Prospective and External Validation of an Ensemble Learning Approach to Sensitively Detect Intravenous Fluid Contamination in Basic Metabolic Panels.

Background: Intravenous (IV) fluid contamination within clinical specimens causes an operational burden on the laboratory when detected, and potential patient harm when undetected. Even mild contamination is often sufficient to meaningfully alter results across multiple analytes. A recently reported unsupervised learning approach was more sensitive than routine workflows, but still lacked sensitivity to mild but significant contamination.

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.

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.

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.

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 (SARS‑CoV‑2) 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.

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.

Artificial Intelligence Applications in Clinical Chemistry.

Artificial intelligence (AI) applications are an area of active investigation in clinical chemistry. Numerous publications have demonstrated the promise of AI across all phases of testing including preanalytic, analytic, and postanalytic phases; this includes novel methods for detecting common specimen collection errors, predicting laboratory results and diagnoses, and enhancing autoverification workflows. Although AI applications pose several ethical and operational challenges, these technologies are expected to transform the practice of the clinical chemistry laboratory in the near future.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Clinical characteristics and outcomes for 7,995 patients with SARS-CoV-2 infection.

Objective: Severe acute respiratory syndrome virus (SARS-CoV-2) has infected millions of people worldwide. Our goal was to identify risk factors associated with admission and disease severity in patients with SARS-CoV-2.

Design: This was an observational, retrospective study based on real-world data for 7,995 patients with SARS-CoV-2 from a clinical data repository.