An artificial intelligence-powered PD-L1 combined positive score (CPS) analyser in urothelial carcinoma alleviating interobserver and intersite variability.

AIMS: Immune checkpoint inhibitors targeting programmed death-ligand 1 (PD-L1) have shown promising clinical outcomes in urothelial carcinoma (UC). The combined positive score (CPS) quantifies PD-L1 22C3 expression in UC, but it can vary between pathologists due to the consideration of both immune and tumour cell positivity. METHODS AND RESULTS: An artificial intelligence (AI)-powered PD-L1 CPS analyser was developed using 1,275,907 cells and 6175.42 mm2 of tissue annotated by pathologists, extracted from 400 PD-L1 22C3-stained whole slide images of UC. We validated the AI model on 543 UC PD-L1 22C3 cases collected from three institutions. There were 446 cases (82.1%) where the CPS results (CPS ≥10 or <10) were in complete agreement between three pathologists, and 486 cases (89.5%) where the AI-powered CPS results matched the consensus of two or more pathologists. In the pathologist's assessment of the CPS, statistically significant differences were noted depending on the source hospital (P = 0.003). Three pathologists reevaluated discrepancy cases with AI-powered CPS results. After using the AI as a guide and revising, the complete agreement increased to 93.9%. The AI model contributed to improving the concordance between pathologists across various factors including hospital, specimen type, pathologic T stage, histologic subtypes, and dominant PD-L1-positive cell type. In the revised results, the evaluation discordance among slides from different hospitals was mitigated. CONCLUSION: This study suggests that AI models can help pathologists to reduce discrepancies between pathologists in quantifying immunohistochemistry including PD-L1 22C3 CPS, especially when evaluating data from different institutions, such as in a telepathology setting.

Augmented interpretation of HER2, ER, and PR in breast cancer by artificial intelligence analyzer: enhancing interobserver agreement through a reader study of 201 cases.

BACKGROUND: Accurate classification of breast cancer molecular subtypes is crucial in determining treatment strategies and predicting clinical outcomes. This classification largely depends on the assessment of human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), and progesterone receptor (PR) status. However, variability in interpretation among pathologists pose challenges to the accuracy of this classification. This study evaluates the role of artificial intelligence (AI) in enhancing the consistency of these evaluations. METHODS: AI-powered HER2 and ER/PR analyzers, consisting of cell and tissue models, were developed using 1,259 HER2, 744 ER, and 466 PR-stained immunohistochemistry (IHC) whole-slide images of breast cancer. External validation cohort comprising HER2, ER, and PR IHCs of 201 breast cancer cases were analyzed with these AI-powered analyzers. Three board-certified pathologists independently assessed these cases without AI annotation. Then, cases with differing interpretations between pathologists and the AI analyzer were revisited with AI assistance, focusing on evaluating the influence of AI assistance on the concordance among pathologists during the revised evaluation compared to the initial assessment. RESULTS: Reevaluation was required in 61 (30.3%), 42 (20.9%), and 80 (39.8%) of HER2, in 15 (7.5%), 17 (8.5%), and 11 (5.5%) of ER, and in 26 (12.9%), 24 (11.9%), and 28 (13.9%) of PR evaluations by the pathologists, respectively. Compared to initial interpretations, the assistance of AI led to a notable increase in the agreement among three pathologists on the status of HER2 (from 49.3 to 74.1%, p < 0.001), ER (from 93.0 to 96.5%, p = 0.096), and PR (from 84.6 to 91.5%, p = 0.006). This improvement was especially evident in cases of HER2 2+ and 1+, where the concordance significantly increased from 46.2 to 68.4% and from 26.5 to 70.7%, respectively. Consequently, a refinement in the classification of breast cancer molecular subtypes (from 58.2 to 78.6%, p < 0.001) was achieved with AI assistance. CONCLUSIONS: This study underscores the significant role of AI analyzers in improving pathologists' concordance in the classification of breast cancer molecular subtypes.

Reduced economic disparity in biologics use for psoriasis after introducing the reducing copayment program.

