Developing and validating a pre-operative risk score to predict post-hepatectomy liver failure

Significance of Prediction Models for Post-Hepatectomy Liver Failure Based on Type IV Collagen 7s Domain in Patients with Hepatocellular Carcinoma

Background: Previous studies have attempted to establish predictive models for post-hepatectomy liver failure (PHLF) in patients with hepatocellular carcinoma (HCC) undergoing liver resection. However, a versatile and useful predictive model for PHLF remains to be developed. Therefore, we aimed to develop predictive models for PHLF based on type IV collagen 7s domain (7s collagen) in patients with HCC. Methods: We retrospectively collected data from 972 patients with HCC who had undergone initial curative liver resection between February 2000 and December 2020 at our hospital. Multivariate logistic regression analysis using a restricted cubic spline was performed to evaluate the effect of 7s collagen on the incidence of PHLF. A nomogram was developed based on 7s collagen. Results: PHLF grades B or C were identified in 104 patients (11%): 98 (10%) and 6 (1%) PHLF grades B and C, respectively. Multivariate logistic regression analysis revealed that the preoperative serum level of 7s collagen was significantly associated with a proportional increase in the risk of PHLF, which was confirmed in both laparoscopic and open liver resections. A nomogram was developed based on 7s collagen, with a concordance index of 0.768. The inclusion of 7s collagen values in the predictive model increased the predictive accuracy. Conclusion: The findings highlight the efficacy of the serum level of 7s collagen as a predictive factor for PHLF. Our novel nomogram using 7s collagen may be useful for predicting the risk of PHLF.

Development and Validation of Prediction Models and Risk Calculators for Posthepatectomy Liver Failure and Postoperative Complications Using a Diverse International Cohort of Major Hepatectomies

OBJECTIVE

The study aim was to develop and validate models to predict clinically significant posthepatectomy liver failure (PHLF) and serious complications [a Comprehensive Complication Index (CCI)>40] using preoperative and intraoperative variables.

BACKGROUND

PHLF is a serious complication after major hepatectomy but does not comprehensively capture a patient's postoperative course. Adding the CCI as an additional metric can account for complications unrelated to liver function.

METHODS

The cohort included adult patients who underwent major hepatectomies at 12 international centers (2010-2020). After splitting the data into training and validation sets (70:30), models for PHLF and a CCI>40 were fit using logistic regression with a lasso penalty on the training cohort. The models were then evaluated on the validation data set.

RESULTS

Among 2192 patients, 185 (8.4%) had clinically significant PHLF and 160 (7.3%) had a CCI>40. The PHLF model had an area under the curve (AUC) of 0.80, calibration slope of 0.95, and calibration-in-the-large of -0.09, while the CCI model had an AUC of 0.76, calibration slope of 0.88, and calibration-in-the-large of 0.02. When the models were provided only preoperative variables to predict PHLF and a CCI>40, this resulted in similar AUCs of 0.78 and 0.71, respectively. Both models were used to build 2 risk calculators with the option to include or exclude intraoperative variables ( PHLF Risk Calculator; CCI>40 Risk Calculator ).

CONCLUSIONS

Using an international cohort of major hepatectomy patients, we used preoperative and intraoperative variables to develop and internally validate multivariable models to predict clinically significant PHLF and a CCI>40 with good discrimination and calibration.

Comparing Outcomes of Minimally Invasive and Open Hepatectomy for Primary Liver Malignancies in Patients with Low-MELD Cirrhosis

INTRODUCTION

Cirrhotic patients with primary liver cancer may undergo curative-intent resection when selected appropriately. Patients with T1 tumors and low-MELD are generally referred for resection. We aim to evaluate whether minimally invasive hepatectomy (MIH) is associated with improved outcomes versus open hepatectomy (OH).

METHODS

NSQIP hepatectomy database 2014-2021 was used to select patients with T1 Hepatocellular Carcinoma (HCC) or Intra-hepatic Cholangiocarcionoma (IHCC) and low-MELD cirrhosis (MELD ≤ 10) who underwent partial hepatectomy. Propensity score matching was applied between OH and MIH patients, and 30-day postoperative outcomes were compared. Multivariable regression was used to identify predictors of post-hepatectomy liver failure (PHLF) in the selected population.

RESULTS

There were 922 patients: 494 (53.6%) OH, 372 (40.3%) MIH, and 56 (6.1%) began MIH converted to OH (analyzed with the OH cohort). We matched 354 pairs of patients with an adequate balance between the groups. MIH was associated with lower rates of bile leak (HR 0.37 [0.19-0.72)], PHLF (HR 0.36 [0.15-0.86]), collections requiring drainage (HR 0.30 [0.15-0.63]), postoperative transfusion (HR 0.36 [0.21-0.61]), major (HR 0.45 [0.27-0.77]), and overall morbidity (HR 0.44 [0.31-0.63]), and a two-day shorter median hospitalization (3 vs. 5 days; HR 0.61 [0.45-0.82]). No difference was noted in operative time, wound, respiratory, and septic complications, or mortality. Regression analysis identified ascites, prior portal vein embolization (PVE), additional hepatectomies, Pringle's maneuver, and OH (vs. MIH) as independent predictors of PHLF.

CONCLUSION

MIH for early-stage HCC/IHCC in low-MELD cirrhotic patients was associated with improved postoperative outcomes over OH. These findings suggest that MIH should be considered an acceptable approach in this population of patients.

