Patient Data Research Articles

Implications of Large Language Models for Quality and Efficiency of Neurologic Care: Emerging Issues in Neurology.

Large language models (LLMs) are advanced artificial intelligence (AI) systems that excel in recognizing and generating human-like language, possibly serving as valuable tools for neurology-related information tasks. Although LLMs have shown remarkable potential in various areas, their performance in the dynamic environment of daily clinical practice remains uncertain. This article outlines multiple limitations and challenges of using LLMs in clinical settings that need to be addressed, including limited clinical reasoning, variable reliability and accuracy, reproducibility bias, self-serving bias, sponsorship bias, and potential for exacerbating health care disparities. These challenges are further compounded by practical business considerations and infrastructure requirements, including associated costs. To overcome these hurdles and harness the potential of LLMs effectively, this article includes considerations for health care organizations, researchers, and neurologists contemplating the use of LLMs in clinical practice. It is essential for health care organizations to cultivate a culture that welcomes AI solutions and aligns them seamlessly with health care operations. Clear objectives and business plans should guide the selection of AI solutions, ensuring they meet organizational needs and budget considerations. Engaging both clinical and nonclinical stakeholders can help secure necessary resources, foster trust, and ensure the long-term sustainability of AI implementations. Testing, validation, training, and ongoing monitoring are pivotal for successful integration. For neurologists, safeguarding patient data privacy is paramount. Seeking guidance from institutional information technology resources for informed, compliant decisions, and remaining vigilant against biases in LLM outputs are essential practices in responsible and unbiased utilization of AI tools. In research, obtaining institutional review board approval is crucial when dealing with patient data, even if deidentified, to ensure ethical use. Compliance with established guidelines like SPIRIT-AI, MI-CLAIM, and CONSORT-AI is necessary to maintain consistency and mitigate biases in AI research. In summary, the integration of LLMs into clinical neurology offers immense promise while presenting formidable challenges. Awareness of these considerations is vital for harnessing the potential of AI in neurologic care effectively and enhancing patient care quality and safety. The article serves as a guide for health care organizations, researchers, and neurologists navigating this transformative landscape.

A new framework to enhance healthcare monitoring using patient-centric predictive analysis

In the contemporary healthcare landscape, various intelligent automated approaches are revolutionizing healthcare tasks. Learning concepts are pivotal for activities like comprehending acquired data and monitoring patient behavior. Among patient-centric concerns, addressing data heterogeneity, extraction, and prediction challenges is crucial. To enhance patient monitoring using care indicators like cost and length of stay at healthcare centers, many researchers found a model for automated tools, but do not have the artificial intelligence (AI) based models as of now. Therefore, this research study will propose an AI and internet of things (IoT) integrated automated approach with smart sensors called the “PatientE” framework with heterogeneity and patient data. Employing certain rules for data extraction to form a distinct representation, our model integrates pre-treatment information and employs a modified combined random forest, long-short term memory (LSTM), and bidirectional long-short term memory (BiLSTM) algorithm for predictive post-treatment monitoring. This framework, synergizing AI, IoT, and advanced neural networks, facilitates real-time health monitoring, especially focusing on breast cancer patients. Embracing pre-treatment, in-treatment, and post-treatment phases, our model aims for accurate diagnosis, improved cost-efficiency, and extended stays. The evaluation underscores scalability, reliability enhancement, and validates the framework's efficacy in transforming healthcare practices.

Changes in nutritional status and the development of obesity and metabolic syndrome following pediatric heart transplantation.

