Retrospective evaluation of clinical decision support for within-laboratory optimization of SARS-CoV-2 NAAT workflow.

The unprecedented demand for severe acute respiratory syndrome coronavirus 2 (SARS­CoV­2) testing led to challenges in prioritizing and processing specimens efficiently. We describe and evaluate a novel workflow using provider- and patient-facing ask at order entry (AOE) questions to generate distinctive icons on specimen labels for within-laboratory clinical decision support (CDS) for specimen triaging. A multidisciplinary committee established target turnaround times (TATs) for SARS-CoV-2 nucleic acid amplification test (NAAT) based on common clinical scenarios. A set of AOE questions was used to collect relevant clinical information that prompted icon generation for triaging SARS-CoV-2 NAAT specimens. We assessed the collect-to-verify TATs among relevant clinical scenarios. Our study included a total of 1,385,813 SARS-CoV-2 NAAT conducted from March 2020 to June 2022. Most testing met the TAT targets established by institutional committees, but deviations from target TATs occurred during periods of high demand and supply shortages. Median TATs for emergency department (ED) and inpatient specimens and ambulatory pre-procedure populations were stable over the pandemic. However, healthcare worker and other ambulatory test TATs varied substantially, depending on testing volume and community transmission rates. Median TAT significantly differed throughout the pandemic for ED and inpatient clinical scenarios, and there were significant differences in TAT among label icon-signified ambulatory clinical scenarios. We describe a novel approach to CDS for triaging specimens within the laboratory. The use of CDS tools could help clinical laboratories prioritize and process specimens efficiently, especially during times of high demand. Further studies are needed to evaluate the impact of our CDS tool on overall laboratory efficiency and patient outcomes. IMPORTANCE We describe a novel approach to clinical decision support (CDS) for triaging specimens within the clinical laboratory for severe acute respiratory syndrome coronavirus 2 (SARS­CoV­2) nucleic acid amplification tests (NAAT). The use of our CDS tool could help clinical laboratories prioritize and process specimens efficiently, especially during times of high demand. There were significant differences in the turnaround time for specimens differentiated by icons on specimen labels. Further studies are needed to evaluate the impact of our CDS tool on overall laboratory efficiency and patient outcomes.

Biomarkers vs Machines: The Race to Predict Acute Kidney Injury.

BACKGROUND: Acute kidney injury (AKI) is a serious complication affecting up to 15% of hospitalized patients. Early diagnosis is critical to prevent irreversible kidney damage that could otherwise lead to significant morbidity and mortality. However, AKI is a clinically silent syndrome, and current detection primarily relies on measuring a rise in serum creatinine, an imperfect marker that can be slow to react to developing AKI. Over the past decade, new innovations have emerged in the form of biomarkers and artificial intelligence tools to aid in the early diagnosis and prediction of imminent AKI. CONTENT: This review summarizes and critically evaluates the latest developments in AKI detection and prediction by emerging biomarkers and artificial intelligence. Main guidelines and studies discussed herein include those evaluating clinical utilitiy of alternate filtration markers such as cystatin C and structural injury markers such as neutrophil gelatinase-associated lipocalin and tissue inhibitor of metalloprotease 2 with insulin-like growth factor binding protein 7 and machine learning algorithms for the detection and prediction of AKI in adult and pediatric populations. Recommendations for clinical practices considering the adoption of these new tools are also provided. SUMMARY: The race to detect AKI is heating up. Regulatory approval of select biomarkers for clinical use and the emergence of machine learning algorithms that can predict imminent AKI with high accuracy are all promising developments. But the race is far from being won. Future research focusing on clinical outcome studies that demonstrate the utility and validity of implementing these new tools into clinical practice is needed.

From Data to Wisdom: Biomedical Knowledge Graphs for Real-World Data Insights.

Graph data models are an emerging approach to structure clinical and biomedical information. These models offer intriguing opportunities for novel approaches in healthcare, such as disease phenotyping, risk prediction, and personalized precision care. The combination of data and information in a graph model to create knowledge graphs has rapidly expanded in biomedical research, but the integration of real-world data from the electronic health record has been limited. To broadly apply knowledge graphs to EHR and other real-world data, a deeper understanding of how to represent these data in a standardized graph model is needed. We provide an overview of the state-of-the-art research for clinical and biomedical data integration and summarize the potential to accelerate healthcare and precision medicine research through insight generation from integrated knowledge graphs.

