Identifying Predictors of PASI100 Responses up to Month 12 in Patients with Moderate-to-severe Psoriasis Receiving Biologics in the Psoriasis Study of Health Outcomes (PSoHO).

Despite the abundance of data concerning biologic treatments for patients with psoriasis, clinicians are often challenged with discerning the optimal treatment for each patient. To inform this selection, this study explored whether a patient's baseline characteristics or disease profile could predict the likelihood of achieving complete skin clearance with biologic treatment. Machine-learning and other statistical methods were applied to the substantial data collected from patients with moderate-to-severe psoriasis in the ongoing, international, prospective, observational Psoriasis Study of Health Outcomes (PSoHO). The 3 measures of complete skin clearance were a psoriasis area and severity index (PASI)100 response at (a) week 12, (b) month 12, and (c) week 12 and maintain ed at month 6 and month 12 (PASI100 durability). From these real-world data, the absence of nail psoriasis emerged  as the most consistent feature that may be used by clinicians to predict high-level treatment responses with biologic treatment. Other significant predictors of skin clearance with biologic treatments were the absence of hypertension and a lower body surface area affected by psoriasis. Overall, this study evidences the substantial challenge of identifying reliable clinical markers of treatment response for patients with psoriasis and highlights the importance of regular screening for psoriatic nail involvement.

The Effect of Teriparatide Treatment on the Risk of Fragility Fractures in Postmenopausal Women with Osteoporosis: Results from the Asian and Latin America Fracture Observational Study (ALAFOS).

The Asian and Latin America Fracture Observational Study (ALAFOS) is a prospective, observational, single-arm study conducted in 20 countries across Asia, Latin America and the Middle East. ALAFOS evaluated new clinical vertebral and non-vertebral fragility fractures in relation to time on teriparatide, in postmenopausal women with osteoporosis in real-life clinical practice. Clinical fragility fractures, back pain, and health-related quality of life (HRQoL) were recorded in 6-month intervals for ≤ 24 months during teriparatide treatment and up to 12-months post-treatment. Data were analysed with piecewise exponential regression with inverse probability weighting for time to event outcomes and mixed-model repeated measures for back pain and HRQoL. 3054 postmenopausal women started teriparatide and attended ≥ one follow-up visit (mean [SD] age 72.5 [10.4] years). The median (95% CI) time to treatment discontinuation was 22.0 months (21.2, 22.8). During the treatment period, 111 patients (3.6%) sustained 126 clinical fractures (2.98 fractures/100 patient-years). Rates of new clinical fragility fractures were significantly decreased during the > 6-12, > 12-18, and > 18-24-month periods, as compared with the first 6 months of treatment (hazard ratio [HR] 0.57; 95% CI 0.37, 0.88; p = 0.012; HR 0.35; 95% CI 0.19, 0.62; p < 0.001; HR 0.43; 95% CI 0.23, 0.83; p = 0.011; respectively). Patients also reported an improvement in back pain and HRQoL (p < 0.001). These results provide data on the real-world effectiveness of teriparatide in the ALAFOS regions and are consistent with other studies showing reduction of fractures after 6 months of teriparatide treatment. These results should be interpreted in the context of the noncontrolled design of this observational study.

Comparative effectiveness of biologics in clinical practice: week 12 primary outcomes from an international observational psoriasis study of health outcomes (PSoHO).

BACKGROUND: Clinical trials study treatment outcomes under stringent conditions, capturing incompletely the heterogeneity of patient populations and treatment complexities encountered in real-world practice. OBJECTIVES: To compare the effectiveness of anti-interleukin (IL)-17A biologics relative to other approved biologics in patients with moderate-to-severe psoriasis. METHODS: The Psoriasis Study of Health Outcomes (PSoHO) is an ongoing 3-year observational cohort study in adults with chronic moderate-to-severe plaque psoriasis initiating or switching to a new biologic. Primary study endpoint is the proportion of patients achieving 90% improvement in Psoriasis Area and Severity Index (PASI 90) and/or static Physician Global Assessment (sPGA) 0/1 at Week 12 (W12) in the anti-IL-17A cohort (ixekizumab [IXE], secukinumab) vs. all other approved biologics. Secondary outcomes include the proportion of patients who achieve PASI 75/90/100, absolute PASI scores ≤5, ≤2 and ≤1, Dermatology Life Quality Index (DLQI) score of 0/1 at W12 between the two cohorts and among the individual biologics. Comparative effectiveness analyses were conducted using Frequentist Model Averaging (FMA), a novel causal inference machine learning approach. Missing data for binary outcomes were imputed as non-response. RESULTS: Patient profiles in the anti-IL-17A cohort and other biologics cohort were similar, with more frequent comorbid psoriatic arthritis and less frequent exposure to conventional treatments in the patients receiving anti-IL-17A biologics. At W12, 71.4% of patients who received an anti-IL-17A biologic achieved PASI 90 and/or sPGA 0/1 compared to 58.6% of patients who received other biologics (odds ratios [OR], 1.9; 95% confidence intervals [CI], [1.6, 2.4]). Similar findings were observed for secondary outcomes. CONCLUSIONS: These results reflect the high efficacy and early onset of skin clearance of IL-17A inhibitors observed in randomized clinical trials and confirm the effectiveness of anti-IL-17A biologics in the real-world setting.

Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making.

BACKGROUND: Machine learning is a broad term encompassing a number of methods that allow the investigator to learn from the data. These methods may permit large real-world databases to be more rapidly translated to applications to inform patient-provider decision making. METHODS: This systematic literature review was conducted to identify published observational research of employed machine learning to inform decision making at the patient-provider level. The search strategy was implemented and studies meeting eligibility criteria were evaluated by two independent reviewers. Relevant data related to study design, statistical methods and strengths and limitations were identified; study quality was assessed using a modified version of the Luo checklist. RESULTS: A total of 34 publications from January 2014 to September 2020 were identified and evaluated for this review. There were diverse methods, statistical packages and approaches used across identified studies. The most common methods included decision tree and random forest approaches. Most studies applied internal validation but only two conducted external validation. Most studies utilized one algorithm, and only eight studies applied multiple machine learning algorithms to the data. Seven items on the Luo checklist failed to be met by more than 50% of published studies. CONCLUSIONS: A wide variety of approaches, algorithms, statistical software, and validation strategies were employed in the application of machine learning methods to inform patient-provider decision making. There is a need to ensure that multiple machine learning approaches are used, the model selection strategy is clearly defined, and both internal and external validation are necessary to be sure that decisions for patient care are being made with the highest quality evidence. Future work should routinely employ ensemble methods incorporating multiple machine learning algorithms.

Comparison of cumulative clinical benefits of biologics for the treatment of psoriasis over 16 weeks: Results from a network meta-analysis.

BACKGROUND: Cumulative clinical improvement and speed of improvement are important to psoriasis patients. OBJECTIVE: Compare cumulative benefits of biologics over 12 to 16 weeks in the treatment of moderate to severe psoriasis. METHODS: A systematic literature review identified phase III trial data on Psoriasis Area and Severity Index (PASI) responses for biologics during 12 and 16 weeks of treatment. Cumulative clinical benefit, measured by the area under the curve for PASI ≥75% improvement (PASI 75), ≥90% improvement (PASI 90), and 100% improvement (PASI 100), was compared using the network meta-analysis and Bayesian methodology on the relative probability of achieving percentage of maximum area under the curve. RESULTS: Among biologics approved for psoriasis treatment, anti-interleukin-17 biologics demonstrated consistently greater cumulative clinical benefits on PASI 75, PASI 90, and PASI 100 over the 12- or 16-week period than anti-interleukin-23 and other biologics. For biologics with 12-week data, ixekizumab and brodalumab showed greater cumulative benefits for PASI 75, PASI 90, and PASI 100 than secukinumab, followed by guselkumab, infliximab, adalimumab, ustekinumab, and etanercept. Ixekizumab showed greater cumulative benefits than all other biologics reporting 16-week data. LIMITATIONS: Recently approved biologics were not included. CONCLUSION: Ixekizumab (at 12 weeks and 16 weeks) and brodalumab (at 12 weeks) had greater cumulative clinical benefit than all of other biologics studied.

Combination of several matching adjusted indirect comparisons (MAICs) with an application in psoriasis.

In health technology assessment (HTA), beside network meta-analysis (NMA), indirect comparisons (IC) have become an important tool used to provide evidence between two treatments when no head-to-head data are available. Researchers may use the adjusted indirect comparison based on the Bucher method (AIC) or the matching-adjusted indirect comparison (MAIC). While the Bucher method may provide biased results when included trials differ in baseline characteristics that influence the treatment outcome (treatment effect modifier), this issue may be addressed by applying the MAIC method if individual patient data (IPD) for at least one part of the AIC is available. Here, IPD is reweighted to match baseline characteristics and/or treatment effect modifiers of published data. However, the MAIC method does not provide a solution for situations when several common comparators are available. In these situations, assuming that the indirect comparison via the different common comparators is homogeneous, we propose merging these results by using meta-analysis methodology to provide a single, potentially more precise, treatment effect estimate. This paper introduces the method to combine several MAIC networks using classic meta-analysis techniques, it discusses the advantages and limitations of this approach, as well as demonstrates a practical application to combine several (M)AIC networks using data from Phase III psoriasis randomized control trials (RCT).

