Reconfiguring traditional EKG interpretation with artificial intelligence – a reliable, time-saving alternative?

Background

Time and accuracy are key factors that may make or break an efficient triage and management in most medical premises, particularly so when expedited diagnosis saves lives - a not so uncommon scenario in the field of cardiology. By studying the different variables involved in cardiologist-EKG interactions that lead to the identification and management of different cardiovascular entities, we delved into the applications of Artificial Intelligence (AI) algorithms in order to improve upon the classic, but dated, EKG methodology. With this study, we pit our algorithm against cardiologists to perform a thorough analysis of the time invested to diagnose an EKG as Normal, as well as an assessment of the accuracy of said label.

Purpose

To present a faster and reliable AI-guided EKG interpretation methodology that outperforms cardiologists' capabilities in identifying Normal EKG records.

Methods

The International Telemedical System (ITMS) developed and tested an EKG assessing AI algorithm and incorporated it into the workflow of their Telemedicine Integrated Platform, a digital EKG reading program where cardiologists continuously report their findings remotely in real time. During the month of April 2019; 35 ITMS cardiologists reported a grand total of 61,441 EKG records, later subjecting them to the AI algorithm, implemented through the “One Click Report” process. Through this simple 2-step approach, the algorithm provides a suggestion of “Normal” or “Abnormal” to the cardiologist based on the patterns of the fiducial points included in said EKG reports. A comparison of the time of normal EKG diagnosis is made and the correlation between AI and cardiologists is assessed.

Results

On average, our AI algorithm discerned a normal EKG within 30.63s (95% CI 26.51s to 34.75s), in solid contrast with cardiologists' interpretations alone, which amounted to 83.54s (95% CI from 69.43s to 97.65s). This accounts for an overall saving of 52.91s (95% CI 42.45s to 63.83s) by implementing this innovative methodology in a cardiologist practice. In addition, this method correctly reported 23,213 Normal EKG records out of the total 25,013 AI output, reaching a 92.8% correlation between man and machine. The total average time saved in normal EKG readings with AI (23,213) would accrue an approximate of 20,470 minutes (ie, 42 8-hours work shifts worth of time dedicated to diagnosing a normal EKG).

Conclusions

The implementation of automated AI-driven technologies into daily EKG interpretation tasks poses an attractive time-saving alternative for faster and accurate results in a modern cardiology practice. By further expanding on the concept of an intelligent EKG characterization device, a more efficient and patient-centered clinical exercise will ensue.

Funding Acknowledgement

Type of funding source: None

Enhancing AI-guided STEMI detection algorithms by incorporating higher quality fiduciary EKG elements

Background

As EKG interpretation paradigms to a physician-free milieu, accumulating massive quantities of distilled pre-processed data becomes a must for machine learning techniques. In our pursuit of reducing ischemic times in STEMI management, we have improved our Artificial Intelligence (AI)-guided diagnostic tool by following a three-step approach: 1) Increase accuracy by adding larger clusters of data. 2) Increase the breadth of EKG classifications to provide more precise feedback and further refine the inputs which ultimately reflects in better and more accurate outputs. 3) Improving the algorithms' ability to discern between cardiovascular entities reflected in the EKG records.

Purpose

To bolster our algorithm's accuracy and reliability for electrocardiographic STEMI recognition.

Methods

Dataset: A total of 7,286 12-lead EKG records of 10-seconds length with a sampling frequency of 500 Hz obtained from Latin America Telemedicine Infarct Network from April 2014 to December 2019. This included the following balanced classes: angiographically confirmed STEMI, branch blocks, non-specific ST-T abnormalities, normal, and abnormal (200+ CPT codes, excluding the ones included in other classes). Labels of each record were manually checked by cardiologists to ensure precision (Ground truth). Pre-processing: First and last 250 samples were discarded to avoid a standardization pulse. Order 5 digital low pass filters with a 35 Hz cut-off was applied. For each record, the mean was subtracted to each individual lead. Classification: 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: 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 complete dataset of unconfirmed STEMI. Performance indicators (accuracy, sensitivity, and specificity) were calculated for each model and results were compared with our previous findings from past experiments.

