Categorical Agreement Research Articles

The robustness of the flow-gradient classification of severe aortic stenosis

ObjectivesA flow-gradient classification is used to determine the indication for intervention for patients with severe aortic stenosis (AS) with discordant echocardiographic parameters. We investigated the agreement in flow-gradient classification by stroke volume (SV) measurement at the left ventricular outflow tract (LVOT) and at the left ventricle. MethodsData were used from a prospective cohort study and patients with severe AS (aortic valve area index ≤0.6 cm2/m2) with preserved ejection fraction (>50%) were selected. SV was determined by an echocardiographic core laboratory at the LVOT and by subtracting the 2-dimensional left ventricle end-systolic from the end-diastolic volume (volumetric). Patients were stratified into 4 groups based on SV index (35 mL/m2) and mean gradient (40 mm Hg). The group composition was compared and the agreement between the SV measurements was investigated using regression, correlation, and limits of agreement. In addition, a systematic LVOT diameter overestimation of 1 mm was simulated to study flow-gradient reclassification. ResultsOf 1118 patients, 699 were eligible. The group composition changed considerably as agreement on flow state occurred in only 50% of the measurements. LVOT SV was on average 15.1 mL (95% limits of agreement −24.9:55.1 mL) greater than volumetric SV. When a systematic 1-mm LVOT diameter overestimation was introduced, the low-flow groups halved. ConclusionsThere was poor agreement in the flow-gradient classification of severe AS as a result of large differences between LVOT and volumetric SV. Furthermore, this classification was sensitive to small measurement errors. These results stress that parameters beyond the flow-gradient classification should be considered to ensure accurate recommendations for intervention.

Performance of current versus modified CEUS LI-RADS in the diagnosis of non-hepatocellular carcinoma malignancies.

The high proportion of HCC in CEUS LR-M decreases the sensitivity of LR-5 for the diagnosis of HCC. However, when modifying LR-M criteria to further improve the sensitivity of LR-5, it is also important not to compromise the diagnostic performance (especially sensitivity) of LR-M for non-hepatocellular carcinoma malignancies (non-HCCMs). The purpose of this study was to evaluate the diagnostic performance of CEUS LI-RADS (2017 version) for non-HCCMs and to explore the impact of modified CEUS LI-RADS on the diagnostic performance of LR-M. In this retrospective study, patients with pathologically confirmed non-HCCMs were evaluated. Two radiologists independently interpreted the major CEUS features and categorized the liver lesions. New LR-M criteria were applied: early washout (< 45 s) or marked washout (< 5 min). The sensitivity values of the current and modified CEUS LR-M were assessed and then compared using a paired χ2 test. Cohen's κ was used to compare the inter-reader agreement of the LI-RADS categories. A total of 131 non-HCCMs were ultimately selected, including 71 intrahepatic cholangiocarcinomas, 26 combined hepatocellular cholangiocarcinomas, 29 metastases, and 5 other non-HCCMs. The numbers of LR-M, LR-5, LR-4, and LR-3 in liver lesions were 111, 18, 1, and 1, respectively. The inter-reader agreement of the LI-RADS categories for non-HCCMs was 0.59. The sensitivity of the current CEUS LR-M in diagnosing non-HCCMs was 84.7%. By adjusting the early washout time to < 45 s, the sensitivity of LR-M was 80.9%. By adjusting the marked washout time within 5 min, the sensitivity of LR-M was 72.5%. CEUS LR-M has high sensitivity in diagnosing non-HCCMs. For LR-M nodules with nonrim arterial phase hyperenhancement and early washout, advancing the time of early washout to < 45 s has a minimal impact on the sensitivity of LR-M in diagnosing non-HCCMs compared to the condition of increasing the marked washout within 5 min.

Integrated Microfluidic Chip for Rapid Antimicrobial Susceptibility Testing Directly from Positive Blood Cultures.

