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Predictors of oral anticoagulant-associated adverse events in seniors transitioning from hospital to home: a retrospective cohort study protocol

Introduction Oral anticoagulants (OACs) are widely prescribed in older adults. High OAC-related adverse event rates in the early period following hospital discharge argue for an analysis to identify predictors. Our objective is to identify and validate clinical and continuity of care variables among seniors discharged from hospital on an OAC, which are independently associated with OAC-related adverse events within 30 days. Methods and analysis We propose a population-based retrospective cohort study of all adults aged 66 years or older who were discharged from hospital on an OAC from September 2010 to March 2015 in Ontario, Canada. The primary outcome is a composite of the first hospitalisation or emergency department visit for a haemorrhage or thromboembolic event or mortality within 30 days of hospital discharge. A Cox proportional hazards model will be used to determine the association between the composite outcome and a set of prespecified covariates. A split sample method will be adopted to validate the variables associated with OAC-related adverse events. Ethics and dissemination The use of data in this project was authorised under section 45 of Ontario’s Personal Health Information Protection Act, which does not require review by a research ethics board. Results will be disseminated via peer-reviewed publications and presentations at conferences and will determine intervention targets to improve OAC management in upcoming randomised trials. Trial registration number ClinicalTrials.gov Identifier: NCT02777047; Pre-results.


GENERAL COMMENTS
Overview: The authors of this manuscript detail a study protocol to identify predictors of adverse clinical events among older adults discharged on oral anticoagulants. The authors note that anticoagulants have a high rate of adverse drug events following hospital discharge. They posit that a mix of clinical and continuity factors identify at-risk patients.
To identify predictors, the authors intend to use the ICES database, which contains an impressive number of clinical and administrative databases. Overall, the investigators present a thoughtful study protocol that utilizes many best practices of epidemiologic research. For example, the authors discuss reasoned approaches to model the competing risk of death, the use of many potentially colinear predictors, and the use of a blanking period. Below I outline a few areas that the authors may consider.
Major considerations 1. Use of a composite outcome. The investigators describe a primary outcome as a composite of hemorrhage, thromboembolic event, or death from any cause. While this may be reasonable, I am concerned that the predictors and mechanisms that lead to hemorrhagic complications are different from those that may lead to thromboembolic events. There are at least two potential problems. First, this approach may result in a poor model fit. Second, readers may find it difficult to implement the model because the model is both predicting an adverse event (hemorrhage) and therapeutic failure (thromboembolism). There are potential solutions to this problem. If the number of clinical events allows, the authors may consider deriving one model for bleeding and another for thromboembolism. Alternatively, as a sensitivity analysis, the authors could create a single model and then assess the discrimination and fit for each outcome separately in the validation dataset. 2. Rx on day one as inclusion criteria. The authors note that for patients to be included in this study cohort, there must be an OAC dispensing noted within one day of discharge. My concern with this inclusion criteria revolves around the assumption that all discharge prescriptions are picked up immediately following hospital discharge. For example, consider a patient who was using anticoagulants before hospitalization and resumes use on discharge but they do not require a refill immediately after discharge. Ideally, this patient should be included in the cohort. Perhaps this criterion was chosen because it is a very specific definition that ensures that the cohort contains patients using anticoagulant and does not include those who may not be using anticoagulants. However, this approach biases the findings to be most applicable to new anticoagulant starts, where I suspected many patients using anticoagulants after discharge were also using anticoagulants before hospitalization. Unless these patients also filled a prescription on the day following discharge, they would be excluded from the analysis as described. There are several potential solutions the authors may consider. First, the authors may consider including patients who fill a prescription at any point during the blanking period (7 days as noted). Second, the authors may consider examining how many people who were using anticoagulants before their hospitalization would meet the entry criteria. If many are excluded, they may consider a different approach for patients who were using anticoagulants before hospitalization.
Minor considerations 1. The authors note that administrative data reduce selection bias.
In the brief description provided it was not clear how selection bias is mitigated using administrative data. The authors may wish to expand on their reasoning more (page 6) 2. The authors do not discuss how they will handle discharges to facilities or institutional care settings. Will these patients be included? 3. To preface this comment, I am not a statistician. The authors note that they will examine the characteristics of the discharging facility. It seems that there is a nested structure to the observations (patients within discharging physicians within hospitalization). If this is an accurate reading, the authors may consider addressing whether they will account for the clustering of observations.

