Background: Immune checkpoint inhibitors (ICIs) are standard treatments for advanced non-small cell lung cancer (NSCLC); however, evidence regarding their relative efficacy and safety is lacking. This study compared the efficacy and safety of all currently available ICI treatments in patients with advanced NSCLC to identify optimal treatment regimens. Methods: PubMed, Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, and Embase databases were systematically searched for randomized controlled trials (RCTs) published up to August 8, 2022. The primary outcomes were overall survival (OS) and progression-free survival (PFS). Secondary outcomes included objective response rate (ORR) and adverse events (AEs). Results: Forty RCTs involving 22,526 patients were selected, and a total of 26 treatment regimens were identified. Treatment with anti-programmed cell death protein-1 (anti-PD-1) provided superior OS compared with anti-programmed death ligand 1 (anti-PD-L1) treatment. ICIs plus platinum-based chemotherapy (PBC) were superior to ICIs treatment alone, although the addition of PBC increased treatment toxicity. Cemiplimab ranked first for OS and lowest for any-grade AEs in advanced NSCLC patients without PD-L1 selection. Regarding grade ≥3 AEs, the toxicity of ICI monotherapy or ICI–ICI combination was consistently lower than that of the other treatments. For patients without PD-L1 selection, cemiplimab showed the best OS, pembrolizumab plus docetaxel (Pem-DXT) showed the best PFS, and atezolizumab plus bevacizumab and PBC (Atezo-Beva-PBC) showed the best ORR. Pembrolizumab plus PBC and Atezo-Beva-PBC were the most likely optimal treatments for OS and PFS in patients with PD-L1 expression <1%, respectively. In patients with PD-L1 expression ≥1%, treatment regimens containing anti-PD-1 provided superior OS benefits compared with those of anti-PD-L1 treatment, and sintilimab plus PBC (Sint-PBC) provided the best OS benefit; as for PFS, ICI plus PBC consistently showed greater PFS benefits than ICI or PBC alone. For patients with anti-PD-L1 expression of 1–49%, camrelizumab plus PBC provided the best benefit for OS and PFS among included treatment. Durvalumab-tremelimumab-PBC and Atezo-Beva-PBC respectively presented the highest OS and PFS for patients with PD-L1 expression ≥50%. Moreover, cemiplimab and Atezo-Beva-PBC yielded the best OS and PFS benefits as first-line treatments for patients with advanced NSCLC, respectively. Conclusions: Although ICI plus PBC likely resulted in superior survival outcomes compared to ICI treatment alone, it did increase toxicity. Cemiplimab presented a well-balanced efficacy and safety profile in advanced NSCLC treatment. Our findings with the current ICIs comparisons will aid future trials for cancer immunotherapy. Registration: PROSPERO, https://www.crd.york.ac.uk/PROSPERO/, CRD42022323879.

Objective: Nivolumab improves overall survival (OS) and is associated with fewer adverse events than sorafenib for the treatment of advanced hepatocellular carcinoma (aHCC). However, the cost-effectiveness of nivolumab compared with sorafenib treatment for aHCC remains unclear. This study evaluated the cost-effectiveness of nivolumab and sorafenib in the treatment of aHCC.Materials and methods: A partitioned survival model that included three mutually exclusive health states was used to evaluate the cost-effectiveness of nivolumab and sorafenib for treating aHCC. The clinical characteristics and outcomes of the patients in the model were obtained from the CheckMate 459. We performed deterministic one-way sensitivity and probabilistic sensitivity analyses to evaluate the robustness of the model. Subgroup analyses were also performed. Costs, life-years, quality-adjusted life-years (QALYs), incremental cost-effectiveness ratio (ICER), incremental net health benefits (INHB), and incremental net monetary benefits (INMB) were measured.Results: The base case analysis showed that compared with sorafenib, treatment with nivolumab was associated with an increment of 0.50 (2.45 vs. 1.95) life-years and an increment of 0.32 (1.59 vs. 1.27) QALYs, as well as a $69,762 increase in cost per patient. The ICER was $220,864/QALY. The INHB and INMB were −0.15 QALYs and −$22,362 at a willingness-to-pay (WTP) threshold of $150,000/QALY, respectively. The probabilistic sensitivity analysis demonstrated that the probability of nivolumab being cost-effective was only 10.38% at a WTP threshold of $150,000/QALY. The model was most sensitive to the costs of sorafenib and nivolumab according to the one-way sensitivity analysis. When the price of sorafenib exceeded $0.93/mg or nivolumab was less than $24.23/mg, nivolumab was more cost-effective. The subgroup analysis illustrated that the probability of cost-effectiveness was >50% in the Barcelona Clinic Liver Cancer Stage B subgroups for nivolumab at a WTP threshold of $150,000/QALY. This study also showed that the probability of cost-effectiveness was <50% in most subgroups.Conclusion: Nivolumab was not cost-effective, although it was associated with better clinical benefit and a favorable safety profile for the treatment of aHCC compared with sorafenib from the third-party payer perspective in the United States. If the price of nivolumab is substantially reduced, favorable cost-effectiveness can be achieved among patients with aHCC.

