Exploration Strategy Research Articles

Depositional facies and evolution of sponge-microbial-constructed carbonate platforms in the lower Cambrian (Stage 3), Tarim basin, China: Understanding from anatomy

The early Cambrian was generally considered the earliest time interval while the archaeocyaths could act as the constructors in building reef framework although commonly coexisting with the microbial constructors (Epiphyton and Renalcis) in the course of the “Cambrian explosion”; however, this kind of metazoan-microbial buildups has not yet been reported in Tarim Basin. To delineate facies (associations), spatiotemporal variations, reef-building processes of metazoan-microbial constructed ramps/shelves and evolution, detailed anatomy was carried out upon the lower Cambrian Xiaoerbulak Formation (or Stage 3) exposed continuously along a marginal transect (∼2 km wide) in Aksu area, northwestern Tarim Basin. Three platform types with distinctive facies associations are distinguished: (1) sponge-microbial biostrome/mound-colonized homoclinal and distally-steepened ramps recorded respectively in the Lower Member and basal Middle Member, (2) Archaeocyath-Epiphyton reef- and microbial reef-rimmed platforms (or shelves) in the mid-upper Middle Member, and (3) microbial biostrome-colonized ramp in the Upper Member, thus illustrating the evolution from the ramp to reef-rimmed platform and final reversion to the ramp system. Four and a half third-order depositional sequences are further identified: one in the Lower Member, two in the Middle Member and one and half (TST) in the Upper Member, although, at least, one sequence was absent in the platform/ramp interior. All these can be well correlated with those in equivalent successions identified elsewhere around the world, implying a basic control of eustatic changes on platform development and evolution. Moreover, syndepositional faulting/sliding that occurred off the ramp margin, as demonstrated by slope failure/collapse, may have enhanced the relief difference between the inner and mid-outer ramps, promoting metazoan-microbes to colonize and build up wave-resistant architecture on the uplifting margin, forming the reef-rimmed platform (or shelf) system as typified in the Middle Member. Meanwhile, the development of wave-resistant, metazoan-microbial reef complexes also benefited from their concurrently-gained calcifying ability, which likely resulted from the instinctive biotic responses to the changes in climate and oceanic chemistry (i.e., pCO2, seawater Mg/Ca ratio) in the early Cambrian. The new datasets of this study fill previous information gap on the early Cambrian metazoan-involved buildups that had controlled the platform configuration and evolution in Tarim Basin, which thus are critically important for better understanding of climatic/environmental controls on their occurrence and evolution of the time-specific reef complexes in the course of “Cambrian Explosion”. Additionally, these datasets would provide a useful analogue for perceiving the facies-constrained porosity and heterogeneity in the deeply buried counterparts in the basin, helpful for making exploration strategy for the deeply-buried hydrocarbon resources.

Integrated 3D Reservoir Modeling and Tectonic Evaluation of the Upper Cretaceous Bahariya Formation in the Burg El Arab Area, Egypt: Implications for Hydrocarbon Exploration and Production Strategies

Integrated 3D Reservoir Modeling and Tectonic Evaluation of the Upper Cretaceous Bahariya Formation in the Burg El Arab Area, Egypt: Implications for Hydrocarbon Exploration and Production Strategies

Estimation of the dolomite content of carbonate rock outcrops based on spectral knowledge and machine learning

Accurately estimating the dolomite content in carbonate rocks is crucial for optimizing oil and gas exploration and production strategies. Hyperspectral techniques for estimating dolomite content have advantages in terms of efficiency, cost-effectiveness, and non-destructiveness compared with traditional laboratory methods. Despite the abundance of hyperspectral data, feature selection and extraction remain challenging. In this study, hyperspectral data collected from surface outcrop in the field using the analytical spectral device (ASD) were applied to construct model for estimating dolomite content. Firstly, the data were preprocessed via outlier analysis and continuum transformation. Next, a hybrid approach integrating spectral knowledge with machine learning was proposed and applied to facilitate efficient and precise feature selection of the hyperspectral data; in this approach, preliminary screening based on spectral knowledge is followed by further hyperspectral data feature selection using a random forest algorithm. The selected features were then combined using a support vector regression algorithm to obtain the estimation model. Finally, the accuracy of the model was evaluated using the hyperspectral data from field outcrop samples. To further verify the effectiveness of this method, various combinations of eight input variables and four machine learning algorithms were compared. Among all combinations, our model achieved the highest accuracy with a test R2 value of 0.91 and a root-mean-square error of only 0.122. The proposed method is practical and efficient and provides precise quantitative data for field geologists to identify the mineral distribution in outcrops. Thus, our method provides robust support for understanding reservoir characteristics and has significant practical value in geological surveys and mineral exploration.

