Heparin is an effective treatment for preventing liver failure after hepatectomy

Abstract

BACKGROUND

Posthepatectomy liver failure (PHLF) is one of the most important causes of death following liver resection. Heparin, an established anticoagulant, can protect liver function through a number of mechanisms, and thus, prevent liver failure.

AIM

To look at the safety and efficacy of heparin in preventing hepatic dysfunction after hepatectomy.

METHODS

The data was extracted from Multiparameter Intelligent Monitoring in Intensive Care III (MIMIC-III) v1. 4 pinpointed patients who had undergone hepatectomy for liver cancer, subdividing them into two cohorts: Those who were injected with heparin and those who were not. The statistical evaluations used were unpaired t-tests, Mann-Whitney U tests, chi-square tests, and Fisher’s exact tests to assess the effect of heparin administration on PHLF, duration of intensive care unit (ICU) stay, need for mechanical ventilation, use of continuous renal replacement therapy (CRRT), incidence of hypoxemia, development of acute kidney injury, and ICU mortality. Logistic regression was utilized to analyze the factors related to PHLF, with propensity score matching (PSM) aiming to balance the preoperative disparities between the two groups.

RESULTS

In this study, 1388 patients who underwent liver cancer hepatectomy were analyzed. PSM yielded 213 matched pairs from the heparin-treated and control groups. Initial univariate analyses indicated that heparin potentially reduces the risk of PHLF in both matched and unmatched samples. Further analysis in the matched cohorts confirmed a significant association, with heparin reducing the risk of PHLF (odds ratio: 0.518; 95% confidence interval: 0.295-0.910; P = 0.022). Additionally, heparin treatment correlated with improved short-term postoperative outcomes such as reduced ICU stay durations, diminished requirements for respiratory support and CRRT, and lower incidences of hypoxemia and ICU mortality.

CONCLUSION

Liver failure is an important hazard following hepatic surgery. During ICU care heparin administration has been proved to decrease the occurrence of hepatectomy induced liver failure. This indicates that heparin may provide a hopeful option for controlling PHLF.

Key Words: Liver resection, Posthepatectomy liver failure, Prophylactic treatment, Heparin, Prognosis of hepatectomy

Core Tip: This study emphasizes that heparin, which is commonly identified with its anticoagulant characteristics, also offers benefits in prevention of posthepatectomy liver failure (PHLF). Application of the Multiparameter Intelligent Monitoring in Intensive Care III database shows that the administration of heparin in the postoperative intensive care unit (ICU) setting is linked to a decreased occurrence of PHLF, shortened ICU stays, and lesser need for mechanical ventilation and renal support. These outcomes underscore heparin’s potential as a valuable therapeutic option to enhance short-term postoperative results for patients undergoing liver surgery.

INTRODUCTION

Posthepatectomy liver failure (PHLF) stands as one of the most critical complications after liver surgery, marked by significant rates of morbidity and mortality[1,2]. Studies recently published indicate that the occurrence of PHLF fluctuates between 4.9% and 9.0%[3]. The International Study Group of Liver Surgery defines PHLF with a grading system: Grade A involves an elevation in international normalized ratio (INR) or total bilirubin (TBIL) without altering the clinical pathway; Grade B includes clinical deviations that are managed non-invasively; and Grade C encompasses deviations requiring invasive interventions[4]. An alternative diagnostic measure, the 50-50 criterion, is applied on the fifth postoperative day, characterizing PHLF by serum total bilirubin levels exceeding 50 μmol/L and a prothrombin time (PT) index below 50%[4]. The onset of PHLF indicates a decline in the liver’s ability to synthesize, excrete, and detoxify, evidenced by heightened levels of INR and bilirubin shortly after surgery[5]. The regeneration of the liver remnant is crucial for a patient’s prognosis following hepatectomy. Factors such as hepatic hemodynamic disturbances, immune-inflammatory responses, and metabolic dysfunctions can exacerbate hepatocyte death, thereby impairing the function of the liver remnant and precipitating liver failure[6]. In the context of liver resection, predominant risk factors encompass pre-existing liver conditions, the extent of resection, and the specifics of the intraoperative procedures. Mitigating the risk of PHLF hinges on thorough preoperative assessment and preparation, choosing the optimal surgical techniques, and implementing rigorous postoperative surveillance and management[7]. Despite these measures, comprehensive strategies to address PHLF remain incomplete, necessitating further investigations to bridge these gaps and enhance the understanding of contributory factors to PHLF.

