Validation of a Nomogram to Predict the Risk of Perioperative Blood Transfusion for Liver Resection

Risk factors of major bleeding detected by machine learning method in patients undergoing liver resection with controlled low central venous pressure technique

BACKGROUND

Controlled low central venous pressure (CLCVP) technique has been extensively validated in clinical practices to decrease intraoperative bleeding during liver resection process; however, no studies to date have attempted to propose a scoring method to better understand what risk factors might still be responsible for bleeding when CLCVP technique was implemented.

METHODS

We aimed to use machine learning to develop a model for detecting the risk factors of major bleeding in patients who underwent liver resection using CLCVP technique. We reviewed the medical records of 1077 patients who underwent liver surgery between January 2017 and June 2020. We evaluated the XGBoost model and logistic regression model using stratified K-fold cross-validation (K = 5), and the area under the receiver operating characteristic curve, the recall rate, precision rate, and accuracy score were calculated and compared. The SHapley Additive exPlanations was employed to identify the most influencing factors and their contribution to the prediction.

RESULTS

The XGBoost classifier with an accuracy of 0.80 and precision of 0.89 outperformed the logistic regression model with an accuracy of 0.76 and precision of 0.79. According to the SHapley Additive exPlanations summary plot, the top six variables ranked from most to least important included intraoperative hematocrit, surgery duration, intraoperative lactate, preoperative hemoglobin, preoperative aspartate transaminase, and Pringle maneuver duration.

CONCLUSIONS

Anesthesiologists should be aware of the potential impact of increased Pringle maneuver duration and lactate levels on intraoperative major bleeding in patients undergoing liver resection with CLCVP technique.   What is already known on this topic-Low central venous pressure technique has already been extensively validated in clinical practices, with no prediction model for major bleeding. What this study adds-The XGBoost classifier outperformed logistic regression model for the prediction of major bleeding during liver resection with low central venous pressure technique. How this study might affect research, practice, or policy-anesthesiologists should be aware of the potential impact of increased PM duration and lactate levels on intraoperative major bleeding in patients undergoing liver resection with CLCVP technique.

Development and validation of a prediction tool for intraoperative blood transfusion in brain tumor resection surgery: a retrospective analysis

Early identification of a patient with a high risk of blood transfusion during brain tumor resection surgery is difficult but critical for implementing preoperative blood-saving strategies. This study aims to develop and validate a machine learning prediction tool for intraoperative blood transfusion in brain tumor resection surgery. A total of 541 patients who underwent brain tumor resection surgery in our hospital from January 2019 to December 2021 were retrospectively enrolled in this study. We incorporated demographics, preoperative comorbidities, and laboratory risk factors. Features were selected using the least absolute shrinkage and selection operator (LASSO). Eight machine learning algorithms were benchmarked to identify the best model to predict intraoperative blood transfusion. The prediction tool was established based on the best algorithm and evaluated with discriminative ability. The data were randomly split into training and test groups at a ratio of 7:3. LASSO identified seven preoperative relevant factors in the training group: hemoglobin, diameter, prothrombin time, white blood cell count (WBC), age, physical status of the American Society of Anesthesiologists (ASA) classification, and heart function. Logistic regression, linear discriminant analysis, supporter vector machine, and ranger all performed better in the eight machine learning algorithms with classification errors of 0.185, 0.193, 0.199, and 0.196, respectively. A nomogram was then established, and the model showed a better discrimination ability [0.817, 95% CI (0.739, 0.895)] than hemoglobin [0.663, 95% CI (0.557, 0.770)] alone in the test group (P = 0.000). Hemoglobin, diameter, prothrombin time, WBC, age, ASA status, and heart function are risk factors of intraoperative blood transfusion in brain tumor resection surgery. The prediction tool established using the logistic regression algorithm showed a good discriminative ability than hemoglobin alone for predicting intraoperative blood transfusion in brain tumor resection surgery.

