7
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: not found
      • Article: not found

      Safely Identifying Emergency Department Patients With Acute Chest Pain for Early Discharge : HEART Pathway Accelerated Diagnostic Protocol

      Read this article at

      ScienceOpenPublisher
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          <div class="section"> <a class="named-anchor" id="S1"> <!-- named anchor --> </a> <h5 class="section-title" id="d599239e242">Background:</h5> <p id="P1">The HEART Pathway is an accelerated diagnostic protocol (ADP) designed to identify low-risk Emergency Department (ED) patients with chest pain for early discharge without stress testing or angiography. The objective of this study was to determine whether implementation of the HEART Pathway is safe (30 day death and myocardial infarction rate &lt;1% in low-risk patients) and effective (reduces 30 day hospitalizations) in ED patients with possible acute coronary syndrome (ACS). </p> </div><div class="section"> <a class="named-anchor" id="S2"> <!-- named anchor --> </a> <h5 class="section-title" id="d599239e247">Methods:</h5> <p id="P2">A prospective pre/post study was conducted at three US sites among 8,474 adult ED patients with possible ACS. Patients included were ≥21 years old, investigated for possible ACS, and had no evidence of ST-segment elevation myocardial infarction on electrocardiography. Accrual occurred for 12 months before and after HEART Pathway implementation from November 2013- January 2016. The HEART Pathway ADP was integrated into each site’s electronic health record as an interactive clinical decision support tool. Following ADP integration, ED providers prospectively utilized the HEART Pathway to identify patients with possible ACS as low-risk (appropriate for early discharge without stress testing or angiography) or non-low-risk (appropriate for further in-hospital evaluation). The primary safety and effectiveness outcomes, death and myocardial infarction (MI) and hospitalization rates at 30 days, were determined from health records, insurance claims, and death index data. </p> </div><div class="section"> <a class="named-anchor" id="S3"> <!-- named anchor --> </a> <h5 class="section-title" id="d599239e252">Results:</h5> <p id="P3">Pre- and post-implementation cohorts included 3713 and 4761 patients, respectively. The HEART Pathway identified 30.7% as low-risk; 0.4% of these patients experienced death or MI within 30 days. Hospitalization at 30 days was reduced by 6% in the post- vs pre-implementation cohort (55.6% vs 61.6%; aOR: 0.79, 95%CI: 0.71–0.87). During the index visit more MIs were detected in the post-implementation cohort (6.6% vs 5.7%; aOR: 1.36, 95%CI: 1.12–1.65). Rates of death or MI during follow-up were similar (1.1% vs 1.3%; aOR: 0.88, 95% CI: 0.58–1.33). </p> </div><div class="section"> <a class="named-anchor" id="S4"> <!-- named anchor --> </a> <h5 class="section-title" id="d599239e257">Conclusions:</h5> <p id="P4">HEART Pathway implementation was associated with decreased hospitalizations, increased identification of index visit MIs, and a very low death and MI rate among low-risk patients. These findings support use of the HEART Pathway to identify low-risk patients that can be safely discharged without stress testing or angiography. </p> </div><div class="section"> <a class="named-anchor" id="S5"> <!-- named anchor --> </a> <h5 class="section-title" id="d599239e262">Clinical Trial Registration:</h5> <p id="P5"> <a data-untrusted="" href="http://clinicaltrials.gov" id="d599239e266" target="xrefwindow">clinicaltrials.gov</a> Identifier: NCT02056964 </p> </div>

          Related collections

          Most cited references18

          • Record: found
          • Abstract: found
          • Article: not found

          Using the outcome for imputation of missing predictor values was preferred.

