Healthcare analytics (New York, N.Y.)最新文献

Optimized early fusion of handcrafted and deep learning descriptors for voice pathology detection and classification

Healthcare analytics (New York, N.Y.)

Pub Date : 2024-12-01 DOI: 10.1016/j.health.2024.100369

Roohum Jegan, R. Jayagowri

This study presents an automated noninvasive voice disorder detection and classification approach using an optimized fusion of modified glottal source estimation and deep transfer learning neural network descriptors. A new set of modified descriptors based on a glottal source estimator and pre-trained Inception-ResNet-v2 convolutional neural network-based features are proposed for the speech disorder detection and classification task. The modified feature set is obtained using mel-cepstral coefficients, harmonic model, phase discrimination means, distortion deviation descriptors, conventional wavelet, and glottal source estimation features. Early descriptor-level fusion is employed in this study for performance enhancement-however, the fusion results in higher feature vector dimensionality. A nature-inspired slime mould algorithm is utilized to remove redundant and select the best discriminating features. Finally, the classification is performed using the K-nearest neighbor (KNN) classifier. The proposed algorithm was evaluated using extensive experiments with different feature combinations, with and without feature selection, and with two popular datasets: the Arabic Voice Pathology Database (AVPD) and the Saarbrucken Voice Database (SVD). We show that the proposed optimized fusion method attained an enhanced voice pathology detection accuracy of 98.46%, encompassing a wide spectrum of voice disorders on the SVD database. Furthermore, compared to traditional handcrafted and deep neural network-based techniques, the proposed method demonstrates competitive performance with fewer features.

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引用次数: 0

A deep neural network model with spectral correlation function for electrocardiogram classification and diagnosis of atrial fibrillation 用于心电图分类和心房颤动诊断的带频谱相关函数的深度神经网络模型

Healthcare analytics (New York, N.Y.)

Pub Date : 2024-11-23 DOI: 10.1016/j.health.2024.100370

Sara Mihandoost

Atrial Fibrillation (AF) is a common type of irregular heartbeat, and early detection can significantly improve treatment outcomes and prognoses. Single-lead Electrocardiogram (ECG) devices are under extensive scrutiny for monitoring patients' heart health worldwide. Standardized ECG signal monitoring has demonstrated a significant reduction in mortality rates associated with severe cardiovascular diseases. However, the automatic detection method for AF requires significant improvement. This study presents a novel approach that utilizes the cyclostationary analysis of ECG signals, uncovering a spectral hidden periodicity between the QRS-T (the main wave components representing electrical activity in the heart) complexes of the ECG signal through the Spectral Correlation Function (SCF). To validate the proposed method's performance, the single ECG's SCF coefficients are applied to the Convolutional Recurrent Neural Network (CRNN), which consists of convolutional and long short-term memory (LSTM) layers, on the 2017 PhysioNet challenge dataset. The obtained results demonstrate that the proposed approach efficiently represents ECG signals through SCF coefficients, leading to the accurate detection of AF with an average accuracy of 92.76% and an average F1-score of 89.1%.

心房颤动（房颤）是一种常见的心律不齐类型，早期检测可显著改善治疗效果和预后。单导联心电图（ECG）设备在监测全球患者心脏健康方面受到广泛关注。标准化的心电信号监测已证明可显著降低与严重心血管疾病相关的死亡率。然而，心房颤动的自动检测方法还需要大力改进。本研究提出了一种新方法，利用心电信号的周期性分析，通过频谱相关函数（SCF）揭示心电信号 QRS-T（代表心脏电活动的主要波形成分）复合体之间的频谱隐藏周期性。为验证所提方法的性能，在 2017 PhysioNet 挑战赛数据集上，将单个心电图的 SCF 系数应用于卷积递归神经网络（CRNN），该网络由卷积层和长短期记忆层组成。结果表明，所提出的方法能通过 SCF 系数有效地表示心电信号，从而准确检测出房颤，平均准确率为 92.76%，平均 F1 分数为 89.1%。

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引用次数: 0

An ensemble convolutional neural network model for brain stroke prediction using brain computed tomography images 利用脑计算机断层扫描图像预测脑中风的集合卷积神经网络模型

Healthcare analytics (New York, N.Y.)

