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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. [ABSTRACT FROM AUTHOR]

In the context of exploratory data analysis and machine learning, standardization of laboratory results is an important pre-processing step. Variable proportions of pathological results in routine datasets lead to changes of the mean (µ) and standard deviation (σ), and thus cause problems in the classical z-score transformation. Therefore, this study investigates whether the zlog transformation compensates these disadvantages and makes the results more meaningful from a medical perspective. The results presented here were obtained with the statistical software environment R, and the underlying data set was obtained from the UC Irvine Machine Learning Repository. We compare the differences of the zlog and z-score transformation for five different dimension reduction methods, hierarchical clustering and four supervised classification methods. With the zlog transformation, we obtain better results in this study than with the z-score transformation for dimension reduction, clustering and classification methods. By compensating the disadvantages of the z-score transformation, the zlog transformation allows more meaningful medical conclusions. We recommend using the zlog transformation of laboratory results for pre-processing when exploratory data analysis and machine learning techniques are applied. [ABSTRACT FROM AUTHOR]

Pseudocyst formation is common in many patients with acute pancreatitis during follow-up. Many risk factors have been proposed to be associated with the development of PP, but the predictive factors are still underexplored. The focus of this study was to investigate whether early laboratory indicators could effectively predict the occurrence of PP. 2811 AP patients hospitalized in the Second Affiliated Hospital of Soochow University between November 2008 and September 2020 were retrospectively studied. Univariate and multivariate analyses were used to screen the risk variables. The nomograms of those risk factors were validated and evaluated by logistic analysis. AP patients had a 6.1 % (172/2811) incidence of PP. In a univariate analysis, the development of PP was correlated with serum lactate dehydrogenase (LDH), albumin (ALB), calcium (Ca), hemoglobin (Hb), organ dysfunction, CT severity index (CTSI), etiology, age, etc. Further logistic regression analysis showed that the risk factors were different between hyperlipidemic pancreatitis patients (LDH, ALB and Ca) and non-hyperlipidemic pancreatitis patients (LDH, Hb, ALB and Ca). A nomogram based on the identified risk factors was developed. Our model showed good discrimination ability, with a boostrap - corrected C index of 0.905 (95 % CI = 0.875–0.935), and had well-fitted calibration curves. The area under the curve (AUC) of the nomogram were 0.905 (95 % CI = 0.875–0.935) and 0.933 (95 % CI = 0.890–0.975) in the training and validation groups, respectively. The results of DCA indicated that the nomogram may have clinic usefulness. The nomogram that incorporates early laboratory data (LDH, Hb, ALB, and Ca) in AP patients is able to predict the incidence of PP with greater accuracy than the CTSI and AP severity. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract 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.

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. [ABSTRACT FROM AUTHOR]

Abstract 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.

The paper provides a clinical case of physical and medical rehabilitation of patient D born in 1963. Principal diagnosis established in 2018 according to ICD-10: I42.0 dilated cardiomyopathy. Laboratory testing revealed DNA of herpes virus 6 (HHV6), positive test for cytomegalovirus (CMV) DNA, 6.0×102 copies/mL, positive test for the Epstein–Barr virus (EBV) DNA, 7.2×103 copies/mL. In April 2018, the patient underwent successful surgery, implantation of the biventricular bypass system supplemented by membrane oxygenation. Physical rehabilitation was performed that yielded positive results.

Abstract 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

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.

Background: A summary of clinical findings and laboratory data is collected in patient summary sheets. The aim of this study was to determine the frequency of laboratory data reported in the summary sheets based on the classification of diagnoses in ICD-10. Methods: A descriptive-cross sectional study was performed on summary sheets of patients admitted to the pediatric ward from March 2015 to February 2016.The laboratory data of these sheets were extracted using a data collection form. Because of high number of data in each sheet and given that, depending on the diagnosis, the reported laboratory data were different, to simplify the presentation of the results the data were categorized based on patients’ diagnoses which were coded by ICD-10. Results: A total of 10224 laboratory data were extracted, which included 47 unique data elements. The highest number of laboratory data was related to hematology (N = 14) and the lowest number was related to urine and stool analysis (N = 6). Conclusions: Laboratory data are recorded in summary sheets in accordance with patient's diagnosis. Therefore, it is possible to process the existing summary sheets and extract the registration rules, then implement these rules in hospital information systems to generate electronic summary sheets.