Incidence and characteristics of the nocebo response from meta-analyses of the placebo arms of clinical trials of olanzapine for bipolar disorder.

OBJECTIVES: In the clinical setting, the nocebo phenomenon is where clinical worsening or adverse events occur as a response to a treatment, in a situation in which conditioning from previous treatment exposure and/or expectations of sickness or symptoms lead to sickness and symptoms in a conditioned or expectant individual. The nocebo response may thus be a confounder in clinical treatment and clinical research. There is a need to know how to predict if an individual is likely to be a nocebo responder, and how significant and commonplace the nocebo effect might be. METHODS: An analysis was conducted on nine placebo-controlled, randomized clinical trials of olanzapine for the treatment of bipolar disorder using data from placebo-treated study participants only. Data were analysed to identify participant or study characteristics associated with a nocebo event, defined as any treatment-emergent adverse event (TEAE) or an increase in score from baseline to endpoint for primary measures of clinical symptoms. RESULTS: A total of 1185 participants were randomized to placebo, of whom 806 (68%) reported a TEAE. Hamilton Depression Rating Scale (HDRS) data were only available for 649 placebo-treated participants, of whom 321 (49.5%) demonstrated worsening. Nocebo events were significantly associated with: not being treatment-naïve, younger age, being located in the USA, being a participant in an earlier study, and being classified as obese compared with normal weight. CONCLUSIONS: A pattern to identify nocebo responders did not emerge, although some prognostic variables were associated with a greater probability of nocebo response. There was some evidence to support the role of expectancy as a cause of nocebo reactions.

Alternative Weighting Approaches for Anchored Matching-Adjusted Indirect Comparisons via a Common Comparator.

BACKGROUND: Adjusted indirect comparisons (anchored via a common comparator) are an integral part of health technology assessment. These methods are challenged when differences between studies exist, including inclusion/exclusion criteria, outcome definitions, patient characteristics, as well as ensuring the choice of a common comparator. OBJECTIVES: Matching-adjusted indirect comparison (MAIC) can address these challenges, but the appropriate application of MAICs is uncertain. Examples include whether to match between individual-level data and aggregate-level data studies separately for treatment arms or to combine the arms, which matching algorithm should be used, and whether to include the control treatment outcome and/or covariates present in individual-level data. RESULTS: Results from seven matching approaches applied to a continuous outcome in six simulated scenarios demonstrated that when no effect modifiers were present, the matching methods were equivalent to the unmatched Bucher approach. When effect modifiers were present, matching methods (regardless of approach) outperformed the Bucher method. Matching on arms separately produced more precise estimates compared with matching on total moments, and for certain scenarios, matching including the control treatment outcome did not produce the expected effect size. The entropy balancing approach was used to determine whether there were any notable advantages over the method proposed by Signorovitch et al. When unmeasured effect modifiers were present, no approach was able to estimate the true treatment effect. CONCLUSIONS: Compared with the Bucher approach (no matching), the MAICs examined demonstrated more accurate estimates, but further research is required to understand these methods across an array of situations.

Respiratory disease in the Asia-Pacific region: Cough as a key symptom.

BACKGROUND: Respiratory diseases represent a significant impact on health care. A cross-sectional, multicountry (India, Korea, Malaysia, Singapore, Taiwan, and Thailand) observational study was conducted to investigate the proportion of adult patients who received care for a primary diagnosis of asthma, allergic rhinitis (AR), chronic obstructive pulmonary disease (COPD), or rhinosinusitis. OBJECTIVE: To determine the proportion of patients who received care for asthma, AR, COPD, and rhinosinusitis, and the frequency and main symptoms reported. METHODS: Patients ages ≥18 years, who presented to a physician with symptoms that met the diagnostic criteria for a primary diagnosis of asthma, AR, COPD, or rhinosinusitis were enrolled. Patients and physicians completed a survey that contained questions related to demographics and respiratory symptoms. RESULTS: A total of 13,902 patients with a respiratory disorder were screened, of whom 7030 were eligible and 5250 enrolled. The highest percentage of patients who received care had a primary diagnosis of AR (14.0% [95% confidence interval {CI}, 13.4-14.6%]), followed by asthma (13.5% [95% CI, 12.9-14.1%]), rhinosinusitis (5.4% [95% CI, 4.6-5.3%]), and COPD (4.9% [95% CI, 5.0-5.7%]). Patients with a primary diagnosis of COPD (73%), followed by asthma (61%), rhinosinusitis (59%), and AR (47%) most frequently reported cough as a symptom. Cough was the main reason for seeking medical care among patients with a primary diagnosis of COPD (43%), asthma (33%), rhinosinusitis (13%), and AR (11%). CONCLUSION: Asthma, AR, COPD, and rhinosinusitis represent a significant proportion of respiratory disorders in patients who presented to health care professionals in the Asia-Pacific region, many with concomitant disease. Cough was a prominent symptom and the major reason for patients with respiratory diseases to seek medical care.

