Post-analytical Phase Research Articles

Neural networks pipeline for quality management in IVF laboratory

This study introduces a novel neural network-based pipeline for predicting clinical pregnancy rates in IVF treatments, integrating both clinical and laboratory data. We developed a metamodel combining deep neural networks and Kolmogorov-Arnold networks, leveraging their complementary strengths to enhance predictive accuracy and interpretability. The metamodel achieved robust performance metrics after training and fitting on 11500 clinical cases: accuracy = 0.72, AUC = 0.75, F1 score = 0.60, and Matthews Correlation Coefficient of 0.42. According to morpho-kinetical embryo evaluation, our model’s PRC of 0.66 significantly improves over existing time-lapse systems for pregnancy prediction, demonstrating better handling of imbalanced clinical data. The metamodel’s calibration metrics (Brier score = 0.20, expected calibration error = 0.06, maximum calibration error = 0.12, Hosmer-Lemeshow test p-value = 0.06) indicate robust reliability in predicting clinical pregnancy outcomes. We validated the model’s reproducibility using an independent dataset of 665 treatment cycles, showing close alignment between predicted and actual pregnancy rates (58.9% vs. 59.1%). With the Bayesian method, we proposed a robust framework for integrating historical data with real-time predictions from neural networks, enabling a transition from retrospective to prospective analysis. Our approach extends beyond conventional embryo selection, incorporating post-analytical phase evaluation in the IVF laboratory. This comprehensive framework enables detailed analysis across different patient subpopulations and time periods, facilitating the identification of systemic issues and IVF protocol optimization. The model’s ability to track pregnancy probabilities over time and staff members allows for both outcome prediction and retrospective and prospective assessment of IVF treatment efficacy, providing a data-driven strategy for continuous improvement in assisted reproductive technology.

What is the journey that follows our personal training analyses? Reflections on the post-analytic phase of development and some of the challenges of the modern NHS workplace

ABSTRACT In this paper, the author further explores and theorises themes from a recent autoethnographical essay ‘Dangerous journey: memories of containment in cuba’ in which he detailed the immediate demanding period that followed the end of his own analysis – an event which occurred some time after the completion of his formal training. To study this very particular time-period in professional life, he draws on literature on endings in child psychotherapy and the progression of personal training analyses in the psychoanalytic professions, while also referencing discussions held with a range of child psychotherapy colleagues. The reader of this paper is invited to enquire why there are no child psychotherapy publications and a near-void in wider psychoanalytic publications on this important caesura, coupled with a lack of descriptions of the challenges of working in current NHS systems. The author also draws on his experiences to provide a brief, general commentary on such challenges. He hopes that the paper may encourage further writing in the profession on the place of personal analyses within (and often for a time after) the training – especially the possible meanings when ending them – and more broadly of their enduring place in the lives of child psychotherapists post-terminus.

Diagnostic role of urine human epididymis protein 4 in ovarian cancer.

Ovarian cancer is the 8th most common malignancy in women and the deadliest gynecological cancer. Due to the non-specific symptoms and the lack of effective diagnostic methods, late diagnosis remains the main barrier for improving the poor prognosis. Human epididymis protein 4 (HE4) is a protein overexpressed in ovarian cancer, but not in healthy individuals or benign conditions. The aim of this review article is to evaluate the laboratory aspect and potential clinical application of urine HE4. The methodology is presented, together with discussion on preanalytical, analytical and postanalytical phase of HE4 detection using urine. Moreover, we present the diagnostic role of urine HE4 in differential diagnosis, chemotherapy response, detection of recurrence and detection of low-malignant potential tumors. It has been found that urine HE4 presents as a promising, non-invasive tumor marker for detection and monitoring of ovarian cancer. However, standardization of the HE4 detection process is needed prior to implementation in clinical diagnostics.