Coagulation abnormalities are widely acknowledged as strong indicators predicting adverse outcomes in liver diseases. Heparin, a prevalent anticoagulant, not only shields endothelial cells and prevents the thrombosis of hepatic vessels but also mitigates hepatic hemodynamic abnormalities[8]. It further serves as a hepatoprotective agent by modulating cellular metabolism and the inflammatory response, which in turn reduces hepatocyte damage. Dr. Silva’s recent in vivo research highlighted that heparin significantly reduces hepatic cell apoptosis during hemorrhagic shock and reperfusion injuries[9]. Additionally, another investigation illustrated that heparin influences lipoprotein processing within the liver[10]. Despite these findings, the use of heparin as a prophylactic treatment in liver surgery is scarcely documented. The clinical consensus on the early administration of heparin post-liver surgery is still under debate. Consequently, this retrospective study was designed to assess heparin’s efficacy in preventing PHLF.

MATERIALS AND METHODS

Data source

Data for this study were sourced from version 1.4 of the Multiparameter Intelligent Monitoring in Intensive Care III (MIMIC-III) database. MIMIC-III, which is freely accessible, contains records for over 50000 critical care patients who were treated at Beth Israel Deaconess Medical Center between 2001 and 2012. Prior to accessing the database, completion of the “Protecting Human Research Participants” course offered by the National Institutes of Health was mandatory (record ID: 11186516). Both the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center’s Institutional Review Boards approved the use and creation of this database. The need for informed consent was waived due to the de-identification of all data.

Study population

To be included in the study, patients were required to meet several criteria: They needed to be between 18 and 79 years old, undergoing their initial admission to the intensive care unit (ICU), with a postoperative stay of more than two days following a hepatectomy. We established exclusion criteria to refine the study population further: patients diagnosed with additional cancer types, those undergoing treatment with warfarin or other anticoagulants, individuals with pre-existing liver failure or dysfunction in other organs, a history of embolic or thrombotic events, or other significant hematological disorders, and any cases where more than 10% of the essential data were missing.

Data collected

For the study, comprehensive baseline characteristics and pre-surgical laboratory values were meticulously recorded. These included demographic details such as gender, age, height, and weight, along with clinical data encompassing the presence of malignant tumors, the use of laparoscopic techniques, smoking status, ethnic background, and medical history of conditions like hypertension and portal vein tumor thrombosis (PVTT). Preoperative liver conditions noted were cirrhosis and portal hypertension, the latter defined by a hepatic venous pressure gradient exceeding 6 mmHg[11]. Additionally, patient histories of chronic obstructive pulmonary disease, chronic kidney disease, and essential hematological indices such as red blood cell (RBC), plasma, and laboratory diagnostics including TBIL, aspartate transaminase (AST), alanine transaminase (ALT), lactate dehydrogenase, albumin (ALB), serum creatinine (Cr), blood urea nitrogen, glomerular filtration rate, white blood cell, platelets (PLT), INR, PT, and activated partial thromboplastin time (APTT) were also systematically collected.

Groups and outcomes

Study participants were divided into two cohorts: The heparin group consisted of patients who were administered heparin either subcutaneously or via continuous infusion in preventive or therapeutic doses (either 0.1 U/mL or 0.2 U/mL or higher) for a duration of over five consecutive days; the control group included patients who did not receive anticoagulant therapy or received it for fewer than five days[12]. The principal measure of the study was the rate of PHLF. Liver failure was identified by a TBIL level of 5 mg/dL or higher, or a PT of less than 40%, indicative of a worsening underlying liver condition. Secondary outcomes measured were the duration of ICU stay, the need for respiratory support, continuous renal replacement therapy (CRRT), occurrences of hypoxemia, incidents of acute kidney injury, and deaths in the ICU.

