Utilisation of an operative difficulty grading scale for laparoscopic cholecystectomy

Background A reliable system for grading operative difficulty of laparoscopic cholecystectomy would standardise description of findings and reporting of outcomes. The aim of this study was to validate a difficulty grading system (Nassar scale), testing its applicability and consistency in two large prospective datasets. Methods Patient and disease-related variables and 30-day outcomes were identified in two prospective cholecystectomy databases: the multi-centre prospective cohort of 8820 patients from the recent CholeS Study and the single-surgeon series containing 4089 patients. Operative data and patient outcomes were correlated with Nassar operative difficultly scale, using Kendall’s tau for dichotomous variables, or Jonckheere–Terpstra tests for continuous variables. A ROC curve analysis was performed, to quantify the predictive accuracy of the scale for each outcome, with continuous outcomes dichotomised, prior to analysis. Results A higher operative difficulty grade was consistently associated with worse outcomes for the patients in both the reference and CholeS cohorts. The median length of stay increased from 0 to 4 days, and the 30-day complication rate from 7.6 to 24.4% as the difficulty grade increased from 1 to 4/5 (both p < 0.001). In the CholeS cohort, a higher difficulty grade was found to be most strongly associated with conversion to open and 30-day mortality (AUROC = 0.903, 0.822, respectively). On multivariable analysis, the Nassar operative difficultly scale was found to be a significant independent predictor of operative duration, conversion to open surgery, 30-day complications and 30-day reintervention (all p < 0.001). Conclusion We have shown that an operative difficulty scale can standardise the description of operative findings by multiple grades of surgeons to facilitate audit, training assessment and research. It provides a tool for reporting operative findings, disease severity and technical difficulty and can be utilised in future research to reliably compare outcomes according to case mix and intra-operative difficulty

Population‐based cohort study of outcomes following cholecystectomy for benign gallbladder diseases

Background The aim was to describe the management of benign gallbladder disease and identify characteristics associated with all‐cause 30‐day readmissions and complications in a prospective population‐based cohort. Methods Data were collected on consecutive patients undergoing cholecystectomy in acute UK and Irish hospitals between 1 March and 1 May 2014. Potential explanatory variables influencing all‐cause 30‐day readmissions and complications were analysed by means of multilevel, multivariable logistic regression modelling using a two‐level hierarchical structure with patients (level 1) nested within hospitals (level 2). Results Data were collected on 8909 patients undergoing cholecystectomy from 167 hospitals. Some 1451 cholecystectomies (16·3 per cent) were performed as an emergency, 4165 (46·8 per cent) as elective operations, and 3293 patients (37·0 per cent) had had at least one previous emergency admission, but had surgery on a delayed basis. The readmission and complication rates at 30 days were 7·1 per cent (633 of 8909) and 10·8 per cent (962 of 8909) respectively. Both readmissions and complications were independently associated with increasing ASA fitness grade, duration of surgery, and increasing numbers of emergency admissions with gallbladder disease before cholecystectomy. No identifiable hospital characteristics were linked to readmissions and complications. Conclusion Readmissions and complications following cholecystectomy are common and associated with patient and disease characteristics

Application of fuzzy sets and other statistical techniques in landslide hazard zonation mapping

The objectives of this research are, (I). establish a methodology that quantifies the severity levels of causative factor attributes that are rated subjectively, and (2). optimize weights and severity levels of the causative factor attributes in order to achieve maximum agreement between the predicted landslide hazard potential and the actual landslide intensities observed in the field. The landslide hazard potential is evaluated based on a decision tree consisting of two levels of attributes identified as primary and secondary. It is assumed that the primary and secondary attributes have independent contributions towards the final result. The relative importance of each component attribute (factor) among the other factors that are connected to a nodal point of this tree is indicated by a subjective (or linguistic) description. Fuzzy sets mathematics which is an effective tool for mathematical representation of subjective expressions such as "quite high", "medium" or ."not that high" etc. are used to describe the relative importance of component attributes. Moreover, linguistic ratings used to describe the severity of each secondary factor are also considered as fuzzy numbers in order to account for the vagueness or the subjectivity in the rating procedure. Computer routines are developed to express the fuzzy sets that describe the subjective ratings and weights and then manipulate them using a technique resembling the Monte-Carlo simulation in order to determine the overall hazard potential of a given site. Eventually, the resulting overall degree of hazard, which itself is a fuzzy set, is converted back to an appropriate linguistic scale containing 'very high', 'high', 'medium', 'low', or 'very low' designations, in order to interpret the overall landslide potential of the site. Once the hazard potential estimates are predicted for every zone demarcated on a selected study area, they are compared with actual landslide intensities estimated for the same zones based on available field data. Then, through an iterative procedure the initially assigned weights of the primary and secondary attributes are systematically adjusted until the field observed landslide intensity matches at a satisfactory level with the predicted hazard potential. When the developed methodology is applied to the remaining study areas in Sri Lanka, and weights are refined further, it would become an effective tool in accurate landslide hazard zonation

Analytical determination of landslide potential using fuzzy sets and other statistical techniques

Recognizing the importance of identifying the potential slope instability, different methods have been developed by the experts in this field all over the world. However, because of the complex nature of this problem a method that is applicable to one region may not be applicable to another. Therefore, it is important to identify the causative attributes of landslides and accurately quantify their contribution in order to develop a methodology for a specific region. This paper presents a fuzzy set based integration system to assess the landslide intensities, incorporating the studies made on Sri Lankan landslides