Dynamic Nomogram for Predicting the Risk of Perioperative Neurocognitive Disorders in Adults

Protocol to develop a chemotherapy drug screening process by constructing a cancer prognostic model using public databases

Drug resistance is currently the biggest challenge in cancer chemotherapy. Here, we present a protocol to develop a chemotherapy drug screening process by constructing a cancer prognostic model (PM) using public databases. We describe steps for downloading code and data, preparing the expression matrix and metadata for analysis, screening modeling genes, and constructing a PM. We then detail procedures for constructing predictive websites for cancer patients' survival based on their age, tumor stage, gene expression levels, and risk scores. For complete details on the use and execution of this protocol, please refer to Bai et al.1.

Development and Validation of a Nomogram for the Failed Conversion of Labor Analgesia to Cesarean Section Anesthesia

Purpose

The conversion of epidural labor analgesia (ELA) to epidural surgical anesthesia (ESA) for intrapartum cesarean section (CS) often encounters failures. This study aimed to develop a nomogram for predicting the failure rate of this conversion.

Patients and Methods

A retrospective analysis was conducted on data from the Fujian Maternity and Child Health Hospital. Pregnant women (n=214) who underwent cesarean section after receiving labor analgesia. We performed correlation heat map and Lasso regression in terms of exclusion confounding factors and screening independent variables. A nomogram was developed to predict the occurrence.

Results

The developed nomogram incorporated variables such as pregnant history, weight, premature rupture of membranes (PROM), dural puncture epidural (DPE), anesthesiologist level of cesarean section (ALOCS), and Anesthesiologist level of labor analgesia (ALOLA). The model demonstrated good predictive performance, providing a practical tool for assessing the risk of failure in converting labor analgesia to cesarean section anesthesia.

Conclusion

The nomogram can aid anesthesiologists in making informed decisions and optimizing patient care. By utilizing the nomogram, clinicians can estimate the probability of conversion failure based on individual patient characteristics and clinical factors.