Automated calculation and reporting of vancomycin area under the concentration-time curve: a simplified single-trough concentration-based equation approach.

Vancomycin, a crucial antibiotic for Gram-positive bacterial infections, requires therapeutic drug monitoring (TDM). Contemporary guidelines advocate for AUC-based monitoring; however, using Bayesian programs for AUC estimation poses challenges. We aimed to develop and evaluate a simplified AUC estimation equation using a steady-state trough concentration (Ctrough) value. Utilizing 1,034 TDM records from 580 general hospitalized patients at a university-affiliated hospital in Ulsan, we created an equation named SSTA that calculates the AUC by applying Ctrough, body weight, and single dose as input variables. External validation included 326 records from 163 patients at a university-affiliated hospital in Seoul (EWUSH) and literature data from 20 patients at a university-affiliated hospital in Bangkok (MUSI). It was compared with other AUC estimation models based on the Ctrough, including a linear regression model (LR), a sophisticated model based on the first-order equation (VancoPK), and a Bayesian model (BSCt). Evaluation metrics, such as median absolute percentage error (MdAPE) and the percentage of observations within ±20% error (P20), were calculated. External validation using the EWUSH data set showed that SSTA, LR, VancoPK, and BSCt had MdAPE values of 6.4, 10.1, 6.6, and 7.5% and P20 values of 87.1, 82.5, 87.7, and 83.4%, respectively. External validation using the MUSI data set showed that SSTA, LR, and VancoPK had MdAPEs of 5.2, 9.4, and 7.2%, and P20 of 95, 90, and 95%, respectively. Owing to its decent AUC prediction performance, simplicity, and convenience for automated calculation and reporting, SSTA could be used as an adjunctive tool for the AUC-based TDM.

Updated Reference Intervals for Alanine Aminotransferase in a Metabolically and Histologically Normal Population.

BACKGROUND & AIMS: This study aims to reevaluate upper reference limit (URL) for alanine aminotransferase (ALT) by considering the changing epidemiology of major liver diseases. We employed histological and metabolic parameters in Asian living liver donors. METHODS: We performed a retrospective analysis of 5455 potential living liver donors from 2005 to 2019. Participants were screened for hepatitis B, C, HIV, and alcohol use. Histologically and metabolically healthy participants were assessed using the Prati criteria (body mass index <23 kg/m2, triglyceride ≤200 mg/dL, fasting glucose ≤105 mg/dL, total cholesterol ≤220 mg/dL). The updated ALT-URL was determined as the 95th percentile among participants without hepatic steatosis and who met the Prati criteria. RESULTS: The median age was 30 years, with a male predominance (66.2%). Among 5455 participants, 3162 (58.0%) showed no hepatic steatosis, with 1553 (49.1%) meeting both the criteria for no steatosis and the Prati criteria for metabolic health. The updated URL for ALT in these participants was 34 U/L for males and 22 U/L for females, which was significantly lower than conventionally accepted values. Using this revised ALT-URL, 72.8% of males with ALT levels ≥34 U/L and 55.0% of females with ALT levels ≥22 U/L showed signs of steatosis, whereas 32.7% of males and 22.2% of females met the criteria for metabolic syndrome. CONCLUSIONS: Our study provided the newly established reference intervals for ALT levels in a metabolically and histologically verified Asian population. The proposed URL for ALT are 34 U/L and 22 U/L for males and females, respectively.

Machine Learning-Based Sample Misidentification Error Detection in Clinical Laboratory Tests: A Retrospective Multicenter Study.

BACKGROUND: In clinical laboratories, the precision and sensitivity of autoverification technologies are crucial for ensuring reliable diagnostics. Conventional methods have limited sensitivity and applicability, making error detection challenging and reducing laboratory efficiency. This study introduces a machine learning (ML)-based autoverification technology to enhance tumor marker test error detection. METHODS: The effectiveness of various ML models was evaluated by analyzing a large data set of 397 751 for model training and internal validation and 215 339 for external validation. Sample misidentification was simulated by random shuffling error-free test results with a 1% error rate to achieve a real-world approximation. The ML models were developed with Bayesian optimization for tuning. Model validation was performed internally at the primary institution and externally at other institutions, comparing the ML models' performance with conventional delta check methods. RESULTS: Deep neural networks and extreme gradient boosting achieved an area under the receiver operating characteristic curve of 0.834 to 0.903, outperforming that of conventional methods (0.705 to 0.816). External validation by 3 independent laboratories showed that the balanced accuracy of the ML model ranged from 0.760 to 0.836, outperforming the balanced accuracy of 0.670 to 0.773 of the conventional models. CONCLUSIONS: This study addresses limitations regarding the sensitivity of current delta check methods for detection of sample misidentification errors and provides versatile models that mitigate the operational challenges faced by smaller laboratories. Our findings offer a pathway toward more efficient and reliable clinical laboratory testing.

