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.

Estimation of gestational age-specific reference intervals for coagulation assays in a neonatal intensive care unit using real-world data.

Background: Interpretation of coagulation testing in neonates currently relies on reference intervals (RIs) defined from older patient cohorts. Direct RI studies are difficult, but indirect estimation may allow us to infer normative neonatal distributions from routinely collected clinical data.

Objective: Assess the utility of indirect reference interval methods in estimating coagulation reference intervals in critically ill neonates.

Validating, Implementing, and Monitoring Machine Learning Solutions in the Clinical Laboratory Safely and Effectively.

Background: Machine learning solutions offer tremendous promise for improving clinical and laboratory operations in pathology. Proof-of-concept descriptions of these approaches have become commonplace in laboratory medicine literature, but only a scant few of these have been implemented within clinical laboratories, owing to the often substantial barriers in validating, implementing, and monitoring these applications in practice. This mini-review aims to highlight the key considerations in each of these steps.

Cardiac Troponin to Adjudicate Subclinical Heart Failure in Diabetic Patients and a Murine Model of Metabolic Syndrome.

Background: Cardiovascular disease, kidney health, and metabolic disease (CKM) syndrome is associated with significant morbidity and mortality, particularly from congestive heart failure (CHF). Guidelines recommend measurement of cardiac troponin (cTn) to identify subclinical heart failure (HF) in diabetics/CKM. However, appropriate thresholds and the impact from routine screening have not been elucidated.

Worse fibro-inflammatory activity on diagnostic liver biopsy adversely impacts biochemical remission in autoimmune hepatitis.

Background: Autoimmune hepatitis (AIH) patients can present with advanced fibrosis at diagnosis or may progress to the same if biochemical remission on treatment is not achieved.

Methods: We conducted a single-center retrospective analysis of 34 pediatrics and 39 adult AIH patients. Three pathologists, blinded to clinical information, reviewed the diagnostic liver biopsy (DLB) slides of AIH patients.

Process improvement in thyroid fine needle aspiration: Standardizing number of smears for enhanced adequacy and diagnosis.

Fine-needle aspiration (FNA) is a safe, cost-effective diagnostic procedure used in the evaluation of thyroid nodules. The number of thyroid FNAs has dramatically increased over the past few years. In the absence of standardized procedures regarding the number of needle passes needed for diagnosis and the lack of clarity on the use of conventional smears (CS) versus liquid-based preparations (LBP), the demand of thyroid FNAs has led to increased workload on cytology laboratories, which can negatively affect patient safety.

Automating the Detection of IV Fluid Contamination Using Unsupervised Machine Learning.

Background: Intravenous (IV) fluid contamination is a common cause of preanalytical error that can delay or misguide treatment decisions, leading to patient harm. Current approaches for detecting contamination rely on delta checks, which require a prior result, or manual technologist intervention, which is inefficient and vulnerable to human error. Supervised machine learning may provide a means to detect contamination, but its implementation is hindered by its reliance on expert-labeled training data.

Generalizability of a Machine Learning Model for Improving Utilization of Parathyroid Hormone-Related Peptide Testing across Multiple Clinical Centers.

Background: Measuring parathyroid hormone-related peptide (PTHrP) helps diagnose the humoral hypercalcemia of malignancy, but is often ordered for patients with low pretest probability, resulting in poor test utilization. Manual review of results to identify inappropriate PTHrP orders is a cumbersome process.

Methods: Using a dataset of 1330 patients from a single institute, we developed a machine learning (ML) model to predict abnormal PTHrP results.

GPT-4 Underperforms Experts in Detecting IV Fluid Contamination.

Background: Specimens contaminated with intravenous (IV) fluids are common in clinical laboratories. Current methods for detecting contamination rely on insensitive and workflow-disrupting delta checks or manual technologist review. Herein, we assessed the utility of large language models for detecting contamination by IV crystalloids and compared its performance to multiple, but variably trained healthcare personnel (HCP).

