A novel risk classifier to predict the in-hospital death risk of nosocomial infections in elderly cancer patients

Development and validation of a clinical prediction model for early ventilator weaning in post-cardiac surgery

Background

Weaning patients from mechanical ventilation is a critical clinical challenge post cardiac surgery. The effective liberation of patients from the ventilator significantly improves their recovery and survival rates. This study aimed to develop and validate a clinical prediction model to evaluate the likelihood of successful extubation in post-cardiac surgery patients.

Method

A predictive nomogram was constructed for extubation success in individual patients, and receiver operating characteristic (ROC) and calibration curves were generated to assess its predictive capability. The superior performance of the model was confirmed using Delong's test in the ROC analysis. A decision curve analysis (DCA) was conducted to evaluate the clinical utility of the nomogram.

Results

Among 270 adults included in our study, 107 (28.84%) experienced delayed extubation. A predictive nomogram system was derived based on five identified risk factors, including the proportion of male patients, EuroSCORE II, operation time, pump time, bleeding during operation, and brain natriuretic peptide (BNP) level. Based on the predictive system, five independent predictors were used to construct a full nomogram. The area under the curve values of the nomogram were 0.880 and 0.753 for the training and validation cohorts, respectively. The DCA and clinical impact curves showed good clinical utility of this model.

Conclusion

Delayed extubation and weaning failure, common and potentially hazardous complications following cardiac surgery, vary in timing based on factors such as sex, EuroSCORE II, pump duration, bleeding, and postoperative BNP reduction. The nomogram developed and validated in this study can accurately predict when extubation should occur in these patients. This tool is vital for assessing risks on an individual basis and making well-informed clinical decisions.

Development and validation of a risk prediction model for arthritis in community-dwelling middle-aged and older adults in China

Background

Considering its high prevalence, estimating the risk of arthritis in middle-aged and older Chinese adults is of particular interest. This study was conducted to develop a risk prediction model for arthritis in community-dwelling middle-aged and older adults in China.

Methods

Our study included a total of 9599 participants utilising data from the China Health and Retirement Longitudinal Study (CHARLS). Participants were randomly assigned to training and validation groups at a 7:3 ratio. Univariate and multivariate binary logistic regression analyses were used to identify the potential predictors of arthritis. Based on the results of the multivariate binary logistic regression, a nomogram was constructed, and its predictive performance was evaluated using the receiver operating characteristic (ROC) curve. The accuracy and discrimination ability were assessed using calibration curve analysis, while decision curve analysis (DCA) was performed to evaluate the net clinical benefit rate.

Results

A total of 9599 participants were included in the study, of which 6716 and 2883 were assigned to the training and validation groups, respectively. A nomogram was constructed to include age, hypertension, heart diseases, gender, sleep time, body mass index (BMI), residence address, the parts of joint pain, and trouble with body pains. The results of the ROC curve suggested that the prediction model had a moderate discrimination ability (AUC >0.7). The calibration curve of the prediction model demonstrated a good predictive accuracy. The DCA curves revealed a favourable net benefit for the prediction model.

Conclusions

The predictive model demonstrated good discrimination, calibration, and clinical validity, and can help community physicians and clinicians to preliminarily assess the risk of arthritis in middle-aged and older community-dwelling adults.