Revolutionary New UTI Test Using Machine Learning: No Microscopy Needed!
Primary Care gets a Boost: NoMicro Classifier for UTI Detection.
Urinary tract infections (UTIs) are a common kind of infection that happens in the urinary system. They can happen to men and women and are often treated with antibiotics. However, doctors are worried about using too many antibiotics, which can make it harder to treat infections in the future. There are different ways to test for UTIs, but a urine culture is the best way to know if someone has a UTI. However, this test takes a long time to get the results, so doctors sometimes need to make a quick decision about treatment.
Some people have made special rules and tools to help doctors determine if someone has a UTI, like using a dipstick test or asking about symptoms. Recently, scientists have used computers to make models that can predict if someone has a UTI based on different information. These models have been able to do a good job of figuring out who has a UTI, but they depend on having test results from a microscope, which is not always available in doctors’ offices. The researchers in this study wanted to see if they could make a new model that does not depend on microscope test results and still works well to predict UTIs. They tested their new model using data from a hospital emergency department and from a doctor’s office, and it worked well in both places.
How was the model trained and tested?
- Two data sources were used: one with over 80,000 patients seen in an emergency department (ED) and one with 472 patients seen at a primary care department (PD)
- The ED data set was divided into a training set (80%) and an internal validation set (20%)
- The PC data set was used exclusively for external validation
- Two models were trained: a microscopy-required model and a microscopy-independent model
- The models were trained using XGBoost, random forests, and artificial neural networks
- The models were internally validated using the emergency department 20% holdout validation set and externally validated on the primary care data set
- The performance of the models was determined at specific cutoffs for the prediction of pathogenic and nonpathogenic cultures
- The models were also evaluated for their ability to decrease antibiotic overuse by retrospectively applying decision rules to the primary care data set.
To explore the code and data, you can visit the below Github repository.
The Inference
- The study investigated whether a urine culture prediction model previously successful in an emergency department (ED) could be adapted for use in environments without the ability to evaluate urine microscopically.
- Internal validation showed that the new model (NoMicro) performed similarly to the original model (NeedMicro) in accuracy.
- Both NoMicro and NeedMicro achieved high performance, with ROC-AUC scores greater than 0.85. (Note- A high ROC-AUC score means that the model can distinguish between positive and negative examples correctly, and it means that the model is performing well)
- Cutoff-dependent performance measures (sensitivity, specificity, PPV, and NPV) were also comparable between the two models.
- The overall performance suggests that the NoMicro classifier is a viable alternative to the NeedMicro classifier and is not significantly affected by the lack of urine microscopy features.
What Next?
The study found that a new way of testing urine in the emergency room and primary care clinics, called the NoMicro model, worked well without needing to look at the urine under a microscope. But, this new way needs a special computer program to be created and tested before it can be used by doctors. The study also found that in areas where there are more people with bad urine infections, the NoMicro model may not be as accurate. The study suggests that more research is needed before using this new way of testing urine in real clinics.
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