Clinical implications of inaccurate potassium determination in hemolyzed pediatric blood specimens.

BACKGROUND: Analysis of whole blood specimens is rapid and saves blood, but hemolysis may go undetected and compromise the accuracy of potassium measurement. We aimed to define the frequency and magnitude of error in whole blood potassium measurement. METHODS: 34 months of whole blood and plasma potassium data were extracted from patients aged less than 2 years at the time of sample acquisition. Hemolysis was detected using the plasma "H index." The magnitude of potassium bias was estimated from the difference between paired whole blood and plasma measurement separated by less than 2 h. RESULTS: 56,000 of the 105,000 data points were from plasma and 20 % of these had significant hemolysis. Rates of hemolysis (nearing 50 %) were greatest in the neonatal nursery. Of 662 proximal whole blood and plasma paired results, 8 % had elevated whole blood potassium with a normal plasma value and 4 % had a normal whole blood potassium with reduced plasma potassium. The bias between whole blood and plasma potassium ranged from -1.0 to 4.0 mmol/L. CONCLUSIONS: The use of whole blood analysis brings with it significant risk for error in potassium measurement. Better tools to detect hemolysis in these types of specimens are indicated.

Acute pulmonary injury in hematology patients supported with pathogen-reduced and conventional platelet components.

ABSTRACT: Patients treated with antineoplastic therapy often develop thrombocytopenia requiring platelet transfusion, which has potential to exacerbate pulmonary injury. This study tested the hypothesis that amotosalen-UVA pathogen-reduced platelet components (PRPCs) do not potentiate pulmonary dysfunction compared with conventional platelet components (CPCs). A prospective, multicenter, open-label, sequential cohort study evaluated the incidence of treatment-emergent assisted mechanical ventilation initiated for pulmonary dysfunction (TEAMV-PD). The first cohort received CPC. After the CPC cohort, each site enrolled a second cohort transfused with PRPC. Other outcomes included clinically significant pulmonary adverse events (CSPAE) and the incidence of treatment-emergent acute respiratory distress syndrome (TEARDS) diagnosed by blinded expert adjudication. The incidence of TEAMV-PD in all patients (1068 PRPC and 1223 CPC) was less for PRPC (1.7 %) than CPC (3.1%) with a treatment difference of -1.5% (95% confidence interval [CI], -2.7 to -0.2). In patients requiring ≥2 PCs, the incidence of TEAMV-PD was reduced for PRPC recipients compared with CPC recipients (treatment difference, -2.4%; 95% CI, -4.2 to -0.6). CSPAE increased with increasing PC exposure but were not significantly different between the cohorts. For patients receiving ≥2 platelet transfusions, TEARDS occurred in 1.3% PRPC and 2.6% CPC recipients (P = .086). Bayesian analysis demonstrated PRPC may be superior in reducing TEAMV-PD and TEARDS for platelet transfusion recipients compared with CPC recipients, with 99.2% and 88.8% probability, respectively. In this study, PRPC compared with CPC demonstrated high probability of reduced severe pulmonary injury requiring assisted mechanical ventilation in patients with hematology disorders dependent on platelet transfusion. This trial was registered at www.ClinicalTrials.gov as #NCT02549222.

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. An automated approach that is accurate, reproducible, and practical is needed. METHODS: A total of 25 747 291 basic metabolic panel (BMP) results from 312 721 patients were obtained from the laboratory information system (LIS). A Uniform Manifold Approximation and Projection (UMAP) model was trained and tested using a combination of real patient data and simulated IV fluid contamination. To provide an objective metric for classification, an "enrichment score" was derived and its performance assessed. Our current workflow was compared to UMAP predictions using expert chart review. RESULTS: UMAP embeddings from real patient results demonstrated outliers suspicious for IV fluid contamination when compared with the simulated contamination's embeddings. At a flag rate of 3 per 1000 results, the positive predictive value (PPV) was adjudicated to be 0.78 from 100 consecutive positive predictions. Of these, 58 were previously undetected by our current clinical workflows, with 49 BMPs displaying a total of 56 critical results. CONCLUSIONS: Accurate and automatable detection of IV fluid contamination in BMP results is achievable without curating expertly labeled training data.

