Introduction
Although left ventricular assist device (LVAD) therapy offers a solution for patients with end-stage heart failure with respect to overall survival and improvements in quality of life, complications after implantation remains a persistent source of morbidity and mortality.1–3 To decrease the risk of adverse events in the postoperative setting, guideline recommendations include hemodynamic optimization of pulmonary vascular impedance, right ventricular (RV) filling pressures, and cardiac index, but data on goals of optimization remain uncertain.4 An association between poor RV function, as measured by indices of systolic function or elevated central venous pressure, and post-implant complications, such as the need for right ventricular assist device (RVAD), renal failure, and death has been observed.5–8 In fact, the Interagency Registry for Mechanically Assisted Circulatory Support (INTERMACS) definition of RV failure includes the development of renal failure because of congestion as a supportive criterion.9
Despite the general consensus that hemodynamic optimization pre-LVAD implantation is associated with improved outcomes, relatively few data guide decisions about optimization. Goals for preimplant renal function (creatinine < 2.5, blood urea nitrogen [BUN] < 40) and hemodynamics (right atrial pressure < 15 mmHg and pulmonary capillary wedge pressure < 24 mmHg) have been suggested10 based on post-hoc trial analysis.11 However, whether preimplant risk markers are most prognostic at the time of initial hemodynamic catheterization or immediately pre-LVAD implant is unknown. Furthermore, data on preimplant optimization utilizing aggressive mechanical circulatory support—often required in the context of cardiogenic shock—suggest impaired outcomes.12–14 Therefore, further data are required to understand the implications and goals of optimization.
The current study evaluates the three of the most significant complications after continuous-flow LVAD (CF-LVAD) implantation: RV failure requiring temporary RVAD, chronic hemodialysis at 90 days, and 30-day mortality. We explored potentially modifiable preoperative parameters, including hemodynamics at multiple time points and laboratory studies of end-organ function to determine whether optimal parameters were associated with improved outcomes after LVAD implant.
Methods
Patient Population
A retrospective cohort study of consecutive adult patients undergoing first-time CF-LVAD implantation at a single institution was conducted. Patients were implanted between 2007 and 2017. The Institutional Review Board approved the current study.
Comprehensive hemodynamic values at the time of initial right heart catheterization and pulmonary artery catheter placement, 6 h after placement, and immediately pre-LVAD were obtained. All patients had invasive hemodynamics at these time points. These included all directly measured and routinely derived indices from pulmonary artery catheterization as well as percent and absolute changes between each time point. Laboratory data representing end-organ function before and closest to the time of LVAD implantation were extracted for analysis. Outcome data were similarly extracted through chart review.
A total of 174 (48%) of patients were supported on intra-aortic balloon pump therapy before LVAD. Only 12 patients (3.3%) were supported on extracorporeal membrane oxygenation (ECMO) therapy. ECMO was implanted as salvage therapy for patients failing medical or percutaneous mechanical circulatory support therapy or for cardiogenic shock at the time of another cardiac surgery but never purely for optimization alone. Five patients (1.4%) were supported on Impella therapy (Abiomed, Inc., Danvers, Massachusetts), which was for LV venting in three patients on ECMO and for optimization in the context of failing medical therapy and worsening end-organ function in two patients. Four patients (1.1%) were supported on TandemHeart therapy (CardiacAssist, Inc.; Pittsburgh, Pennsylvania). Two of these patients were declining despite medical therapy, one patient was implanted with a ProtekDuo percutaneous RVAD for biventricular failure at the time of LVAD implant, and one patient arrived to our institution with TandemHeart in place for post-myocardial infarction cardiogenic shock. The median duration of percutaneous mechanical support was 2 days (IQR 1.25–4 days).
During the study period, there was no formal protocol for hemodynamic optimization so all decisions regarding initiation and titration of medical therapy and support devices were made collaboratively by the cardiac intensive care unit (CICU) team in coordination with the medical and surgical LVAD teams. However, in general, primary goals included: normalization of the cardiac index, achievement of a right atrial pressure less than 10–12 mmHg, and improvement in pulmonary capillary wedge pressure or pulmonary artery (PA) diastolic values to less than 20 mmHg with a combination of inotropic therapy and mechanical circulatory support as needed. The use of intra-aortic balloon pump therapy was not only dependent on hemodynamics but also on end-organ function and desire for aggressive optimization, as well as the preferences of the implanting surgeon. Hemodialysis or ultrafiltration was not used except in cases of acute renal failure.