Explanation strategies in humans versus current explainable artificial intelligence: Insights from image classification

AbstractExplainable AI (XAI) methods provide explanations of AI models, but our understanding of how they compare with human explanations remains limited. Here, we examined human participants' attention strategies when classifying images and when explaining how they classified the images through eye‐tracking and compared their attention strategies with saliency‐based explanations from current XAI methods. We found that humans adopted more explorative attention strategies for the explanation task than the classification task itself. Two representative explanation strategies were identified through clustering: One involved focused visual scanning on foreground objects with more conceptual explanations, which contained more specific information for inferring class labels, whereas the other involved explorative scanning with more visual explanations, which were rated higher in effectiveness for early category learning. Interestingly, XAI saliency map explanations had the highest similarity to the explorative attention strategy in humans, and explanations highlighting discriminative features from invoking observable causality through perturbation had higher similarity to human strategies than those highlighting internal features associated with higher class score. Thus, humans use both visual and conceptual information during explanation, which serve different purposes, and XAI methods that highlight features informing observable causality match better with human explanations, potentially more accessible to users.

Fault system dynamics and their impact on ordovician carbonate karst reservoirs: Outcrop analogs and 3D seismic analysis in the Tabei region, Tarim Basin, NW China

The Tabei Uplift in the Tarim Basin of NW China represents an area with significant hydrocarbon exploration potential. This paper describes the effects of multi-phase faults on structural and hydrocarbon systems in the Tabei Uplift utilizing high-quality 3-D seismic, geological outcrops and drilling and production data. The interpretation and analysis of 10 fault systems from the Cambrian through the Cretaceous, shows that the deep, middle and shallow faults developed in four stages: the Early Caledonian, Mid-Late Caledonian, Hercynian and Himalayan. Specifically, the Early Caledonian fault system is favorable for the hydrocarbon expulsion of Cambrian source rocks, while Mid-Late Caledonian strike-slip faults facilitate hydrocarbon migration and accumulation. Hercynian and Himalayan strike-slip faults were formed as a result of the reactivation of the Mid-Late Caledonian strike-slip faults and have a destructive effect on the older oil reservoirs. Similarly, Permian volcanic activity also caused varying degrees of damage to Ordovician reservoirs. Oil production along fault zones more than 100 km show that the complex fracture-vuggy (FV) structure is more likely to trap oil and gas when it is damaged than the simple FV structure. Fault-controlled, fracture-vuggy carbonate reservoirs (FVCRs) were classified into six grades, which correlate to the productivity of oil wells in the region. Using this classification system, several drilling locations were selected and resulted in 80% success rate. This classification provides a novel approach for assessing the interplay between fault activity and reservoir formation, enhancing the prediction of hydrocarbon potential in varying strike-slip fault zones. This analysis not only refines our understanding of the region's geodynamic history but also aids in optimizing exploration and development strategies.

Improving PID Controller Performance in Nonlinear Oscillatory Automatic Generation Control Systems Using a Multi-objective Marine Predator Algorithm with Enhanced Diversity

Power systems are pivotal in providing sustainable energy across various sectors. However, optimizing their performance to meet modern demands remains a significant challenge. This paper introduces an innovative strategy to improve the optimization of PID controllers within nonlinear oscillatory Automatic Generation Control (AGC) systems, essential for the stability of power systems. Our approach aims to reduce the integrated time squared error, the integrated time absolute error, and the rate of change in deviation, facilitating faster convergence, diminished overshoot, and decreased oscillations. By incorporating the spiral model from the Whale Optimization Algorithm (WOA) into the Multi-Objective Marine Predator Algorithm (MOMPA), our method effectively broadens the diversity of solution sets and finely tunes the balance between exploration and exploitation strategies. Furthermore, the QQSMOMPA framework integrates quasi-oppositional learning and Q-learning to overcome local optima, thereby generating optimal Pareto solutions. When applied to nonlinear AGC systems featuring governor dead zones, the PID controllers optimized by QQSMOMPA not only achieve 14%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} reduction in the frequency settling time but also exhibit robustness against uncertainties in load disturbance inputs.

