Development and validation of a tool to stratify the treatment effect of low-dose aspirin in patients with cardiovascular disease: VISTA (Vascular Intervention Stratification Tool for Aspirin)

Development and validation of a tool to stratify the treatment effect of low-dose aspirin in patients with cardiovascular disease: VISTA (Vascular Intervention Stratification Tool for Aspirin) | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Development and validation of a tool to stratify the treatment effect of low-dose aspirin in patients with cardiovascular disease: VISTA (Vascular Intervention Stratification Tool for Aspirin) View ORCID Profile Yekai Zhou , View ORCID Profile Joseph Edgar Blais , View ORCID Profile Esther Wai Yin Chan , Tak-Wah Lam , View ORCID Profile Kai Hang Yiu , View ORCID Profile Hung Fat Tse , Celine SL Chui , View ORCID Profile Ruibang Luo doi: https://doi.org/10.1101/2024.06.07.24308636 Yekai Zhou 1 Department of Computer Science, The University of Hong Kong , Hong Kong Special Administration Region, China BSc Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Yekai Zhou Joseph Edgar Blais 2 Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, The University of Hong Kong , Hong Kong Special Administrative Region, China BScPharm, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Joseph Edgar Blais Esther Wai Yin Chan 2 Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, The University of Hong Kong , Hong Kong Special Administrative Region, China 3 Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong Science and Technology Park , Hong Kong Special Administrative Region, China BPharm(Hons), PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Esther Wai Yin Chan Tak-Wah Lam 1 Department of Computer Science, The University of Hong Kong , Hong Kong Special Administration Region, China PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kai Hang Yiu 4 Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong , Hong Kong Special Administration Region, China MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kai Hang Yiu Hung Fat Tse 4 Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong , Hong Kong Special Administration Region, China MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Hung Fat Tse Celine SL Chui 3 Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong Science and Technology Park , Hong Kong Special Administrative Region, China 5 School of Nursing, The University of Hong Kong , 5/F Academic Building, 3 Sassoon Road, Pokfulam, Hong Kong Special Administrative Region, China 6 School of Public Health, The University of Hong Kong , Hong Kong Special Administrative Region, China PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: cslchui{at}hku.hk rbluo{at}cs.hku.hk Ruibang Luo 1 Department of Computer Science, The University of Hong Kong , Hong Kong Special Administration Region, China PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ruibang Luo For correspondence: cslchui{at}hku.hk rbluo{at}cs.hku.hk Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Background Aspirin resistance, as determined by measuring platelet aggregation function, has been investigated as a proxy outcome for clinical treatment failure with low-dose aspirin (occurrence of cardiovascular events despite regular aspirin intake). However, among adults with cardiovascular disease (CVD), there is no method to predict aspirin treatment failure using routinely available clinical data. We aimed to develop and internally validate the Vascular Intervention Stratification Tool for Aspirin (VISTA), a model that predicts inter-individual variability in clinical response to low-dose aspirin. Methods We used electronic health records of the Hong Kong Hospital Authority to identify derivation (n=48,743) and validation (n=322,731) cohorts consisting of individuals diagnosed with CVD between January 1, 2015 to December 31, 2018. The composite outcome of recurrent CVD event included the diagnosis of coronary heart disease, ischemic stroke, and peripheral artery disease after low-dose aspirin initiation (≤ 100 mg). Cox proportional hazards regression with the least absolute shrinkage and selection operator regularization was used to identify the most strongly associated and relevant risk factors for aspirin treatment failure. One-year hazard ratio (HR) was estimated across different risk categories for aspirin treated vs. untreated individuals. Results The derivation cohort included 1,623 individuals who initiated low-dose aspirin after their CVD diagnosis. Among 109 variables available, six were selected as model inputs: atrial fibrillation, dyslipidemia, hyperglycemia, polypharmacy, neutrophilia, and elevated serum creatine kinase. In the model validation cohort, we identified 22,192 individuals who initiated low-dose aspirin and 3,747 individuals without aspirin, other antiplatelets, or anticoagulants. Results of the model validation demonstrated a strong graded association between the number of VISTA risk factors and the one-year risk of CVD. Compared to untreated individuals, low-dose aspirin use with no VISTA risk factors had the lowest HR for CVD (0.68, 95% CI of 0.57 to 0.81). For low-dose aspirin user with 1-2 VISTA risk factors, HRs was 0.87 (0.81 to 0.93). The presence of 3-6 VISTA risk factors was associated with aspirin treatment failure (HR 0.99; 95% CI of 0.88 to 1.12), which occurred in approximately 20% of patients in our validation cohort. Conclusions VISTA can predict the heterogeneity of low-dose aspirin’s treatment effect against recurrent CVD. VISTA could be used to stratify patients based on six readily available risk factors and inform patients and clinicians about the potential benefits of aspirin therapy and the potential for alternate antiplatelet treatments. What is new? We developed VISTA (Vascular Intervention Stratification Tool for Aspirin), the first tool that enables the stratification of low-dose aspirin treatment effect for the secondary prevention of cardiovascular disease (CVD). By utilizing six easily accessible clinical risk factors (atrial