E-AHPBA-ESSO-ESSR Innsbruck consensus guidelines for preoperative liver function assessment before hepatectomy

BACKGROUND

Posthepatectomy liver failure (PHLF) contributes significantly to morbidity and mortality after liver surgery. Standardized assessment of preoperative liver function is crucial to identify patients at risk. These European consensus guidelines provide guidance for preoperative patient assessment.

METHODS

A modified Delphi approach was used to achieve consensus. The expert panel consisted of hepatobiliary surgeons, radiologists, nuclear medicine specialists, and hepatologists. The guideline process was supervised by a methodologist and reviewed by a patient representative. A systematic literature search was performed in PubMed/MEDLINE, the Cochrane library, and the WHO International Clinical Trials Registry. Evidence assessment and statement development followed Scottish Intercollegiate Guidelines Network methodology.

RESULTS

Based on 271 publications covering 4 key areas, 21 statements (at least 85 per cent agreement) were produced (median level of evidence 2- to 2+). Only a few systematic reviews (2++) and one RCT (1+) were identified. Preoperative liver function assessment should be considered before complex resections, and in patients with suspected or known underlying liver disease, or chemotherapy-associated or drug-induced liver injury. Clinical assessment and blood-based scores reflecting liver function or portal hypertension (for example albumin/bilirubin, platelet count) aid in identifying risk of PHLF. Volumetry of the future liver remnant represents the foundation for assessment, and can be combined with indocyanine green clearance or LiMAx® according to local expertise and availability. Functional MRI and liver scintigraphy are alternatives, combining FLR volume and function in one examination.

CONCLUSION

These guidelines reflect established methods to assess preoperative liver function and PHLF risk, and have uncovered evidence gaps of interest for future research.

Assessing textbook outcome after single large hepatocellular carcinoma resection

BACKGROUND

This study aimed to investigate the impact and predictors of an ideal surgical care following SLHCC resection.

METHODS

SLHCC patients who underwent LR in two tertiary hepatobiliary centers between 2000 and 2021 were retrieved from prospectively maintained databases. The quality of surgical care was measured by the textbook outcome (TO). Tumor burden was defined by the tumor burden score (TBS). Factors associated with TO were determined on multivariate analysis. The impact of TO on oncological outcomes was assessed using Cox regressions.

RESULTS

Overall, 103 SLHCC patients were included. Laparoscopic approach was considered in 65 (63.1%) patients and 79 (76.7%) patients presented with moderate TBS. TO was achieved in 54 (52.4%) patients. Laparoscopic approach was independently associated with TO (OR 2.57; 95% CI 1.03-6.64; p = 0.045). Within 19 (6-38) months of median follow up, patients who achieved TO had better OS compared to non-TO patients (1-year OS: 91.7% vs. 66.9%; 5-year OS: 83.4% vs. 37.0%, p < 0.0001). On multivariate analysis, TO was independently associated with improved OS, especially in non-cirrhotic patients (HR 0.11; 95% CI 0.02-0.52, p = 0.005).

CONCLUSIONS

TO achievement could be a relevant surrogate marker of improved oncological care following SLHCC resection in non-cirrhotic patients.

Significance of predicted future liver remnant volume on liver failure risk after major hepatectomy: a case matched comparative study

Introduction

Future liver remnant volume (FLRV), a risk factor for liver failure (PHLF) after major hepatectomy (MH), is not routinely measured. This study aimed to evaluate the association between FLRV and PHLF.

Patients and methods

All patients undergoing MH (4 + segments) between 2011 and 2018 were identified from a prospectively maintained single-centre database. Perioperative data were collected for patients with PHLF, who were matched (1:2) with non-PHLF controls. FLRV and FLRV_% (i.e., % of total liver volume) were calculated retrospectively from preoperative CT scans using Synapse-3D software, and compared between the PHLF and matched control groups.

Results

Of 711 patients undergoing MH, PHLF occurred in 27 (3.8%), of whom 24 had preoperative CT scans available. These patients were matched to 48 non-PHLF controls, 98% of whom were classified as being at high risk of PHLF on preoperative risk scoring. FLRV_% was significantly lower in the PHLF group, compared to matched controls (median: 28.7 vs. 35.2%, p = 0.010), with FLRV% < 30% in 58% and 29% of patients, respectively. Assessment of the ability of FLRV_% to differentiate between PHLF and matched controls returned an area under the ROC curve of 0.69, and an optimal cut-off value of FLRV_% < 31.5%, which yielded 79% sensitivity and 67% specificity.

Conclusions

FLRV_% is significantly predictive of PHLF after MH, with over half of patients with PHLF having FLRV_% < 30%. In light of this, we propose that all patients should undergo risk stratification prior to MH, with the high risk patients additionally being assessed with CT volumetry.

Clinical application of preoperative shear wave dispersion for prediction of post liver failure in patients with hepatocellular carcinoma after hepatectomy

OBJECTIVE

The aim in this study was to determine the efficacy of shear wave dispersion (SWD) technique for the prediction of post hepatectomy liver failure (PHLF) in patients with hepatocellular carcinoma after hepatectomy and develop an SWD based risk prediction model.

METHODS & MATERIALS

We prospectively enrolled 205 consecutive patients who were scheduled to undergo hepatectomy for hepatocellular carcinoma (HCC), pre-operative SWD examination, laboratory data and some other clinicopathological tests were collected. The risk factors of PHLF were identified according to univariate and multivariate analysis, a predictive model was established based on logistic regression analyses.

