Real-world Data Research Articles

Artificial Intelligence and Machine Learning in Hepatocellular Carcinoma Screening, Diagnosis and Treatment - A Comprehensive Systematic Review

Background: Medical image analysis plays a crucial role in the screening, monitoring, diagnosis, and prognosis of diseases. Hepatocellular Carcinoma (HCC) often remain asymptomatic in their early stages. Timely identification and intervention are crucial to prevent the progression to decompensated liver diseases and advanced-stage HCC, minimizing morbidity and mortality. Methodology: This study examined twenty relevant research articles that met the inclusion criteria. Utilizing the PRISMA criteria, a systematic search was conducted on PubMed/MEDLINE and Google Scholar for studies on HCC screening employing Artificial Intelligence (AI) and Machine Learning (ML). The search focused on the keywords "artificial intelligence" and "hepatocellular carcinoma," with inclusion criteria specifying studies in the English language published in and after 2020. Exclusions were made for histology, animal research, and investigations conducted before 2020. Titles and abstracts were thoroughly reviewed, and any discrepancies were discussed. Results: The comprehensive review reveals the transformative impact of AI and ML on HCC screening, diagnosis, and therapy. ML models demonstrated effectiveness in early HCC diagnosis, distinguishing hepatic lesions, predicting treatment responses, and assessing recurrence risks across various techniques. Integration of mass spectrometry-based technologies, advanced imaging, and real-world data significantly improved diagnostic accuracy and clinical decision support. AI models exhibited diagnostic expertise with potential applications in therapy suggestions, personalized surveillance, and prognostic evaluations. However, further validation and seamless integration into clinical practice are essential for realizing their full potential. Conclusion: This systematic study underscores the progress made in the application of AI and ML in HCC screening, diagnosis, and treatment. Numerous studies highlight the capability of AI and ML systems to enhance hepatocellular carcinoma diagnosis, predict outcomes, and optimize therapy. The enduring and versatile nature of these technologies points towards a revolutionary future in personalized and efficient HCC management.

Validation of algorithms in studies based on routinely collected health data: general principles.

Clinicians, researchers, regulators, and other decision-makers increasingly rely on evidence from real-world data (RWD), including data routinely accumulating in health and administrative databases. RWD studies often rely on algorithms to operationalize variable definitions. An algorithm is a combination of codes or concepts used to identify persons with a specific health condition or characteristic. Establishing the validity of algorithms is a prerequisite for generating valid study findings that can ultimately inform evidence-based health care. This paper aims to systematize terminology, methods, and practical considerations relevant to the conduct of validation studies of RWD-based algorithms. We discuss measures of algorithm accuracy; gold/reference standard; study size; prioritizing accuracy measures; algorithm portability; and implication for interpretation. Information bias is common in epidemiologic studies, underscoring the importance of transparency in decisions regarding choice and prioritizing measures of algorithm validity. The validity of an algorithm should be judged in the context of a data source, and one size does not fit all. Prioritizing validity measures within a given data source depends on the role of a given variable in the analysis (eligibility criterion, exposure, outcome or covariate). Validation work should be part of routine maintenance of RWD sources.

Effectiveness, safety and discontinuation rates of bictegravir/emtricitabine/tenofovir alafenamide (BIC/FTC/TAF) in people with HIV using real-world data: a systematic review and meta-analysis.

The use of bictegravir/emtricitabine/tenofovir alafenamide (BIC/FTC/TAF) is based on the results of robust clinical trials. To assess the effectiveness and safety of BIC/FTC/TAF in treatment-naïve (TN) and treatment-experienced (TE) people with HIV using available real-world cohort studies. Systematic review and meta-analysis of publications and communications identified via Boolean search in Medline, PubMed and Embase, and conference abstracts reporting retrospective real-world use of BIC/FTC/TAF, published until 31 January 2024. The primary endpoint was the proportion of TN and TE people with HIV with viral load (VL) < 50 copies/mL at 48 weeks while on treatment. Of the 38 identified publications and conference abstracts, for the present analysis we included 12 publications (comprising 792 TN and 6732 TE individuals). For the three publications including 507 TN participants reporting the primary outcome, VL suppression was 97% [95% confidence intervals (CI): 89-100]. For the nine publications including 4946 TE participants reporting the primary outcome, VL suppression was 95% (95% CI: 94-96), with suppression >93% in all studies. Total discontinuations at 48 weeks in TE individuals were 3% (95% CI: 2-5), 1% (95% CI: 0-2) due to side effects. A total of four publications with 151 TE individuals with previous presence of M184V substitution were identified, reporting a suppression rate at 48 weeks of 95% (95% CI: 88-100). Real-world studies demonstrate low discontinuation rates and high rates of virologic suppression in individuals treated with BIC/FTC/TAF, both TN and TE with and without previous detection of M184V substitution.