Nutritional status in pediatric patients undergoing heart transplantation (HT) is frequently a focus of clinical management and requires high resource utilization. Pre-operative nutrition status has been shown to affect post-operative mortality but no studies have been performed to assess how nutritional status may change and the risk of developing nutritional comorbidities long-term in the post-transplant period. A single-center retrospective chart review of patients ≥2 years of age who underwent heart transplantation between 1/1/2005 and 4/30/2020 was performed. Patient data were collected at listing, time of transplant, 1-year, and 3-year follow-up post-transplant. Nutrition status was classified based on body mass index (BMI) percentile in the primary analysis. Alternative nutritional indices, namely the nutrition risk index (NRI), prognostic nutrition index (PNI), and BMI z-score, were utilized in secondary analyses. Of the 63 patients included, the proportion of patients with overweight/obese status increased from 21% at listing to 41% at 3-year follow-up. No underweight patients at listing became overweight/obese at follow-up. Of patients who were overweight/obese at listing, 88% maintained that status at 3-year follow-up. Overweight/obese status at listing, 1-year, and 3-year post-transplantation were significantly associated with developing metabolic syndrome. In comparison to the alternative nutritional indices, BMI percentile best predicted post-transplant metabolic syndrome. The results suggest that pediatric patients who undergo heart transplantation are at risk of developing overweight/obesity and related nutritional sequelae (ie, metabolic syndrome). Improved surveillance and interventions targeted toward overweight/obese HT patients should be investigated to reduce the burden of associated comorbidities.

Skin cancer diagnosis using the deep learning advancements: a technical review

It is vital in today's technologically advanced society to combat skin cancer using machines rather than human intervention. Any time the look of the skin changes abnormally, there is a danger that the person might be at risk for skin cancer. Dermatology expertise and computer vision methods must be merged to diagnose melanoma more effectively. Because of this, it is necessary to learn about numerous detection methods to help doctors discover skin cancer at an early stage. This research paper provides a comprehensive technical review of the advancements in using deep learning techniques for the diagnosis of skin cancer. Since skin cancer is so prevalent, early identification is essential for better treatment results. Among the medical uses where deep learning, a kind of machine learning, has shown promise is in the identification of skin cancer. This research investigates the most cutting-edge skin cancer diagnostic deep-learning approaches, datasets, and assessment metrics currently in use. This study discusses the benefits and drawbacks of using deep learning for skin cancer detection. Challenges include ethical and privacy considerations about patient data, the incorporation of models into clinical procedures, and problems with dataset bias and generalisation.

Nanocarbon tracer applied to explore the relationship between the number of exposed parathyroid glands and parathyroid function

After nanocarbon tracer was applied to thyroidectomy, we aimed to investigate the risk factors of hypoparathyroidism and the relationship between the number of exposed parathyroid glands and parathyroid function. This study retrospectively collected clinical data of patients who underwent thyroidectomy at Haining People’s Hospital from January 2019 to December 2022. Levels of parathyroid hormone (PTH) and blood calcium were observed before surgery, 1 day after surgery, and one month after surgery to assess the occurrence of postoperative parathyroid hypofunction. Patients were grouped based on their postoperative PTH levels. Furthermore, basic patient data and surgical data were collected to analyze the risk factors associated with hypoparathyroidism after thyroidectomy using single factor analysis. ANOVA analysis was used to compare the correlation between intraoperative parathyroid exposure and postoperative PTH and blood calcium levels. This study involved 80 patients. On the first day after surgery, the serum PTH and blood calcium levels of patients decreased, and 59 patients (73.75%) had normal serum PTH level after surgery, which was the normal group. Twenty-one patients (26.25%) developed hypoparathyroidism after operation and were divided into hypoparathyroidism group. Single factor analysis showed that the main risk factors of postoperative hypoparathyroidism were lesion location, lymph node dissection, nano carbon, number of lymph node metastasis, intraoperative parathyroid exposure, and inaccurate parathyroidectomy. ANOVA analysis showed that the more parathyroid exposure during operation was accompanied by the more decrease of serum PTH and blood calcium levels after operation. Therefore, the location of thyroid lesions, lymph node dissection, nano-carbon, number of lymph node metastases, intraoperative parathyroid exposure, and parathyroidectomy are risk factors for transient hypoparathyroidism after thyroid surgery. Moreover, intraoperative parathyroid exposure significantly affected serum PTH and blood calcium levels. Performing more accurate thyroidectomy and reducing parathyroid exposure help reduce the occurrence of postoperative hypoparathyroidism and hypocalcemia.

