Potential harm caused by physicians' a-priori beliefs in the clinical effectiveness of hydroxychloroquine and its impact on clinical and economic outcome - A simulation approach

Abstract

Background: Despite growing controversies around Hydroxychloroquine's effectiveness, the drug is still widely prescribed by clinicians to treat COVID19 patients. Therapeutic judgment under uncertainty and imperfect information may be influenced by personal preference, whereby individuals, to confirm a-priori beliefs, may propose drugs without knowing the clinical benefit. To estimate this disconnect between available evidence and prescribing behavior, we created a Bayesian model analyzing a-priori optimistic belief of physicians in Hydroxychloroquine's effectiveness.

Methodology: We created a Bayesian model to simulate the impact of different a-priori beliefs related to Hydroxychloroquine's effectiveness on clinical and economic outcome.

Results: Our hypothetical results indicate no significant difference in treatment effect (combined survival benefit and harm) up to a presumed drug's effectiveness level of 20%, with younger individuals being negatively affected by the treatment (RR 0.82, 0.55-1.2; (0.95 (1.1) % expected adverse events versus 0.05 (0.98) % expected death prevented). Simulated cost data indicate overall hospital cost (medicine, hospital stay, complication) of 18.361,41€ per hospitalized patient receiving Hydroxychloroquine treatment.

Conclusion: Off-label use of Hydroxychloroquine needs a rational, objective and datadriven evaluation, as personal preferences may be flawed and cause harm to patients and to society.

Keywords: Bayesian modeling, health economics; Cognitive bias; Cost-effectiveness; Hydroxychloroquine; Off-label drug use; Simulation model.

Fig. 1

Simulation Model. The model simulates the pathway of a cohort exposed to the SARS COV-2, receiving a therapeutic course of Hydroxychloroquine once exposed to COVID-19.

Fig. 2

Population Pyramid. Population pyramid of the Italian sample population. Age distribution in both standard treatment and intervention [source ISTAT 2015].

Fig. 3

Distribution of the relative risk effect across age groups. Posterior distribution of the relative risk effect across a) age groups, b) general population stratified by a-priori beliefs.

Fig. 4

Outcome parameter at a presumed effectiveness of 20%. Results of 1500 simulations based on a starting population of 1 million. A) Box plot of absolute change in hospital death following intervention, stratified by age; each box represent the distribution of expected excess deaths prevented by the intervention in that age class; B) Box plot of overall in hospital deaths as percentage of total hospitalized; C) Box plot of absolute adverse event due to intervention, stratified by age; each box represent the distribution of expected adverse events due to intervention in that age class; D) Box plot of adverse events due to intervention, percentage of total hospitalized.