Laboratory Data Research Articles

Exploration of the factors affecting different delirium subtypes in hospitalized COVID-19 patients: a multicentre cross-sectional study

During the COVID-19 pandemic, delirium became a major complication that worsened patient outcomes. However, the factors influencing the severity of delirium in patients with COVID-19 have not been determined. We conducted this study to detect influencing factors associated with subtypes of delirium in patients with COVID-19. We included 1774 adult inpatients with COVID-19 from January to February 2023 at 7 sites in China. And used the 3 min Confusion Assessment Method and the Richmond Agitation-Sedation Scale for site assessment to identify and classify subtypes of delirium. Laboratory data were obtained from the Hospital Information System. After multivariate analysis, hypoactive delirium was significantly associated with age, the serum albumin concentration, frailty and sarcopenia, and health and nutritional status. Mixed delirium was significantly associated with age, D-dimer level, sarcopenia, health status and nutritional status. Additionally, hyperactive delirium was significantly associated with age, procalcitonin levels, frailty status and health status. Our findings suggest that poor nutritional status and low serum albumin concentration can help detect patients at high risk of developing hypoactive and mixed delirium. Additionally, clinical staff should pay more attention to patients with inflammatory conditions to assess and detect delirium because many influencing factors are involved in the common pathological mechanism of inflammation.

Machine Learning Model for Anesthetic Risk Stratification for Gynecologic and Obstetric Patients: Cross-Sectional Study Outlining a Novel Approach for Early Detection.

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. 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. 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. 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. By combining comorbidity information and clinical laboratory data, our methodology based on the LightGBM model provides more accurate predictions for anesthetic risk classification. Research Ethics Committee of the National Taiwan University Hospital 202204010RINB; https://www.ntuh.gov.tw/RECO/Index.action.

Retrospective Longitudinal Observational Study on the Long-Term Effects of Sodium-Glucose Cotransporter-2 Inhibitors on the Development of Metabolic Dysfunction-Associated Steatotic Liver Disease in Type 2 Diabetic Japanese Patients.

Background/Objectives: The health burden of metabolic dysfunction-associated fatty liver disease (MASLD) has been increasing lately. Cardiovascular disease (CVD) is the main cause of death in MASLD patients; therefore, the treatments for MASLD should improve both CV risk factors such as obesity, diabetes, and dyslipidemia, in addition to an improvement in liver function. The evidence on the long-term effects of sodium-glucose cotransporter 2 inhibitors (SGLT2is) on the progression of MASLD in Asian populations is very limited. Methods: The retrospective longitudinal study was performed by using the medical records at our institute. We picked up patients with type 2 diabetes who had taken SGLT2is for at least 3 years or longer between 1 April 2014 and 31 March 2018. We collected the data on metabolic parameters, including laboratory data and anthropometric parameters, and compared the data before and after the initiation of SGLT2is treatment. Results: During the observation period, 324 patients had taken SGLT2is for 3 years. Three-year SGLT2is treatment significantly reduced body weight, hemoglobin A1c, low-density lipoprotein cholesterol, triglyceride, and non-high-density lipoprotein cholesterol (non-HDL-C). Such favorable changes in serum lipids were remarkable in patients with statins. Furthermore, this treatment significantly improved liver function and the markers for hepatic steatosis and hepatic fibrosis. Conclusions: Considering that the development of CVD determines the prognosis of MASLD patients, long-term SGLT2is treatment may be an ideal therapy for MASLD patients.

Oxidised low-density lipoprotein and antibodies against oxidised low-density lipoprotein in patients with antiphospholipid syndrome.