Impact of Academia-Government Collaboration on Laboratory Medicine Standardization in South Korea: analysis of eight years creatinine proficiency testing experience.

OBJECTIVES: To evaluate the performance of the Academia-Government Collaboration for Laboratory Medicine Standardization in Korea (KR-STDZN) based on data from KR-STDZN proficiency testing (KR-STDZN-PT) for creatinine over eight years (2015-2022). METHODS: We used KR-STDZN-PT data of creatinine tests from 2015 to 2022. Acceptance of the participating institutions' test results was assessed by calculating the acceptance performance as absolute bias (absBias%), total coefficient of variance (tCV%), and total error (TE%) for each sample using six measurements from each institution and true values of each reference material. The test result was considered acceptable when absBias%, tCV%, and TE% were <5.10, <3.20, and <11.40 %, respectively. The proportion of acceptable institutions among all participating institutions in each round was defined as the acceptance rate. Improvements in absBias%, tCV%, and TE% were analyzed using creatinine concentration ranges in samples. RESULTS: The number of participating institutions increased from 2015 to 2017 but remained consistent since 2018. The acceptance rates for absBias% and TE% increased from 52.2 and 77.6 %, in 2015 and to 90.7 and 96.3 %, in 2022, respectively. The acceptance rate for tCV% remained in the 90 % range for eight years. When creatinine <3 mg/dL, mean absBias%, and mean TE% improved significantly in 2021-2022 compared to 2015-2016 (p<0.05). When creatinine >3 mg/dL, acceptance performance did not improve. Mean tCV% remained consistent annually regardless of creatinine concentration. No significant variations in test methods were observed. CONCLUSIONS: The collaboration between academia and the government improved creatinine testing quality. Nevertheless, KR-STDZN must be expanded and refined.

Machine learning-based delta check method for detecting misidentification errors in tumor marker tests.

OBJECTIVES: Misidentification errors in tumor marker tests can lead to serious diagnostic and treatment errors. This study aims to develop a method for detecting these errors using a machine learning (ML)-based delta check approach, overcoming limitations of conventional methods. METHODS: We analyzed five tumor marker test results: alpha-fetoprotein (AFP), cancer antigen 19-9 (CA19-9), cancer antigen 125 (CA125), carcinoembryonic antigen (CEA), and prostate-specific antigen (PSA). A total of 246,261 records were used in the analysis. Of these, 179,929 records were used for model training and 66,332 records for performance evaluation. We developed a misidentification error detection model based on the random forest (RF) and deep neural network (DNN) methods. We performed an in silico simulation with 1 % random sample shuffling. The performance of the developed models was evaluated and compared to conventional delta check methods such as delta percent change (DPC), absolute DPC (absDPC), and reference change values (RCV). RESULTS: The DNN model outperformed the RF, DPC, absDPC, and RCV methods in detecting sample misidentification errors. It achieved balanced accuracies of 0.828, 0.842, 0.792, 0.818, and 0.833 for AFP, CA19-9, CA125, CEA, and PSA, respectively. Although the RF method performed better than DPC and absDPC, it showed similar or lower performance compared to RCV. CONCLUSIONS: Our research results demonstrate that an ML-based delta check method can more effectively detect sample misidentification errors compared to conventional delta check methods. In particular, the DNN model demonstrated superior and stable detection performance compared to the RF, DPC, absDPC, and RCV methods.

Practical delta check limits for tumour markers in different clinical settings.

