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.

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.

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.

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.

Cybersecurity and Information Assurance for the Clinical Laboratory.

BACKGROUND: Network-connected medical devices have rapidly proliferated in the wake of recent global catalysts, leaving clinical laboratories and healthcare organizations vulnerable to malicious actors seeking to ransom sensitive healthcare information. As organizations become increasingly dependent on integrated systems and data-driven patient care operations, a sudden cyberattack and the associated downtime can have a devastating impact on patient care and the institution as a whole. Cybersecurity, information security, and information assurance principles are, therefore, vital for clinical laboratories to fully prepare for what has now become inevitable, future cyberattacks. CONTENT: This review aims to provide a basic understanding of cybersecurity, information security, and information assurance principles as they relate to healthcare and the clinical laboratories. Common cybersecurity risks and threats are defined in addition to current proactive and reactive cybersecurity controls. Information assurance strategies are reviewed, including traditional castle-and-moat and zero-trust security models. Finally, ways in which clinical laboratories can prepare for an eventual cyberattack with extended downtime are discussed. SUMMARY: The future of healthcare is intimately tied to technology, interoperability, and data to deliver the highest quality of patient care. Understanding cybersecurity and information assurance is just the first preparative step for clinical laboratories as they ensure the protection of patient data and the continuity of their operations.

Advances in molecular infectious diseases testing in the time of COVID-19.

The Coronavirus Disease of 2019 (COVID-19) pandemic has been a challenging event for laboratory medicine and diagnostics manufacturers. We have had to confront numerous unique and previously unthinkable issues on a daily basis in order to continue offering diagnostic testing for not only Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), but other testing that was significantly impacted by supply chain and staffing disruptions related to COVID-19. Out of this tremendously stressful and, at times, chaotic environment, decades of innovations and advances in testing methodologies and instrumentation became essential to handle the overwhelming volume of samples with clinically appropriate turn-around-time. Additionally, a number of novel testing approaches and technological innovations emerged to address laboratory and public health needs for widespread testing. In this review we consider both technological advances in infectious diseases testing and other innovations in sample collection, processing, automation, workflow, and testing that have embodied the laboratory response to the COVID-19 pandemic.

A preliminary study of hydrocodone and hydromorphone to oxycodone ratios for distinguishing impurities from independent opioid use.

BACKGROUND: Urine drug testing (UDT) monitors prescription compliance and/or drug abuse. However, interpretation of UDT results obtained by liquid chromatography-tandem mass spectrometry (LC-MS-MS) can be complicated by the presence of drug impurities that are detected by highly sensitive methods. Hydrocodone is a drug impurity that can be found as high as 1% in oxycodone pills. OBJECTIVES: We evaluated the frequency and concentration of hydrocodone and its metabolite, hydromorphone, in patients taking oxycodone to check if the ratio of hydrocodone or hydromorphone to oxycodone could distinguish between oxycodone only use from those consuming additional opiates. DESIGN & METHODS: We correlated LC-MS/MS results with medication records of 319 patients with positive oxycodone results over 7 months (4/2021-11/2021). RESULTS: Fifteen of 319 patients with positive oxycodone results were taking oxycodone only. For these 15 patients, the mean ratio of hydrocodone to oxycodone was 0.57% (range 0.05%-3.35%), and the mean ratio of hydromorphone to oxycodone was 0.81% (range 0.18-3.51%). CONCLUSIONS: Hydrocodone and/or hydromorphone are detectable in patients taking only oxycodone and can likely be identified as an impurity if their calculated ratio to oxycodone is <1 %. Further validation of the ratios in a larger sample size is recommended.

Contamination of clinical blood samples with crystalloid solutions: An experimental approach to derive multianalyte delta checks.

BACKGROUND: Laboratorians are left unguided by a paucity of literature on how to configure rules for the detection of intravenous (IV) fluid contamination in blood samples. We designed a study to determine the in vitro effect of increasing blood sample contamination from commonly used crystalloid solutions and how these observations can guide the derivation of multianalyte delta checks to detect such pre-analytical error. METHODS: In this study, we spiked increasing volumes of commonly used IV fluids (normal saline (NS), lactated ringers (LR), and 5% dextrose) into blood samples that were collected from healthy donors. Routine chemistry analytes were measured and compared between neat and contrived samples. From these observations, we derived several permutations of multianalyte delta checks using the basic metabolic panel framework and evaluated rule performance using retrospective data. RESULTS: The wet chemistry experiments showed that increasing the volume of crystalloid solution contamination significantly changed several analytes. Subsequently derived multianalyte delta check procedures were applied to retrospective data. For all IV fluids tested, smaller magnitudes of analyte change resulted in more samples flagged. CONCLUSION: Multianalyte delta checks may be an effective method for the detection of IV fluid contamination.

