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.

Association between procurement biopsy findings and deceased donor kidney outcomes: a paired kidney analysis.

Unfavourable procurement biopsy findings are the most common reason for deceased donor kidney discard in the United States. We sought to assess the association between biopsy findings and post-transplant outcomes when donor characteristics are accounted for. We used registry data to identify 1566 deceased donors of 3132 transplanted kidneys (2015-2020) with discordant right/left procurement biopsy classification and performed time-to-event analyses to determine the association between optimal histology and hazard of death-censored graft failure or death. We then repeated all analyses using a local cohort of 147 donors of kidney pairs with detailed procurement histology data available (2006-2016). Among transplanted kidney pairs in the national cohort, there were no significant differences in incidence of delayed graft function or primary nonfunction. Time to death-censored graft failure was not significantly different between recipients of optimal versus suboptimal kidneys. Results were similar in analyses using the local cohort. Regarding recipient survival, analysis of the national, but not local, cohort showed optimal kidneys were associated with a lower hazard of death (adjusted HR 0.68, 95% CI 0.52-0.90, P = 0.006). In conclusion, in a large national cohort of deceased donor kidney pairs with discordant right/left procurement biopsy findings, we found no association between histology and death-censored graft survival.

Inverse-Weighted Survival Games.

Deep models trained through maximum likelihood have achieved state-of-the-art results for survival analysis. Despite this training scheme, practitioners evaluate models under other criteria, such as binary classification losses at a chosen set of time horizons, e.g. Brier score (BS) and Bernoulli log likelihood (BLL). Models trained with maximum likelihood may have poor BS or BLL since maximum likelihood does not directly optimize these criteria. Directly optimizing criteria like BS requires inverse-weighting by the censoring distribution. However, estimating the censoring model under these metrics requires inverse-weighting by the failure distribution. The objective for each model requires the other, but neither are known. To resolve this dilemma, we introduce Inverse-Weighted Survival Games. In these games, objectives for each model are built from re-weighted estimates featuring the other model, where the latter is held fixed during training. When the loss is proper, we show that the games always have the true failure and censoring distributions as a stationary point. This means models in the game do not leave the correct distributions once reached. We construct one case where this stationary point is unique. We show that these games optimize BS on simulations and then apply these principles on real world cancer and critically-ill patient data.

Admission Cardiac Diagnostic Testing with Electrocardiography and Troponin Measurement Prognosticates Increased 30-Day Mortality in COVID-19.

Background Cardiovascular involvement in coronavirus disease 2019 (COVID-19) is common and leads to worsened mortality. Diagnostic cardiovascular studies may be helpful for resource appropriation and identifying patients at increased risk for death. Methods and Results We analyzed 887 patients (aged 64±17 years) admitted with COVID-19 from March 1 to April 3, 2020 in New York City with 12 lead electrocardiography within 2 days of diagnosis. Demographics, comorbidities, and laboratory testing, including high sensitivity cardiac troponin T (hs-cTnT), were abstracted. At 30 days follow-up, 556 patients (63%) were living without requiring mechanical ventilation, 123 (14%) were living and required mechanical ventilation, and 203 (23%) had expired. Electrocardiography findings included atrial fibrillation or atrial flutter (AF/AFL) in 46 (5%) and ST-T wave changes in 306 (38%). 27 (59%) patients with AF/AFL expired as compared to 181 (21%) of 841 with other non-life-threatening rhythms (P<0.001). Multivariable analysis incorporating age, comorbidities, AF/AFL, QRS abnormalities, and ST-T wave changes, and initial hs-cTnT ≥20 ng/L showed that increased age (HR 1.04/year), elevated hs-cTnT (HR 4.57), AF/AFL (HR 2.07), and a history of coronary artery disease (HR 1.56) and active cancer (HR 1.87) were associated with increased mortality. Conclusions Myocardial injury with hs-cTnT ≥20 ng/L, in addition to cardiac conduction perturbations, especially AF/AFL, upon hospital admission for COVID-19 infection is associated with markedly increased risk for mortality than either diagnostic abnormality alone.

Diagnosing Transthyretin Cardiac Amyloidosis by Technetium Tc 99m Pyrophosphate: A Test in Evolution.

