UNASSIGNED: At the beginning of December 2022, the Chinese government made major adjustments to the epidemic prevention and control measures. The epidemic infection data and laboratory makers for infected patients based on this period may help with the management and prognostication of COVID-19 patients.
UNASSIGNED: The COVID-19 patients hospitalized during December 2022 were enrolled. Logistic regression analysis was used to screen significant factors associated with mortality in patients with COVID-19. Candidate variables were screened by LASSO and stepwise logistic regression methods and were used to construct logistic regression as the prognostic model. The performance of the models was evaluated by discrimination, calibration, and net benefit.
UNASSIGNED: 888 patients were eligible, consisting of 715 survivors and 173 all-cause deaths. Factors significantly associated with mortality in COVID-19 patients were: lactate dehydrogenase (LDH), albumin (ALB), procalcitonin (PCT), age, smoking history, malignancy history, high density lipoprotein cholesterol (HDL-C), lactate, vaccine status and urea. 335 of the 888 eligible patients were defined as ICU cases. Seven predictors, including neutrophil to lymphocyte ratio, D-dimer, PCT, C-reactive protein, ALB, bicarbonate, and LDH, were finally selected to establish the prognostic model and generate a nomogram. The area under the curve of the receiver operating curve in the training and validation cohorts were respectively 0.842 and 0.853. In terms of calibration, predicted probabilities and observed proportions displayed high agreements. Decision curve analysis showed high clinical net benefit in the risk threshold of 0.10-0.85. A cutoff value of 81.220 was determined to predict the outcome of COVID-19 patients via this nomogram.
UNASSIGNED: The laboratory model established in this study showed high discrimination, calibration, and net benefit. It may be used for early identification of severe patients with COVID-19.

BACKGROUND: Spinal muscular atrophy (SMA) is a progressive neurodegenerative disorder that can be treated with intrathecal nusinersen, an antisense oligonucleotide. In addition to efficacy, safety is a determining factor in the success of any therapy. Here, we aim to assess the safety of nusinersen therapy in paediatric patients with SMA.
METHODS: Laboratory data of paediatric patients with SMA who received nusinersen between October 2019 and May 2022 were retrospectively analysed.
RESULTS: During the observation period, 46 infants and children aged 2.9 months to 13.6 years received a total of 213 nusinersen doses without safety concerns. Inflammatory markers were stable throughout the study. International normalized ratio was increased by 0.09 per injection. Urea levels were increased by 0.108 mmol/L, and cystatin C decreased by 0.029 mg/L per injection. There were no significant changes in platelet count, activated partial thrombin time, creatinine levels or liver enzyme levels during treatment. The cerebrospinal fluid (CSF) leukocyte count remained stable, and total protein increased by 24.038 mg/L per injection.
CONCLUSIONS: Our data showed that nusinersen therapy is generally safe in children with SMA. Laboratory monitoring did not identify any persistent or significantly abnormal findings. CSF protein should be monitored to gain more insights.

Host immune dysfunction plays a crucial role in the onset, progression, and outcome of hemophagocytic lymphohistiocytosis (HLH). This study aimed to comprehensively evaluate the peripheral immune profiles in patients with newly diagnosed secondary hemophagocytic lymphohistiocytosis (sHLH), and explore their predictive value for patient prognosis. A total of 77 patients with sHLH were enrolled in this study, with 31 of them experiencing mortality. Flow cytometry was used to assess the percentages, absolute numbers, and phenotypes of lymphocyte subsets. Simultaneously, cytokine levels and routine laboratory indicators were also collected. In sHLH patients, lymphocyte subset absolute numbers were significantly impaired, accompanied by T cell hyperactivation, B cell hyperactivation, and increased plasmablast proliferation. Prognostic analysis revealed that lower CD8+ T cell percentages, elevated APTT, IL-6, IL-10 levels, and increased CD4+CD28null T cell proportions were associated with poor patient outcomes. The study demonstrates dysregulation in the counts and phenotypes of lymphocyte subsets in sHLH patients. Several key factors, including IL-6, IL-10, APTT, and various T cell percentages, have potential as prognostic markers and therapeutic targets in sHLH.