Propensity score matching

Due to notable differences in baseline characteristics between the two groups, propensity score matching (PSM) was utilized to mitigate the influence of confounding variables. The propensity score was developed considering all the variables listed in Table 1. Then, the caliper 0.05 was used where patients of both cohorts were matched one to one through nearest neighbor matching.

Table 1 Baseline of patients between two groups.

	Before matching			After matching
	Heparin-free group (n = 421)	Heparin group (n = 967)	P value	Heparin-free group (n = 213)	Heparin group (n = 213)	P value
Gender (male/female)	243/178	507/409	0.069	133/80	122/91	0.277
Age (yr)	56.70 ± 14.88	58.10 ± 14.46	0.100	56.00 ± 14.90	57.58 ± 15.58	0.274
Height (cm)	169.20 ± 8.82	168.30 ± 8.57	0.080	170.00 ± 9.09	169.50 ± 9.10	0.551
Weight (kg)	68.10 ± 14.55	66.30 ± 13.28	0.820	69.00 ± 14.36	68.81 ± 14.03	0.870
Malignant tumor (Yes/No)	119 (28.3%)	421 (43.5%)	0.000	55 (25.8%)	74 (34.7%)	0.045
Laparoscopic (Yes/No)	379 (90.0%)	802 (82.9%)	0.001	206 (96.7%)	198 (93%)	0.080
Smoking (Yes/No)	50 (11.9%)	100 (10.3%)	0.397	38 (17.8%)	39 (18.3%)	0.900
Ethnicity (white/not white)	296 (70.3%)	625 (64.6%)	0.040	146 (68.5%)	161 (75.6%)	0.105
Hypertension (Yes/No)	190 (45.1%)	231 (54.9%)	0.787	94 (44.1%)	97 (45.5%)	0.770
PVTT (Yes/No)	21 (5.0%)	37 (3.8%)	0.320	12 (5.6%)	14 (6.6%)	0.686
Diabetes (Yes/No)	5 (1.2%)	61 (6.3%)	0.000	1 (0.5%)	2 (0.9%)	0.562
Cirrhosis (Yes/No)	81 (19.2%)	137 (14.2%)	0.017	56 (26.3%)	50 (23.5%)	0.501
Portal hypertension (Yes/No)	61 (14.5%)	75 (7.8%)	0.000	40 (18.8%)	25 (11.7%)	0.043
COPD (Yes/No)	25 (5.9%)	45 (4.7%)	0.315	14 (6.6%)	10 (4.7%)	0.401
CKD (Yes/No)	39 (9.3%)	40 (4.1%)	0.000	23 (10.8%)	19 (8.9%)	0.516
Transfusion (Yes/No)	83 (19.7%)	92 (9.5%)	0.000	48 (22.5%)	44 (20.7%)	0.638
Laboratory tests
TBIL (mg/mL)	1.1 [0.5, 3.9]	0.9 [0.5, 1.8]	0.002	1.3 [0.5, 4.7]	1.1 [0.6, 3.8]	0.808
AST (U/L)	65 [33, 158]	116 [53, 277]	0.000	68.0 [35.0, 186.5]	89.0 [44.0, 240.0]	0.704
ALT (U/L)	54 [27, 158]	101 [43, 237]	0.000	55.0 [28.0, 168.5]	76.0 [30.0, 210.5]	0.847
LDH (U/L)	263.0 [192.0, 405.0]	272.0 [198.8, 403.5]	0.882	274.0 [199.5, 437.5]	288.0 [201.0, 440.5]	0.487
ALB (g/dL)	3.4 [2.8, 3.8]	3.3 [2.9, 3.7]	0.724	3.4 [2.8, 3.8]	3.2 [2.8, 3.7]	0.639
Cr (mg/dL)	0.9 [0.7, 1.3]	1 [1.0, 1.0]	0.000	0.9 [0.7, 1.4]	1.0 [0.7, 1.4]	0.901
BUN (mg/dL)	16 [11, 28]	14 [11, 20]	0.000	18 [12, 31]	16 [11, 26]	0.629
GFR (mg/dL)	16 [11, 28]	14 [11, 20]	0.000	18 [12, 31]	16 [11, 26]	0.629
WBC (K/μL)	7.3 [5.0, 10.0]	10.0 [7.0, 14.0]	0.000	6.9 [4.7, 9.7]	8.1 [5.0, 12.3]	0.241
PLT (K/μL)	177 [113, 261]	205 [145, 266]	0.001	160.0 [96.0, 235.5]	187.0 [117.5, 245.0]	0.054
INR	1.2 [1.1, 1.6]	1.2 [1.1, 1.4]	0.000	1.2 [1.1, 1.6]	1.3 [1.1, 1.6]	0.408
PT (s)	13.6 [12.1, 18.1]	13.4 [12.2, 15.3]	0.000	13.5 [11.9, 17.8]	14.2 [12.4, 17.3]	0.436
APTT (s)	32 [28.1, 38.7]	29.9 [27.2, 33.9]	0.004	31.6 [28.2, 37.3]	31.0 [27.6, 37.0]	0.202

