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Vitacca M, Paneroni M. Use simple tools to improve complex conditions. Eur J Intern Med 2024; 125:39-40. [PMID: 38762434 DOI: 10.1016/j.ejim.2024.05.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Accepted: 05/14/2024] [Indexed: 05/20/2024]
Affiliation(s)
- Michele Vitacca
- Respiratory Rehabilitation Unit, Istituti Clinici Scientifici Maugeri IRCCS, Institute of Lumezzane (Bs), Italy.
| | - Mara Paneroni
- Respiratory Rehabilitation Unit, Istituti Clinici Scientifici Maugeri IRCCS, Institute of Lumezzane (Bs), Italy
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Wu J, Lu Y, Dong S, Wu L, Shen X. Predicting COPD exacerbations based on quantitative CT analysis: an external validation study. Front Med (Lausanne) 2024; 11:1370917. [PMID: 38933101 PMCID: PMC11199769 DOI: 10.3389/fmed.2024.1370917] [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: 01/16/2024] [Accepted: 05/30/2024] [Indexed: 06/28/2024] Open
Abstract
Purpose Quantitative computed tomography (CT) analysis is an important method for diagnosis and severity evaluation of lung diseases. However, the association between CT-derived biomarkers and chronic obstructive pulmonary disease (COPD) exacerbations remains unclear. We aimed to investigate its potential in predicting COPD exacerbations. Methods Patients with COPD were consecutively enrolled, and their data were analyzed in this retrospective study. Body composition and thoracic abnormalities were analyzed from chest CT scans. Logistic regression analysis was performed to identify independent risk factors of exacerbation. Based on 2-year follow-up data, the deep learning system (DLS) was developed to predict future exacerbations. Receiver operating characteristic (ROC) curve analysis was conducted to assess the diagnostic performance. Finally, the survival analysis was performed to further evaluate the potential of the DLS in risk stratification. Results A total of 1,150 eligible patients were included and followed up for 2 years. Multivariate analysis revealed that CT-derived high affected lung volume/total lung capacity (ALV/TLC) ratio, high visceral adipose tissue area (VAT), and low pectoralis muscle cross-sectional area (CSA) were independent risk factors causing COPD exacerbations. The DLS outperformed exacerbation history and the BMI, airflow obstruction, dyspnea, and exercise capacity (BODE) index, with an area under the ROC (AUC) value of 0.88 (95%CI, 0.82-0.92) in the internal cohort and 0.86 (95%CI, 0.81-0.89) in the external cohort. The DeLong test revealed significance between this system and conventional scores in the test cohorts (p < 0.05). In the survival analysis, patients with higher risk were susceptible to exacerbation events. Conclusion The DLS could allow accurate prediction of COPD exacerbations. The newly identified CT biomarkers (ALV/TLC ratio, VAT, and pectoralis muscle CSA) could potentially enable investigation into underlying mechanisms responsible for exacerbations.
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Affiliation(s)
- Ji Wu
- Department of General Surgery, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, China
| | - Yao Lu
- Department of Anesthesia, Fifth People's Hospital of Wujiang District, Suzhou, China
| | - Sunbin Dong
- Department of General Medicine, Municipal Hospital, Suzhou, China
| | - Luyang Wu
- Department of General Medicine, Municipal Hospital, Suzhou, China
| | - Xiping Shen
- Department of General Surgery, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, China
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Sun W, Li Y, Tan S. Development and Validation of an In-Hospital Mortality Prediction Model for Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease. Int J Chron Obstruct Pulmon Dis 2024; 19:1303-1314. [PMID: 38881715 PMCID: PMC11180450 DOI: 10.2147/copd.s461269] [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: 01/24/2024] [Accepted: 05/28/2024] [Indexed: 06/18/2024] Open
Abstract
Purpose Patients with chronic obstructive pulmonary disease (COPD) often face unknown risks during acute exacerbation of the disease (AECOPD), which could potentially result in mortality. This study aimed to develop and validate a nomogram model for predicting the risk of in-hospital mortality in AECOPD patients. Patients and Methods Clinical data of patients hospitalized at The Second People's Hospital of Wuhu City for AECOPD between January 2013 and December 2022 were retrospectively collected. Variables underwent selection through LASSO regression and multivariable logistic regression to develop a nomogram model. The model's predictive performance was assessed using the concordance index, calibration curve, and decision curve analysis (DCA), with internal validation conducted using the bootstrap method. Results A total of 1224 patients were included in this study, with 98 (8%) deaths occurring during hospitalization. LASSO regression identified 11 variables, used to construct model A. Further multivariable logistic regression was conducted to select variables with P < 0.05 to establish model B. model B was selected as the final model based on discrimination, calibration, and clinical utility, encompassing variables including acute respiratory failure, lung cancer, heart rate, hemoglobin, absolute neutrophil count, serum albumin, blood urea nitrogen, and serum chloride. The nomogram model achieved a concordance index of 0.858. Internal validation of the model was conducted using the bootstrap method with 500 repetitions, resulting in a concordance index of 0.851 (95% CI: 0.805, 0.893). The calibration curve demonstrated a good fit, with a Hosmer-Lemeshow goodness-of-fit test P-value of 0.520. Moreover, DCA findings suggested patient benefit within a threshold probability range of 0.02 to 0.73, with a maximum net benefit of 0.07. Conclusion The model constructed in this study has good predictive performance, which helps clinical doctors identify patients at high risk of death early.
