Turning data into decisions : clinical decision support in orthopaedic oncology

Background: The treatment of patients with skeletal metastases is predicated on each patient’s estimated survival. In order to maximize function and quality of life, orthopaedic surgeons must carefully avoid over- or undertreatment of the disease. Unfortunately, physician estimates are notoriously inaccurate and there are no validated means by which to estimate patient survival in patients with long-bone skeletal metastases. The purpose of this thesis is to apply machine learning (ML) approaches to (1) develop a clinical decision support (CDS) tool capable of estimating survival in patients with operable skeletal metastases, and (2) establish guidelines so that this approach may be used in other relevant topics within the field of orthopaedics. Methods: We first defined the scope of the problem using data from the Karolinska Skeletal Metastasis Registry. We then developed objective criteria by which to estimate patient survival using data gleaned from the Memorial Sloan-Kettering Skeletal Metastasis Database (n=189). We employed ML techniques to find patterns within the data associated with short- and long-term survival. We chose three and 12 months because they are widely accepted to guide orthopaedic surgical decisionmaking. We developed an Artificial Neural Network (ANN), a Bayesian Belief Network (BBN), and a traditional Logistic Regression (LR) model. Each resulting model was internally validated and compared using Receiver Operator Characteristic (ROC) analysis. In addition, we performed decision analysis to determine which model, if any, was suited for clinical use. Next, we externally validated the models using Scandinavian Registry data (n=815), and again using data collected by the Societ. Italiana di Ortopedia e Traumatologia (SIOT) (n=287). We then created a web-based CDS tool as well as the infrastructure to collect prospective data on a global scale, so the models could be improved over time. Finally, we used BBN modeling to describe the hierarchical relationships between features associated with the treatment of highgrade soft tissue sarcomas (STS), and codify this complex information into a graphical representation to promote a more thorough understanding of the disease process. Results: We found that implant failures in patients with skeletal metastases remain relatively common—even in the revision setting—as patients outlive their implants. On the other hand, perioperative deaths are relatively common, indicating that an estimation of life expectancy should be part of the surgical decision making process. Using ML approaches, we found several criteria that can be used to estimate longevity in this patient population. When compared to other techniques, the ANN model was most accurate, and also resulted in highest net benefit on decision analysis, compared to the BBN and LR models. However, the BBN is the best suited to accommodate missing data, which is common in the clinical setting. The three- and 12-month BBN models were successfully externally validated using the SSMR database (Area under the ROC curve (AUC) of 0.79 and 0.76, respectively), and again using SIOT data (AUC 0.80 and 0.77). In the setting of high-grade, completely excised STS, BBN Modeling identified the first-degree associates of disease-specific survival to be the size of the primary tumor, and the presence and timing of local and distant recurrence. Conclusions: We successfully developed and validated a CDS tool designed to estimate survival in patients with operable skeletal metastases. In addition, we made this tool available to orthopaedic surgeons, worldwide, at www.pathfx.org. We also created an international skeletal metastasis registry to continue to collect data on patients with skeletal metastases. Within this framework, prognostic models have the capacity to improve over time, as treatment philosophies evolve and more effective systemic therapies become available. These techniques may now be applied to other disciplines, in an effort to turn quality data into decision support tools

Reducing adverse impacts of Amazon hydropower expansion

Proposed hydropower dams at more than 350 sites throughout the Amazon require strategic evaluation of trade-offs between the numerous ecosystem services provided by Earth\u27s largest and most biodiverse river basin. These services are spatially variable, hence collective impacts of newly built dams depend strongly on their configuration. We use multiobjective optimization to identify portfolios of sites that simultaneously minimize impacts on river flow, river connectivity, sediment transport, fish diversity, and greenhouse gas emissions while achieving energy production goals. We find that uncoordinated, dam-by-dam hydropower expansion has resulted in forgone ecosystem service benefits. Minimizing further damage from hydropower development requires considering diverse environmental impacts across the entire basin, as well as cooperation among Amazonian nations. Our findings offer a transferable model for the evaluation of hydropower expansion in transboundary basins

How do we estimate survival? External validation of a tool for survival estimation in patients with metastatic bone disease-decision analysis and comparison of three international patient populations

How do we estimate survival? External validation of a tool for survival estimation in patients with metastatic bone disease-decision analysis and comparison of three international patient population

Reducing greenhouse gas emissions of Amazon hydropower with strategic dam planning

Hundreds of dams have been proposed throughout the Amazon basin, one of the world’s largest untapped hydropower frontiers. While hydropower is a potentially clean source of renewable energy, some projects produce high greenhouse gas (GHG) emissions per unit electricity generated (carbon intensity). Here we show how carbon intensities of proposed Amazon upland dams (median = 39 kg CO2eq MWh−1, 100-year horizon) are often comparable with solar and wind energy, whereas some lowland dams (median = 133 kg CO2eq MWh−1) may exceed carbon intensities of fossil-fuel power plants. Based on 158 existing and 351 proposed dams, we present a multi-objective optimization framework showing that low-carbon expansion of Amazon hydropower relies on strategic planning, which is generally linked to placing dams in higher elevations and smaller streams. Ultimately, basin-scale dam planning that considers GHG emissions along with social and ecological externalities will be decisive for sustainable energy development where new hydropower is contemplated. © 2019, The Author(s)

Diving into the vertical dimension of elasmobranch movement ecology

Knowledge of the three-dimensional movement patterns of elasmobranchs is vital to understand their ecological roles and exposure to anthropogenic pressures. To date, comparative studies among species at global scales have mostly focused on horizontal movements. Our study addresses the knowledge gap of vertical movements by compiling the first global synthesis of vertical habitat use by elasmobranchs from data obtained by deployment of 989 biotelemetry tags on 38 elasmobranch species. Elasmobranchs displayed high intra- and interspecific variability in vertical movement patterns. Substantial vertical overlap was observed for many epipelagic elasmobranchs, indicating an increased likelihood to display spatial overlap, biologically interact, and share similar risk to anthropogenic threats that vary on a vertical gradient. We highlight the critical next steps toward incorporating vertical movement into global management and monitoring strategies for elasmobranchs, emphasizing the need to address geographic and taxonomic biases in deployments and to concurrently consider both horizontal and vertical movements