Retrospective study on the occurrence of Salmonella serotypes in veterinary specimens of Atlantic Canada (2012-2021).

AIM: This study aimed to summarize the frequency and the antimicrobial susceptibility profiles of the Salmonella serotypes identified from the specimens of companion animals, livestock, avian, wildlife and exotic species within Atlantic Canada. MATERIALS AND METHODS: The retrospective electronic laboratory data of microbiological analyses of a selected subset of samples from 03 January 2012 to 29 December 2021 submitted from various animal species were retrieved. The frequency of Salmonella serotypes identified, and their antimicrobial susceptibility results obtained using the disk diffusion or broth method were analysed. The test results were interpreted according to the Clinical and Laboratory Standards Institute standard. The Salmonella serotypes were identified by slide agglutination (Kauffman-White-Le-Minor Scheme) and/or the Whole Genome Sequencing for the Salmonella in silico Serovar Typing Resource-based identification. RESULTS: Of the cases included in this study, 4.6% (n = 154) had at least one Salmonella isolate, corresponding to 55 different serovars. Salmonella isolation was highest from exotic animal species (n = 40, 1.20%), followed by porcine (n = 26, 0.78%), and canine (n = 23, 0.69%). Salmonella subsp. enterica serovar Typhimurium was predominant among exotic mammals, porcine and caprine samples, whereas S. Enteritidis was mostly identified in bovine and canine samples. S. Typhimurium of porcine origin was frequently resistant (>70.0%) to ampicillin. In contrast, S. Typhimurium isolates from porcine and caprine samples were susceptible (>70.0%) to florfenicol. S. Oranienburg from equine samples was susceptible to chloramphenicol, but frequently resistant (>90.0%) to azithromycin. In avian samples, S. Copenhagen was susceptible (>90.0%) to florfenicol, whereas Muenchen was frequently resistant (>90.0%) to florfenicol. S. subsp. diarizonae serovar IIIb:61:k:1,5 of ovine origin was resistant (50.0% isolates) to sulfadimethoxine. No significant changes were observed in the antibiotic resistance profiles across the study years. CONCLUSIONS: This report provides data for surveillance studies, distribution of Salmonella serotypes and their antimicrobial resistance among veterinary specimens of Atlantic Canada.

A SARS-CoV-2 minimum data standard to support national serology reporting.

BACKGROUND: Healthcare laboratory systems produce and capture a vast array of information, yet do not always report all of this to the national infrastructure within the United Kingdom. The global COVID-19 pandemic brought about a much greater need for detailed healthcare data, one such instance being laboratory testing data. The reporting of qualitative laboratory test results (e.g. positive, negative or indeterminate) provides a basic understanding of levels of seropositivity. However, to better understand and interpret seropositivity, how it is determined and other factors that affect its calculation (i.e. levels of antibodies), quantitative laboratory test data are needed. METHOD: 36 data attributes were collected from 3 NHS laboratories and 29 CO-CONNECT project partner organisations. These were assessed against the need for a minimum dataset to determine data attribute importance. An NHS laboratory feasibility study was undertaken to assess the minimum data standard, together with a literature review of national and international data standards and healthcare reports. RESULTS: A COVID serology minimum data standard (CSMDS) comprising 12 data attributes was created and verified by 3 NHS laboratories to allow national granular reporting of COVID serology results. To support this, a standardised set of vocabulary terms was developed to represent laboratory analyser systems and laboratory information management systems. CONCLUSIONS: This paper puts forward a minimum viable standard for COVID-19 serology data attributes to enhance its granularity and augment the national reporting of COVID-19 serology laboratory results, with implications for future pandemics.

Longitudinal HbA1c patterns before the first treatment of diabetes in routine clinical practice: A latent class trajectory analysis.