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Affiliation(s)
- Wenjie Sun
- Graduate School, Wannan Medical College, Wuhu, Anhui, People's Republic of China
| | - Yeshan Li
- Respiratory Department, The Second People's Hospital of Wuhu City, Wuhu, Anhui, People's Republic of China
| | - Shuxin Tan
- Graduate School, Wannan Medical College, Wuhu, Anhui, People's Republic of China
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Xu H, Yan R, Ye C, Li J, Ji G. Specific mortality in patients with diffuse large B-cell lymphoma: a retrospective analysis based on the surveillance, epidemiology, and end results database. Eur J Med Res 2024; 29:241. [PMID: 38643217 PMCID: PMC11031870 DOI: 10.1186/s40001-024-01833-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 04/06/2024] [Indexed: 04/22/2024] Open
Abstract
BACKGROUND The full potential of competing risk modeling approaches in the context of diffuse large B-cell lymphoma (DLBCL) patients has yet to be fully harnessed. This study aims to address this gap by developing a sophisticated competing risk model specifically designed to predict specific mortality in DLBCL patients. METHODS We extracted DLBCL patients' data from the SEER (Surveillance, Epidemiology, and End Results) database. To identify relevant variables, we conducted a two-step screening process using univariate and multivariate Fine and Gray regression analyses. Subsequently, a nomogram was constructed based on the results. The model's consistency index (C-index) was calculated to assess its performance. Additionally, calibration curves and receiver operator characteristic (ROC) curves were generated to validate the model's effectiveness. RESULTS This study enrolled a total of 24,402 patients. The feature selection analysis identified 13 variables that were statistically significant and therefore included in the model. The model validation results demonstrated that the area under the receiver operating characteristic (ROC) curve (AUC) for predicting 6-month, 1-year, and 3-year DLBCL-specific mortality was 0.748, 0.718, and 0.698, respectively, in the training cohort. In the validation cohort, the AUC values were 0.747, 0.721, and 0.697. The calibration curves indicated good consistency between the training and validation cohorts. CONCLUSION The most significant predictor of DLBCL-specific mortality is the age of the patient, followed by the Ann Arbor stage and the administration of chemotherapy. This predictive model has the potential to facilitate the identification of high-risk DLBCL patients by clinicians, ultimately leading to improved prognosis.
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Affiliation(s)
- Hui Xu
- Department of Hematology, Taixing People's Hospital, No. 98, Runtai South Road, Taixing, 225400, Jiangsu, China
| | - Rong Yan
- Taixing People's Hospital, Taixing, Jiangsu, China
| | - Chunmei Ye
- Department of Hematology, Taixing People's Hospital, No. 98, Runtai South Road, Taixing, 225400, Jiangsu, China
| | - Jun Li
- Department of Hematology, Taixing People's Hospital, No. 98, Runtai South Road, Taixing, 225400, Jiangsu, China
| | - Guo Ji
- Department of Hematology, Taixing People's Hospital, No. 98, Runtai South Road, Taixing, 225400, Jiangsu, China.
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Zhu Z, Zhao S, Li J, Wang Y, Xu L, Jia Y, Li Z, Li W, Chen G, Wu X. Development and application of a deep learning-based comprehensive early diagnostic model for chronic obstructive pulmonary disease. Respir Res 2024; 25:167. [PMID: 38637823 PMCID: PMC11027407 DOI: 10.1186/s12931-024-02793-3] [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: 12/06/2023] [Accepted: 03/28/2024] [Indexed: 04/20/2024] Open
Abstract
BACKGROUND Chronic obstructive pulmonary disease (COPD) is a frequently diagnosed yet treatable condition, provided it is identified early and managed effectively. This study aims to develop an advanced COPD diagnostic model by integrating deep learning and radiomics features. METHODS We utilized a dataset comprising CT images from 2,983 participants, of which 2,317 participants also provided epidemiological data through questionnaires. Deep learning features were extracted using a Variational Autoencoder, and radiomics features were obtained using the PyRadiomics package. Multi-Layer Perceptrons were used to construct models based on deep learning and radiomics features independently, as well as a fusion model integrating both. Subsequently, epidemiological questionnaire data were incorporated to establish a more comprehensive model. The diagnostic performance of standalone models, the fusion model and the comprehensive model was evaluated and compared using metrics including accuracy, precision, recall, F1-score, Brier score, receiver operating characteristic curves, and area under the curve (AUC). RESULTS The fusion model exhibited outstanding performance with an AUC of 0.952, surpassing the standalone models based solely on deep learning features (AUC = 0.844) or radiomics features (AUC = 0.944). Notably, the comprehensive model, incorporating deep learning features, radiomics features, and questionnaire variables demonstrated the highest diagnostic performance among all models, yielding an AUC of 0.971. CONCLUSION We developed and implemented a data fusion strategy to construct a state-of-the-art COPD diagnostic model integrating deep learning features, radiomics features, and questionnaire variables. Our data fusion strategy proved effective, and the model can be easily deployed in clinical settings. TRIAL REGISTRATION Not applicable. This study is NOT a clinical trial, it does not report the results of a health care intervention on human participants.
