Real-World Treatment Patterns and Outcomes of First-Line Immunotherapy Among Patients With Advanced Nonsquamous NSCLC Harboring BRAF, MET, or HER2 Alterations.

Introduction: Data on utilization and clinical outcomes of programmed cell death protein or programmed death-ligand 1 (PD-[L]1) inhibitors in NSCLC with uncommon oncogenic alterations is limited. Methods: This retrospective study used a deidentified U.S. nationwide clinicogenomic database to select patients with advanced nonsquamous NSCLC without EGFR, ALK, or ROS1 alterations, diagnosed from January 1, 2016 to September 30, 2020, who initiated first-line therapy. Our objectives were to summarize characteristics and treatment patterns for patients with four little-studied genomic alterations or driver-negative NSCLC. We estimated Kaplan-Meier real-world time on treatment (rwTOT) and time to next treatment for patients receiving PD-(L)1 inhibitors. The data cutoff was September 30, 2021. Results: Of the 3971 eligible patients, 84 (2%) had NSCLC with BRAF V600E mutation, 117 (3%) had MET exon 14 skipping mutation, 130 (3%) had MET amplification, 91 (2%) had ERBB2 activation mutation, and 691 patients (17%) had driver-negative NSCLC. Patient characteristics differed among cohorts as expected. The most common first-line regimen in each cohort was a PD-(L)1 inhibitor as monotherapy or in combination with chemotherapy. The median rwTOT with anti-PD-(L)1 monotherapy was 4.6 months in the driver-negative cohort and ranged from 2.9 months (ERBB2 mutation) to 7.6 months (BRAF V600E mutation). The median rwTOT with anti-PD-(L)1-chemotherapy combination was 5.2 months in the driver-negative cohort and 6 months in all but the BRAF V600E cohort (17.5 mo). The patterns of real-world time to next treatment results were similar. Conclusions: Substantial use of anti-PD-(L)1 therapy and associated clinical outcomes are consistent with previous real-world findings and suggest no detriment from PD-(L)1 inhibitors for advanced nonsquamous NSCLC harboring one of these four genomic alterations relative to driver-negative NSCLC.

Development and Temporal Validation of an Electronic Medical Record-Based Insomnia Prediction Model Using Data from a Statewide Health Information Exchange.

This study aimed to develop and temporally validate an electronic medical record (EMR)-based insomnia prediction model. In this nested case-control study, we analyzed EMR data from 2011-2018 obtained from a statewide health information exchange. The study sample included 19,843 insomnia cases and 19,843 controls matched by age, sex, and race. Models using different ML techniques were trained to predict insomnia using demographics, diagnosis, and medication order data from two surveillance periods: -1 to -365 days and -180 to -365 days before the first documentation of insomnia. Separate models were also trained with patient data from three time periods (2011-2013, 2011-2015, and 2011-2017). After selecting the best model, predictive performance was evaluated on holdout patients as well as patients from subsequent years to assess the temporal validity of the models. An extreme gradient boosting (XGBoost) model outperformed all other classifiers. XGboost models trained on 2011-2017 data from -1 to -365 and -180 to -365 days before index had AUCs of 0.80 (SD 0.005) and 0.70 (SD 0.006), respectively, on the holdout set. On patients with data from subsequent years, a drop of at most 4% in AUC is observed for all models, even when there is a five-year difference between the collection period of the training and the temporal validation data. The proposed EMR-based prediction models can be used to identify insomnia up to six months before clinical detection. These models may provide an inexpensive, scalable, and longitudinally viable method to screen for individuals at high risk of insomnia.

Modeling acute care utilization: practical implications for insomnia patients.

