Predictive Modeling of Early Mortality After Left Ventricular Assist Device Implantation: A Single-Center Exploratory Analysis

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Accurate preoperative identification of high-risk patients could optimize candidate selection and improve perioperative management. Methods: From February 2021 to March 2025, 60 patients who underwent LVAD implantation at Zhongshan Hospital, Fudan University were included in the present study. Early mortality was defined as death within 2 months postoperatively, a threshold derived from survival analysis showing a distinct early hazard phase. Preoperative demographic, echocardiographic, hemodynamic, and laboratory variables were evaluated. Predictor selection used least absolute shrinkage and selection operator (LASSO) logistic regression with 10-fold cross-validation, followed by multivariable Firth logistic regression. Internal validation employed bootstrap resampling (1,000 iterations). Results: The mean age was 55 ± 10 years, and 50 (83%) patients had dilated cardiomyopathy. Kaplan–Meier analysis indicated an early hazard phase, with all deaths occurring by ~ 1.7 months after implantation. LASSO selected age, MELD-XI, passive cardiac index (PasCI), and mixed venous oxygen saturation (SvO₂) for multivariable modeling. In the final Firth model, lower SvO₂ (odds ratio [OR] 0.92; 95% CI 0.83–1.00; p = 0.048) and older age (OR 1.13; 95% CI 1.00–1.35; p = 0.059) were associated with higher early mortality. Internal validation yielded an optimism-adjusted AUC of 0.84. Conclusions: In this exploratory study, lower preoperative SvO₂ and older age were the principal predictors of early mortality after LVAD implantation. Our internally validated risk assessment model supports preoperative risk prediction and stratification; however, confirmation in larger, multicenter cohorts is warranted before broader adoption. (246/250) Left ventricular assist device Heart failure Early mortality Risk prediction model Firth logistic regression LASSO regression Exploratory analysis Figures Figure 1 Figure 2 Figure 3 Background Left ventricular assist devices (LVADs) have become an established therapy for patients with end-stage heart failure (HF) in Western countries. In the early period, LVADs were primarily used as a bridge to transplantation (BTT) to stabilize terminal HF patients while waiting for donor hearts. However, with the persistent shortage of donor organs and the fact that many candidates do not meet transplantation criteria[ 1 ], LVAD therapy has gradually evolved from a BTT strategy into a destination therapy (DT) for selected non-transplant candidates, supported by decades of clinical experience demonstrating improved survival and quality of life[ 2 – 5 ]. In China, the prevalence of HF remains high, yet most patients rely predominantly on pharmacologic treatment[ 6 , 7 ]. The shortage of donor hearts and the high cost of imported devices such as the HeartMate 3 have limited access to durable mechanical support. Over the past decade, several domestically developed LVADs have entered clinical trials, such as the CH-VAD, showing encouraging short-term outcomes[ 8 ]. Despite continuous advancements in LVAD technology, surgical techniques, and perioperative management, early mortality after implantation remains a major challenge. Data from the EUROMACS registry indicate that approximately one in five patients die within 90 days, most often from multiorgan failure or sepsis[ 9 ]. Subsequent studies have further identified right ventricular failure and postoperative infection as key contributors to early mortality[ 10 ]. This issue is particularly relevant in China, where patients often present with more advanced disease and multiple comorbidities, and national experience with durable LVADs is still accumulating. Although established risk models, such as the HeartMate Risk Score and EUROMACS, provide valuable reference frameworks, these models were primarily derived from Western populations[ 9 , 11 ]. Significant demographic and clinical differences exist between Western and Asian patients, and the current models focus mainly on long-term or overall mortality rather than early outcomes[ 12 ]. Evidences specific to Chinese LVAD recipients remain scarce. Therefore, we conducted an exploratory, data-driven study to identify preoperative predictors of early mortality after LVAD implantation and to develop an internally validated preliminary risk model tailored to this population. Methods Study Design and Population This retrospective, single-center exploratory study aimed to develop and internally validate a predictive model for early mortality after durable LVAD implantation. Sixty consecutive adult patients who underwent LVAD implantation between February 2021 and March 2025 at Zhongshan Hospital, Fudan University, were included. Procedures were performed as destination therapy or BTT according to institutional criteria. Patients with reimplantation, concomitant transplantation, emergency ECMO support, or incomplete records were excluded. The overall workflow of variable selection and model validation is illustrated in Fig. 1 . The study complied with the Declaration of Helsinki and was approved by the institutional review board (B2021-823); informed consent was waived owing to the retrospective design. Outcome Definition The primary outcome was early postoperative mortality, defined as all-cause death within two months after surgery, based on the early hazard phase observed on survival analysis. Patients alive beyond this period were classified as survivors. Survival status was verified through inpatient, outpatient, and follow-up records up to 12 months post-implantation. Data Collection and Variables Preoperative demographic, echocardiographic, catheter-derived hemodynamic, and laboratory variables were extracted from electronic medical records. Variables included patient characteristics, comorbidities, cardiac function indices, and right-heart catheterization measures. Composite indices such as the MELD-XI score and passive cardiac index (PasCI) were used to reflect hepatic–renal and hemodynamic function, respectively. Detailed variable definitions, formulas, and handling of missing data are provided in Supplementary Table S1 . Statistical Analysis Continuous variables are presented as mean ± SD or median (IQR) and categorical variables as counts (%). Kaplan–Meier analysis was used to identify the early mortality cluster. Predictor screening was performed using least absolute shrinkage and selection operator (LASSO) logistic regression, followed by multivariable Firth penalized logistic regression for model construction. Multivariable modeling used a complete-case cohort; calibration and discrimination were assessed via calibration plots, the Brier score, and ROC analysis, and internal validation used 1,000 bootstrap resamples to obtain optimism-corrected performance. Detailed modeling steps, parameter tuning, and R implementation are described in the Supplementary Methods. Results Baseline Demographic, Echocardiographic and Hemodynamic Characteristics The study included 60 LVAD recipients with a mean age of 55.2 ± 10.4 years; 86.7% were male. Most patients had dilated cardiomyopathy (83.3%), with ischemic etiology in 16.7%. The mean LVEF was 26.3 ± 4.9%, indicating severe LV dysfunction. Moderate or greater mitral and tricuspid regurgitation were present in 50.0% and 20.0% of patients, respectively. Mean total bilirubin and creatinine were 17.8 ± 8.5 µmol/L and 114.2 ± 39.2 µmol/L, corresponding to a MELD-XI score of 13.3 ± 3.2. Invasive hemodynamic assessment showed elevated filling pressures (RAP 8.2 ± 5.8 mmHg; PCWP 19.6 ± 10.9 mmHg) and reduced perfusion (PasCI 