Rapid and accurate antimicrobial prescriptions are critical for bloodstream infection (BSI) patients, as they can guide drug use and decrease mortality significantly. The traditional antimicrobial susceptibility testing (AST) for BSI is time-consuming and tedious, taking 2-3 days. Avoiding lengthy monoclonal cultures and shortening the drug sensitivity incubation time are keys to accelerating the AST. Here, we introduced a bacteria separation integrated AST (BSI-AST) chip, which could extract bacteria directly from positive blood cultures (PBCs) within 10 min and quickly give susceptibility information within 3 h. The integrated chip includes a bacteria separation chamber, multiple AST chambers, and connection channels. The separator gel was first preloaded into the bacteria separation chamber, enabling the swift separation of bacteria cells from PBCs through on-chip centrifugation. Then, the bacteria suspension was distributed in the AST chambers with preloaded antibiotics through a quick vacuum-assisted aliquoting strategy. Through centrifuge-assisted on-chip enrichment, detectable growth of the phenotype under different antibiotics could be easily observed in the taper tips of AST chambers within a few hours. As a proof of concept, direct AST from artificial PBCs with Escherichia coli against 18 antibiotics was performed on the BSI-AST chip, and the whole process from bacteria extraction to AST result output was less than 3.5 h. Moreover, the integrated chip was successfully applied to the diagnosis of clinical PBCs, showing 93.3% categorical agreement with clinical standard methods. The reliable and fast pathogen characterization of the integrated chip suggested its great potential application in clinical diagnosis.

Short incubation of disc diffusion for Streptococcus pneumoniae and Haemophilus influenzae to reduce time to susceptibility report.

Rapidly instituted antimicrobial therapy is important in severe infections, and reduced time to the antimicrobial susceptibility testing (AST) report is thus of importance. Disc diffusion (DD) is a cheap, rapidly adaptable, flexible and comprehensive method for phenotypic AST. Previous studies have shown that early reading of inhibition zones for non-fastidious species is possible. To evaluate zone reading after short incubation of DD in Haemophilus influenzae (n = 73) and Streptococcus pneumoniae (n = 112). The readability was evaluated and susceptibility interpretation (SIR) was performed, using the EUCAST 18 ± 2 h incubation breakpoint table (version 12.0), after 6 and 8 h of incubation. Categorical agreement (CA) and error rates were calculated using standard DD and broth microdilution as reference. The proportion of readable zones in H. influenzae was 19% (6 h) and 89% (8 h). The CA was 98% after 8 h. The corresponding readability in S. pneumoniae was 63%/98% and CA was 95%/97% after 6 and 8 h, respectively. Early reading of the screening discs (benzylpenicillin 1 unit in H. influenzae and oxacillin 1 µg in S. pneumoniae) correctly identified 18/22 of the H. influenzae isolates and all the readable S. pneumoniae isolates with reduced β-lactam susceptibility. For non-β-lactam agents, very major errors were most common for quinolones in S. pneumoniae. Introduction of areas of technical uncertainty (ATUs) reduced the error rate to ≤1.1%. We conclude that shortened incubation is feasible for H. influenzae and S. pneumoniae. To reduce the risk of false categorization a buffer zone (i.e. ATU) near the breakpoints must be used.

Artificial intelligence evaluation of coronary computed tomography angiography for coronary stenosis classification and diagnosis.

Ruling out obstructive coronary artery disease (CAD) using coronary computed tomography angiography (CCTA) is time-consuming and challenging. This study developed a deep learning (DL) model to assist in detecting obstructive CAD on CCTA to streamline workflows. In total, 2929 DICOM files and 7945 labels were extracted from curved planar reformatted CCTA images. A modified Inception V3 model was adopted. To validate the artificial intelligence (AI) model, two cardiologists labelled and adjudicated the classification of coronary stenosis on CCTA. The model was trained to differentiate the coronary artery into binary stenosis classifications <50% and ≥50% stenosis. Using the quantitative coronary angiography (QCA) consensus results as a reference standard, the performance of the AI model and CCTA radiology readers was compared by calculating Cohen's kappa coefficients at patient and vessel levels. The net reclassification index was used to evaluate the net benefit of the DL model. The diagnostic accuracy of the AI model was 92.3% and 88.4% at the patient and vessel levels, respectively. Compared with CCTA radiology readers, the AI model had a better agreement for binary stenosis classification at both patient and vessel levels (Cohen kappa coefficient: .79 vs. .39 and .77 vs. .40, p < .0001). The AI model also exhibited significantly improved model discrimination and reclassification (Net reclassification index = .350; Z = 4.194; p < .001). The developed AI model identified obstructive CAD, and the model results correlated well with QCA results. Incorporating the model into the reporting system of CCTA may improve workflows.