Biologics for psoriasis are efficient and safe, but very expensive. We investigated the association of the reducing copayment program (RCP) with changes in biologics use patterns depending on the income levels of patients with moderate-to-severe psoriasis. This nationwide cohort study included patients identified as having moderate-to-severe psoriasis between 2014 and 2020. Logistic regression models were used to estimate the odds ratio for the use of biologics according to income levels. Among 57,139 patients with moderate-to-severe psoriasis, 3464 (6.1%) used biologics for psoriasis from 2014 to 2020. After the introduction of RCP in 2017, the proportion of patients with moderate-to-severe psoriasis using biologics rapidly increased from 5.0% in 2016 to 19.2% in 2020; the increase was more remarkable in patients with the lowest or mid-low income compared to those with Medical Aid. Drug survival of biologics was higher in patients with the highest income before the RCP, but became comparable between those with high and low incomes after RCP introduction. The introduction of RCP was associated with an increased use of biologics in patients with moderate-to-severe psoriasis of all income levels; however, the effect was more pronounced in low-income patients. The RCP may contribute to alleviating the disparity in access to biologics.

Discontinuation risk from adverse events: immunotherapy alone vs. combined with chemotherapy: a systematic review and network meta-analysis.

BACKGROUND: While immunotherapy combined with chemotherapy (Chemo-IO) is generally recognized for providing superior outcomes compared to monotherapy (mono-IO), it is associated with a higher incidence of treatment-related adverse events (TRAEs), which may lead to treatment discontinuation. In this study, we compared the rates of treatment discontinuation between mono-IO and Chemo-IO as first-line treatments for various solid tumors. METHODS: We systematically reviewed clinical trials from databases (PubMed, Embase, Cochrane Library, and an additional source) published from January 1, 2018, to July 10, 2023. We included phase III randomized controlled trials (RCTs) that utilized immunotherapy agents in at least one arm as first-line treatments for a variety of solid tumors. Data extraction followed the Preferred Reporting Items for Systematic Reviews (PRISMA) extension statement for network meta-analysis. A random effects model was used for the network meta-analysis, with the risk of bias assessed using the Cochrane risk-of-bias tool II. The primary outcomes encompassed treatment discontinuation rates due to TRAEs among patients who underwent immunotherapy, either alone or combined with chemotherapy, for various solid tumors. Pooled relative risks (RRs) with 95% confidence intervals (CIs) were calculated to compare between treatment groups. RESULTS: From 29 RCTs, a total of 21,677 patients and 5 types of treatment were analyzed. Compared to mono-IO, Chemo-IO showed a significantly higher rate of discontinuation due to TRAEs (RR 2.68, 95% CI 1.98-3.63). Subgroup analysis for non-small cell lung cancer (NSCLC) patients also exhibited a greater risk of discontinuation due to TRAEs with Chemo-IO compared to mono-IO (RR 2.93, 95% CI 1.67-5.14). Additional analyses evaluating discontinuation rates due to either treatment emergent adverse events (TEAEs) or AEs regardless of causality (any AEs) consistently revealed an elevated risk associated with Chemo-IO. CONCLUSIONS: Chemo-IO was associated with an elevated risk of treatment discontinuation not only due to TRAEs but also any AEs or TEAEs. Given that the treatment duration can impact clinical outcomes, a subset of patients might benefit more from mono-IO than combination therapy. Further research is imperative to identify and characterize this subset.

Endocrine and metabolic comorbidities in primary cicatricial alopecia: A nationwide population-based study.