Variables are shown as “mean (SD)” or “median [IQR]”. Comparisons were made by unpaired student’s test, chi-square test or Mann-Whitney test according to their distribution before propensity score matching (PSM), while paired student’s test, paired chi-square test or Mann-Whitney test are then used after PSM. The bold indicates statistical significance. PVTT: Portal vein tumor thrombus; PSM: Propensity score matching; COPD: Chronic obstructive pulmonary disease; CKD: Chronic kidney disease; RBC: Red blood cell; TBIL: Total bilirubin; AST: Aspartate transaminase; ALT: Alanine transaminase; LDH: Lactate dehydrogenase; ALB: Albumin; Cr: Serum creatinine; BUN: Blood urea nitrogen; GFR: Glomerular filtration rate; WBC: White blood cell; INR: International normalized ratio; PLT: Platelets; PT: Prothrombin time; APTT: Activated partial thromboplastin time.

Statistical analysis

The representation of continuous variables in the study was adjusted to their distribution, which were either presented as means plus or minus standard deviations (SD) or as medians with interquartile ranges (IQR). Categorical variables were shown by means of their counts and corresponding percentages. Analysis of covariates and interactions across these variables was performed using various statistical tests such as unpaired Student’s t test, Mann-Whitney test, Two-way ANOVA for group comparisons, chi-square and Fisher’s exact tests for nominal data, chosen based on appropriateness to data type. Logistic regression analyses were performed before and after introduction of the PSM to look deeper into the association between different factors and PHLF. At first, all the variables underwent a univariate logistic regression to identify the variables mainly associated with PHLF, where the variables with P value less than 0.1 were evaluated using multivariable with a backward stepwise selection strategy. The investigation extended to examining the influence of heparin on other adverse clinical outcomes employing a bivariate logistic regression framework. Detailed subgroup analyses were also performed to evaluate the differential effects of heparin on PHLF across various stratified groups. Statistical significance for all tests was determined at a P value of less than 0.05, using a two-tailed hypothesis test. These analyses were carried out utilizing SPSS software, version 25.0 (IBM SPSS, Chicago, IL, United States), and R software, version 4.2.1 (Institute for Statistics and Mathematics, Vienna, Austria; http://www.r-project.org/).

RESULTS

A review of 1388 patients who underwent liver resection identified the same number for inclusion in the final study cohort. Of these, 967 were treated with heparin while the remaining 421 constituted the heparin-free group, as depicted in Figure 1A. Baseline characteristics and laboratory findings are detailed in Table 1. Prior to PSM, significant imbalances in several factors were noted between the groups. For example, the heparin group showed a higher incidence of malignant tumors and a reduced use of laparoscopic procedures. Additionally, notable differences were observed across most laboratory parameters. These disparities suggest that those in the heparin group were typically more severely ill. To address these imbalances, PSM was employed, resulting in 213 matched pairs. Post-PSM analysis showed that most variables were well-balanced between the two groups, with portal hypertension being the primary exception.

Figure 1 Study pipeline, prediction model performance, and effect of heparin on post-hepatectomy liver failure. A: Study pipeline showing the selection of hepatic disease patients from the Multiparameter Intelligent Monitoring in Intensive Care III database, criteria for inclusion and exclusion, and the final cohort of patients who underwent hepatectomy. Propensity score matching was used to balance the heparin and heparin-free groups; B: Receiver operating characteristic curve showing the performance of the prediction model; C: Forest plot illustrating the effect of heparin on various clinical outcomes in the post-hepatectomy liver failure cohort. MIMIC: Multiparameter Intelligent Monitoring in Intensive Care; ICU: Intensive care unit; HR: Hazard ratio; 95%CI: 95% confidence interval; AUC: Area under the curve.

