The influence of undetected hemolysis on POCT potassium results in the emergency department.

OBJECTIVES: This study aimed to evaluate discrepancies in potassium measurements between point-of-care testing (POCT) and central laboratory (CL) methods, focusing on the impact of hemolysis on these measurements and its impact in the clinical practice in the emergency department (ED). METHODS: A retrospective analysis was conducted using data from three European university hospitals: Technische Universitat Munchen (Germany), Hospital Universitario La Paz (Spain), and Erasmus University Medical Center (The Netherlands). The study compared POCT potassium measurements in EDs with CL measurements. Data normalization was performed in categories for potassium levels (kalemia) and hemolysis. The severity of discrepancies between POCT and CL potassium measurements was assessed using the reference change value (RCV). RESULTS: The study identified significant discrepancies in potassium between POCT and CL methods. In comparing POCT normo- and mild hypokalemia against CL results, differences of -4.20 % and +4.88 % were noted respectively. The largest variance in the CL was a +4.14 % difference in the mild hyperkalemia category. Additionally, the RCV was calculated to quantify the severity of discrepancies between paired potassium measurements from POCT and CL methods. The overall hemolysis characteristics, as defined by the hemolysis gradient, showed considerable variation between the testing sites, significantly affecting the reliability of potassium measurements in POCT. CONCLUSIONS: The study highlighted the challenges in achieving consistent potassium measurement results between POCT and CL methods, particularly in the presence of hemolysis. It emphasised the need for integrated hemolysis detection systems in future blood gas analysis devices to minimise discrepancies and ensure accurate POCT results.

Diagnostic Accuracy of Portable, Handheld Point-of-Care Tests vs Laboratory-Based Bilirubin Quantification in Neonates: A Systematic Review and Meta-analysis.

Importance: Quantification of bilirubin in blood is essential for early diagnosis and timely treatment of neonatal hyperbilirubinemia. Handheld point-of-care (POC) devices may overcome the current issues with conventional laboratory-based bilirubin (LBB) quantification. Objective: To systematically evaluate the reported diagnostic accuracy of POC devices compared with LBB quantification. Data Sources: A systematic literature search was conducted in 6 electronic databases (Ovid MEDLINE, Embase, Web of Science Core Collection, Cochrane Central Register of Controlled Trials, CINAHL, and Google Scholar) up to December 5, 2022. Study Selection: Studies were included in this systematic review and meta-analysis if they had a prospective cohort, retrospective cohort, or cross-sectional design and reported on the comparison between POC device(s) and LBB quantification in neonates aged 0 to 28 days. Point-of-care devices needed the following characteristics: portable, handheld, and able to provide a result within 30 minutes. This study was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-analyses reporting guideline. Data Extraction and Synthesis: Data extraction was performed by 2 independent reviewers into a prespecified, customized form. Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Meta-analysis was performed of multiple Bland-Altman studies using the Tipton and Shuster method for the main outcome. Main Outcomes and Measures: The main outcome was mean difference and limits of agreement in bilirubin levels between POC device and LBB quantification. Secondary outcomes were (1) turnaround time (TAT), (2) blood volumes, and (3) percentage of failed quantifications. Results: Ten studies met the inclusion criteria (9 cross-sectional studies and 1 prospective cohort study), representing 3122 neonates. Three studies were considered to have a high risk of bias. The Bilistick was evaluated as the index test in 8 studies and the BiliSpec in 2. A total of 3122 paired measurements showed a pooled mean difference in total bilirubin levels of -14 µmol/L, with pooled 95% CBs of -106 to 78 µmol/L. For the Bilistick, the pooled mean difference was -17 µmol/L (95% CBs, -114 to 80 µmol/L). Point-of-care devices were faster in returning results compared with LBB quantification, whereas blood volume needed was less. The Bilistick was more likely to have a failed quantification compared with LBB. Conclusions and Relevance: Despite the advantages that handheld POC devices offer, these findings suggest that the imprecision for measurement of neonatal bilirubin needs improvement to tailor neonatal jaundice management.

Characterization of Laboratory Flow and Performance for Process Improvements via Application of Process Mining.

