Explore the vast range of titles available.

Background . Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) is the etiological agent of the coronavirus disease 2019 (COVID-19) pandemic. Among the risk factors associated with the severity of this disease is the presence of several metabolic disorders. For this reason, the aim of this research was to identify the comorbidities and laboratory parameters among COVID-19 patients admitted to the intensive care unit (ICU), comparing the patients who required invasive mechanical ventilation (IMV) with those who did not require IMV, in order to determine the clinical characteristics associated with the COVID-19 severity. Methods. We carried out a cross-sectional study among 152 patients who were admitted to the ICU from April 1 st to July 31 st, 2021, in whom the comorbidities and laboratory parameters associated with the SARS-CoV-2 infection severity were identified. The data of these patients was grouped into two main groups: \"patients who required IMV\" and \"patients who did not require IMV\". The nonparametric Mann-Whitney U test for continuous data and the χ 2 test for categorical data were used to compare the variables between both groups. Results. Of the 152 COVID-19 patients who were admitted to the ICU, 66 required IMV and 86 did not require IMV. Regarding the comorbidities found in these patients, a higher prevalence of type 2 diabetes mellitus (T2DM), hypertension and obesity was observed among patients who required IMV vs. those who did not require IMV ( p<0.05). Concerning laboratory parameters, only glucose, Interleukin 6 (IL-6), lactate dehydrogenase (LDH) and C-reactive protein (CRP) were significantly higher among patients who required IMV than in those who did not require IMV ( p<0.05). Conclusion. This study performed in a Mexican population indicates that comorbidities such as: T2DM, hypertension and obesity, as well as elevated levels of glucose, IL-6, LDH and CRP are associated with the COVID-19 severity.

To better understand the progression of severe fever with thrombocytopenia syndrome (SFTS), identify early predictors of mortality, and improve the cure rate, the present study aimed to analyze the demographic feature, clinical characteristics, and laboratory parameters of patients with SFTS and to explore the risk factors associated with fatal outcome. We retrospectively analyzed demographic feature, clinical characteristics, and laboratory parameters of 216 laboratory-confirmed SFTS patients in Shandong province between January 2015 and December 2019. Univariate analysis was used to assess the relevance between these factors and fatal outcome. Factors with P  < 0.05 in univariate analysis were further analyzed using multivariable logistic regression analysis to identify the independent risk factors for mortality of SFTS. Age, five complications (including CNS symptoms, pulmonary infection, heart failure, arrhythmia, and bleeding events), and ten abnormal laboratory parameters (including serum viral load, blood platelet, ALT, AST, LDH, CK, CK-MB, Cr, serum Ca 2+ , and APTT) were statistically significant by univariate analysis. These factors were further analyzed by multivariable logistic regression analysis, and the results indicated that coma, pulmonary infection, high viral load, and prolonged APTT were associated with fatal outcome in SFTS patients. Our study identified four independent risk factors associated with fatal outcome for SFTS patients. The results were hoped to provide help for active treatment of SFTS. However, the identification of risk factors is not absolutely associated with fatal outcome. Patients’ risk should be assessed by dynamic observation of the changes in risk factor indicators.

Background Self-rated health (SRH), a subjective assessment of health status, is extensively used in the public health field. However, whether SRH can reflect the objective health status is still debatable. We aim to reveal the relationship between SRH and objective health status in the general population. Methods We assessed the relationship between SRH and objective health status by examining the prevalence of diseases, laboratory parameters, and some health-related factors in different SRH groups. Data were collected from 18,000 residents randomly sampled from the general population in five cities of China (3,600 in each city). SRH was assessed by a single-item health measure with five options: “very good,” “good,” “fair,” “bad,” and “very bad.” The differences in prevalence of diseases, laboratory parameters, and health-related factors between the “healthy” (very good plus good), “relatively healthy” (fair), and “unhealthy” (bad plus very bad) groups were examined. The odds ratios (ORs) referenced by the healthy group were calculated using logistic regression analysis. Results The prevalence of all diseases was associated with poorer SRH. The tendency was more prominent in cardio-cerebral vascular diseases, visual impairment, and mental illnesses with larger ORs. Residents with abnormalities in laboratory parameters tended to have poorer SRH, with ORs ranging from 1.62 (for triglyceride) to 3.48 (for hemoglobin among men) in a comparison of the unhealthy and healthy groups. Most of the health-related factors regarded as risks were associated with poorer SRH. Among them, life and work pressure, poor spiritual status, and poor quality of interpersonal relationships were the most significant factors. Conclusions SRH is consistent with objective health status and can serve as a global measure of health status in the general population.

