Biochemistry laboratory errors and patient safety: Turkey data

Objectives Our aim in this study is to provide information about the rate of errors in the process of the biochemistry laboratories in the hospitals in Turkey with the “Indicators”. Methods The hospitals calculate their own data according to the indicator cards defined by the Ministry of Health of Turkey and enter into the system once in a year. In this study we examined the quality indicators related to the disruptions in the biochemistry laboratory of hospitals for the year of 2018. Results All indicators except “Non-timely reported result rate in biochemistry laboratory” are found to be significantly higher in university hospitals. This indicator is found to be significantly higher in private hospitals(p:0.030) “Lost sample rate in biochemistry laboratory” is found to be significantly higher in Eastern Anatolia Region (p:0.000) and “Non-timely reported result rate in biochemistry laboratory” is found to be significantly higher in Aegean Region (p:0.008). Conclusions The ratio of non-timely reported result rate is the most seen disruption in biochemistry laboratories. It may be due to lots of reasons; lack of biochemistry equipment, lack of staff, problems in transportation, etc. The management of hospitals and the staff should take measures and regulations about problems.

Contribution to Lab Errors as a Healthcare Professional

With the emergence of new diagnostic markers every day, laboratory investigations have become an essential and integral part of healthcare. It is prudent to ensure that this dependence on diagnosis and treatment in laboratories is serious and responsible. This responsibility does not lie solely within the confines of a laboratory but extends to any healthcare personnel involved in the process of report generation. Reviews revolving around this topic focus on the laboratory's roles and conclude with the emphasis on paying attention to the extra-analytical phases. In this review, we attempt to expand our audience to include all healthcare professionals and highlight their role in increasing or minimizing laboratory errors. The process of creating a reliable report will be viewed as a shared responsibility. This includes the patient who has the responsibility to follow the direction given before specimen collection and extends to the doctor who interprets the results, keeping in mind all the inherent limitations that a test encompasses.

Development and Application of Computerized Risk Registry and Management Tool Based on FMEA and FRACAS for Total Testing Process

Background and Objectives: Risk management is considered an integral part of laboratory medicine to assure laboratory quality and patient safety. However, the concept of risk management is philosophical, so actually performing risk management in a clinical laboratory can be challenging. Therefore, we would like to develop a sustainable, practical system for continuous total laboratory risk management. Materials and Methods: This study was composed of two phases: the development phase in 2019 and the application phase in 2020. A concept flow diagram for the computerized risk registry and management tool (RRMT) was designed using the failure mode and effects analysis (FMEA) and the failure reporting, analysis, and corrective action system (FRACAS) methods. The failure stage was divided into six according to the testing sequence. We applied laboratory errors to this system over one year in 2020. The risk priority number (RPN) score was calculated by multiplying the severity of the failure mode, frequency (or probability) of occurrence, and detection difficulty. Results: 103 cases were reported to RRMT during one year. Among them, 32 cases (31.1%) were summarized using the FMEA method, and the remaining 71 cases (68.9%) were evaluated using the FRACAS method. There was no failure in the patient registration phase. Chemistry units accounted for the highest proportion of failure with 18 cases (17.5%), while urine test units accounted for the lowest portion of failure with two cases (1.9%). Conclusion: We developed and applied a practical computerized risk-management tool based on FMEA and FRACAS methods for the entire testing process. RRMT was useful to detect, evaluate, and report failures. This system might be a great example of a risk management system optimized for clinical laboratories.