How power shapes behavior: Evidence from physicians.

Power-the asymmetric control of valued resources-affects most human interactions. Although power is challenging to study with real-world data, a distinctive dataset allowed us to do so within the critical context of doctor-patient relationships. Using 1.5 million quasi-random assignments in US military emergency departments, we examined how power differentials between doctor and patient (measured by using differences in military ranks) affect physician behavior. Our findings indicate that power confers nontrivial advantages: "High-power" patients (who outrank their physician) receive more resources and have better outcomes than equivalently ranked "low-power" patients. Patient promotions even increase physician effort. Furthermore, low-power patients suffer if their physician concurrently cares for a high-power patient. Doctor-patient concordance on race and sex also matters. Overall, power-driven variation in behavior can harm the most vulnerable populations in health care settings.

Longitudinal Changes in Diagnostic Accuracy of a Differential Diagnosis List Developed by an AI-Based Symptom Checker: Retrospective Observational Study.

Artificial intelligence (AI) symptom checker models should be trained using real-world patient data to improve their diagnostic accuracy. Given that AI-based symptom checkers are currently used in clinical practice, their performance should improve over time. However, longitudinal evaluations of the diagnostic accuracy of these symptom checkers are limited. This study aimed to assess the longitudinal changes in the accuracy of differential diagnosis lists created by an AI-based symptom checker used in the real world. This was a single-center, retrospective, observational study. Patients who visited an outpatient clinic without an appointment between May 1, 2019, and April 30, 2022, and who were admitted to a community hospital in Japan within 30 days of their index visit were considered eligible. We only included patients who underwent an AI-based symptom checkup at the index visit, and the diagnosis was finally confirmed during follow-up. Final diagnoses were categorized as common or uncommon, and all cases were categorized as typical or atypical. The primary outcome measure was the accuracy of the differential diagnosis list created by the AI-based symptom checker, defined as the final diagnosis in a list of 10 differential diagnoses created by the symptom checker. To assess the change in the symptom checker's diagnostic accuracy over 3 years, we used a chi-square test to compare the primary outcome over 3 periods: from May 1, 2019, to April 30, 2020 (first year); from May 1, 2020, to April 30, 2021 (second year); and from May 1, 2021, to April 30, 2022 (third year). A total of 381 patients were included. Common diseases comprised 257 (67.5%) cases, and typical presentations were observed in 298 (78.2%) cases. Overall, the accuracy of the differential diagnosis list created by the AI-based symptom checker was 172 (45.1%), which did not differ across the 3 years (first year: 97/219, 44.3%; second year: 32/72, 44.4%; and third year: 43/90, 47.7%; P=.85). The accuracy of the differential diagnosis list created by the symptom checker was low in those with uncommon diseases (30/124, 24.2%) and atypical presentations (12/83, 14.5%). In the multivariate logistic regression model, common disease (P<.001; odds ratio 4.13, 95% CI 2.50-6.98) and typical presentation (P<.001; odds ratio 6.92, 95% CI 3.62-14.2) were significantly associated with the accuracy of the differential diagnosis list created by the symptom checker. A 3-year longitudinal survey of the diagnostic accuracy of differential diagnosis lists developed by an AI-based symptom checker, which has been implemented in real-world clinical practice settings, showed no improvement over time. Uncommon diseases and atypical presentations were independently associated with a lower diagnostic accuracy. In the future, symptom checkers should be trained to recognize uncommon conditions.

Avoiding Delays in Reporting Time-to-Event Randomized Trials: Calendar Backstops and Other Approaches.