Results

Complete STEMI data: Accuracy: 95.9% Sensitivity: 95.7% Specificity: 96.5%; Confirmed STEMI: Accuracy: 98.1% Sensitivity: 98.1% Specificity: 98.1%; Prior Data obtained in our previous experiments are shown below for comparison.

Conclusion(s)

After the addition of clustered pre-processed data, all performance indicators for STEMI detection increased considerably between both Confirmed STEMI datasets. On the other hand, the Complete STEMI dataset kept a strong and steady set of performance metrics when compared with past results. These findings not only validate the consistency and reliability of our algorithm but also connotes the importance of creating a pristine dataset for this and any other AI-derived medical tools.

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

Moving in sync – concordance betweena artificial intelligence and cardiologist on detecting normal electrocardiograms

Background

Merging modern technologies with classic diagnostic tests often results in a sense of insecurity within the medical community, particularly so with potentially life-saving studies such as the electrocardiogram (EKG). In order to provide a greater sense of trust between Artificial Intelligence (AI) and cardiologists, we provide an AI-driven algorithm capable of accurately and reliably characterize an EKG as normal within a highly complex, cardiologist-reviewed EKG database and report the degree of concordance between this machine vs physician scenario.

Purpose

To provide a dependable and accurate AI algorithm that conducts EKG interpretation in a cardiologist-tier manner.

Methods

The International Telemedical System (ITMS) developed and tested an EKG assessing AI algorithm and incorporated it into the workflow of their Telemedicine Integrated Platform, a digital EKG reading program where cardiologists continuously report their findings remotely in real-time. During the month of April 2,019; 35 ITMS cardiologists reported a grand total of 61,441 EKG records, later submitting them to the AI algorithm implemented through the “One Click Report” process. Through this simple 2-step approach, the algorithm provides a suggestion of “Normal” or “Abnormal” to the cardiologist based on the patterns of the fiducial points included in said EKG reports. Confirmation of these suggestions by the cardiologists ensued.

Results

Overall, cardiologists confirmed 23,213 out of 25,013 AI outputs for “Normal” EKGs, demonstrating a concordance of 92.8% for Normal diagnosis.

Conclusion

Through this methodology, we provide an AI technology that can be reliably applied and trusted in EKG digital platforms to identify and suitably label a normal EKG. Further testing will accrue into a multi label algorithm compatible with abnormal cardiovascular entities, potentially precluding the role of the cardiologist for triaging, particularly in the prehospital setting. We anticipate that this approach will become a promising methodology in modern cardiology practice.

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

Comparison of Effectiveness and Sensitivity Using Two In-Office Bleaching Protocols for a 6% Hydrogen Peroxide Gel in a Randomized Clinical Trial

Objective:

The aim of this blinded and randomized clinical trial was to compare two application protocols (one 36-minute application vs three 12-minute applications). We then assessed the effectiveness of the bleaching and any increase in sensitivity that was induced by bleaching via a split-mouth design.

Methods and Materials:

Thirty patients were treated. One group had a half arch of teeth treated with a traditional application protocol (group A: 3 × 12 minutes for two sessions). The other received an abbreviated protocol (group B: 1 × 36 minutes over two sessions). Two sessions were appointed with a two-day interval between them. The tooth color was registered at each session, as well as one week and one month after completing the treatment via a spectrophotometer. This measured L*, a*, and b*. This was also evaluated subjectively using the VITA classical A1-D4 guide and VITA Bleachedguide 3D-MASTER. Tooth sensitivity was registered according to the visual analogue scale (VAS) scale. Tooth color variation and sensitivity were compared between groups.

Results:

Both treatments changed tooth color vs baseline. The ΔE* = 5.71 ± 2.62 in group A, and ΔE* = 4.93 ± 2.09 in group B one month after completing the bleaching (p=0.20). No statistical differences were seen via subjective evaluations. There were no differences in tooth sensitivity between the groups. The absolute risk of sensitivity reported for both groups was 6.25% (p=0.298). The intensity by VAS was mild (p=1.00).