Outcomes
The primary composite outcome of the study, selected based on the clinical significance of the component outcomes, was a composite of requirement for postimplantation surgical RVAD, 30-day mortality, and requirement for continued hemodialysis at 90 days postimplantation. The secondary outcome of the study was 1-year overall survival.
Statistical Methods
Distribution of continuous variables is described by mean ± standard deviation or median (IQR) as appropriate. Continuous variables were compared using Student’s t-test or Ranked sums test (Kruskal–Wallis test) and categorical variables were compared using the likelihood ratio test.
To identify variables significantly associated with the primary outcome, univariate logistic regression models were constructed for all hemodynamic and laboratory data as continuous variables. To correct for multiple comparisons, the associations between variables and the composite endpoint were adjusted for the false discovery rate using the Benjamini and Hochberg methodology.15 Variables significantly associated with the composite outcome after correction (−log10[adjusted p-value] > 2) were input into a multivariate logistic regression model to determine whether the associations persisted after adjustment. This was similarly done for predictors of achieving optimal parameters at the time of LVAD implant.
Utilizing the variables significantly and independently associated with the composite outcome, receiver-operator curves were constructed and C-statistic was calculated. Optimal cut-points for the continuous variables were defined by maximizing the sensitivity + specificity-1 function for each variable (Youden’s J index). After dichotomization, odds ratios (OR) for the composite outcome were calculated based on the number of parameters optimized. Finally, a Kaplan–Meier analysis was performed to stratify the secondary outcome by the number of optimized parameters. Data analysis was performed using JMP 14.1 (SAS Institute, Inc., Cary, North Carolina) and GraphPad Prism Version 8 (GraphPad Software, Inc., San Diego, California).
Results
A total of 359 patients were included in the study. The mean age was 59 ± 13 years, and 54% had a nonischemic cardiomyopathy as the indication for LVAD implant, and 229 patients (64%) were implanted with destination therapy intent. Table 1 displays baseline characteristics, including the distribution of INTERMACS scores at implant, preimplant left ventricular echocardiography data, preimplant laboratory studies, preimplant hemodynamic data, and preimplant hemodynamic support. There were notable but expected differences in hemodynamics and hemodynamic support. A period of optimization in the CICU occurred with a median duration of optimization pre-LVAD implantation was 2 days (IQR 2–5 days), depending on both surgical timing and success with hemodynamic optimization.
Table 1. -
Characteristics of the Cohort Before
left ventricular assist device Implant (
N = 359)
| Characteristic |
No. (%), Mean (SD), or Median (IQR) |
Met Primary Endpoint |
Did not meet Primary Endpoint |
p-value |
| Age, mean (SD), y |
59 (13) |
58 (14) |
59 (13) |
0.59 |
| Female, no. (%) |
74 (21) |
13 (30) |
57 (20) |
0.14 |
| Non-ischemic, no. (%) |
193 (54) |
27 (61) |
153 (53) |
