QC Constellation: a cutting-edge solution for risk and patient-based quality control in clinical laboratories.

OBJECTIVES: Clinical laboratories face limitations in implementing advanced quality control (QC) methods with existing systems. This study aimed to develop a web-based application to addresses this gap, and improve QC practices. METHODS: QC Constellation, a web application built using Python 3.11, integrates various statistical QC modules. These include Levey-Jennings charts with Westgard rules, sigma-metric calculations, exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) charts, and method decision charts. Additionally, it offers a risk-based QC section and a patient-based QC module aligning with modern QC practices. The codes and the web application links for QC Constellation were shared at https://github.com/hikmetc/QC_Constellation, and http://qcconstellation.com, respectively. RESULTS: Using synthetic data, QC Constellation demonstrated effective implementation of Levey-Jennings charts with user-friendly features like checkboxes for Westgard rules and customizable moving averages graphs. Sigma-metric calculations for hypothetical performance values of serum total cholesterol were successfully performed using allowable total error and maximum allowable measurement uncertainty goals, and displayed on method decision charts. The utility of the risk-based QC module was exemplified by assessing QC plans for serum total cholesterol, showcasing the application's capability in calculating risk-based QC parameters including maximum unreliable final patient results, risk management index, and maximum run size and offering risk-based QC recommendations. Similarly, the patient-based QC and optimization modules were demonstrated using simulated sodium results. CONCLUSIONS: In conclusion, QC Constellation emerges as a pivotal tool for laboratory professionals, streamlining the management of quality control and analytical performance monitoring, while enhancing patient safety through optimized QC processes.

Validation of SMA screening kits with SMN1 gene analysis in a Turkish cohort.

OBJECTIVE: It is crucial to start early treatment in Spinal Muscular Atrophy (SMA) with available drugs to stop the progression of the disease, therefore making SMA screening preferable. This study assessed Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) compared to Multiplex Ligation-dependent Probe Amplification (MLPA) for detecting Spinal Muscular Atrophy (SMA) through SMN1 gene copy number analysis in a Turkish cohort. METHODS: We analyzed 249 DNA samples, previously tested for SMN1 and SMN2 gene deletion via MLPA, using qRT-PCR kits from three different companies. Accuracy, sensitivity, and specificity of qRT-PCR in identifying deletions of SMN1 copy number variations. RESULTS: High accuracy (96.2-98.7%) achieved with qRT-PCR for detecting homozygous deletions, heterozygous deletions, and copy number variations in the SMN1 gene. Minor discrepancies between qRT-PCR and MLPA were observed, possibly due to single nucleotide polymorphisms affecting primer binding. CONCLUSIONS: The qRT-PCR method proved to be a rapid, cost-effective, and accurate technique, aligning well with the demands of routine SMA screening, suggesting its general suitability for application in SMA screening programs. This research highlights the importance of improving molecular methodologies and the value of collaborations between government and relevant sectors to overcome rare diseases, particularly through the enhancement of screening initiatives which is the first and most effective strategy to protect the public health.

Machine learning-based clinical decision support using laboratory data.

Artificial intelligence (AI) and machine learning (ML) are becoming vital in laboratory medicine and the broader context of healthcare. In this review article, we summarized the development of ML models and how they contribute to clinical laboratory workflow and improve patient outcomes. The process of ML model development involves data collection, data cleansing, feature engineering, model development, and optimization. These models, once finalized, are subjected to thorough performance assessments and validations. Recently, due to the complexity inherent in model development, automated ML tools were also introduced to streamline the process, enabling non-experts to create models. Clinical Decision Support Systems (CDSS) use ML techniques on large datasets to aid healthcare professionals in test result interpretation. They are revolutionizing laboratory medicine, enabling labs to work more efficiently with less human supervision across pre-analytical, analytical, and post-analytical phases. Despite contributions of the ML tools at all analytical phases, their integration presents challenges like potential model uncertainties, black-box algorithms, and deskilling of professionals. Additionally, acquiring diverse datasets is hard, and models' complexity can limit clinical use. In conclusion, ML-based CDSS in healthcare can greatly enhance clinical decision-making. However, successful adoption demands collaboration among professionals and stakeholders, utilizing hybrid intelligence, external validation, and performance assessments.