New oncology therapies that extend patients' lives beyond initial expectations and improving later-line treatments can lead to complications in clinical trial design and conduct. In particular, for trials with event-based analyses, the time to observe all the protocol-specified events can exceed the finite follow-up of a clinical trial or can lead to much delayed release of outcome data. With the advent of multiple classes of oncology therapies leading to much longer survival than in the past, this issue in clinical trial design and conduct has become increasingly important in recent years. We propose a straightforward prespecified backstop rule for trials with a time-to-event analysis and evaluate the impact of the rule with both simulated and real-world trial data. We then provide recommendations for implementing the rule across a range of oncology clinical trial settings.

Remission Factors for Ustekinumab Treatment of Ulcerative Colitis: A Multicenter Retrospective Study of Real-World Data in Japan

Remission Factors for Ustekinumab Treatment of Ulcerative Colitis: A Multicenter Retrospective Study of Real-World Data in Japan

Sustained Effectiveness and Safety Over Time of Teriflunomide in Chinese Patients with Relapsing Multiple Sclerosis in the Greater Bay Area of China: Insights from Real-World Data.

The real-world data on the medium- to long-term effectiveness and safety of teriflunomide in Chinese patients with relapsing multiple sclerosis (MS) is limited. Therefore, this study aims to assess the treatment outcomes of teriflunomide in Chinese patients with MS over a medium- to long-term period. This cohort study was carried out in three tertiary hospitals and regional MS centers located in the Greater Bay Area of China. We obtained the historical clinical data of patients who underwent teriflunomide treatment for at least 6months. The primary objective was to evaluate the proportion of patients achieving no evidence of disease activity (NEDA)-3 status, which is characterized by the absence of relapses, confirmed disability worsening, and new or enlarging MRI lesions, over time. Secondary objectives included assessing the proportion of patients meeting each NEDA-3 criterion, changes in motor and cognitive function, as well as the incidence of adverse events and treatment discontinuations. A total of 160 patients with MS were enrolled, including 125 patients treated with teriflunomide for at least 1year (≥ 1-year completers) and 71 patients treated for at least 2years (≥ 2-year completers). A total of 85.63% of the overall population achieved clinical NEDA-3 status at 6months of teriflunomide treatment, and 71.20% of ≥ 1-year completers achieved NEDA-3 status at 12months of teriflunomide treatment. The median timed 25-foot walk test (T25FW), nine-hole peg test (9-HPT), and paced auditory serial addition test (PASAT) results were relatively stable before and after treatment. Medium- to long-term MS disease activity, as indicated by NEDA-3 status, is well controlled in patients treated with continuous teriflunomide treatment in real-world settings.

Real World Data are Not Always Big Data: The Case for Primary Data Collection on Medication Use in Pregnancy in the Context of Birth Defects Research.

Many examples of the use of real-world data in the area of pharmacoepidemiology include "big data" such as insurance claims, medical records, or hospital discharge databases. However, "big" is not always better, particularly when studying outcomes with narrow windows of etiologic relevance. Birth defects are one such outcome, where specificity of exposure timing is critical. Studies with primary data collection can be designed to query details on the timing of medication use, as well as type, dose, frequency, duration, and indication, that can better characterize the "real world". Because birth defects are rare, etiologic studies are typically case-control in design, like the National Birth Defects Prevention Study, Birth Defects Study to Evaluate Pregnancy exposureS, and Slone Birth Defects Study. Recall bias can be a concern, but the ability to collect detailed information on both prescription and over-the-counter medication use and on other exposures such as diet, family history, and sociodemographic factors is a distinct advantage over claims and medical record data sources. Case-control studies with primary data collection are essential to advancing the pharmacoepidemiology of birth defects.

Effectiveness and tolerability of eptinezumab in treating patients with migraine resistant to conventional preventive medications and CGRP (receptor) antibodies: a multicentre retrospective real-world analysis from Germany.