OBJECTIVES: Few studies have reported on delta checks for tumour markers, even though these markers are often evaluated serially. Therefore, this study aimed to establish a practical delta check limit in different clinical settings for five tumour markers: alpha-fetoprotein, cancer antigen 19-9, cancer antigen 125, carcinoembryonic antigen, and prostate-specific antigen. METHODS: Pairs of patients' results (current and previous) for five tumour markers between 2020 and 2021 were retrospectively collected from three university hospitals. The data were classified into three subgroups, namely: health check-up recipient (subgroup H), outpatient (subgroup O), and inpatient (subgroup I) clinics. The check limits of delta percent change (DPC), absolute DPC (absDPC), and reference change value (RCV) for each test were determined using the development set (the first 18 months, n=179,929) and then validated and simulated by applying the validation set (the last 6 months, n=66,332). RESULTS: The check limits of DPC and absDPC for most tests varied significantly among the subgroups. Likewise, the proportions of samples requiring further evaluation, calculated by excluding samples with both current and previous results within the reference intervals, were 0.2-2.9% (lower limit of DPC), 0.2-2.7% (upper limit of DPC), 0.3-5.6% (absDPC), and 0.8-35.3% (RCV99.9%). Furthermore, high negative predictive values >0.99 were observed in all subgroups in the in silico simulation. CONCLUSIONS: Using real-world data, we found that DPC was the most appropriate delta-check method for tumour markers. Moreover, Delta-check limits for tumour markers should be applied based on clinical settings.

Comparison between polynomial regression and weighted least squares regression analysis for verification of analytical measurement range.

OBJECTIVES: Recently, the linearity evaluation protocol by the Clinical & Laboratory Standards Institute (CLSI) has been revised from EP6-A to EP6-ED2, with the statistical method of interpreting linearity evaluation data being changed from polynomial regression to weighted least squares linear regression (WLS). We analyzed and compared the analytical measurement range (AMR) verification results according to the present and prior linearity evaluation guidelines. METHODS: The verification of AMR of clinical chemistry tests was performed using five samples with two replicates in three different laboratories. After analyzing the same evaluation data in each laboratory by the polynomial regression analysis and WLS methods, results were compared to determine whether linearity was verified across the five sample concentrations. In addition, whether the 90% confidence interval of deviation from linearity by WLS was included in the allowable deviation from linearity (ADL) was compared. RESULTS: A linearity of 42.3-56.8% of the chemistry items was verified by polynomial regression analysis in three laboratories. For analysis of the same data by WLS, a linearity of 63.5-78.3% of the test items was verified where the deviation from linearity of all five samples was within the ADL criteria, and the cases where the 90% confidence interval of all deviation from linearity overlapped the ADL was 78.8-91.3%. CONCLUSIONS: Interpreting AMR verification data by the WLS method according to the newly revised CLSI document EP6-ED2 could reduce laboratory workload, enabling efficient laboratory practice.

Effect of usual source of care on receiving smoking cessation advice: Korean National Health Panel data analysis.

BACKGROUND: Despite various anti-smoking policies, the smoking rate in adults is still high in Korea. Doctors' advice is known to increase the smoking cessation success rate. However, few studies have reported the effect of having a usual source of care (USC) on receiving smoking cessation advice. OBJECTIVE: To determine the effect of USC on receiving smoking cessation advice. METHODS: We performed multiple panel logistic regression analyses to identify the effect of having a USC on the rate of receiving a doctor's smoking cessation advice using 2009, 2012 and 2013 datasets from the Korea Health Panel database. Only people who responded to questions regarding a USC and smoking cessation advice were analysed. Eventually, 5243 observations were included in the final analysis. RESULTS: A higher percentage of people with a USC received smoking cessation advice from doctors (58.4% in 2009, 64.0% in 2012 and 59.6% in 2013) than those not having a USC (28.6% in 2009, 37.5% in 2012 and 34.8% in 2013). The odds ratios (ORs) of receiving smoking cessation advice in people with a USC were higher than those of people without a USC after performing multiple panel logistic regression analysis with random effects (OR: 2.24; 95% confidence interval: 1.90-2.63). CONCLUSIONS: Having a USC increased the odds of receiving a doctor's smoking cessation advice in Koreans. The results of this study suggest that a health care policy that encourages having a USC is useful in receiving more smoking cessation advice in a Korean population.

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.

An Algorithmic Approach Is Superior to the 99th Percentile Upper Reference Limits of High Sensitivity Troponin as a Threshold for Safe Discharge from the Emergency Department.

