Reserve Estimation Research Articles

Stochastic Chain-Ladder Reserving with Modeled General Inflation

We consider two possible approaches to the problem of incorporating explicit general (i.e., economic) inflation in the non-life claims reserve estimates and the corresponding reserve SCR, defined—as in Solvency II—under the one-year view. What we call the actuarial approach provides a simplified solution to the problem, obtained under the assumption of deterministic interest rates and absence of inflation risk premia. The market approach seeks to eliminate these shortcomings by combining a stochastic claims reserving model with a stochastic market model for nominal and real interest rates. The problem is studied in details referring to the stochastic chain-ladder provided by the Over-dispersed Poisson model. The application of the two approaches is illustrated by a worked example based on market data.

Integration of a Low Gas-Saturated Zone in Creating a 3D Model of the Medvezhye Field

3D geological modeling is an integral part of reserves estimation and project documentation for the development of gas and oil fields. The developed 3D geological model of the Cenomanian productive complex including the low gas-saturated zone of the Medvezhye field represents a combination of data from the drilling of 350 wells, the structural features of the reservoir, permeability properties determined on cores, seismic surveys, and the results of geophysical well surveys. Based on examining the available well logs recorded in the Medvezhye field wells, the low gas-saturated zone is integrated into the construction of the 3D geological model of the Medvezhye field of the Cenomanian productive complex. A total of three models are constructed, and a 400*400*0.4 m grid is recommended for further hydrodynamic modeling. A comparison of the three grids suggests that a more detailed model delivers more thorough results, however, heavy models cannot be applied to hydrodynamic modeling. Resource estimations do not differ across the three grids significantly.

Reliable Technologies Combine To Establish Reasonable Certainty for Reserves Estimates

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 210358, “Establishing Reasonable Certainty for Reserves Estimates by Using a Combination of Reliable Technologies,” by Keshav Narayanan, SPE, Peter Gale, SPE, and J.P. Blangy, SPE, Woodside Energy, et al. The paper has not been peer reviewed. The Petroleum Resources Management System (PRMS) and many regulatory agencies require what is termed “reasonable certainty” for Proved Reserves estimates. The guidance provided by the PRMS or the US Securities and Exchange Commission (SEC) for establishing reasonable certainty is general in nature because of the difficulty in explicitly describing all possible scenarios and also allows leeway to use new technologies in the future. The complete paper addresses the complex challenge of establishing reasonable certainty in reserves and resource assessments and discusses how multiple reliable technologies may be used in concert to establish reasonable certainty for reserves estimates through the flexibility provided by the PRMS. Introduction The authors write that a common response to many questions seeking clarification on reserves-related matters is that “it depends.” Likewise, regarding the question of what constitutes reliable technology, the answer depends on the specific circumstances and the data being considered. The use of reliable technology as a method to improve certainty in the estimates of oil and gas reserves was formally recognized in the 2008 modernization of oil and gas reporting. The PRMS issued in 2007 and further updated in 2018 also recognized the use of appropriate technologies that can lead to more-reliable estimates of reserves. However, the SEC or the PRMS guidelines also seem to recognize that it might not be possible to prescribe a definitive approach that could work for any set of circumstances. They broadly describe the framework for what constitutes reliable technology and how it can be used to establish reasonable certainty. Operators have an obligation to use a scientific method to build a robust case to justify categorization of volumes. Previous Research Previous work reviewed how the industry, in the years from 2008 to 2016, had been using reliable technologies for reserves estimates and how industry practitioners were demonstrating and describing that these applications were consistent with the SEC and PRMS guidelines. The material surveyed by previous researchers can be grouped into the following broad themes: - Seismic applications, which establish water contacts downdip of well controls and explore how seismic amplitude processes can be relied on to improve probability of geologic success - Dynamic simulations, which investigate when reasonable certainty can be demonstrated when simulation models are used - Unconventional applications, which include how reasonable certainty should be established when booking proved, undeveloped reserves (PUD) away from well control - SEC comment letters, which can require registrant companies to more-clearly spell out the demonstration of reasonable certainty - Applications of multiple reliable technologies increase confidence in free-water level determined by use of pressure data or seismic amplitude shutoffs, seal-capacity analysis, well logs, pressure data, and seismic to support proved reserves estimates

Characteristics of iron-manganese nodules containing nickel and cobalt in chromite deposits in Co Dinh, Thanh Hoa province