0.54 ± 0.41 L/min/m²; SvO₂ 64 ± 12%), consistent with advanced heart-failure physiology. Detailed baseline characteristics are summarized in Table 1 . Of the 60 patients, 49 with complete preoperative data with 6 early deaths comprised the modeling cohort (see Table 3 and Table 4 ). Table 1 Baseline Clinical and Hemodynamic Characteristics (n = 60) Category Variable Value (Mean ± SD, n (%)) Demographics and Etiology Age (years) 55.2 ± 10.4 Gender (male) 52 (86.7%) BMI (kg/m²) 24.5 ± 5.4 Dilated cardiomyopathy 50 (83.3%) Ischemic cardiomyopathy 10 (16.7%) Diabetes mellitus 12 (20%) Hypertension 8 (13.3%) Renal failure 6 (10%) Echocardiographic Findings LVEF (%) 26.3 ± 4.9 TAPSE (mm) 15.6 ± 2.7 AR (≥ moderate) 2 (3.3%) MR (≥ moderate) 30 (50%) TR (≥ moderate) 12 (20%) Laboratory and Organ Function Total bilirubin (µmol/L) 17.8 ± 8.5 Creatinine (µmol/L) 114.2 ± 39.2 ALT (U/L) 48.6 ± 57.8 AST (U/L) 35.2 ± 34.2 LDH (U/L) 241.2 ± 102.5 MELD-XI score 13.3 ± 3.2 Pulmonary Artery Catheter–Derived Hemodynamics RAP (mmHg) 8.22 ± 5.79 mPAP (mmHg) 29.96 ± 14.4 PCWP (mmHg) 19.63 ± 10.9 PasCI (L/min/m²) 0.54 ± 0.41 SvO₂ (%) 64.22 ± 12.37 PVR (dyn·s·cm⁻⁵) 271.04 ± 175.86 PAPI 4.12 ± 2.57 Continuous variables are presented as mean ± SD; categorical variables as n (%). Abbreviations: See list of abbreviations. Table 3 Multivariable Firth Logistic Regression for Early Mortality (≤ 2 Months) After LVAD Implantation Variable OR (95% CI) p value Age (per year) 1.13 (1.00–1.35) 0.059 MELD-XI (per point) 0.93 (0.64–1.30) 0.680 Passive Cardiac Index (per L/min/m²) 4.72 (0.24–60.50) 0.269 Mixed venous O₂ saturation (per %) 0.92 (0.83–1.00) 0.048 Model sample: n = 49; events = 6. Abbreviations: See list of abbreviations. Table 4 Internal Validation of the Calibrated Risk Equation for Early Mortality After LVAD Implantation Outcome Sample size (n) Mean predicted risk SD Min Max Survivor 43 0.101 0.102 0.003 0.551 Death 6 0.273 0.197 0.115 0.560 Internal validation was performed using bootstrap resampling (1,000 iterations). The mean predicted risk was higher among deaths (0.27 ± 0.20) compared with survivors (0.10 ± 0.10), indicating good model discrimination. Early Postoperative Survival Pattern Kaplan–Meier analysis demonstrated that all deaths occurred within approximately 1.7 months after LVAD implantation, indicating a distinct early postoperative hazard phase (Fig. 2 ). Beyond this period, overall survival remained stable up to 12 months of follow-up. The estimated 1-month survival was 90.0%, and survival thereafter remained approximately unchanged once the early hazard phase had passed. Based on this temporal pattern, subsequent analyses focused on identifying preoperative predictors of early mortality. Feature Selection and Model Development Univariable Firth logistic regression was first performed to examine the relationship between preoperative variables and early mortality (Supplementary Table S2). None of the variables reached statistical significance, although mixed venous oxygen saturation (SvO₂) showed a near-significant association. To account for potential multicollinearity and small sample size, all candidate variables were subsequently entered into a LASSO logistic regression with ten-fold cross-validation. The optimal penalty parameter (λmin = 0.00407) was selected at the minimum binomial deviance (Fig. 3 A). Variables retained in the λmin model included Age, MELD–XI, Passive Cardiac Index (PasCI), and mixed venous oxygen saturation (SvO₂), along with several hemodynamic and instability parameters (Table 2 ). At this feature-selection stage, cross-validated discrimination was high (AUC = 0.992; Fig. 3 B); threshold-dependent metrics from this stage are reported with Fig. 3 . Table 2 LASSO Feature Selection and Cross-Validated Model Performance Variable Coefficient Selected predictors at λ.min Intercept 4.4736 Age 0.2995 MELD-XI -0.4142 Passive Cardiac Index 7.2252 Mixed venous O₂ saturation -0.2853 PVR -0.0112 PAPI 0.2999 MAP -0.0432 PAPI < 1.5 -7.7532 Pre-op instability -1.6375 — CV folds 10 Lambda (min) 0.00407 Lambda (1se) 0.0198 AUC (λ.min) 0.992 Model n / events 49 / 6 Coefficients are shown for the model at λmin (0.00407) determined by 10-fold cross-validation. Model discrimination (AUC = 0.992) was internally validated. Abbreviations: See list of abbreviations. Guided by the LASSO selection, we fit the multivariable Firth logistic model in the complete-case cohort (n = 49; 6 deaths within ≤ 2 months). In the final model, lower SvO₂ (OR 0.92, 95% CI 0.83–1.00; p = 0.048) and older age (OR 1.13, 95% CI 1.00–1.35; p = 0.059) were associated with higher odds of early mortality, whereas MELD–XI and PasCI were not statistically significant (Table 3 ). Model Calibration, Validation and Performance The final Firth model included age, MELD–XI, PasCI, and SvO₂. The calibrated equation was: \(\:\text{logit}\left(p\right)=-3.890+0.122\times\:\text{Age}-0.073\times\:\text{MELD--XI}+1.552\times\:\text{PasCI}-0.083\times\:\text{SvO}\text{₂}.\) The predicted probability was calculated as \(\:p=\frac{{e}^{\text{logit}\left(p\right)}}{1+{e}^{\text{logit}\left(p\right)}}.\) Internal validation using bootstrap resampling (1,000 iterations) demonstrated good model performance. The mean predicted risk was 0.27 ± 0.20 among patients who died (n = 6) and 0.10 ± 0.10 among survivors (n = 43), corresponding to an optimism-adjusted AUC of 0.84 (Table 4 , Supplementary Figure S1 ). The ROC curve confirmed adequate discrimination between survivors and early deaths (≤ 2 months). Given the limited number of events, this internally validated model should be regarded as a hypothesis-generating tool, providing a simple and interpretable framework for estimating early mortality risk in LVAD candidates. Discussion In this exploratory single-center study, lower preoperative SvO₂ and older age emerged as the main predictors of early postoperative mortality after LVAD implantation. The internally validated model demonstrated good discrimination, suggesting that even within a relatively small cohort, key hemodynamic and clinical parameters may help identify high-risk patients. SvO₂ as a global marker of oxygen balance plays a pivotal role in understanding early postoperative outcomes in LVAD recipients[ 13 ]. Physiologically, SvO₂ reflects the proportion of oxygenated hemoglobin in the blood returning to the right side of the heart after systemic circulation, thereby representing the balance between oxygen delivery and consumption[ 14 ]. A decrease in SvO₂ indicates insufficient oxygen supply or increased oxygen demand, whereas an elevated SvO₂ may occur when oxygen delivery is adequate, but tissues are unable to extract oxygen effectively. In patients supported with LVADs, SvO₂ must be interpreted in conjunction with other hemodynamic and clinical parameters, as mechanical support can mask underlying circulatory abnormalities. For example, SvO₂ can remain abnormally high in cases of impaired oxygen extraction due to mitochondrial dysfunction or systemic infection. Nevertheless, SvO₂ remains a valuable clinical marker of global tissue perfusion, and our findings are generally consistent with previous reports highlighting its prognostic significance in LVAD populations. Age, another major determinant identified in our model, reflects the inherent vulnerability and reduced physiological reserve of the elderly population. Numerous studies have consistently identified older age as an independent predictor of mortality after LVAD implantation[ 15 – 18 ]. The inclusion of age in our model aligns with these prior findings, reinforcing its role as a robust and reproducible risk factor for adverse outcomes following mechanical circulatory support. These observations collectively suggest