The association between the use of heparin and the subsequent clinical outcomes is systematically detailed in Table 2. In the broader patient sample, 142 individuals experienced liver failure following surgery. Notably, the incidence of PHLF was less prevalent among patients who did not receive heparin, with figures reported at 15.7% compared to 7.9% in the non-heparin group, resulting in an odds ratio (OR) of 2.180 and a 95% confidence interval (CI) ranging from 1.533 to 3.099, with a significant P value of less than 0.001. Following the application of PSM, the trend persisted, though with a narrower margin (21.1% vs 13.1%, OR: 0.530; 95%CI: 0.303-0.928; P = 0.026). For secondary outcomes, unadjusted logistic regression analysis of the entire cohort demonstrated that patients not treated with heparin experienced longer durations in the ICU [hazard ratio (HR): 1.501; 95%CI: 1.104-2.040; P = 0.01]. Additionally, this group required more extensive respiratory support (HR: 2.479; 95%CI: 1.745, 3.523; P < 0.001) and were more likely to undergo CRRT (HR: 5.044; 95%CI: 2.160, 11.782; P < 0.001). They also faced higher risks of developing hypoxemia (HR: 1.260; 95%CI: 1.955-3.032; P = 0.003) and increased chances of ICU mortality (HR: 2.354; 95%CI: 1.543-3.593; P < 0.001). Post-operative blood tests aimed at evaluating liver function and the coagulation system revealed no significant variations in total bilirubin, ALT, and AST levels as shown in Figure 2A-F. However, the INR values significantly improved in the heparin group, both before and after PSM (Figure 2G and H, P < 0.001). The results related to other coagulation indicators such as PT and PLT initially showed poorer outcomes in patients treated with heparin pre-PSM, but these markers improved post-PSM, as depicted in Figure 2I-L, likely indicating the effectiveness of the PSM in balancing these groups.

Figure 2 Baseline and postoperative liver function and coagulation results before and after propensity score matching. A: Baseline total bilirubin levels in the heparin and non-heparin groups before propensity score matching (PSM); B: Baseline total bilirubin levels in the heparin and non-heparin groups after PSM; C: Baseline alanine transaminase (ALT) levels in the heparin and non-heparin groups before PSM; D: Baseline ALT levels in the heparin and non-heparin groups after PSM; E: Baseline aspartate transaminase (AST) levels in the heparin and non-heparin groups before PSM; F: Baseline AST levels in the heparin and non-heparin groups after PSM; G: Baseline international normalized ratio (INR) values in the heparin and non-heparin groups before PSM; H: Baseline INR values in the heparin and non-heparin groups after PSM; I: Baseline platelets (PLT) levels in the heparin and non-heparin groups before PSM; J: Baseline PLT levels in the heparin and non-heparin groups after PSM; K: Baseline prothrombin time (PT) levels in the heparin and non-heparin groups before PSM; L: Baseline PT levels in the heparin and non-heparin groups after PSM. ^aP < 0.05; ^bP < 0.01; ^cP < 0.001. PSM: Propensity score matching; AST: Aspartate transaminase; ALT: Alanine transaminase; INR: International normalized ratio; PLT: Platelets; PT: Prothrombin time.

Table 2 Association between heparin use and clinical outcomes.