Pub Date : 2024-10-29 DOI: 10.1016/j.health.2024.100368

Most. Jannatul Ferdous, Rifat Shahriyar

A stroke is a potentially fatal brain attack that causes an interruption in the blood supply to the brain. As a result, brain cells start to die due to a lack of oxygen and nutrients. After a stroke, every minute is critical. A million or more brain cells perish every minute during a stroke. The prompt identification of a stroke can prevent lasting brain damage or even save the patient’s life. Doctors advise computed tomography (CT) images of the brain for earlier stroke detection. If doctors delay CT diagnosis or may make erroneous diagnoses, this can be life-threatening. For that reason, an automatic diagnosis of stroke from a brain CT scan image will be beneficial for stroke patients. This study moderates three pre-trained convolutional neural network (CNN) models named Inceptionv3, MobileNetv2, and Xception by updating the top layer of those models using the transfer-learning technique based on CT images of the brain. A new ensemble convolutional neural network (ENSNET) model is proposed for automatic brain stroke prediction from brain CT scan images. ENSNET is the average of two improved CNN models named InceptionV3 and Xception. We have relied on the following metrics: accuracy, precision, recall, f1-score, confusion matrix, accuracy versus epoch, loss versus epoch, and the receiver operating characteristic (ROC) curve to assess performance matrices. The accuracy of the moderated Inceptionv3 is 97.48%, the moderated MobileNetv2 is 83.29%, and the moderated Xception is 96.11%. Nonetheless, the suggested ensemble model ENSNET performs better than the other models when it comes to the diagnosis of stroke from brain CT scans, providing 98.86% accuracy, 97.71% precision, 98.46% recall, 98.08% f1-score, and 98.74% area under the ROC curve(AUC). Therefore, the proposed model ENSNET can detect strokes from computed tomography images of the brain more successfully than other models.

中风是一种可能致命的脑部疾病，会导致大脑供血中断。因此，脑细胞会因缺氧和缺乏营养而开始死亡。中风后，每一分钟都至关重要。在中风期间，每分钟都有一百万或更多的脑细胞死亡。及时发现中风可以避免对大脑造成持久伤害，甚至挽救患者的生命。医生建议通过脑部计算机断层扫描（CT）图像来尽早发现中风。如果医生延误 CT 诊断或做出错误诊断，可能会危及生命。因此，通过脑部 CT 扫描图像自动诊断中风将对中风患者有益。本研究基于脑部 CT 图像，利用迁移学习技术更新了三个预先训练好的卷积神经网络（CNN）模型，分别命名为 Inceptionv3、MobileNetv2 和 Xception。本文提出了一种新的集合卷积神经网络（ENSNET）模型，用于从脑部 CT 扫描图像自动预测脑中风。ENSNET 是名为 InceptionV3 和 Xception 的两个改进 CNN 模型的平均值。我们采用以下指标来评估性能矩阵：准确度、精确度、召回率、f1-分数、混淆矩阵、准确度与历时的关系、损失与历时的关系以及接收者操作特征曲线（ROC）。经调节的 Inceptionv3 的准确率为 97.48%，经调节的 MobileNetv2 的准确率为 83.29%，经调节的 Xception 的准确率为 96.11%。尽管如此，建议的集合模型 ENSNET 在通过脑 CT 扫描诊断中风方面的表现优于其他模型，准确率为 98.86%，精确率为 97.71%，召回率为 98.46%，f1 分数为 98.08%，ROC 曲线下面积（AUC）为 98.74%。因此，与其他模型相比，所提出的 ENSNET 模型能更成功地从脑部计算机断层扫描图像中检测出脑卒中。

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引用次数: 0

A hierarchical Bayesian approach for identifying socioeconomic factors influencing self-rated health in Japan 用分层贝叶斯方法确定影响日本自我健康评价的社会经济因素

Healthcare analytics (New York, N.Y.)

