Evaluating the implementation of Nuevo Amanecer-II in rural community settings using mixed methods and equity frameworks

BACKGROUND

The 10-week Nuevo Amanecer-II intervention, tested through a randomized controlled trial, reduced anxiety and improved stress management skills among Spanish-speaking Latina breast cancer survivors. This paper describes the implementation and equity evaluation outcomes of the Nuevo Amanecer-II intervention delivered in three California rural communities.

METHODS

Using implementation and equity frameworks, concurrent convergent mixed methods were applied to evaluate implementation (feasibility, fidelity, acceptability, adoption, appropriateness, and sustainability) and equity (shared power and capacity building) outcomes. Quantitative data were collected using tracking forms, fidelity rating forms, and program evaluation surveys; qualitative data were collected using semi-structured in-depth interviews. Respondents included community-based organization (CBO) administrators, recruiters, compañeras (interventionists), and program participants.

RESULTS

Of 76 women randomized to the intervention, 65 (86%) completed at least 7 of 10 sessions. Participants' knowledge (85% correct of 7 questions) and skills mastery were high (85% able to correctly perform 14 skills). Mean fidelity ratings across compañeras ranged from 3.8 (modeled skills) to 5.0 (used supportive/caring communication); 1-5 scale. The program was rated as very good/excellent by 90% of participants. Participants and compañeras suggested including family members; compañeras suggested expanding content on managing thoughts and mood and healthy living and having access to participant's survivorship care plan to tailor breast cancer information. CBOs adopted the program because it aligned with their priority populations and mission. Building on CBOs' knowledge, resources, and infrastructure, implementation success was due to shared power, learning, responsibility, and co-ownership, resulting in a co-created tailored program for community and organizational contexts. Building intervention capacity prior to implementation, providing funding, and ongoing technical support to CBOs were vital for fidelity and enhancement of recruiter and compañera professional skills. Two of three CBOs created plans for program sustainability beyond the clinical trial; all administrators discussed the need for new funding sources to sustain the program as delivered.

CONCLUSIONS

Building on community assets and using equitable participatory research processes were central to the successful implementation of a peer-delivered psychosocial intervention in three rural communities among Spanish-speaking Latinas with breast cancer.

Depression Symptoms, Perceived Stress, and Loneliness During the COVID-19 Pandemic Among Diverse US Racial-Ethnic Groups

Introduction

Studies have reported increases in psychological distress during the COVID-19 pandemic. This study aimed to estimate associations between race-ethnicity and psychological distress during the COVID-19 pandemic among nationally representative samples of all major racial-ethnic groups in the United States.

Methods

We conducted a nationally representative cross-sectional survey between December 2020 and February 2021 of Asian, black/African American, Latino (English and Spanish speaking), American Indian/Alaska Native, Native Hawaiian/Pacific Islander, white, and multiracial adults (n=5500). Distress measures included: anxiety-depression (Patient Health Questionnaire-4 [PHQ-4]), stress (modified Perceived Stress Scale), and loneliness-isolation (frequency felt lonely and isolated). Multinomial logistic regression models estimated associations between race-ethnicity and psychological distress, adjusting for demographic and health characteristics.

Results

Overall, 23.7% reported moderate/severe anxiety-depression symptoms, 34.3% reported moderate/severe stress, and 21.3% reported feeling lonely-isolated fairly/very often. Compared with white adults and adjusting for covariates, the prevalence of moderate/severe anxiety-depression was significantly lower among Asian (adjusted odds ratio [aOR]=0.44, 95% confidence interval [CI]=0.34-0.58), black (aOR=0.49, 95% CI=0.38-0.63), English-speaking Latino (aOR=0.62, 95% CI=0.45-0.85), Spanish-speaking Latino (aOR=0.31, 95% CI=0.22-0.44), and Native Hawaiian/Pacific Islander (aOR=0.66, 95% CI=0.49-0.90) adults. Similar trends were seen for moderate/severe stress and feeling lonely-isolated fairly/very often. Worse distress profiles of American Indian/Alaska Native and multiracial adults were attenuated after adjustment.

Conclusions

Minoritized groups tended to have less distress than white adults. Collective experiences of cumulative disadvantage could engender shared resiliency/normalization among these groups.

Lung lymphatic thrombosis and dysfunction caused by cigarette smoke exposure precedes emphysema in mice

The lymphatic vasculature is critical for lung function, but defects in lymphatic function in the pathogenesis of lung disease is understudied. In mice, lymphatic dysfunction alone is sufficient to cause lung injury that resembles human emphysema. Whether lymphatic function is disrupted in cigarette smoke (CS)-induced emphysema is unknown. In this study, we investigated the effect of CS on lung lymphatic function. Analysis of human lung tissue revealed significant lung lymphatic thrombosis in patients with emphysema compared to control smokers that increased with disease severity. In a mouse model, CS exposure led to lung lymphatic thrombosis, decreased lymphatic drainage, and impaired leukocyte trafficking that all preceded the development of emphysema. Proteomic analysis demonstrated an increased abundance of coagulation factors in the lymph draining from the lungs of CS-exposed mice compared to control mice. In addition, in vitro assays demonstrated a direct effect of CS on lymphatic endothelial cell integrity. These data show that CS exposure results in lung lymphatic dysfunction and a shift in thoracic lymph towards a prothrombic state. Furthermore, our data suggest that lymphatic dysfunction is due to effects of CS on the lymphatic vasculature that precede emphysema. These studies demonstrate a novel component of CS-induced lung injury that occurs early in the pathogenesis of emphysema.

Substantial concordance between transient elastography and APRI and FIB-4 combination amongst hepatitis C inmates with non advanced liver fibrosis

Objectives

To establish concordance between transient elastography (TE) and non invasive markers (NIM) APRI and FIB-4 combination in cronic hepatitis C (HC) patients with non-advanced liver fibrosis (NALF).

Material and method

Multi-centre retrospective study carried out at two different Barcelona Prisons HC inmates who had the TE done at 2019. We compared the ET vs. NIM results. The NALF consideration was ≤2 (≤12.5 Kilopascal (kPa) in TE). In the NALF cases was calculated de NIM APRI and FIB-4 and the kappa index agreement was established between TE and NIM.

Results

107 cases were included, but only 82 were assessable. The average age was 42 (DS: ±3.2) years. The 96.5% were men, 51.2% spanish, 70.7% drug users and 39% HIV co infected. The 45.1% of those HC infected had genotipe 1. The 90.2% of the evaluated patients by TE the ALD was not detected. The kappa index was 0.78. 65 (79.3%) studied inmates got HC treatment. The 20.7% could not be treated because the evaluation was not completed.

Conclusion

Most of the HC infected inmates have no ALD, and in such cases concordance between NIM/TE is substantial. The NIM can be used to shorten the evaluation time and prescribe the treatment faster, especially if the length of stay in prison is short and risk of transmission is high.

Racial/ethnic disparities in intent to obtain a COVID-19 vaccine: A nationally representative United States survey

Black, Latino, Pacific Islander, and American Indian/Alaska Native adults are more likely than White adults to experience SARS-CoV-2-related infections, hospitalizations, and mortality. We assessed intent to be vaccinated and concerns among 7 U.S. racial/ethnic groups (1,000 Black/African American, 500 American Indian/Alaska Native, 1,000 Asian, 1,000 Latino (500 English- and 500 Spanish-speaking), 500 Pacific Islander, 500 multiracial, and 1,000 White adults) in a cross-sectional online survey conducted December 2020-February 2021, weighted to be nationally representative within groups. Intent to be vaccinated was ascertained with: "If a COVID-19 vaccine becomes available, how likely are you to get vaccinated?" (not at all/slightly/moderately/very/extremely likely). Respondents identified which concerns would keep them from being vaccinated: cost, not knowing where, safety, effectiveness, side-effects, and other. Multinomial logistic regression models assessed associations of race/ethnicity with odds of being extremely/very/moderately, slightly likely to be vaccinated (ref = not at all), controlling for demographics and health. Overall, 30% were extremely likely, 22% not at all likely, and 48% unsure. Compared to White respondents, American Indian/Alaska Native (Adjusted Odds Ratio (AOR) = 0.66, 95% CI, 0.47-0.92) and Black/African American (AOR = 0.54, 95% CI, 0.41-0.72) respondents were less likely, and Asian (AOR = 2.21, 95% CI, 1.61-3.02) and Spanish-speaking Latino respondents (AOR = 3.74, 95% CI, 2.51-5.55) were more likely to report being extremely likely to be vaccinated. Side-effects (52%) and safety (45%) were overriding concerns. Intent and vaccination rates are changing rapidly; these results constitute a comprehensive baseline for ongoing vaccination efforts among U.S. racial and ethnic groups.