	Heparin-free group, n (%)	Heparin group, n (%)	OR (95%CI)	P value
Before PSM	n = 421	n = 967
PHLF	66 (15.7)	76 (7.9)	2.180 (1.533, 3.099)	< 0.001
ICU stay	79 (18.8)	129 (13.3)	1.501 (1.104, 2.040)	0.010
Respiratory support	70 (16.6)	72 (7.4)	2.479 (1.745, 3.523)	0.000
CRRT	17 (4)	8 (0.8)	5.044 (2.160, 11.782)	< 0.001
Hypoxemia	39 (9.3)	48 (5.0)	1.955 (1.260, 3.032)	0.003
AKI	40 (9.5)	66 (6.8)	1.433 (0.951, 2.161)	0.086
ICU death	43 (10.2)	43 (4.4)	2.354 (1.543, 3.593)	< 0.001
After PSM	n = 213	n = 213
PHFL	45 (21.1)	28 (13.1)	0.530 (0.303, 0.928)	0.026
ICU stay	49 (23.0)	51 (23.9)	1.722 (0.933, 3.177)	0.082
Respiratory support	43 (20.2)	30 (14.1)	0.502 (0.262, 0.960)	0.037
CRRT	5 (2.3)	4 (1.9)	1.059 (0.249, 4.496)	0.939
Hypoxemia	23 (10.8)	23 (10.8)	1.181 (0.598, 2.334)	0.631
AKI	22 (10.3)	21 (9.9)	1.017 (0.525, 1.971)	0.960
ICU death	27 (12.7)	24 (11.3)	0.956 (0.469, 1.949)	0.901

Bivariate logistic regression analysis with patients before and post propensity score matching was all applied to investigate variables potentially associated with all secondary clinical outcomes. PSM: Propensity score matching; CRRT: Continuous renal replacement therapy; PHFL: Post-hepatectomy liver failure; AKI: Acute kidney injury.

In the univariate analysis conducted post-PSM, 13 variables emerged as potential risk factors for PHLF, each with an unadjusted P value below 0.1. These variables were subsequently incorporated into a multivariate model, from which five factors were identified as significantly correlated with PHLF. These include treatment with heparin, which demonstrated a protective effect (OR: 0.518; 95%CI: 0.295-0.910, P = 0.022), diagnosis of PVTT (OR: 3.825; 95%CI: 1.486-9.844; P = 0.005), blood transfusion (OR: 3.316; 95%CI: 1.851-5.940; P < 0.001), total bilirubin (TBIL) (OR: 1.050; 95%CI: 1.011-1.089; P = 0.010), and ALB levels (OR: 0.473; 95%CI: 0.296, 0.755; P = 0.002) as detailed in Table 3. The receiver operating characteristic curves for this refined model are displayed in Figure 1B, highlighting its promising predictive capacity for PHLF. Conversely, the regression analysis conducted on patients before PSM identified 17 variables with an unadjusted P value below 0.1, as detailed in Supplementary Table 1. Following the multivariate selection process, only four factors were retained in the final model, including PVTT, transfusion, PLT, and ALB levels. Notably, in this pre-PSM analysis, heparin did not emerge as a significant prognostic factor for PHLF.

Table 3 Univariate and multivariate analyses of factors associated with liver failure in matched groups.