Machine learning models can help improve health care services. However, they need to be practical to gain wide-adoption. In this study, we investigate the practical utility of different data modalities and cohort segmentation strategies when designing models for emergency department (ED) and inpatient hospital (IH) visits. The data modalities include socio-demographics, diagnosis and medications. Segmentation compares a cohort of insomnia patients to a cohort of general non-insomnia patients under varying age and disease severity criteria. Transfer testing between the two cohorts is introduced to demonstrate that an insomnia-specific model is not necessary when predicting future ED visits, but may have merit when predicting IH visits especially for patients with an insomnia diagnosis. The results also indicate that using both diagnosis and medications as a source of data does not generally improve model performance and may increase its overhead. Based on these findings, the proposed evaluation methodologies are recommended to ascertain the utility of disease-specific models in addition to the traditional intra-cohort testing.

Species clustering, climate effects, and introduced species in 5 million city trees across 63 US cities.

Sustainable cities depend on urban forests. City trees-pillars of urban forests-improve our health, clean the air, store CO2, and cool local temperatures. Comparatively less is known about city tree communities as ecosystems, particularly regarding spatial composition, species diversity, tree health, and the abundance of introduced species. Here, we assembled and standardized a new dataset of N = 5,660,237 trees from 63 of the largest US cities with detailed information on location, health, species, and whether a species is introduced or naturally occurring (i.e., "native"). We further designed new tools to analyze spatial clustering and the abundance of introduced species. We show that trees significantly cluster by species in 98% of cities, potentially increasing pest vulnerability (even in species-diverse cities). Further, introduced species significantly homogenize tree communities across cities, while naturally occurring trees (i.e., "native" trees) comprise 0.51-87.4% (median = 45.6%) of city tree populations. Introduced species are more common in drier cities, and climate also shapes tree species diversity across urban forests. Parks have greater tree species diversity than urban settings. Compared to past work which focused on canopy cover and species richness, we show the importance of analyzing spatial composition and introduced species in urban ecosystems (and we develop new tools and datasets to do so). Future work could analyze city trees alongside sociodemographic variables or bird, insect, and plant diversity (e.g., from citizen-science initiatives). With these tools, we may evaluate existing city trees in new, nuanced ways and design future plantings to maximize resistance to pests and climate change. We depend on city trees.

Trees in towns and cities provide critical services to humans, animals and other living things. They help prevent climate change by capturing and storing carbon dioxide; they provide food and shelter to other species, they scrub the air of microscopic pollutants, cool local temperatures, and improve the mental and physical health of those who have access to them. In general, naturally occurring (so called native) plant species support richer local ecosystems ­ such as bird and butterfly communities ­ than plants that have been introduced from other areas. However, relatively little is known about which species of trees are found in towns and cities or how these species are distributed. Here, McCoy, Goulet-Scott et al. assembled a dataset of 5.6 million city trees from 63 cities in the United States. This dataset contained rich data on the exact location, species, and health of individual city trees ­ including park trees, those in urban forests, and trees that line city streets. In nearly all of the cities, the same tree species were found clustered next to each other, even in cities that had many different species of tree overall. This tendency of tree species to flock together may make these communities more vulnerable to disease and pest outbreaks. Trees in more developed environments, like those that line streets, were much less species diverse than trees spread across parks. Cities with wetter, cooler climates tended to have higher percentages of native tree species compared to cities with drier, hotter climates. Younger cities also had a greater percentage of native tree species than older cities, which may reflect increased awareness of the importance of native tree species among urban planners in more recent years. The cities that had planted non-native tree species tended to select the same species, which contributed to tree communities in different cities looking more alike. McCoy, Goulet-Scott et al. provide easy-to-use tools academics and urban foresters can use to assess how diverse tree communities in individual cities are. This work may help local decision-makers to select and plant trees that build resilience against climate change, pest and disease outbreaks, and maximize the health benefits trees provide all city dwellers.

A social and news media benchmark dataset for topic modeling.