Along with the chromite ore bodies, very early on, geologists recorded the presence of cobalt (Co) and nickel (Ni) metal. The resource of cobalt and nickel is forecasted with over 3 million tons of Ni and over 260 thousand tons of Co metals. Until now, however, knowledge about the existence form and distribution of Ni and Co in chromite ore bodies is very limited. Researching the sludge in the chromite mines and in comparison with manganese nodules found in the ocean floor helps to detect the presence of particles of iron and manganese (Fe-Mn nodules) in granular sewage sludges and in primary chromite ore bodies. Fe-Mn hydroxides, including todorokite mineral, are believed to be the adsorption medium and forming of Ni and Co in the Co Dinh placer chromite. Determining the existence form and distribution of Ni and Co in chromite placer deposits has scientific and practical significance in sampling orientation, sample processing, geological exploration, and reserve and resource estimations, sorting and recovering technology of Co and Ni metals.

An estimation of reservoir pressure dynamics and recoverable gas reserves based on the data of production well surveys

In the development of gas fields, reservoir pressure can be used to estimate gas reserves by means of the material balance methodology and to calculate drained reserves. This provides an indication of the effectiveness of the development system. The authors of the article suggest a methodology for determining drained reserves in wells without reservoir pressure measurements. This approach is useful if the well stock is put into operation concurrently or within a brief period of time. Numerical modelling and analysis of field development data have led the authors to conclude that the current flow rate of wells, the hydrodynamic parameters of the productive formation and the interference of nearby wells are the most significant factors controlling the amount of drained reserves. The use of this technique will improve the understanding of the current processes occurring in the reservoir, clarify the assessment of the energy of the deposit and the value of the initial gas reserves, and also create an additional source of information for timely and more accurate decision-making in field development. Adopting this methodology can improve the understanding of ongoing processes in the reservoir, enhance evaluations of reservoir energy and initial gas reserve sizes, and provide additional information resources for making decisions on field development in a more timely and precise manner.

Assessment of Bedugul Geothermal Prospect Using A Numerical Reservoir Modeling

A numerical modeling was carried out to generate a numerical model of the Bedugul geothermal field located in Bali Island, Indonesia. This study presents a natural state model and updates the conceptual model of Bedugul based on published geological, geophysical, geochemical, and well data. The numerical simulation of the Bedugul geothermal field has been developed using the previous conceptual model and well data. The Bedugul reservoir model is properly aligned with actual Pressure and Temperature well data. Thus, the natural state model is used to update the Bedugul conceptual model. The main updated points of the conceptual model were the location of the heat source to be beneath Mount Tapak, adding the flow fluid pattern as outflow and upflow location, caprock, reservoir, recharge, discharge, and adding iso-temperature distribution. Based on the numerical model result, the Bedugul geothermal field is a waterdominated geothermal system. The results of this model have been applied to the heat stored method with the Monte Carlo simulation to generate a probabilistic distribution of the reserve potential estimation. The probabilistic for P50 of probable and proven reserve estimation for thirty years are 136.3 MW and 33.4 MW, respectively. Keywords: Bedugul, numerical model, natural state, resource assessment, geothermal

Unconventional Reservoir Characterization and Formation Evaluation: A Case Study of a Tight Sandstone Reservoir in West Africa

Unconventional reservoirs, including gas shales and tight gas sands, have gained prominence in the energy sector due to technological advancements and escalating energy demands. The oil industry is eagerly refining techniques to decipher these reservoirs, aiming to reduce data collection costs and uncertainties in reserve estimations. Characteristically, tight reservoirs exhibit low matrix porosity and ultra-low permeability, necessitating artificial stimulation for enhanced production. The efficacy of the stimulation hinges on the organic material distribution, the rock’s mechanical attributes, and the prevailing stress field. Comprehensive petrophysical analysis, integrating standard and specialized logs, core analyses, and dynamic data, is pivotal for a nuanced understanding of these reservoirs. This ensures a reduction in prediction uncertainties, with parameters like shale volume, porosity, and permeability being vital. This article delves into an intricate petrophysical evaluation of the Nene field, a West African unconventional reservoir. It underscores the geological intricacies of the field, the pivotal role of data acquisition, and introduces avant-garde methodologies for depth matching, rock typing, and the estimation of permeability. This research highlights the significance of unconventional reservoir exploration in today’s energy milieu, offering a granular understanding of the Nene field’s geological challenges and proffering a blueprint for analogous future endeavours in unconventional reservoirs.