Factors	B	SE	Wald	OR (95%CI)	P value
Univariate
Heparin	-0.571	0.263	4.703	0.565 [0.337, 0.947]	0.030
Gender	-0.231	0.267	0.749	0.794 [0.470, 1.340]	0.387
Age	-0.009	0.008	1.099	0.991 [0.975, 1.008]	0.294
Height	0.021	0.014	2.055	1.021 [0.992, 1.050]	0.152
Weight	0.026	0.008	9.392	1.026 [1.009, 1.043]	0.002
Malignant tumor	-0.169	0.287	0.347	0.845 [0.482, 1.481]	0.556
Laparoscopic	-0.802	0.468	2.929	0.449 [0.179, 1.123]	0.087
Smoking	0.032	0.252	0.016	1.032 [0.630, 1.691]	0.900
Ethnicity	-0.361	0.274	1.732	0.697 [0.407, 1.193]	0.188
Hypertension	0.085	0.258	0.108	1.088 [0.656, 1.804]	0.743
PVTT	1.207	0.426	8.003	3.343 [1.451, 7.703]	0.005
Diabetes	0.891	1.232	0.523	2.437 [0.218, 27.243]	0.469
Cirrhosis	0.635	0.276	5.319	1.888 [1.100, 3.239]	0.021
Portal hypertension	0.854	0.310	7.579	2.348 [1.279, 4.312]	0.006
COPD	0.389	0.631	0.380	0.678 [0.197, 2.334]	0.537
CKD	0.465	0.388	1.440	1.592 [0.745, 3.404]	0.230
Transfusion	1.565	0.275	32.311	4.783 [2.788, 8.205]	0.000
Laboratory tests
TBIL	0.068	0.017	15.985	1.070 [1.035, 1.107]	0.000
AST	0.000	0.000	2.462	1.000 [1.000, 1.001]	0.117
ALT	0.000	0.000	0.308	1.000 [1.000, 1.001]	0.579
LDH	0.000	0.000	3.279	1.000 [1.000, 1.000]	0.070
ALB	-1.020	0.218	21.989	0.360 [0.235, 0.552]	0.000
Cr	0.054	0.105	0.264	1.056 [0.859, 1.298]	0.608
BUN	-0.005	0.008	0.457	0.995 [0.979, 1.010]	0.499
GFR	-0.005	0.008	0.457	0.995 [0.979, 1.010]	0.499
WBC	0.025	0.016	2.278	1.025 [0.993, 1.058]	0.131
PLT	-0.006	0.002	13.876	0.994 [0.991, 0.997]	0.000
INR	0.529	0.158	11.266	1.697 [1.246, 2.312]	0.001
PT	0.049	0.014	11.643	1.050 [1.021, 1.081]	0.001
APTT	0.010	0.007	1.797	1.010 [0.996, 1.024]	0.180
Multivariate
Heparin	-0.657	0.287	5.236	0.518 [0.295, 0.910]	0.022
PVTT	1.342	0.482	7.734	3.825 [1.486, 9.844]	0.005
Transfusion	1.199	0.297	16.248	3.316 [1.851, 5.940]	< 0.000
TBIL	0.048	0.019	6.558	1.050 [1.011, 1.089]	0.010
ALB	-0.749	0.239	9.815	0.473 [0.296, 0.755]	0.002

The initial screening via univariable logistic regression analysis; all variables with P < 0.1 are considered as potential risk factors, and then allowed in the multivariable analysis with a backward stepwise selection process. Factors with P < 0.05 (the bold) in the final multivariate model means significance. PVTT: Portal vein tumor thrombus; PSM: Propensity score matching; COPD: Chronic obstructive pulmonary disease; CKD: Chronic kidney disease; RBC: Red blood cell; TBIL: Total bilirubin; AST: Aspartate transaminase; ALT: Alanine transaminase; LDH: Lactate dehydrogenase; ALB: Albumin; Cr: Serum creatinine; BUN: Blood urea nitrogen; GFR: Glomerular filtration rate; WBC: White blood cell; INR: International normalized ratio; PLT: Platelets; PT: Prothrombin time; APTT: Activated partial thromboplastin time.

In the detailed subgroup analysis conducted, regardless of the patients’ cirrhosis status, the type of surgical approach (laparoscopic or not), their ethnicity, or whether they had portal hypertension, those who were administered heparin showed consistently lower rates of PHLF across all categories compared to their counterparts who did not receive heparin treatment. This was statistically significant, as indicated in Figure 1C, where all P values were below 0.05. However, a deeper examination of the data stratified by tumor presence revealed a more nuanced relationship. Specifically, the protective effects of heparin were predominantly observed in patients undergoing liver surgeries for benign diseases, with these patients showing a significantly reduced risk of developing PHLF (OR: 0.19; 95%C: 0.38-0.76; P = 0.006).

DISCUSSION

Coagulation disturbances, commonly observed following liver surgery, are often linked to liver failure and a negative prognosis due to microvascular thrombosis[13]. Therefore, moderating the excessively activated coagulation cascade post-hepatectomy could serve as an effective strategy to mitigate the risk of PHLF. This retrospective study, utilizing clinical data from a publicly accessible database, illustrates that short-term heparin administration post-liver surgery or during ICU stays can decrease the incidence of PHLF and enhance overall clinical outcomes, including organ functionality. Moreover, our analysis, which incorporates both multivariable analysis and PSM, substantiates heparin’s independent association with reduced rates of PHLF[14].