An efficient healthcare system by cloud computing and clustering-based hybrid machine learning algorithm

Cloud computing, deep learning, clustering, genetic, and ensemble algorithms in healthcare are gaining popularity. This research highlights the relevance and complex repercussions of this integration. Cloud computing is transforming healthcare by providing scalable data storage and application access. It streamlines data exchange between hospitals, researchers, and institutions. Deep learning allows healthcare systems to use artificial intelligence for diagnostics, predictive analytics, and customized medication. Clustering algorithms segment patients, improving therapy and intervention customization. Genetic algorithms can optimize healthcare processes like treatment planning and resource allocation. Ensemble algorithms combine multiple models to improve predicted accuracy, enabling strong healthcare decision-making. This connection has several benefits. Healthcare systems become more efficient and scalable, resulting in cost-effective resource allocation. Access to patient data and apps promotes collaborative research and real-time healthcare. Deep learning algorithms can recognize complex medical data patterns, improving illness diagnosis and treatment results. Clustering algorithms streamline customized healthcare by stratifying individuals by clinical variables. Genetic algorithms optimize resource allocation, assuring healthcare resource efficiency. Ensemble algorithms improve predicted accuracy and clinical decision support system dependability. Its efficiency, accessibility, and prediction accuracy are positives, but security, resource constraints, interpretability, and ethical issues are obstacles.

Ceftaroline fosamil treatment patterns and outcomes in adults with community-acquired pneumonia: a real-world multinational, retrospective study.

This multicentre, observational, retrospective chart review study assessed ceftaroline fosamil treatment patterns and outcomes in adults hospitalized with community-acquired pneumonia (CAP) in usual care settings. Anonymized patient data were extracted from hospital records of adults with CAP who received ≥4 consecutive IV ceftaroline fosamil doses at sites in Brazil, Colombia, France, Greece, Italy, Russia and Spain. The dataset included 185 patients (58.9% male; mean age 62.2 years), of whom 128 (69.2%) had severe CAP defined by CURB-65, PSI/PORT score or physician judgement. Streptococcus pneumoniae (n = 44; 23.8%) and Staphylococcus aureus [MSSA (n = 15) and MRSA (n = 14)] were the most frequently identified pathogens. Clinical response occurred in 151 (81.6%) patients overall, and in 104 (81.3%) severe CAP patients. Response within ≤4 and >4 days occurred in 79 (42.7%) and 62 (33.5%) patients (unknown, n = 10), respectively. Twenty (10.8%) patients required readmission within 30 days. Thirty-day all-cause mortality rates were 9.7% (n = 18) overall and 10.2% (n = 13) in severe CAP. In sensitivity analysis using ICU admission as a more objective marker of severe CAP (n = 75), clinical response and 30 day mortality occurred in 57 (76.0%) and 10 (13.3%) patients, respectively. Overall, clinical response to ceftaroline fosamil was associated with >60% shorter length of ICU stay (3.6 versus 9.2 days), and >30% lower hospital costs ($8449 versus $12 559) versus non-responders. Ceftaroline fosamil was effective in treating adults with CAP, including severe CAP, in Europe and Latin America. Clinical response to ceftaroline fosamil was associated with reductions in healthcare resource use compared with non-responders.