A-188 Modal Failure and Effects Analysis, FMEA methodology, as a strategy to increase patient safety

Abstract Background Patient safety during the clinical care process is considered a priority in healthcare. Failure Mode and Effects Analysis (FMEA) is a proactive and systematic method of process evaluation and is used to identifying where and how each phase of the analytical process could fail allowing the detection of the causes and the effects they produce on the quality of the results that are issued. A regular use of this tool makes possible to identify the most critical steps, propose improvement strategies and evaluate their impact. The aim of the study is to evaluate applicability of the FMEA tool to identify errors in the pre analytical, analytical, and post analytical phase of a clinical laboratory as well as the main causes and effects, to reduce them by proposing improvement strategies Methods For each phase into which the laboratory activity is divided, the frequency and type of errors that are made and their final consequences on the patient are evaluated. A risk index (NPR=OxDxS) is calculated for each error considering the frequency with which it occurs (O), the ease with which it is detected (D) and the severity of the consequences on the patient (S). Values goes from 1 (rarely) to 10 (often). Improvement actions are proposed for all errors with a risk index greater than 100 and this index is re-evaluated after the implementation of that improvement measures. This same analysis is performed for three times over the last 10 years (in 2014, 2019 and 2023). Results In FMEA analysis carried out in 2014, a total of 40 errors were identified in the pre-analytical phase (63.5% of the total), 12 in the analytical phase and 11 in the post-analytical phase. 50% of the total errors detected required improvement strategies because they had an NPR≥100 value. In the 2023 evaluation, a total of 27 errors were identified in the pre-analytical phase, 10 in the analytical phase, and 5 in the post-analytical phase but only 6 required improvement strategies, which represents an 80% reduction in the most critical errors for patient safety. However, the pre-analytical phase continues to be the most critical, as it continues to account for 64% of the total errors identified, including the lack of identification of the patient or sample and the inadequate prior preparation of the patient. In analytical and post-analytical phases, the NPR values obtained in 2023 decrease significantly over analysis carried out in 2014 and its not needed to apply improvement strategies. Automation and the IT tools significantly reduce the number of errors with high NPR values, but it is undoubtedly the involvement of service professionals that makes the difference over the years. Conclusions The use of the FMEA methodological tool allows the design of appropriate indicators to detect and reduce errors in the clinical laboratory. Its application as part of a good quality control allows a continuous improvement in the laboratory process and consequently a guarantee of quality for the patient

A-209 Preanalytical Phase Errors Constitute the Vast Majority of Errors in Clinical Laboratory Testing

Abstract Background Laboratory errors can occur during the preanalytical, analytic, or post-analytic phase of testing and may pose a considerable threat to patient safety. Studies have estimated that pre-analytical error encapsulates ∼45-68% of laboratory errors and occurs in up to 1.52% of specimens. However, limitations of previously published studies include the use of manual error detection, reliance on manual transcription, small study sizes, and exclusion of common errors. Herein, we aim to identify the types and frequency of errors occurring in each phase of clinical laboratory testing using a large retrospective dataset across a broad range of errors detected in clinical laboratories. Methods This study was performed in the Barnes-Jewish Hospital core laboratory which provides testing for a 1,400-bed tertiary care hospital and included all tests performed for clinical chemistry, special chemistry, toxicology, automated hematology, blood gas testing, coagulation, non-infectious serology, infectious disease serology, urinalysis, and send out testing between January 2022 and May 2023. Errors were compiled from two separate sources. The first source of errors was captured from the laboratory information system (LIS, Cerner). These errors were flagged manually by laboratory staff in real time. The second source of error was a daily report manually curated by a trained medial laboratory scientist as part of routine, laboratory operating procedures and standard workflow. Results There were ∼40,000,000 individual results from ∼11,000,000 specimens during the study period and 87,317 errors observed (∼0.8% of specimens). Of these errors, specimens that were hemolyzed sufficiently to impact result but still reportable were the majority (41,047, 47.2%). When excluding minimal hemolysis, there were 46,270 errors documented. Among these, 44,847 (97.0%) were in the preanalytical phase, 451 (1.0%) were in the analytical phase, and 972 (2.0%) occurred in the postanalytical phase. Subcategorization of preanalytical errors revealed that most of the errors were related to hemolyzed samples requiring results to be suppressed (n=19,701), quantity not sufficient samples (n=8,068), clotted specimens (n=5,840), collection errors (e.g., wrong tube type, n=5,780), not on ice (n=1,369) and IV fluid contamination (n=1,122). In the analytical phase, the causes of error were interfering substances (n=279), QC out of range (n=65), instrument problems (n=58) and reagent issues (n=49). Among the postanalytical errors, 581 were comment errors, 216 were results entry errors, 131 were comments errors with potential impact on patient care, and 44 were dilution errors. Conclusions This study demonstrates that the vast majority of lab errors (∼97.0%) occurring during the preanalytical phase. The high incidence of preanalytical errors, especially errors related to sample integrity implies a need for enhanced tools and training to minimizing errors from specimen collections, maintenance of sample stability, transportation, and processing.

