Impact of Immunosuppressants and Vaccination on COVID-19 Outcomes in Autoimmune Patients and Solid Organ Transplant Recipients: A Nationwide Propensity Score-Matched Study.

PURPOSE: This study investigates the impact of varying degrees of immunosuppression on the clinical outcomes of immunocompromised individuals, particularly those with autoimmune diseases or post-solid organ transplant statuses, in the context of COVID-19. By focusing on these highly vulnerable populations, the study underscores the significant health inequalities faced by immunocompromised patients, who experience disproportionately worse outcomes in comparison to the general population. METHODS: A retrospective cohort analysis of the K-COV-N dataset was conducted, comparing the effects of immunosuppression in autoimmune and transplant groups with matched control groups. Propensity score matching was employed to minimize inequalities in baseline characteristics, ensuring a more equitable comparison between immunocompromised and non-immunocompromised individuals. Outcomes included COVID-19-related in-hospital mortality, 28-day mortality, ICU admissions, and the need for respiratory support among 323,890 adults in the Republic of Korea. Patients with cancer or other immunosuppressive conditions, such as HIV, were excluded. Subgroup analyses assessed the influence of specific immunosuppressive medications and vaccination extent. RESULTS: Significantly elevated in-hospital mortality was found for patients with autoimmune diseases (adjusted Odds Ratio [aOR] 2.749) and transplant recipients (aOR 7.567), with similar patterns in other outcomes. High-dose steroid use and a greater number of immunosuppressant medications markedly increased the risk of poor outcomes. Vaccination emerged as a protective factor, with a single dose substantially improving outcomes for autoimmune patients and at least two doses necessary for transplant recipients. CONCLUSIONS: Immunocompromised patients, particularly those with autoimmune diseases and transplant recipients, are highly vulnerable to severe COVID-19 outcomes. High-dose steroid use and multiple immunosuppressants further increase risks. Vaccination significantly improves outcomes, with at least one dose benefiting autoimmune patients and two doses necessary for transplant recipients. Personalized vaccination schedules based on immunosuppression levels are essential to mitigate healthcare inequalities and improve outcomes, particularly in underserved populations, informing both clinical and public health strategies.

PPFL: A personalized progressive federated learning method for leveraging different healthcare institution-specific features.

Federated learning (FL) in healthcare allows the collaborative training of models on distributed data sources, while ensuring privacy and leveraging collective knowledge. However, as each institution collects data separately, conventional FL cannot leverage the different features depending on the institution. We proposed a personalized progressive FL (PPFL) approach that leverages client-specific features and evaluated with real-world datasets. We compared the performance of in-hospital mortality prediction between our model and conventional models based on accuracy and area under the receiver operating characteristic (AUROC). PPFL achieved an accuracy of 0.941 and AUROC of 0.948, which were higher than the scores of the local models and FedAvg algorithm. We also observed that PPFL achieved a similar performance for cancer data. We identified client-specific features that can contribute to mortality. PPFL is a personalized federated algorithm for heterogeneously distributed clients that expands the feature space for client-specific vertical feature information.

Three-dimensional label-free morphology of CD8 + T cells as a sepsis biomarker.

Sepsis is a dysregulated immune response to infection that leads to organ dysfunction and is associated with a high incidence and mortality rate. The lack of reliable biomarkers for diagnosing and prognosis of sepsis is a major challenge in its management. We aimed to investigate the potential of three-dimensional label-free CD8 + T cell morphology as a biomarker for sepsis. This study included three-time points in the sepsis recovery cohort (N = 8) and healthy controls (N = 20). Morphological features and spatial distribution within cells were compared among the patients' statuses. We developed a deep learning model to predict the diagnosis and prognosis of sepsis using the internal cell morphology. Correlation between the morphological features and clinical indices were analysed. Cell morphological features and spatial distribution differed significantly between patients with sepsis and healthy controls and between the survival and non-survival groups. The model for predicting the diagnosis and prognosis of sepsis showed an area under the receiver operating characteristic curve of nearly 100% with only a few cells, and a strong correlation between the morphological features and clinical indices was observed. Our study highlights the potential of three-dimensional label-free CD8 + T cell morphology as a promising biomarker for sepsis. This approach is rapid, requires a minimum amount of blood samples, and has the potential to provide valuable information for the early diagnosis and prognosis of sepsis.