Conclusions:

We used hydrogen peroxide (6%) that was light activated with a hybrid LED/laser and two different protocols (one 36-minute application vs three 12-minute applications each for two sessions). These approaches were equally effective. There were no differences in absolute risk of sensitivity; both groups reported mild sensitivity.

Outbreak of urinary tract infections by Salmonella spp. after cystoscopic manipulation

INTRODUCTION

Cystoscopes are used for diagnostic and therapeutic purposes and can be vehicles for transmitting healthcare-associated infections. Performing urine cultures before manipulation or administering prophylaxis is determined by the presence or not of risk factors for urinary tract infection.

METHODS

Between October and November 2014, we identified an unusual aggregation of Salmonella spp. isolates in urine cultures at the University Hospital Santa Lucía of Cartagena (Murcia). An epidemiological investigation was conducted to assess the possible relationship between the cases.

RESULTS

Four patients had a urinary tract infection by Salmonella spp. within a short period, which suggests the presence of an outbreak. All of the patients had undergone cystoscopy. The index case had a urinary colonisation by Salmonella spp. prior to the procedure, and none of the reported cases had received prophylaxis. The environmental control cultures and the involved material cultures resulted negative. Intensification of the cystoscope cleaning and disinfection protocol achieved eradication of the outbreak.

CONCLUSION

This is the first reported outbreak of Salmonella spp. related to the use of cystoscopes. The indication for a urine culture should be carefully assessed before conducting invasive urological procedures, as should the need for antibiotic prophylaxis, for patients with risk factors for severe infection. Strict control in the cleaning and disinfection of endoscopy material can prevent the transmission of infections related to this type of procedure.

Molecular characterization of infectious bursal disease virus (IBDV) isolated in Argentina indicates a regional lineage

In Argentina, classical vaccines are used to control infectious bursal disease virus (IBDV); however, outbreaks of IBDV are frequently observed. This could be due to failures in the vaccination programs or to the emergence of new strains, which would be able to break through the protection given by vaccines. Hence, genetic characterization of the viruses responsible for the outbreaks that occurred in recent years is crucial for the evaluation of the control programs and the understanding of the epidemiology and evolution of IBDV. In this study, we characterized 51 field samples collected in Argentina (previously identified as IBDV positive) through the analysis of previously identified apomorphic sequences. Phylogenetic analysis of regVP2 showed that 42 samples formed a unique cluster (Argentinean lineage), seven samples were typical classical strains (one of them was a vaccine strain), and two belonged to the very virulent lineage (vvIBDV). Interestingly, when the analysis was performed on the regVP1 sequences, the field samples segregated similarly to regVP2; thus, we observed no evidence of a reassortment event in the Argentinean samples. Amino acid sequence analysis of regVP2 showed a particular pattern of residues in the Argentinean lineage, particularly the presence of T272, P289 and F296, which had not been reported before as signature sequences for any IBDV phenotype. Notably, the residue S254, characteristic of the antigenic variant, was not present in any of the Argentinean samples.

[Functional dyspepsia and the satiety test: its usefulness in clinical practice]

INTRODUCTION

According to the Rome III Criteria, functional dyspepsia (FD) is classified as postprandial distress syndrome (PDS) and epigastric pain syndrome (EPS). On the other hand, the satiety test (ST) has been used to evaluate gastric accommodation and emptying, distinguishing healthy individuals from those with dyspepsia.

AIMS

To determine whether the ST can distinguish dyspeptic individuals from healthy ones and to evaluate its usefulness in differentiating the two FD subtypes.

METHODS

Adults with FD were consecutively enrolled in a cross-sectional study within the time frame of August 2011 and October 2012. Healthy subjects participated as controls. The ST consisted of the intake of a nutritional supplement (Fortisip®, Nutricia Bagó®) at a constant speed; satiety was graded at 5-minute intervals (1 to 5 points). Intake was suspended when the maximum score was reported. The total ingested volume and caloric intake was recorded and the Mann-Whitney U test was used in the statistical analysis.