Pub Date : 2024-10-25 DOI: 10.1016/j.health.2024.100367

Makoto Nakakita , Teruo Nakatsuma

This study identifies socioeconomic factors that potentially influence self-rated health (SRH), an important indicator of health status, in the Japanese population. We used a panel data logit model to simultaneously estimate the effects of personal attributes, living environment, and social conditions. To achieve a stable estimation of the panel data logit model, we applied hierarchical Bayesian modeling and the Markov Chain Monte Carlo (MCMC) method to obtain its estimation. Furthermore, we used the ancillary-sufficiency interweaving strategy (ASIS) algorithm to improve the efficiency of the MCMC method for the panel data logit model. The results indicate that SRH within the Japanese population is affected by demographic and socioeconomic factors (e.g., age, marital status, educational background, and employment status) and daily habits such as frequency of drinking alcohol. We also obtained results that differed from previous studies in the research literature. Differences in the national character among countries may be reflected in these results. Since SRH is a subjective measure of health status and often differs from actual health status, it is crucial to remove the influences of the national character on SRH in evaluating the actual health status of individuals within a population. The study findings provide important insights into addressing these factors to understand SRH in the Japanese context better.

本研究确定了可能影响日本人口自评健康（SRH）这一健康状况重要指标的社会经济因素。我们使用面板数据 logit 模型来同时估计个人属性、生活环境和社会条件的影响。为了实现面板数据 logit 模型的稳定估计，我们采用了分层贝叶斯建模和马尔可夫链蒙特卡罗（MCMC）方法来进行估计。此外，我们还使用了辅助-效率交织策略（ASIS）算法来提高面板数据 logit 模型的 MCMC 方法的效率。结果表明，日本人口的性健康和生殖健康受到人口和社会经济因素（如年龄、婚姻状况、教育背景和就业状况）以及日常习惯（如饮酒频率）的影响。我们还得出了与以往研究文献不同的结果。这些结果可能反映了各国在国民性方面的差异。由于性健康和生殖健康是对健康状况的主观衡量，往往与实际健康状况存在差异，因此在评估人口中个人的实际健康状况时，剔除民族特色对性健康和生殖健康的影响至关重要。研究结果为解决这些因素提供了重要启示，以便更好地了解日本的性健康和生殖健康状况。

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引用次数: 0

An electrocardiogram signal classification using a hybrid machine learning and deep learning approach 利用机器学习和深度学习混合方法进行心电图信号分类

Healthcare analytics (New York, N.Y.)

Pub Date : 2024-10-09 DOI: 10.1016/j.health.2024.100366

Faramarz Zabihi , Fatemeh Safara , Behrouz Ahadzadeh

An electrocardiogram (ECG) is a diagnostic tool that captures the electrical activity of the heart. Any irregularity in the heart's electrical system is referred to as an arrhythmia, which can be identified through the analysis of ECG signals. Timely diagnosis of cardiac arrhythmias is crucial in order to mitigate their potentially harmful consequences. However, manual analysis of ECG signals is time-consuming and prone to inaccuracies. Therefore, researchers have developed medical decision support systems that utilize machine learning techniques to automate the analysis of ECG signals. In this study, we propose a novel method for classifying ECG signals into four distinct types of heartbeats: normal, supraventricular, ventricular, and fusion. Our method consists of two subsystems that integrate both machine learning and deep learning approaches. The first subsystem uses a residual network block to extract features from the input ECG signal, followed by an LSTM network for learning and classification of these features. The second subsystem uses several feature extraction methods and a random forest to classify the ECG signals. Furthermore, it employs a Synthetic Minority Over-Sampling Technique to improve dataset balance and overall performance. The ultimate result is achieved by merging the results of both subsystems together. An assessment of our approach was carried out on the MIT-BIH dataset, which acts as a recognized ECG signal classification benchmark. Our technique attained an impressive accuracy rate of 99.26%, ranking it as one of the most superior methods in the current literature. Our findings demonstrate the effectiveness and efficiency of our approach in accurately classifying ECG signals for arrhythmia detection.