Quality Management to Improve CSF Gram Stain Turnaround Time.

BACKGROUND: With appropriate turnaround time (TAT), cerebrospinal fluid (CSF) Gram stains can provide rapid, clinically actionable information in patients with meningitis. Monitoring CSF Gram stain TAT at our institution revealed workflow gaps that were causing result reporting delays. We then implemented a new quality management program to improve TAT. METHODS: We reviewed the TAT of all CSF specimens submitted for bacterial culture received between August 1, 2016, and July 31, 2020, and began prospectively monitoring CSF Gram stain TAT in January 2019. We then implemented the following changes in April 2019: (i) monthly reviews of TAT with staff, (ii) hand-off sheets for shift transitions, and (iii) pre- and post-shift walk throughs including centrifuge checks. RESULTS: A total of 6913 samples were included in the analysis. CSF samples with TAT > 60 min decreased from 27.3% to 9.89% (P < 0.0001), and median TAT decreased by 9 min (P < 0.0001) with significantly reduced variability. These changes were sustained throughout the follow-up period across all shifts and shift transitions. CONCLUSIONS: A new monthly quality metric allowed us to track CSF Gram stain TAT, identify barriers to TAT goals, and implement workflow changes that significantly improved TAT without the need for costly interventions.

Supervised machine learning in the mass spectrometry laboratory: A tutorial.

As the demand for laboratory testing by mass spectrometry increases, so does the need for automated methods for data analysis. Clinical mass spectrometry (MS) data is particularly well-suited for machine learning (ML) methods, which deal nicely with structured and discrete data elements. The alignment of these two fields offers a promising synergy that can be used to optimize workflows, improve result quality, and enhance our understanding of high-dimensional datasets and their inherent relationship with disease. In recent years, there has been an increasing number of publications that examine the capabilities of ML-based software in the context of chromatography and MS. However, given the historically distant nature between the fields of clinical chemistry and computer science, there is an opportunity to improve technological literacy of ML-based software within the clinical laboratory scientist community. To this end, we present a basic overview of ML and a tutorial of an ML-based experiment using a previously published MS dataset. The purpose of this paper is to describe the fundamental principles of supervised ML, outline the steps that are classically involved in an ML-based experiment, and discuss the purpose of good ML practice in the context of a binary MS classification problem.

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.

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.

Preventing pseudohyponatremia: Intralipid®-based lipemia cutoffs for sodium are inappropriate.

BACKGROUND: Pseudohyponatremia describes an artifactual decrease in plasma sodium result in samples with high proteins and/or lipids when measured by an indirect ion-selective electrode (ISE) method. We suspected that Intralipid®-based lipemia cutoffs are inappropriate for detecting interfering lipids in human samples and a major contributing factor to the existence of pseudohyponatremia. METHODS: We evaluated 2 approaches to derive a lipemia cutoff for sodium, one in which patient plasma samples were pooled and spiked to simulate hyperlipidemia using Intralipid® (commonly used approach by in-vitro diagnostics manufacturers), and another in which endogenous hyperlipidemic samples (n = 31) were measured by methods not affected by hyperlipidemia (i.e., direct ISE and post-ultracentrifugation indirect ISE). Triglycerides, lipemic index (L-index) and indirect ISE sodium concentrations of samples were measured on Roche Cobas® 8000 and direct ISE on Radiometer® ABL835 Flex analyzers. Endogenous hyperlipidemic samples were also ultracentrifuged on Beckman Coulter® Airfuge to clear excess lipids and re-analyzed for sodium by indirect ISE. RESULTS: We discovered that Intralipid® is not an accurate emulation of the lipemic interference seen in pseudohyponatremia because it showed no effect up to the maximum level of lipemia tested (L-index = 2000). By contrast, endogenous hyperlipidemic samples demonstrated significant deviations in sodium concentration (≥4 mmol/l) when L-index approached or exceeded 700, and a strong positive correlation between L-index and the difference between the indirect and direct methods (i.e., extent of pseudohyponatremia). CONCLUSIONS: Clinical laboratories should lower their tolerance for lipemia from the currently recommended L-index cutoff of 2000 on Roche Cobas 8000®. We recommend reflexing to direct ISE when L-index exceeds 700. Manufacturers and laboratories with other indirect ISE methods should evaluate the effect of lipid interference on their method using hyperlipidemic human samples not Intralipid®.