Artificial Intelligence and Mapping a New Direction in Laboratory Medicine: A Review.

BACKGROUND: Modern artificial intelligence (AI) and machine learning (ML) methods are now capable of completing tasks with performance characteristics that are comparable to those of expert human operators. As a result, many areas throughout healthcare are incorporating these technologies, including in vitro diagnostics and, more broadly, laboratory medicine. However, there are limited literature reviews of the landscape, likely future, and challenges of the application of AI/ML in laboratory medicine. CONTENT: In this review, we begin with a brief introduction to AI and its subfield of ML. The ensuing sections describe ML systems that are currently in clinical laboratory practice or are being proposed for such use in recent literature, ML systems that use laboratory data outside the clinical laboratory, challenges to the adoption of ML, and future opportunities for ML in laboratory medicine. SUMMARY: AI and ML have and will continue to influence the practice and scope of laboratory medicine dramatically. This has been made possible by advancements in modern computing and the widespread digitization of health information. These technologies are being rapidly developed and described, but in comparison, their implementation thus far has been modest. To spur the implementation of reliable and sophisticated ML-based technologies, we need to establish best practices further and improve our information system and communication infrastructure. The participation of the clinical laboratory community is essential to ensure that laboratory data are sufficiently available and incorporated conscientiously into robust, safe, and clinically effective ML-supported clinical diagnostics.

Applications of Digital Microscopy and Densely Connected Convolutional Neural Networks for Automated Quantification of Babesia-Infected Erythrocytes.

BACKGROUND: Clinical babesiosis is diagnosed, and parasite burden is determined, by microscopic inspection of a thick or thin Giemsa-stained peripheral blood smear. However, quantitative analysis by manual microscopy is subject to error. As such, methods for the automated measurement of percent parasitemia in digital microscopic images of peripheral blood smears could improve clinical accuracy, relative to the predicate method. METHODS: Individual erythrocyte images were manually labeled as "parasite" or "normal" and were used to train a model for binary image classification. The best model was then used to calculate percent parasitemia from a clinical validation dataset, and values were compared to a clinical reference value. Lastly, model interpretability was examined using an integrated gradient to identify pixels most likely to influence classification decisions. RESULTS: The precision and recall of the model during development testing were 0.92 and 1.00, respectively. In clinical validation, the model returned increasing positive signal with increasing mean reference value. However, there were 2 highly erroneous false positive values returned by the model. Further, the model incorrectly assessed 3 cases well above the clinical threshold of 10%. The integrated gradient suggested potential sources of false positives including rouleaux formations, cell boundaries, and precipitate as deterministic factors in negative erythrocyte images. CONCLUSIONS: While the model demonstrated highly accurate single cell classification and correctly assessed most slides, several false positives were highly incorrect. This project highlights the need for integrated testing of machine learning-based models, even when models in the development phase perform well.

Agile Health Care Analytics: Enabling Real-Time Disease Surveillance With a Computational Health Platform.

The ongoing coronavirus disease outbreak demonstrates the need for novel applications of real-time data to produce timely information about incident cases. Using health information technology (HIT) and real-world data, we sought to produce an interface that could, in near real time, identify patients presenting with suspected respiratory tract infection and enable monitoring of test results related to specific pathogens, including severe acute respiratory syndrome coronavirus 2. This tool was built upon our computational health platform, which provides access to near real-time data from disparate HIT sources across our health system. This combination of technology allowed us to rapidly prototype, iterate, and deploy a platform to support a cohesive organizational response to a rapidly evolving outbreak. Platforms that allow for agile analytics are needed to keep pace with evolving needs within the health care system.

Optimization of Turnaround Time for Group A Streptococcus PCR.

