Machine learning-based prediction model for hypofibrinogenemia after tigecycline therapy.

BACKGROUND: In clinical practice, the incidence of hypofibrinogenemia (HF) after tigecycline (TGC) treatment significantly exceeds the probability claimed by drug manufacturers. OBJECTIVE: We aimed to identify the risk factors for TGC-associated HF and develop prediction and survival models for TGC-associated HF and the timing of TGC-associated HF. METHODS: This single-center retrospective cohort study included 222 patients who were prescribed TGC. First, we used binary logistic regression to screen the independent factors influencing TGC-associated HF, which were used as predictors to train the extreme gradient boosting (XGBoost) model. Receiver operating characteristic curve (ROC), calibration curve, decision curve analysis (DCA), and clinical impact curve analysis (CICA) were used to evaluate the performance of the model in the verification cohort. Subsequently, we conducted survival analysis using the random survival forest (RSF) algorithm. A consistency index (C-index) was used to evaluate the accuracy of the RSF model in the verification cohort. RESULTS: Binary logistic regression identified nine independent factors influencing TGC-associated HF, and the XGBoost model was constructed using these nine predictors. The ROC and calibration curves showed that the model had good discrimination (areas under the ROC curves (AUC) = 0.792 [95% confidence interval (CI), 0.668-0.915]) and calibration ability. In addition, DCA and CICA demonstrated good clinical practicability of this model. Notably, the RSF model showed good accuracy (C-index = 0.746 [95%CI, 0.652-0.820]) in the verification cohort. Stratifying patients treated with TGC based on the RSF model revealed a statistically significant difference in the mean survival time between the low- and high-risk groups. CONCLUSIONS: The XGBoost model effectively predicts the risk of TGC-associated HF, whereas the RSF model has advantages in risk stratification. These two models have significant clinical practical value, with the potential to reduce the risk of TGC therapy.

Cost-effectiveness of nivolumab plus gemcitabine-cisplatin as first-line treatment for advanced urothelial carcinoma in China and the United States.

Objective: Nivolumab, recently proven in a phase 3 clinical trial (CheckMate 901) to enhance survival when combined with gemcitabine-cisplatin for advanced urothelial carcinoma. This study aimed to assess its cost-effectiveness against gemcitabine-cisplatin alone, from US and Chinese payers' perspectives. Methods: A partitioned survival model was established to assess the life-years, quality-adjusted life-years (QALYs), lifetime costs, and incremental cost-effectiveness ratios (ICERs) of nivolumab plus gemcitabine-cisplatin versus gemcitabine-cisplatin alone as first-line treatment for advanced urothelial carcinoma. Univariate, two-way, and probabilistic sensitivity analyses were conducted to assess the model's robustness. Additionally, subgroup analyses were performed. Results: Nivolumab plus gemcitabine-cisplatin and gemcitabine-cisplatin achieved survival benefits of 4.238 life-years and 2.979 life-years for patients with advanced urothelial carcinoma, respectively. Compared with gemcitabine-cisplatin, nivolumab plus gemcitabine-cisplatin resulted in ICERs of $116,856/QALY in the US and $51,997/QALY in China. The probabilities of achieving cost-effectiveness at the current willingness-to-pay thresholds were 77.5% in the US and 16.5% in China. Cost-effectiveness could be reached if the price of nivolumab were reduced to $920.87/100mg in China. Subgroup analyses indicated that the combination had the highest probability of cost-effectiveness in patients under 65 or with an Eastern Cooperative Oncology Group (ECOG) performance-status score of 0 in the US and China. Conclusion: Nivolumab plus gemcitabine-cisplatin first-line treatment for advanced urothelial carcinoma results in longer life expectancy than gemcitabine-cisplatin, but is not cost-effective in China at current price. However, cost-effectiveness is likely to be achieved in most patient subgroups in the US.

The Economic Impact of Treatment Sequencing in Chronic Lymphocytic Leukemia in Canada Using Venetoclax plus Obinutuzumab.