Structural interpretation of the basement beneath the Southern Desert of Iraq based on aeromagnetic data

The Southern Desert (SD) of Iraq is located in the southernmost region of Iraq and is tectonically located on the stable part (proximal domain) of the Arabian Platform. The area is characterized by a considerable thickness of nonmagnetic Phanerozoic sediments overlying a reworked Proterozoic basement. Due to the intense nature of the karst near-surface geology, the basement has neither been seismically imaged nor drilled. In such situations, potential field (gravity and magnetic) exploration methods can often help to identify and map the deep structure of the basement. Here, the aeromagnetic field data, which cover all of the SD, are used to determine the structure and approximate depth of the basement. We use the combined results of the tilt derivative and phase preserving dynamic range compression (PPDRC) methods to qualitatively delineate the main basement structures and then use the source parameter imaging (SPI), tilt-depth (TD), and finite SPI (FSPI) methods to determine the basement depths. The qualitative interpretation of the tilt derivative and PPDRC methods identifies three north–south to northeast–southwest-trending linear negative anomalies that could represent extensional grabens in the basement surface. These grabens divide the basement into three blocks: the northwest block, the central block, and the southeast block. The magnetic anomalies over the basement blocks suggest that the northwest and central blocks are cut by a set of north–south to north-northwest–south-southeast and northeast–southwest faults. Depth estimation methods over the uplifted blocks have minimum depths between 4 and 5 km, whereas, over the graben, the depths range from 7 km to in excess of 12 km. The FSPI method, unlike the SPI and TD methods that use an infinite depth source body, gives depths generally deeper by up to 1.1 km if the assumed Curie-point depth is 21 km. A more realistic Curie-point depth of 32 km is used in the final interpretation model. These inferred basement blocks, grabens, and subbasin structures agree in general with the regional structures associated with the Arabian Peninsula and could provide an important framework for developing future hydrocarbon exploration strategies of the SD.

CohortNet: Empowering Cohort Discovery for Interpretable Healthcare Analytics

Cohort studies are of significant importance in the field of healthcare analytics. However, existing methods typically involve manual, labor-intensive, and expert-driven pattern definitions or rely on simplistic clustering techniques that lack medical relevance. Automating cohort studies with interpretable patterns has great potential to facilitate healthcare analytics and data management but remains an unmet need in prior research efforts. In this paper, we present a cohort auto-discovery framework for interpretable healthcare analytics. It focuses on the effective identification, representation, and exploitation of cohorts characterized by medically meaningful patterns. In the framework, we propose CohortNet, a core model that can learn fine-grained patient representations by separately processing each feature, considering both individual feature trends and feature interactions at each time step. Subsequently, it employs K-Means in an adaptive manner to classify each feature into distinct states and a heuristic cohort exploration strategy to effectively discover substantial cohorts with concrete patterns. For each identified cohort, it learns comprehensive cohort representations with credible evidence through associated patient retrieval. Ultimately, given a new patient, CohortNet can leverage relevant cohorts with distinguished importance which can provide a more holistic understanding of the patient's conditions. Extensive experiments on three real-world datasets demonstrate that it consistently outperforms state-of-the-art approaches, resulting in improvements in AUC-PR scores ranging from 2.8% to 4.1%, and offers interpretable insights from diverse perspectives in a top-down fashion.

Design and application of deep reinforcement learning algorithms based on unbiased exploration strategies for value functions

Deep Q-networks, as a representation of several classical techniques, have emerged as one of the primary branches in the field of value function-based reinforcement learning. The paper addresses two issues that come up in the realm of reinforcement learning for value function solving: estimating bias and maximizing projected action value function evaluation. By treating the estimation of the highest expected action value as a random selection estimation problem, the suggested approach addresses the estimation bias issue from the standpoint of random selection. A random choice estimate procedure forms the basis of the technique. Firstly, a proposed random choice estimator is presented and its theoretical fairness is established. Second, the estimator is applied to create a reinforcement learning method in a different application. Two techniques, namely stochastic two-depth Q-networks and double-Q learning, are suggested based on the random choice estimation technique. The main parameters of the suggested algorithms are then investigated, and parameter formulas for both predictable and unpredictable scenarios are created. Lastly, a random choice estimation perspective suggests a stochastic two-depth Q-network. The new approach may effectively remove bias in value function estimate, enhance learning performance, and stabilise the learning process, according to simulation findings on Grid World and Atari games.

