Risk prediction models for symptomatic patients with bladder and kidney cancer: a systematic review

Area of Science:

• Urology
• Oncology
• Primary Care Medicine

Background:

• Early diagnosis of bladder and kidney cancer is crucial for better patient outcomes.
• Clinical symptoms and signs can aid primary care physicians in triaging patients suspected of having these cancers.

Purpose of the Study:

• To systematically identify and compare existing models that utilize clinical signs and symptoms for predicting the risk of undiagnosed bladder or kidney cancer.

Main Methods:

• A systematic review was conducted by searching MEDLINE and EMBASE databases.
• Twenty studies, encompassing 29 distinct risk models, were identified and analyzed.
• Models were assessed using TRIPOD guidelines and the PROBAST tool for risk of bias and validation.

Main Results:

• All identified models incorporated hematuria; seven included additional symptoms like abdominal pain.
• Thirteen models demonstrated good predictive discrimination (AUC >0.8), but only eight had undergone external validation.
• All included studies presented a high or unclear risk of bias.

Conclusions:

• Models incorporating clinical features show potential for guiding referrals in primary care, particularly for stratifying patients with visible or non-visible hematuria.
• External validation in diverse patient populations is essential before these models can be reliably implemented in general practice.