Organizational preparedness for the use of large language models in pathology informatics.

In this paper, we consider the current and potential role of the latest generation of Large Language Models (LLMs) in medical informatics, particularly within the realms of clinical and anatomic pathology. We aim to provide a thorough understanding of the considerations that arise when employing LLMs in healthcare settings, such as determining appropriate use cases and evaluating the advantages and limitations of these models. Furthermore, this paper will consider the infrastructural and organizational requirements necessary for the successful implementation and utilization of LLMs in healthcare environments. We will discuss the importance of addressing education, security, bias, and privacy concerns associated with LLMs in clinical informatics, as well as the need for a robust framework to overcome regulatory, compliance, and legal challenges.

Proceedings of the Association for Pathology Informatics Bootcamp 2022.

The Pathology Informatics Bootcamp, held annually at the Pathology Informatics Summit, provides pathology trainees with essential knowledge in the rapidly evolving field of Pathology Informatics. With a focus on data analytics, data science, and data management in 2022, the bootcamp addressed the growing importance of data analysis in pathology and laboratory medicine practice. The expansion of data-related subjects in Pathology Informatics Essentials for Residents (PIER) and the Clinical Informatics fellowship examinations highlights the increasing significance of these skills in pathology practice in particular and medicine in general. The curriculum included lectures on databases, programming, analytics, machine learning basics, and specialized topics like anatomic pathology data analysis and dashboarding.

The next phase in patient safety education: Towards a standardized, tools-based pathology patient safety curriculum: A call to action from the Association of Pathology Chairs' Residency Program Directors Section Training Residents in Patient Safety Workgroup.

Patient safety education is a mandated Common Program Requirement of the Accreditation Council for Graduate Medical Education and for the Royal College of Physicians and Surgeons of Canada in all medical residency and fellowship programs. Although many hospitals and healthcare environments have general patient safety education tools for trainees, few to none focus on the unique training milieu of pathologists, including a mix of highly automated and manual error-prone processes, frequent multiplicity of events, and lack of direct patient relationships for error disclosure. We established a national Association of Pathology Chairs-Program Directors Section Workgroup focused on patient safety education for pathology trainees entitled Training Residents in Patient Safety (TRIPS). TRIPS included diverse representatives from across the United States, as well as representatives from pathology organizations including the American Board of Pathology, the American Society for Clinical Pathology, the United States and Canadian Academy of Pathology, the College of American Pathologists, and the Society to Improve Diagnosis in Medicine. Objectives of the workgroup included developing a standardized patient safety curriculum, designing teaching and assessment tools, and refining them with pilot sites. Here we report the establishment of TRIPS as well as data from national needs assessment of Program Directors across the country, who confirmed the need for a standardized patient safety curriculum.

A Single Reference Interval for Interpreting Serum Free Light Chains across Patients with Varying Renal Function.

BACKGROUND: Serum free light chain (sFLC) assays are interpreted using a sFLC-ratio-based reference interval (manufacturer's interval) that was defined using a cohort of healthy patients. However, renal impairment elevates the sFLC-ratio, leading to a high false positive rate when using the manufacturer's interval. Prior studies have developed renal-specific reference intervals; however, this approach has not been widely adopted due to practical limitations. Thus, there remains a critical need for a renally robust sFLC interpretation method. METHODS: Retrospective data mining was used to define patient cohorts that reflect the spectrum of renal function seen in clinical practice. Two new reference intervals, one based on the sFLC-ratio and one based on a novel principal component analysis (PCA)-based metric, were developed for the FREELITE assay (Binding Site) on the Roche Cobas c501 instrument (Roche). RESULTS: Compared to the manufacturer's reference interval, both new methods exhibited significantly lower false positive rates and greater robustness to renal function while maintaining equivalent sensitivity for monoclonal gammopathy (MG) diagnosis. While not significantly different, the point estimate for sensitivity was highest for the PCA-based approach. CONCLUSION: Renally robust sFLC interpretation using a single reference interval is possible given a reference cohort that reflects the variation in renal function observed in practice. Further studies are needed to achieve sufficient power and determine if the novel PCA-based metric offers superior sensitivity for MG diagnosis. These new methods offer the practical advantages of not requiring an estimated glomerular filtration rate result or multiple reference intervals, thereby lowering practical barriers to implementation.