Eptinezumab is a monoclonal antibody that targets calcitonin gene-related peptide (CGRP mAb) and is used for migraine prophylaxis. Efficacy data are mainly from clinical trials, real-world data are hardly available yet. Reimbursement policy in Germany leads to eptinezumab mainly being used in patients having failed pre-treatment with other CGRP mAb. To date, it is unclear whether eptinezumab is efficacious and well tolerated in this population and how the treatment response differs from patients who are naive to CGRP mAbs. We analysed clinical routine data of 79 patients (episodic migraine (EM): n = 19; chronic migraine (CM): n = 60) from four different centres in Germany. All patients were treated with eptinezumab (100mg). Differences in monthly headache (MHD), migraine (MMD) and acute medication days (AMD) after three months were analysed. The correlation of response with the number of CGRP mAb failures was evaluated. Significance level has been corrected (alpha = 0.017). After three months MHD, MMD and AMD were significantly reduced. In EM, the median reduction for MHD was 4.0 days (IQR: -6.5 to -1.0; p = 0.001), for MMD 3.0 days (IQR: -5.5 to -1.5; p < 0.001) and for AMD 2.0 days (IQR: -5.0 to -0.5; p = 0.006). In CM, median reduction of MHD was 4 days (IQR: -8.0 to 0.0; p < 0.001), 3.0 days (IQR: -6.0 to-1.0; p < 0.001) for MMD and 1.0 day (IQR: -5.0 to 0.0; p < 0.001) for AMD. All patients were resistant to conventional preventive therapies and most to CGRP mAbs. Fourteen patients had never received a CGRP mAb and 65 patients had received at least one mAb without sufficient effectiveness and/or intolerability (one: n = 20, two: n = 28, three: n = 17). There was a significant association between the number of prior therapies and the 30% MHD responder rate (none: 78.6%, one: 45.0%, two: 32.1%, three: 23.5%, p = 0.010). Regarding tolerability, 10.4% (8/77) reported mild side effects. The effectiveness of eptinezumab is significantly reduced in patients who have not previously responded to other CGRP mAbs. However, limitations such as the retrospective nature of the analysis, the small sample size and the short treatment period with only the lower dose of eptinezumab must be considered when interpreting the results.

Al-BERT: a semi-supervised denoising technique for disease prediction.

Medical records are a valuable source for understanding patient health conditions. Doctors often use these records to assess health without solely depending on time-consuming and complex examinations. However, these records may not always be directly relevant to a patient's current health issue. For instance, information about common colds may not be relevant to a more specific health condition. While experienced doctors can effectively navigate through unnecessary details in medical records, this excess information presents a challenge for machine learning models in predicting diseases electronically. To address this, we have developed 'al-BERT', a new disease prediction model that leverages the BERT framework. This model is designed to identify crucial information from medical records and use it to predict diseases. 'al-BERT' operates on the principle that the structure of sentences in diagnostic records is similar to regular linguistic patterns. However, just as stuttering in speech can introduce 'noise' or irrelevant information, similar issues can arise in written records, complicating model training. To overcome this, 'al-BERT' incorporates a semi-supervised layer that filters out irrelevant data from patient visitation records. This process aims to refine the data, resulting in more reliable indicators for disease correlationsand enhancing the model's predictive accuracy and utility in medical diagnostics. To discern noise diseases within patient records, especially those resembling influenza-like illnesses, our approach employs a customized semi-supervised learning algorithm equipped with a focused attention mechanism. This mechanism is specifically calibrated to enhance the model's sensitivity to chronic conditions while concurrently distilling salient features from patient records, thereby augmenting the predictive accuracy and utility of the model in clinical settings. We evaluate the performance of al-BERT using real-world health insurance data provided by Taiwan's National Health Insurance. In our study, we evaluated our model against two others: one based on BERT that uses complete disease records, and another variant that includes extra filtering techniques. Our findings show that models incorporating filtering mechanisms typically perform better than those using the entire, unfiltered dataset. Our approach resulted in improved outcomes across several key measures: AUC-ROC (an indicator of a model's ability to distinguish between classes), precision (the accuracy of positive predictions), recall (the model's ability to find all relevant cases), and overall accuracy. Most notably, our model showed a 15% improvement in recall compared to the current best-performing method in the field of disease prediction. The conducted ablation study affirms the advantages of our attention mechanism and underscores the crucial role of the selection module within al-BERT.

Machine Learning Models for Pancreatic Cancer Risk Prediction Using Electronic Health Record Data - A Systematic Review and Assessment.