Toward Dynamic Risk Prediction of Outcomes After Coronary Artery Bypass Graft: Improving Risk Prediction With Intraoperative Events Using Gradient Boosting.

BACKGROUND: Intraoperative data may improve models predicting postoperative events. We evaluated the effect of incorporating intraoperative variables to the existing preoperative model on the predictive performance of the model for coronary artery bypass graft. METHODS: We analyzed 378 572 isolated coronary artery bypass graft cases performed across 1083 centers, using the national Society of Thoracic Surgeons Adult Cardiac Surgery Database between 2014 and 2016. Outcomes were operative mortality, 5 postoperative complications, and composite representation of all events. We fitted models by logistic regression or extreme gradient boosting (XGBoost). For each modeling approach, we used preoperative only, intraoperative only, or pre+intraoperative variables. We developed 84 models with unique combinations of the 3 variable sets, 2 variable selection methods, 2 modeling approaches, and 7 outcomes. Each model was tested in 20 iterations of 70:30 stratified random splitting into development/testing samples. Model performances were evaluated on the testing dataset using the C statistic, area under the precision-recall curve, and calibration metrics, including the Brier score. RESULTS: The mean patient age was 65.3 years, and 24.7% were women. Operative mortality, excluding intraoperative death, occurred in 1.9%. In all outcomes, models that considered pre+intraoperative variables demonstrated significantly improved Brier score and area under the precision-recall curve compared with models considering pre or intraoperative variables alone. XGBoost without external variable selection had the best C statistics, Brier score, and area under the precision-recall curve values in 4 of the 7 outcomes (mortality, renal failure, prolonged ventilation, and composite) compared with logistic regression models with or without variable selection. Based on the calibration plots, risk restratification for mortality showed that the logistic regression model underestimated the risk in 11 114 patients (9.8%) and overestimated in 12 005 patients (10.6%). In contrast, the XGBoost model underestimated the risk in 7218 patients (6.4%) and overestimated in 0 patients (0%). CONCLUSIONS: In isolated coronary artery bypass graft, adding intraoperative variables to preoperative variables resulted in improved predictions of all 7 outcomes. Risk models based on XGBoost may provide a better prediction of adverse events to guide clinical care.

Longitudinal Assessment of SARS-CoV-2 Antinucleocapsid and Antispike-1-RBD Antibody Testing Following PCR-Detected SARS-CoV-2 Infection.

BACKGROUND: SARS-CoV-2 serologic assays are becoming increasingly available and may serve as a diagnostic aid in a multitude of settings relating to past infection status. However, there is limited literature detailing the longitudinal performance of EUA-cleared serologic assays in US populations, particularly in cohorts with a remote history of PCR-confirmed SARS-CoV-2 infection (e.g., >2 months). METHODS: We evaluated the diagnostic sensitivities and specificities of the Elecsys® Anti-SARS-CoV-2 (anti-N) and Elecsys Anti-SARS-CoV-2 S (anti-S1-RBD) assays, using 174 residual clinical samples up to 267 days post-PCR diagnosis of SARS-CoV-2 infection (n = 154) and a subset of samples obtained prior to the COVID-19 pandemic as negative controls (n = 20). RESULTS: The calculated diagnostic sensitivities for the anti-N and anti-S1-RBD assays were 89% and 93%, respectively. Of the 154 samples in the SARS-CoV-2-positive cohort, there were 6 discrepant results between the anti-N and anti-S1-RBD assays, 5 of which were specimens collected ≥200 days post-PCR positivity and only had detectable levels of anti-S1-RBD antibodies. When only considering specimens collected ≥100 days post-PCR positivity (n = 41), the sensitivities for the anti-N and anti-S1-RBD assays were 85% and 98%, respectively. CONCLUSIONS: The anti-S1-RBD assay demonstrated superior sensitivity at time points more remote to the PCR detection date, with 6 more specimens from the SARS-CoV-2-positive cohort detected, 5 of which were collected more than 200 days post-PCR positivity. While analytical differences and reagent lot-to-lot variability are possible, this may indicate that, in some instances, anti-S1-RBD antibodies may persist longer in vivo and may be a better target for detecting remote SARS-CoV-2 infection.