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. CONTENT: This review will prepare the reader to appraise anomaly detection literature, identify common sources of anomalous results in the clinical laboratory, and offer potential solutions for common shortcomings in current laboratory practices. SUMMARY: Laboratories should implement data-driven approaches to detect technical anomalies and keep them from entering the medical record, while also using the full array of clinical metadata available in the laboratory information system for context-dependent, patient-centered result interpretations.

The effects of preconception and early gestation SARS-CoV-2 infection on pregnancy outcomes and placental pathology.

OBJECTIVE: To evaluate if peri-pregnancy timing of a PCR+ test for SARS-CoV-2 RNA affects pregnancy outcomes and placental pathology. METHODS: This is a retrospective cohort study conducted in a tertiary center. Pregnancy outcomes and placental pathology were compiled for women who tested positive for SARS-CoV-2 RNA from a nasopharyngeal swab assessed by RT-PCR. The population comprised four groups that were PCR+ preconception (T0) or in the 1st (T1), 2nd (T2), or 3rd (T3) trimester of pregnancy. A fifth, control group (TC) tested PCR- for SARS-CoV-2 before delivery. RESULTS: Seventy-one pregnancies were studied. The T0 group exhibited lower gestational ages at delivery, had infants with the lowest birth weights, the highest rate of pregnancy loss before 20 weeks. Features of maternal vascular malperfusion and accelerated villous maturation were prominent findings in the histopathology of placentas from women PCR+ for SARS-CoV-2 RNA, especially in the T0 and the T1 groups. CONCLUSION: Women at highest risk for pregnancy complications are those who test PCR+ for viral RNA preconception or during first trimester of pregnancy.

Comparative risk of pulmonary adverse events with transfusion of pathogen reduced and conventional platelet components.

BACKGROUND: Platelet transfusion carries risk of transfusion-transmitted infection (TTI). Pathogen reduction of platelet components (PRPC) is designed to reduce TTI. Pulmonary adverse events (AEs), including transfusion-related acute lung injury and acute respiratory distress syndrome (ARDS) occur with platelet transfusion. STUDY DESIGN: An open label, sequential cohort study of transfusion-dependent hematology-oncology patients was conducted to compare pulmonary safety of PRPC with conventional PC (CPC). The primary outcome was the incidence of treatment-emergent assisted mechanical ventilation (TEAMV) by non-inferiority. Secondary outcomes included: time to TEAMV, ARDS, pulmonary AEs, peri-transfusion AE, hemorrhagic AE, transfusion reactions (TRs), PC and red blood cell (RBC) use, and mortality. RESULTS: By modified intent-to-treat (mITT), 1068 patients received 5277 PRPC and 1223 patients received 5487 CPC. The cohorts had similar demographics, primary disease, and primary therapy. PRPC were non-inferior to CPC for TEAMV (treatment difference -1.7%, 95% CI: (-3.3% to -0.1%); odds ratio = 0.53, 95% CI: (0.30, 0.94). The cumulative incidence of TEAMV for PRPC (2.9%) was significantly less than CPC (4.6%, p = .039). The incidence of ARDS was less, but not significantly different, for PRPC (1.0% vs. 1.8%, p = .151; odds ratio = 0.57, 95% CI: (0.27, 1.18). AE, pulmonary AE, and mortality were not different between cohorts. TRs were similar for PRPC and CPC (8.3% vs. 9.7%, p = .256); and allergic TR were significantly less with PRPC (p = .006). PC and RBC use were not increased with PRPC. DISCUSSION: PRPC demonstrated reduced TEAMV with no excess treatment-related pulmonary morbidity.

Use of laboratory data for illicit drug use surveillance and identification of socioeconomic risk factors.