BACKGROUND: Bruton tyrosine kinase inhibitors (BTKis) represent an advancement in chronic lymphocytic leukemia; however, these agents are administered continuously until disease progression or unacceptable toxicity, raising concerns about their affordability. Venetoclax in combination with obinutuzumab (VO) is a fixed-duration (12-month) treatment, approved in Canada in 2020. This study estimated the total cumulative cost of different treatment sequences and evaluated the economic impact of introducing treatment sequences with/without VO, from a Canadian health care system perspective. METHODS: A 10-year partitioned survival model was developed, considering key clinical parameters and direct medical costs. Results were stratified by TP53 aberration. RESULTS: Treatment sequences starting with first-line (1L) VO resulted in lower 10-year cumulative costs compared to sequences starting with BTKis administered until disease progression, across both TP53 aberration subgroups. With a maximum of three lines of treatment over a 10-year period, cumulative costs were largely determined by the first two lines of treatment. When comparing sequences with the same 1L treatment, sequences with BTKis in second-line incurred greater costs compared to fixed-duration regimens. CONCLUSIONS: Overall, the economic impact of treating all patients with VO led to 10-year cumulative savings of CAD 169,341 and CAD 293,731 per patient, without and with TP53 aberration, respectively. These savings are mainly due to reductions in treatment costs associated with fixed treatment duration.

Immunotherapy Improves the Survival of Stage 4 Non-Small Cell Lung Cancer Patients at the US Population Level: The Real-World Evidence.

INTRODUCTION: Immunotherapy has revolutionized the management of lung cancer and improved lung cancer survival in trials, but its real-world impact at the population level remains unclear. METHODS: Using data obtained from eight Surveillance, Epidemiology, and End Results (SEER) registries from 2004 through 2019, we addressed the long-term trends in the incidence, incidence-based mortality (IBM), and survival of lung cancer patients in the United States. RESULTS: The incidence and IBM of both non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) all significantly decreased steadily from 2004 to 2019. The 1-year survival (1-YS) of both NSCLC and SCLC improved over time, with the best improvement observed for Stage 4 NSCLC. Two significant turning points of Stage 4 NSCLC 1-YS were observed over the years: 0.63% (95% confidence interval [CI]: 0.33%-0.93%) from 2004 to 2010, 0.81% (95% CI: 0.41%-1.21%) from 2010 to 2014 and a striking 2.09% (95% CI: 1.70%-2.47%) from 2014 to 2019. The same two turning points in 1-YS were pronounced for Stage 4 NSCLC in women, which were coincident with the introduction of epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) and immunotherapy. However, for Stage 4 NSCLC in men, only one significant turning point in the 1-YS starting in 2014 was found, which might only correspond to immunotherapy. Significant period effects in reduced IBM were also observed for both Stage 4 AD and Stage 4 SQCC during the period. CONCLUSION: This SEER analysis found that immunotherapy improved the survival of Stage 4 NSCLC patients at the population level in the United States. This real-world evidence confirms that immunotherapy has truly revolutionized the management of lung cancer.

An interpretable survival model for diffuse large B-cell lymphoma patients using a biologically informed visible neural network.

Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma (NHL) and is characterized by high heterogeneity. Assessment of its prognosis and genetic subtyping hold significant clinical implications. However, existing DLBCL prognostic models are mainly based on transcriptomic profiles, while genetic variation detection is more commonly used in clinical practice. In addition, current clustering-based subtyping methods mostly focus on genes with high mutation frequencies, providing insufficient explanations for the heterogeneity of DLBCL. Here, we proposed VNNSurv (https://bio-web1.nscc-gz.cn/app/VNNSurv), a survival model for DLBCL patients based on a biologically informed visible neural network (VNN). VNNSurv achieved an average C-index of 0.72 on the cross-validation set (HMRN cohort, n = 928), outperforming the baseline methods. The remarkable interpretability of VNNSurv facilitated the identification of the most impactful genes and the underlying pathways through which they act on patient outcomes. When only the 30 highest-impact genes were used as genetic input, the overall performance of VNNSurv improved, and a C-index of 0.70 was achieved on the external TCGA cohort (n = 48). Leveraging these high-impact genes, including 16 genes with low (<5 %) alteration frequencies, we devised a genetic-based prognostic index (GPI) for risk stratification and a subtype identification method. We stratified the patient group according to the International Prognostic Index (IPI) into three risk grades with significant prognostic differences. Furthermore, the defined subtypes exhibited greater prognostic consistency than clustering-based methods. Broadly, VNNSurv is a valuable DLBCL survival model. Its high interpretability has significant value for precision medicine, and its framework is scalable to other diseases.