that even simple demographic factors can provide valuable prognostic information when combined with physiologic indicators such as SvO₂. In contrast, the predictive performance of the PasCI and MELD-XI score did not reach statistical significance in our analysis. Both parameters, however, remain physiologically relevant. PasCI provides a more stable hemodynamic assessment compared with conventional cardiac index and has been linked to postoperative right heart failure after LVAD implantation[ 19 ]. The MELD-XI score reflects combined hepatic and renal dysfunction, both of which are well known to influence surgical and post-implant outcomes in LVAD patients[ 20 , 21 ]. Prior studies in Western LVAD populations have shown a relationship between elevated MELD-XI (or related MELD scores) and increased morbidity and mortality[ 22 ], although some cohorts did not find a significant association (likely due to small sample sizes or low event rates)[ 23 ]. We postulate that the absence of significance in our cohort may similarly reflect a limited number of early mortality events rather than a true lack of predictive value. Given the paucity of data in Asian LVAD populations, larger multicenter studies are warranted to establish the predictive utility of MELD-XI in this demographic. Despite the limited cohort size, this study offers several unique contributions. First, it represents one of the early analyses of Chinese patients implanted with domestically manufactured LVADs, a field that remains in its early stages of development. Data on Asian populations are sparse, and demographic as well as clinical characteristics differ markedly from those reported in Western registries such as INTERMACS[ 24 ] and EUROMACS[ 9 ]. Second, our study specifically focused on the early postoperative period, revealing that most deaths occurred within the first two months after implantation, whereas patients surviving beyond this phase achieved stable mid-term outcomes. This pattern underscores the critical importance of perioperative management in determining early survival. Third, despite a limited number of events, we applied a rigorous methodological approach using LASSO regularization followed by Firth logistic regression, improving model stability and interpretability[ 25 ]. This analytical framework provides meaningful preliminary evidence for risk assessment in an evolving field with limited clinical experience. Nevertheless, several limitations should be acknowledged. The retrospective, single-center design and modest sample size restrict the generalizability of our findings. Although internal validation by bootstrap resampling demonstrated reasonable performance, external validation in larger, multicenter cohorts will be essential to confirm the robustness and clinical applicability of our model. In conclusion, despite its exploratory nature, this study offers preliminary evidence on preoperative risk stratification in Chinese LVAD candidates and may guide perioperative strategies to lower early mortality, pending confirmation in larger multicenter studies. Abbreviations ALT alanine aminotransferase AR aortic regurgitation AST aspartate aminotransferase BTT bridge to transplantation CI confidence interval (in regression models); cardiac index (in hemodynamic formula) CV cross–validation CVP central venous pressure LASSO least absolute shrinkage and selection operator LDH lactate dehydrogenase LVAD left ventricular assist device MAP mean arterial pressure MELD Model for End–Stage Liver Disease mPAP mean pulmonary artery pressure MR mitral regurgitation OR odds ratio PAPI pulmonary artery pulsatility index PasCI passive cardiac index PCWP pulmonary capillary wedge pressure PVR pulmonary vascular resistance RAP right atrial pressure SvO₂ mixed venous oxygen saturation TR tricuspid regurgitation Declarations # These authors contributed equally to the work. Acknowledgements: We thank Austin Todd and Jie Yang for assistance with statistical analysis. Author contributions: Shuyang Lu drafted the manuscript; Junjiang Liu and Dingqian Liu collected the data; Guangwei Hao and Guowei Tu provided perioperative patient care; Kefang Guo and Lili Dong performed intraoperative catheterization and echocardiographic examinations; Ran Huo and Aurora Lee conducted statistical analysis and language editing; Xiaoning Sun and Chunsheng Wang performed the surgeries, conceived the study, and critically revised the manuscript. Conflicts of interest: The authors declare no competing interests. Ethics approval: The study design and a waiver of informed consent were approved by Zhongshan Hospital Fudan University Institutional Review Board (B2021-823, approved August 2021). Funding statement: This project was supported by the Shanghai Municipal Hospital Emerging Frontier Joint Research Project (SHDC12024146). Clinical Trial Number: Not applicable. Human Ethics and Consent to Participate Declarations: Not applicable. 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Supplementary Files Supplementarydata.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 18 May, 2026 Reviews received at journal 15 May, 2026 Reviews received at journal 11 May, 2026 Reviewers agreed at journal 06 May, 2026 Reviewers agreed at journal 04 May, 2026 Reviewers agreed at journal 03 May, 2026 Reviews received at journal 01 May, 2026 Reviewers agreed at journal 01 May, 2026 Reviewers agreed at journal 01 May, 2026 Reviewers invited by journal 01 May, 2026 Editor assigned by journal 29 Apr, 2026 Editor invited by journal 27 Apr, 2026 Submission checks completed at journal 25 Apr, 2026 First submitted to journal 25 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Sun","email":"data:image/png;base64,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","orcid":"","institution":"Fudan University","correspondingAuthor":true,"prefix":"","firstName":"Xiaoning","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2026-04-12 02:53:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9391241/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9391241/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109118317,"identity":"2a493cd9-8d2f-42d9-a3c8-372ca8bf60fd","added_by":"auto","created_at":"2026-05-12 16:52:23","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":252536,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of model development and internal validation for early mortality after LVAD implantation.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9391241/v1/e5c8bcbce5dbbc64a3233153.jpeg"},{"id":109118320,"identity":"9b35fdfb-a273-4d0b-929f-c5a2f391de48","added_by":"auto","created_at":"2026-05-12 16:52:23","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":210913,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan–Meier survival curve of the study cohort after LVAD implantation. \u003c/strong\u003eSurvival analysis demonstrated clustering of deaths within the first two postoperative months (turning point ≈ 1.7 months), after which the survival curve plateaued.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9391241/v1/1421993ee7ed3a81ac4a9209.jpeg"},{"id":109205001,"identity":"13856663-e25d-4911-86f4-cce2f89a57d3","added_by":"auto","created_at":"2026-05-13 15:03:10","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":254562,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFeature selection and performance of the predictive model for early post-LVAD mortality. \u003c/strong\u003e(A) Ten-fold cross-validation identifying the optimal penalty parameter (λmin = 0.00407). (B) Receiver operating characteristic (ROC) curve demonstrating excellent model discrimination (AUC = 0.99, sensitivity = 1.00, specificity = 0.80).