Inadvertent severe hypercapnia during general anesthesia: drop-in oxygen saturation or electrocardiography changes; which one warns us earlier?

Background During general anesthesia especially when the nurse or anesthesiologist forgets to change manual to controlled mode after successful endotracheal intubation, capnography shows End-tidal Co2 above 20 mmHg after checking the place of the tracheal tube and will remain on the screen permanently. In this scenario, the patient receives a high concentration of oxygen, and Spo2 (oxygen saturation) does not drop for a long time which is too late to intervene. It has been all-time questionable which one of the cardiac dysrhythmias or Spo2 dropping occurs earlier. Results Medical records of seven deceased patients reviewed. All of them had electrocardiogram changes including premature ventricular contraction or bradycardia as a first warning sign. Oxygen saturation remains above 95% even with cardiac dysrhythmia. Conclusions Bradycardia and premature ventricular contraction were the first warning findings for severe hypercapnia during general anesthesia and occurred earlier than dropping oxygen saturation. Furthermore, the normal capnography waveform is more reliable than the End-tidal Co2 number for monitoring.

Smartwatch helps detects lung cancer: Using personal technology to advance healthcare

Summary The prevalence of smartwatches and other wearable medical technology has been increasing yearly. These watches offer a sensitive tool for capturing cardiac dysrhythmias and can lead to patients seeking earlier medical advice. This case report highlights the importance of clinicians seeking and using the information provided by wearable medical technology which in this case resulted in both the timely treatment of non-sustained ventricular tachycardia and lung adenocarcinoma.

Abstract 1122‐000140: National Trends in Readmission after Mechanical Thrombectomy in Acute Ischemic Stroke

Introduction : Mechanical thrombectomy (MT) has become the standard of care in patients with large vessel occlusion after trials have demonstrated (MT) improved outcomes in acute ischemic stroke (AIS) as compared to medical therapy. Despite leading to high reperfusion rates, MT patients are at high risk for recurrent ischemic events and complications of stroke. We performed an analysis to evaluate temporal trends in readmission of post‐MT among stroke patients over a three‐year period. Methods : From the Healthcare Cost and Utilization Project Nationwide Readmission Database, we obtained in‐hospital adult patient data with a principal diagnosis of AIS in the US from 2016 to 2018. AIS, MT, thrombolysis treatment and other diagnosis were identified based on International Classification of Diseases, 10th Revision, Clinical Modification codes. We compared the trend of 30‐day readmission in AIS patients who received MT, thrombolysis only and neither treatment with linear regression. Using Clinical Classifications Software Refined tool, we categorized the readmission principal diagnoses of patients underwent MT into groups. All analyses were performed in Stata/SE 15.1 software. Results : Of the 1,271,958 patients admitted from throughout the US with AIS within the study period, 1,130,737 (88.90%) did not receive thrombolysis nor MT, 100,737 (7.92%) received thrombolysis only, and 40,849 (3.21%) underwent MT with or without thrombolysis. The endovascular treatment rate doubled from 2016 (2.40%) to 2018 (4.11%, p < 0.0001). From 2016 to 2018, the readmission rate has significantly decreased from 15.00% to 12.04% (absolute risk reduction (ARR) 2.96%, p = 0.0001) in patients who underwent MT, decreased from 10.46% to 9.51% (ARR 0.95%, p = 0.0097) in patients who received thrombolysis only, and decreased from 11.96% to 11.56% (ARR 0.40%, p = 0.0130) in patients received neither therapy. Among all the patients who underwent MT during the three‐year period, sepsis (1.88%), cerebral infarction (1.59%), sequelae of cerebral infarction (0.82%), cardiac dysrhythmias (0.67%) and heart failure (0.49%) were the most common principal readmission diagnoses. From 2016 to 2018, there were significant decreases in rate of readmissions with septic infection (p = 0.0001), sequelae of cerebral infarction (p < 0.0001), and heart failure (p = 0.0123), but no significant change in cerebral infarction (p = 0.4853) and cardiac dysrhythmias (p = 0.1834). Conclusions : Over three years, the rate of readmissions in AIS patients receiving MT significantly declined, particularly in rate of readmissions in sepsis, sequelae of cerebral infarction, and heart failure. Improved reperfusion rate and better outcomes may explain the reduction in post‐MT complication rate, which needs further studies.

Application of a time-series deep learning model to predict cardiac dysrhythmias in electronic health records

Background Cardiac dysrhythmias (CD) affect millions of Americans in the United States (US), and are associated with considerable morbidity and mortality. New strategies to combat this growing problem are urgently needed. Objectives Predicting CD using electronic health record (EHR) data would allow for earlier diagnosis and treatment of the condition, thus improving overall cardiovascular outcomes. The Guideline Advantage (TGA) is an American Heart Association ambulatory quality clinical data registry of EHR data representing 70 clinics distributed throughout the US, and has been used to monitor outpatient prevention and disease management outcome measures across populations and for longitudinal research on the impact of preventative care. Methods For this study, we represented all time-series cardiovascular health (CVH) measures and the corresponding data collection time points for each patient by numerical embedding vectors. We then employed a deep learning technique–long-short term memory (LSTM) model–to predict CD from the vector of time-series CVH measures by 5-fold cross validation and compared the performance of this model to the results of deep neural networks, logistic regression, random forest, and Naïve Bayes models. Results We demonstrated that the LSTM model outperformed other traditional machine learning models and achieved the best prediction performance as measured by the average area under the receiver operator curve (AUROC): 0.76 for LSTM, 0.71 for deep neural networks, 0.66 for logistic regression, 0.67 for random forest, and 0.59 for Naïve Bayes. The most influential feature from the LSTM model were blood pressure. Conclusions These findings may be used to prevent CD in the outpatient setting by encouraging appropriate surveillance and management of CVH.

