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Research Article Assessment of Time to Hospital Encounter after an Initial Hospitalization for Heart Failure: Results from a Tertiary Medical Center Nicolas W. Shammas , 1,2 Ryan Kelly, 1 Jon Lemke, 1 Ram Niwas, 1 Sarah Castro, 1 Christine Beuthin, 1 Jackie Carlson, 1 Marti Cox, 1 Gail A. Shammas, 2 Terri DeClerck, 1 Kathy Lenaghan, 1 Sunny Arikat, 1 and Marcia Erickson 1 1 Genesis Health System, Davenport, IA, USA 2 Midwest Cardiovascular Research Foundation, Davenport, IA, USA CorrespondenceshouldbeaddressedtoNicolasW.Shammas;[email protected] Received 1 October 2017; Revised 8 December 2017; Accepted 6 February 2018; Published 1 April 2018 AcademicEditor:MichaelS.Wolin Copyright©2018NicolasW.Shammasetal.isisanopenaccessarticledistributedundertheCreativeCommonsAttribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background. Hospitalinpatientreadmissionsforpatientsadmittedinitiallywiththeprimarydiagnosisofheartfailure(HF)canbe as high as 20–25% within 30 days of discharge. is, however, does not include admissions for observations or emergency department(ED)visitswithinthesametimeframeanddoesnotshowatime-dependenthospitalencounterfollowingdischarge after an index admission. We present data on time-dependent hospital encounter of HF patients discharged after an index admissionforaprimarydiagnosisofHF. Methods.estudyrecruitedpatientsfrom2hospitalswithinthesamehealthsystem. 500consecutiveadmissionswiththeICDdiagnosisofHFwerereviewedbyinclusionandexclusionscreeningcriteria.e166 eligible remaining patients were tracked for post hospital discharge encounters consisting of hospital admissions, observation stays, and ED visits. Only those with a primary diagnosis of heart failure were included. Demographics were recorded on all patients.DaysuntilhospitalinpatientreadmissionsorhospitalencountersweredisplayedinKaplan–Meierplots. Results.Atotal of166patientsmetinclusioncriteria(meanage79.3years,males54%).Forthefirst90daysfollowingtheindexadmission,there wereatotalof287follow-upvisits(1.7perpatient),1158totalhospitalizationdays(2.6pervisit,7.0perpatient,and8.6per100 daysatrisk),and21deaths(12.7%).At30days,25%and52%ofpatientshadaninpatientreadmissionorahospitalencounter, respectively. e median time to inpatient readmission was 117 days and to hospital encounter was 27 days. Conclusion. Time- dependent excess days in acute care (unplanned inpatient admission, outpatient observation, and ED visit) rather than 30-day hospital inpatient readmission rate is a more realistic measure of the intensity of care required for HF patients after index admission. 1. Introduction Heart failure (HF) is the second leading risk factor for a cardiovascular hospital inpatient readmission for patients admitted initially with the primary diagnosis of HF. It is estimatedthatthisreadmissionratecanbeashighas20–25% within 30 days of discharge, creating significant direct and indirectcoststoourhealthcaresystem[1–4].Inanattempt to reduce this 30-day readmission rate, the Centers for Medicare and Medicaid Services (CMS) financially pe- nalizes hospitals with higher than expected 30-day read- mission rates. ese risk models developed at Yale Universityarebaseduponunderlyingdiseasesandcomorbid conditions and generate risk-adjusted readmission rates that are compared to the National crude rate to determine readmission penalties [5, 6]. Hindawi Cardiology Research and Practice Volume 2018, Article ID 6087367, 4 pages https://doi.org/10.1155/2018/6087367

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  • Research ArticleAssessment of Time to Hospital Encounter after an InitialHospitalization for Heart Failure: Results from a TertiaryMedical Center

    Nicolas W. Shammas ,1,2 Ryan Kelly,1 Jon Lemke,1 Ram Niwas,1 Sarah Castro,1

    Christine Beuthin,1 Jackie Carlson,1 Marti Cox,1 Gail A. Shammas,2 Terri DeClerck,1