APS calculator: a data-driven tool for setting outcome-based analytical performance specifications for measurement uncertainty using specific clinical requirements and population data.

OBJECTIVES: According to ISO 15189:2022, analytical performance specifications (APS) should relate to intended clinical use and impact on patient care. Therefore, we aimed to develop a web application for laboratory professionals to calculate APS based on a simulation of the impact of measurement uncertainty (MU) on the outcome using the chosen decision limits, agreement thresholds, and data of the population of interest. METHODS: We developed the "APS Calculator" allowing users to upload and select data of concern, specify decision limits and agreement thresholds, and conduct simulations to determine APS for MU. The simulation involved categorizing original measurand concentrations, generating measured (simulated) results by introducing different degrees of MU, and recategorizing measured concentrations based on clinical decision limits and acceptable clinical misclassification rates. The agreements between original and simulated result categories were assessed, and values that met or exceeded user-specified agreement thresholds that set goals for the between-category agreement were considered acceptable. The application generates contour plots of agreement rates and corresponding MU values. We tested the application using National Health and Nutrition Examination Survey data, with decision limits from relevant guidelines. RESULTS: We determined APS for MU of six measurands (blood total hemoglobin, plasma fasting glucose, serum total and high-density lipoprotein cholesterol, triglycerides, and total folate) to demonstrate the potential of the application to generate APS. CONCLUSIONS: The developed data-driven web application offers a flexible tool for laboratory professionals to calculate APS for MU using their chosen decision limits and agreement thresholds, and the data of the population of interest.

Toward the detection of reduced emotion expression intensity: an autism sibling study.

INTRODUCTION: Expressing emotions through spontaneous facial expression is an important nonverbal social communication skill. In our study, we aimed to demonstrate that both children with autism spectrum disorder (ASD) and the non-ASD siblings of children with ASD have deficits in this skill. METHOD: In this study, we analyzed the six core facial emotion expressions of three distinct groups of children - those diagnosed with ASD (n = 60), non-ASD siblings (n = 60), and typically developed children (n = 60). To analyze facial expressions, we employed a computer vision program that uses machine learning algorithms to detect facial features and conducted an evidence-based task that involved assessing participants' ability to recognize facial emotion expressions. RESULTS: Deficits in spontaneous emotion expression were shown in the children with ASD and in non-ASD siblings when compared with typically developed children. Interestingly, it was determined that these deficits were not related to the severity of the autism symptoms in the ASD group. CONCLUSIONS: The results of the study suggest that computer-based automated analysis of facial expressions with contextual social scenes task holds potential for measuring limitations in the ability to express emotions, and they supplement the traditional clinical assessment of social phenotypical behavior deficits. This applies both to children with ASD and especially, to the non-ASD siblings of children with ASD. This study adds a novel approach to previous literature examining the emotion expression skills.

ISO 15189 is a sufficient instrument to guarantee high-quality manufacture of laboratory developed tests for in-house-use conform requirements of the European In-Vitro-Diagnostics Regulation.

The EU In-Vitro Diagnostic Device Regulation (IVDR) aims for transparent risk-and purpose-based validation of diagnostic devices, traceability of results to uniquely identified devices, and post-market surveillance. The IVDR regulates design, manufacture and putting into use of devices, but not medical services using these devices. In the absence of suitable commercial devices, the laboratory can resort to laboratory-developed tests (LDT) for in-house use. Documentary obligations (IVDR Art 5.5), the performance and safety specifications of ANNEX I, and development and manufacture under an ISO 15189-equivalent quality system apply. LDTs serve specific clinical needs, often for low volume niche applications, or correspond to the translational phase of new tests and treatments, often extremely relevant for patient care. As some commercial tests may disappear with the IVDR roll-out, many will require urgent LDT replacement. The workload will also depend on which modifications to commercial tests turns them into an LDT, and on how national legislators and competent authorities (CA) will handle new competences and responsibilities. We discuss appropriate interpretation of ISO 15189 to cover IVDR requirements. Selected cases illustrate LDT implementation covering medical needs with commensurate management of risk emanating from intended use and/or design of devices. Unintended collateral damage of the IVDR comprises loss of non-profitable niche applications, increases of costs and wasted resources, and migration of innovative research to more cost-efficient environments. Taking into account local specifics, the legislative framework should reduce the burden on and associated opportunity costs for the health care system, by making diligent use of existing frameworks.