Background and Objectives: High-sensitivity cardiac troponin I (hs-TnI) is an important indicator of acute myocardial infarction (AMI) among patients presenting with chest discomfort at the emergency department (ED). We aimed to determine a reliable hs-TnI cut-off by comparing various values for a baseline single measurement and an algorithmic approach. Materials and Methods: We retrospectively reviewed the hs-TnI values of patients who presented to our ED with chest discomfort between June 2019 and June 2020. We evaluated the diagnostic accuracy of AMI with the Beckman Coulter Access hs-TnI assay by comparing the 99th percentile upper reference limits (URLs) based on the manufacturer's claims, the newly designated URLs in the Korean population, and an algorithmic approach. Results: A total of 1296 patients who underwent hs-TnI testing in the ED were reviewed and 155 (12.0%) were diagnosed with AMI. With a single measurement, a baseline hs-TnI cut-off of 18.4 ng/L showed the best performance for the whole population with a sensitivity of 78.7%, specificity of 95.7%, negative predictive value (NPV) of 97.1%, and positive predictive value (PPV) of 71.3%. An algorithm using baseline and 2-3 h hs-TnI values showed an 100% sensitivity, 97.7% specificity, an NPV of 100%, and a PPV of 90.1%. This algorithm used a cut-off of <4 ng/L for a single measurement 3 h after symptom onset or an initial level of <5 ng/L and a change of <5 ng/L to rule a patient out, and a cut-off of ≥50 ng/L for a single measurement or a change of ≥20 ng/L to rule a patient in. Conclusions: The algorithmic approach using serial measurements could help differentiate AMI patients from patients who could be safely discharged from the ED, ensuring that patients were triaged accurately and did not undergo unnecessary testing. The cut-off values from previous studies in different countries were effective in the Korean population.

Sample size and rejection limits for detecting reagent lot variability: analysis of the applicability of the Clinical and Laboratory Standards Institute (CLSI) EP26-A protocol to real-world clinical chemistry data.

Objectives: To maintain the consistency of laboratory test results, between-reagent lot variation should be verified before using new reagent lots in clinical laboratory. Although the Clinical and Laboratory Standards Institute (CLSI) document EP26-A deals with this issue, evaluation of reagent lot-to-lot difference is challenging in reality. We aim to investigate a practical way for determining between-reagent lot variation using real-world data in clinical chemistry. Methods: The CLSI EP26-A protocol was applied to 83 chemistry tests in three clinical labs. Three criteria were used to define the critical difference (CD) of each test as follows: reference change value and total allowable error, which are based on biological variation, and acceptable limits by external quality assurance agencies. The sample size and rejection limits that could detect CD between-reagent lots were determined. Results: For more than half of chemistry tests, reagent lot-to-lot differences could be evaluated using only one patient sample per decision level. In many cases, the rejection limit that could detect reagent lot-to-lot difference with ≥90% probability was 0.6 times CD. However, the sample size and rejection limits vary depending on how the CD is defined. In some cases, impractical sample size or rejection limits were obtained. In some cases, information on sample size and rejection limit that met intended statistical power was not found in EP26-A. Conclusions: The CLSI EP26-A did not provide all necessary answers. Alternative practical approaches are suggested when CLSI EP26-A does not provide guidance.

Validation of a liquid chromatography tandem mass spectrometry (LC-MS/MS) method to detect cannabinoids in whole blood and breath.

Background The widespread availability of cannabis raises concerns regarding its effect on driving performance and operation of complex equipment. Currently, there are no established safe driving limits regarding ∆9-tetrahydrocannabinol (THC) concentrations in blood or breath. Daily cannabis users build up a large body burden of THC with residual excretion for days or weeks after the start of abstinence. Therefore, it is critical to have a sensitive and specific analytical assay that quantifies THC, the main psychoactive component of cannabis, and multiple metabolites to improve interpretation of cannabinoids in blood; some analytes may indicate recent use. Methods A liquid chromatography tandem mass spectrometry (LC-MS/MS) method was developed to quantify THC, cannabinol (CBN), cannabidiol (CBD), 11-hydroxy-THC (11-OH-THC), (±)-11-nor-9-carboxy-Δ9-THC (THCCOOH), (+)-11-nor-Δ9-THC-9-carboxylic acid glucuronide (THCCOOH-gluc), cannabigerol (CBG), and tetrahydrocannabivarin (THCV) in whole blood (WB). WB samples were prepared by solid-phase extraction (SPE) and quantified by LC-MS/MS. A rapid and simple method involving methanol elution of THC in breath collected in SensAbues® devices was optimized. Results Lower limits of quantification ranged from 0.5 to 2 µg/L in WB. An LLOQ of 80 pg/pad was achieved for THC concentrations in breath. Calibration curves were linear (R2>0.995) with calibrator concentrations within ±15% of their target and quality control (QC) bias and imprecision ≤15%. No major matrix effects or drug interferences were observed. Conclusions The methods were robust and adequately quantified cannabinoids in biological blood and breath samples. These methods will be used to identify cannabinoid concentrations in an upcoming study of the effects of cannabis on driving.

