Babesiosis Surveillance - Wisconsin, 2001-2015.

Babesiosis is an emerging zoonotic disease caused primarily by Babesia microti, an intraerythocytic protozoan. Babesia microti, like the causal agents for Lyme disease and anaplasmosis, is endemic to the northeastern and upper midwestern United States where it is usually transmitted by the blacklegged tick, Ixodes scapularis. Although babesiosis is usually a mild to moderate illness, older or immunocompromised persons can develop a serious malaria-like illness that can be fatal without prompt treatment. The most common initial clinical signs and symptoms of babesiosis (fever, fatigue, chills, and diaphoresis) are nonspecific and present diagnostic challenges that can contribute to delays in diagnosis and effective treatment with atovaquone and azithromycin (1). Results of one study revealed a mean delay of 12-14 days from symptom onset to treatment (2). Knowledge of the incidence and geographic distribution of babesiosis can raise the index of clinical suspicion and facilitate more prompt diagnosis and lifesaving treatment (1). The first known case of babesiosis in Wisconsin was detected in 1985 (3), and babesiosis became officially reportable in the state in 2001. Wisconsin babesiosis surveillance data for 2001-2015 were analyzed in 3-year intervals to compare demographic, epidemiologic, and laboratory features among patients with cases of reported babesiosis. To determine possible reasons for an increase in reported Babesia infection, trends in electronic laboratory reporting and diagnosis by polymerase chain reaction testing (PCR) were examined. Between the first and last 3-year analysis intervals, there was a 26-fold increase in the incidence of confirmed babesiosis, in addition to geographic expansion. These trends might be generalizable to other states with endemic disease, similar suburbanization and forest fragmentation patterns, and warming average temperatures (4). Accurate surveillance in states where babesiosis is endemic is necessary to estimate the increasing burden of babesiosis and other tickborne diseases and to develop appropriate public health interventions for prevention and practice.

A normalization method for combination of laboratory test results from different electronic healthcare databases in a distributed research network.

PURPOSE: Distributed research networks (DRNs) afford statistical power by integrating observational data from multiple partners for retrospective studies. However, laboratory test results across care sites are derived using different assays from varying patient populations, making it difficult to simply combine data for analysis. Additionally, existing normalization methods are not suitable for retrospective studies. We normalized laboratory results from different data sources by adjusting for heterogeneous clinico-epidemiologic characteristics of the data and called this the subgroup-adjusted normalization (SAN) method. METHODS: Subgroup-adjusted normalization renders the means and standard deviations of distributions identical under population structure-adjusted conditions. To evaluate its performance, we compared SAN with existing methods for simulated and real datasets consisting of blood urea nitrogen, serum creatinine, hematocrit, hemoglobin, serum potassium, and total bilirubin. Various clinico-epidemiologic characteristics can be applied together in SAN. For simplicity of comparison, age and gender were used to adjust population heterogeneity in this study. RESULTS: In simulations, SAN had the lowest standardized difference in means (SDM) and Kolmogorov-Smirnov values for all tests (p < 0.05). In a real dataset, SAN had the lowest SDM and Kolmogorov-Smirnov values for blood urea nitrogen, hematocrit, hemoglobin, and serum potassium, and the lowest SDM for serum creatinine (p < 0.05). CONCLUSION: Subgroup-adjusted normalization performed better than normalization using other methods. The SAN method is applicable in a DRN environment and should facilitate analysis of data integrated across DRN partners for retrospective observational studies.

Customizing Laboratory Information Systems: Closing the Functionality Gap.

Highly customizable laboratory information systems help to address great variations in laboratory workflows, typical in Pathology. Often, however, built-in customization tools are not sufficient to add all of the desired functionality and improve systems interoperability. Emerging technologies and advances in medicine often create a void in functionality that we call a functionality gap. These gaps have distinct characteristics­a persuasive need to change the way a pathology group operates, the general availability of technology to address the missing functionality, the absence of this technology from your laboratory information system, and inability of built-in customization tools to address it. We emphasize the pervasive nature of these gaps, the role of pathology informatics in closing them, and suggest methods on how to achieve that. We found that a large number of the papers in the Journal of Pathology Informatics are concerned with these functionality gaps, and an even larger proportion of electronic posters and abstracts presented at the Pathology Informatics Summit conference each year deal directly with these unmet needs in pathology practice. A rapid, continuous, and sustainable approach to closing these gaps is critical for Pathology to provide the highest quality of care, adopt new technologies, and meet regulatory and financial challenges. The key element of successfully addressing functionality gaps is gap ownership­the ability to control the entire pathology information infrastructure with access to complementary systems and components. In addition, software developers with detailed domain expertise, equipped with right tools and methodology can effectively address these needs as they emerge.

Electronic clinical laboratory test results data tables: lessons from Mini-Sentinel.