B-324 Pilot Study Result of EQA for Fecal Immunochemical Test for Hemoglobin Using a Novel Ready-to-Use Sample Material

Abstract Background The primary screening method for colorectal cancer is fecal immunochemical test (FIT) detecting human hemoglobin (Hb) in feces. There are several tests on the market both for automated analyzers and point-of-care testing (POCT). To ensure high quality results, the tests should be monitored with appropriate quality assessment procedures including participation in external quality assessment (EQA) programs. By participating in an EQA program using a stool-like material, clinical laboratories and POCT sites can fulfil the ISO 15189 requirements of monitoring the complete laboratory process from pre- to postanalytical phases in addition to the quantitative analytical phase of FIT. Here we present the first results of an international EQA pilot study for quantitative FIT using a novel ready-to-use stool-like material, performed by EQA provider Labquality. Methods 40 participants from 8 countries using 9 different FIT reagents were recruited for the pilot study performed in 2023. A set of artificial stool samples containing human hemoglobin uniformly was shipped to the participants at ambient temperature. The sample material is developed by HECTEF (Health Care Technology Foundation, Japan). The participants were instructed to collect the sample with their test specific sample collection kit and perform analysis according to the manufacturer’s instructions as a regular patient sample. Quantitative results (µg Hb/g feces) and qualitative interpretations (positive/negative) were reported in Labquality’s electronic platform LabScala. The quantitative target value was a method group consensus mean. A questionnaire regarding the usability of the sample material was sent to all participants. Results Sample S001 contained no Hb and all participants (40/40) reported a result corresponding to the lower detection limit of their test in use or a low numeric result most likely explained by unspecific background. All reported a negative interpretation. Sample S002 was a high positive sample for Hb. The measured value exceeded the upper detection limit of 4/9 tests (Exdia iFOB, Precision Biosensor; Fecal Occult Blood (iFOB) Neo, Boditech; FOB Test, VedaLab; NADAL FOB Quant test, Nal von Minden). The mean values and CV% were 116 µg/g and 8% for iFOBT, Aidian; 192 ug/g and 4% for Standard iFOB FIA, SD Biosensor; 144 µg/g and 45% for FOB assay kit FOB Gold, Sentinel Diagnostics (different clinical chemistry analyzers used); 91 µg/g and 26% for OC-Sensor FIT, Eiken Chemical. All participants responded a positive qualitative clinical interpretation. The majority of the responders on the feedback questionnaire (26/35) found the samples to be easy to use including comments of the sample being user-friendly and ready-to-use, resembling a patient sample and easy to apply with the test specific sample collection kit. Conclusions The fecal-like sample used in this Labquality’s EQA pilot study evaluates the complete analytical process including the preanalytical, analytical and postanalytical phases. An EQA scheme using this sample material is more compliant to ISO 15189 requirements compared to schemes where liquid or lyophilized samples are used. The new sample material used on this pilot study was positively received by most of the participants and it was compatible with all tests included in the study.

Antiphospholipid antibodies in patients with antiphospholipid syndrome.