Recurrent thrombosis is one of the main clinical features of antiphospholipid syndrome (APS), and recent studies revealed that APS shares similar pathophysiological mechanisms with atherosclerosis. Oxidised low-density lipoprotein (OxLDL) and antibodies against OxLDL (anti-OxLDL) are involved in the development of atherosclerosis. This study aims to investigate the clinical significance of OxLDL and anti-OxLDL in APS patients. One hundred and seventy APS patients and 39 healthy controls (HC) were enrolled. Clinical and laboratory data were collected from Clinical Data Center of Peking University People's Hospital. OxLDL and anti-OxLDL were detected using enzyme-linked immunosorbent assay. Among the 170 APS patients, 106 had isolated thrombotic APS. Compared with HC, APS patients exhibited higher titres of OxLDL [413.86 (220.11-853.67) ng/mL vs. 45.54 (0-105.98) ng/mL, p<0.001] and anti-OxLDL [107.62 (75.68-174.18) U/L vs. 44.13 (18.44-79.76) U/L, p<0.001]. Also, APS patients exhibited a higher positivity rate for OxLDL (88.2% vs. 5.1%, p<0.001) and anti-OxLDL (84.1% vs. 36.5%, p<0.001) compared to HC. APS patients with elevated levels of OxLDL had a higher rate of LAC positivity (68.0% vs. 45.0%, p=0.042). Furthermore, APS patients with positive anti-OxLDL demonstrated a higher occurrence of venous thrombosis (46.2% vs. 18.5%, p=0.008) and a lower rate of Coomb's test positivity (52.6% vs. 76.2%, p=0.049). Multivariate logistic regression revealed that anti-OxLDL positivity (OR 12.424, 95%CI 1.108-139.330, p=0.041) were risk factors for venous thrombotic APS. This study indicates that the presence of anti-OxLDL may serve as potential markers for venous thrombosis in APS patients. OxLDL and anti-OxLDL may function as valuable biomarkers for monitoring APS.

Predicting protein conformational motions using energetic frustration analysis and AlphaFold2

Proteins perform their biological functions through motion. Although high throughput prediction of the three-dimensional static structures of proteins has proved feasible using deep-learning-based methods, predicting the conformational motions remains a challenge. Purely data-driven machine learning methods encounter difficulty for addressing such motions because available laboratory data on conformational motions are still limited. In this work, we develop a method for generating protein allosteric motions by integrating physical energy landscape information into deep-learning-based methods. We show that local energetic frustration, which represents a quantification of the local features of the energy landscape governing protein allosteric dynamics, can be utilized to empower AlphaFold2 (AF2) to predict protein conformational motions. Starting from ground state static structures, this integrative method generates alternative structures as well as pathways of protein conformational motions, using a progressive enhancement of the energetic frustration features in the input multiple sequence alignment sequences. For a model protein adenylate kinase, we show that the generated conformational motions are consistent with available experimental and molecular dynamics simulation data. Applying the method to another two proteins KaiB and ribose-binding protein, which involve large-amplitude conformational changes, can also successfully generate the alternative conformations. We also show how to extract overall features of the AF2 energy landscape topography, which has been considered by many to be black box. Incorporating physical knowledge into deep-learning-based structure prediction algorithms provides a useful strategy to address the challenges of dynamic structure prediction of allosteric proteins.

The Incidence of Clostridioides difficile Infection in the Post-COVID-19 Era in a Hospital in Northern Greece.

Clostridioides difficile infection (CDI) has evolved to be the most significant cause of healthcare-associated diarrhoea and one of the leading representatives of healthcare-associated infections, with a high associated mortality. The aim of this retrospective study was to record the incidence rates and the epidemiological and clinical features of CDI in a large tertiary hospital of northern Greece in the years 2022-2023. All patients with CDI-compatible symptomatology and a positive CDI diagnostic test (GDH-glutamate dehydrogenase and toxin-positive FIA-Fluorescent Immuno-chromatography-SD Biosensor, and/or film array) were included (104 from a total of 4560 admitted patients). Their demographic, laboratory, and clinical data were recorded and analysed. The incidence of CDI in admitted patients was found to be higher than previous reports in the geographical area, reaching 54.6/10,000 patient days and following a rising trend over the course of the study. Thirty-day mortality was high (39.4%), potentially related to new emerging hypervirulent C. difficile strains. In view of the high prevalence of multidrug-resistant organisms in the region, and the significant mortality associated with this infection, these findings particularly point to the need for the implementation of organized surveillance and infection prevention protocols.

Risk of Major Bleeding, Stroke/Systemic Embolism, and Death Associated With Different Oral Anticoagulants in Patients With Atrial Fibrillation and Severe Chronic Kidney Disease.