OBJECTIVES: This study aimed to characterize trends in technetium Tc 99m pyrophosphate (99mTc-PYP) scanning for amyloid transthyretin cardiac amyloidosis (ATTR-CA) diagnosis, to determine whether patients underwent appropriate assessment with monoclonal protein and genetic testing, to evaluate use of single-photon emission computed tomography (SPECT) in addition to planar imaging, and to identify predictive factors for ATTR-CA. BACKGROUND: 99mTc-PYP scintigraphy has been repurposed for noninvasive diagnosis of ATTR-CA. Increasing use of 99mTc-PYP can facilitate identification of ATTR-CA, but appropriate use is critical for accurate diagnosis in an era of high-cost targeted therapeutics. METHODS: Patients undergoing 99mTc-PYP scanning 1 h after injection at a quaternary care center from 2010 to 2019 were analyzed; clinical information was abstracted; and SPECT results were analyzed. RESULTS: Over the decade, endomyocardial biopsy rates remained stable with scanning rates peaking at 132 in 2019 (p < 0.001). Among 753 patients (516 men, mean age 77 years), 307 (41%) had a visual score of 0, 177 (23%) of 1, and 269 (36%) of 2 or 3. Of 751 patients with analyzable heart to contralateral chest ratios, 249 (33%) had a ratio ≥1.5. Monoclonal protein testing status was assessed in 550 patients, of these, 174 (32%) did not undergo both serum immunofixation and serum free light chain analysis tests, and 331 (60%) did not undergo all 3 tests-serum immunofixation, serum free light chain analysis, and urine protein electrophoresis. Of 196 patients with confirmed ATTR-CA, 143 (73%) had genetic testing for transthyretin mutations. In 103 patients undergoing cardiac biopsy, grades 2 and 3 99mTc-PYP had sensitivity of 94% and specificity of 89% for ATTR-CA with 100% specificity for grade 3 scans. With respect to SPECT as a reference standard, planar imaging had false positive results in 16 of 25 (64%) grade 2 scans. CONCLUSIONS: Use of noninvasive testing with 99mTc-PYP scanning for evaluation of ATTR-CA is increasing, and the inclusion of monoclonal protein testing and SPECT imaging is crucial to rule out amyloid light chain amyloidosis and distinguish myocardial retention from blood pooling.

Impact of Deceased Donor Kidney Procurement Biopsy Technique on Histologic Accuracy.

INTRODUCTION: The factors that influence deceased donor kidney procurement biopsy reliability are not well established. We examined the impact of biopsy technique and pathologist training on procurement biopsy accuracy. METHODS: We retrospectively identified all deceased donor kidney-only transplants at our center from 2006 to 2016 with both procurement and reperfusion biopsies performed and information available on procurement biopsy technique and pathologist (n = 392). Biopsies were scored using a previously validated system, classifying "suboptimal" histology as the presence of at least 1 of the following: glomerulosclerosis ≥11%, moderate/severe interstitial fibrosis/tubular atrophy, or moderate/severe vascular disease. We calculated relative risk ratios (RRR) to determine the influence of technique (core vs. wedge) and pathologist (renal vs. nonrenal) on concordance between procurement and reperfusion biopsy histologic classification. RESULTS: A total of 171 (44%) procurement biopsies used wedge technique, and 221 (56%) used core technique. Results of only 36 biopsies (9%) were interpreted by renal pathologists. Correlation between procurement and reperfusion glomerulosclerosis was poor for both wedge (r 2 = 0.11) and core (r 2 = 0.14) biopsies. Overall, 34% of kidneys had discordant classification on procurement versus reperfusion biopsy. Neither biopsy technique nor pathologist training was associated with concordance between procurement and reperfusion histology, but a larger number of sampled glomeruli was associated with a higher likelihood of concordance (adjusted RRR = 1.12 per 10 glomeruli, 95% confidence interval = 1.04-1.22). CONCLUSIONS: Biopsy technique and pathologist training were not associated with procurement biopsy histologic accuracy in this retrospective study. Prospective trials are needed to determine how to optimize procurement biopsy practices.

Characterizing non-heroin opioid overdoses using electronic health records.

INTRODUCTION: The opioid epidemic is a modern public health emergency. Common interventions to alleviate the opioid epidemic aim to discourage excessive prescription of opioids. However, these methods often take place over large municipal areas (state-level) and may fail to address the diversity that exists within each opioid case (individual-level). An intervention to combat the opioid epidemic that takes place at the individual-level would be preferable. METHODS: This research leverages computational tools and methods to characterize the opioid epidemic at the individual-level using the electronic health record data from a large, academic medical center. To better understand the characteristics of patients with opioid use disorder (OUD) we leveraged a self-controlled analysis to compare the healthcare encounters before and after an individual's first overdose event recorded within the data. We further contrast these patients with matched, non-OUD controls to demonstrate the unique qualities of the OUD cohort. RESULTS: Our research confirms that the rate of opioid overdoses in our hospital significantly increased between 2006 and 2015 (P < 0.001), at an average rate of 9% per year. We further found that the period just prior to the first overdose is marked by conditions of pain or malignancy, which may suggest that overdose stems from pharmaceutical opioids prescribed for these conditions. CONCLUSIONS: Informatics-based methodologies, like those presented here, may play a role in better understanding those individuals who suffer from opioid dependency and overdose, and may lead to future research and interventions that could successfully prevent morbidity and mortality associated with this epidemic.