0.27 |
| INTERMACS classifications |
|
|
|
0.008 |
| INTERMACS 1, No. (%) |
29 (8) |
9 (20) |
18 (6) |
|
| INTERMACS 2, No. (%) |
76 (21) |
13 (30) |
55 (19) |
|
| INTERMACS 3, No. (%) |
54 (15) |
5 (11) |
46 (16) |
|
| INTERMACS 4, No. (%) |
124 (35) |
12 (27) |
104 (36) |
|
| INTERMACS 5–7, No. (%) |
76 (21) |
5 (11) |
68 (23) |
|
| Pre-implant LV indices |
|
|
|
|
| LVEDd, mean (SD), mm |
69 (11) |
66 (13) |
70 (11) |
0.08 |
| LVEF, mean (SD), % |
19 (8) |
22 (11) |
19 (8) |
0.05 |
| Pre-implant laboratory studies |
|
|
|
|
| Hemoglobin, mean (SD), g/dL |
11.7 (1.9) |
11.0 (2.0) |
11.8 (1.9) |
0.009 |
| WBC, mean (SD), 109 cell/L |
8.0 (3.3) |
8.0 (4.3) |
8.0 (3.1) |
0.98 |
| Platelets, mean (SD), 109 cell/L |
176 (69) |
154 (56) |
178 (67) |
0.01 |
| INR, mean (SD) |
1.5 (0.7) |
1.7 (1.1) |
1.5 (.6) |
0.32 |
| Total bilirubin, mean (SD), mg/dL |
1.4 (1.3) |
2.0 (2.7) |
1.3 (0.9) |
0.09 |
| Albumin, mean (SD), g/dL |
3.8 (.6) |
3.8 (.6) |
3.8 (.6) |
0.58 |
| NT-proBNP, median (IQR), pg/mL |
4422 (2586–8538) |
5332 (3336–10232) |
4377 (2560–8323) |
0.07 |
| Sodium, mean (SD), mg/dL |
136 (5) |
136 (6) |
136 (5) |
0.71 |
| BUN, mean (SD), mg/dL |
32 (19) |
42 (23) |
30 (18) |
0.002 |
| Creatinine, mean (SD), mg/dL |
1.3 (0.5) |
1.6 (0.7) |
1.2 (0.4) |
0.002 |
| eGFR, mean (SD), mL/min/m2
|
68 (44) |
55 (28) |
71 (47) |
0.002 |
| Preimplant hemodynamics |
|
|
|
|
| RA pressure, mean (SD), mmHg |
11 (7) |
15 (8) |
10 (6) |
0.003 |
| PA systolic, mean (SD), mmHg |
47 (13) |
47 (14) |
47 (13) |
0.76 |
| Mean PA, mean (SD), mmHg |
32 (9) |
32 (10) |
32 (9) |
0.98 |
| PCWP, mean (SD), mmHg |
20 (6) |
19 (6) |
19 (6) |
0.98 |
| CI, mean (SD), L*min−1*m−2
|
2.4 (0.5) |
2.2 (0.7) |
2.4 (0.5) |
0.19 |
| SVR, mean (SD), dynes*s*cm-5
|
1120 (390) |
1210 (630) |
1110 (350) |
0.38 |
| PVR, mean (SD), WU |
3.8 (2.7) |
4.2 (2.3) |
3.8 (2.7) |
0.34 |
| RVSWI, mean (SD), mmHg*ml*m-2
|
630 (300) |
450 (280) |
660 (290) |
0.0003 |
| PAPi, mean (SD), unitless |
4.0 (4.7) |
2.3 (1.9) |
4.4 (5.1) |
<0.0001 |
| CPO, mean (SD), W |
0.8 (0.2) |
0.8 (0.3) |
0.8 (0.2) |
0.26 |
| Preimplant hemodynamic support |
|
|
|
|
| Inotropic therapy, no. (%) |
248 (74) |
36 (82) |
212 (73) |
0.27 |
| VIS, mean (SD) |
3.7 (6.6) |
7.0 (12) |
3.2 (5.5) |
0.04 |
| Change in VIS from PA catheter insertion to LVAD, mean (SD) |
2.0 (7.1) |
4.8 (14.1) |
1.7 (5.5) |
0.16 |
| Two inotropes, no (%) |
76 (23) |
17 (39) |
59 (20) |
0.01 |
| Pre-implant IABP, no. (%) |
174 (48) |
25 (57%) |
134 (46%) |
0.18 |
| Pre-implant other MCS, no (%) |
18 (5) |
7 (16) |
11 (4) |
0.005 |
CI, cardiac index; CPO, cardiac power output; eGFR, estimated glomerular filtration rate; HR, heart rate; IABP, intraaortic balloon pump; INTERMACS, Interagency Registry for Mechanically Assisted Circulatory Support; IQR, interquartile range; LVAD, left ventricular assist device; LVEDd, left ventricular end-diastolic diameter; LVEF, left ventricular ejection fraction; MAP, mean arterial pressure; PA, pulmonary artery; PAPi, pulmonary artery pulsatility index; PCWP, pulmonary capillary wedge pressure; PVR, pulmonary vascular resistance; RAP, right atrial pressure; RVSWI, right ventricular stroke work index; SD, standard deviation; SVR, systemic vascular resistance; VIS, vasoactive-inotropic score.
