A Prediction Rule to Identify Low-Risk Patients with Heart Failure
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514 Auble et al. d HEART FAILURE PREDICTION RULE A Prediction Rule to Identify Low-risk Patients with Heart Failure Thomas E. Auble, PhD, Margaret Hsieh, MD, William Gardner, PhD, Gregory F. Cooper, MD, Roslyn A. Stone, PhD, Julie B. McCausland, MD, Donald M. Yealy, MD Abstract Objectives: To derive a prediction rule using data available was chosen. Results: Within the entire cohort, 4.5% of in the emergency department (ED) to identify a group of patients died and 6.8% survived to hospital discharge after patients hospitalized for the treatment of heart failure who experiencing a serious medical complication. The prediction are at low risk of death and serious complications. Methods: rule used 21 prognostic factors to classify 17.2% of patients The authors analyzed data for all 33,533 patients with a as low risk; 19 (0.3%) died and 59 (1.0%) survived to hospital primary hospital discharge diagnosis of heart failure in 1999 discharge after experiencing a serious medical complication. who were admitted from EDs in Pennsylvania. Candidate Conclusions: This clinical prediction rule identified a group predictors were demographic and medical history variables of patients hospitalized from the ED for the treatment of and the most abnormal examination or diagnostic test values heart failure who were at low risk of adverse inpatient measured in the ED (vital signs only) or on the first day of outcomes. Model performance needs to be examined in a hospitalization. The authors constructed classification trees cohort of patients with an ED diagnosis of heart failure and to identify a subgroup of patients with an observed rate of treated as outpatients or hospitalized. Key words: heart death or serious medical complications before discharge failure; decision support techniques; emergency service; ,2%; the tree that identified the subgroup with the lowest hospital. ACADEMIC EMERGENCY MEDICINE 2005; rate of this outcome and an inpatient mortality rate ,1% 12:514–521. Heart failure affects five million people in the United gests that emergency physicians greatly overestimate States,1 leading to one million hospital admissions the probability of short-term death or severe compli- each year with a primary discharge diagnosis of heart cations for patients with heart failure.8 Moreover, failure and another two million with this as a second- higher estimates of risk were associated with patient ary discharge diagnosis.2 The annual health care treatment in more intense care settings.8 An evidence- expenditure for heart failure is estimated to be $28 based clinical prediction rule to assess severity of billion, most of which is attributable to hospital care.1 illness in patients presenting with heart failure might These costs will likely increase over the next several improve physician risk assessment and the appropri- decades as the elder population increases3 and hos- ateness of initial-site-of-treatment decisions. pitalization rates for heart failure in the population The emergency department (ED) is an ideal site for aged 65 years and older continue to climb.1 the use of a heart failure prediction rule because the Hospital admission rates for patients with heart majority of hospitalized patients with a primary failure vary widely across geographic regions and discharge diagnosis of heart failure are admitted within small areas.4–7 This variation is not explained from this source.9,10 However, existing heart failure fully by differences in disease severity,6,7 suggesting medical practice guidelines have limited use in this that clinicians make hospital admission decisions in setting because they are based on narrowly defined an inconsistent manner. In addition, evidence sug- patient subgroups rather than the broad spectrum of heart failure patients treated in the ED,11–13 they rely on clinical data unavailable in this setting,14 or they From the Department of Emergency Medicine (TEA, MH, JBM, do not report nonfatal serious medical complications DMY), Department of Medicine (WG, GFC, DMY), and Department 6,9,11–13,15–18 of Biostatistics, Graduate School of Public Health (RAS), University that would require hospitalization. Our of Pittsburgh, Pittsburgh, PA. aim in this study was to derive a clinical prediction Received September 17, 2004; revision received November 16, 2004; rule based on data readily available in the ED to accepted November 21, 2004. identify patients with heart failure who are at low risk Presented at the SAEM annual meeting, Boston, MA, May 2003. of inpatient death or serious medical complications. Address for correspondence and reprints: Donald M. Yealy, MD, 230 McKee Place, Suite 500, Pittsburgh, PA 15213. Fax: 412-647-4670; A secondary aim was to examine the rates of death e-mail: [email protected]. and readmission within 30 days of the index hospital- doi:10.1197/j.aem.2004.11.026 ization for patients identified by the rule as low risk. ACAD EMERG MED d June 2005, Vol. 12, No. 6 d