fibrillation, dyslipidemia, hyperglycemia, polypharmacy, neutrophilia, and elevated serum creatine kinase), VISTA allows for the assessment of aspirin suitability before prescription. VISTA can differentiate patients who are likely to benefit significantly from aspirin treatment (0 risk factors, hazard ratio [HR] of 0.68) from those who may experience aspirin treatment failure (3-6 risk factors, HR of 0.99). What are the clinical implications? Estimated from a high-quality contemporary validation cohort, there are approximately 20% of patients with CVD who may experience aspirin treatment failure. By utilizing VISTA, healthcare providers can personalize aspirin treatment, optimizing its effectiveness and minimizing the potential for treatment failure. This tool empowers clinicians to make more accurate and tailored decisions in prescribing low-dose aspirin for secondary prevention of cardiovascular disease, ultimately leading to improved patient outcomes. Introduction Cardiovascular diseases (CVD), such as coronary heart disease and stroke, are the leading cause of death worldwide, resulting in an estimated 18.6 million fatalities in 2019. 1 , 2 Aspirin, or acetylsalicylic acid, has long been recognized as a crucial intervention to improve CVD outcomes due to its antiplatelet properties, particularly in secondary prevention of CVD. 3 , 4 However, the occurrence of occlusive CVD events despite regular aspirin intake, a phenomenon known as aspirin treatment failure, poses a significant clinical challenge. 5 – 10 Identifying patients who may have limited benefit from CVD prevention with aspirin but are exposed to an elevated risk of major organ bleeding is essential for appropriate aspirin prescription and selection of suitable antiplatelet treatments for CVD prevention. 11 Although methods exist to indirectly query aspirin effectiveness via platelet aggregation function tests, there are currently no solutions available to predict aspirin treatment failure in clinical settings. 5 , 12 – 15 Existing methods for evaluating aspirin effectiveness involve assays that test a laboratory phenomenon called aspirin resistance. 5 These assays, including the platelet function analyzer (PFA-100), whole blood aggregometry (WBA), and VerifyNow aspirin assay (VerifyNow), quantify and compare the platelet aggregation function before and after the administration of aspirin. 5 , 12 – 15 However, these assays have limitations when it comes to aspirin prescriptions. First, they serve only as risk indicators, similar to age and lipid levels; they cannot measure the treatment effect of aspirin. 16 , 17 Second, these assays utilize different techniques, resulting in poor agreement even among the same group of patients. 18 – 25 This lack of consistency adds to the difficulty for clinicians in selecting the most appropriate assay. Third, these assays are not endorsed by clinical guidelines and are not typically available in general hospital settings, limiting their use as routine tests for aspirin prescription reference. 26 , 27 Therefore, there is an urgent need for a method based on easily accessible patient characteristics that can directly estimate the treatment effect of aspirin in CVD prevention. In this study, we present the development and validation of the Vascular Intervention Stratification Tool for Aspirin (VISTA), which was designed to stratify the treatment effect of aspirin for the prevention of recurrent CVD events. VISTA is based on six easily accessible patient characteristics that can be assessed prior to aspirin prescription in actual clinical settings. We conducted a large, comprehensive cohort study in Hong Kong, encompassing a wide range of potential risk factors. By employing advanced big data approaches, we analyzed this extensive dataset to identify the most significant risk factors contributing to the variation in aspirin treatment effects. The validity of the identified risk factors was then confirmed in a separate validation cohort, which demonstrated their accuracy in stratifying the treatment effects of aspirin. Our method provides a straightforward estimation of aspirin’s treatment effect, serving as a tool to assess its potential benefits for individual patients. Leveraging VISTA’s capabilities, clinicians gain early access to this information, enabling informed decisions about aspirin therapy suitability. Methods Data source Two contemporary cohorts were identified for model derivation and validation from the electronic health records of the Hong Kong Hospital Authority (HA) with rigorous inclusion criteria ( Figure 1 ). The HA is a statutory body and the largest public healthcare provider of Hong Kong. It provides government subsidized primary, secondary, and tertiary care for all residents who want it, capturing over 70% of all hospitalizations in Hong Kong. 28 Previous studies demonstrated the high validity of the data source, with a positive predictive value of 85% for myocardial infarction (MI) and 91% for stroke. 29 Because patients are provided outpatient medications at very low out-of-pocket cost, dispensing records for low-dose aspirin are recorded in the HA database. This makes it suitable to investigate the chronic effects of low-dose aspirin, which may not be feasible in many other health systems where patients purchase aspirin as an over-the-counter medication without prescription or recording. 