OBJECTIVE: We aimed to validate and refine the encephalitis criteria proposed by the International Encephalitis Consortium in a cohort of adults initially suspected of a central nervous system (CNS) infection.
METHODS: We included patients from two prospective cohort studies consisting of adults suspected of a CNS infection whom underwent a diagnostic lumbar puncture. We evaluated the test characteristics of the criteria for both possible and probable encephalitis. The reference standard was a final clinical diagnosis of encephalitis. Recalibration of the criteria was done by adjusting the weight of each criterion based on their respective odds.
RESULTS: In total 1446 episodes were evaluated, of whom 162 (11%) had a clinical diagnosis of encephalitis. Possible encephalitis had a sensitivity of 41% (95% CI 33-49) and a specificity of 88% (95% CI 86-90). Probable encephalitis had a sensitivity and specificity of respectively 27% (95% CI 20-34) and 95% (95% CI 94-96). Through odds-based weighting, we recalibrated the weight of each individual criterion, resulting in a model consisting of an altered mental status (weight of 2), seizures (weight of 3), elevated CSF leukocytes (weight of 5) and abnormalities on neuroimaging (weight of 9). We proposed a cut-off at 5 for possible encephalitis, (sensitivity 93% [95% CI 88-96]; specificity 51% [95% 49-54]), and at 8 for probable encephalitis (sensitivity 51% [95% CI 44-59]; specificity 91% [95% CI 89-92]).
CONCLUSIONS: We validated and refined the existing diagnostic criteria for encephalitis, leading to a substantially enhanced sensitivity. These updated criteria hold promise to facilitate the accurate identification of encephalitis.

Arrhythmogenic right ventricular cardiomyopathy (ARVC) can lead to sudden cardiac death and life-threatening heart failure. Due to its high fatality rate and limited therapies, the pathogenesis and diagnosis biomarker of ARVC needs to be explored urgently. This study aimed to explore the lncRNA-miRNA-mRNA competitive endogenous RNA (ceRNA) network in ARVC. The mRNA and lncRNA expression datasets obtained from the Gene Expression Omnibus (GEO) database were used to analyze differentially expressed mRNA (DEM) and lncRNA (DElnc) between ARVC and non-failing controls. Differentially expressed miRNAs (DEmiRs) were obtained from the previous profiling work. Using starBase to predict targets of DEmiRs and intersecting with DEM and DElnc, a ceRNA network of lncRNA-miRNA-mRNA was constructed. The DEM and DElnc were validated by real-time quantitative PCR in human heart tissue. Protein-protein interaction network and weighted gene co-expression network analyses were used to identify hub genes. A logistic regression model for ARVC diagnostic prediction was established with the hub genes and their ceRNA pairs in the network. A total of 448 DEMs (282 upregulated and 166 downregulated) were identified, mainly enriched in extracellular matrix and fibrosis-related GO terms and KEGG pathways, such as extracellular matrix organization and collagen fibril organization. Four mRNAs and two lncRNAs, including COL1A1, COL5A1, FBN1, BGN, XIST, and LINC00173 identified through the ceRNA network, were validated by real-time quantitative PCR in human heart tissue and used to construct a logistic regression model. Good ARVC diagnostic prediction performance for the model was shown in both the training set and the validation set. The potential lncRNA-miRNA-mRNA regulatory network and logistic regression model established in our study may provide promising diagnostic methods for ARVC.

