Patrick D. Tyler

Image-guided local delivery strategies enhance therapeutic nanoparticle uptake in solid tumors.

Nanoparticles (NP) have emerged as a novel class of therapeutic agents that overcome many of the limitations of current cancer chemotherapeutics. However, a major challenge to many current NP platforms is unfavorable biodistribution, and limited tumor uptake, upon systemic delivery. Delivery, therefore, remains a critical barrier to widespread clinical adoption of NP therapeutics. To overcome these limitations, we have adapted the techniques of image-guided local drug delivery to develop nanoablation and nanoembolization. Nanoablation is a tumor ablative strategy that employs image-guided placement of electrodes into tumor tissue to electroporate tumor cells, resulting in a rapid influx of NPs that is not dependent on cellular uptake machinery or stage of the cell cycle. Nanoembolization involves the image-guided delivery of NPs and embolic agents directly into the blood supply of tumors. We describe the design and testing of our innovative local delivery strategies using doxorubicin-functionalized superparamagnetic iron oxide nanoparticles (DOX-SPIOs) in cell culture, and the N1S1 hepatoma and VX2 tumor models, imaged by high resolution 7T MRI. We demonstrate that local delivery techniques result in significantly increased intratumoral DOX-SPIO uptake, with limited off-target delivery in tumor-bearing animal models. The techniques described are versatile enough to be extended to any NP platform, targeting any solid organ malignancy that can be accessed via imaging guidance.

Rapid dramatic alterations to the tumor microstructure in pancreatic cancer following irreversible electroporation ablation.

AIM NanoKnife(®) (Angiodynamics, Inc., NY, USA) or irreversible electroporation (IRE) is a newly available ablation technique to induce the formation of nanoscale pores within the cell membrane in targeted tissues. The purpose of this study was to elucidate morphological alterations following 30 min of IRE ablation in a mouse model of pancreatic cancer. MATERIALS & METHODS Immunohistochemistry markers were compared with diffusion-weighted MRI apparent diffusion coefficient measurements before and after IRE ablation. RESULTS Immunohistochemistry apoptosis index measurements were significantly higher in IRE-treated tumors than in controls. Rapid tissue alterations after 30 min of IRE ablation procedures (structural and morphological alterations along with significantly elevated apoptosis markers) were consistently observed and well correlated to apparent diffusion coefficient measurements. DISCUSSION This imaging assay offers the potential to serve as an in vivo biomarker for noninvasive detection of tumor response following IRE ablation.

Robotic-assisted transoral removal of a submandibular megalith†

The majority of salivary stones are less than 8 mm in size and most frequently occur in the submandibular gland. Traditional management of larger stones involves gland resection. Sialendoscopy combined with an external or a transoral sialolithotomy, also called the combined approach technique, permits stone removal and gland preservation. A 31‐year‐old male presented to our service with a 20‐mm megalith in the left submandibular gland. Here we report the first description of a combined approach using the da Vinci Si Surgical System to facilitate transoral stone removal and salivary duct repair. Laryngoscope, 2011

Should Medical Students Track Former Patients in the Electronic Health Record? An Emerging Ethical Conflict

Medical students are increasingly using electronic health records (EHRs) in clerkships, and medical educators should seek opportunities to use this new technology to improve training. One such opportunity is the ability to “track” former patients in the EHR, defined as following up on patients in the EHR for educational purposes for a defined period of time after they have left one’s direct care. This activity offers great promise in clinical training by enabling students to audit their diagnostic impressions and follow the clinical history of illness in a manner not possible in the era of paper charting. However, tracking raises important questions about the ethical use of protected health information, including concerns about compromising patient autonomy, resulting in a conflict between medical education and patient privacy. The authors offer critical analysis of arguments on both sides and discuss strategies to balance the ethical conflict by optimizing outcomes and mitigating harms. They observe that tracking improves training, thus offering long-lasting benefits to society, and is supported by the principle of distributive justice. They conclude that students should be permitted to track for educational purposes, but only with defined limits to safeguard patient autonomy, including obtaining permission from patients, having legitimate educational intent, and self-restricting review of records to those essential for training. Lastly, the authors observe that this conflict will become increasingly important with completion of the planned Nationwide Health Information Network and emphasize the need for national guidelines on tracking patients in an ethically appropriate manner.