\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9391241/v1/662c1e897b63cc8db51d642a.jpeg"},{"id":109249409,"identity":"0dc164f7-472c-428c-8235-c61356fa5e44","added_by":"auto","created_at":"2026-05-14 08:51:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":981737,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9391241/v1/7b1a1fd4-6b81-45a3-9b8d-7d00ceda9024.pdf"},{"id":109118318,"identity":"011429bf-6808-49cf-93a8-c01babe2aaef","added_by":"auto","created_at":"2026-05-12 16:52:23","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":4771545,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydata.docx","url":"https://assets-eu.researchsquare.com/files/rs-9391241/v1/f5668867032f044174efc190.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictive Modeling of Early Mortality After Left Ventricular Assist Device Implantation: A Single-Center Exploratory Analysis","fulltext":[{"header":"Background","content":"\u003cp\u003eLeft ventricular assist devices (LVADs) have become an established therapy for patients with end-stage heart failure (HF) in Western countries. In the early period, LVADs were primarily used as a bridge to transplantation (BTT) to stabilize terminal HF patients while waiting for donor hearts. However, with the persistent shortage of donor organs and the fact that many candidates do not meet transplantation criteria[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], LVAD therapy has gradually evolved from a BTT strategy into a destination therapy (DT) for selected non-transplant candidates, supported by decades of clinical experience demonstrating improved survival and quality of life[\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn China, the prevalence of HF remains high, yet most patients rely predominantly on pharmacologic treatment[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The shortage of donor hearts and the high cost of imported devices such as the HeartMate 3 have limited access to durable mechanical support. Over the past decade, several domestically developed LVADs have entered clinical trials, such as the CH-VAD, showing encouraging short-term outcomes[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite continuous advancements in LVAD technology, surgical techniques, and perioperative management, early mortality after implantation remains a major challenge. Data from the EUROMACS registry indicate that approximately one in five patients die within 90 days, most often from multiorgan failure or sepsis[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Subsequent studies have further identified right ventricular failure and postoperative infection as key contributors to early mortality[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This issue is particularly relevant in China, where patients often present with more advanced disease and multiple comorbidities, and national experience with durable LVADs is still accumulating.\u003c/p\u003e \u003cp\u003eAlthough established risk models, such as the HeartMate Risk Score and EUROMACS, provide valuable reference frameworks, these models were primarily derived from Western populations[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Significant demographic and clinical differences exist between Western and Asian patients, and the current models focus mainly on long-term or overall mortality rather than early outcomes[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Evidences specific to Chinese LVAD recipients remain scarce. Therefore, we conducted an exploratory, data-driven study to identify preoperative predictors of early mortality after LVAD implantation and to develop an internally validated preliminary risk model tailored to this population.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Population\u003c/h2\u003e \u003cp\u003eThis retrospective, single-center exploratory study aimed to develop and internally validate a predictive model for early mortality after durable LVAD implantation. Sixty consecutive adult patients who underwent LVAD implantation between February 2021 and March 2025 at Zhongshan Hospital, Fudan University, were included. Procedures were performed as destination therapy or BTT according to institutional criteria. Patients with reimplantation, concomitant transplantation, emergency ECMO support, or incomplete records were excluded. The overall workflow of variable selection and model validation is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The study complied with the Declaration of Helsinki and was approved by the institutional review board (B2021-823); informed consent was waived owing to the retrospective design.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eOutcome Definition\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was early postoperative mortality, defined as all-cause death within two months after surgery, based on the early hazard phase observed on survival analysis. Patients alive beyond this period were classified as survivors. Survival status was verified through inpatient, outpatient, and follow-up records up to 12 months post-implantation.\u003c/p\u003e\n\u003ch3\u003eData Collection and Variables\u003c/h3\u003e\n\u003cp\u003ePreoperative demographic, echocardiographic, catheter-derived hemodynamic, and laboratory variables were extracted from electronic medical records. Variables included patient characteristics, comorbidities, cardiac function indices, and right-heart catheterization measures. Composite indices such as the MELD-XI score and passive cardiac index (PasCI) were used to reflect hepatic\u0026ndash;renal and hemodynamic function, respectively. Detailed variable definitions, formulas, and handling of missing data are provided in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eContinuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or median (IQR) and categorical variables as counts (%). Kaplan\u0026ndash;Meier analysis was used to identify the early mortality cluster. Predictor screening was performed using least absolute shrinkage and selection operator (LASSO) logistic regression, followed by multivariable Firth penalized logistic regression for model construction. Multivariable modeling used a complete-case cohort; calibration and discrimination were assessed via calibration plots, the Brier score, and ROC analysis, and internal validation used 1,000 bootstrap resamples to obtain optimism-corrected performance. Detailed modeling steps, parameter tuning, and R implementation are described in the Supplementary Methods.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBaseline Demographic, Echocardiographic and Hemodynamic Characteristics\u003c/h2\u003e \u003cp\u003eThe study included 60 LVAD recipients with a mean age of 55.2\u0026thinsp;\u0026plusmn;\u0026thinsp;10.4 years; 86.7% were male. Most patients had dilated cardiomyopathy (83.3%), with ischemic etiology in 16.7%. The mean LVEF was 26.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9%, indicating severe LV dysfunction. Moderate or greater mitral and tricuspid regurgitation were present in 50.0% and 20.0% of patients, respectively. Mean total bilirubin and creatinine were 17.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5 \u0026micro;mol/L and 114.2\u0026thinsp;\u0026plusmn;\u0026thinsp;39.2 \u0026micro;mol/L, corresponding to a MELD-XI score of 13.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2. Invasive hemodynamic assessment showed elevated filling pressures (RAP 8.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8 mmHg; PCWP 19.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9 mmHg) and reduced perfusion (PasCI 0.