Systematic review highlights high risk of bias of clinical prediction models for blood transfusion in patients undergoing elective surgery

BACKGROUND

Blood transfusion can be a lifesaving intervention after perioperative blood loss. Many prediction models have been developed to identify patients most likely to require blood transfusion during elective surgery, but it is unclear whether any are suitable for clinical practice.

STUDY DESIGN AND SETTING

We conducted a systematic review, searching MEDLINE, Embase, PubMed, The Cochrane Library, Transfusion Evidence Library, Scopus, and Web of Science databases for studies reporting the development or validation of a blood transfusion prediction model in elective surgery patients between January 1, 2000 and June 30, 2021. We extracted study characteristics, discrimination performance (c-statistics) of final models, and data, which we used to perform risk of bias assessment using the Prediction model risk of bias assessment tool (PROBAST).

RESULTS

We reviewed 66 studies (72 developed and 48 externally validated models). Pooled c-statistics of externally validated models ranged from 0.67 to 0.78. Most developed and validated models were at high risk of bias due to handling of predictors, validation methods, and too small sample sizes.

CONCLUSION

Most blood transfusion prediction models are at high risk of bias and suffer from poor reporting and methodological quality, which must be addressed before they can be safely used in clinical practice.

Establishment and evaluation of nomogram for predicting intraventricular hemorrhage in neonatal acute respiratory distress syndrome

BACKGROUND

Intraventricular hemorrhage (IVH) is the most common type of brain injury in newborns, especially in newborns with Neonatal acute respiratory distress syndrome (ARDS). IVH can cause brain parenchyma damage and long-term neurological sequelae in children. Early identification and prevention of sequelae are essential. This study aims to establish a predictive nomogram for the early prediction of IVH in newborns with ARDS.

METHODS

From 2019 to 2021, we collected data from 222 infants diagnosed with ARDS in the Department of Neonatology, First Affiliated Hospital of Xinjiang Medical University. Infants have been randomly assigned to the training set (n = 161) or the validation set (n = 61) at a ratio of 7:3. Variables were screened using the Least Absolute Contract and Selection Operator (LASSO) regression to create a risk model for IVH in infants with ARDS. The variables chosen in the LASSO regression model were used to establish the prediction model using multivariate logistic regression analysis.

RESULTS

We recognized 4 variables as independent risk factors for IVH in newborns with ARDS via LASSO analysis, consisting of premature rupture of membranes (PROM), pulmonary surfactant (PS) dosage, PH¹ and Arterial partial pressure of oxygen (PaO₂¹). The C-Index for this dataset is 0.868 (95% CI: 0.837-0.940) and the C index in bootstrap verification is 0.852 respectively. The analysis of the decision curve shows that the model can significantly improve clinical efficiency in predicting IVH. We also provide a website based on the model and open it to users for free, so that the model can be better applied to clinical practice.

CONCLUSION

In conclusion, the nomogram based on 4 factors shows good identification, calibration and clinical practicability. Our nomographs can help clinicians make clinical decisions, screen high-risk ARDS newborns, and facilitate early identification and management of IVH patients.

Predictive Model of Cerebral Vasospasm in Subarachnoid Hemorrhage Based on Regression Equation

In order to explore the regression equation for the prediction model of subarachnoid hemorrhage and cerebral vasospasm, the nomogram prediction model of SCVS occurrence was established. This study is a retrospective analysis of 125 cases of aSAH admitted to a hospital; the patients were divided into SCVS group and non-SCVS group. Select SIRI as a simple and reliable marker of inflammation, analyze its correlation with SCVS and its predictive value, and analyze the predictive value of SIRI to SCVS through ROC curve. Based on the SIRI inflammation level and other related risk factors, a nomogram prediction model for the occurrence of SCVS was built. The experimental results show that the SIRI level of patients in the SCVS group was significantly higher than that of the non-SCVS group, and logistic regression analysis found that SIRI is an independent risk factor for SCVS. SIRI = 3.63 × 10⁹/L is the best cutoff value for diagnosing the occurrence of SCVS. When TC = 2.24 mmol/L and SIRI = 3.63 × 10%/L, its Youden Index is the largest (0.312, 0.296) and is the best cutoff value for predicting the occurrence of SCVS; at the same time, its prediction accuracy (area under the ROC curve (AUC)), sensitivity, specificity, the positive predictive value, and negative predictive value are 0.743, 72.70%, 80.10%, 77.53%, and 94.24% and 0.725, 70.60%, 76.90%, 73.49%, and 93.59%. Nomogram prediction model establishment and evaluation combined with the results of multifactor analysis are used to build an individual nomogram prediction model. The model has good prediction consistency (C-index = 0.685, P < 0.01). ROC analysis results showed that the model that combined SIRI and other standard variables (AUC = 0.896, 95% CI was 0.803-0.929, P < 0.001) was better than the model that did not combine SIRI (AUC = 0.859, 95% CI was 0.759-0.912, P < 0.001) and the model based only on SIRI (AUC = 0.725, 95% CI was 0.586-0.793, P = 0.001) has better predictive value for SCVS. Joint SIRI will optimize the prediction performance of the nomogram model and improve the early recognition and screening capabilities of SCVS.

