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Left Ventricular Diastolic Dysfunction Increases the Risk of Medium-term Mortality in Patients with Cirrhotic Ascites Cover

Left Ventricular Diastolic Dysfunction Increases the Risk of Medium-term Mortality in Patients with Cirrhotic Ascites

Open Access
|Nov 2025

Figures & Tables

Fig. 1

Lasso regression for screening potential influencing factors of 1-year mortality risk. (A) and (B) belong to total cohort, and (C) and (D) belong to subgroup without hepatic malignancy. (A) and (C) are lasso coefficient plots. The vertical axis represents model coefficients, the lower horizontal axis shows the logarithm of λ, and the upper horizontal axis indicates the number of variables corresponding to each λ. λ is the regularization parameter, which controls the degree of penalty applied to model coefficients, thereby balancing the model's fitting ability and complexity. As λ increases, the penalty on model coefficients intensifies, compressing more coefficients toward zero or even setting them to zero. This achieves variable selection by eliminating relatively unimportant predictors, while variables not compressed to zero become the final retained model variables. (B) and (D) are 10-fold cross-validation plots. The vertical axis, Binomial Deviance (also known as Log loss), represents model error. This loss function measures the discrepancy between predicted and observed outcomes, quantifying the quality of model predictions. The lower horizontal axis displays the logarithm of λ, while the upper horizontal axis shows the number of variables corresponding to each λ. The left dashed vertical line indicates λ.min, where model error is minimized, but this model may be relatively complex and carry some risk of overfitting. The right dashed line corresponds to λ.1 standard error (λ.1se), the λ value where the evaluation metric is one standard error larger than that at λ.min. Models at λ.1se sacrifice some predictive accuracy (slightly higher than optimal prediction error) in exchange for a relatively simpler, more stable model with reliable generalization capabilities. Using 1-year mortality risk as the dependent variable, this study selected predictive models from 75 candidate variables across five dimensions (including demographics, basic clinical information, liver functions, laboratory findings, and cardiac parameters of TTE). After Lasso regression, the log(λ.min) and log(λ.1se) for the total cohort were −3.56 and −2.90, corresponding to 19 and 13 model variables, respectively, therefore, the 13 variables associated with log(λ.1se) were identified as potential influencing factors. For the subgroup without hepatic malignancy, log(λ.min) and log(λ.1se) were −3.10 and −2.54, corresponding to 12 and 10 model variables, respectively, so this study adopted the 10 variables corresponding to log(λ.1se) as potential influencing factors.
Lasso regression for screening potential influencing factors of 1-year mortality risk. (A) and (B) belong to total cohort, and (C) and (D) belong to subgroup without hepatic malignancy. (A) and (C) are lasso coefficient plots. The vertical axis represents model coefficients, the lower horizontal axis shows the logarithm of λ, and the upper horizontal axis indicates the number of variables corresponding to each λ. λ is the regularization parameter, which controls the degree of penalty applied to model coefficients, thereby balancing the model's fitting ability and complexity. As λ increases, the penalty on model coefficients intensifies, compressing more coefficients toward zero or even setting them to zero. This achieves variable selection by eliminating relatively unimportant predictors, while variables not compressed to zero become the final retained model variables. (B) and (D) are 10-fold cross-validation plots. The vertical axis, Binomial Deviance (also known as Log loss), represents model error. This loss function measures the discrepancy between predicted and observed outcomes, quantifying the quality of model predictions. The lower horizontal axis displays the logarithm of λ, while the upper horizontal axis shows the number of variables corresponding to each λ. The left dashed vertical line indicates λ.min, where model error is minimized, but this model may be relatively complex and carry some risk of overfitting. The right dashed line corresponds to λ.1 standard error (λ.1se), the λ value where the evaluation metric is one standard error larger than that at λ.min. Models at λ.1se sacrifice some predictive accuracy (slightly higher than optimal prediction error) in exchange for a relatively simpler, more stable model with reliable generalization capabilities. Using 1-year mortality risk as the dependent variable, this study selected predictive models from 75 candidate variables across five dimensions (including demographics, basic clinical information, liver functions, laboratory findings, and cardiac parameters of TTE). After Lasso regression, the log(λ.min) and log(λ.1se) for the total cohort were −3.56 and −2.90, corresponding to 19 and 13 model variables, respectively, therefore, the 13 variables associated with log(λ.1se) were identified as potential influencing factors. For the subgroup without hepatic malignancy, log(λ.min) and log(λ.1se) were −3.10 and −2.54, corresponding to 12 and 10 model variables, respectively, so this study adopted the 10 variables corresponding to log(λ.1se) as potential influencing factors.