Primary cicatricial alopecia (PCA) is a rare, scarring, hair loss disorder. Due to its low incidence, little is known about endocrine and metabolic comorbidities in patients with PCA. Thus, we aimed to investigate the association between PCA and endocrine and metabolic disorders. This nationwide, population-based, cross-sectional study included patients diagnosed with PCA or non-cicatricial alopecia (NCA) and normal individuals without history of alopecia registered in the Korean National Health Insurance Service database between January 1, 2011, and December 31, 2020. We calculated the odds ratios of endocrine and metabolic comorbidities of patients with PCA compared to all patients or age- and sex-matched patients with NCA or normal individuals using multivariable logistic regression models. A total of 3 021 483 individuals (mean age [SD], 38.7 [15.0] years, 1 607 380 [53.2%] men), including 11 956 patients with PCA, 601 852 patients with NCA, and 2 407 675 normal participants, were identified. Patients with PCA had an increased risk for dyslipidemia (adjusted odds ratio [aOR] 1.14, 95% confidence interval [CI] 1.06-1.24), diabetes (aOR 1.38, 95% CI 1.24-1.53), and hypertension (aOR 1.10, 95% CI 1.02-1.19) compared to matched patients with NCA. Regarding PCA subtypes, lichen planopilaris/frontal fibrosing alopecia was positively associated with hypothyroidism (aOR 2.03, 95% CI 1.44-2.86) compared to NCA. Folliculitis decalvans and dissecting cellulitis were positively associated with dyslipidemia (aOR 1.16, 95% CI 1.05-1.28 and aOR 1.16, 95% CI 1.04-1.29, respectively), diabetes (aOR 1.38, 95% CI 1.20-1.58 and aOR 1.52, 95% CI 1.32-1.74, respectively), and hypertension (aOR 1.10, 95% CI 1.00-1.20 and aOR 1.14, 95% CI 1.02-1.27, respectively). Similar trends were observed when each PCA subgroup was compared with the normal control group. This study demonstrates that patients with PCA are more likely to have endocrine and metabolic comorbidities than patients without PCA. Further research on these comorbidities may improve the understanding of PCA.

Artificial Intelligence in Breast Cancer Diagnosis and Personalized Medicine.

Breast cancer is a significant cause of cancer-related mortality in women worldwide. Early and precise diagnosis is crucial, and clinical outcomes can be markedly enhanced. The rise of artificial intelligence (AI) has ushered in a new era, notably in image analysis, paving the way for major advancements in breast cancer diagnosis and individualized treatment regimens. In the diagnostic workflow for patients with breast cancer, the role of AI encompasses screening, diagnosis, staging, biomarker evaluation, prognostication, and therapeutic response prediction. Although its potential is immense, its complete integration into clinical practice is challenging. Particularly, these challenges include the imperatives for extensive clinical validation, model generalizability, navigating the "black-box" conundrum, and pragmatic considerations of embedding AI into everyday clinical environments. In this review, we comprehensively explored the diverse applications of AI in breast cancer care, underlining its transformative promise and existing impediments. In radiology, we specifically address AI in mammography, tomosynthesis, risk prediction models, and supplementary imaging methods, including magnetic resonance imaging and ultrasound. In pathology, our focus is on AI applications for pathologic diagnosis, evaluation of biomarkers, and predictions related to genetic alterations, treatment response, and prognosis in the context of breast cancer diagnosis and treatment. Our discussion underscores the transformative potential of AI in breast cancer management and emphasizes the importance of focused research to realize the full spectrum of benefits of AI in patient care.

Generation of a Melanoma and Nevus Data Set From Unstandardized Clinical Photographs on the Internet.

Importance: Artificial intelligence (AI) training for diagnosing dermatologic images requires large amounts of clean data. Dermatologic images have different compositions, and many are inaccessible due to privacy concerns, which hinder the development of AI. Objective: To build a training data set for discriminative and generative AI from unstandardized internet images of melanoma and nevus. Design, Setting, and Participants: In this diagnostic study, a total of 5619 (CAN5600 data set) and 2006 (CAN2000 data set; a manually revised subset of CAN5600) cropped lesion images of either melanoma or nevus were semiautomatically annotated from approximately 500 000 photographs on the internet using convolutional neural networks (CNNs), region-based CNNs, and large mask inpainting. For unsupervised pretraining, 132 673 possible lesions (LESION130k data set) were also created with diversity by collecting images from 18 482 websites in approximately 80 countries. A total of 5000 synthetic images (GAN5000 data set) were generated using the generative adversarial network (StyleGAN2-ADA; training, CAN2000 data set; pretraining, LESION130k data set). Main Outcomes and Measures: The area under the receiver operating characteristic curve (AUROC) for determining malignant neoplasms was analyzed. In each test, 1 of the 7 preexisting public data sets (total of 2312 images; including Edinburgh, an SNU subset, Asan test, Waterloo, 7-point criteria evaluation, PAD-UFES-20, and MED-NODE) was used as the test data set. Subsequently, a comparative study was conducted between the performance of the EfficientNet Lite0 CNN on the proposed data set and that trained on the remaining 6 preexisting data sets. Results: The EfficientNet Lite0 CNN trained on the annotated or synthetic images achieved higher or equivalent mean (SD) AUROCs to the EfficientNet Lite0 trained using the pathologically confirmed public data sets, including CAN5600 (0.874 [0.042]; P = .02), CAN2000 (0.848 [0.027]; P = .08), and GAN5000 (0.838 [0.040]; P = .31 [Wilcoxon signed rank test]) and the preexisting data sets combined (0.809 [0.063]) by the benefits of increased size of the training data set. Conclusions and Relevance: The synthetic data set in this diagnostic study was created using various AI technologies from internet images. A neural network trained on the created data set (CAN5600) performed better than the same network trained on preexisting data sets combined. Both the annotated (CAN5600 and LESION130k) and synthetic (GAN5000) data sets could be shared for AI training and consensus between physicians.