Performance of Norudia Glycated Albumin Assay on Multiple Analytical Platforms and Comparison to Lucica Assay.

BACKGROUND: The Norudia glycated albumin (GA) assay was evaluated for analytical performance and assay applicability on multiple analytical platforms. METHODS: The evaluation included precision, linearity, reference interval, and comparison with Lucica GA assay. A multicenter study was conducted to compare the results of Norudia GA assay on five kinds of widely used automated clinical chemistry analyzers. RESULTS: Within-laboratory imprecisions for GA% presented 1.3 - 3.3% and 0.8 - 2.6% for low- and high-level control materials, respectively, on different analyzers. GA assay was linear from 20.0 to 680.0 µmol/L of GA. The claimed reference range (12 - 16 GA%) was verified. Norudia GA showed a good GA% correlation with Lucica GA (correlation coefficient 0.999). GA% from each analyzer showed good correlation with the consensus mean of the results of five analyzers (correlation coefficient 0.997 - 0.999). CONCLUSIONS: The Norudia GA assay can successfully be implemented in all the tested platforms, with good GA% correlation.

Preanalytical Errors in the Central Laboratory of a University Hospital Based on the Analysis of Year-Round Data.

BACKGROUND: Preanalytical errors cause a decrease in the accuracy of clinical laboratory results. We analyzed preanalytical errors (preAEs) made in the clinical laboratory of a university hospital. METHODS: All samples received in a centralized laboratory from January 1, to December 31, 2018, were analyzed retrospectively. The categories of preAEs were improper request, incorrect labeling, improper collection/transport, inadequate sample volume, inappropriate container, hemolysis, and sample clotting. The rates of preAEs in these categories were calculated according to sample type, laboratory subunit, department, sampling place, sampling time, and patient age. RESULTS: Of 1,082,014 samples received and analyzed by the laboratory, 6,848 (0.63%) were classified as having preAEs. The most frequent categories of preAE were hemolysis (44.6%), sample clotting (30.8%), and inadequate volume (16.7%). The most frequent preAE category for whole-blood and serum/plasma was clotting and hemolysis, respectively. The most frequent preAE category in the blood bank, clinical chemistry, immunology, and test referral service laboratory subunits was hemolysis, in the hematology subunit it was sample clotting, and in the microbiology and urinalysis subunits it was inadequate sample volume. Surgical departments had a higher rate of preAEs than did non-surgical departments (p < 0.0001). Samples drawn in the sampling room showed the lowest frequencies of preAEs (0.01%). Samples drawn on general wards from 5 pm to 5 am, when duty nurses perform sampling, showed a preAE rate of 2.80%. The rate of preAEs increased with patient age. CONCLUSIONS: This analysis of preAEs is the most comprehensive to date. Our findings will promote the provision of high-quality laboratory services to clinicians and their patients.

Real-time display of laboratory testing status improves satisfaction levels in an emergency department: A pilot study.

BACKGROUND: Clinicians need to know timelines of requested laboratory tests to provide effective patient management. We developed a real-time laboratory progress checking system and measured its effectiveness using appropriate indicators in an emergency room setting. METHODS: In our original in-house health information system display, blank spaces, which were generated for test results when tests were ordered, remained empty until the final results reported. We upgraded the laboratory reporting system to show real-time testing information. The stages included requests for test, label printing, sampling, laboratory receipts, performance of tests, verification of results, and interpretation of results and final report by laboratory physician. To assess the usefulness of the function, we measured the emergency department healthcare workers' satisfaction and compared the number of phone calls about test status before and after implementation. RESULTS: After the system upgrade, the healthcare workers' understanding of the testing process increased significantly as follows. More clinicians could estimate the time of final test results through the real-time testing status information (61.9% and 85.7%, P = .002), and respondents reported that the upgraded system was more convenient than the original system (41.3% and 22.2%, respectively, P = .022). The number of phone calls about the test status decreased after implementation of the upgrade; however, the difference was not statistically significant (before, 0.13% [63 calls/48 637 tests] and after, 0.09% [42/46 666]; P = .066). CONCLUSIONS: The real-time display of laboratory testing status increased understanding of testing process among healthcare workers in emergency room, which ultimately may increase the usefulness and efficiency of the laboratory service use.

