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Background Preanalytical phase of laboratory testing is defined as that which includes clinician’s request for examination, patient preparation, sample collection, and transportation of specimen to the laboratory. Any errors occurring during this phase are referred to as preanalytical errors (PAEs). Objective The objective is to study the effect of training on improvement in the PAEs in venous blood sampling at a tertiary care center. Materials and Methods An observational prospective study was carried out in two phases of 6 months each in the outpatient department collection center, Gandhi Hospital, Secunderabad. First, venous blood samples collected were observed for the following errors: (1) incorrect volume of specimen, (2) hemolyzed samples, (3) clotted samples, (4) wrong vacutainer used, and (5) samples without requisition forms. The evaluation for performance based on quality indicators given by the International Federation of Clinical Chemistry Laboratory Medicine working group on Laboratory Errors and Patient Safety was done. Later, training classes were conducted on the correct phlebotomy techniques for the staff, and then venous blood samples were observed again for any errors. Results Of 43,286 samples collected in the first 6 months, 2208 (5.1%) were found to have errors. The most common cause for rejection was hemolysis followed next by samples collected in inappropriate tubes. Following the training session, a total of 41,064 samples were observed in the next phase. Of these, only 4.6% were found to have errors. Conclusion After training the laboratory technicians on the correct techniques of sample collection, there was a significant reduction in the percentage of errors. Therefore, it is important to give continuous training and education to reduce the frequency of errors and improve the quality of lab testing.

Blood Sampling Guidelines have been developed to target European emergency medicine-related professionals involved in the blood sampling process (e.g. physicians, nurses, phlebotomists working in the ED), as well as laboratory physicians and other related professionals. The guidelines population focus on adult patients. The development of these blood sampling guidelines for the ED setting is based on the collaboration of three European scientific societies that have a role to play in the preanalytical phase process: EuSEN, EFLM, and EUSEM. The elaboration of the questions was done using the PICO procedure, literature search and appraisal was based on the GRADE methodology. The final recommendations were reviewed by an international multidisciplinary external review group. The document includes the elaborated recommendations for the selected sixteen questions. Three in pre-sampling, eight regarding sampling, three post-sampling, and two focus on quality assurance. In general, the quality of the evidence is very low, and the strength of the recommendation in all the questions has been rated as weak. The working group in four questions elaborate the recommendations, based mainly on group experience, rating as good practice. The multidisciplinary working group was considered one of the major contributors to this guideline. The lack of quality information highlights the need for research in this area of the patient care process. The peculiarities of the emergency medical areas need specific considerations to minimise the possibility of errors in the preanalytical phase. [ABSTRACT FROM AUTHOR]

This comprehensive review explores the critical importance of the preanalytical phase in hematology laboratory testing and its significant impact on the overall quality of results. The preanalytical phase encompasses all the processes from sample collection to sample preparation, and any errors during this stage can lead to inaccurate and unreliable hematological data. By systematically examining the various sources of preanalytical errors, including specimen collection, transportation, and handling, this review provides insights into the potential challenges faced in the hematology laboratory. Furthermore, the article discusses a range of effective prevention strategies to mitigate preanalytical phase errors and enhance the reliability of hematological test results. These strategies encompass improvements in staff training, standardization of collection procedures, implementation of quality control measures, and advancements in technology and automation. The review emphasizes the importance of fostering a culture of awareness and adherence to best practices in the laboratory setting. Preanalytical errors remain a major concern in every laboratory because the majority of the stages leading up to these errors are not within the direct control of the laboratory. Even though they cannot be eliminated, they can definitely be minimized. The preanalytical errors of a hematology laboratory can be reduced with the help of quality management systems, personnel training, regular audits, and automation in the preanalytical stage, thus increasing the accuracy of the results and providing better patient care.

This comprehensive review explores the critical importance of the preanalytical phase in hematology laboratory testing and its significant impact on the overall quality of results. The preanalytical phase encompasses all the processes from sample collection to sample preparation, and any errors during this stage can lead to inaccurate and unreliable hematological data. By systematically examining the various sources of preanalytical errors, including specimen collection, transportation, and handling, this review provides insights into the potential challenges faced in the hematology laboratory. Furthermore, the article discusses a range of effective prevention strategies to mitigate preanalytical phase errors and enhance the reliability of hematological test results. These strategies encompass improvements in staff training, standardization of collection procedures, implementation of quality control measures, and advancements in technology and automation. The review emphasizes the importance of fostering a culture of awareness and adherence to best practices in the laboratory setting. Preanalytical errors remain a major concern in every laboratory because the majority of the stages leading up to these errors are not within the direct control of the laboratory. Even though they cannot be eliminated, they can definitely be minimized. The preanalytical errors of a hematology laboratory can be reduced with the help of quality management systems, personnel training, regular audits, and automation in the preanalytical stage, thus increasing the accuracy of the results and providing better patient care. [ABSTRACT FROM AUTHOR]

Prevention of pre-analytical issues in coagulation testing is of paramount importance for good laboratory performance. In addition to common issues like hemolysed, icteric, or lipemic samples, some specific pre-analytical errors of coagulation testing include clotted specimens, improper blood-to-anticoagulant ratio, contamination with other anticoagulants, etc. Prothrombin time (PT) and activated partial thromboplastin time (aPTT) are very commonly affected tests due to pre-analytical variables. The impact these parameters possess on surgical decision-making and various life-saving interventions are substantial therefore we cannot afford laxity and casual mistakes in carrying out these critical investigations at all. In this case series, a total of 4 cases of unexpectedly deranged coagulation profiles have been described which were reported incorrectly due to the overall casual approach towards these critical investigations. We have also mentioned how the treating clinician and lab physician retrospectively accessed relevant information in the nick of time to bring back reassurance. Like every other critical investigation, analytical errors can occur in coagulation parameters due to various avoidable pre-analytical variables. The release of spurious results for coagulation parameters sets alarm bells ringing causing much agony to the treating doctor and patient. Only a disciplined and careful approach taken by hospital and lab staff towards each sample regardless of its criticality can negate these stressful errors to a large extent. [ABSTRACT FROM AUTHOR]