Prevalence and prognostic effects of comorbid coronary artery disease detected in patients undergoing carotid endarterectomy

Abstract Introduction Coronary artery disease (CAD) shares common risk factors with carotid artery stenosis and is a known perioperative risk factor for carotid endarterectomy (CEA). It is feasible to perform CEA with continuation of antiplatelet drugs, including dual antiplatelet therapy. Differing opinions exist with respect to detection of CAD prior to CEA. To support the discussions, we aimed to assess recent prevalence and prognostic effects of comorbid CAD in patients undergoing CEA without known CAD. Methods This single-center prospective cohort study included patients scheduled for elective CEA between 2013 and 2021. Of these, 108 patients without a medical history of CAD (mean age 76.6±7.3 years, 73.1% men) underwent coronary computed tomography angiography (CCTA). Coronary angiography (CAG) and subsequent fractional flow reserve (FFR) estimation were performed where necessary. CAD was defined as total or subtotal occlusion observed on CCTA, ≥75% stenosis on CAG, or lesions with FFR ≤ 0.80 in the major coronary arteries. Optimal medical therapy and/or coronary revascularization was added according to the presence, severity, and extent of CAD. The primary endpoint was MACE (the composite of all-cause mortality, myocardial infarction, or stroke) after CEA. Results The prevalence of comorbid CAD was 19.4% (21 of 108 patients). During a mean follow-up of 4.0 years, the incidence of MACE and stroke was significantly higher in patients with CAD (adjusted HR: 3.10, 95% CI: 1.43–6.71, p=0.0042 and HR: 2.76, 95% CI 1.08–7.31, p=0.041, respectively). Other than patient age, only CAD was a significant predictor of MACE. Conclusions CAD is commonly detected in patients undergoing CEA, and it predicts future MACE and stroke event.

Abstract 15577: Diagnostic Performance of Cadmium-Zinc-Telluride Single-Photon Emission Computed Tomography versus Coronary Angiography Fractional Flow Reserve in Estimating Myocardial Blood Flow and Flow Reserve: A Diagnostic Accuracy Meta-Analysis

Introduction: Precisely estimating myocardial blood flow and flow reserve is crucial in diagnosing and managing ischemic heart disease. Coronary angiography fractional flow reserve (FFR) has been a standard tool. However, the recent emergence of Cadmium-Zinc-Telluride Single-Photon Emission Computed Tomography (CZT-SPECT) has provided an innovative diagnostic approach. Consequently, a comprehensive comparative analysis of their efficacies in evaluating myocardial blood flow and flow reserve is necessary. Hypothesis: This study aims to determine the diagnostic accuracy of CZT-SPECT compared to coronary angiography FFR in estimating myocardial blood flow and flow reserve. Methods: A systematic search until July 2022 was conducted across databases such as PubMed, Embase, and Web of Science, focusing on studies comparing CZT-SPECT and coronary angiography FFR in the estimation of myocardial blood flow and flow reserve. Utilizing the mada package in R software (version 4.0.3), we conducted diagnostic accuracy analysis by pooling metrics like sensitivity, specificity, false-positive rate estimates, diagnostic odds ratio, and positive and negative Likelihood Ratios (LR), all of which were presented with a 95% Confidence Interval (CI). Results: The meta-analysis incorporated 6 studies with a total patient population of 346. Our findings reveal that CZT-SPECT exhibited a pooled sensitivity of 79.1% (95% CI: 71.8-84.9%, I^2=0) and a specificity of 78.5% (95% CI: 70.4-84.8%, I^2=25.3%). The false-positive rate was 21.5% (95% CI: 15.2-29.6%). Moreover, CZT-SPECT demonstrated a diagnostic odds ratio of 13.75 (95% CI: 7.67-24.65), while the positive likelihood ratio was 3.67 (95% CI: 2.59-5.19), and the negative likelihood ratio stood at 0.26 (95% CI: 0.19-0.37). Conclusions: This study provides valuable insights into the diagnostic accuracy of CZT-SPECT compared to coronary angiography FFR in estimating myocardial blood flow and flow reserve. These findings indicate that while CZT-SPECT shows promising diagnostic potential, its performance is similar to coronary angiography FFR. The results will be instrumental in informing clinical decision-making and shaping future research directions in the field of cardiovascular imaging.

