Validation of an algorithm based on administrative data to detect new onset of atrial fibrillation after cardiac surgery

Abstract

Introduction: Postoperative atrial fibrillation (POAF) is a frequent complication of cardiac surgery associated with important morbidity, mortality, and costs. To assess the effectiveness of preventive interventions, an important prerequisite is to have access to accurate measures of POAF incidence. The aim of this study was to develop and validate such a measure. Methods: A validation study was conducted at two large Canadian university health centers. First, a random sample of 976 (10.4%) patients who had cardiac surgery at these sites between 2010 and 2016 was generated. Then, a reference standard assessment of their medical records was performed to determine their true POAF status on discharge (positive/negative). The accuracy of various algorithms combining diagnostic and procedure codes from: 1) the current hospitalization, and 2) hospitalizations up to 6 years before the current hospitalization was assessed in comparison with the reference standard. Overall and site-specific estimates of sensitivity, specificity, positive (PPV), and negative (NPV) predictive values were generated, along with their 95%CIs. Results: Upon manual review, 324 (33.2%) patients were POAF-positive. Our best-performing algorithm combining data from both sites used a look-back window of 6 years to exclude patients previously known for AF. This algorithm achieved 70.4% sensitivity (95%CI: 65.1-75.3), 86.0% specificity (95%CI: 83.1-88.6), 71.5% PPV (95%CI: 66.2-76.4), and 85.4% NPV (95%CI: 82.5-88.0). However, significant site-specific differences in sensitivity and NPV were observed. Conclusion: An algorithm based on administrative data can identify POAF patients with moderate accuracy. However, site-specific variations in coding practices have significant impact on accuracy.

Nurse Staffing Practices and Postoperative Atrial Fibrillation Among Cardiac Surgery Patients: A Multisite Cohort Study

“…28 This algorithm achieved 70.4% sensitivity (95% CI: 65.1% -75.3%), 86.0% specificity (95% CI: 83.1% -88.6%), and 71.5% PPV (95% CI: 66.2% -76.4%) in identifying incident POAF cases. 28…”

“…The occurrence of POAF, defined as new-onset atrial fibrillation (AF) in the immediate postoperative period in a patient without a prior diagnosis of AF, 1,4 was assessed from discharge diagnostic codes using a method previously validated at the study sites by our research group. 28 Briefly, to be considered POAF-positive, patients needed to have: 1) International Classification of Diseases, 10 th revision (ICD-10), diagnostic codes I48.0 (paroxysmal atrial fibrillation), I48.1 (persistent atrial fibrillation), I48.9 (unspecified atrial fibrillation and atrial flutter) or I48.90 (unspecified atrial fibrillation) in the discharge abstract of their current hospitalization and 2) no evidence of ICD-10 codes I48.0, I48.1, I48.2 (chronic atrial fibrillation), I48.9 or I48.90 in the discharge abstracts of their previous hospitalizations over the past 6 years. 28 This algorithm achieved 70.4% sensitivity (95% CI: 65.1% -75.3%), 86.0% specificity (95% CI: 83.1% -88.6%), and 71.5% PPV (95% CI: 66.2% -76.4%) in identifying incident POAF cases.…”

“…Second, although the accuracy of our risk adjustment model compares to that of other commonly used risk scores, the reported c-statistics are somewhat low, and residual confounding could be at play. Third, while we used a validated measure of POAF occurrence, 28 its accuracy was not perfect, and measurement errors may have influenced the estimated effects of the nurse staffing exposures in either direction. Fourth, because our outcome measure is based on discharge diagnostic codes, the timing of POAF occurrence could not be determined.…”

The level of adherence to best-practice guidelines by interprofessional teams with and without acute care nurse practitioners in cardiac surgery: A study protocol

“…Less attention has been paid to ACNP practice in acute care settings; however, within these settings, ACNPs work in collaboration with cardiac surgeons, nursing teams, rehabilitation teams, social workers, and other members of interprofessional teams to ensure the patient’s optimal recovery, and reduce adverse events and postoperative complications. For patients and families, hospitalization in acute care represents a crucial phase of postoperative recovery associated with several physical, psychosocial, and emotional stressors [ 30 ]. Additional studies are needed to substantiate the contribution of ACNPs in interprofessional teams within acute care settings, and identify the benefits of their practice for patients, families, interprofessional teams, and healthcare organizations.…”

Accuracy of Computable Phenotyping Approaches for SARS-CoV-2 Infection and COVID-19 Hospitalizations from the Electronic Health Record

“…The adoption of health information technology systems has positioned health systems to allow such validation by incorporating more detailed clinical data from the electronic health record (EHR) with diagnosis codes, which allows for the development of more robust computable phenotypes. [13][14][15][16][17][18] While there remain concerns about the use of EHR data given potential inaccuracies in data and risk for missing data, these issues are also reflected in data derived from them, such as administrative databases. As such, the EHR represents a potential advance over the use of administrative data alone for both case identification and phenotype validation.…”