Background: For patients with extensive-stage small cell lung cancer (ES-SCLC), serplulimab plus chemotherapy is beneficial as the first-line treatment. It is uncertain whether serplulimab plus chemotherapy will be more cost-effective. The aim of this study was to evaluate from the perspective of the Chinese healthcare system to assess the cost-effectiveness of serplulimab plus chemotherapy for patients with ES-SCLC.Materials and methods: This study employed a partitioned survival model. Patients in the model were selected from ASTRUM-005 for their clinical characteristics and outcomes. In order to assess the robustness of the model, we conducted deterministic one-way sensitivity analyzes as well as probabilistic sensitivity analyzes. Subgroup analyzes were also conducted. Costs, quality-adjusted life-years (QALYs), life-years, incremental cost-effectiveness ratio (ICER), incremental net health benefits (INHB), and incremental net monetary benefits (INMB) were analyzed.Results: Based on the base-case analysis, serplulimab plus chemotherapy contributed to an increase in 0.826 life-years and 0.436 QALYs; an incremental cost of $52,331, yielded ICER of $120,149/QALY. Based on the willingness to pay (WTP) threshold of $37,669/QALY and $86,569/QALY, the INHB was −0.954 QALYs and − 0.169 QALYs and the INMB was -$35,924 and -$14,626, respectively. Based on the probabilistic sensitivity analysis results, serplulimab plus chemotherapy was unlikely to be cost-effective at a WTP threshold of $37,669/QALY and $86,569/QALY. One-way sensitivity analysis indicated that cost of serplulimab and body weight had the greatest impact on the model. Serplulimab plus chemotherapy could be cost-effective at a WTP threshold of $86,569/QALY when the cost of serplulimab was less than $5.24/mg or when the weight of the patient was less than 40.96 kg. Regardless of the WTP threshold at $37,669/QALY or $86,569. Serplulimab plus chemotherapy was not cost-effective in all subgroups.Conclusion: Serplulimab plus chemotherapy was not cost-effective, despite having a prior clinical benefical and a relative safety profile compared with chemotherapy. With the reduction in the price of serplulimab, ES-SCLC patients treated with serplulimab plus chemotherapy may be able to achieve a favorable cost-effectiveness rate.

There is limited evidence that heavy metals exposure contributes to osteoporosis. Multi-parameter scoring machine learning (ML) techniques were developed using National Health and Nutrition Examination Survey data to predict osteoporosis based on heavy metal exposure levels. For generating an optimal predictive model for osteoporosis, 12 ML models were used. Identification was carried out using the model that performed the best. For interpretation of models, Shapley additive explanation (SHAP) methods and partial dependence plots (PDP) were integrated into a pipeline and incorporated into the ML pipeline. By regressing osteoporosis on survey cycles, logistic regression was used to evaluate linear trends in osteoporosis over time. For the purpose of training and validating predictive models, 5745 eligible participants were randomly selected into training and testing set. It was evident from the results that the gradient boosting decision tree model performed the best among the predictive models, attributing to an accuracy rate of 89.40 % in the testing set. Based on the model results, the area under the curve and F1 score were 0.88 and 0.39, respectively. As a result of the SHAP analysis, urinary Co, urinary Tu, blood Cd, and urinary Hg levels were identified as the most influential factors influencing osteoporosis. Urinary Co (0.20-6.10 μg/mg creatinine), urinary Tu (0.06-1.93 μg/mg creatinine), blood Cd (0.07-0.50 μg/L), and urinary Hg (0.06-0.75 μg/mg creatinine) levels displayed a distinctive upward trend with risk of osteoporosis as values increased. Our analysis revealed that urinary Co, urinary Tu, blood Cd, and urinary Hg played a significant role in predictability.