AIMS: To examine the longitudinal heterogeneity of HbA1c preceding the initiation of diabetes treatment in clinical practice. METHODS: In this population-based study, we used HbA1c from routine laboratory and healthcare databases. Latent class trajectory analysis was used to classify individuals according to their longitudinal HbA1c patterns before first glucose-lowering drug prescription irrespective of type of diabetes. RESULTS: Among 21,556 individuals initiating diabetes treatment during 2017-2018, 20,733 (96 %) had HbA1c measured (median 4 measurements [IQR 2-7]) in the 5 years preceding treatment initiation. Four classes with distinct HbA1c trajectories were identified, with varying steepness of increase in HbA1c. The largest class (74 % of the individuals) had mean HbA1c above the 48 mmol/mol threshold 9 months before treatment initiation. Mean HbA1c was 52 mmol/mol (95 % CI 52-52) at treatment initiation. In the remaining three classes, mean HbA1c exceeded 48 mmol/mol almost 1.5 years before treatment initiation and reached 79 mmol/mol (95 % CI 78-80), 105 mmol/mol (95 % CI 104-106), and 137 mmol/mol (95 % CI 135-140) before treatment initiation. CONCLUSION: We identified four distinct longitudinal HbA1c patterns before initiation of diabetes treatment in clinical practice. All had mean HbA1c levels exceeding the diagnostic threshold many months before treatment initiation, indicating therapeutic inertia.

Attrition Rates in HIV Viral Load Monitoring and Factors Associated With Overdue Testing Among Children Within South Africa's Antiretroviral Treatment Program: Retrospective Descriptive Analysis.

BACKGROUND: Numerous studies in South Africa have reported low HIV viral load (VL) suppression and high attrition rates within the pediatric HIV treatment program. OBJECTIVE: Using routine laboratory data, we evaluated HIV VL monitoring, including mobility and overdue VL (OVL) testing, within 5 priority districts in South Africa. METHODS: We performed a retrospective descriptive analysis of National Health Laboratory Service (NHLS) data for children and adolescents aged 1-15 years having undergone HIV VL testing between May 1, 2019, and April 30, 2020, from 152 facilities within the City of Johannesburg, City of Tshwane, eThekwini, uMgungundlovu, and Zululand. HIV VL test-level data were deduplicated to patient-level data using the NHLS CDW (Corporate Data Warehouse) probabilistic record-linking algorithm and then further manually deduplicated. An OVL was defined as no subsequent VL determined within 18 months of the last test. Variables associated with the last VL test, including age, sex, VL findings, district type, and facility type, are described. A multivariate logistic regression analysis was performed to identify variables associated with an OVL test. RESULTS: Among 21,338 children and adolescents aged 1-15 years who had an HIV VL test, 72.70% (n=15,512) had a follow-up VL test within 18 months. Furthermore, 13.33% (n=2194) of them were followed up at a different facility, of whom 3.79% (n=624) were in a different district and 1.71% (n=281) were in a different province. Among patients with a VL of ≥1000 RNA copies/mL of plasma, the median time to subsequent testing was 6 (IQR 4-10) months. The younger the age of the patient, the greater the proportion with an OVL, ranging from a peak of 52% among 1-year-olds to a trough of 21% among 14-year-olds. On multivariate analysis, 2 consecutive HIV VL findings of ≥1000 RNA copies/mL of plasma were associated with an increased adjusted odds ratio (AOR) of having an OVL (AOR 2.07, 95% CI 1.71-2.51). Conversely, patients examined at a hospital (AOR 0.86, 95% CI 0.77-0.96), those with ≥2 previous tests (AOR 0.78, 95% CI 0.70-0.86), those examined in a rural district (AOR 0.63, 95% CI 0.54-0.73), and older age groups of 5-9 years (AOR 0.56, 95% CI 0.47-0.65) and 10-14 years (AOR 0.51, 95% CI 0.44-0.59) compared to 1-4 years were associated with a significantly decreased odds of having an OVL test. CONCLUSIONS: Considerable attrition occurs within South Africa's pediatric HIV treatment program, with over one-fourth of children having an OVL test 18 months subsequent to their previous test. In particular, younger children and those with virological failure were found to be at increased risk of having an OVL test. Improved HIV VL monitoring is essential for improving outcomes within South Africa's pediatric antiretroviral treatment program.

Considerations for the use of biochemical laboratory registry data in clinical and public health research.