A cost-effectiveness analysis of sensor-augmented insulin pump therapy and automated insulin suspension versus standard pump therapy for hypoglycemic unaware patients with type 1 diabetes.

OBJECTIVE: To assess the cost-effectiveness of sensor-augmented insulin pump therapy with "Low Glucose Suspend" (LGS) functionality versus standard pump therapy with self-monitoring of blood glucose in patients with type 1 diabetes who have impaired awareness of hypoglycemia. METHODS: A clinical trial-based economic evaluation was performed in which the net costs and effectiveness of the two treatment modalities were calculated and expressed as an incremental cost-effectiveness ratio (ICER). The clinical outcome of interest for the evaluation was the rate of severe hypoglycemia in each arm of the LGS study. Quality-of-life utility scores were calculated using the three-level EuroQol five-dimensional questionnaire. Resource use costs were estimated using public sources. RESULTS: After 6 months, the use of sensor-augmented insulin pump therapy with LGS significantly reduced the incidence of severe hypoglycemia compared with standard pump therapy (incident rate difference 1.85 [0.17-3.53]; P = 0.037). Based on a primary randomized study, the ICER per severe hypoglycemic event avoided was $18,257 for all patients and $14,944 for those aged 12 years and older. Including all major medical resource costs (e.g., hospital admissions), the ICERs were $17,602 and $14,289, respectively. Over the 6-month period, the cost per quality-adjusted life-year gained was $40,803 for patients aged 12 years and older. CONCLUSIONS: Based on the Australian experience evaluating new interventions across a broad range of therapeutic areas, sensor-augmented insulin pump therapy with LGS may be considered a cost-effective alternative to standard pump therapy with self-monitoring of blood glucose in hypoglycemia unaware patients with type 1 diabetes.

Applying machine learning approaches for predicting obesity risk using US health administrative claims database.

INTRODUCTION: Body mass index (BMI) is inadequately recorded in US administrative claims databases. We aimed to validate the sensitivity and positive predictive value (PPV) of BMI-related diagnosis codes using an electronic medical records (EMR) claims-linked database. Additionally, we applied machine learning (ML) to identify features in US claims databases to predict obesity status. RESEARCH DESIGN AND METHODS: This observational, retrospective analysis included 692 119 people ≥18 years of age, with ≥1 BMI reading in MarketScan Explorys Claims-EMR data (January 2013-December 2019). Claims-based obesity status was compared with EMR-based BMI (gold standard) to assess BMI-related diagnosis code sensitivity and PPV. Logistic regression (LR), penalized LR with L1 penalty (Least Absolute Shrinkage and Selection Operator), extreme gradient boosting (XGBoost) and random forest, with features drawn from insurance claims, were trained to predict obesity status (BMI≥30 kg/m2) from EMR as the gold standard. Model performance was compared using several metrics, including the area under the receiver operating characteristic curve. The best-performing model was applied to assess feature importance. Obesity risk scores were computed from the best model generated from the claims database and compared against the BMI recorded in the EMR. RESULTS: The PPV of diagnosis codes from claims alone remained high over the study period (85.4-89.2%); sensitivity was low (16.8-44.8%). XGBoost performed the best at predicting obesity with the highest area under the curve (AUC; 79.4%) and the lowest Brier score. The number of obesity diagnoses and obesity diagnoses from inpatient settings were the most important predictors of obesity. XGBoost showed an AUC of 74.1% when trained without an obesity diagnosis. CONCLUSIONS: Obesity prevalence is under-reported in claims databases. ML models, with or without explicit obesity, show promise in improving obesity prediction accuracy compared with obesity codes alone. Improved obesity status prediction may assist practitioners and payors to estimate the burden of obesity and investigate the potential unmet needs of current treatments.

A machine learning-based algorithm to identify U-500R insulin candidates among adults with type 2 diabetes mellitus in US retrospective databases.

