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Yang Q, Jiang LL, Li YF, Huang D. Prediction of the SF-6D utility score from Lung cancer FACT-L: a mapping study in China. Health Qual Life Outcomes 2023; 21:122. [PMID: 37964348 PMCID: PMC10648360 DOI: 10.1186/s12955-023-02209-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 11/07/2023] [Indexed: 11/16/2023] Open
Abstract
OBJECTIVE To develop a mapping algorithm for generating the Short Form Six-Dimension (SF-6D) utility score based on the Functional Assessment of Cancer Therapy-Lung (FACT-L) of lung cancer patients. METHODS Data were collected from 625 lung cancer patients in mainland China. The Spearman rank correlation coefficient and principal component analysis were used to evaluate the conceptual overlap between the FACT-L and SF-6D. Five model specifications and four statistical techniques were used to derive mapping algorithms, including ordinary least squares (OLS), Tobit and beta-mixture regression models, which were used to directly estimate health utility, and ordered probit regression was used to predict the response level. The prediction performance was evaluated using the correlations between the root mean square error (RMSE), mean absolute error (MAE), concordance correlation coefficient (CCC), Akaike information criterion (AIC) and Bayesian information criterion (BIC) and the observed and predicted SF-6D scores. A five-fold cross-validation method was used to test the universality of each model and select the best model. RESULTS The average FACT-L score was 103.024. The average SF-6D score was 0.774. A strong correlation was found between FACT-L and SF-6D scores (ρ = 0.797). The ordered probit regression model with the total score of each dimension and its square term, as well as age and sex as covariates, was most suitable for mapping FACT-L to SF-6D scores (5-fold cross-validation: RMSE = 0.0854; MAE = 0.0655; CCC = 0.8197; AEs > 0.1 (%) = 53.44; AEs > 0.05 (%) = 21.76), followed by beta-mixture regression for direct mapping. The Bland‒Altman plots showed that the ordered probit regression M5 had the lowest proportion of prediction scores outside the 95% agreement limit (-0.166, 0.163) at 4.96%. CONCLUSIONS The algorithm reported in this paper enables lung cancer data from the FACT-L to be mapped to the utility of the SF-6D. The algorithm allows the calculation of quality-adjusted life years for cost-utility analyses of lung cancer.
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Affiliation(s)
- Qing Yang
- Nursing Department, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, 610041, Chengdu, China.
| | - Long Lin Jiang
- Nursing Department, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, 610041, Chengdu, China
| | - Yin Feng Li
- Nursing Department, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, 610041, Chengdu, China
| | - Deyu Huang
- School of Nursing, Chengdu Medical College, 610500, Chengdu, China
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Jo YS, Han S, Lee D, Min KH, Park SJ, Yoon HK, Lee WY, Yoo KH, Jung KS, Rhee CK. Development of a daily predictive model for the exacerbation of chronic obstructive pulmonary disease. Sci Rep 2023; 13:18669. [PMID: 37907619 PMCID: PMC10618439 DOI: 10.1038/s41598-023-45835-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 10/24/2023] [Indexed: 11/02/2023] Open
Abstract
Acute exacerbation (AE) of chronic obstructive pulmonary disease (COPD) compromises health status; it increases disease progression and the risk of future exacerbations. We aimed to develop a model to predict COPD exacerbation. We merged the Korean COPD subgroup study (KOCOSS) dataset with nationwide medical claims data, information regarding weather, air pollution, and epidemic respiratory virus data. The Korean National Health and Nutrition Examination Survey (KNHANES) dataset was used for validation. Several machine learning methods were employed to increase the predictive power. The development dataset consisted of 590 COPD patients enrolled in the KOCOSS cohort; these were randomly divided into training and internal validation subsets on the basis of the individual claims data. We selected demographic and spirometry data, medications for COPD and hospital visit for AE, air pollution data and meteorological data, and influenza virus data as contributing factors for the final model. Six machine learning and logistic regression tools were used to evaluate the performance of the model. A light gradient boosted machine (LGBM) afforded the best predictive power with an area under the curve (AUC) of 0.935 and an F1 score of 0.653. Similar favorable predictive performance was observed for the 2151 individuals in the external validation dataset. Daily prediction of the COPD exacerbation risk may help patients to rapidly assess their risk of exacerbation and will guide them to take appropriate intervention in advance. This might lead to reduction of the personal and socioeconomic burdens associated with exacerbation.
