Chan Chen

Mild cognitive impairment (MCI) is a transitional stage to dementia, with cerebral small vessel disease (SVD) as a key contributor. Since retinal microvascular changes may mirror cerebral pathology, we investigated the associations between retinal microvasculature, neuroimaging, and plasma biomarkers in individuals with MCI. 61 MCI patients and 57 controls (CU) underwent retinal imaging, Neuroimaging (peak width of skeletonized diffusivity [PSMD], white matter hyperintensities [WMH]), plasma biomarker analysis, and cognitive assessment. Partial correlations and mediation analyses were adjusted for confounders. MCI exhibited greater retinal tortuosity (p < 0.05) and sparser vasculature (p < 0.001), correlating with higher PSMD, WMH volumes, and adverse plasma biomarkers (all p < 0.05). Mediation analysis revealed PSMD and WMH volumes partially mediated retinal-cognitive associations (p for all mediation < 0.05). Retinal microvascular dysfunction in MCI reflects concurrent cerebral SVD and neurodegeneration, supporting its potential as a non-invasive biomarker for early cognitive decline.

Background Invasive fungal infections (IFI) represent a significant contributor to mortality among sepsis patients in the Intensive Care Unit (ICU). Early diagnosis of IFI is challenging, and currently, there are no predictive tools for identifying sepsis patients who may develop IFI. Our study aims to develop a predictive scoring system to assess the risk of IFI in patients with sepsis admitted to the ICU. Methods A retrospective collection of data from a total of 549 patients was conducted. Data-driven, clinically knowledge-driven, and decision tree models were used to identify predictive variables for risk of IFI in ICU patients with sepsis. Demographic data, vital signs, laboratory values, comorbidities, medication use, and clinical outcomes were all collected. The optimal model was selected based on model performance and clinical utility to establish a risk score. Results Among adult patients with sepsis admitted to the ICU, 127 patients (23.1%) developed IFI. The final data-driven model included four predictive factors, the clinically knowledge-driven model included three predictive factors, and the decision tree model included two. Based on the good performance and clinical utility of the clinically knowledge-driven model, it was chosen as the optimal risk scoring model (C-statistics: 0.79 (95% confidence interval (CI): 0.75–0.83); Hosmer–Lemeshow (H–L) test P = 0.884). The ICU sepsis patient invasive fungal infection risk (AMI) score, created based on the clinically knowledge-driven model, includes mechanical ventilation, application of immunosuppressants, and the types of antibiotics used. The C-statistics for this risk score was 0.79 (95% CI:0.75–0.84) with good calibration (H-L test P = 0.992 and see calibration curve: Fig. 2). Moreover, in terms of clinical utility, the decision curve analysis for AMI showed a favorable net benefit. Conclusions The application of the AMI score can effectively distinguish whether ICU sepsis patients will develop IFI, which is beneficial for clinicians to formulate targeted and timely preventive and treatment measures based on the risk of IFI.

To determine the optimal fluid resuscitation volume in septic patients with acutely decompensated heart failure (ADHF). Septic patients with ADHF were identified from a tertiary urban medical center. The generalized additive models were used to explore the association between fluid resuscitation volume and endpoints, and the initial 3 h fluid resuscitation volume was divided into four groups according to this model: < 10 mL/kg group, ≥ 10 to ≤ 15 mL/kg group, > 15 to ≤ 20 mL/kg group, and > 20 mL/kg group. Logistic and Cox regression models were employed to explore the association between resuscitation volume and primary endpoint, in-hospital mortality, as well as secondary endpoints including 30-day mortality, 1-year mortality, invasive ventilation, and ICU admission. A total of 598 septic patients with a well-documented history of HF were enrolled in the study; 405 patients (68.8%) had sepsis-induced hypoperfusion. Patients with NYHA functional class III and IV were 494 (83.9%) and 22 (3.74%), respectively. Resuscitation volumes above 20 mL/kg (OR 3.19, 95% CI 1.31–8.15) or below 10 mL/kg (OR 2.33, 95% CI 1.14–5.20) significantly increased the risk of in-hospital mortality in septic patients, while resuscitation volumes between 15 and 20 mL/kg were not associated with the risk of in-hospital death in septic patients (OR 1.79, 95% CI 0.68–4.81). In the multivariable Cox models, the effect of resuscitation volume on 30-day and 1-year mortality in septic patients was similar to the effect on in-hospital mortality. Resuscitation volume exceeds 15 mL/kg significantly increased the risk of tracheal intubation, while fluid resuscitation volume was not associated with ICU admission in the septic patients. In septic patients with hypoperfusion, these fluid resuscitation volumes have similar effects on patient outcomes. This association was consistent across the three subgroups with worsened cardiac function, as well as in sensitivity analyses. Our study observed that an initial fluid resuscitation volume of 10–15 mL/kg in the first 3 h was optimal for early resuscitation in septic patients with ADHF, particularly those with worsened cardiac function. These results need to be confirmed in randomized controlled trials with larger sample sizes.