OBJECTIVE: To develop a machine learning-based predictive algorithm to identify patients with type 2 diabetes mellitus (T2DM) who are candidates for initiation of U-500R insulin (U-500R). METHODS: A retrospective cohort of patients with T2DM was used from a large US administrative claims and electronic health records (EHR) database affiliated with Optum. Predictor variables derived from the data were used to identify appropriate supervised machine learning models including least absolute shrinkage and selection operator (LASSO) and extreme gradient boosted (XGBoost) methods. Predictive performance was assessed using precision-recall (PR) and receiver operating characteristic (ROC) area under the curve (AUC). The clinical interpretation of the final model was supported by fitting the final set of variables from the LASSO and XGBoost models to a traditional logistic regression model. Model choice was determined by comparing Akaike Information Criterion (AIC), residual deviances, and scaled Brier scores. RESULTS: Among 81,242 patients who met the study eligibility criteria, 577 initiated U-500R and were assigned to the positive class. Predictors of U-500R initiation included overweight/obesity, neuropathy, HbA1c ≥9% and 8%-9%, BUN 23.8 to <112 mg/dl, ALT 35.9-2056.2 U/L, no radiological chest exams, no GFR labs, and gait/mobility abnormalities. The best performing model was the LASSO model with an ROC AUC of 0.776 on the hold-out test set. CONCLUSION: This study successfully developed and validated a machine learning-based algorithm to identify U-500R candidates among patients with T2DM. This may help health care providers and decision-makers to understand important characteristics of patients who could use U-500R therapies which in turn could support policies and guidelines for optimal patient management.

Incidence of type 2 diabetes, cardiovascular disease and chronic kidney disease in patients with multiple sclerosis initiating disease-modifying therapies: Retrospective cohort study using a frequentist model averaging statistical framework.

Researchers are increasingly using insights derived from large-scale, electronic healthcare data to inform drug development and provide human validation of novel treatment pathways and aid in drug repurposing/repositioning. The objective of this study was to determine whether treatment of patients with multiple sclerosis with dimethyl fumarate, an activator of the nuclear factor erythroid 2-related factor 2 (Nrf2) pathway, results in a change in incidence of type 2 diabetes and its complications. This retrospective cohort study used administrative claims data to derive four cohorts of adults with multiple sclerosis initiating dimethyl fumarate, teriflunomide, glatiramer acetate or fingolimod between January 2013 and December 2018. A causal inference frequentist model averaging framework based on machine learning was used to compare the time to first occurrence of a composite endpoint of type 2 diabetes, cardiovascular disease or chronic kidney disease, as well as each individual outcome, across the four treatment cohorts. There was a statistically significantly lower risk of incidence for dimethyl fumarate versus teriflunomide for the composite endpoint (restricted hazard ratio [95% confidence interval] 0.70 [0.55, 0.90]) and type 2 diabetes (0.65 [0.49, 0.98]), myocardial infarction (0.59 [0.35, 0.97]) and chronic kidney disease (0.52 [0.28, 0.86]). No differences for other individual outcomes or for dimethyl fumarate versus the other two cohorts were observed. This study effectively demonstrated the use of an innovative statistical methodology to test a clinical hypothesis using real-world data to perform early target validation for drug discovery. Although there was a trend among patients treated with dimethyl fumarate towards a decreased incidence of type 2 diabetes, cardiovascular disease and chronic kidney disease relative to other disease-modifying therapies-which was statistically significant for the comparison with teriflunomide-this study did not definitively support the hypothesis that Nrf2 activation provided additional metabolic disease benefit in patients with multiple sclerosis.

Baseline Characteristics and mNAPSI Change from Baseline Scores Through Month 12 for Patients with Moderate-to-Severe Plaque Psoriasis and Concomitant Nail Psoriasis Treated with Biologics from PSoHO.

INTRODUCTION: Nail psoriasis is highly prevalent among patients with psoriasis yet remains one of the most challenging areas to treat. To better understand the treatment landscape for psoriatic nail disease, more studies are needed that compare the effectiveness of different biologics for patients with nail psoriasis. This study contributes to this objective by directly comparing the effectiveness of approved biologics in improving nail psoriasis for patients up to month 12 in a real-world setting. METHODS: Psoriasis Study of Health Outcomes (PSoHO) is an ongoing 3-year, prospective, non-interventional cohort study of adults with chronic moderate-to-severe plaque psoriasis initiating or switching to a new biologic. This study assessed the change in modified Nail Psoriasis Severity Index (mNAPSI) score from baseline to months 3, 6 and 12 for 763 patients and compared the effectiveness of anti-interleukin (IL)-17A biologics versus other approved biologics, as well as ixekizumab versus secukinumab, guselkumab, risankizumab and adalimumab. Comparative adjusted analyses used frequentist model averaging (FMA). Least square mean difference (LSMD) in mNAPSI scores are presented as observed. RESULTS: Irrespective of the severity of nail psoriasis at baseline, the anti-IL-17A cohort had greater mean mNAPSI reductions from baseline compared to the other biologics cohort through month 12, reaching significance at months 3 and 6 in the adjusted analysis. For patients with moderate-to-severe nail psoriasis, ixekizumab showed numerically higher mean reductions in mNAPSI scores compared to all other studied biologics, reaching significance versus guselkumab at all timepoints and risankizumab at month 6. CONCLUSION: This real-world study showed that patients with moderate-to-severe psoriasis and any severity of concomitant nail involvement had significantly faster and more substantial improvements in nail psoriasis up to month 6 in the anti-IL-17A cohort compared to the other biologics cohort. Of the individual biologics studied, ixekizumab showed the highest numerical improvements in nail psoriasis at month 12. TRIAL REGISTRATION: EUPAS24207.

