Malnutrition, Cancer Stage and Gastrostomy Timing as Markers of Poor Outcomes in Gastrostomy-Fed Head and Neck Cancer Patients

A computational model to analyze the impact of birth weight-nutritional status pair on disease development and disease recovery

Purpose

The purpose of this work is to analyse the combined impacts of birth weight and nutritional status on development and recovery of various types of diseases. This work aims to computationally establish the facts about the effects of individual birth weight-nutritional status pairs on disease development and disease recovery.

Methods

This work designs a computational model to analyze the impact of birth weight-nutritional status pairs on disease development and disease recovery. Our model works in two phases. The first phase finds the best machine learning model to predict birth weight from "Child Birth Weight Dataset" available at IEEE Dataport (https://dx.doi.org/10.21227/dvd4-3232). The second phase combines the predicted birth weight labels with nutritional status labels and establishes the effects using differential equations.

Results

The experimental results find Gradient boosting (GB) to work the best with Information gain (IGT) and Support Vector Machine (SVM) with Chi-square test (CST) for predicting the birth weights. The simulated results establish that "normal birth weight and normal nutritional status" is the best pair for resisting disease development as well as enhancing disease recovery. The results also depict that "low birth weight and malnutrition" is the worst pair for disease development while "high birth weight and malnutrition" is the worst combination for disease recovery.

Conclusion

The findings computationally establish the facts about the effects of birth weight-nutritional status pairs on disease development and disease recovery. As a social implication, this study can spread awareness about the importance of birth weight and nutritional status. The outcome can be helpful for the concerned authority in making decisions on healthcare cost and expenditure.

Assessment of the albumin-bilirubin score in breast cancer patients with liver metastasis after surgery

Objective

This study aims to investigate the potential prognostic value of albumin-bilirubin (ALBI) score in breast cancer patients with liver metastasis after surgery.

Methods

This was a retrospective study of 178 breast cancer patients with liver metastasis after surgery. ALBI score was calculated by the following formula: (log10 bilirubin × 0.66) - (albumin × 0.085). The optimal cutoff value of ALBI score was assessed by X-tile. The clinical influence of ALBI score on survival outcomes using Kaplan-Meier method, Log-rank test, Cox proportional hazards regression model. The calibration curves, decision curve analysis and time-dependent ROC curve were used to assess the predictive performance of the nomogram's models.

Results

The classifications of 178 breast cancer patients with liver metastasis after surgery were as follows: low ALBI score group (＜-3.36) vs. high ALBI score group (≥-3.36). The Cox proportional hazards regression model indicated that ALBI score was a potential predictor. Kaplan-Meier survival curve performed that the median disease free survival (p = 0.0029) and overall survival (p＜0.0001) in low ALBI score group were longer than in high ALBI score group. The ALBI-based nomograms had good predictive performance.

Conclusions

The ALBI score has high prognostic ability for survival time in breast cancer with liver metastasis after surgery. These models will be valuable in discriminating patients at high risks of liver metastasis.