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.

P1742Is time to telemedicine diagnosis (TTD) analogous to door to balloon time?

Background

Telemedicine is a powerful, cost-efficient, and scalable tool for population-based AMI management. Traditional metrics of D2N, D2B do not gauge telemedicine effectiveness. We explored the utility of TTD in 784,947-screened patients within the Latin America Telemedicine Infarct Network (LATIN).

Purpose

To evaluate the competence of TTD as an efficiency indicator in telemedicine.

Methods

LATIN employed a spoke-hub strategy to expand access in Brazil, Colombia, Mexico, and Argentina. Small clinics (spokes) in remote areas were strategically connected to PCI-capable facilities (hubs). Experts at 4 remote locations provided urgent EKG diagnosis via tele-consultation, additionally, they triggered ambulance dispatch and implementation of guidelines-based protocols. Investing in updated telemedicine technology provided a system-wide TTD reduction.

Results

714,450 patients were screened for AMI at 350 LATIN centers (Brazil 143, Colombia 118, Mexico 82, Argentina 7). Within our territories 8,448 (1.08%) patients were diagnosed as STEMI; 3,911 (46.3%) were urgently reperfused, of those 3,049 (78%) underwent Primary PCI. TTD was 3 min, demonstrating 98.9% tele-accuracy. D2B was 51 min; in-hospital mortality 5.2%. We encountered a linear correlation between D2B and TTD. The latter was, also, inversely related to the number of screened patients - both associations are favorable for LATIN.

Conclusions

TTD is an important indicator of telemedicine efficiency. LATIN will continue to explore this value's strength and other important associations.

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.

P1751LATIN - A template for effective AMI management in developing countries

Background

In resource-constrained nations, population-based AMI coverage is daunting. Telemedicine can transform the situation through an efficient, cost-effective and scalable program called the Latin America Telemedicine Infarct Network (LATIN). We present our innovative hub-spoke strategy, that has served >780,000 patients.

Purpose

To use telemedicine protocols to demonstrate appropriate access to quality AMI care, encompassing remote areas.

Methods

LATIN required technology and process metrics optimization as well as a scrupulous site selection, during a 12-month pilot. Spokes represent our strategy's nucleus; they consist of small, rural clinics and resource-limited facilities that are connected to PCI-capable hubs. Spokes require constant (3-T) training: Triage, Telemedicine, and Transportation. The latter two categories are the most challenging because they demand constant upgrading.

Results

784,395 patients were screened at 350 LATIN centers (Brazil 143, Colombia 118, Mexico 82, Argentina 7). A total of 8,440 (1.08%) patients were diagnosed with AMI; 3,924 (46.5%) were urgently reperfused including 3,048 (77.7%) who underwent PCI. Globally, Time to Telemedicine Diagnosis (TTD) was 3 min exhibiting 98.9% tele-accuracy, D2B was 51 min, additionally, in-hospital mortality was 5.2%. Major reasons for non-treatment of patients were insurance, lack of ICU beds and delayed presentation.

Conclusions

LATIN is a valuable healthcare system prototype for developing countries. Our hub-spoke strategy focuses on providing adequate AMI management for populations. However, aspects such as ambulance availability, insurance denial and lack of ICU beds must be targeted to improve performance.

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.

P6142LATIN telemedicine - expanded umbrella of cost-effective ami coverage for 100 million people

Background

AMI is a unique entity where the immediate diagnosis can be made by a single test, the EKG. Despite this matchless attribute of easy diagnosis, developing (and some developed) countries lack resources and efficient pathways for urgent and reliable diagnosis of AMI. With Latin Telemedicine Infarct Network (LATIN), we have previously presented Telemedicine as a pragmatic solution for urgent and accurate diagnosis of AMI. In this work, we reveal pathways of scalable population-based AMI management models.

Purpose

To utilize telemedicine as a foundation pillar for creating cost-effective and global models of AMI management.