Antiphospholipid syndrome (APS) is a rare systemic autoimmune disease characterized by recurrent pregnancy morbidity or thrombosis in combination with the persistent presence of antiphospholipid antibodies (aPLs) in plasma/serum. Antiphospholipid antibodies are a heterogeneous, overlapping group of autoantibodies, of which anti-β2-glycoprotein I (aβ2GPI), anticardiolipin (aCL) antibodies and antibodies that prolong plasma clotting time in tests in vitro known as lupus anticoagulant (LAC) are included in the laboratory criteria for the diagnosis of APS. The presence of LAC antibodies in plasma is indirectly determined by measuring the length of coagulation in two tests - activated partial thromboplastin time (aPTT) and diluted Russell's viper venom time (dRVVT). The concentration of aβ2GPI and aCL (immunglobulin G (IgG) and immunoglobulin M (IgM) isotypes) in serum is directly determined by solid-phase immunoassays, either by enzyme-linked immunosorbent assay (ELISA), fluoroimmunoassay (FIA), immunochemiluminescence (CLIA) or multiplex flow immunoassay (MFIA). For patient safety, it is extremely important to control all three phases of laboratory testing, i.e. preanalytical, analytical and postanalytical phase. Specialists in laboratory medicine must be aware of interferences in all three phases of laboratory testing, in order to minimize these interferences. The aim of this review was to show the current pathophysiological aspects of APS, the importance of determining aPLs-a in plasma/serum, with an emphasis on possible interferences that should be taken into account when interpreting laboratory findings.

Beyond the numbers unveiling the errors & critical importance of the pre-analytical, analytical & post-analytical phase in lab total test processes

Beyond the numbers unveiling the errors & critical importance of the pre-analytical, analytical & post-analytical phase in lab total test processes

Navigating through current ml applications in clinical labs: From preanalytical to post-analytical phases

Navigating through current ml applications in clinical labs: From preanalytical to post-analytical phases

Evaluation of quality indicators in microbiology and infectious disease serology laboratory: A study from a tertiary cancer care centre in Gujarat, India

Introduction: Quality indicators (QIs) are important for the monitoring and evaluation of laboratory performance at pre-analytical, analytical, and post-analytical phases. Errors in the laboratory arise more frequently before and after the analysis of samples. The total testing process (TTP) therefore needs to be evaluated for good laboratory performance. A good quality indicator should give information about the performance of a process, thereby governing the quality of services.Objective: To analyse TTP and QIs for a period of six years of screening of laboratory performance.Methods: This retrospective analysis was conducted for the period 2017 to 2022. QIs at pre-analytical (12), analytical (04) and post-analytical (03) phases were defined and analysed on a monthly basis. The laboratory followed the guidelines of the International Organization for Standardization (ISO) 15189:2012 to identify errors in all sections.Results: A total of 259,694 samples were received during the study period. A total of 628,817 tests were performed. The overall error rate was 1.25/1000 tests. Error in the pre-analytic phase was 0.96/1000 tests, the commonest being haemolysed samples. Error in the analytical phase was 0.33/1000 tests. The error of postanalytical phase was 0.48/1000 tests with turn round time (TAT) outliers being the commonest.Conclusion: Amongst all phases of QIs, errors were minimum in the analytical phase. Continuous monitoring and analysis of QIs helps in identification of common errors in the laboratory. Corrective measures will help to improve quality of the laboratory services, and hence the patient outcome. Regular training, evaluation of technical staff, regular preventive maintenance and calibration of all analytical instruments helps in reducing the errors.

Assessing post-analytical phase harmonization in European laboratories: a survey promoted by the EFLM Working Group on Harmonization.

Harmonization of the laboratory total testing process (TTP) is critical to improving patient outcome. In2016, an EFLM survey on the harmonization of TTP underlined the serious shortcomings pertaining to the post-analytical phase. In 2023, the WG-H conducted a new survey aiming to update information in the 2016 harmonization report in order to ascertain whether countries that had declared they were keen to adopt SI units had continued with this program, the aim being to verify the state-of art inharmonization units in areas of laboratory medicine notincluded in the previous survey. Questionnaires were distributed to the Presidents and National Representatives of EFLM Full Member Societies and EFLM affiliate Members. The survey questions were grouped into three categories: measurement units, reference intervals, and nomenclature/terminology, and results were evaluated using Survey Monkey software andExcel. A total of 123 questionnaires from 31 countries wereanalyzed. A trend (+19.3 %) was observed toward a wider use of SI units for general clinical biochemistry parameters. The results for tests not included in the 2016 survey (i.e.,endocrinology diagnostics and coagulation panels), demonstrated that for reports on hormones, responses were satisfactory, 70-90 % of the responders adopting the recommended units, whereas for coagulation test panels, a serious lack of harmonization was found, "seconds", which are inaccurate and not recommended, being widely used units (91 %). The findings made in the 2023 survey demonstrated a progressive, albeit slow, improvement inharmonization reports. However, further efforts at improvement are mandatory.

