Comparison of On-Label Treatment Persistence in Real-World Patients with Psoriatic Arthritis Receiving Guselkumab Versus Subcutaneous Interleukin-17A Inhibitors.

Psoriatic arthritis (PsA) is a chronic, multidomain, inflammatory disease requiring long-term treatment. Guselkumab, a fully human interleukin [IL]-23p19-subunit inhibitor, and the IL-17A inhibitors (IL-17Ai) ixekizumab and secukinumab are approved by the US Food and Drug Administration (FDA) for adults with active PsA. Real-world data evaluating on-label treatment persistence is an important consideration for patients. This retrospective claim-based analysis (IQVIA PharMetrics® Plus) included adults with PsA receiving guselkumab or their first subcutaneous (SC) IL-17Ai (ixekizumab/secukinumab) per FDA label ("on-label") between July 14, 2020, and June 30, 2022. Baseline demographic and disease characteristics were collected in the 12 months preceding the index date (date of first guselkumab or SC IL-17Ai claim); follow-up extended through the earlier of the end of continuous insurance eligibility or end of data availability. Baseline characteristics were balanced between the cohorts by propensity score weighting (standardized mortality ratio [SMR]). Discontinuation was defined as a gap 2 × the FDA-approved maintenance dosing interval (guselkumab:112 days; SC IL-17Ai: 56 days); on-label persistence in the weighted cohorts was assessed using Kaplan-Meier curves and compared with a Cox proportional hazards model. Weighted demographic and disease characteristics were well balanced between the cohorts (guselkumab: N = 910, mean age = 50.4 years, 60.4% female; SC IL-17Ai: N = 2740, mean age = 50.2, 59.4% female). At 12 months, the guselkumab cohort was 1.85 × more likely to remain persistent with on-label therapy vs the SC IL-17Ai cohort (p < 0.001); median time to discontinuation was not reached for guselkumab and was 12.3 months for SC IL-17Ai. At 3, 6, 9, and 12 months, persistence rates in the weighted cohorts were higher with guselkumab than with SC IL-17Ai (p < 0.001). In this real-world claims data analysis in adults with PsA, on-label persistence rates were statistically significantly higher with guselkumab, as early as 3 months, with ~ 2 × greater likelihood of persistence at 12 months relative to SC IL-17Ai.

Mease PJ ，Ferrante SA ，Shiff NJ ，Fitzgerald TP ，Chakravarty SD ，Walsh JA ... -  《-》

Comparison of Real-World On-Label Treatment Persistence in Patients with Psoriatic Arthritis Receiving Guselkumab Versus Subcutaneous Tumor Necrosis Factor Inhibitors.

Treatment persistence among patients with psoriatic arthritis (PsA) is essential for achieving optimal treatment outcomes. Guselkumab, a fully human interleukin-23p19-subunit inhibitor, was approved by the United States (US) Food and Drug Administration for the treatment of active PsA in July 2020, with a dosing regimen of 100 mg at week 0, week 4, then every 8 weeks. In the Phase 3 DISCOVER-1 and DISCOVER-2 studies of patients with active PsA, 94% of guselkumab-randomized patients completed treatment through 1 year and 90% did so through 2 years (DISCOVER-2). Real-world evidence is needed to compare treatment persistence while following US prescribing guidelines (i.e., on-label persistence) for guselkumab versus subcutaneous (SC) tumor necrosis factor inhibitors (TNFis). Adults with PsA receiving guselkumab or their first SC TNFi (i.e., adalimumab, certolizumab pegol, etanercept, or golimumab) between 14 July 2020 and 31 March 2022 were identified in the IQVIA PharMetrics® Plus database (first claim defined the treatment start date [index date]). Baseline characteristics and biologic use (biologic-naïve/biologic-experienced) were assessed during the 12-month period preceding the index date. Baseline characteristics were balanced between cohorts using propensity-score weighting based on the standardized mortality ratio approach. The follow-up period spanned from the index date until the earlier of the end of continuous insurance eligibility or end of data availability. On-label persistence, defined as the absence of treatment discontinuation (based on a gap of 112 days for guselkumab or 56 days for SC TNFi) or any dose escalation/reduction during follow-up, was assessed in the weighted treatment cohorts using Kaplan-Meier (KM) curves. A Cox proportional hazards model, further adjusted for baseline biologic use, was used to compare on-label persistence between the weighted cohorts. The guselkumab cohort included 526 patients (mean age 49.8 years; 61.2% female) and the SC TNFi cohort included 1953 patients (mean age: 48.5 years; 60.2% female). After weighting, baseline characteristics were well balanced with a mean follow-up of 12.3-12.4 months across cohorts; 51.5% of patients in the guselkumab cohort and 16.7% in the SC TNFi cohort received biologics in the 12-month baseline period. Respective rates of treatment persistence at 3, 6, 9, and 12 months were 91.2%, 84.1%, 75.9%, and 71.5% for the guselkumab cohort versus 77.3%, 61.6%, 50.0%, and 43.7% for the SC TNFi cohort (all log-rank p < 0.001). At 12 months, patients in the guselkumab cohort were 3.0 times more likely than patients in the SC TNFi cohort to remain persistent on treatment (p < 0.001). Median time to discontinuation was not reached for the guselkumab cohort and was 8.9 months for the SC TNFi cohort. This real-world study employing US commercial health-plan claims data to assess on-label treatment persistence in PsA demonstrated that, at 12 months, guselkumab was associated with a 3 times greater likelihood of persistence compared with SC TNFi.

