Collateral damage from antigen receptor gene diversification.

Chromosomal translocations that juxtapose antigen receptor genes and oncogenes are frequently associated with lymphoid malignancies. In this issue, Robbiani et al. (2008) show that activation-induced deaminase (AID), an enzyme involved in antigen receptor gene diversification, generates DNA double-strand breaks (DSBs) in oncogenes, and Tsai et al. (2008) propose that AID and the recombinase-activating gene (RAG) endonuclease may collaborate to generate off-target DSBs.

Use of Routine Complete Blood Count Results to Rule Out Anaplasmosis Without the Need for Specific Diagnostic Testing.

BACKGROUND: Anaplasmosis presents with fever, headache, and laboratory abnormalities including leukopenia and thrombocytopenia. Polymerase chain reaction (PCR) is the preferred diagnostic but is overutilized. We determined if routine laboratory tests could exclude anaplasmosis, improving PCR utilization. METHODS: Anaplasma PCR results from a 3-year period, with associated complete blood count (CBC) and liver function test results, were retrospectively reviewed. PCR rejection criteria, based on white blood cell (WBC) and platelet (PLT) counts, were developed and prospectively applied in a mock stewardship program. If rejection criteria were met, a committee mock-refused PCR unless the patient was clinically unstable or immunocompromised. RESULTS: WBC and PLT counts were the most actionable routine tests for excluding anaplasmosis. Retrospective review demonstrated that rejection criteria of WBC ≥11 000 cells/µL or PLT ≥300 000 cells/µL would have led to PCR refusal in 428 of 1685 true-negative cases (25%) and 3 of 66 true-positive cases (5%) involving clinically unstable or immunocompromised patients. In the prospective phase, 155 of 663 PCR requests (23%) met rejection criteria and were reviewed by committee, which endorsed refusal in 110 of 155 cases (71%) and approval in 45 (29%), based on clinical criteria. PCR was negative in all 45 committee-approved cases. Only 1 of 110 mock-refused requests yielded a positive PCR result; this patient was already receiving doxycycline at the time of testing. CONCLUSIONS: A CBC-based stewardship algorithm would reduce unnecessary Anaplasma PCR testing, without missing active cases. Although the prospectively evaluated screening approach involved medical record review, this was unnecessary to prevent errors and could be replaced by a rejection comment specifying clinical situations that might warrant overriding the algorithm.

Utilization Management of High-Cost Imaging in an Outpatient Setting in a Large Stable Patient and Provider Cohort over 7 Years.

Purpose To quantify the effect of a comprehensive, long-term, provider-led utilization management (UM) program on high-cost imaging (computed tomography, magnetic resonance imaging, nuclear imaging, and positron emission tomography) performed on an outpatient basis. Materials and Methods This retrospective, 7-year cohort study included all patients regularly seen by primary care physicians (PCPs) at an urban academic medical center. The main outcome was the number of outpatient high-cost imaging examinations per patient per year ordered by the patient's PCP or by any specialist. The authors determined the probability of a patient undergoing any high-cost imaging procedure during a study year and the number of examinations per patient per year (intensity) in patients who underwent high-cost imaging. Risk-adjusted hierarchical models were used to directly quantify the physician component of variation in probability and intensity of high-cost imaging use, and clinicians were provided with regular comparative feedback on the basis of the results. Observed trends in high-cost imaging use and provider variation were compared with the same measures for outpatient laboratory studies because laboratory use was not subject to UM during this period. Finally, per-member per-year high-cost imaging use data were compared with statewide high-cost imaging use data from a major private payer on the basis of the same claim set. Results The patient cohort steadily increased in size from 88 959 in 2007 to 109 823 in 2013. Overall high-cost imaging utilization went from 0.43 examinations per year in 2007 to 0.34 examinations per year in 2013, a decrease of 21.33% (P < .0001). At the same time, similarly adjusted routine laboratory study utilization decreased by less than half that rate (9.4%, P < .0001). On the basis of unadjusted data, outpatient high-cost imaging utilization in this cohort decreased 28%, compared with a 20% decrease in statewide utilization (P = .0023). Conclusion Analysis of high-cost imaging utilization in a stable cohort of patients cared for by PCPs during a 7-year period showed that comprehensive UM can produce a significant and sustained reduction in risk-adjusted per-patient year outpatient high-cost imaging volume. © RSNA, 2017.

Applying Lean methodologies reduces ED laboratory turnaround times.