Ionized Magnesium Correlates With Total Blood Magnesium in Pediatric Patients Following Kidney Transplant.

Abnormal serum magnesium (Mg) concentrations are common and associated with worse mortality in kidney-transplant recipients. Many kidney and transplant-related factors affect Mg homeostasis. The concentration of the active form, ionized Mg (iMg), is not measured clinically, and total Mg (tMg) and iMg correlations have conflicted. We hypothesized that iMg and tMg concentrations show poor categorical agreement (i.e., low, normal, and high) in kidney-transplant recipients but that ionized calcium (iCa) correlates with iMg. We retrospectively evaluated hypomagnesemia in kidney-transplant recipients over a 2-yr period. We prospectively collected blood at 0-28 days post-transplant to measure correlations between iMg and iCa/tMg. iMg and iCa concentrations in the reference ranges of 0.44-0.65 and 1.0-1.3 mmol/L, respectively, were considered normal. Fisher's exact test and unweighted kappa statistics revealed category agreements. Pearson's correlation coefficients and linear regression measured correlations. Among 58 retrospective kidney-transplant recipients, 54 (93%) had tMg<0.66 mmol/L, 28/58 (48%) received Mg supplementation, and 20/28 (71%) had tacrolimus dose adjustments during supplementation. In 13 prospective transplant recipients (N=43 samples), iMg and tMg showed strong category agreement (P=0.0003) and correlation (r=0.71, P<0.001), whereas iMg and iCa did not (P=0.7; r=-0.25, P=0.103, respectively). tMg and iMg exhibited strong correlation following kidney transplantation. However, iCa may not be an accurate surrogate for iMg. Determining the effect of Mg supplementation and the Mg concentration where supplementation is clinically necessary are important next steps.

Evaluation of Within- and Between-Site Agreement for Direct Observation of Physical Behavior Across Four Research Groups

Direct observation (DO) is a widely accepted ground-truth measure, but the field lacks standard operational definitions. Research groups develop project-specific annotation platforms, limiting the utility of DO if labels are not consistent. Purpose: The purpose was to evaluate within- and between-site agreement for DO taxonomies (e.g., activity intensity category) across four independent research groups who have used video-recorded DO. Methods: Each site contributed video files (508 min) and had two trained research assistants annotate the shared video files according to their existing annotation protocols. The authors calculated (a) within-site agreement for the two coders at the same site expressed as intraclass correlation and (b) between-site agreement, the proportion of seconds that agree between any two coders regardless of site. Results: Within-site agreement at all sites was good–excellent for both activity intensity categories (intraclass correlation range: .82–.9) and posture/whole-body movement (intraclass correlation range: .77–.98). Between-site agreement for intensity categories was 94.6% for sedentary, 80.9% for light, and 82.8% for moderate–vigorous. Three of the four sites had common labels for eight posture/whole-body movements and had within-site agreements of 94.5% and between-site agreements of 86.1%. Conclusions: Distinct research groups can annotate key features of physical behavior with good-to-excellent interrater reliability. Operational definitions are provided for core metrics for researchers to consider in future studies to facilitate between-study comparisons and data pooling, enabling the deployment of deep learning approaches to wearable device algorithm calibration.