RESULTS

SWD examination was successfully performed in 205 patients. PHLF occurred in 51 patients (24.9%), including 37/11/3 patients with Grade A/B/C, respectively. There was a high correlation between SWD value of liver and liver fibrosis stage (r = 0.873, p < 0.05). Patients with PHLF has a higher median SWD value of liver than patients without PHLF [17.4 vs 14.7 (m/s)/kHz, p < 0.05]. The SWD value of liver, total bilirubin (TB), international normalized ratio of prothrombin time (INR) and splenomegaly were significantly related to PHLF based on the multivariate analysis. A new prediction model (PM) for PHLF was established (PM = -12.918 + 0.183× SWD + 6.668× INR +0.100×TB+1.240×splenomegaly). The optimal cutoff value of SWD for predicting PHLF was 16.7 (m/s)/kHz. The area under the curve (AUC) of the PM for PHLF was 0.833, which was higher than that of SWD, INR, Forns, FIB4, APRI (p < 0.005, respectively).

CONCLUSION

SWD is a promising and reliable method for PHLF prediction in patients with HCC who were undergoing hepatectomy. Compared with SWD, Forns, APRI and FIB-4, PM demonstrate better efficacy for preoperative PHLF prediction.

Post-hepatectomy liver failure prediction and prevention: Development of a nomogram containing postoperative anticoagulants as a risk factor

INTRODUCTION AND OBJECTIVES

Posthepatectomy liver failure (PHLF) is a serious complication after hepatectomy, and its effective methods for preoperative prediction are lacking. Here, we aim to identify predictive factors and build a nomogram to evaluate patients' risk of developing PHLF.

PATIENTS AND METHODS

A retrospective review of a training cohort, including 199 patients who underwent hepatectomy at the Shanghai Eastern Hepatobiliary Surgery Hospital, was conducted. Independent risk variables for PHLF were identified using multivariate analysis of perioperative variables, and a nomogram was used to build a predictive model. To test the predictive power, a prospective study in which a validation cohort of 71 patients was evaluated using the nomogram. The prognostic value of this nomogram was evaluated by the C-index.

RESULTS

Independent risk variables for PHLF were identified from perioperative variables. In multivariate analysis of the training cohort, tumor number, Pringle maneuver, blood loss, preoperative platelet count, postoperative ascites and use of anticoagulant medications were determined to be key risk factors for the development of PHLF, and they were selected for inclusion in our nomogram. The nomogram showed a 0.911 C-index for the training cohort. In the validation cohort, the nomogram also showed good prognostic value for predicting PHLF. The validation cohort was used with similarly successful results to evaluate risk in two previously published study models with calculated C-indexes of 0.718 and 0.711.

CONCLUSION

Our study establishes for the first time a novel nomogram that can be used to identify patients at risk of developing PHLF.

Current evidence on posthepatectomy liver failure: comprehensive review

INTRODUCTION

Despite important advances in many areas of hepatobiliary surgical practice during the past decades, posthepatectomy liver failure (PHLF) still represents an important clinical challenge for the hepatobiliary surgeon. The aim of this review is to present the current body of evidence regarding different aspects of PHLF.

METHODS

A literature review was conducted to identify relevant articles for each topic of PHLF covered in this review. The literature search was performed using Medical Subject Heading terms on PubMed for articles on PHLF in English until May 2022.

RESULTS

Uniform reporting on PHLF is lacking due to the use of various definitions in the literature. There is no consensus on optimal preoperative assessment before major hepatectomy to avoid PHLF, although many try to estimate future liver remnant function. Once PHLF occurs, there is still no effective treatment, except liver transplantation, where the reported experience is limited.

DISCUSSION

Strict adherence to one definition is advised when reporting data on PHLF. The use of the International Study Group of Liver Surgery criteria of PHLF is recommended. There is still no widespread established method for future liver remnant function assessment. Liver transplantation is currently the only effective way to treat severe, intractable PHLF, but for many indications, this treatment is not available in most countries.

Machine learning prediction model for post- hepatectomy liver failure in hepatocellular carcinoma: A multicenter study

Introduction

Post-hepatectomy liver failure (PHLF) is one of the most serious complications and causes of death in patients with hepatocellular carcinoma (HCC) after hepatectomy. This study aimed to develop a novel machine learning (ML) model based on the light gradient boosting machines (LightGBM) algorithm for predicting PHLF.

Methods

A total of 875 patients with HCC who underwent hepatectomy were randomized into a training cohort (n=612), a validation cohort (n=88), and a testing cohort (n=175). Shapley additive explanation (SHAP) was performed to determine the importance of individual variables. By combining these independent risk factors, an ML model for predicting PHLF was established. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value, and decision curve analyses (DCA) were used to evaluate the accuracy of the ML model and compare it to that of other noninvasive models.

Results

The AUCs of the ML model for predicting PHLF in the training cohort, validation cohort, and testing cohort were 0.944, 0.870, and 0.822, respectively. The ML model had a higher AUC for predicting PHLF than did other non-invasive models. The ML model for predicting PHLF was found to be more valuable than other noninvasive models.

Conclusion

A novel ML model for the prediction of PHLF using common clinical parameters was constructed and validated. The novel ML model performed better than did existing noninvasive models for the prediction of PHLF.