Incidental Pheochromocytoma: Silent but Violent

Background: Pheochromocytoma is a rare catecholamine secreting neuroendocrine tumor. It arises from the chromaffin cells of adrenal medulla. It is diagnosed in 5–6.5% of adrenal incidentalomas which is not common. The usual clinical presentation includes the classic triad of sweating, headache and tachycardia. However, asymptomatic cases are seen in 8% of the patients with pheochromocytoma. We present a clinically asymptomatic patient diagnosed during work up of adrenal incidentaloma. The possible etiology for silent presentation includes one of the following:(i) Presence of a smaller functional tissue (ii)Accelerated turnover of the tumor causing release of the unmetabolized catecholamines in small amounts (iii) Pulsatile tumor secretion (iv)Tumors triggered by stress (v) Laboratory errors due to inappropriate handling of specimen at high-temperature (vi) False negative test results secondary to caffeine ingestion in the prior 24 hours. Clinical Case: 59 years old Caucasian female with past medical history of type 2 diabetes mellitus, obesity, essential hypertension, nonischemic cardiomyopathy, and asthma presented to the emergency room with complaints of worsening shortness of breath and pedal edema for 1 month. Physical exam: Blood pressure 146/78 mm of Hg and heart rate 82 beats/min, mild pedal edema, no pulmonary crackles. On imaging, CT angio chest showed irregularly enhancing right adrenal mass measuring 3.4 cm. This adrenal incidentaloma was not visualized on imaging done 5 years ago. Further, MRI abdomen revealed 4.1 cm right adrenal mass. Laboratory testing showed high total plasma metanephrines: 890 pg/ml (< or = 205), 24-hour urine metanephrines: 2337 (140–785), A1C: 10%. This confirmed the diagnosis of adrenal pheochromocytoma. Preoperatively, she was started on phenoxybenzamine 10 mg BID and encouraged on liberal salt intake. During the course, her blood pressure and heart rate were monitored daily. She underwent right adrenalectomy. Surgical pathology revealed 4.1 cm pheochromocytoma, negative margins with extension to the adipose tissues and vascular invasion, PASS score = 4. Post operatively, patient declined to get labs done. Due to high risk behavior of the tumor, patient needs to be monitored annually for lifelong. Conclusion: Pheochromocytoma is an uncommon tumor with varied clinical presentation. It can manifest itself widely from being silent to aggressive disease. This warrants high suspicion, early detection and management, thereby reducing the morbidity and mortality. Lately, there has been increased incidence of adrenal incidentalomas owing to widespread use of radiological investigations. We report a case of incidental pheochromocytoma which is biochemically active but clinically asymptomatic. This emphasizes the importance of being more vigilant during the evaluation of adrenal incidentalomas.

Evaluation of pre-analytical phase performance of emergency laboratory by harmonized quality indicators and six sigma

Background: Pre-analytical errors account for up to 70% of all the errors made in laboratory diagnostics which are mostly not directly under laboratory control. Laboratories across the world have been using different Quality indicators (QIs) for identifying and quantification of pre-analytical errors. Objective of the present study is to identify the different pre-analytical errors with their frequency and to assess the pre-analytical phase performance of emergency laboratory by using harmonized Quality Indicators and six sigma metrics. Methods and material: A prospective observational study was conducted from January 2019 to December 2019 to monitor the inappropriateness of samples and test request forms. We have quantified the performance of pre-analytical phase of our emergency laboratory based on the harmonized QIs proposed by The International Federation of Clinical Chemistry Working Group on Laboratory Errors and Patient Safety (IFCC- WGLEPS) and six sigma metrics. Results: Emergency laboratory received a total of 55431 samples during Jan- 2019 to Dec- 2019. Number of pre-analytical errors were 1089 which accounted for 1.96% of total samples received. Haemolysed samples, clotted samples and samples with insufficient volume were contributed to 37%, 26% and 15% of the total pre-analytical errors respectively. Conclusions: Pre-analytical phase performance of our emergency laboratory complies with the quality specifications laid by the International Federation of Clinical Chemistry Working Group on Laboratory Errors and Patient Safety (IFCC-WGLEPS). Implementation of harmonised QIs assures the comparability of laboratory findings with different laboratories across the world.

Estimation of change limits (deltacheck) in clinical laboratory

Objectives Change limits, more commonly called delta check, are those in which a change in a patient’s measured result in relation to their corresponding preceding measurement is suspected of being erroneous and should be considered as a doubtful result. The aim of this study was to provide change limits for some biochemical and haematological quantities to detect doubtful measured results and to assess its effectiveness to detect erroneous results for their application in and the standardization of the plausibility control. Methods Change limits have been estimated for 13 biochemical and 6 haematological quantities. For each quantity, relative differences (D), expressed as a percentage between the two consecutive measured results from the same patient (from scheduled laboratory requests), were calculated. From these differences (D), the p5 and p95 percentiles of the data distribution were calculated. To assess the effectiveness of the change limits to detect laboratory errors, 43 erroneous laboratory reports, containing different biochemical and haematological quantities, were obtained from the standard laboratory plausibility control procedure. Results From the 43 erroneous laboratory reports, 31 (72%) were due to endovenous administration errors and 12 (28%) were due to mislabeling errors. All erroneous laboratory reports were detected when the change limits of the quantities were combined and applied together. Conclusions The best combination of quantities, which detect all the erroneous reports in the same specimen were: potassium, albumin, creatinine, glucose and haemoglobin.

Risk identification of a hospital laboratory pre-analytics through failure mode and effect analysis

Background: Implementing an active system to identify, monitor and manage risk from laboratory errors can enhance patient safety and quality of care. Aims and Objectives: Failure Mode and Effect Analysis (FMEA) technique allows evaluating and measuring the hazards of a process malfunction, to decide where to execute improvement actions, and to measure the outcome of those actions. The aim of this study was to assess pre analytical phase of laboratory testing, mitigate risk and thereby increase patient safety. Materials and Methods: Steps followed in the study were: planning the study, selecting team members, analysis of the processes, risk analysis, and developing a risk reduction protocol by incorporating corrective actions. A Fault Tree Analysis diagram was used to plot the cascade of faults leading to the pre analytical errors. Risk Priority Number (RPN) was assigned. A minimum cut- off 40 RPN was considered for interventions and highest RPN errors were prioritized with corrective actions. Post intervention RPN score was calculated. Results: Eight failure modes had the highest RPN. Corrective actions were prioritized against these errors. RPN scores of test ordering error, sample collection error, transport errors, error in patient identification, site selection, urine samples not received, sample accessioning and sample processing errors decreased, post intervention. Conclusion: With thorough planning, we can use FMEA as a common standard to analyze risk in pre analytical phase of laboratory testing.