To achieve preimplantation hemodynamic values, changes in hemodynamics and therapies across the optimization period were documented. Between PA catheter placement and preimplant hemodynamics, the mean heart rate increased by 4 ± 15 beats per minute, mean arterial pressure rose by 1 ± 13 mmHg, right atrial pressure (RAP) fell by 4.0 ± 7 mmHg, mean PA pressure fell by 4.4 ± 9.5 mmHg, and pulmonary capillary wedge pressure fell by 4.5 ± 5.5 mmHg. The mean cardiac index rose by 0.6 ± 0.6 L*min−1*m−2. Systemic vascular resistance fell by 300 ± 500 dynes*s*cm−5. To achieve these improvements, inotrope usage increased in 103 (29%) subjects at baseline to 265 (74%) on inotropic therapy just before implantation. Additionally, diuresis was used to achieve improved hemodynamics, and the mean diuresis (net negative values accounting for intake and urine output) in the 3 days preceding implantation was −650 ± 1230 ml, −670 ± 1180 ml, and −800 ± 1290 ml on days −3 through −1, respectively.
In total 44 patients (13%) met the primary composite endpoint, including 16 patients (4.4%) who required an RVAD post-implant for severe RV failure, 17 (5.4%) who required dialysis extended out to 90 days, and 18 (5.0%) who experienced death within 30 days postimplant. Among the 16 patients requiring an RVAD, 4 (25%) died within 30 days of implant. Median survival after RVAD was 5.3 months. Ultimately, 3 (33% of the 9 patients alive at 90-days post-RVAD) required dialysis at 90 days.
The results of the initial univariate analysis are shown in Table 2. Of 227 variables evaluated, only 5 emerged as significantly associated with the primary composite outcome, including BUN, RAP, pressure-adjusted heart rate (HR*RAP/MAP), Model for End-stage Liver Disease score, and right ventricular stroke work index (RVSWI). Notably, no differences in outcomes were observed among specific devices (unadjusted p = 0.13). Similarly, although there was a trend toward significance, year of implantation was not associated with the primary outcome (unadjusted p = 0.051); importantly, this trend was not consistent over consecutive years (data not shown), and no effect was seen when stratified into three eras (p = 0.16). The five variables were entered into multivariate analysis (Table 2), and BUN, RAP, and RVSWI emerged as significant independent factors associated with the composite outcome (p < 0.01 for each). All changes between initial and final hemodynamic values were not associated with the primary outcome.
Table 2. -
Univariate and Multivariate Associations with the Composite Endpoint
| Variable |
Univariate Model
OR (95% CI)* |
p-value† |
Multivariate Model OR (95% CI)* |
p-value |
| BUN |
1.03 (1.01–1.04) |
0.0045 |
1.02 (1.00–1.04) |
0.029 |
| RAP |
1.10 (1.05–1.16) |
0.0045 |
1.06 (1.00–1.12) |
0.048 |
| HR*RAP*MAP-1
|
1.07 (1.03–1.11) |
0.0045 |
0.99 (0.91–1.08) |
0.81 |
| MELD Score |
1.09 (1.04–1.14) |
0.0048 |
1.06 (1.0–1.13) |
0.06 |
| RVSWI/100 |
0.74 (0.62–0.87) |
<0.0001 |
0.80 (0.67–0.95) |
0.0063 |
*Odds ratios are per unit increase in given variable.
†Adjusted for false discovery rate.
BUN, blood urea nitrogen; HR, heart rate; MAP, mean arterial pressure; MELD, Model for End-stage Liver Disease; RVSWI, right ventricular stroke work index; RAP, right atrial pressure.
BUN, RAP, and RVSWI were then analyzed with univariate logistic regression modeling to determine their individual accuracy at predicting the composite endpoint and to determine optimal cut-points using receiver–operator curves. The individual performance characteristics are shown in Figure 1. Each variable was associated with a fair or good discrimination ability, but the predictive accuracy was improved when variables were combined in a multivariate logistic model (C-statistic = 0.77, p < 0.0001). Optimal cutoffs for prediction of the primary composite outcomes were defined for each variable: BUN (38 mg/dl, rounded to 40 mg/dl), RAP (12 mmHg), and RVSWI (487 mmHg*ml*m−2, rounded to 500 mmHg*ml*m−2).