Early postoperative serum aspartate aminotransferase for prediction of post-hepatectomy liver failure

Background

Post-hepatectomy liver failure (PHLF) is a serious complication of hepatectomy. The current criteria for PHLF diagnosis (ISGLS consensus) require laboratory data on or after postoperative day (POD) 5, which may delay treatment for patients at risk. The present study aimed to determine the associations between early postoperative (POD1) serum aminotransferase levels and PHLF.

Methods

The medical records of patients who underwent hepatectomy at Ramathibodi Hospital from January 2008 to December 2019 were retrospectively examined. Patients were classified into PHLF and non-PHLF groups. Preoperative characteristics, intraoperative findings, and early postoperative laboratory data (serum AST, ALT, bilirubin, and international normalized ratio (INR) on POD0 to POD5) were analyzed.

Results

A total of 890 patients were included, of whom 31 (3.4%) had PHLF. Cut-off points for AST of 260 U/L and ALT of 270 U/L on POD1 were predictive of PHLF. In multivariate analysis, AST > 260 U/L on POD1, ICG-R15, major hepatectomy, blood loss, and INR were independently associated with PHLF.

Conclusions

Early warning from elevated serum AST on POD1, before a definitive diagnosis of PHLF is made on POD5, can help alert physicians that a patient is at risk, meaning that active management and vigilant monitoring can be initiated as soon as possible.

A Novel Nomogram for Prediction of Post-Hepatectomy Liver Failure in Patients with Resectable Hepatocellular Carcinoma: A Multicenter Study

Objective

To develop a nomogram for predicting post-hepatectomy liver failure (PHLF) in patients with resectable hepatocellular carcinoma (HCC) based on portal hypertension, the extent of resection, ALT, total bilirubin, and platelet count.

Methods

Patients with HCC hospitalized from January 2015 to December 2020 were included in a retrospective cohort study. 595 HCC patients were divided into a training cohort (n=416) and a validation cohort (n=179) by random sampling. Univariate and multivariable analyses were performed to identify the independent variables to predict PHLF. The nomogram models for predicting the overall risk of PHLF and the risk of PHLF B+C were constructed based on the independent variables. Comparisons were made by using receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) with traditional models, such as FIB-4 score, APRI score, CP class (Child-Pugh), MELD score (model of end-stage liver disease), and ALBI score (albumin-bilirubin) to analyze the accuracy and superiority of the nomogram.

Results

We discovered that portal hypertension (yes vs no) (OR=1.677,95% CI:1.817–4.083, p=0.002), the extent of liver resection (OR=1.872,95% CI:3.937–47.096, p=0.001), ALT (OR=1.003,95% CI:1.003–1.016, P=0.003), total bilirubin (OR=1.036,95% CI:1.031–1.184, p=0.005), and platelet count (OR= 1.004, 95% CI:0.982–0.998, p=0.020) were independent risk factors for PHLF using multifactorial analysis. The nomogram models were constructed using well-fit calibration curves for each of these five covariates. When compared to the FIB4, ALBI, MELD, and CP score, our nomogram models have a better predictive value for predicting the overall risk of PHLF or the risk of PHLF B+C. The validation cohort’s results were consistent. DCA also confirmed the conclusion.

Conclusion

Our models, in the form of static nomogram or web application, were developed to predict PHLF overall risk and PHLF B+C risk in patients with HCC, with a high prediction sensitivity and specificity performance than other commonly used scoring systems.

A Novel Nomogram for Predicting Postoperative Liver Failure After Major Hepatectomy for Hepatocellular Carcinoma

Background

Post-hepatectomy liver failure (PHLF) is the most common cause of mortality after major hepatectomy in hepatocellular carcinoma (HCC) patients. We aim to develop a nomogram to preoperatively predict grade B/C PHLF defined by the International Study Group on Liver Surgery Grading (ISGLS) in HCC patients undergoing major hepatectomy.

Study Design

The consecutive HCC patients who underwent major hepatectomy at the Eastern Hepatobiliary Surgery Hospital between 2008 and 2013 served as a training cohort to develop a preoperative nomogram, and patients from 2 other hospitals comprised an external validation cohort. Least absolute shrinkage and selection operator (LASSO) logistic regression was applied to identify preoperative predictors of grade B/C PHLF. Multivariable logistic regression was utilized to establish a nomogram model. Internal and external validations were used to verify the performance of the nomogram. The accuracy of the nomogram was also compared with the conventional scoring models, including MELD and ALBI score.

Results

A total of 880 patients who underwent major hepatectomy (668 in the training cohort and 192 in the validation cohort) were enrolled in this study. The independent risk factors of grade B/C PHLF were age, gender, prothrombin time, total bilirubin, and CSPH, which were incorporated into the nomogram. Good prediction discrimination was achieved in the training (AUROC: 0.73) and validation (AUROC: 0.72) cohorts. The calibration curve also showed good agreement in both training and validation cohorts. The nomogram has a better performance than MELD and ALBI score models.

Conclusion

The proposed nomogram showed more accurate ability to individually predict grade B/C PHLF after major hepatectomy in HCC patients than MELD and ALBI scores.

Appraisal of multivariable prognostic models for post-operative liver decompensation following partial hepatectomy: a systematic review

BACKGROUND

Few reports have evaluated prognostic modelling studies of tools used for surgical decision-making. This systematic review aimed to describe and critically appraise studies that have developed or validated multivariable prognostic models for post-operative liver decompensation following partial hepatectomy.