OBJECTIVES: To inform researchers of central considerations and limitations when applying biochemical laboratory-generated registry data in clinical and public health research. STUDY DESIGN AND SETTING: After review of literature on registry-based studies and the utilization of clinical laboratory registry data, relevant paragraphs and their applicability toward the creation of considerations for the use of biochemical registry data in research were evaluated. This led to the creation of an initial ten considerations. These were elaborated, edited, and merged after several read-throughs by all authors and discussed thoroughly under influence by the authors' personal experiences with laboratory databases and research registries in Denmark, leading to the formulation of five central considerations with corresponding items and illustrative examples. RESULTS: We recommend that the following considerations should be addressed in studies relying on biochemical laboratory-generated registry data: why are biochemical laboratory data relevant to examine the hypothesis, and how were the variable(s) utilized in the study? What were the primary indications for specimen collection in the study population of interest? Were there any pre-analytical circumstances that could influence the test results? Are data comparable between producing laboratories and within the single laboratory over time? Is the database representative in terms of completeness of study populations and key variables? CONCLUSION: It is crucial to address key errors in laboratory registry data and acknowledge potential limitations.

Text analysis framework for identifying mutations among non-small cell lung cancer patients from laboratory data.

BACKGROUND: Laboratory data can provide great value to support research aimed at reducing the incidence, prolonging survival and enhancing outcomes of cancer. Data is characterized by the information it carries and the format it holds. Data captured in Alberta's biomarker laboratory repository is free text, cluttered and rouge. Such data format limits its utility and prohibits broader adoption and research development. Text analysis for information extraction of unstructured data can change this and lead to more complete analyses. Previous work on extracting relevant information from free text, unstructured data employed Natural Language Processing (NLP), Machine Learning (ML), rule-based Information Extraction (IE) methods, or a hybrid combination between them. METHODS: In our study, text analysis was performed on Alberta Precision Laboratories data which consisted of 95,854 entries from the Southern Alberta Dataset (SAD) and 6944 entries from the Northern Alberta Dataset (NAD). The data covers all of Alberta and is completely population-based. Our proposed framework is built around rule-based IE methods. It incorporates topics such as Syntax and Lexical analyses to achieve deterministic extraction of data from biomarker laboratory data (i.e., Epidermal Growth Factor Receptor (EGFR) test results). Lexical analysis compromises of data cleaning and pre-processing, Rich Text Format text conversion into readable plain text format, and normalization and tokenization of text. The framework then passes the text into the Syntax analysis stage which includes the rule-based method of extracting relevant data. Rule-based patterns of the test result are identified, and a Context Free Grammar then generates the rules of information extraction. Finally, the results are linked with the Alberta Cancer Registry to support real-world cancer research studies. RESULTS: Of the original 5512 entries in the SAD dataset and 5017 entries in the NAD dataset which were filtered for EGFR, the framework yielded 5129 and 3388 extracted EGFR test results from the SAD and NAD datasets, respectively. An accuracy of 97.5% was achieved on a random sample of 362 tests. CONCLUSIONS: We presented a text analysis framework to extract specific information from unstructured clinical data. Our proposed framework has shown that it can successfully extract relevant information from EGFR test results.

Using time-series chest radiographs and laboratory data by machine learning for identifying pulmonary infection and colonization of Acinetobacter baumannii.

BACKGROUND: Accurately distinguishing between pulmonary infection and colonization in patients with Acinetobacter baumannii is of utmost importance to optimize treatment and prevent antibiotic abuse or inadequate therapy. An efficient automated sorting tool could prompt individualized interventions and enhance overall patient outcomes. This study aims to develop a robust machine learning classification model using a combination of time-series chest radiographs and laboratory data to accurately classify pulmonary status caused by Acinetobacter baumannii. METHODS: We proposed nested logistic regression models based on different time-series data to automatically classify the pulmonary status of patients with Acinetobacter baumannii. Advanced features were extracted from the time-series data of hospitalized patients, encompassing dynamic pneumonia indicators observed on chest radiographs and laboratory indicator values recorded at three specific time points. RESULTS: Data of 152 patients with Acinetobacter baumannii cultured from sputum or alveolar lavage fluid were retrospectively analyzed. Our model with multiple time-series data demonstrated a higher performance of AUC (0.850, with a 95% confidence interval of [0.638-0.873]), an accuracy of 0.761, a sensitivity of 0.833. The model, which only incorporated a single time point feature, achieved an AUC of 0.741. The influential model variables included difference in the chest radiograph pneumonia score. CONCLUSION: Dynamic assessment of time-series chest radiographs and laboratory data using machine learning allowed for accurate classification of colonization and infection with Acinetobacter baumannii. This demonstrates the potential to help clinicians provide individualized treatment through early detection.