    Kathy Lenaghan,1 Sunny Arikat,1 and Marcia Erickson1

    1Genesis Health System, Davenport, IA, USA2Midwest Cardiovascular Research Foundation, Davenport, IA, USA

    Correspondence should be addressed to Nicolas W. Shammas; [email protected]

    Received 1 October 2017; Revised 8 December 2017; Accepted 6 February 2018; Published 1 April 2018

    Academic Editor: Michael S. Wolin

    Copyright © 2018 Nicolas W. Shammas et al. *is is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work isproperly cited.

    Background.Hospital inpatient readmissions for patients admitted initially with the primary diagnosis of heart failure (HF) can beas high as 20–25% within 30 days of discharge. *is, however, does not include admissions for observations or emergencydepartment (ED) visits within the same time frame and does not show a time-dependent hospital encounter following dischargeafter an index admission. We present data on time-dependent hospital encounter of HF patients discharged after an indexadmission for a primary diagnosis of HF. Methods. *e study recruited patients from 2 hospitals within the same health system.500 consecutive admissions with the ICD diagnosis of HF were reviewed by inclusion and exclusion screening criteria. *e 166eligible remaining patients were tracked for post hospital discharge encounters consisting of hospital admissions, observationstays, and ED visits. Only those with a primary diagnosis of heart failure were included. Demographics were recorded on allpatients. Days until hospital inpatient readmissions or hospital encounters were displayed in Kaplan–Meier plots. Results. A totalof 166 patients met inclusion criteria (mean age 79.3 years, males 54%). For the first 90 days following the index admission, therewere a total of 287 follow-up visits (1.7 per patient), 1158 total hospitalization days (2.6 per visit, 7.0 per patient, and 8.6 per 100days at risk), and 21 deaths (12.7%). At 30 days, 25% and 52% of patients had an inpatient readmission or a hospital encounter,respectively. *e median time to inpatient readmission was 117 days and to hospital encounter was 27 days. Conclusion. Time-dependent excess days in acute care (unplanned inpatient admission, outpatient observation, and ED visit) rather than 30-dayhospital inpatient readmission rate is a more realistic measure of the intensity of care required for HF patients afterindex admission.

    1. Introduction

    Heart failure (HF) is the second leading risk factor fora cardiovascular hospital inpatient readmission for patientsadmitted initially with the primary diagnosis of HF. It isestimated that this readmission rate can be as high as 20–25%within 30 days of discharge, creating significant direct andindirect costs to our health care system [1–4]. In an attempt

    to reduce this 30-day readmission rate, the Centers forMedicare and Medicaid Services (CMS) financially pe-nalizes hospitals with higher than expected 30-day read-mission rates. *ese risk models developed at YaleUniversity are based upon underlying diseases and comorbidconditions and generate risk-adjusted readmission rates thatare compared to the National crude rate to determinereadmission penalties [5, 6].

    HindawiCardiology Research and PracticeVolume 2018, Article ID 6087367, 4 pageshttps://doi.org/10.1155/2018/6087367

    mailto:[email protected]://orcid.org/0000-0001-8279-0111https://doi.org/10.1155/2018/6087367

  • *e 30-day readmission rate is, however, an arbitraryendpoint. Many sites that are penalized hit the Nationalcrude rate in 29 or 28 days, and many sites not penalized hitthe National crude rate in 31 or 32 days. In addition, CMSincludes in their models the 30-day readmissions that occuroutside the institution, which cannot be tracked until ap-proximately 9 months later with the CMS audits. Further-more, with global bundled fees, sites have to betterunderstand how initiatives to reduce readmissions affectreadmission rates and encounter rates across time. Cur-rently, readmission rates do not include admissions forobservations or emergency department (ED) visits withinthe same time frame and do not show a time-dependenthospital encounter following discharge after an indexadmission.

