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Operative classification of ventral abdominal hernias: new and practical classification. Yasser Selim. From the Ministry of Health.Background: Ventral hernias of the abdomen are defined as a noninguinal, nonhiatal defect in the fascia of the abdominal wall. Unfortunately, there is not currently a universal classification system for ventral hernias. One of the more accepted classification systems is that of the European Hernia Society (EHS). Its limitation is that it does not include individual patient risk factors and wound classification. The aim of this work was to find out the basic principles of hernia etiology and pathogenesis, clarify the factors that are important in treatment of ventral hernias, and categorize hernia patients according to those factors. Methods: This retrospective study included 238 patients who presented to our surgery department between 2010 and 2020. A full description of ventral hernias was made, including their type according to the EHS. In addition, abdominal wall components were assessed, including strength of rectus muscles, lateral abdominal muscles, and abdominal fascia, namely the linea alba. Patients with spontaneous hernias were grouped according to the size of the defect and the condition of the rectus abdominis muscles, the fascia and other abdominal muscles. Results: Patients were put into 6 clinical categories: type 1A, type 1B, type 2, type 3, type 4, and type 5. The grouping of patients was done according to the factors we believed affect the choice of surgical procedure and the prognosis of repair. Patients with types 1 and 2 have normal abdominal muscles, whereas those with types 3 and 4 have weak muscles and weak stretched fascia (linea alba). Type 5 includes incisional hernias. Conclusion: The primary purpose of any classification should be to improve the possibility of comparing different studies and their results. By describing hernias in a standardized way, different patient populations can be compared. Numerous classifications for groin and ventral hernias have been proposed over the past 5-6 decades. For primary abdominal wall hernias, there was agreement with EHS classification on the use of localization and size as classification variables.

ObjectiveAccurate estimates of survival guide decision-making for patients and oncologists. Advances in the capacity to measure complex tumour biology and patient factors allow for concurrent consideration of clinical, pathological, molecular, and biological markers for prognostication. Clinical prediction tools are a mechanism to combine and personalize these increasingly large amounts of complex information for prognostication. ApproachWe describe the process of linking routinely collected health data, cancer registry, and pathology report data in two provinces to develop (Ontario, Canada) and validate (Manitoba, Canada) a clinical prediction tool in esophageal cancer. We compared the performance of a base model restricted to patient and disease characteristics available prior to surgical resection (e.g., age, sex, histology, comorbidities), and a more complex model including pathology specimen details (e.g., tumour stage). Cox proportional hazards models were fit to predict death at three years following resection. Internal and external validity was assessed using overall calibration and optimism corrected c-statistics. Equity was assessed through calibration in predefined patient subgroups. Results2124 patients who underwent surgical resection for esophageal cancer between May 1, 2004 and June 30, 2016 for whom a pathology record was available were included in the study cohort. Median age was 66, with 80% males and 85% adenocarcinomas. Survival data were available until March 31, 2020.  The model with pathology data had superior discrimination and calibration (calibration slope of 1.02 and intercept -0.01, and optimism-corrected c-statistic 0.77), compared to the base model (calibration slope of 0.95, intercept 0.02, and c-statistic 0.60).  External validation is ongoing. ConclusionOur study demonstrates that prediction models for cancer prognosis built solely on data from health administrative databases may be unreliable. The addition of high-quality pathology report data from electronic medical records or population-based cancer registries is necessary for accurate estimation. Our work provides a framework for combining administrative and clinical data which could be applied to the development of other clinical prediction models.

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