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.

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.

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.

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.

Iron studies and transferrin, a source of test ordering confusion highly amenable to clinical decision support.

INTRODUCTION: An important cause of laboratory test misordering and overutilization is clinician confusion between tests with similar sounding names or similar indications. We identified an area of test ordering confusion with iron studies that involves total iron binding capacity (TIBC), transferrin, and transferrin saturation. We observed concurrent ordering of direct transferrin along with TIBC at many hospitals within our health system and suspected this was unnecessary. METHODS: We extracted patient test results for transferrin, TIBC and other biomarkers. Using these data, we evaluated both patterns of test utilization and test result concordance. We implemented a clinical decision support (CDS) alert to discourage unnecessary orders for direct transferrin. RESULTS: Using linear regression, we were able to predict transferrin from either TIBC alone or TIBC with other analytes with a high degree of accuracy, demonstrating that in most cases, direct transferrin in combination with TIBC provides little if any additional diagnostic information beyond TIBC alone. The CDS alert proved highly effective in reducing transferrin test utilization at four different hospitals. CONCLUSIONS: Concurrent ordering of direct transferrin and TIBC should usually be avoided. Removal of transferrin or TIBC from the test menu or implementation of CDS may improve utilization of these tests.

Machine Learning Models Improve the Diagnostic Yield of Peripheral Blood Flow Cytometry.

OBJECTIVES: Peripheral blood flow cytometry (PBFC) is useful for evaluating circulating hematologic malignancies (HM) but has limited diagnostic value for screening. We used machine learning to evaluate whether clinical history and CBC/differential parameters could improve PBFC utilization. METHODS: PBFC cases with concurrent/recent CBC/differential were split into training (n = 626) and test (n = 159) cohorts. We classified PBFC results with abnormal blast/lymphoid populations as positive and used two models to predict results. RESULTS: Positive PBFC results were seen in 58% and 21% of training cases with and without prior HM (P < .001). % neutrophils, absolute lymphocyte count, and % blasts/other cells differed significantly between positive and negative PBFC groups (areas under the curve [AUC] > 0.7). Among test cases, a decision tree model achieved 98% sensitivity and 65% specificity (AUC = 0.906). A logistic regression model achieved 100% sensitivity and 54% specificity (AUC = 0.919). CONCLUSIONS: We outline machine learning-based triaging strategies to decrease unnecessary utilization of PBFC by 35% to 40%.

Validation and Implementation of an Ordering Alert to Improve the Efficiency of Monoclonal Gammopathy Evaluation.

OBJECTIVES: To evaluate the use of a provider ordering alert to improve laboratory efficiency and reduce costs. METHODS: We conducted a retrospective study to assess the use of an institutional reflex panel for monoclonal gammopathy evaluation. We then created a clinical decision support (CDS) alert to educate and encourage providers to change their less-efficient orders to the reflex panel. RESULTS: Our retrospective analysis demonstrated that an institutional reflex panel could be safely substituted for a less-efficient and higher-cost panel. The implemented CDS alert resulted in 79% of providers changing their high-cost order panel to an order panel based on the reflex algorithm. CONCLUSIONS: The validated decision support alert demonstrated high levels of provider acceptance and directly led to operational and cost savings within the laboratory. Furthermore, these studies highlight the value of laboratory involvement with CDS efforts to provide agile and targeted provider ordering assistance.

Order Indication Solicitation to Assess Clinical Laboratory Test Utilization: D-Dimer Order Patterns as an Illustrative Case.

BACKGROUND: A common challenge in the development of laboratory clinical decision support (CDS) and laboratory utilization management (UM) initiatives stems from the fact that many laboratory tests have multiple potential indications, limiting the ability to develop context-specific alerts. As a potential solution, we designed a CDS alert that asks the ordering clinician to provide the indication for testing, using D-dimer as an exemplar. Using data collected over a nearly 3-year period, we sought to determine whether the indication capture was a useful feature within the CDS alert and whether it provided actionable intelligence to guide the development of an UM strategy. METHODS: We extracted results and ordering data for D-dimer testing performed in our laboratory over a 35-month period. We analyzed order patterns by clinical indication, hospital service, and length of hospitalization. RESULTS: Our final data set included 13,971 result-order combinations and indeed provided actionable intelligence regarding test utilization patterns. For example, pulmonary embolism was the most common emergency department indication (86%), while disseminated intravascular coagulation was the most common inpatient indication (56%). D-dimer positivity rates increased with the duration of hospitalization and our data suggested limited utility for ordering this test in the setting of suspected venous thromboembolic disease in admitted patients. In addition, we found that D-dimer was ordered for unexpected indications including the assessment of stroke, dissection, and extracorporeal membrane oxygenation. CONCLUSIONS: Indication capture within a CDS alert and correlation with result data can provide insight into order patterns which can be used to develop future CDS strategies to guide appropriate test use by clinical indication.

Machine Learning and Other Emerging Decision Support Tools.

Emerging applications of machine learning and artificial intelligence offer the opportunity to discover new clinical knowledge through secondary exploration of existing patient medical records. This new knowledge may in turn offer a foundation to build new types of clinical decision support (CDS) that provide patient-specific insights and guidance across a wide range of clinical questions and settings. This article will provide an overview of these emerging approaches to CDS, discussing both existing technologies as well as challenges that health systems and informaticists will need to address to allow these emerging approaches to reach their full potential.