Deep learning model improves tumor-infiltrating lymphocyte evaluation and therapeutic response prediction in breast cancer.

Tumor-infiltrating lymphocytes (TILs) have been recognized as key players in the tumor microenvironment of breast cancer, but substantial interobserver variability among pathologists has impeded its utility as a biomarker. We developed a deep learning (DL)-based TIL analyzer to evaluate stromal TILs (sTILs) in breast cancer. Three pathologists evaluated 402 whole slide images of breast cancer and interpreted the sTIL scores. A standalone performance of the DL model was evaluated in the 210 cases (52.2%) exhibiting sTIL score differences of less than 10 percentage points, yielding a concordance correlation coefficient of 0.755 (95% confidence interval [CI], 0.693-0.805) in comparison to the pathologists' scores. For the 226 slides (56.2%) showing a 10 percentage points or greater variance between pathologists and the DL model, revisions were made. The number of discordant cases was reduced to 116 (28.9%) with the DL assistance (p < 0.001). The DL assistance also increased the concordance correlation coefficient of the sTIL score among every two pathologists. In triple-negative and human epidermal growth factor receptor 2 (HER2)-positive breast cancer patients who underwent the neoadjuvant chemotherapy, the DL-assisted revision notably accentuated higher sTIL scores in responders (26.8 ± 19.6 vs. 19.0 ± 16.4, p = 0.003). Furthermore, the DL-assistant revision disclosed the correlation of sTIL-high tumors (sTIL ≥ 50) with the chemotherapeutic response (odd ratio 1.28 [95% confidence interval, 1.01-1.63], p = 0.039). Through enhancing inter-pathologist concordance in sTIL interpretation and predicting neoadjuvant chemotherapy response, here we report the utility of the DL-based tool as a reference for sTIL scoring in breast cancer assessment.

Explore highly relevant questions in the Baumann skin type questionnaire through the digital skin analyzer: A retrospective single-center study in South Korea.

BACKGROUND: Baumann skin type questionnaire (BSTQ) has been widely used for evaluating skin types in dermatology. However, it requires excessive assessment time and lacks sufficient clinical validation for the Asian population. AIMS: We aimed to establish optimized BSTQ based on dermatological assessment of the Asian population. METHODS: This was a single-center retrospective study, where the patient completed a modified BSTQ and a digital photography examination. The answers to four question groups for evaluating skin properties, including oily versus dry (O-D), sensitive versus resistant (S-R), pigmented versus non-pigmented (P-N), and wrinkled versus tight (W-T) were compared with the measurements. Highly relevant questions are selected using two different strategies and used to determine the threshold level, which was compared with skin-type measurement. RESULTS: In O-D, S-R, P-N, and W-T, 3-5 out of 6, 2-6 out of 9, 3-6 out of 7, and 4-9 out of 11 questions were selected, respectively. As a result, skin type scores from two strategies and measurements showed similar Pearson correlation coefficient values compared to modified BSTQ (for O-D and sebum, 0.236/0.266 vs. 0.232; for O-D and porphyrin, 0.230/0.267 vs. 0.230; for S-R and redness, 0.157/0.175 vs. 0.095; for S-R and porphyrin, 0.061 vs. 0.051; for P-N and melanin pigmentation, 0.156/0.208 vs. 0.150; for W-T and wrinkle, 0.265/0.269 vs. 0.217). CONCLUSION: Two strategies for optimizing BSTQ are proposed and validated for Asian patients. Compared to the BSTQ, our methods show comparable performance with a significantly reduced number of questions.