The use of some nucleic acid amplification tests (NAATs) for the diagnosis of group A Streptococcus (GAS) pharyngitis allows laboratories to adopt single-tiered testing without reflex culture. However, centralization may delay the delivery of actionable information to the bedside, particularly in the outpatient setting. We describe two novel workflows at our institution and their effect on in-lab turnaround time (TAT) at a tertiary care microbiology lab. Laboratory records were extracted, and relevant data were analyzed after the implementation of qualitative in vitro diagnostic testing for GAS with the Xpert Xpress Strep A assay, performed using the GeneXpert Infinity-48s. Workflow optimization steps studied included: (i) direct specimen submission to the microbiology laboratory via the pneumatic tube system and (ii) autoverification of GAS NAAT results in the laboratory information system. Between April 2018 and October 2018, 2,595 unique specimens were tested for GAS by PCR. Of these, 2,523 were included in the final analysis. Linear regression established that the total in-lab TAT was significantly reduced by direct specimen submission to the microbiology laboratory, autoverification, and processing during the night shift. We describe two workflow optimization methods that reduced the in-lab TAT for GAS NAAT. Although microbiology labs historically use manual processes, the advent of total laboratory automation and the adoption of on-demand NAATs will allow for more streamlined processing of microbiology specimens. It may be beneficial to consider instrument interfacing and specimen processing optimization during the early phases of implementation planning for NAATs in the microbiology laboratory.

Very Deep Convolutional Neural Networks for Morphologic Classification of Erythrocytes.

BACKGROUND: Morphologic profiling of the erythrocyte population is a widely used and clinically valuable diagnostic modality, but one that relies on a slow manual process associated with significant labor cost and limited reproducibility. Automated profiling of erythrocytes from digital images by capable machine learning approaches would augment the throughput and value of morphologic analysis. To this end, we sought to evaluate the performance of leading implementation strategies for convolutional neural networks (CNNs) when applied to classification of erythrocytes based on morphology. METHODS: Erythrocytes were manually classified into 1 of 10 classes using a custom-developed Web application. Using recent literature to guide architectural considerations for neural network design, we implemented a "very deep" CNN, consisting of >150 layers, with dense shortcut connections. RESULTS: The final database comprised 3737 labeled cells. Ensemble model predictions on unseen data demonstrated a harmonic mean of recall and precision metrics of 92.70% and 89.39%, respectively. Of the 748 cells in the test set, 23 misclassification errors were made, with a correct classification frequency of 90.60%, represented as a harmonic mean across the 10 morphologic classes. CONCLUSIONS: These findings indicate that erythrocyte morphology profiles could be measured with a high degree of accuracy with "very deep" CNNs. Further, these data support future efforts to expand classes and optimize practical performance in a clinical environment as a prelude to full implementation as a clinical tool.

Evaluation of relational and NoSQL database architectures to manage genomic annotations.

While the adoption of next generation sequencing has rapidly expanded, the informatics infrastructure used to manage the data generated by this technology has not kept pace. Historically, relational databases have provided much of the framework for data storage and retrieval. Newer technologies based on NoSQL architectures may provide significant advantages in storage and query efficiency, thereby reducing the cost of data management. But their relative advantage when applied to biomedical data sets, such as genetic data, has not been characterized. To this end, we compared the storage, indexing, and query efficiency of a common relational database (MySQL), a document-oriented NoSQL database (MongoDB), and a relational database with NoSQL support (PostgreSQL). When used to store genomic annotations from the dbSNP database, we found the NoSQL architectures to outperform traditional, relational models for speed of data storage, indexing, and query retrieval in nearly every operation. These findings strongly support the use of novel database technologies to improve the efficiency of data management within the biological sciences.

Perinephric Abscess with Fistula Formation to Descending Colon: A Case Report and a Review of the Literature.

UNLABELLED: A perinephric abscess (PA) is an uncommon clinical entity. We present a case of a patient with aknown chronic perinephric hematoma, complicated by infection, subsequently developing a fistula to the adjacent descending colon. To the knowledge of these authors, there are no reported cases of perinephric abscess fistula formation with the descending colon. CASE REPORT: A 50-year old woman with multiple medical comorbidities presented with gram negative sepsis and was subsequently found to have a cyst on her left kidney. Following CT-guided biopsy, an iatrogenic hematoma was formed and subsequently became infected. Radiographic evaluation revealed fistula formation between the abscess and the adjacent descending colon. Conservative management resulted in closure of the fistula without the need for further surgical management. DISCUSSION/CONCLUSION: Perinephric abscess (PA) formation can result in significant morbidity and mortalitywith complications including sepsis, renal failure and fistula formation. We present a first case report in which a PA fistualized with the descending colon following percutaneous drainage.