Seven-tesla magnetic resonance imaging accurately quantifies intratumoral uptake of therapeutic nanoparticles in the McA rat model of hepatocellular carcinoma: Preclinical study in a rodent model

ObjectivesAfter inducing McA tumors in Sprague-Dawley rats (McA-SD), the following hypotheses were tested: first, that hypervascular McA tumors grown in Sprague-Dawley rats provide a suitable platform to investigate drug delivery; and second, that high-field MRI can be used to measure intratumoral uptake of DOX-SPIOs. Materials and MethodsMcA cells were implanted into the livers of 18 Sprague-Dawley rats. In successfully inoculated animals, 220-&mgr;L DOX-SPIOs were delivered to tumors via the intravenous or intra-arterial route. Pretreatment and posttreatment T2*-weighted images were obtained using 7-T MRI, and change in R2* value (&Dgr;R2*) was obtained from mean signal intensities of tumors in these images. Tumor iron concentration ([Fe]), an indicator of DOX-SPIO uptake, was measured using mass spectroscopy. The primary outcome variable was the Pearson correlation between &Dgr;R2* and [Fe]. ResultsTumors grew successfully in 13 of the 18 animals (72%). Mean (SD) maximum tumor diameter was 0.83 (0.25) cm. The results of phantom studies revealed a strong positive correlation between &Dgr;R2* and [Fe], with r = 0.98 (P < 0.01). The results of in vivo drug uptake studies demonstrated a positive correlation between &Dgr;R2* and [Fe], with r = 0.72 (P = 0.0004). ConclusionsThe McA tumors grown in the Sprague-Dawley rats demonstrated uptake of nanoparticle-based therapeutic agents. Magnetic resonance imaging quantification of intratumoral uptake strongly correlated with iron concentrations in pathological specimens, suggesting that MRI may be used to quantify uptake of iron-oxide nanotherapeutics.

Privacy Versus Confidentiality: More on the Use of the Electronic Health Record for Learning

Privacy Versus Confidentiality: More on the Use of the Electronic Health Record for Learning To the Editor: We appreciate the Commentary by McLaughlin and Coderre on our Perspective addressing ethical concerns associated with tracking former patients in electronic health records (EHRs). Their focus on learning theory is an important contribution to the discussion of how best to use EHRs in training. However, tracking is associated with important ethical concerns, and their conclusion that these are adequately managed through the rule of confidentiality leaves unaddressed the concomitant risk to patient privacy.

Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives

In secondary analysis of electronic health records, a crucial task consists in correctly identifying the patient cohort under investigation. In many cases, the most valuable and relevant information for an accurate classification of medical conditions exist only in clinical narratives. Therefore, it is necessary to use natural language processing (NLP) techniques to extract and evaluate these narratives. The most commonly used approach to this problem relies on extracting a number of clinician-defined medical concepts from text and using machine learning techniques to identify whether a particular patient has a certain condition. However, recent advances in deep learning and NLP enable models to learn a rich representation of (medical) language. Convolutional neural networks (CNN) for text classification can augment the existing techniques by leveraging the representation of language to learn which phrases in a text are relevant for a given medical condition. In this work, we compare concept extraction based methods with CNNs and other commonly used models in NLP in ten phenotyping tasks using 1,610 discharge summaries from the MIMIC-III database. We show that CNNs outperform concept extraction based methods in almost all of the tasks, with an improvement in F1-score of up to 26 and up to 7 percentage points in area under the ROC curve (AUC). We additionally assess the interpretability of both approaches by presenting and evaluating methods that calculate and extract the most salient phrases for a prediction. The results indicate that CNNs are a valid alternative to existing approaches in patient phenotyping and cohort identification, and should be further investigated. Moreover, the deep learning approach presented in this paper can be used to assist clinicians during chart review or support the extraction of billing codes from text by identifying and highlighting relevant phrases for various medical conditions.