Ex vivo expansion of natural killer cells for hematological cancer immunotherapy: a systematic review and meta-analysis.

The present systematic review aimed to investigate natural killer (NK) cell ex vivo expansion protocols within the scope of clinical trials targeting hematological cancer and to conduct a meta-analysis to assess the effect of NK cell infusion on survival. Research articles of clinical studies in which cell products produced by ex vivo expansion, consisting of a certain amount of NK cells and infused to patients with hematological cancer, were included in the systematic review. We conducted a proportion analysis with random effects for product purity and viability values. Studies having control groups were included in the survival meta-analysis. Among 11.028 identified records, 21 were included in the systematic review. We observed statistically significant heterogeneity for viability (I2 = 97.83%, p < 0.001) and purity values (I2 = 99.95%, p < 0.001), which was attributed to the diversity among isolation and expansion protocols. In addition, the survival meta-analysis findings suggested that NK cell therapy favors disease-free survival (DFS) of patients with myeloid malignancies but limited to only two clinical studies (odds ratio = 3.40 (confidence interval:1.27-9.10), p = 0.01). While included protocols yielded cell products with acceptable viability, the utility of immunomagnetic methods; feeder cells such as K562 expressing membrane-bound IL15 and 4-1BBL or expressing membrane-bound IL21 and 4-1BBL might be preferable to achieve better purity. In conclusion, NK cell therapy has a potential to improve DFS of patients with myeloid malignancies.

Voluntary imitation of dynamic facial expressions in attention deficit hyperactivity disorder: a facial-behavior analysis.

OBJECTIVE: The difficulties involved in social interaction among children with attention deficit hyperactivity disorder (ADHD) have been shown in many studies. Based on the knowledge that the imitation of facial expressions is a key factor in social interaction and functionality, the focus of prior studies has been on the evaluation of facial expressions in individuals with ADHD. However, little is known about voluntary facial mimicry in individuals with ADHD. In this context, we aimed to evaluate the voluntary-facial-imitation intensity of dynamic facial expressions in children with ADHD. METHOD: Forty-one children with ADHD and 53 typically developing children were included in the study. Participants were presented with a video of six basic emotions and neutral facial expressions selected from the EU-Emotion Stimulus Set via a screen. After each emotion, the instruction "now imitate it" was given. While the children watched the video, their faces were recorded with a webcam. The intensity of the children's voluntary facial imitations was examined with a computer vision program (Openface) that performs facial analysis on recorded videos. RESULTS: There was no significant difference between the groups in terms of facial emotion recognition accuracy. In group comparisons of voluntary facial mimicry, children with ADHD showed a significantly higher imitation intensity after emotional expressions of sadness, surprise and fear. There was no difference between the groups after the emotions of happiness, anger and disgust. CONCLUSION: This non-obtrusive, noninvasive, and cost-effective method allowed us to measure the quantitative differences in facial mimicry between children with ADHD and typically developing children. Our results contribute new information to the literature by indicating which emotions can be used in the evaluation of social communication skills, as well as intervention targets for these skills, in children with ADHD.