Background: This study was aimed to identify the independent risk factors for falls n hospitalized older patients and develop a corresponding predictive model.Methods: A retrospective observational study design was adopted, comprising 440 older patients with falls history and 510 older patients without falls history during hospitalization. Data collected included demographic information, vital signs, comorbidities, psychiatric disorder, function absent, current medication, other clinical indicators.Results: Mobility disability, high-risk medications use, frequency of hospitalizations, psychiatric disorder, visual impairment are independent risk factors for falls in older patients. The A-M2-HPV scoring system was developed. The AUC value of the nomogram was 0.884, indicating the model has excellent discriminative ability. The AUC value of the A-M2-HPV score was 0.788, demonstrating better discrimination and stratification capabilities.Conclusion: The A-M2-HPV scoring system provides a valuable tool to assess the risk of falls in hospitalized older patients and to aid in the implementation of preventive measures.

The performance of host immune responses biomarkers and clinical scores was compared to identify infection patient populations at risk of progression to sepsis, ICU admission and mortality. Immune response biomarkers were measured and NEWS, SIRS, and MEWS. Logistic and Cox regression models were employed to evaluate the strength of association. IL-10 and NEWS had the strongest association with sepsis development, whereas IL-6 and CRP had the strongest association with ICU admission and in-hospital mortality. IL-6 [HR (95%CI) = 2.68 (1.61-4.46)] was associated with 28-day mortality. Patient subgroups with high IL-10 (≥ 5.03 pg/ml) and high NEWS (> 5 points) values had significantly higher rates of sepsis development (88.3% vs 61.1%; p < 0.001), in-hospital mortality (35.0% vs. 16.7%; p < 0.001), 28-day mortality (25.0% vs. 5.6%; p < 0.001), and ICU admission (66.7% vs. 38.9%; p < 0.001). Patients exhibiting low severity signs of infection but high IL-10 levels showed an elevated probability of developing sepsis. Combining IL-10 with the NEWS score provides a reliable tool for predicting the progression from infection to sepsis at an early stage. Utilizing IL-6 in the emergency room can help identify patients with low NEWS or SIRS scores.

Background: Oxidative Balance Score (OBS) is a tool for assessing the oxidative stress-related exposures of diet and lifestyle. The study aimed to investigate the association between OBS and low muscle mass.Methods: Overall, 6,307 individuals over the age of 18 were assessed using data from the 2011 to 2018 National Health and Nutrition Examination Survey (NHANES). Weighted logistic regression and models were used, together with adjusted models.Results: There was a negative relationship between OBS and low muscle mass [odds ratio (OR): 0.96, 95% confidence interval (CI): 0.94–0.97, p< 0.0001] using the first OBS level as reference. The values (all 95% CI) were 0.745 (0.527–1.054) for the second level, 0.650 (0.456–0.927) for the third level, and 0.326 (0.206–0.514) for the fourth level (P for trend <0.0001). Independent links with low muscle mass were found for diet and lifestyle factors. A restricted cubic spline model indicated a non-linear association between OBS and low muscle mass risk (P for non-linearity<0.05). In addition, the inflection points of the nonlinear curves for the relationship between OBS and risk of low muscle mass were 20.Conclusion: OBS and low muscle mass were found to be significantly negatively correlated. By modulating oxidative balance, a healthy lifestyle and antioxidant rich diet could be a preventive strategy for low muscle mass.

Purpose The objective of this study is to investigate the associated risk factors of pulmonary infection in individuals diagnosed with chronic kidney disease (CKD). The primary goal is to develop a predictive model that can anticipate the likelihood of pulmonary infection during hospitalization among CKD patients. Methods This retrospective cohort study was conducted at two prominent tertiary teaching hospitals. Three distinct models were formulated employing three different approaches: (1) the statistics-driven model, (2) the clinical knowledge-driven model, and (3) the decision tree model. The simplest and most efficient model was obtained by comparing their predictive power, stability, and practicability. Results This study involved a total of 971 patients, with 388 individuals comprising the modeling group and 583 individuals comprising the validation group. Three different models, namely Models A, B, and C, were utilized, resulting in the identification of seven, four, and eleven predictors, respectively. Ultimately, a statistical knowledge-driven model was selected, which exhibited a C-statistic of 0.891 (0.855–0.927) and a Brier score of 0.012. Furthermore, the Hosmer–Lemeshow test indicated that the model demonstrated good calibration. Additionally, Model A displayed a satisfactory C-statistic of 0.883 (0.856–0.911) during external validation. The statistical-driven model, known as the A-C2GH2S risk score (which incorporates factors such as albumin, C2 [previous COPD history, blood calcium], random venous blood glucose, H2 [hemoglobin, high-density lipoprotein], and smoking), was utilized to determine the risk score for the incidence rate of lung infection in patients with CKD. The findings revealed a gradual increase in the occurrence of pulmonary infections, ranging from 1.84% for individuals with an A-C2GH2S Risk Score ≤ 6, to 93.96% for those with an A-C2GH2S Risk Score ≥ 18.5. Conclusion A predictive model comprising seven predictors was developed to forecast pulmonary infection in patients with CKD. This model is characterized by its simplicity, practicality, and it also has good specificity and sensitivity after verification.