Walsh JA ，Lin I ，Zhao R ，Shiff NJ ，Morrison L ，Emond B ，Yu LH ，Schwartzbein S ，Lefebvre P ，Pilon D ，Chakravarty SD ，Mease P ... -  《-》

Erratum: Eyestalk Ablation to Increase Ovarian Maturation in Mud Crabs.

《Jove-Journal of Visualized Experiments》

Comparison of Two Modern Survival Prediction Tools, SORG-MLA and METSSS, in Patients With Symptomatic Long-bone Metastases Who Underwent Local Treatment With Surgery Followed by Radiotherapy and With Radiotherapy Alone.

Survival estimation for patients with symptomatic skeletal metastases ideally should be made before a type of local treatment has already been determined. Currently available survival prediction tools, however, were generated using data from patients treated either operatively or with local radiation alone, raising concerns about whether they would generalize well to all patients presenting for assessment. The Skeletal Oncology Research Group machine-learning algorithm (SORG-MLA), trained with institution-based data of surgically treated patients, and the Metastases location, Elderly, Tumor primary, Sex, Sickness/comorbidity, and Site of radiotherapy model (METSSS), trained with registry-based data of patients treated with radiotherapy alone, are two of the most recently developed survival prediction models, but they have not been tested on patients whose local treatment strategy is not yet decided. (1) Which of these two survival prediction models performed better in a mixed cohort made up both of patients who received local treatment with surgery followed by radiotherapy and who had radiation alone for symptomatic bone metastases? (2) Which model performed better among patients whose local treatment consisted of only palliative radiotherapy? (3) Are laboratory values used by SORG-MLA, which are not included in METSSS, independently associated with survival after controlling for predictions made by METSSS? Between 2010 and 2018, we provided local treatment for 2113 adult patients with skeletal metastases in the extremities at an urban tertiary referral academic medical center using one of two strategies: (1) surgery followed by postoperative radiotherapy or (2) palliative radiotherapy alone. Every patient's survivorship status was ascertained either by their medical records or the national death registry from the Taiwanese National Health Insurance Administration. After applying a priori designated exclusion criteria, 91% (1920) were analyzed here. Among them, 48% (920) of the patients were female, and the median (IQR) age was 62 years (53 to 70 years). Lung was the most common primary tumor site (41% [782]), and 59% (1128) of patients had other skeletal metastases in addition to the treated lesion(s). In general, the indications for surgery were the presence of a complete pathologic fracture or an impending pathologic fracture, defined as having a Mirels score of ≥ 9, in patients with an American Society of Anesthesiologists (ASA) classification of less than or equal to IV and who were considered fit for surgery. The indications for radiotherapy were relief of pain, local tumor control, prevention of skeletal-related events, and any combination of the above. In all, 84% (1610) of the patients received palliative radiotherapy alone as local treatment for the target lesion(s), and 16% (310) underwent surgery followed by postoperative radiotherapy. Neither METSSS nor SORG-MLA was used at the point of care to aid clinical decision-making during the treatment period. Survival was retrospectively estimated by these two models to test their potential for providing survival probabilities. We first compared SORG to METSSS in the entire population. Then, we repeated the comparison in patients who received local treatment with palliative radiation alone. We assessed model performance by area under the receiver operating characteristic curve (AUROC), calibration analysis, Brier score, and decision curve analysis (DCA). The AUROC measures discrimination, which is the ability to distinguish patients with the event of interest (such as death at a particular time point) from those without. AUROC typically ranges from 0.5 to 1.0, with 0.5 indicating random guessing and 1.0 a perfect prediction, and in general, an AUROC of ≥ 0.7 indicates adequate discrimination for clinical use. Calibration refers to the agreement between the predicted outcomes (in this case, survival probabilities) and the actual outcomes, with a perfect calibration curve having an intercept of 0 and a slope of 1. A positive intercept indicates that the actual survival is generally underestimated by