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Affiliation(s)
- Zecheng Zhu
- Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Shunjin Zhao
- Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Lanxi Branch (Lanxi People's Hospital), Hangzhou, Zhejiang, China
| | - Jiahui Li
- Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yuting Wang
- Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Luopiao Xu
- Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yubing Jia
- Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Zihan Li
- Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Wenyuan Li
- Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Gang Chen
- College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Xifeng Wu
- Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
- National Institute for Data Science in Health and Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China.
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Chen X, Yang Q, Gao L, Chen W, Gao X, Li Y, Ao L, Sun D. Association Between Serum Anion Gap and Mortality in Critically Ill Patients with COPD in ICU: Data from the MIMIC IV Database. Int J Chron Obstruct Pulmon Dis 2024; 19:579-587. [PMID: 38444550 PMCID: PMC10911976 DOI: 10.2147/copd.s433619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 02/15/2024] [Indexed: 03/07/2024] Open
Abstract
Background Serum anion gap (AG) has been proven to be associated with prognosis in critically ill patients. However, few studies have investigated the association between AG and all-cause mortality in critically ill patients with chronic obstructive pulmonary disease (COPD). Objective We hypothesized that the initial AG level would predict the mortality risk in critically ill patients with COPD. Methods This retrospective cohort study was based on the Medical Information Mart for Intensive Care (MIMIC) IV database. We extracted demographics, vital signs, laboratory tests, comorbidity, and scoring systems from the first 24 hours after patient ICU admission. Multivariable logistic regression analysis models were used to explore the association between serum AG levels and mortality. Interaction and stratified analyses were conducted including age, gender and comorbidity. Results A total of 5531 critically ill patients with COPD were enrolled, composed of 53.6% male and 46.4% female with a median age of 73 years. The all-cause mortality of these patients during ICU hospitalization was 13.7%. The risk of all-cause mortality increased as the AG level increased in the univariate logistic regression analysis (OR=1.13, 95% CI: 1.11-1.15, p<0.01). After adjusting for all the covariates in multivariate logistic regression analysis, the odds ratio was 1.06 (95% CI: 1.04-1.09, p<0.01). Compared with the lowest AG group Q1 (≤11mmol/L), the adjusted OR value for AG and mortality in Q2 (12-13mmol/L) was 0.89 (95% CI: 0.63-1.25, p=0.502), Q3 (14-15mmol/L) was 0.95 (95% CI: 0.68-1.34, p=0.788), and Q4 (≥16mmol/L) was 1.49 (95% CI: 1.10-2.02, p=0.009) respectively. In addition, the results of the subgroup and stratified analyses were robust. Conclusion AG is positively related to all-cause mortality in critically ill patients with COPD.
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Affiliation(s)
- Xiaojing Chen
- Department of Respiratory and Critical Care Medicine, Inner Mongolia People’s Hospital, Hohhot, 010017, People’s Republic of China
- NHC Key Laboratory of Diagnosis & Treatment of COPD, Inner Mongolia People’s Hospital, Hohhot, 010017, People’s Republic of China
- Inner Mongolia Key Laboratory of Respiratory Diseases, Inner Mongolia People’s Hospital, Hohhot, 010017, People’s Republic of China
| | - Qilin Yang
- Department of Critical Care, the Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Li Gao
- Department of Respiratory and Critical Care Medicine, Inner Mongolia People’s Hospital, Hohhot, 010017, People’s Republic of China
- NHC Key Laboratory of Diagnosis & Treatment of COPD, Inner Mongolia People’s Hospital, Hohhot, 010017, People’s Republic of China
- Inner Mongolia Key Laboratory of Respiratory Diseases, Inner Mongolia People’s Hospital, Hohhot, 010017, People’s Republic of China
| | - Weinan Chen
- Department of Respiratory and Critical Care Medicine, Inner Mongolia People’s Hospital, Hohhot, 010017, People’s Republic of China
| | - Xiaoyu Gao
- Department of Respiratory and Critical Care Medicine, Inner Mongolia People’s Hospital, Hohhot, 010017, People’s Republic of China
- NHC Key Laboratory of Diagnosis & Treatment of COPD, Inner Mongolia People’s Hospital, Hohhot, 010017, People’s Republic of China
- Inner Mongolia Key Laboratory of Respiratory Diseases, Inner Mongolia People’s Hospital, Hohhot, 010017, People’s Republic of China
| | - Yameng Li
- Department of Respiratory and Critical Care Medicine, Inner Mongolia People’s Hospital, Hohhot, 010017, People’s Republic of China
- NHC Key Laboratory of Diagnosis & Treatment of COPD, Inner Mongolia People’s Hospital, Hohhot, 010017, People’s Republic of China
- Inner Mongolia Key Laboratory of Respiratory Diseases, Inner Mongolia People’s Hospital, Hohhot, 010017, People’s Republic of China
| | - Liying Ao
- Department of Otolaryngology, Inner Mongolia People’s Hospital, Hohhot, 010017, People’s Republic of China
| | - Dejun Sun
- Department of Respiratory and Critical Care Medicine, Inner Mongolia People’s Hospital, Hohhot, 010017, People’s Republic of China
- NHC Key Laboratory of Diagnosis & Treatment of COPD, Inner Mongolia People’s Hospital, Hohhot, 010017, People’s Republic of China
- Inner Mongolia Key Laboratory of Respiratory Diseases, Inner Mongolia People’s Hospital, Hohhot, 010017, People’s Republic of China
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Tang J, Huang J, He X, Zou S, Gong L, Yuan Q, Peng Z. The prediction of in-hospital mortality in elderly patients with sepsis-associated acute kidney injury utilizing machine learning models. Heliyon 2024; 10:e26570. [PMID: 38420451 PMCID: PMC10901004 DOI: 10.1016/j.heliyon.2024.e26570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 02/14/2024] [Accepted: 02/15/2024] [Indexed: 03/02/2024] Open