Chloroquine Ingestion to Prevent SARS-CoV-2 Infection: A Report of Two Cases

Introduction: Amid the global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), chloroquine and hydroxychloroquine were being studied as agents to prevent and treat coronavirus disease 2019. Information about these agents and their effects circulated throughout the general public media, raising the concern for self-directed consumption of both pharmaceutical and non-pharmaceutical products. Case Report: We present two cases of chloroquine toxicity that occurred after ingestion of an aquarium disinfectant that contained chloroquine phosphate in a misguided attempt to prevent infection by SARS-CoV-2. One patient had repeated emesis and survived, while the other was unable to vomit, despite attempts, and suffered fatal cardiac dysrhythmias. Conclusion: These cases illustrate the spectrum of toxicity, varied presentations, and importance of early recognition and management of chloroquine poisoning. In addition, we can see the importance of sound medical guidance in an era of social confusion compounded by the extremes of public and social media.

Design and Optimization of ECG Modeling for Generating Different Cardiac Dysrhythmias

The electrocardiogram (ECG) has significant clinical importance for analyzing most cardiovascular diseases. ECGs beat morphologies, beat durations, and amplitudes vary from subject to subject and diseases to diseases. Therefore, ECG morphology-based modeling has long-standing research interests. This work aims to develop a simplified ECG model based on a minimum number of parameters that could correctly represent ECG morphology in different cardiac dysrhythmias. A simple mathematical model based on the sum of two Gaussian functions is proposed. However, fitting more than one Gaussian function in a deterministic way has accuracy and localization problems. To solve these fitting problems, two hybrid optimization methods have been developed to select the optimal ECG model parameters. The first method is the combination of an approximation and global search technique (ApproxiGlo), and the second method is the combination of an approximation and multi-start search technique (ApproxiMul). The proposed model and optimization methods have been applied to real ECGs in different cardiac dysrhythmias, and the effectiveness of the model performance was measured in time, frequency, and the time-frequency domain. The model fit different types of ECG beats representing different cardiac dysrhythmias with high correlation coefficients (>0.98). Compared to the nonlinear fitting method, ApproxiGlo and ApproxiMul are 3.32 and 7.88 times better in terms of root mean square error (RMSE), respectively. Regarding optimization, the ApproxiMul performs better than the ApproxiGlo method in many metrics. Different uses of this model are possible, such as a syntactic ECG generator using a graphical user interface has been developed and tested. In addition, the model can be used as a lossy compression with a variable compression rate. A compression ratio of 20:1 can be achieved with 1 kHz sampling frequency and 75 beats per minute. These optimization methods can be used in different engineering fields where the sum of Gaussians is used.

Abstract 16107: Electrocardiographic Findings and Their Associations With Adverse Outcomes in Hospitalized Patients With COVID-19

Introduction: Although primarily a respiratory illness, COVID-19 frequently involves the cardiovascular system. Hypothesis: Accordingly, we assessed whether abnormal electrocardiographic findings, including cardiac dysrhythmias, ST/T wave changes, and QTc interval prolongation, predict adverse outcomes in patients with COVID-19. Methods: A multi-center retrospective study of hospitalized patients with COVID-19 was performed across 4 hospitals in Texas and 1 in New York. Initial and subsequent ECG findings among patients with uncomplicated disease course, patients with adverse clinical outcomes (need for vasopressors, mechanical ventilation or renal replacement therapy) and patients who died were analyzed. Univariable and multivariable Cox analyses were performed. Results: We identified 297 consecutive patients with COVID-19. Of these patients, 91% had available ECGs. Median heart rate on initial ECG was 92 bpm (IQR: 27), and median QTc interval was 442 msec (IQR: 40). Longer QTc interval on initial ECG was associated with adverse clinical outcomes (443 msec, IQR: 43) and death (457 msec, IQR: 52, vs 441.5 msec, IQR: 36; p=0.033). Cardiac dysrhythmias or ST/T wave changes were noted in subsequent ECGs of 46% of patients and were associated with adverse clinical outcomes (58%) or death (68% vs 33%; HR: 2.2, 95% CI: 1.3-3.6; p=0.002). Sixty-four patients (23%) developed a QTc interval >500msec during their hospitalization. In multivariable Cox analysis, history of coronary artery disease was independently associated with development of QTc>500msec (HR 2.1, 95% CI 1.0-4.4; p=0.047) while other baseline comorbidities were not. Patients with QTc>500msec were more commonly treated with azithromycin (91% vs 77%; p=0.048); no other differences in COVID-19 treatment were identified. In-hospital mortality among patients with QTc>500msec was 37% versus 13% among patients with shorter QTc intervals (HR: 2.6, 95% CI 1.5-4.5; p=0.001). Conclusions: Prolonged QTc interval on initial ECG of COVID-19 patients predicted adverse outcomes and death. Azithromycin was associated with development of QTc>500msec. Patients with a QTc interval >500msec had a 2.6 times higher risk for in-hospital mortality compared to patients with shorter QTc intervals.