Outcomes of Ventilated Patients With Sepsis Who Undergo Interhospital Transfer: A Nationwide Linked Analysis*

Objectives: The outcomes of critically ill patients who undergo interhospital transfer are not well understood. Physicians assume that patients who undergo interhospital transfer will receive more advanced care that may translate into decreased morbidity or mortality relative to a similar patient who is not transferred. However, there is little empirical evidence to support this assumption. We examined country-level U.S. data from the Nationwide Readmissions Database to examine whether, in mechanically ventilated patients with sepsis, interhospital transfer is associated with a mortality benefit. Design: Retrospective data analysis using complex survey design regression methods with propensity score matching. Setting: The Nationwide Readmissions Database contains information about hospital admissions from 22 States, accounting for roughly half of U.S. hospitalizations; the database contains linkage numbers so that admissions and transfers for the same patient can be linked across 1 year of follow-up. Patients: From the 2013 Nationwide Readmission Database Sample, 14,325,172 hospital admissions were analyzed. There were 61,493 patients with sepsis and on mechanical ventilation. Of these, 1,630 patients (2.7%) were transferred during their hospitalization. A propensity-matched cohort of 1,630 patients who did not undergo interhospital transfer was identified. Interventions: None. Measurements and Main Results: The exposure of interest was interhospital transfer to an acute care facility. The primary outcome was hospital mortality; the secondary outcome was hospital length of stay. The propensity score included age, gender, insurance coverage, do not resuscitate status, use of renal replacement therapy, presence of shock, and Elixhauser comorbidities index. After propensity matching, interhospital transfer was not associated with a difference in in-hospital mortality (12.3% interhospital transfer vs 12.7% non–interhospital transfer; p = 0.74). However, interhospital transfer was associated with a longer total hospital length of stay (12.8 d interquartile range, 7.7–21.6 for interhospital transfer vs 9.1 d interquartile range, 5.1–17.0 for non–interhospital transfer; p < 0.01). Conclusions: Patients with sepsis requiring mechanical ventilation who underwent interhospital transfer did not have improved outcomes compared with a cohort with matched characteristics who were not transferred. The study raises questions about the risk-benefit profile of interhospital transfer as an intervention.

Behind the Scenes: A Medical Natural Language Processing Project

Advancement of Artificial Intelligence (AI) capabilities in medicine can help address many pressing problems in healthcare. However, AI research endeavors in healthcare may not be clinically relevant, may have unrealistic expectations, or may not be explicit enough about their limitations. A diverse and well-functioning multidisciplinary team (MDT) can help identify appropriate and achievable AI research agendas in healthcare, and advance medical AI technologies by developing AI algorithms as well as addressing the shortage of appropriately labeled datasets for machine learning. In this paper, our team of engineers, clinicians and machine learning experts share their experience and lessons learned from their two-year-long collaboration on a natural language processing (NLP) research project. We highlight specific challenges encountered in cross-disciplinary teamwork, dataset creation for NLP research, and expectation setting for current medical AI technologies.

Access to Prescription Opioids-Primum Non Nocere: A Teachable Moment.

Story From the Front Lines A 14-year-old boy found acetaminophen-hydrocodone in his parents’ medicine cabinet and took it out of curiosity. He liked how the pills made him feel and progressed to daily use of prescription opioids. At age 15 years, he was prescribed a short course of acetaminophen-oxycodone for a back injury due to wrestling. After the prescription ended, he continued to seek prescription opioids from illicit sources, taking them almost daily. He was briefly sent to juvenile detention after being caught selling opioids. For the next 3 years he abstained from opioids but drank alcohol socially and smoked cigarettes. At age 19 years, he tried heroin with a friend and began using the drug daily. Today, he describes the experience as “spiritual... when I took the drug, it felt like I had found a deep calling.” He began selling heroin to fund his habit. At age 21 years, he overdosed on heroin. He was initially revived by emergency medical services with intranasal naloxone, but owing to compartment syndrome of the right thigh, he developed hyperkalemia and experienced cardiac arrest. After 18 minutes of advanced cardiac life support, a pulse returned. He was intubated and admitted to the intensive care unit. His course was complicated by renal failure requiring renal replacement therapy, stress cardiomyopathy, deep venous thrombosis, and anoxic brain injury. His cardiac and renal function normalized. Although his left-sided motor function is now normal, he still has spastic movements, weakness, and limited range of motion in the right upper and lower extremities. He can now ambulate without a walker or cane, and he has normal thought and speech. Since leaving the hospital, he has been attending weekly Alcoholics Anonymous, Heroin Anonymous, and Narcotics Anonymous meetings. Unfortunately, he has had several relapses, and contracted hepatitis C after sharing injection paraphernalia.