Fig. 2

K-M curves showing the predictive value of LVDD vs. non-LVDD for 1-year mortality risk. (A) Total participants; (B) Without hepatic malignancy; (C) With hepatic malignancy.
K-M curves showing the predictive value of LVDD vs. non-LVDD for 1-year mortality risk. (A) Total participants; (B) Without hepatic malignancy; (C) With hepatic malignancy.

Fig. 3

Time-dependent ROC curves showing the efficacy of LVDD in predicting 1-year mortality. (A) Overall participants; (B) Subgroup without hepatic malignancy
Time-dependent ROC curves showing the efficacy of LVDD in predicting 1-year mortality. (A) Overall participants; (B) Subgroup without hepatic malignancy

Fig. 4

The intrinsic relationship between cirrhosis and LVDD and corresponding mechanisms.
The intrinsic relationship between cirrhosis and LVDD and corresponding mechanisms.

Univariate and multivariate Cox regression of 1-year mortality risk

ParticipantsFactorsUnivariate CoxMultivariate Cox


HR95% CIP-valueHR95% CIP-value
TotalBMI0.9170.851–0.9870.0210.8760.808–0.9500.001
Hepatic malignancy2.5791.635–4.069<0.0011.8631.107–3.1410.019
MAD1.0690.999–1.1550.088
RVOTD1.0871.039–1.137<0.0011.0771.018–1.1390.008
LVDD2.4471.522–3.933<0.0012.1091.279–3.4780.003
WBC1.1141.065–1.165<0.001
PLT1.0041.002–1.006<0.0011.0031.0002–1.00620.035
Na0.8860.839–0.936<0.001
DBIL1.0041.001–1.0070.001
GGT1.0011.001–1.0020.007
ALP1.0021.001–1.003<0.001
CHE0.3520.213–0.580<0.001
INR4.7132.197–10.110<0.0017.0312.504–19.748<0.001
Without hepatic malignancyEtiology of cirrhosis 3.8741.482–10.1290.006
  viral hepatitis1--
  alcohol2.1690.929–5.0640.074
  virus+alcohol0.4230.056–3.1060.398
  others3.1501.332–7.4480.009
RVOTD1.0611.017–1.1070.006
LVDD3.0201.441–6.3320.0032.3511.040–5.3130.040
RDW1.2151.063–1.3880.004
CO20.8860.828–0.948<0.0010.8400.764–0.922<0.001
CREA1.0031.001–1.0040.003
TBIL1.0051.002–1.008<0.0011.0071.003–1.0110.001
CHE0.2480.114–0.540<0.001
INR10.4002.963–36.510<0.001
CTP score1.2911.116–1.494<0.001

Demographic, basic clinical characteristics, and baseline information

FactorsTotal (n = 194)Non-LVDD group (n = 102)LVDD group (n = 92)t/Z/χ2P
Demographics