Engaged with the heart – the EKG ring STEMI detector

Background

Our previous experience in telemedicine-centered STEMI management networks has shown delayed presentations as one of the most relevant factors in the reduction of Symptom-to-Baloon times. In order to further improve outcomes, we delved into the applications of mathematical vector algebra and engineering to incorporate an innovative Artificial Intelligence-guided Single Lead EKG methodology into a wearable ring to provide a self-administered alternative to reliable and expedite STEMI screening.

Purpose

To provide preliminary results of the application of ultra-wearable technology in a ring for accurate STEMI detection.

Methods

Our present work was done in two steps – 1) Applying mathematical vector algebra to construct an accurate and practical AI-guided Single Lead EKG algorithm for STEMI detection compatible with wearable devices, and 2) To engineer this algorithm into a wearable ring for quick and reliable STEMI detection. Throughout our first step, we provided a group of new lead waveforms (Vn') by positioning a single lead-capable wearable device into the chest positions Cn (C1, C2,..., C6) while touching the second electrode with a right-hand finger in the same device, all of which corresponded to the difference in electric potential between Right Arm (RA) and the correspondent conventional precordial Vn chest position. By using vector algebra, we recognized Vn' as the sum of (-aVR + Vn). Vector mathematical analysis was performed for 5,783 STEMI (50%) and 5,784 Not-STEMI (50%) EKG from a proprietary dataset, obtaining their corresponding new Vn' precordial leads. Finally, the AI-guided STEMI detector model was trained with 10,410 EKG records (90%) and tested with 1,157 EKG records (10%). Performance metrics were calculated to determine best new Lead for STEMI detection. In the second step, we engineered this methodology into a wearable ring device. When a patient presents chest discomfort or oppression, the most common reaction is to move the hands towards the chest. By mimicking this behavior and having our EKG-capable ring technology on the right hand, we replicate our methodology by positioning said ring to chest positions Cn to register an EKG trace of new Vn' precordial leads and calculated performance metrics to evaluate the correlation with previous experiment.

Results

Test results shows Lead V2' as the best overall lead in detecting STEMI with 91.2% Accuracy, 89.6% Sensibility, and 92.9% Specificity. These results were reproduced with both methodologies.

Conclusions

Preliminary outcomes of the implementation of our innovative Single Lead EKG methodology into an ultra-portable ring yielded promising results. Prospective studies will be needed to further validate this neoteric methodology for STEMI detection, nevertheless, we envision the potential future applications of this technology in the clinical setting, particularly with swift screening and activation of remote STEMI management networks.

Funding Acknowledgement

Type of funding source: None

Waddling beyond door to balloon times and impinging true ischemic times with artificial intelligence-guided single lead EKG for STEMI detection

Background

The present process of STEMI detection is cumbersome as it utilizes outdated equipment and requires a trained technician and an expert cardiologist. We have developed a patient-administered, Artificial Intelligence (AI) guided, Single Lead EKG for early STEMI detection.

Purpose

To answer the question “Is early STEMI detection possible with a Single Lead EKG?”

Methods

We experimented with an AI-guided algorithm for a single-lead EKG for STEMI detection with the following step-wise developments: 1) An AI algorithm that predictably interprets STEMI using a 12-lead EKG; 2) An AI algorithm for STEMI detection using a single-lead EKG; 3) A methodology for identifying the best single lead to detect STEMI; 4) Advanced AI algorithms for STEMI localization with a single-lead EKG. The AI methodology was as follows: Sample: The mammoth Latin American Telemedicine Infarct Network telemedicine database that provides an umbrella of AMI management to 100 million patients in Brazil, Colombia, Mexico, Chile, and Argentina was queried for cardiologist annotated EKG. A total of 8,511 EKG and 90,592 classified heartbeats were selected for the experiments. Preprocessing: segmentation of each ECG into individual heartbeats. Training & Testing: 90% and 10%, respectively, of the total dataset. Classification: 1-D Convolutional Neural Network; classes were construed for each heartbeat. Performance indicators were calculated per lead.

Results

The algorithm was able to provide an accuracy of 91.9%. Lead V2 yielded the best results among individual leads for STEMI detection.

Conclusions

Early experiments provide a framework for augmenting STEMI detection with the use of AI-guided, single lead techniques. Such approaches seem rational as we target the reduction of true STEMI ischemic times.

Funding Acknowledgement

Type of funding source: None

Countdown to physician-free EKG interpretation

Background

With the introduction of electronic medical records and other digital platforms, the classification and coding of different medical entities have become a complex, cumbersome task that is prone to diagnostic inconsistencies and errors. By incorporating Artificial Intelligence (AI) to a massive database of EKG records, we have developed an innovative methodology to accurately discriminate an EKG as “normal” or “abnormal”. We firmly believe that this algorithm sets up medicine on a path of complete computer-aided EKG interpretation.

Purpose

To present a viable AI-guided filter that can accurately discriminate between normal and abnormal EKG within a cardiologist-annotated EKG database.

Methods

An observational, retrospective, case-control study. Samples: A total of 140,000 randomly sampled 12-lead ECG of 10-seconds length with a sampling frequency of 500 [Hz] from Brazil (BR) and Colombia (CO) (divided as 70,000 normal and 70,000 abnormal EKG records per country dataset) were derived from the private International Telemedical System (ITMS) database from September 2018 to July 2019. Only de-identified records were used, records with artifacts were excluded. Preprocessing: Only the first 2s of each short lead and 9s of the long lead were considered. This data includes mobile (MOB) and transtelephonic (TTP) EKGs (50/50 ratio). Limb leads I, II and III and precordial leads V1, V2, V3 and V5 were used. The mean was removed from each lead. Training Sets: Four models were trained as depicted in the figure below. Each training dataset has 25,000 Normal and 25,000 Abnormal records, where 10% of the total records were used as a validation set. The test sets included 10,000 normal, and 10,000 abnormal records each. Testing and Class Assigning: An inception convolutional neural network was implemented; Each model was tested with 5,000 normal and 5,000 abnormal records of the corresponding country and transmission type with which they were trained. “Normal” or “Abnormal” labels were assigned to each EKG record and were compared to the cardiologists' reports; performance indicators (accuracy, sensitivity, and specificity) were calculated for each model.

Results

An overall accuracy of 82.4%; sensitivity of 88.7%; and specificity of 76.2% was achieved amongst the 4 testing models (Separate results of each training set are shown below).

Conclusion(s)

AI enables the interpretation of digital EKG records to be exercised in an organized, accurate, and straightforward manner, taking into consideration the multiple potential entities that can be diagnosed through this historical triage tool. By quickly identifying the normal records, the cardiologist is able to invest efforts in treating patients in a timely manner.

Funding Acknowledgement

Type of funding source: None

Maximum artificial intelligence and complete reconstruct of population-based AMI care

Background

After creating a behemoth hub and spoke AMI network that encompasses more than 100 million patients in 5 countries, we have begun to incorporate Artificial Intelligence (AI) algorithms into our telemedicine strategy with the goal of creating comprehensive, very early AMI diagnosis and physician-free triage. In doing so, we have replaced door-to-balloon times (d2b) with symptom-to-balloon times (s2b) as an immutable objective.

Purpose

To incorporate AI attributes for very early AMI detection, triage, and management.

Methods

We expanded our effective telemedicine strategy (100 million population; 877,178 telemedicine encounters; 55% overall mortality reduction; $291 million cost savings) with a logistic reset to impact s2b. To do this, we incorporated our Single Lead 1.0 (lead I) and Single Lead 2.0 (lead V2) technology for self-administered AMI detection with our physician-free STEMI diagnosis and triage AI algorithms. Single Lead algorithms and physician-free protocols were generated by utilizing Machine Learning from our mammoth annotated EKG repository.

Results

In addition to three logistic markers of efficiency Time-to-Telemedicine Diagnosis (TTD), Door-In-Door-Out (DIDO) and Transfer Times (TT); we are monitoring s2b. A gradual release of the algorithms and single lead is occurring at the telemedicine spokes. Detailed results will be available at the time of presentation.

Conclusions

Impacting s2b, the Achilles Heel of Primary PCI, may be achieved with the use of patient-administered AMI detection tools. Incorporation of these technologies into AI algorithms will add to telemedicine efficiencies for population-based AMI care.