;)
Figure 1.: Receiver operator curves for variables associated with composite outcome. Each of three variables derived from multivariate model plotted by sensitivity and 1-specificity for discrimination of the composite outcome. Area under the curve (AUC) was calculated for each parameter and p-values represent significance of the logistic regression analysis. Dashed lines refer to optimal cut point maximizing the function, sensitivity+specificity-1 (Youden’s J index).
These dichotomized parameters were then evaluated for the prediction of the primary composite outcome. Using the cutoff identified, BUN > 40, RAP > 12, and RVSWI < 500 were associated with the increased risk of the primary outcome with ORs of 4.4 (95% CI, 2.2–8.9), 2.7 (95% CI, 1.3–5.8), and 5.2 (95% CI, 2.3–11.3), respectively (all p ≤ 0.01). Associated sensitivities (Se) and specificities (Sp) for these dichotomized variables were as follows: BUN (Se 0.56, Sp 0.78), RA (Se 0.67, Sp 0.61), and RVSWI (Se 0.69, Sp 0.73).
A strong and incremental stepwise relationship was observed between the number of optimized parameters (agnostic to which parameter) and the primary outcome with ORs of 3.5 (95% CI, 1.1–11.7), 7.2 (95% CI, 2.1–24.2), and 20.6 (95% CI, 5.3–80.6) for 2/3, 1/3, and 0/3 parameters optimized, respectively, relative to patients with 3/3 optimized parameters (Figure 2). Importantly, the 112 (41%) patients who had all three parameters optimized before LVAD implantation had a 4% rate of the primary composite endpoint, including a 2%, 30-day mortality. Not surprisingly, the number of optimized parameters was associated with differences in hemodynamics and most notably in indices that include RAP or RVSWI (Table 3). However, there were also significant differences across the number of optimized parameters with respect to heart rate, mean arterial pressure, systemic vascular resistance, and cardiac index. Notably, there were no differences in left-sided filling pressure or cardiac power.
Table 3. -
Immediate Pre-Implant Variables by Number of Optimized Parameters
| Variable |
3/3 Optimized |
2/3 optimized |
1/3 optimized |
0/3 optimized |
p-value |
| Clinical Variables |
|
|
|
|
|
| INTERMACS classifications |
|
|
|
|
0.003 |
| INTERMACS 1, No. (%) |
5 (4) |
7 (8) |
6 (12) |
4 (19) |
|
| INTERMACS 2, No. (%) |
16 (140 |
21 (23) |
12 (24) |
9 (43) |
|
| INTERMACS 3, No. (%) |
13 (12) |
17 (18) |
14 (28) |
2 (10) |
|
| INTERMACS 4, No. (%) |
51 (46) |
36 (39) |
12 (24) |
6 (29) |
|
| INTERMACS 5–7, No. (%) |
27 (24) |
12 (13) |
6 (12) |
0 (0) |
|
| IABP Therapy, No. (%) |
53 (47) |
58 (62) |
31 (62) |
17 (81) |
0.01 |
| Hemodynamic Variables |
|
|
|
|
|
| HR, mean (SD), beat*min−1
|
78 (12) |
85 (15) |
88 (17) |
85 (14) |
0.0005 |
| MAP, mean (SD), mmHg |
74 (10) |
77 (10) |
77 (13) |
70 (11) |
0.003 |
| RAP, mean (SD), mmHg |
7 (3) |
11 (5) |
16 (8) |
20 (5) |
<0.0001 |
| PA systolic, mean (SD), mmHg |
49 (12) |
47 (15) |
46 (15) |
44 (10) |
0.31 |
| Mean PA, mean (SD), mmHg |
32 (7) |
31 (11) |
32 (10) |
31 (7) |
0.98 |
| PCWP, mean (SD), mmHg |
19 (5) |
20 (7) |
19 (7) |
20 (9) |
0.996 |
| CI, mean (SD), L*min−1*m−2
|
2.5 (.5) |
2.3 (.6) |
2.3 (.6) |
2.3 (.6) |
0.02 |
| PVR, mean (SD), WU |
3.0 (1.4) |
2.8 (2.3) |
2.9 (1.6) |
2.1 (1.5) |
0.84 |
| SVR, mean (SD), dynes*s*cm−5
|
1100 (340) |
1200 (440) |
1100 (410) |
900 (340) |
0.04 |
| RAP/PCWP, mean (SD), unitless |
0.40 (.19) |
0.60 (.29) |
1.0 (.84) |
1.1 (.53) |
<0.0001 |
| HR*RAP/MAP, mean (SD), beat/min |
7.5 (4.0) |
12.0 (6.4) |
18.6 (9.1) |
25.8 (10.7) |
<0.0001 |
| RVSWI, mean (SD), mm Hg*ml*m−2
|
800 (250) |
590 (250) |
430 (260) |
290 (120) |
<0.0001 |
| PAPi, mean (SD), unitless |
5.8 (5.2) |
3.7 (4.9) |
1.9 (1.8) |
1.1 (0.50) |
<0.0001 |
| CPO, mean (SD), W |