Prognostic value of baseline and early treatment response of neutrophil-lymphocyte ratio, C-reactive protein, and lactate dehydrogenase in non-small cell lung cancer patients undergoing immunotherapy.

Background: Not all non-small cell lung cancer (NSCLC) patients will benefit from immune checkpoint therapy and use of these medications carry serious autoimmune adverse effects. Therefore, biomarkers are needed to better identify patients who will benefit from its use. Here, the correlation of overall survival (OS) with baseline and early treatment period serum biomarker responses was evaluated in patients with NSCLC undergoing immunotherapy. Methods: Patients diagnosed with NSCLC undergoing immunotherapy (n=597) at a tertiary academic medical center in South Korea were identified between January 2010 and November 2021. The neutrophil-lymphocyte ratio (NLR), C-reactive protein (CRP), and lactate dehydrogenase (LDH) levels in the survival and non-survival groups were examined at baseline and early treatment periods. Additionally, aberrant laboratory parameters at each period were used to stratify survival curves and examine their correlation with one-year OS. Results: In the non-survival group, the NLR, CRP, and LDH levels at the early treatment period were higher than those at the baseline (P<0.001). The survival curves stratified based on aberrant laboratory findings in each period varied (log-rank test P<0.001). Multivariate Cox regression analysis revealed that having prescribed more than 3rd line of chemotherapy [hazard ratio (HR) =3.19, 95% confidence interval (CI): 1.04-9.82; P=0.043] and early treatment period CRP (HR =3.88; 95% CI: 1.55-9.72; P=0.004) and LDH (HR =4.04; 95% CI: 2.01-8.12; P<0.001) levels were significant predictors of one-year OS. Conclusions: Early treatment period CRP and LDH levels were significant predictors of OS in patients with NSCLC undergoing immunotherapy.

Development and validation of an artificial intelligence model for the early classification of the aetiology of meningitis and encephalitis: a retrospective observational study.

Background: Early diagnosis and appropriate treatment are essential in meningitis and encephalitis management. We aimed to implement and verify an artificial intelligence (AI) model for early aetiological determination of patients with encephalitis and meningitis, and identify important variables in the classification process. Methods: In this retrospective observational study, patients older than 18 years old with meningitis or encephalitis at two centres in South Korea were enrolled for development (n = 283) and external validation (n = 220) of AI models, respectively. Their clinical variables within 24 h after admission were used for the multi-classification of four aetiologies including autoimmunity, bacteria, virus, and tuberculosis. The aetiology was determined based on the laboratory test results of cerebrospinal fluid conducted during hospitalization. Model performance was assessed using classification metrics, including the area under the receiver operating characteristic curve (AUROC), recall, precision, accuracy, and F1 score. Comparisons were performed between the AI model and three clinicians with varying neurology experience. Several techniques (eg, Shapley values, F score, permutation feature importance, and local interpretable model-agnostic explanations weights) were used for the explainability of the AI model. Findings: Between January 1, 2006, and June 30, 2021, 283 patients were enrolled in the training/test dataset. An ensemble model with extreme gradient boosting and TabNet showed the best performance among the eight AI models with various settings in the external validation dataset (n = 220); accuracy, 0.8909; precision, 0.8987; recall, 0.8909; F1 score, 0.8948; AUROC, 0.9163. The AI model outperformed all clinicians who achieved a maximum F1 score of 0.7582, by demonstrating a performance of F1 score greater than 0.9264. Interpretation: This is the first multiclass classification study for the early determination of the aetiology of meningitis and encephalitis based on the initial 24-h data using an AI model, which showed high performance metrics. Future studies can improve upon this model by securing and inputting time-series variables and setting various features about patients, and including a survival analysis for prognosis prediction. Funding: MD-PhD/Medical Scientist Training Program through the Korea Health Industry Development Institute, funded by the Ministry of Health & Welfare, Republic of Korea.

