Accuracy of pre-operative parameters in predicting severe cholecystitis-A systematic review.

INTRODUCTION: Severity of cholecystitis can be defined by the presence of histopathological changes such as gangrene, perforation, and empyema. Severe cholecystitis correlates with higher morbidity and longer hospital stay. The present review aimed to identify the predictors of severe cholecystitis. METHODS: Electronic databases including PubMed, Scopus, and Cochrane library were searched in the period of January 1980 to March 2019. The main outcome of this review was to assess the predictability of pre-operative parameters such as Leukocytosis, fever, tachycardia, gallbladder wall edema, gallbladder distension, serum platelet count, and gallbladder mural striation. The role of patients' characteristics including age, gender, and diabetes mellitus in predicting severe cholecystitis was also assessed. RESULTS: A total of 8823 patients were analysed. The mean age of patients was 67.14 ± 4.17. The parameters that had the highest Odds ratio in predicting severe cholecystitis were all findings on CT scanning and included attenuation of arterial phase, mural striation of the gallbladder, and decreased gallbladder wall enhancement. CONCLUSION: We conclude that CT findings were the most significant predictors of severe cholecystitis. Patients with clinical and laboratory predictors of severe cholecystitis should be urgently evaluated with contrast CT scan to rule out any severe complications.

Artificial intelligence-enhanced electrocardiogram for the diagnosis of cardiac amyloidosis: A systemic review and meta-analysis.

BACKGROUND: Diagnosis of cardiac amyloidosis (CA) is often delayed due to variability in clinical presentation. The electrocardiogram (ECG) is one of the most common and widely available tools for assessing cardiovascular diseases. Artificial intelligence (AI) models analyzing ECG have recently been developed to detect CA, but their pooled accuracy is yet to be evaluated. METHODS: We searched the Scopus, MEDLINE, and Cochrane CENTRAL databases until April 2024 for studies assessing AI-enhanced ECG diagnosis of CA. Studies reporting findings from derivation and validation cohorts were included. Studies combining other diagnostic modalities, such as echocardiography, were excluded. The outcome of interest was the area under the receiver operating characteristic curve (AUC) for overall CA and subtypes transthyretin amyloidosis (ATTR) and light chain amyloidosis (AL). Analysis was done using RevMan 5.4.1 general inverse variance random effects model, pooling data for AUC and 95 % confidence intervals (CI). RESULTS: Five studies comprising seven cohorts met the eligibility criteria. The total derivation and validation cohorts were 8,639 and 3,843, respectively, although one study did not describe this data. The AUC was 0.89 (95 % CI, 0.86-0.91) for cardiac amyloidosis, 0.90 (95 % CI, 0.86-0.95) for ATTR amyloidosis, and 0.80 (95 % CI, 0.80-0.93) for AL amyloidosis. CONCLUSION: AI-enhanced ECG models effectively detect CA and may provide a valuable tool for the early detection and intervention of this disease.

Frequency of Three-Vessel Disease Among Patients With Non-ST Segment Elevation Myocardial Infarction.

BACKGROUND: Though the presence of three-vessel disease (3VD) among patients with non-ST Segment Elevation Myocardial Infarction (NSTEMI) is relatively common, very limited data is available regarding its clinical significance. The current study aimed to determine the frequency of 3VD among NSTEMI patients presenting at the tertiary care hospital of Karachi, Pakistan. METHODOLOGY: This cross-sectional study was conducted at the National Institute of Cardiovascular Diseases, Karachi from August 15 2015 to February 15 2016 over a sample of 139 NSTEMI patients. Data regarding patients' baseline characteristics were recorded in a proforma. Coronary angiography was performed to determine the presence of 3VD. The frequency of occluded arteries and 3VD was also recorded and the collected data was then analyzed using Statistical Package for Social Sciences (SPSS) version 20.0 (IBM Corp., Armonk, NY, USA). RESULTS: A total of 139 NSTEMI patients were enrolled in the study with a mean age of 50.47 ± 12.47 years. The majority of them were males (70.5%), mostly ≥ 40 years of age (67.6%). Among the comorbidities, 50.4% of patients had diabetes mellitus (DM), 61.9% were hypertensive and 30.9% had dyslipidemia. The overall frequency of 3VD among the enrolled NSTEMI patients was 30.2%. Three major arteries were found to be occluded; 68.3% NSTEMI patients had occlusion in the left anterior descending (LAD) artery, followed by right coronary artery (RCA) among 49.6%, 40.3% had left circumflex (LCX) artery occlusion, and 50.4% had diagonal and obtuse marginal (OM) artery occlusion respectively. Among the effect modifiers, no significant impact of age, gender, and smoking habits was observed on the frequency of 3VD among NSTEMI patients (p > 0.05). Among the reported comorbidities, DM was significant among NSTEMI patients with 3VD (p < 0.05). CONCLUSION: Our study results indicated that a significant proportion of NSTEMI patients had 3VD, independent of the effect of age and gender.