Development and validation of novel interpretable survival prediction models based on drug exposures for severe heart failure during vulnerable period.

BACKGROUND: Severe heart failure (HF) has a higher mortality during vulnerable period while targeted predictive tools, especially based on drug exposures, to accurately assess its prognoses remain largely unexplored. Therefore, this study aimed to utilize drug information as the main predictor to develop and validate survival models for severe HF patients during this period. METHODS: We extracted severe HF patients from the MIMIC-IV database (as training and internal validation cohorts) as well as from the MIMIC-III database and local hospital (as external validation cohorts). Three algorithms, including Cox proportional hazards model (CoxPH), random survival forest (RSF), and deep learning survival prediction (DeepSurv), were applied to incorporate the parameters (partial hospitalization information and exposure durations of drugs) for constructing survival prediction models. The model performance was assessed mainly using area under the receiver operator characteristic curve (AUC), brier score (BS), and decision curve analysis (DCA). The model interpretability was determined by the permutation importance and Shapley additive explanations values. RESULTS: A total of 11,590 patients were included in this study. Among the 3 models, the CoxPH model ultimately included 10 variables, while RSF and DeepSurv models incorporated 24 variables, respectively. All of the 3 models achieved respectable performance metrics while the DeepSurv model exhibited the highest AUC values and relatively lower BS among these models. The DCA also verified that the DeepSurv model had the best clinical practicality. CONCLUSIONS: The survival prediction tools established in this study can be applied to severe HF patients during vulnerable period by mainly inputting drug treatment duration, thus contributing to optimal clinical decisions prospectively.

Toripalimab plus chemotherapy in the treatment of metastatic triple-negative breast cancer: a cost-effectiveness analysis.

Objective: This study focuses on assessing the cost-effectiveness of incorporating toripalimab alongside chemotherapy for the treatment of patients diagnosed with metastatic triple-negative breast cancer from the perspective of the Chinese healthcare system. Methods: A partitioned survival model was constructed to simulate the costs and health outcomes over the lifetime of patients with mTNBC. Clinical data regarding overall survival, progression-free survival, and treatment-related adverse events were derived from the TORCHLIGHT clinical trials. Incremental cost-effectiveness ratio (ICER) were calculated based on the gains in quality-adjusted life-year (QALY). The willingness-to-pay (WTP) threshold was defined as $39,855.79 per QALY. Additionally, sensitivity analyses were conducted to examine the robustness of the model. Results: The total cost incurred by the group receiving toripalimab was $38,040.62, while the placebo plus chemotherapy was $26,102.07. The utilization of the toripalimab regimen resulted in an increase of 0.74 QALYs and an incremental cost of $11,938.55 compared to the placebo plus chemotherapy group. The ICER was $16,133.18/QALY, indicating that toripalimab plus chemotherapy is a cost-effective strategy according to the WTP threshold. Sensitivity analyses confirmed the robustness of the results. Conclusion: This study suggests that the addition of toripalimab to chemotherapy for the treatment of mTNBC is a cost-effective strategy. The findings provide valuable evidence to guide decision-making regarding treatment selection for patients with mTNBC in China.

Cost-effectiveness of utidelone and capecitabine versus monotherapy in anthracycline- and taxane-refractory metastatic breast cancer.

Background: This study aimed to assess the cost-effectiveness of combining utidelone with capecitabine, compared to capecitabine monotherapy, for the treatment of anthracycline- and taxane-refractory metastatic breast cancer within the Chinese healthcare system. Methods: A partitioned survival model was formulated based on patient characteristics from the NCT02253459 trial. Efficacy, safety, and health economics data were sourced from the trial and real-world clinical practices. We derived estimates for costs, quality-adjusted life years (QALYs), and the incremental cost-effectiveness ratio (ICER) for the two treatment strategies. Sensitivity and subgroup analyses were conducted to rigorously evaluate uncertainties' impact. Results: Over a 5-year span, the combination therapy manifested substantially higher costs than capecitabine monotherapy, with a differential of US$ 26,370.63. This combined approach conferred an additional 0.49 QALYs, resulting in an ICER of US$ 53,874.17/QALY. Utilizing the established willingness-to-pay threshold, the combination might not consistently be deemed cost-effective when juxtaposed against monotherapy. However, at an ICER of US$ 53,874.4/QALY, the probability of the combination being cost-effective increased to 48.97%. Subgroup analysis revealed that the combination was more cost-effective than capecitabine alone in specific patient groups, including those <60 years, patients with more than two chemotherapy rounds, patients lacking certain metastases, patients having limited metastatic sites, patients with an Eastern Cooperative Oncology Group status of 0, and patients with particular hormone receptor profiles. Conclusion: Although the combination of utidelone and capecitabine may not be an economically viable universal choice for anthracycline- and taxane-refractory metastatic breast cancer, it could be more cost-effective in specific patient subgroups than capecitabine monotherapy.