Aspergillus Galactomannan Titer as a Diagnostic Marker of Invasive Pulmonary Aspergillosis in Lung Transplant Recipients: A Single-Center Retrospective Cohort Study.

Invasive pulmonary aspergillosis (IPA) can occur in immunocompromised patients, and an early detection and intensive treatment are crucial. We sought to determine the potential of Aspergillus galactomannan antigen titer (AGT) in serum and bronchoalveolar lavage fluid (BALF) and serum titers of beta-D-glucan (BDG) to predict IPA in lung transplantation recipients, as opposed to pneumonia unrelated to IPA. We retrospectively reviewed the medical records of 192 lung transplant recipients. Overall, 26 recipients had been diagnosed with proven IPA, 40 recipients with probable IPA, and 75 recipients with pneumonia unrelated to IPA. We analyzed AGT levels in IPA and non-IPA pneumonia patients and used ROC curves to determine the diagnostic cutoff value. The Serum AGT cutoff value was 0.560 (index level), with a sensitivity of 50%, specificity of 91%, and AUC of 0.724, and the BALF AGT cutoff value was 0.600, with a sensitivity of 85%, specificity of 85%, and AUC of 0.895. Revised EORTC suggests a diagnostic cutoff value of 1.0 in both serum and BALF AGT when IPA is highly suspicious. In our group, serum AGT of 1.0 showed a sensitivity of 27% and a specificity of 97%, and BALF AGT of 1.0 showed a sensitivity of 60% and a specificity of 95%. The result suggested that a lower cutoff could be beneficial in the lung transplant group. In multivariable analysis, serum and BALF AGT, with a minimal correlation between the two, showed a correlation with a history of diabetes mellitus.

Analyzing adverse drug reaction using statistical and machine learning methods: A systematic review.

BACKGROUND: Adverse drug reactions (ADRs) are unintended negative drug-induced responses. Determining the association between drugs and ADRs is crucial, and several methods have been proposed to demonstrate this association. This systematic review aimed to examine the analytical tools by considering original articles that utilized statistical and machine learning methods for detecting ADRs. METHODS: A systematic literature review was conducted based on articles published between 2015 and 2020. The keywords used were statistical, machine learning, and deep learning methods for detecting ADR signals. The study was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement (PRISMA) guidelines. RESULTS: We reviewed 72 articles, of which 51 and 21 addressed statistical and machine learning methods, respectively. Electronic medical record (EMR) data were exclusively analyzed using the regression method. For FDA Adverse Event Reporting System (FAERS) data, components of the disproportionality method were preferable. DrugBank was the most used database for machine learning. Other methods accounted for the highest and supervised methods accounted for the second highest. CONCLUSIONS: Using the 72 main articles, this review provides guidelines on which databases are frequently utilized and which analysis methods can be connected. For statistical analysis, >90% of the cases were analyzed by disproportionate or regression analysis with each spontaneous reporting system (SRS) data or electronic medical record (EMR) data; for machine learning research, however, there was a strong tendency to analyze various data combinations. Only half of the DrugBank database was occupied, and the k-nearest neighbor method accounted for the greatest proportion.

Event Prediction Model Considering Time and Input Error Using Electronic Medical Records in the Intensive Care Unit: Retrospective Study.

