Clinical dichotomania: A major cause of over-diagnosis and over-treatment?

TL;DR

This paper argues that the inappropriate dichotomisation of diagnostic results is the main cause of over-diagnosis and over-treatment in medicine, and proposes a more nuanced interpretation of numerical data to improve decision-making.

Contribution

It introduces a method using logistic regression curves to interpret diagnostic results, reducing over-diagnosis and overtreatment by better assessing disease severity.

Findings

Over 36% of treated patients are over-diagnosed in the 20-40 mcg/min AER range.

Fewer nephropathy cases occur with higher AER levels on treatment.

Calibrated logistic models enable personalized risk assessment for treatment decisions.

Abstract

Introduction: There have been many warnings that inappropriate dichotomisation of results into positive or negative, high, or normal etc., during medical research could be very damaging. The aim of this paper is to argue that this is the main cause of over-diagnosis and over-treatment. Methods: Illustrative data were taken from a randomised control trial (RCT) that compared the frequency of nephropathy within 2 years in those on treatment with an angiotensin receptor blocker and a control and on patients in whom the numerical value of the albumin excretion rate (AER) was available on all patients before they are randomised. Results: When the RCT results were divided into AER ranges, a negligible proportion developed nephropathy within 2 years and benefited from treatment in the range 20 to 40mcg/min in which 36% of currently treated patients fall (and are thus over-diagnosed and overtreated). Above an AER of 40mcg/min, there was a gradual increase in proportions with nephropathy in each range, with fewer developing nephropathy in each range on irbesartan 150mg daily than on control and fewer still developing nephropathy on 300mg daily. Interpretation: When logistic regression functions were fitted to the data and calibrated, curves were created that allowed outcome probabilities and absolute risk reductions to be estimated for use in shared decision making (illustrated by application to an example patient). This could avoid much overdiagnosis and overtreatment. Conclusion: Careful attention to disease severity by interpreting each numerical diagnostic result provides better application of the principles of diagnosis and treatment decisions that can prevent over-diagnosis and over-treatment.