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41 L/min/m\u0026sup2;; SvO₂ 64\u0026thinsp;\u0026plusmn;\u0026thinsp;12%), consistent with advanced heart-failure physiology. Detailed baseline characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Of the 60 patients, 49 with complete preoperative data with 6 early deaths comprised the modeling cohort (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Clinical and Hemodynamic Characteristics (n\u0026thinsp;=\u0026thinsp;60)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValue (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, n (%))\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographics and Etiology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.2\u0026thinsp;\u0026plusmn;\u0026thinsp;10.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender (male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (86.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDilated cardiomyopathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (83.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIschemic cardiomyopathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (20%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (13.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRenal failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (10%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEchocardiographic Findings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLVEF (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTAPSE (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAR (\u0026ge;\u0026thinsp;moderate)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMR (\u0026ge;\u0026thinsp;moderate)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (50%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR (\u0026ge;\u0026thinsp;moderate)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (20%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaboratory and Organ Function\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal bilirubin (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCreatinine (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114.2\u0026thinsp;\u0026plusmn;\u0026thinsp;39.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eALT (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.6\u0026thinsp;\u0026plusmn;\u0026thinsp;57.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAST (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.2\u0026thinsp;\u0026plusmn;\u0026thinsp;34.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLDH (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e241.2\u0026thinsp;\u0026plusmn;\u0026thinsp;102.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMELD-XI score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulmonary Artery Catheter\u0026ndash;Derived Hemodynamics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRAP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.22\u0026thinsp;\u0026plusmn;\u0026thinsp;5.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emPAP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.96\u0026thinsp;\u0026plusmn;\u0026thinsp;14.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePCWP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.63\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePasCI (L/min/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSvO₂ (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.22\u0026thinsp;\u0026plusmn;\u0026thinsp;12.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePVR (dyn\u0026middot;s\u0026middot;cm⁻⁵)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e271.04\u0026thinsp;\u0026plusmn;\u0026thinsp;175.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePAPI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.12\u0026thinsp;\u0026plusmn;\u0026thinsp;2.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eContinuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD; categorical variables as n (%).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eAbbreviations: See list of abbreviations.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable Firth Logistic Regression for Early Mortality (\u0026le;\u0026thinsp;2 Months) After LVAD Implantation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (per year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.13 (1.00\u0026ndash;1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMELD-XI (per point)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.93 (0.64\u0026ndash;1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.680\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePassive Cardiac Index (per L/min/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.72 (0.24\u0026ndash;60.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMixed venous O₂ saturation (per %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.92 (0.83\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eModel sample: n\u0026thinsp;=\u0026thinsp;49; events\u0026thinsp;=\u0026thinsp;6.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eAbbreviations: See list of abbreviations.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInternal Validation of the Calibrated Risk Equation for Early Mortality After LVAD Implantation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSample size (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean predicted risk\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurvivor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.551\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeath\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.560\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eInternal validation was performed using bootstrap resampling (1,000 iterations). The mean predicted risk was higher among deaths (0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20) compared with survivors (0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10), indicating good model discrimination.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEarly Postoperative Survival Pattern\u003c/h3\u003e\n\u003cp\u003eKaplan\u0026ndash;Meier analysis demonstrated that all deaths occurred within approximately 1.7 months after LVAD implantation, indicating a distinct early postoperative hazard phase (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Beyond this period, overall survival remained stable up to 12 months of follow-up. The estimated 1-month survival was 90.0%, and survival thereafter remained approximately unchanged once the early hazard phase had passed. Based on this temporal pattern, subsequent analyses focused on identifying preoperative predictors of early mortality.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eFeature Selection and Model Development\u003c/h3\u003e\n\u003cp\u003eUnivariable Firth logistic regression was first performed to examine the relationship between preoperative variables and early mortality (Supplementary Table S2). None of the variables reached statistical significance, although mixed venous oxygen saturation (SvO₂) showed a near-significant association.