Impact of deintensifying hypoglycaemic drugs in older adults with type 2 diabetes: protocol for an emulation of a target trial

Abstract

Introduction: In older adults with type 2 diabetes (T2D), overtreatment with hypoglycaemic drugs (HDs: sulfonylureas, glinides and/or insulins) is frequent and associated with increased 1-year mortality. Deintensification of HD is thus a key issue, for which evidence is though limited. The primary objective of this study will be to estimate the effect of deintensifying HD on clinical outcomes (hospital admission or death) within 3 months in older adults (≥75 years) with T2D.

Methods: We will emulate with real-world data a target trial, within The Health Improvement Network cohort, a large-scale database of data collected from electronic medical records of 2000 general practitioners in France. From 1 January 2010 to 28 February 2019, we will include eligible patients ≥75 years who will have T2D, a stable dose of HDs, glycated haemoglobin A1c (HbA1c) value <75 mmol/mol (9.0%) and no deintensification in the past year. The target trial will be sequentially emulated (ie, eligibility assessed) every month in the database. Patients will be classified at baseline of each sequential trial in the intervention arm (deintensification of HDs: decrease of ≥50% in the total dose of HDs, including complete cessation) or control arm (no deintensification of HDs). The pooled dataset for all sequential emulated trials will be analysed. The primary outcome will be time to first occurrence of hospital admission or death, within 3 months. Secondary outcomes will be hospitalisation, death, appropriateness of glycaemic control and occurrence of HbA1c >75 mmol/mol within 1 year. Participants will be followed from baseline to 12 months after randomisation, administrative censoring, or death, whichever occurs first. A pooled logistic regression will be used to estimate the treatment effect on the incidence of the outcomes.

Dissemination and ethics: No ethical approval is needed for using retrospectively this fully anonymised database. The results will be disseminated during conferences and through publications in scientific journals.

Keywords: Aged; CLINICAL PHARMACOLOGY; General diabetes; Patient-Centered Care; Primary Care.

Figure 1

Timeframe for observations in the study.¹Glycated haemoglobin A1c (HbA1c) level ≥ 75 mmol/mol (9.0%) and with an increase greater than or equal to 5% from the HbA1c value at baseline. ²Censored on the occurrence of the outcome of interest, death, administrative censoring (cut-off date on 28 February 2020), or up to 12 months following baseline. HD: hypoglycaemic drugs (sulfonylureas, glinides, and/or insulins); no deintensification of HD: defined according to the treatment strategies (ie, increase of total dose of HD, same dose of HD, or decrease of < 50% of the total dose of HD). Covariates assessed during the first window (during the entire period of data availability up to and including the baseline) will be age at diabetes diagnosis, complications of diabetes (microangiopathy, including diabetic polyneuropathy and diabetic retinopathy), hypertension, other comorbidities (including those of the Charlson Comorbidity Index: myocardial infarction, congestive heart failure, peripheral vascular disease, cerebrovascular accident or transient ischaemic attack, severe neurocognitive troubles, chronic obstructive pulmonary disease, connective tissue disease, peptic ulcer disease, liver disease, hemiplegia, moderate-to-severe chronic kidney disease, solid tumour, leukaemia, lymphoma and acquired immunodeficiency syndrome), claim-based frailty index, number of hospitalisation in the year before baseline, and number of general practitioner contacts in the year before baseline. Covariate assessed during the second window (during the 3 months before baseline) will be the HbA1c value. The covariates assessed during the third window (at baseline) will be: age, sex, physician’s practice region, place of residence (home vs nursing home), total number of drugs per day and duration of diabetes.

Figure 2

Theoretical example of the sequential emulations of the target trial. (A) In this theoretical example, five patients (P1–P5) were included from the database. Available data for these patients are represented on the timeline. (B) The target trial is sequentially emulated each month, and examples of these sequential emulations are provided for January (emulation #1), February (emulation #2), March (emulation #3), and June (emulation #x). For emulation #1, one cloned copy of patient (P5) started deintensification, which can be compared with three cloned copies of patients (P1, P2 and P3). If the cloned copies meet the eligible criteria of the target trial, they are kept for analysis. Treatment assignment is based on the prescription data compatible with the treatment strategy, and confounders at baseline of the sequential trial (ie, calendar month in the database) are also measured for each cloned copy (eg, comedication). To ensure coinciding of eligibility, treatment assignment and start of follow-up, thus avoiding immortal-time and selection biases, the follow-up of each cloned copy is restarted at the beginning of the sequential trial. For emulation #2, one cloned copy of patient P2 that started deintensification can be compared with two cloned copies of patients P1 and P3 that did not. Cloned copies of patients (P5″) are no longer eligible for the sequential emulated trial, according to eligibility criteria of the target trial: the patient already began deintensification in the past year (or in other words, a new-user design is applied). Assignment to treatment strategy, confounders measurement and follow-up reinitialisation are the same as for emulation #1. For emulation #3, no participant started deintensification in the database, so the comparison between deintensification and no deintensification is not feasible, and the cloned copies are not kept for analysis. These steps are repeated as many times as the target trial can be emulated, that is, each month when at least one eligible cloned copy starts the deintensification strategy and can be compared with a cloned copy belonging to the no-intensification arm. (C) Finally, the cloned copies of all sequential emulated trials are stacked in the final dataset for pooled analysis. HD, hypoglycaemic drug; M, months.