Christopher A. Aakre

Stroke or transient ischemic attack in patients with transvenous pacemaker or defibrillator and echocardiographically detected patent foramen ovale

Background— A patent foramen ovale (PFO) may permit arterial embolization of thrombi that accumulate on the leads of cardiac implantable electronic devices in the right-sided cardiac chambers. We sought to determine whether a PFO increases the risk of stroke/transient ischemic attack (TIA) in patients with endocardial leads. Methods and Results— We retrospectively evaluated all patients who had endocardial leads implanted between January 1, 2000, and October 25, 2010, at Mayo Clinic Rochester. Echocardiography was used to establish definite PFO and non-PFO cohorts. The primary end point of stroke/TIA consistent with a cardioembolic etiology and the secondary end point of mortality during postimplantation follow-up were compared in PFO versus non-PFO patients with the use of Cox proportional hazards models. We analyzed 6075 patients (364 with PFO) followed for a mean 4.7±3.1 years. The primary end point of stroke/TIA was met in 30/364 (8.2%) PFO versus 117/5711 (2.0%) non-PFO patients (hazard ratio, 3.49; 95% confidence interval, 2.33–5.25; P<0.0001). The association of PFO with stroke/TIA remained significant after multivariable adjustment for age, sex, history of stroke/TIA, atrial fibrillation, and baseline aspirin/warfarin use (hazard ratio, 3.30; 95% confidence interval, 2.19–4.96; P<0.0001). There was no significant difference in all-cause mortality between PFO and non-PFO patients (hazard ratio, 0.91; 95% confidence interval, 0.77–1.07; P=0.25). Conclusions— In patients with endocardial leads, the presence of a PFO on routine echocardiography is associated with a substantially increased risk of embolic stroke/TIA. This finding suggests a role of screening for PFOs in patients who require cardiac implantable electronic devices; if a PFO is detected, PFO closure, anticoagulation, or nonvascular lead placement may be considered.

Comparison of Clinical Risk Stratification for Predicting Stroke and Thromboembolism in Atrial Fibrillation

Background and Purpose— Several accepted algorithms exist to characterize the risk of thromboembolism in atrial fibrillation. We performed a comparative analysis to assess the predictive value of 9 such schemes. Methods— In a longitudinal community-based cohort study from Olmsted County, Minnesota, 2720 residents with atrial fibrillation were followed up for 4.4±3.6 years±SD from 1990 to 2004. Risk factors were identified using a diagnostic index integrated with the electronic medical record. Thromboembolism and cardiovascular event data were collected and analyzed. Results— We identified 350 validated thromboembolic events in our cohort. Multivariable analysis identified age >75 years (odds ratio, 2.08; P<0.0001), female sex (odds ratio, 1.45; P=0.0015), history of hypertension (odds ratio, 3.07; P<0.0001), diabetes mellitus (odds ratio, 1.58; P=0.0003), and history of heart failure (odds ratio, 1.50; P=0.0102) as significant predictors of clinical thromboembolism. The Stroke Prevention in Atrial Fibrillation (SPAF; hazard ratio, 2.75; c=0.659), CHADS2-revised (hazard ratio, 3.48; c=0.654), and CHADS2-classical (hazard ratio, 2.90; c=0.653) risk schemes were most accurate in risk stratification. The low-risk cohort within the CHA2DS2-VASc scheme had the lowest event rate among all low-risk cohorts (0.11 per 100 person-years). Conclusions— A direct comparison of 9 risk schemes reveals no profound differences in risk stratification accuracy for high-risk patients. Accurate prediction of low-risk patients is perhaps more valuable in determining those unlikely to benefit from oral anticoagulation therapy. Among our cohort, CHA2DS2-VASc performed best in this purpose.