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Affiliation(s)
- Yong Suk Jo
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Seoul St. Mary's Hospital, The Catholic University of Korea, 222 Banpo-daero, Seocho-Gu, Seoul, 06591, Republic of Korea
| | - Solji Han
- Department of Statistics and Data Science, Yonsei University, Seoul, Republic of Korea
| | - Daeun Lee
- Department of Applied Statistics, Yonsei University, Seoul, Republic of Korea
| | - Kyung Hoon Min
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Internal Medicine, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Seoung Ju Park
- Department of Internal Medicine, Jeonbuk National University Medical School, Jeonju, Republic of Korea
| | - Hyoung Kyu Yoon
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Yeouido St Mary's Hospital, The Catholic University of Korea, Seoul, Republic of Korea
| | - Won-Yeon Lee
- Department of Internal Medicine, Yonsei University Wonju College of Medicine, Wonju, Gangwon, Republic of Korea
| | - Kwang Ha Yoo
- Division of Pulmonary and Allergy Medicine, Department of Internal Medicine, Konkuk University School of Medicine, Seoul, Republic of Korea
| | - Ki-Suck Jung
- Division of Pulmonary Medicine, Department of Internal Medicine, Hallym University Sacred Heart Hospital, Hallym University Medical School, Anyang, Republic of Korea
| | - Chin Kook Rhee
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Seoul St. Mary's Hospital, The Catholic University of Korea, 222 Banpo-daero, Seocho-Gu, Seoul, 06591, Republic of Korea.
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Yang Q, Huang D, Jiang L, Tang Y, Zeng D. Obtaining SF-6D utilities from FACT-H&N in thyroid carcinoma patients: development and results from a mapping study. Front Endocrinol (Lausanne) 2023; 14:1160882. [PMID: 37664851 PMCID: PMC10470082 DOI: 10.3389/fendo.2023.1160882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 07/31/2023] [Indexed: 09/05/2023] Open
Abstract
Objective There is limited evidence for mapping clinical tools to preference-based generic tools in the Chinese thyroid cancer patient population. The current study aims to map the FACT-H&N (Functional Assessment of Cancer Therapy-Head and Neck Cancer) to the SF-6D (Short Form Six-Dimension), which will inform future cost-utility analyses related to thyroid cancer treatment. Methods A total of 1050 participants who completed the FACT-H&N and SF-6D questionnaires were included in the analysis. Four methods of direct and indirect mapping were estimated: OLS regression, Tobit regression, ordered probit regression, and beta mixture regression. We evaluated the predictive performance in terms of root mean square error (RMSE), mean absolute error (MAE), concordance correlation coefficient (CCC), Akaike information criterion (AIC) and Bayesian information criterion (BIC) and the correlation between the observed and predicted SF-6D scores. Results The mean value of SF-6D was 0.690 (SD = 0.128). The RMSE values for the fivefold cross-validation as well as the 30% random sample validation for multiple models in this study were 0.0833-0.0909, MAE values were 0.0676-0.0782, and CCC values were 0.6940-0.7161. SF-6D utility scores were best predicted by a regression model consisting of the total score of each dimension of the FACT-H&N, the square of the total score of each dimension, and covariates including age and gender. We proposed to use direct mapping (OLS regression) and indirect mapping (ordered probit regression) to establish a mapping model of FACT-H&N to SF-6D. The mean SF-6D and cumulative distribution functions simulated from the recommended mapping algorithm generally matched the observed ones. Conclusions In the absence of preference-based quality of life tools, obtaining the health status utility of thyroid cancer patients from directly mapped OLS regression and indirectly mapped ordered probit regression is an effective alternative.