Increased Incidence of Vestibular Disorders in Patients With SARS-CoV-2

Objective: Determine the incidence of vestibular disorders in patients with SARS-CoV-2 compared to the control population. Study Design: Retrospective. Setting: Clinical data in the National COVID Cohort Collaborative database (N3C). Methods: Deidentified patient data from the National COVID Cohort Collaborative database (N3C) were queried based on variant peak prevalence (untyped, alpha, delta, omicron 21K, and omicron 23A) from covariants.org to retrospectively analyze the incidence of vestibular disorders in patients with SARS-CoV-2 compared to control population, consisting of patients without documented evidence of COVID infection during the same period. Results: Patients testing positive for COVID-19 were significantly more likely to have a vestibular disorder compared to the control population. Compared to control patients, the odds ratio of vestibular disorders was significantly elevated in patients with untyped (odds ratio [OR], 2.39; confidence intervals [CI], 2.29–2.50; P < 0.001), alpha (OR, 3.63; CI, 3.48–3.78; P < 0.001), delta (OR, 3.03; CI, 2.94–3.12; P < 0.001), omicron 21K variant (OR, 2.97; CI, 2.90–3.04; P < 0.001), and omicron 23A variant (OR, 8.80; CI, 8.35–9.27; P < 0.001). Conclusions: The incidence of vestibular disorders differed between COVID-19 variants and was significantly elevated in COVID-19-positive patients compared to the control population. These findings have implications for patient counseling and further research is needed to discern the long-term effects of these findings.

A decentralized federated learning-based cancer survival prediction method with privacy protection

BackgroundSurvival prediction is one of the crucial goals in precision medicine, as accurate survival assessment can aid physicians in selecting appropriate treatment for individual patients. To achieve this aim, extensive data must be utilized to train the prediction model and prevent overfitting. However, the collection of patient data for disease prediction is challenging due to potential variations in data sources across institutions and concerns regarding privacy and ownership issues in data sharing. To facilitate the integration of cancer data from different institutions without violating privacy laws, we developed a federated learning-based data integration framework called AdFed, which can be used to evaluate patients’ survival while considering the privacy protection problem by utilizing the decentralized federated learning technology and regularization method. ResultsAdFed was tested on different cancer datasets that contain the patients’ information from different institutions. The experimental results show that AdFed using distributed data can achieve better performance in cancer survival prediction (AUC=0.605) than the compared federated-learning-based methods (average AUC=0.554). Additionally, to assess the biological interpretability of our method, in the case study we list 10 identified genes related to liver cancer selected by AdFed, among which 5 genes have been proved by literature review. ConclusionsThe results indicate that AdFed outperforms better than other federated-learning-based methods, and the interpretable algorithm can select biologically significant genes and pathways while ensuring the confidentiality and integrity of data.

The Future of Patient Data Security: Exploring Emerging Technologies and Collaborative Approaches

Abstract: The protection of sensitive patient data has become a paramount concern in the increasingly digital landscape of the healthcare industry. As healthcare organizations adopt electronic health records and face a growing array of cyber threats, they must navigate a complex regulatory environment, including the Health Insurance Portability and Accountability Act (HIPAA), while implementing robust strategies to safeguard personal health information (PHI). This article provides a comprehensive overview of the challenges and best practices in healthcare data protection, examining the current regulatory framework, the various threats to patient data—such as cyber-attacks, insider threats, and human error—and the key strategies and technologies for enhancing security, including encryption, access controls, and employee training. The article also explores the potential of emerging technologies, such as block chain and artificial intelligence, in revolutionizing healthcare data management and security, and emphasizes the importance of collaboration among stakeholders, including healthcare providers, technology vendors, policymakers, and patients. By recapping the main challenges and strategies discussed, offering insights into the future of data protection in healthcare, and issuing a call to action for prioritizing patient data security, this article serves as a valuable resource for healthcare organizations seeking to strengthen their data protection measures and maintain patient trust in the digital age.

Karyotyping and Down Syndrome: Insights from Cytogenetic Analysis and Patient Data

Abstract: Down syndrome is one of the most commonly occurring chromosomal disorders, and in a rapidly growing population like India, cytogenetic evaluation is crucial for accurate diagnosis and medical intervention. Karyotyping remains the most relied upon cytogenetic analysis method due to its affordability and extensive experience. This paper provides an overview of Down syndrome, chromosomal aberrations, karyotype analysis, its requirements, and the processes involved. Additionally, it presents data collected in our laboratory over a year, facilitating data driven decision making. While Down syndrome has no cure, early detection through karyotyping enables better management and access to medical care, offering affected individuals the opportunity for a fulfilling life.