Examining the relationship between peer feedback classified by deep learning and online learning burnout

Students are prone to experiencing learning burnout while engaged in online learning due to the lack of interaction. However, peer feedback can play an important role in alleviating learning burnout. This study uses a combination of deep learning (DL) and statistical analysis to investigate the relationship between online peer feedback and learning burnout. Several DL algorithms, including Bidirectional Encoder Representations from Transformers (BERT), Bi-directional Long Short-Term Memory (Bi-LSTM), and BERT-Bi-LSTM, are compared to classify peer comments in terms of cognitive content and affective state. Moreover, by using the feedback classified by DL and survey data from 116 participants, multiple linear regression (MLR) analysis is employed to explore the relationships between online learning burnout and feedback messages. The results show that the BERT model achieves the best performance in terms of classification performance and agreement with manual coding. Further, we find that student burnout is significantly influenced by peer feedback. Individuals who receive more suggestive feedback experience a greater reduction in emotional exhaustion. However, when receiving more reinforcing feedback without guidance, learners' behavior tends to deteriorate. From an affective perspective, the more positive feedback the learners receive, the more they tend to exhibit higher levels of emotional exhaustion and a diminished sense of achievement. And when receiving more negative feedback, learners tend to have a worse emotional experience and demonstrate poorer self-learning behavior. This study contributes to our understanding of the application of DL in peer assessment and the impact of received peer feedback on preventing and alleviating learning burnout.

Detection of colistin resistance via four methods among Gram-negative bacteria

Objective: We aimed to find the most accurate and robust method for the detection of colistin resistance among Gram-negative bacteria by comparing the performance of four methods. Methods: Colistin resistantance was determine by disk diffusion (DD), Etest, colistin broth disk elution (CBDE) and compared with standard broth microdilution (BMD) method. Results: An exploratory study was conducted in a tertiary hospital in Jaipur, India, from November 2021 to November 2022. Of the 384 isolates tested, 41 (10.7%) were resistant against colistin by BMD and CBDE testing. Compared with BMD method, the categorical agreement of DD and Etest methods were 65.9% and 78.1%, respectively, and the rates of very major error were 34.1% and 21.9%, respectively. Conclusions: Due to the high frequency of very major error, the use of DD and Etest are not recommended by Clinical laboratory and standards institute (CLSI) for colistin susceptibility testing while CBDE is the recommended method which yields a categorical agreement of 100% with BMD method. The CBDE method can be used in routine laboratories for the detection of colistin resistance.

Rapid Identification and Antimicrobial Susceptibility Testing of Gram Positive Bacteria by Direct Inoculation from Positive Blood Culture Bottles

Abstract Background Rapid identification of the causative agent for sepsis and determination of its susceptibility profile by direct inoculation of the biochemical and susceptibility testing media with the blood-broth mixtures from signal positive bottles might help reducing the time needed for provision of results compared to the standard isolated-colony based method and hence would help the rapid initiation of effective and targeted antimicrobial therapy and reduce the bacteremia-related morbidity and mortality Aim of the Work To validate the direct inoculation method as a rapid method for the identification of Gram-positive bacteremic isolates and determination of their antimicrobial susceptibility profile by comparing its results to that of the standard isolate-based method routinely used in our laboratories. Patients and Methods This study included a total of 100 positive blood culture bottles submitted to the Main Microbiology laboratory for identification and antimicrobial susceptibility testing via disk diffusion method directly from blood culture bottles and according to standard isolate-based identification method. Results There was a 100% categorical agreement between the results of the direct (primary) biochemical identification method and those of the standard isolate-based identification method. As regard AST the overall categorical agreement between both methods was 96.9%, the minor error rate was 0.8% and the major error rate was 2.3%. Conclusion Rapid Identification and Antimicrobial Susceptibility Testing of Gram-positive Bacteria is a time saving method and can help in reducing turnaround time to obtain AST results.

Deep learning-based detection and classification of lumbar disc herniation on magnetic resonance images.

