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.