30 Download figure Open in new tab Figure 1. Selection of patients. Hong Kong West Cluster is a part of Hong Kong Island. CVD = cardiovascular diseases. Study cohorts and eligibility criteria We included patients who 1) had their first CVD diagnosis recorded between 1 January 2015 and 31 December 2018, 2) did not have an aspirin prescription dispensed before the index CVD event, and 3) had a prescription record for low-dose (≤ 100mg) aspirin daily until the end of study follow-up, or did not have any prescription record for aspirin at any dose until the end point of their study (as the control group to compare with the treated patients to calculate the treatment effect of aspirin).The cohort entry date was the date of their first diagnosis of CVD in any inpatient or outpatient setting. Patients were followed until the earliest occurrence of the composite recurrent CVD outcome, date of registered death, one year after their index CVD diagnosis, or the study end date (31 December 2019). We did not include patients with CVD diagnosis within one year before the study end date to ensure the patients would have sufficient exposure to aspirin therapy. Daily aspirin users whose most frequently visited healthcare facility within the study period was on Hong Kong Island were categorized as the Hong Kong Island cohort for model derivation. Patients with their most frequently visited healthcare facility within the study period was on Kowloon and New Territories were categorized as the Kowloon and New Territories cohort for model validation. The daily aspirin users were in the treatment group and the non-aspirin users in the untreated (control) group. Exposure, outcomes and risk factor variables The included patients had a CVD diagnosis (coronary heart disease, ischemic stroke, and peripheral artery disease) between January 1, 2015, and December 31, 2018. Patients were not prescribed low-dose (≤ 100 mg) aspirin before the CVD event. The control group patients were not treated with low-dose aspirin before their censored date. The control group patients received sufficient daily low-dose aspirin treatment from the date of CVD event to their censored date. The outcome was the diagnosis of CVD defined by the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes. The outcome was a composite of coronary heart disease, ischaemic or haemorrhagic stroke, and peripheral artery disease (Supplementary Table 1). We compiled an extensive list of over 100 variables to ensure the selection of representative risk factors. The list encompassed clinical laboratory tests, disease and medication history, family history of disease, demographic factors, and healthcare utilization ( Table 2 ). Diagnoses and procedures were defined by ICD-9-CM codes (Supplementary Table 2), and medication history was defined by the British National Formulary (BNF) sections (Supplementary Table 3). Statistical analysis A robust feature selection pipeline was applied to identify the risk variables for model derivation. 31 , 32 Multivariate imputation with chained equations (MICE) was used to generate one imputed dataset to replace the missing values of clinical laboratory tests. 33 MICE is a principled method for dealing with missing data and is extremely reliable on high-dimensional datasets with various missing patterns. 34 For better statistical reliability and clinical utility, risk variables with missing rates below 20% (e.g., clinical laboratory tests) and an event rate above 1% (e.g., disease and medication history) were passed for feature selection. We employed a Cox proportional hazards model (CPH) with the least absolute shrinkage and selection operator (LASSO) regularization to shortlist statistically significant (p value<0.05) risk variables. 35 , 36 CPH is the most widely used multivariate statistical model for survival analysis. 37 , 38 Its regression coefficients can be interpreted as hazard ratios which can be easily understood by clinicians for better decision-making. LASSO is a robust feature selection method. It selects the most representative but independent set of risk variables, which is reliable when downstream manual prioritization is required. The final list of risk variables was also determined based on current clinical evidence to ensure the final set of risk variables are comprehensive and relevant to CVD prognosis. The identified continuous risk variables were converted to categorical form based on clinical diagnosis criteria. Each risk variable was weighted as one point, and the risk category was defined as the total number of risk factors for each patient. Patients in the validation cohort were assigned risk categories based on the identified risk variables. One-year CVD risk was calculated for patients within different risk categories in the treatment and control groups respectively using the Kaplan-Meier method. 39 The treatment effect and the confidence interval across different risk categories were calculated as the coefficient in CPH, which was trained on the composite of treatment and control group patients with treatment as a covariate in each risk category. All analyses were conducted using Python (version 3.9.1), with its add-on package lifelines. 40 This study report is in accordance with the TRIPOD statement. 41 Ethical approval for this study was granted by the Institutional Review Board of The University of Hong Kong/HA Hong Kong West Cluster (UW24-124). Results Study cohorts For the derivation cohort, we initially identified 48,743 patients aged 18 or above with lipid test records and diagnosis of CVD at hospitals in the Hong Kong West Cluster. We excluded 39,262 patients whose first CVD event occurred before January 1, 2015, or after December 31, 2018. We excluded 3,169 patients with low dose aspirin treatment before their first CVD. We excluded 4,546 patients with no daily aspirin treatment after CVD. We excluded 140 patients with anticoagulant or other antiplatelet treatments before or after CVD. We excluded 3 patients with the first CVD diagnosis of hemorrhagic stroke. Overall, 1,623 patients (selected out of 48,743 patients) who took aspirin daily were included in the Hong Kong Island cohort for model derivation. For the validation cohort, we initially identified a cohort of 322,731 patients aged 35 or above with blood pressure records and diagnosis of CVD by the Hospital Authority. Similar exclusion criteria were applied to the validation cohort. A total of 245,188 patients were excluded who had their first CVD event before January 1, 2015 or after December 31, 2018. A total of 28,902 of patients were excluded who had low dose aspirin treatment before their first CVD. A total of 8,372 patients were excluded who had received aspirin treatment after CVD, but not daily. A total of 580 patients were excluded because of immediate CVD death. To ensure no overlap with the