Automated Facial Acne Lesion Detecting and Counting Algorithm for Acne Severity Evaluation and Its Utility in Assisting Dermatologists.

BACKGROUND: Although lesion counting is an evaluation method that effectively analyzes facial acne severity, its usage is limited because of difficult implementation. OBJECTIVES: We aimed to develop and validate an automated algorithm that detects and counts acne lesions by type, and to evaluate its clinical applicability as an assistance tool through a reader test. METHODS: A total of 20,699 lesions (closed and open comedones, papules, nodules/cysts, and pustules) were manually labeled on 1213 facial images of 398 facial acne photography sets (frontal and both lateral views) acquired from 258 patients and used for training and validating algorithms based on a convolutional neural network for classifying five classes of acne lesions or for binary classification into noninflammatory and inflammatory lesions. RESULTS: In the validation dataset, the highest mean average precision was 28.48 for the binary classification algorithm. Pearson's correlation of lesion counts between algorithm and ground-truth was 0.72 (noninflammatory) and 0.90 (inflammatory), respectively. In the reader test, eight readers (100.0%) detected and counted lesions more accurately using the algorithm compared with the reader-alone evaluation. CONCLUSIONS: Overall, our algorithm demonstrated clinically applicable performance in detecting and counting facial acne lesions by type and its utility as an assistance tool for evaluating acne severity.

Practical Training Approaches for Discordant Atopic Dermatitis Severity Datasets: Merging Methods With Soft-Label and Train-Set Pruning.

Objective assessment of atopic dermatitis (AD) is essential for choosing proper management strategies. This study investigated the performance of convolutional neural networks (CNN) models in grading the severity of AD. Five board-certified dermatologists independently evaluated the severity of 9,192 AD images. The severity of AD was evaluated based on an Investigator's Global Assessment (IGA) and six signs of AD. For CNN training, we applied three distinct approaches: 1) ensemble vs. integration 2) hard-label vs. soft-label and 3) train-set pruning. For the IGA prediction, the two best models were chosen based on the macro-averaged AUROC and F-1 score. The ensemble-soft-label-pruning model was chosen based on AUROC 0.943, 0.927 for the internal and external validation set respectively, and integration-soft-label-whole dataset model was chosen based on the F1-score 0.750, 0.721 for the internal and external validation set respectively. CNN models trained by multi-evaluator dataset outperformed the models by an individual evaluator dataset, and they performed better to the dataset in which the assessment of dermatologists was concordant. In conclusion, CNN models for AD could be improved by labeled dataset from multiple evaluators, merging methods with soft-label and train-set pruning.

Risk of Skin Cancer and Actinic Keratosis in Patients with Rosacea: A Nationwide Population-based Cohort Study.

The association between rosacea and skin cancer remains inconclusive, with conflicting reports. The aim of this nationwide population-based cohort study was to determine the risk of skin cancer in patients with rosacea. A rosacea cohort (n = 11,420) was formulated and evaluated from 2010 to 2019. The incidence rate ratios of actinic keratosis, cutaneous melanoma, keratinocyte carcinoma and gastric, colorectal, and liver cancer were analysed in comparison with a matched control group, and multivariable stratified Cox proportional hazards model analysis was performed. The risk of actinic keratosis and keratinocyte carcinoma was increased in the rosacea group compared with the control group, with adjusted hazard ratios of 6.05 (95% confidence interval 3.63-10.09) and 2.66 (1.53-4.61), respectively. The risk of cutaneous melanoma and gastric, colorectal and liver cancer was not increased, with adjusted hazard ratios of 1.69 (0.25-11.37), 0.81 (0.59-1.10), 0.91 (0.69-1.18) and 1.32 (0.89-1.95), respectively. These results reveal an increased risk of actinic keratosis and keratinocyte carcinoma in patients with rosacea.

Artificial Intelligence-Powered Whole-Slide Image Analyzer Reveals a Distinctive Distribution of Tumor-Infiltrating Lymphocytes in Neuroendocrine Neoplasms.

