Medical equations have enjoyed a lot of love by internists for a good reason. They are a neat solution to a complicated problem.

The complicated problem is that molecules in our blood are interdependent when they exert their physiological actions. For example, the same total calcium level can be interpreted as both high or low, depending on the albumin level. Theoretically, what we need to do is have different reference ranges for calcium depending on albumin level. But a large table of numbers, like that, would be hard to remember. What we do instead is take the calcium level that was actually measured, modify it based on the albumin level, then test the modified calcium level against a unified reference range.

These mathematical equations are not based on any particular scientific principles. They are simply “designed” to simulate a graph that was observed in an epidemiological study. They are imperfect and may lack generalizability. For example, in the case of “corrected” calcium, subsequent studies have found that those equations underestimate the prevalence of hypocalcemia in critically ill patients.

They may also introduce conceptual errors. In my concrete understanding of the English language, calling the modified calcium a “corrected” calcium implies that the calcium level that was actually measured is “incorrect”, which is far from the truth.

Similar problems plague all other equations, including the Holy Grail of medical equations: the Henderson-Hasselbalch equation. The Henderson-Hasselbalch equation can be very inaccurate even in relatively common clinical scenarios like lactic acidosis and formic acid accumulation secondary to methanol ingestion. My personal experience is that this is not emphasized enough when discussing the equation during rounds.

The same problem affects prognostic scoring systems, like the CHADS2 score. Similar to “corrected” calciums, the CAHDS2 score is an artificial score that was designed to simulate a graph that was observed in epidemiological studies. It is a simplification that leads to loss of data. In fact, a group of people felt that there was too much loss of data and their solution was to expand the score with more letters from the alphabet until it became the CHADS2-VaSC score.

Despite all of their imperfections, these calculators and scoring systems were our best compromises. Theoretically, we could get a better estimate of a patients risk of stroke by getting as much information about them as possible then figuring out the closest cohort possible to this patient and using that cohort as the basis to calculate our patients risk of stroke. While this was both possible and more accurate, it was impractical.

Until now.

I believe that a recent change is going obsolete all of these equations and scoring systems. It is the fact that every physician today holds in their hands an advanced portable computer in the form of a smartphone.

With this much computational power and computer storage in our hands, the compromises that we had to make to allow us to practice medicine depending solely on our memories do not apply anymore. We can now plug in as much patient detail as we have into an iPhone application and get the most accurate estimate possible of their prognosis.

A good example of this type of prognostic scores is the FRAX tool, which estimates the 10-year risk of fractures due to osteoporosis. There are no arbitrary cut-offs or categories. All numbers are entered as continuous variables. The tool uses the best data and best statistical methods available to give the best estimate possible.

Another, older example, is the 10-year CVD risk calculators derived from the Framingham Heart Study.

Until we are fully into this next revolution, evolution has presented us with a variety of nice to use iPhone medical equation applications that allows us to easily calculate values and scores. This should make them more enjoyable in their last days with us.