\u003c/p\u003e \u003cp\u003eTo account for potential multicollinearity and small sample size, all candidate variables were subsequently entered into a LASSO logistic regression with ten-fold cross-validation. The optimal penalty parameter (λmin\u0026thinsp;=\u0026thinsp;0.00407) was selected at the minimum binomial deviance (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Variables retained in the λmin model included Age, MELD\u0026ndash;XI, Passive Cardiac Index (PasCI), and mixed venous oxygen saturation (SvO₂), along with several hemodynamic and instability parameters (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e2\u003c/span\u003e). At this feature-selection stage, cross-validated discrimination was high (AUC\u0026thinsp;=\u0026thinsp;0.992; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB); threshold-dependent metrics from this stage are reported with Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLASSO Feature Selection and Cross-Validated Model Performance\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelected predictors at λ.min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.4736\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2995\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMELD-XI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.4142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePassive Cardiac Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.2252\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMixed venous O₂ saturation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.2853\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePVR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePAPI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0432\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePAPI\u0026thinsp;\u0026lt;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.7532\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-op instability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.6375\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCV folds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLambda (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00407\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLambda (1se)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0198\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAUC (λ.min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel n / events\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 / 6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eCoefficients are shown for the model at λmin (0.00407) determined by 10-fold cross-validation. Model discrimination (AUC\u0026thinsp;=\u0026thinsp;0.992) was internally validated.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eAbbreviations: See list of abbreviations.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eGuided by the LASSO selection, we fit the multivariable Firth logistic model in the complete-case cohort (n\u0026thinsp;=\u0026thinsp;49; 6 deaths within \u0026le;\u0026thinsp;2 months). In the final model, lower SvO₂ (OR 0.92, 95% CI 0.83\u0026ndash;1.00; p\u0026thinsp;=\u0026thinsp;0.048) and older age (OR 1.13, 95% CI 1.00\u0026ndash;1.35; p\u0026thinsp;=\u0026thinsp;0.059) were associated with higher odds of early mortality, whereas MELD\u0026ndash;XI and PasCI were not statistically significant (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eModel Calibration, Validation and Performance\u003c/h2\u003e \u003cp\u003eThe final Firth model included age, MELD\u0026ndash;XI, PasCI, and SvO₂. The calibrated equation was:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\text{logit}\\left(p\\right)=-3.890+0.122\\times\\:\\text{Age}-0.073\\times\\:\\text{MELD--XI}+1.552\\times\\:\\text{PasCI}-0.083\\times\\:\\text{SvO}\\text{₂}.\\)\u003c/span\u003e \u003c/span\u003eThe predicted probability was calculated as\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:p=\\frac{{e}^{\\text{logit}\\left(p\\right)}}{1+{e}^{\\text{logit}\\left(p\\right)}}.\\)\u003c/span\u003e \u003c/span\u003eInternal validation using bootstrap resampling (1,000 iterations) demonstrated good model performance. The mean predicted risk was 0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20 among patients who died (n\u0026thinsp;=\u0026thinsp;6) and 0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10 among survivors (n\u0026thinsp;=\u0026thinsp;43), corresponding to an optimism-adjusted AUC of 0.84 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The ROC curve confirmed adequate discrimination between survivors and early deaths (\u0026le;\u0026thinsp;2 months). Given the limited number of events, this internally validated model should be regarded as a hypothesis-generating tool, providing a simple and interpretable framework for estimating early mortality risk in LVAD candidates.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this exploratory single-center study, lower preoperative SvO₂ and older age emerged as the main predictors of early postoperative mortality after LVAD implantation. The internally validated model demonstrated good discrimination, suggesting that even within a relatively small cohort, key hemodynamic and clinical parameters may help identify high-risk patients.\u003c/p\u003e \u003cp\u003eSvO₂ as a global marker of oxygen balance plays a pivotal role in understanding early postoperative outcomes in LVAD recipients[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Physiologically, SvO₂ reflects the proportion of oxygenated hemoglobin in the blood returning to the right side of the heart after systemic circulation, thereby representing the balance between oxygen delivery and consumption[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. A decrease in SvO₂ indicates insufficient oxygen supply or increased oxygen demand, whereas an elevated SvO₂ may occur when oxygen delivery is adequate, but tissues are unable to extract oxygen effectively. In patients supported with LVADs, SvO₂ must be interpreted in conjunction with other hemodynamic and clinical parameters, as mechanical support can mask underlying circulatory abnormalities. For example, SvO₂ can remain abnormally high in cases of impaired oxygen extraction due to mitochondrial dysfunction or systemic infection. Nevertheless, SvO₂ remains a valuable clinical marker of global tissue perfusion, and our findings are generally consistent with previous reports highlighting its prognostic significance in LVAD populations.\u003c/p\u003e \u003cp\u003eAge, another major determinant identified in our model, reflects the inherent vulnerability and reduced physiological reserve of the elderly population. Numerous studies have consistently identified older age as an independent predictor of mortality after LVAD implantation[\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The inclusion of age in our model aligns with these prior findings, reinforcing its role as a robust and reproducible risk factor for adverse outcomes following mechanical circulatory support. These observations collectively suggest that even simple demographic factors can provide valuable prognostic information when combined with physiologic indicators such as SvO₂.\u003c/p\u003e \u003cp\u003eIn contrast, the predictive performance of the PasCI and MELD-XI score did not reach statistical significance in our analysis. Both parameters, however, remain physiologically relevant. PasCI provides a more stable hemodynamic assessment compared with conventional cardiac index and has been linked to postoperative right heart failure after LVAD implantation[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The MELD-XI score reflects combined hepatic and renal dysfunction, both of which are well known to influence surgical and post-implant outcomes in LVAD patients[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Prior studies in Western LVAD populations have shown a relationship between elevated MELD-XI (or related MELD scores) and increased morbidity and mortality[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], although some cohorts did not find a significant association (likely due to small sample sizes or low event rates)[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. We postulate that the absence of significance in our cohort may similarly reflect a limited number of early mortality events rather than a true lack of predictive value. Given the paucity of data in Asian LVAD populations, larger multicenter studies are warranted to establish the predictive utility of MELD-XI in this demographic.