Sintilimab plus chemotherapy for first-line treatment of advanced or metastatic nonsquamous non-small-cell lung cancer: network meta-analysis.

Aim: This systematic literature review and network meta-analysis evaluated the efficacy and safety of sintilimab + pemetrexed + platinum versus US FDA-approved/National Comprehensive Cancer Network-recommended immune checkpoint inhibitor (ICI) combination therapies for untreated advanced/metastatic non-squamous non-small-cell lung cancer without EGFR/ALK aberrations. Methods: Bayesian network meta-analysis was the base-case analysis and included assessment of fixed and random effects, and independent and simultaneous models, adjusting for baseline risk (placebo response). Chemotherapy was the common comparator. Results: Sintilimab + pemetrexed + platinum was associated with significantly longer progression-free survival than atezolizumab + platinum + nab-paclitaxel (hazard ratio [HR]: 0.57; 95% credible interval [CrI]: 0.40-0.82) and nivolumab + ipilimumab + pemetrexed + platinum (HR: 0.66; 95% CrI: 0.48-0.92). Sintilimab + pemetrexed + platinum and pembrolizumab + pemetrexed + platinum showed comparable progression-free survival (HR: 0.96; 95% CrI: 0.71-1.30). There was no significant difference in overall survival (HR range: 0.61-0.81) or overall response rates (odds ratio [OR] range: 0.29-0.75) between sintilimab + pemetrexed + platinum and the other ICI combinations. The incidence of high-grade adverse events was higher with sintilimab + pemetrexed + platinum than with nivolumab + ipilimumab (OR: 0.46; 95% CrI: 0.33-0.64) or without chemotherapy (OR: 0.25; 95% CrI: 0.19-0.34), with no significant difference between sintilimab + pemetrexed + platinum and the other ICI combinations. Conclusion: Sintilimab + pemetrexed + platinum showed comparable efficacy and safety versus US standard-of-care first-line ICI combinations for advanced/metastatic non-squamous non-small-cell lung cancer.

Sintilimab is an immunotherapy drug that was successfully developed and tested in China to treat a kind of lung cancer that has spread, called advanced non-squamous non-small-cell lung cancer (NSCLC). The ORIENT-11 clinical study showed that adding sintilimab to two types of chemotherapy (pemetrexed and platinum) as the first treatment for people in China with advanced non-squamous NSCLC was safe and effective in reducing the risk of cancer spreading, growing or getting worse, compared with chemotherapy alone. Our study combined and analyzed the results from 11 clinical studies to look at how well sintilimab with chemotherapy may work compared with immunotherapy drugs approved in the USA. The results showed that sintilimab with chemotherapy is as effective and safe as immunotherapy drugs approved in the USA to treat people with advanced non-squamous NSCLC. These results may help doctors and payers when deciding how to treat people with this disease.

Validating the Assumptions of Population Adjustment: Application of Multilevel Network Meta-regression to a Network of Treatments for Plaque Psoriasis.

BACKGROUND: Network meta-analysis (NMA) and indirect comparisons combine aggregate data (AgD) from multiple studies on treatments of interest but may give biased estimates if study populations differ. Population adjustment methods such as multilevel network meta-regression (ML-NMR) aim to reduce bias by adjusting for differences in study populations using individual patient data (IPD) from 1 or more studies under the conditional constancy assumption. A shared effect modifier assumption may also be necessary for identifiability. This article aims to demonstrate how the assumptions made by ML-NMR can be assessed in practice to obtain reliable treatment effect estimates in a target population. METHODS: We apply ML-NMR to a network of evidence on treatments for plaque psoriasis with a mix of IPD and AgD trials reporting ordered categorical outcomes. Relative treatment effects are estimated for each trial population and for 3 external target populations represented by a registry and 2 cohort studies. We examine residual heterogeneity and inconsistency and relax the shared effect modifier assumption for each covariate in turn. RESULTS: Estimated population-average treatment effects were similar across study populations, as differences in the distributions of effect modifiers were small. Better fit was achieved with ML-NMR than with NMA, and uncertainty was reduced by explaining within- and between-study variation. We found little evidence that the conditional constancy or shared effect modifier assumptions were invalid. CONCLUSIONS: ML-NMR extends the NMA framework and addresses issues with previous population adjustment approaches. It coherently synthesizes evidence from IPD and AgD studies in networks of any size while avoiding aggregation bias and noncollapsibility bias, allows for key assumptions to be assessed or relaxed, and can produce estimates relevant to a target population for decision-making. HIGHLIGHTS: Multilevel network meta-regression (ML-NMR) extends the network meta-analysis framework to synthesize evidence from networks of studies providing individual patient data or aggregate data while adjusting for differences in effect modifiers between studies (population adjustment). We apply ML-NMR to a network of treatments for plaque psoriasis with ordered categorical outcomes.We demonstrate for the first time how ML-NMR allows key assumptions to be assessed. We check for violations of conditional constancy of relative effects (such as unobserved effect modifiers) through residual heterogeneity and inconsistency and the shared effect modifier assumption by relaxing this for each covariate in turn.Crucially for decision making, population-adjusted treatment effects can be produced in any relevant target population. We produce population-average estimates for 3 external target populations, represented by the PsoBest registry and the PROSPECT and Chiricozzi 2019 cohort studies.

