Development of a Method to Risk Stratify Patients With Heart Failure for 30-Day Readmission Using Implantable Device Diagnostics

被引:24
|
作者
Whellan, David J. [1 ]
Sarkar, Shantanu [2 ]
Koehler, Jodi [2 ]
Small, Roy S. [3 ]
Boyle, Andrew [4 ]
Warman, Eduardo N. [2 ]
Abraham, William T. [5 ]
机构
[1] Thomas Jefferson Univ, Dept Med, Philadelphia, PA 19107 USA
[2] Medtronic Inc, Mounds View, MN USA
[3] Heart Grp, Lancaster, PA USA
[4] Aurora St Lukes Med Ctr, Div Cardiol, Milwaukee, WI USA
[5] Ohio State Univ, Div Cardiovasc Med, Columbus, OH 43210 USA
关键词
ATRIAL-FIBRILLATION; INTRATHORACIC IMPEDANCE; RATE-VARIABILITY; TRIAL; HOSPITALIZATION; ARRHYTHMIAS; MANAGEMENT; MORTALITY; ADMISSION; SECONDARY;
D O I
10.1016/j.amjcard.2012.08.050
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
The aim of the present study was to evaluate whether diagnostic data collected after a heart failure (HF) hospitalization can identify patients with HF at risk of early readmission. The diagnostic data from cardiac resynchronization therapy defibrillator (CRT-D) devices can identify outpatient HF patients at risk of future HF events. In the present retrospective analysis of 4 studies, we identified patients with CRT-D devices, with a HF admission, and 30-day postdischarge follow-up data. The evaluation of the diagnostic data for impedance, atrial fibrillation, ventricular heart rate during atrial fibrillation, loss of CRT-D pacing, night heart rate, and heart rate variability was modeled to simulate a review of the first 7 days after discharge on the seventh day. Using a combined score created from the device parameters that were significant univariate predictors of 30-day HF readmission, 3 risk groups were created. A Cox proportional hazards model adjusting for age, gender, New York Heart Association class, and length of stay during the index hospitalization was used to compare the groups. The study cohort of 166 patients experienced a total of 254 HF hospitalizations, with 34 readmissions within 30 days. Daily impedance, high atrial fibrillation burden with poor rate control (>90 beat/min) or reduced CRT-D pacing (<90% pacing), and night heart rate >80 beats/min were significant univariate predictors of 30-day HF readmission. Patients in the "high"-risk group for the combined diagnostic had a significantly greater risk (hazard ratio. 25.4, 95% confidence interval 3.6 to 179.0, p = 0.001) compared to the "low"-risk group for 30-day readmission for HF. In conclusion, device-derived HF diagnostic criteria evaluated 7 days after discharge identified patients at significantly greater risk of a HF event within 30 days after discharge. (c) 2013 Elsevier Inc. All rights reserved. (Am J Cardiol 2013;111:79-84)
引用
收藏
页码:79 / 84
页数:6
相关论文
共 50 条
  • [1] Implantable device diagnostics on day of discharge identify heart failure patients at increased risk for early readmission for heart failure
    Small, Roy S.
    Whellan, David J.
    Boyle, Andrew
    Sarkar, Shantanu
    Koehler, Jodi
    Warman, Eduardo N.
    Abraham, William T.
    EUROPEAN JOURNAL OF HEART FAILURE, 2014, 16 (04) : 419 - 425
  • [2] Predictors of 30-Day Readmission in Patients Hospitalized With Decompensated Heart Failure
    Hernandez, Marlow B.
    Schwartz, Randall S.
    Asher, Craig R.
    Navas, Elsy V.
    Totfalusi, Victor
    Buitrago, Ivan
    Lahoti, Ankush
    Novaro, Gian M.
    CLINICAL CARDIOLOGY, 2013, 36 (09) : 542 - 547
  • [3] Risk factors for 30-day readmission in patients with congestive heart failure
    Mirkin, Katelin A.
    Enomoto, Laura M.
    Caputo, Gregory M.
    Hollenbeak, Christopher S.
    HEART & LUNG, 2017, 46 (05): : 357 - 362
  • [4] Worse Prognosis in Heart Failure Patients with 30-Day Readmission
    Tung, Ying-Chang
    Chou, Shing-Hsien
    Liu, Kuan-Liang
    Hsieh, I-Chang
    Wu, Lung-Sheng
    Lin, Chia-Pin
    Wen, Ming-Shien
    Chu, Pao-Hsien
    ACTA CARDIOLOGICA SINICA, 2016, 32 (06) : 698 - 707
  • [5] Identification of Emergency Department Patients With Acute Heart Failure at Low Risk for 30-Day Adverse Events The STRATIFY Decision Tool
    Collins, Sean P.
    Jenkins, Cathy A.
    Harrell, Frank E., Jr.
    Liu, Dandan
    Miller, Karen F.
    Lindsell, Christopher J.
    Naftilan, Allen J.
    McPherson, John A.
    Maron, David J.
    Sawyer, Douglas B.
    Weintraub, Neal L.
    Fermann, Gregory J.
    Roll, Susan K.
    Sperling, Matthew
    Storrow, Alan B.
    JACC-HEART FAILURE, 2015, 3 (10) : 737 - 747
  • [6] Derivation and Validation of a 30-Day Heart Failure Readmission Model
    Fleming, Lisa M.
    Gavin, Michael
    Piatkowski, Gail
    Chang, James D.
    Mukamal, Kenneth J.
    AMERICAN JOURNAL OF CARDIOLOGY, 2014, 114 (09) : 1379 - 1382
  • [7] Etiologies, Trends, and Predictors of 30-Day Readmission in Patients With Heart Failure
    Arora, Shilpkumar
    Patel, Prashant
    Lahewala, Sopan
    Patel, Nilay
    Patel, Nileshkumar J.
    Thakore, Kosha
    Amin, Aditi
    Tripathi, Byomesh
    Kumar, Varun
    Shah, Harshil
    Shah, Mahek
    Panaich, Sidakpal
    Deshmukh, Abhishek
    Badheka, Apurva
    Gidwani, Umesh
    Gopalan, Radha
    AMERICAN JOURNAL OF CARDIOLOGY, 2017, 119 (05) : 760 - 769
  • [8] Updates in heart failure 30-day readmission prevention
    Goldgrab, David
    Balakumaran, Kathir
    Kim, Min Jung
    Tabtabai, Sara R.
    HEART FAILURE REVIEWS, 2019, 24 (02) : 177 - 187
  • [9] Explainable machine learning for predicting 30-day readmission in acute heart failure patients
    Zhang, Yang
    Xiang, Tianyu
    Wang, Yanqing
    Shu, Tingting
    Yin, Chengliang
    Li, Huan
    Duan, Minjie
    Sun, Mengyan
    Zhao, Binyi
    Kadier, Kaisaierjiang
    Xu, Qian
    Ling, Tao
    Kong, Fanqi
    Liu, Xiaozhu
    ISCIENCE, 2024, 27 (07)
  • [10] Impact of Hyponatremia Correction on the Risk for 30-Day Readmission and Death in Patients with Congestive Heart Failure
    Donze, Jacques D.
    Beeler, Patrick E.
    Bates, David W.
    AMERICAN JOURNAL OF MEDICINE, 2016, 129 (08) : 836 - 842