Laboratory Data Management Research Articles

Surveillance of Antimicrobial Resistance in the ECOWAS Region: Setting the Scene for Critical Interventions Needed.

Antimicrobial resistance poses a significant challenge to public health globally, leading to increased morbidity and mortality. AMR surveillance involves the systematic collection, analysis, and interpretation of data on the occurrence and distribution of AMR in humans, animals, and the environment for action. The West African Health Organization, part of the Economic Community of West African States (ECOWAS), is committed to addressing AMR in the region. This paper examines the status of AMR surveillance in ECOWAS countries using available WHO data from the TrACSS survey and GLASS enrollments. The analysis reveals that while progress has been made, significant challenges remain. Twelve of the fifteen ECOWAS countries are enrolled in GLASS, and ten have developed national action plans (NAPs) for AMR. However, there is a need to ensure all countries fully implement their NAPs, continue reporting to GLASS, and use the data for evidence-based actions and decision making. Surveillance systems for AMR and antimicrobial consumption/use vary across countries with some demonstrating limited capacity. All countries, except Cabo Verde, reported having a reference laboratory for AMR testing. Strengthening laboratory capabilities, data management and use, and multisectoral coordination are crucial for effective AMR surveillance and response. Based on the findings and the regional context, it is essential to prioritize capacity building, data utilization, and the adoption of standardized guidelines for AMR surveillance. Collaboration among ECOWAS countries, the WAHO, and international partners is essential to address AMR comprehensively. Ensuring a consistent supply of essential antimicrobial medications and reagents is vital.

Experiences, Lessons, and Challenges With Adapting REDCap for COVID-19 Laboratory Data Management in a Resource-Limited Country: Descriptive Study.

The COVID-19 pandemic brought challenges requiring timely health data sharing to inform accurate decision-making at national levels. In Botswana, we adapted and integrated the Research Electronic Data Capture (REDCap) and the District Health Information System version 2 (DHIS2) platforms to support timely collection and reporting of COVID-19 cases. We focused on establishing an effective COVID-19 data flow at the national public health laboratory, being guided by the needs of health care professionals at the National Health Laboratory (NHL). This integration contributed to automated centralized reporting of COVID-19 results at the Ministry of Health (MOH). This paper reports the experiences, challenges, and lessons learned while designing, adapting, and implementing the REDCap and DHIS2 platforms to support COVID-19 data management at the NHL in Botswana. A participatory design approach was adopted to guide the design, customization, and implementation of the REDCap platform in support of COVID-19 data management at the NHL. Study participants included 29 NHL and 4 MOH personnel, and the study was conducted from March 2, 2020, to June 30, 2020. Participants' requirements for an ideal COVID-19 data management system were established. NVivo 11 software supported thematic analysis of the challenges and resolutions identified during this study. These were categorized according to the 4 themes of infrastructure, capacity development, platform constraints, and interoperability. Overall, REDCap supported the majority of perceived technical and nontechnical requirements for an ideal COVID-19 data management system at the NHL. Although some implementation challenges were identified, each had mitigation strategies such as procurement of mobile Internet routers, engagement of senior management to resolve conflicting policies, continuous REDCap training, and the development of a third-party web application to enhance REDCap's capabilities. Lessons learned informed next steps and further refinement of the REDCap platform. Implementation of REDCap at the NHL to streamline COVID-19 data collection and integration with the DHIS2 platform was feasible despite the urgency of implementation during the pandemic. By implementing the REDCap platform at the NHL, we demonstrated the possibility of achieving a centralized reporting system of COVID-19 cases, hence enabling timely and informed decision-making at a national level. Challenges faced presented lessons learned to inform sustainable implementation of digital health innovations in Botswana and similar resource-limited countries.

Laboratory Data Timeliness and Completeness Improves Following Implementation of an Electronic Laboratory Information System in Côte d'Ivoire: Quasi-Experimental Study on 21 Clinical Laboratories From 2014 to 2020.