Heparin, a heterogeneous mixture of heparan sulfate glycosaminoglycans isolated from porcine intestines, exerts a potent anticoagulant effect through selective interactions with numerous proteins. Among these proteins is the serine protease inhibitor antithrombin-III (AT III), which influences thrombin, factors Xa, IXa, XIa, XIIa, and tissue plasminogen activator. Extensive clinical research has confirmed the efficacy and safety of heparin for treating patients at high risk of coagulation disorders. Notably, a retrospective analysis by Peng et al[15] demonstrated that un-fractioned heparin could enhance outcomes for patients with sepsis-induced coagulopathy. However, the benefits of heparin therapy following surgery are still under debate, with some studies indicating reduced hospital mortality[16], while others report no impact on short-term surgical outcomes[17]. A primary limitation of these studies is the lack of a predefined target population for heparin use and the absence of a universally recognized clinical biomarker for its application. Moreover, the selection of anticoagulation or hemostasis strategies after liver surgery remains complicated due to the heterogeneity of patient conditions and the complexity of surgical techniques[18]. Our findings suggest that heparin therapy serves as a valuable organ protection strategy in major surgeries, reducing dysfunction in both respiratory and urinary systems, and even decreasing ICU mortality rates. Initially, our multivariable model before PSM showed no statistically significant association between heparin treatment and PHLF, likely due to confounding factors. This aligns with findings from other randomized clinical trials suggesting that less critically ill patients might not benefit from anticoagulant therapy[19]. Following PSM, two matched cohorts were established, featuring patients with lower severity and reduced PHLF rates.

The evidence gathered supports the premise that initiating anticoagulation with heparin immediately following hepatic surgery can significantly prevent the onset of liver failure, likely attributed to the elevated risk of microthrombosis following extensive liver resections. Extensive surgical and occlusion times have been associated with increased instances of liver failure and mortality. These conditions facilitate hypothermia or ischemia-reperfusion injury, which stimulates Kupffer cells to produce oxygen-free radicals, initiating inflammatory responses that ultimately lead to endothelial damage and impairments in coagulation mechanisms[20]. These findings underscore the necessity for assertive anticoagulation therapy following hepatic operations. However, the use of heparin in the early postoperative period is hampered by concerns over potential severe hemorrhagic events, a risk exacerbated by the partially understood mechanisms of thrombosis[21]. Recent clinical studies have disclosed an unexpected prevalence of bleeding complications with conventional heparin used as prophylaxis against postoperative thrombosis, particularly highlighting that patients predisposed to heparin allergies or heparin-induced thrombocytopenia are at an increased risk of experiencing significant hemorrhagic events[22]. On the contrary, prior meta-analyses have shown that heparin does not elevate the risk of major bleeding events in patients with sepsis[23,24]. Clinical decisions regarding the application of heparin are therefore frequently influenced by the surgeon’s evaluation of blood loss during the operation. An additional concern remains the paucity of definitive evidence regarding the efficacy of alternatives such as heparin derivatives, heparinoids, or other anticoagulants compared to standard heparin. Research utilizing animal models has indicated that low molecular weight heparins are less prone to cause hemorrhagic complications[25].

Several significant limitations are inherent to this study and merit discussion. Given the retrospective nature of this analysis, there is a potential for both selection and ascertainment biases. Table 1 illustrates marked discrepancies across numerous variables between the groups, with those receiving heparin typically exhibiting more severe medical conditions. In response, we applied both multivariate regression analysis and PSM to mitigate these confounding influences effectively. Furthermore, the database from which this study draws its data lacks essential peri-operative variables, including intra-operative blood loss records, and fails to analyze methodologies related to ICU treatments or interventions. Another critical gap is the absence of data on tumor stage and the impact of preoperative treatments such as radiotherapy or chemotherapy, which significantly influence the residual liver volume: An essential factor in determining the likelihood of PHLF. This gap highlights a significant limitation of the MINIC database, which is primarily geared towards gathering data from patients within ICU settings. As such, interpretations of our findings should be approached with caution due to these dataset constraints.

CONCLUSION

In summarizing the findings from the MIMIC-3 database analysis, it was determined that administration of heparin not only diminishes the rate of post-hepatectomy liver failure but may also contribute to improved clinical outcomes overall. Interestingly, heparin application was associated with enhanced INR values within the treatment group and did not elevate bleeding risks. To substantiate these observations and elucidate the underlying mechanisms, future prospective clinical trials are warranted.