Multi-Institutional Development and External Validation of a Nomogram for Prediction of Extrahepatic Recurrence After Curative-Intent Resection for Hepatocellular Carcinoma

BACKGROUNDS

Extrahepatic recurrence of hepatocellular carcinoma (HCC) after surgical resection is associated with unfavorable prognosis. The objectives of the current study were to identify the risk factors and develop a nomogram for the prediction of extrahepatic recurrence after initial curative surgery.

METHODS

A total of 635 patients who underwent curative-intent resection for HCC between 2000 and 2017 were identified from an international multi-institutional database. The clinicopathological characteristics, risk factors, and long-term survival of patients with extrahepatic recurrence were analyzed. A nomogram for the prediction of extrahepatic recurrence was established and validated in 144 patients from an external cohort.

RESULTS

Among the 635 patients in the derivative cohort, 283 (44.6%) experienced recurrence. Among patients who recurred, 80 (28.3%) patients had extrahepatic ± intrahepatic recurrence, whereas 203 (71.7%) had intrahepatic recurrence only. Extrahepatic recurrence was associated with more advanced initial tumor characteristics, early recurrence, and worse prognosis versus non-extrahepatic recurrence. A nomogram for the prediction of extrahepatic recurrence was developed using the β-coefficients from the identified risk factors, including neutrophil-to-lymphocyte ratio, multiple lesions, tumor size, and microvascular invasion. The nomogram demonstrated good ability to predict extrahepatic recurrence (c-index: training cohort 0.786; validation cohort: 0.845). The calibration plots demonstrated good agreement between estimated and observed extrahepatic recurrence (p = 0.658).

CONCLUSIONS

An externally validated nomogram was developed with good accuracy to predict extrahepatic recurrence following curative-intent resection of HCC. This nomogram may help identify patients at high risk of extrahepatic recurrence and guide surveillance protocols as well as adjuvant treatments.

Development and validation of a nomogram to predict intraoperative blood transfusion for gastric cancer surgery

OBJECTIVE

To construct and validate a nomogram composed of preoperative variables to predict intraoperative blood transfusion for gastric cancer surgery.

BACKGROUND

Intraoperative transfusion for gastric cancer surgery is a common medical procedure that is associated with increased postoperative complications.

METHODS

A total of 999 patients who underwent gastrectomy between January 2010 and June 2019 were randomly allocated into the primary and validation cohorts in a 2:1 ratio. In the primary cohort, logistic analyses were performed to identify independent predictors for transfusion. Using the Akaike information criterion, selected variables were incorporated to construct a nomogram. Validations of the nomogram were performed in the primary and validation cohorts. The discrimination ability of the nomogram was assessed by the concordance index (C-index), and calibration was assessed by calibration curves and the Hosmer-Lemeshow goodness-of-fit test.