The Ministry of Health in Côte d'Ivoire and the International Training and Education Center for Health at the University of Washington, funded by the United States President's Emergency Plan for AIDS Relief, have been collaborating to develop and implement the Open-Source Enterprise-Level Laboratory Information System (OpenELIS). The system is designed to improve HIV-related laboratory data management and strengthen quality management and capacity at clinical laboratories across the nation. This evaluation aimed to quantify the effects of implementing OpenELIS on data quality for laboratory tests related to HIV care and treatment. This evaluation used a quasi-experimental design to perform an interrupted time-series analysis to estimate the changes in the level and slope of 3 data quality indicators (timeliness, completeness, and validity) after OpenELIS implementation. We collected paper and electronic records on clusters of differentiation 4 (CD4) testing for 48 weeks before OpenELIS adoption until 72 weeks after. Data collection took place at 21 laboratories in 13 health regions that started using OpenELIS between 2014 and 2020. We analyzed the data at the laboratory level. We estimated odds ratios (ORs) by comparing the observed outcomes with modeled counterfactual ones when the laboratories did not adopt OpenELIS. There was an immediate 5-fold increase in timeliness (OR 5.27, 95% CI 4.33-6.41; P<.001) and an immediate 3.6-fold increase in completeness (OR 3.59, 95% CI 2.40-5.37; P<.001). These immediate improvements were observed starting after OpenELIS installation and then maintained until 72 weeks after OpenELIS adoption. The weekly improvement in the postimplementation trend of completeness was significant (OR 1.03, 95% CI 1.02-1.05; P<.001). The improvement in validity was not statistically significant (OR 1.34, 95% CI 0.69-2.60; P=.38), but validity did not fall below pre-OpenELIS levels. These results demonstrate the value of electronic laboratory information systems in improving laboratory data quality and supporting evidence-based decision-making in health care. These findings highlight the importance of OpenELIS in Côte d'Ivoire and the potential for adoption in other low- and middle-income countries with similar health systems.

University consortium to address public health priorities and research capacity building in the Caribbean

The State University of New York (SUNY) – University of the West Indies (UWI) Health Research Consortium (HRC) was implemented in 2015 by the SUNY Global Health Institute (GHI) and the SUNY-UWI Center for Leadership and Sustainable Development. The goal was to advance public health in the Caribbean through collaborative research and education among faculty and students at SUNY and UWI. The Consortium is now a dynamic matrix addressing health priorities that were initially agreed upon with the Jamaica Ministry of Health and Wellness. The HRC has built a foundational matrix that is planning for cutting edge laboratory instrumentation, biomedical informatics system, seamless electronic medical records network, national laboratory data management system, and novel biotechnology (e.g., robotic surgery cluster). The SUNY-UWI partnership fills existing gaps through collaborative programs with the SUNY GHI that facilitate UWI faculty interactions with SUNY faculty and core resources and incubators that encourage collaborations while UWI infrastructure expands. The Health Research Consortium utilizes existing academic models at UWI and SUNY to promote collaboration, capacity building, and program implementation. Consortium teams develop sound business development models that foster sustained economic growth and form the pipeline for workforce development and career opportunities. The Caribbean diaspora and UWI alumni are engaged in working together on this effort. In addition, mentoring in K-12 and beyond is needed to create a vision for the next generations.

Registered Data-Centered Lab Management System Based on Data Ownership Safety Architecture

University and college laboratories are important places to train professional and technical personnel. Various regulatory departments in colleges and universities still rely on traditional laboratory management in research projects, which are prone to problems such as untimely information and data transmission. The present study aimed to propose a new method to solve the problem of data islands, explicit ownership, conditional sharing, data safety, and efficiency during laboratory data management. Hence, this study aimed to develop a data-centered lab management system that enhances the safety of lab data management and allows the data owners of the labs to control data sharing with other users. The architecture ensures data privacy by binding data ownership with a person using a key management method. To achieve data flow safely, data ownership conversion through the process of authorization and confirmation was introduced. The designed lab management system enables laboratory regulatory departments to receive data in a secure form by using this platform, which could solve data sharing barriers. Finally, the proposed system was applied and run in different server environments by implementing data security registration, authorization, confirmation, and conditional sharing using SM2, SM4, RSA, and AES algorithms. The system was evaluated in terms of the execution time for several lab data with different sizes. The findings of this study indicate that the proposed strategy is safe and efficient for lab data sharing across domains.