OBJECTIVE: To investigate the difference in risk factors between non-arteritic anterior ischaemic optic neuropathy (NAION) and central retinal artery occlusion (CRAO) and develop a predictive diagnostic nomogram.
METHODS: The study included 37 patients with monocular NAION, 20 with monocular CRAO, and 24 with hypertension. Gender, age, and systemic diseases were recorded. Blood routine, lipids, hemorheology, carotid and brachial artery doppler ultrasound, and echocardiography were collected. The optic disc area, cup area, and cup-to-disc ratio (C/D) of the unaffected eye in the NAION and CRAO group and the right eye in the hypertension group were measured.
RESULTS: The carotid artery intimal medial thickness (C-IMT) of the affected side of the CRAO group was thicker (P=0.039) and its flow-mediated dilation (FMD) was lower (P=0.049) than the NAION group. Compared with hypertension patients, NAION patients had higher whole blood reduced viscosity low-shear (WBRV-L) and erythrocyte aggregation index (EAI; P=0.045, 0.037), and CRAO patients had higher index of rigidity of erythrocyte (IR) and erythrocyte deformation index (EDI; P=0.004, 0.001). The optic cup and the C/D of the NAION group were smaller than the other two groups (P<0.0001). The diagnostic prediction model showed high diagnostic specificity (83.7%) and sensitivity (85.6%), which was highly related to hypertension, the C-IMT of the affected side, FMD, platelet (PLT), EAI, and C/D.
CONCLUSIONS: CRAO patients show thicker C-IMT and worse endothelial function than NAION. NAION and CRAO may be related to abnormal hemorheology. A small cup and small C/D may be involved in NAION. The diagnostic nomogram can be used to preliminarily identify NAION and CRAO.

Systemic inflammation and reciprocal organ interactions are associated with the pathophysiology of heart failure with preserved ejection fraction (HFpEF). However, the clinical value, especially the diagnositc prediction power of inflammation and extra-cardiac organ dysfunction for HfpEF is not explored. In this cross-sectional study, 1808 hospitalized patients from January 2014 to June 2022 in ChiHFpEF cohort were totally enrolled according to inclusion and exclusion criteria. A diagnostic model with markers from routine blood test as well as liver and renal dysfunction for HFpEF was developed using data from ChiHFpEF-cohort by logistic regression and assessed by receiver operating characteristic curve (ROC) and Brier score. Then, the model was validated by the tenfold cross-validation and presented as nomogram and a web-based online risk calculator as well. Multivariate and LASSO regression analysis revealed that age, hemoglobin, neutrophil to lymphocyte ratio, AST/ALT ratio, creatinine, uric acid, atrial fibrillation, and pulmonary hypertension were associated with HFpEF. The predictive model exhibited reasonably accurate discrimination (ROC, 0.753, 95% CI 0.732-0.772) and calibration (Brier score was 0.200). Subsequent internal validation showed good discrimination and calibration (AUC = 0.750, Brier score was 0.202). In additoin to participating in pathophysiology of HFpEF, inflammation and multi-organ interactions have diagnostic prediction value for HFpEF. Screening and optimizing biomarkers of inflammation and multi-organ interactions stand for a new field to improve noninvasive diagnostic tool for HFpEF.

OBJECTIVE: Periventricular-intraventricular hemorrhage is the most common type of intracranial bleeding in newborns, especially in the first 3 days after birth. Severe periventricular-intraventricular hemorrhage is considered a progression from mild periventricular-intraventricular hemorrhage and is often closely associated with severe neurological sequelae. However, no specific indicators are available to predict the progression from mild to severe periventricular-intraventricular in early admission. This study aims to establish an early diagnostic prediction model for severe PIVH.
METHODS: This study was a retrospective cohort study with data collected from the MIMIC-III (v1.4) database. Laboratory and clinical data collected within the first 24 h of NICU admission have been used as variables for both univariate and multivariate logistic regression analyses to construct a nomogram-based early prediction model for severe periventricular-intraventricular hemorrhage and subsequently validated.
RESULTS: A predictive model was established and represented by a nomogram, it comprised three variables: output, lowest platelet count and use of vasoactive drugs within 24 h of NICU admission. The model\'s predictive performance showed by the calculated area under the curve was 0.792, indicating good discriminatory power. The calibration plot demonstrated good calibration between observed and predicted outcomes, and the Hosmer-Lemeshow test showed high consistency (p = 0.990). Internal validation showed the calculated area under a curve of 0.788.
CONCLUSIONS: This severe PIVH predictive model, established by three easily obtainable indicators within the NICU, demonstrated good predictive ability. It offered a more user-friendly and convenient option for neonatologists.