derivation cohort, we excluded 6,845 patients with most frequently visited healthcare utilization on the Hong Kong Island. We excluded 1,619 patients with anticoagulant or other antiplatelet treatments before or after CVD. We excluded 5,286 patients with the first CVD diagnosis of hemorrhagic stroke. Overall, 25,939 patients (selected out of 322,731 patients) were included in the Kowloon and New Territories cohort for model validation; 22,192 daily aspirin users were assigned to the treatment group and 3,747 non aspirin users were assigned to the control group. Baseline characteristics The baseline characteristics were similar across the cohorts and subgroups, to ensure the reliability of our selection criteria and support the subsequent model derivation and validation ( Table 1 ). The median age at the cohort entry date was 67 to 75, and 35–48% of the patients were female. A considerable proportion of the patients had comorbidities: 12-23% with dyslipidemia, 30-40% with hypertension, 19-20% with diabetes, 3-10% with atrial fibrillation, 3-5% with chronic kidney disease, and 6-7% with congestive heart failure. Regarding medication history, 18-21% of patients had received statin therapy, 15-22% of patients had taken antidiabetic drugs, and 42-65% of patients were on antihypertensive drugs. The median number of concurrent medications was 6-7. View this table: View inline View popup Download powerpoint Table 1. Baseline characteristics of the derivation and validation cohorts Model development The complete list of all available variables and the details of the feature selection process are shown in Table 2 . There were initially 109 risk variables under consideration. We excluded 38 of them with a high missing rate (>20%) or low event rate (<1%) from subsequent LASSO regression for better statistical reliability and clinical utility. The LASSO regression on the remaining variables identified 7 statistically significant (p value < 0.05) variables. We shortlisted 4 of them based on clinical evidence. We replaced the use of antiplatelet drugs with fasting glucose level as a measure of diabetic inclination due to the higher data completeness of the latter. We added atrial fibrillation to the final list considering its established role in suboptimal aspirin effectiveness. 42 , 43 We translated the selected numerical variables, namely neutrophil count, creatine kinase, glucose levels, and concurrent medication, into categorical variables based on specific diagnostic criteria. Neutrophil count was categorized as neutrophilia (neutrophil count > 7.5 * 10^9/L), creatine kinase was categorized as elevated CK (defined as CK > 400 IU/L), glucose levels were categorized as hyperglycemia (glucose > 7 mmol/L), and concurrent medication was categorized as polypharmacy (concurrent medication > 4). View this table: View inline View popup Download powerpoint Table 2. Summary of included 109 variables assessed in the model derivation cohort The identified risk factors encompassed a comprehensive spectrum of direct and indirect causes that contributed aspirin suboptimal effects, which were supported by the literature. First, while aspirin is more effective in atherosclerotic CVD, its effectiveness in other types of CVD may be limited. 44 Aspirin has limited efficacy for stroke prevention in patients with atrial fibrillation. 43 Neutrophilia may serve as an indicator of inflammation signaling arteritis which compromise the efficacy of aspirin. 44 , 45 Elevated CK, an indicator of muscle damage, is a possible sign of cardiomyopathy or myonecrosis, which could potentially impact the response to aspirin treatment. 46 , 47 Specific comorbidities and prevalent drug use may also affect the treatment effect of aspirin. Polypharmacy can lead to drug interactions that interfere with the effect of aspirin. Hyperglycemia and dyslipidemia may hinder the intended antiplatelet effects of aspirin by increasing the production of prostaglandin F2-like compounds. 48 – 50 The final list of the 6 risk factors was then utilized in the CPH. The parameter estimates for each of the 6 predictor variables showed statistical significance and were of similar magnitude ( Table 3 ). View this table: View inline View popup Download powerpoint Table 3. Multivariate stratification model for aspirin treatment effect To facilitate practical implementation, we adopted a pragmatic approach by categorizing the suboptimal aspirin treatment effect based on the number of risk factors present in an individual. This risk stratification strategy allows for a straightforward assessment of the likelihood and extent of suboptimal aspirin response based on the number and nature of identified risk factors. Model validation We compared the outcomes in the treatment and control group across different risk categories with different number of risk factors in the validation cohort ( Figure 2 ). For the ease of clinical use and model stability, we defined 3 risk categories regarding the total number of 0, 1-2, and 3-6 risk factors (out of 6) for aspirin treatment effect stratification. Around 80% of patients (79.7% in the treatment group and 78.7% in the control group) were in the risk categories with less than three risk factors. The results showed that patients had similar baseline one-year CVD recurrence risk regardless of the risk category (from 43.5% with 0 risk factors to 52.4% with 3-6 risk factors in the control group). In contrast, the recurrence risk with aspirin treatment showed strong graded association with the risk categories (from 34.0% with 0 risk factors to 54.5% with −6 risk factors in the aspirin treatment group). The stable baseline risk and the graded risk after treatment showed the robustness of the risk factors that we identified. As the number of risk factors increased, there was a gradual approaching tendency in the CVD risk between the treatment and control group. This indicated that aspirin treatment might be more effective in individuals with fewer risk factors than those with multiple risk factors. Download figure Open in new tab Figure 2. Outcomes by risk categories and treatment