Re-Print: Importance of Quality in the Pre-Analytical Phase

The laboratory examinations in the diagnostic medicine assist doctors in clinical decisions, discoveries of diseases and, finally, in verifying the evolution of t h e patient in the face of treatments, being the responsibility of the laboratory, the performance and delivery of the result in a precise and reliable way. For this, we divided the analysis into three phases: pre-analytical phase encompassing the choice of the exam by the doctor, until the transport of the sample to the analysis site, the analytical phase where the analysis takes place and finally, the post-analytical phase where it is released and interpreted the results. The pre-analytical phase is the least automated and has the participation of the largest number of professionals from different areas, so the possibility of errors is greater, impairing the quality of the sample and interfering with the results of the exams, causing a great impact on the patient's life. This study aims to inform health professionals about the importance of a well-executed pre-analytical phase, preventing future errors.

Analysis of toxicological findings related to immunofluorescence investigations.

The subject of this article is the role of forensic toxicology in post-mortem examinations using immunofluorescence methods, its implications and its role in providing conclusive evidence for both criminal and civil proceedings. The aim of the study is to verify the correlation between the mode of death and the ingestion of exogenous substances and, if positive, to identify the category of substances ingested and assess their role in the cause of death. A laboratory study was carried out, consisting of several phases: pre-analytical phase; analytical phase; post-analytical phase. The variables analyzed were sex, cause of death, age. Abused substances tested: amphetamines, methamphetamines, barbiturates, benzodiazepines, cocaine, methadone, opiates, tricyclic antidepressants, delta-9-tetrahydrocannabinol (cannabis), alcohol. Retrospective analysis was performed on a total sample of 55 cases. The most relevant data emerged: cocaine with an incidence of 7.3% (4 cases out of 55), amphetamines with 5.4% (3 cases in total). The results of the screening tests were then subjected to confirmatory tests. There is an association between the use of certain exogenous substances and an increased risk of certain causes of death, such as overdose, traffic accidents, cardiovascular deaths, etc. This paper has highlighted the possibility of using first level immunological tests, such as immunofluorescence, to provide preliminary answers to the judicial authority immediately after autopsy, and a quantitative deepening with further second level tests, such as gas chromatography, as a gold standard to determine the cause of death.

Harmonizing the post-analytical phase: focus on the laboratory report.

The final, post-analytical, phase of laboratory testing is increasingly recognized as a fundamental step in maximizing quality and effectiveness of laboratory information. There is a need to close the loop of the total testing cycle by improving upon the laboratory report, and its notification to users. The harmonization of the post-analytical phase is somewhat complicated, mainly because it calls for communication that involves parties speaking different languages, including laboratorians, physicians, information technology specialists, and patients. Recently, increasing interest has been expressed in integrated diagnostics, defined as convergence of imaging, pathology, and laboratory tests with advanced information technology (IT). In particular, a common laboratory, radiology and pathology diagnostic reporting system that integrates text, sentinel images and molecular diagnostic data to an integrated, coherent interpretation enhances management decisions and improves quality of care.

Assessment of hematology laboratory performance in the total testing process using quality indicators and sigma metrics in the northwest of Ethiopia: A cross-sectional study.