Retrospective Analysis of Six Years of Acute Flaccid Paralysis Surveillance and Polio Vaccine Coverage Reported by Italy, Serbia, Bosnia and Herzegovina, Montenegro, Bulgaria, Kosovo, Albania, North Macedonia, Malta, and Greece.

Here we analyzed six years of acute flaccid paralysis (AFP) surveillance, from 2015 to 2020, of 10 countries linked to the WHO Regional Reference Laboratory, at the Istituto Superiore di Sanità, Italy. The analysis also comprises the polio vaccine coverage available (2015–2019) and enterovirus (EV) identification and typing data. Centralized Information System for Infectious Diseases and Laboratory Data Management System databases were used to obtain data on AFP indicators and laboratory performance and countries’ vaccine coverage from 2015 to 2019. EV isolation, identification, and typing were performed by each country according to WHO protocols. Overall, a general AFP underreporting was observed. Non-Polio Enterovirus (NPEV) typing showed a high heterogeneity: over the years, several genotypes of coxsackievirus and echovirus have been identified. The polio vaccine coverage, for the data available, differs among countries. This evaluation allows for the collection, for the first time, of data from the countries of the Balkan area regarding AFP surveillance and polio vaccine coverage. The need, for some countries, to enhance the surveillance systems and to promote the polio vaccine uptake, in order to maintain the polio-free status, is evident.

Effective Virtual Teaching through the Interactive and Inexpensive Teaching Laboratory Data Management (TLDM) System

The COVID-19 pandemic has shifted a significant number of on-campus and in-person activitiesto an online, virtual setting. This has caused difficulty in achieving the same learning outcomes in the absence of in-person interaction, particularly for lab courses. In 2014, the Chemical and Biological Engineering department at The University of British Columbia developed and implemented a Teaching Laboratory Data Management (TLDM) system to improve the delivery andeffectiveness of lab-based courses. The TLDM system guides students through experimental calculations and automates a significant amount of grading for instructors. The TLDM system aided the department to better adapt to online lab courses and was integral to the virtual instruction of lab courses during the COVID-19 pandemic. Through conducting surveys and group feedback sessions with students, teaching assistants (TAs) and instructors, we found that the TLDM systemhas been well received by all three groups of stakeholders for use in online lab courses, and provides several key benefits. Namely, it helps students understand the calculations involved in experiments and provides an effective substitute for in-person lab activities, while reducing workload for TAs and instructors. The TLDM system can potentially be a great tool to complementonline courses around the world in an inexpensive way.

MO110ARE BIG-DATA TECHNIQUES USEFUL IN SCREENING FOR PRIMARY HYPERALDOSTERONISM? RESULTS OF A PILOT STUDY AND THEIR PROSPECTIVE APPLICATION