in the validation cohort. Kaplan-Meier curves stratified by different risk categories and treatment groups (aspirin versus control). Number of patients in aspirin group and control group in each risk category were shown after each subtitle (aspirin, control). One year Kaplan-Meier CVD recurrence probability (1y KM) in the two groups, the corresponding absolute risk reduction (ARR), and the hazard ratio (HR) with 95% confidence interval for aspirin versus control groups are shown. The HR of aspirin treatment, estimated based on both groups, exhibited a similar trend ( Table 4 ). Specifically, patients with no risk factors (14.2% of patients) had an aspirin treatment HR of 0.68. This suggested that aspirin treatment significantly reduced the risk of CVD events in this low-risk subgroup. For patients with 1-2 risk factors, which constituted 64.8% of the cohort, the aspirin treatment HR is 0.87. Although the effect size was slightly attenuated compared to the no-risk factor group, these individuals still benefited from aspirin treatment as indicated by the reduced HR. In contrast, the remaining 21.0% of patients with 3-6 risk factors exhibited an aspirin treatment HR of 0.99. This group of high-risk patients may be referred to as had aspirin treatment failure, since aspirin therapy did not provide an additional protective effect against CVD events. View this table: View inline View popup Download powerpoint Table 4. Treatment effect stratification for low-dose aspirin compared to no aspirin treatment after the diagnosis of CVD in the validation cohort (n=25,939) Website design A website employed VISTA at www.bio8.cs.hku.hk/vista.html (Supplementary Information 1) was deployed for demonstration, and to enhance utility and clinician-patient discussions. Discussion Our study represents the first investigation, to the best of our knowledge, into the impact of patient characteristics on the effectiveness of low-dose aspirin for preventing recurrent CVD events in secondary prevention. We conducted rigorous statistical testing on a large pool of variables and identified six relevant risk factors, which encompass potential mechanisms underlying suboptimal treatment effects of aspirin. The results of our model validation further demonstrated the strong performance of our model in stratifying aspirin treatment effects. By leveraging VISTA’s capabilities, clinicians gain early access to the potential benefits of aspirin therapy as a treatment option for each patient, allowing them to make informed decisions regarding its suitability. The tool enables clinicians to assess a patient’s risk of treatment failure and proactively adjust the medication regimen by considering alternative therapeutic approaches. For instance, if a patient exhibits a high risk of treatment failure, the clinician can opt to rely on other medications, such as setting lower targets for blood pressure and LDL cholesterol with a higher intensity of antihypertensive and lipid-lowering agents. By optimizing the combination of medications based on individual patient characteristics, this decision support tool has the potential to enhance treatment efficacy and improve outcomes in the secondary prevention of CVD. Optimizing the prescription of low-dose aspirin for CVD prevention requires a comprehensive evaluation of both the potential bleeding risk and the treatment effect. While a study has attempted on this objective, it only predicted the bleeding risk, without assessing the treatment benefit. 51 In scenarios where the predicted treatment benefit is unknown, clinicians may only rely on the bleeding risk as a reference. If the bleeding risk is not high, aspirin may be considered as a viable option, even though the benefit remains uncertain. Conversely, if the estimated benefit from aspirin is modest, regardless of how low the bleeding risk is, it is advisable to avoid prescribing aspirin and instead prioritize other medications to achieve the targeted treatment objective. Our study addresses this limitation by directly assessing the treatment effect of aspirin, offering a more comprehensive perspective. Moreover, the bleeding risk study identified 20 risk factors that were associated with the bleeding risk. However, only one of them overlapped with our identified risk factors (diabetes), while the other 19 of them were not associated with treatment failure. This disparity suggests that the treatment benefit and bleeding risk operate through distinct mechanisms and systems. It emphasizes the significance of our study in prompting clinicians to consider the benefit-risk scenario differently when prescribing aspirin. Aspirin treatment failure has long been recognized as important consideration in clinical practice. Existing methods for assessing aspirin’s antiplatelet effects rely on specific laboratory tests, such as PFA-100, WBA, and VerifyNow, which measure individual platelet function to predict the effectiveness of aspirin. Several case studies have shown that aspirin resistance, as indicated by these assays, is associated with a higher rate of CVD events. 16 , 52 However, these tests are not readily available in general hospital settings and have modest correlation and agreement. For instance, in a study of 201 patients with stable coronary artery disease receiving >80mg/day of aspirin, the prevalence of aspirin resistance varied greatly: 7% from VerifyNow, 18% from WBA, and 60% from PFA-100. 