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.

Role of digital health in detection and management of atrial fibrillation.

Atrial fibrillation is a common arrhythmia associated with significant morbidity, mortality and decreased quality of life. Mobile health devices marketed directly to consumers capable of detecting atrial fibrillation through methods including photoplethysmography, single-lead ECG as well as contactless methods are becoming ubiquitous. Large-scale screening for atrial fibrillation is feasible and has been shown to detect more cases than usual care-however, controversy still exists surrounding screening even in older higher risk populations. Given widespread use of mobile health devices, consumer-driven screening is happening on a large scale in both low-risk and high-risk populations. Given that young people make up a large portion of early adopters of mobile health devices, there is the potential that many more patients with early onset atrial fibrillation will come to clinical attention requiring possible referral to genetic arrythmia clinic. Physicians need to be familiar with these technologies, and understand their risks, and limitations. In the current review, we discuss current mobile health devices used to detect atrial fibrillation, recent and upcoming trials using them for diagnosis of atrial fibrillation, practical recommendations for patients with atrial fibrillation diagnosed by a mobile health device and special consideration in young patients.

Deep learning evaluation of biomarkers from echocardiogram videos.

BACKGROUND: Laboratory testing is routinely used to assay blood biomarkers to provide information on physiologic state beyond what clinicians can evaluate from interpreting medical imaging. We hypothesized that deep learning interpretation of echocardiogram videos can provide additional value in understanding disease states and can evaluate common biomarkers results. METHODS: We developed EchoNet-Labs, a video-based deep learning algorithm to detect evidence of anemia, elevated B-type natriuretic peptide (BNP), troponin I, and blood urea nitrogen (BUN), as well as values of ten additional lab tests directly from echocardiograms. We included patients (n = 39,460) aged 18 years or older with one or more apical-4-chamber echocardiogram videos (n = 70,066) from Stanford Healthcare for training and internal testing of EchoNet-Lab's performance in estimating the most proximal biomarker result. Without fine-tuning, the performance of EchoNet-Labs was further evaluated on an additional external test dataset (n = 1,301) from Cedars-Sinai Medical Center. We calculated the area under the curve (AUC) of the receiver operating characteristic curve for the internal and external test datasets. FINDINGS: On the held-out test set of Stanford patients not previously seen during model training, EchoNet-Labs achieved an AUC of 0.80 (0.79-0.81) in detecting anemia (low hemoglobin), 0.86 (0.85-0.88) in detecting elevated BNP, 0.75 (0.73-0.78) in detecting elevated troponin I, and 0.74 (0.72-0.76) in detecting elevated BUN. On the external test dataset from Cedars-Sinai, EchoNet-Labs achieved an AUC of 0.80 (0.77-0.82) in detecting anemia, of 0.82 (0.79-0.84) in detecting elevated BNP, of 0.75 (0.72-0.78) in detecting elevated troponin I, and of 0.69 (0.66-0.71) in detecting elevated BUN. We further demonstrate the utility of the model in detecting abnormalities in 10 additional lab tests. We investigate the features necessary for EchoNet-Labs to make successful detection and identify potential mechanisms for each biomarker using well-known and novel explainability techniques. INTERPRETATION: These results show that deep learning applied to diagnostic imaging can provide additional clinical value and identify phenotypic information beyond current imaging interpretation methods. FUNDING: J.W.H. and B.H. are supported by the NSF Graduate Research Fellowship. D.O. is supported by NIH K99 HL157421-01. J.Y.Z. is supported by NSF CAREER 1942926, NIH R21 MD012867-01, NIH P30AG059307 and by a Chan-Zuckerberg Biohub Fellowship.

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.

Changes in T-cell subsets identify responders to FcR-nonbinding anti-CD3 mAb (teplizumab) in patients with type 1 diabetes.

The mechanisms whereby immune therapies affect progression of type 1 diabetes (T1D) are not well understood. Teplizumab, an FcR nonbinding anti-CD3 mAb, has shown efficacy in multiple randomized clinical trials. We previously reported an increase in the frequency of circulating CD8(+) central memory (CD8CM) T cells in clinical responders, but the generalizability of this finding and the molecular effects of teplizumab on these T cells have not been evaluated. We analyzed data from two randomized clinical studies of teplizumab in patients with new- and recent-onset T1D. At the conclusion of therapy, clinical responders showed a significant reduction in circulating CD4(+) effector memory T cells. Afterward, there was an increase in the frequency and absolute number of CD8CM T cells. In vitro, teplizumab expanded CD8CM T cells by proliferation and conversion of non-CM T cells. Nanostring analysis of gene expression of CD8CM T cells from responders and nonresponders versus placebo-treated control subjects identified decreases in expression of genes associated with immune activation and increases in expression of genes associated with T-cell differentiation and regulation. We conclude that CD8CM T cells with decreased activation and regulatory gene expression are associated with clinical responses to teplizumab in patients with T1D.

Biomarkers in type 1 diabetes: application to the clinical trial setting.

PURPOSE OF REVIEW: Biomarkers of type 1 diabetes (T1D) are important for assessing risk of developing disease, monitoring disease progression, and determining responses to clinical treatments. Here we review recent advances in the development of biomarkers of T1D with a focus on their utility in clinical trials. RECENT FINDINGS: Measurements of autoantibodies and metabolic outcomes have been the foundation of monitoring T1D for the past 20 years. Recent advancements have led to improvements in T-cell-specific assays that have been used in large-scale clinical trials to measure antigen-specific T cell responses. Additionally, new tools are being developed for the measurement of ß cell mass and death that will allow for more direct measurement of disease activity. Lastly, recent studies have used both immunologic and nonimmunologic biomarkers to identify responders to treatments in clinical trials. SUMMARY: Use of biomarkers in the study of T1D has largely not changed over the past 20 years; however, recent advancements in the field are establishing new techniques that allow for more precise monitoring of disease progression. These new tools will ultimately lead to an improvement in understanding of disease and will be utilized in clinical trials.

New and future immunomodulatory therapy in type 1 diabetes.

Type 1 diabetes is a common autoimmune disease that affects millions of people worldwide and has an incidence that is increasing at a striking rate, especially in young children. It results from the targeted self-destruction of the insulin-secreting ß cells of the pancreas and requires lifelong insulin treatment. The effects of chronic hyperglycemia - the result of insulin deficiency - include secondary endorgan complications. Over the past two decades our increased understanding of the pathogenesis of this disease has led to the development of new immunomodulatory treatments. None have yet received regulatory approval, but this report highlights recent progress in this area.

The 1.75 Å resolution structure of fission protein Fis1 from Saccharomyces cerevisiae reveals elusive interactions of the autoinhibitory domain.

Fis1 mediates mitochondrial and peroxisomal fission. It is tail-anchored to these organelles by a transmembrane domain, exposing a soluble cytoplasmic domain. Previous studies suggested that Fis1 is autoinhibited by its N-terminal region. Here, a 1.75 Å resolution crystal structure of the Fis1 cytoplasmic domain from Saccharomyces cerevisiae is reported which adopts a tetratricopeptide-repeat fold. It is observed that this fold creates a concave surface important for fission, but is sterically occluded by its N-terminal region. Thus, this structure provides a physical basis for autoinhibition and allows a detailed examination of the interactions that stabilize the inhibited state of this molecule.