0.83 (.22) |
0.85 (.27) |
0.81 (.25) |
0.71 (.18) |
0.12 |
CI, cardiac index; CPO, cardiac power output; HR, heart rate; INTERMACS, Interagency Registry for Mechanically Assisted Circulatory Support; IABP, intraaortic balloon pump; MAP, mean arterial pressure; PA, pulmonary artery; PAPi, pulmonary artery pulsatility index; PCWP, pulmonary capillary wedge pressure; PVR, pulmonary vascular resistance; RAP, right atrial pressure; RVSWI, right ventricular stroke work index; SD, standard deviation; SVR, systemic vascular resistance.
Figure 2.: Relationship between composite outcome and number of parameters optimized. Using three of three parameters optimized as the referent, rate of the composite endpoint (A) and odds ratio for composite endpoint (B) for two parameters, one parameter, and no parameters optimized are shown. Error bars in (B) represent 95% CI.
Baseline covariates associated with the inability to achieve optimal parameters were then assessed. Aside from baseline hemodynamic variables at the time of right heart catheterization, factors associated with optimized parameters included: tricuspid regurgitant (TR) velocity on echocardiography (OR for optimized values 3.9, 95% CI, 2.3–6.7, p < 0.0001), INTERMACS class 1–3 vs 4–7 (OR 0.35, 95% CI, 0.21–0.59, p < 0.0001), and RV systolic pressure on echocardiography (OR 1.04, 95% CI, 1.02–1.06, p = 0.0002 per 1 mmHg increase). On multivariate analysis, TR velocity and INTERMACS 1–3 status remained significant predictors of optimized values (OR 8.4, 95% CI, 2.3–30.6, p = 0.001 per 1 m/s increase and OR 0.37, 95% CI, 0.21–0.65, p = 0.0006 vs INTERMACS 4–7). There was no difference observed in the duration of optimization with respect to the achievement of optimal parameters (Ranked sums, χ2 = 1.86, p = 0.60 across the number of optimized parameters). The secondary endpoint was evaluated with Kaplan–Meier analysis, stratifying outcomes by the number of optimized variables. As shown in Figure 3, the number of optimized parameters pre-implant was associated with 1-year overall survival (p = 0.02) with the patients who were not optimized in any parameter experiencing significantly worse outcomes than other cohorts.
Figure 3.: Kaplan-Meier curve for overall 1 year survival. Number of optimized parameters was associated with overall 1-year survival (p = 0.02). Number of patients at risk is shown.
Discussion
This study identifies three variables that are highly associated with patient-oriented adverse events early post-CF-LVAD-implantation: the need for RVAD, the requirement for hemodialysis at 90 days, or 30-day mortality. These endpoints are those of marked clinical significance and each represents outcomes to be avoided, whereas prior analyses have focused on individual endpoints. We observed a strong relationship between a high BUN >40 mg/dL, a high RAP >12 mmHg, and a low RVSWI <500 mmHg*ml*m−2 and risk of post-LVAD adverse outcomes. These variables could be used to assess the adequacy of optimization given a strong inverse relationship between the number of optimized parameters and both the primary composite endpoint and ultimately 1-year overall survival after LVAD implant. Although this retrospective analysis cannot be used to determine whether treatment strategies targeting the optimization of these three parameters will reduce the risk of post-LVAD adverse events, our data clearly show that patients who achieve low BUN, low central venous pressure and high RVSWI values before LVAD implantation will be at low risk of postoperative adverse events. In contrast to prior studies, which have focused on RV failure as a sole outcome,5,11,16 we herein report a patient-centered composite outcome, including early death and the long-term need for dialysis.