Funding Acknowledgement

Type of funding source: None

AI and telemedicine: total remote guidance of AMI management

Background

For a decade, Latin American Telemedicine Infarct Network (LATIN) Telemedicine has transformed AMI management in Brazil, Colombia, Mexico, Chile, and Argentina. With a hub and spoke strategy, AMI coverage was expanded to 100 million population and 877,177 telemedicine encounters were performed. Cost savings from avoiding unnecessary transfer of patients was $291 million. We are now rapidly escalating on a path to making the telemedicine process “physician-free” by utilizing Artificial Intelligence (AI) protocols.

Purpose

To demonstrate that AI can replace a cardiologist for remote AMI telemedicine guidance.

Methods

The process of total AI guidance focused on both aspects of our telemedicine strategy – accurate AMI diagnosis and tele-guidance of the entire STEMI process. We developed our innovative approach by initially creating AI algorithms for computer-aided diagnosis. Next, we incorporated logistic variables (duration of chest pain, transfer times to LATIN hub, etc) to the algorithm for physician-free triage into thrombolysis, primary PCI and pharmaco-invasive management. The intent of creating AI algorithms was early STEMI detection and triage. After the patient was efficiently transferred to the hub, a final treatment decision was made by the hub cardiologists.

Results

Three crucial areas of telemedicine efficiency are being monitored – Time-to-Telemedicine Diagnosis (TTD), Door-In-Door-Out (DIDO) and Transfer Times (TT). All are showing improvements. Detailed results will be available at the time of presentation.

Conclusions

We are encouraged with the possibility of making the entire telemedicine guidance of AMI management “physician-free”. Next-Gen improvements are being contemplated by including a Single Lead EKG for AMI detection that will impact symptom-to-balloon times.

Funding Acknowledgement

Type of funding source: None

Enriching artificial intelligence ST-elevation myocardial infarction (STEMI) detection algorithms with differential diagnoses

Background

STEMI outcomes, although improved with systems of care, are hamstrung by delayed presentation and prevaricates of a 12-lead ECG. We report an artificial intelligence (AI) guided, single lead EKG algorithm for a self-administered tool to reliably detect STEMI and trigger ambulance dispatch.

Purpose

To provide a reliable and improved AI-guided Single Lead EKG methodology.

Methods

From our cardiologist-annotated repository, we assigned a dataset of 11,118 classified ECG. Ontology organized 5 groups apportioned for an interclass balance among commoner STEMI differential diagnoses. This anonymous, pre-classified data included 5,549 STEMI, 1,391 normal, 1,393 Bundle Branch Block, 1,393 non-specific ST-T changes and 1,392 miscellaneous. Each ECG was fragmented into individual 1-lead strips. Algorithm: 1-D Convolutional Neural Networks. Gender and age were included before the last dense layer. Training and Testing: Preset 90% dataset (10,008 ECG) train, 10% test (1,110 ECG). Statistical Analysis and ROC curves: Digitized dataset, 500 samples/second, 10s duration, total 5,000 samples per lead. Statistical mean for each lead was calculated and subtracted from the original lead. Statistical values and ROC curves were assessed.

Results

Most Accurate: Lead V2 – 91%; Most Sensitive: Lead I – 92% Most Specific: Lead III – 96%. Best AUC: Lead V2 – 91%.

Conclusions

Incorporating subtypes of STEMI differential diagnosis enriches the single lead AI algorithm. Validating the derived algorithm with our entire database of 18 million ECG will further strengthen the results.

Funding Acknowledgement

Type of funding source: None

Baby steps in the path of modifying the role of cardiologists for interpreting EKG for AMI

Background

EKG interpretation is slowly transitioning to a physician-free, Artificial Intelligence (AI)-driven endeavor. Our continued efforts to innovate follow a carefully laid stepwise approach, as follows: 1) Create an AI algorithm that accurately identifies STEMI against non-STEMI using a 12-lead EKG; 2) Challenging said algorithm by including different EKG diagnosis to the previous experiment, and now 3) To further validate the accuracy and reliability of our algorithm while also improving performance in a prehospital and hospital settings.

Purpose

To provide an accurate, reliable, and cost-effective tool for STEMI detection with the potential to redirect human resources into other clinically relevant tasks and save the need for human resources.

Methods

Database: EKG records obtained from Latin America Telemedicine Infarct Network (Mexico, Colombia, Argentina, and Brazil) from April 2014 to December 2019. Dataset: A total of 11,567 12-lead EKG records of 10-seconds length with sampling frequency of 500 [Hz], including the following balanced classes: unconfirmed and angiographically confirmed STEMI, branch blocks, non-specific ST-T abnormalities, normal and abnormal (200+ CPT codes, excluding the ones included in other classes). The label of each record was manually checked by cardiologists to ensure precision (Ground truth). Pre-processing: The first and last 250 samples were discarded as they may contain a standardization pulse. An order 5 digital low pass filter with a 35 Hz cut-off was applied. For each record, the mean was subtracted to each individual lead. Classification: The determined classes were STEMI (STEMI in different locations of the myocardium – anterior, inferior and lateral); Not-STEMI (A combination of randomly sampled normal, branch blocks, non-specific ST-T abnormalities and abnormal records – 25% of each subclass). Training & Testing: A 1-D Convolutional Neural Network was trained and tested with a dataset proportion of 90/10; respectively. The last dense layer outputs a probability for each record of being STEMI or Not-STEMI. Additional testing was performed with a subset of the original dataset of angiographically confirmed STEMI.

Results

See Figure Attached – Preliminary STEMI Dataset Accuracy: 96.4%; Sensitivity: 95.3%; Specificity: 97.4% – Confirmed STEMI Dataset: Accuracy: 97.6%; Sensitivity: 98.1%; Specificity: 97.2%.

Conclusions

Our results remain consistent with our previous experience. By further increasing the amount and complexity of the data, the performance of the model improves. Future implementations of this technology in clinical settings look promising, not only in performing swift screening and diagnostic steps but also partaking in complex STEMI management triage.

Funding Acknowledgement

Type of funding source: None

Innovative techniques to construct powerful artificial intelligence algorithms for st-elevation myocardial infarction

Background

With the sudden advent of Artificial Intelligence (AI), incorporation of these technologies into key aspects of our working environment has become an ever so delicate task, especially so when dealing with time-sensitive and potentially lethal scenarios such as ST-Elevation Myocardial Infarction (STEMI) management. By further expanding into our successful experiences with AI-guided algorithms for STEMI detection, we implemented an innovative ensemble method into our methodology as we seek to improve the algorithm's predictive capabilities.

Purpose

Through the ensemble method, we combined two ML techniques to boost our previous experiments' accuracy and reliability.

Methods

Database: EKG records obtained from Latin America Telemedicine Infarct Network (Mexico, Colombia, Argentina, and Brazil) from April 2014 to December 2019. Dataset: Two separate datasets were used to train and test two sets of AI algorithms. The first comprised of 11,567 records and the second 7,286 records, each composed of 12-lead EKG records of 10-second length with sampling frequency of 500 Hz, including the following balanced classes: unconfirmed & angiographically confirmed STEMI (first model); angiographically confirmed STEMI only (second model); and, for both models, we included branch blocks, non-specific ST-T abnormalities, normal, and abnormal (200+ CPT codes, excluding the ones included in other classes). Label per record was manually checked by cardiologists to ensure precision (Ground truth). Pre-processing: First and last 250 samples were discarded to avoid a standardization pulse. An order 5 digital low pass filter with a 35 Hz cut-off was applied. For each record, the mean was subtracted to each individual lead. Classification: The determined classes were STEMI and Not-STEMI (A combination of randomly sampled normal, branch blocks, non-specific ST-T abnormalities and abnormal records – 25% of each subclass). Training & Testing: The last dense layer outputs a probability for each record of being STEMI or Not-STEMI. These probabilities were calculated for each model (Model 1 trained with Complete STEMI dataset and Model 2 trained with confirmed STEMI only dataset) and aggregated using the mean aggregation to generate the final label for each record. A 1-D Convolutional Neural Network was trained and tested with a dataset proportion of 90%/10%; respectively. Results are reported for both testing datasets (Complete and confirmed STEMI only records).