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. [ABSTRACT FROM AUTHOR]

Background: Reference intervals (RIs) of serum calcium vary based on age, population demographics, and methods of assessment. However, conventional approaches to establish serum calcium (Ca)RIs pose ethical and practical challenges, especially in the pediatric population. Hence, the use of indirect approaches is beneficial. This study was carried out to estimate the RIs of serum Ca using three indirect approaches in the pediatric and adolescent population of Pakistan. Methods: Data mining laboratory information systems, for serum Ca results from 2013 - 2021 was done on a target population ranging from birth to 18 years of age. The population was grouped into three categories based on age (birth - 1 year, 2 - 4 years, and 5 - 18 years), as defined previously by Tahmasebi et al. in the CALIPER cohort. Pre-validated indirect algorithms, 'KOSMIC', Bhattacharya, and Hoffman, were used for analyzing the RIs. Results: A total of non-duplicate 40,914 serum Ca tests were retrieved over a period of 6 years, including 38.7% (n = 15,830) from birth - 1 year, 16.3% (n = 6,641) from > 1 - 4 years, and 45.2% (n = 18443) from > 4 - 18 years respectively. The three methods revealed comparable performance with the direct RIs reported by Tahmasebi et al. in the CALIPER cohort. Keeping a stringent total allowable error of 1 mg/dl for Serum Ca as given by Clinical Laboratory Improvement Amendments (CLIA) the KOSMIC method outperformed the other two when compared to Tahmasebi, Houman, et al. Conclusions: The study advocates the use of the indirect approach for calculating RIs for serum calcium in the pediatric population, especially to aid clinical decision making in a low resource setting, due to its ability to reproduce results in line with the direct approach in a more economical, practical, and feasible way. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract 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.

Abstract Background Frailty can be operationalized based on the accumulation of deficits using a frailty index (FI) and is associated with an increased risk of adverse health outcomes. Here, we aim to compare validity of a FI from laboratory data with that of the common clinical FI for prediction of mortality in adults aged 55 + years, also examine whether combined FI could improve identification of adults aged 55 + years at increased risk of death. Methods Data for this analysis were obtained from the Beijing Longitudinal Study of Aging that involved 1,257 community-dwelling Chinese people, aged 55 + years at baseline. The main outcome measure was 5-year mortality. An FI-self-report based on 30 self-reported health-related data was constructed. An FI-lab was developed using laboratory data, in addition to pulse, systolic and diastolic blood pressure, pulse pressure, body mass index (BMI) and waist. A combined FI comprised all items from each FI. Kaplan–Meier survival curve and Cox proportional hazards models were performed to evaluate the risk of each FI on death. The area under receiver operating characteristic(ROC) curves were used to compare the discriminative performance of each FI. Results Of 1257 participants, 155 died and 156 lost at the end of the 5-year follow-up. The mean FI-self-report score was 0.11 ± 0.10, the FI-lab score was 0.33 ± 0.14 and FI-combined score was 0.19 ± 0.09. Higher frailty level defined by each FI was associated with higher risk of death. After adjustment for age and sex, Cox proportional hazards models showed that the higher scores of frailty were associated with a higher risk of mortality for each FI, the hazard ratios for the FI-self-report and FI-lab and FI-combined were 1.04 (1.03 to 1.05) and 1.02 (1.01 to 1.03) and 1.05 (1.04 to 1.07), respectively. The areas under the ROC curve were 0.79 (0.77–0.82) for the FI-self-report, 0.77(0.75–0.80) for the FI-lab and 0.81(0.78–0.82) for FI-combined. Conclusions A FI from laboratory data can stratify older adults at increased risk of death alone and in combination with FI based on self-report data. Assessment in clinical settings of creating an FI using routine collected laboratory data needs to be further developed.

Abstract Introduction Response to ADP P2Y12 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/P2Y12 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 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/P2Y12 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 (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/P2Y12 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/P2Y12 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/P2Y12 and PFA-P2Y is necessary for optimal detection of HPR.

While desalination of water is energy intensive, traditional use of fossil fuels makes it not only costly, but also unsustainable and environmentally unfriendly; utilization of solar energy for desalination is therefore highly encouraged. This paper presents a study on the technical performance of a solar desalination unit located in Tehran city. The process is modeled using standard energy equations and performance indices are defined. The model simulation results are compared with experimental data from laboratory tests. This study observed that there is good agreement between the experimental data and the model simulation results. It is observed that the maximum solar radiation intensity is found in June and the highest evaporation rate in this month is about 5 × 10 - 3 m 3 . It is found that the treatment efficacy of the plant decreases with increasing depth of wastewater. Performance of the desalination unit is found to be satisfactory. [ABSTRACT FROM AUTHOR]

Introduction: Neuropsychological manifestations are increasingly reported in patients with COVID-19, but the subject is poorly understood and our knowledge about cognitive impairment is limited. This study aimed to investigate neuropsychological involvement of COVID-19 and its relation to laboratory data. Material and methods: This study was conducted among patients admitted to a tertiary medical center in Iran, in the period March 2020 to March 2021. Neuropsychiatric assessments were carried out by the Addenbrooke’s Cognitive Examination (ACE) test in all patients and interpreted by a specialist. It was performed using the Persian version of ACE in six domains consisting of attention, orientation, memory, language, visual perception and visuospatial skills. Laboratory data were assessed among all patients. Statistical analyses were performed using SPSS. Results: Among 114 patients with COVID-19, the mean age was 48.46 ±13.61 years, and the male/female ratio was 1.32. The mean values of some abnormal laboratory data were: erythrocyte sedimentation rate (ESR) 57.2 ±29.8, C-reactive protein (CRP) 81.7 ±59.8 and D-dimer 459.3 ±395.4. Cognitive impairment was detected in 41 patients (63%). The mean score of ACE was 80.68 ±19.29. Statistical analysis showed correlations between total result of the ACE test and laboratory findings that were not statistically significant. Conclusions: In our study, no relation was found between laboratory test results and the overall cognitive examination score but statistical analysis showed a correlation between CRP and verbal fluency, between hemoglobin level and language and visuospatial domains, and also between total level of bilirubin and memory. These relations may suggest that we should follow the neurocognitive features in COVID-19 patients earlier. [ABSTRACT FROM AUTHOR]