Accurate risk prediction can facilitate screening and early detection of pancreatic cancer (PC). We conducted a systematic review to critically evaluate effectiveness of machine learning (ML) and artificial intelligence (AI) techniques applied to Electronic Health Records (EHR) for PC risk prediction. Ovid MEDLINE(R), Ovid EMBASE, Ovid Cochrane Central Register of Controlled Trials, Ovid Cochrane Database of Systematic Reviews, Scopus, and Web of Science were searched for articles that utilized ML/AI techniques to predict PC, published between January 1st, 2012 to February 1st, 2024. Study selection and data extraction were conducted by two independent reviewers. Critical appraisal and data extraction was performed using CHARMS checklist. Risk of bias and applicability was examined using PROBAST. Thirty studies including 169,149 PC cases were identified. Logistic regression was the most frequent modeling method. Twenty studies utilized a curated set of known PC risk predictors or those identified by clinical experts. ML model discrimination performance (C-index) ranged from 0.57 to 1.0. Missing data was underreported, and most studies did not implement explainable-AI techniques or report exclusion time intervals. AI/ML models for PC risk prediction using known risk factors perform reasonably well and may have near-term applications in identifying cohorts for targeted PC screening if validated in real-world data sets. The combined use of structured and unstructured EHR data using emerging language models while incorporating explainable-AI techniques has the potential to identify novel PC risk factors and this approach merits further study.

An innovative method to strengthen evidence for potential drug safety signals using Electronic Health Records.

Reports from spontaneous reporting systems (SRS) are hypothesis generating. Additional evidence such as more reports is required to determine whether the generated drug-event associations are in fact safety signals. However, underreporting of adverse drug reactions (ADRs) delays signal detection. Through the use of natural language processing, different sources of real-world data can be used to proactively collect additional evidence for potential safety signals. This study aims to explore the feasibility of using Electronic Health Records (EHRs) to identify additional cases based on initial indications from spontaneous ADR reports, with the goal of strengthening the evidence base for potential safety signals. For two confirmed and two potential signals generated by the SRS of the Netherlands Pharmacovigilance Centre Lareb, targeted searches in the EHR of the Leiden University Medical Centre were performed using a text-mining based tool, CTcue. The search for additional cases was done by constructing and running queries in the structured and free-text fields of the EHRs. We identified at least five additional cases for the confirmed signals and one additional case for each potential safety signal. The majority of the identified cases for the confirmed signals were documented in the EHRs before signal detection by the Dutch Medicines Evaluation Board. The identified cases for the potential signals were reported to Lareb as further evidence for signal detection. Our findings highlight the feasibility of performing targeted searches in the EHR based on an underlying hypothesis to provide further evidence for signal generation.

Evaluating distributed-learning on real-world obstetrics data: comparing distributed, centralized and local models

This study focused on comparing distributed learning models with centralized and local models, assessing their efficacy in predicting specific delivery and patient-related outcomes in obstetrics using real-world data. The predictions focus on key moments in the obstetric care process, including discharge and various stages of hospitalization. Our analysis: using 6 different machine learning methods like Decision Trees, Bayesian methods, Stochastic Gradient Descent, K-nearest neighbors, AdaBoost, and Multi-layer Perceptron and 19 different variables with various distributions and types, revealed that distributed models were at least equal, and often superior, to centralized versions and local versions. We also describe thoroughly the preprocessing stage in order to help others implement this method in real-world scenarios. The preprocessing steps included cleaning and harmonizing missing values, handling missing data and encoding categorical variables with multisite logic. Even though the type of machine learning model and the distribution of the outcome variable can impact the result, we reached results of 66% being superior to the centralized and local counterpart and 77% being better than the centralized with AdaBoost. Our experiments also shed light in the preprocessing steps required to implement distributed models in a real-world scenario. Our results advocate for distributed learning as a promising tool for applying machine learning in clinical settings, particularly when privacy and data security are paramount, thus offering a robust solution for privacy-concerned clinical applications.

Data Science Methods for Real-World Evidence Generation in Real-World Data.

In the healthcare landscape, data science (DS) methods have emerged as indispensable tools to harness real-world data (RWD) from various data sources such as electronic health records, claim and registry data, and data gathered from digital health technologies. Real-world evidence (RWE) generated from RWD empowers researchers, clinicians, and policymakers with a more comprehensive understanding of real-world patient outcomes. Nevertheless, persistent challenges in RWD (e.g., messiness, voluminousness, heterogeneity, multimodality) and a growing awareness of the need for trustworthy and reliable RWE demand innovative, robust, and valid DS methods for analyzing RWD. In this article, I review some common current DS methods for extracting RWE and valuable insights from complex and diverse RWD. This article encompasses the entire RWE-generation pipeline, from study design with RWD to data preprocessing, exploratory analysis, methods for analyzing RWD, and trustworthiness and reliability guarantees, along with data ethics considerations and open-source tools. This review, tailored for an audience that may not be experts in DS, aspires to offer a systematic review of DS methods and assists readers in selecting suitable DS methods and enhancing the process of RWE generation for addressing their specific challenges.

