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Machine learning risk prediction of mortality for patients undergoing surgery with perioperative SARS-CoV-2: the COVIDSurg mortality score

COVIDSurg Collaborative
To support the global restart of elective surgery, data from an international prospective cohort study of 8492 patients (69 countries) was analysed using artificial intelligence (machine learning techniques) to develop a predictive score for mortality in surgical patients with SARS-CoV-2. We found that patient rather than operation factors were the best predictors and used these to create the COVIDsurg Mortality Score ( Our data demonstrates that it is safe to restart a wide range of surgical services for selected patients.
PMID: 34227657
ISSN: 1365-2168
CID: 5561492

Endoscopic submucosal myotomy for the treatment of achalasia (with video) [Case Report]

Stavropoulos, Stavros N; Harris, Michael D; Hida, Sven; Brathwaite, Colin; Demetriou, Christopher; Grendell, James
PMID: 21111876
ISSN: 1097-6779
CID: 3411902

Comparison of the 48 hour bravo capsule versus the traditional 24 hour dual channel pH probe in the evaluation of extraesophageal GERD [Meeting Abstract]

Demetriou, CA; Kongara, K; Grendell, J; Stampe, M
ISSN: 0002-9270
CID: 3412782

Serratia marcescens bacteremia after carotid endarterectomy and coronary artery bypass grafting [Case Report]

Demetriou, C A; Cunha, B A
Serratia Marcescens is a common, water-borne hospital colonizer. Respiratory secretions, wounds, and urine are frequently recognized areas of Serratia colonization. Serratia bacteremias usually occur nosocomially and are associated with high mortality and morbidity rates. Serratia bacteremias may be primary or secondary from an identifiable source. Hospital-acquired S marcescens bacteremias have no known source in half of the cases. We present a case of nosocomial primary S marcescens bacteremia in a surgical patient successfully treated with levofloxacin.
PMID: 10409316
ISSN: 0147-9563
CID: 3436362