RESULTS

The following risk factors for transfusion were identified and used to construct the nomogram: ASA status (III-IV vs I-II: odds ratio [OR] 1.74), comorbidities (yes vs no: OR 1.57), tumour location (diffuse vs lower: OR 4.05), cTNM stage (III vs I: OR 1.95), and a preoperative haemoglobin level less than 80 g/L (vs over 120 g/L: OR 35.30). The C-index was 0.859 and 0.850 in the primary and validation cohorts, respectively, which both indicated good discrimination of the nomogram. Additionally, both calibration curves and Hosmer-Lemeshow tests (p-value 0.184 and 0.887, respectively) demonstrated high agreement between the predictions and actual outcomes.

CONCLUSION

A nomogram composed of preoperative variables to predict blood transfusion for gastric cancer surgery was effectively developed and validated. This nomogram could be used to improve the utilisation of red blood cells for gastrectomy.

Machine Learning for the Prediction of Red Blood Cell Transfusion in Patients During or After Liver Transplantation Surgery

Aim: This study aimed to use machine learning algorithms to identify critical preoperative variables and predict the red blood cell (RBC) transfusion during or after liver transplantation surgery. Study Design and Methods: A total of 1,193 patients undergoing liver transplantation in three large tertiary hospitals in China were examined. Twenty-four preoperative variables were collected, including essential population characteristics, diagnosis, symptoms, and laboratory parameters. The cohort was randomly split into a train set (70%) and a validation set (30%). The Recursive Feature Elimination and eXtreme Gradient Boosting algorithms (XGBOOST) were used to select variables and build machine learning prediction models, respectively. Besides, seven other machine learning models and logistic regression were developed. The area under the receiver operating characteristic (AUROC) was used to compare the prediction performance of different models. The SHapley Additive exPlanations package was applied to interpret the XGBOOST model. Data from 31 patients at one of the hospitals were prospectively collected for model validation. Results: In this study, 72.1% of patients in the training set and 73.2% in the validation set underwent RBC transfusion during or after the surgery. Nine vital preoperative variables were finally selected, including the presence of portal hypertension, age, hemoglobin, diagnosis, direct bilirubin, activated partial thromboplastin time, globulin, aspartate aminotransferase, and alanine aminotransferase. The XGBOOST model presented significantly better predictive performance (AUROC: 0.813) than other models and also performed well in the prospective dataset (accuracy: 76.9%). Discussion: A model for predicting RBC transfusion during or after liver transplantation was successfully developed using a machine learning algorithm based on nine preoperative variables, which could guide high-risk patients to take appropriate preventive measures.

Establishment and assessment of a nomogram for predicting blood transfusion risk in posterior lumbar spinal fusion

Background

The aim of this study was to determine the risk factors and develop a nomogram for blood transfusions after posterior lumbar spinal fusion (PSL).

Methods

We conducted a retrospective, single-center study based on 885 patients receiving PSL, and data was obtained from May 2015 to September 2019. Univariable and multivariable logistics regression analysis were conducted to identify risk factors for blood transfusion, and a nomogram was constructed to individually evaluate the risk of blood transfusion. Discrimination, calibration, and clinical usefulness were validated by the receiver operating characteristics (ROC), C-index, calibration plot, and decision curve analysis, respectively. Bootstrapping validation was performed to assess the performance of the model.

Results

Of 885 patients, 885 were enrolled in the final study population, and 289 received blood transfusion. Statistical analyses showed that low preoperative hemoglobin (Hb), longer time to surgery, operative time, levels of fusion > 1, longer surgery duration, and higher total intraoperative blood loss (IBL) were the risk factors for transfusion. The C-index was 0.898 (95% CI 0.847–0.949) in this dataset and 0.895 in bootstrapping validation, respectively. Calibration curve showed satisfied discrimination and calibration of the nomogram. Decision curve analysis (DCA) shown that the nomogram was clinical utility.

Conclusions

In summary, we investigated the relationship between the blood transfusion requirement and predictors: levels of fusion, operative time, time to surgery, total intraoperative EBL, and preoperative Hb level. Our nomogram with a robust performance in the assessment of risk of transfusion can contribute to clinicians in making clinical decision. However, external validation is still needed in the further.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13018-020-02053-2.