Not surprisingly, markers of RV systolic dysfunction and high central venous pressures were associated with the composite endpoint given prior observations of the relationships between RV failure and need for postoperative RVAD5,6,11,16 as well as post-implant renal failure.8,17,18 RVSWI as an index of RV systolic function has been previously identified as a risk factor, and we found that RVSWI was a more robust predictor than the frequently discussed pulmonary artery pulsatility index (PAPI).6,16,18,19 This does not discount the importance of PAPi; instead, the collinearity of PAPi with RVSWI resulted in its nonsignificance of the former in the multivariate model.
Similarly, the markers of renal dysfunction including elevated levels of BUN and creatinine have frequently been associated with adverse post-LVAD outcomes including the need for RVAD.7,11,20 These associations have been consistently identified in the literature despite potential confounding by variable utilization of inotropic therapies and even circulatory support strategies that may affect the markers of RV dysfunction and renal function. The association between these markers and 30-day mortality likely reflects the severity of illness and may identify a high-risk population that cannot be well-optimized before implantation.
Hemodynamic Optimization Strategies
In addition to medical therapy, one strategy that has been employed to prevent postoperative end-organ dysfunction and improve outcomes for patients with or at risk of RV failure after LVAD implant is the judicious use of planned RVAD therapy. However, this approach has not been rigorously studied and available data suggest that planned RVAD therapy may not be associated with improved outcomes compared to unplanned therapy.21,22 This observation may be driven by the severe illness of any patient requiring RVAD therapy as previous data suggest that inability to wean from RVAD therapy was a greater predictor of poor outcome than unplanned therapy itself.22 Weaning from RVAD depends on the population studied but planned percutaneous therapy in the modern era may be associated with an ability to wean in as many as 75%, whereas other experiences have suggested weaning could occur in as few as 20%.21,23,24 Our cohort would suggest a reasonable rate of weaning from RVAD support with 75% 30-day survival, although overall outcomes remained poor with median survival after implant of only 5.3 months.
The frequency of RV failure has prompted consideration of durable biventricular assist devices (BiVADs), and adequate outcomes have been observed in select populations,27–29 including through a novel RA to PA configuration.30 However, the surgical challenges, RV geometry, and complexity of management limit the broad utilization of BiVADs for severe biventricular failure.
Given that RV failure mediates many other serious adverse events after LVAD, multiple forms of mechanical circulatory support as bridge to durable CF-LVAD have been explored. Unfortunately, few data have suggested that optimization with mechanical support before LVAD implant is associated with improved outcomes. In a small retrospective series, prophylactic intra-aortic balloon pump (IABP) therapy was associated with the improved postimplant course including improved markers of end-organ function.25 However, the use of IABP therapy has also been previously associated with the increased risk of RV failure after LVAD implant.26 Furthermore, the use of more aggressive forms of mechanical support, such as ECMO, have been associated with worse early outcomes despite propensity matching, which is observed herein.12–14 Frequent IABP use remains a common practice at our institution for preimplant optimization in spite of this lack of data, and an important distinction must be made between prophylactic IABP placement to facilitate hemodynamic optimization versus bail-out IABP placement for patients failing medical therapy.
We were unable to identify any changes in hemodynamic parameters over the period of optimization during which a PA catheter was implanted that were associated with the primary composite endpoint. This may reflect some refractory RV dysfunction that predisposes to the requirement for RVAD or a series of events perioperatively that may result in the increased risk of both RV and renal failure as well as mortality.31 These latter factors may not be as modifiable in the preoperative setting though continued work to identify factors intraoperatively and early postoperatively that may predispose to these adverse outcomes is critical to improving outcomes. To begin to identify markers that might suggest the ability to optimize, we analyzed baseline variables that might be associated with the achievement of these goal parameters. We found that higher TR velocity and higher INTERMACS class status were most associated with the ability to achieve these parameters. The former likely represents adequate RV function to generate a high PA pressure and the latter represents a sicker patient population, possibly experiencing more systemic illness and end-organ dysfunction but a greater capacity to respond to medical therapy during optimization.
