Ranking as a Procedure for Selecting a Replacement Variable in the Score Predicting the Survival of Patients Treated with Curative Intent for Colorectal Liver Metastases.

Background and Objectives: The issue of a missing variable precludes the external validation of many prognostic models. For example, the Liverpool score predicts the survival of patients undergoing surgical therapy for colorectal liver metastases, but it includes the neutrophil-lymphocyte ratio, which cannot be measured retrospectively. Materials and Methods: We aimed to find the most appropriate replacement for the neutrophil-lymphocyte ratio. Survival analysis was performed on data representing 632 liver resections for colorectal liver metastases from 2000 to 2020. Variables associated with the Liverpool score, C-reactive protein, albumins, and fibrinogen were ranked. The rankings were performed in four ways: The first two were based on the Kaplan-Meier method (log-rank statistics and the definite integral IS between two survival curves). The next method of ranking was based on univariate and multivariate Cox regression analyses. Results: The ranks were as follows: the radicality of liver resection (rank 1), lymph node infiltration of primary colorectal cancer (rank 2), elevated C-reactive protein (rank 3), the American Society of Anesthesiologists Classification grade (rank 4), the right-sidedness of primary colorectal cancer (rank 5), the multiplicity of colorectal liver metastases (rank 6), the size of colorectal liver metastases (rank 7), albumins (rank 8), and fibrinogen (rank 9). Conclusions: The ranking methodologies resulted in almost the same ranking order of the variables. Elevated C-reactive protein was ranked highly and can be considered a relevant replacement for the neutrophil-lymphocyte ratio in the Liverpool score. These methods are suitable for ranking variables in similar models for medical research.

The learning curve of laparoscopic liver resection utilising a difficulty score.

BACKGROUND: This study aimed to quantitatively evaluate the learning curve of laparoscopic liver resection (LLR) of a single surgeon. PATIENTS AND METHODS: A retrospective review of a prospectively maintained database of liver resections was conducted. 171 patients undergoing pure LLRs between April 2008 and April 2021 were analysed. The Halls difficulty score (HDS) for theoretical predictions of intraoperative complications (IOC) during LLR was applied. IOC was defined as blood loss over 775 mL, unintentional damage to the surrounding structures, and conversion to an open approach. Theoretical association between HDS and the predicted probability of IOC was utilised to objectify the shape of the learning curve. RESULTS: The obtained learning curve has resulted from thirteen years of surgical effort of a single surgeon. It consists of an absolute and a relative part in the mathematical description of the additive function described by the logarithmic function (absolute complexity) and fifth-degree regression curve (relative complexity). The obtained learning curve determines the functional dependency of the learning outcome versus time and indicates several local extreme values (peaks and valleys) in the learning process until proficiency is achieved. CONCLUSIONS: This learning curve indicates an ongoing learning process for LLR. The proposed mathematical model can be applied for any surgical procedure with an existing difficulty score and a known theoretically predicted association between the difficulty score and given outcome (for example, IOC).

Evaluation of the Iwate Model for Predicting the Difficulty of Laparoscopic Liver Resection: Does Tumor Size Matter?

BACKGROUND: This study aimed to externally validate the Iwate scoring model and its prognostic value for predicting the risks of intra- and postoperative complications of laparoscopic liver resection. METHODS: Consecutive patients who underwent pure laparoscopic liver resection between 2008 and 2019 at a single tertiary center were included. The Iwate scores were calculated according to the original proposition (four difficulty levels based on six indices). Intra- and postoperative complications were compared across difficulty levels. Fitting the obtained data to the cumulative density function of the Weibull distribution and a linear function provided the mean risk curves for intra- and postoperative complications, respectively. RESULTS: The difficulty levels of 142 laparoscopic liver resections were scored as low, intermediate, advanced, and expert level in 41 (28.9%), 53 (37.3%), 32 (22.5%), and 16 (11.3%) patients, respectively. Intraoperative complications were detected in 26 (18.3%) patients and its rates (2.4%, 7.5%, 34.3%, and 62.5%) increased gradually with statistically significant values among difficulty levels (P Ë‚ 0.001). Major postoperative complications occurred in 21 (14.8%) patients and its rates (4.8%, 5.6%, 28.1%, 43.7%; P Ë‚ 0.001) showed the same trend as for intraoperative complications. Then, the mean risk curves of both complications were obtained. Due to outliers, a new threshold for a tumor size index was proposed at 38 mm. The repeated analysis showed improved results. CONCLUSIONS: The Iwate scoring model predicts the probability of complications across difficulty levels. Our proposed tumor size threshold (38 mm) improves the quality of the prediction. The model is upgraded by a probability of complications for every difficulty score.

The external validation of a difficulty scoring system for predicting the risk of intraoperative complications during laparoscopic liver resection.

BACKGROUND: This study aimed to externally validate and upgrade the recent difficulty scoring system (DSS) proposed by Halls et al. to predict intraoperative complications (IOC) during laparoscopic liver resection (LLR). METHODS: The DSS was validated in a cohort of 128 consecutive patients undergoing pure LLRs between 2008 and 2019 at a single tertiary referral center. The validated DSS includes four difficulty levels based on five risk factors (neoadjuvant chemotherapy, previous open liver resection, lesion type, lesion size and classification of resection). As established by the validated DSS, IOC was defined as excessive blood loss (> 775 mL), conversion to an open approach and unintentional damage to surrounding structures. Additionally, intra- and postoperative outcomes were compared according to the difficulty levels with usual statistic methods. The same five risk factors were used for validation done by linear and advanced nonlinear (artificial neural network) models. The study was supported by mathematical computations to obtain a mean risk curve predicting the probability of IOC for every difficulty score. RESULTS: The difficulty level of LLR was rated as low, moderate, high and extremely high in 36 (28.1%), 63 (49.2%), 27 (21.1%) and 2 (1.6%) patients, respectively. IOC was present in 23 (17.9%) patients. Blood loss of >775 mL occurred in 8 (6.2%) patients. Conversion to open approach was required in 18 (14.0%) patients. No patients suffered from unintentional damage to surrounding structures. Rates of IOC (0, 9.5, 55.5 and 100%) increased gradually with statistically significant value among difficulty levels (P < 0.001). The relations between the difficulty level, need for transfusion, operative time, hepatic pedicle clamping, and major postoperative morbidity were statistically significant (P < 0.05). Linear and nonlinear validation models showed a strong correlation (correlation coefficients 0.914 and 0.948, respectively) with the validated DSS. The Weibull cumulative distribution function was used for predicting the mean risk probability curve of IOC. CONCLUSION: This external validation proved this DSS based on patient's, tumor and surgical factors enables us to estimate the risk of intra- and postoperative complications. A surgeon should be aware of an increased risk of complications before starting with more complex procedures.