Cost-effectiveness analysis of CYP3A5 genotype-guided tacrolimus dosing in solid organ transplantation using real-world data.

The objective of this study was to estimate the cost-effectiveness of CYP3A5 genotype-guided tacrolimus dosing in kidney, liver, heart, and lung transplant recipients relative to standard of care (SOC) tacrolimus dosing, from a US healthcare payer perspective. We developed decision-tree models to compare economic and clinical outcomes between CYP3A5 genotype-guided and SOC tacrolimus therapy in the first six months post-transplant. We derived inputs for CYP3A5 phenotype frequencies and physician use of genotype test results to inform clinical care from literature; tacrolimus exposure [high vs low tacrolimus time in therapeutic range using the Rosendaal algorithm (TAC TTR-Rosendaal)] and outcomes (incidences of acute tacrolimus nephrotoxicity, acute cellular rejection, and death) from real-world data; and costs from the Medicare Fee Schedule and literature. We calculated cost per avoided event and performed sensitivity analyses to evaluate the robustness of the results to changes in inputs. Incremental costs per avoided event for CYP3A5 genotype-guided vs SOC tacrolimus dosing were $176,667 for kidney recipients, $364,000 for liver recipients, $12,982 for heart recipients, and $93,333 for lung recipients. The likelihood of CYP3A5 genotype-guided tacrolimus dosing leading to cost-savings was 19.8% in kidney, 32.3% in liver, 51.8% in heart, and 54.1% in lung transplant recipients. Physician use of genotype results to guide clinical care and the proportion of patients with a high TAC TTR-Rosendaal were key parameters driving the cost-effectiveness of CYP3A5 genotype-guided tacrolimus therapy. Relative to SOC, CYP3A5 genotype-guided tacrolimus dosing resulted in a slightly greater benefit at a higher cost. Further economic evaluations examining intermediary outcomes (e.g., dose modifications) are needed, particularly in populations with higher frequencies of CYP3A5 expressers.

Joint optimization of the inventory routing problem considering the recycling of broken bikes in the bike-sharing system

Bike-sharing system has become an indispensable element of sustainable urban transportation, effectively resolving the "last mile" transportation challenge for city dwellers. A major daily operational task in these systems is planning a fleet to rebalance the bikes over time, ensuring the optimal availability of bikes and docks to users. Recycling is also a daily work with the an increase in the number of broken bikes. However, rebalancing or recycling operation is always regarded as an independent tasks. They are separately studied in existing papers. Thus, this paper develops an operational strategy for recycling broken bikes during the rebalancing process, and studies the combination of the station inventory and vehicle routing problems. First, an inventory routing model is constructed with the aim of minimizing the total costs including procurement, expected user loss, inventory and transportation costs. Then, a two-stage iterative algorithm is developed with both exact and heuristic algorithms. We use real-world data from Capital Bikeshare to test our proposed model and approach, which shows the two-stage iterative algorithm is efficient and outperforms existing solutions in reducing total costs. Finally, the sensitivity analysis is performed on key parameters such as the vehicle’s capacity, unit penalty costs for customer dissatisfaction events, unit inventory holding costs and the observation period of rebalancing. It shows that enterprises can reduce the total cost by altering vehicle’s capacity, reducing the unit inventory holding costs or changing the observation period of rebalancing.

Long-Term Effectiveness and Safety of Ixekizumab for the Treatment of Moderate-to-Severe Plaque Psoriasis: A Five-Year Multicenter Retrospective Study-IL PSO (Italian Landscape Psoriasis).