Results

Complete STEMI Dataset: Accuracy: 96.5% Sensitivity: 96.2% Specificity: 96.9% – Confirmed STEMI only Dataset: Accuracy: 98.5% Sensitivity: 98.3% Specificity: 98.6%'

Conclusion(s)

While Model 1 and Model 2 achieved similar performances with promising results on their own, applying a combination of both through the ensemble model exhibits a clear improvement in performance when applied to both datasets. This provides a blueprint for advanced automated STEMI detection through wearable devices.

Funding Acknowledgement

Type of funding source: None

Artificial intelligence methodology: multi-label classification of abnormal EKG records

Background

Our previous experience with Artificial Intelligence (AI)-conducted EKG characterization displayed outstanding results in fast and reliable identification of Normal EKGs within the International Telemedical System (ITMS)'s massive record repository. By expanding the array of recognizable cardiovascular entities, we upgraded our methodology to accurately discriminate an anomaly amongst a highly complex database of EKG records.

Purpose

To present a feasible AI-guided filter that can accurately discriminate and classify Normal and Abnormal EKG records within a multilabeled cardiologist-annotated EKG database.

Methods

ITMS developed and tested the “One Click”' process, a “Normal/Abnormal” EKG assessing AI algorithm, by incorporating it into their digital EKG reading platform where cardiologists continuously report their findings remotely in real time. To ameliorate the diagnostic range of the algorithm, a separate dataset of 121,641 12-lead EKG records was consolidated from the ITMS database from October 2011 to January 2019. Only de-identified data was used. Preprocessing: The first 2s of each short lead and 9s of the long lead were considered. Limb leads I, II and III; and precordial leads V1, V2, V3, and V5 were used. The mean was removed from each lead. AI models/Classification: Two models were created and tested independently based on the method of EKG acquisition (69,852 records transtelephonic [TTP]; 52,259 mobile transmission [MOB]). Each record is categorized into six disjoint classes based on the most common types of cardiac disorders (Low/null co-occurrence pathologies in these datasets were grouped into analogous groups). Training/Testing: Distribution of both sets per transmission type was performed through a greedy algorithm, which identified multiple diagnoses per EKG record and labeled it separately to the corresponding group, ensuring sufficient samples per class. Detailed class distribution is shown below. An inception convolutional neural network was implemented; “Normal” or “Abnormal” labels were assigned to each EKG record independently and were compared to cardiologists' reports; performance indicators were calculated for each model and group.

Results

MOB model accrued an average accuracy of 86.7%; sensitivity of 90.5%; and specificity of 83.9%. TTP model yielded an average accuracy of 77.2%; sensitivity of 91.1%; and specificity of 69.4% (Lower values were attributed to the “Ventricular Complexes” group, which challenged the algorithm by having a smaller ratio of abnormal exams). Detailed results of each training set are shown below.

Conclusion

Providing an effective and reliable multilabel-capable EKG triaging tool remains a challenging but attainable goal. Continuous systematic enhancement of our AI-driven methodology has led us to satisfactory, yet imperfect results which compel us to further study and improve our efforts to provide a trustworthy cardiologist-friendly triage device.

Funding Acknowledgement

Type of funding source: None

Novel wearable sensor device methodology for STEMI detection

Background

Novel STEMI detection tools using wearable Single Lead EKG methodologies demonstrate vast potential in many clinical scenarios. Recent research suggests that smartwatches and other wearable devices can be repositioned to acquire “new” chest leads that have similar, but not equal, waveforms when compared to traditional precordial leads. Throughout our previous research, only Lead I data had been used to train our Machine Learning (ML) models due to a lack of datasets from these “new” leads. We now propose an innovative methodology to tackle these limitations and compare it with our previous experience.

Purpose

To demonstrate that mathematical vector algebra can reliably transform EKG STEMI databases into different, ML-ready datasets useful to train models with entirely new leads, mainly to be used in the development and training of reliable STEMI detection tools.

Methods

Our previous research has demonstrated that the most accurate (91.2%) ML model was achieved through precordial lead 2 (V2). By definition, V2 corresponds to the difference in electric potential between the Wilson Central Terminal (Wt) and the Chest terminal 2 (C2). To obtain the Wt, at least three electrodes must be used (Right Arm [RA], Left Arm [LA], Left Leg [LL]). Due to practical reasons, we discarded this methodology and worked with Lead I instead, which needs only two body contacts (RA, LA), and provides waveforms that are compatible with the majority of wearable devices (smartwatches, rings, among others). New waveforms (Vn') were obtained by positioning a single lead-capable wearable device (Smartwatch) to chest positions Cn (C1, C2,...,C6) and touching a second electrode with a right-hand finger, which corresponds to the difference in electric potential between RA and the correspondent conventional Vn chest position, respectively. Using vector algebra, we observe that Vn' corresponds to the sum of −aVR + Vn. Vector mathematical analysis was performed for 5,783 STEMI (50%) and 5,784 Not-STEMI (50%) EKG dataset, obtaining their corresponding new precordial leads Vn'. Following this, the ML Heart Attack Detector model was trained with 10,410 EKG (90%) and tested utilizing 1,157 (10%) EKG. Performance metrics were calculated for each new Lead and compared with our Prior Data.

Results

A 1:1 correlation was seen between our previous and current experiments, with Lead V2' performing as the best overall lead with 91.2% Accuracy, 89.6% Sensibility, and 92.9% Specificity. Complete information on prior and new data are provided below.

Conclusions

With the use of this new methodology, we overcame the inherent limitations of using our best Lead (V2) in a single lead approach for STEMI screening. Further prospective data is needed to validate this approach, but it provides a promising blueprint for automated STEMI detection and management triage through the use of wearable devices.

Funding Acknowledgement

Type of funding source: None

P1464Adoption of feedback to validate a machine learning model for single lead STEMI detection

Background

We have explored the performance of a single lead EKG with Artificial Intelligence (AI) based algorithms in STEMI diagnosis, thus far lead V2 has yielded the best results. Anticipating the performance of the LUMENGT-AI model, we designed a feedback strategy with healthcare centers to expand the validation of our work.

Purpose

To create a pragmatic alternative to the existing gold standard, a 12-lead EKG, for STEMI diagnosis.

Methods

An observational, retrospective, case-control study. Sample: 2,543 exclusively STEMI (anterior, inferior and lateral wall) diagnosis, EKG records. Feedback: From healthcare centers, confirming STEMI diagnosis and location, was obtained (thrombolysis, primary Percutaneous Coronary Intervention (PCI), pharmaco invasive therapy or coronary artery bypass surgery). Records excluded other patient and medical information. Sample was derived from the private International Telemedical Systems (ITMS) database. LUMENGT-AI Algorithm was employed. Preprocessing: detection of QRS complexes using the wavelet system, segmentation of each EKG into individual heartbeats (53,667 total beats) with fixed window of 0.4s to the left and 0.9s to the right of main QRS; Classification: A 1-D convolutional neural network was implemented; “STEMI” and “Not-STEMI” classes were considered for each heartbeat per lead; individual probabilities were aggregated to generate the final label for each record. Training & Testing: 90% and 10% of the sample were used, respectively. Experiments: Intel PC i7 8750H processor at 2.21GHz, 16GB RAM, Windows 10 OS with NVidia GTX 1070 GPU, 8GB RAM.

Results

V2 was the most precise lead with an Accuracy of 93.6%, a Sensitivity of 89%, and a Specificity of 94.7%.

Conclusions

The strategic adoption of feedback from healthcare centers provided strong validation of our model. The results of AI-augmented, single lead EKG are encouraging. We anticipate that this approach will become a promising methodology in STEMI detection.

P6421Can cardiologists rely on artificial intelligence to identify the culprit vessel in STEMI?

Background

The importance of culprit lesion identification is critical for risk stratification of a patient with an ST-Elevation Myocardial Infarction (STEMI). The aforementioned provide patients with a more elaborated strategy of management and treatment either they are treated with PCI or less invasive techniques such as thrombolysis. We report a novel approach that employs AI-guided electrocardiogram (EKG) algorithms for rapid and accurate identification of the culprit STEMI vessel.

Purpose

To create an innovative, machine learning tool for a more effective risk stratification of STEMI patients.