Sepsis Prediction

Abstract: Sepsis manifests as a life-threatening condition wherein the body's response to infection triggers organ dysfunction. Typically, this immune response inadvertently damages the body's tissues while combating the infection. Within the overall range of sepsis, septic shock represents a critical subset characterized by notable circulatory, cellular, and metabolic abnormalities, resulting in a increased mortality rate than standard sepsis. Noteworthy is the consensus from various studies highlighting the crucial role of early intervention in enhancing the survival chances of sepsis patients. Given the urgency of timely recognition and treatment, there is a heightened focus on sepsis prediction. Traditional scoring systems such as APACHE, SAPS, and SOFA provide insights into disease severity and prognosis but lack the capacity for early sepsis detection. This study uses ML techniques and analyzes extensive patient data, which includes vital signs and medical history, to identify important indicators and prognostic patterns that find the occurrence of sepsis. By applying advanced algorithms like neural networks and ensemble methods, the model shows impressive accuracy in predicting the progression of sepsis. In addition, detailed analysis of trait significance helps to understand key prognostic markers, allowing clinicians to strategically prioritize interventions. Successful integration of this prognostic tool into the clinical setting offers a promising opportunity for preventive management of sepsis, which can reduce mortality and improve patient outcomes. We built a (LSTM) network to distinguish the internal association between different. indicators in clinical data. Analysis includes use of electronic health records, multicenter collaboration, and challenge development. Our goal is to support the research of accurate and timely sepsis predictors, ultimately improving patient outcomes.

Revolutionizing Healthcare: The Transformative Power of Artificial Intelligence

Abstract: The field of radiology is changing due to artificial intelligence (AI), which presents hitherto unheard-of chances to improve diagnostic efficiency and accuracy. The transformational potential of AI in radiology is examined in this research, with particular attention to how it might expedite clinical workflows and completely change picture interpretation. The first section of the abstract emphasizes the rising need for radiological services as well as the difficulties radiologists have in organizing massive amounts of patient data while maintaining prompt and precise diagnosis. The article goes on to discuss AI as a potent tool that radiologists may use to analyze pictures more confidently, spot abnormalities, and make clinical choices more quickly. The transformational potential of artificial intelligence (AI) in radiology is examined in this research, with a focus on how AI might enhance diagnostic efficiency and accuracy. It presents the potential of artificial intelligence (AI) to transform radiological practice by highlighting its strengths in image interpretation, anomaly detection, and clinical decision assistance. But there are drawbacks to using AI in radiology, including concerns about data privacy and algorithm transparency. In order to guarantee patient safety and confidence in AI-enabled radiological techniques, the study highlights the significance of responsible AI implementation. The study concludes by highlighting the revolutionary effects of AI on radiology and highlighting its potential as a tool to improve healthcare delivery and diagnostic accuracy.

Streamlining Dental Clinic Operations Introducing the Web Application for Efficient Dental Clinic Management

Abstract: The Dental Clinic Management System web application is a comprehensive software program designed to automate and optimize the various tasks and operations that occur in a dental clinic. Historically, dental clinics have kept their records manually and done their paperwork on paper, which has led to many errors, inefficiencies, and delays.To tackle these problems, the Web Application for Dental Clinic Management System provides dental professionals and personnel with an easy-to-use interface that makes daily operations more efficient. The DCMS web application assists patients in scheduling a dentist appointment at a dental clinic. The system simplifies the administration of patient data, encompassing personal data, medical history, and treatment documentation. Precise diagnosis and treatment planning are ensured by the easily accessible and updated information by authorized staff. Only the patient that have been registered in Dental Clinic Management System be able to use the system. It is run by highly qualified and experienced health professionals.This website covers Admin panel, Dentist panel and Patient panel. User can perform search by dentist specialty, gender, and dentist name. User can browse through dentist profile and view their specialty information. The register user can choose and book an appointment at the flexible time and date. Dentist can view the patient appointment and they can confirm or cancel their reservation. Admin can able control and maintain Dentist and patient information through a computerized process. And Admin can view and replay the unregistered users queries, also view the medical report of the patient.