BACKGROUND: The rising level of laboratory automation provides an increasing number of logged events that can be used for the characterization of laboratory performance and process improvements. This abundance of data is often underutilized for improving laboratory efficiency. OBJECTIVES: The first aim of this descriptive study is to provide a structured approach for transforming raw laboratory data to data that is suitable for process mining. The second aim is to describe a process mining approach for mapping and characterizing the sample flow in a clinical chemistry laboratory to identify areas for improvement in the testing process. METHODS: Data were extracted from instrument log files and the middleware between laboratory instruments and information technology infrastructure. Process mining was used for automated process discovery and analysis. Laboratory performance was quantified in terms of relevant key performance indicators (KPIs): turnaround time, timeliness, workload, work-in-process, and machine downtime. RESULTS: The method was applied to two Dutch university hospital clinical chemistry laboratories. We identified areas where alternative routes might increase laboratory efficiency and observed the negative effects of machine downtime on laboratory performance. This encourages the laboratory to review sample routes in its analyzer lines, the routes of high priority samples during instrument downtime, as well as the preventive maintenance policy. CONCLUSION: This article provides the first application of process mining to event data from a medical diagnostic laboratory for automated process model discovery. Our study shows that process mining, with the use of relevant KPIs, provides valuable insights for laboratories that motivates the disclosure and increased utilization of laboratory event data, which in turn drive the analytical staff to intervene in the process to achieve the set performance goals. Our approach is vendor independent and widely applicable for all medical diagnostic laboratories.

Turnaround time prediction for clinical chemistry samples using machine learning.

OBJECTIVES: Turnaround time (TAT) is an essential performance indicator of a medical diagnostic laboratory. Accurate TAT prediction is crucial for taking timely action in case of prolonged TAT and is important for efficient organization of healthcare. The objective was to develop a model to accurately predict TAT, focusing on the automated pre-analytical and analytical phase. METHODS: A total of 90,543 clinical chemistry samples from Erasmus MC were included and 39 features were analyzed, including priority level and workload in the different stages upon sample arrival. PyCaret was used to evaluate and compare multiple regression models, including the Extra Trees (ET) Regressor, Ridge Regression and K Neighbors Regressor, to determine the best model for TAT prediction. The relative residual and SHAP (SHapley Additive exPlanations) values were plotted for model evaluation. RESULTS: The regression-tree-based method ET Regressor performed best with an R2 of 0.63, a mean absolute error of 2.42 min and a mean absolute percentage error of 7.35%, where the average TAT was 30.09 min. Of the test set samples, 77% had a relative residual error of at most 10%. SHAP value analysis indicated that TAT was mainly influenced by the workload in pre-analysis upon sample arrival and the number of modules visited. CONCLUSIONS: Accurate TAT predictions were attained with the ET Regressor and features with the biggest impact on TAT were identified, enabling the laboratory to take timely action in case of prolonged TAT and helping healthcare providers to improve planning of scarce resources to increase healthcare efficiency.

Development and validation of an early warning model for hospitalized COVID-19 patients: a multi-center retrospective cohort study.

BACKGROUND: Timely identification of deteriorating COVID-19 patients is needed to guide changes in clinical management and admission to intensive care units (ICUs). There is significant concern that widely used Early warning scores (EWSs) underestimate illness severity in COVID-19 patients and therefore, we developed an early warning model specifically for COVID-19 patients. METHODS: We retrospectively collected electronic medical record data to extract predictors and used these to fit a random forest model. To simulate the situation in which the model would have been developed after the first and implemented during the second COVID-19 'wave' in the Netherlands, we performed a temporal validation by splitting all included patients into groups admitted before and after August 1, 2020. Furthermore, we propose a method for dynamic model updating to retain model performance over time. We evaluated model discrimination and calibration, performed a decision curve analysis, and quantified the importance of predictors using SHapley Additive exPlanations values. RESULTS: We included 3514 COVID-19 patient admissions from six Dutch hospitals between February 2020 and May 2021, and included a total of 18 predictors for model fitting. The model showed a higher discriminative performance in terms of partial area under the receiver operating characteristic curve (0.82 [0.80-0.84]) compared to the National early warning score (0.72 [0.69-0.74]) and the Modified early warning score (0.67 [0.65-0.69]), a greater net benefit over a range of clinically relevant model thresholds, and relatively good calibration (intercept = 0.03 [- 0.09 to 0.14], slope = 0.79 [0.73-0.86]). CONCLUSIONS: This study shows the potential benefit of moving from early warning models for the general inpatient population to models for specific patient groups. Further (independent) validation of the model is needed.