The severity assessment of lumbar disc herniation (LDH) on MR images is crucial for selecting suitable surgical candidates. However, the interpretation of MR images is time-consuming and requires repetitive work. This study aims to develop and evaluate a deep learning-based diagnostic model for automated LDH detection and classification on lumbar axial T2-weighted MR images. A total of 1115 patients were analyzed in this retrospective study; both a development dataset (1015 patients, 15 249 images) and an external test dataset (100 patients, 1273 images) were utilized. According to the Michigan State University (MSU) classification criterion, experts labeled all images with consensus, and the final labeled results were regarded as the reference standard. The automated diagnostic model comprised Faster R-CNN and ResNeXt101 as the detection and classification network, respectively. The deep learning-based diagnostic performance was evaluated by calculating mean intersection over union (IoU), accuracy, precision, sensitivity, specificity, F1 score, the area under the receiver operating characteristics curve (AUC), and intraclass correlation coefficient (ICC) with 95% confidence intervals (CIs). High detection consistency was obtained in the internal test dataset (mean IoU = 0.82, precision = 98.4%, sensitivity = 99.4%) and external test dataset (mean IoU = 0.70, precision = 96.3%, sensitivity = 97.8%). Overall accuracy for LDH classification was 87.70% (95% CI: 86.59%-88.86%) and 74.23% (95% CI: 71.83%-76.75%) in the internal and external test datasets, respectively. For internal testing, the proposed model achieved a high agreement in classification (ICC = 0.87, 95% CI: 0.86-0.88, P < 0.001), which was higher than that of external testing (ICC = 0.79, 95% CI: 0.76-0.81, P < 0.001). The AUC for model classification was 0.965 (95% CI: 0.962-0.968) and 0.916 (95% CI: 0.908-0.925) in the internal and external test datasets, respectively. The automated diagnostic model achieved high performance in detecting and classifying LDH and exhibited considerable consistency with experts' classification.

Analysis of the Intra- and Inter-observer Reliabilities of Delbet Classification: Is Simplification Required.

This study aims to investigate the intra- and inter-reliabilities of Delbet classification and whether simplifying the classification improves reliability. The Radiographs of 58 patients under 16years old diagnosed with pediatric hip fractures between January 2012 and January 2020 were evaluated. Two separate Microsoft® PowerPoint (PPT) presentations consisting of 58 slides for Delbet's classification and its simplified form were prepared in which the order of cases was randomized and blinded. Then five orthopedic surgeons with different years of experience (more than 15years of surgical experience were accepted experienced group) in pediatric trauma surgery evaluated the presentations two times one month apart. In the inter-observer reliability analysis, a moderate agreement for the classical Delbet classification and a substantial agreement for the simplified Delbet classification was found in both evaluations. As a result of the intra-observer reliability analysis, there was substantial agreement in four observers, moderate agreement in one observer for the classical Delbet classification and substantial agreement in two observers, very good agreement in three observers for the simplified Delbet classification. The experience was not effective on agreement levels in the intra-observer reliability analysis of the classical Delbet classification system. The inter-reliability values of the less experienced group in the simplified classification were much higher than the classical Delbet classification. The simplification of the Delbet classification provides a significant increase in inter-observer reliability levels, especially in less experienced orthopedics and traumatology specialists, and a significant increase in intra-observer reliability levels. Level III, diagnostic study.

Laser-Based 3D Body Scanning Reveals a Higher Prevalence of Abdominal Obesity than Tape Measurements: Results from a Population-Based Sample.