Abstract
Background Sepsis-associated acute kidney injury (SA-AKI) is a severe complication associated with poorer prognosis and increased mortality, particularly in elderly patients. Currently, there is a lack of accurate mortality risk prediction models for these patients in clinic. Objectives This study aimed to develop and validate machine learning models for predicting in-hospital mortality risk in elderly patients with SA-AKI. Methods Machine learning models were developed and validated using the public, high-quality Medical Information Mart for Intensive Care (MIMIC)-IV critically ill database. The recursive feature elimination (RFE) algorithm was employed for key feature selection. Eleven predictive models were compared, with the best one selected for further validation. Shapley Additive Explanations (SHAP) values were used for visualization and interpretation, making the machine learning models clinically interpretable. Results There were 16,154 patients with SA-AKI in the MIMIC-IV database, and 8426 SA-AKI patients were included in this study (median age: 77.0 years; female: 45%). 7728 patients excluded based on these criteria. They were randomly divided into a training cohort (5,934, 70%) and a validation cohort (2,492, 30%). Nine key features were selected by the RFE algorithm. The CatBoost model achieved the best performance, with an AUC of 0.844 in the training cohort and 0.804 in the validation cohort. SHAP values revealed that AKI stage, PaO2, and lactate were the top three most important features contributing to the CatBoost model. Conclusion We developed a model capable of predicting the risk of in-hospital mortality in elderly patients with SA-AKI.
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Affiliation(s)
- Jie Tang
- Department of Gastroenterology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Organ Fibrosis Key Lab of Hunan Province, Central South University, Changsha, Hunan, China
| | - Jian Huang
- Department of Diagnostic Ultrasound and Echocardiography, Sir Run Run Shaw Hospital, Zhejiang University College of Medicine, Hangzhou, Zhejiang, China
| | - Xin He
- Organ Fibrosis Key Lab of Hunan Province, Central South University, Changsha, Hunan, China
- Department of Nephrology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Sijue Zou
- Organ Fibrosis Key Lab of Hunan Province, Central South University, Changsha, Hunan, China
- Department of Nephrology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Li Gong
- Organ Fibrosis Key Lab of Hunan Province, Central South University, Changsha, Hunan, China
- Department of Nephrology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Qiongjing Yuan
- Organ Fibrosis Key Lab of Hunan Province, Central South University, Changsha, Hunan, China
- Department of Nephrology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National International Joint Research Center for Medical Metabolomices, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Medical Research Center for Geriatric Diseases, Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Zhangzhe Peng
- Organ Fibrosis Key Lab of Hunan Province, Central South University, Changsha, Hunan, China
- Department of Nephrology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National International Joint Research Center for Medical Metabolomices, Xiangya Hospital, Central South University, Changsha, Hunan, China
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Cheng W, Zhou A, Song Q, Zeng Y, Lin L, Liu C, Shi J, Zhou Z, Peng Y, Li J, Deng D, Yang M, Yang L, Chen Y, Cai S, Chen P. Development and validation of a nomogram model for mortality prediction in stable chronic obstructive pulmonary disease patients: A prospective observational study in the RealDTC cohort. J Glob Health 2024; 14:04049. [PMID: 38385363 PMCID: PMC10905054 DOI: 10.7189/jogh.14.04049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2024] Open
Abstract
Background Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide. There is no nomogram model available for mortality prediction of stable COPD. We intended to develop and validate a nomogram model to predict mortality risk in stable COPD patients for personalised prognostic assessment. Methods A prospective observational study was made of COPD outpatients registered in the RealDTC study between December 2016 and December 2019. Patients were randomly assigned to the training cohort and validation cohort in a ratio of 7:3. We used Lasso regression to screen predicted variables. Further, we evaluated the prognostic performance using the area under the time-dependent receiver operating characteristic curve (AUC) and calibration curve. We used the AUC, concordance index, and decision curve analysis to evaluate the net benefits and utility of the nomogram compared with three earlier prediction models. Results Of 2499 patients, the median follow-up was 38 months. The characteristics of the patients between the training cohort (n = 1743) and the validation cohort (n = 756) were similar. ABEODS nomogram model, combining age, body mass index, educational level, airflow obstruction, dyspnoea, and severe exacerbation in the first year, was constructed to predict mortality in stable COPD patients. In the integrative analysis of training and validation cohorts of the nomogram model, the three-year mortality prediction achieved AUC = 0.84; 95% confidence interval (CI) = 0.81, 0.88 and AUC = 0.80; 95% CI = 0.74, 0.86, respectively. The ABEODS nomogram model preserved excellent calibration in both the training cohort and validation cohort. The time-dependent AUC, concordance index, and net benefit of the nomogram model were higher than those of BODEx, updated ADO, and DOSE, respectively. Conclusions We developed and validated a prognostic nomogram model that accurately predicts mortality across the COPD severity spectrum. The proposed ABEODS nomogram model performed better than earlier models, including BODEx, updated ADO, and DOSE in Chinese patients with COPD. Registration ChiCTR-POC-17010431.