\u003c/p\u003e \u003cp\u003eDespite the limited cohort size, this study offers several unique contributions. First, it represents one of the early analyses of Chinese patients implanted with domestically manufactured LVADs, a field that remains in its early stages of development. Data on Asian populations are sparse, and demographic as well as clinical characteristics differ markedly from those reported in Western registries such as INTERMACS[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] and EUROMACS[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Second, our study specifically focused on the early postoperative period, revealing that most deaths occurred within the first two months after implantation, whereas patients surviving beyond this phase achieved stable mid-term outcomes. This pattern underscores the critical importance of perioperative management in determining early survival. Third, despite a limited number of events, we applied a rigorous methodological approach using LASSO regularization followed by Firth logistic regression, improving model stability and interpretability[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This analytical framework provides meaningful preliminary evidence for risk assessment in an evolving field with limited clinical experience.\u003c/p\u003e \u003cp\u003eNevertheless, several limitations should be acknowledged. The retrospective, single-center design and modest sample size restrict the generalizability of our findings. Although internal validation by bootstrap resampling demonstrated reasonable performance, external validation in larger, multicenter cohorts will be essential to confirm the robustness and clinical applicability of our model.\u003c/p\u003e \u003cp\u003eIn conclusion, despite its exploratory nature, this study offers preliminary evidence on preoperative risk stratification in Chinese LVAD candidates and may guide perioperative strategies to lower early mortality, pending confirmation in larger multicenter studies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eALT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ealanine aminotransferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eaortic regurgitation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003easpartate aminotransferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBTT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebridge to transplantation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfidence interval (in regression models); cardiac index (in hemodynamic formula)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecross\u0026ndash;validation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCVP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecentral venous pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLASSO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eleast absolute shrinkage and selection operator\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLDH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elactate dehydrogenase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLVAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eleft ventricular assist device\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMAP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emean arterial pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMELD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eModel for End\u0026ndash;Stage Liver Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003emPAP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emean pulmonary artery pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emitral regurgitation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eodds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePAPI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epulmonary artery pulsatility index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePasCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epassive cardiac index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCWP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epulmonary capillary wedge pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePVR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epulmonary vascular resistance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRAP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eright atrial pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSvO₂\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emixed venous oxygen saturation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etricuspid regurgitation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e# These authors contributed equally to the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e We thank Austin Todd and Jie Yang for assistance with statistical analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u0026nbsp;\u003c/strong\u003eShuyang Lu drafted the manuscript; Junjiang Liu and Dingqian Liu collected the data; Guangwei Hao and Guowei Tu provided perioperative patient care; Kefang Guo and Lili Dong performed intraoperative catheterization and echocardiographic examinations; Ran Huo and Aurora Lee conducted statistical analysis and language editing; Xiaoning Sun and Chunsheng Wang performed the surgeries, conceived the study, and critically revised the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest:\u003c/strong\u003e The authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u003c/strong\u003e The study design and a waiver of informed consent were approved by Zhongshan Hospital Fudan University Institutional Review Board (B2021-823, approved August 2021).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement:\u003c/strong\u003e This project was supported by the Shanghai Municipal Hospital Emerging Frontier Joint Research Project (SHDC12024146).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate Declarations:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCameli M, Pastore MC, Campora A, Lisi M, Mandoli GE. Donor shortage in heart transplantation: How can we overcome this challenge? Front Cardiovasc Med. 2022;9:1001002.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLahpor JR, de Jonge N, van Swieten HA, Wesenhagen H, Klopping C, Geertman JH, et al. Left ventricular assist device as bridge to transplantation in patients with end-stage heart failure: Eight-year experience with the implantable HeartMate LVAS. Neth Heart J. 2002;10:267\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKitada S, Schulze PC, Jin Z, Clerkin K, Homma S, Mancini DM. Comparison of early versus delayed timing of left ventricular assist device implantation as a bridge-to-transplantation: An analysis of the UNOS dataset. Int J Cardiol. 2016;203:929\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFendler TJ, Nassif ME, Kennedy KF, Joseph SM, Silvestry SC, Ewald GA, et al. Global Outcome in Patients With Left Ventricular Assist Devices. Am J Cardiol. 2017;119:1069\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMehra MR, Uriel N, Naka Y, Cleveland JC Jr., Yuzefpolskaya M, Salerno CT, et al. A Fully Magnetically Levitated Left Ventricular Assist Device - Final Report. N Engl J Med. 2019;380:1618\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang X, Zhao ZP, Shi Y, Han GY, Xu Y, Li YC, et al. The evolving burden of heart failure in China: a 34-year subnational analysis of trends and causes from the Global Burden of Disease Study 2023. Mil Med Res. 2025;12:65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKang K, Wang Q, Li Y, Liu C, Yu H, Li N. Global and Chinese perspectives on the growing burden of heart failure: trends, gender, and age-related differences (1990\u0026ndash;2021) based on GBD 2021 data. BMC Cardiovasc Disord. 