Methods

LATIN pilot tested the hypothesis of remote guidance of AMI management and expanded access by creating a hub and spoke, STEMI systems of care that exploited regional resources. A highly efficient, web-based, cloud-computing prototype was developed and scrupulously monitored with a new metric of time to telemedicine diagnosis (TTD). STEMI systems of care were created to efficiently triage the diagnosed patients for being treated with thrombolysis, pharmaco-invasive management or Primary PCI. This stratagem had enormous provincial variability and was constrained mainly by ambulance structure. Telemedicine and IT costs were forced lower and enabled a cost-effective process to hugely provide access to 100 million patients located in poorer regions of Colombia, Brazil, Mexico, and Argentina. Education and training have formed the mantra for LATIN and stakeholder development, and ambulance systems development has remained immutable goals.

Results

Almost 800,000 patients were successfully screening through LATIN with a cost for accurate STEMI diagnosis of < $3, a tele accuracy that exceeded 95% and with TTD <4 minutes. A total of 8,440 (1.1%) of patients were diagnosed with AMI in this manner and 3,924 (46.5%) urgently reperfused, mainly with Primary PCI (3,048, 77.8%). D2B times have been lowered now to 51 minutes but this is fortuitous, as several PCI-capable facilities are small, and direct transfer to the catheterization laboratory is easy. Door in and Door out times and transport times remain high as a large number of patients are denied by insurance and other payers for treatment. Overall, mortality is 5.2%.

Conclusions

Global financial and philanthropic institutions should contemplate models analogous to LATIN for saving the lives of millions of poor patients in developing countries from AMI.

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.

P2426Validating the diagnostic value of a machine learning algorithm for STEMI detection

Background

We have previously reported the use of Artificial Intelligence (AI) guided EKG analysis for detection of ST-Elevation Myocardial Infarction (STEMI). To demonstrate the diagnostic value of our algorithm, we compared AI predictions with reports that were confirmed as STEMI.

Purpose

To demonstrate the absolute proficiency of AI for detecting STEMI in a standard12-lead EKG.

Methods

An observational, retrospective, case-control study. Sample: 5,087 EKG records, including 2,543 confirmed STEMI cases obtained via feedback from health centers following appropriate patient management (thrombolysis, primary Percutaneous Coronary Intervention (PCI), pharmacoinvasive therapy or coronary artery bypass surgery). Records excluded patient and medical information. The sample was derived from the 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 (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, 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

The model yielded an accuracy of 97.2%, a sensitivity of 95.8%, and a specificity of 98.5%.

Conclusion(s)

Our AI-based algorithm can reliably diagnose STEMI and will preclude the role of a cardiologist for screening and diagnosis, especially in the pre-hospital setting.

P5237Continued financial benefits of LATIN telemedicine program from avoiding unnecessary transfer of patients

Background

Latin America Telemedicine Infarct Network (LATIN) employed telemedicine to construct a population-based AMI program in Brazil, Colombia, Mexico, and Argentina. It increased access, accuracy and guidelines-based care and addressed fiscal issues. Previously, we demonstrated a cost and benefit analysis (CBA) of LATIN based upon avoiding unnecessary transfers and hospitalization. We have performed a scrupulous follow up of this initial observation with a long-term follow up from all expanded LATIN sites.

Purpose

To demonstrate that telemedicine avoids unnecessary transfer of patients.

Methods

784,947 patients at LATIN spokes (small clinics in remote areas) were screened and CBA measured at hubs, spokes and telemedicine centers. Technology, transfer, inpatient, and procedure-related costs were included. A sensitivity analysis was performed for worst and best scenarios of costs, revenues, and savings. A comparison with Avera e-Emergency (Sioux Falls, SD) Telemedicine program, involving 85 rural hospitals in 7 states, is provided (13% transfer avoidance).

Results

Of 784,947 screened patients, 8,448 had STEMI (1.08%); 3,911 (46.3%) were urgently reperfused, 3,049 (78%) with PPCI. Time to Telemedicine Diagnosis was 3 min. With efficient triage, costs for non-AMI patients was controlled. LATIN expenses, including for IT and experts, were $272, and for transfer and indirect care, $1,068. Net savings/patient were $796. Savings, till date, range between $187.4 million and $62.4 million (Best scenario −30% transfer avoidance; Worse scenario −10% transfer avoidance).

Conclusions

Longitudinal analysis firms the trend of enormous cost savings with LATIN. Telemedicine avoids unnecessary transfers and hospitalization and it is a cost-effective strategy for population-based AMI programs.