          Epidemiologic studies commonly estimate associations between predictors (risk factors) and outcome. Most software automatically exclude subjects with missing values. This commonly causes bias because missing values seldom occur completely at random (MCAR) but rather selectively based on other (observed) variables, missing at random (MAR). Multiple imputation (MI) of missing predictor values using all observed information including outcome is advocated to deal with selective missing values. This seems a self-fulfilling prophecy. We tested this hypothesis using data from a study on diagnosis of pulmonary embolism. We selected five predictors of pulmonary embolism without missing values. Their regression coefficients and standard errors (SEs) estimated from the original sample were considered as "true" values. We assigned missing values to these predictors--both MCAR and MAR--and repeated this 1,000 times using simulations. Per simulation we multiple imputed the missing values without and with the outcome, and compared the regression coefficients and SEs to the truth. Regression coefficients based on MI including outcome were close to the truth. MI without outcome yielded very biased--underestimated--coefficients. SEs and coverage of the 90% confidence intervals were not different between MI with and without outcome. Results were the same for MCAR and MAR. For all types of missing values, imputation of missing predictor values using the outcome is preferred over imputation without outcome and is no self-fulfilling prophecy.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Methods for identifying 30 chronic conditions: application to administrative data

            Background Multimorbidity is common and associated with poor clinical outcomes and high health care costs. Administrative data are a promising tool for studying the epidemiology of multimorbidity. Our goal was to derive and apply a new scheme for using administrative data to identify the presence of chronic conditions and multimorbidity. Methods We identified validated algorithms that use ICD-9 CM/ICD-10 data to ascertain the presence or absence of 40 morbidities. Algorithms with both positive predictive value and sensitivity ≥70% were graded as “high validity”; those with positive predictive value ≥70% and sensitivity <70% were graded as “moderate validity”. To show proof of concept, we applied identified algorithms with high to moderate validity to inpatient and outpatient claims and utilization data from 574,409 people residing in Edmonton, Canada during the 2008/2009 fiscal year. Results Of the 40 morbidities, we identified 30 that could be identified with high to moderate validity. Approximately one quarter of participants had identified multimorbidity (2 or more conditions), one quarter had a single identified morbidity and the remaining participants were not identified as having any of the 30 morbidities. Conclusions We identified a panel of 30 chronic conditions that can be identified from administrative data using validated algorithms, facilitating the study and surveillance of multimorbidity. We encourage other groups to use this scheme, to facilitate comparisons between settings and jurisdictions. Electronic supplementary material The online version of this article (doi:10.1186/s12911-015-0155-5) contains supplementary material, which is available to authorized users.
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              What is an acceptable risk of major adverse cardiac event in chest pain patients soon after discharge from the Emergency Department?: a clinical survey.

                Bookmark

                Author and article information

                Journal
                Circulation
                Circulation
                Ovid Technologies (Wolters Kluwer Health)
                0009-7322
                1524-4539
                November 27 2018
                November 27 2018
                : 138
                : 22
                : 2456-2468
                Affiliations
                [1 ]Department of Emergency Medicine (S.A.M., B.C.H., C.D.M.), Wake Forest School of Medicine, Winston-Salem, NC.
                [2 ]Department of Implementation Science (S.A.M.), Wake Forest School of Medicine, Winston-Salem, NC.
                [3 ]Department of Epidemiology and Prevention (S.A.M.), Wake Forest School of Medicine, Winston-Salem, NC.
                [4 ]Department of Biostatistical Sciences (K.M.L., B.J.W., L.D.C.), Wake Forest School of Medicine, Winston-Salem, NC.
                [5 ]Public Health Sciences (G.L.B.), Wake Forest School of Medicine, Winston-Salem, NC.
                [6 ]Departments of Neurology, Sticht Center on Aging, Gerontology, and Geriatric Medicine (P.W.D.), Wake Forest School of Medicine, Winston-Salem, NC.
                [7 ]Department of Internal Medicine, Division of Cardiovascular Medicine (D.M.H.), Wake Forest School of Medicine, Winston-Salem, NC.
                [8 ]Department of Physiology and Pharmacology (J.-F.D.-G.), Wake Forest School of Medicine, Winston-Salem, NC.
                [9 ]Clinical and Translational Science Institute (J.-F.D.-G., W.M.F.), Wake Forest School of Medicine, Winston-Salem, NC.
                Article
                10.1161/CIRCULATIONAHA.118.036528
                6c4f0f01-1f64-486b-a76c-aa2afc062729
                © 2018
                History

                Comments

                Comment on this article