Efficacy and Safety of Multiple Dupilumab Dose Regimens in Patients with Moderate-To-Severe Atopic Dermatitis: A Systematic Review and Network Meta-Analysis of Randomized Controlled Trials

BACKGROUND

Dupilumab ameliorates the signs and symptoms of atopic dermatitis (AD) and improves the patient's quality of life. Multiple-dose regimens of dupilumab have been applied by clinicians, but the efficacy of some regimens remains unclear.

OBJECTIVES

The aim of the study was to systematically evaluate the efficacy and safety of multiple dupilumab dose regimens in patients with moderate-to-severe AD in terms of comprehensive outcomes.

METHODS

We searched electronic databases and subjected the selected studies to risk-of-bias assessment and network meta-analysis (NMA). Efficacy and safety outcomes were compared using a random-effects NMA to estimate pooled relative risk ratio (RR) of direct and indirect comparisons among multiple dupilumab dose regimens. The Eczema Area Severity Index, Investigator's Global Assessment, and pruritus numerical rating scale were analyzed to assess the efficacy, while adverse events (AEs) and serious adverse events to represent the safety.

RESULTS

Eight randomized controlled trials involving 3,679 patients were identified. Most patients received therapy for 16 weeks. Multiple dupilumab dose regimens, including 300 mg weekly (QW), 300 mg every 2 weeks (Q2W), 200 mg Q2W, 300 mg monthly (QM), 300 mg every 2 months (Q2M), and 100 mg QM were analyzed. All regimens, except 100 mg QM, had significantly better efficacy than placebo. 300 mg QW and 300 mg Q2W appeared to have similar efficacy. Notably, both 300 mg QW and 300 mg Q2W had no significantly better efficacy than 300 mg QM. As for 300 mg Q2M, significantly reduced efficacy was noted in only one efficacy outcome when compared to 300 mg QW and 300 mg Q2W. In terms of safety outcomes, AEs occurring with any of the regimens were comparable with the placebo. No significant inconsistency was noted within the network in all efficacy outcomes.

CONCLUSIONS

Our NMA indicated that the administration of the following dupilumab regimens was effective for patients with moderate-to-severe AD: 300 mg QW, 300 mg Q2W, 200 mg Q2W, 300 mg QM, and 300 mg Q2M. Our data can improve the understanding of the relative efficacy and safety of multiple dupilumab dose regimens, which will help in shared decision-making between clinicians and patients.

Artificial Intelligence-Based Prediction of Lung Cancer Risk Using Nonimaging Electronic Medical Records: Deep Learning Approach

Background

Artificial intelligence approaches can integrate complex features and can be used to predict a patient’s risk of developing lung cancer, thereby decreasing the need for unnecessary and expensive diagnostic interventions.

Objective

The aim of this study was to use electronic medical records to prescreen patients who are at risk of developing lung cancer.

Methods

We randomly selected 2 million participants from the Taiwan National Health Insurance Research Database who received care between 1999 and 2013. We built a predictive lung cancer screening model with neural networks that were trained and validated using pre-2012 data, and we tested the model prospectively on post-2012 data. An age- and gender-matched subgroup that was 10 times larger than the original lung cancer group was used to assess the predictive power of the electronic medical record. Discrimination (area under the receiver operating characteristic curve [AUC]) and calibration analyses were performed.

Results

The analysis included 11,617 patients with lung cancer and 1,423,154 control patients. The model achieved AUCs of 0.90 for the overall population and 0.87 in patients ≥55 years of age. The AUC in the matched subgroup was 0.82. The positive predictive value was highest (14.3%) among people aged ≥55 years with a pre-existing history of lung disease.

Conclusions

Our model achieved excellent performance in predicting lung cancer within 1 year and has potential to be deployed for digital patient screening. Convolution neural networks facilitate the effective use of EMRs to identify individuals at high risk for developing lung cancer.