Pressure waves, while traveling along pressurized pipes, collect precious information about possible faults (e.g., leaks and partial blockages). In fact, the characteristics of the pressure wave reflected by the fault are strongly related to it. To encourage the use of the transient test-based technologies (TTBTs) for partial blockage (PB) detection in pressurized pipe systems, it can be of interest to critically analyze the available experimental results and to point out the aspects that need to be investigated in more detail, since no review has been executed so far. Such a deficiency has two negative consequences. The first one is that TTBTs are still relegated to limbo by technicians. The second one is that not enough material is available for refining tools to extract all the information contained in the acquired pressure signals and then to pursue an effective PB detection. As main results of the executed analysis, the following issues can be counted: (i) the lack of tests carried out in large diameter and concrete pipes; (ii) the absence of tests carried out in complex pipe systems (e.g., looped networks); and (iii) the extreme need for considering real pipe systems. The fulfillment of the last issue will greatly contribute to the solutions of the other ones. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Since the 1990s, pressure waves propagating in pressurized pipes during a transient event have been re-evaluated both in the laboratory and in real systems as a way to obtain information about location and size of leaks. However, a systematic analysis of the obtained results has not been offered yet in the literature. In this paper, a review of the available experimental data concerning the use of transient tests for leak identification in pressurized pipe systems is presented. The selected data are examined with regard to the main features (categories) influencing the transient response of a leaky pipe system: layout, modality of transient generation, material, diameter, pre-transient pressure and flow condition, inserted pressure wave, and leak characteristics. Moreover, for each of such categories, possible specific issues to address for future research are proposed. [ABSTRACT FROM AUTHOR]

Abstract Background Only a few studies have performed comprehensive comparisons between hospitalized patients from different waves of COVID-19. Thus, we aimed to compare the clinical characteristics and laboratory data of patients admitted to the western part of Denmark during the first and second waves of COVID-19 in 2020. Furthermore, we aimed to identify risk factors for critical COVID-19 disease and to describe the available information on the sources of infection. Methods We performed a retrospective study of medical records from 311 consecutive hospitalized patients, 157 patients from wave 1 and 154 patients from wave 2. The period from March 7 to June 30, 2020, was considered wave 1, and the period from July 1st to December 31, 2020, was considered wave 2. Data are presented as the total study population, as a comparison between waves 1 and 2, and as a comparison between patients with and without critical COVID-19 disease (nonsurvivors and patients admitted to the intensive care unit (ICU)). Results Patients admitted during the first COVID-19 wave experienced a more severe course of disease than patients admitted during wave 2. Admissions to the ICU and fatal disease were significantly higher among patients admitted during wave 1 compared to wave 2. The percentage of patients infected at hospital decreased in wave 2 compared to wave 1, whereas more patients were infected at home during wave 2. We found no significant differences in sociodemographics, lifestyle information, or laboratory data in the comparison of patients from waves 1 and 2. However, age, sex, smoking status, comorbidities, fever, and dyspnea were identified as risk factors for critical COVID-19 disease. Furthermore, we observed significantly increased levels of C-reactive protein and creatinine, and lower hemoglobin levels among patients with critical disease. Conclusions At admission, patients were more severely ill during wave 1 than during wave 2, and the outcomes were worse during wave 1. We confirmed previously identified risk factors for critical COVID-19 disease. In addition, we found that most COVID-19 infections were acquired at home.

COVID-19 disease has been the most important disease recently and has affected serious number of people in the world. There is not proven treatment method yet and early diagnosis of COVID-19 is crucial to prevent spread of the disease. Laboratory data can be easily accessed in about 15 minutes, and cheaper than the cost of other COVID-19 detection methods such as CT imaging and RT-PCR test. In this study, we perform a comparative study for COVID-19 prediction using machine learning and deep learning algorithms from laboratory findings. For this purpose, nine different machine learning algorithms including different structures as well as deep neural network classifier are evaluated and compared. Experimental results conduct that cosine k-nearest neighbor classifier achieves better accuracy with 89% among other machine learning algorithms. Furthermore, deep neural network classifier achieves an accuracy of 90.3% when one hidden layer including 60 neurons is used to detect COVID-19 disease from laboratory findings data.