Objective Lipemia is an important cause of preanalytical errors in laboratory results. They affect the specimen integrity and trustworthiness of laboratory results. The present study was to assess the impact of lipemia on routine clinical chemistry analytes. Methods Anonymous leftover serum samples with normal levels of routine biochemical parameters were pooled. Twenty such pooled serum samples were used for the study. The samples were spiked with commercially available intralipid solution (20%) to produce lipemic concentrations of 0, 400 (mild, 20 μL), 1,000 (moderate, 50 μL), and 2,000 mg/dL (severe, 100 μL). Glucose, renal function test, electrolytes, and liver function test were estimated in all the samples. Baseline data without the effect of interference was considered as true value and percentage bias for the spiked samples was calculated. Interference was considered significant if the interference bias percentage exceeded 10%. Result Parameters like glucose, urea, creatinine, direct bilirubin, sodium, potassium, and chloride showed negative interference at mild and moderate lipemic concentration and positive interference at severe lipemic concentration. Parameters like aspartate transaminase (AST) and alanine transaminase (ALT) showed negative interference at mild and positive interference at moderate and severe lipemic concentration. Whereas uric acid, total protein, albumin, total bilirubin, alkaline phosphatase, gamma-glutamyl transferase, calcium, magnesium, and phosphorous showed positive interference at all concentrations. Significant interference (> 10%) was shown for magnesium (mild lipemia), albumin, direct bilirubin, ALT, and AST at moderate lipemic concentration. All parameters showed significant interference at severe lipemic concentration. Conclusion All the study parameters are affected by lipemic interference at varying levels. Laboratory-specific data regarding lipemic interference at various concentrations on the clinical biochemistry parameters is needed.

Introduction PTS (pneumatic transport system) is extensively being used in modern hospitals for rapid transportation of blood samples and other specimens. However, it has a potential impact on blood components, which should be investigated and nullified accordingly. This study was part of a correction program aimed at reducing hemolysis. It was done by comparing paired samples transported manually and by PTS.

Objective Lipemia is an important cause of preanalytical errors in laboratory results. They affect the specimen integrity and trustworthiness of laboratory results. The present study was to assess the impact of lipemia on routine clinical chemistry analytes. Methods Anonymous leftover serum samples with normal levels of routine biochemical parameters were pooled. Twenty such pooled serum samples were used for the study. The samples were spiked with commercially available intralipid solution (20%) to produce lipemic concentrations of 0, 400 (mild, 20 μL), 1,000 (moderate, 50 μL), and 2,000 mg/dL (severe, 100 μL). Glucose, renal function test, electrolytes, and liver function test were estimated in all the samples. Baseline data without the effect of interference was considered as true value and percentage bias for the spiked samples was calculated. Interference was considered significant if the interference bias percentage exceeded 10%. Result Parameters like glucose, urea, creatinine, direct bilirubin, sodium, potassium, and chloride showed negative interference at mild and moderate lipemic concentration and positive interference at severe lipemic concentration. Parameters like aspartate transaminase (AST) and alanine transaminase (ALT) showed negative interference at mild and positive interference at moderate and severe lipemic concentration. Whereas uric acid, total protein, albumin, total bilirubin, alkaline phosphatase, gamma-glutamyl transferase, calcium, magnesium, and phosphorous showed positive interference at all concentrations. Significant interference (> 10%) was shown for magnesium (mild lipemia), albumin, direct bilirubin, ALT, and AST at moderate lipemic concentration. All parameters showed significant interference at severe lipemic concentration. Conclusion All the study parameters are affected by lipemic interference at varying levels. Laboratory-specific data regarding lipemic interference at various concentrations on the clinical biochemistry parameters is needed. [ABSTRACT FROM AUTHOR]

Introduction PTS (pneumatic transport system) is extensively being used in modern hospitals for rapid transportation of blood samples and other specimens. However, it has a potential impact on blood components, which should be investigated and nullified accordingly. This study was part of a correction program aimed at reducing hemolysis. It was done by comparing paired samples transported manually and by PTS. Materials and Methods This study was initiated to monitor the impact of PTS on hemolysis of clinical biochemistry blood samples. It was performed in two phases—before and after the corrective action taken. Phase I: done after PTS installation but before the corrective action was taken. Duplicate samples from 100 healthy individuals were collected, one set transported by PTS and the other by human carriers. Both sets were assessed for 25 biochemistry analytes, hemolysis index (HI), and acceleration profiles using a data logger. Corrective measures were then taken, followed by phase II of the study. In phase II, the sample size and study design remained the same as phase I. All the test results of PTS and hand-carried samples were statistically analyzed for any significant difference. Result In phase I, all the hemolysis-manifesting parameters, LDH (lactate dehydrogenase), potassium, AST (aspartate transaminase), and phosphorus, were raised in PTS samples as compared with the manual samples. Their differences were significant as the p -values were 0.001, 0.000, 0.025, and 0.047, respectively. The differences for LDH and potassium were clinically significant as well. HI (9%) and peak acceleration (15.7 g) were high in PTS samples. In phase II, no statistically significant difference between paired samples was found for all biochemistry parameters except for a few which were clinically nonsignificant. For PTS samples, HI was 2.5% and the peak acceleration was 11.2 g, whereas for manual samples, HI was 2%. Conclusion Evidence of hemolysis was found in PTS samples as compared with handheld samples, which was resolved after several corrective actions were taken. Thereafter, PTS became reliable for sample delivery in a routine biochemistry laboratory. Hence, each hospital should scrutinize their PTS for its effects on sample integrity to get rid of PTS-induced preanalytical errors. [ABSTRACT FROM AUTHOR]

Background and objectives: Laboratory medicine is equally challenged by economic and new technological pressures. Clinical laboratories have undergone major change due to advancement of technology, which has improved the decision making of clinicians but introduced the risk of errors. This study aimed to evaluate the errors that occurred in the pre-analytical phase of laboratory testing. Methods: This was a prospective observational study that was done in the Clinical Biochemistry Laboratory of a tertiary care center from June 2016 to May 2017. The path of the sample was analyzed from sample collection to transport. Frequency of deficiencies in the request forms and different types of pre-analytical errors were recorded. Results: During the study period, the frequency of pre-analytical errors was about 3.1%. Sample hemolysis was the predominant error in sample collected from both indoor and outdoor patients. Conclusion: Proper management of pre-analytical errors requires continuous evaluation of source of errors, taking corrective measures, and significant interdepartmental cooperation.