Methods

An observational, retrospective, case-control study. Sample: 2,542 exclusively STEMI diagnosis EKG records that included post discharge feedback from healthcare centers, confirming diagnosis and culprit vessel (Left Main Coronary Artery [LMCA]; Left Anterior Descending [LAD]; Right Coronary Artery [RCA]; Left Circumflex Artery [LCX]; Saphenous Vein Graft [SVG]). Records excluded other patient and medical information. The sample was derived from the private International Telemedical Systems (ITMS) database. LUMENGT-AI Algorithm was employed. Preprocessing: detection of QRS complexes using a wavelet system, segmentation of each EKG into individual heartbeats (27,125 total beats) with fixed window of 0.4s to the left and 0.9s to the right of main QRS; Classification: A 1-D convolutional neural network was implemented; “LCMA”, “LAD”, “LCX”, “RCA”, “SVG”, and “No Information” classes were considered for each heartbeat; individual probabilities were aggregated to generate the final label for each record. Training & Testing: 90% and 10% of the sample was used, respectively. Experiments: Intel PC i7 8750H processor at 2.21GHz, 16GB RAM, Windows 10 OS with NVidia GTX 1070 GPU, 8GB RAM.

Results

Global Accuracy: 79.4%; LAD: Sensitivity 86.2%; Specificity 84.8%. RCA: Sensitivity 85.7%; Specificity 83.7%. LCX: Sensitivity 43.5%; Specificity 96.9%.

Conclusions

Coupling an AI-augmented algorithm and 12-lead EKG provides encouraging results for STEMI culprit vessel localization. Overall, risk stratification is possible for individual lesions located in the LAD and RCA. However, our approach yielded uncertain results in the LCX territory. We plan to continue to exploring variables for improvement of our results.

P1928Global telemedicine initiatives for combating ami

Background

Major disparities exist between developed and developing countries in Acute Myocardial Infarction (AMI) outcomes. Telemedicine has emerged as a powerful, cost-efficient, and scalable tool for population-based AMI management. We propose efficient telemedicine protocols as frontline AMI strategies for resource-constrained developing countries.

Purpose

To create a global template of using telemedicine protocols for treating AMI.

Methods

A hub and spoke strategy was utilized for Latin America Telemedicine Infarct Network (LATIN) to expand access in Brazil, Colombia, Mexico, and Argentina. Small clinics and primary care health centers in remote areas were strategically connected with 24/7 primary PCI facilities. Experts at 4 remote sites provided urgent EKG diagnosis and tele-consultation that triggered ambulance dispatch and implementation of standardized AMI protocols.

Results

784,947 patients were screened for AMI at 350 LATIN centers (Brazil 143, Colombia 118, Mexico 82, Argentina 7). With this expanded geographic reach, 8,448 (1.08%) patients were diagnosed with STEMI and 3,911 (46.3%) urgently reperfused, including 3,049 (78%) with Primary PCI. Time to Telemedicine Diagnosis (TTD) was 3 min, tele-accuracy 98.9%, D2B 51 min, and in-hospital mortality 5.2%. Major reasons for non-treatment were insurance denials, lack of ICU beds and chest pain >12 hours.

Conclusions

LATIN demonstrates the feasibility of a population-based and telemedicine guided AMI strategy that can hugely expand access. Telemedicine has important public health implications as a global approach to AMI care in developing countries.

P3352Telemedicine transcends national boundaries in quest of creating a behemoth ami program

Background

The Latin America Telemedicine Infarct Network (LATIN) has exploited the remarkable competence of telemedicine for remote guidance. In doing so, LATIN created a mammoth population-based AMI network that employed experts located several hundred miles away to guide the reperfusion strategies for almost 800,000 screened patients. In this pioneering project, telemedicine was initially utilized to guide AMI management within national confines. We speculated whether LATIN telemedicine navigation could outstrip countrywide borders.

Purpose

To maximally harness the vast possibilities of telemedicine for improving AMI care.

Methods

During its pilot phase, LATIN began as a hub and spoke, AMI system in Colombia where 20 spokes (small community health centers and rural clinics) were configured with 3 hubs that could perform Primary PCI. These sites were linked through web-based connectivity. Expert cardiologists, located 50–250 miles away in Bogota, Colombia, used sophisticated telemedicine platforms for urgent EKG diagnosis and teleconsultation of the entire AMI process. Based upon the duration of chest pain and travel time to the hub, these experts guided patients through guideline-based strategies of thrombolysis, pharmaco invasive management or primary PCI. Efficiency of the telemedicine process was measured with the new metric of time to telemedicine diagnosis (TTD). Cloud computing, GPS navigation, and numerous business intelligent tools were gradually incorporated into LATIN telemedicine. As systems became more scalable, the program was expanded to Brazil, where LATIN flourished. Over the last 18 months, LATIN telemedicine capabilities have been pressed across national boundaries. Presently, all 82 LATIN centers in Mexico are guided by experts located in Bogota, Colombia and the 7 Argentina centers channeled through Santiago, Chile.

Results

784,947 patients were screened for AMI at 350 LATIN centers (Brazil 143, Colombia 118, Mexico 82, Argentina 7). Navigation pathways are depicted in the attached figure. TTD remains extremely low in all four countries, and comparable efficiency and tele-accuracy have been achieved. With expanded geographic reach, 8,448 (1.08%) patients were diagnosed with STEMI and 3,911 (46.3%) urgently reperfused, including 3,049 (78%) with Primary PCI. Time to TTD ranged between 2.8 to 5.8 minutes, with a mean of 3.5 min. Tele-accuracy was 98.5%, D2B 51 min, and in-hospital mortality 5.2%. Various other comparative metrics for the 4 countries are being gathered and will be available at the time of presentation.

Conclusions

LATIN demonstrates the robust ability of telemedicine to transcend national boundaries to guide AMI management. This strategy can be adopted in under-developed countries in Asia and Africa to provide an umbrella of AMI care for the millions of disadvantaged patients.

P1466Can physicians trust a machine learning algorithm to diagnose ST elevation myocardial infarction?

Background

For the past years, the medical field has been taking advantage of the endless possibilities that Artificial Intelligence (AI) provides. Using computer-aided devices that can perform and interpret electrocardiograms (EKG) accurately pushes current healthcare boundaries. We present the LUMENGT-AI, this model can handle large datasets, multiclass diagnoses, complex EKG morphology, and still detect ST Elevation MI (STEMI) accurately.

Purpose

To develop an innovative AI-based system for automated STEMI specific EKG analysis.

Methods

An observational, retrospective, case-control study. Sample: 8,511 EKG records, previously diagnosed as “normal”, “abnormal” (over 200 conditions) or “STEMI” (4,255 cases). Records excluded patient and medical information. The sample was derived from the private International Telemedical Systems (ITMS) database. LUMENGT-AI Algorithm was employed. Preprocessing: detection of QRS complexes by wavelet system, segmentation of each EKG into individual heartbeats (90,592 total beats) with fixed window of 0.4s to the left and 0.9s to the right of main QRS; Classification: A 1-D convolutional neural network was implemented, “STEMI” and “Not-STEMI” classes were considered for each heartbeat, individual probabilities were aggregated to generate the final label for each record. Training & Testing: 90% and 10% of the sample were used, respectively. Experiments: Intel PC i7 8750H processor at 2.21GHz, 16GB RAM, Windows 10 OS with NVidia GTX 1070 GPU, 8GB RAM.

Results

Ground Truth Score – Accuracy (94.1%), Sensitivity (87.8%), Specificity (98.1%) – see the comparison to published data in Table.

Conclusions

A statistical analysis allowed us to compare STEMI recognition efficiency between physicians and our model. The LUMENGT algorithm results secured its place as a reliable tool to diagnose STEMI faster and more accurately than physicians.

P6417Increasing the accuracy of a machine learning algorithm for STEMI diagnosis by incorporating demographic variables

Background

Our previous work demonstrated the diagnostic value of Artificial Intelligence (AI) -driven algorithms for ST-Elevation Myocardial Infarction (STEMI). In the present research, we explore the importance of demographic data inclusion, in order to achieve a more accurate diagnosis.

Purpose

To demonstrate that incorporation of demographic variables into the sample records will augment the accuracy of AI-based protocols for STEMI diagnosis.

Methods

An observational, retrospective, case-control study. Demographic data (age and gender) male/female ratio 1.3, ages 98–18 years was added to the sample records. Sample: 8,511 EKG records, previously diagnosed as normal, abnormal (over 200 conditions) or STEMI. Records excluded other patient and medical information. The sample was derived from the private International Telemedical Systems (ITMS) database. LUMENGT-AI Algorithm was employed. Preprocessing: detection of QRS complexes by wavelet system, segmentation of each EKG into individual heartbeats (90,592 total beats) with fixed window of 0.4s to the left and 0.9s to the right of main QRS; Classification: A 1-D convolutional neural network was implemented, “STEMI” and “Not-STEMI” classes were considered for each heartbeat, individual probabilities were aggregated to generate the final label for each record. Training & Testing: 90% and 10% of the sample was used, respectively. Experiments: Intel PC i7 8750H processor at 2.21GHz, 16GB RAM, Windows 10 OS with Nvidia GTX 1070GPU, 8GB RAM.