Data-Driven Anomaly Detection in Laboratory Medicine: Past, Present, and Future.

Background: Anomaly detection is an integral component of operating a clinical laboratory. It covers both the recognition of laboratory errors and the rapid reporting of clinically impactful results. Procedures for identifying laboratory errors and highlighting critical results can be improved by applying modern data-driven approaches.

Distinct clonal identities of B-ALLs arising after lenolidomide therapy for multiple myeloma.

Patients with multiple myeloma (MM) who are treated with lenalidomide rarely develop a secondary B-cell acute lymphoblastic leukemia (B-ALL). The clonal and biological relationship between these sequential malignancies is not yet clear. We identified 17 patients with MM treated with lenalidomide, who subsequently developed B-ALL.

A community approach to the cancer-variant-interpretation bottleneck.

As guidelines, therapies, and literature on cancer variants expand, the lack of consensus variant interpretations impedes clinical applications. CIViC is a public domain, crowd-sourced, and adaptable knowledgebase of evidence for the Clinical Interpretation of Variants in Cancer, designed to reduce barriers to knowledge sharing and alleviate the variant interpretation bottleneck.

Neoadjuvant and Adjuvant Pembrolizumab in Resectable Locally Advanced, Human Papillomavirus-Unrelated Head and Neck Cancer: A Multicenter, Phase II Trial.

Purpose: Pembrolizumab improved survival in patients with recurrent or metastatic head and neck squamous-cell carcinoma (HNSCC). The aims of this study were to determine if pembrolizumab would be safe, result in pathologic tumor response (pTR), and lower the relapse rate in patients with resectable human papillomavirus (HPV)-unrelated HNSCC.

Patients And Methods: Neoadjuvant pembrolizumab (200 mg) was administered and followed 2 to 3 weeks later by surgical tumor ablation.

Open-Sourced CIViC Annotation Pipeline to Identify and Annotate Clinically Relevant Variants Using Single-Molecule Molecular Inversion Probes.

Purpose: Clinical targeted sequencing panels are important for identifying actionable variants for patients with cancer; however, existing approaches do not provide transparent and rationally designed clinical panels to accommodate the rapidly growing knowledge within oncology.

Materials And Methods: We used the Clinical Interpretations of Variants in Cancer (CIViC) database to develop an Open-Sourced CIViC Annotation Pipeline (OpenCAP). OpenCAP provides methods to identify variants within the CIViC database, build probes for variant capture, use probes on prospective samples, and link somatic variants to CIViC clinical relevance statements.

Author Correction: The prognostic effects of somatic mutations in ER-positive breast cancer.

The original version of this Article contained errors in the depiction of confidence intervals in the NF1 BCSS data illustrated in Figure 3b. These have now been corrected in both the PDF and HTML versions of the Article. The incorrect version of Figure 3b is presented in the associated Author Correction.

Standard operating procedure for somatic variant refinement of sequencing data with paired tumor and normal samples.

Purpose: Following automated variant calling, manual review of aligned read sequences is required to identify a high-quality list of somatic variants. Despite widespread use in analyzing sequence data, methods to standardize manual review have not been described, resulting in high inter- and intralab variability.

Methods: This manual review standard operating procedure (SOP) consists of methods to annotate variants with four different calls and 19 tags.

The prognostic effects of somatic mutations in ER-positive breast cancer.

Here we report targeted sequencing of 83 genes using DNA from primary breast cancer samples from 625 postmenopausal (UBC-TAM series) and 328 premenopausal (MA12 trial) hormone receptor-positive (HR+) patients to determine interactions between somatic mutation and prognosis. Independent validation of prognostic interactions was achieved using data from the METABRIC study. Previously established associations between MAP3K1 and PIK3CA mutations with luminal A status/favorable prognosis and TP53 mutations with Luminal B/non-luminal tumors/poor prognosis were observed, validating the methodological approach.