RESULTS

The study included 39 dyspeptic patients and 20 control individuals. The patients were predominantly women (84.6 vs. 25%; p < 0.0001) and they were similar in age (39.59 ± 13.53 vs. 34.70 ± 9.85 years) and BMI (24.32 ± 3.52 vs. 25.82 ± 3.34 kg/m2) with respect to the controls. The FD subtype percentages were PDS: 61%, EPS: 31%, and Mixed syndrome: 8%. There was a lower ingested volume and caloric intake on the part of the dyspeptic patients (185 vs. 300 ml and 277 vs. 520 Kcal, respectively. Both: P<.001). No differences in the ST were observed between the two pure dyspepsia subtypes.

CONCLUSIONS

There was a difference in the ST between healthy individuals and those with dyspepsia, but the ingested volume and caloric intake in the two FD subtypes were similar.

Computed Tomographic Screening of Pulmonary Arterial Hypertension in Candidates for Lung Transplantation

INTRODUCTION

Computed tomography (CT) is essential to evaluate candidates for lung transplantation (LT) We sought to correlate identification of pulmonary arterial hypertension (PAH) via CT to echocardiographic (Eco) and/or hemodynamic observations (Hd) as well as the pathological findings (Path) at posttransplant pneumonectomy.

MATERIALS AND METHODS

The study included 24 consecutive months follow-up of 71 patients who were referred for LT evaluation with 54 undergoing LT. Study parameters included CT [diameter of the principal pulmonary artery (PPA) >or= 29 mm), PPA/diameter of the ascending aorta (AA) ratio > 1; diameter of the lobar artery (LA)/diameter of the lobar bronchiole (LB) ratio > 1 in three or more lobes; thickening of the anterosuperior pericardial recess (>15 mm); mosaic lung density pattern]; PAPs via Eco and/or Hd, and findings Path of PAH. Statistical analysis included chi-square and Pearson correlation coefficient.

RESULTS

Thirty-five patients had PAH. Significant relationships (P < .05) were observed between PAH (Eco and/or Hd) and PPA, PPA/AA, and LA/LB. The sensitivity of the finding PPA >or= 29 mm was 65.9%, and 85.7% when combined with LA/LB > 1. In the Path study, 14/54 patients revealed alterations compatible with PAH. Significant relationships (P < .05) were observed between PAH via Path and PPA (CT) and between PAH via Path and PAH (Eco and/or Hd).

CONCLUSIONS

CT is useful for the detection of PAH. Findings of PAH via CT were indicative of the need for further confirmatory studies. In contrast, the absence of findings with CT does not exclude the possible presence of PAH.

[Mesangial glomerulonephritis and intermediate uveitis]

Uveitis in children are less frequent than in adults. Their prognosis is variable because it may be found as an isolated and idiophatic condition or in association with definite clinical entities. The associated noninfectious diseases with predominantly renal involvement are tubulointerstitial nephritis and uveitis syndrome (TINU syndrome), mesangial glomerulonephritis isolated or in association with Behçet's disease. A case of 14-years-old girl with intermediate uveitis (pars planitis) and mesangial glomerulonephritis is presented. The ocular symptoms was eye redness and ocular pain and she has snow-banks in pars plana. She showed microscopic hematuria and intermitent proteinuria that increased during the ocular clinical exacerbation. Renal biopsy revealed both mild mesangial matrix increase and mesangial celullarity with normal tubulointerstitial structure and mesangial deposition of IgA and IgG immunoglobulins. This case is de first pediatric patient report in the literature with intermediate uveitis and mesangial glomerulonephritis with immune deposition. Mesangial glomerulonephritis were observed in patients whit Behçet disease, known etiological cause of uveitis in adults and children. These findings may suggest that uveitis and glomerulonephritis have common immunological pathogenesis including circulatory immune complexes. In uveitis patients, screening for associated extra-ocular and renal manifestations is mandatory and should have careful long-term follow-up with regular systemic evaluation.