UNASSIGNED: This work aimed to explore the prognostic risk factors of lung cancer (LC) patients and establish a line chart prediction model.
UNASSIGNED: A total of 322 LC patients were taken as the study subjects. They were randomly divided into a training set (n = 202) and a validation set (n = 120). Basic information and laboratory indicators were collected, and the progression-free survival (PFS) and overall survival (OS) were followed up. Single-factor and cyclooxygenase (COX) multivariate analyses were performed on the training set to construct a Nomogram prediction model, which was validated with 120 patients in the validation set, and Harrell\'s consistency was analyzed.
UNASSIGNED: Single-factor analysis revealed significant differences in PFS (P<0.05) between genders, body mass index (BMI), carcinoembryonic antigen (CEA), cancer antigen 125 (CA125), squamous cell carcinoma antigen (SCCA), treatment methods, treatment response evaluation, smoking status, presence of pericardial effusion, and programmed death ligand 1 (PD-L1) at 0 and 1-50%. Significant differences in OS (P<0.05) were observed for age, tumor location, treatment methods, White blood cells (WBC), uric acid (UA), CA125, pro-gastrin-releasing peptide (ProGRP), SCCA, cytokeratin fragment 21 (CYFRA21), and smoking status. COX analysis identified male gender, progressive disease (PD) as treatment response, and SCCA > 1.6 as risk factors for LC PFS. The consistency indices of the line chart models for predicting PFS and OS were 0.782 and 0.772, respectively.
UNASSIGNED: Male gender, treatment response of PD, and SCCA > 1.6 are independent risk factors affecting the survival of LC patients. The PFS line chart model demonstrates good concordance.

BACKGROUND: The location of gastrointestinal perforation is essential for severity evaluation and optimizing the treatment approach. We aimed to retrospectively analyze the clinical characteristics, laboratory parameters, and imaging features of patients with gastrointestinal perforation and construct a predictive model to distinguish the location of upper and lower gastrointestinal perforation.
METHODS: A total of 367 patients with gastrointestinal perforation admitted to the department of emergency surgery in Fujian Medical University Union Hospital between March 2014 and December 2020 were collected. Patients were randomly divided into training set and test set in a ratio of 7:3 to establish and verify the prediction model by logistic regression. The receiver operating characteristic curve, calibration map, and clinical decision curve were used to evaluate the discrimination, calibration, and clinical applicability of the prediction model, respectively. The multiomics model was validated by stratification analysis in the prediction of severity and prognosis of patients with gastrointestinal perforation.
RESULTS: The following variables were identified as independent predictors in lower gastrointestinal perforation: monocyte absolute value, mean platelet volume, albumin, fibrinogen, pain duration, rebound tenderness, free air in peritoneal cavity by univariate logistic regression analysis and stepwise regression analysis. The area under the receiver operating characteristic curve of the prediction model was 0.886 (95% confidence interval, 0.840-0.933). The calibration curve shows that the prediction accuracy and the calibration ability of the prediction model are effective. Meanwhile, the decision curve results show that the net benefits of the training and test sets are greater than those of the two extreme models as the threshold probability is 20-100%. The multiomics model score can be calculated via nomogram. The higher the stratification of risk score array, the higher the number of transferred patients who were admitted to the intensive care unit (P < 0.001).
CONCLUSIONS: The developed multiomics model including monocyte absolute value, mean platelet volume, albumin, fibrinogen, pain duration, rebound tenderness, and free air in the peritoneal cavity has good discrimination and calibration. This model can assist surgeons in distinguishing between upper and lower gastrointestinal perforation and to assess the severity of the condition.

BACKGROUND: Although results from in vitro studies and small randomized controlled trials have shown positive effects of Dazhu hongjingtian injection (DZHJTI) on acute ischemic stroke (AIS), their generalizability to routine clinical practice remains to be established.
OBJECTIVE: The primary aim of this study is to evaluate the effectiveness of DZHJTI treatment for AIS with regard to changes in the stroke-related neurological deficit from baseline to outpatient follow-up, mortality, subsequent vascular events, disability, and traditional Chinese medicine syndrome in real-world clinical settings. By monitoring for adverse events or significant changes in vital signs and laboratory parameters, we also aim to assess the safety of DZHJTI.
METHODS: This prospective, multicenter cohort study plans to enroll 2000 patients with AIS within 14 days of symptom onset from 30 hospitals across China. Eligible patients will be followed up for 6 months after initiating medication treatments. The primary outcome will be the change in the National Institute of Health Stroke Scale score from baseline to outpatient follow-up. The secondary outcomes include overall mortality, stroke recurrence, new-onset major vascular events, global disability, and improvement of traditional Chinese medicine syndrome in 6 months. Adverse events or clinically significant changes in vital signs and laboratory parameters, regardless of the severity, will be recorded during the trial to assess the safety of DZHJTI. An augmented inverse propensity weighted estimator will be used to reduce variability and improve accuracy in average treatment effects estimation.
RESULTS: The clinical trial registration was approved in October 2022, and the recruitment and enrollment of participants started in November 2022. The study\'s outcomes are expected to be published in 2025 in reputable, peer-reviewed health-related research journals.
CONCLUSIONS: This real-world cohort study is the first to assess the effectiveness and safety of DZHJTI in treating AIS. It may provide additional clinical evidence, including the duration of response, long-term drug effectiveness, and subgroup efficacy data. The study results will be valuable for clinicians and patients seeking optimal treatment for AIS and could lead to better use of DZHJTI and improved patient outcomes.
BACKGROUND: ITMCTR ITMCTR2022000005; http://tinyurl.com/554ns8m5.
UNASSIGNED: DERR1-10.2196/52447.