METHODS

This study was designed using the CHARMS checklist. Following a comprehensive literature search, two reviewers independently screened candidate references for inclusion and abstracted relevant study details. Qualitative assessment was performed using the PROBAST tool.

RESULTS

We identified 36 prognostic modelling studies; 25 focused on development only, 3 developed and validated models, and 8 validated pre-existing models. None compared routine use of a prognostic model against standard clinical practice. Most studies used single-institution, retrospective cohort designs, conducted in Eastern populations. In total, 15 different outcome definitions for post-operative liver decompensation events were used. Statistical concerns surrounding model overfitting, performance assessment, and internal validation led to high risk of bias for all studies.

CONCLUSIONS

Current prognostic models for post-operative liver decompensation following partial hepatectomy may not be valid for routine clinical use due to design and methodologic concerns. Landmark resources and reporting guidelines such as the TRIPOD statement may assist researchers, and additionally, model impact assessment studies represent opportunities for future research.

Variations between the anatomical and functional distribution, based on 99m technetium -mebrofinate SPECT-CT scan, in patients at risk of post hepatectomy liver failure

BACKGROUND

The aim of the current study is to investigate the variations of anatomical (LV_Rem%) and functional remnant volumes (fLV_Rem%) and the dynamic uptake of Technetium-Mebrofinate (FRLF) measured from 99^m Technetium-Mebrofinate SPECT-CT scan (TMSCT) in patients at high risk of post-hepatectomy liver failure (PHLF).

METHODS

Variations in the measures of LV_Rem% and fLV_Rem% were assessed. The predictive accuracies of LV_Rem%, fLV_Rem% and FRLF with respect to PHLF were reported.

RESULTS

From the N = 92 scans performed, LV_Rem% and fLV_Rem% returned identical results in 15% of cases, and ±10 percentage points in 79% of cases. Some patients had larger discrepancies, with difference of >10 percentage points in 21% of cases. The difference was significant in those with primary liver cancers (-4.4 ± 9.2, p = 0.002). For the N = 29 patients that underwent surgery as planned on TMSCT, FRLF was a strong predictor of PHLF, with an AUROC of 0.83 (p = 0.005).

CONCLUSION

TMSCT is emerging as a useful modality in pre-operative assessment of patients undergoing major liver resection. For those with primary liver cancer, there is a significant variation in the anatomical and functional distributions that needs considered in surgical planning. Reduced FRLF, measured as the dynamic uptake in the future liver remnant, is a strong predictor of PHLF.

Early postoperative arterial lactate concentrations to stratify risk of post-hepatectomy liver failure

BACKGROUND

Post-hepatectomy liver failure (PHLF) represents the major determinant for death after liver resection. Early recognition is essential. Perioperative lactate dynamics for risk assessment of PHLF and associated morbidity were evaluated.

METHODS

This was a multicentre observational study of patients undergoing hepatectomy with validation in international high-volume units. Receiver operating characteristics analysis and cut-off calculation for the predictive value of lactate for clinically relevant International Study Group of Liver Surgery grade B/C PHLF (clinically relevant PHLF (CR-PHLF)) were performed. Lactate and other perioperative factors were assessed in a multivariable CR-PHLF regression model.

RESULTS

The exploratory cohort comprised 509 patients. CR-PHLF, death, overall morbidity and severe morbidity occurred in 7.7, 3.3, 40.9 and 29.3 per cent of patients respectively. The areas under the curve (AUCs) regarding CR-PHLF were 0.829 (95 per cent c.i. 0.770 to 0.888) for maximum lactate within 24 h (Lactate_Max) and 0.870 (95 per cent c.i. 0.818 to 0.922) for postoperative day 1 levels (Lactate_POD1). The respective AUCs in the validation cohort (482 patients) were 0.812 and 0.751 and optimal Lactate_Max cut-offs were identical in both cohorts. Exploration cohort patients with Lactate_Max 50 mg/dl or greater more often developed CR-PHLF (50.0 per cent) than those with Lactate_Max between 20 and 49.9 mg/dl (7.4 per cent) or less than 20 mg/dl (0.5 per cent; P < 0.001). This also applied to death (18.4, 2.7 and 1.4 per cent), severe morbidity (71.1, 35.7 and 14.1 per cent) and associated complications such as acute kidney injury (26.3, 3.1 and 2.3 per cent) and haemorrhage (15.8, 3.1 and 1.4 per cent). These results were confirmed in the validation group. Combining Lactate_Max with Lactate_POD1 further increased AUC (ΔAUC = 0.053) utilizing lactate dynamics for risk assessment. Lactate_Max, major resections, age, cirrhosis and chronic kidney disease were independent risk factors for CR-PHLF. A freely available calculator facilitates clinical risk stratification (www.liver-calculator.com).

CONCLUSION

Early postoperative lactate values are powerful, readily available markers for CR-PHLF and associated complications after hepatectomy with potential for guiding postoperative care.Presented in part as an oral video abstract at the 2020 online Congress of the European Society for Surgical Research and the 2021 Congress of the Austrian Surgical Society.

A Nomogram Based on Preoperative Inflammatory Indices and ICG-R15 for Prediction of Liver Failure After Hepatectomy in HCC Patients

Objective

To establish a nomogram based on inflammatory indices and ICG-R15 for predicting post-hepatectomy liver failure (PHLF) among patients with resectable hepatocellular carcinoma (HCC).