Abstract Background and Aims Primary aldosteronism is a rare cause of high blood pressure (BP). Guidelines recommend a screening in several situations, such as hypertension with hypokalemia, sleeping apnea or adrenal mass, by determining the baseline aldosterone/renin ratio. Recent studies point out that hyperaldosteronism may be more frequent than expected in patients who do not have an arbitrary high aldosterone/renin ratio. The application of big data techniques could generate “red flags” that help clinicians to anticipate the diagnosis. We have evaluated the usefulness of big data in the diagnosis of hyperaldosteronism. Method We have used 3 data sources: patient management, laboratory data management and clinical record. With them, Oracle database management system extracts demographic data, blood pressure and analytical values in a single collection. This data are stored in a MongoDB database, where two collections are generated: "patients" (data we are collecting) and "variables" (values obtained from the identifiers of the latest data obtained from the source systems are saved to guide the collection process). Since MongoDB is a schemaless system, it is possible to easily add new variables and their results to the collection "patients", if it is necessary, in order to generate alerts. In order to diagnose hyperaldosteronism, age between 18 and 55 years, BP &amp;gt; 139/89 mmHg, K 3.5 mEq/l and plasma bicarbonate &amp;gt; 26 mEq/L were used. Results From January 2019 to 30th March 2020, 952,520 data were collected and 281,661 outcome variables from 80,410 patients were analyzed. The method generated alerts of possible hyperaldosteronism in 33 patients (18 men, mean age 46.8 + 6.8 years old). The retrospective study of the clinical record of these patients showed that 11 (33%) had sustained hypertension and 54.5% of them needed 3 antihypertensive drugs. 2 patients suffered a severe cardiovascular event (intracranial hemorrhage). Of these 33 patients, aldosterone/renin screening was performed in 2, 2 other patients had an abdominal CT scan, in which no adrenal masses were found. No specific studies were performed to definitively rule out hyperaldosteronism in any patient. In this period of application of big data, the hospital archive coded 3 hyperaldosteronisms: 1 of them was due to Bartter syndrome, another was a misdiagnosis (androgenic syndrome) and the third one was due to medical background of hyperaldosteronism which had been operated years ago. These results were followed by a prospective screening carried on from 30th March 2020 to 1th January 2021. 776,878 supplementary data were extracted, and 232,425 variables from 13,958 patients were analyzed. The system have generated an alert for 12 patients: 2 of them have been diagnosed of primary hyperaldosteronism, another one have an adrenal mass, which is in surgical waiting list, and 3 patients (total: 50%) are waiting medical tests due to a high suspicion of hyperaldosteronism. Conclusion Big data techniques allow us to create “red flags” for the screening of rare diseases and might be an important tool in their diagnosis. Its systematic application guides us to perform specific diagnostic tests in at least 50% of the cases, in order to improve the diagnostic accuracy in these selected patients.

Developing and Piloting a Standardized European Protocol for Hepatitis C Prevalence Surveys in the General Population (2016-2019).

Background: A robust estimate of the number of people with chronic hepatitis C virus (HCV) infection is essential for an appropriate public health response and for monitoring progress toward the WHO goal of eliminating viral hepatitis. Existing HCV prevalence studies in the European Union (EU)/European Economic Area (EEA) countries are heterogeneous and often of poor quality due to non-probability based sampling methods, small sample sizes and lack of standardization, leading to poor national representativeness. This project aimed to develop and pilot standardized protocols for undertaking nationally representative HCV prevalence surveys in the general adult population.Methods: From 2016 to 2019 a team from the Robert Koch-Institute contracted by the European Centre for Disease Prevention and Control synthesized evidence on existing HCV prevalence surveys and survey methodology and drafted a protocol. The methodological elements of the protocol were piloted and evaluated in Bulgaria, Finland and Italy, and lessons learnt from the pilots were integrated in the final protocol. An international multidisciplinary expert group was consulted regularly.Results: The protocol includes three alternative study approaches: a stand-alone survey; a “nested” survey within an existing health survey; and a retrospective testing survey approach. A decision algorithm advising which approach to use was developed. The protocol was piloted and finalized covering minimum and gold standards for all steps to be implemented from sampling, data protection and ethical issues, recruitment, specimen collection and laboratory testing options, staff training, data management and analysis and budget considerations. Through piloting, the survey approaches were effectively implemented to produce HCV prevalence estimates and the pilots highlighted the strengths and limitations of each approach and key lessons learnt were used to improve the protocol.Conclusions: An evidence-based protocol for undertaking HCV prevalence serosurveys in the general population reflecting the different needs, resources and epidemiological situations has been developed, effectively implemented and refined through piloting. This technical guidance supports EU/EEA countries in their efforts to estimate their national hepatitis C burden as part of monitoring progress toward the elimination targets.