Improving pressure injury risk assessment using real-world data from skilled nursing facilities: A cohort study.

This study aimed to improve the predictive accuracy of the Braden assessment for pressure injury risk in skilled nursing facilities (SNFs) by incorporating real-world data and training a survival model. A comprehensive analysis of 126 384 SNF stays and 62 253 in-house pressure injuries was conducted using a large calibrated wound database. This study employed a time-varying Cox Proportional Hazards model, focusing on variations in Braden scores, demographic data and the history of pressure injuries. Feature selection was executed through a forward-backward process to identify significant predictive factors. The study found that sensory and moisture Braden subscores were minimally contributive and were consequently discarded. The most significant predictors of increased pressure injury risk were identified as a recent (within 21 days) decrease in Braden score, low subscores in nutrition, friction and activity, and a history of pressure injuries. The model demonstrated a 10.4% increase in predictive accuracy compared with traditional Braden scores, indicating a significant improvement. The study suggests that disaggregating Braden scores and incorporating detailed wound histories and demographic data can substantially enhance the accuracy of pressure injury risk assessments in SNFs. This approach aligns with the evolving trend towards more personalized and detailed patient care. These findings propose a new direction in pressure injury risk assessment, potentially leading to more effective and individualized care strategies in SNFs. The study highlights the value of large-scale data in wound care, suggesting its potential to enhance quantitative approaches for pressure injury risk assessment and supporting more accurate, data-driven clinical decision-making.

First-line serplulimab plus chemotherapy versus chemotherapy in PD-L1-positive esophageal squamous-cell carcinoma: a cost-effectiveness analysis.

Patients with PD-L1-positive esophageal squamous-cell carcinoma (ESCC) were significantly more likely to survive when treated with serplulimab plus cisplatin plus 5-fluorouracil (serplulimab-CF). At this point, it is unknown whether this expensive therapy is cost-effective. From the Chinese healthcare system's perspective, we aimed to evaluate serplulimab-CF versus CF alone for cost-effectiveness. A partitioned survival model was constructed based on the ASTRUM-007 trial. Quality-adjusted life-years (QALYs) and incremental cost-effectiveness ratio (ICER) were calculated. A further analysis of subgroups and scenarios was conducted. The willingness to pay (WTP) threshold of $38,258/QALY or $84,866/QALY is defined as three times the per capita gross domestic product value of the general region or affluent region. Compared with CF alone, in the overall (scenario 1), patients with PD-L1 expression level of 1 ≤ CPS < 10 (scenario 2), and patients with PD-L1 CPS ≥ 10 (scenario 3) populations, the ICERs were $69,025/QALY, $82,533/QALY, and $75,436/QALY for serplulimab-CF. Nevertheless, the probability of serplulimab-CF becoming cost-effective based on scenarios 1, 2, and 3 is only 2.71%, 0.94%, and 2.84%, respectively, at a WTP threshold of $38,258/QALY. When serplulimab costs < $4.84/mg, serplulimab-CF may be cost-effective at the WTP threshold of $38,258/QALY; otherwise, CF was preferred. Similar results were obtained from sensitivity analyses, suggesting the robustness of these findings. There was no cost-effectiveness in general regions of China for serplulimab-CF in PD-L1-positive ESCC compared to CF, although it is probably considered cost-effective in affluent regions. Serplulimab-CF may achieve favorable cost-effectiveness by lowering the price of serplulimab.

Deep learning survival model predicts outcome after intracerebral hemorrhage from initial CT scan.

