A Clinical Score for Predicting Atrial Fibrillation in Patients with Cryptogenic Stroke or Transient Ischemic Attack

ObjectivesDetection of atrial fibrillation (AF) in post-cryptogenic stroke (CS) or transient ischemic attack (TIA) patients carries important therapeutic implications.MethodsTo risk stratify CS/TIA patients for later development of AF, we conducted a retrospective cohort study using data from 1995 to 2015 in the Stanford Translational Research Integrated Database Environment (STRIDE).ResultsOf the 9,589 adult patients (age ≥40 years) with CS/TIA included, 482 (5%) patients developed AF post CS/TIA. Of those patients, 28.4, 26.3, and 45.3% were diagnosed with AF 1-12 months, 1-3 years, and >3 years after the index CS/TIA, respectively. Age (≥75 years), obesity, congestive heart failure, hypertension, coronary artery disease, peripheral vascular disease, and valve disease are significant risk factors, with the following respective odds ratios (95% CI): 1.73 (1.39-2.16), 1.53 (1.05-2.18), 3.34 (2.61-4.28), 2.01 (1.53-2.68), 1.72 (1.35-2.19), 1.37 (1.02-1.84), and 2.05 (1.55-2.69). A risk-scoring system, i.e., the HAVOC score, was constructed using these 7 clinical variables that successfully stratify patients into 3 risk groups, with good model discrimination (area under the curve = 0.77).ConclusionsFindings from this study support the strategy of looking longer and harder for AF in post-CS/TIA patients. The HAVOC score identifies different levels of AF risk and may be used to select patients for extended rhythm monitoring

Baseline characteristics before and after inverse probability of selection weighting (IOPW).

Baseline characteristics before and after inverse probability of selection weighting (IOPW).</p

Fig 2 -

Kaplan Meier Curves in a) IPTW adjusted (for confounding only) cohort of second-line patients b) IPTW and IOPW adjusted (for both confounding and non-representativeness) cohort of first-line patients. Time is shown in days. Note that sample size was impacted by weighting in our analyses.</p

Comparison of variables involved in Step 1 and Step 2 analyses.

Comparison of variables involved in Step 1 and Step 2 analyses.</p

Directed acyclic graph to illustrate our two-step approach to adjust for confounding with inverse probability of weighting (IPTW) and non-representativeness with inverse probability of selection weighting (IOPW).

A = treatment assignment; Y = outcome; S = selection into original population from target population; C = confounders; E = treatment effect modifiers.</p

Baseline characteristics before and after inverse probability of treatment weighting (IPTW) among the second-line population.

Baseline characteristics before and after inverse probability of treatment weighting (IPTW) among the second-line population.</p