Patients with atrial fibrillation and severe chronic kidney disease have higher risks of bleeding, thromboembolism, and mortality. However, optimal anticoagulant choice in these high-risk patients remains unclear. Using deidentified electronic health records from the Optum Labs Data Warehouse, adults with atrial fibrillation and severe chronic kidney disease (estimated glomerular filtration rate <30 mL/min per 1.73 m2) initiating warfarin, apixaban, or rivaroxaban between 2011 and 2021 were included. Using inverse probability of treatment weighting, adjusted risks of major bleeding, stroke/systemic embolism, and death were compared among agents. A total of 6794 patients were included (mean age, 78.5 years; mean estimated glomerular filtration rate, 24.7 mL/min per 1.73 m2; 51% women). Apixaban versus warfarin was associated with a lower risk of major bleeding (incidence rate, 1.5 versus 2.9 per 100 person-years; subdistribution hazard ratio [sub-HR], 0.53 [95% CI, 0.39-0.70]), and similar risks for stroke/systemic embolism (incidence rate, 1.9 versus 2.4 per 100 person-years; sub-HR, 0.80 [95% CI, 0.59-1.09]) and death (incidence rate, 4.6 versus 4.5 per 100 person-years; HR, 1.03 [95% CI, 0.82-1.29]). Rivaroxaban versus warfarin was associated with a higher risk of major bleeding (incidence rate, 4.9 versus 2.9 per 100 person-years; sub-HR, 1.65 [95% CI, 1.10-2.48]), with no difference in risks for stroke/systemic embolism and death. Apixaban versus rivaroxaban was associated with a lower risk of major bleeding (sub-HR, 0.53 [95% CI, 0.36-0.78]). These real-world findings are consistent with potential safety advantages of apixaban over warfarin and rivaroxaban for patients with atrial fibrillation and severe chronic kidney disease. Further randomized trials comparing individual oral anticoagulants are warranted.

How Gaussian mixture modelling can help to verify reference intervals from laboratory data with a high proportion of pathological values

Abstract Objectives Although there are several indirect methods that can be used to verify reference limits, they have a common weakness in that they assume a low proportion of pathological values. This paper investigates whether a Gaussian decomposition algorithm can identify the non-pathological fraction even if it is not the main subset of mixed data. Methods All investigations are carried out in the R programming environment. The mclust package is used for Gaussian mixture modelling via the expectation maximization (EM) algorithm. For right-skewed distributions, logarithms of the original values are taken to approximate the Gaussian model. We use the Bayesian information criterion (BIC) for evaluation of the results. The reflimR and refineR packages serve as comparison procedures. Results We generate synthetic data mixtures with known normal distributions to demonstrate the feasibility and reliability of our approach. Application of the algorithm to real data from a Nigerian and a German population produces results, which help to interpret reference intervals of reflimR and refineR that are obviously too wide. In the first example, the mclust analysis of hemoglobin in Nigerian women supports the medical hypothesis that an anemia rate of more than 50 % leads to falsely low reference limits. Our algorithm proposes various scenarios based on the BIC values, one of which suggests reference limits that are close to published data for Nigeria but significantly lower than those established for the Caucasian population. In the second example, the standard statistical analysis of creatine kinase in German patients with predominantly cardiac diseases yields a reference interval that is clearly too wide. With mclust we identify overlapping fractions that explain this false result. Conclusions Gaussian mixture modelling does not replace standard methods for reference interval estimation but is a valuable adjunct when these methods produce discrepant or implausible results.

Risk and impact of cytomegalovirus infection in lymphoma patients treated with bendamustine.

Bendamustine is used to treat lymphoma with excellent efficacy but is known for its immunosuppressive effect. Cytomegalovirus (CMV) reactivation after bendamustine use has been reported. We aim to address the impact of CMV infection in lymphoma patients treated with bendamustine-containing regimens. We retrospectively analyzed lymphoma patients at Taipei Veterans General Hospital in Taiwan between September 1, 2010, and April 30, 2022. Clinically significant CMV infection (CS-CMVi) was defined as the first CMV reactivation after bendamustine use necessitating CMV therapy. Patients' baseline characteristics and laboratory data were recorded. The primary endpoint of the study was CS-CMVi. A time-dependent covariate Cox regression model was used to estimate the risk factors of CS-CMVi and mortality. A total of 211 lymphoma patients treated with bendamustine were enrolled. Twenty-seven (12.8%) had CS-CMVi. The cumulative incidence was 10.1 per 100 person-years during the three-year follow-up period. In the multivariate analysis, lines of therapy before bendamustine≥ 1 (95% CI 1.10-24.76), serum albumin < 3.5g/dL (95% CI 2.63-52.93), and liver disease (95% CI 1.51-28.61) were risk factors for CS-CMVi. In conclusion,CS-CMVi (95% confidence interval [CI] 1.23-10.73) was one of the major independent risk factors of mortality. Lines of therapy before bendamustine≥ 1, hypoalbuminemia, and liver disease were risk factors for CS-CMVi in lymphoma patients treated with bendamustine.