Results

The model yielded an accuracy of 97.1%, a sensitivity of 96.8%, and a specificity of 97.5%.

Conclusions

The ability of AI-guided algorithms to diagnose STEMI is increased by expanding the morphological variables with demographic data. This approach may be applied to improve the EKG diagnosis of other cardiovascular entities and improve clinical management.

3035Synergy of artificial intelligence and single lead EKG to detect and localize STEMI

Background

The cumbersome, standard 12-lead electrocardiogram (EKG) challenges an efficient detection of ST-Elevation Myocardial Infarction (STEMI) in pre-hospital (ambulances) and hospital (portable devices) settings. We believe that our machine-learning algorithm embedded into a single lead EKG will be successful in acute care settings.

Purpose

To incorporate Artificial Intelligence-guided, single lead EKG interpretation, to facilitate easy and accurate STEMI detection in urgent situations.

Methods

This is an observational, retrospective, case-control study. A subset sample was generated from the International Telemedical Systems (ITMS) database that contains cardiologist annotated EKG records. Subset: A total of 2,542 exclusively confirmed STEMI diagnosis EKG records from enrolled healthcare centers in Mexico, Colombia, and Brazil; including specific ischemic heart wall (anterior, inferior, and lateral). Following discharge of treated patients, confirmation of STEMI diagnosis was obtained as feedback from healthcare centers. Records were anonymized EKG that excluded all medical information. Sample: A Standard 12 lead, 10-seconds length, 500Hz sampling frequency EKG was fed to the LUMENGT-AI STEMI detecting algorithm. Preprocessing: Detection of QRS complexes by wavelet system, segmentation of each EKG record into individual heartbeats (total dataset 27,152 beats) with fixed window of 0.4s to the left and 0.9s to the right of main QRS; Classification: A 1-D convolutional neural network was implemented, three classes were considered for individual heartbeats: “Anterior”, “Inferior” and “Lateral”, each corresponding to the heart wall affected. These individual probabilities were aggregated to generate the final label for each of the 12 leads. Training & Testing: 90% and 10% of the dataset was used respectively. Experiments: Intel PC i7 8750H processor at 2.21GHz, 16GB RAM, Windows 10 OS with a NVidia GTX 1070 GPU, 8GB RAM.

Results

Accuracy – Lead V2 (91.7%); Sensitivity Anterior wall – Lead V2 (97.4%); Sensitivity for Lateral wall – Lead I (10.0%); and Sensitivity for Inferior wall – Lead V2 (93.6%).

Conclusions

AI algorithms merged with a Single lead approach detect and localize STEMI within any setting. The V2 lead yields superior results for mapping of ischemic areas of the heart among the anterior and inferior walls. In contrast, diagnosis remains suboptimal for identifying the lateral wall. The usage of synergistic technologies facilitates easy, fast and early STEMI triage and management.

P6418The continued proficiency of artificial intelligence for interpreting EKG: single lead EKG for STEMI culprit lesion localization

Background

Traditionally, the 12-lead electrocardiogram (EKG) has been used for diagnosing ST-Elevation Myocardial Infarction (STEMI) and for identifying the culprit lesion. We have previously demonstrated the impact of combining a Single Lead approach with Artificial Intelligence (AI) to replace tasks previously dominated by the 12 lead EKG. This research explores the role of the single lead EKG in identifying a culprit lesion.

Purpose

To test the use of a single lead approach to accurately locate the culprit vessel.

Methods

An observational, retrospective, case-control study. Sample: 2,542 exclusively STEMI diagnosis EKG records that included post discharge feedback from healthcare centers, confirming diagnosis and culprit vessel (Left Main Coronary Artery [LMCA]; Left Anterior Descending [LAD]; Right Coronary Artery [RCA]; Left Circumflex Artery [LCX]; Saphenous Vein Graft [SVG]). Records excluded other patient and medical information. The sample was derived from the private International Telemedical Systems (ITMS) database. LUMENGT-AI Algorithm was employed. Preprocessing:detection of QRS complexes using a wavelet system, segmentation of each EKG into individual heartbeats (27,125 total beats) with fixed window of 0.4s to the left and 0.9s to the right of main QRS; Classification: A 1-D convolutional neural network was implemented; “LCMA”, “LAD”, “CX”, “RCA”, “SVG”, and “No Information” classes were considered for each heartbeat per lead; individual probabilities were aggregated to generate the final label for each record. Training & Testing: 90% and 10% of the sample was used, respectively. Experiments: Intel PC i7 8750H processor at 2.21GHz, 16GB RAM, Windows 10 OS with NVidia GTX 1070 GPU, 8GB RAM.

Results

Accuracy: 77.4% Lead III; Sensitivity: LMCA (Lead aVL 25%); LAD (Lead aVF 87.8%); RCA (Leads V1, V3 92.9%); LCX (Lead aVL 21.7%).

Conclusions

Our results yielded the dominance of a specific single lead to each culprit vessels, aVF for LAD and V1 and V3 for RCA. We continue testing with different algorithms to search for reliable results for the LMCA and LCX. Nonetheless, conjugating a Single Lead EKG with an AI-augmented algorithm enables faster and easier management for patients that present with STEMI affecting the LAD and RCA territories.

Prevalence of latent tuberculosis infection in inmates recently incarcerated in a men's prison in Barcelona

OBJECTIVE

To study the prevalence of latent tuberculosis infection (LTBI) in prisoners.

METHODS

Among inmates admitted to a men's preventive detention prison in Barcelona during May-June 2009, without a previous positive tuberculin skin test (TST), a ≥ 10 mm TST was considered positive (5 mm in human immunodeficiency virus [HIV] infected persons). A multivariate logistic regression was performed, calculating odds ratios (ORs) and 95% confidence intervals (CIs).

RESULTS

A total of 221 individuals were included. The average age was 33.5 years (± 8.9 SD); 61.6% were foreigners and 45.2% were heroine and/or cocaine users; 40.3% had LTBI. The infection was associated with age >40 years (OR 3.10, 95%CI 1.51-6.35) and having been born in Eastern Europe (OR 4.3, 95%CI 1.4-12.8), North Africa (OR 2.2, 95%CI 1.01-4.7), sub-Saharan Africa (OR 7.6, 95%CI 1.3-44) or Latin America (OR 3.8, 95%CI 1.5-9.3). Subjects infected with HIV had a lower risk of a positive TST (OR 0.22, 95%CI 0.04-1.07). Only 31 (14%) did not present any of these risk factors, and 8 (25.8%) had LTBI.

CONCLUSIONS

The prevalence of LTBI was very high in this study, and systematic screening of all inmates at the time of entry into the prison is therefore recommended. Excluding those who do not fall in any of the high-risk prevalence groups from the evaluation complicates the screening and is not very effective.

[Prevalence of latent tuberculosis infection amongst immigrants entering prison]

OBJECTIVE

To study the prevalence of latent tuberculosis infection (LTBI) and the predictive factors amongst immigrants entering prison.

METHODS

prospective study conducted in May and June of 2009. The tuberculin skin test (TST) was performed, with induration of ≥ 10 mm being regarded as positive. Variables collected were: age, origin, number of incarcerations, length of time living in Spain, heroin and cocaine consumption, intravenous drug use and HIV infection. The rate of LTBI was calculated and the overall infection rate (ITL and history of TB). To study predictable factors, a bivariant and multivariant analysis were carried out using logistic regression.

RESULTS

152 male immigrants. Average age: 31.9 years ± 7.8; 37.2% of them with heroin or cocaine consumption and 7.5% IDU. 12 patients were previously TST positive and 6 patients had history of TB. TST was performed on 134 people, 63 with positive results and 71 with negative ones. ITL rate: 49.3. Overall infection rate: 53.3%. Bivariate associated with LTBI: more than one incarceration (67.4% vs. 36.4% in primary, p=0.001), age (76% ≥ 40 vs. 40.4% under this age and heroin and cocaine consumption (60% consumers vs. 39.3% non consumers; p=0.02. Multivariate analysis only confirmed the association with age (p=0.001; OR: 2.34, IC=1.39-3.94).

CONCLUSIONS

The LTBI rate amongst immigrants entering prison is very high. A complete study is recommended for all of them, with special attention being paid to the most vulnerable ones, such as older people.