Assuring laboratory quality by minimizing the magnitude of errors is essential. Therefore, this study aimed to assess hematology laboratory performance in the total testing process using quality indicators and sigma metrics. A cross-sectional study was conducted from April to June 2022. The study included a total of 13,546 samples. Data on included variables were collected using a checklist. Descriptive statistics were used to present the overall distribution of errors. Binary logistic regression models were applied. Furthermore, using a Sigma scale, the percentage of errors was converted to defects per million opportunities to assess laboratory performance. Finally, the defect per million opportunities was converted to a sigma value using a sigma calculator. Of the 13,546 samples and corresponding requests, the overall error rate was 123,296/474,234 (26%): 93,412/47,234 (19.7%) pre-analytical, 2364/474,234 (0.5%) analytical, and 27,520/474,234 (5.8%) post-analytical. Of the overall errors, 93,412/123,296 (75.8%), 2364/123,296 (1.9%), and 27,520/123,296 (22.3%) were pre-analytical, analytical, and post-analytical errors, respectively. The overall sigma value of the laboratory was 2.2. The sigma values of the pre-analytical, analytical, and post-analytical phases were 2.4, 4.1, and 3.1, respectively. The sample from the inpatient department and collected without adherence to the standard operating procedures (SOPs) had a significantly higher (p < 0.05) rejection rate as compared to the outpatient department and collected with adherence to SOPs, respectively. In addition, an association between prolonged turnaround times and manual recording, inpatient departments, and morning work shifts was observed. The current study found that the overall performance of the laboratory was very poor (less than three sigma). Therefore, the hospital leadership should change the manual system of ordering tests and release of results to a computerized system and give need-based training for all professionals involved in hematology laboratory sample collection and processing.

Biosafety considerations for laboratory work with SARS-CoV-2

Today, the world is affected by a COVID-19 epidemic. Early diagnosis can play an important role in the control and prevention of this disease, so the study and practice of biosafety among staff of diagnostic laboratories have a key role in the healthiness of health fighters against COVID-19. In this study, we will review laboratory biosafety considerations for the diagnosis process of SARS-CoV-2 in three phases: (1) the preanalytical phase, (2) the analytical phase, and (3) the postanalytical phase. It should be noted that employees with high-risk underlying health conditions, like lung or heart disease or diabetes, should be moved to lower-risk environments. We believe that this review will help to restrict the risk of developing COVID-19, while working in clinical laboratories.

RISK MANAGEMENT STRATEGIES IN MEDICAL LABORATORY PRACTICE

Introduction: Risk management is crucial in medical laboratory practice to ensure patient safety, maintain quality standards, and mitigate potential errors. This comprehensive review explores various risk management strategies implemented in medical laboratories to enhance understanding and effectiveness in practice. Methods: To identify and analyze risk management strategies employed in medical laboratory settings, a thorough examination of existing literature, guidelines, and best practices was conducted. Key themes and approaches were synthesized to overview current practices and emerging trends comprehensively. Results: The review highlights various risk management strategies utilized in medical laboratories, including quality control measures, staff training and competency assessment, standardized protocols, and procedures, utilization of technology and automation, error reporting systems, and participation in external quality assessment schemes. These strategies aim to identify, assess, and mitigate risks across all stages of the laboratory testing process, from pre-analytical to post-analytical phases. Conclusion: Effective risk management is essential for maintaining the integrity and reliability of laboratory testing, ultimately contributing to improved patient care outcomes. By implementing comprehensive risk management strategies, medical laboratories can enhance operational efficiency, minimize errors, and ensure compliance with regulatory requirements. Continued vigilance, ongoing education, and integration of new technologies are essential for adapting to evolving risks and challenges in laboratory practice.

Continual Improvement in Clinical Bacteriology Laboratory with Quality Indicators: A Retrospective Observational Study