PENERAPAN APLIKASI MANAJEMEN LABORATORIUM PADA DINAS PERTANIAN DAN PERIKANAN KOTA BANJARMASIN

The Banjarmasin City Agriculture and Fisheries Office in providing innovation desperately needs a management that can manage an organization to be more structured with the specified procedures. The problem in this study is that there is no application program for processing data. The purpose of this research is to create and implement management applications that can help facilitate and accelerate the management of laboratory data at the Banjarmasin City Agriculture and Fisheries Office. The data collection methods used are interviews, observations, and review literature while the research methods used use SDLC waterfall. The result of this research is that the application that has been created can be used to facilitate and speed up the processing of data at the Office of Agriculture and Fisheries Banjarmasin City.

MinMLST: machine learning for optimization of bacterial strain typing.

High-resolution microbial strain typing is essential for various clinical purposes, including disease outbreak investigation, tracking of microbial transmission events and epidemiological surveillance of bacterial infections. The widely used approach for multilocus sequence typing (MLST) that is based on the core genome, cgMLST, has the advantage of a high level of typeability and maximal discriminatory power. Yet, the transition from a seven loci-based scheme to cgMLST involves several challenges, that include the need by some users to maintain backward compatibility, growing difficulties in the day-to-day communication within the microbiology community with respect to nomenclature and ontology, issues with typeability, especially if a more stringent approach to loci presence is used, and computational requirements concerning laboratory data management and sharing with end-users. Hence, methods for optimizing cgMLST schemes through careful reduction of the number of loci are expected to be beneficial for practical needs in different settings. We present a new machine learning-based methodology, minMLST, for minimizing the number of genes in cgMLST schemes by identifying subsets of informative genes and analyzing the trade-off between gene reduction and typing performance. The results achieved with minMLST over eight bacterial species show that despite the reduction in the number of genes up to a factor of 10, the typing performance remains very high and significant with an Adjusted Rand Index that ranges between 0.4 and 0.93 in different species and a P-value < 10-3. The identification of such optimized MLST schemes for bacterial strain typing is expected to improve the implementation of cgMLST by improving interlaboratory agreement and communication. The python package minMLST is available at https://PyPi.org/project/minmlst/PyPI and supported on Linux and Windows. Supplementary data are available at Bioinformatics online.

Early Experience With COVID-19 and Solid Organ Transplantation at a US High-volume Transplant Center.

Background.The novel coronavirus severe acute respiratory syndrome coronavirus 2 [coronavirus disease 2019 (COVID-19)] poses unique challenges for immunosuppressed patients. Solid organ transplant (SOT) recipients comprise a large proportion of this group, yet there is limited knowledge about the presentation, clinical course, and immunosuppression management of this novel infection among heart, lung, liver, pancreas, and kidney transplant recipients.Methods.We present 21 SOT recipients diagnosed with COVID-19 between January 1, 2020 and April 22, 2020 at a US high-volume transplant center. Diagnostic workup, clinical course, immunosuppression/antiviral management, and immediate outcomes are described.Results.Twenty-one (15.9%) of 132 symptomatic patients tested were positive. Mean age at diagnosis was 54.8 ± 10.9 y. Median time from transplant was 5.58 y (interquartile range 2.25, 7.33). Median follow-up was 18 d (interquartile range 13, 30). Fourteen patients required inpatient management, with 7 (50%) placed in the intensive care unit (ICU). All transplant types were represented. Nearly 43% exhibited GI symptoms. Over half (56.2%) presented with elevated serum creatinine suggestive of acute kidney injury. The majority of patients (5/7) with concomitant infections at baseline required the ICU. Eighty percent received hydroxychloroquine ± azithromycin. Ten received toclizumab and/or ribavirin; 1 received remdesivir. Antimetabolites ± calcineurin inhibitors were held or reduced. Over half of hospitalized patients (8/14) were discharged home. Only 1 mortality (4.8%) to date, in a critically ill heart/kidney patient who had been in the ICU before diagnosis.Conclusions.COVID-19 positive SOT at our institution had favorable short-term outcomes. Those with concomitant infections had more severe illness. More data will be available to evaluate long-term outcomes and disease impact on graft function.