心电图（ECG）是一种捕捉心脏电活动的诊断工具。心电系统中的任何不规则现象都被称为心律失常，可通过分析心电图信号加以识别。及时诊断心律失常对于减轻其潜在的有害后果至关重要。然而，人工分析心电图信号既费时又容易出错。因此，研究人员开发了利用机器学习技术自动分析心电图信号的医疗决策支持系统。在本研究中，我们提出了一种将心电图信号分为四种不同类型心跳的新方法：正常、室上性、心室和融合。我们的方法由两个整合了机器学习和深度学习方法的子系统组成。第一个子系统使用残差网络块从输入心电信号中提取特征，然后使用 LSTM 网络对这些特征进行学习和分类。第二个子系统使用多种特征提取方法和随机森林对心电图信号进行分类。此外，它还采用了合成少数群体过度采样技术，以提高数据集的平衡性和整体性能。最终的结果是将两个子系统的结果合并在一起。我们在 MIT-BIH 数据集上对我们的方法进行了评估，该数据集是公认的心电信号分类基准。我们的技术达到了令人印象深刻的 99.26% 的准确率，是目前文献中最优秀的方法之一。我们的研究结果证明了我们的方法在准确分类心电信号以检测心律失常方面的有效性和效率。

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引用次数: 0

An inter-hospital performance assessment model for evaluating hospitals performing hip arthroplasty 用于评估髋关节置换术医院的医院间绩效评估模型

Healthcare analytics (New York, N.Y.)

Pub Date : 2024-09-24 DOI: 10.1016/j.health.2024.100365

Fabian Dehanne , Magali Pirson , Etienne Cuvelier , Frédéric Bielen , Pol Leclercq , Benoît Libert , Maximilien Gourdin

The value of hospital care to patients is expressed as a combination of reduced healthcare costs, fewer medical complications, and improved patient satisfaction. Few studies highlight the value hospitals provide to their patients through hip replacement surgery.

This study aims to define a methodology for inter-hospital comparison purposes that can assess the value of hip replacement management to patients by using indicators of costs, medical complications, and patient outcomes.

We identified medical complications and costs from medico-administrative data collected by three hospitals. We associated a Disability Adjusted Life Years (DALYs) impact with medical complications, readmissions (within 30 days), and hospital mortality. Costs were analysed from a social security perspective. Patient outcomes were collected through a questionnaire-based survey after hip surgery. To compare the three hospitals, we created a composite indicator by standardizing each dependent variable and combining a weighting of importance provided by patients.

This study analysed 342 hospital stays. The mean (standard deviation) number of DALYs per stay was estimated to be more than 0.0028 (0.016) for a mean (standard deviation) cost of €4,834 (€3,665). The composite indicator allowed hospitals to be ranked and areas for improvement to be identified. In our case mix, Hospital 3 is the lowest-ranked hospital, with excessively high costs and a relatively low level of satisfaction compared to the others.

The simultaneous evaluation of medical complications, patient outcomes, and costs is a prerequisite for quality improvement efforts by managers and practitioners. In our opinion, this experiment, which sought to estimate the value hospitals bring to patients, may be viewed as the first step towards value-based purchasing in Belgium.

医院护理对患者的价值体现在降低医疗成本、减少医疗并发症和提高患者满意度等方面。很少有研究强调医院通过髋关节置换手术为患者带来的价值。本研究旨在确定一种用于医院间比较的方法，该方法可通过成本、医疗并发症和患者预后等指标评估髋关节置换术管理对患者的价值。我们从三家医院收集的医疗行政数据中确定了医疗并发症和成本，并将残疾调整生命年（DALYs）影响与医疗并发症、再入院（30 天内）和住院死亡率联系起来。我们从社会保障的角度对成本进行了分析。髋关节手术后，我们通过问卷调查收集了患者的治疗效果。为了对三家医院进行比较，我们对每个因变量进行了标准化处理，并结合患者提供的重要性加权，创建了一个综合指标。每次住院的平均（标准差）残疾调整寿命年数估计超过0.0028（0.016），平均（标准差）费用为4834欧元（3665欧元）。综合指标可以对医院进行排名，并确定需要改进的地方。在我们的病例组合中，第 3 医院是排名最低的医院，与其他医院相比，其费用过高，满意度相对较低。我们认为，这项旨在估算医院为患者带来的价值的实验，可以被视为比利时向基于价值的采购迈出的第一步。

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引用次数: 0

A data envelopment analysis model for optimizing transfer time of ischemic stroke patients under endovascular thrombectomy 优化血管内血栓切除术下缺血性脑卒中患者转院时间的数据包络分析模型

Healthcare analytics (New York, N.Y.)