Smart Autonomous Nurse Assistant Robot (SANAR)

Abstract: In the evolving landscape of healthcare, the integration of innovative technologies has become paramount to enhance efficiency, accessibility, and usability across all sectors, particularly in the medical industry. This project proposes the development of a Smart Autonomous Nurse Assistant Robot (SANAR) aimed at revolutionizing medical care delivery, especially during emergencies and pandemics like Covid-19. SANAR utilizes cutting-edge technology to streamline disease diagnosis, monitor patient health in real-time, and facilitate drug delivery. The core of SANAR's functionality lies in its utilization of a health tracker system-based IoT platform, which continuously analyzes vital patient data, including oxygen levels, pulse rate, and temperature. Through simple text commands and a timer-based medicine scheduler, SANAR ensures timely medication administration, leveraging a cloud server for seamless data access and management with utmost security. Equipped with advanced sensors such as oximeter, heartbeat, ultrasonic sensors, and a voice module, SANAR leverages Internet of Things (IoT) technology to enable data storage and retrieval over HTTP and MQTT protocols, facilitating connectivity and scalability. The primary goal of this project is to democratize smart automation in healthcare, making it accessible and affordable to all segments of society, ultimately empowering medical staff to deliver efficient and effective care.

Prediction and Analysis of Liver Patient Data

Abstract: Prediction and Analysis of Liver Patient Data is a project that aims to gain insights into liver diseases and develop a system for predicting the likelihood of liver-related health conditions by using data analysis techniques and predictive modeling. This project examines data from liver patients, focusing on the relationships between a key list of liver enzymes, proteins, age, and gender in order to predict the likelihood of liver disease. In this case, we are constructing a model by employing various machine learning algorithms to find the most accurate model. And connect to a flask-based web application. The user can predict the disease by entering parameters into the web application. The project's goal is to uncover significant indicators and build a predictive model to aid in diagnosing liver diseases and identifying potential risks by exploring patterns and relationships within the data.

Impact of technological innovations on healthcare delivery: A literature review of efficiency, patient care, and operational challenges

The rapid integration of technology in healthcare systems globally has ushered in transformative changes in patient care, operational efficiency, and data management. This literature review assesses the impact of emerging technologies such as blockchain, information technology, electronic medical records (EMRs), and artificial intelligence (AI) within healthcare settings. Drawing from a wide range of peer-reviewed articles and empirical studies, this paper synthesizes current research findings to understand the multifaceted effects of these technologies on healthcare delivery. The review highlights how these innovations enhance healthcare quality, streamline operations, and tackle prevalent challenges, particularly focusing on their implementation in Saudi Arabian healthcare contexts. Key findings indicate that while technological advancements offer significant improvements in patient care and system management, they also introduce challenges related to privacy, security, and the need for significant infrastructure investments. The review underscores the necessity for ongoing research to navigate technology adoption’s complexities and optimize healthcare practices effectively. This paper contributes to academic and practical discussions on leveraging technology to foster more efficient and responsive healthcare systems worldwide.\

Integrated Diagnosis, Treatment and Prognosis in Healthcare using Artificial Intelligence

Artificial Intelligence (AI) has revolutionized healthcare by integrating treatment, diagnosis, and prognosis into a cohesive and patient-centric approach. This study examines how utilising AI technology in healthcare might improve patient management and have a transformational impact. Huge volumes of patient data, including as genetic data, medical records, and treatment outcomes are analysed by AI algorithms, allowing for the creation of individualised treatment regimens based on precise prognostic assessments and diagnoses. Utilising AI-driven decision-making promotes proactive and preventative actions, improving healthcare outcomes. To ensure ethical AI adoption, however, concerns about data privacy, algorithmic bias, and ethical issues must be addressed. In order to demonstrate how AI-driven therapy approaches are successful, case examples are reviewed in this article, demonstrating how they might potentially enhance patient care. As AI develops, its seamless integration with healthcare systems has enormous promise for revolutionising medical practise. It will usher in a new era of accurate, effective, and data-driven patient management, which will ultimately be advantageous to both patients and healthcare professionals.its capacity to enhance patient care.