Integrating GDPR in ISO 15189 for Medical Laboratories: Major Aspects and Perspectives.

Medical laboratories process and store sensitive data during four major phases: arrival of patients in the laboratory premises and registration of their data, pre-analytical, analytical and post-analytical phases. ISO 15189 has specific requirements concerning the management of the laboratory data in terms of security, availability and protection. The aim of the present study was to examine major aspects of the General Data Protection Regulation (GDPR) integration in medical laboratories that comply with the ISO 15189 standard, including data breach and informed consent. To the best of our knowledge, this is the first study dealing with this subject in the healthcare sector. Accredited medical laboratories need to modify their ISO 15189 Quality System documentation and processes applying appropriate additions and adjustments in order to incorporate GDPR requirements in a clear manner.

Assessing the Concepts and Designs of 58 Mobile Apps for the Management of the 2014-2015 West Africa Ebola Outbreak: Systematic Review.

BackgroundThe use of mobile phone information technology (IT) in the health sector has received much attention especially during the 2014-2015 Ebola virus disease (EVD) outbreak. mHealth can be attributed to a major improvement in EVD control, but there lacks an overview of what kinds of tools were available and used based on the functionalities they offer.ObjectiveWe aimed to conduct a systematic review of mHealth tools in the context of the recent EVD outbreak to identify the most promising approaches and guide further mHealth developments for infectious disease control.MethodsFollowing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we searched for all reports on mHealth tools developed in the context of the 2014-2015 EVD outbreak published between January 1, 2014 and December 31, 2015 on Google Scholar, MEDLINE, CAB s (Global Health), POPLINE, and Web of Science in any language using the search strategy: (“outbreak” OR “epidemic”) AND (“mobile phone” OR “smartphone” OR “smart phone” OR “mobile phone” OR “tablet” OR “mHealth”) AND (“Ebola” OR ”EVD” OR “VHF” OR “Ebola virus disease” OR “viral hemorrhagic fever”) AND (“2014” OR “2015”). The relevant publications were selected by 2 independent reviewers who applied a standardized data extraction form on the tools’ functionalities.ResultsWe identified 1220 publications through the search strategy, of which 6.31% (77/1220) were original publications reporting on 58 specific mHealth tools in the context of the EVD outbreak. Of these, 62% (34/55) offered functionalities for surveillance, 22% (10/45) for case management, 18% (7/38) for contact tracing, and 6% (3/51) for laboratory data management. Only 3 tools, namely Community Care, Sense Ebola Followup, and Surveillance and Outbreak Response Management and Analysis System supported all four of these functionalities.ConclusionsAmong the 58 identified tools related to EVD management in 2014 and 2015, only 3 appeared to contain all 4 key functionalities relevant for the response to EVD outbreaks and may be most promising for further development.

Patient facing decision support system for interpretation of laboratory test results

BackgroundIn some healthcare systems, it is common that patients address laboratory test centers directly without a physician’s recommendation. This practice is widely spread in Russia with about 28% of patients who visiting laboratory test centers for diagnostics. This causes an issue when patients get no help from the physician in understanding the results.Computer decision support systems proved to efficiently solve a resource consuming task of interpretation of the test results. So, a decision support system can be implemented to rise motivation and empower the patients who visit a laboratory service without a doctor’s referral.MethodsWe have developed a clinical decision support system for patients that solves a classification task and finds a set of diagnoses for the provided laboratory tests results.The Wilson and Lankton’s assessment model was applied to measure patients’ acceptance of the solution.ResultsA first order predicates-based decision support system has been implemented to analyze laboratory test results and deliver reports in natural language to patients. The evaluation of the system showed a high acceptance of the decision support system and of the reports that it generates.ConclusionsDetailed notification of the laboratory service patients with elements of the decision support is significant for the laboratory data management, and for patients’ empowerment and safety.