Topic modeling is an active research area with several unanswered questions. The focus of recent research in this area is on the use of a vector embedding representation of the input text with both generative and evolutionary topic modeling techniques. Unfortunately, it is hard to compare different techniques when the underlying data and preprocessing steps that were used to develop the models are not available. This paper presents two secondary datasets that can help address this gap. These datasets are derived from two primary datasets. The first consists of 8145 posts from the r/Cancer health forum and the second consists of 18,294 messages submitted to 20 different news groups. The same preprocessing procedure is applied to both datasets by removing punctuation, stop words and high frequency words. Each dataset is then clustered using three different topic modeling techniques: pPSO, ETM and NVDM and three topic numbers: 10, 20, 30. In addition, for pPSO two text embeddings representation are considered: sBERT and Skipgram. The secondary datasets were originally developed in support of a comparative analysis of the aforementioned topic modeling techniques in a study titled "Comparing PSO-based Clustering over Contextual Vector Embeddings to Modern Topic Modeling" submitted to the Journal of Information Processing and Management. The present paper provides a detailed description of the two secondary datasets including the unique identifier that can be used to retrieve the original documents, the pre-processing scripts, the topic keywords generated by the three topic modeling techniques with varying topic numbers and embedding representations. As such, the datasets allow direct comparison with other topic modeling techniques. To further facilitate this process, the algorithm underlying the evolutionary topic modeling technique, pPSO, proposed by the authors is also provided.

Exosomal PD-L1 induces osteogenic differentiation and promotes fracture healing by acting as an immunosuppressant.

A moderate inflammatory response at the early stages of fracture healing is necessary for callus formation. Over-active and continuous inflammation, however, impairs fracture healing and leads to excessive tissue damage. Adequate fracture healing could be promoted through suppression of local over-active immune cells in the fracture site. In the present study, we achieved an enriched concentration of PD-L1 from exosomes (Exos) of a genetically engineered Human Umbilical Vein Endothelial Cell (HUVECs), and demonstrated that exosomes overexpressing PD-L1 specifically bind to PD-1 on the T cell surface, suppressing the activation of T cells. Furthermore, exosomal PD-L1 induced Mesenchymal Stem Cells (MSCs) towards osteogenic differentiation when pre-cultured with T cells. Moreover, embedding of Exos into an injectable hydrogel allowed Exos delivery to the surrounding microenvironment in a time-released manner. Additionally, exosomal PD-L1, embedded in a hydrogel, markedly promoted callus formation and fracture healing in a murine model at the early over-active inflammation phase. Importantly, our results suggested that activation of T cells in the peripheral lymphatic tissues was inhibited after local administration of PD-L1-enriched Exos to the fracture sites, while T cells in distant immune organs such as the spleen were not affected. In summary, this study provides the first example of using PD-L1-enriched Exos for bone fracture repair, and highlights the potential of Hydrogel@Exos systems for bone fracture therapy through immune inhibitory effects.

Inferring the patient's age from implicit age clues in health forum posts.

Broader patient-reported experiences in oncology are largely unknown due to the lack of available information from traditional data sources. Online health community data provide an exploratory way to uncover these experiences at a large scale. Analyzing these data can guide further studies towards understanding patients' needs and experiences. However, analysis of online health data is inherently difficult due to the unstructured nature of these data and the variety of ways information can be expressed over text. Specifically, subscribers may not disclose critical information such as the age of the patient in their posts. In fact, the number of health forum posts that explicitly mention the age of the patient is significantly lower than the number of posts that do not include this information in the Reddit r/Cancer health forum under consideration in the present paper. Health-focused studies often need to consider or control for age as a confounder, hence the importance of having sufficient age data. This paper presents a methodology that can help classify health forum posts according to four age groups (0-17, 18-39, 40-64 and 65 + years) even when the posts do not contain explicit mention of the age of the patient. First, the subset of the posts that include explicit mention of the age of the patient is identified. Second, the explicit age clues are removed from these posts and used to train the proposed age classifier. The resulting classifier is able to infer the age of the patient using only implicit age clues with an average true positive rate (TPR) of 71%. This TPR is comparable to the average TPR of 69% obtained from human annotations for the same set of posts.

An Automated Line-of-Therapy Algorithm for Adults With Metastatic Non-Small Cell Lung Cancer: Validation Study Using Blinded Manual Chart Review.