Failure Mode and Effects Analysis (FMEA) at the preanalytical phase for POCT blood gas analysis: proposal for a shared proactive risk analysis model.

OBJECTIVES: Proposal of a risk analysis model to diminish negative impact on patient care by preanalytical errors in blood gas analysis (BGA). METHODS: Here we designed a Failure Mode and Effects Analysis (FMEA) risk assessment template for BGA, based on literature references and expertise of an international team of laboratory and clinical health care professionals. RESULTS: The FMEA identifies pre-analytical process steps, errors that may occur whilst performing BGA (potential failure mode), possible consequences (potential failure effect) and preventive/corrective actions (current controls). Probability of failure occurrence (OCC), severity of failure (SEV) and probability of failure detection (DET) are scored per potential failure mode. OCC and DET depend on test setting and patient population e.g., they differ in primary community health centres as compared to secondary community hospitals and third line university or specialized hospitals. OCC and DET also differ between stand-alone and networked instruments, manual and automated patient identification, and whether results are automatically transmitted to the patient's electronic health record. The risk priority number (RPN = SEV × OCC × DET) can be applied to determine the sequence in which risks are addressed. RPN can be recalculated after implementing changes to decrease OCC and/or increase DET. Key performance indicators are also proposed to evaluate changes. CONCLUSIONS: This FMEA model will help health care professionals manage and minimize the risk of preanalytical errors in BGA.

Real-time monitoring of drug laboratory test interactions: a proof of concept.

OBJECTIVES: For the correct interpretation of test results, it is important to be aware of drug-laboratory test interactions (DLTIs). If DLTIs are not taken into account by clinicians, erroneous interpretation of test results may lead to a delayed or incorrect diagnosis, unnecessary diagnostic testing or therapy with possible harm for patients. A DLTI alert accompanying a laboratory test result could be a solution. The aim of this study was to test a multicentre proof of concept of an electronic clinical decision support system (CDSS) for real-time monitoring of DLTIs. METHODS: CDSS was implemented in three Dutch hospitals. So-called 'clinical rules' were programmed to alert medical specialists for possible DLTIs based on laboratory test results outside the reference range in combination with prescribed drugs. A selection of interactions from the DLTI database of the Dutch society of clinical chemistry and laboratory medicine were integrated in 43 clinical rules, including 24 tests and 25 drugs. During the period of one month all generated DTLI alerts were registered in the laboratory information system. RESULTS: Approximately 65 DLTI alerts per day were detected in each hospital. Most DLTI alerts were generated in patients from the internal medicine and intensive care departments. The most frequently reported DLTI alerts were potassium-proton pump inhibitors (16%), potassium-beta blockers (11%) and creatine kinase-statins (11%). CONCLUSIONS: This study shows that it is possible to alert for potential DLTIs in real-time with a CDSS. The CDSS was successfully implemented in three hospitals. Further research must reveal its usefulness in clinical practice.

Clinical usefulness of drug-laboratory test interaction alerts: a multicentre survey.