Laboratory Data Connectivity and Management: the Intersection of Laboratory Information Management Systems, Ancillary Open-Source Software, and Consumer Technologies.

Clinical laboratories must balance the need for efficient workflows with acutely managing exceptional specimens to prevent errors and ensure patient safety. These goals have conventionally been met by programmatic modifications to, or investment in new, laboratory information systems (LIS)2. However, we are now seeing a shift to more capable middleware and laboratory information management software (LIMS) to handle the dynamic nature of clinical processes and the data collected. In the clinical laboratories, only a fraction of the data generated actually makes it into the electronic health record …

APLIKASI PENGELOLAAN DATA LABORATORIUM DI PDAM BANDARMASIH KOTA BANJARMASIN

At PDAM Bandarmasih of Banjarmasin city, the laboratory division of data management system and report still use simple application. It is considered less effective and efficient in supporting the work productivity of employees in it, so it will take a little time in the process of data management laboratory, search data and provide information. From these problems raised the proposal to create a web-based application, in which it can perform the management of laboratory data. The methodology used in making this application is the programming language is PHP. for display using CSS and Jquery. Database using MySQL. Tools and Editor used are Xammp and Notepad ++. Supported by the availability of local internet network (intranet) in PDAM Bandarmasih Banjarmasin. This application is used as a media input, editing, and data deletion, data management with the Application of data management Laboratory PDAM Bandarmasih Banjarmasin City is expected to facilitate employees in managing laboratory data and data collection process so that the results of reporting data can be known easily, quickly and accurately . Keywords: PDAM Bandarmasih, Laboratory, Php, Css (Cascading Style Sheets), Xampp, MySql.

PerkinElmer pushes further into diagnostics

PerkinElmer has signed a definitive agreement to acquire the German clinical diagnostics firm Euroimmun Medical Laboratory Diagnostics for $1.3 billion. Expected to close by the end of the year, the deal adds Euroimmun’s autoimmune, infectious disease, and allergy testing capabilities to PerkinElmer’s well-established newborn-disease testing services and other disease diagnostics businesses. The transaction continues a trend among traditional instrumentation firms to expand into medical diagnostics. Founded in 1987, Euroimmun employs 2,400 people, 1,600 of whom are in Germany. Its annual sales are about $310 million—45% from China and 30% from Europe. In the past five years, Euroimmun enjoyed average annual sales growth of 19%, PerkinElmer says. Euroimmun uses a variety of immunohistochemical and biochemical methods to diagnose disease. It also offers automated equipment and software for laboratory data management. PerkinElmer CEO Robert Friel says the two firms will leverage their combined detection, ima...

The 2015 New York City Legionnaires' Disease Outbreak: A Case Study on a History-Making Outbreak.

The 2015 New York City Legionnaires' Disease Outbreak: A Case Study on a History-Making Outbreak.

Reporting altered test results in hemolyzed samples: is the cure worse than the disease?

The management of laboratory data in unsuitable (hemolyzed) samples remains an almost unresolved dilemma. Whether or not laboratory test results obtained by measuring unsuitable specimens should be made available to the clinicians has been the matter of fierce debates over the past decades. Recently, an intriguing alternative to suppressing test results and recollecting the specimen has been put forward, entailing the definition and implementation of specific algorithms that would finally allow reporting a preanalytically altered laboratory value within a specific comment about its uncertainty of measurement. This approach carries some advantages, namely the timely communication of potentially life-threatening laboratory values, but also some drawbacks. These especially include the challenging definition of validated performance specifications for hemolyzed samples, the need to producing reliable data with the lowest possible uncertainty, the short turnaround time for repeating most laboratory tests, the risk that the comments may be overlooked in short-stay and frequently overcrowded units (e.g. the emergency department), as well as the many clinical advantages of a direct communication with the physician in charge of the patient. Despite the debate remains open, we continue supporting the suggestion that suppressing data in unsuitable (hemolyzed) samples and promptly notifying the clinicians about the need to recollect the samples remains the most (clinically and analytically) safe practice.