• Autoverification-based quality rules can prevent the release of potentially erroneous results. • Turbid specimens may account for the majority of lipemia index alarms. • Some cases of pseudohypoglycemia or pseudohyperkalemia may be identified via autoverification-based quality rules. The preanalytical phase of testing accounts for the majority of the errors. Software-based quality rules, such as autoverification, can assist in preanalytical error detection; therefore, preventing erroneous results from being reported. Two autoverification rules, turbidity/lipemia, and pseudohypoglycemia/pseudohyperkalemia alarms, are highlighted. Increased sample turbidity may arise from several causes outside of lipemia. Typically, this can be resolved by clarifying the sample with standard centrifugation. Truly lipemic specimens typically require higher centrifugation speeds and greater centrifugation time. At our facility, 96% of direct bilirubin (DBIL), 95% of aspartate transaminase (AST), and 98% of alanine transaminase (ALT) turbidity/lipemia alarms were found to be from sample turbidity versus lipemia. Secondly, a pseudohypoglycemia/pseudohyperkalemia rule was employed for specimens with delayed separation from cellular material. Of the total potassium results >6.0 mmol/L or glucose results <40 mg/dL (2.2 mmol/L), 30% and 50% respectively were noted to have delayed sample separation. [ABSTRACT FROM AUTHOR]

Hemolysis is associated with erroneous or delayed results. Objectives of the study were to compare four different methods for obtaining hemolysis in vitro on three different analyzers. Hemolysis was prepared with addition of pure hemoglobin into serum pool, osmotic shock, aspiration through blood collection needle, freezing/thawing of whole blood. Biochemistry parameters were measured in duplicate at Architect c8000 (Abbott, Abbott Park, USA), Beckman Coulter AU680 (Beckman Coulter, Brea, USA) and Cobas 6000 c501 (Roche, Mannheim, Germany), according to manufacturers' declarations. Cut-off value was defined as the highest value of H index with corresponding bias lower than acceptance criteria. We were not able to obtain results with freezing protocol. On all three platforms, lowest number of analytes were sensitive to hemolysis at H=0.5 using method of adding free hemoglobin. When osmotic shock was used, cut-off values for the most analytes were generally met at lower values. Hemolysis significantly interfered with measurement of potassium and lactate dehydrogenase (LD) at H=0.5 on all platforms. The most of the tested analytes had the lowest acceptable H index when aspiration method was used. At the low level of hemolysis (H=0.8) glucose, sodium, potassium, chloride, phosphate, and LD were affected on all analyzers, with some additional analytes depending on the manufacturer. Hemolysis interference differs on different analyzers and according to protocol for obtaining hemolysis. Aspiration method was generally the most sensitive to hemolysis interference, while addition of free Hb was the most resistant. [ABSTRACT FROM AUTHOR]

Introduction: Medical and laboratory errors can be caused due to many reasons, including communication problem, inadequate training of the staff members, improper identification. Quality indicators can help in objective measurement of errors in various crucial steps. Aim: To determine the nature and frequency of preanalytical, some of the analytical and postanalytical errors in the clinical laboratory with the help of quality indicators. Materials and Methods: This was a retrospective study and data was collected for preanalytical, some of analytical and postanalytical errors from December 2020 to May 2021, from the central laboratory and were classified under various quality indicators. MS excel was used to analyse the data and descriptive statistics such as number and percentage were used to present the data. Results: Out of the total 677,887 samples received from both Outpatient Department (OPD) and Inpatient Department (IPD) in the central laboratory for clinical chemistry, preanalytical error was found in 482 samples (0.071%) and most common was hemolysis and billing errors. Out of total 677,887 samples received repeat testing was done in 287 samples (0.042%), turnaround time (TAT) exceeded in total 2,29,629 samples (33.87%) and transcription errors/amended report were seen in 41 (0.006%). Conclusion: Sample haemolysis, billing errors, insufficient sample and clotted sample are the most common preanalytical errors encountered in clinical laboratory. turnaround time (TAT) was exceeded in one third of the samples. These errors can be minimised by repeated training, annual competency assessment and more automation in preanalytical phase. [ABSTRACT FROM AUTHOR]

Purpose: The blood sampling procedure is complex and prone to failure, as reflected by preanalytical errors in pediatric hospital care. The primary aim was to evaluate if the risk of preanalytical errors was higher with capillary blood sampling than with venous blood sampling, and secondary, explore specific factors associated with preanalytical errors, both overall and stratified by capillary and venous blood sampling. Design and Methods: This observational pediatric hospital study collected outcomes from medical records and blood sampling surveys from year 2014 to 2016. The risk of preanalytical errors was analyzed with adjusted‐odds ratio (adj‐OR) by multivariable logistic regression with 95% confidence intervals (CIs). Results: Overall, 128 (13%) preanalytical errors were identified among 951 blood samples. The proportion and adj‐OR of errors was significantly higher in capillary compared with venous blood samples, 72 (20%) of 354 versus 56 (9.4%) of 597, p =.001, adj‐OR 2.88 (CI 1.79–4.64). Blood collection with multiple sample tubes was significantly associated with increased risk of preanalytical errors (n = 97 of 601, 16%), while log weight (kg) significantly decreased the risk of preanalytical errors adj‐OR 0.66 (CI 0.50–0.86), indicating a protective effect of increasing weight. However, stratified analyses indicated a protective effect of increasing log weight for venous blood sampling adj‐OR 0.52 (CI 0.38–0.72), but not capillary blood sampling, adj‐OR 1.08 (CI 0.76–1.55). Conclusion: This study indicates that capillary blood sampling collection increases the risk of preanalytical errors. Further, a child's increasing body weight reduced the risk of preanalytical errors, while multiple sample tube collections significantly increased the risk of preanalytical errors. Practice Implications: This new information may help nurses improve their knowledge concerning blood sampling collection in pediatrics. Altogether, this study also indicates that implementing more venous blood sampling and improve the cases of capillary sampling could reduce the number of preanalytical errors in pediatric hospitals. [ABSTRACT FROM AUTHOR]

Introduction: Medical and laboratory errors can be caused due to many reasons, including communication problem, inadequate training of the staff members, improper identification. Quality indicators can help in objective measurement of errors in various crucial steps. Aim: To determine the nature and frequency of preanalytical, some of the analytical and postanalytical errors in the clinical laboratory with the help of quality indicators. Materials and Methods: This was a retrospective study and data was collected for preanalytical, some of analytical and postanalytical errors from December 2020 to May 2021, from the central laboratory and were classified under various quality indicators. MS Excel was used to analyse the data and descriptive statistics such as number and percentage were used to present the data. Results: Out of the total 677,887 samples received from both Outpatient Department (OPD) and Inpatient Department (IPD) in the central laboratory for clinical chemistry, preanalytical error was found in 482 samples (0.071%) and most common was haemolysis and billing errors. Out of total 677,887 samples received repeat testing was done in 287 samples (0.042%), Turnaround Time (TAT) exceeded in total 2,29,629 samples (33.87%) and transcription errors/amended report were seen in 41 (0.006%). Conclusion: Sample haemolysis, billing errors, insufficient sample and clotted sample are the most common preanalytical errors encountered in clinical laboratory. The TAT was exceeded in one third of the samples. These errors can be minimised by repeated training, annual competency assessment and more automation in preanalytical phase.