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Affiliation(s)
- Wei Cheng
- Department of Pulmonary and Critical Care Medicine, Second Xiangya Hospital; Research Unit of Respiratory Disease; Diagnosis and Treatment Centre of Respiratory Disease, Central South University, Changsha, Hunan, China
- Clinical Medical Research Centre for Respiratory and Critical Care Medicine in Hunan Province, China
| | - Aiyuan Zhou
- Department of Pulmonary and Critical Care Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Qing Song
- Department of Pulmonary and Critical Care Medicine, Second Xiangya Hospital; Research Unit of Respiratory Disease; Diagnosis and Treatment Centre of Respiratory Disease, Central South University, Changsha, Hunan, China
- Clinical Medical Research Centre for Respiratory and Critical Care Medicine in Hunan Province, China
| | - Yuqin Zeng
- Department of Pulmonary and Critical Care Medicine, Second Xiangya Hospital; Research Unit of Respiratory Disease; Diagnosis and Treatment Centre of Respiratory Disease, Central South University, Changsha, Hunan, China
- Clinical Medical Research Centre for Respiratory and Critical Care Medicine in Hunan Province, China
| | - Ling Lin
- Department of Pulmonary and Critical Care Medicine, Second Xiangya Hospital; Research Unit of Respiratory Disease; Diagnosis and Treatment Centre of Respiratory Disease, Central South University, Changsha, Hunan, China
- Clinical Medical Research Centre for Respiratory and Critical Care Medicine in Hunan Province, China
| | - Cong Liu
- Department of Pulmonary and Critical Care Medicine, Second Xiangya Hospital; Research Unit of Respiratory Disease; Diagnosis and Treatment Centre of Respiratory Disease, Central South University, Changsha, Hunan, China
- Clinical Medical Research Centre for Respiratory and Critical Care Medicine in Hunan Province, China
| | - Jingcheng Shi
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China
| | - Zijing Zhou
- Department of Pulmonary and Critical Care Medicine, Second Xiangya Hospital; Research Unit of Respiratory Disease; Diagnosis and Treatment Centre of Respiratory Disease, Central South University, Changsha, Hunan, China
- Clinical Medical Research Centre for Respiratory and Critical Care Medicine in Hunan Province, China
| | - Yating Peng
- Department of Pulmonary and Critical Care Medicine, Second Xiangya Hospital; Research Unit of Respiratory Disease; Diagnosis and Treatment Centre of Respiratory Disease, Central South University, Changsha, Hunan, China
- Clinical Medical Research Centre for Respiratory and Critical Care Medicine in Hunan Province, China
| | - Jing Li
- Department of Pulmonary and Critical Care Medicine, Second Xiangya Hospital; Research Unit of Respiratory Disease; Diagnosis and Treatment Centre of Respiratory Disease, Central South University, Changsha, Hunan, China
- Clinical Medical Research Centre for Respiratory and Critical Care Medicine in Hunan Province, China
| | - DingDing Deng
- Department of Respiratory Medicine, The First Affiliated People's Hospital, Shaoyang College, Shaoyang, Hunan, China
| | - Min Yang
- Department of Pulmonary and Critical Care Medicine, Second Xiangya Hospital; Research Unit of Respiratory Disease; Diagnosis and Treatment Centre of Respiratory Disease, Central South University, Changsha, Hunan, China
- Clinical Medical Research Centre for Respiratory and Critical Care Medicine in Hunan Province, China
| | - Lizhen Yang
- Department of Pulmonary and Critical Care Medicine, Second Xiangya Hospital; Research Unit of Respiratory Disease; Diagnosis and Treatment Centre of Respiratory Disease, Central South University, Changsha, Hunan, China
- Clinical Medical Research Centre for Respiratory and Critical Care Medicine in Hunan Province, China
| | - Yan Chen
- Department of Pulmonary and Critical Care Medicine, Second Xiangya Hospital; Research Unit of Respiratory Disease; Diagnosis and Treatment Centre of Respiratory Disease, Central South University, Changsha, Hunan, China
- Clinical Medical Research Centre for Respiratory and Critical Care Medicine in Hunan Province, China
| | - Shan Cai
- Department of Pulmonary and Critical Care Medicine, Second Xiangya Hospital; Research Unit of Respiratory Disease; Diagnosis and Treatment Centre of Respiratory Disease, Central South University, Changsha, Hunan, China
- Clinical Medical Research Centre for Respiratory and Critical Care Medicine in Hunan Province, China
| | - Ping Chen
- Department of Pulmonary and Critical Care Medicine, Second Xiangya Hospital; Research Unit of Respiratory Disease; Diagnosis and Treatment Centre of Respiratory Disease, Central South University, Changsha, Hunan, China
- Clinical Medical Research Centre for Respiratory and Critical Care Medicine in Hunan Province, China
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Chang TH, Chen YD, Lu HHS, Wu JL, Mak K, Yu CS. Specific patterns and potential risk factors to predict 3-year risk of death among non-cancer patients with advanced chronic kidney disease by machine learning. Medicine (Baltimore) 2024; 103:e37112. [PMID: 38363886 PMCID: PMC10869094 DOI: 10.1097/md.0000000000037112] [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: 10/17/2023] [Accepted: 01/09/2024] [Indexed: 02/18/2024] Open
Abstract