BACKGROUND: Predicting functional impairment after intracerebral hemorrhage (ICH) provides valuable information for planning of patient care and rehabilitation strategies. Current prognostic tools are limited in making long term predictions and require multiple expert-defined inputs and interpretation that make their clinical implementation challenging. This study aimed to predict long term functional impairment of ICH patients from admission non-contrast CT scans, leveraging deep learning models in a survival analysis framework. METHODS: We used the admission non-contrast CT scans from 882 patients from the Massachusetts General Hospital ICH Study for training, hyperparameter optimization, and model selection, and 146 patients from the Yale New Haven ICH Study for external validation of a deep learning model predicting functional outcome. Disability (modified Rankin scale [mRS] > 2), severe disability (mRS > 4), and dependent living status were assessed via telephone interviews after 6, 12, and 24 months. The prediction methods were evaluated by the c-index and compared with ICH score and FUNC score. RESULTS: Using non-contrast CT, our deep learning model achieved higher prediction accuracy of post-ICH dependent living, disability, and severe disability by 6, 12, and 24 months (c-index 0.742 [95% CI -0.700 to 0.778], 0.712 [95% CI -0.674 to 0.752], 0.779 [95% CI -0.733 to 0.832] respectively) compared with the ICH score (c-index 0.673 [95% CI -0.662 to 0.688], 0.647 [95% CI -0.637 to 0.661] and 0.697 [95% CI -0.675 to 0.717]) and FUNC score (c-index 0.701 [95% CI- 0.698 to 0.723], 0.668 [95% CI -0.657 to 0.680] and 0.727 [95% CI -0.708 to 0.753]). In the external independent Yale-ICH cohort, similar performance metrics were obtained for disability and severe disability (c-index 0.725 [95% CI -0.673 to 0.781] and 0.747 [95% CI -0.676 to 0.807], respectively). Similar AUC of predicting each outcome at 6 months, 1 and 2 years after ICH was achieved compared with ICH score and FUNC score. CONCLUSION: We developed a generalizable deep learning model to predict onset of dependent living and disability after ICH, which could help to guide treatment decisions, advise relatives in the acute setting, optimize rehabilitation strategies, and anticipate long-term care needs.

Novel prognostic biomarkers in nasopharyngeal carcinoma unveiled by mega-data bioinformatics analysis.

Nasopharyngeal carcinoma (NPC) is commonly diagnosed at an advanced stage with a high incidence rate in Southeast Asia and Southeast China. However, the limited availability of NPC patient survival data in public databases has resulted in less rigorous studies examining the prediction of NPC survival through construction of Kaplan-Meier curves. These studies have primarily relied on small samples of NPC patients with progression-free survival (PFS) information or data from head and neck squamous cell carcinoma (HNSCC) studies almost without NPC patients. Thus, we coanalyzed RNA expression profiles in eleven datasets (46 normal (control) vs 160 tumor (NPC)) downloaded from the Gene Expression Omnibus (GEO) database and survival data provided by Jun Ma from Sun Yat-sen University. Then, differential analysis, gene ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and network analysis were performed using STRING database. After that, 2142 upregulated differentially expressed genes (DEGs) and 3857 downregulated DEGs were screened. Twenty-five of them were identified as hub genes, which were enriched in several pathways (cilium movement, extracellular matrix structural constituent, homologous recombination and cell cycle). Utilizing the comprehensive dataset we amassed from GEO database, we conducted a survival analysis of DEGs and subsequently constructed survival models. Seven DEGs (RASGRP2, MOCOS, TTC9, ARHGAP4, DPM3, CD37, and CD72) were identified and closely related to the survival prognosis of NPC. Finally, qRT-PCR, WB and IHC were performed to confirm the elevated expression of RASGRP2 and the decreased expression of TTC9, CD37, DPM3 and ARHGAP4, consistent with the DEG analysis. Conclusively, our findings provide insights into the novel prognostic biomarkers of NPC by mega-data bioinformatics analysis, which suggests that they may serve special targets in the treatment of NPC.

Cost-utility analysis of second-line axicabtagene ciloleucel versus standard of care in Japan based on the ZUMA-7 trial.