INTRODUCTION: The management of preanalytical errors is an important role in contributing to the more effective and reliable study of patient outcomes. The aim of this study is to determine the frequency of the sample rejection of the preanalytical turn, to classify, to evaluate the effect of the education on errors. METHODS: : The rejected samples for the first six months of 2016 were analyzed. Red rates were classified according to the preanalytical errors and the place where they were analyzed. After the training, the same analysis was conducted again in the second half of the year and the effect on the sample rejection rates was followed. RESULTS: The first half of 2016 was 641.218, the last half 603.688 samples 1.244.906 samples were accepted in 1 year. In the first 6 months 7908 the last 6 months 8150, at the end of 1 year 16058 samples were rejected. The total sample rejection rate was 1.29%, 1.23% in the first 6 months and 1.35% in the last 6 months. The most frequent cause was the clotted sample (62.73%) and the insufficient sample (18.8%). Clotted samples were often seen in blood gas and hemogram (whole blood count) samples. There was a decrease in the rejection rates of the samples in biochemistry, hemogram and HbA1c after training (p

Introduction: The Key incident monitoring and management system program (KIMMS) program collects data for 19 quality indicators (QIs) from Australian medical laboratories. This paper aims to review the data submitted to see whether the number of errors with a higher risk priority number (RPN) have been reduced in preference to those with a lower RPN, and to calculate the cost of these errors. Materials and methods: Data for QIs from 60 laboratories collected through the KIMMS program from 2015 until 2018 were retrospectively reviewed. The results for each QI were averaged for the four-year average and coefficient of variation. To review the changes in QI frequency, the yearly averages for 2015 and 2018 were compared. By dividing the total RPN by 4 and multiplying that number by the cost of recollection of 30 AUD, it was possible to assign the risk cost of these errors. Results: The analysis showed a drop in the overall frequency of incidents (6.5%), but a larger drop in risk (9.4%) over the period investigated. Recollections per year in Australia cost the healthcare industry 27 million AUD. If the RPN data is used, this cost increases to 66 million AUD per year. Conclusions: Errors with a higher RPN have fallen more than those with lower RPN. The data shows that the errors associated with phlebotomy are the ones that have most improved. Further improvements require a better understanding of the root cause of the errors and to achieve this, work is required in the collection of the data to establish best-practice guidelines. [ABSTRACT FROM AUTHOR]

Objectives: Preanalytical phase quality, which begins with a request for testing and continues with patient preparation, sample collection, transport, processing and storage, is essential for coagulation tests. The Turkish Biochemical Society Preanalytical Phase Working Group created a survey to evaluate the preanalytical phase practices for coagulation laboratories in our country. Methods: The survey consisted of a total of 26 questions and included almost all steps of the preanalytical phase. Results: Fifty-four laboratory specialists have participated in the study. The survey results showed that participants have different practices for most stages of the preanalytical phase for coagulation tests. Conclusion: According to the survey results, a national guideline may help standardisation of the preanalytical phase for coagulation tests in our country and increasing training in this respect would contribute to achieving accurate test results. [ABSTRACT FROM AUTHOR]

BACKGROUND: An important component of laboratory medicine is preanalytical phase. Since laboratory report plays a major role in patient management, more importance should be given to the quality of laboratory tests. AIM: The present study was undertaken to find the prevalence and types of preanalytical errors at a tertiary care hospital in South India. MATERIALS AND METHODS: In this cross-sectional study, a total of 118,732 samples ([62,474 outpatient department [OPD] and 56,258 inpatient department [IPD]) were received in hematology laboratory. These samples were analyzed for preanalytical errors such as misidentification, incorrect vials, inadequate samples, clotted samples, diluted samples, and hemolyzed samples. RESULTS: The overall prevalence of preanalytical errors found was 513 samples, which is 0.43% of the total number of samples received. The most common preanalytical error observed was inadequate samples followed by clotted samples. Overall frequencies (both OPD and IPD) of preanalytical errors such as misidentification, incorrect vials, inadequate samples, clotted samples, diluted samples, and hemolyzed samples were 0.02%, 0.05%, 0.2%, 0.12%, 0.02%, and 0.03%, respectively. CONCLUSION: The present study concluded that incorrect phlebotomy techniques due to lack of awareness is the main reason for preanalytical errors. This can be avoided by proper communication and coordination between laboratory and wards, proper training and continuing medical education programs for laboratory and paramedical staffs, and knowledge of the intervening factors that can influence laboratory results.

Background: For Coronavirus Disease 2019 (Covid-19) infection, clinical laboratories provide essential contributions in the diagnosis of infection, stage prognostication, and evaluation of disease severity. We aimed to show laboratory problems including changes of test numbers, changes of test panels, and differences of preanalytical errors during Covid-19 pandemic and, in the current study, we also intended to give solutions for the obstacles to guide other possible pandemics. Methods: Our study was based on data between January 10, 2020, and May 10, 2020. The first Covid-19 case of the Republic of Turkey was seen March 10, 2020, which was determined as the threshold date for comparisons. This was a single center, data mining, retrospective study. Results: The number of patients admitted to hospital were 34,260 and 15,573, the number of total tests were 66,263 and 42,066 before and after pandemic, respectively, for the two-month interval. Test percentage changes were increased for D-dimer 136%, fibrinogen 3,113%, troponin 6%, and LDH 17%. Test percentage changes were decreased for CBC 37%, sedimentation 45%, aPTT 30%, PT 37%, CRP 28%, ProCT 10%, ferritin 29%, CK-MB 27%, blood gases 47%, ALT 43%, AST 42%, urea 42%, creatinine 42%, triglycerides 45%, sodium 42%, potassium 41%, chloride 21%, urine culture 58%, and blood culture 44%. When preanalytical sources of errors were investigated no differences were found. Conclusions: Laboratories must take quick action and be prepared for changes in patient services during pandemics. The most reliable ways for this are past experiences, statistical analysis, co-operation with administrations, high quality communication skills, and a risk-based management system. [ABSTRACT FROM AUTHOR]