Association of 28-day mortality with non-high-density lipoprotein cholesterol and the high-density lipoprotein cholesterol ratio (NHHR) in patients with sepsis: Results of MIMIC-IV database analysis

BackgroundThe correlation between lipid profiles and sepsis has received increasing attention. The ratio of non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol (NHHR) is one of the key lipid profiles. However, in-depth exploration of the correlation between NHHR and the mortality risk of patients with sepsis is limited.MethodsData from the MIMIC-IV (v2.2) database, we review the NHHR relevance and the sepsis severity index using Spearman’s correlation analysis. Additionally, we research NHHR associated with sepsis patients’ survival rate of 28 days using Cox regression analyses of continuous and categorical models. To further validate our findings, we conducted subgroup and sensitivity analyses.ResultsThe study involved 3,142 patients diagnosed with sepsis, according to 28 days after in-hospital survival condition, divided into two groups. In this study, 2932 patients were in the survival group and 210 patients died within 28 days (mortality group). Of note, the mean NHHR of patients in the mortality group exceeded that of the survival group (3.5 vs. 2.9). Additionally, NHHR was positively correlated with the severity index. After adjusting for demographic and laboratory data, an increased NHHR was positively correlated with higher sepsis mortality risk (OR = 1.06; 95% CI: 1.02–1.11; P = 0.013). Subgroup analysis shown the same results. Contributors were be categorized into two groups based on NHHR levels, with a threshold of 2.61. Contrast the mortality risk between low-NHHR group and high-NHHR group, high-NHHR show greater mortality risk on 28-day, 60-day, 90-day, in ICU, and in hospital.ConclusionElevated NHHR is to be correlated with an increased risk of mortality in patients with sepsis. Further research on NHHR may contribute to advancements in sepsis prevention and treatment.

Prognostic value of morning blood pressure surge in chronic kidney disease.

This was a retrospective study. This study investigated the occurrence of a composite endpoints (cardiovascular and cerebrovascular events, end-stage renal disease, and death) in 153 patients (aged≥18years) with a diagnosis of in chronic kidney disease (CKD). Based on morning blood pressure surge (MBPS) defined as ≥35mm Hg, patients were divided into two groups: with MBPS (n=50) and without MBPS (n=103). All patients were followed up for at least 1year. Baseline demographic, laboratory and follow-up data were collected. The clinical characteristics of the two groups were compared. The relationships between MBPS and endpoint events were analyzed using the Kaplan-Meier method and Cox regression model. In total, 153 patients (mean age 41.8years; 56.86% males) were included in this study. During the follow-up period (mean 4.3years), 34 endpoint events occurred. After adjustment for the covariates, the risk of cardiovascular and cerebrovascular events, end-stage renal disease and death remained significantly higher in patients with MBPS (hazard ratio [HR] and 95% confidence interval [CI] 3.124 [1.096-9.130]]) Among the other variables, systolic blood pressure, and night-time and daytime pulse pressures remained significantly associated with outcome in patients of CKD (1.789 [1.205-2.654], 1.710 [1.200-2.437], and 1.318 [1.096-1.586], respectively]. In conclusions, MBPS was identified as an independent prognostic factor for composite endpoint events (cardiovascular and cerebrovascular events, end-stage renal disease and death) patients with chronic kidney disease patients.

Unsupervised machine learning to identify subphenotypes among cardiac intensive care unit patients with heart failure.