Effectiveness of Mesenchymal Stem Cell Therapy for COVID-19 Patients in the Intensive Care Unit: A Case-Control Study

Objective: Many methods are used in the treatment of coronavirus disease 2019 (COVID-19), which causes acute respiratory distress syndrome (ARDS), and there are conflicting reports in the literature regarding the results of mesenchymal stem cell (MSC) therapy, which is one of those methods. The aim of our study is to evaluate the effect of MSC treatment applied together with standard treatments on survival. Materials and Methods: This retrospective case-control study evaluates the survival effect of MSC treatment administered to patients treated in intensive care after the development of ARDS due to COVID-19 between March 2020 and March 2021. The age, gender, comorbid disease status, APACHE II score, and overall and comorbidity-based survival rates were compared between patients who received standard medical treatment (SMT) and patients who received MSC treatment together with SMT. Results: There were 62 patients in the group receiving only SMT and 81 patients in the group receiving SMT and MSC. No difference was observed between the groups in terms of age, gender, presence of comorbid diseases, or APACHE II scores. There were also no differences according to Kaplan-Maier analysis for the survival statuses of the groups. There was no serious adverse effect due to MSC treatment among these patients. Conclusion: Our study presents the largest case series in the literature, and it was observed that MSC treatment may not significantly affect overall survival or comorbid disease-based survival, in contrast to many other studies in the literature.

An approach for determining allowable between reagent lot variation.

Clinicians trust medical laboratories to provide reliable results on which they rely for clinical decisions. Laboratories fulfil their responsibility for accurate and consistent results by utilizing an arsenal of approaches, ranging from validation and verification experiments to daily quality control procedures. All these procedures verify, on different moments, that the results of a certain examination procedure have analytical performance characteristics (APC) that meet analytical performance specifications (APS) set for a particular intended use. The APC can in part be determined by estimating the measurement uncertainty component under conditions of within-laboratory precision (uRw), which comprises all components influencing the measurement uncertainty of random sources. To maintain the adequacy of their measurement procedures, laboratories need to distinguish aspects that are manageable vs. those that are not. One of the aspects that may influence uRw is the momentary significant bias caused by shifts in reagent and/or calibrator lots, which, when accepted or unnoticed, become a factor of the APC. In this paper, we postulate a model for allocating a part of allowable uRw to between-reagent lot variation, based on the need for long-term consistency of the measurement variability for that specific measurand. The allocation manages the ratio between short-term and long-term variation and indicates laboratories when to reject or correct certain variations due to reagent lots.

Estimation of Low-Density Lipoprotein Cholesterol Concentration Using Machine Learning.

OBJECTIVE: Low-density lipoprotein cholesterol (LDL-C) can be estimated using the Friedewald and Martin-Hopkins formulas. We developed LDL-C prediction models using multiple machine learning methods and investigated the validity of the new models along with the former formulas. METHODS: Laboratory data (n = 59,415) on measured LDL-C, high-density lipoprotein cholesterol, triglycerides (TG), and total cholesterol were partitioned into training and test data sets. Linear regression, gradient-boosted trees, and artificial neural network (ANN) models were formed based on the training data. Paired-group comparisons were performed using a t-test and the Wilcoxon signed-rank test. We considered P values <.001 with an effect size >.2 to be statistically significant. RESULTS: For TG ≥177 mg/dL, the Friedewald formula underestimated and the Martin-Hopkins formula overestimated the LDL-C (P <.001), which was more significant for LDL-C <70 mg/dL. The linear regression, gradient-boosted trees, and ANN models outperformed the aforementioned formulas for TG ≥177 mg/dL and LDL-C <70 mg/dL based on a comparison with a homogeneous assay (P >.001 vs. P <.001) and classification accuracy. CONCLUSION: Linear regression, gradient-boosted trees, and ANN models offer more accurate alternatives to the aforementioned formulas, especially for TG 177 to 399 mg/dL and LDL-C <70 mg/dL.

Detection of COVID-19 by Machine Learning Using Routine Laboratory Tests.