Effectiveness of Biologics, Patient-Reported Outcomes, and Clinical Photography in a Subset of Patients with Moderate-to-Severe Psoriasis: Week 12 Results from the Psoriasis Study of Health Outcomes (PSoHO).

Purpose: Since skin is highly accessible, clinical photography is a useful tool to visually substantiate the real-world effectiveness outcomes of biologic-treated adults with moderate-to-severe psoriasis (PsO). We report the effectiveness and patient-reported outcomes at Week 12 between anti-interleukin (IL)-17A biologics and other biologics as well as ixekizumab and guselkumab in patients with available clinical photography at baseline and Week 12. Patients and Methods: The Psoriasis Study of Health Outcomes (PSoHO) is an international, non-interventional, cohort study investigating the effectiveness of biologics in adults with moderate-to-severe psoriasis at Week 12. Outcomes included the proportion of patients who achieved 90% improvement in Psoriasis Area and Severity Index (PASI90) and/or static Physician Global Assessment (sPGA) 0/1 (primary endpoint), PASI100, PASI90, Dermatology Life Quality Index (DLQI), and Itch Numeric Rating Scale (NRS) (secondary endpoints) at Week 12. Data are reported descriptively. Results: This analysis included 59 biologic-treated (23 anti-IL-17A; 36 other biologics) patients with available clinical photographs from the overall PSoHO study (n=1981). At baseline, the mean (standard deviation [SD]) age was 45.7 (11.1) years, 71.2% were male, 52.5% were bio-experienced and the median (interquartile range) duration of disease was 10.5 (12.4) years. Mean (SD) PASI was 16.9 (9.3) and sPGA was 3.5 (0.8). At Week 12, 65.2%/47.2% of the anti-IL-17A/other biologics cohort achieved the primary outcome. Response rates for PASI90/100 were numerically higher with anti-IL-17A than with other biologics. Patients receiving anti-IL-17A had numerically better outcomes for DLQI 0/1 and Itch NRS than those receiving other biologics at Week 12. Clinical photographs confirmed skin improvements in ixekizumab- and guselkumab-treated patients. Conclusion: This subgroup analysis showed that anti-IL-17A biologics are effective at rapidly improving signs and symptoms of PsO and improving quality of life. Additionally, serial photography provided visual evidence of biologic treatment response over time.

Predictors of insulin pump initiation among people with type 2 diabetes from a US claims database using machine learning.

OBJECTIVE: Insulin pump use is increasing among people with type 2 diabetes (T2D), albeit at a slower rate compared to people with type 1 diabetes (T1D). Factors associated with insulin pump initiation among people with T2D in the real-world are understudied. METHODS: This retrospective, nested case-control study aimed to identify predictors of insulin pump initiation among people with T2D in the United States (US). Adults with T2D who were new to bolus insulin use were identified from the IBM MarketScan Commercial database (2015-2020). Candidate variables of pump initiation were entered into conditional logistic regression (CLR) and penalized CLR models. RESULTS: Of the 32,104 eligible adults with T2D, 726 insulin pump initiators were identified and matched to 2,904 non-pump initiators using incidence density sampling. Consistent predictors of insulin pump initiation across the base case, sensitivity, and post hoc analyses included continuous glucose monitor (CGM) use, visiting an endocrinologist, acute metabolic complications, higher count of HbA1c tests, lower age, and fewer diabetes-related medication classes. CONCLUSIONS: Many of these predictors could represent a clinical indication for treatment intensification, greater patient engagement in diabetes management, or proactive management by healthcare providers. Improved understanding of predictors for pump initiation may lead to more targeted efforts to improve access and acceptance of insulin pumps among persons with T2D.

Comparative Effectiveness, Time to Discontinuation, and Patient-Reported Outcomes with Baricitinib in Rheumatoid Arthritis: 2-Year Data from the Multinational, Prospective Observational RA-BE-REAL Study in European Patients.