Pulmonary embolism in patients with transvenous cardiac implantable electronic device leads

BACKGROUND Cardiac implantable electronic devices (CIEDs) are commonly associated with transvenous lead-related thrombi that can cause pulmonary embolism (PE). METHODS AND RESULTS We retrospectively evaluated all patients with transvenous CIED leads implanted at Mayo Clinic Rochester between 1 January 2000, and 25 October 2010. Pulmonary embolism outcomes during follow-up were screened using diagnosis codes and confirmed with imaging study reports. Of 5646 CIED patients (age 67.3 ± 16.3 years, 64% men, mean follow-up 4.69 years) 88 developed PE (1.6%), incidence 3.32 [95% confidence interval (CI) 2.68-4.07] per 1000 person-years [men: 3.04 (95% CI 2.29-3.96) per 1000 person-years; women: 3.81 (95% CI 2.72-5.20) per 1000 person-years]. Other than transvenous CIED lead(s), 84% had another established risk factor for PE such as deep vein thrombosis (28%), recent surgery (27%), malignancy (25%), or prior history of venous thromboembolism (15%). At the time of PE, 22% had been hospitalized for ≥ 48 h, and 59% had been hospitalized in the preceding 30 days. Pulmonary embolism occurred in 22% despite being on systemic anticoagulation therapy. Out of 88 patients with PE, 45 subsequently died, mortality rate 93 (95% CI 67-123) per 1000 person-years (hazard ratio 2.0, 95% CI 1.5-2.7, P < 0.0001). CONCLUSIONS Though lead-related thrombus is commonly seen in patients with transvenous CIED leads, clinical PE occurs with a low incidence. It is possible that embolism of lead thrombus is uncommon or emboli are too small to cause consequential pulmonary infarction.

Prospective validation of a near real-time EHR-integrated automated SOFA score calculator

OBJECTIVES We created an algorithm for automated Sequential Organ Failure Assessment (SOFA) score calculation within the Electronic Health Record (EHR) to facilitate detection of sepsis based on the Third International Consensus Definitions for Sepsis and Septic Shock (SEPSIS-3) clinical definition. We evaluated the accuracy of near real-time and daily automated SOFA score calculation compared with manual score calculation. METHODS Automated SOFA scoring computer programs were developed using available EHR data sources and integrated into a critical care focused patient care dashboard at Mayo Clinic in Rochester, Minnesota. We prospectively compared the accuracy of automated versus manual calculation for a sample of patients admitted to the medical intensive care unit at Mayo Clinic Hospitals in Rochester, Minnesota and Jacksonville, Florida. Agreement was calculated with Cohens kappa statistic. Reason for discrepancy was tabulated during manual review. RESULTS Random spot check comparisons were performed 134 times on 27 unique patients, and daily SOFA score comparisons were performed for 215 patients over a total of 1206 patient days. Agreement between automatically scored and manually scored SOFA components for both random spot checks (696 pairs, κ=0.89) and daily calculation (5972 pairs, κ=0.89) was high. The most common discrepancies were in the respiratory component (inaccurate fraction of inspired oxygen retrieval; 200/1206) and creatinine (normal creatinine in patients with no urine output on dialysis; 128/1094). 147 patients were at risk of developing sepsis after intensive care unit admission, 10 later developed sepsis confirmed by chart review. All were identified before onset of sepsis with the ΔSOFA≥2 point criterion and 46 patients were false-positives. CONCLUSIONS Near real-time automated SOFA scoring was found to have strong agreement with manual score calculation and may be useful for the detection of sepsis utilizing the new SEPSIS-3 definition.

Comparison of methods of alert acknowledgement by critical care clinicians in the ICU setting

Background Electronic Health Record (EHR)-based sepsis alert systems have failed to demonstrate improvements in clinically meaningful endpoints. However, the effect of implementation barriers on the success of new sepsis alert systems is rarely explored. Objective To test the hypothesis time to severe sepsis alert acknowledgement by critical care clinicians in the ICU setting would be reduced using an EHR-based alert acknowledgement system compared to a text paging-based system. Study Design In one arm of this simulation study, real alerts for patients in the medical ICU were delivered to critical care clinicians through the EHR. In the other arm, simulated alerts were delivered through text paging. The primary outcome was time to alert acknowledgement. The secondary outcomes were a structured, mixed quantitative/qualitative survey and informal group interview. Results The alert acknowledgement rate from the severe sepsis alert system was 3% (N = 148) and 51% (N = 156) from simulated severe sepsis alerts through traditional text paging. Time to alert acknowledgement from the severe sepsis alert system was median 274 min (N = 5) and median 2 min (N = 80) from text paging. The response rate from the EHR-based alert system was insufficient to compare primary measures. However, secondary measures revealed important barriers. Conclusion Alert fatigue, interruption, human error, and information overload are barriers to alert and simulation studies in the ICU setting.