Despite the importance of tumor-infiltrating lymphocytes (TIL) and PD-L1 expression to the immune checkpoint inhibitor (ICI) response, a comprehensive assessment of these biomarkers has not yet been conducted in neuroendocrine neoplasm (NEN). We collected 218 NENs from multiple organs, including 190 low/intermediate-grade NENs and 28 high-grade NENs. TIL distribution was derived from Lunit SCOPE IO, an artificial intelligence (AI)-powered hematoxylin and eosin (H&E) analyzer, as developed from 17,849 whole slide images. The proportion of intra-tumoral TIL-high cases was significantly higher in high-grade NEN (75.0% vs. 46.3%, p = 0.008). The proportion of PD-L1 combined positive score (CPS) ≥ 1 case was higher in high-grade NEN (85.7% vs. 33.2%, p < 0.001). The PD-L1 CPS ≥ 1 group showed higher intra-tumoral, stromal, and combined TIL densities, compared to the CPS < 1 group (7.13 vs. 2.95, p < 0.001; 200.9 vs. 120.5, p < 0.001; 86.7 vs. 56.1, p = 0.004). A significant correlation was observed between TIL density and PD-L1 CPS (r = 0.37, p < 0.001 for intra-tumoral TIL; r = 0.24, p = 0.002 for stromal TIL and combined TIL). AI-powered TIL analysis reveals that intra-tumoral TIL density is significantly higher in high-grade NEN, and PD-L1 CPS has a positive correlation with TIL densities, thus showing its value as predictive biomarkers for ICI response in NEN.

Artificial intelligence-powered programmed death ligand 1 analyser reduces interobserver variation in tumour proportion score for non-small cell lung cancer with better prediction of immunotherapy response.

BACKGROUND: Manual evaluation of programmed death ligand 1 (PD-L1) tumour proportion score (TPS) by pathologists is associated with interobserver bias. OBJECTIVE: This study explored the role of artificial intelligence (AI)-powered TPS analyser in minimisation of interobserver variation and enhancement of therapeutic response prediction. METHODS: A prototype model of an AI-powered TPS analyser was developed with a total of 802 non-small cell lung cancer (NSCLC) whole-slide images. Three independent board-certified pathologists labelled PD-L1 TPS in an external cohort of 479 NSCLC slides. For cases of disagreement between each pathologist and the AI model, the pathologists were asked to revise the TPS grade (<1%, 1%-49% and ≥50%) with AI assistance. The concordance rates among the pathologists with or without AI assistance and the effect of the AI-assisted revision on clinical outcome upon immune checkpoint inhibitor (ICI) treatment were evaluated. RESULTS: Without AI assistance, pathologists concordantly classified TPS in 81.4% of the cases. They revised their initial interpretation by using the AI model for the disagreement cases between the pathologist and the AI model (N = 91, 93 and 107 for each pathologist). The overall concordance rate among the pathologists was increased to 90.2% after the AI assistance (P < 0.001). A reduction in hazard ratio for overall survival and progression-free survival upon ICI treatment was identified in the TPS subgroups after the AI-assisted TPS revision. CONCLUSION: The AI-powered TPS analyser assistance improves the pathologists' consensus of reading and prediction of the therapeutic response, raising a possibility of standardised approach for the accurate interpretation.

Risks of Comorbidities in Patients With Palmoplantar Pustulosis vs Patients With Psoriasis Vulgaris or Pompholyx in Korea.

