Changshu No. 2 People's Hospital

This study aimed to investigate the prognostic impact of completing 30 mL/kg fluid resuscitation within 1 h in elderly septic shock patients.

This was a multicenter prospective observational cohort study. We applied logistic regression to assess the impact of completing 30 mL/kg fluid resuscitation within 1 h on respiratory support escalation including new-onset mechanical ventilation, bilevel positive airway pressure (BiPAP), and high-flow nasal cannula (HFNC) as well as heart failure (HF). We plotted Kaplan-Meier (K-M) curves to evaluate survival in patients completing 30 mL/kg fluid resuscitation within 1 h. We performed mediation analyses to determine the influence of HF on mortality associated with completing 30 mL/kg fluid resuscitation within 1 h.

Completing 30 mL/kg fluid resuscitation within 1 h increased the odds ratios of new-onset BiPAP (adjusted OR = 3.411; 95% confidence interval (CI) = [1.526, 7.620]) and HFNC (adjusted OR = 2.576; 95% CI = [1.252, 5.297]) within 24 h as well as the odds ratio of HF (adjusted OR = 2.291; 95% CI = [1.266, 4.149]). The adjusted K-M curve showed that patients completing 30 mL/kg fluid resuscitation within 1 h had higher 30-d mortality than those completing it over longer periods. The mediation effect suggested that completing 30 mL/kg fluid resuscitation within 1 h could be fatal primarily because it increased the risk of HF.

For elderly patients with septic shock, completing 30 ml/kg of fluid resuscitation within 1 h ought to be more cautious, particularly considering the patient's cardiac function and overall clinical status.

Intracardiopulmonary migration of an inferior vena cava (IVC) filter is an uncommon but potentially life-threatening complication. A previous systematic review including data through 2008 found that the most common cause for migration was operator error and that open thoracotomy was the best first option for management. The aim of this study was to assess the clinical presentation and causes of intracardiopulmonary filter migration, as well as the most commonly used management strategies over the past 15 years.

A systematic search of the literature was conducted to identify studies pertaining to intracardiopulmonary IVC filter migration that were published between November 2008 and June 2024, and data were collected regarding clinical presentation, complications, type and location of filter migration, and management strategies.

A total of 156 cases of intracardiopulmonary migration of IVC filters were identified in 88 publications. Of the 66 patients whose symptoms status was listed, 50 patients had symptoms and 16 patients were asymptomatic. In the remaining patients, the symptom status was not listed. The most frequently reported symptom was chest pain (68.0%; 34/50). Of the 156 patients, 34 (22.8%) experienced intracardiopulmonary migration of the entire filter body, and the remaining 122 (78.2%) experienced filter fracture with migration of the fractured struts. Complications were reported in 30.1% of patients (47/156); these complications included myocardial injury (n = 37), damage of tricuspid valve (n = 12), and hemopericardium (n = 21). Endovascular (16 filters and 56 struts) or surgical (13 filters and 16 struts) retrieval was the primary management strategy for intracardiopulmonary migration of the filter or fractured strut.

The main cause of intracardiopulmonary migration of an IVC filter is filter fracture. Endovascular retrieval is gradually becoming the preferred management option for intracardiopulmonary migration of IVC filters.

Accurate estimation of recurrence risk for cervical cancer plays a pivot role in making individualized treatment plans. We aimed to develop and externally validate an end-to-end deep learning model for predicting recurrence risk in cervical cancer patients following surgery by using multiparametric MRI images.

The clinicopathologic data and multiparametric MRI images of 406 cervical cancer patients from three institutions were collected. We designed a novel deep learning model called "ConvXGB" for predicting recurrence risk by combining the convolutional neural network (CNN) and eXtreme Gradient Boost (XGBoost). The predictive performance of the ConvXGB model was evaluated using time-dependent area under curve (AUC), compared with the deep learning radio-clinical model, clinical model, conventional radiomics nomogram and an existing histology-specific tool. The potential of the ConvXGB model in predicting the recurrence-free survival (RFS) and overall survival (OS) was assessed.

The ConvXGB model outperformed other models in predicting recurrence risk, with AUCs for 1 and 3 year-RFS of 0.872(95% CI, 0.857-0.906) and 0.882(95% CI, 0.860-0.904) respectively in the test cohort. This model showed better discrimination, calibration and clinical utility. Grad-CAM analysis was adopted to help clinicians better understand the predictive results. Moreover, Kaplan-Meier survival analysis revealed that patients who were stratified into high-risk group by the ConvXGB model were significantly susceptible to higher cumulative recurrence risk rates and worse outcome.

The ConvXGB model allowed for predicting postoperative recurrence risk in cervical cancer patients and for stratifying the risk of RFS and OS.