Background: Unsuitable samples are common problem for laboratories. The blood collection tubes need to be validated or verified prior to their being used in the routine laboratory for reducing this situation. Objective: We aim to compare the technical qualifications of routinely used BD Vacutainer® Serum Separator Tubes™ II Advance Plus with BD Vacutainer® Barricor™ LH Plasma Tubes for local technical validation. Materials and methods: Apparently healthy 150 voluntary subjects were enrolled in the study. Samples were collected in two separated tubes by a single phlebotomist. Twelve quality indicators were used to compare these two different types of tubes for local technical validation. Differences (%) between them were calculated with the formula proposed by EFLM. In case of any difference of less than 1% for each indicator, the evaluated tube was considered as non-inferior. Results: Indicators, such as tubes with physical defects, that fail to create vacuum, not properly fitting into the blood collection device, under filling (10%), cracked tubes, tubes exterior surface contaminated with blood, hemolysed specimens, including fibrin strand/mass in the sample, red blood cell adhesion, poor/incomplete barrier formation were found non-inferior in Barricor™ tubes. White particulate matter (WPM) was observed in 24.6% of Barricor™. Therefore, the last indicator (tubes including gel/foreign material/WPM in sample after centrifugation) was found inferior for Barricor™. Conclusion: Technical local validation studies should be encouraged in terms of quality management. It was thought that WPM would not cause any interference in a properly filled tube. In addition to, Barricor™ was also found to be technically acceptable when evaluated through using all other indicators. [ABSTRACT FROM AUTHOR]

Introduction: The Key incident monitoring and management system program (KIMMS) program collects data for 19 quality indicators (QIs) from Australian medical laboratories. This paper aims to review the data submitted to see whether the number of errors with a higher risk priority number (RPN) have been reduced in preference to those with a lower RPN, and to calculate the cost of these errors. Materials and methods: Data for QIs from 60 laboratories collected through the KIMMS program from 2015 until 2018 were retrospectively reviewed. The results for each QI were averaged for the four-year average and coefficient of variation. To review the changes in QI frequency, the yearly averages for 2015 and 2018 were compared. By dividing the total RPN by 4 and multiplying that number by the cost of recollection of 30 AUD, it was possible to assign the risk cost of these errors. Results: The analysis showed a drop in the overall frequency of incidents (6.5%), but a larger drop in risk (9.4%) over the period investigated. Recollections per year in Australia cost the healthcare industry 27 million AUD. If the RPN data is used, this cost increases to 66 million AUD per year. Conclusions: Errors with a higher RPN have fallen more than those with lower RPN. The data shows that the errors associated with phlebotomy are the ones that have most improved. Further improvements require a better understanding of the root cause of the errors and to achieve this, work is required in the collection of the data to establish best-practice guidelines. [ABSTRACT FROM AUTHOR]

Objective: Vast majority of laboratory errors occurs in preanalytical phase and in vitro hemolysis is the most common among preanalytical errors. Automated serum index measurement is being used in routine biochemical analysis in Antalya Public Health Care Laboratory, since June 2014. Our aim in this study is to reveal the impact of serum index usage on rejected samples and rejected test rates due to hemolysis. Materials and methods: Hemolysis, icterus and lipemia (HIL) spectral interference reagent and program have been used in our laboratory since June 2014. In the current study, the number of samples and tests that were rejected due to hemolysis in June-August 2014 were compared with those rejected in the same period of 2013. Results: In 2014, the sample rejection rate was 2.53% and the rejected test rate was 0.48%. In 2013, the sample rejection rate was 0.56% and the rejected test rate was 0.55%. When compared two periods, statistically significant increase in rejected sample number due to hemolysis in 2014 is result of, visually undetectable hemolyzed samples previously can be identified by HIL method (p < 0.05). Conclusion: Usage of hemolysis index program in automated systems for detecting hemolysis was evaluated as a method which is standardized, semi-quantitative, with high reproducibility and allows test based rejection. [ABSTRACT FROM AUTHOR]

Aim: Blood analyses containing preanalytical errors (PAEs) are hazardous for patients. This study investigated the frequency of PAEs in blood analysis and the corresponding quality indicators of the sampling process in Swedish paediatric tertiary care. Methods: Data were retrieved from the laboratory at Astrid Lindgren Children's Hospital between 2013 and 2014. Preanalytical blood sampling performance was analysed according to the Six Sigma scale, ranging from 0 to 6 (933 137–3.4 defects per million [DPM]). Results: Of the 1 148 716 analyses, 61 656 (5.4%) were rejected due to PAEs. The PAEs ranged between hospital specialities from 1.9 to 9.4% (p < 0.001) and work shift times, from 6.0% in the day to 5.7% in the evening and 4.3% at night (p values <0.001). Clotting was the most prominent error (51.3%), affecting mostly haematology and coagulation analyses. Incorrectly filled samples represented almost 25% of all PAEs, with effects on chemistry, haematology and coagulation analyses. The sigma score for the overall preanalytical phase (3.2) corresponded to 44 565 DPM. Conclusion: Samples with PAEs were frequently clotted and insufficiently filled, and the distribution of errors varied within working shifts and specific analyses. The overall quality control in paediatric blood sampling was barely acceptable. [ABSTRACT FROM AUTHOR]

The presence of cold agglutinins (CAs) in samples intended for complete blood count (CBC) using automated haematology analysers might cause serious preanalytical errors. In this report we describe the case of a 90-year old female patient admitted to the Emergency department following trauma injuries. A blood testing on admission revealed surprisingly low red blood cell count (0.99 x 1012/L), low haematocrit (0.102 L/L) which did not correlate with haemoglobin concentration (100 g/L), and high erythrocytes indices (mean corpuscular haemoglobin, 101 pg; mean corpuscular haemoglobin concentration, 980 g/L). In the second sample, after repeated collection, almost equal results were observed. Blood smear examination under the microscope revealed clusters of erythrocytes. Cold agglutinins presence was suspected and, in order to get valid results, sample was warmed to 37 °C. Correction of CBC was observed. Furthermore, we performed some additional analysis to confirm the presence of CAs in this patient. The aim of this report was to present the laboratory findings in a case of CAs and propose a laboratory procedure for whole blood samples with suspected CAs. [ABSTRACT FROM AUTHOR]