The global obesity epidemic is a major public health concern, and accurate diagnosis is essential for identifying at-risk individuals. Three-dimensional (3D) body scanning technology offers several advantages over the standard practice of tape measurements for diagnosing obesity. This study was conducted to validate body scan data from a German population-based cohort and explore clinical implications of this technology in the context of metabolic syndrome. We performed a cross-sectional analysis of 354 participants from the Study of Health in Pomerania that completed a 3D body scanning examination. The agreement of anthropometric data obtained from 3D body scanning with manual tape measurements was analyzed using correlation analysis and Bland-Altman plots. Classification agreement regarding abdominal obesity based on IDF guidelines was assessed using Cohen's kappa. The association of body scan measures with metabolic syndrome components was explored using correlation analysis. Three-dimensional body scanning showed excellent validity with slightly larger values that presumably reflect the true circumferences more accurately. Metabolic syndrome was highly prevalent in the sample (31%) and showed strong associations with central obesity. Using body scan vs. tape measurements of waist circumference for classification resulted in a 16% relative increase in the prevalence of abdominal obesity (61.3% vs. 52.8%). These results suggest that the prevalence of obesity may be underestimated using the standard method of tape measurements, highlighting the need for more accurate approaches.

The Development of a Short Form of the Indonesian Version of the Wechsler Adult Intelligence Scale-Fourth Edition.

(1) Background: The Wechsler intelligence scales are very popular in clinical practice and for research purposes. However, they are time consuming to administer. Therefore, researchers and psychologists have explored the possibility of shorter test battery compositions. (2) Methods: In this study, we investigated 13 potential short forms of the Indonesian version of the Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV-ID). An existing standardization data set of 1745 Indonesian participants collected for the validation of the WAIS-IV-ID was used to examine the short forms' validity. These ranged from 2-subtest versions to 7-subtest versions. Regression analyses with goodness-of-fit measures were performed, and regression equations were determined for each short form to estimate the Full Scale IQ (FSIQ) score. Discrepancies between the FSIQ and the estimated FSIQ (FSIQEst) scores were examined and classification accuracies were calculated for each short form (% agreement of intelligence classification between the FSIQEst and FSIQ). (3) Results: None of the 13 short form FSIQEst values significantly differed from the FSIQ scores based on the full WAIS-IV-ID, and strong correlations were observed between each of these values. The classification accuracies of the short forms were between 56.8% and 81.0%. The 4-subtest short form of the WAIS-IV-ID consisting of the subtests Matrix Reasoning, Information, Arithmetic, and Coding had the optimal balance between best classification values and a short administration duration. The validity of this short form was demonstrated in a second study in an independent sample (N = 20). (4) Conclusions: Based on the results presented here, the WAIS-IV-ID short forms are able to reliably estimate the FSIQ, with a significant shorter administration duration. The WAIS-IV-ID short form consisting of four subtests, Matrix Reasoning, Information, Arithmetic, and Coding, was the best version according to our criteria.

Raman-Based Antimicrobial Susceptibility Testing on Antibiotics of Last Resort.

Antibiotic resistance represents a serious global health challenge, particularly with the emergence of strains resistant to last-resort antibiotics such as tigecycline, polymyxin B, and vancomycin. Urgent measures are required to alleviate this situation. To facilitate the judicious use of antibiotics, rapid and precise antimicrobial susceptibility testing (AST) is essential. Heavy water (deuterium oxide, D2O)-labeled Raman spectroscopy has emerged as a promising time-saving tool for microbiological testing. Deuterium incorporation and experimental conditions were examined to develop and apply a Raman-based AST method to evaluate the efficacy of last-resort antibiotics, including tigecycline, polymyxin B, and vancomycin, against Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Enterococcus faecium. Essential agreement and categorical agreement were used to assess the metabolism inactivation concentration based on Raman spectroscopy (R-MIC)-a new metric developed in this study-and minimum inhibitory concentration (MIC) determined via the traditional microdilution broth method. Spearman's rank correlation coefficient was employed to measure the association between R-MIC and MIC values. The Raman-based AST method achieved a 100% categorical agreement (92/92) with the traditional microdilution broth method within five hours, while the traditional method required approximately 24 h. The R-MIC values shared 68.5% (63/92) consistency with the MIC values. In addition, the R-MIC and MIC values were highly correlated (Spearman's r=0.96), resulting in an essential agreement of 100%. Our optimized experimental method and conditions indicate that Raman-based AST holds great promise as a solution to overcome the time-consuming challenges of traditional AST methods.