P575Merging technologies to provide Mexico an innovative nationwide AMI management network

Background

“Cόdigo Infarto”, the vast patient-centric, app-based, educational crusade of the Mexican Society of Interventional Cardiology (SOCIME) has been combined with the Latin America Telemedicine Infarct Network (LATIN) to create a comprehensive, national AMI program for Mexico.

Purpose

To demonstrate the benefits of amalgamating educational initiatives of national cardiology societies with a global telemedicine program for improving AMI management.

Methods

“Cόdigo Infarto” App connects patients to a network of several hundred cardiologists and 250 Primary PCI-capable labs. LATIN provides the partnership with its robust telemedicine platform, a hub-spoke strategy that supports patient's access to appropriate medical management. Remotely located experts, in Colombia, provide urgent EKG diagnosis via tele-consultation to the entire LATIN Mexico network. They activate ambulance dispatch and implement guideline-based protocols.

Results

Numerous “Cόdigo Infarto” sites have incorporated LATIN to provide a simple and accelerated management of AMI patients. Currently, the partnership (7 hubs, 78 spokes) has screened 19,886 patients. A total of 359 STEMI cases (1.8%) have been diagnosed; 118 patients (33%) were urgently reperfused, Primary PCI was performed in 74% of the latter cases. D2B time was 41 min. Reasons for the lack of treatment include delayed presentation, lack of ICU beds and insurance denials. Currently, these constraints are being methodically probed. Updated results will be available at time of presentation.

Conclusions

The synergy of the AMI initiative for Mexico provides a template for similar initiatives in developing countries.

P1741Hitting the wall in converting diagnosed ST-elevation myocardial infarction patients to treating them: a humbling analysis from LATIN telemedicine

Background

The behemoth telemedicine program, Latin America Telemedicine Infarct Network (LATIN) has exponentially grown in 4 countries in Central and South America. It has provided AMI coverage to >100 million patients and it has contributed to transforming AMI care in the continent by its “halo” effect. We continue our meticulous search in evaluating the impact of LATIN and in doing so, we have confronted a sobering reality.

Purpose

To make continued improvements in population-based AMI management, the continued success of the initiative requires participation from healthcare policy makers, health economists, and payers.

Methods

LATIN was created as a hub and spoke model to hugely increase access (>100 million population coverage) to quality AMI treatment primarily with short door to balloon time (D2B) PCI. Innovative telemedicine platforms were created and networked at all 350 centers that were located in small clinics and primary health centers in poor sections of the countries (spokes) and at 24/7 PCI capable institutions (hubs). Remote cardiologists, located in 3 central locations, provided immediate EKG diagnosis (time to telemedicine diagnosis, TTD <3.5 minutes) and they provided expert guidance for the entire STEMI process, Door in Door Out (DIDO), and transport times (TT). LATIN performance metrics, under its strict control, and including process metrics at the hubs, spokes, and at the command telemedicine sites, were measured and plotted. The macroeconomic variables of insurance approvals, ambulance structure, and availability of ICU beds were determined and incorporated into performance variables of the LATIN program.

Results

784,395 patients were screened at 350 LATIN centers (Brazil 143, Colombia 118, Mexico 82, Argentina 7). With expanded reach, 8,440 (1.08%) patients were diagnosed and 3,924 (46.5%) urgently reperfused, including 3,048 (77.7%) with PCI. Time to Telemedicine Diagnosis (TTD) was 3 min, tele-accuracy 98.9%, D2B 51 min, and in-hospital mortality 5.2%. Over 4 years of operation, the proportion of reperfused STEMI patients has ranged between 41–48% - the major reasons for non-treatment were insurance, lack of ICU beds and delayed presentation.

Conclusions

Sustained improvements, as a result of stringent QA processes and continuous education, have resulted in reduced D2B, TTD, DIDO, TT, and in overall mortality. However, LATIN remains constrained with a large proportion of patients that are diagnosed but not treated, largely because of payer denials. Although this metric is showing improvement from broad dissemination of LATIN benefits, further gains from LATIN will result mainly from improved reimbursements.