OBJECTIVES: The present study aimed to develop a clinical decision support tool to assist coronavirus disease 2019 (COVID-19) diagnoses with machine learning (ML) models using routine laboratory test results. METHODS: We developed ML models using laboratory data (n = 1,391) composed of six clinical chemistry (CC) results, 14 CBC parameter results, and results of a severe acute respiratory syndrome coronavirus 2 real-time reverse transcription-polymerase chain reaction as a gold standard method. Four ML algorithms, including random forest (RF), gradient boosting (XGBoost), support vector machine (SVM), and logistic regression, were used to build eight ML models using CBC and a combination of CC and CBC parameters. Performance evaluation was conducted on the test data set and external validation data set from Brazil. RESULTS: The accuracy values of all models ranged from 74% to 91%. The RF model trained from CC and CBC analytes showed the best performance on the present study's data set (accuracy, 85.3%; sensitivity, 79.6%; specificity, 91.2%). The RF model trained from only CBC parameters detected COVID-19 cases with 82.8% accuracy. The best performance on the external validation data set belonged to the SVM model trained from CC and CBC parameters (accuracy, 91.18%; sensitivity, 100%; specificity, 84.21%). CONCLUSIONS: ML models presented in this study can be used as clinical decision support tools to contribute to physicians' clinical judgment for COVID-19 diagnoses.

Xanthine oxidase enzyme activity in keratoconic corneal epithelium.

PURPOSE: To assess the activity of xanthine oxidase (XO) enzyme in keratoconic corneal epithelium and to evaluate its relationship with the keratoconus (KC) severity. METHODS: This prospective and randomized study included 66 eyes of 54 KC patients who received corneal collagen cross-linking treatment and 43 eyes of 32 patients who underwent photorefractive keratectomy due to their refractive error. During surgical procedures, the corneal epithelium was mechanically scraped and gathered to analyze the XO enzyme activity spectrophotometrically. The KC group was subdivided into three groups (stages 1, 2, and 3) according to the Amsler-Krumeich classification. The results were compared between the KC and the control group and in between KC subgroups. RESULTS: No significant differences in age and gender were found between the KC and control groups (p = 0.064 and p = 0.296, respectively). The mean XO activity levels of the KC and control groups were 173.57 ± 87.61 and 223.70 ± 99.52 mIU/mg, respectively (p < 0.001). In KC group, 33 eyes were at stage 1, 19 were at stage 2, and 14 were at stage 3. No significant difference was observed between KC subgroups regarding XO activity levels (p = 0.681). CONCLUSION: In this study, our findings revealed that ultraviolet-related pro-oxidant XO enzyme may have a role in the etiopathogenesis of KC. Further studies are needed to support our result. CLINICAL TRIALS REGISTRATION: When we started this study in 2018, we did not have a "Clinical Trials Registration." However, we have ethics committee approval (date: 21. 02. 2018/No: 22).

Utilization of biological variation data in the interpretation of laboratory test results - survey about clinicians' opinion and knowledge.

INTRODUCTION: To interpret test results correctly, understanding of the variations that affect test results is essential. The aim of this study is: 1) to evaluate the clinicians' knowledge and opinion concerning biological variation (BV), and 2) to investigate if clinicians use BV in the interpretation of test results. MATERIALS AND METHODS: This study uses a questionnaire comprising open-ended and close-ended questions. Questions were selected from the real-life numerical examples of interpretation of test results, the knowledge about main sources of variations in laboratories and the opinion of clinicians on BV. A total of 399 clinicians were interviewed, and the answers were evaluated using a scoring system ranked from A (clinician has the highest level of knowledge and the ability of using BV data) to D (clinician has no knowledge about variations in laboratory). The results were presented as number (N) and percentage (%). RESULTS: Altogether, 60.4% of clinicians have knowledge of pre-analytical and analytical variations; but only 3.5% of them have knowledge related to BV. The number of clinicians using BV data or reference change value (RCV) to interpret measurements results was zero, while 79.4% of clinicians accepted that the difference between two measurements results located within the reference interval may be significant. CONCLUSIONS: Clinicians do not use BV data or tools derived from BV such as RCV to interpret test results. It is recommended that BV should be included in the medical school curriculum, and clinicians should be encouraged to use BV data for safe and valid interpretation of test results.

The use of preanalytical quality indicators: a Turkish preliminary survey study.