Performance of the Disc Diffusion Method, MTS Gradient Tests and Two Commercially Available Microdilution Tests for the Determination of Cefiderocol Susceptibility in Acinetobacter spp.

Cefiderocol is a siderophore-conjugated cephalosporin with potent activity against multidrug-resistant Gram-negative pathogens including Acinetobacter baumannii. The aim of this study was to evaluate cefiderocol testing methods on a relevant collection of 97 Acinetobacter spp. isolates. Commercialized broth microdilution methods (ComASP®, Liofilchem and UMIC®, Bruker), MIC test strips (Liofilchem) and disc diffusion using discs of three different brands (Mast Diagnostic, Liofilchem and Oxoid-Thermo Fisher Scientific) were compared with the broth microdilution reference method. None of the methods tested fulfilled acceptable criteria (essential agreement [EA] ≥ 90%; bias = ±30%) but both BMD methods achieved acceptable categorical agreement rates (CA = 95.9% [93/97, 95% CI 89.9-98.4] and CA = 93.8% [91/97, 95% CI 87.2-97.1] for ComASP® and UMIC®, respectively) and bias < 30% (-7.2% and -25.2% for ComASP® and UMIC®, respectively). The use of MIC gradient testing is strongly discouraged due to misclassification of 55% (n = 23/42) of resistant strains. Finally, the disc diffusion method could be used to rapidly screen for susceptible strains by setting a critical diameter of 22 mm.

Comparison of the Etest and Sensititre methods for anaerobe susceptibility testing

Background and Aims: Antimicrobial susceptibility testing of anaerobic clinical isolates is of paramount importance for patient therapy and resistance monitoring. In our laboratory the MIC gradient Etest method and broth microdilution with Sensititre trays are used for susceptibility testing of anaerobes and the aim of this study was to compare the two methods on a panel of anaerobes routinely isolated from patients in the province of Bolzano, Italy. Materials and Methods: Totally, 74 non-repetitive Gram-positive and Gram-negative patient isolates were tested with Etest strips on Fastidious Anaerobe Agar (F.A.A.) and with Sensititre trays, according to manufacturer’s instructions. Interpretation of MICs was by EUCAST or CLSI criteria, resistance percentages were calculated and Categorical Agreement (CA) and Essential Agreement (EA) between the two methods were determined. Results: Of the 74 isolates, 68 (91.9%) grew on both systems and agreement for these was compared in the study. CA for all isolates was ≥90% for all tested antibiotics except moxifloxacin, whereas EA was generally lower. Resistance was generally low, except for clindamycin in all isolates and tigecycline in Gram-negatives. In our study Etest was a superior and more handy method. Conclusions: To conclude, we believe the Etest method is more suitable for routine diagnostic laboratory usage. Nevertheless, multicenter studies are required to evaluate the two methods for anaerobic susceptibility testing.

Clinical implications of changing thyroglobulin and antithyroglobulin antibodies analytical methods in the follow-up of patients with differentiated thyroid carcinoma

Background and aimsPatients’ response to treatment in differentiated thyroid cancer (DTC) is classified according to serum thyroglobulin concentrations (Tg), usually using the American Thyroid Association guidelines and considering potential interfering anti-thyroglobulin antibodies (Ab-Tg). We aim to evaluate the clinical implications of changing Tg and Ab-Tg quantification method. Material and methodsTg and Ab-Tg were quantified in 82 serum samples (60 from DTC patients) by Elecsys and Access immunoassays. ResultsElecsys immunoassay rendered higher values of Tg than Access: mean bias 5.03 ng/mL (95%CI:-14.14–24.21). In DTC patients, there was an almost perfect agreement for response classification (kappa index = 0.833). Discrepancies appeared in patients with undetermined response, with a more tendency to subclassification with Access. Ab-Tg showed a poor correlation (r = 0.5394). When Elecsys cut-off was reduced to 43 IU/mL, agreement for positive/negative classification improved from a kappa index of 0.607 to 0.650. Prospective study with personalized follow-up showed that only 6.3% of Tg results required an analytical confirmation, being confirmed 93% of them. ConclusionsDespite the biases observed, clinical impact of an analytical change is minimal in patients’ management. However, cautious and personalized follow-up period after the change is still mandatory, especially in patients with Tg levels between 0.2 and 1 ng/mL.