    We present data on time-dependent hospital encountersof HF patients discharged after an index admission fora primary diagnosis of HF at a large tertiary health caresystem.

    2. Methods

    *is is a historical cohort study of patients treated at eitherGenesis Medical Center-Davenport or Genesis MedicalCenter-Silvis for their initial qualifying primary diagnosis ofheart failure (HF). *e study includes admissions betweenJuly 2010 and June 2012 with up to 180 days for additionalreadmissions through December 31, 2012. *e primaryendpoint is the time to hospital patient encounter afterhospital discharge for qualifying patients with the diagnosisof HF. A hospital encounter was defined as either a hospitaladmission, observation status, or an emergency department(ED) visit. *is study was conducted following all applicablelocal and federal regulations. *e study was approved by theInstitutional Review Board (IRB) of the Genesis HealthSystem. A waiver of informed consent and a waiver ofHIPPA authorization were obtained from the IRB. All in-formation and data concerning subjects or their participa-tion in this study were considered confidential. All data usedin the analysis and reporting of this evaluation were used ina manner without identifiable reference to the subject.

    2.1. Inclusion Criteria. Subjects had to meet all of the fol-lowing criteria to be included in the study:

    (1) Patients had their initial admission with primarydiagnosis of HF whether systolic or diastolic on orafter July 1, 2010.

    (2) Patients resided within the Genesis Health System17-county service area. *ey resided in the 9-countyprimary service area including Scott, Jackson,Clinton, Muscatine, and Des Moines counties inIowa and Rock Island,Mercer, Henry, andWhitesidecounties in Illinois, or in the secondary 8-countyservice area including Cedar, Louisa, Henry, and Leecounties in Iowa and Carroll, Henderson, Knox, andWarren in Illinois. Patients were also included iftheir reported zip code was outside this area but theywere admitted from a local long-term care facility.

    2.2. Exclusion Criteria. Subjects were not included in thestudy if any of the following conditions were present:

    (1) HF admissions with a history of HF present onadmission (POA) prior to July 1, 2010

    (2) Hospital death at index visit(3) Patients who transferred from one acute care setting

    to another acute care setting(4) Patients who chose to leave against medical advice

    2.3. Study Rules(1) All patients with an initial qualifying primary di-

    agnosis (as coded at Genesis Medical Centers inDavenport and Silvis) of HF with an admission fromJuly 1, 2010, through June 30, 2012, were evaluated.

    (2) All medical records for the index admission wereretrieved as well as records of all readmissions toa hospital in the Genesis Health System prior toDecember 31, 2012.

    (3) All patients had one qualifying hospitalization butmay have had multiple discharges that followed forreadmission.

    (4) A good faith effort was used to identify dates of deathfrom public records and Genesis records in order tocensor patients from further risk of admission. *esources included Genesis medical records, the SocialSecurity National Death Index which is updated atGenesis on a regular basis, and finally throughobituaries. Genesis records are also supplemented byexpired patients identified by CMS through theiraudits.

    2.4. Study Sample Size. All 166 patients with a heart failureindex visit meeting the screening criteria were tracked andanalyzed. No patient was considered lost to follow-up unlessthere was evidence that they had been admitted to an acutecare hospital outside of the Genesis Health System.

    2.5. Study Variables. Demographic and hospital variablescollected are listed in Tables 1 and 2.

    2.6. Statistical Analysis. Monitoring of data was performedby a committee from the investigators who reviewed andadjudicated all reasons for readmission after index admis-sion and reconciled any differences seen by the codingdepartment and the reviewer as to the reason of HF ad-mission at index (primary versus secondary). Monitoringconsisted of the review of subject records, source documents,and other required documentation as needed.

    Baseline data were contrasted across strata and primaryand secondary service areas. Age and sex were screened todetermine whether there were differences in baseline data.Analyses included both parametric and nonparametric aswas appropriate for the underlying distribution. For sparsedata, exact tests from the CYTEL Studio (Cytel, Cambridge,

    2 Cardiology Research and Practice

  • MA) were used and for others MINITAB (Minitab Inc.) wasused.