Multi-Strategy-Improved Growth Optimizer and Its Applications

The growth optimizer (GO) is a novel metaheuristic algorithm designed to tackle complex optimization problems. Despite its advantages of simplicity and high efficiency, GO often encounters localized stagnation when dealing with discretized, high-dimensional, and multi-constraint problems. To address these issues, this paper proposes an enhanced version of GO called CODGBGO. This algorithm incorporates three strategies to enhance its performance. Firstly, the Circle-OBL initialization strategy is employed to enhance the quality of the initial population. Secondly, an exploration strategy is implemented to improve population diversity and the algorithm’s ability to escape local optimum traps. Finally, the exploitation strategy is utilized to enhance the convergence speed and accuracy of the algorithm. To validate the performance of CODGBGO, it is applied to solve the CEC2017, CEC2020, 18 feature selection problems, and 4 real engineering optimization problems. The experiments demonstrate that the novel CODGBGO algorithm effectively addresses the challenges posed by complex optimization problems, offering a promising approach.

An adaptive uniform search framework for constrained multi-objective optimization

This paper proposes an adaptive uniform search framework designed for constrained multi-objective optimization. The framework comprises three key components: a global uniform exploration strategy, a local greedy exploitation strategy, and a search switch mechanism. These components work together to facilitate comprehensive exploration of promising areas while maintaining a balance between global exploration and local exploitation. Specifically, the global uniform exploration strategy ensures even distribution within promising areas, preventing any oversights during exploration. The local greedy exploitation strategy divides these areas into sub-areas and employs a feasibility-led constraint handling technique to enhance efficiency in identifying optimal solutions. Additionally, the search switch dynamically adjusts the search strategy between global exploration and local exploitation. Numerical simulations on various benchmark suites and real-world problem demonstrate the strong performance of the framework in addressing constrained multi-objective optimization problems. The comparison results show that compared with eight recently proposed algorithms, the proposed framework is more robust in solving diverse constrained multi-objective optimization problems.

In Silico Exploration of Molecular Mechanisms for Inhibiting Inflammatory Responses by 3Н-Thiazolo[4,5-b]pyridin-2-one Derivatives.

Combined in silico strategy for molecular mechanisms exploration of a series 3H-thiazolo[4,5-b]pyridin-2-ones exhibiting strong anti-exudative action through QSAR analysis, molecular docking and pharmacophore modelling is reported. GA-ML technique was used for QSAR models generation with 2D autocorrelation descriptors. One- and two-parameter regressions revealed that certain structural patterns or heteroatoms contribute mutually to the anti-exudative activity potentiation. Possible action mechanisms were discovered through flexible docking simulations with cyclooxygenase pathway enzymes (COX-1, COX-2, mPGES-1). Docking results indicated the possibility of stable complexes formation with the effective docking scores and proper orientation of ligands within the enzymes active sites. Pharmacophore modelling was carried out using protein-ligand interaction fingerprints methodology. Two- and three-centre 3D pharmacophore queries were constructed. Their analysis indicated the functionality of bicyclic thiazolopyridine scaffold proved by the steric placement of heteroatoms in the corresponding pharmacophore centres.

A novel exploration strategy for the YUKI algorithm for topology optimization with metaheuristic structural binary distribution

The YUKI algorithm is an optimization approach based on search space reduction, adjusting its exploration and exploitation strategies based on optimization progress. This article suggests an update to the exploration strategy to enhance its efficacy in engineering design problems and maintain the simplicity of the algorithm. Performance evaluations were conducted on challenging composite functions and engineering design problems. In addition, the metaheuristic structural binary-distribution technique is introduced, integrating the YUKI algorithm with gradient descent for topology optimization. This technique is tested on the problem of maximizing structural stiffness, based on strain energy, across low and high dimensions, and in the problem of heat dissipation. The results suggest a significant improvement in exploration performance. MATLAB and Python source codes for the YUKI algorithm are publicly accessible at https://brahimbenaissa.com/posts/Yuki-Algorithm-02/

The role of geophysics in geologic hydrogen resources

Abstract Transition to cleaner energy sources is crucial for reducing carbon emissions to zero. Among these new clean energy types, there is a growing awareness of the potential for naturally occurring geologic hydrogen (H2) as a primary energy resource that can be readily introduced into the existing energy supply. It is anticipated that geophysics will play a critical role in such endeavors. There are two major different types of geologic H2. One is natural H2 (referred to as gold H2), which is primarily accumulating naturally in reservoirs in certain geological setting; and the other is stimulated H2 (referred to as orange H2), which is produced artificially from source rocks through chemical and physical stimulations. We will first introduce geophysics in geologic H2 in comparison and contrast to the scenarios of blue and green H2. We will then discuss the significance of geophysics in both natural H2 and stimulated H2 in term of both exploration and monitoring tools. Comparing and contrasting the current geophysical tools in hydrocarbon exploration and production, we envision the innovative geophysical technologies and strategies for geologic H2 resources based on our current understanding of both natural and stimulated geologic hydrogen systems. The strategies for H2 exploration will involve a shift from reservoir- to source rock-centered approaches. Last, we believe that the geophysical methods including integration of multi-geophysics, efficient data acquisition, and machine learning in geologic H2 could be potentially provide sufficient new directions and significant opportunities to pursue research for the next one or two decades.