Aim: To perform a cost-effectiveness analysis comparing axicabtagene ciloleucel (axi-cel) with standard of care (SoC; salvage chemoimmunotherapy, followed by high-dose therapy with autologous stem cell rescue for responders) for second-line (2L) treatment of adults with relapsed or refractory large B-cell lymphoma (r/r LBCL) in the pivotal ZUMA-7 trial data from a Japanese payer perspective. Materials & methods: A three-state partitioned survival model was utilized using population and clinical inputs from the ZUMA-7 trial data over a lifetime horizon. Results: Axi-cel was associated with greater incremental quality-adjusted life-years (2.06) and higher incremental total costs ($48,685.59/¥6.9 million) leading to an incremental cost-effectiveness ratio of $23,590.34/¥3.3 million per quality-adjusted life-years compared with SoC. Conclusion: Axi-cel is a cost-effective treatment alternative to SoC for 2L treatment of adults with r/r LBCL.

[Box: see text].

Multi-state Markov model for time to treatment changes for HIV/AIDS patients: a retrospective cohort national datasets, Ethiopia.

BACKGROUND: Virological failure, drug resistance, toxicities, and other issues make it difficult for ART to maintain long-term sustainability. These issues would force a modification in the patient's treatment plan. The aim of this research was to determine whether first-line antiretroviral therapy is durable and to identify the factors that lead to patients on HAART changing their first highly active antiretroviral therapy regimen. METHODS: A retrospective cohort study was conducted from October, 2019-March, 2020 across all regional states including Addis Ababa and Dire Dawa administrative cities. The target population is from all health facilities that have been providing ART service for at least the past 6 months as of October 2019. Multi-stage clustered sampling method was used to select study facilities and participants. Simple random selected ART medical records of patients ever enrolled in ART treatment services. We adopted a multi-state survival modelling (msm) approach assuming each treatment regimen as state. We estimate the transition probability of patients to move from one regimen to another for time to treatment change/switch. We estimated the transition probability, prediction probabilities and length of stay and factor associated with treatment modification of patients to move from one regimen to another. RESULTS: Any of the six therapy combinations (14.4%) altered their treatment at least once during the follow-up period for a variety of reasons. Of the patients, 4,834 (13.26%) changed their treatments just once, while 371 (1.1%) changed it more than once. For 38.6% of the time, a treatment change was undertaken due to toxicity, another infection or comorbidity, or another factor, followed by New drugs were then made accessible and other factors 18.3% of the time, a drug was out of supply; 2.6% of those instances involved pregnancy; and 43.1% involved something else. Highly active anti-retroviral therapy (HAART) combinations TDF + 3TC + NVP, d4T + 3TC + NVP, and TDF + 3TC + EFV were high to treatment alterations in all reasons of treatment modifications, with 29.74%, 26.52%, and 19.52% treatment changes, respectively. Early treatment modification or regime change is one of the treatment combinations that include the d4T medication that creates major concern. The likelihood of staying and moving at the the start of s = 0 and 30-month transitions increased, but the likelihood of staying were declined. For this cohort dataset, the presence of opportunistic disease, low body weight, baseline CD4 count, and baseline TB positive were risk factors for therapy adjustment. CONCLUSION: Given that the current study took into account a national dataset, it provides a solid basis for ART drug status and management. The patient had a higher likelihood of adjusting their treatment at some point during the follow-up period due to drug toxicity, comorbidity, drug not being available, and other factors, according to the prediction probability once more. Baseline TB positivity, low CD4 count, opportunistic disease, and low body weight were risk factors for therapy adjustment in this cohort dataset.

Economic evaluation of camrelizumab plus rivoceranib versus sorafenib as first-line therapy for unresectable hepatocellular carcinoma in the United States and China.