Chronic kidney disease (CKD) is a major public health concern. But there are limited machine learning studies on non-cancer patients with advanced CKD, and the results of machine learning studies on cancer patients with CKD may not apply directly on non-cancer patients. We aimed to conduct a comprehensive investigation of risk factors for a 3-year risk of death among non-cancer advanced CKD patients with an estimated glomerular filtration rate < 60.0 mL/min/1.73m2 by several machine learning algorithms. In this retrospective cohort study, we collected data from in-hospital and emergency care patients from 2 hospitals in Taiwan from 2009 to 2019, including their international classification of disease at admission and laboratory data from the hospital's electronic medical records (EMRs). Several machine learning algorithms were used to analyze the potential impact and degree of influence of each factor on mortality and survival. Data from 2 hospitals in northern Taiwan were collected with 6565 enrolled patients. After data cleaning, 26 risk factors and approximately 3887 advanced CKD patients from Shuang Ho Hospital were used as the training set. The validation set contained 2299 patients from Taipei Medical University Hospital. Predictive variables, such as albumin, PT-INR, and age, were the top 3 significant risk factors with paramount influence on mortality prediction. In the receiver operating characteristic curve, the random forest had the highest values for accuracy above 0.80. MLP, and Adaboost had better performance on sensitivity and F1-score compared to other methods. Additionally, SVM with linear kernel function had the highest specificity of 0.9983, while its sensitivity and F1-score were poor. Logistic regression had the best performance, with an area under the curve of 0.8527. Evaluating Taiwanese advanced CKD patients' EMRs could provide physicians with a good approximation of the patients' 3-year risk of death by machine learning algorithms.
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Affiliation(s)
- Tzu-Hao Chang
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei, Taiwan
| | - Yu-Da Chen
- Department of Family Medicine, Taipei Medical University Hospital, Taipei, Taiwan
- School of Health Care Administration, College of Management, Taipei Medical University, Taipei, Taiwan
- Department of Family Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Henry Horng-Shing Lu
- Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
- Institute of Data Science and Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Jenny L. Wu
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | | | - Cheng-Sheng Yu
- Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei, Taiwan
- Clinical Data Center, Office of Data Science, Taipei Medical University, Taipei, Taiwan
- Fintech RD Center, Nan Shan Life Insurance Co., Ltd
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Zhu X, Xie B, Chen Y, Zeng H, Hu J. Machine learning in the prediction of in-hospital mortality in patients with first acute myocardial infarction. Clin Chim Acta 2024; 554:117776. [PMID: 38216028 DOI: 10.1016/j.cca.2024.117776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 01/03/2024] [Accepted: 01/08/2024] [Indexed: 01/14/2024]
Abstract
BACKGROUND Persistent efforts are required to further reduce the in-hospital mortality of patients suffering from acute myocardial infarction (AMI), even in the face of a global trend of declining AMI-related fatalities. We investigated deep machine learning models for in-hospital death prediction in patients on their first AMI. METHOD In this 2-center retrospective analysis, first AMI patients from Hospital I and Hospital II were included; 4783 patients from Hospital 1 were split in a 7:3 ratio between the training and testing sets. Data from 1053 AMI patients in Hospital II was used for further validation. 70 clinical information and laboratory examination parameters as predictive indicators were included. Logistic Regression Classifier (LR), Random Forests Classifier (RF), eXtreme Gradient Boosting (XGB), Support Vector Machine Classifier (SVM), Multilayer Perceptron (MLP), Gradient Boosting Machine (GBM), Bootstrapped Aggregation (Bagging) models with AMI patients were developed. The importance of selected variables was obtained through the Shapley Additive exPlanations (SHAP) method. The performance of each model was shown using the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (Average Precision; AP). RESULT The in-hospital mortality for AMI in the training, testing, and validation sets were 5.7 %, 5.6 %, and 6.0 %, respectively. The top 8 predictors were D-dimer, brain natriuretic peptide, cardiogenic shock, neutrophil, prothrombin time, blood urea nitrogen, cardiac arrest, and phosphorus. In the testing cohort, the models of LR, RF, XGB, SVM, MLP, GBM, and Bagging yielded AUROC values of 0.929, 0.931, 0.907, 0.868, 0.907, 0.923, and 0.932, respectively. Bagging has good predictive value and certain clinical value in external validation with AUROC 0.893. CONCLUSION In order to improve the forecasting accuracy of the risk of AMI patients, guide clinical nursing practice, and lower AMI inpatient mortality, this study looked into significant indicators and the optimal models for predicting AMI inpatient mortality.