Summary. The prevalence of type 2 diabetes and diffuse non-toxic goiter increases worldwide. It has been reported that patients with impaired glucose metabolism have larger thyroid volumes. The researchers have increasingly focused on studying the relationship between insulin resistance and abnormal thyroid function and morphology. The aim of the study – to evaluate laboratory data in patients with comorbidity of type 2 diabetes mellitus and diffuse non-toxic goiter and identify possible markers of their comorbid course. Materials and Methods. The study included medical records of 541 patients. Two experimental groups were formed: 40 patients with diffuse non-toxic goiter and type 2 diabetes mellitus and 501 patients with type 2 diabetes only. Each patient had ultrasound examination of thyroid gland. The size of thyroid gland was calculated by the Bruno method. Data of lipid profile, general blood analysis, level of insulin, glucose, biochemical blood analysis, thyroid hormones (TSH, T4) were defined. Statistical analysis was performed by calculating the median (Me), lower (Lq) and upper (Uq) quartiles, Mann-Whitney U-test. Results. The average age of patients was 56 years. General blood analysis had significantly higher ESR levels by 63.6 % in the group of T2D + diffuse non-toxic goiter in comparison with T2D. Significantly higher levels of HbA1c by 7.4 %, non-HDL-C by 3.57 % in the group T2D + diffuse non-toxic goiter in comparison with T2D. Conclusions. Patients with combined type 2 diabetes mellitus and diffuse non-toxic goiter have significantly higher levels of ESR, HbA1c, and progressive dyslipidemia due to non-HDL-C, compared with patients with type 2 diabetes mellitus only. There is a tendency to increase total and residual cholesterol in the group of T2D + diffuse non-toxic goiter compared with T2D

Objectives: The aim of this study was to develop and assess a risk nomogram of bacterial infection in patients with ANCA-associated vasculitis (AAV) in southwest China. Method: We established a prediction model based on a training dataset of 249 AAV patients. The least absolute shrinkage and selection operator (Lasso) was used to screen feature variables. Multivariate logistic regression analysis was used to build a prediction model for feature variables. Nomogram was used to predict the risk of bacterial infection in AAV patients. Receiver operating characteristic (ROC) curve was used to evaluate and verify the prediction accuracy of the model. Calibration and clinical useful range was assessed using calibration curve and decision curve analysis, respectively. Results: Bactericidal permeability enhancement protein of ANCAs (BPI-ANCAs), procalcitonin (PCT), and white blood cell (WBC) were the characteristic variables in this study. Nomogram showed that positive BPI-ANCAs and PCT had higher positive predictive value for bacterial infection in AAV patients. The area under curve (AUC) of the model was 0.703 (95% confidence interval: 0.640–0.766). In the validation model, the AUC was 0.745 (95% confidence interval: 0.617–0.872). Decision curve analysis showed that the nonadherence nomogram was clinically useful within the threshold probability range of 0.31–0.85. Conclusions: Nomogram combined with BPI-ANCAs and PCT has the guiding significance for predicting bacterial infection risk in AAV. As an ANCA-specific autoantibody, BPI-ANCAs is helpful for clinicians to understand the role of specific autoantibodies in the pathogenesis of AAV. Key Points • BPI-ANCAs, PCT, and WBC could predict bacterial infection in AAV patients. • Nomogram showed that positive BPI-ANCAs had a high positive predictive value for bacterial infection in AAV patients. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Henoch-Schönlein purpura (HSP) is a common systemic vasculitis in children, involving the skin, musculoskeletal system, gastrointestinal tract and kidneys. Some studies in children have shown possible risk factors linked with the development and severity of HSP Nephritis (HSPN). The aim of this study was to research predicting factors for the development of HSPN. Methods: We retrospectively evaluated 132 pediatric patients with HSP, according to EULAR/PRINTO/PRESS criteria. All patients were screened for HSPN by urinalysis. Finally, we compared demographic, clinical and laboratory data in HSP patients with and without nephritis. Results: The median age at HSP diagnosis [6.2 (2.6–17.5) vs. 5.5 (0.8–15.4) years, p = 0.03] and the incidence of abdominal pain (48 vs. 27%, p = 0.01) were significantly higher in HSPN patients. No differences were evidenced regarding gender, allergic diseases, skin recurrences, gastrointestinal involvement, musculoskeletal involvement, scrotal involvement, and laboratory data (white blood cell count, neutrophil count, lymphocyte count, platelet count, C-reactive protein, erythrocyte sedimentation rate, and blood concentration of IgA). Conclusions: The age at diagnosis and abdominal pain were independent risk factors for renal involvement in HSP patients. However, due to the retrospective nature of this study, further long-term and prospective studies will be necessary. [ABSTRACT FROM AUTHOR]