www.aemj.org 515 METHODS demographic information, hospital charges, and di- agnosis and procedure codes using ICD-9-CM. Study Design. We used a retrospective cohort study Pennsylvania hospitals are also required to use the design to derive a clinical prediction rule using MediQual-Atlas System to abstract more than 300 key patient data from existing databases. The study was clinical findings (KCFs) for each patient, including approved for exempt status by our institutional re- demographic, historical, physical examination, labo- view board. ratory, electrocardiographic, and imaging data. All hospitals in Pennsylvania were required to abstract Study Population. We identified our study cohort KCF data using MediQual-Atlas System standardized using two proprietary statewide databases for pa- data collection instruments and documentation; chart tients discharged from all Pennsylvania general acute reviewers were first required to achieve 95% agree- ment with data abstracted by a MediQual-Atlas Sys- care hospitals in 1999 with a diagnosis of heart failure. 26 Heart failure was defined as an International Classi- tem instructor. There were 192 general acute care fication of Diseases, Ninth Revision, Clinical Modifi- hospitals required to submit quarterly hospital dis- cation (ICD-9-CM)19 hospital primary discharge charge data to PHC4 in 1999. PHC4 excluded all diagnosis code consistent with heart failure (398.91, discharges from 14 hospitals for one or more quarters 402.11, 402.91, 404.01, 404.03, 404.11, 404.13, 404.91, because the data were noncompliant with submission 404.93, 428.0, 428.1, or 428.9). We included patients criteria for UB-92 or Atlas data; these included 235 with these diagnoses if they were 18 years of age or patients from seven hospitals that had at least one older, Pennsylvania residents, and hospitalized from patient with a primary discharge diagnosis of heart the ED during the study period. Only the first hospi- failure (estimated from PHC4 documentation). The talization of each patient in 1999 was included to 1999 database we received from PHC4 consisted of obtain independent observations for the statistical 68,240 cases (99.5%) from 192 hospitals with at least modeling. We excluded patients who did not have one patient with a primary discharge diagnosis of pulse, systolic blood pressure, and respiratory rate heart failure. We excluded 2,545 discharges (3.5%) data available from the ED. from the PHC4 database because they could not be We used administrative discharge diagnosis codes linked to the Atlas database. to identify patients with heart failure rather than The Pennsylvania Department of Health Division of standardized clinical criteria because the only widely Vital Statistics matched mortality data for 1999 and accepted clinical criteria20,21 were designed for appar- the first 30 days of 2000 by linking patient social ently healthy populations; their diagnostic accuracy is security number, age, and gender with PHC4 data. We limited in patients seeking emergent treatment for an used the state death index rather than the national acute episode of heart failure.22,23 The validity of our death index because the former was more current, patient identification strategy is supported by studies because the former could be linked to the PHC4 that confirmed, through independent review of med- database, and because 98% of state resident deaths ical charts, the diagnosis of heart failure in 83%–96% occurred within the state. We suspect even fewer of patients assigned a primary hospital discharge residents hospitalized for the treatment of heart fail- diagnosis code of heart failure.23–25 We assumed that ure would travel and die out of state so soon after ICD-9-CM codes for heart failure assigned to patients discharge. PHC4 stripped the final data set of any in our database by attending clinicians with access to patient identifiers before forwarding it to the study their medical records, test results, and responses to team. heart failure treatment were similarly reliable. Exclu- sion of patient identifiers from our database pre- Predictor Variables. Table 1 lists the candidate pre- cluded independent review of medical records in dictor variables we selected to develop the rule; other this study. studies found them to be prognostic of short-term or long-term adverse outcomes, and most are readily available in the ED. We included arterial blood gas Study Databases. We used the most recently available findings even though they are not routinely ordered data from the Pennsylvania Health Care Cost Contain- in the ED because this test has been found to be ment Council (PHC4), Cardinal Health Information prognostic of adverse outcomes in other settings and Companies (CHIC)-MediQual Systems Atlas Severity can be readily obtained in the ED. We used the KCF of Illness System (hereafter referred to as MediQual- values for pulse, systolic blood pressure, and respira- Atlas System) databases, and Pennsylvania Depart- tory rate collected in the ED for all patients and KCF ment of Health Division of Vital Statistics.