BACKGROUND: Extraction of line-of-therapy (LOT) information from electronic health record and claims data is essential for determining longitudinal changes in systemic anticancer therapy in real-world clinical settings. OBJECTIVE: The aim of this retrospective cohort analysis is to validate and refine our previously described open-source LOT algorithm by comparing the output of the algorithm with results obtained through blinded manual chart review. METHODS: We used structured electronic health record data and clinical documents to identify 500 adult patients treated for metastatic non-small cell lung cancer with systemic anticancer therapy from 2011 to mid-2018; we assigned patients to training (n=350) and test (n=150) cohorts, randomly divided proportional to the overall ratio of simple:complex cases (n=254:246). Simple cases were patients who received one LOT and no maintenance therapy; complex cases were patients who received more than one LOT and/or maintenance therapy. Algorithmic changes were performed using the training cohort data, after which the refined algorithm was evaluated against the test cohort. RESULTS: For simple cases, 16 instances of discordance between the LOT algorithm and chart review prerefinement were reduced to 8 instances postrefinement; in the test cohort, there was no discordance between algorithm and chart review. For complex cases, algorithm refinement reduced the discordance from 68 to 62 instances, with 37 instances in the test cohort. The percentage agreement between LOT algorithm output and chart review for patients who received one LOT was 89% prerefinement, 93% postrefinement, and 93% for the test cohort, whereas the likelihood of precise matching between algorithm output and chart review decreased with an increasing number of unique regimens. Several areas of discordance that arose from differing definitions of LOTs and maintenance therapy could not be objectively resolved because of a lack of precise definitions in the medical literature. CONCLUSIONS: Our findings identify common sources of discordance between the LOT algorithm and clinician documentation, providing the possibility of targeted algorithm refinement.

Changes in Healthcare Resource Use and Costs in Commercially Insured Insomnia Patients Initiating Suvorexant.

INTRODUCTION: Insomnia diagnosis has been associated with a significant clinical and economic burden on patients and healthcare systems. This study examined changes in healthcare resource use (HCRU) and costs in insomnia patients before and after initiation of suvorexant treatment. METHODS: This retrospective cohort study analyzed Optum Clinformatics Data Mart claims data (Jan 2010-Dec 2018). Patients with ≥ 2 insomnia diagnosis claims and ≥ 1 prescription for suvorexant were included. Prevalent and incident insomnia patients were analyzed separately. The change in the trends of HCRU and costs were examined for 12 months before and 12 months after suvorexant initiation. An interrupted time series (ITS) analysis was conducted to assess the level and slope changes. Subgroups of patients with mental health comorbidities were examined. RESULTS: The study included 18,919 and 5939 patients in the prevalent and incident insomnia cohorts, respectively. For the prevalent cohort, mean (SD) age was 64.5 (14.1) years, 65% were female, 74% had Medicare Advantage coverage, and 61% had a Charlson comorbidity index score ≥ 1. Characteristics for the incident cohort were similar. The ITS results suggested that the trend for monthly total healthcare cost (THC) was increasing before suvorexant initiation (US$52.51 in the prevalent cohort, $74.93 in incident insomnia cohort), but, after suvorexant initiation, the monthly total cost showed a decreasing trend in both cohorts. The decrease in slope for THC after suvorexant initiation were $72.66 and $112.07 per month in the prevalent and incident cohorts, respectively. The monthly trends in HCRU rates also decreased. The subgroup analysis showed that decreases were 1.5-3 times greater for patients with mental health comorbidities. CONCLUSIONS: In this real-world study, suvorexant initiation was associated with immediate and continued decreases in HCRU and costs in insomnia patients. Further research is needed to understand the effect of suvorexant initiation on direct medical costs as well as costs associated with lost productivity in other real-world settings.

Disparity in Access to Oncology Precision Care: A Geospatial Analysis of Driving Distances to Genetic Counselors in the U.S.