BACKGROUND: Increasing the value of health care delivery is a national priority, and providers face growing pressure to reduce cost while improving quality. Ample opportunity exists to increase efficiency and quality simultaneously through the application of systems engineering science. OBJECTIVE: We examined the hypothesis that Lean-based reorganization of laboratory process flow would improve laboratory turnaround times (TAT) and reduce waste in the system. METHODS: This study was a prospective, before-after analysis of laboratory process improvement in a teaching hospital emergency department (ED). The intervention included a reorganization of laboratory sample flow based in systems engineering science and Lean methodologies, with no additional resources. The primary outcome was the median TAT from sample collection to result for 6 tests previously performed in an ED kiosk. RESULTS: After the intervention, median laboratory TAT decreased across most tests. The greatest decreases were found in "reflex tests" performed after an initial screening test: troponin T TAT was reduced by 33 minutes (86 to 53 minutes; 99% confidence interval, 30-35 minutes) and urine sedimentation TAT by 88 minutes (117 to 29 minutes; 99% confidence interval, 87-90 minutes). In addition, troponin I TAT was reduced by 12 minutes, urinalysis by 9 minutes, and urine human chorionic gonadotropin by 10 minutes. Microbiology rapid testing TAT, a "control," did not change. CONCLUSIONS: In this study, Lean-based reorganization of laboratory process flow significantly increased process efficiency. Broader application of systems engineering science might further improve health care quality and capacity while reducing waste and cost.

Using machine learning to develop smart reflex testing protocols.

OBJECTIVE: Reflex testing protocols allow clinical laboratories to perform second line diagnostic tests on existing specimens based on the results of initially ordered tests. Reflex testing can support optimal clinical laboratory test ordering and diagnosis. In current clinical practice, reflex testing typically relies on simple "if-then" rules; however, this limits the opportunities for reflex testing since most test ordering decisions involve more complexity than traditional rule-based approaches would allow. Here, using the analyte ferritin as an example, we propose an alternative machine learning-based approach to "smart" reflex testing. METHODS: Using deidentified patient data, we developed a machine learning model to predict whether a patient getting CBC testing will also have ferritin testing ordered. We evaluate applications of this model to reflex testing by assessing its performance in comparison to possible rule-based approaches. RESULTS: Our underlying machine learning models performed moderately well in predicting ferritin test ordering (AUC=0.731 in reference to actual ordering) and demonstrated promising potential to underlie key clinical applications. In contrast, none of the many traditionally framed, rule-based, hypothetical reflex protocols we evaluated offered sufficient agreement with actual ordering to be clinically feasible. Using chart review, we further demonstrated that the strategic deployment of our model could avoid important ferritin test ordering errors. CONCLUSIONS: Machine learning may provide a foundation for new types of reflex testing with enhanced benefits for clinical diagnosis.

High Rates of Missed HIV Testing Among Oral PrEP Users in the United States From 2018-2021: A National Assessment on Compliance With HIV Testing Recommendations of the CDC PrEP Guidelines.

Background: The US Centers for Disease Control and Prevention recommends HIV testing every 3 months in oral PrEP users. We performed a national assessment of HIV testing compliance among oral PrEP users. Methods: We analyzed 408 910 PrEP prescriptions issued to 39 809 PrEP users using a national insurance claims database that contained commercial and Medicaid claims. We identified PrEP use based on pharmacy claims and outpatient diagnostic coding. We evaluated the percentage of PrEP prescription refills without HIV testing (identified by CPT codes) within the prior 3, 6, and 12 months using time to event methods. We performed subgroup and multivariate analyses by age, gender, race, insurance type, and geography. Results: Of 39 809 persons, 36 197 were commercially insured, 3612 were Medicaid-insured, and 96% identified as male; the median age (interquartile range) was 34 (29-44) years, and the Medicaid-insured PrEP users were 24% Black/African American, 44% White, and 9% Hispanic/Latinx. Within the prior 3, 6, and 12 months, respectively, the percentage of PrEP prescription fills in individuals without HIV Ag/Ab testing was 34.3% (95% CI, 34.2%-34.5%), 23.8% (95% CI, 23.7%-23.9%), and 16.6% (95% CI, 16.4%-16.7%), and the percentage without any type of HIV test was 25.8% (95% CI, 25.6%-25.9%), 14.6% (95% CI, 14.5%-14.7%), and 7.8% (95% CI, 7.7%-7.9%). Conclusions: Approximately 1 in 3 oral PrEP prescriptions were filled in persons who had not received an HIV Ag/Ab test within the prior 3 months, with evidence of health disparities. These findings inform clinical PrEP monitoring efforts and compliance with national HIV testing guidance to monitor PrEP users.