THP: Tensor-field-driven hierarchical path planning for autonomous scene exploration with depth sensors

It is challenging to automatically explore an unknown 3D environment with a robot only equipped with depth sensors due to the limited field of view. We introduce THP, a tensor field-based framework for efficient environment exploration which can better utilize the encoded depth information through the geometric characteristics of tensor fields. Specifically, a corresponding tensor field is constructed incrementally and guides the robot to formulate optimal global exploration paths and a collision-free local movement strategy. Degenerate points generated during the exploration are adopted as anchors to formulate a hierarchical TSP for global path optimization. This novel strategy can help the robot avoid long-distance round trips more effectively while maintaining scanning completeness. Furthermore, the tensor field also enables a local movement strategy to avoid collision based on particle advection. As a result, the framework can eliminate massive, time-consuming recalculations of local movement paths. We have experimentally evaluate our method with a ground robot in 8 complex indoor scenes. Our method can on average achieve 14% better exploration efficiency and 21% better exploration completeness than state-of-the-art alternatives using LiDAR scans. Moreover, compared to similar methods, our method makes path decisions 39% faster due to our hierarchical exploration strategy.

A Transformer and LSTM-Based Approach for Blind Well Lithology Prediction

Petrographic prediction is crucial in identifying target areas and understanding reservoir lithology in oil and gas exploration. Traditional logging methods often rely on manual interpretation and experiential judgment, which can introduce subjectivity and constraints due to data quality and geological variability. To enhance the precision and efficacy of lithology prediction, this study employed a Savitzky–Golay filter with a symmetric window for anomaly data processing, coupled with a residual temporal convolutional network (ResTCN) model tasked with completing missing logging data segments. A comparative analysis against the support vector regression and random forest regression model revealed that the ResTCN achieves the smallest MAE, at 0.030, and the highest coefficient of determination, at 0.716, which are indicative of its proximity to the ground truth. These methodologies significantly enhance the quality of the training data. Subsequently, a Transformer–long short-term memory (T-LS) model was applied to identify and classify the lithology of unexplored wells. The input layer of the Transformer model follows an embedding-like principle for data preprocessing, while the encoding block encompasses multi-head attention, Add & Norm, and feedforward components, integrating the multi-head attention mechanism. The output layer interfaces with the LSTM layer through dropout. A performance evaluation of the T-LS model against established rocky prediction techniques such as logistic regression, k-nearest neighbor, and random forest demonstrated its superior identification and classification capabilities. Specifically, the T-LS model achieved a precision of 0.88 and a recall of 0.89 across nine distinct lithology features. A Shapley analysis of the T-LS model underscored the utility of amalgamating multiple logging data sources for lithology classification predictions. This advancement partially addresses the challenges associated with imprecise predictions and limited generalization abilities inherent in traditional machine learning and deep learning models applied to lithology identification, and it also helps to optimize oil and gas exploration and development strategies and improve the efficiency of resource extraction.

Early exploration modelling of natural hydrogen systems through the use of existing open source data

Natural hydrogen’s viability as a sustainable energy source will be enhanced through comprehensive geological modelling. This paper integrates existing open-source data to delve into the geological aspects of natural hydrogen exploration and proposes possible workflows. Geological and geophysical modelling entails characterising subsurface formations conducive to natural hydrogen generation and trapping. Utilising geological surveys, field observation, geophysical seismic and gravity data, alongside existing well logs, this analysis looks to identify regions with favourable geological conditions for the generation, migration and structures for accumulation of natural hydrogen. Furthermore, understanding the subsurface geology aids in the development of safe and efficient extraction techniques. By incorporating geological modelling into the evaluation of natural hydrogen, this paper provides a comprehensive overview of its potential as a sustainable energy solution. Leveraging existing open-source data alongside geological insights ensures a robust foundation for decision-making in exploration, production, storage, and utilisation strategies. This integrated approach empowers stakeholders to make informed choices in shaping a greener, more sustainable energy landscape.