Diabetes related phenotypes and their influence on outcomes of patients with corona virus disease 2019 (COVID-19).

INTRODUCTION: Diabetes mellitus (DM) is associated with severe forms of COVID-19 but little is known about the diabetes-related phenotype considering pre-admission, on-admission and data covering the entire hospitalization period. METHODS: We analyzed COVID-19 inpatients (n = 3327) aged 61.2(48.2-71.4) years attended from March to September 2020 in a public hospital. RESULTS: DM group (n = 1218) differed from Non-DM group (n = 2109) by higher age, body mass index (BMI), systolic blood pressure and lower O2 saturation on admission. Gender, ethnicity and COVID-19-related symptoms were similar. Glucose and several markers of inflammation, tissue injury and organ dysfunction were higher among patients with diabetes: troponin, lactate dehydrogenase, creatine phosphokinase (CPK), C-reactive protein (CRP), lactate, brain natriuretic peptide, urea, creatinine, sodium, potassium but lower albumin levels. Hospital (12 × 11 days) and intensive care unit permanence (10 × 9 days) were similar but DM group needed more vasoactive, anticoagulant and anti-platelet drugs, oxygen therapy, endotracheal intubation and dialysis. Lethality was higher in patients with diabetes (39.3% × 30.7%) and increased with glucose levels and age, in male sex and with BMI < 30 kg/m2 in both groups (obesity paradox). It was lower with previous treatment with ACEi/BRA in both groups. Ethnicity and education level did not result in different outcomes between groups. Higher frequency of comorbidities (hypertension, cardiovascular/renal disease, stroke), of inflammatory (higher leucocyte number, RCP, LDH, troponin) and renal markers (urea, creatinine, potassium levels and lower sodium, magnesium) differentiated lethality risk between patients with and without diabetes. CONCLUSIONS: Comorbidities, inflammatory markers and renal disfunction but not Covid-19-related symptoms, obesity, ethnicity and education level differentiated lethality risk between patients with and without diabetes.

Q fever and toxoplasmosis in South African livestock and wildlife: a retrospective study on seropositivity, sporadic abortion, and stillbirth cases in livestock caused by Coxiella burnetii.

BACKGROUND: Q fever and toxoplasmosis are economically important zoonoses as they cause considerable losses in livestock (cattle, sheep and goats) and wildlife (antelopes, giraffes, lions, and cheetahs) through reproductive disorders such as abortions and stillbirths. Q fever and toxoplasmosis testing in South Africa is conducted by the Agricultural Research Council-Onderstepoort Veterinary Research (ARC-OVR). However, both zoonoses are understudied and not monitored in South Africa as they are not considered controlled or notifiable diseases in the Animal Disease Act 35 of 1984. A retrospective study was conducted on Q fever (2007-2009) and toxoplasmosis (2007-2017) using diagnostic laboratory data at the ARC-OVR. Also, we report on sporadic abortion and stillbirth cases in livestock from diagnostic tissue samples submitted for Coxiella burnetii polymerase chain reaction (PCR) detection at the ARC-OVR. RESULTS: During 2007 to 2009, 766 animal samples were tested for C. burnetii antibodies and seropositivity was 0.9% (95%CI: 0.3-1.7) with sheep (1.9%; 95%CI: 0.6-4.4) having the highest seropositivity followed by cattle (0.7%; 95%CI: 0.09-2.6), while all goats (0.0%; 95%CI: 0.0-4.2) and wildlife (0.0%; 95%CI: 0.0-2.5) tested were negative. From 2007 to 2017, 567 sera were tested for T. gondii antibodies; overall seropositivity was 12.2% (95%CI: 9.6-15). Wildlife had highest seropositivity to T. gondii antibodies (13.9%; 95%CI: 9.0-19.7) followed by goats (12.9%; 95%CI: 9.2-17.4) and sheep (12.3%; 95%CI: 5.1-23.8) while seropositivity in cattle was 2.4% (95%CI: 0.06-12.9). Of 11 animals tested by C. burnetii PCR detection (2021-2022), 10 (91.0%) were positive. The amplicon sequences showed similarity to Coxiella burnetii strain 54T1 transposase gene partial coding sequence. CONCLUSIONS: We have confirmed the occurrence of the causative agents of Q fever and toxoplasmosis in livestock and wildlife in South Africa, with data limitations. These zoonoses remain of importance with limited information about them in South Africa. This study provides baseline information for future studies on Q fever and toxoplasmosis in South African livestock and wildlife, as well other African countries. Due to limited data collection experienced in this study, it is recommended that improvements in data collection samples tested should include associated factors such as sex, age, and breed of the animals.