Predictive factors in advanced head and neck cancer treated by radio-chemotherapy and hypoxia modification

6085

Background: Anemia and tumor hypoxia are known factors for resistance to radio-chemotherapy (RT-CT). In a previous report we have suggested that spinal cord stimulation (SCS) can modify tumor oxygenation and regional blood flow in head and neck cancer (HNC). The aim of the present prospective study was to test the predictive value of pO2 measurement in HNC treated by RT-CT and hypoxia modification using SCS. Methods: Twelve male patients with advanced HNC were analyzed. Stage IVb-IVa: 8–4; mean age 58 + 7.6 years (46–70). Scheduled therapy was hyperfractionated RT (120 cGy/fraction, two fractions/day, total dose 81.6 Gy) from a Co- 60 source, and tegafur 800 mg/day. SCS devices were placed before RT-CT under local anesthesia. During treatment, SCS was connected from 20–30 min before to 20–30 min after each radiotherapy session. Before treatment, they were assessed: Hemoglobin levels and tumor oxygenation pre-SCS and pos-SCS (measured by a polarographic probe system ‘pO2 Histograph‘), expressed as median-pO2, and the fraction of pO2 values less than 5 mmHg (HF5) and less than 2.5 mmHg (HF2.5). Correlations were assessed using Pearson and Spearman tests, and actuarial survival using Kaplan-Meier estimates and Log-rank test. Results: Hemoglobin levels were correlated with oxygenation pre-SCS and pos-SCS: median-pO2 (p=0.005 and p=0.011), HF5 (p=0.048 and p=0.005) respectively. Anemia was associated with more advanced stage (IVb vs IVa, p=0.022), higher HF5 pos-SCS (p=0.028) and lower disease-free survival (p=0.019). The HF2.5 pos-SCS was adversely correlated with the 2 years actuarial: disease-free survival (p=0.027), cause-specific survival (p=0.008) and overall survival (p=0.008). HF2.5 was also correlated with hematocrit (p=0.044). Conclusions: Low hemoglobin levels and anemia are associated with more hypoxic and more advanced tumors. Pre-treatment tumor hypoxia (assessed by the fraction of pO2 values less than 2.5 mmHg during-SCS) is a strong predictive factor for survival in advanced HNC. Patients with highly hypoxic tumors should be selected for more aggressive treatments. Partially supported by: Grant ‘FUNCIS: PI 31–98‘. Scientific supervision was carried out by GICOR.

No significant financial relationships to disclose.

Nipple care, sore nipples, and breastfeeding: a randomized trial

Sore and cracked nipples are common and may represent an obstacle to successful breastfeeding. In Italy, it is customary for health professionals to prescribe some type of ointment to prevent or treat sore and cracked nipples. The efficacy of these ointments is insufficiently documented. The incidence of sore and cracked nipples was compared between mothers given routine nipple care, including an ointment (control group), and mothers instructed to avoid the use of nipple creams and other products (intervention group). Breastfeeding duration was also compared between the two groups. Eligible mothers were randomly assigned, after informed consent, to one of the two groups. No difference was found between the control (n = 96) and the intervention group (n = 123) in the incidence of sore and cracked nipples and in breastfeeding duration. However, several factors were associated with sore nipples and with breastfeeding duration. The use of a pacifier and of a feeding bottle in the hospital were both associated with sore nipples at discharge (p = 0.02 and p = 0.03, respectively). Full breastfeeding up to 4 months postpartum was significantly associated with the following early practices: breastfeeding on demand, rooming-in at least 20 hours/day, non-use of formula and pacifier, no test-weighing at each breastfeed. The incidence of sore and cracked nipples and the duration of breastfeeding were not influenced by the use of a nipple ointment. Other interventions, such as providing the mother with guidance and support on positioning and latching, and modifications of hospital practices may be more effective in reducing nipple problems.

Kangaroo mother care for low birthweight infants: a randomized controlled trial in different settings

A randomized controlled trial was carried out for 1 y in three tertiary and teaching hospitals, in Addis Ababa (Ethiopia), Yogyakarta (Indonesia) and Merida (Mexico), to study the effectiveness, feasibility, acceptability and cost of kangaroo mother care (KMC) when compared to conventional methods of care (CMC). About 29% of 649 low birthweight infants (LBWI; 1000-1999 g) died before eligibility. Of the survivors, 38% were excluded for various reasons, 149 were randomly assigned to KMC (almost exclusive skin-to-skin care after stabilization), and 136 to CMC (warm room or incubator care). There were three deaths in each group and no difference in the incidence of severe disease. Hypothermia was significantly less common in KMC infants in Merida (13.5 vs 31.5 episodes/100 infants/d) and overall (10.8 vs 14.6). Exclusive breastfeeding at discharge was more common in KMC infants in Merida (80% vs 16%) and overall (88% vs 70%). KMC infants had a higher mean daily weight gain (21.3 g vs 17.7 g) and were discharged earlier (13.4 vs 16.3 d after enrolment). KMC was considered feasible and presented advantages over CMC in terms of maintenance of equipment. Mothers expressed a clear preference for KMC and health workers found it safe and convenient. KMC was cheaper than CMC in terms of salaries (US$ 11,788 vs US$ 29,888) and other running costs (US$ 7501 vs US$ 9876). This study confirms that hospital KMC for stabilized LBWI 1000-1999 g is at least as effective and safe as CMC, and shows that it is feasible in different settings, acceptable to mothers of different cultures, and less expensive. Where exclusive breastfeeding is uncommon among LBWI, KMC may bring about an increase in its prevalence and duration, with consequent benefits for health and growth. For hospitals in low-income countries KMC may represent an appropriate use of scarce resources.

Heterogenous expression of the EGF-receptor in human breast carcinoma

The epidermal growth factor receptor was analyzed in the membrane fraction from a series of human breast carcinomas using a radioligand assay. The results were compared to immunostaining with monoclonal antibodies on tissue sections. All tumors with detectable receptor levels as measured by ligand binding showed a positive staining with monoclonal antibodies. In such tumors more than 50 percent of the cells were receptor positive with varying intensity. Tumors classified as receptor negative with the radioligand assay could be divided into two major groups based on their immunostaining. The largest group showed no reactivity with monoclonal antibodies. In a second smaller group, a positivity was observed in small clusters of tumor cells while the majority of cells were negative. The staining intensity in such clusters was usually low. It seems possible that this heterogenous expression of the epidermal growth factor receptor is of significance with respect to clonal selection in tumor spreading.

Rigid gas-permeable contact lenses for myopia control: effects of discontinuation of lens wear

Conventionally fitted Paraperm O2plus contact lenses were worn for 44 months by 23 myopic children, who discontinued lens wear for 2.5 months and then resumed lens wear with Fluoroperm 30 lenses for a period of 8 months. Mean changes in myopia were: (1) an increase of 0.76 D during the initial 44 months of lens wear, (2) a further increase of 0.27 D during the 2.5 months when lenses were not worn, and (3) a decrease of 0.02 D during the 8-month period of Fluoroperm 30 lens wear. These results show that the effect of rigid gas-permeable lenses on myopia progression is diminished if lens wear is discontinued; however, the mean increase in myopia for these children was significantly less than would have been expected if glasses had been worn for the entire 54.5-month period. The results after discontinuation and then resumption of lens wear show that the effect of contact lenses in controlling the progression of myopia could not be accounted for entirely on the basis of corneal flattening as measured by the keratometer, therefore reinforcing the conclusion that corneal flattening due to rigid lens wear takes place primarily at the corneal apex, rather than in the zone of the cornea measured by the keratometer.

Silicone-acrylate contact lenses for myopia control: 3-year results

One hundred myopic children between the ages of 8 and 13 years were fitted with Paraperm O2plus silicone-acrylate contact lenses. After 3 years of lens wear, the mean increase in myopia for the 56 subjects remaining in the study was 0.48 D (+/- 0.70) D as compared with 1.53 (+/- 0.81) D for a group of spectacle-wearing myopes matched for initial age and initial refractive error. The mean change in corneal refracting power for the contact lens wearers was a decrease (corneal flattening) of 0.37 (+/- 0.32) D. Assuming that little or no corneal change would have occurred in the absence of the contact lenses, we may conclude that corneal flattening (as measured by the keratometer) accounts for less than half of the effect of contact lenses in controlling myopia progression. A possible explanation for this disparity is that although the keratometer provides a valid measurement of corneal refracting power for a "normal" cornea, it fails to provide a valid measurement for a cornea that has been flattened by wearing a contact lens.