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Affiliation(s)
- Qing Yang
- Nursing Department, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Deyu Huang
- School of Nursing, Chengdu Medical College, Chengdu, China
| | - Longlin Jiang
- Nursing Department, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Yuan Tang
- Nursing Department, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Dingfen Zeng
- Nursing Department, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
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Huang D, Peng J, Chen N, Yang Q, Jiang L. Mapping study of papillary thyroid carcinoma in China: Predicting EQ-5D-5L utility values from FACT-H&N. Front Public Health 2023; 11:1076879. [PMID: 36908441 PMCID: PMC9998072 DOI: 10.3389/fpubh.2023.1076879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 02/02/2023] [Indexed: 02/25/2023] Open
Abstract
Objective To develop a mapping algorithm that can be used to predict EQ-5D-5L health utility scores from FACT-H&N and obtain health utility parameters for Chinese patients with papillary thyroid carcinoma (PTC), which can be used for cost-utility analysis in health economic. Methods A total of 1,050 patients with PTC from a tertiary hospital in China were included, and they completed FACT-H&N and EQ-5D-5L. Four mapping algorithms of direct mapping functions were used to derive the models: Ordinary least squares (OLS), Tobit model (Tobit), Two-part model (TPM), and Beta mixture regression model (Beta). The goodness-of-fit of models was assessed by the mean absolute error (MAE), root mean square error (RMSE), Akaike information criteria (AIC), Bayesian information criteria (BIC), and absolute error (AE). A fivefold cross-validation method was used to test the stability of the models. Results The mean utility value of the EQ-5D-5L was 0.870 ± 0.094. The mean EQ-VAS score was 76.5 ± 13.0. The Beta mixture regression model mapping FACT-H&N to EQ-5D-5L achieved the best performance [fivefold cross-validation MAE = 0.04612, RMSE = 0.06829, AIC = -2480.538, BIC = -2381.137, AE > 0.05 (%) = 32.48, AE > 0.1 (%) = 8.95]. The independent variables in this model were Physical Well-Being (PWB), Emotional Well-Being (EWB), Head & Neck Cancer Subscale (HNCS) scores and its square term and interaction term scores. Conclusions This study calculated the health utility score of Chinese patients with PTC. The reported algorithms can be used to map the FACT-H&N into the EQ-5D-5L, which can be applied in the cost-utility related study of patients with PTC.
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Affiliation(s)
- Deyu Huang
- School of Nursing, Chengdu Medical College, Chengdu, China
| | - Jialing Peng
- School of Nursing, Chengdu Medical College, Chengdu, China
| | - Na Chen
- School of Nursing, Chengdu Medical College, Chengdu, China
| | - Qing Yang
- Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Longlin Jiang
- Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
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Mapping algorithms for predicting EuroQol-5D-3L utilities from the assessment test of chronic obstructive pulmonary disease. Sci Rep 2022; 12:20930. [PMID: 36463253 PMCID: PMC9719462 DOI: 10.1038/s41598-022-24956-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 11/22/2022] [Indexed: 12/07/2022] Open
Abstract
To predict 3-Level version of European Quality of Life-5 Dimensions (EQ-5D-3L) questionnaire utility from the chronic obstructive pulmonary disease (COPD) assessment test (CAT), the study attempts to collect EQ-5D-3L and CAT data from COPD patients. Response mapping under a backward elimination procedure was used for EQ-5D score predictions from CAT. A multinomial logistic regression (MLR) model was used to identify the association between the score and the covariates. Afterwards, the predicted scores were transformed into the utility. The developed formula was compared with ordinary least squares (OLS) regression models and models using Mean Rank Method (MRM). The MLR models performed as well as other models according to mean absolute error (MAE) and root mean squared error (RMSE) evaluations. Besides, the overestimation for low utility patients (utility ≤ 0.6) and underestimation for near health (utility > 0.9) in the OLS method was improved through the means of the MLR model based on bubble chart analysis. In conclusion, response mapping with the MLR model led to performance comparable to the OLS and MRM models for predicting EQ-5D utility from CAT data. Additionally, the bubble charts analysis revealed that the model constructed in this study and MRM could be a better predictive model.
