to Epic's genomic import specification and to support alternative input formats. An automated pipeline for pushing standards-based structured genomic data directly into the EHR was successfully implemented, where genetic variant data and the clinical annotations are now both available to be viewed in the EHR through Epic's genomics module. Issues encountered in the development and deployment of the HL7v2 converter primarily revolved around data variability issues, primarily lack of a standardized representation of data elements within various genomic interpretation report files. The technical implementation of a HL7v2 message transformation to feed genomic variant and clinical annotation data into an EHR has been successful. In addition to genetic variant data, the implementation described here releases the valuable asset of clinically relevant genomic annotations provided by labs from static PDFs to calculable, structured data in EHR systems.

Evaluation of miniaturized Illumina DNA preparation protocols for SARS-CoV-2 whole genome sequencing

The global pandemic caused by SARS-CoV-2 has increased the demand for scalable sequencing and diagnostic methods, especially for genomic surveillance. Although next-generation sequencing has enabled large-scale genomic surveillance, the ability to sequence SARS-CoV-2 in some settings has been limited by the cost of sequencing kits and the time-consuming preparations of sequencing libraries. We compared the sequencing outcomes, cost and turn-around times obtained using the standard Illumina DNA Prep kit protocol to three modified protocols with fewer clean-up steps and different reagent volumes (full volume, half volume, one-tenth volume). We processed a single run of 47 samples under each protocol and compared the yield and mean sequence coverage. The sequencing success rate and quality for the four different reactions were as follows: the full reaction was 98.2%, the one-tenth reaction was 98.0%, the full rapid reaction was 97.5% and the half-reaction, was 97.1%. As a result, uniformity of sequence quality indicated that libraries were not affected by the change in protocol. The cost of sequencing was reduced approximately seven-fold and the time taken to prepare the library was reduced from 6.5 hours to 3 hours. The sequencing results obtained using the miniaturised volumes showed comparability to the results obtained using full volumes as described by the manufacturer. The adaptation of the protocol represents a lower-cost, streamlined approach for SARS-CoV-2 sequencing, which can be used to produce genomic data quickly and more affordably, especially in resource-constrained settings.

Current Developments of Artificial Intelligence in Digital Pathology and Its Future Clinical Applications in Gastrointestinal Cancers

The implementation of DP will revolutionize current practice by providing pathologists with additional tools and algorithms to improve workflow. Furthermore, DP will open up opportunities for development of AI-based tools for more precise and reproducible diagnosis through computational pathology. One of the key features of AI is its capability to generate perceptions and recognize patterns beyond the human senses. Thus, the incorporation of AI into DP can reveal additional morphological features and information. At the current rate of AI development and adoption of DP, the interest in computational pathology is expected to rise in tandem. There have already been promising developments related to AI-based solutions in prostate cancer detection; however, in the GI tract, development of more sophisticated algorithms is required to facilitate histological assessment of GI specimens for early and accurate diagnosis. In this review, we aim to provide an overview of the current histological practices in AP laboratories with respect to challenges faced in image preprocessing, present the existing AI-based algorithms, discuss their limitations and present clinical insight with respect to the application of AI in early detection and diagnosis of GI cancer.

The Hologic Aptima SARS-CoV-2 assay enables high ratio pooling saving reagents and improving turnaround time

BACKGROUND

The Hologic Aptima™ TMA SARS-CoV-2 assay was employed to test pooled nasopharyngeal (NP) samples to evaluate the performance of pooled sample testing and characterize variables influencing results.

METHODS

Results on 1033 previously tested NP samples were retrieved to characterize the relative light units (RLU) of SARS-CoV-2-positive samples in the tested population. The pooling strategy of combining 10 SARS-CoV-2 samples into one pool (10/1) was used in this study. The results were compared with neat sample testing using the same Aptima™ TMA SARS-CoV-2 assay and also the CDC RT-PCR and the Cepheid SARS-CoV-2 assays.

RESULTS

The Aptima assay compares favorably with both CDC RT-PCR and the Cepheid SARS-CoV-2 assays. Once samples are pooled 10 to 1 as in our experiments, the resulting signal strength of the assay suffers. A divide opens between pools assembled from strong-positive versus only weak-positive samples. Pools of the former can be reliably detected with positive percent agreement (PPA) of 95.2%, while pools of the latter are frequently misclassified as negative with PPA of 40%. When the weak-positive samples with kRLU value lower than 1012 constitute 3.4% of the total sample profile, the assay PPA approaches 93.4% suggesting that 10/1 pooled sample testing by the Aptima assay is an effective screening tool for SARS-CoV-2.

CONCLUSION

Performing pooled testing, one should monitor the weak positives with kRLU lower than 1012 or quantification cycle (Cq) value higher than 35 on an ongoing basis and adjust pooling approaches to avoid reporting false negatives.