Development and Validation of a Nomogram for Preoperative Prediction of Localization of Neonatal Gastrointestinal Perforation

Background: Information regarding the localization of gastrointestinal perforation is crucial for the following surgical procedure. This study was to determine the key indicators and develop a prediction model for the localization in neonates with gastrointestinal perforation. Methods: A nomogram to predict the location of neonatal gastrointestinal perforation was developed using a cohort of patients who underwent surgery between July 2009 and May 2021. Baseline variables were analyzed using logistics regression and nomogram developed using significant predictors. The predictive performance of the nomogram was assessed by the concordance index (C-index), calibration curve, and area under the receiver operating characteristic (ROC) curve (AUC). The nomogram was further validated in an integrated external cohort. Results: We investigated the data of 201 patients, of which 65 (32.3%) were confirmed with upper gastrointestinal perforation by surgery. Multivariate logistic regression analysis identified the following as independent predictors: preterm [OR: 5.014 (1.492-18.922)], time of onset [OR: 0.705 (0.582-0.829)], preoperative hemoglobin [OR:1.017 (1.001-1.033)], bloody stool: No [OR: 4.860 (1.270-23.588)], shock [OR: 5.790 (1.683-22.455)] and sepsis: No [OR 3.044 (1.124-8.581)]. Furthermore, the nomogram was effective in predicting the perforation site, with an AUC of 0.876 [95% confidence interval (CI): 0.830-0.923]. Internal validation showed that the average AUC was 0.861. Additionally, the model achieved satisfactory discrimination (AUC, 0.900; 95% CI, 0.826-0.974) and calibration (Hosmer-Lemeshow test, P = 0.4802) in external validation. Conclusions: The nomogram based on the six factors revealed good discrimination and calibration, suggesting good clinical utility. The nomogram could help surgeons predict the location of gastrointestinal perforation before surgery to make a surgical plan.

Prognostic Impact of Pedicle Clamping during Liver Resection for Colorectal Metastases

Pedicle clamping (PC) during liver resection for colorectal metastases (CRLM) is used to reduce blood loss and allogeneic blood transfusion (ABT). The effect on long-term oncologic outcomes is still under debate. A retrospective analysis of the impact of PC on ABT-demand regarding overall (OS) and recurrence-free survival (RFS) in 336 patients undergoing curative resection for CRLM was carried out. Survival analysis was performed by both univariate and multivariate methods and propensity-score (PS) matching. PC was employed in 75 patients (22%). No increased postoperative morbidity was monitored. While the overall ABT-rate was comparable (35% vs. 37%, p = 0.786), a reduced demand for more than two ABT-units was observed (p = 0.046). PC-patients had better median OS (78 vs. 47 months, p = 0.005) and RFS (36 vs. 23 months, p = 0.006). Multivariate analysis revealed PC as an independent prognostic factor for OS (HR = 0.60; p = 0.009) and RFS (HR = 0.67; p = 0.017). For PC-patients, 1:2 PS-matching (N = 174) showed no differences in the overall ABT-rate compared to no-PC-patients (35% vs. 40%, p = 0.619), but a trend towards reduced transfusion requirement (>2 ABT-units: 9% vs. 21%, p = 0.052; >4 ABT-units: 2% vs. 11%, p = 0.037) and better survival (OS: 78 vs. 44 months, p = 0.088; RFS: 36 vs. 24 months; p = 0.029). Favorable long-term outcomes and lower rates of increased transfusion demand were observed in patients with PC undergoing resection for CRLM. Further prospective evaluation of potential oncologic benefits of PC in these patients may be meaningful.

Phlebotomy resulting in controlled hypovolaemia to prevent blood loss in major hepatic resections (PRICE-1): a pilot randomized clinical trial for feasibility

BACKGROUND

Major liver resection is associated with blood loss and transfusion. Observational data suggest that hypovolaemic phlebotomy can reduce these risks. This feasibility RCT compared hypovolaemic phlebotomy with the standard of care, to inform a future multicentre trial.