OBJECTIVES: The utilization of reliable quality indicators (QIs) proven to be suitable for monitoring and improvement tools is one of the best choices to minimize of the risk of errors in all laboratory processes called as total testing process (TTP). In 2008, a Working Group "Laboratory Errors and Patient Safety" (WG-LEPS) established by International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) developed the Model of Quality Indicators (MQI) complying with requirements of the ISO 15189:2012 standard for laboratory accreditation. They have also been dealing with harmonizing the QIs in most laboratories worldwide since then. The present study was set out to investigate the frequency of using IFCC WG-LEPS' pre-QIs by Turkish laboratories and to assess the conformity of them, by taking into account Turkey's conditions. METHODS: A survey consisting nine questions was applied in 81 laboratories using SurveyMonkey. RESULTS: According to the survey results, most of the laboratories reported they have used pre-QIs in the quality standards of health prepared by Turkish Ministry of Health (MOH). A part of IFCC WG-LEPS' pre-QIs were being utilized by more than 80% of the laboratories, the rest of which only used by 10% of laboratories. CONCLUSIONS: The majority of the medical laboratories have been using the pre-QIs included in the guidelines of Quality Standards prepared by the MOH. The pre-QIs are partially compatible with IFCC WG-LEPS' pre-QIs. The definitions of IFCC WG-LEPS' pre-QIs may also be revised to make them more clear and understandable by IFCC WG-LEPS. The insufficiency of Health Information Management Systems (HIMS) limits the use of pre-QIs proposed by IFCC WG-LEPS. Finally, the education of relevant personnel about the use of HIMS and pre-QIs is very crucial to harmonize and to extend the use of IFCC WG-LEPS' pre-QIs in Turkish medical biochemistry laboratories.

Different effects of heating and freezing treatments on the antioxidant properties of broccoli, cauliflower, garlic and onion. An experimental in vitro study.

BACKGROUND: Vegetables have some beneficial effects on human health due to their antioxidant compounds, like polyphenols. Cooking leads to many physical and chemical changes to plant structure that can alter the phytochemical compounds of vegetables. OBJECTIVES: To investigate the effects of heat treatment and freezing on the antioxidant properties of garlic, onion, broccoli and cauliflower. DESIGN AND SETTING: Experimental in vitro study in a university laboratory. METHODS: Fresh broccoli (Brassica oleracea var. italica), cauliflower (Brassica oleracea var. botrytis), garlic (Allium sativum) and onion (Allium cepa) were obtained from a local store. These vegetables were divided into three treatment groups: raw, heated and frozen. The heat treatment consisted of heating them in a drying oven at 150 °C for 20 minutes. The freezing treatment consisted of keeping them frozen at -20 °C until analysis. The total phenolic content, antioxidant activity and malondialdehyde levels of the vegetables were measured using the Folin-Ciocalteu phenol reagent, 2,2-diphenyl-1-picrylhydrazyl radical scavenging activity and thiobarbituric acid reactive substances, respectively. RESULTS: Heat treatment had deleterious effects on the antioxidant properties of onion and garlic; and it decreased the antioxidant activity of broccoli. Freezing improved the antioxidant activity of broccoli and garlic, but had detrimental effects for cauliflower and onion. CONCLUSIONS: Heat treatment and freezing exhibit different effects on the antioxidant properties of broccoli, cauliflower, garlic and onion. Convenient cooking and storage patterns should be identified for each vegetable, to obtain the best nutritional benefit from the antioxidant compounds of vegetables.