A retrospective study of myelin oligodendrocyte glycoprotein antibody-associated disease from a clinical laboratory perspective.

Objectives: To analyze the differences in laboratory data between patients with myelin oligodendrocyte glycoprotein (MOG) antibody-associated disease (MOGAD), multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD). Methods: The study included 26 MOGAD patients who visited Beijing Tiantan Hospital from 2018 to 2021. MS and NMOSD patients who visited the clinic during the same period were selected as controls. Relevant indicators were compared between the MOGAD group and the MS/NMOSD groups, and the diagnostic performance of meaningful markers was assessed. Results: The MOGAD group showed a slight female preponderance of 57.7%, with an average onset age of 29.8 years. The absolute and relative counts of neutrophils were higher in the MOGAD group than in the MS group, while the proportion of lymphocytes was lower. The cerebrospinal fluid (CSF) IgG level, IgG index, 24-h IgG synthesis rate, and positive rate of oligoclonal bands (OCB) were lower in MOGAD patients than in the MS group. The area under ROC curve (AUC) was 0.939 when combining the relative lymphocyte count and IgG index. Compared to the NMOSD group, the MOGAD group had higher levels of serum complement C4 and lower levels of serum IgG. The AUC of serum C4 combined with FT4 was 0.783. Conclusion: Statistically significant markers were observed in the laboratory data of MOGAD patients compared to MS/NMOSD patients. The relative lymphocyte count combined with IgG index had excellent diagnostic efficacy for MOGAD and MS, while serum C4 combined with FT4 had better diagnostic efficacy for MOGAD and NMOSD.

Improving protein therapeutic development through cloud-based data integration.

Pharma 4.0 is a digital evolution of the pharmaceutical industry that automates scientists' traditional workflows with the implementation of modern technologies like cloud pipelines, artificial intelligence, robotic platforms, and augmented reality. Lab data capture (LDC) is an essential strategy for initiating Pharma 4.0 that aggregates and harmonizes siloed lab data from analytical instruments, reporting systems, and operational platforms. This publication describes the execution of LDC within a quantitative PCR (qPCR) workflow using the Tetra Data Platform (TDP). We selected this workflow because the qPCR instrument, the ViiA7, generates discrete file-based data that documents execution of individual assays for quantifying residual DNA throughout biologics process development and product profiling. TDP executes LDC through the deployment of file scanning software agents, scanning and ingestion processes, and a cloud-based parsing pipeline that harmonizes source data. Web applications were developed to query, visualize, and interpret harmonized qPCR data for automated experiment data processing and process control charting from the TDP platform. Our implementation of LDC enables analytical researchers to harness FAIR (Findable, Accessible, Interoperable, Reproducible) data practices across the organization and establishes a "compliance-by-code" culture in development labs.

HbA1c-defined prediabetes and progression to type 2 diabetes in Denmark: A population-based study based on routine clinical care laboratory data.

AIMS: To estimate the prevalence, incidence, mortality, and risk of progression to type 2 diabetes for individuals with HbA1c-defined prediabetes based on Danish nationwide population-based laboratory databases. METHODS: We included all HbA1c measurements from general practice and hospitals during 2012 to 2018. We estimated the cumulative incidence of having at least one HbA1c measurement. The prevalence and incidence rates of prediabetes (HbA1c 42-47 mmol/mol) were examined in the adult Danish population. The 5-year cumulative incidence of progression to type 2 diabetes was estimated with death as competing event. RESULTS: Among 4,979,590 adult Danes, 70.8% (95% CI 70.8-70.9) had at least one HbA1c measurement during 2012 to 2018. The prevalence of prediabetes was 7.1% (95% CI 7.1-7.1) in 2018. The incidence rate was 14.2 (95% CI 14.1-14.3) per 1,000 person-years, with median age 66.9 years (IQR 56.7-75.7) and median HbA1c 43 mmol/mol (IQR 42-44) at prediabetes diagnosis. Within five years, 17.5% (95% CI 17.3-17.7) died and the 5-year cumulative incidence of type 2 diabetes was 21.3% (95% CI 21.1-21.5). CONCLUSIONS: Out of 100 Danish adults, 1.4 develop prediabetes each year and they can be identified at an early stage in laboratory databases. Within five years, one in five individuals with prediabetes progresses to diabetes and one in six dies.