Artificial Intelligence in the Clinical Laboratory: An Overview with Frequently Asked Questions.

This article provides an overview of machine learning fundamentals and some applications of machine learning to clinical laboratory diagnostics and patient management. A key goal of this article is to provide a basic foundation in clinical machine learning for readers with clinical laboratory experience that will set them up for more in-depth study of the topic and/or to become a better collaborator with computational colleagues in the development and deployment of machine learning-based solutions.

Using Machine Learning to Develop Smart Reflex Testing Protocols.

Objective: Reflex testing protocols allow clinical laboratories to perform second line diagnostic tests on existing specimens based on the results of initially ordered tests. Reflex testing can support optimal clinical laboratory test ordering and diagnosis. In current clinical practice, reflex testing typically relies on simple "if-then" rules; however, this limits their scope since most test ordering decisions involve more complexity than a simple rule will allow. Here, using the analyte ferritin as an example, we propose an alternative machine learning-based approach to "smart" reflex testing with a wider scope and greater impact than traditional rule-based approaches. Methods: Using patient data, we developed a machine learning model to predict whether a patient getting CBC testing will also have ferritin testing ordered, consider applications of this model to "smart" reflex testing, and evaluate the model by comparing its performance to possible rule-based approaches. Results: Our underlying machine learning models performed moderately well in predicting ferritin test ordering and demonstrated greater suitability to reflex testing than rule-based approaches. Using chart review, we demonstrate that our model may improve ferritin test ordering. Finally, as a secondary goal, we demonstrate that ferritin test results are missing not at random (MNAR), a finding with implications for unbiased imputation of missing test results. Conclusions: Machine learning may provide a foundation for new types of reflex testing with enhanced benefits for clinical diagnosis and laboratory utilization management.

Aberrantly resolved RAG-mediated DNA breaks in Atm-deficient lymphocytes target chromosomal breakpoints in cis.

Canonical chromosomal translocations juxtaposing antigen receptor genes and oncogenes are a hallmark of many lymphoid malignancies. These translocations frequently form through the joining of DNA ends from double-strand breaks (DSBs) generated by the recombinase activating gene (RAG)-1 and -2 proteins at lymphocyte antigen receptor loci and breakpoint targets near oncogenes. Our understanding of chromosomal breakpoint target selection comes primarily from the analyses of these lesions, which are selected based on their transforming properties. RAG DSBs are rarely resolved aberrantly in wild-type developing lymphocytes. However, in ataxia telangiectasia mutated (ATM)-deficient lymphocytes, RAG breaks are frequently joined aberrantly, forming chromosomal lesions such as translocations that predispose (ATM)-deficient mice and humans to the development of lymphoid malignancies. Here, an approach that minimizes selection biases is used to isolate a large cohort of breakpoint targets of aberrantly resolved RAG DSBs in Atm-deficient lymphocytes. Analyses of this cohort revealed that frequently, the breakpoint targets for aberrantly resolved RAG breaks are other DSBs. Moreover, these nonselected lesions exhibit a bias for using breakpoints in cis, forming small chromosomal deletions, rather than breakpoints in trans, forming chromosomal translocations.

A revised timeline for the origin of Plasmodium falciparum as a human pathogen.

While Plasmodium falciparum is known to have had a strong effect on human evolution, the time period when P. falciparum first infected ancestors of modern humans has remained uncertain. Recent advances demonstrated that P. falciparum evolved from ancestors of gorilla parasites via host switching. Here, we estimate the range of dates during which this host switch may have occurred. DNA sequences of portions of the mitochondrial cytochrome b gene obtained from gorilla parasites closely related to human P. falciparum were aligned and compared against similar sequences from human P. falciparum. Time estimates were calculated by applying a previously established parasite cytochrome b gene mutation rate (0.012 mutations per site per million years) and by modeling uncertainty in a Monte-Carlo simulation. We estimate a 95% confidence interval for when P. falciparum first infected ancestors of modern humans to be 112,000 and 1,036,000 years ago (median estimate, 365,000 years ago). This confidence interval suggests that P. falciparum first infected human ancestors much more recently than the previous recognized estimate of 2.5 million years ago. The revised estimate may inform our understanding of certain aspects of human-malaria co-evolution. For example, this revised date suggests a closer relationship between the entry of P. falciparum in humans and the appearance of many red blood cell polymorphisms considered to be genetic adaptations to malaria. In addition, the confidence interval lies within the timeframe dating the dawn of Homo sapiens, suggesting that P. falciparum may have undergone host switching as a Plasmodia adaptation specific for our species.