That perioperative events may be associated with poor outcomes does not refute the hypothesis that optimization may also improve outcomes. Unfortunately, we cannot determine whether the outcome is affected by the methods used to achieve hemodynamic optimization, though patients here requiring more vasoactive support generally experienced a worse outcome, as has been demonstrated previously in similar cohorts.32,33
Most importantly, our study provides easily measured and straightforward parameters to target during preoperative hemodynamic optimization before planned LVAD implant. Although we cannot state that titrating therapy to achieve these goals before implant will necessarily reduce the risk of postoperative adverse events, the data provided herein establishes a construct by which to guide future clinical trial design to test this hypothesis. Similarly, different strategies for achieving hemodynamic optimization could be compared, using the variables identified in our study as targets for treatment. Importantly, this work suggests that if LVAD implantation can safely be deferred, these parameters are preoperatively optimized; this may be reasonable to consider. Alternatively, patients who cannot achieve optimization of these parameters despite aggressive preoperative therapy should be counseled of their higher risk of postoperative complications, and other potential mitigation strategies should be explored including the sustained period of inotropic support followed by reassessment. The optimal duration of preoperative optimization is unknown, and future work should explore the extent to which optimization can be accomplished and analyze the necessary time for this to occur prospectively.
Renal Failure and Optimization
In general, renal function tends to improve after LVAD implantation.34,35 However, chronic renal failure before implant is associated with severely impaired postimplant outcomes.36 Markers of poor end-organ function at baseline have been previously identified as risk factors for postimplant RV failure and worsening renal function,11,20 and development of more advanced stages of acute kidney injury and need for renal replacement therapy after implant are associated with poor overall survival.37
The eventual development of end-stage renal disease or need for chronic hemodialysis may occur in approximately 5% (as reflected in the present dataset) and has been associated with a high risk of mortality (up to a 10-fold greater odds of inpatient death), incident stroke, cost of care, and length of stay.38 The inclusion of 90-day hemodialysis in our composite endpoint reflects additional patient-level issues aside from mortality including the observations that intolerance to dialysis that may occur because of intradialytic hypotension, dialysis centers are not universally equipped to receive LVAD patients, and quality of life may be significantly impaired as in the general population.39 Similar to some previous data,11 preimplant BUN emerged as a more significant risk factor for renal failure than absolute creatinine, which may reflect the poor relationship between creatinine and true renal function in patients with cardiac cachexia.40
Limitations
The current study is limited by the single center, retrospective nature which could introduce selection bias because of the characteristics of patients referred for LVAD therapy and result in reduced generalizability. However, all consecutive patients undergoing CF-LVAD implantation were included in the current study during the timeframe of enrollment, and our cohort is generally similar to other published contemporaneous CF-LVAD cohorts. Our distribution of INTERMACS scores may reflect a lower implantation rate of class I patients, reflecting institutional preferences regarding patient selection. Additionally, the use of inotropic therapy and temporary mechanical support may reflect institutional practices that are not universal and could have influenced the observed outcome relationships. Crucially, this study cannot be used to imply causation, and we observed that patients who achieved more optimized parameters differed substantially from patients who did not achieve optimization. Presumably, sicker patients could not be optimized and therefore had adverse outcomes, and we cannot conclude that adjusting therapies to achieve preoperative optimization will necessarily prevent postoperative complications. Lastly, the long study period and inclusion of HM2 patients decreases the generalizability to more contemporary pumps though the hemodynamic principles remain similar.
Conclusion
The indices of low RVSWI (< 500 mmHg*ml*m−2), high RAP (>12 mmHg), and high BUN (>40 mg/dL) were independently, and in a stepwise fashion, associated with the risk of a composite endpoint of requirement for RVAD, hemodialysis at 90 days postimplant, and 30 day mortality as well as 1-year overall mortality. These powerful prognostic markers should be evaluated to determine whether prospective achievement of hemodynamic or laboratory endpoints is associated with improved post-CF-LVAD implant outcomes.
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