Pub Date : 2024-09-19 DOI: 10.1016/j.health.2024.100364

Mirpouya Mirmozaffari, Noreen Kamal

This study applies Data Envelopment Analysis (DEA) to optimize transfer times and futile transfers of eligible ischemic stroke patients receiving Endovascular Thrombosis (EVT) in Primary Stroke Centers (PSC) in Nova Scotia. The study aims to assess healthcare delivery in Nova Scotia over two periods. It seeks to improve stroke care for rural populations by examining nine inputs, including age and distance between PSCs and the Comprehensive Stroke Centre (CSC) that provided EVT treatment, concerning a single output variable: whether EVT is performed or not. In the first phase, 115 patients were treated as Decision-Making Units (DMUs) for ten PSCs by applying an input-oriented Variable Returns to Scale (VRS) assisted by super-efficiency analysis using the Python-based PyDEA tool. This tool is known for its unrestricted capacity to handle DMUs, inputs, and outputs. In the second phase, eight PSCs with low patient numbers were merged into four DMUs, each consisting of two PSCs. These two merged PSCs have limited patients, and the selected PSCs are also geographically close. Two PSCs have been kept separate because they had sufficient patient volume. In the first phase, VRS generated more reasonable efficiency scores for evaluation, while in the second phase, Constant Returns to Scale (CRS) outperformed VRS, yielding better results. In the initial stage of the second phase, ten PSCs were considered as six DMUs using the input-oriented CRS and VRS for 115 patients. Super-efficiency measures were applied in this stage to improve the evaluation process further. In the second part of the second phase, a comparison between the first period (2018–2019) and the second period (2020–2021) was conducted using the Malmquist Productivity Index (MPI), considering CRS and VRS to evaluate the relative efficiency and productivity change of six DMUs over time.

本研究应用数据包络分析法（DEA）对新斯科舍省初级卒中中心（PSC）接受血管内血栓治疗（EVT）的合格缺血性卒中患者的转院时间和无效转院进行优化。该研究旨在评估新斯科舍省两个时期的医疗服务提供情况。该研究通过对九个输入变量（包括年龄、初级卒中中心与提供 EVT 治疗的综合卒中中心 (CSC) 之间的距离）和一个输出变量（是否实施 EVT）进行研究，力求改善农村人口的卒中治疗。在第一阶段，通过使用基于 Python- 的 PyDEA 工具，在超效率分析的辅助下，应用以输入为导向的规模收益率变量（VRS），将 115 名患者作为 10 个 PSC 的决策单元（DMU）进行处理。该工具以其处理 DMU、输入和输出的无限制能力而著称。在第二阶段，8 个患者人数较少的 PSC 被合并为 4 个 DMU，每个 DMU 由两个 PSC 组成。这两家合并后的初级保健中心的病人数量有限，所选的初级保健中心在地理位置上也很接近。有两家初级保健中心因病人数量充足而被分开。在第一阶段，VRS 得出了更合理的效率评估分数，而在第二阶段，规模恒定收益法（CRS）优于 VRS，取得了更好的结果。在第二阶段的初始阶段，十家初级保健中心被视为六个 DMU，对 115 名患者使用了以投入为导向的 CRS 和 VRS。在这一阶段采用了超效率措施，以进一步改进评估过程。在第二阶段的第二部分，使用马尔奎斯特生产力指数（MPI）对第一阶段（2018-2019 年）和第二阶段（2020-2021 年）进行了比较，考虑了 CRS 和 VRS，以评估六个 DMU 随时间推移的相对效率和生产力变化。

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引用次数: 0

An investigation of Susceptible–Exposed–Infectious–Recovered (SEIR) tuberculosis model dynamics with pseudo-recovery and psychological effect 带假康复和心理效应的易感-暴露-感染-康复（SEIR）结核病模型动力学研究

Healthcare analytics (New York, N.Y.)