    Cox proportional hazards analysis was used for theprimary analysis of time to readmission and time to en-counter. Patients were censored at the time of their death,a disposition that was against medical advice, or a transferfrom another acute care hospital. *ese analyses werestratified by sites performed using the TIBCO S-Plus soft-ware (TIBCO Software Co., Palo Alto, CA). Kaplan–Meiersurvival analyses with run tests were used to both screen theabove factors and demonstrate their impact on time toreadmission (MINITAB or S-Plus).

    3. Results

    A total of 500 admissions between 10/1/2011 and 9/30/2013who had an ICD diagnosis of HF were reviewed usingmedical records, and all patient hospital encounters wererecorded until 12/31/2013. A total of 166 patients met in-clusion criteria (mean age 79.3 years, males 54%). Types ofheart failure were systolic 33%, diastolic 33%, both 7%, andunspecified 27%. Exclusions were mostly accounted for bypatients who had prior HF and those transferred from otherinstitutions. Table 1 illustrates patients’ demographics.

    For the first 90 days following the index admission, therewere a total of 287 follow-up visits (1.7 per patient), 1158total hospitalization days (2.6 per visit, 7.0 per patient, and

    8.6 per 100 days at risk), and 21 deaths (12.7%). Figure 1 isa survival curve illustrating freedom from inpatient ad-missions for HF after hospital discharge with the primarydiagnosis of HF.

    Table 2 illustrates the predictors of encounter afterhospital discharge with the primary diagnosis of HF. Amongthe several significant predictors, noncardiac causes such asdepression and chronic obstructive pulmonary disease havethe highest risk of an encounter within 30 days after an initialhospital discharge.

    At 30 days, 25% and 52% of patients had an inpatientreadmission or a hospital encounter, respectively. *emedian time to inpatient readmission was 117 days and tohospital encounter was 27 days. Figure 2 is a survival curveillustrating freedom from encounters for HF after hospitaldischarge with the primary diagnosis of HF.

    100

    80

    60

    30

    40

    20

    010

    25

    50

    75

    0 100 200 300Days a�er initial discharge

    Perc

    ent o

    f pat

    ient

    s not

    read

    mitt

    ed

    400 500 600 700 800

    Table of statisticsMean: 213.869Median: 117IQR: 299

    Figure 1: A survival curve illustrating freedom from inpatientadmissions for HF after hospital discharge with the primary di-agnosis of HF.

    100

    80

    60

    30

    40

    20

    010

    25

    50

    75

    0 100 200 300Days a�er initial discharge

    Perc

    ent o

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    no

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    400 500 600 700 800

    Table of statisticsMean: 126.708Median: 27IQR: 151

    Figure 2: A survival curve illustrating freedom from encounters forHF after hospital discharge with the primary diagnosis of HF.

    Table 1: Patients’ demographics.Mean age (years) 79.3Sex (male) 54%Heart failure typeDiastolic 33%Systolic 33%Systolic and diastolic 7%Unspecified 27%

    Site of index visitDavenport 61%Silvis 39%

    Table 2: Predictors of encounter after initial hospital discharge.

    Variable Relative risk p valueDepression 3.31 0Chronic obstructive pulmonary disease 2.83 0Cardiorespiratory disorder 2.53 0Other heart diseases 2.24 0.02Fluid disorder 1.9 0Atherosclerosis 1.77 0Pneumonia 1.55 0Anemia 1.55 0Gastrointestinal disorder 1.49 0Drug abuse 1.46 0.01Arrhythmia 0.77 0Male 0.71 0Urinary tract disorder 0.53 0.01Asthma 0.38 0.01Dementia 0.72 0.03

    Cardiology Research and Practice 3

  • In our study, the fitted probability of 30-day inpatientreadmission versus CMS probability correlated significantly(p � 0.024).

