OBJECTIVES: The aim of this study was to derive and validate a model to predict survival in candidates for HeartMate II (HMII) (Thoratec, Pleasanton, California) left ventricular assist device (LVAD) support. BACKGROUND: LVAD mortality risk prediction is important for candidate selection and communicating expectations to patients and clinicians. With the evolution of LVAD support, prior risk prediction models have become less valid. METHODS:Patients enrolled into the HMII bridge to transplantation and destination therapy trials (N = 1,122) were randomly divided into derivation (DC) (n = 583) and validation cohorts (VC) (n = 539). Pre-operative candidate predictors of 90-day mortality were examined in the DC with logistic regression, from which the HMII Risk Score (HMRS) was derived. The HMRS was then applied to the VC. RESULTS: There were 149 (13%) deaths within 90 days. In the DC, mortality (n = 80) was higher in older patients (odds ratio [OR]: 1.3, 95% confidence interval [CI]: 1.1 to 1.7 per 10 years), those with greater hypoalbuminemia (OR: 0.49, 95% CI: 0.31 to 0.76 per mg/dl of albumin), renal dysfunction (OR: 2.1, 95% CI: 1.4 to 3.2 per mg/dl creatinine), coagulopathy (OR: 3.1, 95% CI: 1.7 to 5.8 per international normalized ratio unit), and in those receiving LVAD support at less experienced centers (OR: 2.2, 95% CI: 1.2 to 4.4 for <15 trial patients). Mortality in the DC low, medium, and high HMRS groups was 4%, 16%, and 29%, respectively (p < 0.001). In the VC, corresponding mortality was 8%, 11%, and 25%, respectively (p < 0.001). HMRS discrimination was good (area under the receiver-operating characteristic curve: 0.71, 95% CI: 0.66 to 0.75). CONCLUSIONS: The HMRS might be useful for mortality risk stratification in HMII candidates and may serve as an additional tool in the patient selection process.
RCT Entities:
OBJECTIVES: The aim of this study was to derive and validate a model to predict survival in candidates for HeartMate II (HMII) (Thoratec, Pleasanton, California) left ventricular assist device (LVAD) support. BACKGROUND: LVAD mortality risk prediction is important for candidate selection and communicating expectations to patients and clinicians. With the evolution of LVAD support, prior risk prediction models have become less valid. METHODS:Patients enrolled into the HMII bridge to transplantation and destination therapy trials (N = 1,122) were randomly divided into derivation (DC) (n = 583) and validation cohorts (VC) (n = 539). Pre-operative candidate predictors of 90-day mortality were examined in the DC with logistic regression, from which the HMII Risk Score (HMRS) was derived. The HMRS was then applied to the VC. RESULTS: There were 149 (13%) deaths within 90 days. In the DC, mortality (n = 80) was higher in older patients (odds ratio [OR]: 1.3, 95% confidence interval [CI]: 1.1 to 1.7 per 10 years), those with greater hypoalbuminemia (OR: 0.49, 95% CI: 0.31 to 0.76 per mg/dl of albumin), renal dysfunction (OR: 2.1, 95% CI: 1.4 to 3.2 per mg/dl creatinine), coagulopathy (OR: 3.1, 95% CI: 1.7 to 5.8 per international normalized ratio unit), and in those receiving LVAD support at less experienced centers (OR: 2.2, 95% CI: 1.2 to 4.4 for <15 trial patients). Mortality in the DC low, medium, and high HMRS groups was 4%, 16%, and 29%, respectively (p < 0.001). In the VC, corresponding mortality was 8%, 11%, and 25%, respectively (p < 0.001). HMRS discrimination was good (area under the receiver-operating characteristic curve: 0.71, 95% CI: 0.66 to 0.75). CONCLUSIONS: The HMRS might be useful for mortality risk stratification in HMII candidates and may serve as an additional tool in the patient selection process.
Authors: Deborah D Ascheim; Annetine C Gelijns; Daniel Goldstein; Lemuel A Moye; Nicholas Smedira; Sangjin Lee; Charles T Klodell; Anita Szady; Michael K Parides; Neal O Jeffries; Donna Skerrett; Doris A Taylor; J Eduardo Rame; Carmelo Milano; Joseph G Rogers; Janine Lynch; Todd Dewey; Eric Eichhorn; Benjamin Sun; David Feldman; Robert Simari; Patrick T O'Gara; Wendy C Taddei-Peters; Marissa A Miller; Yoshifumi Naka; Emilia Bagiella; Eric A Rose; Y Joseph Woo Journal: Circulation Date: 2014-03-28 Impact factor: 29.690
Authors: Jennifer Cowger; Palak Shah; John Stulak; Simon Maltais; Keith D Aaronson; James K Kirklin; Francis D Pagani; Christopher Salerno Journal: J Heart Lung Transplant Date: 2015-11-06 Impact factor: 10.247
Authors: Lauren K Truby; Lakshmi Sridharan; Raul J Flores; A Reshad Garan; Douglas Jennings; Melana Yuzefpolskaya; Koji Takeda; Hiroo Takayama; Yoshifumi Naka; Paolo C Colombo; Veli K Topkara Journal: ASAIO J Date: 2019 Mar/Apr Impact factor: 2.872
Authors: Michael E Nassif; Jayendrakumar S Patel; Jerrica E Shuster; David S Raymer; Ronald Jackups; Eric Novak; Brian F Gage; Sunil Prasad; Scott C Silvestry; Gregory A Ewald; Shane J LaRue Journal: JACC Heart Fail Date: 2015-02 Impact factor: 12.035
Authors: Eileen M Hsich; Eugene H Blackstone; Lucy Thuita; Dennis M McNamara; Joseph G Rogers; Hemant Ishwaran; Jesse D Schold Journal: Circ Heart Fail Date: 2017-06 Impact factor: 8.790
Authors: Anuradha Lala; John C Rowland; Bart S Ferket; Annetine C Gelijns; Emilia Bagiella; Sean P Pinney; Alan J Moskowitz; Marissa A Miller; Francis D Pagani; Donna M Mancini Journal: JAMA Cardiol Date: 2020-06-01 Impact factor: 14.676
Authors: Maria Chiara Todaro; Bijoy K Khandheria; Timothy E Paterick; Matt M Umland; Vinay Thohan Journal: Curr Cardiol Rep Date: 2014-04 Impact factor: 2.931