Different effects of heating and freezing treatments on the antioxidant properties of broccoli, cauliflower, garlic and onion. An experimental in vitro study

ABSTRACT BACKGROUND: Vegetables have some beneficial effects on human health due to their antioxidant compounds, like polyphenols. Cooking leads to many physical and chemical changes to plant structure that can alter the phytochemical compounds of vegetables. OBJECTIVES: To investigate the effects of heat treatment and freezing on the antioxidant properties of garlic, onion, broccoli and cauliflower. DESIGN AND SETTING: Experimental in vitro study in a university laboratory. METHODS: Fresh broccoli (Brassica oleracea var. italica), cauliflower (Brassica oleracea var. botrytis), garlic (Allium sativum) and onion (Allium cepa) were obtained from a local store. These vegetables were divided into three treatment groups: raw, heated and frozen. The heat treatment consisted of heating them in a drying oven at 150 °C for 20 minutes. The freezing treatment consisted of keeping them frozen at -20 °C until analysis. The total phenolic content, antioxidant activity and malondialdehyde levels of the vegetables were measured using the Folin-Ciocalteu phenol reagent, 2,2-diphenyl-1-picrylhydrazyl radical scavenging activity and thiobarbituric acid reactive substances, respectively. RESULTS: Heat treatment had deleterious effects on the antioxidant properties of onion and garlic; and it decreased the antioxidant activity of broccoli. Freezing improved the antioxidant activity of broccoli and garlic, but had detrimental effects for cauliflower and onion. CONCLUSIONS: Heat treatment and freezing exhibit different effects on the antioxidant properties of broccoli, cauliflower, garlic and onion. Convenient cooking and storage patterns should be identified for each vegetable, to obtain the best nutritional benefit from the antioxidant compounds of vegetables.

Uncertainty of measurement for 14 immunoassay analytes: application to laboratory result interpretation.

Laboratory tests are an integral part of clinical decision making. Therefore, measurement uncertainty comes into prominence in the context of the accuracy of the laboratory result. This study aims to investigate measurement uncertainty of 14 immunoassay analytes, to compare them with different quality goals and to utilize them in the result interpretation. Measurement uncertainties of 14 immunoassay analytes were estimated by using internal and external quality control data by using Nordtest approach. Expanded uncertainties (U) were compared with allowable total error (TEa%), permissible relative deviation in the external quality assessment (PRDEQA%) and permissible expanded uncertainty for external quality assessment (pUEQAS%). Uncertainties were incorporated into the calculation of reference change values (RCV) and uncertainty adjusted reference intervals. RCVs of 14 analytes were calculated by three different methods reported by Harris, Clinical Laboratory Standards Institute (CLSI), and National Pathology Accreditation Advisory Council (NPAAC). Measurement uncertainties of TSH, estradiol, LH, progesterone, prolactin, and vitamin B12 were within defined allowable limits. Uone-sided FT3 and Uone-sided ferritin exceeded defined TEa% but UFT3 and Uferritin were found below the limits of pUEQAS%. Measurement uncertainties of FT4, cortisol, DHEAS, FSH, testosterone, and folate did not meet the specification limits. Recently defined permissible expanded uncertainty promises new targets to compare estimated measurement uncertainty. Measurement uncertainty should be applied to the laboratory result interpretation within the scope of RCV and reference interval to obviate misdiagnosis. Furthermore, we suggest that laboratories should inform clinicians about the tests with high uncertainties to assist them making the right clinical diagnosis. Abbreviations CLSI: Clinical Laboratory Standards Institute; CV: coefficient of variation; CVA: analytic coefficient of variation; CVG: inter-individual coefficient of variation; CVI: intra-individual coefficient of variation; DHEAS: dehydroepiandrosterone sulfate; FSH: follicle-stimulating hormone; FT3: free triiodothyronine; FT4: free thyroxine; k: coverage factor; LH: luteinizing hormone; LRL: lower reference limit; MD: minimal difference; NPAAC: National Pathology Accreditation Advisory Council; PRDEQA%: permissible relative deviation in the external quality assessment; pUEQAS%: permissible expanded uncertainty for external quality assessment; RCV: reference change value; RCV': uncertainty-adjusted reference change value; TSH: thyroid-stimulating hormone; Rw: within-laboratory reproducibility; RMSbias: root mean square of biases; u(Cref): the uncertainty of nominal values; u(bias): uncertainty component for bias; uc: combined standard uncertainty; TEa%: allowable total error; U: expanded uncertainty; Uone-sided%: one sided estimation of expanded measurement uncertainty using coverage factor "1.65"; URL: upper reference limit.