Background: The clinical pathogenesis of COVID-19 necessitates a comprehensive and homogeneous study to understand the disease mechanisms. Identifying clinical symptoms and laboratory parameters as key predictors can guide prognosis and inform effective treatment strategies. This study analyzed comorbidities and laboratory metrics to predict COVID-19 mortality using a homogeneous model., Method: A retrospective cohort study was conducted on 7500 COVID-19 patients admitted to Rasoul Akram Hospital between 2022 and 2022. Clinical and laboratory data, along with comorbidity information, were collected and analyzed using advanced coding, data alignment, and regression analyses. Machine learning algorithms were employed to identify relevant features and calculate predictive probability scores., Results: The frequency and mortality rates of COVID-19 among males (19.3 %) were higher than those among females (17 %) (p = 0.01, OR = 0.85, 95 % CI = 0.76-0.96). Cancer (p < 0.05, OR = 1.9, 95 % CI = 1.48-2.4) and Alzheimer's (p < 0.05, OR = 2.36, 95 % CI = 1.89-2.9) were the two most common comorbidities associated with long-term hospitalization (LTH). Kidney disease (KD) was identified as the most lethal comorbidity (45 % of KD patients) (OR = 5.6, 95 % CI = 5.05-6.04, p < 0.001). Age > 55 was the most predictive parameter for mortality (p < 0.001, OR = 6.5, 95 % CI = 1.03-1.04), and the CT scan score showed no predictive value for death (p > 0.05). WBC, Cr, CRP, ALP, and VBG-HCO3 were the most significant critical data associated with death prediction across all comorbidities (p < 0.05)., Conclusion: COVID-19 is particularly lethal for elderly adults; thus, age plays a crucial role in disease prognosis. Regarding death prediction, various comorbidities rank differently, with KD having a significant impact on mortality outcomes., Competing Interests: Declaration of Competing Interest I, Nazanin Mojtabavi, on behalf of all co-authors, hereby declare that there is no conflict of interest regarding the research presented in this manuscript entitled "[Retrospective Analysis of COVID-19 Clinical and Laboratory Data: Constructing a Multivariable Model Across Different Comorbidities]". I affirm that neither myself nor any of my co-authors have any financial, personal, or professional interests that could influence the interpretation or presentation of the research findings contained within this manuscript., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: Reaching meaningful interoperability between proprietary health care systems is a ubiquitous task in medical informatics, where communication servers are traditionally used for referring and transforming data from the source to target systems. The Mirth Connect Server, an open-source communication server, offers, in addition to the exchange functionality, functions for simultaneous manipulation of data. The standard Fast Healthcare Interoperability Resources (FHIR) has recently become increasingly prevalent in national health care systems. FHIR specifies its own standardized mechanisms for transforming data structures using StructureMaps and the FHIR mapping language (FML)., Objective: In this study, a generic approach is developed, which allows for the application of declarative mapping rules defined using FML in an exchangeable manner. A transformation engine is required to execute the mapping rules., Methods: FHIR natively defines resources to support the conversion of instance data, such as an FHIR StructureMap. This resource encodes all information required to transform data from a source system to a target system. In our approach, this information is defined in an implementation-independent manner using FML. Once the mapping has been defined, executable Mirth channels are automatically generated from the resources containing the mapping in JavaScript format. These channels can then be deployed to the Mirth Connect Server., Results: The resulting tool is called FML2Mirth, a Java-based transformer that derives Mirth channels from detailed declarative mapping rules based on the underlying StructureMaps. Implementation of the translate functionality is provided by the integration of a terminology server, and to achieve conformity with existing profiles, validation via the FHIR validator is built in. The system was evaluated for its practical use by transforming Labordatenträger version 2 (LDTv.2) laboratory results into Medical Information Object (Medizinisches Informationsobjekt) laboratory reports in accordance with the National Association of Statutory Health Insurance Physicians' specifications and into the HL7 (Health Level Seven) Europe Laboratory Report. The system could generate complex structures, but LDTv.2 lacks some information to fully comply with the specification., Conclusions: The tool for the auto-generation of Mirth channels was successfully presented. Our tests reveal the feasibility of using the complex structures of the mapping language in combination with a terminology server to transform instance data. Although the Mirth Server and the FHIR are well established in medical informatics, the combination offers space for more research, especially with regard to FML. Simultaneously, it can be stated that the mapping language still has implementation-related shortcomings that can be compensated by Mirth Connect as a base technology., (© Jesse Kruse, Joshua Wiedekopf, Ann-Kristin Kock-Schoppenhauer, Andrea Essenwanger, Josef Ingenerf, Hannes Ulrich. Originally published in JMIR Medical Informatics (https://medinform.jmir.org).)