the prediction model, and a negative intercept suggests the opposite (overestimation). When comparing models, an intercept closer to 0 typically indicates better calibration. Calibration can also be summarized as log(O:E), the logarithm scale of the ratio of observed (O) to expected (E) survivors. A log(O:E) > 0 signals an underestimation (the observed survival is greater than the predicted survival); and a log(O:E) < 0 indicates the opposite (the observed survival is lower than the predicted survival). A model with a log(O:E) closer to 0 is generally considered better calibrated. The Brier score is the mean squared difference between the model predictions and the observed outcomes, and it ranges from 0 (best prediction) to 1 (worst prediction). The Brier score captures both discrimination and calibration, and it is considered a measure of overall model performance. In Brier score analysis, the "null model" assigns a predicted probability equal to the prevalence of the outcome and represents a model that adds no new information. A prediction model should achieve a Brier score at least lower than the null-model Brier score to be considered as useful. The DCA was developed as a method to determine whether using a model to inform treatment decisions would do more good than harm. It plots the net benefit of making decisions based on the model's predictions across all possible risk thresholds (or cost-to-benefit ratios) in relation to the two default strategies of treating all or no patients. The care provider can decide on an acceptable risk threshold for the proposed treatment in an individual and assess the corresponding net benefit to determine whether consulting with the model is superior to adopting the default strategies. Finally, we examined whether laboratory data, which were not included in the METSSS model, would have been independently associated with survival after controlling for the METSSS model's predictions by using the multivariable logistic and Cox proportional hazards regression analyses. Between the two models, only SORG-MLA achieved adequate discrimination (an AUROC of > 0.7) in the entire cohort (of patients treated operatively or with radiation alone) and in the subgroup of patients treated with palliative radiotherapy alone. SORG-MLA outperformed METSSS by a wide margin on discrimination, calibration, and Brier score analyses in not only the entire cohort but also the subgroup of patients whose local treatment consisted of radiotherapy alone. In both the entire cohort and the subgroup, DCA demonstrated that SORG-MLA provided more net benefit compared with the two default strategies (of treating all or no patients) and compared with METSSS when risk thresholds ranged from 0.2 to 0.9 at both 90 days and 1 year, indicating that using SORG-MLA as a decision-making aid was beneficial when a patient's individualized risk threshold for opting for treatment was 0.2 to 0.9. Higher albumin, lower alkaline phosphatase, lower calcium, higher hemoglobin, lower international normalized ratio, higher lymphocytes, lower neutrophils, lower neutrophil-to-lymphocyte ratio, lower platelet-to-lymphocyte ratio, higher sodium, and lower white blood cells were independently associated with better 1-year and overall survival after adjusting for the predictions made by METSSS. Based on these discoveries, clinicians might choose to consult SORG-MLA instead of METSSS for survival estimation in patients with long-bone metastases presenting for evaluation of local treatment. Basing a treatment decision on the predictions of SORG-MLA could be beneficial when a patient's individualized risk threshold for opting to undergo a particular treatment strategy ranged from 0.2 to 0.9. Future studies might investigate relevant laboratory items when constructing or refining a survival estimation model because these data demonstrated prognostic value independent of the predictions of the METSSS model, and future studies might also seek to keep these models up to date using data from diverse, contemporary patients undergoing both modern operative and nonoperative treatments. Level III, diagnostic study.

Lee CC ，Chen CW ，Yen HK ，Lin YP ，Lai CY ，Wang JL ，Groot OQ ，Janssen SJ ，Schwab JH ，Hsu FM ，Lin WH ... -  《-》

Defining the optimum strategy for identifying adults and children with coeliac disease: systematic review and economic modelling.

Elwenspoek MM ，Thom H ，Sheppard AL ，Keeney E ，O'Donnell R ，Jackson J ，Roadevin C ，Dawson S ，Lane D ，Stubbs J ，Everitt H ，Watson JC ，Hay AD ，Gillett P ，Robins G ，Jones HE ，Mallett S ，Whiting PF ... -  《-》