BACKGROUND: Drug overdose is the leading cause of death among people 25-44 years of age in the United States. Existing drug surveillance methods are important for prevention and directing treatment, but are limited by delayed reporting and lack of geographic granularity. METHODS: Laboratory urine drug screen and complete metabolic panel data from patients presenting to the emergency department was used to observe long-term and short-term temporal and geospatial changes at the zip code-level in St. Louis. Multivariate linear regression was performed to investigate associations between zip code-level socioeconomic factors and drug screening positivity rates. RESULTS: An increase in the fentanyl positive drug screens was seen during the initial COVID-19 shutdown period in the spring of 2020. A decrease in cocaine positivity was seen in the fall and winter of 2020, with a return to baseline coinciding with the second major COVID-19 shutdown in the summer of 2021. These changes appeared to be independent of changes in emergency department utilization as measured by complete metabolic panels ordered. Significant short-term changes in fentanyl and cocaine positivity rates between specific time periods were able to be localized to individual zip codes. Zip code-level multivariate analysis demonstrated independent associations between socioeconomic/demographic factors and fentanyl/cocaine positivity rates as determined by laboratory drug screening data. CONCLUSIONS: Analyzing clinical laboratory drug screening data can enable a more temporally and geographically granular view of population-level drug use surveillance. Additionally, laboratory data can be utilized to find population-level socioeconomic associations with illicit drug use, presenting a potential avenue for the use of this data to guide public health and healthcare policy decisions.

Clinical Decision Support for Laboratory Testing.

BACKGROUND: As technology enables new and increasingly complex laboratory tests, test utilization presents a growing challenge for healthcare systems. Clinical decision support (CDS) refers to digital tools that present providers with clinically relevant information and recommendations, which have been shown to improve test utilization. Nevertheless, individual CDS applications often fail, and implementation remains challenging. CONTENT: We review common classes of CDS tools grounded in examples from the literature as well as our own institutional experience. In addition, we present a practical framework and specific recommendations for effective CDS implementation. SUMMARY: CDS encompasses a rich set of tools that have the potential to drive significant improvements in laboratory testing, especially with respect to test utilization. Deploying CDS effectively requires thoughtful design and careful maintenance, and structured processes focused on quality improvement and change management play an important role in achieving these goals.

Transfusion Medicine Informatics: A Review of Current Practice and a Glimpse into the Future.

Clinical informatics has been described as the "relentless pursuit of assisting people" by using data and information technology to improve health care. A core principle is that a person supported by information technology is superior to either a person or machine alone. Striving toward this harmonization, the transfusion medicine field has had a significant number of accomplishments. Clinical informatics interventions have helped achieve better quality, efficiency, and safety in nearly all aspects of transfusion medicine. This review summarizes these accomplishments and provides a preview of novel ideas that could transform transfusion medicine into a proactive, data-driven, and patient-centered discipline.

Using information theory to optimize a diagnostic threshold to match physician-ordering practice.

OBJECTIVE: Clinicians order laboratory tests in an effort to reduce diagnostic or therapeutic uncertainty. Information theory provides the opportunity to quantify the degree to which a test result is expected to reduce diagnostic uncertainty. We sought to apply information theory toward the evaluation and optimization of a diagnostic test threshold and to determine if the results would differ from those of conventional methodologies. We used a heparin/PF4 immunoassay (PF4 ELISA) as a case study. MATERIALS AND METHODS: The laboratory database was queried for PF4 ELISA and serotonin release assay (SRA) results during the study period, with the latter serving as the gold standard for the disease heparin-induced thrombocytopenia (HIT). The optimized diagnostic threshold of the PF4 ELISA test was compared using conventional versus information theoretic approaches under idealized (pretest probability = 50%) and realistic (pretest probability = 2.4%) testing conditions. RESULTS: Under ideal testing conditions, both analyses yielded a similar optimized optical density (OD) threshold of OD > 0.79. Under realistic testing conditions, information theory suggested a higher threshold, OD > 1.5 versus OD > 0.6. Increasing the diagnostic threshold improved the global information value, the value of a positive test and the noise content with only a minute change in the negative test value. DISCUSSION: Our information theoretic approach suggested that the current FDA approved cutoff (OD > 0.4) is overly permissive leading to loss of test value and injection of noise into an already complex diagnostic dilemma. Because our approach is purely statistical and takes as input data that are readily accessible in the clinical laboratory it offers a scalable and data-driven strategy for optimizing test value that may be widely applicable in the domain of laboratory medicine. CONCLUSION: Information theory provides more meaningful measures of test value than the widely used accuracy-based metrics.