Assessment of nutritional status and clinical outcomes: A comprehensive retrospective analysis of critically ill patients.

Assessing nutritional status is vital for understanding and managing patients' clinical conditions. Controlling Nutritional Status (CONUT) score is one of the most frequently used scores that evaluate caloric status, protein reserve, and immunological response estimation. This study assesses the critically ill patients' nutritional status and its relationship with patient-related factors and mortality. Electronic medical records of 1687 Intensive Care Unit (ICU) patient files were reviewed from January 2019 to May 2023 retrospectively. Patient demographic information, clinical characteristics, and laboratory data were extracted and anonymized for analysis. The patients were divided according to the CONUT scores, and retrieved data were compared. The study population's demographic features and clinical outcomes were stratified by CONUT scores. The gender distribution was similar; however, there was a significant age difference between the Low-CONUT and High-CONUT groups (P < .001). High CONUT values are linked to an increased risk of death, intubation, age, APACHE-II scores, and the number of comorbidities. Malnutrition is prevalent in ICU patients with gastrointestinal illnesses and urogenital system disorders, including infections. Patients with a high CONUT score at admission had an increased risk of ICU death even after correcting for covariates. The CONUT score is a trustworthy and practical tool for assessing ICU patients' nutritional status, which is highly associated with ICU mortality. Patients admitted to the ICU with gastrointestinal, urogenital, or infectious disorders should have their nutritional condition assessed carefully. According to our study, the rate of intubated patients admitted to the intensive care unit is higher in malnourished patients. This suggests it should be reviewed in clinical settings, particularly in elderly, frail patients and those with numerous comorbidities.

Mineral resource assessment through geostatistical analysis in a phosphate deposit

Purpose. The selection of an appropriate variographic model is crucial in geostatistics to obtain accurate estimates of mineral reserves. The aim of this work is to develop a reserve estimation tool using a geostatistical approach. Methodology. The geostatistical approach is based on selecting the most representative variographic models for the studied variables. The model selection is done by applying a cross-validation procedure leave-one-out (LOOCV). LOOCV is a resampling technique used in statistical analysis and machine learning to estimate the generalization error of a model and compare the performance of different models. The studied variables are then estimated using ordinary kriging. Findings. The application of the proposed approach has resulted in satisfactory results in terms of dispersion of grades and thicknesses of mineralized layers in a phosphate deposit. To evaluate the quality of the adjustment models obtained, efficiency factors such as Nash-Sutcliffe, and RMSE (Root Mean Square Error), were employed. These factors provide quantitative measures of the agreement between the observed and predicted values. The NSE (Nash-Sutcliffe efficiency) and RMSE (root mean square error) values of 0.572 and 6.599, respectively, indicate a better fit and greater accuracy of the adjustment models. The accuracy and efficiency criteria of the studied variables have acceptable values, with a mean square error (MSE) of 1.54 · 10-7. Originality. The combination of the least squares and LOOCV methods in the geostatistical analysis leads to improved estimation precision, greater reliability in representing the spatial variability of the parameters, and enhanced confidence in the validity of the adjustment models. Practical value. The development of a computer code for this geostatistical approach provides a practical tool for decision-makers to use in the management and exploitation of mining sites. Overall, this study has contributed to the advancement of geostatistical techniques and their application in the mining industry.

Model Error (or Ambiguity) and Its Estimation, with Particular Application to Loss Reserving

This paper is concerned with the estimation of forecast error, particularly in relation to insurance loss reserving. Forecast error is generally regarded as consisting of three components, namely parameter, process and model errors. The first two of these components, and their estimation, are well understood, but less so model error. Model error itself is considered in two parts: one part that is capable of estimation from past data (internal model error), and another part that is not (external model error). Attention is focused here on internal model error. Estimation of this error component is approached by means of Bayesian model averaging, using the Bayesian interpretation of the LASSO. This is used to generate a set of admissible models, each with its prior probability and likelihood of observed data. A posterior on the model set, conditional on the data, may then be calculated. An estimate of model error (for a loss reserve estimate) is obtained as the variance of the loss reserve according to this posterior. The population of models entering materially into the support of the posterior may turn out to be “thinner” than desired, and bootstrapping of the LASSO is used to increase this population. This also provides the bonus of an estimate of parameter error. It turns out that the estimates of parameter and model errors are entangled, and dissociation of them is at least difficult, and possibly not even meaningful. These matters are discussed. The majority of the discussion applies to forecasting generally, but numerical illustration of the concepts is given in relation to insurance data and the problem of insurance loss reserving.