Hidradenitis suppurativa (HS) is a chronic, recurrent inflammatory skin disorder. Laboratory parameters may serve as an assessment tool, providing a potential objective window into disease activity. To systematically evaluate the utility of commonly obtained laboratory parameters in the objective assessment of disease severity in patients with HS. This single-center retrospective study included patients who were clinically diagnosed with HS, acne inversa, or follicular occlusion triad at Peking Union Medical College Hospital between January 1, 2020, and July 1, 2025. Study data were extracted from clinical examination records and corresponding laboratory test results. Ordinal logistic regression models were constructed to evaluate the associations between laboratory parameters and Hurley stages. Heterogeneity analyses were performed to assess the consistency of these associations across anatomical regions. The study included 1750 clinical visits from 583 HS patients, with a male predominance (85.7%) and a mean age of 31.15±12.11 years. By systematically comparing laboratory parameters across Hurley stages, this study identified significant differences in ten markers, including white blood cell (WBC), neutrophil ratio (NEU%), platelet (PLT), plateletcrit (PCT), mean platelet volume (MPV), hemoglobin (Hb), gamma-glutamyl transferase (GGT), high-density lipoprotein cholesterol (HDL-C), erythrocyte sedimentation rate (ESR), and high-sensitivity C-reactive protein (hsCRP). Ordinal logistic regression analyses demonstrated that WBC, NEU%, PLT, PCT, platelet distribution width (PDW), GGT, ESR, and hsCRP were significantly and positively associated with increasing Hurley stage, while Hb, MPV, and HDL-C exhibited inverse associations. Heterogeneity analysis found that systemic inflammation-related markers (WBC, PDW, ESR, and hsCRP) demonstrated highly consistent associations with HS severity across anatomical locations, while metabolic parameters (HDL-C, triglycerides (TG), alanine aminotransferase (ALT), and aspartate aminotransferase (AST)) exhibited significant site-specific heterogeneity. This study supports the use of readily available laboratory markers as an objective adjunct to conventional clinical severity assessments, while highlighting the anatomical specificity of HS pathophysiology.

IntroductionHeadache is one of the most frequent neurologic manifestations in COVID-19. We aimed to analyze which symptoms and laboratory abnormalities were associated with the presence of headache and to evaluate if patients with headache had a higher adjusted in-hospital risk of mortality.MethodsRetrospective cohort study. We included all consecutive patients admitted to the Hospital with confirmed SARS-CoV-2 infection between March 8th and April 11th, 2020. We collected demographic data, clinical variables and laboratory abnormalities. We used multivariate regression analysis.ResultsDuring the study period, 576 patients were included, aged 67.2 (SD: 14.7), and 250/576 (43.3%) being female. Presence of headache was described by 137 (23.7%) patients. The all-cause in-hospital mortality rate was 127/576 (20.0%). In the multivariate analysis, patients with headache had a lower risk of mortality (OR: 0.39, 95% CI: 0.17–0.88, p = 0.007). After adjusting for multiple comparisons in a multivariate analysis, variables that were independently associated with a higher odds of having headache in COVID-19 patients were anosmia, myalgia, female sex and fever; variables that were associated with a lower odds of having headache were younger age, lower score on modified Rankin scale, and, regarding laboratory variables on admission, increased C-reactive protein, abnormal platelet values, lymphopenia and increased D-dimer.ConclusionHeadache is a frequent symptom in COVID-19 patients and its presence is an independent predictor of lower risk of mortality in COVID-19 hospitalized patients.

