Developing an Agnostic Risk Prediction Model for Early AKI Detection in Cancer Patients.

Acute kidney injury (AKI) is a common complication among oncology patients associated with lower remission rates and higher mortality. To reduce the impact of this condition, we aimed to predict AKI earlier than existing tools, to allow clinical intervention before occurrence. We trained a random forest model on 597,403 routinely collected blood test results from 48,865 patients undergoing cancer treatment at The Christie NHS Foundation Trust between January 2017 and May 2020, to identify AKI events upcoming in the next 30 days. AKI risk levels were assigned to upcoming AKI events and tested through a prospective analysis between June and August 2020. The trained model gave an AUROC of 0.881 (95% CI 0.878-0.883), when assessing predictions per blood test for AKI occurrences within 30 days. Assigning risk levels and testing the model through prospective validation from the 1st June to the 31st August identified 73.8% of patients with an AKI event before at least one AKI occurrence, 61.2% of AKI occurrences. Our results suggest that around 60% of AKI occurrences experienced by patients undergoing cancer treatment could be identified using routinely collected blood results, allowing clinical remedial action to be taken and disruption to treatment by AKI to be minimised.

Unknotting the interactive effects of learning processes on cultural evolutionary dynamics.

Forms of non-random copying error provide sources of inherited variation yet their effects on cultural evolutionary dynamics are poorly understood. Focusing on variation in granny and reef knot forms, we present a mathematical model that specifies how these variant frequencies are affected by non-linear interactions between copying fidelity, mirroring, handedness and repetition biases. Experiments on adult humans allowed these effects to be estimated using approximate Bayesian computation and the model is iterated to explain the prevalence of granny over reef knots in the wild. Our study system also serves to show conditions under which copying fidelity drives heterogeneity in cultural variants at equilibrium, and that interaction between unbiased forms of copying error can skew cultural variation.