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Background The multidisciplinary perioperative and anaesthetic management of patients undergoing pelvic exenteration is essential for good surgical outcomes. No clear guidelines have been established, and there is wide variation in clinical practice internationally. This consensus statement consolidates clinical experience and best practice collectively, and systematically addresses key domains in the perioperative and anaesthetic management. Methods The modified Delphi methodology was used to achieve consensus from the PelvEx Collaborative. The process included one round of online questionnaire involving controlled feedback and structured participant response, two rounds of editing, and one round of web-based voting. It was held from December 2019 to February 2020. Consensus was defined as more than 80 per cent agreement, whereas less than 80 per cent agreement indicated low consensus. Results The final consensus document contained 47 voted statements, across six key domains of perioperative and anaesthetic management in pelvic exenteration, comprising preoperative assessment and preparation, anaesthetic considerations, perioperative management, anticipating possible massive haemorrhage, stress response and postoperative critical care, and pain management. Consensus recommendations were developed, based on consensus agreement achieved on 34 statements. Conclusion The perioperative and anaesthetic management of patients undergoing pelvic exenteration is best accomplished by a dedicated multidisciplinary team with relevant domain expertise in the setting of a specialized tertiary unit. This consensus statement has addressed key domains within the framework of current perioperative and anaesthetic management among patients undergoing pelvic exenteration, with an international perspective, to guide clinical practice, and has outlined areas for future clinical research.

A new concept of multisystem disease has emerged as a long-term condition following mild-severe COVID-19 infection. The main symptoms of this affection are breathlessness, chest pain, and fatigue. We present here the clinical case of four COVID-19 patients during hospitalization and 60 days after hospital discharge. Physiological impairment of all patients was assessed by spirometry, dyspnea score, arterial blood gas, and 6-minute walk test 60 days after hospital discharge, and computed tomographic scan 90 days after discharge. All patients had fatigue, which was not related to hypoxemia or impaired spirometry values, and interstitial lung alterations, which occurred in both mechanically ventilated and non-mechanically ventilated patients. In conclusion, identifying the prevalence and patterns of permanent lung damage is paramount in preventing and treating COVID-19-induced fibrotic lung disease. Additionally, and based on our preliminary results, it will be also relevant to establish long-term outpatient programs for these individuals.

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Aim We aim to compare machine learning with neural network performance in predicting R0 resection (R0), length of stay > 14 days (LOS), major complication rates at 30 days postoperatively (COMP) and survival greater than 1 year (SURV) for patients having pelvic exenteration for locally advanced and recurrent rectal cancer. Method A deep learning computer was built and the programming environment was established. The PelvEx Collaborative database was used which contains anonymized data on patients who underwent pelvic exenteration for locally advanced or locally recurrent colorectal cancer between 2004 and 2014. Logistic regression, a support vector machine and an artificial neural network (ANN) were trained. Twenty per cent of the data were used as a test set for calculating prediction accuracy for R0, LOS, COMP and SURV. Model performance was measured by plotting receiver operating characteristic (ROC) curves and calculating the area under the ROC curve (AUROC). Results Machine learning models and ANNs were trained on 1147 cases. The AUROC for all outcome predictions ranged from 0.608 to 0.793 indicating modest to moderate predictive ability. The models performed best at predicting LOS > 14 days with an AUROC of 0.793 using preoperative and operative data. Visualized logistic regression model weights indicate a varying impact of variables on the outcome in question. Conclusion This paper highlights the potential for predictive modelling of large international databases. Current data allow moderate predictive ability of both complex ANNs and more classic methods.

Introduction: heart failure is a crippling disease that reduces the quality of life; therefore, it is a serious public health problem. Objectives: to analyze the epidemiological and assistance care profile of heart failure patients admitted to a regional reference hospital. Statistically correlate clinical signs to diagnostic criteria and admissions to primary care services. To verify consistency between the treatment used and heart failure guidelines. Patients and methods: this was a prevalence, cross-sectional, and an exploratory study conducted through the reading of medical charts from a regional reference hospital from patients whose cause for hospitalization was heart failure in 2010. The data were analyzed in the Epi-Info 3.5 software. Frequency analysis and Odds Ratio (OR) with 95% confidence interval were calculated taking into account the P-value calculated through the Fisher's exact test. The project was approved by the University Ethics Committee (Protocol 159/2011). Results: 54 medical records were analyzed; 81% of patients had access to a primary care unit in the area of their residence. Dyslipidemia was associated with the highest number of hospitalizations (OR = 16/P = 0.034). The primary etiology of heart failure was systemic hypertensive heart disease (72.2%). The main risk factors found were hypertension (66.7%), smoking (48.1%), diabetes mellitus (44.4%), and dyslipidemia (40.7%). Out of the heart failure diagnoses, 68.52% could have been made from the Framingham criteria. Conclusions: permanent education programs are needed for addressing heart failure risk factors, evaluation and adherence to treatment, and active search for cases in the primary care as well as diagnosis of heart failure and its proper management.

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