Accounting for differences between serum and plasma: An indirect approach to derive reference intervals for total protein, albumin, and globulin.

BACKGROUND: Observable quantitative variations exist between plasma and serum in routine protein measurements, often not reflected in standard reference intervals. In this study, we describe an indirect approach for estimating a combined reference interval (RI) (i.e., serum and plasma), for commonly ordered protein measurands: total protein, albumin, and globulin. METHODS: We applied an indirect reference interval estimation for protein measurements in serum and plasma using data from July 2018 to February 2024. The data were divided into three Epochs based on a period of plasma separator tube shortage during the COVID-19 pandemic. Bootstrap resampling was used to calculate RIs and corresponding 95% confidence intervals for each month. RESULTS: Our results demonstrate notable changes in RI limits for total protein, albumin, and globulin between Epochs, reflecting the influence of changing sample matrix. A combined RI was identified for all components and verified using plasma and serum samples from 20 healthy individuals and retrospective analysis of flagging rates on our outpatient population using new and historical RIs. CONCLUSION: The study demonstrates notable differences in the RIs for total protein, albumin, and globulin when container type changes. In addition, the results demonstrate the effectiveness of big data analytics in deriving RIs and highlights the necessity of continuous RI assessment and adjustment based on the patient population and acceptable specimen types.

A primer for quantum computing and its applications to healthcare and biomedical research.

OBJECTIVES: To introduce quantum computing technologies as a tool for biomedical research and highlight future applications within healthcare, focusing on its capabilities, benefits, and limitations. TARGET AUDIENCE: Investigators seeking to explore quantum computing and create quantum-based applications for healthcare and biomedical research. SCOPE: Quantum computing requires specialized hardware, known as quantum processing units, that use quantum bits (qubits) instead of classical bits to perform computations. This article will cover (1) proposed applications where quantum computing offers advantages to classical computing in biomedicine; (2) an introduction to how quantum computers operate, tailored for biomedical researchers; (3) recent progress that has expanded access to quantum computing; and (4) challenges, opportunities, and proposed solutions to integrate quantum computing in biomedical applications.

Comparison of mesenchymal stem cells (osteoprogenitors) harvested from proximal humerus and distal femur during arthroscopic surgery.

PURPOSE: The aim of this study was to examine the relations between age, gender, and number of viable mesenchymal stem cells (MSCs) in concentrated bone marrow (BM) obtained from the proximal humerus and distal femur during arthroscopic surgery. METHODS: BM was aspirated from either the proximal humerus (n = 55) or distal femur (n = 29) during arthroscopic surgery in 84 patients (51.3 ± 11.6 years). MSCs were obtained from fractionated bone marrow after a 5-minute spin at 1,500 rpm. Volume of BM and number of nucleated cells (NCs) were calculated, and samples were cultured for 6 days, after which point colony-forming units (CFUs) were quantified and fluorescence-activated cell sorting (FACS) analysis was performed. Simple linear regression was used to explore relations between age, gender, volume of aspirated BM, and MSCs per milliliter. RESULTS: BM aspirations yielded a mean quantity of 22.6 ± 12.3 mL. After centrifugation, 30.0 ± 16.7 × 10(6) nucleated cells/mL of concentrated BM were harvested. The proximal humerus provided 38.7 ± 52.6 × 10(6), and the distal femur, 25.9 ± 14.3 × 10(6), for an overall 766.3 ± 545.3 MSCs/mL of concentrated BM (proximal humerus: 883.9 ± 577.6, distal femur: 551.3 ± 408.1). Values did not significantly differ by age, gender, or donor site. CONCLUSIONS: Arthroscopic aspiration of bone marrow from the proximal humerus and distal femur is a reproducible technique and yields reliable concentrations of MSCs. The use of an intraoperative concentration method resulted in consistent amounts of MSCs in all clinically relevant age groups without a significant drop of the number of isolated MSCs. CLINICAL RELEVANCE: Human MSCs derived from concentrated bone marrow aspirate are a promising biological addition that may have practical use in the future of soft tissue augmentation. Arthroscopic techniques for bone marrow aspiration that do not require an additional surgical site for aspiration (e.g., iliac crest) or a second operative procedure may facilitate future use of MSCs in arthroscopic surgery.