The introduction of biological therapies has revolutionized the treatment of moderate-to-severe plaque psoriasis. In particular, ixekizumab, an inhibitor of interleukin-17A, has shown great results in terms of efficacy and safety in both clinical trials and real-world experiences. However, there is a lack of long-term real-world data available for ixekizumab. We conducted a multicenter real-life study to evaluate the effectiveness and safety of ixekizumab in patients with moderate-to-severe plaque psoriasis. Psoriasis Area and Severity Index score (PASI) was collected at baseline and after 1, 2, 3, 4, and 5years. The occurrence of any adverse events was recorded at each time point. We enrolled 1096 patients treated with ixekizumab for at least 1year. At week52, the percentages of PASI90 and PASI100 were 85.04% and 69.07%, respectively. After 5years of treatment with ixekizumab, out of 145 patients, a PASI90 response was achieved by 86.90% of patients, while complete skin clearance was reached by 68.28% of patients. We did not observe any new significant safety findings throughout the study period. This study supports the long-term effectiveness and safety of ixekizumab in a real-world setting.

Comparative analysis of persistence and remission with guselkumab versus secukinumab and ixekizumab in the United States

Purpose: Real-world data comparing long-term performance of interleukin (IL)-23 and IL-17 inhibitors in psoriasis are limited. This study compared treatment persistence and remission among patients initiating guselkumab versus IL-17 inhibitors. Methods: Adults with psoriasis initiating guselkumab, secukinumab, or ixekizumab treatment (index date) were identified from Merative™ MarketScan® Research Databases (01/01/2016–10/31/2021). Persistence was defined as no index biologic supply gaps of twice the labeled maintenance dosing interval. Remission was defined using an exploratory approach as index biologic discontinuation for ≥6 months without psoriasis-related inpatient admissions and treatments. Results: There were 3516 and 6066 patients in the guselkumab versus secukinumab comparison, and 3805 and 4674 patients in guselkumab versus ixekizumab comparison. At 18 months, the guselkumab cohort demonstrated about twice the persistence rate as secukinumab (hazard ratio [HR] = 2.15; p < 0.001) and ixekizumab cohorts (HR = 1.77; p < 0.001). At 6 months after index biologic discontinuation, the guselkumab cohort was 31% and 40% more likely to achieve remission than secukinumab (rate ratio [RR] = 1.31; p < 0.001) and ixekizumab cohorts (RR = 1.40; p < 0.001). Conclusions: Guselkumab was associated with greater persistence and likelihood of remission than IL-17 inhibitors, indicating greater disease control and modification potential.

The double burden of severe mental illness and cancer: a population-based study on colorectal cancer care pathways from screening to end-of-life care.

Cancer is one of the main causes of death in persons with severe mental illness (SMI). Although their cancer incidence is similar, or sometimes even potentially lower compared to the general population, their cancer mortality remains higher. The role of healthcare provision and care equity in this mortality is increasingly being addressed in research, but available studies are limited in their scope. In this context, our aim was to compare colorectal cancer (CRC) care pathways from screening to end-of-life care in patients with and without pre-existing SMI on a national scale. This research leverages real-world data from the French national health claims database, covering the entire population, to assess cancer screening, diagnosis, treatment and post-treatment follow-up as well as quality of care (QOC) pathways among patients with incident CRC in 2015-2018, considering whether they had pre-existing SMI. We matched patients with SMI with three patients without - on age, sex, region of residence, year of cancer incidence and cancer type and location at presentation - as well as nationally established quality of CRC care indicators and regression models adjusting for relevant socio-economic, clinical and care provider-related covariates. Among patients with incident CRC, 1,532 individuals with pre-existing SMI were matched with individuals without SMI. After adjusting for covariates, both colon and rectal cancer patients with SMI were less likely to participate in the national CRC screening programme and to receive advanced diagnostic examinations (e.g., colonoscopies and several complementary diagnostic examinations). They also had lower odds of receiving combined treatments (e.g., neoadjuvant chemotherapy, radiotherapy and excision) and of having access to targeted therapy or capecitabine but higher odds for invasive care (e.g., stoma). Colon cancer patients with SMI were also more likely to have no treatment at all, and rectal cancer patients with SMI were less likely to receive post-treatment follow-up. Suboptimal QOC was observed for both groups of patients, but to a higher extent for patients with SMI, with statistically significant differences for indicators focusing on diagnosis and post-treatment follow-up. Our findings reveal discrepancies across the care continuum of CRC between individuals with and without SMI and provide initial avenues on where to focus future efforts to address them, notably at the entry and exit stages of cancer care pathways, while calling for further research on the mechanisms preventing equity of physical healthcare for individuals with SMI.