18 Also, they do not directly predict the effectiveness of aspirin in preventing CVD, limiting their practical usefulness for clinicians’ reference. In contrast, our study proposes a practical method that utilizes easily accessible variables to predict the treatment effect of aspirin on CVD prevention. According to the validation results, approximately 80% of patients are expected to experience a considerable treatment effect from aspirin, with hazard ratios ranging from 0.67 to 0.83. However, approximately 20% of patients may have treatment failure (HR close to 1.00) from aspirin if two or more risk factors are present. The findings from our research provide valuable guidance for clinicians in the decision-making process of prescribing aspirin. By offering a straightforward estimation of aspirin’s treatment effect, our method can be used as a tool to assess the potential benefits of aspirin as a treatment option for each patient. Our work introduced a novel lens to investigate treatment recommendation methods in the era of big data. Traditional approaches to evaluate treatment effects rely on randomized controlled trials (RCTs). RCTs face practical constraints, cost considerations, and data management complexities, which often lead to the collection of a limited number of patient characteristics. While RCTs are reliable in yielding population-level outcomes, stratifying individual benefits based on these limited covariates is challenging. In our research, we demonstrated the potential of high-quality retrospective cohort studies with a comprehensive set of covariates in treatment effect stratification. We utilized a robust feature selection process to identify key risk factors in aspirin efficacy variation and applied rigorous validation to estimate the variation of the treatment effects across various risk groups. In the era of big data and with advances in data curation, there is an opportunity to develop more robust treatment effect stratification that complements the population-level results obtained from RCTs. Our approach has proved to be robust in addressing this task and can be extended to analyze the efficacy of various therapies. By leveraging the vast amount of available data, we can gain valuable insights into individual treatment responses and enhance personalized medicine approaches. There are limitations in our study. First, both our derivation and validation cohorts were developed using data from Hong Kong. Although Hong Kong is a multi-ethnic city, its population is predominantly Chinese. Consequently, the generalizability of our model’s performance to other ethnicities and regions may be uncertain. Future research should aim to apply our model to validation cohorts comprising diverse ethnic populations to assess its performance across different racial groups. Second, our estimation of treatment effects across risk groups was based on a retrospective cohort study design. This introduces the possibility of selection bias in the selection of the treatment and control groups. To further validate our model, it would be beneficial to conduct more rigorous investigations, such as prospective clinical studies that incorporate the collection of the identified key risk factors. This independent validation of each risk factor and evaluation of the performance of risk group treatment stratification would provide stronger evidence for the effectiveness and reliability of our model. Conclusions The variability in aspirin treatment effects poses a significant challenge in optimizing therapeutic outcomes for patients with CVD. VISTA can be helpful in the secondary prevention of CVD which can identify patients who may be at risk of treatment failure thus help clinicians with treatment planning. We identified six key risk factors, history of atrial fibrillation, dyslipidemia, hyperglycemia, polypharmacy, neutrophilia, and elevated CK, that contribute to the variation in aspirin treatment effects. The big-data approach employed in our study also holds promise across various therapeutic areas beyond aspirin therapy. This research represents a significant step towards advancing personalized medicine in aspirin therapy, ultimately enhancing therapeutic effectiveness and improving CVD prevention on an individualized basis. Data Availability Data will not be available for others as the data custodians have not given permission. Sources of funding Nil. Disclosures EWYC reports honorarium from Hospital Authority; and grants from Research Grants Council (RGC, Hong Kong), Research Fund Secretariat of the Food and Health Bureau, National Natural Science Fund of China, Wellcome Trust, Bayer, Bristol-Myers Squibb, Pfizer, Janssen, Amgen, Takeda, and Narcotics Division of the Security Bureau of the Hong Kong Special Administrative Region, outside the submitted work. CSLC has received grants from the Food and Health Bureau of the Hong Kong Government, Hong Kong Research Grant Council, Hong Kong Innovation and Technology Commission, Pfizer, IQVIA, MSD, and Amgen; and personal fees from PrimeVigilance; outside the submitted work. RL has received grants from Hong Kong Research Grant Council, Hong Kong Innovation and Technology Commission; and research donations from Oxford Nanopore Technologies, outside the submitted work. All other authors declare no competing interests. Acknowledgements Nil. Footnotes ↵ * Co-first authors References 1. ↵ Roth GA , Mensah GA , Johnson CO , et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990-2019: Update From the GBD 2019 Study . J Am Coll Cardiol 2020 ; 76 ( 25 ): 2982 – 3021 . OpenUrl CrossRef PubMed 2. ↵ GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019 . Lancet 2020 ; 396 ( 10258 ): 1204 – 22 . OpenUrl CrossRef PubMed 3. ↵ Collaborative overview of randomised trials of antiplatelet therapy--I: Prevention of death, myocardial infarction, and stroke by prolonged antiplatelet therapy in various categories of patients. Antiplatelet Trialists’ Collaboration . BMJ 1994 ; 308 ( 6921 ): 81 – 106 . OpenUrl Abstract / FREE Full Text 4. ↵ Antithrombotic Trialists C . Collaborative meta-analysis of randomised trials of antiplatelet therapy for prevention of death, myocardial infarction, and stroke in high risk patients . BMJ 2002 ; 324 ( 7329 ): 71 – 86 . OpenUrl Abstract / FREE Full Text 5. ↵ Bhatt DL , Topol EJ . Scientific and therapeutic advances in antiplatelet therapy . Nat Rev Drug Discov 2003 ; 2 ( 1 ): 15 – 28 . OpenUrl CrossRef PubMed Web of Science 6. Neergaard-Petersen S , Ajjan R , Hvas AM , et al. Fibrin clot structure and platelet aggregation in patients with aspirin treatment failure . PLoS One 2013 ; 8 ( 8 ): e71150 . OpenUrl 7. Chu JW , Wong CK , Chambers J , Wout JV , Herbison P , Tang EW . Aspirin resistance determined from a bed-side test in patients