OS8.1 A phase 0/2 clinical trial of a CDK4/6 inhibitor in aggressive meningioma patients

BACKGROUND

New approaches are urgently needed for aggressive meningiomas, which remain largely incurable. Forkhead Box M1 (FOXM1) has been identified as a master transcription factor in aggressive meningiomas and Cyclin D-dependent Kinases (CDK) are positive regulators of cell-cycle entry, promoting tumorigenesis through FOXM1 activation. We evaluated the tumor pharmacokinetics (PK), tumor pharmacodynamics (PD), and preliminary clinical response of ribociclib, a selective CDK4/6-inhibitor, in aggressive meningioma patients.

MATERIAL AND METHODS

Eight aggressive WHO Grade II/III meningioma patients with intact RB expression were enrolled and administered oral ribociclib daily (900mg) for 5 days prior to tumor resection. Plasma, tumor, and cerebrospinal fluid (CSF) samples were collected at 2, 8, or 24 h after the last dose. Total and unbound drug concentrations were determined using a validated LC-MS/MS method. PD effects, including RB and FoxM1 phosphorylation, were compared to matched archival tissue. Patients with PK and PD responses in tumor tissue, defined as unbound ribociclib concentration > 5-fold in vitro IC50 (0.04µM) and >20% decrease in pRB levels, respectively, were enrolled into an exploratory Phase 2 cohort.

RESULTS

The median CSF concentration of ribociclib was 0.25 µM. In tumor tissue, the median unbound ribociclib concentration was 1.36 µM and the median unbound tumor-to-plasma ratio was 5.34. Suppression of G1-to-S phase was inferred in tumors with declining FoxM1 phosphorylation (50%), RB phosphorylation (38%), and cellular proliferation (75%). Four patients demonstrated concurrent PK and PD responses and were graduated to continuous ribociclib therapy. At one year, two of these patients (one Grade II and one Grade III) demonstrate partial responses per RANO criteria.

CONCLUSION

Ribociclib achieves pharmacologically-active concentrations in aggressive meningioma tissue. Target modulation was demonstrated by a decrease in FOXM1-mediated tumor proliferation. Further investigation of ribociclib as a therapeutic strategy for aggressive meningiomas is warranted.

OS4.2 Phase 0 trial of Ceritinib in brain metastasis and recurrent glioblastoma

BACKGROUND

Ceritinib is an orally bioavailable, small molecule inhibitor for ALK/IGFR1/FAK, which are highly expressed in glioblastoma and brain metastases. Preclinical and clinical data suggest that ceritinib has activity in central nervous system (CNS) malignancies, but to date there is no direct evidence in patients. This study assessed the pharmacokinetics (PK) and pharmacodynamics (PD) of ceritinib in recurrent glioblastoma and brain metastasis patients.

MATERIALS AND METHODS

Three brain metastasis and seven glioblastoma patients with high expression of pSTAT5b/pFAK/pIGFR1 were enrolled and treated with oral ceritinib daily (750 mg) for 10 days prior to tumor resection. Plasma, tumor, and cerebrospinal fluid (CSF) samples were collected at ~ 4 and 24 h following the last dose. Total and unbound drug concentrations were determined using LC-MS/MS. PD response was assessed by immunohistochemical analysis of pALK, pFAK, pIGFR1, and pIRS1 staining in treated tumor and matched archival tissues.

RESULTS

Ceritinib was highly bound to human plasma protein (median fraction unbound (Fu), 1.4%) and to brain tumor tissue (median Fu, 0.073% and 0.14% in enhancing and non-enhancing regions respectively). There was a large interindividual variability in drug CNS penetration, with the median unbound concentrations in enhancing, non-enhancing, and CSF of 0.040, 0.006, and 0.012 µM, respectively. The median unbound tumor-to-plasma ratio was 2.44 and 0.33 in enhancing and non-enhancing areas, respectively. In one patient with brain metastasis, drug binding to enhancing tumor was significantly lower (Fu, 1.62%), resulting in a higher unbound drug tumor concentration and CSF concentration as compared to those in glioblastoma patients. In all patients, no changes in PD markers were detected.