Comparison between Zone 2 and Zone 3 distal anastomoses for aortic arch replacement in terms of invasiveness.

Zone 2 anastomosis with total cervical branch reconstruction for acute type A aortic dissection and aortic arch aneurysms became possible after stent-graft introduction. This may be an easier procedure and reduce the risk of recurrent laryngeal nerve palsy. Therefore, this study aimed to compare the outcomes between Zone 2 and Zone 3 distal anastomoses. After evaluating the patient data in our institute between April 2016 and April 2022, the patients in whom distal anastomosis was performed at Zone 2 with a stent-graft were defined as the Zone 2 group (n = 70). The patients in whom distal anastomosis was performed at Zone 3 were defined as the Zone 3 group (n = 24). The incidence of new-onset recurrent nerve palsy was one patient (1.4%) in the Zone 2 group and six patients (25.0%) in the Zone 3 group (p < 0.001). The lower body perfusion arrest time was 44.3 ± 9.1min in the Zone 2 group and 52.9 ± 12.8min in the Zone 3 group (p = 0.005). There were no significant differences in in-hospital mortality and morbidities. Multivariable analysis showed that only age was an independent predictor of overall mortality. Performing distal anastomosis at Zone 2 with a frozen elephant trunk or stent-graft reduced the lower body perfusion arrest time and possibly prevented recurrent nerve palsy.

Cost-effectiveness analysis of tislelizumab vs. camrelizumab for the treatment of second-line locally advanced or metastatic esophageal squamous cell carcinoma

BackgroundEsophageal carcinoma is a type of cancer that occurs in the esophagus. For patients with locally advanced or metastatic esophageal squamous cell carcinoma who have either experienced disease progression following first-line standard chemotherapy or are intolerant to it, the prognosis is typically poor. Additionally, these patients often bear a substantial economic burden during the course of their treatment. Tislelizumab is a selective PD-1 inhibitor with efficacy proven in locally advanced or metastatic esophageal squamous cell carcinoma. The study aims to evaluate the cost-effectiveness of tislelizumab versus camrelizumab as the second-line treatment in locally advanced or metastatic esophageal squamous cell carcinoma (ESCC) patients in China.MethodsFrom the perspective of China’s healthcare system, the partitioned survival model with three health states was established in a 3-week cycle and a lifetime horizon. Anchored matching adjusted indirect comparison was used for survival analyses based on individual patient data from RATIONALE 302 trial and the published ESCORT study due to the lack of head-to-head clinical trials. Only direct medical costs were included. Costs and utility values were derived from local charges, the published literature, and related databases. Sensitivity analyses and a scenario analysis were also performed to verify the robustness of the model results.ResultsCompared with camrelizumab monotherapy, tislelizumab monotherapy incurred a lower lifetime cost ($8,346 vs. $8,851) and yielded higher quality-adjusted life-years (QALYs) (0.87 vs. 0.63), which resulted in an incremental cost-effectiveness ratio (ICER) of -$2,051/QALY. Tislelizumab monotherapy is a dominant option over camrelizumab monotherapy in China. The three primary parameters upon which this result was most sensitive were the unit cost of camrelizumab, the unit cost of tislelizumab, and the duration of reactive cutaneous capillary endothelial proliferation (RCCEP). According to the probabilistic sensitivity analysis (PSA), tislelizumab monotherapy was 100% cost-effective when the WTP was 1–3 times GDP per capita in China($11,207/QALY∼$33,621/QALY). Scenario analysis showed that the result was consistent.ConclusionTislelizumab monotherapy is a dominant option compared with camrelizumab monotherapy as the second-line treatment for locally advanced or metastatic ESCC in China.