Background: Pseudocyst formation is common in many patients with acute pancreatitis during follow-up. Many risk factors have been proposed to be associated with the development of PP, but the predictive factors are still underexplored. The focus of this study was to investigate whether early laboratory indicators could effectively predict the occurrence of PP., Methods: 2811 AP patients hospitalized in the Second Affiliated Hospital of Soochow University between November 2008 and September 2020 were retrospectively studied. Univariate and multivariate analyses were used to screen the risk variables. The nomograms of those risk factors were validated and evaluated by logistic analysis., Results: AP patients had a 6.1 % (172/2811) incidence of PP. In a univariate analysis, the development of PP was correlated with serum lactate dehydrogenase (LDH), albumin (ALB), calcium (Ca), hemoglobin (Hb), organ dysfunction, CT severity index (CTSI), etiology, age, etc. Further logistic regression analysis showed that the risk factors were different between hyperlipidemic pancreatitis patients (LDH, ALB and Ca) and non-hyperlipidemic pancreatitis patients (LDH, Hb, ALB and Ca). A nomogram based on the identified risk factors was developed. Our model showed good discrimination ability, with a boostrap - corrected C index of 0.905 (95 % CI = 0.875-0.935), and had well-fitted calibration curves. The area under the curve (AUC) of the nomogram were 0.905 (95 % CI = 0.875-0.935) and 0.933 (95 % CI = 0.890-0.975) in the training and validation groups, respectively. The results of DCA indicated that the nomogram may have clinic usefulness., Conclusions: The nomogram that incorporates early laboratory data (LDH, Hb, ALB, and Ca) in AP patients is able to predict the incidence of PP with greater accuracy than the CTSI and AP severity., Competing Interests: Declaration of competing interest The authors declare no competing interests., (Copyright © 2024. Published by Elsevier B.V.)

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. [ABSTRACT FROM AUTHOR]

Background: Frailty can be operationalized based on the accumulation of deficits using a frailty index (FI) and is associated with an increased risk of adverse health outcomes. Here, we aim to compare validity of a FI from laboratory data with that of the common clinical FI for prediction of mortality in adults aged 55 + years, also examine whether combined FI could improve identification of adults aged 55 + years at increased risk of death.Methods: Data for this analysis were obtained from the Beijing Longitudinal Study of Aging that involved 1,257 community-dwelling Chinese people, aged 55 + years at baseline. The main outcome measure was 5-year mortality. An FI-self-report based on 30 self-reported health-related data was constructed. An FI-lab was developed using laboratory data, in addition to pulse, systolic and diastolic blood pressure, pulse pressure, body mass index (BMI) and waist. A combined FI comprised all items from each FI. Kaplan-Meier survival curve and Cox proportional hazards models were performed to evaluate the risk of each FI on death. The area under receiver operating characteristic(ROC) curves were used to compare the discriminative performance of each FI.Results: Of 1257 participants, 155 died and 156 lost at the end of the 5-year follow-up. The mean FI-self-report score was 0.11 ± 0.10, the FI-lab score was 0.33 ± 0.14 and FI-combined score was 0.19 ± 0.09. Higher frailty level defined by each FI was associated with higher risk of death. After adjustment for age and sex, Cox proportional hazards models showed that the higher scores of frailty were associated with a higher risk of mortality for each FI, the hazard ratios for the FI-self-report and FI-lab and FI-combined were 1.04 (1.03 to 1.05) and 1.02 (1.01 to 1.03) and 1.05 (1.04 to 1.07), respectively. The areas under the ROC curve were 0.79 (0.77-0.82) for the FI-self-report, 0.77(0.75-0.80) for the FI-lab and 0.81(0.78-0.82) for FI-combined.Conclusions: A FI from laboratory data can stratify older adults at increased risk of death alone and in combination with FI based on self-report data. Assessment in clinical settings of creating an FI using routine collected laboratory data needs to be further developed. [ABSTRACT FROM AUTHOR]

• A latent class trajectory analysis of longitudinal HbA1c data in clinical practice. • We found four distinct HbA1c trajectories preceding diabetes treatment initiation. • The four trajectories varied in steepness of increase in HbA1c. • The 48 mmol/mol diagnostic threshold is exceeded 9–17 months before first treatment. • The high HbA1c values before first treatment initiation imply therapeutic inertia. To examine the longitudinal heterogeneity of HbA1c preceding the initiation of diabetes treatment in clinical practice. 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. 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. 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. [ABSTRACT FROM AUTHOR]

Background: Only a few studies have performed comprehensive comparisons between hospitalized patients from different waves of COVID-19. Thus, we aimed to compare the clinical characteristics and laboratory data of patients admitted to the western part of Denmark during the first and second waves of COVID-19 in 2020. Furthermore, we aimed to identify risk factors for critical COVID-19 disease and to describe the available information on the sources of infection.Methods: We performed a retrospective study of medical records from 311 consecutive hospitalized patients, 157 patients from wave 1 and 154 patients from wave 2. The period from March 7 to June 30, 2020, was considered wave 1, and the period from July 1st to December 31, 2020, was considered wave 2. Data are presented as the total study population, as a comparison between waves 1 and 2, and as a comparison between patients with and without critical COVID-19 disease (nonsurvivors and patients admitted to the intensive care unit (ICU)).Results: Patients admitted during the first COVID-19 wave experienced a more severe course of disease than patients admitted during wave 2. Admissions to the ICU and fatal disease were significantly higher among patients admitted during wave 1 compared to wave 2. The percentage of patients infected at hospital decreased in wave 2 compared to wave 1, whereas more patients were infected at home during wave 2. We found no significant differences in sociodemographics, lifestyle information, or laboratory data in the comparison of patients from waves 1 and 2. However, age, sex, smoking status, comorbidities, fever, and dyspnea were identified as risk factors for critical COVID-19 disease. Furthermore, we observed significantly increased levels of C-reactive protein and creatinine, and lower hemoglobin levels among patients with critical disease.Conclusions: At admission, patients were more severely ill during wave 1 than during wave 2, and the outcomes were worse during wave 1. We confirmed previously identified risk factors for critical COVID-19 disease. In addition, we found that most COVID-19 infections were acquired at home. [ABSTRACT FROM AUTHOR]