Identification of types and frequency of pre-analytical errors in hematology laboratory at a tertiary hospital of Nepal

Background: In the laboratory, errors can occur at any stage of sample processing; pre-analytical, analytical, and post-analytical. Since the pre-analytical phase is the most common source of laboratory errors, the goal of this study is to identify the types and frequency of pre-analytical errors in the hematology laboratory.Materials and Methods: This is a cross-sectional descriptive study done at Nepal Medical College Teaching hospital for a duration of nine months (January 2020 to September 2020). All blood samples received at the hematology laboratory were included whereas biochemistry and special tests blood samples were excluded. Samples were checked for misidentification (incorrectly labeled vials/vials without labels/incorrectly filled forms), incorrect samples (wrong choice of vials), clotted samples, inadequate samples, diluted samples, hemolyzed samples. The errors that occurred in these samples (both inpatient and outpatient) were noted down and measures were taken accordingly before analyzing the sample.Results: The total number of samples received was 15,337. Pre-analytical errors were seen in 857 samples (5.5%). Inadequate samples (25%) were the most common error followed by incorrect samples (20%), hemolyzed samples (20%), misidentification (14%), clotted samples (12%), and diluted samples (9%). Complete blood count test was most affected. Samples from the inpatient department were most affected.Conclusions: The preanalytical error rate in the hematology unit was 5.5% with an inadequate sample being the commonest error. Most of the errors were seen in the test requested for a complete blood count. Samples from the inpatient department showed the most errors.

Modelling Soluble Solids Content Accumulation in ‘Braeburn’ Apples

Optical sensor data can be used to determine changes in anthocyanins, chlorophyll and soluble solids content (SSC) in apple production. In this study, visible and near-infrared spectra (729 to 975 nm) were transformed to SSC values by advanced multivariate calibration models i.e., partial least square regression (PLSR) in order to test the substitution of destructive chemical analyses through non-destructive optical measurements. Spectral field scans were carried out from 2016 to 2018 on marked ‘Braeburn’ apples in Southwest Germany. The study combines an in-depth statistical analyses of longitudinal SSC values with horticultural knowledge to set guidelines for further applied use of SSC predictions in the orchard to gain insights into apple carbohydrate physiology. The PLSR models were investigated with respect to sample size, seasonal variation, laboratory errors and the explanatory power of PLSR models when applied to independent samples. As a result of Monte Carlo simulations, PLSR modelled SSC only depended to a minor extent on the absolute number and accuracy of the wet chemistry laboratory calibration measurements. The comparison between non-destructive SSC determinations in the orchard with standard destructive lab testing at harvest on an independent sample showed mean differences of 0.5% SSC over all study years. SSC modelling with longitudinal linear mixed-effect models linked high crop loads to lower SSC values at harvest and higher SSC values for fruit from the top part of a tree.

Modelling Soluble Solids Content Accumulation in ’Braeburn’ Apples

Optical sensor data can be used to determine changes in anthocyanins, chlorophyll and soluble solids content (SSC) in apple production. In this study, visible and near-infrared spectra (729 to 975 nm) were transformed to SSC values by advanced multivariate calibration models i.e. partial least square regression (PLSR) in order to test the substitution of destructive chemical analyses through non-destructive optical measurements. Spectral field scans were carried out from 2016 to 2018 on marked ’Braeburn’ apples in Southwest Germany. The study combines an in-depth statistical analyses of longitudinal SSC values with horticultural knowledge to set guidelines for further applied use of SSC predictions in the orchard to gain insights into apple carbohydrate physiology. The PLSR models were investigated with respect to sample size, seasonal variation, laboratory errors and the explanatory power of PLSR models when applied to independent samples. As a result of Monte Carlo simulations, PLSR modelled SSC only depended to a minor extent on the absolute number and accuracy of the wet chemistry laboratory calibration measurements. The comparison between non-destructive SSC determinations in the orchard with standard destructive lab testing at harvest on an independent sample showed mean differences of 0.5 % SSC over all study years. SSC modelling with longitudinal linear mixed-effect (LME) models linked high crop loads to lower SSC values at harvest and higher SSC values for fruit from the top part of a tree.