Hospitalized patients with heart failure (HF) are a heterogeneous population, with multiple phenotypes proposed. Prior studies have not examined the biological phenotypes of critically ill patients with HF admitted to the contemporary cardiac intensive care unit (CICU). We aimed to leverage unsupervised machine learning to identify previously unknown HF phenotypes in a large and diverse cohort of patients with HF admitted to the CICU. We screened 6008 Mayo Clinic CICU patients with an admission diagnosis of HF from 2007 to 2018 and included those without missing values for common laboratory tests. Consensus k-means clustering was performed based on 10 common admission laboratory values (potassium, chloride, anion gap, blood urea nitrogen, haemoglobin, red blood cell distribution width, mean corpuscular volume, platelet count, white blood cell count and neutrophil-to-lymphocyte ratio). In-hospital mortality was evaluated using logistic regression, and 1year mortality was evaluated using Cox proportional hazard models after multivariable adjustment. Among 4877 CICU patients with HF who had complete admission laboratory data (mean age 69.4years, 38.4% females), we identified five clusters with divergent demographics, comorbidities, laboratory values, admission diagnoses and use of critical care therapies. We labelled these clusters based on the characteristic laboratory profile of each group: uncomplicated (25.7%), iron-deficient (14.5%), cardiorenal (18.4%), inflamed (22.3%) and hypoperfused (19.2%). In-hospital mortality occurred in 10.7% and differed between the phenotypes: uncomplicated, 2.7% (reference); iron-deficient, 8.1% [adjusted odds ratio (OR) 2.18 (1.38-3.48), P<0.001]; cardiorenal, 10.3% [adjusted OR 2.11 (1.37-3.32), P<0.001]; inflamed, 12.5% [adjusted OR 1.79 (1.18-2.76), P=0.007]; and hypoperfused, 21.9% [adjusted OR 4.32 (2.89-6.62), P<0.001]. These differences in mortality between phenotypes were consistent when patients were stratified based on demographics, aetiology, admission diagnoses, mortality risk scores, shock severity and systolic function. One-year mortality occurred in 31.5% and differed between the phenotypes: uncomplicated, 11.9% (reference); inflamed, 26.8% [adjusted hazard ratio (HR) 1.56 (1.27-1.92), P<0.001]; iron-deficient, 33.8% [adjusted HR 2.47 (2.00-3.04), P<0.001]; cardiorenal, 41.2% [adjusted HR 2.41 (1.97-2.95), P<0.001]; and hypoperfused, 52.3% [adjusted HR 3.43 (2.82-4.18), P<0.001]. Similar findings were observed for post-discharge 1year mortality. Unsupervised machine learning clustering can identify multiple distinct clinical HF phenotypes within the CICU population that display differing mortality profiles both in-hospital and at 1year. Mortality was lowest for the uncomplicated HF phenotype and highest for the hypoperfused phenotype. The inflamed phenotype had comparatively higher in-hospital mortality yet lower post-discharge mortality, suggesting divergent short-term and long-term prognosis.

Characterizing the Asthma Phenotype of Military Personnel.

Asthma is the most common diagnosis in military personnel who endorse chronic dyspnea. Service members have unique occupational risk factors, and there is concern that airborne exposures in the deployed environment as well as other occupational exposures may contribute to the development of asthma or exacerbate pre-existing disease. Asthma phenotyping with clinical biomarkers such as serum immunoglobulin E (IgE) levels and eosinophil (EOS) counts is useful in defining treatment strategies for the management of asthma. This study sought to characterize the phenotype of medically separated military personnel with career-limiting asthma to define potential management strategies and guide future research evaluating the unexplained prevalence of asthma in this population. A retrospective chart review of active duty service members (ADSM) who underwent fitness for duty evaluation via medical evaluation board between 2005 and 2016 and were separated with a minimum 30% conditional disability rating for asthma was performed. Only ADSM who were diagnosed with asthma by a pulmonologist and had spirometry data available were included in the analysis. Demographics, spirometry data, and laboratory data to include IgE levels, radioallergosorbent panels, and EOS counts were analyzed from the DoD electronic medical record. A total of 141 service members were evaluated with a mean age of 42 ± 6.8 years, mean serum EOS count of 300 ± 358 cells/μL, and mean IgE level of 305 ± 363 IU/mL. The patients were further categorized into 4 subgroups based on serum EOS count and IgE level: group A with IgE < 100 IU/mL and EOS < 300 cells/μL (n = 45; 33%), group B with IgE > 100 IU/mL and EOS < 300 cells/μL (n = 44; 32%), group C with IgE < 100 IU/mL and EOS > 300 cells/μL (n = 6; 1%), and group D with IgE > 100 IU/mL, EOS > 300 cells/μL (n = 46; 34%). Among the cohorts, there were no statistically significant differences in demographics, body mass index, spirometry, smoking history, or disability rating. The majority of ADSM with a defined asthma history do not have concordant elevations in serum IgE and blood EOS suggestive of a Th2-high phenotype. Asthma in this population is heterogeneous, and phenotyping using clinical biomarkers may be useful to define optimal treatment strategies.

