T-tube management of late esophageal perforation

Esophageal perforation is a serious condition with a high mortality rate. Management strategy of such a patient depends on the extent of perforation and the time interval between perforation and diagnosis. The use of a T tube to treat delayed esophageal perforation with complete resolution and no need for future definitive surgery has been less frequently described. We adapted this principle in successful management of a 73 year old patient with four days history of fever, shortness of breath, chest pain and radiological evidence of perforation

Surgical correction of aorto-pulmonary window: a rare and lethal cause of pulmonary hypertension

Aorto-pulmonary window is a relatively rare congenital cardiac malformation with an overall incidence of 0.1%. Pulmonary hypertension develops quickly if the lesion is left untreated hence early surgical intervention is warranted after diagnosis. The surgery for correction of APW has evolved over years, currently an open repair on cardiopulmonary bypass (CPB) with a single patch technique yields good results. Mortality is affected by association of pulmonary hypertension and other cardiac malformations. We present a case of an infant with a large type II APW with a relatively low pulmonary vascular resistance. Hospital stay was complicated because of pulmonary arterial disease making it an important reason for correction in the first few months of life

Outcome and factors associated with hospital mortality in patients with impaired left ventricular function undergoing coronary artery bypass grafting: where do we stand?

Objective: Impaired ventricular function is a known risk factor for mortality after coronary artery bypass grafting however increasingly more patients with impaired ventricular function are referred for surgery. Currently no large data is available from Pakistan regarding this aspect of coronary surgery. Our objectives were to find out the hospital mortality and mid term functional improvement in patients with impaired ventricular function undergoing coronary artery by pass grafting and identify the risk factors for mortality. Methodology: Retrospective analysis of preoperative, operative and postoperative variables of patients with impaired ventricular function who were operated for isolated first time coronary artery bypass between October 2006 to April 2009. Results: Total 190 patients with impaired ventricular function underwent isolated first time coronary artery bypass grafting during this period with a male predominance (82.6%). Mean ejection fraction of the group was 25.4±5.3%. Mean predicted mortality on logistic Euro score was 10.9±2.7%. Actual in hospital mortality of the group was 4.7% which is comparable to contemporary published results. Multivariate analysis identified use of intra aortic balloon pump, non use of internal mammary artery and preoperative NYHA functional class as factors associated with mortality. Conclusion: Coronary artery bypass grafting can be performed in patients with impaired ventricular function with acceptable hospital mortality and mid term functional improvement

Peri-operative determinants of prolonged CICU stay after coronary artery bypass graft surgery in elderly at a private tertiary care hospital: a case control study

Abstract OBJECTIVE: To explore peri-operative risk factors associated with prolonged stay in cardiac intensive care unit among patients undergoing isolated coronary artery bypass grafting. METHODS: This retrospective case control study was conducted at the Aga Khan University Hospital, Karachi, comprised medical records of patients who had undergone cardiothoracic revascularisation surgery from January 2006 to December 2013. The patients were grouped into cases and controls at a ratio of 1:2 on the basis of length of stay at cardiac intensive care unit, i.e. \u3e72 hours andanalysis. RESULTS: Of the 999 patients, 333(33.3%) were cases and 666(66.6%) were controls. The mean age of cases was 62.5±9.7 years and that of controls was 60.8±9.6 years (p=0.007). The number of males was 280(84.1%) among the cases and 489(73.4%)among the controls. Adjusted odds ratio and 95% confidence interval for age and male gender were 1.02 (1.0,1.03) and [1.90 (1.32,2.74)]; diabetics were at high risk of staying longer [1.51 (1.13,2.02)]; previous cardiovascular interventions [1.65 (1.05,2.59)], intra-aortic balloon pump insertion [1.45 (1.01,2.08)], initial ventilation time and post-operative bleeding tamponade were independently associated with prolonged cardiac intensive care unit stay [1.01 (1.00, 1.01)] and [1.9 (1.13,3.2)], respectively. The risk of dying among the cases was three times more after adjusting for all covariates in the model [3.1 (1.52,6.31)]. CONCLUSION: Advanced age, male gender, diabetes, previous cardiovascular interventions, post-operative intra-aortic balloon pump insertion, initial ventilation support and post-op bleeding tamponade were found to be the independent risk factors for prolonged cardiac intensive care unit stay

CT-based automatic spine segmentation using patch-based deep learning

CT vertebral segmentation plays an essential role in various clinical applications, such as computer-assisted surgical interventions, assessment of spinal abnormalities, and vertebral compression fractures. Automatic CT vertebral segmentation is challenging due to the overlapping shadows of thoracoabdominal structures such as the lungs, bony structures such as the ribs, and other issues such as ambiguous object borders, complicated spine architecture, patient variability, and fluctuations in image contrast. Deep learning is an emerging technique for disease diagnosis in the medical field. This study proposes a patch-based deep learning approach to extract the discriminative features from unlabeled data using a stacked sparse autoencoder (SSAE). 2D slices from a CT volume are divided into overlapping patches fed into the model for training. A random under sampling (RUS)-module is applied to balance the training data by selecting a subset of the majority class. SSAE uses pixel intensities alone to learn high-level features to recognize distinctive features from image patches. Each image is subjected to a sliding window operation to express image patches using autoencoder high-level features, which are then fed into a sigmoid layer to classify whether each patch is a vertebra or not. We validate our approach on three diverse publicly available datasets: VerSe, CSI-Seg, and the Lumbar CT dataset. Our proposed method outperformed other models after configuration optimization by achieving 89.9% in precision, 90.2% in recall, 98.9% in accuracy, 90.4% in F-score, 82.6% in intersection over union (IoU), and 90.2% in Dice coefficient (DC). The results of this study demonstrate that our model's performance consistency using a variety of validation strategies is flexible, fast, and generalizable, making it suited for clinical application