Importance of Susceptibility Rate of 'the First' Isolate: Evidence of Real-World Data.

Background and objectives: For proper antimicrobial therapy, cumulative antibiograms should be representative of geographic region and be accurate. Clinical and Laboratory Standards Institute (CLSI) guidelines recommend that only the first isolates (FI) of a species per patient are used when reporting cumulative antibiograms. However, >50% of hospitals in the United States report antibiograms of all isolates. We compared antibiograms from the FI with those from total isolates (TI). Materials and Methods: Antimicrobial data of all isolates identified in the Microbiology unit of Ilsan Paik Hospital in 2019 were retrospectively acquired from the hospital information system. The susceptibility rates to antimicrobials of Escherichia coli, Acinetobacter baumannii, Klebsiella pneumoniae, Pseudomonas aeruginosa, Staphylococcus aureus, Enterococcus faecium, and Enterococcus faecalis were analyzed by FI and TI, respectively. Isolate counts and susceptibility rates of each species for the reported antimicrobials were compared. Results: The numbers of isolates by FI/TI were as follows: 1824/2692 E. coli, 480/1611 A. baumannii, and 662/1306 K. pneumoniae, and 407/953 P. aeruginosa for gram-negative bacteria and 649/1364 S. aureus, 211/313 E. faecium, and 323/394 E. faecalis for gram-positive bacteria. All antimicrobial agents showed higher susceptibility rates when calculated as FI than as TI in gram-negative bacteria except colistin: 3.7% for E. coli, 14.5% for A. baumannii, 8.3% for K. pneumoniae, and 7.9% for P. aeruginosa. In S. aureus, 8/11 antimicrobial agents revealed higher susceptibility rates for FI than for TI. E. faecalis and E. faecium showed lower susceptibility rates for 7/10 antimicrobial agents for FI than for TI. The oxacillin susceptibility rates of S. aureus were 36.6%/30.2% with FI/TI and vancomycin susceptibility rates for E. faecium were 54.1% and 49.5%, respectively. Conclusions: When comparing cumulative antibiograms by FI with TI using real-world data, there is a large gap for critical species requiring hospital infection control. Although FI calculation is difficult, antibiograms must be calculated as FI for proper preemptive antimicrobial therapy because FI provides proper antimicrobial susceptibility data.

Effect of Having a Usual Source of Care on Medical Expenses - Using the Korea Health Panel Data.

BACKGROUND: There is a controversy about the effect of having a usual source of care on medical expenses. Although many studies have shown lower medical expenses in a group with a usual source of care, some have shown higher medical expenses in such a group. This study aimed to empirically demonstrate the effect of having a usual source of care on medical expenses. METHODS: The participants included those aged 20 years and older who responded to the questionnaire about "having a usual source of care" from the Korean Health Panel Data of 2012, 2013, and 2016 (6,120; 6,593; and 7,598 respectively). Those who responded with "I do not get sick easily" or "I rarely visit medical institutions" as the reasons for not having a usual source of care were excluded. The panel regression with random effects model was performed to analyze the effect of having a usual source of care on medical expenses. RESULTS: The group having a usual source of care spent 20% less on inpatient expenses and 25% less on clinic expenses than the group without a usual source of care. Particularly, the group having a clinic-level usual source of care spent 12% less on total medical expenses, 9% less on outpatient expenses, 35% less on inpatient expenses, and 74% less on hospital expenses, but 29% more on clinic expenses than the group without a usual source of care. CONCLUSION: This study confirmed that medical expenses decreased in the group with a usual source of care, especially a clinic-level usual source of care (USC), than in the group without a usual source of care. Encouraging people to have a clinic-level USC can control excessive medical expenses and induce desirable medical care utilization.

Predictive Value of Dental Maturity for a Positive Gonadotropin-Releasing Hormone Stimulation Test Result in Girls with Precocious Puberty.