Methods

A retrospective cohort of 407 patients with HCC hospitalized at Xiangya Hospital of Central South University between January 2015 and December 2020, and 81 patients with HCC hospitalized at the Second Xiangya Hospital of Central South University between January 2019 and January 2020 were included in the study. Totally 488 HCC patients were divided into the training cohort (n=378) and the validation cohort (n=110) by random sampling. Univariate and multivariate analysis was performed to identify the independent risk factors. Through combining these independent risk factors, a nomogram was established for the prediction of PHLF. The accuracy of the nomogram was evaluated and compared with traditional models, like CP score (Child-Pugh), MELD score (Model of End-Stage Liver Disease), and ALBI score (albumin-bilirubin) by using receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).

Results

Cirrhosis (OR=2.203, 95%CI:1.070-3.824, P=0.030), prothrombin time (PT) (OR=1.886, 95%CI: 1.107-3.211, P=0.020), tumor size (OR=1.107, 95%CI: 1.022-1.200, P=0.013), ICG-R15% (OR=1.141, 95%CI: 1.070-1.216, P<0.001), blood loss (OR=2.415, 95%CI: 1.306-4.468, P=0.005) and AST-to-platelet ratio index (APRI) (OR=4.652, 95%CI: 1.432-15.112, P=0.011) were independent risk factors of PHLF. Nomogram was built with well-fitted calibration curves on the of these 6 factors. Comparing with CP score (C-index=0.582, 95%CI, 0.523-0.640), ALBI score (C-index=0.670, 95%CI, 0.615-0.725) and MELD score (C-ibasedndex=0.661, 95%CI, 0.606-0.716), the nomogram showed a better predictive value, with a C-index of 0.845 (95%CI, 0.806-0.884). The results were consistent in the validation cohort. DCA confirmed the conclusion as well.

Conclusion

A novel nomogram was established to predict PHLF in HCC patients. The nomogram showed a strong predictive efficiency and would be a convenient tool for us to facilitate clinical decisions.

Timing of Complication and Failure to Rescue after Hepatectomy: Single-Institution Analysis of 28 Years of Hepatic Surgery

BACKGROUND

Morbidity after hepatectomy remains a significant, potentially preventable, outcome. Understanding the pattern of complications and rescue pathways is critical for the development of targeted initiatives intended to salvage patients after operative morbidity.

STUDY DESIGN

Patients undergoing liver resection from 1991 to 2018 at a single institution were analyzed. Failure to rescue (FTR) was defined as percentage of deaths in patients with a complication within 30 days. Generalized estimating equations with log-link function assessed associations between clinical characteristics and major complications and between complications at fewer than 30 days and 30 to 90 days. Logistic regression assessed associations between complications and FTR.

RESULTS

A total of 6,191 patients and 6,668 operations were identified, of which 55.6% were performed for management of metastatic colorectal cancer. Major complications (grade ≥3) occurred in 20.2% of operations (1,346 of 6,668). Ninety-day mortality was 2.2%. The most common complication was intra-abdominal abscess at 9.0% (95% CI, 8.3% to 9.7%). Ten percent of patients with a complication at 30 days had another complication between 30 and 90 days compared with 2% without an early complication (odds ratio [OR] 5.09; 95% CI, 3.97 to 6.54; p < 0.001). FTR for liver failure, cardiac arrest, abscess, and hemorrhage was 36%, 56%, 3%, and 6%, respectively. Risk of 90-day mortality was higher in patients with liver failure (53% vs 2%; OR 61.42; 95% CI, 37.47 to 100.67; p < 0.001), cardiac arrest (69% vs 2%; OR 96.95; 95% CI, 33.23 to 283.80; p < 0.001), hemorrhage (11% vs 2%; OR 5.51; 95% CI, 2.59 to 11.73; p < 0.001), and abscess (7% vs 2%; OR 4.05; 95% CI, 2.76 to 5.94; p < 0.001) compared with those without these complications.

CONCLUSIONS

Morbidity after hepatectomy is frequent despite low mortality. This study identifies targets for improvement in morbidity and failure to rescue after hepatectomy. Efforts to improve recognition and intervention for infections and early complications are needed to improve outcomes.

The use of neoadjuvant lobar radioembolization prior to major hepatic resection for malignancy results in a low rate of post hepatectomy liver failure

Background

Neoadjuvant yttrium-90 transarterial radioembolization (TARE) is increasingly being used as a strategy to facilitate resection of otherwise unresectable tumors due to its ability to generate both tumor response and remnant liver hypertrophy. Perioperative outcomes after the use of neoadjuvant lobar TARE remain underinvestigated.

Methods

A single center retrospective review of patients who underwent lobar TARE prior to major hepatectomy for primary or metastatic liver cancer between 2007 and 2018 was conducted. Baseline demographics, radioembolization parameters, pre- and post-radioembolization volumetrics, intra-operative surgical data, adverse events, and post-operative outcomes were analyzed.

Results

Twenty-six patients underwent major hepatectomy after neoadjuvant lobar TARE. The mean age was 58.3 years (17-88 years). 62% of patients (n=16) had primary liver malignancies while the remainder had metastatic disease. Liver resection included right hepatectomy or trisegmentectomy, left or extended left hepatectomy, and sectorectomy/segmentectomy in 77% (n=20), 8% (n=2), and 15% (n=4) of patients, respectively. The mean length of stay was 8.3 days (range, 3-33 days) and there were no grade IV morbidities or 90-day mortalities. The incidence of post hepatectomy liver failure (PHLF) was 3.8% (n=1). The median time to progression after resection was 4.5 months (range, 3.3-10 months). Twenty-three percent (n=6) of patients had no recurrence. The median survival was 28.9 months (range, 16.9-46.8 months) from major hepatectomy and 37.6 months (range, 25.2-53.1 months) from TARE.