Radioimmunometric assay of B-type natriuretic peptide (BNP) in heart transplantation: correlation between BNP determinations and biopsy grading of rejection

The aim of this study was to determine whether elevated brain natriuretic peptide (BNP) levels after heart transplantation are correlated with the severity of rejection by using endomyocardial biopsy (EMB) and echocardiographic parameters indicative of ventricular function of the transplanted heart. This was an observational study of 80 orthotopic heart transplant recipients (11 women and 69 men; mean age 53+/-11 years). BNP determinations were performed within 48 h of endomyocardial biopsy. The echocardiographic study and BNP determination were also performed in a group of healthy volunteers. We found significantly higher BNP mean levels in heart transplant patients than in healthy volunteers (volunteers, 16.7+/-16.2 pg.ml-1; transplant, 213.4+/-268.6 pg.ml-1; P<0.001). Transplant recipients with rejection grades 2, 3 and 4 on EMB had significantly higher BNP levels than those with rejection grades 0 and 1 (higher rejection grade, 162.5+/-168.4 pg.ml-1; lower rejection grade, 292+/-361.8 pg.ml-1; P<0.01). BNP values of patients with good left ventricular function (LVF) were significantly lower than in patients with mildly and moderately impaired LVF and patients with severely impaired LVF (good function, 199.76+/-233.6 pg.ml-1; mildly/moderately impaired LVF, 937+/-644.5 pg.ml-1; severely impaired LVF, 1038+/-491.2 pg.ml-1; P<0.001). It is concluded that BNP plasma levels are elevated in heart transplant patients compared to the normal population. The distribution of BNP levels in heart transplanted patients show a wide range. BNP elevation is greater in patients with higher rejection grades on EMB and greater impairment of left ventricular function.

Severe lung involvement in systemic scleromyxoedema: a highly unusual finding

Scleromyxoedema is a rare systemic disorder characterized by a lichenoid papular rash. Although scleromyxoedema can involve any organ, very few cases of pulmonary involvement have been reported. Moreover, there are no reports in the literature on treatment of this condition, especially with lung transplantation. The authors report a case of scleromyxoedema in a young man with neurological, skin and respiratory involvement, the latter being mainly characterized by pulmonary emphysema. Due to the serious respiratory compromise, and to the stability of the systemic lesions, a bilateral lung transplantation was performed with successful results at 4.5 yrs. In conclusion, lung transplantation may be required in patients with severe respiratory failure caused by scleromyxoedema.

Nerve sheath myxoma (neurothekeoma) in the tongue of a newborn

Nerve sheath myxoma is a benign peripheral nerve sheath tumor that rarely occurs in the oral cavity; experience with these lesions is therefore limited. The lesion described in this report appeared clinically as a gradually enlarging, painless growth arising on the tongue of a newborn girl. Microscopically, the lesion was characterized by nodules of spindle-shaped cells with abundant myxoid stroma. Immunohistochemical studies were consistent with a nerve sheath neoplasm.

[Incidence of radicular cysts in a series of 125 chronic periapical lesions. Histopathologic study]

The incidence of radicular cysts in 125 chronic periapical lesions was studied in 36 cases of periapical surgery (28.8%), and to 89 tooth extractions (71.2%). Histopathology revealed 18 radicular cysts (14.4%) and 107 lesions corresponding to chronic apical periodontitis (C.A.P.) or granulomas (85.6%). Cholesterol clefts, a fibrous capsule and presence of a cavity were more common in the cysts, significant differences being observed with respect to chronic apical periodontitis.

[Factors related to diagnostic reliability of bronchial biopsy in primary bronchogenic carcinoma]

To analyze the diagnostic reliability of bronchial biopsy (BB) in bronchogenic carcinoma and the impact of several factors, among them patient symptoms and condition, tumor characteristics and the endoscopist's and pathologist's experience. One hundred eighty-four BB from 151 patients diagnosed of bronchogenic carcinoma in our hospital in the years 1993 and 1994 were reviewed. We first performed single variable analysis, and later logistical regression analysis taking BB positivity or negativity as the dependent variable. The independent variables were age, tumor stage, histological type, lesion necrosis, number of biopsy fragments collected, size of the largest fragment, the endoscopist who performed the BB and the pathologist who studied the specimen. The diagnosis sensitivity of BB was 69.6%. The variables that significantly influenced diagnostic accuracy, in both the single variable and multiple factorial analyses, were clinical status (p < 0.0004) and necrosis (p < 0.0057) with odds ratios of 4.6088 and 0.3766, respectively. The patient's clinical status and the presence or absence of necrosis are the factors that most influence diagnostic accuracy in BB for bronchogenic carcinoma. The likelihood of obtaining a diagnosis is 4.6 greater when clinical status is severe, and 2.7 times greater in the absence of necrosis. The experience of the bronchoscopist, after a learning period, and of the examining pathologist, do not appear to have a decisive effect on diagnostic reliability in this technique.