Response to Reviewers
Reviewer 1:

Comment Response
1. This manuscript presents the study protocol aimed to explore the predictors of oral Anticoagulant-associated Adverse Events in seniors transitioning from hospital to home. This study is important for clinical practice. The study is well design and the manuscript is well written. I only have some minor suggestions.
Thank you for the kind words.
2. Why set the age cut point at >66 years? This is not common in other studies. Why not at ≥ 60 or 65 years？ The age threshold of 66 years will be applied to ensure that we can obtain adequate past history of medications, as automatic Ontario Drug Benefits program eligibility begins at the age of 65. Adults younger than 66 years of age will be excluded to avoid incomplete or missing prescription drug data. Revised accordingly (location of change: Section-Participants, Page 8).
3. Any subgroup analysis is planned? eg. by sex, indications of OAC, types of OAC?
This would be a valid option, however in the present study no subgroup analyses are planned so that we keep straightforward clinical interpretability of study results. This will be a consideration for future work.

4.
A brief discussion may be needed to present the clinical impact of this study.

Reviewer 2:
Comment Response 3. Rx on day one as inclusion criteria. The authors note that for patients to be included in this study cohort, there must be an OAC dispensing noted within one day of discharge. My concern with this inclusion criteria revolves around the assumption that all discharge prescriptions are picked up immediately following hospital discharge. For example, consider a patient who was using anticoagulants before hospitalization and resumes use on discharge but they do not require a refill immediately after discharge. Ideally, this patient should be included in the cohort. Perhaps this criterion was chosen because it is a very specific definition that ensures that the cohort contains patients using anticoagulant and does not include those who may not be using anticoagulants. However, this approach biases the findings to be most applicable to new anticoagulant starts, where I suspected many patients using anticoagulants after discharge were also using anticoagulants before We agree with the reviewer's comments that we use a specific but non-sensitive definition of OAC users. Given our current inclusion criteria we do bias the cohort to consist more of new OAC starts. However, we decided to only include patients with an OAC dispensing within 1 day of hospital discharge as we are extremely interested in determining the impact of the hospitalization, a high-risk time period for patients, on outcomes. Upon extracting our cohort based on the current definition, 43% of the study participants are prevalent OAC users, those who were dispensed an OAC in the year prior to index hospitalization. This suggests that prevalent OAC users will be captured in our study. The reviewer's suggestions to deal with this bias are extremely reasonable and provide considerations for future work. Revised accordingly (location of change: Section-Bias, Page 13).
hospitalization. Unless these patients also filled a prescription on the day following discharge, they would be excluded from the analysis as described. There are several potential solutions the authors may consider. First, the authors may consider including patients who fill a prescription at any point during the blanking period (7 days as noted). Second, the authors may consider examining how many people who were using anticoagulants before their hospitalization would meet the entry criteria. If many are excluded, they may consider a different approach for patients who were using anticoagulants before hospitalization. 4. The authors note that administrative data reduce selection bias. In the brief description provided it was not clear how selection bias is mitigated using administrative data. The authors may wish to expand on their reasoning more (page 6) Revised accordingly (location of change: Section-Study Design, Page 6).
5. The authors do not discuss how they will handle discharges to facilities or institutional care settings. Will these patients be included?
These patients will be included in the cohort of patients used in the present study. Discharge to facilities or institutional care settings (Table 5) will be explored as independent variables/predictors in the models.
6. To preface this comment, I am not a statistician. The authors note that they will examine the characteristics of the discharging facility. It seems that there is a nested structure to the observations (patients within discharging physicians within hospitalization). If this is an accurate reading, the authors may consider addressing whether they will account for the clustering of observations. This is a valid comment and we did consider the potential clustering effect when designing the protocol. Currently, the type of hospital including teaching, community, or small hospital, and the physician speciality responsible for the index OAC prescription are included in our model as independent variables. The type of hierarchical modelling proposed by the reviewer is much more complex, and we decided to not pursue it for the current study. The reviewer's suggestions are reasonable and provide considerations for future work.

REVIEWER
Yuli Huang Shunde hospital, Southern medical university, China REVIEW RETURNED 09-May-2020

GENERAL COMMENTS
The authors have revised their manuscript according to my previous suggestions, no further suggestions

REVIEWER
Sachin J Shah University of California, San Francisco