    4. Discussion

    HF readmissions impose a health burden on the health caresystem costing billions of dollars in direct and indirect costs.Currently, CMS penalizes hospitals for 30-day readmissionof a patient discharged with the primary diagnosis of HF.Several measures have been attempted to reduce 30-dayreadmission for HF with various levels of success. *esemeasures, however, focused on hospital readmissionrather than patient encounter, which also include EDvisits and admission for observations. *e 30-day ad-mission or encounter rates are arbitrary time points.Hospitals may project more accurately their readmissionrate after a HF discharge if a time-dependent admissionrate is generated.

    In this study, the 30-day readmission rate was consistentwith the national average of about 25%. *is correlatedsignificantly with the CMS-fitted probability, indicating thatour study’s sample is a true reflection of the overall HF CMSpopulation. *e encounter rates were significantly higherthan the readmission rates, indicating that patients dis-charged with the primary diagnosis of HF seek more healthcare resources than projected by the 30-day readmissionrate. *erefore, any bundled global fees for readmissionshould take into consideration the near doubling of resourcerequirements than what is projected by the CMS 30-dayreadmission rates.

    It must be noted that these models treat males and fe-males the same and that there are no interactions found.*ere are several limitations, however. All patients used tobuild these models were at least 65 years old, and therefore,conclusions derived from these data do not apply to youngerpatients. Also, patients readmitted outside the health systemwere not captured, and consequently, the current projectedreadmission or encounter rates may well be underestimated.In addition, patients with prior several readmissions werenot included because they potentially may create a con-founding factor that would influence future readmission orencounter rates. *erefore, our data only apply to thosepatients with first HF hospitalization. Finally, the study isrelatively small in numbers and needs to be confirmed bya larger multicenter cohort of patients.

    Disclosure

    *e abstract of this paper was presented at the ACC 2016Meeting as a poster presentation with interim findingson April 03, 2016. *e poster’s abstract was published in“Poster Abstracts” in Journal of the American College ofCardiology, vol. 13, no. 67, p. 1407, http://www.sciencedirect.com/science/article/pii/S0735109716314085.

    Conflicts of Interest

    *e authors declare that they have no conflicts of interest.

    Acknowledgments

    *is study was supported by Genesis Research and GrantAdministration Office.

    References

    [1] M. Hallerbach, A. Francoeur, S. C. Pomerantz et al., “Patternsand predictors of early hospital readmission in patients withcongestive heart failure,” American Journal of Medical Quality,vol. 23, no. 1, pp. 18–23, 2008.

    [2] J. B. O’Connell and M. Bristow, “Economic impact of heartfailure in the United States: time for a different approach,”Journal of Heart and Lung Transplantation, vol. 13, pp. S107–S112, 1993.

    [3] L. Fernandez-Gasso, L. Hernando-Arizaleta, J. A. Palomar-Rodŕıguez et al., “Trends, causes and timing of 30-day read-missions after hospitalization for heart failure: 11-yearpopulation-based analysis with linked data,” InternationalJournal of Cardiology, vol. 248, pp. 246–251, 2017.

    [4] K. A. Mirkin, L. M. Enomoto, G. M. Caputo, andC. S. Hollenbeak, “Risk factors for 30-day readmission inpatients with congestive heart failure,” Heart & Lung: 5eJournal of Acute and Critical Care, vol. 46, no. 5, pp. 357–362,2017.

    [5] P. S. Keenan, S. L. Normand, Z. Lin et al., “An administrativeclaims measure suitable for profiling hospital performance onthe basis of 30-day all-cause readmission rates among patientswith heart failure,” Circulation: Cardiovascular Quality andOutcomes, vol. 1, no. 1, pp. 29–37, 2008.

    [6] C. K. McIlvennan, Z. J. Eapen, and L. A. Allen, “Hospitalreadmissions reduction program,” Circulation, vol. 131, no. 20,pp. 1796–1803, 2015.

    4 Cardiology Research and Practice

    http://www.sciencedirect.com/science/article/pii/S0735109716314085http://www.sciencedirect.com/science/article/pii/S0735109716314085

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