In the US, the growing demand for precision medicine, particularly in oncology, continues to put pressure on the availability of genetic counselors to meet that demand. This is especially true in certain geographic locations due to the uneven distribution of genetic counselors throughout the US. To assess these disparities, access to genetic counselors of all specialties is explored by geography, cancer type, and social determinants of health. Geospatial technology was used to combine and analyze genetic counselor locations and cancer incidence at the county level across the US, with a particular focus on tumors associated with BRCA mutations including ovarian, pancreatic, prostate and breast. Access distributions were quantified, and associations with region, cancer type, and socioeconomic variables were investigated using correlational tests. Nationally, in 2020, there were 4,813 genetic counselors, or 1.49 genetic counselors per 100,000 people, varying between 0.17 to 5.7 per 100,000 at the state level. Seventy-one percent of U.S. residents live within a 30-minute drive-time to a genetic counselor. Drive-times, however, are not equally distributed across the country - while 82% of people in metropolitan areas are 30 minutes from a genetic counselor, only 6% of people in nonmetro areas live within 30 minutes' drive time. There are statistically significant differences in access across geographical regions, socioeconomics and cancer types. Access to genetic counselors for cancer patients differs across groups, including regional, socioeconomic, and cancer type. These findings highlight areas of the country that may benefit from increased genetic counseling provider supply, by increasing the number of genetic counselors in a region or by expanding the use of telegenetics a term used to describe virtual genetic counseling consults that occur via videoconference. Policy intervention to allow genetic counselors to bill for their services may be an effective route for increasing availability of genetic counselors' services However, genetic counselors in direct patient care settings also face other challenges such as salary, job satisfaction, job recognition, overwork/burnout, and appropriate administrative/clinical support, and addressing these issues should also be considered along with policy support. These results could support targeted policy reform and alternative service models to increase access to identified pockets of unmet need, such as telemedicine. Data and analysis are available to the public through an interactive dashboard.

Temporal phenotyping by mining healthcare data to derive lines of therapy for cancer.

Lines of therapy (LOT) derived from real-world healthcare data not only depict real-world cancer treatment sequences, but also help define patient phenotypes along the course of disease progression and therapeutic interventions. The sequence of prescribed anticancer therapies can be defined as temporal phenotyping resulting from changes in morphological (tumor staging), biochemical (biomarker testing), physiological (disease progression), and behavioral (physician prescribing and patient adherence) parameters. We introduce a novel methodology that is a two-part approach: 1) create an algorithm to derive patient-level LOT and 2) aggregate LOT information via clustering to derive temporal phenotypes, in conjunction with visualization techniques, within a large insurance claims dataset. We demonstrated the methodology using two examples: metastatic non-small cell lung cancer and metastatic melanoma. First, we generated a longitudinal patient cohort for each cancer type and applied a set of rules to derive patient-level LOT. Then the LOT algorithm outputs for each cancer type were visualized using Sankey plots and K-means clusters based on durations of LOT and of gaps in therapy between LOT. We found differential distribution of temporal phenotypes across clusters. Our approach to identify temporal patient phenotypes can increase the quality and utility of analyses conducted using claims datasets, with the potential for application to multiple oncology disease areas across diverse healthcare data sources. The understanding of LOT as defining patients' temporal phenotypes can contribute to continuous health learning of disease progression and its interaction with different treatment pathways; in addition, this understanding can provide new insights that can be applied by tailoring treatment sequences for the patient phenotypes who will benefit.

Single-molecule fluorescence imaging of nanocatalytic processes.

This tutorial review covers recent developments in using single-molecule fluorescence microscopy to study nanoscale catalysis. The single-molecule approach enables following catalytic and electrocatalytic reactions on nanocatalysts, including metal nanoparticles and carbon nanotubes, at single-reaction temporal resolution and nanometer spatial precision. Real-time, in situ, multiplexed measurements are readily achievable under ambient solution conditions. These studies provide unprecedented insights into catalytic mechanism, reactivity, selectivity, and dynamics in spite of the inhomogeneity and temporal variations of catalyst structures. Prospects, generality, and limitations of the single-molecule fluorescence approach for studying nanocatalysis are also discussed.