Prevalence and distribution of carbapenem-resistant Enterobacterales in companion animals: A nationwide study in the United States using commercial laboratory data.

Carbapenem-resistant Enterobacterales (CRE) are a concern in both human and animal medicine globally. Despite extensive research in humans, limited data exist on CRE in companion animals, with a lack of nationwide prevalence estimates. To assess the occurrence and trends of CRE in cats and dogs across the United States by analyzing 4 years of commercial antimicrobial susceptibility testing (AST) data. Between 2019 and 2022, 477 426 ASTs were conducted on Enterobacterales isolates against imipenem. Isolates were derived from 379 598 dogs and 97 828 cats. Animal origin was not disclosed. In this retrospective study, antimicrobial susceptibility test data from IDEXX Laboratories were analyzed. Analysis included resistance estimations to imipenem stratified by sampling site, an assessment of resistance patterns over time and location, and the application of space-time cluster analysis to identify potential outbreaks. Antibiograms were produced for carbapenem-resistant isolates. Susceptibility to imipenem was high, at 98.86%. Temporal analysis indicated stability in susceptibility, with an unexplained reduction in susceptible isolates in June 2019. Spatial analysis identified 2 high-risk clusters along the Western Coast (relative risk [RR]: 23.26; P < .001) and in Texas (RR: 10.72; P < .001) in that month. Three other clusters were found, in Missouri (RR: 39.55; P = .038), Florida (RR: 4.53; P < .001), and New York (RR: 9.20; P < .001). CRE are present at a low prevalence in dogs and cats across the United States. Variations in prevalence across patient-level and environmental factors highlight the need for tailored stewardship programs.

Multiple fractures due to hungry bone syndrome following parathyroidectomy: a clinical case report and review of literature

BackgroundHungry bone syndrome (HBS) is defined as prolonged hypocalcemia caused by a sudden decrease in parathyroid hormone (PTH) levels after parathyroidectomy (PTX). Multiple fractures after PTX due to HBS in an end-stage renal disease (ESRD) patient on chronic hemodialysis (HD) are challenging and rare medical conditions presented in this study.Case PresentationA 42-year-old ESRD patient on HD 3 times a week presented to Shariati Hospital, Tehran, Iran, complaining of worsening bone pain and loss of appetite. Laboratory data revealed an intact parathyroid hormone (iPTH) concentration of 2500 pg/mL, an alkaline phosphatase (Alp) level of 4340 IU/L, a phosphorus (P) level of 9 mg/dL, and a calcium (Ca) concentration of 7.2 mg/dL. Sestamibi scintigraphy revealed parathyroid adenoma. The findings suggested tertiary hyperparathyroidism (HPT-III), and the patient was scheduled for total PTX. Approximately one month after surgery, the patient was referred due to convulsions, leg mobility problems, and worsening bone pain. There was bilateral femoral ecchymosis. The Ca concentration was 5.8 mg/dL, and radiological evaluations revealed multiple skeletal fractures. HBS after PTX was suggested for this patient. After several days of hospitalization, he suffered subcutaneous emphysema followed by rib fractures and passed away.ConclusionsMultiple fractures after PTX due to HBS following HPT-III in ESRD patients are rare and demanding, highlighting the necessity of timely diagnosis and management of patients with HPT-III. Severe hypocalcemia following PTX can cause skeletal disorders. However, the surgical treatment of parathyroid adenomas may be more important than the risk of complications associated with bone health.

A nomogram to predict the occurrence of pseudocyst in patients with acute pancreatitis

BackgroundPseudocyst 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. Methods2811 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. ResultsAP 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. ConclusionsThe 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.

Establishment of a diagnostic model for urinary calculi in pregnant women: A retrospective cohort study.