Automating clinical score calculation within the electronic health record: A Feasibility Assessment

OBJECTIVES Evidence-based clinical scores are used frequently in clinical practice, but data collection and data entry can be time consuming and hinder their use. We investigated the programmability of 168 common clinical calculators for automation within electronic health records. METHODS We manually reviewed and categorized variables from 168 clinical calculators as being extractable from structured data, unstructured data, or both. Advanced data retrieval methods from unstructured data sources were tabulated for diagnoses, non-laboratory test results, clinical history, and examination findings. RESULTS We identified 534 unique variables, of which 203/534 (37.8%) were extractable from structured data and 269/534 (50.4.7%) were potentially extractable using advanced techniques. Nearly half (265/534, 49.6%) of all variables were not retrievable. Only 26/168 (15.5%) of scores were completely programmable using only structured data and 43/168 (25.6%) could potentially be programmable using widely available advanced information retrieval techniques. Scores relying on clinical examination findings or clinical judgments were most often not completely programmable. CONCLUSION Complete automation is not possible for most clinical scores because of the high prevalence of clinical examination findings or clinical judgments - partial automation is the most that can be achieved. The effect of fully or partially automated score calculation on clinical efficiency and clinical guideline adherence requires further study.

Response to Letters Regarding Article, “Stroke or Transient Ischemic Attack in Patients With Transvenous Pacemaker or Defibrillator and Echocardiographically Detected Patent Foramen Ovale”

We acknowledge the letter from Drs Rao and Chan for highlighting the limitations pertinent to clinical applicability of our findings, and we acknowledge Drs Proietti, Thanassoulis, and Essebag for sharing their case of a patient who suffered a stroke after cardiovascular implantable electronic device implantation.1 The former letter expresses concern regarding the potential for misclassification bias attributable to differential diligence in diagnosing patients with patent foramen ovale (PFO) already at high risk for cardioembolism. The latter suggests that characteristics of the PFO themselves, such as the size and degree of shunting, may affect the risk attributable to PFOs. We reviewed all transthoracic and transesophageal echocardiogram (TEE) reports available for patients who had cardiovascular implantable electronic devices implanted at Mayo Clinic Rochester between January 1, 2000, and October 25, 2010, for mention of PFO and whether the PFO was definite or could not be conclusively confirmed or refuted. Only patients with conclusive PFOs whether with TEE, color Doppler, or agitated saline contrast were considered eligible for the PFO group, and the cases with inconclusive reports were excluded from analysis. Because many patients had multiple echocardiograms with >1 clinical indication listed in the reports and with >6000 patients (with some patients with >40 echocardiographic reports available for review), we were unable to systematically analyze …

Towards automated calculation of evidence-based clinical scores

AIM To determine clinical scores important for automated calculation in the inpatient setting. METHODS A modified Delphi methodology was used to create consensus of important clinical scores for inpatient practice. A list of 176 externally validated clinical scores were identified from freely available internet-based services frequently used by clinicians. Scores were categorized based on pertinent specialty and a customized survey was created for each clinician specialty group. Clinicians were asked to rank each score based on importance of automated calculation to their clinical practice in three categories - “not important”, “nice to have”, or “very important”. Surveys were solicited via specialty-group listserv over a 3-mo interval. Respondents must have been practicing physicians with more than 20% clinical time spent in the inpatient setting. Within each specialty, consensus was established for any clinical score with greater than 70% of responses in a single category and a minimum of 10 responses. Logistic regression was performed to determine predictors of automation importance. RESULTS Seventy-nine divided by one hundred and forty-four (54.9%) surveys were completed and 72/144 (50%) surveys were completed by eligible respondents. Only the critical care and internal medicine specialties surpassed the 10-respondent threshold (14 respondents each). For internists, 2/110 (1.8%) of scores were “very important” and 73/110 (66.4%) were “nice to have”. For intensivists, no scores were “very important” and 26/76 (34.2%) were “nice to have”. Only the number of medical history (OR = 2.34; 95%CI: 1.26-4.67; P < 0.05) and vital sign (OR = 1.88; 95%CI: 1.03-3.68; P < 0.05) variables for clinical scores used by internists was predictive of desire for automation. CONCLUSION Few clinical scores were deemed “very important” for automated calculation. Future efforts towards score calculator automation should focus on technically feasible “nice to have” scores.