Objective: The aim of this study is to explore the value of combined detection of ABO blood group and tumor markers in the diagnosis of gastric cancer. Methods: A total of 3650 gastric cancer patients treated in our center from January 2015 to December 2019, and 5822 controls were recruited, and divided into training set and validation set according to 7:3. The diagnostic and predictive model of gastric cancer was constructed by binary logistic regression method in the training set. The diagnostic value of the prediction model for gastric cancer was evaluated by calculating the prediction probability P value and drawing the Receiver operating characteristic (ROC) curve, and was verified in the validation set. Results: The Area under the curve (AUC) of the diagnosis and prediction model in the training set was 0.936 (95%CI: 0.926-0.941), the sensitivity was 81.66%, and the specificity was 98.61%. In the validation set, the AUC was 0.941 (95%CI: 0.932-0.950), the sensitivity was 82.33%, and the specificity was 99.02%. Furthermore, the diagnostic model obtained in this study had a high diagnostic value for early gastric cancer patients in the healthy population (AUC of training set, validation set and total population were 0.906, 0.920 and 0.908, respectively). Conclusions: We constructed a diagnostic model for gastric cancer including blood group and tumor markers, which has high reference value for the diagnosis of gastric cancer patients, and the model can better distinguish early gastric cancer from healthy people.

Non-infectious chronic diseases, especially inflammatory bowel diseases (IBDs), hypertension, and diabetes mellitus, are characterized by a prolonged and multisystemic course, and their incidence increases annually, usually causing serious economic burden and psychological stress for patients. Therefore, these diseases deserve scientific and consistent disease management. In addition, the lack of a comprehensive \"early disease clues tracking-personalized treatment system-follow-up\" model in hospitals also exacerbates this dilemma. Based on these facts, we propose an individualized prediction management system for IBDs based on chronic diseases, focusing on the established IBDs-related prediction models and summarizing their advantages and disadvantages. We call on researchers to pay attention to the integration of models with clinical practice and the continuous correction of models to achieve truly individualized medical treatment for chronic diseases, thus providing substantial value for the rapid diagnosis and adequate treatment of chronic diseases such as IBDs, which follow the \"relapse-remission\" disease model, and realizing long-term drug use and precise disease management for patients. The goal is to achieve a new level of chronic disease management by scientifically improving long-term medication, precise disease management, and individualized medical treatment, effectively prolonging the remission period and reducing morbidity and disability rates.

UNASSIGNED: Wilson\'s disease, also known as hepatolenticular degeneration, is a rare human autosomal recessive inherited disorder of copper metabolism. The clinical manifestations are diverse, and the diagnosis and treatment are often delayed. The purpose of this study is to establish a new predictive diagnostic model of Wilson\'s disease and evaluate its predictive efficacy by multivariate regression analysis of small trauma, good accuracy, low cost, and quantifiable serological indicators, in order to identify Wilson\'s disease early, improve the diagnosis rate, and clarify the treatment plan.
UNASSIGNED: A retrospective analysis was performed on 127 patients with Wilson\'s disease admitted to the First People\'s Hospital of Yunnan Province from January 2003 to May 2022 as the experimental group and 73 patients with normal serological indicators who were not diagnosed with Wilson\'s disease. SPSS version 26.0 software was used for single factor screening and a multivariate binary logistic regression analysis to screen out independent factors. R version 4.1.0 software was used to establish an intuitive nomogram prediction model for the independent influencing factors included. The accuracy of the nomogram prediction model was evaluated and quantified by calculating the concordance index (C-index) and drawing the calibration curve. At the same time, the area under the curve (AUC) of the nomogram prediction model and the receiver operating characteristic (ROC) curve of the Leipzig score was calculated to compare the predictive ability of the nomogram model and the current Leipzig score for Wilson\'s disease.
UNASSIGNED: Alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (AKP), albumin (ALB), uric acid (UA), serum calcium (Ca), serum phosphorus (P), and hemoglobin (HGB) are closely related to the occurrence of Wilson\'s disease (p < 0.1). The prediction model of Wilson\'s disease contains seven independent predictors: ALT, AST, AKP, ALB, UA, Ca, and P. The AUC value of the prediction model was 0.971, and the C-index value was 0.972. The calibration curve was well fitted with the ideal curve. The nomogram prediction model had a good predictive effect on the occurrence of Wilson\'s disease; the ROC curve of Leipzig score was drawn, and the AUC value was calculated. The AUC of the Leipzig score was 0.969, indicating that the prediction model and the scoring system had predictive value, and the nomogram prediction model had a better predictive effect on the research objects of the center.
UNASSIGNED: ALT, AST, AKP, ALB, UA, Ca, and P are independent predictors of Wilson\'s disease, and can be used as early predictors. Based on the nomogram prediction model, the optimal threshold was determined to be 0.698, which was an important reference index for judging Wilson\'s disease. Compared with the Leipzig score, the nomogram prediction model has a relatively high sensitivity and specificity and has a good clinical application value.