Background: The key incident monitoring and management systems (KIMMS) quality assurance program monitors incidents in the pre- and postanalytical phases of testing in medical laboratories. Haemolysed specimens have been found to be the most frequent preanalytical error and have major implications for patient care. The aims of this study were to assess the suitability of KIMMS for quality reporting of haemolysis and to devise a meaningful method for reporting and monitoring haemolysis. Methods: A structured survey of 68 Australian KIMMS laboratory participant organisations was undertaken. Quarterly haemolysis reports (2011-2014) were analysed. Results: Among 110 million accessions reported, haemolysis rates varied according to the reporting methods that participants used for assigning accessions (16% of participants reported haemolysis by specimen and 83% reported by episode) and counting haemolysis rejections (61% by specimen, 35% by episode and 3% by test). More than half of the participants (56%) assigned accessions by episode and counted rejections by specimen. For this group, the average haemolysis rate per 100,000 episodes was 177 rejected specimens with the average rate varying from 100 to 233 over time. The majority of participants (91%) determined rejections using the haemolysis index. Two thirds of participants (66%) recorded the haemolysis manually in laboratory information systems. Conclusions: KIMMS maintains the largest longitudinal haemolysis database in the world. However, as a means of advancing improvements in the quality of the preanalytical laboratory process, there is a need to standardise reporting methods to enable robust comparison of haemolysis rejection rates across participant laboratories. [ABSTRACT FROM AUTHOR]

BACKGROUND: An important component of laboratory medicine is preanalytical phase. Since laboratory report plays a major role in patient management, more importance should be given to the quality of laboratory tests. AIM: The present study was undertaken to find the prevalence and types of preanalytical errors at a tertiary care hospital in South India. MATERIALS AND METHODS: In this cross-sectional study, a total of 118,732 samples ([62,474 outpatient department [OPD] and 56,258 inpatient department [IPD]) were received in hematology laboratory. These samples were analyzed for preanalytical errors such as misidentification, incorrect vials, inadequate samples, clotted samples, diluted samples, and hemolyzed samples. RESULTS: The overall prevalence of preanalytical errors found was 513 samples, which is 0.43% of the total number of samples received. The most common preanalytical error observed was inadequate samples followed by clotted samples. Overall frequencies (both OPD and IPD) of preanalytical errors such as misidentification, incorrect vials, inadequate samples, clotted samples, diluted samples, and hemolyzed samples were 0.02%, 0.05%, 0.2%, 0.12%, 0.02%, and 0.03%, respectively. CONCLUSION: The present study concluded that incorrect phlebotomy techniques due to lack of awareness is the main reason for preanalytical errors. This can be avoided by proper communication and coordination between laboratory and wards, proper training and continuing medical education programs for laboratory and paramedical staffs, and knowledge of the intervening factors that can influence laboratory results. [ABSTRACT FROM AUTHOR]

Background: Patient satisfaction in outpatient phlebotomy settings typically depends on wait time and venipuncture experience, and many patients equate their experiences with their overall satisfaction with the hospital. Methods: We compared patient service times and preanalytical errors pre- and postimplementation of an integrated electronic health record (EHR)-laboratory information system (LIS) and electronic specimen collection module. We also measured patient wait time and assessed patient satisfaction using a 5-question survey. Results: The percentage of patients waiting less than 10 minutes increased from 86% preimplementation to 93% postimplementation of the EHR-LIS (P ≤.001). The median total service time decreased significantly, from 6 minutes (IQR, 4-8 minutes), to 5 minutes (IQR, 3-6 minutes) (P = .005). The preanalytical errors decreased significantly, from 3.20 to 1.93 errors per 1000 specimens (P≤.001). Overall patient satisfaction improved, with an increase in excellent responses for all 5 questions (P ≤.001). Conclusions: We found several benefits of implementing an electronic specimen collection module, including decreased wait and service times, improved patient satisfaction, and a reduction in preanalytical errors. [ABSTRACT FROM AUTHOR]

Background: Preanalytical variables, such as sample collection, handling and transport, may affect patient results. Preanalytical phase quality monitoring should be established in order to minimize laboratory errors and improve patient safety. Methods: A retrospective study (2001-2013) of the results obtained through the Spanish Society of Clinical Biochemistry and Molecular Pathology (SEQC) External quality assessment (preanalytical phase) was performed to summarize data regarding the main factors affecting preanalytical phase quality. Our aim was to compare data from 2006 to 2013 with a previously published manuscript assessing the 2001-2005 period. Results: A significant decrease in rejection rates was observed both for blood and urine samples. For serum samples, the most frequent rejection causes in the first period were non-received samples (37.5%), hemolysis (29.3%) and clotted samples (14.4%). Conversely, in the second period, hemolysis was the main rejection cause (36.2%), followed by non-received samples (34.5%) and clotted samples (11.1%). For urine samples, the main rejection cause overall was a non-received sample (up to 86.1% of cases in the second period, and 81.6% in the first). For blood samples with anticoagulant, the number of rejections also decreased. While plasma-citrate- ESR still showed the highest percentages of rejections (0.980% vs. 1.473%, p < 0.001), the lowest corresponded to whole-blood EDTA (0.296% vs. 0.381%, p < 0.001). Conclusions: For the majority of sample types, a decrease in preanalytical errors was confirmed. Improvements in organization, implementation of standardized procedures in the preanalytical phase, and participation in a Spanish external quality assessment scheme may have notably contributed to error reduction in this phase. [ABSTRACT FROM AUTHOR]

Preanalytical errors have previously been shown to contribute a significant proportion of errors in laboratory processes and contribute to a number of patient safety risks. Accreditation against ISO 15189:2012 requires that laboratory Quality Management Systems consider the impact of preanalytical processes in areas such as the identification and control of non-conformances, continual improvement, internal audit and quality indicators. Previous studies have shown that there is a wide variation in the definition, repertoire and collection methods for preanalytical quality indicators. The International Federation of Clinical Chemistry Working Group on Laboratory Errors and Patient Safety has defined a number of quality indicators for the preanalytical stage, and the adoption of harmonized definitions will support interlaboratory comparisons and continual improvement. There are a variety of data collection methods, including audit, manual recording processes, incident reporting mechanisms and laboratory information systems. Quality management processes such as benchmarking, statistical process control, Pareto analysis and failure mode and effect analysis can be used to review data and should be incorporated into clinical governance mechanisms. In this paper, The Association for Clinical Biochemistry and Laboratory Medicine PreAnalytical Specialist Interest Group review the various data collection methods available. Our recommendation is the use of the laboratory information management systems as a recording mechanism for preanalytical errors as this provides the easiest and most standardized mechanism of data capture. [ABSTRACT FROM AUTHOR]

Phlebotomy is a neglected and ignored area within the health services. Optimal blood sample procedure is essential for diagnosis and treatment. An ever increasing number of nurses are imposed the job of taking blood samples. The purpose of this project has been to gain knowledge about blood sampling procedures, practical training and point of care testing (POCT) received by nursing students and, in addition, their attitude to these activities. The goal was to uncover a possible need for a better training procedure. Three different university colleges participated in a questionnaire-based study attended by 202 2nd and 3rd year nursing students. The subject matter was training, experience and attitude towards phlebotomy and POCT. The questionnaire had both open and closed alternative answers. The nursing students described unstructured training in taking blood samples and POCT. The training appeared coincidental without learning goals, leaving the students feeling they had insufficient competence. The project also shows that many nursing students look upon venous blood collection as a natural part of their professional activity when working in a nursing home, especially because it is more convenient for the patient and the institution. To avoid incorrect analysis results, resulting in unnecessary examinations and delay in treatment, the competence of the nurses should be upgraded. The nursing curriculum needs be improved according to blood collection standards and POCT when carried out by nurses.