suspected to have acute coronary syndrome portends a worse 6 months outcome . QJM 2010 ; 103 ( 6 ): 405 – 12 . OpenUrl CrossRef PubMed 8. Airee A , Draper HM , Finks SW . Aspirin resistance: disparities and clinical implications . Pharmacotherapy 2008 ; 28 ( 8 ): 999 – 1018 . OpenUrl PubMed 9. Patrono C , Garcia Rodriguez LA , Landolfi R , Baigent C . Low-dose aspirin for the prevention of atherothrombosis . N Engl J Med 2005 ; 353 ( 22 ): 2373 – 83 . OpenUrl CrossRef PubMed Web of Science 10. ↵ Grundmann K , Jaschonek K , Kleine B , Dichgans J , Topka H . Aspirin non-responder status in patients with recurrent cerebral ischemic attacks . J Neurol 2003 ; 250 ( 1 ): 63 – 6 . OpenUrl CrossRef PubMed Web of Science 11. ↵ Antithrombotic Trialists C , Baigent C , Blackwell L , et al. Aspirin in the primary and secondary prevention of vascular disease: collaborative meta-analysis of individual participant data from randomised trials . Lancet 2009 ; 373 ( 9678 ): 1849 – 60 . OpenUrl CrossRef PubMed Web of Science 12. ↵ Harrison P . Progress in the assessment of platelet function . Br J Haematol 2000 ; 111 ( 3 ): 733 – 44 . OpenUrl PubMed Web of Science 13. Kundu SK , Heilmann EJ , Sio R , Garcia C , Davidson RM , Ostgaard RA . Description of an in vitro platelet function analyzer--PFA-100 . Semin Thromb Hemost 1995 ; 21 Suppl 2 : 106 – 12 . OpenUrl CrossRef PubMed Web of Science 14. Rand ML , Leung R , Packham MA . Platelet function assays . Transfus Apher Sci 2003 ; 28 ( 3 ): 307 – 17 . OpenUrl 15. ↵ Patrono C . Aspirin resistance: definition, mechanisms and clinical read-outs . J Thromb Haemost 2003 ; 1 ( 8 ): 1710 – 3 . OpenUrl CrossRef PubMed Web of Science 16. ↵ Gum PA , Kottke-Marchant K , Welsh PA , White J , Topol EJ . A prospective, blinded determination of the natural history of aspirin resistance among stable patients with cardiovascular disease . J Am Coll Cardiol 2003 ; 41 ( 6 ): 961 – 5 . OpenUrl FREE Full Text 17. ↵ Chen WH , Cheng X , Lee PY , et al. Aspirin resistance and adverse clinical events in patients with coronary artery disease . Am J Med 2007 ; 120 ( 7 ): 631 – 5 . OpenUrl CrossRef PubMed Web of Science 18. ↵ Lordkipanidze M , Pharand C , Schampaert E , Turgeon J , Palisaitis DA , Diodati JG . A comparison of six major platelet function tests to determine the prevalence of aspirin resistance in patients with stable coronary artery disease . Eur Heart J 2007 ; 28 ( 14 ): 1702 – 8 . OpenUrl CrossRef PubMed Web of Science 19. Breet NJ , van Werkum JW , Bouman HJ , Kelder JC , Ten Berg JM , Hackeng CM . High on-aspirin platelet reactivity as measured with aggregation-based, cyclooxygenase-1 inhibition sensitive platelet function tests is associated with the occurrence of atherothrombotic events . J Thromb Haemost 2010 ; 8 ( 10 ): 2140 – 8 . OpenUrl CrossRef PubMed 20. Grove EL , Hvas AM , Johnsen HL , et al. A comparison of platelet function tests and thromboxane metabolites to evaluate aspirin response in healthy individuals and patients with coronary artery disease . Thromb Haemost 2010 ; 103 ( 6 ): 1245 – 53 . OpenUrl CrossRef PubMed Web of Science 21. Cattaneo M . Laboratory detection of ‘aspirin resistance’: what test should we use (if any)? Eur Heart J 2007 ; 28 ( 14 ): 1673 – 5 . OpenUrl CrossRef PubMed Web of Science 22. Lev EI . Aspirin resistance transient laboratory finding or important clinical entity? J Am Coll Cardiol 2009 ; 53 ( 8 ): 678 – 80 . OpenUrl FREE Full Text 23. Muir AR , McMullin MF , Patterson C , McKeown PP . Assessment of aspirin resistance varies on a temporal basis in patients with ischaemic heart disease . Heart 2009 ; 95 ( 15 ): 1225 – 9 . OpenUrl Abstract / FREE Full Text 24. Santilli F , Rocca B , De Cristofaro R , et al. Platelet cyclooxygenase inhibition by low-dose aspirin is not reflected consistently by platelet function assays: implications for aspirin “resistance” . J Am Coll Cardiol 2009 ; 53 ( 8 ): 667 – 77 . OpenUrl FREE Full Text 25. ↵ Shen H , Herzog W , Drolet M , et al. Aspirin Resistance in healthy drug-naive men versus women (from the Heredity and Phenotype Intervention Heart Study) . Am J Cardiol 2009 ; 104 ( 4 ): 606 – 12 . OpenUrl CrossRef PubMed 26. ↵ Michelson AD , Cattaneo M , Eikelboom JW , et al. Aspirin resistance: position paper of the Working Group on Aspirin Resistance . J Thromb Haemost 2005 ; 3 ( 6 ): 1309 – 11 . OpenUrl CrossRef PubMed Web of Science 27. ↵ Wang TH , Bhatt DL , Topol EJ . Aspirin and clopidogrel resistance: an emerging clinical entity . Eur Heart J 2006 ; 27 ( 6 ): 647 – 54 . OpenUrl CrossRef PubMed Web of Science 28. ↵ Census and Statistics Department . Hong Kong Special Administrative Region. Thematic Household Survey Report No. 68. 2018. https://www.censtatd.gov.hk/en/data/stat_report/product/B1130201/att/B11302682019XXXXB0100.pdf . 29. ↵ Wong AY , Root A , Douglas IJ , et al. Cardiovascular outcomes associated with use of clarithromycin: population based study . bmj 2016 ; 352 . 30. ↵ Shami JJP , Yan VKC , Wei Y , et al. Low-dose aspirin does not lower the risk of colorectal cancer in patients with type 2 diabetes taking metformin . J Intern Med 2023 ; 293 ( 3 ): 371 – 83 . OpenUrl 31. ↵ Zhou Y , Lin CJ , Yu Q , et al. Development and validation of risk prediction model for recurrent cardiovascular events among Chinese: the Personalized CARdiovascular DIsease risk Assessment for Chinese model . European Heart Journal - Digital Health 2024 . 32. ↵ Zhou Y , Lin CJX , Yu Q , et al. Primary Prevention Cardiovascular Disease Risk Prediction Model for Contemporary Chinese (1° P-CARDIAC): Model Derivation and Validation Using a Hybrid Statistical and Machine-Learning Approach. 