Pub Date : 2024-09-17 DOI: 10.1016/j.health.2024.100361

Yudi Ari Adi , Suparman

Tuberculosis is one of the most pressing issues of the modern era, posing a severe health risk to humans in recent decades. This study proposes a Susceptible–Exposed–Infectious–Recovered (SEIR) tuberculosis epidemic transmission model with psychological effects and pseudo-recovery. We consider a compartmental mathematical model in which the entire population is divided into four compartments based on their natural features. The model is validated, and parameter values are estimated using Indonesian data from 2002 to 2022. To investigate their epidemiological significance, we proved the positivity and boundedness of solutions, as well as the local and global stability of equilibria. Sensitivity analysis is used to find the most influential parameters with the most significant influence on the basic reproduction number,

R_{0}

. The bifurcation procedure tools of the center manifold theory are used to conduct a bifurcation study. Mathematical conditions ensure the inferred event of forward bifurcation. We performed numerical simulations that support our theoretical findings.

结核病是当代最紧迫的问题之一，近几十年来严重危害人类健康。本研究提出了一种具有心理效应和伪康复的易感-暴露-感染-康复（SEIR）结核病流行传播模型。我们考虑了一个分区数学模型，其中根据自然特征将整个人群分为四个分区。我们利用 2002 年至 2022 年的印尼数据对模型进行了验证，并估算了参数值。为了研究其流行病学意义，我们证明了解的实在性和有界性，以及平衡点的局部和全局稳定性。通过敏感性分析，我们找到了对基本繁殖数 R0 影响最大的参数。利用中心流形理论的分岔程序工具进行分岔研究。数学条件确保了推断的正向分岔事件。我们进行的数值模拟支持了我们的理论发现。

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引用次数: 0

A novel integrated logistic regression model enhanced with recursive feature elimination and explainable artificial intelligence for dementia prediction 利用递归特征消除和可解释人工智能增强痴呆症预测的新型综合逻辑回归模型

Healthcare analytics (New York, N.Y.)

Pub Date : 2024-09-14 DOI: 10.1016/j.health.2024.100362

Rasel Ahmed , Nafiz Fahad , Md Saef Ullah Miah , Md. Jakir Hossen , Md. Kishor Morol , Mufti Mahmud , M. Mostafizur Rahman

Dementia is a major global health issue that significantly impacts millions of individuals, families, and societies worldwide, creating a substantial burden on healthcare systems. This study introduces a novel approach for predicting dementia by employing the Logistic Regression (LR) model, enhanced with Recursive Feature Elimination (RFE), applied to a unique dataset comprising 1000 patients, with 49.60% male and 50.40% female. The LR model, recognized for its simplicity and effectiveness in binary classification tasks, is optimized through RFE, a technique that iteratively eliminates less significant features to improve model performance. The model’s effectiveness was assessed using comprehensive metrics, including accuracy, precision, recall, F1-score, Matthews Correlation Coefficient (MCC), and Kappa score. Furthermore, SHapley Additive exPlanations (SHAP) values were employed to increase the interpretability of the model, providing insights into the most influential features for dementia prediction. To address the issue of overfitting, a standardization technique was implemented, which enhanced the model’s predictive performance. The findings of this study hold potential implications for early dementia detection, informing intervention strategies, and optimizing healthcare resource allocation.