BACKGROUND: Camrelizumab combined with rivoceranib has been proven effective for treating unresectable hepatocellular carcinoma (uHCC). However, their higher prices than sorafenib could impose a substantial economic burden on patients. AIM: This study aimed to evaluate the relative cost-effectiveness of the combination of camrelizumab and rivoceranib versus sorafenib as first-line therapy for patients with uHCC from the perspective of the US and Chinese payers. METHOD: Using data from the CARES-310 trial, a partitioned survival model (PSM) was developed, considering the perspectives of the US and Chinese payers. The model employed a 15-year time horizon and a biweekly cycle. Direct medical costs and utility data were collected from previous studies and open-access databases. Primary outcomes included quality-adjusted life years (QALYs) and the incremental cost-effectiveness ratio (ICER). Price simulations, sensitivity analyses, and subgroup analyses were conducted. RESULTS: The ICER for the US and China was $122,388.62/QALY and $30,410.56/QALY, respectively, falling below the willingness-to-pay (WTP) thresholds of $150,000/QALY for the US and $35,898.87/QALY for China. Price simulations indicated the cost-effectiveness of camrelizumab plus rivoceranib when the price of camrelizumab (200 mg) remained below $6275.19 in the US and $558.09 in China. The primary determinant of cost-effectiveness in both regions was the cost of camrelizumab. CONCLUSION: The combination of camrelizumab and rivoceranib is a cost-effective first-line therapy for uHCC in both the US and China. Lowering their prices could significantly influence their cost-effectiveness and accessibility to patients. These findings will guide clinicians in treating uHCC and help decision-makers formulate value-based drug pricing strategies.

Weibull parametric model for survival analysis in women with endometrial cancer using clinical and T2-weighted MRI radiomic features.

BACKGROUND: Semiparametric survival analysis such as the Cox proportional hazards (CPH) regression model is commonly employed in endometrial cancer (EC) study. Although this method does not need to know the baseline hazard function, it cannot estimate event time ratio (ETR) which measures relative increase or decrease in survival time. To estimate ETR, the Weibull parametric model needs to be applied. The objective of this study is to develop and evaluate the Weibull parametric model for EC patients' survival analysis. METHODS: Training (n = 411) and testing (n = 80) datasets from EC patients were retrospectively collected to investigate this problem. To determine the optimal CPH model from the training dataset, a bi-level model selection with minimax concave penalty was applied to select clinical and radiomic features which were obtained from T2-weighted MRI images. After the CPH model was built, model diagnostic was carried out to evaluate the proportional hazard assumption with Schoenfeld test. Survival data were fitted into a Weibull model and hazard ratio (HR) and ETR were calculated from the model. Brier score and time-dependent area under the receiver operating characteristic curve (AUC) were compared between CPH and Weibull models. Goodness of the fit was measured with Kolmogorov-Smirnov (KS) statistic. RESULTS: Although the proportional hazard assumption holds for fitting EC survival data, the linearity of the model assumption is suspicious as there are trends in the age and cancer grade predictors. The result also showed that there was a significant relation between the EC survival data and the Weibull distribution. Finally, it showed that Weibull model has a larger AUC value than CPH model in general, and it also has smaller Brier score value for EC survival prediction using both training and testing datasets, suggesting that it is more accurate to use the Weibull model for EC survival analysis. CONCLUSIONS: The Weibull parametric model for EC survival analysis allows simultaneous characterization of the treatment effect in terms of the hazard ratio and the event time ratio (ETR), which is likely to be better understood. This method can be extended to study progression free survival and disease specific survival. TRIAL REGISTRATION: ClinicalTrials.gov NCT03543215, https://clinicaltrials.gov/ , date of registration: 30th June 2017.

Cost-effectiveness of Sacituzumab Govitecan versus Single-agent Chemotherapy for Patients with Metastatic Triple-Negative Breast Cancer in China.

INTRODUCTION: Patients with metastatic triple-negative breast cancer (mTNBC) have poor prognosis and survival outcomes. Sacituzumab govitecan was newly approved into Chinese market for mTNBC. However, whether its price matches the survival benefit still needs exploring. Here, this study aimed to evaluate the cost-effectiveness of sacituzumab govitecan versus chemotherapy in patients with mTNBC from the perspective of Chinese healthcare system. METHODS: A partitioned survival model consisting of three discrete health states was constructed to assess the cost-effectiveness of sacituzumab govitecan versus single-agent chemotherapy. The key clinical data in the model were from the ASCENT trial. Costs and utility inputs were collected from published literatures. Life-years gained, quality adjusted life-years (QALYs), incremental cost-effectiveness ratio (ICER), incremental net health benefits, and incremental net monetary benefits were calculated between 2 treatment strategies. One-way and probabilistic sensitivity analyses were conducted to account for uncertainty and verify model robustness. Subgroup and cost-threshold analysis were also performed. RESULTS: Sacituzumab govitecan provided an additional 0.25 QALYs and an incremental cost of $ 81,778.61 compared with chemotherapy, which was associated with an ICER of $ 323,603.84/QALY. One-way sensitivity analysis revealed that the model was most sensitive to the cost of sacituzumab govitecan, weight, and utility of progression-free survival. The probabilistic sensitivity analysis indicated that the probability of sacituzumab govitecan being cost-effective was 0%. Considering a willingness-to-pay (WTP) of 3 times GDP, the maximum cost of sacituzumab govitecan that would make it cost-effective was $155.65 per unit (180 mg). CONCLUSIONS: Sacituzumab govitecan was not cost-effective for patients with mTNBC compared with chemotherapy at the commonly adopted WTP threshold of 3 times GDP per capita per QALY in China.