33. ↵ van Buuren S , Groothuis-Oudshoorn K. mice: Multivariate Imputation by Chained Equations in R . Journal of Statistical Software 2011 ; 45 ( 3 ): 1 – 67 . OpenUrl 34. ↵ Azur MJ , Stuart EA , Frangakis C , Leaf PJ . Multiple imputation by chained equations: what is it and how does it work? Int J Methods Psychiatr Res 2011 ; 20 ( 1 ): 40 – 9 . OpenUrl CrossRef PubMed 35. ↵ Tibshirani R . Regression Shrinkage and Selection Via the Lasso . Journal of the Royal Statistical Society: Series B (Methodological) 1996 ; 58 ( 1 ): 267 – 88 . OpenUrl CrossRef Web of Science 36. ↵ Cox DR . Regression Models and Life-Tables . Journal of the Royal Statistical Society: Series B (Methodological) 1972 ; 34 ( 2 ): 187 – 202 . OpenUrl Web of Science 37. ↵ Deo SV , Deo V , Sundaram V . Survival analysis—part 2: Cox proportional hazards model . Indian journal of thoracic and cardiovascular surgery 2021 ; 37 ( 2 ): 229 – 33 . OpenUrl 38. ↵ Harrell FE . Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis : Springer ; 2001 . 39. ↵ Kaplan EL , Meier P . Nonparametric Estimation from Incomplete Observations . Journal of the American Statistical Association 1958 ; 53 : 457 – 81 . OpenUrl CrossRef Web of Science 40. ↵ Davidson-Pilon C . lifelines: survival analysis in Python . Journal of Open Source Software 2019 ; 4 ( 40 ): 1317 . OpenUrl 41. ↵ Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPODstatement. The UK EQUATOR Centre. 42. ↵ Warfarin versus aspirin for prevention of thromboembolism in atrial fibrillation: Stroke Prevention in Atrial Fibrillation II Study . Lancet 1994 ; 343 ( 8899 ): 687 – 91 . OpenUrl CrossRef PubMed Web of Science 43. ↵ Lip GY . The role of aspirin for stroke prevention in atrial fibrillation . Nat Rev Cardiol 2011 ; 8 ( 10 ): 602 – 6 . OpenUrl CrossRef PubMed 44. ↵ Sanderson S , Emery J , Baglin T , Kinmonth AL . Narrative review: aspirin resistance and its clinical implications . Ann Intern Med 2005 ; 142 ( 5 ): 370 – 80 . OpenUrl CrossRef PubMed Web of Science 45. ↵ Arruda-Olson AM , Reeder GS , Bell MR , Weston SA , Roger VL . Neutrophilia predicts death and heart failure after myocardial infarction: a community-based study . Circ Cardiovasc Qual Outcomes 2009 ; 2 ( 6 ): 656 – 62 . OpenUrl Abstract / FREE Full Text 46. ↵ Savonitto S , Granger CB , Ardissino D , et al. The prognostic value of creatine kinase elevations extends across the whole spectrum of acute coronary syndromes . J Am Coll Cardiol 2002 ; 39 ( 1 ): 22 – 9 . OpenUrl FREE Full Text 47. ↵ Chen W-H , Lee P-Y , Ng W , Tse H-F , Lau C-P . Aspirin resistance is associated with a high incidence of myonecrosis after non-urgent percutaneous coronary intervention despite clopidogrel pretreatment . Journal of the American College of Cardiology 2004 ; 43 ( 6 ): 1122 – 6 . OpenUrl FREE Full Text 48. ↵ Cambria-Kiely JA , Gandhi PJ . Possible mechanisms of aspirin resistance . J Thromb Thrombolysis 2002 ; 13 ( 1 ): 49 – 56 . OpenUrl CrossRef PubMed Web of Science 49. Davi G , Ciabattoni G , Consoli A , et al. In vivo formation of 8-iso-prostaglandin f2alpha and platelet activation in diabetes mellitus: effects of improved metabolic control and vitamin E supplementation . Circulation 1999 ; 99 ( 2 ): 224 – 9 . OpenUrl Abstract / FREE Full Text 50. ↵ Parker WAE , Sagar R , Kurdee Z , et al. A randomised controlled trial to assess the antithrombotic effects of aspirin in type 1 diabetes: role of dosing and glycaemic control . Cardiovasc Diabetol 2021 ; 20 ( 1 ): 238 . OpenUrl 51. ↵ Selak V , Jackson R , Poppe K , et al. Predicting Bleeding Risk to Guide Aspirin Use for the Primary Prevention of Cardiovascular Disease: A Cohort Study . Ann Intern Med 2019 ; 170 ( 6 ): 357 – 68 . OpenUrl PubMed 52. ↵ Eikelboom JW , Hirsh J , Weitz JI , Johnston M , Yi Q , Yusuf S . Aspirin-resistant thromboxane biosynthesis and the risk of myocardial infarction, stroke, or cardiovascular death in patients at high risk for cardiovascular events . Circulation 2002 ; 105 ( 14 ): 1650 – 5 . OpenUrl Abstract / FREE Full Text View the discussion thread. Back to top Previous Next Posted June 09, 2024. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. 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Share Development and validation of a tool to stratify the treatment effect of low-dose aspirin in patients with cardiovascular disease: VISTA (Vascular Intervention Stratification Tool for Aspirin) Yekai Zhou , Joseph Edgar Blais , Esther Wai Yin Chan , Tak-Wah Lam , Kai Hang Yiu , Hung Fat Tse , Celine SL Chui , Ruibang Luo medRxiv 2024.06.07.24308636; doi: https://doi.org/10.1101/2024.06.07.24308636 Share This Article: Copy Citation Tools Development and validation of a tool to stratify the treatment effect of low-dose aspirin in patients with cardiovascular disease: VISTA (Vascular Intervention Stratification Tool for Aspirin) Yekai Zhou , Joseph Edgar Blais , Esther Wai Yin Chan , Tak-Wah Lam , Kai Hang Yiu , Hung Fat Tse , Celine SL Chui , Ruibang Luo medRxiv 2024.06.07.24308636; doi: https://doi.org/10.1101/2024.06.07.24308636 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Cardiovascular Medicine Subject Areas All Articles Addiction Medicine (572) Allergy and Immunology (864) Anesthesia (302) Cardiovascular Medicine (4448) Dentistry and Oral Medicine (444) Dermatology (383) Emergency Medicine (609) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1515) Epidemiology (15240) Forensic Medicine (30) Gastroenterology (1130) Genetic and Genomic Medicine (6614) Geriatric Medicine (669) Health Economics (1000) Health Informatics (4550) Health Policy (1372) Health Systems and Quality Improvement (1613) Hematology (543) HIV/AIDS (1268) Infectious Diseases (except HIV/AIDS) (15928) Intensive Care and Critical Care Medicine (1106) Medical Education (624) Medical Ethics (147) Nephrology (668) Neurology (6622) Nursing (346) Nutrition (999) Obstetrics and Gynecology (1147) Occupational and Environmental Health (957) Oncology (3343) Ophthalmology (977) Orthopedics (369) Otolaryngology (421) Pain Medicine (436) Palliative Medicine (130) Pathology (665) Pediatrics (1695) Pharmacology and Therapeutics (693) Primary Care Research (714) Psychiatry and Clinical Psychology (5459) Public and Global Health (9249) Radiology and Imaging (2205) Rehabilitation Medicine and Physical Therapy (1371) Respiratory Medicine (1197) Rheumatology (597) Sexual and Reproductive Health (715) Sports Medicine (530) Surgery (714) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a028db6bbf421b23',t:'MTc3OTkyNTE0Nw=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();