痴呆症是一个重大的全球性健康问题，严重影响着全球数百万个人、家庭和社会，给医疗保健系统带来沉重负担。本研究介绍了一种预测痴呆症的新方法，该方法采用逻辑回归（LR）模型，并通过递归特征消除（RFE）进行了增强，适用于由 1000 名患者组成的独特数据集，其中男性占 49.60%，女性占 50.40%。LR 模型因其在二元分类任务中的简便性和有效性而得到认可，该模型通过 RFE 技术进行了优化，RFE 是一种迭代消除不重要特征以提高模型性能的技术。该模型的有效性通过准确度、精确度、召回率、F1 分数、马修斯相关系数（MCC）和 Kappa 分数等综合指标进行评估。此外，还采用了SHAPLEY Additive exPlanations（SHAP）值来提高模型的可解释性，从而深入了解对痴呆症预测最有影响的特征。为了解决过拟合问题，我们采用了标准化技术，从而提高了模型的预测性能。这项研究的结果对早期痴呆症的检测、干预策略的制定和医疗资源的优化分配具有潜在的意义。

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引用次数: 0

A Markov cohort model for Endoscopic surveillance and management of Barrett’s esophagus 内镜监测和管理巴雷特食管的马尔科夫队列模型

Healthcare analytics (New York, N.Y.)

Pub Date : 2024-08-29 DOI: 10.1016/j.health.2024.100360

Ravi Vissapragada , Norma B. Bulamu , Roger Yazbeck , Jonathan Karnon , David I. Watson

Barrett's esophagus is an asymptomatic precursor to esophageal adenocarcinoma. Its rising incidence due to lifestyle factors, coupled with healthcare costs, requires cost-effective alternatives for surveillance. We propose a decision-analytic Markov cohort model to simulate Barrett's esophagus's natural progression to esophageal adenocarcinoma using TreeAge Pro. Health states include metaplasia (non-dysplastic Barrett's esophagus), low-grade dysplasia, high-grade dysplasia, and esophageal adenocarcinoma. Triplicates of these health states represent one non-stratified and two risk-stratified cohorts for devising risk-based strategies. A cycle length of six months and a time horizon of 35 years, totaling 70 cycles, is considered. Model inputs are derived from literature and, when unavailable from an extensive local database of 1087 patients (5081 person-years) from March 2003–2021, cleaned and analyzed with Rstudio (R version 3.6.3). Specific tests included descriptive statistics, Cox-proportional hazard models, and graphing. A seven-step calibration process is performed for risk-stratified and non-stratified groups simultaneously to match the progression to high-grade dysplasia and esophageal adenocarcinoma. This allows comparison between risk- and non-risk-based strategies. The calibration process included input parameterization, optimization, goodness of fit calculation, selection of sets meeting convergence criteria, and integration into probabilistic sensitivity analysis. This process generated 10,187 sets of transition probabilities, with 4358 meeting convergence criteria, ensuring equal model outputs in all groups. Mortality was 10.7% for cancer-related deaths, matching literature values. This process provides a robust framework for evaluating Barrett's esophagus progression and management strategies, supporting informed decision-making in healthcare.

巴雷特食管是食管腺癌的无症状前兆。由于生活方式和医疗成本等因素，其发病率不断上升，因此需要成本效益高的替代监测方法。我们提出了一个决策分析马尔可夫队列模型，利用 TreeAge Pro 模拟巴雷特食管向食管腺癌的自然发展过程。健康状态包括化生期（非增生不良的巴雷特食管）、低度增生不良、高度增生不良和食管腺癌。这些健康状况的三倍代表一个非分层组群和两个风险分层组群，用于制定基于风险的策略。考虑的周期长度为 6 个月，时间跨度为 35 年，共计 70 个周期。模型输入数据来源于文献，如无法获得，则来源于 2003 年 3 月至 2021 年间 1087 名患者（5081 人-年）的庞大本地数据库，并使用 Rstudio（R 3.6.3 版）进行了清理和分析。具体测试包括描述性统计、Cox 比例危险模型和绘图。对风险分层组和非分层组同时进行七步校准，以匹配向高级别发育不良和食管腺癌的进展。这样就可以对基于风险和非基于风险的策略进行比较。校准过程包括输入参数化、优化、拟合优度计算、选择符合收敛标准的集合，以及整合到概率敏感性分析中。这一过程产生了 10187 组过渡概率，其中 4358 组符合收敛标准，确保了所有组的模型输出结果相同。癌症相关死亡的死亡率为 10.7%，与文献值相符。这一过程为评估巴雷特食管的进展和管理策略提供了一个稳健的框架，为医疗保健领域的知情决策提供了支持。

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引用次数: 0