Use of silicone-acrylate contact lenses for the control of myopia: results after two years of lens wear

Although a number of reports have been published concerning the use of polymethyl methacrylate (PMMA) contact lenses for the control of myopia, there have been no reports of the use of gas permeable contact lenses for this purpose. In the study reported here, 100 myopic children between the ages of 8 and 13 years were fitted with Paraperm O2 plus silicone-acrylate contact lenses to be worn for a period of 3 years. Lenses were fitted by the alignment method, most lenses having diameters from 8.5 to 9.0 mm. At the end of 2 years 60 subjects remained in the study, 53 of whom were wearing their lenses on a regular basis and the other 7 were irregular wearers. Mean increases in myopia during the 2-year period were found to be 0.28 D for the subjects who wore their lenses regularly and 0.93 D for the irregular wearers, compared to 0.80 D for a group of 31 age-matched single vision spectacle lens wearers. Mean corneal refracting power was found to decrease (the cornea flattened) 0.33 D for the regular wearers as compared to an increase of 0.14 D for the irregular wearers and a decrease of 0.13 D for the spectacle wearers. Mean changes in axial length were an increase of 0.1 mm for the regular wearers, an increase of 0.4 mm for the irregular wearers, and an increase of 0.6 mm for the spectacle wearers.(ABSTRACT TRUNCATED AT 250 WORDS)

Perioperative blood transfusion associated with infectious complications after colorectal cancer operations

We prospectively studied 168 consecutive patients with colorectal cancer to identify perioperative determinants of infectious complications. All patients received preoperative bowel preparation with laxatives, enemas, oral neomycin and erythromycin base, and intravenous cefazolin. Age, sex, admission hematocrit value, operative procedure, specimen length, duration of operation, blood loss, transfusions, tumor size, tumor differentiation, nodal status, and Dukes' stage were evaluated in relation to infectious complications using multivariate analysis. Infectious complications developed in 24 of the 168 patients in the study (14 percent) and these accounted for the four deaths. Blood transfusion (p = 0.0100) and admission hematocrit value (p = 0.0191) were significantly related to postoperative infectious complications. Low admission hematocrit values appeared to protect patients from infectious complications. Patients who had postoperative infectious complications received 2.14 +/- 2.75 units of blood compared with 0.82 +/- 1.37 units in patients without infectious complications (p = 0.0005). Although blood transfusion was associated with high operative blood loss, prolonged procedures, and large specimens (p less than 0.005), none of these factors was significantly associated with infectious complications (p greater than 0.10). Blood transfusion is immunosuppressive in other clinical situations and may be a more significant factor affecting postoperative immune function and susceptibility to infectious complications than previously recognized.

Septal driving of hippocampal theta rhythm: role of gamma-aminobutyrate-benzodiazepine receptor complex in mediating effects of anxiolytics

In free-moving male rats, when the hippocampal theta rhythm is artificially driven by stimulation in the septum at frequencies between 5 and 10 Hz, the function relating frequency to the threshold current required to drive the theta rhythm has a minimum at 7.7 Hz. This minimum is eliminated by anxiolytic drugs. Dose-response curves for this effect are reported for chlordiazepoxide, diazepam and meprobamate. The effect of meprobamate was reversed by two gamma-aminobutyrateA antagonists, picrotoxin and bicuculline, which have previously been shown to be without effects of their own. The gamma-aminobutyrateB agonist, baclofen, also without effect on its own, blocked the elimination of the 7.7-Hz minimum caused by the gamma-aminobutyrateA agonist, muscimol. The beta-carboline, ethyl-beta-carboline-3-carboxylate, had mixed agonist/antagonist properties, blocking the effects of chlordiazepoxide, diazepam and muscimol (though not sodium amylobarbitone) but itself acting like a benzodiazepine. Coupled with earlier data, these findings support a role for gamma-aminobutyrate receptors in mediating the effects of anxiolytic drugs.

Houston Myopia Control Study: a randomized clinical trial. Part I. Background and design of the study

The Houston Myopia Control Study is a 3-year randomized clinical trial in which each of 213 myopic children was placed in either a single vision (standard treatment) group, a +1.00 D add treatment group, or a +2.00 D add treatment group, on the basis of a randomized procedure. Subjects for the three treatment groups were matched on the basis of sex, age, and the initial amount of myopia. The study involves two groups of investigators: an evaluation team, whose task has been to evaluate candidates before entering the study and to reevaluate each subject on a yearly basis for the 3-year period, and a patient care team, whose task has been to prescribe glasses for each subject as well as to counsel subjects and their parents in the correct use of the glasses and to provide a follow-up examination every six months for the duration of the study. Once the glasses had been prescribed, members of the evaluation team were not permitted to know which subjects wore single vision lenses and which wore bifocals. In the interest of good patient care, members of the patient care team knew which subjects wore single vision lenses and which wore +1.00 D add or +2.00 D add bifocals. In this report, the authors discuss theories concerning the etiology of myopia, methods that have been used in an attempt to control the progression of myopia, and the design of the current study. Further reports will present the results of the study on the basis of the data collected by each of the two study teams.

The effects of compounds related to gamma-aminobutyrate and benzodiazepine receptors on behavioural responses to anxiogenic stimuli in the rat: choice behaviour in the T-maze

Two methods were used to test rats' responses to novelty in the T-maze: (1) a test of spontaneous alternation allowing separate measurement of place and body turn alternation; and (2) a test of entry into an arm of changed brightness ("response to stimulus change"). Chlordiazepoxide reduced spontaneous alternation by specifically weakening body turn alternation and eliminated the response to stimulus change. These findings are similar to those previously reported for the barbiturate sodium amylobarbitone. The same pattern of change in the two tests was seen after a low dose of the GABAA agonist muscimol (0.00125 mg/kg); when the dose of muscimol was raised (0.01 and 0.25 mg/kg), place alternation was also reduced. Picrotoxin but not bicuculline (both GABAA blockers) reversed the effects of muscimol and partially those of chlordiazepoxide on the response to stimulus change; in the spontaneous alternation test picrotoxin only marginally affected the response to 0.25 mg/kg muscimol and actually enhanced the effect of 0.000125 mg/kg. The GABAB agonist baclofen (1 mg/kg) acted in the test of response to stimulus change like chlordiazepoxide and muscimol; however, when baclofen was combined with muscimol, the two drugs tended to show mutual blocking. These results are generally consistent with the hypothesis that GABAergic mechanisms play a role in anxiolytic behavioural activity, but many details are difficult to explain.

The effects of compounds related to gamma-aminobutyrate and benzodiazepine receptors on behavioural responses to anxiogenic stimuli in the rat: punished barpressing

Rats were trained to press a bar for sucrose reward on a random-interval (RI) schedule and footshock punishment was then introduced for 3-min intrusion periods (signalled by a tone) on an independent RI schedule. Shock intensity was individually adjusted to produce stable intermediate levels of response suppression during the tone for each animal. Groups of animals were then allocated to a number of separate experiments in which they were systemically injected with anxiolytics (chlordiazepoxide HCl or sodium amylobarbitone), GABA antagonists (picrotoxin or bicuculline), the GABA (A) agonist muscimol, the GABA(B) agonist baclofen, an antagonist (RO 15-1788) at the benzodiazepine receptor and, an inverse agonist (FG 7142) at this receptor. The results showed that the alleviation of punishment-induced suppression of barpressing produced by chlordiazepoxide was blocked or partially blocked by RO 15-1788, picrotoxin and bicuculline but not by FG 7142; that picrotoxin (but not FG 7142) increased the suppression of responding by punishment; that neither muscimol nor baclofen affected responding on their own, but their combination weakly but reliably released punished responding from suppression; and that the anti-punishment effect of amylobarbitone was unaffected by either picrotoxin or bicuculline, though the barbiturate reversed the punishment-enhancing effect of picrotoxin. These results are discussed in the light of the hypothesis that anxiolytic behavioural effects are due to increased GABAergic inhibition.

Burkitt's lymphoma in Cuba. I. Clinical and morphological features and EBV association

The first 7 cases of histologically and cytochemically confirmed Burkitt's lymphoma have recently been studied in Cuba. Some of them presented with facial bone involvement and high anti-Epstein-Barr virus (EBV) antibody titers characteristic of endemic African cases. Others showed features of nonendemic cases. The distribution of patients in these two groups may be in relation to the behavior of the healthy Cuban population with respect to the EBV virus.