Utility of the Modified Frailty Index to Predict Cardiac Resynchronization Therapy Outcomes and Response

Milner, Aidan; Braunstein, Eric D.; Umadat, Goyal; Ahsan, Hamza; Lin, Juan; Palma, Eugen C.

doi:10.1016/j.amjcard.2019.12.049

Cited by 10 publications

(16 citation statements)

References 19 publications

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“…In our study ScREEN, EAARN and mFI score performed well for the prediction of CRT response (>0.60) and allcause mortality (>0.60) which was in concordance with their validation cohorts 4,5,10 . However, comparing all three scores, mFI was associated with the best predictive power for both CRT response and all-cause mortality (0.70 respectively).…”

Section: Discussionsupporting

confidence: 87%

“…We calculated the ScREEN[ 4 ], EAARN[ 5 ], and mFI[ 10 ] risk scores during the pre-implantation phase of CRT to predict all-cause mortality and CRT response, for each patient as per the equations described in each score’s original study.…”

Section: Methodsmentioning

confidence: 99%

“…Modified Frailty Index (mFI)[ 10 ] used to assess the vulnerability of patients to adverse effects especially in the setting of medical intervention. The 11 variables of the mFI include non-Activities of Daily Living independent, diabetes mellitus, exacerbation of Chronic Obstructive Pulmonary Disease (COPD) or Congestive Heart Failure (CHF) in the last 30 days, myocardial infarction within 6 months, previous Percutaneous Coronary Intervention (PCI)/Coronary Artery Bypass Graft (CABG)/angina, hypertension, Peripheral vascular disease, impaired sensorium, and transient ischemic attack (TIA)/ Cerebrovascular Accident (CVA) with or without deficits.…”

Section: Methodsmentioning

confidence: 99%

“…Recently multiple risks predicting models based on demographic profile, electrocardiographic parameters, biochemical test results, and co-morbid conditions have been proposed and validated. Scores like ScREEN[ 4 ], EAARN[ 5 ], VALID CRT[ 6 ], L2ANDS2[ 7 ], CRT-SCORE[ 8 ], machine learning (ML) algorithms[ 9 ], and Modified Frailty Index (mFI)[ 10 ] have been studied to predict CRT outcome. The ScREEN, EAARN, and mFI scores are derived from easily available clinical and biochemical variables and have performed good for prediction of clinical outcome in their respective derivation cohorts[ 4 , 5 , 10 ], still which one to be used with most reliability is uncertain.…”

Section: Introductionmentioning

confidence: 99%

“…

VALID CRT 6 , L2ANDS2 7 , CRT-SCORE 8 , machine learning (ML) algorithms 9 , and Modified Frailty Index (mFI) 10 have been studied to predict CRT outcome. The ScREEN, EAARN, and mFI scores are derived from easily available clinical and biochemical variables and have performed good for prediction of clinical outcome in their respective derivation cohorts 4,5,10 , still which one to be used with most reliability is uncertain.In this study, we aim to assess prospectively the performance of EAARN, mFI, and ScREEN scores for the prediction of outcome in patients receiving CRT.

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mentioning

confidence: 99%

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Comparing the Modified Frailty Index with conventional scores for prediction of cardiac resynchronization therapy response in patients with heart failure

Raj¹,

Nath²,

Pandit³

et al. 2021

JFSF

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Section: Discussionsupporting

confidence: 87%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…

…”

mentioning

confidence: 99%

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Comparing the Modified Frailty Index with conventional scores for prediction of cardiac resynchronization therapy response in patients with heart failure

Raj¹,

Nath²,

Pandit³

et al. 2021

JFSF

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show abstract

Long-term outcome of cardiac resynchronization therapy patients in the elderly

et al. 2023

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Heart failure (HF) is a leading cause of mortality and hospitalization in the elderly. However, data are scarce about their response to device treatment such as cardiac resynchronization therapy (CRT). We aimed to evaluate the age-related differences in the effectiveness of CRT, procedure-related complications, and long-term outcome. Between 2000 and 2020, 2656 patients undergoing CRT implantation were registered and analyzed retrospectively. Patients were divided into 3 groups according to their age: group I, < 65; group II, 65–75; and group III, > 75 years. The primary endpoint was the echocardiographic response defined as a relative increase > 15% in left ventricular ejection fraction (LVEF) within 6 months, and the secondary endpoint was the composite of all-cause mortality, heart transplantation, or left ventricular assist device implantation. Procedure-related complications were also assessed. After implantation, LVEF showed significant improvement both in the total cohort [28% (IQR 24/33) vs. 35% (IQR 28/40); p < 0.01)] and in each subgroup (27% vs. 34%; p < 0.01, 29% vs. 35%; p < 0.01, 30% vs. 35%; p < 0.01). Response rate was similar in the 3 groups (64% vs. 62% vs. 56%; p = 0.41). During the follow-up, 1574 (59%) patients died. Kaplan–Meier curves revealed a significantly lower survival rate in the older groups (log-rank p < 0.001). The cumulative complication rates were similar among the three age groups (27% vs. 28% vs. 24%; p = 0.15). Our results demonstrate that CRT is as effective and safe therapy in the elderly as for young ones. The present data suggest that patients with appropriate indications benefit from CRT in the long term, regardless of age.

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