The diagnosis of symptomatic urinary stones during pregnancy is challenging; ultrasonography has a low specificity and sensitivity for diagnosing urinary stones. This study aimed to develop a clinical diagnostic model to assist clinicians in distinguishing symptomatic urinary stones in pregnant women. In this retrospective cohort study, we consecutively collected clinical data from pregnant women who presented with acute abdominal, lumbar, and lumbar and abdominal pain at the emergency department of our hospital between January 1, 2017, and December 31, 2019. To distinguish patients with urinary calculi from those without, we reviewed the follow-up records within 2 weeks post-consultation, ultrasonography results within 2 weeks, or self-reports of stone passage within 2 weeks. We selected risk factors from the baseline clinical and laboratory data of patients to establish a diagnostic model. Of the total patients included in the study, 105 patients were diagnosed as having symptomatic urinary stones and 126 were determined to have abdominal pain for reasons other than urinary stones. The initial model had an area under the curve (AUC) of 0.9966. The No-Lab Model had an AUC of 0.9856. The Lab Model had an AUC of 0.832. The Stone Model had an AUC of 0.9952. The simplified Stone Model did not show a decrease in discriminative ability. Of the four diagnostic models that we established for preliminary diagnosis of symptomatic urinary tract stones in pregnant women, the simplified Stone Model demonstrated excellent performance. Users can scan quick response codes to access web-based diagnostic model interfaces, facilitating easy clinical operation.

Impacts of extreme temperature conditions on acute health outcomes using clinical test and laboratory data in Minnesota, USA

Impacts of extreme temperature conditions on acute health outcomes using clinical test and laboratory data in Minnesota, USA

Perspective on Theoretical and Experimental Advances in Atmospheric Photochemistry.

Research that explores the chemistry of Earth's atmosphere is central to the current understanding of global challenges such as climate change, stratospheric ozone depletion, and poor air quality in urban areas. This research is a synergistic combination of three established domains: earth observation, for example, using satellites, and in situ field measurements; computer modeling of the atmosphere and its chemistry; and laboratory measurements of the properties and reactivity of gas-phase molecules and aerosol particles. The complexity of the interconnected chemical and photochemical reactions which determine the composition of the atmosphere challenges the capacity of laboratory studies to provide the spectroscopic, photochemical, and kinetic data required for computer models. Here, we consider whether predictions from computational chemistry using modern electronic structure theory and nonadiabatic dynamics simulations are becoming sufficiently accurate to supplement quantitative laboratory data for wavelength-dependent absorption cross-sections, photochemical quantum yields, and reaction rate coefficients. Drawing on presentations and discussions from the CECAM workshop on Theoretical and Experimental Advances in Atmospheric Photochemistry held in March 2024, we describe key concepts in the theory of photochemistry, survey the state-of-the-art in computational photochemistry methods, and compare their capabilities with modern experimental laboratory techniques. From such considerations, we offer a perspective on the scope of computational (photo)chemistry methods based on rigorous electronic structure theory to become a fourth core domain of research in atmospheric chemistry.

Accuracy study of Angiotensin 1–7 composite index test to predict pulmonary fibrosis and guide treatment

BackgroundPulmonary fibrosis can develop after acute respiratory distress syndrome (ARDS). The hypothesis is we are able to measure phenotypes that lie at the origin of ARDS severity and fibrosis development. The aim is an accuracy study of prognostic circulating biomarkers. MethodsA longitudinal study followed COVID-related ARDS patients with medical imaging, pulmonary function tests and biomarker analysis, generating 444 laboratory data. Comparison to controls used non-parametrical statistics; p < 0·05 was considered significant. Cut-offs were obtained through receiver operating curve. Contingency tables revealed predictive values. Odds ratio was calculated through logistic regression. ResultsAngiotensin 1–7 beneath 138 pg/mL defined Angiotensin imbalance phenotype. Hyper-inflammatory phenotype showed a composite index test above 34, based on high Angiotensin 1–7, C-Reactive Protein, Ferritin and Transforming Growth Factor-β. Analytical study showed conformity to predefined goals. Clinical performance gave a positive predictive value of 95 % (95 % confidence interval, 82 %–99 %), and a negative predictive value of 100 % (95 % confidence interval, 65 %–100 %). Those severe ARDS phenotypes represented 34 (Odds 95 % confidence interval, 3–355) times higher risk for pulmonary fibrosis development (p < 0·001). ConclusionsAngiotensin 1–7 composite index is an early and objective predictor of ARDS evolving to pulmonary fibrosis. It may guide therapeutic decisions in targeted phenotypes.