Convergent Validity of 2 Widely Used Methodologies for Calculating the Hospital Standardized Mortality Ratio in Flanders, Belgium.

To assess their construct validity, we compared results from 2 models used for estimating hospital standardized mortality ratios (HSMRs) in Belgium. The method of the Flemish Hospital Network (FHN) is based on a logistic regression for each of the 64 All Patient Refined Diagnosis-Related Groups that explain 80% of mortality and uses the Elixhauser score to correct for comorbidities. (H)SMRs published on the 3M-Benchmark-Portal are calculated by a simpler indirect standardization for All Patient Refined Diagnosis-Related Groups and risk of mortality (ROM) at discharge. We used administrative data from all eligible hospital admissions in 22 Flemish hospitals between 2016 and 2019 (FHN, n = 682,935; 3M, n = 2,122,305). We evaluated model discrimination and accuracy and assessed agreement in estimated HSMRs between methods. The Spearman correlation between HSMRs generated by the FHN model and the standard 3M model was 0.79. Although 2 of 22 hospitals showed opposite classification results, that is, an HSMR significantly <1 according to the FHN method but significantly >1 according to the 3M model, classification agreement between methods was significant (agreement for 59.1% of hospitals, κ = 0.45). The 3M model (c statistic = 0.96, adjusted Brier score = 26%) outperformed the FHN model (0.87, 17%). However, using ROM at admission instead of at discharge in the 3M model significantly reduced model performance (c statistic = 0.94, adjusted Brier score = 21%), but yielded similar HSMR estimates and eliminated part of the discrepancy with FHN results. Results of both models agreed relatively well, supporting convergent validity. Whereas the FHN method only adjusts for disease severity at admission, the ROM indicator of the 3M model includes diagnoses not present on admission. Although diagnosis codes generated by complications during hospitalization have the tendency to increase the predictive performance of a model, these should not be included in risk adjustment procedures.

Comparison of Disk Diffusion, E-Test, and Broth Microdilution Methods for Testing In Vitro Activity of Cefiderocol in Acinetobacter baumannii.

The reference method for cefiderocol antimicrobial susceptibility testing is broth microdilution (BMD) with iron-depleted-Mueller-Hinton (ID-MH) medium, whereas breakpoints recommended for disk diffusion (DD) are based on MH-agar plates. We aimed to compare the performance of the commercial BMD tests ComASP (Liofilchem) and UMIC (Bruker), and DD and E-test using MH- and ID-MH-agar plates with the reference BMD method using 100 carbapenem-resistant-A. baumannii isolates. Standard BMD was performed according to the EUCAST guidelines; DD and E-test were carried out using two commercial MH-agar plates (BioMérieux and Liofilchem) and an in-house ID-MH-agar plate, while ComASP and UMIC were performed according to the manufacturer's guidelines. DD performed with the ID-MH-agar plates led to a higher categorical agreement (CA, 95.1%) with standard BMD and fewer categorization errors compared to the commercial MH-agar plates (CA BioMérieux 91.1%, Liofilchem 89.2%). E-test on ID-MH-agar plates exhibited a significantly higher essential agreement (EA, 75%) with standard BMD compared to the two MH-agar plates (EA BioMérieux 57%, Liofilchem 44%), and showed a higher performance in detecting high-level resistance than ComASP and UMIC (mean log2 difference with standard BMD for resistant isolates of 0.5, 2.83, and 2.08, respectively). In conclusion, DD and E-test on ID-MH-agar plates exhibit a higher diagnostic performance than on MH-agar plates and the commercial BMD methods. Therefore, we recommend using ID-MH-agar plates for cefiderocol susceptibility testing of A. baumannii.