Specific oncological contribution of cathepsin D and pS2 in human breast cancer: their relationship with TNM status, estradiol receptors, epidermal growth factor receptor and neu amplification

The present study attempts to clarify the specific contribution of cathepsin D (CD) and pS2 to the progression of breast cancer (BC) by examining the relationship between these two factors and TNM status, tumour grade, estradiol receptors (ER) and the prognosis factors epidermal growth factor receptor (EGFR) and neu amplification in a group of 270 BC patients. CD and pS2 were determined by an immunoradiometric procedure in tumour cytosols obtained for ER. Neu amplifications were evaluated by dot-blot, in tumour DNA. EGFR was determined in membrane tumour preparations obtained from ER cytosols by a two-point radiometric saturation assay. CD is basically related to bad prognosis factors and has a direct correlation with tumour size (P = 0.025) and EGFR content (P = 0.007) and is associated with the presence of metastases (P = 0.000). pS2 is mostly related to good prognosis factors and showed an inverse correlation with the Scarff-Bloom Index (P = 0.011) and a direct correlation with ER content (P = 0.014). Finally, pS2 and CD also showed a strong mutual association (P = 0.009) and the fact that both correlated with ER content confirms in tumours the experimental finding that they are estrogen-induced proteins.

[Bronchiolitis obliterans organizing pneumonia. Clinical and evolutive features]

A series is here reported of eleven patients with the histological diagnosis of bronchiolitis obliterans with organized pneumonia (BOOP) and the clinical course after one year of corticosteroid therapy. Four patients had idiopathic BOOP, one patient BOOP secondary to bone marrow transplantation and the other six patients had BOOP secondary to inhalation of toxic substances (Ardystil syndrome). The most common radiological feature at the onset of the disease was the presence of bilateral patchy infiltrates; the most common functional finding was a decreased diffusion capacity for carbon monoxide. After one year of follow-up, seven out of the ten patients still alive had a normal radiology and all showed a significant improvement in ventilatory and gasometric parameters.

Epidemic outbreak of interstitial lung disease in aerographics textile workers--the "Ardystil syndrome": a first year follow up

BACKGROUND

The longer term respiratory effects of massive inhalational exposure of textile printing sprayers to Acramin (the "Ardystil syndrome") are not well established.

METHODS

A 12 month follow up of 27 heavily exposed textile sprayers was performed.

RESULTS

Twenty one patients experienced cough, 18 dyspnoea, and 17 nose bleeding at initial exposure, with histological evidence of organising pneumonia in 13 cases, radiological abnormalities detected by computed tomographic scanning in 20 cases, and diminution of diffusion capacity to below 80% of predicted in seven cases. At one year after exposure symptoms persisted in 15 cases, radiological alterations in six, and diffusion capacity was reduced in nine.

CONCLUSIONS

Whilst most of our patients showed improvement at one year, evidence of persistent lung involvement was present in an appreciable minority of exposed cases.

Squamous cell carcinoma of the oral cavity: a follow up study of 85 cases and analysis of prognostic variables

We have studied clinical and morphological variables of 85 patients with squamous cell carcinoma of the oral cavity. After a follow up which varied between 2 months and 6 years, we carried out an analysis of the survival rate, and obtained significant differences (p < 0.05) for the Breslow and Mantel-Cox tests in relation to the clinical stage, size and presence of lymphadenopathy. Furthermore we have carried out a predictive-prognostic statistical analysis through a multiple regression study, from which we have concluded that the size of the lesion and the number of peritumoral eosinophils were the variables with prognostic significance with respect to the survival rate of the patients. Furthermore, once the variables in relation to the incidence of relapse were analyzed, we found that the size, the presence of lymphadenopathy, the number of peritumoral eosinophils and the number of mitoses were those variables considered to be of the greatest prognostic value.