BACKGROUND: In the era of artificial intelligence, event prediction models are abundant. However, considering the limitation of the electronic medical record-based model, including the temporally skewed prediction and the record itself, these models could be delayed or could yield errors. OBJECTIVE: In this study, we aim to develop multiple event prediction models in intensive care units to overcome their temporal skewness and evaluate their robustness against delayed and erroneous input. METHODS: A total of 21,738 patients were included in the development cohort. Three events-death, sepsis, and acute kidney injury-were predicted. To overcome the temporal skewness, we developed three models for each event, which predicted the events in advance of three prespecified timepoints. Additionally, to evaluate the robustness against input error and delays, we added simulated errors and delayed input and calculated changes in the area under the receiver operating characteristic curve (AUROC) values. RESULTS: Most of the AUROC and area under the precision-recall curve values of each model were higher than those of the conventional scores, as well as other machine learning models previously used. In the error input experiment, except for our proposed model, an increase in the noise added to the model lowered the resulting AUROC value. However, the delayed input did not show the performance decreased in this experiment. CONCLUSIONS: For a prediction model that was applicable in the real world, we considered not only performance but also temporal skewness, delayed input, and input error.

Local Differential Privacy in the Medical Domain to Protect Sensitive Information: Algorithm Development and Real-World Validation.

BACKGROUND: Privacy is of increasing interest in the present big data era, particularly the privacy of medical data. Specifically, differential privacy has emerged as the standard method for preservation of privacy during data analysis and publishing. OBJECTIVE: Using machine learning techniques, we applied differential privacy to medical data with diverse parameters and checked the feasibility of our algorithms with synthetic data as well as the balance between data privacy and utility. METHODS: All data were normalized to a range between -1 and 1, and the bounded Laplacian method was applied to prevent the generation of out-of-bound values after applying the differential privacy algorithm. To preserve the cardinality of the categorical variables, we performed postprocessing via discretization. The algorithm was evaluated using both synthetic and real-world data (from the eICU Collaborative Research Database). We evaluated the difference between the original data and the perturbated data using misclassification rates and the mean squared error for categorical data and continuous data, respectively. Further, we compared the performance of classification models that predict in-hospital mortality using real-world data. RESULTS: The misclassification rate of categorical variables ranged between 0.49 and 0.85 when the value of ε was 0.1, and it converged to 0 as ε increased. When ε was between 102 and 103, the misclassification rate rapidly dropped to 0. Similarly, the mean squared error of the continuous variables decreased as ε increased. The performance of the model developed from perturbed data converged to that of the model developed from original data as ε increased. In particular, the accuracy of a random forest model developed from the original data was 0.801, and this value ranged from 0.757 to 0.81 when ε was 10-1 and 104, respectively. CONCLUSIONS: We applied local differential privacy to medical domain data, which are diverse and high dimensional. Higher noise may offer enhanced privacy, but it simultaneously hinders utility. We should choose an appropriate degree of noise for data perturbation to balance privacy and utility depending on specific situations.

Implementing Vertical Federated Learning Using Autoencoders: Practical Application, Generalizability, and Utility Study.

BACKGROUND: Machine learning (ML) is now widely deployed in our everyday lives. Building robust ML models requires a massive amount of data for training. Traditional ML algorithms require training data centralization, which raises privacy and data governance issues. Federated learning (FL) is an approach to overcome this issue. We focused on applying FL on vertically partitioned data, in which an individual's record is scattered among different sites. OBJECTIVE: The aim of this study was to perform FL on vertically partitioned data to achieve performance comparable to that of centralized models without exposing the raw data. METHODS: We used three different datasets (Adult income, Schwannoma, and eICU datasets) and vertically divided each dataset into different pieces. Following the vertical division of data, overcomplete autoencoder-based model training was performed for each site. Following training, each site's data were transformed into latent data, which were aggregated for training. A tabular neural network model with categorical embedding was used for training. A centrally based model was used as a baseline model, which was compared to that of FL in terms of accuracy and area under the receiver operating characteristic curve (AUROC). RESULTS: The autoencoder-based network successfully transformed the original data into latent representations with no domain knowledge applied. These altered data were different from the original data in terms of the feature space and data distributions, indicating appropriate data security. The loss of performance was minimal when using an overcomplete autoencoder; accuracy loss was 1.2%, 8.89%, and 1.23%, and AUROC loss was 1.1%, 0%, and 1.12% in the Adult income, Schwannoma, and eICU dataset, respectively. CONCLUSIONS: We proposed an autoencoder-based ML model for vertically incomplete data. Since our model is based on unsupervised learning, no domain-specific knowledge is required in individual sites. Under the circumstances where direct data sharing is not available, our approach may be a practical solution enabling both data protection and building a robust model.