Clinical prediction models determine the pre-test probability of pulmonary embolism (PE) and assess the need for tests for these patients. Coronavirus infection is associated with a greater risk of PE, increasing its severity and conferring a worse prognosis. The pathogenesis of PE appears to be different in patients with and without SARS-CoV-2 infection. This systematic review aims to discover the utility of probability models developed for PE in patients with COVID-19 by reviewing the available literature.
A literature search on the PubMed, Scopus, and EMBASE databases was carried out. All studies that reported data on the use of clinical prediction models for PE in patients with COVID-19 were included. Study quality was assessed using the Newcastle-Ottawa scale for non-randomized studies.
Thirteen studies that evaluated five prediction models (Wells score, Geneva score, YEARS algorithm, and PERC and PEGeD clinical decision rules) were included. The different scales were used in 1,187 patients with COVID-19. Overall, the models showed limited predictive ability. The two-level Wells score with low (or unlikely) clinical probability in combination with a D-dimer level <3000ng/mL or a normal bedside lung ultrasound showed an adequate correlation for ruling out PE.
Our systematic review suggests that the clinical prediction models available for PE that were developed in the general population are not applicable to patients with COVID-19. Therefore, their use is in clinical practice as the only diagnostic screening tool is not recommended. New clinical probability models for PE that are validated in these patients are needed.

UNASSIGNED: Clinical prediction models determine the pre-test probability of pulmonary embolism (PE) and assess the need for tests for these patients. Coronavirus infection is associated with a greater risk of PE, increasing its severity and conferring a worse prognosis. The pathogenesis of PE appears to be different in patients with and without SARS-CoV-2 infection. This systematic review aims to discover the utility of probability models developed for PE in patients with COVID-19 by reviewing the available literature.
UNASSIGNED: A literature search on the PubMed, Scopus, and EMBASE databases was carried out. All studies that reported data on the use of clinical prediction models for PE in patients with COVID-19 were included. Study quality was assessed using the Newcastle-Ottawa scale for non-randomized studies.
UNASSIGNED: Thirteen studies that evaluated five prediction models (Wells score, Geneva score, YEARS algorithm, and PERC and PEGeD clinical decision rules) were included. The different scales were used in 1,187 patients with COVID-19. Overall, the models showed limited predictive ability. The two-level Wells score with low (or unlikely) clinical probability in combination with a D-dimer level < 3000 ng/mL or a normal bedside lung ultrasound showed an adequate correlation for ruling out PE.
UNASSIGNED: Our systematic review suggests that the clinical prediction models available for PE that were developed in the general population are not applicable to patients with COVID-19. Therefore, their use is in clinical practice as the only diagnostic screening tool is not recommended. New clinical probability models for PE that are validated in these patients are needed.