Electrocardiographic deep learning for predicting post-procedural mortality: a model development and validation study.

BACKGROUND: Preoperative risk assessments used in clinical practice are insufficient in their ability to identify risk for postoperative mortality. Deep-learning analysis of electrocardiography can identify hidden risk markers that can help to prognosticate postoperative mortality. We aimed to develop a prognostic model that accurately predicts postoperative mortality in patients undergoing medical procedures and who had received preoperative electrocardiographic diagnostic testing. METHODS: In a derivation cohort of preoperative patients with available electrocardiograms (ECGs) from Cedars-Sinai Medical Center (Los Angeles, CA, USA) between Jan 1, 2015 and Dec 31, 2019, a deep-learning algorithm was developed to leverage waveform signals to discriminate postoperative mortality. We randomly split patients (8:1:1) into subsets for training, internal validation, and final algorithm test analyses. Model performance was assessed using area under the receiver operating characteristic curve (AUC) values in the hold-out test dataset and in two external hospital cohorts and compared with the established Revised Cardiac Risk Index (RCRI) score. The primary outcome was post-procedural mortality across three health-care systems. FINDINGS: 45 969 patients had a complete ECG waveform image available for at least one 12-lead ECG performed within the 30 days before the procedure date (59 975 inpatient procedures and 112 794 ECGs): 36 839 patients in the training dataset, 4549 in the internal validation dataset, and 4581 in the internal test dataset. In the held-out internal test cohort, the algorithm discriminates mortality with an AUC value of 0·83 (95% CI 0·79-0·87), surpassing the discrimination of the RCRI score with an AUC of 0·67 (0·61-0·72). The algorithm similarly discriminated risk for mortality in two independent US health-care systems, with AUCs of 0·79 (0·75-0·83) and 0·75 (0·74-0·76), respectively. Patients determined to be high risk by the deep-learning model had an unadjusted odds ratio (OR) of 8·83 (5·57-13·20) for postoperative mortality compared with an unadjusted OR of 2·08 (0·77-3·50) for postoperative mortality for RCRI scores of more than 2. The deep-learning algorithm performed similarly for patients undergoing cardiac surgery (AUC 0·85 [0·77-0·92]), non-cardiac surgery (AUC 0·83 [0·79-0·88]), and catheterisation or endoscopy suite procedures (AUC 0·76 [0·72-0·81]). INTERPRETATION: A deep-learning algorithm interpreting preoperative ECGs can improve discrimination of postoperative mortality. The deep-learning algorithm worked equally well for risk stratification of cardiac surgeries, non-cardiac surgeries, and catheterisation laboratory procedures, and was validated in three independent health-care systems. This algorithm can provide additional information to clinicians making the decision to perform medical procedures and stratify the risk of future complications. FUNDING: National Heart, Lung, and Blood Institute.

Frequency and timing of short-term complications following abdominoperineal resection.

BACKGROUND: Abdominoperineal resection (APR) is primarily used for rectal cancer and is associated with a high rate of complications. Though the majority of APRs are performed as open procedures, laparoscopic APRs have become more popular. The differences in short-term complications between open and laparoscopic APR are poorly characterized. METHODS: We conducted a retrospective cohort study using the American College of Surgeons National Surgical Quality Improvement Program database to determine the frequency and timing of onset of 30-d postoperative complications after APR and identify differences between open and laparoscopic APR. RESULTS: A total of 7681 patients undergoing laparoscopic or open APR between 2011 and 2015 were identified. The total complication rate for APR was high (45.4%). APRs were commonly complicated by blood transfusion (20.1%), surgical site infection (19.3%), and readmission (12.3%). Laparoscopic APR was associated with a 14% lower total complication rate compared to open APR (36.0% versus 50.1%, P < 0.001). This was primarily driven by a decreased rate of transfusion (10.7% versus 24.9%, P < 0.001) and surgical site infection (15.5% versus 21.2%, P < 0.001). Laparoscopic APR had shorter length of stay and decreased reoperation rate but similar rates of readmission and death. Cardiopulmonary complications occurred earlier in the postoperative period after APR, whereas infectious complications occurred later. CONCLUSIONS: Short-term complications following APR are common and occur more frequently in patients who undergo open APR. This, along with factors such as risk of positive pathologic margins, surgeon skill set, and patient characteristics, should contribute to the decision-making process when planning rectal cancer surgery.

Perioperative outcomes of coronary artery bypass graft in renal transplant recipients in the United States: results from the Nationwide Inpatient Sample.

BACKGROUND: Cardiovascular disease is the leading cause of morbidity and mortality in patients with chronic kidney disease (CKD). In fact, death from cardiovascular disease is the number one cause of graft loss in kidney transplant (KTx) patients. Compared to patients on dialysis, CKD patients with KTx have increased quality and length of life. It is not known, however, whether outcomes of coronary artery bypass graft (CABG) surgery differ between CKD patients with KTx or on dialysis. METHODS: This was a retrospective cohort study comparing CKD patients with KTx or on dialysis undergoing CABG surgery included in the Nationwide Inpatient Sample from 2002 to 2011. Logistic and linear regression models were used to estimate the adjusted associations of KTx on all-cause in-hospital mortality, length of stay, cost of hospitalization, and rate of complications in CABG surgery. RESULTS: CKD patients with KTx had decreased all-cause in-hospital mortality (2.68% vs 5.86%, odds ratio (OR)=0.56, 95% confidence interval (CI)=0.32 to 0.99, P=.046), length of stay (ß=-2.96, 95% CI=-3.67 to -2.46, P<.001), and total hospital charges (difference=-$38 884, 95% CI=-$48 173 to -29 596, P<.001). They also had decreased rate of a number of perioperative complications. CONCLUSIONS: CKD patient with KTx have better perioperative outcomes in CABG surgery compared to patients on dialysis.