Trends in clinical laboratory practice place more demands on the quality of patient specimens. Advances in analytical performance (i.e., increased automation, reduced sample volume, increased assay sensitivity), as well as efficiency and cost reduction gains (i.e., throughput, test turnaround time) have improved medical practice. These changes, however, have caused an increased incidence of preanalytical errors, dictating the need for higher-quality specimens. The following paper addresses these errors in addition to methodologies for improving the quality of the preanalytical phase.

Objectives: Most preanalytical errors at our institution occur during nonphlebotomy blood draws. We implemented an electronic health record (EHR), interfaced the EHR to the laboratory information system, and designed a new specimen collection module. We studied the effects of the new system on nonphlebotomy preanalytical errors.Methods: We used an electronic database of preanalytical errors and calculated the number and type of the most common errors in the emergency department (ED) and inpatient nursing for 3-month periods before (August-October 2014) and after (August-October 2015) implementation. The level of staff compliance with the new system was also assessed.Results: The average monthly preanalytical errors decreased significantly from 7.95 to 1.45 per 1,000 specimens in the ED (P < 0001) and 11.75 to 3.25 per 1,000 specimens in inpatient nursing (P < 0001). The rate of decrease was similar for mislabeled, unlabeled, wrong specimen received and no specimen received errors. Most residual errors (80% in the ED and 67% in inpatient nursing) occurred when providers did not use the new system as designed.Conclusions: Implementation of a customized specimen collection module led to a significant reduction in preanalytical errors. Improved compliance with the system may lead to further reductions in error rates. [ABSTRACT FROM AUTHOR]

Background: Phlebotomy, a commonly performed medical procedure in healthcare, is essential for disease diagnosis and patient management. However, poorly performed phlebotomy can compromise patient safety, healthcare worker (HCW) safety, and specimen quality. We carried out a study between June and July 2010 to assess knowledge, quality and safety of phlebotomy before implementation of a public-private partnership between Becton, Dickinson and Company and the US President's Emergency Plan for AIDS Relief.Methods: This was a cross-sectional observational study in 8 healthcare facilities within 4 regions of Kenya. HCWs were observed conducting venous and capillary blood collections, and pre- and posttests were offered during HCW training.Results: Of 283 blood samples obtained, 194 were venous draws conducted by 72 HCWs and 89 were capillary draws performed by 33 HCWs. Based on 12 preset quality-associated criteria, none of the 194 observed phlebotomies met the standard. In total, 91 HCWs were trained in phlebotomy. The mean knowledge increase between pre- and posttraining test was 41%, ranging from 39% to 45% (95% confidence interval, 29.3%-53.5%;P< .001).Conclusions: Inadequate knowledge and imperfect phlebotomy procedures were noted. This formed the basis for the safe phlebotomy partnership to address these deficiencies. To ensure sustainability, safe phlebotomy practices were integrated into preservice training. [ABSTRACT FROM AUTHOR]

Background: Inadequately completed laboratory test request forms contribute to preanalytical errors and limit the advice of pathologists when interpreting laboratory test results. Educating clinicians about this has been proposed by several studies as a strategy to reduce the occurrence. Aim: We aimed to determine the effectiveness of such education on the prevalence of adequately completed laboratory test request forms. Subjects and Methods: This was a quasi.experimental study conducted at the chemical pathology laboratory of the Lagos University Teaching Hospital, Nigeria. Incoming laboratory request forms were audited for a period of 1 month looking out for eight data variables. Subsequently, intensive clinician education was undertaken via seminars, publications, and orientation programs on 670 clinicians for 6 weeks duration. After that, a repeat audit for the same data variables was conducted for another period of 1 month. A Z.test of significance for the comparison of independent proportions was conducted for form errors pre. and post.intervention. Results: Error rates for missing variables pre. and post.clinician education were: Name pre = 0 (0%), post = 0 (0%); age pre = 330 (21.6%), post = 28 (1.9%), P < 0.001; gender pre = 64 (4.2%), post = 53 (3.6%), P = 0.37; hospital number pre = 848 (55.6%), post = 524 (35.3%), P < 0.001; clinician name pre = 165 (10.8%), post = 64 (4.3%), P < 0.001; ward/clinic pre = 311 (20.4%), post = 235 (15.8%), P < 0.01; clinical diagnosis pre = 220 (14.4%), post = 33 (2.2%), P < 0.001; specimen type pre = 169 (11.1%), post = 116 (7.8%), P < 0.01, respectively. Conclusion: There was an improvement in the inadequate completion of laboratory request forms after clinicians were educated on proper completion using various interactive media, showing that it is an effective strategy. However, further studies are required to identify which educational strategy is most effective in reducing error rates in laboratory test request forms. [ABSTRACT FROM AUTHOR]

The present paper aims to be an example of the clinical laboratory risk assessment, though it does not include all the possible risks. This paper presents the evaluation technique FMEA (failure modes and effects analysis) as a tool for risk management and quality improvement of the clinical laboratory analyses. The purpose of FMEA is to aid the clinical laboratory in raising awareness and in identification of the possible hazardous situations of a testing system. Once the hazardous event has been identified, the risk can be estimated, analyzed and treated. Using the standard CLSI EP18-A guidelines, the table FMEA has been laid and, thus the errors from the pre-analytical process, especially, have been ranked according to criticality. [ABSTRACT FROM AUTHOR]

Objectives Errors in preanalytical phase occupied for almost half of total errors in clinical laboratory, and the causes are related to medical staff's quality awareness and behaviors. In order to reduce the preanalytical errors in our hospital, we established and applied a training system to improve the situation. Methods The disqualified sample types and major causes of errors in the preanalytical phase were investigated in clinical laboratory department from September 2008 to August 2009. In the following year, we established and applied a training system to affect the quality awareness and behaviors of medical staff. Questionnaire investigation was analyzed to illustrate the changes of respondents' quality awareness and behavior, and the preanalytical errors were reanalyzed according to different departments to evaluate the effects of the intervention measures. Results The total disqualification rate in the preanalytical phase obtained from September 2008 to August 2009 was 1.36%, and the major types of disqualified samples were coagulation of anticoagulant sample, sample inadequacy, sample container error, sample information error, and sample type error. After application of established training system, respondents' quality awareness on preanalytical samples changed dominantly, and respondents' own behavior and behavior to others also changed notably. The total disqualification rate in preanalytical phase dropped to 0.94%, among 33 clinical departments, the preanalytical errors in 25 departments decreased to various degrees, and 10 departments had overall decreasing amplitude over 50%. Conclusions The overall effect of the application of established training system is very good, and the disqualification rate of the major departments decrease to various degrees. [ABSTRACT FROM AUTHOR]