Evaluation of the Privacy Risks of Personal Health Identifiers and Quasi-Identifiers in a Distributed Research Network: Development and Validation Study.

BACKGROUND: Privacy should be protected in medical data that include patient information. A distributed research network (DRN) is one of the challenges in privacy protection and in the encouragement of multi-institutional clinical research. A DRN standardizes multi-institutional data into a common structure and terminology called a common data model (CDM), and it only shares analysis results. It is necessary to measure how a DRN protects patient information privacy even without sharing data in practice. OBJECTIVE: This study aimed to quantify the privacy risk of a DRN by comparing different deidentification levels focusing on personal health identifiers (PHIs) and quasi-identifiers (QIs). METHODS: We detected PHIs and QIs in an Observational Medical Outcomes Partnership (OMOP) CDM as threatening privacy, based on 18 Health Insurance Portability and Accountability Act of 1996 (HIPPA) identifiers and previous studies. To compare the privacy risk according to the different privacy policies, we generated limited and safe harbor data sets based on 16 PHIs and 12 QIs as threatening privacy from the Synthetic Public Use File 5 Percent (SynPUF5PCT) data set, which is a public data set of the OMOP CDM. With minimum cell size and equivalence class methods, we measured the privacy risk reduction with a trust differential gap obtained by comparing the two data sets. We also measured the gap in randomly sampled records from the two data sets to adjust the number of PHI or QI records. RESULTS: The gaps averaged 31.448% and 73.798% for PHIs and QIs, respectively, with a minimum cell size of one, which represents a unique record in a data set. Among PHIs, the national provider identifier had the highest gap of 71.236% (71.244% and 0.007% in the limited and safe harbor data sets, respectively). The maximum size of the equivalence class, which has the largest size of an indistinguishable set of records, averaged 771. In 1000 random samples of PHIs, Device_exposure_start_date had the highest gap of 33.730% (87.705% and 53.975% in the data sets). Among QIs, Death had the highest gap of 99.212% (99.997% and 0.784% in the data sets). In 1000, 10,000, and 100,000 random samples of QIs, Device_treatment had the highest gaps of 12.980% (99.980% and 87.000% in the data sets), 60.118% (99.831% and 39.713%), and 93.597% (98.805% and 5.207%), respectively, and in 1 million random samples, Death had the highest gap of 99.063% (99.998% and 0.934% in the data sets). CONCLUSIONS: In this study, we verified and quantified the privacy risk of PHIs and QIs in the DRN. Although this study used limited PHIs and QIs for verification, the privacy limitations found in this study could be used as a quality measurement index for deidentification of multi-institutional collaboration research, thereby increasing DRN safety.

A Patient Self-Checkup App for COVID-19: Development and Usage Pattern Analysis.

BACKGROUND: Clear guidelines for a patient with suspected COVID-19 infection are unavailable. Many countries rely on assessments through a national hotline or telecommunications, but this only adds to the burden of an already overwhelmed health care system. In this study, we developed an algorithm and a web application to help patients get screened. OBJECTIVE: This study aims to aid the general public by developing a web-based application that helps patients decide when to seek medical care during a novel disease outbreak. METHODS: The algorithm was developed via consultations with 6 physicians who directly screened, diagnosed, and/or treated patients with COVID-19. The algorithm mainly focused on when to test a patient in order to allocate limited resources more efficiently. The application was designed to be mobile-friendly and deployed on the web. We collected the application usage pattern data from March 1 to March 27, 2020. We evaluated the association between the usage pattern and the numbers of COVID-19 confirmed, screened, and mortality cases by access location and digital literacy by age group. RESULTS: The algorithm used epidemiological factors, presence of fever, and other symptoms. In total, 83,460 users accessed the application 105,508 times. Despite the lack of advertisement, almost half of the users accessed the application from outside of Korea. Even though the digital literacy of the 60+ years age group is half of that of individuals in their 50s, the number of users in both groups was similar for our application. CONCLUSIONS: We developed an expert-opinion-based algorithm and web-based application for screening patients. This innovation can be helpful in circumstances where information on a novel disease is insufficient and may facilitate efficient medical resource allocation.