OBJECTIVES: Knowledge of possible drug-laboratory test interactions (DLTIs) is important for the interpretation of laboratory test results. Failure to recognize these interactions may lead to misinterpretation, a delayed or erroneous diagnosis, or unnecessary extra diagnostic tests or therapy, which may harm patients. The aim of this multicentre survey was to evaluate the clinical value of DLTI alerts. METHODS: A survey was designed with six predefined clinical cases selected from the clinical laboratory practice with a potential DLTI. Physicians from several departments, including internal medicine, cardiology, intensive care, surgery and geriatrics in six participating hospitals were recruited to fill in the survey. The survey addressed their knowledge of DLTIs, motivation to receive an alert and opinion on the potential influence on medical decision making. RESULTS: A total of 210 physicians completed the survey. Of these respondents 93% had a positive attitude towards receiving DLTI alerts; however, the reported value differed per case and per respondent's background. In each clinical case, medical decision making was influenced as a consequence of the reported DLTI message (ranging from 3 to 45% of respondents per case). CONCLUSIONS: In this multicentre survey, most physicians stated DLTI messages to be useful in laboratory test interpretation. Medical decision making was influenced by reporting DLTI alerts in each case. Alerts should be adjusted according to the needs and preferences of the receiving physicians.

A critical review of laboratory performance indicators.

Healthcare budgets worldwide are under constant pressure to reduce costs while improving efficiency and quality. This phenomenon is also visible in clinical laboratories. Efficiency gains can be achieved by reducing the error rate and by improving the laboratory's layout and logistics. Performance indicators (PIs) play a crucial role in this process as they allow for performance assessment. This review aids in the process for selecting laboratory PIs-which is not trivial-by providing an overview of frequently used PIs in the literature that can also be used in clinical laboratories. We conducted a systematic review of the laboratory medicine literature on PIs. As the testing process in clinical laboratories can be viewed as a production process, we also reviewed the production processes literature on PIs. The reviewed literature relates to the design, optimization or performance assessment of such processes. The most frequently cited PIs relate to pre-analytical errors, timeliness, resource utilization, cost, and the amount of congestion. Their citation frequency in the literature is used as a proxy for their importance. PIs are discussed in terms of their definition, measurability and impact. The use of suitable PIs is crucial in production processes, including clinical laboratories. By also reviewing the production processes literature, additional relevant PIs for clinical laboratories were found. The PIs in the laboratory medicine literature mostly relate to laboratory errors, while the PIs in the production processes literature relate to the amount of congestion in the process.

Pre-implementation guidelines for infectious disease point-of-care testing in medical institutions.

Infectious disease point-of-care test (ID-POCT) devices are becoming widely available, and in this respect, international quality standards and guidelines are available for consultation once ID-POCT has been implemented into medical institutions. However, specific guidelines for consultation during the initial pre-implementation decision-making process are currently lacking. Further, there exist pre-implementation issues specific to ID-POCT. Here we present pre-implementation guidelines for consultation when considering the implementation of ID-POCT in medical institutions.

Uterine vascular function in a transgenic preeclampsia rat model.

We investigated intrauterine growth restriction, endothelial function, and uterine artery blood flow characteristics in a transgenic preeclampsia rat model with an activated renin-angiotensin system. We compared preeclamptic Sprague-Dawley (SD-PE) rats with normal pregnant Sprague-Dawley and nonpregnant Sprague-Dawley rats. We used transabdominal ultrasound and found that SD-PE rat embryos developed intrauterine growth restriction. Isolated uterine arteries from SD-PE rats incubated with phenylephrine exhibited an increased contractile response, whereas a single high dose of acetylcholine resulted in an impaired vasorelaxation compared with controls. Incremental acetylcholine doses increased relaxation of SD-PE vessels at low acetylcholine doses but caused a paradoxical contraction at higher acetylcholine doses. Indomethacin and a thromboxane-receptor antagonist (SQ 29,548) blocked this effect, suggesting maternal prostanoid-dependent endothelial dysfunction. SD-PE rats had a decreased prostacyclin (6-keto-prostaglandin F1alpha):thromboxane ratio in the serum compared with normal pregnant Sprague-Dawley rats or nonpregnant Sprague-Dawley. Surprisingly, the Doppler resistance index decreased during pregnancy in SD-PE compared with normal pregnant Sprague-Dawley rats, suggesting unimpaired uteroplacental flow in the uterine artery. Umbilical flow was unchanged with absent end-diastolic flow in all of the groups. Renin-angiotensin system activation-induced preeclampsia is associated with altered placentation, modified resistance index, and endothelial dysfunction. A disturbed prostacyclin:thromboxane ratio could be an important mediator.