[Cardiac rhabdomyosarcoma. Its echographic diagnosis]

We report a case of cardiac rhabdomyosarcoma whose initial clinical features were fever and palpitations due to documented ventricular tachycardia. Sequential two-dimensional echocardiographic studies pointed out the presumed diagnosis, showing intracavitary masses at multiple sites appearing within a short period of time. The postmortem examination confirmed the diagnosis of rhabdomyosarcoma.

[Epidermoid carcinoma of the oral cavity: a histopathological study of 85 cases]

In 85 cases of intraoral squamous cell carcinoma we have carried out a descriptive and statistical analysis of 8 parameters or morphological aspects: structural growth pattern, degree of keratinization, differentiation or similarity, nuclear pleomorphism, frequency of mitoses, inflammatory response, number of eosinophils and vascular invasion. We have found that in 65% of the cases there was a cord type growth pattern, and 51% evidenced a maximum degree of keratinization. The neoplastic cells were characterized by a moderate differentiation and pleomorphism, 69% and 73% respectively, there being 1-3 mitoses/8 fields in 54% of the cases. We did not find very marked differences between degrees 1, 2, and 3 of inflammatory response (39%, 25%, 33% respectively). We observed vascular invasion in only 8.5% of the cases. The average value of intratumoral and peritumoral eosinophils was 18 and 37.

[Validity of a score of clinical parameters as a screening method in acute bacterial diarrhea in childhood]

We report a prospective study of the cases of acute diarrhea admitted to our pediatric service during one year (n = 172) to elaborate a screening method to identify patients with bacterial diarrhea. A bacterial causative organism was identified in 31.4%. The most common species was Salmonella, followed by Campylobacter. We evaluated the clinical presentation parameters, looking for differences between the group were a bacterial organism was isolated and in the group where it was not. We designated a score valid as a first level screening for bacterial diarrhea. With a score greater than or equal to 7 the sensitivity was 81.5% and the specificity 60.2%. The latter increased to 95% when occult blood in feces was associated with the greater than or equal to 7 score (second screening level). We propose this clinical score as a criterion for the indication of fecal cultures in children with acute gastroenteritis.

[Histologic study of the effect of the CO2 laser and the cicatrization of the tongue in the rat]

In the present paper we analyze the effect of CO2 laser in the mouse tongue, in relation with the intensity and exposition time. We study the histology of the lesions and their healing. We show that with 15 w of intensity the effect is bigger tan supposed affecting nearly to the hole tongue. This experience make us to conclude that there is the possibility of harming the perichondrium and cartilage when using the laser in the laryngeal mucosa.

[Familial childhood cortical hyperostosis]

We present three cases of Caffey's disease, which have been observed in a family and a previous one former generation of the same family. A review of the literature upon family cases is carried out (35 families with 143 patients) prevailing the hypothesis of the type of autosomal dominant trait with incomplete penetrance and variable expressivity. HLA system is studied in such a family without common haplotypes being found and therefore the trait does not seem to be linked to genes of this system.

[Visceral and cutaneous leishmaniasis in Huesca. Epidemiologic aspects]

The authors present a study of twelve children affected with leishmaniasis, observed in the province of Huesca (Spain). Three of the patients presented visceral form and the other nine cutaneous form. The clinical, evolutive and therapeutic aspects of this disease are analyzed, with special reference to the epidemiological aspects, since the incidence has been found to be high in an area in which the disease is not considered endemic.

[Mucolipidosis III. Apropos of a case]

Authors present a case of mucolipidosis III diagnosed in a boy suffering from articular retractions, platyspondylia and a normal mucolysacchariduria. Biochemical characteristics of the lisosomial enzyme, reduced in fibroblasts in culture and increased in biological fluids allow such diagnose. Primary enzyme lack being not yet explained, authors comment upon the hypothesis trying to explain biochemical findings.