Hypoproteinemia being a manifestation of immunotherapy-related liver dysfunction

Case report of atypical undernutrition of hypoproteinemia type

Albumin and prealbumin serve as vital markers reflecting hepatic synthesis activity and overall body nutrient status. Hypoproteinemia can result from various etiological factors, with reduced blood inflow into the liver due to portal vein thrombosis being one such cause. However, literature addressing this specific association remains limited. This report presents an atypical case of malnutrition involving a patient who experienced prolonged hypoproteinemia attributable to a gradual decline in hepatic blood perfusion caused by progressive portal thrombosis and cavernous transformation of the portal vein (CTPV). The case encompasses an in-depth analysis of the factors contributing to undernutrition, the etiology and diagnosis of hypoproteinemia, and its clinical implications. Vigilance for the presence of hypoproteinemia is essential in the management of patients afflicted by progressive portal vein thrombosis complicated by CTPV. Timely and effective interventions aimed at rectifying hypoproteinemia can significantly enhance clinical outcomes. Moreover, reduced hepatic blood flow should be considered a plausible underlying cause in cases of unexplained hypoproteinemia, warranting thorough evaluation. This case underscores the importance of recognizing the intricate interplay between hepatic vascular pathology and protein homeostasis in clinical practice.

Machine learning-assisted ensemble analysis for the prediction of urinary tract infection in elderly patients with ovarian cancer after cytoreductive surgery

BACKGROUND

Urinary tract infection (UTI) is a common type of postoperative infection following cytoreductive surgery for ovarian cancer, which severely impacts the prognosis and quality of life of patients.

AIM

To develop a machine learning assistant model for the prevention and control of nosocomial infection.

METHODS

A total of 674 elderly patients with ovarian cancer who were treated at the Department of Gynaecology at Jingzhou Central Hospital between January 31, 2016 and January 31, 2022 and met the inclusion criteria of the study were selected as the research subjects. A retrospective analysis of the postoperative UTI and related factors was performed by reviewing the medical records. Five machine learning-assisted models were developed using two-step estimation methods from the candidate predictive variables. The robustness and clinical applicability of each model were assessed using the receiver operating characteristic curve, decision curve analysis and clinical impact curve.

RESULTS

A total of 12 candidate variables were eventually included in the UTI prediction model. Models constructed using the random forest classifier, support vector machine, extreme gradient boosting, and artificial neural network and decision tree had areas under the receiver operating characteristic curve ranging from 0.776 to 0.925. The random forest classifier model, which incorporated factors such as age, body mass index, catheter, catheter intubation times, blood loss, diabetes and hypoproteinaemia, had the highest predictive accuracy.

CONCLUSION

These findings demonstrate that the machine learning-based prediction model developed using the random forest classifier can be used to identify elderly patients with ovarian cancer who may have postoperative UTI. This can help with treatment decisions and enhance clinical outcomes.