Importance: Palmoplantar pustulosis (PPP) has been reported to be accompanied by systemic conditions. However, the risks of comorbidities in patients with PPP have rarely been evaluated. Objective: To assess the risks of comorbidities in patients with PPP compared with patients with psoriasis vulgaris or pompholyx. Design, Setting, and Participants: This nationwide population-based cross-sectional study used data from the Korean National Health Insurance database and the National Health Screening Program collected from January 1, 2010, to December 31, 2019. Data were analyzed from July 1, 2020, to October 31, 2021. Korean patients diagnosed with PPP, psoriasis vulgaris, or pompholyx who visited a dermatologist between January 1, 2010, and December 31, 2019, were enrolled. Exposures: Presence of PPP. Main Outcomes and Measures: The risks of comorbidities among patients with PPP vs patients with psoriasis vulgaris or pompholyx were evaluated using a multivariable logistic regression model. Results: A total of 37 399 patients with PPP (mean [SD] age, 48.98 [17.20] years; 51.7% female), 332 279 patients with psoriasis vulgaris (mean [SD] age, 47.29 [18.34] years; 58.7% male), and 365 415 patients with pompholyx (mean [SD] age, 40.92 [17.63] years; 57.4% female) were included in the analyses. Compared with patients with pompholyx, those with PPP had significantly higher risks of developing psoriasis vulgaris (adjusted odds ratio [aOR], 72.96; 95% CI, 68.19-78.05; P < .001), psoriatic arthritis (aOR, 8.06; 95% CI, 6.55-9.92; P < .001), ankylosing spondylitis (aOR, 1.91; 95% CI, 1.61-2.27; P < .001), type 1 diabetes (aOR, 1.33; 95% CI, 1.16-1.52; P < .001), type 2 diabetes (aOR, 1.33; 95% CI, 1.29-1.38; P < .001), Graves disease (aOR, 1.25; 95% CI, 1.11-1.42; P < .001), Crohn disease (aOR, 1.63; 95% CI, 1.11-2.40; P = .01), and vitiligo (aOR, 1.87; 95% CI, 1.65-2.12; P < .001) after adjusting for demographic covariates. The risks of ankylosing spondylitis (aOR, 1.37; 95% CI, 1.16-1.62; P < .001) and Graves disease (aOR, 1.40; 95% CI, 1.23-1.58; P < .001) were significantly higher among patients with PPP vs psoriasis vulgaris. However, the risks of psoriatic arthritis (aOR, 0.54; 95% CI, 0.47-0.63; P < .001), systemic lupus erythematosus (aOR, 0.67; 95% CI, 0.46-0.97; P = .04), Sjögren syndrome (aOR, 0.70; 95% CI, 0.50-0.96; P = .03), systemic sclerosis (aOR, 0.29; 95% CI, 0.11-0.77; P = .01), vitiligo (aOR, 0.53; 95% CI, 0.47-0.60; P < .001), and alopecia areata (aOR, 0.88; 95% CI, 0.81-0.95; P = .001) were significantly lower among those with PPP vs psoriasis vulgaris. Conclusions and Relevance: The results of this cross-sectional study suggest that patients with PPP have an overlapping comorbidity profile with patients with psoriasis vulgaris but not patients with pompholyx. However, the risks of comorbidities among patients with PPP may be substantially different from those among patients with psoriasis vulgaris.

An Analysis of Judicial Cases Concerning Analgesic-Related Medication Errors in the Republic of Korea.

OBJECTIVES: Analgesic-related medication errors can be a threat to patient safety. This study aimed to identify and describe medication errors that can cause serious adverse drug events (ADEs) related to analgesic use. METHODS: This retrospective, observational, medicolegal study analyzed closed cases concerning complications induced by medication errors involving 3 commonly used analgesics: opioids, nonsteroidal anti-inflammatory drugs (NSAIDs), and acetaminophen (AAP). Cases closed between 1994 and 2019 that were available in the Korean Supreme Court judgment database system were included. Medication errors were categorized using a classification system (developed by our group) based on the stage of drug administration. Clinical characteristics and judgment statuses were analyzed. RESULTS: A total of 71 cases were included in the final analysis (opioids, n = 30; NSAIDs, n = 35; AAP, n = 6). Among them, 43 claims (60.6%) resulted in payments to the plaintiffs, with a median payment of $86,607 (interquartile range, $34,554-$193,782). The severity of ADEs was high (National Association of Insurance Commissioners scale ≥6) in 88.7% (n = 63) of claims, with a total of 44 (62%) deaths. The most common types of ADEs associated with opioid, NSAID, and AAP use were respiratory depression, anaphylactic shock, and fulminant hepatitis, respectively. The most common recognized medication errors associated with opioid, NSAIDs, and AAP were inappropriate patient monitoring (n = 10; 33.3%), improper analgesic choice (n = 15; 42.9%), and inappropriate treatment after ADEs (n = 3; 50%), respectively. CONCLUSIONS: Our findings indicate that efforts should be made to reduce medication errors related to analgesic use to prevent permanent injury and potential malpractice claims.