Conclusions

Major hepatectomy after neoadjuvant lobar radioembolization is safe with a low incidence of PHLF.

Defining the risk of liver failure after minor hepatectomy: a NSQIP analysis of 7029 patients

BACKGROUND

Post-hepatectomy liver failure (PHLF) remains a significant complication after hepatic resection. This study aims to determine the rate of PHLF in patients undergoing resection of 3 or fewer segments and analyze the association of PHLF with perioperative characteristics and postoperative complications.

METHODS

The American College of Surgeons hepatectomy-targeted National Surgical Quality Improvement Program database was queried for patients undergoing left hemi-hepatectomy or partial resection from 2014 to 2018. The primary outcome was PHLF, defined by ISGLS. Multivariable logistic regression models assessed the association between PHLF, preoperative and operative variables and postoperative complications.

RESULTS

Among 7029 patients, 187 (2.7%) experienced PHLF, with clinically significant (grade B/C) PHLF in 1.4%. PHLF was associated with older age, male gender, higher ASA classification, ascites, and elevated SGOT. Preoperative ascites (OR 4.94, 95%CI: 2.45-9.94, p < 0.001) had the strongest association with PHLF. There was no association between PHLF and concurrent colorectal resection, neoadjuvant therapy, or concurrent ablation. Surgical site infection (OR 3.64, 95%CI: 2.40-5.54, p < 0.001), sepsis (OR 3.78, 95%CI: 2.16-6.61, p < 0.001), postoperative invasive procedure (OR 6.92, 95%CI: 4.91-9.76, p < 0.001), and bile leak (OR 4.65, 95%CI: 3.04-7.12, p < 0.001) were associated with PHLF.

CONCLUSION

PHLF after minor hepatectomy is rare and associated with signs of preoperative liver dysfunction. The association with infectious complications suggests a multifactorial etiology and provides targets for quality improvement.

Prediction of Post-hepatectomy Liver Failure in Patients With Hepatocellular Carcinoma Based on Radiomics Using Gd-EOB-DTPA-Enhanced MRI: The Liver Failure Model

Objectives: Preoperative prediction of post-hepatectomy liver failure (PHLF) in patients with hepatocellular carcinoma (HCC) is significant for developing appropriate treatment strategies. We aimed to establish a radiomics-based clinical model for preoperative prediction of PHLF in HCC patients using gadolinium-ethoxybenzyl-diethylenetriamine (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI).

Methods: A total of 144 HCC patients from two medical centers were included, with 111 patients as the training cohort and 33 patients as the test cohort, respectively. Radiomics features and clinical variables were selected to construct a radiomics model and a clinical model, respectively. A combined logistic regression model, the liver failure (LF) model that incorporated the developed radiomics signature and clinical risk factors was then constructed. The performance of these models was evaluated and compared by plotting the receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC) with 95% confidence interval (CI).

Results: The radiomics model showed a higher AUC than the clinical model in the training cohort and the test cohort for predicting PHLF in HCC patients. Moreover, the LF model had the highest AUCs in both cohorts [0.956 (95% CI: 0.955–0.962) and 0.844 (95% CI: 0.833–0.886), respectively], compared with the radiomics model and the clinical model.

Conclusions: We evaluated quantitative radiomics features from MRI images and presented an externally validated radiomics-based clinical model, the LF model for the prediction of PHLF in HCC patients, which could assist clinicians in making treatment strategies before surgery.

A cohort study of hepatectomy-related complications and prediction model for postoperative liver failure after major liver resection in 1,441 patients without obstructive jaundice

Background

This cohort study, based on a large sample of extensive hepatectomy cases, aimed to analyze the distribution of hepatectomy-related complications and to develop a predictive model of posthepatectomy liver failure (PHLF).

Methods

Data of patients who underwent hepatectomy of ≥3 liver segments at the Eastern Hepatobiliary Surgery Hospital from 2000 to 2016 were collected and analyzed. Information on hepatectomy-related complications was collected and risk factors were analyzed. A total of 1,441 eligible patients were randomly assigned at 3:1 ratio into the derivation (n=1,080) and validation (n=361) cohorts. The multivariable logistic regression model was used to establish the prediction model of PHLF in the derivation cohort.

Results

The incidence rates of PHLF, ascites, bile leakage, intra-abdominal bleeding, and abscesses were 58.22%, 10.76%, 11.17%, 9.71%, and 4.16%, respectively. The 90-day perioperative mortality rate was 1.32%. Multivariate analyses found that age, gender, platelet, creatinine, gamma-glutamyltransferase, thrombin time, fibrinogen, hepatitis B e (HBe) antigen positive, and number of resected liver segments were independent prognostic factors of PHLF in the derivation cohort and included in the nomogram. The prediction model demonstrated good discrimination [area under the curve =0.726, 95% confidence interval (CI), 0.696–0.760, P<0.0001] and calibration.

Conclusions

Our study showed a high perioperative safety and a low risk of serious complications in patients who underwent major liver resection (MLR) at a large hepatobiliary surgery center. Routine preoperative clinical information can be used to develop a postoperative liver failure risk prediction model for rational planning of surgery.