  Age, M(P25, P75) years55.0(47.0, 60.2)49.0(43.0, 57.3)59.0(53.2, 65.7)−5.936<0.001
  Male sex, n(%)135(69.6)77(75.5)58(63)3.5410.060
  Han ethnicity, n(%)177(91.2)96(94.1)81(88)2.2320.135
  BMI, M(P25, P75) kg/cm222.0(20.3, 24.3)22.0(20.0, 23.9)22.4(20.4, 24.6)−0.8070.420
  Smoking history, n(%)
    Yes103(53.1)60(58.9)43(46.7)3.2040.202
    No84(43.3)38(37.3)46(50)
    Quit7(3.6)4(3.9)3(3.3)
  Alcohol consumption, n(%)
    Yes82(42.3)48(47.1)34(37)2.8490.241
    No91(46.9)42(41.2)49(53.3)
    Quit21(10.8)12(11.8)9(9.8)
Family history of hepatic malignant, n(%)22(11.3)13(12.7)9(9.8)0.4220.516
Basic clinical information
  Etiology of cirrhosis, n(%) 7.6030.055
    Viral hepatitis144(74.2)83(81.4)61(66.2)
    Alcohol17(8.8)6(5.9)11(12)
    Virus + Alcohol18(9.3)9(8.8)9(9.8)
    Others15(7.7)4(3.9)11(12)
  Hepatic malignant, n(%)124(63.9)31(30.4)39(42.4)3.0200.082
  EGVB, n(%)22(11.3)12(11.8)10(10.9)0.0390.844
  Systolic blood pressure, M(P25, P75) mmHg115.0(104.0, 128.0)115.0(101.7, 122.0)117.0(105.0, 134.0)−1.7520.080
  Diastolic blood pressure, M(P25, P75) mmHg72.0(66.0, 82.0)71.0(65.0, 78.0)74.0(68.0, 85.0)−2.0660.039
  Heart rate, M(P25, P75) bpm82.0(72.0, 92.0)80.0(72.0, 91.0)83.5(71.3, 92.0)−0.3340.738.
Liver function
  CTP, M(P25, P75) scores9.0(8.0, 11.0)9.0(7.0, 11.0)9.0(8.0, 11.0)−0.8890.374
  MELD, M(P25, P75) scores12.6(10.0, 17.0)12.4(10.1, 16.4)12.6(9.5, 15.7)−0.5170.605
Laboratory findings
  TBIL, M(P25, P75) μmol/L31.9(18.2, 64.5)32.2(18.0, 77.8)31.6(18.2, 58.1)−0.1420.887
  DBIL, M(P25, P75) μmol/L14.4(7.8, 36.0)13.3(7.5, 44.6)14.6(7.8, 28.8)−0.1870.852
  ALT, M(P25, P75) U/L35.0(23.0, 75.0)37.0(24.8, 72.3)32.5(23.0, 78.0)−0.9400.347
  AST, M(P25, P75) U/L54.0(37.8, 120.0)58.5(37.0, 109.75)51.0(38.0, 132.7)−00690.945
  GGT, M(P25, P75) U/L96.0(40.8, 169.6)80.5(38.7, 156.3)103.5(46.4, 162.7)−1.2470.212
  ALP, M(P25, P75) U/L142.0(102.8, 201.2)142.0(105.2, 195.0)153(100.0, 217.0)−0.5130.608
  TP, M(P25, P75) g/L61.6(57.5, 67.4)61.8(57.0, 67.1)61.3(57.7, 68.2)−0.1330.894
  PALB, M(P25, P75) g/L76.1(49.1, 110.3)77.0(46.4, 105.4)71.1(52.1, 112.8)−0.4280.669
  ALB, M(P25, P75) g/L29.5(25.7, 33.3)30.2(27.3, 35.0)28.2(24.8, 31.8)−2.5960.009