In this study, we retrospectively investigated patients aged 18 years or older who received outpatient prescriptions in Department of Dentistry of Health Sciences University of Hokkaido Hospital based on laboratory data related to renal function from the Department of Internal Medicine. A total of 768 patients received outpatient prescriptions in the Department of Dentistry, of which 114 patients had a history of medical examination. In addition, laboratory data related to renal function from the Department of Internal Medicine was available for 71 patients, accounting for 9.2％ of the total. Six of 71 patients （8.5％） had serum creatinine levels above the reference value, which were evaluated as decreased renal function. Eighteen out of 71 patients（ 25.4％） showed a eGFR value of 59 mL/min/1.73 m² or less, which is one of the indicators for a diagnosis of CKD. All 18 patients were over 60 years old, and it was considered important to design the dental prescriptions in consideration of the deterioration of renal function due to aging. Of the 71 patients with renal function laboratory data, 18 patients with CKD stage G3a （mild to moderately decreased） to G4 （highly decreased） were investigated for dental prescription medicines. Three analgesics and 2 antibiotics were prescribed with the necessary precautions, such as dosage adjustment, as described in Clinical Practice Guideline for Drug-induced Kidney Injury in 2016. These analgesics and antibiotics were prescribed to 12 out of 18 patients, two of whom had CKD stage G3b disease（ moderately to highly decreased kidney function）, and considerations such as dose adjustments were necessary in terms of their prescriptions. As a result of this study, the necessity for prescription design based on laboratory data for renal function was again demonstrated in relation to dental prescriptions. Furthermore, it was suggested that by utilizing laboratory data from departments of internal medicine, it is possible to use medicines properly without burdening patients with the need for blood sampling [ABSTRACT FROM AUTHOR]

Background and aims: Diabetes mellitus (DM) rates have reached endemic levels, which is a major challenge for the modern health care system; the vast majority are patients with type 2 diabetes mellitus (T2DM). The presence of other comorbidities drastically affects diabetic patient outcomes, treatment, and management options. The aim of our study was to analyze routine laboratory data of type 2 diabetic patients with thyroid dysfunction and search for possible markers of comorbid course of T2DM, hypothyroidism (HT), and diffuse nontoxic goiter (DNG). Material and method: We analyzed 596 medical records of T2DM patients without thyroid dysfunction and with comorbid HT and/or DNG. Results and conclusions: Type 2 diabetic patients with comorbid HT or DNG have significantly higher BMI, increased level of HbA1c and increased ESR compared with patients with T2DM without thyroid dysfunction. The progression of lipid metabolism disorders in patients with a comorbid course of t T2DM and HT is characterized by a significant decrease in HDL-C level, as well as an increase of RC and TG levels compared with T2DM only, as well as the comorbidity of T2DM and DNG. [ABSTRACT FROM AUTHOR]

Purpose: Steroid hormones in hormonal contraceptives influence a variety of metabolic parameters. Mainly, ethinyloestradiol (EE) in combined hormonal contraceptives (CHC) is a potent inducer of hepatic coagulation factors, has an impact on cholesterol and triglyceride levels and glucose tolerance. Progestins in CHC modify the oestrogen effects in different ways, depending on their pharmacologic properties. The metabolic impact of progestin-only contraceptives is generally considered low. Methods: The influence of novel drospirenone (DRSP)-only pill (4 mg DRSP in 24/4 intake) on a variety of lipid-, carbohydrate- and bone metabolic parameters and on haemostatic variables including clotting factors and D-Dimer level was evaluated in comparison to 0.075 mg desogestrel (DSG) during a multicentric, prospective, double-blind, double-dummy clinical trial with 1190 participants over nine treatment cycles. Results: For both DRSP and DSG, there was a decrease in cholesterol (total, HDL, and LDL) and triglyceride levels. No relevant influence on glucose, insulin, and c-peptide levels or bone remodelling markers were detected in both treatment groups. Considering the coagulatory parameters, there was no impact on hemostasis. Conclusions: The results confirm the beneficial properties of the drospirenone-only pill. EudraCT Number: 2011-002396-42 [ABSTRACT FROM AUTHOR]