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Affiliation(s)
- Xiaoli Zhu
- Department of Laboratory Medicine, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, People's Republic of China
| | - Bojian Xie
- Department of Oncological Surgery, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, People's Republic of China
| | - Yijun Chen
- Department of Laboratory Medicine, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, People's Republic of China
| | - Hanqian Zeng
- Department of Oncological Surgery, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, People's Republic of China
| | - Jinxi Hu
- Department of Oncological Surgery, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, People's Republic of China.
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11
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Chen C, Zhang W, Yan G, Tang C. Identifying metabolic dysfunction-associated steatotic liver disease in patients with hypertension and pre-hypertension: An interpretable machine learning approach. Digit Health 2024; 10:20552076241233135. [PMID: 38389508 PMCID: PMC10883118 DOI: 10.1177/20552076241233135] [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: 09/23/2023] [Accepted: 01/30/2024] [Indexed: 02/24/2024] Open
Abstract
Objective Metabolic dysfunction-associated steatotic liver disease (MASLD) is one of the most prevalent liver diseases and is associated with pre-hypertension and hypertension. Our research aims to develop interpretable machine learning (ML) models to accurately identify MASLD in hypertensive and pre-hypertensive populations. Methods The dataset for 4722 hypertensive and pre-hypertensive patients is from subjects in the NAGALA study. Six ML models, including the decision tree, K-nearest neighbor, gradient boosting, naive Bayes, support vector machine, and random forest (RF) models, were used in this study. The optimal model was constructed according to the performances of models evaluated by K-fold cross-validation (k = 5), the area under the receiver operating characteristic curve (AUC), average precision (AP), accuracy, sensitivity, specificity, and F1. Shapley additive explanation (SHAP) values were employed for both global and local interpretation of the model results. Results The prevalence of MASLD in hypertensive and pre-hypertensive patients was 44.3% (362 cases) and 28.3% (1107 cases), respectively. The RF model outperformed the other five models with an AUC of 0.889, AP of 0.800, accuracy of 0.819, sensitivity of 0.816, specificity of 0.821, and F1 of 0.729. According to the SHAP analysis, the top five important features were alanine aminotransferase, body mass index, waist circumference, high-density lipoprotein cholesterol, and total cholesterol. Further analysis of the feature selection in the RF model revealed that incorporating all features leads to optimal model performance. Conclusions ML algorithms, especially RF algorithm, improve the accuracy of MASLD identification, and the global and local interpretation of the RF model results enables us to intuitively understand how various features affect the chances of MASLD in patients with hypertension and pre-hypertension.
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Affiliation(s)
- Chen Chen
- School of Cyber Science and Engineering, Southeast University, Nanjing, Jiangsu, China
- School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu, China
| | - Wenkang Zhang
- Department of Cardiology, Zhongda Hospital, Southeast University, Nanjing, Jiangsu, China
- School of Medicine, Southeast University, Nanjing, Jiangsu, China
| | - Gaoliang Yan
- Department of Cardiology, Zhongda Hospital, Southeast University, Nanjing, Jiangsu, China
| | - Chengchun Tang
- Department of Cardiology, Zhongda Hospital, Southeast University, Nanjing, Jiangsu, China
- School of Medicine, Southeast University, Nanjing, Jiangsu, China
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12
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Sharma M, Wyszkiewicz PV, Matheson AM, McCormack DG, Parraga G. Chest MRI and CT Predictors of 10-Year All-Cause Mortality in COPD. COPD 2023; 20:307-320. [PMID: 37737132 DOI: 10.1080/15412555.2023.2259224] [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: 05/11/2023] [Accepted: 09/11/2023] [Indexed: 09/23/2023]
Abstract
Pulmonary imaging measurements using magnetic resonance imaging (MRI) and computed tomography (CT) have the potential to deepen our understanding of chronic obstructive pulmonary disease (COPD) by measuring airway and parenchymal pathologic information that cannot be provided by spirometry. Currently, MRI and CT measurements are not included in mortality risk predictions, diagnosis, or COPD staging. We evaluated baseline pulmonary function, MRI and CT measurements alongside imaging texture-features to predict 10-year all-cause mortality in ex-smokers with (n = 93; 31 females; 70 ± 9years) and without (n = 69; 29 females, 69 ± 9years) COPD. CT airway and vessel measurements, helium-3 (3He) MRI ventilation defect percent (VDP) and apparent diffusion coefficients (ADC) were quantified. MRI and CT texture-features were extracted using PyRadiomics (version2.2.0). Associations between 10-year all-cause mortality and all clinical and imaging measurements were evaluated using multivariable regression model odds-ratios. Machine-learning predictive models for 10-year all-cause mortality were evaluated using area-under-receiver-operator-characteristic-curve (AUC), sensitivity and specificity analyses. DLCO (%pred) (HR = 0.955, 95%CI: 0.934-0.976, p < 0.001), MRI ADC (HR = 1.843, 95%CI: 1.260-2.871, p < 0.001), and CT informational-measure-of-correlation (HR = 3.546, 95% CI: 1.660-7.573, p = 0.001) were the strongest predictors of 10-year mortality. A machine-learning model trained on clinical, imaging, and imaging textures was the best predictive model (AUC = 0.82, sensitivity = 83%, specificity = 84%) and outperformed the solely clinical model (AUC = 0.76, sensitivity = 77%, specificity = 79%). In ex-smokers, regardless of COPD status, addition of CT and MR imaging texture measurements to clinical models provided unique prognostic information of mortality risk that can allow for better clinical management.Clinical Trial Registration: www.clinicaltrials.gov NCT02279329.