Dental maturity is associated with skeletal maturity, which is advanced in girls with central precocious puberty (CPP). We investigated the performance of dental maturity as a screening method for CPP using mandibular second premolar and molar calcification stages, assessed the associated anthropometric and laboratory factors, and evaluated pubertal response predictors using the gonadotropin-releasing hormone stimulation test (GnRHST) in prepubertal and pubertal girls. A prospective case-control study was conducted in girls, aged 7.0-8.9 years, classified into pubertal (peak luteinizing hormone [LH] after GnRHST ≥ 5 IU/L), prepubertal (peak LH < 5 IU/L), and control groups. Auxological and biochemical tests, panoramic radiographs, and GnRHSTs in participants with breast development were conducted. Dental maturity was assessed using the Demirjian index (DI). We included 103 girls (pubertal, 40; prepubertal, 19; control, 44). Chronological age (CA) was not significantly different between groups. Bone age (BA) and BA advancement was higher in the pubertal and prepubertal groups. Increased DI values at the mandibular second premolar and molar were significantly associated with CA, BA, BA advancement, height standard deviation score (SDS), peak LH after GnRHST, and insulin-like growth factor-I (IGF-I) (all P < 0.05). Moreover, odds ratio (OR) of the mandibular second premolar and molar (a DI value of ≥ E) for predicting a positive response to GnRHST was 8.7 (95% confidence intervals [CI], 2.9-26.1) and 5.2 (95% CI, 2.2-12.7), respectively. Dental maturity was a strong predictor for diagnosing CPP. Determining dental maturity in girls with suspected precocious puberty might help determine the performance of GnRHSTs.

Report on the Project for Establishment of the Standardized Korean Laboratory Terminology Database, 2015.

The National Health Information Standards Committee was established in 2004 in Korea. The practical subcommittee for laboratory test terminology was placed in charge of standardizing laboratory medicine terminology in Korean. We aimed to establish a standardized Korean laboratory terminology database, Korea-Logical Observation Identifier Names and Codes (K-LOINC) based on former products sponsored by this committee. The primary product was revised based on the opinions of specialists. Next, we mapped the electronic data interchange (EDI) codes that were revised in 2014, to the corresponding K-LOINC. We established a database of synonyms, including the laboratory codes of three reference laboratories and four tertiary hospitals in Korea. Furthermore, we supplemented the clinical microbiology section of K-LOINC using an alternative mapping strategy. We investigated other systems that utilize laboratory codes in order to investigate the compatibility of K-LOINC with statistical standards for a number of tests. A total of 48,990 laboratory codes were adopted (21,539 new and 16,330 revised). All of the LOINC synonyms were translated into Korean, and 39,347 Korean synonyms were added. Moreover, 21,773 synonyms were added from reference laboratories and tertiary hospitals. Alternative strategies were established for mapping within the microbiology domain. When we applied these to a smaller hospital, the mapping rate was successfully increased. Finally, we confirmed K-LOINC compatibility with other statistical standards, including a newly proposed EDI code system. This project successfully established an up-to-date standardized Korean laboratory terminology database, as well as an updated EDI mapping to facilitate the introduction of standard terminology into institutions.

An approach to standardization of urine sediment analysis via suggestion of a common manual protocol.

INTRODUCTION: The results of urine sediment analysis have been reported semiquantitatively. However, as recent guidelines recommend quantitative reporting of urine sediment, and with the development of automated urine sediment analyzers, there is an increasing need for quantitative analysis of urine sediment. Here, we developed a protocol for urine sediment analysis and quantified the results. METHODS: Based on questionnaires, various reports, guidelines, and experimental results, we developed a protocol for urine sediment analysis. The results of this new protocol were compared with those obtained with a standardized chamber and an automated sediment analyzer. Reference intervals were also estimated using new protocol. RESULTS: We developed a protocol with centrifugation at 400 g for 5 min, with the average concentration factor of 30. The correlation between quantitative results of urine sediment analysis, the standardized chamber, and the automated sediment analyzer were generally good. The conversion factor derived from the new protocol showed a better fit with the results of manual count than the default conversion factor in the automated sediment analyzer. CONCLUSIONS: We developed a protocol for manual urine sediment analysis to quantitatively report the results. This protocol may provide a mean for standardization of urine sediment analysis.