I appreciate sharing of your experience. In my opinion, nothing is more important than the patient’s experience. When I was about 5, my grandfather died abruptly of prostate cancer after having chosen to be conservative in treatment. As I understand it, this was partly due to informal advice from a relative and urologist, who was wary of PSA and the aggressive treatment of prostate cancer, and who would often point out in the years that followed (with some guilt I think) that PSA and its downstream procedures appeared a few years after lithotripsy obviated lithotomy (in 1986, the Watson et al. paper was published). My grandfather with prostate cancer ultimately made his own choice, though. Possibly, things worked out in the way he would have preferred; his family never saw him suffer. However, better screening guidelines and better interpretation of information would have been helpful. As mentioned, we need more studies and better models for interpreting information like PSA to help patients better achieve their individual goals of care.
Overall, it sounds like, based on your experience, there are new technologies that guard against the potential downstream adverse effects of falsely elevated PSA levels. I have also noted other potential fixes—e.g., per UpToDate, one can recheck a moderately elevated PSA after a few months (serial testing).
+1 for the point about not abdicating. Probably my main concern with the editorial had centered around this, stemming from the last line, and maybe from an earlier discussion on thresholds for decision making. The issue is when any threshold becomes one-size-fits-all (as we see with statins, which are recommended if ASCVD>=10%), which often nudges the individual patient out of the conversation. My fear was that the last line in the editorial, which seemed to separate individual patients from risks and benefits, could be read as a call for a global threshold like this. I think it’s unlikely this was the intention, though. The decision curve analysis paper appears to empower individual patient preferences; ie, the Pauker threshold reflects individual patient utility.
In the end, a medical decision can be conceptualized as a collaboration between the provider, who gives the probabilities of events, and the patient, who defines utilities on those events (although in practice the provider sometimes helps with this). Then we need to do some kind of math to optimize. Actually, I think we need to do a better job at abdicating optimization to the math.
Although studying something like prostate cancer can be a moving target, I think we need more systematic, utility-based approaches for studying interventions in general.
Re the ordinal models: thank you Dr. Harrell - still thinking more about this.