Diabetes is a lifelong condition that occurs when the pancreas loses its ability to secrete insulin or experiences a significant reduction in insulin production. Early identification of high-risk patients is crucial for timely interventions and improved outcomes. Traditional clinical risk prediction models rely on regression analysis using clinical, sociodemographic, and anthropometric data; however, they have limitations in terms of accuracy and generalizability. This research proposes a diagnostic strategy leveraging machine learning (ML) techniques, specifically the XGBoost algorithm optimised with Optuna, to enhance high-risk prediction based on laboratory parameters. The study utilises an open-access diabetes dataset incorporating patient demographics, laboratory test results, and clinical outcomes. Data preprocessing, including cleaning, normalisation, and feature extraction, is performed using an Adaptive Tree-Structured Parzen Estimator (ATPE) and XGBoost model. The proposed model outperforms conventional classification models, achieving 83% accuracy, 80% precision, 78% recall, and a 78% F1 score. A comprehensive correlation and confusion matrix evaluation highlights the model’s effectiveness in distinguishing high-risk patients. Findings indicate that integrating machine learning (ML)-based risk classification frameworks with laboratory test-based diagnostic strategies improves predictive accuracy and patient stratification. However, data quality, population diversity, and real-time applicability remain challenges. Future research should explore the integration of real-time data from wearable devices and expand model deployment to other chronic and rare diseases, enhancing adaptability and clinical decision-making.

PurposeTo quantify the severity of 2019 novel coronavirus disease (COVID-19) on chest CT and to determine its relationship with laboratory parameters.MethodsPatients with real-time fluorescence polymerase chain reaction (RT-PCR)–confirmed COVID-19 between January 01 and February 18, 2020, were included in this study. Laboratory parameters were retrospectively collected from medical records. Severity of lung changes on chest CT of early, progressive, peak, and absorption stages was scored according to the percentage of lung involvement (5 lobes, scores 1–5 for each lobe, range 0–20). Relationship between CT scores and laboratory parameters was evaluated by the Spearman rank correlation. The Bonferroni correction adjusted significance level was at 0.05/4 = 0.0125.ResultsA total of 84 patients (mean age, 47.8 ± 12.0 years [standard deviation]; age range, 24–80 years) were evaluated. The patients underwent a total of 339 chest CT scans with a median interval of 4 days (interquartile range, 3–5 days). Median chest CT scores peaked at 4 days after the beginning of treatment and then declined. CT score of the early stage was correlated with neutrophil count (r = 0.531, P = 0.011). CT score of the progressive stage was correlated with neutrophil count (r = 0.502, P < 0.001), white blood cell count (r = 0.414, P = 0.001), C-reactive protein (r = 0.511, P < 0.001), procalcitonin (r = 0.423, P = 0.004), and lactose dehydrogenase (r = 0.369, P = 0.010). However, CT scores of the peak and absorption stages were not correlated with any parameter (P > 0.0125). No sex difference occurred regarding CT score (P > 0.05).ConclusionSeverity of lung abnormalities quantified on chest CT might correlate with laboratory parameters in the early and progressive stages. However, larger cohort studies are necessary.

Pit viper snakebite envenoming remains a critical global health challenge, with tissue necrosis and subsequent amputation posing significant morbidity despite antivenom availability. Existing prediction tools lack integration of dynamic laboratory parameters and iatrogenic factors, limiting their clinical utility. A retrospective cohort study analyzed 1,527 pit viper snakebite envenoming cases from the People’s Hospital of Lichuan City (2012–2025). Data encompassed demographics, bite characteristics, treatment timelines, and laboratory parameters (neutrophil-to-lymphocyte ratio [NLR], D-dimer, fibrinogen [FIB]). Univariate and multivariate logistic regression analyses identified independent predictors, and a nomogram was constructed using R software. Model performance was evaluated via area under the curve (AUC), calibration curves, Hosmer-Lemeshow tests, and decision curve analysis (DCA). Key predictors included tourniquet misuse (OR = 15.45, 95% CI: 9.27–25.77), antivenom injection time (> 6 h; OR = 11.82, 95% CI: 7.18–19.45), the time from injury to admission (> 6 h; OR = 3.90, 95% CI: 2.46–6.20). Elevated NLR (OR = 1.25) and D-dimer (OR = 1.12) predicted amputation risk, whereas higher FIB demonstrated a non-significant protective trend (OR = 0.79, P  = 0.090). The nomogram demonstrated exceptional discrimination (AUC: 0.893 training, 0.881 testing) and calibration (Hosmer-Lemeshow P  > 0.14), with high sensitivity (90–93%) and moderate specificity (68–72%). DCA confirmed clinical utility across risk thresholds (2-100%). This study highlights the interplay of temporal and laboratory parameters in amputation risk. The nomogram provides a robust tool for early risk stratification, emphasizing timely antivenom use and standardized first aid. This model offers a valuable reference for the implementation of prompt preventive and therapeutic interventions.