Refined lithology identification: Methodology, challenges and prospects

Refined lithology identification is the reliable basis for determining the spatial distribution of lithology, reserve estimation and 3D modeling. With the development of artificial intelligence, there have been some intelligent identification methods that can identify coarse-grained classes of rock. However, the research progress of fine-grained lithology identification is slow due to the difficulty of fine-grained identification, the high cost of data collection and complexity of engineering site conditions. Against this background, we conducted research on fine-grained identification based on macroscopic images, analyzed the differences between fine-grained identification and coarse-grained identification, explored the differences between image identification under laboratory conditions and on-site conditions, and discussed the development direction of lithology identification. Specifically, we constructed a laboratory dataset (160 lithologies) and an on-site dataset (13 lithologies); then, we developed a classification model specifically for extracting lithological features; next, we conducted identification experiments using the self-built model and detection model on these two datasets and comparative analysis was carried out. The results show that the self-developed model has an evaluation metric (F1-score) of 0.9764 on the images collected in the laboratory, but an F1-score of only 0.6143 on the images collected in the engineering site. Unlike coarse-grained identification, image augmentation that changes local features degraded the fine-grained lithology identification performance. Factors such as surface contamination caused by geological process, weathering and similarity in appearance due to metamorphism in the engineering site can affect the accuracy of image-based methods. This study explores in detail the potential and existing problems of fine-grained lithology identification based on macroscopic images, which can provide reference for future reservoir-related issues.

Molecular dynamics simulations of methane adsorption and displacement from graphenylene shale reservoir nanochannels

Methane is the main component of shale gas and is adsorbed in shale pores. Methane adsorption not only affects the estimation of shale gas reserves but also reduces extraction efficiency. Therefore, investigating the behavior of methane adsorption in shale reservoirs is important for evaluating shale gas resources, as well as understanding its desorption and displacement from the nanochannels of shale gas reservoirs. In this research, molecular dynamics simulations were used to investigate the adsorption behavior of methane gas in organic shale pores made of graphenylene, followed by its displacement by CO2 and N2 injection gases. The effects of pore size, pressure, and temperature on adsorption were examined. It was observed that increasing the pore size at a constant pressure led to a decrease in the density of adsorbed methane molecules near the pore surface, while a stable free phase with constant density formed in the central region of the nanopore. Moreover, adsorption increased with increasing pressure, and at pressures ranging from 0 to 3 MPa, 15 and 20 Å pores exhibited lower methane adsorption compared to other pores. The amount of adsorption decreased with increasing temperature, and the observed adsorption isotherm followed the Langmuir adsorption isotherm. The mechanism of methane displacement by the two injected gases differed. Carbon dioxide filled both vacant adsorption sites and directly replaced the adsorbed methane. On the other hand, nitrogen only adsorbed onto the vacant sites and, by reducing the partial pressure of methane, facilitated the displacement of methane.

Rock Typing Approaches for Effective Complex Carbonate Reservoir Characterization

For highly heterogeneous complex carbonate reef reservoirs, rock typing with respect to depositional conditions, secondary processes, and permeability and porosity relationships is a useful tool to improve reservoir characterization, modeling, prediction of reservoir volume properties, and estimation of reserves. A review of various rock typing methods has been carried out. The basic methods of rock typing were applied to a carbonate reservoir as an example. The advantages and disadvantages of the presented methods are described. A rock typing method based on a combination of hydraulic flow units and the R35 method is proposed. Clustering methods for rock typing are used. The optimum clustering method is identified, and for each rock type, the permeability–porosity relationships are built and proposed for use in the geomodelling stage.