METHODS

Patients undergoing major liver resections were enrolled between June 2016 and January 2018. Randomization was done during surgery and the surgeons were blinded to the group allocation. For hypovolaemic phlebotomy, 7-10 ml per kg whole blood was removed, without intravenous fluid replacement. Co-primary outcomes were feasibility and estimated blood loss (EBL).

RESULTS

A total of 62 patients were randomized to hypovolaemic phlebotomy (31) or standard care (31), at a rate of 3·1 patients per month, thus meeting the co-primary feasibility endpoint. The median EBL difference was -111 ml (P = 0·456). Among patients at high risk of transfusion, the median EBL difference was -448 ml (P = 0·069). Secondary feasibility endpoints were met: enrolment, blinding and target phlebotomy (mean(s.d.) 7·6(1·9) ml per kg). Blinded surgeons perceived that parenchymal resection was easier with hypovolaemic phlebotomy than standard care (16 of 31 versus 10 of 31 respectively), and guessed that hypovolaemic phlebotomy was being used with an accuracy of 65 per cent (20 of 31). There was no significant difference in overall complications (10 of 31 versus 15 of 31 patients), major complications or transfusion. Among those at high risk, transfusion was required in two of 15 versus three of nine patients (P = 0·326).

CONCLUSION

Endpoints were met successfully, but no difference in EBL was found in this feasibility study. A multicentre trial (PRICE-2) powered to identify a difference in perioperative blood transfusion is justified. Registration number: NCT02548910 ( http://www.clinicaltrials.gov).

Perioperative blood transfusions following hepatic lobectomy: A national analysis of academic medical centers in the modern era

BACKGROUND

The purpose of the study was to characterize the prevalence and impact of perioperative blood use for patients undergoing hepatic lobectomy at academic medical centers.

METHODS

The University HealthSystem Consortium (UHC) database was queried for hepatic lobectomies performed between 2011 and 2014 (n = 6476). Patients were grouped according to transfusion requirements into high (>5 units, 7%), medium (2-5 units, 6%), low (1 unit, 8%), and none (0 units, 79%) during hospital stay for comparison of outcomes.

RESULTS

Over 20% of patients undergoing hepatic lobectomy received blood perioperatively, of which 35% required more than 5 units. Patients with high transfusion requirements had increased severity of illness (p < 0.01). High transfusion requirements correlated with increased readmission rates (23.4% vs. 19.2% vs. 16.6% vs. 13.5%), total direct costs ($31,982 vs. $20,859 vs. $19,457 vs. $16,934), length of stay (9 days vs. 8 vs. 7 vs. 6), and in-hospital mortality (10.8% vs. 2.0% vs. 0.9% vs. 2.0%) compared to medium, low, and no transfusion amounts (all p < 0.01). Neither center nor surgeon volume were associated with transfusion use.

CONCLUSION

High transfusion requirements after hepatic lobectomy in the United States are associated with worse perioperative quality measures, but may not be influenced by center or surgeon volume.

Intraoperative Blood Transfusion and Postoperative Morbidity Following Liver Resection

BACKGROUND Blood transfusion is common during liver resection (LR). The objective of the present study was to investigate the effects of intraoperative transfusion of different blood components on post-LR morbidity. MATERIAL AND METHODS We included 610 patients undergoing LR and grouped them according to intraoperative transfusion of different blood components: packed red blood cells only (PRBC, n=81); frozen fresh plasma, platelets, and cryoprecipitate (FPC, n=38); transfusion only with PRBC + FPC transfusion (n=244); and no blood transfusion (n=247). Propensity score matching (PSM) analysis was used to mitigate selection bias in comparisons. RESULTS The overall blood transfusion rate was 59.5%. In comparison with the no blood transfusion group, PRBC-only and PRBC + FPC transfusion were more common in patients with lower preoperative hemoglobin, worse liver function, larger tumor size, and undergoing a major LR, and thus were associated with increased postoperative morbidity. In contrast, FPC-only transfusion was more frequent in patients with a liver function of Child-Pugh B and lower preoperative albumin vs. the no blood transfusion group. In the propensity model, transfusion of PRBC (PRBC-only and PRBC+FPC) and FPC (FPC-only and FPC+PRBC) were significantly associated with increased postoperative complications vs. the no blood transfusion group (OR and 95% CI, 1.9 [1.2-2.7], p=0.002; OR and 95% CI, 1.6 [1.0-2.4], p=0.029). In contrast, intraoperative PRBC-only or FPC-only transfusion showed no significant adverse effects on postoperative morbidity. CONCLUSIONS Allogenic transfusion of PRBC and FPC blood components was associated with increased postoperative morbidity after liver surgery. Different blood components should be used only when absolutely necessary.