Introduction: Healthcare management is undergoing significant changes with the evolution of new and re-emerging infections. A clinical microbiologist plays an important role in giving an accurate and timely report to the clinicians. Quality Indicators (QIs) act as a measure of the quality of services offered by the laboratory and are tools to monitor and evaluate the laboratory’s performance throughout the Total Testing Process (TTP). Aim: To measure the performance of the clinical bacteriology laboratory using QIs. Materials and Methods: A retrospective study was conducted in the Department of Microbiology at Lokmanya Tilak Municipal Medical College and General Hospital, Mumbai, Maharashtra, India. The study evaluated QIs from the records of 94,624 samples received in the bacteriology section of the clinical Microbiology laboratory between January 2018 and March 2021. Data analysis was conducted over a six-month period from December 2021 to May 2022. In 2018, one QI was identified for each phase, with an additional QI added in each phase to the pre-existing QI in 2019. In 2020, a QI was added in the preanalytical phase only. In 2021, the acceptable limit for one preanalytical QI was reduced from 2% to 1%. Data analysis was performed using an Excel sheet. Results: Data from records of 94,624 clinical bacteriology samples collected over 39 months were analyzed retrospectively. The preanalytical indicators included the number of samples rejected (135, 0.14%) and the number of requisition forms with three patient identifiers (59,645, 93.95%). Analytical phase QIs consisted of the average External Quality Assurance Scheme (EQAS) performance score (97.44% from January 2018 to March 2021) and outliers in the Internal Quality Control (IQC) (25 from January 2019 till March 2021). Failures in the IQC were not assessed in 2018. Postanalytical phase QIs included Turnaround Time (TAT) (average of 2.55 days for aerobic growth) and reporting time for critical alerts, which was within 24 hours of alert finding (100% for smear and culture-positive results). Conclusion: Regular monitoring of QIs helps to identify potential errors. This laboratory chose to analyse and monitor its processes using practically feasible QIs. It was found that the laboratory consistently maintained its performance throughout the study period.

Monkeypox Diagnosis in Clinical Settings: A Comprehensive Review of Best Laboratory Practices.

An outbreak of monkeypox (Mpox) was reported in more than 40 countries in early 2022. Accurate diagnosis of Mpox can be challenging, but history, clinical findings, and laboratory diagnosis can establish the diagnosis. The pre-analytic phase of testing includes collecting, storing, and transporting specimens. It is advised to swab the lesion site with virus transport medium (VTM) containing Dacron or polyester flock swabs from two different sites. Blood, urine, and semen samples may also be used. Timely sampling is necessary to obtain a sufficient amount of virus or antibodies. The analytical phase of infectious disease control involves diagnostic tools to determine the presence of the virus. While polymerase chain reaction (PCR) is the gold standard for detecting Mpox, genome sequencing is for identifying new or modified viruses. As a complement to these methods, isothermal amplification methods have been designed. ELISA assays are also available for the determination of antibodies. Electron microscopy is another effective diagnostic method for tissue identification of the virus. Wastewater fingerprinting provides some of the most effective diagnostic methods for virus identification at the community level. The advantages and disadvantages of these methods are further discussed. Post-analytic phase requires proper interpretation of test results and the preparation of accurate patient reports that include relevant medical history, clinical guidelines, and recommendations for follow-up testing or treatment.

Recommendations for Performance Evaluation of Machine Learning in Pathology: A Concept Paper From the College of American Pathologists.

Machine learning applications in the pathology clinical domain are emerging rapidly. As decision support systems continue to mature, laboratories will increasingly need guidance to evaluate their performance in clinical practice. Currently there are no formal guidelines to assist pathology laboratories in verification and/or validation of such systems. These recommendations are being proposed for the evaluation of machine learning systems in the clinical practice of pathology. To propose recommendations for performance evaluation of in vitro diagnostic tests on patient samples that incorporate machine learning as part of the preanalytical, analytical, or postanalytical phases of the laboratory workflow. Topics described include considerations for machine learning model evaluation including risk assessment, predeployment requirements, data sourcing and curation, verification and validation, change control management, human-computer interaction, practitioner training, and competency evaluation. An expert panel performed a review of the literature, Clinical and Laboratory Standards Institute guidance, and laboratory and government regulatory frameworks. Review of the literature and existing documents enabled the development of proposed recommendations. This white paper pertains to performance evaluation of machine learning systems intended to be implemented for clinical patient testing. Further studies with real-world clinical data are encouraged to support these proposed recommendations. Performance evaluation of machine learning models is critical to verification and/or validation of in vitro diagnostic tests using machine learning intended for clinical practice.