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Hu M, Ding P, Ma J, Yang N, Zheng J, Zhou N. Cost-Effectiveness Analysis of the TCM "Yupingfeng Granules" in the Treatment of Acute Exacerbations of COPD Based on a Randomized Clinical Trial. Int J Chron Obstruct Pulmon Dis 2022; 17:2369-2379. [PMID: 36176739 PMCID: PMC9514780 DOI: 10.2147/copd.s374782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 09/10/2022] [Indexed: 12/04/2022] Open
Abstract
Introduction Traditional Chinese medicine (TCM) is becoming increasingly important as it provides further options for treating many diseases worldwide. The TCM "Yupingfeng" has been used in China for over 800 years, and its clinical efficacy and safety for COPD treatment have been proven in previous studies. The objective of this study was to compare the long-term cost-effectiveness of Yupingfeng granules and the current conventional treatment for COPD patients in China. Methods A Markov model was constructed from the perspective of the Chinese healthcare system using TreeAge Pro 2011. The model cycle length was 12 months, and the cycle time was set to 10 years. Data from a randomized controlled trial were used to generate the number of acute exacerbations, COPD assessment test (CAT) score and actual medication used. The state transition probabilities, costs and quality-adjusted life years (QALYs) were derived from available sources. A threshold of 72,447 yuan per QALY gained was used as a cost-effectiveness criterion. One-way and probabilistic sensitivity analyses were conducted to verify the model. In addition, the cost-effectiveness of a 35-year cycle was evaluated as a scenario analysis. Results In the basic-case analysis, the ICER of adding Yupingfeng granules to the current conventional treatment drugs was ¥2123.04 per QALY, which was less than the threshold (one-time per capita GDP).Sensitivity analyses showed the results to be robust. Probabilistic sensitivity analysis showed that the probability of the ICER being less than the one-time per capita GDP threshold was 100%. In the scenario analysis, the incremental cost-effectiveness was ¥12,051.27 per QALY which was also under the one-time per capita GDP. Conclusion By reducing the number of acute exacerbations of COPD, thereby correspondingly reducing the follow-up treatment cost, Yupingfeng granules combined with conventional treatment were found to provide a cost-effective therapeutic strategy for COPD.
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Affiliation(s)
- Ming Hu
- West China School of Pharmacy Sichuan University, Chengdu, People’s Republic of China
| | - Pan Ding
- West China School of Pharmacy Sichuan University, Chengdu, People’s Republic of China
| | - Jinfang Ma
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Nan Yang
- West China School of Pharmacy Sichuan University, Chengdu, People’s Republic of China
| | - Jinping Zheng
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Naitong Zhou
- West China School of Pharmacy Sichuan University, Chengdu, People’s Republic of China
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Current Status of Research on the Mapping Function of Health Utility Values in the Asia Pacific Region: A Systematic Review. Value Health Reg Issues 2021; 24:224-239. [PMID: 33894684 DOI: 10.1016/j.vhri.2020.12.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 11/11/2020] [Accepted: 12/06/2020] [Indexed: 11/22/2022]
Abstract
OBJECTIVES This systematic review aimed to analyze the published studies on the use of the mapping method between generic scales and disease-specific scales as well as between 2 universal scales. METHODS A systematic literature search was conducted using PubMed, ScienceDirect, Web of Science, CNKI, Weipa Database, Wanfang Database, and HERC Database to collect articles about the application of the mapping method to the measurement of health utility value from January 2000 to December 2019. RESULTS Overall, 59 articles met the inclusion requirements, and most of them were a mapping study between a disease-specific scale and a generic scale. Then all these articles were classified by the following study types: a clear functional relationship; unclear functional relationship; disease-specific scale and universality; mapping between generic scales and disease-specific scales, and mapping between universal scales. Most studies derived the best mapping model from the ordinary least squares regression, and fewer studies chose to use new regression methods. Sample sizes in the retrieved studies generally affected the reliability of the study results. CONCLUSIONS In recent years, as more attention has been paid to the research of the mapping method, a large number of problems have followed, such as the selection of scale types, the coverage of the study sample, and the selection of evaluation index of model performance and sample size. It is hoped that these problems can be properly solved in the future research.
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