Designing a minimum data set of laboratory data for the electronic summary sheet of pediatric ward in Iran: A cross-sectional study.

Background and Aim: Iranian hospitals are provided with hospital information systems (HISs) from different vendors, which make it hardly possible to summarize laboratory data in an consistent manner. Therefore, there is a need to design a minimum data set of laboratory data that will define standard criteria and reduce potential medical errors. The purpose of this study was to design a minimum data set (MDS) of laboratory data for an electronic summary sheet to be used in the pediatric ward of Iranian hospitals. Methods: This study consists of three phases. In the first phase, out of 3997 medical records from the pediatric ward, 604 summary sheets were chosen as sample. The laboratory data of these sheets were examined and the recorded tests were categorized. In the second phase, based on the types of diagnosis we developed a list of tests. Then we asked the physicians of the ward to select which ones should be documented for each patient's diagnosis. In the third phase, the tests that were reported in 21%-80% of the records, and were verified by the same percentage of physicians, were evaluated by the experts' panel. Results: In the first phase, 10,224 laboratory data were extracted. Of these, 144 data elements reported in more than 80% of the records, and more than 80% of experts approved them to be included in the MDS for patients' summary sheet. After data elements were investigated in the experts' panel, 292 items were chosen for the final list of the data set. Conclusions: This MDS was designed such that, if implemented in hospital information systems, it could automatically enable registering data in the summary sheet when patient's diagnosis is registered.

Association between socioeconomic deprivation and incidence of infectious intestinal disease by pathogen and linked transmission route: An ecological analysis in the UK.

Infectious intestinal disease (IID) studies conducted at different levels of the surveillance pyramid have found heterogeneity in the association of socioeconomic deprivation with illness. The aim of this study was to analyse the association between socioeconomic deprivation and incidence of IID by certain gastrointestinal pathogens reported to UKHSA. Data were extracted from 2015 to 2018 for Salmonella, Campylobacter, Shigella, Giardia species, and norovirus. Rates were calculated per 100,000 person-years by the index of multiple deprivation quintile, and an ecological analysis was conducted using univariant and multvariable regression models for each pathogen. Incidence of Campylobacter, and Giardia species decreased with increasing deprivation. Conversely, the incidence of norovirus, non-typhoidal Salmonella, Salmonella typhi/paratyphi, Shigella species increased with increasing deprivation. Multivariable analysis results showed that higher deprivation was significantly associated with higher odds of higher number of cases for Shigella flexneri, norovirus and S. typhi/paratyphi. Infections most associated with deprivation were those transmitted by person-to-person spread, and least associated were those transmitted by zoonotic contamination of the environment. Person-to-person transmission can be contained by implementing policies targeting over-crowding and poor hygiene. This approach is likely to be the most effective solution for the reduction of IID.

Identification of High Platelet Reactivity Despite ADP P2Y 12 Inhibitor Treatment: Two Populations in the Vasodilator-Stimulated Phosphoprotein Assay and Variable PFA-P2Y Shapes of Curve.