Reproducibility of Deceased Donor Kidney Procurement Biopsies.

BACKGROUND AND OBJECTIVES: Unfavorable histology on procurement biopsies is the most common reason for deceased donor kidney discard. We sought to assess the reproducibility of procurement biopsy findings. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We compiled a continuous cohort of deceased donor kidneys transplanted at our institution from 1/1/2006 to 12/31/2016 that had at least one procurement biopsy performed, and excluded cases with missing biopsy reports and those used in multiorgan transplants. Suboptimal histology was defined as the presence of advanced sclerosis in greater than or equal to one biopsy compartment (glomeruli, tubules/interstitium, vessels). We calculated κ coefficients to assess agreement in optimal versus suboptimal classification between sequential biopsy reports for kidneys that underwent multiple procurement biopsies and used time-to-event analysis to evaluate the association between first versus second biopsies and patient and allograft survival. RESULTS: Of the 1011 kidneys included in our cohort, 606 (60%) had multiple procurement biopsies; 98% had first biopsy performed at another organ procurement organization and their second biopsy performed locally. Categorical agreement was highest for vascular disease (κ=0.17) followed by interstitial fibrosis and tubular atrophy (κ=0.12) and glomerulosclerosis (κ=0.12). Overall histologic agreement (optimal versus suboptimal) was κ=0.15. First biopsy histology had no association with allograft survival in unadjusted or adjusted analyses. However, second biopsy optimal histology was associated with a higher probability of death-censored allograft survival, even after adjusting for donor and recipient factors (adjusted hazard ratio, 0.50; 95% confidence interval, 0.34 to 0.75; P=0.001). CONCLUSIONS: Deceased donor kidneys that underwent multiple procurement biopsies often displayed substantial differences in histologic categorization in sequential biopsies, and there was no association between first biopsy findings and post-transplant outcomes.

X-CAL: Explicit Calibration for Survival Analysis.

Survival analysis models the distribution of time until an event of interest, such as discharge from the hospital or admission to the ICU. When a model's predicted number of events within any time interval is similar to the observed number, it is called well-calibrated. A survival model's calibration can be measured using, for instance, distributional calibration (D-CALIBRATION) [Haider et al., 2020] which computes the squared difference between the observed and predicted number of events within different time intervals. Classically, calibration is addressed in post-training analysis. We develop explicit calibration (X-CAL), which turns D-CALIBRATION into a differentiable objective that can be used in survival modeling alongside maximum likelihood estimation and other objectives. X-CAL allows practitioners to directly optimize calibration and strike a desired balance between predictive power and calibration. In our experiments, we fit a variety of shallow and deep models on simulated data, a survival dataset based on MNIST, on length-of-stay prediction using MIMIC-III data, and on brain cancer data from The Cancer Genome Atlas. We show that the models we study can be miscalibrated. We give experimental evidence on these datasets that X-CAL improves D-CALIBRATION without a large decrease in concordance or likelihood.

Social Network Behavior and Engagement Within a Smoking Cessation Facebook Page.

BACKGROUND: Social media platforms are increasingly being used to support individuals in behavior change attempts, including smoking cessation. Examining the interactions of participants in health-related social media groups can help inform our understanding of how these groups can best be leveraged to facilitate behavior change. OBJECTIVE: The aim of this study was to analyze patterns of participation, self-reported smoking cessation length, and interactions within the National Cancer Institutes' Facebook community for smoking cessation support. METHODS: Our sample consisted of approximately 4243 individuals who interacted (eg, posted, commented) on the public Smokefree Women Facebook page during the time of data collection. In Phase 1, social network visualizations and centrality measures were used to evaluate network structure and engagement. In Phase 2, an inductive, thematic qualitative content analysis was conducted with a subsample of 500 individuals, and correlational analysis was used to determine how participant engagement was associated with self-reported session length. RESULTS: Between February 2013 and March 2014, there were 875 posts and 4088 comments from approximately 4243 participants. Social network visualizations revealed the moderator's role in keeping the community together and distributing the most active participants. Correlation analyses suggest that engagement in the network was significantly inversely associated with cessation status (Spearman correlation coefficient = -0.14, P=.03, N=243). The content analysis of 1698 posts from 500 randomly selected participants identified the most frequent interactions in the community as providing support (43%, n=721) and announcing number of days smoke free (41%, n=689). CONCLUSIONS: These findings highlight the importance of the moderator for network engagement and provide helpful insights into the patterns and types of interactions participants are engaging in. This study adds knowledge of how the social network of a smoking cessation community behaves within the confines of a Facebook group.