Developing a Mobile App for Monitoring Medical Record Changes Using Blockchain: Development and Usability Study.

BACKGROUND: Although we are living in an era of transparency, medical documents are often still difficult to access. Blockchain technology allows records to be both immutable and transparent. OBJECTIVE: Using blockchain technology, the aim of this study was to develop a medical document monitoring system that informs patients of changes to their medical documents. We then examined whether patients can effectively verify the monitoring of their primary care clinical medical records in a system based on blockchain technology. METHODS: We enrolled participants who visited two primary care clinics in Korea. Three substudies were performed: (1) a survey of the recognition of blockchain medical records changes and the digital literacy of participants; (2) an observational study on participants using the blockchain-based mobile alert app; and (3) a usability survey study. The participants' medical documents were profiled with HL7 Fast Healthcare Interoperability Resources, hashed, and transacted to the blockchain. The app checked the changes in the documents by querying the blockchain. RESULTS: A total of 70 participants were enrolled in this study. Considering their recognition of changes to their medical records, participants tended to not allow these changes. Participants also generally expressed a desire for a medical record monitoring system. Concerning digital literacy, most questions were answered with "good," indicating fair digital literacy. In the second survey, only 44 participants-those who logged into the app more than once and used the app for more than 28 days-were included in the analysis to determine whether they exhibited usage patterns. The app was accessed a mean of 5.1 (SD 2.6) times for 33.6 (SD 10.0) days. The mean System Usability Scale score was 63.21 (SD 25.06), which indicated satisfactory usability. CONCLUSIONS: Patients showed great interest in a blockchain-based system to monitor changes in their medical records. The blockchain system is useful for informing patients of changes in their records via the app without uploading the medical record itself to the network. This ensures the transparency of medical records as well as patient empowerment.

Developing a Mobile App for Patients to Monitor Medical Record Changes Using Blockchain.

According to recent revisions to medical laws in Korea, changes to electronic medical records are to be documented. To do so, however, a transparent system with which to store original documents and changes thereto is needed. The transparency and immutability of blockchain records are the key characters of blockchain technology. Employing these characteristics, we developed an application with which to monitor changes of medical records using blockchain.

Ex Vivo Interferon Gamma Production by Peripheral Immune Cells Predicts Survival in Lung Adenocarcinoma.

BACKGROUND: Lung cancer is one of the most lethal malignancies, with a 5-year survival rate < 20% in patients with stage IV lung cancer. Impaired host immunity is associated with lung cancer pathogenesis, and interferon gamma (IFN-γ) plays an important role in antitumor immune surveillance. We evaluated the clinical significance of ex vivo production of IFN-γ in patients with lung adenocarcinoma. PATIENTS AND METHODS: We reviewed the medical records of 109 treatment-naive patients with lung adenocarcinoma who had undergone IFN-γ releasing assay. Differences in the IFN-γ level in nil and mitogen tubes were defined as ex vivo IFN-γ production. Correlation analysis was performed to evaluate the correlation between ex vivo IFN-γ production, cancer staging, and Eastern Cooperative Oncology Group performance status. The optimal cutoff values of low and high ex vivo IFN-γ production were estimated using receiver operator characteristic curve analysis. Cox proportional hazard analyses were used to evaluate the prognostic factors of 1-year overall patient survival. RESULTS: Ex vivo IFN-γ production correlated with N stage, M stage, cancer staging, and Eastern Cooperative Oncology Group performance status. Low ex vivo IFN-γ production (ex vivo IFN-γ production ≤ 7.79 IU/mL) was independently associated with 1-year overall survival (odds ratio = 3.289; 95% confidence interval, 1.573-6.872; P = .002). Additionally, low ex vivo IFN-γ production was an independent predictor of 1-year overall survival in patients with stage IV cancer (odds ratio = 3.156; 95% confidence interval, 1.473-6.760; P = .003). CONCLUSION: Ex vivo IFN-γ production before treatment might be a useful biomarker for predicting prognosis in patients with lung adenocarcinoma.