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.

UNASSIGNED: In children with enthesitis-related arthritis (ERA), the hip and sacroiliac joint function might be impaired if not properly treated. We sought to evaluate the effectiveness of anti-tumor necrosis factor-α (TNF-α) therapy using the inflammatory indicators, Juvenile Arthritis Disease Activity Score 27 (JADAS27) and magnetic resonance imaging (MRI).
UNASSIGNED: We conducted a single-center retrospective study of 134 patients with ERA. We evaluated the effect of anti-TNF therapy on the inflammatory indicators, active joint count, MRI quantitative score, and JADAS27 over 18 months. We used the Spondyloarthritis Research Consortium of Canada (SPARCC) and the Hip Inflammation MRI Scoring System (HIMRISS) scoring systems for hip and sacroiliac joints scoring.
UNASSIGNED: The average age of onset of children with ERA was 11.62 ± 1.95 years, and they were treated with disease-modifying antirheumatic drugs (DMARDs) combined with biologics (n = 87, 64.93%). There were no differences in HLA-B27 positivity between the biologics and non-biologics treatment groups [66 (49.25%) vs. 68 (50.75%), P > 0.05]. Children who received anti-TNF (71 received etanercept, 13 adalimumab, 2 golimumab, and 1 infliximab) therapy improved significantly. Children with ERA used DMARDs and biologics at baseline (Group A) were followed up to 18 months, and their active joint count (4.29 ± 1.99 vs. 0.76 ± 1.33, P = 0.000), JADAS27 (13.70 ± 4.80 vs. 4.53 ± 4.52, P = 0.000) and MRI quantitative scores (P = 0.001) were significantly lower than those at baseline. Some of the patients (n = 13, 9.70%) were treated with DMARDs at the onset of the disease, but did not show significant improvement (Group B). After 6-18 months of switching to anti-TNF therapy, related indicators of the children were significantly lower than at baseline and 1 month (P < 0.013). At 18 months, a total of 33 patients (n = 74, 44.59%) in Group A and 7 (n = 13, 53.85%) in Group B reached inactive state.
UNASSIGNED: Eighteen months after diagnosis, anti-TNF therapy was found to be effective in children diagnosed with ERA. MRI is important for the early diagnosis of juvenile idiopathic arthritis. TNF-α inhibitors can significantly improve the clinical manifestations of sacroiliac joint and hip involvement in patients with ERA. Overall, the real-world study provides more evidence for precision diagnosis and treatment for other hospitals, families and patients.

UNASSIGNED: The growth of antibiotic resistance to Mycobacterium TB represents a major barrier to the goal of \"Ending the global TB epidemics\". This study aimed to develop and validate a simple clinical scoring system to predict the unfavorable treatment outcomes (UTO) in multidrug- and rifampicin-resistant tuberculosis (MDR/RR-TB) patients.
UNASSIGNED: A total of 333 MDR/RR-TB patients were recruited retrospectively. The clinical, radiological and laboratory features were gathered and selected by lasso regression. These variables with area under the receiver operating characteristic curve (AUC)>0.6 were subsequently submitted to multivariate logistic analysis. The binomial logistic model was used for establishing a scoring system based on the nomogram at the training set (N = 241). Then, another independent set was used to validate the scoring system (N = 92).
UNASSIGNED: The new scoring system consists of age (8 points), education level (10 points), bronchiectasis (4 points), red blood cell distribution width-coefficient of variation (RDW-CV) (7 points), international normalized ratio (INR) (7 points), albumin to globulin ratio (AGR) (5 points), and C-reactive protein to prealbumin ratio (CPR) (6 points). The scoring system identifying UTO has a discriminatory power of 0.887 (95% CI=0.835-0.939) in the training set, and 0.805 (95% CI=0.714-0.896) in the validation set. In addition, the scoring system is used exclusively to predict the death of MDR/RR-TB and has shown excellent performance in both training and validation sets, with AUC of 0.930 (95% CI=0.872-0.989) and 0.872 (95% CI=0.778-0.967), respectively.
UNASSIGNED: This novel scoring system based on seven accessible predictors has exhibited good predictive performance in predicting UTO, especially in predicting death risk.

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 Ca2+, 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.