2025;25:510.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, Zhou X, Chen H, Du J, Qing P, Zou L, et al. Long-term outcomes of a novel fully magnetically levitated ventricular assist device for the treatment of advanced heart failure in China. J Heart Lung Transpl. 2024;43:1806\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkin S, Soliman O, de By T, Muslem R, Tijssen JGP, Schoenrath F, et al. Causes and predictors of early mortality in patients treated with left ventricular assist device implantation in the European Registry of Mechanical Circulatory Support (EUROMACS). Intensive Care Med. 2020;46:1349\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoburg RS, Marinos SL, Baumgaertner M, Rustenbach CJ, Salewski C, Doll I et al. Nine Years of Continuous Flow LVAD (HeartMate 3): Survival and LVAD-Related Complications before and after Hospital Discharge. J Cardiovasc Dev Dis. 2024;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTibrewala A, Pham DT, Hu M, Petito LC, Rich JD, Gustafsson F, et al. Risk prediction model for waitlist mortality in patients with left ventricular assist devices. JHLT Open. 2025;10:100337.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMehra MR, Goldstein DJ, Cleveland JC, Cowger JA, Hall S, Salerno CT, et al. Five-Year Outcomes in Patients With Fully Magnetically Levitated vs Axial-Flow Left Ventricular Assist Devices in the MOMENTUM 3 Randomized Trial. JAMA. 2022;328:1233\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaakinen TI, Ikalainen T, Erkinaro TM, Karhu JM, Liisanantti JH, Ohtonen PP, et al. Association of low mixed venous oxygen saturations during early ICU stay with increased 30-day and 1-year mortality after cardiac surgery: a single-center retrospective study. BMC Anesthesiol. 2022;22:322.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChetana Shanmukhappa S, Lokeshwaran S. Venous Oxygen Saturation. \u003cem\u003eStatPearls\u003c/em\u003e. Treasure Island (FL); 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAtluri P, Goldstone AB, Kobrin DM, Cohen JE, MacArthur JW, Howard JL, et al. Ventricular assist device implant in the elderly is associated with increased, but respectable risk: a multi-institutional study. Ann Thorac Surg. 2013;96:141\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsleh R, Alnsasra H, Daly RC, Schettle SD, Briasoulis A, Taher R, et al. Predictors and Clinical Outcomes of Vasoplegia in Patients Bridged to Heart Transplantation With Continuous-Flow Left Ventricular Assist Devices. J Am Heart Assoc. 2019;8:e013108.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRadhoe SP, Veenis JF, Jakus N, Timmermans P, Pouleur AC, Rubis P, et al. How does age affect outcomes after left ventricular assist device implantation: results from the PCHF-VAD registry. ESC Heart Fail. 2023;10:884\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan de Vreede EM, van den Berg F, Jahangiri P, Caliskan K, Mattace-Raso F. The Effect of Age on Non-Invasive Hemodynamics in Chronic Heart Failure Patients on Left-Ventricular Assist Device Support: A Pilot Study. J Clin Med. 2022;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang PC, Millar J, Noly PE, Sicim H, Likosky DS, Zhang M et al. Preoperative passive venous pressure-driven cardiac function determines left ventricular assist device outcomes. J Thorac Cardiovasc Surg. 2024;168:133\u0026thinsp;\u0026ndash;\u0026thinsp;44 e5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCritsinelis A, Kurihara C, Volkovicher N, Kawabori M, Sugiura T, Manon M 2, et al. Model of End-Stage Liver Disease-eXcluding International Normalized Ratio (MELD-XI) Scoring System to Predict Outcomes in Patients Who Undergo Left Ventricular Assist Device Implantation. Ann Thorac Surg. 2018;106:513\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmdani S, Boyle GJ, Cantor RS, Conway J, Godown J, Kirklin JK, et al. Significance of pre and post-implant MELD-XI score on survival in children undergoing VAD implantation. J Heart Lung Transpl. 2021;40:1614\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeorge TJ, Van Dinter T, Rawitscher D, DiMaio JM, Kabra N, Afzal A. Impact of Preoperative Liver Function on Short-Term HeartMate 3 Outcomes. Am J Cardiol. 2022;183:62\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhayata M, Al-Kindi S, Panhwar MS, Njoroge L, Deo S, Medalian B, et al. Preoperative MELD-XI is not Associated with Mortality after LVAD. J Card Fail. 2018;24:S106.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMolina EJ, Shah P, Kiernan MS, Cornwell WK 3rd, Copeland H, Takeda K et al. The Society of Thoracic Surgeons Intermacs 2020 Annual Report. Ann Thorac Surg. 2021;111:778\u0026thinsp;\u0026ndash;\u0026thinsp;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeinze G, Schemper M. A solution to the problem of separation in logistic regression. Stat Med. 2002;21:2409\u0026ndash;19.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Left ventricular assist device, Heart failure, Early mortality, Risk prediction model, Firth logistic regression, LASSO regression, Exploratory analysis","lastPublishedDoi":"10.21203/rs.3.rs-9391241/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9391241/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground:\u003c/b\u003e\u003c/p\u003e \u003cp\u003eEarly postoperative mortality remains a major clinical challenge in left ventricular assist device implantation. Accurate preoperative identification of high-risk patients could optimize candidate selection and improve perioperative management.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods:\u003c/b\u003e\u003c/p\u003e \u003cp\u003eFrom February 2021 to March 2025, 60 patients who underwent LVAD implantation at Zhongshan Hospital, Fudan University were included in the present study. Early mortality was defined as death within 2 months postoperatively, a threshold derived from survival analysis showing a distinct early hazard phase. Preoperative demographic, echocardiographic, hemodynamic, and laboratory variables were evaluated. Predictor selection used least absolute shrinkage and selection operator (LASSO) logistic regression with 10-fold cross-validation, followed by multivariable Firth logistic regression. Internal validation employed bootstrap resampling (1,000 iterations).\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults:\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe mean age was 55\u0026thinsp;\u0026plusmn;\u0026thinsp;10 years, and 50 (83%) patients had dilated cardiomyopathy. Kaplan\u0026ndash;Meier analysis indicated an early hazard phase, with all deaths occurring by ~\u0026thinsp;1.7 months after implantation. LASSO selected age, MELD-XI, passive cardiac index (PasCI), and mixed venous oxygen saturation (SvO₂) for multivariable modeling. In the final Firth model, lower SvO₂ (odds ratio [OR] 0.92; 95% CI 0.83\u0026ndash;1.00; p\u0026thinsp;=\u0026thinsp;0.048) and older age (OR 1.13; 95% CI 1.00\u0026ndash;1.35; p\u0026thinsp;=\u0026thinsp;0.059) were associated with higher early mortality. Internal validation yielded an optimism-adjusted AUC of 0.84.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions:\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn this exploratory study, lower preoperative SvO₂ and older age were the principal predictors of early mortality after LVAD implantation. Our internally validated risk assessment model supports preoperative risk prediction and stratification; however, confirmation in larger, multicenter cohorts is warranted before broader adoption. 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