CONCLUSION

Ceritinib is highly bound to plasma proteins and tumor tissues. Unbound drug concentrations achieved in brain metastasis and glioblastoma are unlikely sufficient for target modulation.

Data Resource: Vascular Risk in Adult New Zealanders (VARIANZ) datasets

INTRODUCTION

The Vascular Risk in Adult New Zealanders (VARIANZ) datasets contain a range of routinely-collected New Zealand health data relevant to cardiovascular disease (CVD) and related conditions. The datasets enable exploration of cardiovascular-related treatment, service utilisation, outcomes and prognosis.

PROCESSES

Each dataset is constructed by anonymised individual-level linkage of eight national administrative health databases to identify all New Zealand adults aged ≥20 years who have recorded contact with publicly-funded New Zealand health services during a given year from 2006 onwards, when data quality is considered sufficient.

DATA CONTENTS

Individual-level data for each VARIANZ dataset can include variables covering demography, dispensing of cardiovascular disease (CVD) preventive medications and prior hospitalisations for atherosclerotic CVD, heart failure, atrial fibrillation and diabetes. If required, VARIANZ datasets can be individually linked to follow-up national routinely collected health data in subsequent years, including all-cause mortality events and fatal/non-fatal CVD events, to create VARIANZ longitudinal cohorts. Bespoke linkage can also be undertaken to include other national and regional administrative health data such as non-CVD related hospitalisations in order to explore CVD comorbidities or novel risk factors. Furthermore, a subset of the VARIANZ datasets based on specific health contacts (such as CVD hospitalisations only) can also be identified, and some data can be requested for years prior to 2006. The New Zealand routinely-collected health databases used to construct the VARIANZ datasets do not capture primary care diagnostic classifications or certain CVD risk factor data such as smoking status, blood pressure or lipid profiles.

CONCLUSION

The Vascular Risk in Adult New Zealanders (VARIANZ) datasets capture the majority of the New Zealand population in a given year and are available from 2006 onwards, or earlier than 2006 for some datasets based on specific health contacts. VARIANZ data can be used to explore a range of research questions regarding management, outcomes and prognosis for CVD.

A23 Population level diversification of hepatitis C viral strains over time among people who inject drugs in Baltimore, MD

Hepatitis C virus (HCV) infection occurs in 30–90 per cent of people who inject drugs (PWID). Although cure rates can exceed 95 per cent, treatment access is limited and approximately 400,000 people die each year due to complications of chronic infection. A temporal analysis of cluster networks among PWID can be used to inform strategies to interdict transmission. In Baltimore, PWID have been recruited for The AIDS Linked to the IntraVenous Experience (ALIVE) cohort. A demographic questionnaire was administered and recorded for baseline and recent participants. Viral RNA underwent PCR with primers targeting the core and envelope-1 protein (CE1) and sequenced via Sanger sequencing. Sequences with > 400 bp reads and Q-scores >370 were used for downstream analysis resulting in 322 ALIVE baseline participants (1988–9) and 548 recently diagnosed subjects enrolled approximately two decades later (2005–16). Cluster networks were rendered with a threshold of 4 per cent in MicrobeTRACE, and statistical analyses were performed in R Studio. Of the 1988–9 subjects, the majority (259/317, 81.7%) were a part of cluster. There were nine clusters and fifty-eight singletons, with two large clusters containing most sequences of genotype 1a (73.5%). Two decades later, a minority of recently diagnosed individuals (235/512, 44.1%) were part of a cluster. There were seventeen clusters with 286 singletons with two large clusters containing 1a genotype individuals (21.5%). Additional clustering was done by parsing the two datasets by subtype 1a (n = 714) and 1b (n = 151). The genotype 1a network demonstrates a majority, 65.8 per cent, of participants in clusters. Moreover, two large clusters can be observed with baseline participants towards the center and recent participants on the outskirts indicative of high linkage at baseline. The genotype 1b network produced a single large cluster but subclusters were observed. The sequences between the two time points co-mingled but subclusters were also observed. Interestingly, the two large clusters from 1988 to 1989 were still evident in the 2005–16 viral sequences. We observed greater cluster diversity in more recently diagnosed individuals, indicative of a less connected network of individuals sharing transmission risk, though major viral strains did persist over time in this cohort.