Introduction Response to ADP P2Y 12 receptor inhibition by clopidogrel can be evaluated by various techniques. Here, we compared a functional rapid point-of-care technique (PFA-P2Y) with the degree of biochemical inhibition assessed by the VASP/P2Y 12 assay. Methods Platelet response to clopidogrel was investigated in 173 patients undergoing elective intracerebral stenting (derivation cohort n = 117; validation cohort n = 56). High platelet reactivity (HPR) was defined as PFA-P2Y occlusion time <106 seconds or VASP/P2Y 12 platelet reactivity index (PRI) >50%. Results In the derivation cohort, receiver operator characteristics analysis for the ability of PFA-P2Y to detect biochemical HPR showed high specificity (98.4%) but poor sensitivity (20.0%) and a very low area under the curve (0.59). The VASP/P2Y 12 assay revealed two coexisting platelet populations with different levels of vasodilator-stimulated phosphoprotein (VASP) phosphorylation: a fraction of highly phosphorylated, inhibited platelets and another of poorly phosphorylated, reactive platelets. Analysis of the PFA-P2Y curve shape revealed different types, categorized by time of occlusion (<106 seconds, 106 to 300 seconds, >300 seconds), and pattern (regular, irregular, and atypical). Noteworthy, curves with late occlusion and permeable curves with an irregular or atypical pattern correlated with VASP-PRI >50% and smaller sizes of the inhibited platelet subpopulation. Considering the PFA-P2Y shape of the curve for the detection of HPR improved sensitivity (72.7%) and preserved specificity (91.9%), with a rather high AUC (0.823). The validation cohort confirmed the VASP/P2Y 12 assay data and the usefulness of considering the PFA-P2Y curve shape. Conclusion In patients treated with acetylsalicylic acid and clopidogrel for 7-10 days, the VASP/P2Y 12 assay reveals two coexisting subpopulations of differentially inhibited platelets, whose relative sizes predict global PRI and distinct PFA-P2Y curve patterns, indicating incomplete clopidogrel efficacy. The detailed analysis of both VASP/P2Y 12 and PFA-P2Y is necessary for optimal detection of HPR.

Artificial intelligence and laboratory data in rheumatic diseases.

Artificial intelligence (AI)-based medical technologies are rapidly evolving into actionable solutions for clinical practice. Machine learning (ML) algorithms can process increasing amounts of laboratory data such as gene expression immunophenotyping data and biomarkers. In recent years, the analysis of ML has become particularly useful for the study of complex chronic diseases, such as rheumatic diseases, heterogenous conditions with multiple triggers. Numerous studies have used ML to classify patients and improve diagnosis, to stratify the risk and determine disease subtypes, as well as to discover biomarkers and gene signatures. This review aims to provide examples of ML models for specific rheumatic diseases using laboratory data and some insights into relevant strengths and limitations. A better understanding and future application of these analytical strategies could facilitate the development of precision medicine for rheumatic patients.

Factors Affecting Survival and Local Control in Patients with Bone Metastases Treated with Radiotherapy.

The aim of this study was to evaluate the expected prognosis and factors affecting local control (LC) of the bone metastatic sites treated with palliative external beam radiotherapy (RT). Between December 2010 and April 2019, 420 cases (male/female = 240/180; median age [range]: 66 [12-90] years) with predominantly osteolytic bone metastases received RT and were evaluated. LC was evaluated by follow-up computed tomography (CT) image. Median RT doses (BED10) were 39.0 Gy (range, 14.4-71.7 Gy). The 0.5-year overall survival and LC of RT sites were 71% and 84%, respectively. Local recurrence on CT images was observed in 19% (n = 80) of the RT sites, and the median recurrence time was 3.5 months (range, 1-106 months). In univariate analysis, abnormal laboratory data before RT (platelet count, serum albumin, total bilirubin, lactate dehydrogenase, or serum calcium level), high-risk primary tumor sites (colorectal, esophageal, hepatobiliary/pancreatic, renal/ureter, and non-epithelial cancers), no antineoplastic agents (ATs) administration after RT, and no bone modifying agents (BMAs) administration after RT were significantly unfavorable factors for both survival and LC of RT sites. Sex (male), performance status (≥3), and RT dose (BED10) (<39.0 Gy) were significantly unfavorable factors for only survival, and age (≥70 years) and bone cortex destruction were significantly unfavorable factors for only LC of RT sites. In multivariate analysis, only abnormal laboratory data before RT influenced both unfavorable survival and LC of RT sites. Performance status (≥3), no ATs administration after RT, RT dose (BED10) (<39.0 Gy), and sex (male) were significantly unfavorable factors for survival, and primary tumor sites and BMAs administration after RT were significantly unfavorable factors for LC of RT sites. In conclusion, laboratory data before RT was important factor both prognosis and LC of bone metastases treated with palliative RT. At least in patients with abnormal laboratory data before RT, palliative RT seemed to be focused on the only pain relief.

A Wolf in Sheep's Clothing: Reuse of Routinely Obtained Laboratory Data in Research.

