Some studies have reported to use some predictors before extracorporeal membrane oxygenation (ECMO) initiation to predict the acute kidney injury (AKI) risk. However, injury during the ECMO operation and the response of patients to ECMO may significantly influence the prognosis, and they are unpredictable before ECMO initiation. This study aims to develop a potential model based clinical characteristics at the 2-hour time point during ECMO for the early prediction of AKI in patients receiving ECMO. 139 patients who underwent ECMO were enrolled in this study. The clinical characteristics and the laboratory examinations at 2-hour time point during ECMO were recorded. The least absolute shrinkage and selection operator (LASSO) regression method was performed to select predictors, and logistic regression and a nomogram were used to establish the prediction model. The area under curve (AUC) of the receiver operating characteristic and calibration curve were used to analyze the discrimination and calibration of the model. K-fold cross-validation method was performed to validate the accuracy of this model. Among the 139 patients receiving ECMO, 106 participants (76.26%) developed AKI. Four predictive variables including ECMO model, serum creatinine (Scr-2h), uric acid(UA-2h), and serum lactate (Lac-2h) at the 2-hour time point during ECMO were filtered from 39 clinical parameters by LASSO regression. These four predictors were incorporated to develop a model for predicting AKI risk using logistic regression. The AUC of the model was 0.905 (0.845-0.965), corresponding to 81.1% sensitivity, 90.9% specificity and 83.5% accuracy. Moreover, this model showed good consistency between observed and predicted probability based on the calibration curve (P > 0.05). The validation performed by K-fold cross-validation method showed that the accuracy was 0.874 ± 0.006 in training sets, 0.827 ± 0.053 in test sets, indicating a good capability for AKI risk prediction. Finally, a nomogram based on this model was constructed to facilitate its use in clinical practice. The nomogram incorporating Scr-2h,Lac-2h, UA-2h, and ECMO model may facilitate the individualized prediction of the AKI risk among patients undergoing ECMO.

Objective: Dynamic monitoring of the concentration variation of IgM and IgG in patients with SARS‐CoV‐2 infections and exploring their diagnostic value for coronavirus disease‐19 (COVID‐19).Methods: A total of 15 patients with SARS‐CoV‐2 infection were enrolled as the COVID‐19 group, and 50 patients were enrolled as the control group. The concentrations of SARS‐CoV‐2‐specific antibodies (IgM and IgG) were detected by a chemiluminescence immunoassay (CLIA).Results: According to the cutoff value recommended by the manufacturer (cutoff = 10 AU/mL), the sensitivity, specificity, Youden index (YI), positive predictive value (PPV), and negative predictive value (NPV) of IgM were 60%, 100%, 60%, 100%, and 89.29%, respectively; and 86.67%, 100%, 86.67%, 100%, and 96.15%, respectively, for IgG. We reassessed the cutoff value of IgM. When the cutoff value for SARS‐CoV‐2 IgM was 1.83 AU/mL, the sensitivity, specificity, YI, PPV, and NPV were 93.33%, 98%, 91.33%, 93.33%, and 98%, respectively. During dynamic monitoring of the concentrations of IgM and IgG in COVID‐19 patients, we found the shortest times before a patient became IgM and IgG seropositive after symptom onset were 1.5 and 2 days, respectively. The longest times were 7 and 8 days, respectively. The positive rates of SARS‐CoV‐2 IgM and IgG both reached 100% in 8‐14 days after symptom onset.Conclusion: The IgM cutoff value of 1.83 AU/mL for the diagnosis of COVID‐19 was much better than the cutoff suggested by the manufacturer. SARS‐CoV‐2 infection can be ruled out if antibodies against SARS‐CoV‐2 are still undetectable 14 days after symptom onset.

Background: Previous studies have compared the incidence of pertussis before and during the COVID-19 pandemic, finding that public health measures related to COVID-19 contributed to a temporary decline in reported pertussis cases during the pandemic. However, the post-pandemic period has seen a resurgence in respiratory infections, influenced by relaxed health measures and decreased public vigilance. This study investigates the epidemiological dynamics of pertussis among patients with acute respiratory tract infections (ARTI) in Zhejiang Province, China, providing essential reference information for ongoing public health strategies.Methods: This study analyzed multicenter data from January 2023 to May 2024, involving 8,560 patients with ARTI from three hospitals in Zhejiang Province. Inclusion criteria included patients who presented with cough symptoms and were clinically diagnosed with either acute upper respiratory tract infections (URTI) or acute lower respiratory tract infections (LRTI), and who had undergone at least one Bordetella pertussis DNA test. The study analyzed the epidemiological changes of pertussis positivity rates and their associations with time, age, gender, and diagnosis types (URTI and LRTI).Results: From January 2023 to May 2024, the positivity rate and testing number for pertussis among patients with ARTI generally showed a gradual increasing pattern. In March 2024, the positivity rate reached its peak at 31.58%, followed by a weekly decline. The overall positivity rate was 23.59%, with no significant differences observed between genders. Pertussis incidence was higher in patients with LRTI (24.49%) compared to those with URTI (18.63%, OR = 1.40, 95% CI: 1.20–1.63, p < 0.001) and in outpatients (25.32%) compared to inpatients (6.09%, OR = 4.17, 95% CI: 3.07–5.64, p < 0.001). According to a generalized additive model analysis, there was a wave-shaped, non-linear relationship between age and pertussis incidence, with a relatively high rate observed in the 5 to 17 age group, peaking at age 10 (33.85%). Additionally, the impact of age, patient type, and diagnosis type on the pertussis infection rate varied across different age groups.Conclusion: After the COVID-19 pandemic, the positivity rate of pertussis in Zhejiang Province peaked in early 2024 and then showed a declining pattern. Children and adolescents were particularly affected, emphasizing the need for enhanced vaccination and public health interventions in this population.

Objective: Identifying high-risk children with a poor prognosis in pediatric intensive care units (PICUs) is critical. The aim of this study was to assess the predictive value of early plasma osmolality levels in determining the clinical outcomes of children in PICUs.Methods: We retrospectively assessed critically ill children in a pediatric intensive care database. The locally weighted-regression scatter-plot smoothing (LOWESS) method was used to explore the approximate relationship between plasma osmolality and in-hospital mortality. Linear spline functions and stepwise expansion models were applied in conjunction with a multivariate logistic regression to further analyze this relationship. A subgroup analysis by age and complications was performed.Results: In total, 5,620 pediatric patients were included in this study. An approximately “U”-shaped relationship between plasma osmolality and mortality was detected using LOWESS. In the logistic regression model using a linear spline function, plasma osmolality ≥ 290 mmol/L was significantly associated with in-hospital mortality [odds ratio (OR) 1.020, 95% confidence interval (CI) 1.010–1.031], while plasma osmolality <290 mmol/L was not significantly associated with in-hospital mortality (OR 0.990, 95% CI 0.966–1.014). In the logistic regression model with plasma osmolality as a tri-categorical variable, only high osmolality was significantly associated with in-hospital mortality (OR 1.90, 95% CI 1.38–2.64), whereas low osmolality was not associated with in-hospital mortality (OR 1.28, 95% CI 0.84–1.94). The interactions between plasma osmolality and age or complications were not significant.Conclusion: High osmolality, rather than low osmolality, can predict a poor prognosis in children in PICUs.