  GLOB, M(P25, P75) g/L31.2(27.2, 36.7)30.4(27.0, 35.5)32.1(28.0, 37.8)−1.6290.103
  CHE, M(P25, P75) U/L2978.0(2136.8, 4008.3)2978.0(2214.0, 3999.0)2886.5(2089.7, 4253.5)−0.6360.525
  PT, M(P25, P75) sec16.6(15.3, 18.4)16.6(15.3, 18.4)16.6(15.4, 18.1)−0.2360.814
  PTA, M(P25, P75) %59.8(68.0, 50.7)59.7(50.1, 69.9)59.8(51.0, 68.0)−0.0080.994
  INR, M(P25, P75)1.4(1.3, 1.6)1.4(1.2, 1.6)1.4(1.2, 1.5)−0.0030.998
  WBC, M(P25, P75) ×109/L4.4(3.1, 7.3)4.1(2.7, 7.9)4.5(3.4, 7.4)−1.6940.092
  RBC, (±s) ×1012/L3.6±0.83.6±0.73.5±0.80.5560.579
  HGB, M(P25, P75) g/L115.0(97.8, 133.0)117.5(95.0, 132.3)115.0(99.3, 133.0)0.0800.937
  PLT, M(P25, P75) ×109/L89.5(59.7, 141.5)83.5(53.7, 139.0)100.0(71.2, 142.5)−1.5800.114
  K, (±s) mmol/L3.7±0.53.7±0.53.8±0.5−2.0530.041
  Na, M(P25, P75) mmol/L138.9(136.1, 141.1)138.9(136.5, 141.2)108.9(105.8, 140.8)−0.6900.490
  Ca, (±s) mmol/L2.1±0.22.0±0.12.09±0.2−0.7520.453
  CL, M(P25, P75) mmol/L106.9(136.1, 141.1)106.9(103.9, 109.4)106.5(103.1, 109.4)−0.4910.630
  CO2, M(P25, P75) mmol/L23.3(21.5, 25.2)23.2(21.7, 24.9)23.4(21.3, 25.2)−0.1220.903
  BUN, M(P25, P75) mmol/L4.8(3.8, 7.1)4.5(3.6, 6.5)5.2(4.1, 8.5)−2.7680.006
  Crea, M(P25, P75) μmol/L61.5(52.0, 74.0)60.0(49.0, 73.0)63.5(53.0, 80.7)−1.5150.130
  UA, M(P25, P75) μmol/L295.0(244.0, 375.2)290.5(243.7, 355.2)301(242.2, 406.0)−0.9460.344
  FPG, M(P25, P75) mmol/L5.6(4.9, 7.4)5.5(4.8, 7.1)5.7(5.0, 8.2)−1.3750.169
  TG, M(P25, P75) mmol/L0.8(0.6, 1.3)0.8(0.5, 1.2)0.8(0.6, 1.2)−1.2040.229
  CHOL, M(P25, P75) mmol/L3.2(2.6, 3.9)3.1(2.5, 3.5)3.1(2.7, 4.2)−2.4270.015
  HDL-C, M(P25, P75) mmol/L0.8(0.5, 1.1)0.7(0.4, 1.0)0.7(0.5, 1.0)−0.5520.581
  LDL-C, M(P25, P75) mmol/L1.9(1.4, 2.5)1.8(1.3, 2.2)2.0(1.4, 2.8)−2.9570.003
  CEA, M(P25, P75) μg/L3.2(2.1, 4.8)3.0(2.0, 4.5)3.6(2.3, 5.0)−2.3000.021
  AFP, M(P25, P75) μg/L6.4(3.1, 66.7)6.3(3.2, 38.9)6.8(2.8, 100.4)−0.3910.696
  CA125, M(P25, P75) U/mL107.5(31.5, 276.5)76.8(27.9, 228.2)152.9(40.9, 442.8)−2.9440.003
  CA199, M(P25, P75) U/mL31.8(17.4, 68.5)31.5(16.3, 72.9)34.0(18.3, 67.2)−0.2100.834
  LYMP, M(P25, P75) ×109/L0.9(0.6, 1.3)0.9(0.5, 1.2)0.9(0.5, 1.2)−0.3440.730
  HCT, (±s) %33.0±7.633.0±7.133.0±8.10.0130.990
  RDW, M(P25, P75) fL15.7(14.4, 17.0)15.7(14.4, 17.2)15.3(14.2, 16.9)−0.6130.540