Electronic health records (EHRs) contain valuable data for reuse in science, quality evaluations, and clinical decision support. Because routinely obtained laboratory data are abundantly present, often numeric, generated by certified laboratories, and stored in a structured way, one may assume that they are immediately fit for (re)use in research. However, behind each test result lies an extensive context of choices and considerations, made by both humans and machines, that introduces hidden patterns in the data. If they are unaware, researchers reusing routine laboratory data may eventually draw incorrect conclusions. In this paper, after discussing health care system characteristics on both the macro and micro level, we introduce the reader to hidden aspects of generating structured routine laboratory data in 4 steps (ordering, preanalysis, analysis, and postanalysis) and explain how each of these steps may interfere with the reuse of routine laboratory data. As researchers reusing these data, we underline the importance of domain knowledge of the health care professional, laboratory specialist, data manager, and patient to turn routine laboratory data into meaningful data sets to help obtain relevant insights that create value for clinical care.

Analysis of Clinical Characteristics and Laboratory Data Related to the Prognosis of Korean Severe Fever with Thrombocytopenia Syndrome Patients: A Single-Center Study.

Background: Severe fever with thrombocytopenia syndrome (SFTS), an emerging viral infectious disease, is mainly transmitted by ticks in the surrounding environment. Clinical progress and risk factors for prognosis in SFTS patients were not yet fully understood. Thus, the objective of this study was to analyze clinical progression and laboratory data related to the prognosis of South Korean SFTS patients in a single institution from 2014 to 2021. Materials and Methods: Fifty-three confirmed SFTS patients from August 2014 to September 2021 at Gyeongsang National University Hospital (GNUH) in Jinju, South Korea were enrolled. Electronic medical records of SFTS patients' demographic features, clinical data, and laboratory data were retrospectively reviewed. Risk factors for fatality were statistically analyzed by classifying enrolled patients into fatal and non-fatal groups. Results: The mean age of patients in the fatal group was significantly higher than that in the non-fatal group (p = 0.036). Hemorrhagic manifestations (p = 0.001) and multiple organ dysfunction (MOD) (p < 0.001) were significantly common in the fatal group. Age, hemorrhagic manifestations, and MOD were also associated with death (p = 0.001, p = 0.008, and p = 0.041, respectively), with adjusted hazard ratios (aHRs) of 1.14, 18.25, and 2.36, respectively. Onset of illness to admission was also significantly associated with death (p = 0.005), with aHR of 0.48. Age, interval from onset of illness to admission, hemorrhagic manifestations, and MOD were found to be variables related to the fatality of SFTS patients. Conclusion: Laboratory test results showed a significant difference between the fatal group and the non-fatal group, but they did not have a statistically significant effect on the prognosis of SFTS patients.

What's the difference between lung adenocarcinoma and lung squamous cell carcinoma? Evidence from a retrospective analysis in a cohort of Chinese patients.

Background: Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are the two most common subtypes of lung cancer. Previously, they were categorized into one histological subtype known as non-small cell lung cancer (NSCLC) and often treated similarly. However, increasing evidence suggested that LUAD and LUSC should be classified and treated as different cancers. But yet, detailed differences in clinical features between LUAD and LUSC have not been well described. Methods: A cohort of 142 Chinese patients with 111 LUAD and 31 LUSC cases were consecutively enrolled from April 2019 to October 2020 in Hunan Provincial People's Hospital. The clinical features of the patients were retrospectively analyzed and compared in the terms of general information, clinicopathologic characteristics, imaging findings and laboratory data. Results: In comparison with LUAD, LUSC patients had a significantly higher proportion of males, smokers, drinkers, higher-stage cases. The mean tumor size in LUSC patients was significantly larger than that in LUAD patients. Compared with LUAD patients, more of patients with LUSC had cough, fever and abundant sputum symptoms. Besides that, more bacterial infections and fungal infections were found in LUSC patients than that in LUAD patients. Imaging data shows that ground-glass opacity and patchy shadows in radiological films were more frequent in LUAD patients than that in LUSC patients. In addition to initial laboratory data, LUSC patients had higher levels of leukocytes, platelets, and creatinine that of LUAD patients. Conclusions: Together, these results suggested that there exist distinct differences between LUAD and LUSC subtypes; LUSC may be a more malignant type in comparison with LUAD. Our findings may have potential implications in clinical settings. However, further multicenter studies are needed to validate these findings in a larger sample size.