Three-Dimensional Integrated Geo-Static Modeling for Prospect Identification and Reserve Estimation in the Middle Miocene Multi-Reservoirs: A Case Study from Amal Field, Southern Gulf of Suez Province

Middle Miocene reservoirs in the southern part of the Gulf of Suez province are characterized by geometrical uncertainties due to their structural settings, lateral facies change, different lithologies, and diverse reservoir quality. Therefore, in this study, detailed 3D geo-static models were constructed by integrating multiple datasets, including 2D seismic sections and digital well-logs. The 3D models were constructed for the Belayim Formation (Hammam Faraun Member), Kareem Formation (Markha Member), and Rudies Formation (Upper Rudies Member) with detailed structuration, zonation, and layering for Amal Field in the southern Gulf of Suez province to assess the hydrocarbon potential, calculate accurate reserves, recommend development and exploration plans, and propose locations for future drilling. The resultant structural model exhibited a compartmentalized area of major and minor normal faults trending NW–SE, forming structurally high potential hydrocarbon trapping locations in the study area. The petrophysical models indicated the good potentiality of Hammam Faraun as a reservoir with porosity values of 15–23%, increasing towards the central part of the area, volume of shale (Vsh) of 21–31%, water saturation (Sw) of 34–49%, and sand thickness increasing toward the northeastern part of the area. The Markha Member was also interpreted as a good reservoir, with porosity values of 15–22%, increasing towards the southeastern part of the area, Vsh of 13–29%, Sw of 16–38%, and sandy facies accumulating in the central horst block. Upper Rudies exhibits good reservoir properties with porosity values of 16–23%, Vsh of 29–37%, Sw of 35–40%, and good sandy facies in the central horst block of the area. The study results showed hydrocarbon potential in the central horst block of the study area for the Middle Miocene multi-reservoirs.

On the impact of outliers in loss reserving

The sensitivity of loss reserving techniques to outliers in the data or deviations from model assumptions is a well known challenge. It has been shown that the popular chain-ladder reserving approach is at significant risk to such aberrant observations in that reserve estimates can be significantly shifted in the presence of even one outlier. As a consequence the chain-ladder reserving technique is non-robust. In this paper we investigate the sensitivity of reserves and mean squared errors of prediction under Mack’s Model (ASTIN Bull 23(2):213–225, 1993). This is done through the derivation of impact functions which are calculated by taking the first derivative of the relevant statistic of interest with respect to an observation. We also provide and discuss the impact functions for quantiles when total reserves are assumed to be lognormally distributed. Additionally, comparisons are made between the impact functions for individual accident year reserves under Mack’s Model and the Bornhuetter–Ferguson methodology. It is shown that the impact of incremental claims on these statistics of interest varies widely throughout a loss triangle and is heavily dependent on other cells in the triangle. Results are illustrated using data from a Belgian non-life insurer.

Chronic stress induces meiotic arrest failure and ovarian reserve decline via the cAMP signaling pathway.

Chronic stress is suspected to be a causal factor of female subfertility; however, the underlying mechanisms remain unclear. Here, we found that chronic stress inhibited the cyclic adenosine 3',5'-monophosphate (cAMP) signaling pathway, leading to ovarian reserve decline in mice. A chronic stress model was constructed using restraint stress for 8 weeks. An elongated estrous cycle and a significant increase in the number of atretic follicles were observed in the stress group. We identified a significant increase in meiotic arrest failure (MAF) in oocytes in the stress group, characterized by condensed metaphase chromosomes, assembled spindles, or polar bodies in the oocytes. Whole-mount ovarian reserve estimation at the single-oocyte level using the CUBIC method (clear, unobstructed brain/body imaging cocktails and computational analysis) revealed a significant decrease in quiescent oocytes from 2,261/ovary in the control group to 1,373/ovary in the stress group. The number of growing oocytes also significantly decreased from 220/ovary in the control group to 150/ovary in the stress group. Real-time quantitative polymerase chain reaction (RT-qPCR) analysis of the meiotic arrest maintenance pathways revealed significant downregulation of Gpr3, Nppc, and Npr2 in the stress group. These results indicate that blocking cAMP production contributes to MAF and a decline in ovarian reserve. Overall, we present new insights into the mechanisms underlying chronic-stress-induced oocyte loss and potential targets for ovarian reserve preservation.

High-Speed On-Site Deep Learning-Based FFR-CT Algorithm: Evaluation Using Invasive Angiography as the Reference Standard.