Postoperative Albumin-Bilirubin Grade Change Predicts the Prognosis of Patients with Hepatitis B-Related Hepatocellular Carcinoma Within the Milan Criteria

OBJECTIVE

Albumin-bilirubin (ALBI) grade has been validated as a simple, evidence-based, and objective prognostic tool for patients with hepatocellular carcinoma (HCC). However, minimal information is available concerning postoperative ALBI grade changes in HCC. This study aimed to investigate the prognostic value of postoperative ALBI grade changes in patients with hepatitis B virus (HBV)-related HCC within the Milan criteria after liver resection.

METHODS

Patients with HBV-related HCC within the Milan criteria who underwent liver resection between 2010 and 2016 at West China Hospital were reviewed (N = 258). A change in ALBI grade was defined as first postoperative month ALBI grade-preoperative ALBI grade. If the value was >0, postoperative worsening of ALBI grade was considered; otherwise, stable ALBI grade was considered. Cox proportional hazard regression analyses were used to determine the factors that influence recurrence and survival.

RESULTS

During the follow-up, 130 patients experienced recurrence and 47 patients died. Multivariate analyses revealed that postoperative worsening of ALBI grade (HR 1.541, 95% CI 1.025-2.318, P = 0.038), microvascular invasion (MVI, HR 1.802, 95% CI 1.205-2.695, P = 0.004), and multiple tumors (HR 1.676, 95% CI 1.075-2.615, P = 0.023) were associated with postoperative recurrence, whereas MVI (HR 2.737, 95% CI 1.475-5.080, P = 0.001), postoperative worsening of ALBI grade (HR 2.268, 95% CI 1.227-4.189, P = 0.009), high alpha-fetoprotein level (HR 2.055, 95% CI 1.136-3.716, P = 0.017), and transfusion (HR 2.597, 95% CI 1.395-4.834, P = 0.003) negatively influenced long-term survival. Patients with postoperative worsening of ALBI grade exhibited increased incidence of recurrence and worse long-term survival.

CONCLUSION

Postoperative worsening of ALBI grade was associated with increased recurrence and poorer overall survival for patients with HBV-related HCC within the Milan criteria. We should pay attention to liver function changes in HCC patients after liver resection.

Safety and feasibility of elective liver resection in adult Jehovah's Witnesses: the Henri Mondor Hospital experience

BACKGROUND

Elective liver resection (LR) in Jehovah's Witness (JW) patients, for whom transfusion is not an option, involves complex ethical and medical issues and surgical difficulties.

METHODS

Consecutive data from a LR program for liver tumors in JWs performed between 2014 and 2017 were retrospectively reviewed. A systematic review of the literature with a pooled analysis was performed.

RESULTS

Ten patients were included (median age = 61 years). None needed preoperative erythropoietin. Tumor biopsy was not performed. Major hepatectomy was performed in 4 patients. The median estimated blood loss was 200 mL. A cell-saver was installed in 2 patients, none received saved blood. The median hemoglobin values before and at the end of surgery were 13.4 g/dL and 12.6 g/dL, respectively (p = 0.04). Nine complications occurred in 4 patients, but no postoperative hemorrhage occurred. In-hospital mortality was nil. Nine studies including 35 patients were identified in the literature; there was reported no mortality and low morbidity. None of the patients were transfused.

CONCLUSIONS

By using a variety of blood conservation techniques, the risk/benefit ratio of elective liver resection for liver was maintained in selected adult JW patients. JW faith should not constitute an absolute exclusion from hepatectomy.