The INFLUENCE 3.0 model: Updated predictions of locoregional recurrence and contralateral breast cancer, now also suitable for patients treated with neoadjuvant systemic therapy.

Background: Individual risk prediction of 5-year locoregional recurrence (LRR) and contralateral breast cancer (CBC) supports decisions regarding personalised surveillance. The previously developed INFLUENCE tool was rebuild, including a recent population and patients who received neoadjuvant systemic therapy (NST).

Methods: Women, surgically treated for nonmetastatic breast cancer, diagnosed between 2012 and 2016, were selected from the Netherlands Cancer Registry.

Development and validation of an international preoperative risk assessment model for postoperative delirium.

Background: Postoperative delirium (POD) is a frequent complication in older adults, characterised by disturbances in attention, awareness and cognition, and associated with prolonged hospitalisation, poor functional recovery, cognitive decline, long-term dementia and increased mortality. Early identification of patients at risk of POD can considerably aid prevention.

Methods: We have developed a preoperative POD risk prediction algorithm using data from eight studies identified during a systematic review and providing individual-level data.

External validation of 87 clinical prediction models supporting clinical decisions for breast cancer patients.

Introduction: Numerous prediction models have been developed to support treatment-related decisions for breast cancer patients. External validation, a prerequisite for implementation in clinical practice, has been performed for only a few models. This study aims to externally validate published clinical prediction models using population-based Dutch data.

The majority of 922 prediction models supporting breast cancer decision-making are at high risk of bias.

Objectives: To systematically review the currently available prediction models that may support treatment decision-making in breast cancer.

Study Design And Setting: Literature was systematically searched to identify studies reporting on development of prediction models aiming to support breast cancer treatment decision-making, published between January 2010 and December 2020. Quality and risk of bias were assessed using the Prediction model Risk Of Bias (ROB) Assessment Tool (PROBAST).