Influence of extensional strike-slip fault systems on hydrocarbon accumulation: case studies from Huanghekoudong and Miaoxi’nan sub-sags

Extensional strike-slip basins have unique structural features that influence both sub-sags and traps. Despite previous investigations into the extension-strike-slip fault system, there remains a gap in the comprehensive analysis of its influence on sub-sag formation and trap development. The Huanghekoudong and Miaoxi’nan sub-sags within the Bohai Bay Basin, intersected by the Tan-Lu Fault Zone, offer exemplary cases for examining the interplay between strike-slip and extensional tectonic regimes. The neotectonic strike-slip movements along the Tan-Lu Fault Zone have given rise to various extensional strike-slip overlap zones in these sub-sags, rendering these locales ideal for investigating the dynamics of strike-slip and extensional tectonic processes. Employing three-dimensional seismic data, well logs, and a newly constructed sequence stratigraphic framework, our research delineates the geometric and kinematic phenomena characteristic of the extensional strike-slip fault system, including the dynamics of extensional fault deformation and interaction through time. By scrutinizing the fault system’s vital role in shaping sub-sag evolution and trap genesis, we present a comprehensive model that significantly contributes to our understanding of structural trapping dynamics. This model not only fine-tunes existing trapping models but also offers invaluable insights for future exploration strategies within the Bohai Bay Basin and other similar extensional strike-slip basins worldwide. Our findings highlight the novel and significant implications of the extensional strike-slip fault system in controlling sub-sag and trap features, thus bridging a notable gap in existing geotechnical knowledge.

Groundwater Exploration and Assessment in Arid and Semi-Arid Regions of Basaltic Terrain of Solapur: Lessons Learned and Future Prospects

This research delves into the intricate dynamics of groundwater exploration and assessment in the arid and semi-arid basaltic terrain of Solapur, India. The study investigates the hydrogeological complexities of the region, emphasizing the importance of community involvement and sustainable practices in groundwater management. Employing advanced geophysical surveys, borehole logging, and hydrogeological modeling, the research uncovers the challenges and successes encountered during the study, providing valuable insights for future exploration strategies. Key findings highlight the heterogeneity of basaltic formations, the significance of local community engagement, and the need for adaptive approaches in groundwater exploration. Lessons learned from successes in geophysical surveys and challenges faced in borehole logging contribute to the knowledge base for effective exploration techniques in similar geological settings. The research's significance for Solapur lies in its potential to guide sustainable water management practices, empower local communities, and inform policy formulation. The emphasis on community involvement, awareness, and regulatory measures serves as a foundation for addressing water scarcity challenges in the region. Beyond Solapur, the study holds broader implications for arid and semi-arid regions globally. It contributes to scientific knowledge, informing policy frameworks, and promoting community-centric approaches to groundwater management. The research provides a blueprint for addressing water resource challenges in diverse geographical contexts, ensuring the resilience of communities and the preservation of vital groundwater resources.

Operative management of primary hyperparathyroidism in Europe.

Multicentre studies have previously reported on national outcomes of surgery for primary hyperparathyroidism, but not investigated whether management and outcome are uniform among countries. This study investigated whether there are differences among European countries in operative management and outcome of surgery for primary hyperparathyroidism. Using data from Eurocrine®, a pan-European registry for endocrine surgeries, a retrospective observational cross-sectional multicentre study with 99 participating centres in 14 European countries was performed. Data on age, sex, calcium levels, operative strategy, conversion rate and rate of failed exploration were analysed for patients who underwent initial surgery for sporadic primary hyperparathyroidism. Primary outcome measures were intention to perform limited parathyroidectomy and the rate of hypercalcaemia at first follow-up. A total of 9548 patients were registered between 2015 and 2020. There were 7642 (80%, range 74.5-93.2%) females. There was intention to perform limited parathyroidectomy in 7320 of 9548 (76.7%) operations, ranging from 498 of 1007 (49.5%) to 40 of 41 (97.6%) among countries. Hypercalcaemia at first follow-up (median time to follow-up 15 days) was found in 416 of 9548 (4.4%) operations, ranging from 0 of 119 (0%) to 3 of 38 (7.9%) among countries. This study demonstrated large differences in the intention to perform limited parathyroidectomy for primary hyperparathyroidism among European countries, as well as differences in the rate of postoperative hypercalcaemia. Future studies are needed to evaluate the impact of these different healthcare practices on patient outcomes.