Cardiac Parameters of TTE [M(P25, P75)]

Parameters of TTETotal (n = 194)Non-LVDD group (n = 102)LVDD group (n = 92)t/Z/χ2P
Aortic Valve Ring Internal Diameter, mm23.0(22.0, 25.0)23.0(22.0, 25.0)24.0(21.2, 25.7)−1.3820.173
Ascending Aorta Internal Diameter, mm25.0(23.0, 27.0)25.0(23.0, 26.0)25.5(23.0, 28.0)−1.9470.052
Mitral annulus diameter (MAD), mm20.0(18.0, 22.0)20.0(18.0, 22.0)21.0(19.0, 22.0)−0.8970.370
Left Cardiac Chamber Diameter (LCD), mm28.0(26.0, 31.0)28.0(25.0, 31.0)28.0(26.0, 31.0)−0.4350.663
Intrapericardial Pressure, mmHg42.0(38.7, 46.0)43.0(39.0, 46.0)42.0(39.0, 46.0)−0.7850.432
Right Ventricular Diameter, mm20.0(18.0, 22.0)20.0(18.0, 22.0)20.0(18.0, 21.7)−0.7840.433
Right Atrial Diameter, mm32.0(30.0, 35.0)32.5(30.0, 35.0)32.0(29.0, 34.0)−1.2550.210
right ventricular outflow tract diameter (RVOTD), mm22.0(20.0, 24.0)22.0(20.0, 24.0)23.0(21.0, 25.0)−1.5730.116
Interventricular Septum Thickness, mm9.0(8.0, 10.0)9.0(8.0, 10.0)9.0(8.0, 10.0)−1.3850.166
Left Posterior Wall Thickness, mm9.0(8.0, 10.0)9.0(8.0, 10.0)9.0(8.0, 10.0)−1.0150.310
end-diastolic volume (EDV), ml100.5(89.0, 117.2)102.0(89.0, 113.0)99.5(89.5, 121.0)−0.3200.749
end-systolic volume (ESV), ml31.0(23.0, 40.0)31.0(24.0, 40.0)32.0(23.0, 42.5)−0.2380.812
ejection fraction (EF), %68.0(64.0, 73.0)69.0(64.0, 72.2)67.0(63.0, 73.0)−0.7920.428
Fractional Shortening, %38.0(34.0, 42.0)38.0(35.0, 42.0)37.0(34.0, 42.7)−0.9210.357
stroke volume (SV), ml70.0(61.0, 80.0)69.5(60.0, 78.6)71.0(61.0, 80.0)−0.3530.724
E-point Septal Separation, mm5.0(5.0, 6.0)5.0(5.0, 6.0)5.0(5.0, 6.0)−1.3580.174
Pulmonary Artery Systolic Pressure, mmHg24.0(22.7, 28.0)24.0(21.0, 28.0)24.0(23.0, 28.0)−0.1160.908
Tricuspid Regurgitation (peak velocity), m/s220.0(210.0, 246.0)220.0(200.0, 240.0)220.0(210.0, 240.0)−0.2750.783
Pulmonary Artery Valve Forward Flow Rate, m/s113.0(96.7, 131.0)111.0(94.7, 130.0)113.0(100.0, 134.2)−0.8990.369
Pulmonary Pulmonic Valve Forward Flow Rate, m/s90.0(80.0, 104.5)90.5(80.7, 102.2)90.0(80.0, 106.7)−0.1930.847
E-wave/A-wave ratio (E/A)0.9(0.7, 1.3)1.2(1.0, 1.3)0.7(0.6, 0.8)−9.412<0.001
DOI: https://doi.org/10.2478/rjim-2025-0022 | Journal eISSN: 2501-062X | Journal ISSN: 1220-4749
Language: English
Submitted on: Aug 17, 2025
Published on: Nov 19, 2025
Published by: N.G. Lupu Internal Medicine Foundation
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2025 Sheng-Hao Li, Lu Zhang, Hong-Juan Li, Ye Li, Yan-Min Zheng, Qing-Qing Wang, published by N.G. Lupu Internal Medicine Foundation
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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