Real evidence to assess clinical testing interference risk (REACTIR): A strategy using real world data to assess the prevalence of interfering substances in patients undergoing clinical laboratory testing.

INTRODUCTION: Laboratory test interferences can cause spurious test results and patient harm. Knowing the frequency of various interfering substances in patient populations likely to be tested with a particular laboratory assay may inform test development, test utilization and strategies to mitigate interference risk. METHODS: We developed REACTIR (Real Evidence to Assess Clinical Testing Interference Risk), an approach using real world data to assess the prevalence of various interfering substances in patients tested with a particular type of assay. REACTIR uses administrative real world data to identify and subgroup patient cohorts tested with a particular laboratory test and evaluate interference risk. RESULTS: We demonstrate the application REACTIR to point of care (POC) blood glucose testing. We found that exposure to several substances with the potential to interfere in POC blood glucose tests, including N-acetyl cysteine (NAC) and high dose vitamin C was uncommon in most patients undergoing POC glucose tests with several key exceptions, such as burn patients receiving high dose IV-vitamin C or acetaminophen overdose patients receiving NAC. CONCLUSIONS: Findings from REACTIR may support risk mitigation strategies including targeted clinician education and clinical decision support. Likewise, adaptations of REACTIR to premarket assay development may inform optimal assay design and assessment.

Use of machine learning to predict clinical decision support compliance, reduce alert burden, and evaluate duplicate laboratory test ordering alerts.

OBJECTIVES: While well-designed clinical decision support (CDS) alerts can improve patient care, utilization management, and population health, excessive alerting may be counterproductive, leading to clinician burden and alert fatigue. We sought to develop machine learning models to predict whether a clinician will accept the advice provided by a CDS alert. Such models could reduce alert burden by targeting CDS alerts to specific cases where they are most likely to be effective. MATERIALS AND METHODS: We focused on a set of laboratory test ordering alerts, deployed at 8 hospitals within the Partners Healthcare System. The alerts notified clinicians of duplicate laboratory test orders and advised discontinuation. We captured key attributes surrounding 60 399 alert firings, including clinician and patient variables, and whether the clinician complied with the alert. Using these data, we developed logistic regression models to predict alert compliance. RESULTS: We identified key factors that predicted alert compliance; for example, clinicians were less likely to comply with duplicate test alerts triggered in patients with a prior abnormal result for the test or in the context of a nonvisit-based encounter (eg, phone call). Likewise, differences in practice patterns between clinicians appeared to impact alert compliance. Our best-performing predictive model achieved an area under the receiver operating characteristic curve (AUC) of 0.82. Incorporating this model into the alerting logic could have averted more than 1900 alerts at a cost of fewer than 200 additional duplicate tests. CONCLUSIONS: Deploying predictive models to target CDS alerts may substantially reduce clinician alert burden while maintaining most or all the CDS benefit.

Development of a "meta-model" to address missing data, predict patient-specific cancer survival and provide a foundation for clinical decision support.

OBJECTIVE: Like most real-world data, electronic health record (EHR)-derived data from oncology patients typically exhibits wide interpatient variability in terms of available data elements. This interpatient variability leads to missing data and can present critical challenges in developing and implementing predictive models to underlie clinical decision support for patient-specific oncology care. Here, we sought to develop a novel ensemble approach to addressing missing data that we term the "meta-model" and apply the meta-model to patient-specific cancer prognosis. MATERIALS AND METHODS: Using real-world data, we developed a suite of individual random survival forest models to predict survival in patients with advanced lung cancer, colorectal cancer, and breast cancer. Individual models varied by the predictor data used. We combined models for each cancer type into a meta-model that predicted survival for each patient using a weighted mean of the individual models for which the patient had all requisite predictors. RESULTS: The meta-model significantly outperformed many of the individual models and performed similarly to the best performing individual models. Comparisons of the meta-model to a more traditional imputation-based method of addressing missing data supported the meta-model's utility. CONCLUSIONS: We developed a novel machine learning-based strategy to underlie clinical decision support and predict survival in cancer patients, despite missing data. The meta-model may more generally provide a tool for addressing missing data across a variety of clinical prediction problems. Moreover, the meta-model may address other challenges in clinical predictive modeling including model extensibility and integration of predictive algorithms trained across different institutions and datasets.