PURPOSE: Developing electronic clinical data into a common data model posed substantial challenges unique from those encountered with administrative data. We present here the design, implementation, and use of the Mini-Sentinel Distributed Database laboratory results table (LRT). METHODS: We developed the LRT and guided Mini-Sentinel data partners (DPs) in populating it from their source data. Data sources included electronic health records and internal and contracted clinical laboratory systems databases. We employed the Logical Observation Identifiers, Names, and Codes (LOINC®) results reporting standards. We evaluated transformed results data using data checks and an iterative, ongoing characterization and harmonization process. RESULTS: Key LRT variables included test name, subcategory, specimen source, LOINC, patient location, specimen date and time, result unit, and unique person identifier. Selected blood and urine chemistry, hematology, coagulation, and influenza tests were included. Twelve DPs with outpatient test results participated; four also contributed inpatient test results. As of September 2013, the LRT included 385,516,239 laboratory test results; data are refreshed at least quarterly. LOINC availability and use varied across DP. Multiple data quality and content issues were identified and addressed. CONCLUSION: Developing the LRT brought together disparate data sources with no common coding structure. Clinical laboratory test results obtained during routine healthcare delivery are neither uniformly coded nor documented in a standardized manner. Applying a systematic approach with data harmonization efforts and ongoing oversight and management is necessary for a clinical laboratory results data table to remain valid and useful.

Evaluación multicéntrica del nuevo sistema analítico BioSystems BA 400 ® y comparación de procedimientos de medida / Multicentre evaluation of the new analytical system BioSystems BA 400 ® and comparison of measurement procedures

Objetivo. El objetivo de este trabajo fué evaluar, mediante un estudio multicéntrico, la imprecisión y la veracidad de un elevado número de procedimientos de medida en el nuevo sistema analítico BioSystems BA 400(R). Material y método. El estudio de la imprecisión se llevó a cabo siguiendo recomendaciones establecidas y utilizando sueros control con 2 concentraciones distintas. El estudio de la veracidad se ha realizado mediante la comparación de los procedimientos de medida del nuevo sistema con los utilizados habitualmente en los centros evaluadores. Resultados. Los resultados obtenidos para la imprecisión interdiaria con el nuevo analizador han sido en general excelentes en relación a los errores máximos permitidos. Se han encontrado algunas diferencias no despreciables y estadísticamente significativas entre los distintos procedimientos de medida, que son debidas a diferencias en el mensurando en algunos casos (transaminasas, inmunoanálisis) y a diferencias en los calibradores en otros. Conclusiones. La evaluación ha demostrado las excelentes prestaciones de precisión y veracidad del sistema. El nuevo analizador proporciona resultados en muestras de pacientes que son equivalentes a los obtenidos con otros analizadores (Olympus AU5400 y AU2700, Roche Cobas C711 y Siemens ADVIA 2400, 1800 y BNII) (AU)

Background. The purpose of the study was a multicentre evaluation of the imprecision and of the trueness of a wide variety of measurement procedures with the new analytical system BioSystems BA 400(R). Methods. The imprecision study was performed following established recommendations and using control sera with two different concentrations. The trueness was studied by means of a comparison of the measurement procedures of the new analyser with those of routine use in the evaluating centres. Results. The results obtained for the between-day imprecision with the new analyser have been in general excellent in relation to the maximum allowed errors. Several differences that are not worthless and that are statistically significant have been found between the measurement procedures. The differences are due to measurand differences in some cases (transaminases, immunoanalysis), and to the calibration in other. Conclusion. The evaluation study has demonstrated the excellent performance of the system regarding precision and trueness. The results obtained for patient samples with the new analyzer are equivalent to those obtained with other analyzers (Olympus AU5400 y AU2700, Roche Cobas C711 y Siemens ADVIA 2400, 1800 y BNII) (AU)

Sniffing out acute kidney injury in the ICU: do we have the tools?

PURPOSE OF REVIEW: Acute kidney injury (AKI) is one of the most common complications of critical illnesses, and early detection of AKI can improve its outcome. Using advanced electronic surveillance tools has attracted attention in recent years. RECENT FINDINGS: It is not a secret that information overload in clinical practice, particularly those that are admitted to ICUs, can decrease the ability of practitioners to identify changes in patients' status in a timely manner. On the contrary, knowing the impact of an early and accurate diagnosis of syndromes, such as AKI, on patients' outcomes makes the use of electronic syndromic surveillance (ESS) tools a mandate. In recent years, a number of such tools for early detection of AKI have been developed with variable sensitivity and specificity. Although the impact of using these tools on patients' outcomes is unclear, the time to the appropriate processes required for the care of AKI has been successfully shortened by the use of these devices. SUMMARY: ESS tools (sniffers) for AKI may allow improvement in patient processes of care or more efficient patient recruitment in AKI-related ICU studies.

The ideal laboratory information system.

CONTEXT: Laboratory information systems (LIS) are critical components of the operation of clinical laboratories. However, the functionalities of LIS have lagged significantly behind the capacities of current hardware and software technologies, while the complexity of the information produced by clinical laboratories has been increasing over time and will soon undergo rapid expansion with the use of new, high-throughput and high-dimensionality laboratory tests. In the broadest sense, LIS are essential to manage the flow of information between health care providers, patients, and laboratories and should be designed to optimize not only laboratory operations but also personalized clinical care. OBJECTIVES: To list suggestions for designing LIS with the goal of optimizing the operation of clinical laboratories while improving clinical care by intelligent management of laboratory information. DATA SOURCES: Literature review, interviews with laboratory users, and personal experience and opinion. CONCLUSIONS: Laboratory information systems can improve laboratory operations and improve patient care. Specific suggestions for improving the function of LIS are listed under the following sections: (1) Information Security, (2) Test Ordering, (3) Specimen Collection, Accessioning, and Processing, (4) Analytic Phase, (5) Result Entry and Validation, (6) Result Reporting, (7) Notification Management, (8) Data Mining and Cross-sectional Reports, (9) Method Validation, (10) Quality Management, (11) Administrative and Financial Issues, and (12) Other Operational Issues.