A novel model for predicting posthepatectomy liver failure based on liver function and degree of liver resection in patients with hepatocellular carcinoma

BACKGROUND

The permissible liver resection rate for preventing posthepatectomy liver failure (PHLF) remains unclear. We aimed to develop a novel PHLF-predicting model and to strategize hepatectomy for hepatocellular carcinoma (HCC).

METHODS

This retrospective study included 335 HCC patients who underwent anatomical hepatectomy at eight institutions between 2013 and 2017. Risk factors, including volume-associated liver-estimating parameters, for PHLF grade B-C were analyzed in a training set (n = 122) via multivariate analysis, and a PHLF prediction model was developed. The utility of the model was evaluated in a validation set (n = 213).

RESULTS

Our model was based on the three independent risk factors for PHLF identified in the training set: volume-associated indocyanine green retention rate at 15 min, platelet count, and prothrombin time index (the VIPP score). The areas under the receiver operating characteristic curve of the VIPP scores for severe PHLF in the training and validation sets were 0.864 and 0.794, respectively. In both sets, the VIPP score stratified patients at risk for severe PHLF, with a score of 3 (specificity, 0.92) indicating higher risk.

CONCLUSION

Our model facilitates the selection of the appropriate hepatectomy procedure by providing permissible liver resection rates based on VIPP scores.

An Improved Scoring System Based on Platelet-Albumin-Bilirubin in Predicting Posthepatectomy Liver Failure Outcomes

BACKGROUND

Posthepatectomy liver failure (PHLF) is one of the major complications of liver resection that causes perioperative mortality. Accurate preoperative assessment of PHLF is of great significance to reduce the complication rate after hepatectomy and improve the survival rate.

METHODS

A retrospective study of patients who received hepatectomy from January 2016 to October 2019 at Tang Du Hospital was performed. The area under the receiver operating characteristic (ROC) curve was used to compare the predictive effects of various scoring models on PHLF.

RESULTS

The area under the ROC curve of platelet-albumin-bilirubin (PALBI) score, new platelet-albumin-bilirubin (I-PALBI) score, ALBI score, and MELD score was, respectively, 0.647, 0.772, 0.677, and 0.686 (p < 0.01). The I-PALBI score was significantly better than the other scores.

CONCLUSIONS

I-PALBI score can be used as a predictive score of PHLF, and its prediction accuracy is better than other scoring systems.

A nomogram for prediction of posthepatectomy liver failure in patients with hepatocellular carcinoma: A retrospective study

To generate a nomogram to predict posthepatectomy liver failure (PHLF), we attempted to elucidate salient risk factors in patients with hepatocellular carcinoma (HCC).We performed a retrospective review of 665 patients with HCC who received hepatectomy in 2 academic institutions in China. Independent risk factors for PHLF were identified from putative demographic, intrinsic, biochemical, surgery-related, and volumetric data. A predictive nomogram was formulated based on relevant risk factors, and we compared this with existing models.We identified clinical signs of portal hypertension (P = .023), serum total bilirubin (P = .001), serum creatinine (P = .039), and intraoperative hemorrhage (P = .015) as being important risk factors in predicting PHLF. The nomogram had a C-index of 0.906 for the externally validated data. The nomogram displayed better predictive value than 2 of the other most cited models (C-indices of 0.641 and 0.616, respectively) in the current cohort. Additionally, we were able to patients into low- (<10%), intermediate- (10-30%), and high-risk (≥30%) groups based on the nomogram. This allows us to facilitate person-specific management.Here, we constructed a simple nomogram for prediction of PHLF in patients with HCC weighted by independent risk factors. Further prospective studies are required to confirm the predictive ability of our nomogram.

Development and validation of a brachycephalic risk (BRisk) score to predict the risk of complications in dogs presenting for surgical treatment of brachycephalic obstructive airway syndrome

OBJECTIVE

To develop and validate a preoperative brachycephalic risk (BRisk) score that objectively and accurately predicts the risk of major complications or death in dogs undergoing corrective surgery for brachycephalic obstructive airway syndrome (BOAS).

STUDY DESIGN

Retrospective multicenter cohort study.

SAMPLE POPULATION

Score development n = 233 dogs, validation n = 50 dogs.

METHODS

Data were collected on signalment, medical history, reason for presentation, physical examination, and preoperative diagnostic findings. The primary outcome measures included risk of major complications (requirement for postoperative oxygen support for >48 hours or postoperative temporary/permanent tracheostomy) or death within the hospitalization period. The score was developed by using data from two centers and was validated in a third center. The 10-point BRisk score was modeled on breed, history of previous surgery, concurrent procedures, body condition score, airway status, and admission rectal temperature.

RESULTS

The score was associated with negative outcome (P < .0001) and discriminated well in both the construction (area under the receiver operator characteristic [AUROC] = 0.83) and validation groups (AUROC = 0.84). Dogs with scores >3 were 9.1 times more likely to have a negative outcome (95% CI = 3.9-21.2) compared with dogs with scores ≤3.

CONCLUSION

The BRisk score developed from admission data in this study accurately rated the risk of negative outcome of dogs undergoing corrective surgery for BOAS.

CLINICAL SIGNIFICANCE

Preoperative determination of the BRisk score may assist triage, management of owner expectations, decision making regarding intervention selection, and characterization of populations in clinical research.