INTRODUCTION: RA-BE-REAL is a 3-year, multinational, prospective, observational study of adult patients with rheumatoid arthritis (RA) evaluating time to discontinuation of initial RA treatment along with patient baseline characteristics. This study's primary objective was to assess the time to discontinuation of initial baricitinib, any other targeted synthetic disease-modifying anti-rheumatic drug (tsDMARD), or any biologic disease-modifying anti-rheumatic drug (bDMARD) treatment for all causes (excluding sustained clinical response) over 24 months in a European population. METHODS: Patients initiated treatment with baricitinib (cohort A) or any bDMARD or tsDMARD (cohort B) for the first time. This study's primary objective was to assess the time to discontinuation of initial baricitinib, any other targeted synthetic disease-modifying anti-rheumatic drug (tsDMARD), or any biologic disease-modifying anti-rheumatic drug (bDMARD) treatment for all causes (excluding sustained clinical response) over 24 months in a European population. Comparative effectiveness analyses, over 24 months, included time to treatment discontinuation for all causes (excluding sustained clinical response), percentage of patients achieving Clinical Disease Activity Index (CDAI) remission or low disease activity (LDA), as well as mean changes from baseline for CDAI, pain visual analogue scale, and the Health Assessment Questionnaire-Disability Index (HAQ-DI). For this European subpopulation, comparative analyses were performed using a frequentist model averaging (FMA) framework based on a data-driven machine learning causal inference approach to compare time to discontinuation, effectiveness, rates of remission or LDA, and patient-reported outcomes over 24 months comparing baricitinib with TNFi, as well as non-TNFi and tsDMARD grouped as other mechanism of action (OMA) drugs. RESULTS: In the European sample of RA-BE-REAL, patients with RA treated with baricitinib experienced fewer discontinuations in comparison to those treated with tumour necrosis factor inhibitors or OMA. Overall, patients naïve to b/tsDMARDs achieved a higher rate of LDA and remission compared with experienced patients. A significantly greater proportion of patients treated with baricitinib achieved LDA compared with b/tsDMARDs. CONCLUSION: This real-world data can better inform clinicians about baricitinib effectiveness and drug survival when prescribing treatment for patients with RA across different subpopulations.

Comparative Effectiveness of Biologics Across Subgroups of Patients with Moderate-to-Severe Plaque Psoriasis: Results at Week 12 from the PSoHO Study in a Real-World Setting.

INTRODUCTION: In routine clinical care, important treatment outcomes among patients with moderate-to-severe plaque psoriasis (PsO) have been shown to vary according to patient demographics and disease characteristics. This study aimed to provide direct comparative effectiveness data at week 12 between anti-interleukin (IL)-17A biologics relative to other approved biologics for the treatment of PsO across seven clinically relevant patient subgroups in the real-world setting. METHODS: From the international, non-interventional Psoriasis Study of Health Outcomes (PSoHO), 1981 patients with moderate-to-severe PsO were grouped a priori according to seven clinically relevant demographic and disease variables with binary categories, which were sex (male or female), age (< 65 or ≥ 65 years), body mass index (≤ 30 or > 30 kg/m2), race (White or Asian), PsO disease duration (< 15 or ≥ 15 years), psoriatic arthritis (PsA) comorbidity (present or absent), and prior biologic use (never or ≥ 1). Across these subgroups, effectiveness was compared between the anti-IL-17A cohort (ixekizumab, secukinumab) versus all other approved biologics and ixekizumab versus five individual biologics. The proportion of patients in each subgroup who achieved 90% improvement in Psoriasis Area and Severity Index (PASI90) and/or static Physician Global Assessment (sPGA) 0/1, PASI100, or PASI90 at week 12 were assessed. Comparative analyses were conducted using frequentist model averaging (FMA). Missing data were imputed using non-responder imputation. RESULTS: Patients in each of the seven subgroups achieved similar response rates to those of the overall treatment cohort, apart from patients with PsA treated with other biologics who had 7-10% lower response rates. Consequently, patients with comorbid PsA had significantly higher odds of achieving skin clearance at week 12 with anti-IL-17A biologics compared to other biologics. Patients in all subgroups had significantly higher odds of achieving PASI90 and/or sPGA (0,1), PASI100, and PASI90 in the anti-IL-17A cohort relative to the other biologics cohort, except for the Asian subgroup. No sex- or age-specific differences in treatment effectiveness after 12 weeks were identified, neither between the treatment cohorts nor between the individual treatment comparisons. CONCLUSIONS: Despite relative consistency of comparative treatment effectiveness across subgroups, the presence of comorbid PsA may affect a patient's clinical response to some treatments.