Development and validation of machine learning for early mortality in systemic sclerosis
Abstract Clinical predictors of mortality in systemic sclerosis (SSc) are diversely reported due to different healthcare conditions and populations. A simple predictive model for early mortality among patients with SSc is needed as a precise referral tool for general practitioners. We aimed to devel...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-10-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-22161-9 |
| _version_ | 1811182248007827456 |
|---|---|
| author | Chingching Foocharoen Wilaiphorn Thinkhamrop Nathaphop Chaichaya Ajanee Mahakkanukrauh Siraphop Suwannaroj Bandit Thinkhamrop |
| author_facet | Chingching Foocharoen Wilaiphorn Thinkhamrop Nathaphop Chaichaya Ajanee Mahakkanukrauh Siraphop Suwannaroj Bandit Thinkhamrop |
| author_sort | Chingching Foocharoen |
| collection | DOAJ |
| description | Abstract Clinical predictors of mortality in systemic sclerosis (SSc) are diversely reported due to different healthcare conditions and populations. A simple predictive model for early mortality among patients with SSc is needed as a precise referral tool for general practitioners. We aimed to develop and validate a simple predictive model for predicting mortality among patients with SSc. Prognostic research with a historical cohort study design was conducted between January 1, 2013, and December 31, 2020, in adult SSc patients attending the Scleroderma Clinic at a university hospital in Thailand. The data were extracted from the Scleroderma Registry Database. Early mortality was defined as dying within 5 years after the onset of SSc. Deep learning algorithms with Adam optimizer and different machine learning algorithms (including Logistic Regression, Decision tree, AdaBoost, Random Forest, Gradient Boosting, XGBoost, and Autoencoder neural network) were used to classify SSc mortality. In addition, the model’s performance was evaluated using the area under the receiver operating characteristic curve (auROC) and its 95% confidence interval (CI) and values in the confusion matrix. The predictive model development included 528 SSc patients, 343 (65.0%) were females and 374 (70.8%) had dcSSc. Ninety-five died within 5 years after disease onset. The final 2 models with the highest predictive performance comprise the modified Rodnan skin score (mRSS) and the WHO-FC ≥ II for Model 1 and mRSS and WHO-FC ≥ III for Model 2. Model 1 provided the highest predictive performance, followed by Model 2. After internal validation, the accuracy and auROC were good. The specificity was high in Models 1 and 2 (84.8%, 89.8%, and 98.8% in model 1 vs. 84.8%, 85.6%, and 98.8% in model 2). This simplified machine learning model for predicting early mortality among patients with SSc could guide early referrals to specialists and help rheumatologists with close monitoring and management planning. External validation across multi-SSc clinics should be considered for further study. |
| first_indexed | 2024-04-11T09:29:24Z |
| format | Article |
| id | doaj.art-d861689d14904393ad4efccaf8ec859c |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-11T09:29:24Z |
| publishDate | 2022-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-d861689d14904393ad4efccaf8ec859c2022-12-22T04:31:57ZengNature PortfolioScientific Reports2045-23222022-10-0112111010.1038/s41598-022-22161-9Development and validation of machine learning for early mortality in systemic sclerosisChingching Foocharoen0Wilaiphorn Thinkhamrop1Nathaphop Chaichaya2Ajanee Mahakkanukrauh3Siraphop Suwannaroj4Bandit Thinkhamrop5Department of Medicine, Faculty of Medicine, Khon Kaen UniversityData Management and Statistical Analysis Center, Faculty of Public Health, Khon Kaen UniversityData Management and Statistical Analysis Center, Faculty of Public Health, Khon Kaen UniversityDepartment of Medicine, Faculty of Medicine, Khon Kaen UniversityDepartment of Medicine, Faculty of Medicine, Khon Kaen UniversityDepartment of Epidemiology and Biostatistics, Faculty of Public Health, Khon Kaen UniversityAbstract Clinical predictors of mortality in systemic sclerosis (SSc) are diversely reported due to different healthcare conditions and populations. A simple predictive model for early mortality among patients with SSc is needed as a precise referral tool for general practitioners. We aimed to develop and validate a simple predictive model for predicting mortality among patients with SSc. Prognostic research with a historical cohort study design was conducted between January 1, 2013, and December 31, 2020, in adult SSc patients attending the Scleroderma Clinic at a university hospital in Thailand. The data were extracted from the Scleroderma Registry Database. Early mortality was defined as dying within 5 years after the onset of SSc. Deep learning algorithms with Adam optimizer and different machine learning algorithms (including Logistic Regression, Decision tree, AdaBoost, Random Forest, Gradient Boosting, XGBoost, and Autoencoder neural network) were used to classify SSc mortality. In addition, the model’s performance was evaluated using the area under the receiver operating characteristic curve (auROC) and its 95% confidence interval (CI) and values in the confusion matrix. The predictive model development included 528 SSc patients, 343 (65.0%) were females and 374 (70.8%) had dcSSc. Ninety-five died within 5 years after disease onset. The final 2 models with the highest predictive performance comprise the modified Rodnan skin score (mRSS) and the WHO-FC ≥ II for Model 1 and mRSS and WHO-FC ≥ III for Model 2. Model 1 provided the highest predictive performance, followed by Model 2. After internal validation, the accuracy and auROC were good. The specificity was high in Models 1 and 2 (84.8%, 89.8%, and 98.8% in model 1 vs. 84.8%, 85.6%, and 98.8% in model 2). This simplified machine learning model for predicting early mortality among patients with SSc could guide early referrals to specialists and help rheumatologists with close monitoring and management planning. External validation across multi-SSc clinics should be considered for further study.https://doi.org/10.1038/s41598-022-22161-9 |
| spellingShingle | Chingching Foocharoen Wilaiphorn Thinkhamrop Nathaphop Chaichaya Ajanee Mahakkanukrauh Siraphop Suwannaroj Bandit Thinkhamrop Development and validation of machine learning for early mortality in systemic sclerosis Scientific Reports |
| title | Development and validation of machine learning for early mortality in systemic sclerosis |
| title_full | Development and validation of machine learning for early mortality in systemic sclerosis |
| title_fullStr | Development and validation of machine learning for early mortality in systemic sclerosis |
| title_full_unstemmed | Development and validation of machine learning for early mortality in systemic sclerosis |
| title_short | Development and validation of machine learning for early mortality in systemic sclerosis |
| title_sort | development and validation of machine learning for early mortality in systemic sclerosis |
| url | https://doi.org/10.1038/s41598-022-22161-9 |
| work_keys_str_mv | AT chingchingfoocharoen developmentandvalidationofmachinelearningforearlymortalityinsystemicsclerosis AT wilaiphornthinkhamrop developmentandvalidationofmachinelearningforearlymortalityinsystemicsclerosis AT nathaphopchaichaya developmentandvalidationofmachinelearningforearlymortalityinsystemicsclerosis AT ajaneemahakkanukrauh developmentandvalidationofmachinelearningforearlymortalityinsystemicsclerosis AT siraphopsuwannaroj developmentandvalidationofmachinelearningforearlymortalityinsystemicsclerosis AT banditthinkhamrop developmentandvalidationofmachinelearningforearlymortalityinsystemicsclerosis |