BACKGROUND. Estimation of fractional flow reserve from coronary CTA (FFR-CT) is an established method of assessing the hemodynamic significance of coronary lesions. However, clinical implementation has progressed slowly, partly because of off-site data transfer with long turnaround times for results. OBJECTIVE. The purpose of this study was to evaluate the diagnostic performance of FFR-CT computed on-site with a high-speed deep learning-based algorithm with invasive hemodynamic indexes as the reference standard. METHODS. This retrospective study included 59 patients (46 men, 13 women; mean age, 66.5 ± 10.2 years) who underwent coronary CTA (including calcium scoring) followed within 90 days by invasive angiography with invasive fractional flow reserve (FFR) and/or instantaneous wave-free ratio measurements from December 2014 to October 2021. Coronary artery lesions were considered to have hemodynamically significant stenosis in the presence of invasive FFR of 0.80 or less and/or instantaneous wave-free ratio of 0.89 or less. A single cardiologist evaluated the CTA images using an on-site deep learning-based semiautomated algorithm entailing a 3D computational flow dynamics model to determine FFR-CT for coronary artery lesions detected with invasive angiography. Time for FFR-CT analysis was recorded. FFR-CT analysis was repeated by the same cardiologist in 26 randomly selected examinations and by a different cardiologist in 45 randomly selected examinations. Diagnostic performance and agreement were assessed. RESULTS. A total of 74 lesions were identified with invasive angiography. FFR-CT and invasive FFR had strong correlation (r = 0.81) and, in Bland-Altman analysis, bias of 0.01 and 95% limits of agreement of -0.13 to 0.15. FFR-CT had AUC for hemodynamically significant stenosis of 0.975. At a cutoff of 0.80 or less, FFR-CT had 95.9% accuracy, 93.5% sensitivity, and 97.7% specificity. In 39 lesions with severe calcifications (≥ 400 Agatston units), FFR-CT had AUC of 0.991 and at a cutoff of 0.80, 94.7% sensitivity, 95.0% specificity, and 94.9% accuracy. Mean analysis time per patient was 7 minutes 54 seconds. Intraobserver agreement (intraclass correlation coefficient, 0.85; bias, -0.01; 95% limits of agreement, -0.12 and 0.10) and interobserver agreement (intraclass correlation coefficient, 0.94; bias, -0.01; 95% limits of agreement, -0.08 and 0.07) were good to excellent. CONCLUSION. A high-speed on-site deep learning-based FFR-CT algorithm had excellent diagnostic performance for hemodynamically significant stenosis with high reproducibility. CLINICAL IMPACT. The algorithm should facilitate implementation of FFR-CT technology into routine clinical practice.

PRedicting the EVolution of SubjectIvE Cognitive Decline to Alzheimer’s Disease With machine learning: the PREVIEW study protocol

BackgroundAs disease-modifying therapies (DMTs) for Alzheimer's disease (AD) are becoming a reality, there is an urgent need to select cost-effective tools that can accurately identify patients in the earliest stages of the disease. Subjective Cognitive Decline (SCD) is a condition in which individuals complain of cognitive decline with normal performances on neuropsychological evaluation. Many studies demonstrated a higher prevalence of Alzheimer’s pathology in patients diagnosed with SCD as compared to the general population. Consequently, SCD was suggested as an early symptomatic phase of AD. We will describe the study protocol of a prospective cohort study (PREVIEW) that aim to identify features derived from easily accessible, cost-effective and non-invasive assessment to accurately detect SCD patients who will progress to AD dementia.MethodsWe will include patients who self-referred to our memory clinic and are diagnosed with SCD. Participants will undergo: clinical, neurologic and neuropsychological examination, estimation of cognitive reserve and depression, evaluation of personality traits, APOE and BDNF genotyping, electroencephalography and event-related potential recording, lumbar puncture for measurement of Aβ42, t-tau, and p-tau concentration and Aβ42/Aβ40 ratio. Recruited patients will have follow-up neuropsychological examinations every two years. Collected data will be used to train a machine learning algorithm to define the risk of being carriers of AD and progress to dementia in patients with SCD.DiscussionThis is the first study to investigate the application of machine learning to predict AD in patients with SCD. Since all the features we will consider can be derived from non-invasive and easily accessible assessments, our expected results may provide evidence for defining cost-effective and globally scalable tools to estimate the risk of AD and address the needs of patients with memory complaints. In the era of DMTs, this will have crucial implications for the early identification of patients suitable for treatment in the initial stages of AD.Trial registration number (TRN)NCT05569083.