Background In-hospital cardiac arrest (IHCA) is an acute disease with a high fatality rate that burdens individuals, society, and the economy. This study aimed to develop a machine learning (ML) model using routine laboratory parameters to predict the risk of IHCA in rescue-treated patients. Methods This retrospective cohort study examined all rescue-treated patients hospitalized at the First Medical Center of the PLA General Hospital in Beijing, China, from January 2016 to December 2020. Five machine learning algorithms, including support vector machine, random forest, extra trees classifier (ETC), decision tree, and logistic regression algorithms, were trained to develop models for predicting IHCA. We included blood counts, biochemical markers, and coagulation markers in the model development. We validated model performance using fivefold cross-validation and used the SHapley Additive exPlanation (SHAP) for model interpretation. Results A total of 11,308 participants were included in the study, of which 7779 patients remained. Among these patients, 1796 (23.09%) cases of IHCA occurred. Among five machine learning models for predicting IHCA, the ETC algorithm exhibited better performance, with an AUC of 0.920, compared with the other four machine learning models in the fivefold cross-validation. The SHAP showed that the top ten factors accounting for cardiac arrest in rescue-treated patients are prothrombin activity, platelets, hemoglobin, N-terminal pro-brain natriuretic peptide, neutrophils, prothrombin time, serum albumin, sodium, activated partial thromboplastin time, and potassium. Conclusions We developed a reliable machine learning-derived model that integrates readily available laboratory parameters to predict IHCA in patients treated with rescue therapy.

Mast cell-activating signals in cold urticaria are not yet well defined and are likely to be heterogeneous. Cold agglutinins and cryoglobulins have been described as factors possibly associated with cold urticaria, but their relevance has not been explained. We performed a single-center prospective cohort study of 35 cold urticaria patients. Cold agglutinin and cryoglobulin test results, demographics, detailed history data, cold stimulation test results, complete blood count values, C-reactive protein, total immunoglobulin E levels, and basal serum tryptase levels were analyzed. Forty six percent ( n = 16) of 35 tested patients had a positive cold agglutinin test and 27% ( n = 9) of 33 tested patients had a positive cryoglobulin test. Cold agglutinin positive patients, when compared to cold agglutinin negative ones, were mainly female ( P = 0.030). No gender-association was found for cryoglobulins. A positive cold agglutinin test, but not a positive cryoglobulin test, was associated with a higher rate of reactions triggered by cold ambient air ( P = 0.009) or immersion in cold water ( P = 0.041), and aggravated by increased summer humidity ( P = 0.007). Additionally, patients with a positive cold agglutinin test had a higher frequency of angioedema triggered by ingestion of cold foods or drinks ( P = 0.043), and lower disease control based on Urticaria Control Test ( P = 0.023). Cold agglutinin levels correlated with erythrocyte counts (r = −0.372, P = 0.028) and monocyte counts (r = −0.425, P = 0.011). Cryoglobulin concentrations correlated with basal serum tryptase levels (r = 0.733, P = 0.025) and cold urticaria duration (r = 0.683, P = 0.042). Results of our study suggest that cold agglutinins and cryoglobulins, in a subpopulation of cold urticaria patients, are linked to the course and possibly the pathogenesis of their disease.