Cost-effectiveness of additional serplulimab to chemotherapy in metastatic squamous non-small cell lung cancer patients.

Objective: To estimate the cost-effectiveness of adding serplulimab to chemotherapy for metastatic squamous non-small cell lung cancer (NSCLC) patients in a first-line setting from a Chinese perspective. Methods: A three-health state partitioned survival model was constructed to simulate disease development. The clinical data used in the model were derived from the ASTRUM-004 clinical trial. Only direct medical costs were included, and the utilities were derived from published literature. The quality-adjusted life-years (QALYs) and incremental cost-effectiveness ratio (ICER) were employed to evaluate health outcomes. Additionally, a sensitivity analysis was performed to verify the robustness of the results. Results: Compared with chemotherapy alone, the addition of serplulimab resulted in an increase of 0.63 QALYs with an incremental cost of $5,372.73, leading to an ICER of $8,528.14 per QALY. This ICER was significantly lower than 3 times China's per capita GDP. The one-way sensitivity analysis suggested that the utility of PFS was the most sensitive factor on ICERs, followed by the price of serplulimab. Conclusion: The combination of serplulimab and chemotherapy has been shown to be a cost-effective initial treatment option for patients with metastatic squamous NSCLC with the commonly accepted willingness-to-pay threshold of 3 times the GDP per capita per QALY in China.

Identifying long-term survivors and those at higher or lower risk of relapse among patients with cytogenetically normal acute myeloid leukemia using a high-dimensional mixture cure model.

Patients with cytogenetically normal acute myeloid leukemia (CN-AML) may harbor prognostically relevant gene mutations and thus be categorized into one of the three 2022 European LeukemiaNet (ELN) genetic-risk groups. Nevertheless, there remains heterogeneity with respect to relapse-free survival (RFS) within these genetic-risk groups. Our training set included 306 adults on Alliance for Clinical Trials in Oncology studies with de novo CN-AML aged < 60 years who achieved a complete remission and for whom centrally reviewed cytogenetics, RNA-sequencing, and gene mutation data from diagnostic samples were available (Alliance trial A152010). To overcome deficiencies of the Cox proportional hazards model when long-term survivors are present, we developed a penalized semi-parametric mixture cure model (MCM) to predict RFS where RNA-sequencing data comprised the predictor space. To validate model performance, we employed an independent test set from the German Acute Myeloid Leukemia Cooperative Group (AMLCG) consisting of 40 de novo CN-AML patients aged < 60 years who achieved a complete remission and had RNA-sequencing of their pre-treatment sample. For the training set, there was a significant non-zero cure fraction (p = 0.019) with 28.5% of patients estimated to be cured. Our MCM included 112 genes associated with cure, or long-term RFS, and 87 genes associated with latency, or shorter-term time-to-relapse. The area under the curve and C-statistic were respectively, 0.947 and 0.783 for our training set and 0.837 and 0.718 for our test set. We identified a novel, prognostically relevant molecular signature in CN-AML, which allows identification of patient subgroups independent of 2022 ELN genetic-risk groups.Trial registration Data from companion studies CALGB 8461, 9665 and 20202 (trials registered at www.clinicaltrials.gov as, respectively, NCT00048958, NCT00899223, and NCT00900224) were obtained from Alliance for Clinical Trials in Oncology under data sharing study A152010. Data from the AMLCG 2008 trial was registered at www.clinicaltrials.gov as NCT01382147.