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) (10) (

Pressure waves, while traveling along pressurized pipes, collect precious information about possible faults (e.g., leaks and partial blockages). In fact, the characteristics of the pressure wave reflected by the fault are strongly related to it. To encourage the use of the transient test-based technologies (TTBTs) for partial blockage (PB) detection in pressurized pipe systems, it can be of interest to critically analyze the available experimental results and to point out the aspects that need to be investigated in more detail, since no review has been executed so far. Such a deficiency has two negative consequences. The first one is that TTBTs are still relegated to limbo by technicians. The second one is that not enough material is available for refining tools to extract all the information contained in the acquired pressure signals and then to pursue an effective PB detection. As main results of the executed analysis, the following issues can be counted: (i) the lack of tests carried out in large diameter and concrete pipes; (ii) the absence of tests carried out in complex pipe systems (e.g., looped networks); and (iii) the extreme need for considering real pipe systems. The fulfillment of the last issue will greatly contribute to the solutions of the other ones.

Background: Preoperative evaluation is important, and this study explored the application of machine learning methods for anesthetic risk classification and the evaluation of the contributions of various factors. To minimize the effects of confounding variables during model training, we used a homogenous group with similar physiological states and ages undergoing similar pelvic organ-related procedures not involving malignancies., Objective: Data on women of reproductive age (age 20-50 years) who underwent gestational or gynecological surgery between January 1, 2017, and December 31, 2021, were obtained from the National Taiwan University Hospital Integrated Medical Database., Methods: We first performed an exploratory analysis and selected key features. We then performed data preprocessing to acquire relevant features related to preoperative examination. To further enhance predictive performance, we used the log-likelihood ratio algorithm to generate comorbidity patterns. Finally, we input the processed features into the light gradient boosting machine (LightGBM) model for training and subsequent prediction., Results: A total of 10,892 patients were included. Within this data set, 9893 patients were classified as having low anesthetic risk (American Society of Anesthesiologists physical status score of 1-2), and 999 patients were classified as having high anesthetic risk (American Society of Anesthesiologists physical status score of >2). The area under the receiver operating characteristic curve of the proposed model was 0.6831., Conclusions: By combining comorbidity information and clinical laboratory data, our methodology based on the LightGBM model provides more accurate predictions for anesthetic risk classification., Trial Registration: Research Ethics Committee of the National Taiwan University Hospital 202204010RINB; https://www.ntuh.gov.tw/RECO/Index.action., (©Feng-Fang Tsai, Yung-Chun Chang, Yu-Wen Chiu, Bor-Ching Sheu, Min-Huei Hsu, Huei-Ming Yeh. Originally published in JMIR Formative Research (https://formative.jmir.org), 21.08.2024.)

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., Competing Interests: Declaration of competing interest The authors report no conflicts of interest regarding the study presented in this paper. K.A.D. is now an employee of Novo Nordisk A/S., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Acute encephalopathy with biphasic seizures and late reduced diffusion (AESD) often causes various neurological sequelae, necessitating early and objective differentiation of AESD from a febrile seizure (FS). Therefore, we developed a scoring system that predicts AESD onset using only early laboratory data. Methods: We selected patients with AESD or FS admitted to the Tottori University Hospital between November 2005 and September 2020 and collected laboratory data from onset to discharge in patients with FS and from onset to the second neurological events in patients with AESD. Results: We identified 18 patients with AESD and 181 patients with FS. In comparison with patients with FS, patients with AESD showed statistically significant increases in ammonia (NH3), blood sugar (BS), and serum creatinine (Cr) levels, and the white blood cell (WBC) count, and a significant decrease in pH at <3 h from onset. We set the cut-off values and adjusted the weight of each of these parameters based on data obtained <3 h from onset and proposed a scoring system for predicting AESD. This system showed 91% sensitivity and 94% specificity for distinguishing AESD from FS. These accuracies were only slightly improved by the addition of information related to consciousness and seizure duration (sensitivity, 91%; specificity, 96%). Conclusion: NH3, BS, and Cr levels, WBC count, and pH were significantly different between patients with AESD and patients with FS at <3 h from seizure onset. This scoring system using these data may enable the prediction of AESD onset for patients under sedation or without precise clinical information. [ABSTRACT FROM AUTHOR]

Background: Acute encephalopathy with biphasic seizures and late reduced diffusion (AESD) often causes various neurological sequelae, necessitating early and objective differentiation of AESD from a febrile seizure (FS). Therefore, we developed a scoring system that predicts AESD onset using only early laboratory data.Methods: We selected patients with AESD or FS admitted to the Tottori University Hospital between November 2005 and September 2020 and collected laboratory data from onset to discharge in patients with FS and from onset to the second neurological events in patients with AESD.Results: We identified 18 patients with AESD and 181 patients with FS. In comparison with patients with FS, patients with AESD showed statistically significant increases in ammonia (NH3), blood sugar (BS), and serum creatinine (Cr) levels, and the white blood cell (WBC) count, and a significant decrease in pH at

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., (©Ahmad Haeri Mazanderani, Lebohang Radebe, Gayle G Sherman. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 14.05.2024.)