Risk of Primary Spontaneous Pneumothorax According to Chest Configuration.

BACKGROUND: We compared the chest configurations of patients with primary spontaneous pneumothorax (PSP) and age-sex-matched controls to determine the presence of chest wall deformities in patients with PSP. METHODS: We retrospectively enrolled 166 male patients with PSP (age, 18-19 years) and 85 age-sex-matched controls without PSP, who simultaneously underwent chest computed tomography (CT) and radiography at one of two institutes. After correcting for height, the following thoracic parameters were comparatively evaluated between the two groups: maximal internal transverse (T) and anteroposterior (W) diameters of the chest, maximal internal lung height (H), Haller index (T/W), and T/Height, T/H, W/Height, W/H, and H/Height ratios. RESULTS: Patients were taller than the control subjects (176.5 cm ± 5.9 cm versus 174.4 cm ± 5.6 cm; p = 0.007). After controlling for height, the patient group exhibited lower T and W and greater H and Haller index values than the control group (T: 95% confidence interval [CI], 24.8-25.2 cm versus 25.9-26.5; W: 95% CI, 8.9-9.2 cm versus 10.1-10.6 cm; H: 95% CI, 25.2-25.9 cm versus 23.4-24.4 cm; and Haller index, 95% CI, 2.7-2.9 versus 2.4-2.6; all, p < 0.001). The patient group also exhibited lower T/Height, T/H, W/Height, and W/H ratios and greater H/Height ratio than the control group. CONCLUSIONS: Patients with PSP have an anteroposteriorly flatter, laterally narrower, and craniocaudally taller thorax than subjects without PSP, suggesting that chest configuration is associated with the development of pneumothorax.

Active tuberculosis risk with tumor necrosis factor inhibitors after treating latent tuberculosis.

BACKGROUND: Active tuberculosis (TB) risk increases during anti-tumor necrosis factor (TNF) therapy; therefore, latent TB infection (LTBI) screening is recommended in potential TNF inhibitor users. It is unclear whether anti-TNF therapy increases the risk of active TB infection even after standard LTBI treatment. OBJECTIVE: The objective of this study was to compare the risk of active TB development in LTBI-positive versus LBTI-negative TNF inhibitor users following the current national LTBI treatment guidelines for LTBI. METHODS: We retrospectively studied 949 TNF inhibitor users with immune-mediated inflammatory diseases from 2005 to 2012 at the Yonsei University Health System. We compared the incidence of active TB among LTBI-positive TNF inhibitor users treated according to national guidelines (n = 256) and LTBI-negative TNF inhibitor users (n = 521), using Poisson regression. RESULTS: The active TB incidence was 1107 per 100,000 patient-years in LTBI-positive TNF inhibitor users who received standard LTBI treatment and 490 per 100,000 patient-years in LTBI-negative TNF inhibitor users. Analysis showed that despite this numerical trend active TB risk was not statistically significantly elevated in LTBI-positive versus LTBI-negative TNF inhibitor users (incidence risk ratio, 2.15; P = 0.24; 95% confidence interval, 0.6-7.7). CONCLUSIONS: This study demonstrated no statistically significantly increased risk of active TB in LTBI-positive TNF inhibitor users who received standard LTBI treatment compared with LTBI-negative TNF inhibitor users.