Background: Hemolysis is the main cause of non-quality samples in clinical laboratories, producing the highest percentage of rejections in the external assurance programs of preanalytical quality. The objective was to: 1) study the agreement between the detection methods and quantification of hemolysis; 2) establish comparable hemolysis interference limits for a series of tests and analytical methods; and 3) study the preanalytical variables which most influence hemolysis production. Methods: Different hemoglobin concentration standards were prepared using the reference method. Agreement was studied between automated methods [hemolytic indexes (HI)] and reference method, as well as the interference according to hemolysis degree in various biochemical tests was measured. Preanalytical variables which could influence hemolysis production were studied: type of extraction, type of tubes, transport time, temperature and centrifugation conditions. Results: Good agreement was obtained between hemoglobin concentrations measured using the reference method and HI, for the most of studied analyzers, particularly those giving quantitative HI. The hemolysis interference cut-off points obtained for the majority of tests studied (except LDH, K) are dependent on the method/analyzer utilized. Furthermore, discrepancies have been observed between interference limits recommended by the manufacturer. The preanalytical variables which produce a lower percentage of hemolysis rejections were: centrifugation at the extraction site, the use of lower volume tubes and a transport time under 15 min at room temperature. Conclusions: The setting of interference limits (cut-off) for each used test/method, and the study of preanalytical variability will assist to the results harmonization for this quality indicator. [ABSTRACT FROM AUTHOR]

Objective Errors in preanalytical phase decrease the accuracy of reports from clinical laboratory department. Considering the disqualified rate of preanalytical sample in our hospital, we performed several intervention measures to improve the situation. Methods The disqualified sample types and major causes of errors in the preanalytical phase were investigated in clinical laboratory department from September 2008 to August 2009. In the following year, we utilized multiple measures to properly intervene the key points of whole sample collection process, and the preanalytical errors were reanalyzed trimonthly, then the disqualification rate of total, major disqualified sample types and different test groups were calculated to evaluate the effects of the intervention measures. Results The total disqualification rate in the preanalytical phase obtained from September 2008 to August 2009 was 1.36%, and the major types of disqualified samples were coagulation of anticoagulant sample, sample inadequacy, sample container error, sample information error and sample type error. After one year intervention through key points of whole preanalytical sample collection process, the total disqualification rate dropped to 0.94%, and the disqualification rate of coagulation of anticoagulant sample, sample inadequacy, sample container error, sample information error, and sample type error decreased by 20.45%, 28.00%, 25.00%, 76.92%, and 66.66%, respectively. As for test groups, the decreasing amplitude of biochemical, routine, immunological, microbiological and emergency test group was 47.36%, 33.33%, 20.00%, 50.00%, and 21.43%, respectively. Conclusions The overall effect of the interventions is very good, and the disqualification rate of the main causes decreases to various degrees. [ABSTRACT FROM AUTHOR]

Background and Objectives Errors in sample identification contribute to patient mismanagement and erroneous administration of blood and blood products. This report describes the rate of sample identification errors over 2 years in a multispecialty hospital in India and possible measures to decrease these errors. Materials and Methods Various measures were taken to reduce the risk of identification errors during sample collection, laboratory processing and administration of blood. The bedside blood grouping method by the slide agglutination technique was also introduced along with other measures in December 2011 as a risk mitigation step to prevent an ABO incompatible transfusion, as well as to provide a method of surveillance for possible errors during transfusion. Results The rate of sample identification errors was 48 and 45 per 1 000 000 among the total tests billed in 2011 and 2012, respectively. In the blood bank alone, the sample identification error rate was 0·96 in 2011 and 0·46 in 2012 per 1000 bags of blood and blood components issued after the various steps to prevent such errors in the blood bank were introduced. 81% of these errors (26 out of 32) have been reported in the inpatient setting. 15·6% (5 out of 32) were repeat errors made by the same technicians. Conclusions Among the various measures used to reduce sample identification errors, bedside blood grouping allows prevention of ABO incompatible transfusions when performed by trained technicians and may also be used as a method of active surveillance for sample identification errors in hospitals. [ABSTRACT FROM AUTHOR]

Objective  Lipemia is an important cause of preanalytical errors in laboratory results. They affect the specimen integrity and trustworthiness of laboratory results. The present study was to assess the impact of lipemia on routine clinical chemistry analytes. Methods  Anonymous leftover serum samples with normal levels of routine biochemical parameters were pooled. Twenty such pooled serum samples were used for the study. The samples were spiked with commercially available intralipid solution (20%) to produce lipemic concentrations of 0, 400 (mild, 20 μL), 1,000 (moderate, 50 μL), and 2,000 mg/dL (severe, 100 μL). Glucose, renal function test, electrolytes, and liver function test were estimated in all the samples. Baseline data without the effect of interference was considered as true value and percentage bias for the spiked samples was calculated. Interference was considered significant if the interference bias percentage exceeded 10%. Result  Parameters like glucose, urea, creatinine, direct bilirubin, sodium, potassium, and chloride showed negative interference at mild and moderate lipemic concentration and positive interference at severe lipemic concentration. Parameters like aspartate transaminase (AST) and alanine transaminase (ALT) showed negative interference at mild and positive interference at moderate and severe lipemic concentration. Whereas uric acid, total protein, albumin, total bilirubin, alkaline phosphatase, gamma-glutamyl transferase, calcium, magnesium, and phosphorous showed positive interference at all concentrations. Significant interference (> 10%) was shown for magnesium (mild lipemia), albumin, direct bilirubin, ALT, and AST at moderate lipemic concentration. All parameters showed significant interference at severe lipemic concentration. Conclusion  All the study parameters are affected by lipemic interference at varying levels. Laboratory-specific data regarding lipemic interference at various concentrations on the clinical biochemistry parameters is needed., Competing Interests: Conflict of Interest None declared., (The Indian Association of Laboratory Physicians. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. ( https://creativecommons.org/licenses/by-nc-nd/4.0/ ).)