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Affiliation(s)
- Maksym Sharma
- Robarts Research Institute, Western University, London, Canada
- Department of Medical Biophysics, Western University, London, Canada
| | - Paulina V Wyszkiewicz
- Robarts Research Institute, Western University, London, Canada
- Department of Medical Biophysics, Western University, London, Canada
| | - Alexander M Matheson
- Robarts Research Institute, Western University, London, Canada
- Department of Medical Biophysics, Western University, London, Canada
| | - David G McCormack
- Division of Respirology, Department of Medicine, Western University, London, Canada
| | - Grace Parraga
- Robarts Research Institute, Western University, London, Canada
- Department of Medical Biophysics, Western University, London, Canada
- Division of Respirology, Department of Medicine, Western University, London, Canada
- School of Biomedical Engineering, Western University, London, Canada
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13
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Smith LA, Oakden-Rayner L, Bird A, Zeng M, To MS, Mukherjee S, Palmer LJ. Machine learning and deep learning predictive models for long-term prognosis in patients with chronic obstructive pulmonary disease: a systematic review and meta-analysis. Lancet Digit Health 2023; 5:e872-e881. [PMID: 38000872 DOI: 10.1016/s2589-7500(23)00177-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Revised: 06/26/2023] [Accepted: 08/29/2023] [Indexed: 11/26/2023]
Abstract
BACKGROUND Machine learning and deep learning models have been increasingly used to predict long-term disease progression in patients with chronic obstructive pulmonary disease (COPD). We aimed to summarise the performance of such prognostic models for COPD, compare their relative performances, and identify key research gaps. METHODS We conducted a systematic review and meta-analysis to compare the performance of machine learning and deep learning prognostic models and identify pathways for future research. We searched PubMed, Embase, the Cochrane Library, ProQuest, Scopus, and Web of Science from database inception to April 6, 2023, for studies in English using machine learning or deep learning to predict patient outcomes at least 6 months after initial clinical presentation in those with COPD. We included studies comprising human adults aged 18-90 years and allowed for any input modalities. We reported area under the receiver operator characteristic curve (AUC) with 95% CI for predictions of mortality, exacerbation, and decline in forced expiratory volume in 1 s (FEV1). We reported the degree of interstudy heterogeneity using Cochran's Q test (significant heterogeneity was defined as p≤0·10 or I2>50%). Reporting quality was assessed using the TRIPOD checklist and a risk-of-bias assessment was done using the PROBAST checklist. This study was registered with PROSPERO (CRD42022323052). FINDINGS We identified 3620 studies in the initial search. 18 studies were eligible, and, of these, 12 used conventional machine learning and six used deep learning models. Seven models analysed exacerbation risk, with only six reporting AUC and 95% CI on internal validation datasets (pooled AUC 0·77 [95% CI 0·69-0·85]) and there was significant heterogeneity (I2 97%, p<0·0001). 11 models analysed mortality risk, with only six reporting AUC and 95% CI on internal validation datasets (pooled AUC 0·77 [95% CI 0·74-0·80]) with significant degrees of heterogeneity (I2 60%, p=0·027). Two studies assessed decline in lung function and were unable to be pooled. Machine learning and deep learning models did not show significant improvement over pre-existing disease severity scores in predicting exacerbations (p=0·24). Three studies directly compared machine learning models against pre-existing severity scores for predicting mortality and pooled performance did not differ (p=0·57). Of the five studies that performed external validation, performance was worse than or equal to regression models. Incorrect handling of missing data, not reporting model uncertainty, and use of datasets that were too small relative to the number of predictive features included provided the largest risks of bias. INTERPRETATION There is limited evidence that conventional machine learning and deep learning prognostic models demonstrate superior performance to pre-existing disease severity scores. More rigorous adherence to reporting guidelines would reduce the risk of bias in future studies and aid study reproducibility. FUNDING None.
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Affiliation(s)
- Luke A Smith
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia; School of Public Health, University of Adelaide, Adelaide, SA, Australia.
| | - Lauren Oakden-Rayner
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia; School of Public Health, University of Adelaide, Adelaide, SA, Australia
| | - Alix Bird
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia; School of Public Health, University of Adelaide, Adelaide, SA, Australia
| | - Minyan Zeng
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia; School of Public Health, University of Adelaide, Adelaide, SA, Australia
| | - Minh-Son To
- Health Data and Clinical Trials, Flinders University, Bedford Park, SA, Australia; South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, SA, Australia
| | - Sutapa Mukherjee
- Department of Respiratory and Sleep Medicine, Southern Adelaide Local Health Network (SALHN), Bedford Park, SA, Australia; Adelaide Institute for Sleep Health/Flinders Health and Medical Research Institute, College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
| | - Lyle J Palmer
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia; School of Public Health, University of Adelaide, Adelaide, SA, Australia
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14
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Antão J, de Mast J, Marques A, Franssen FME, Spruit MA, Deng Q. Demystification of artificial intelligence for respiratory clinicians managing patients with obstructive lung diseases. Expert Rev Respir Med 2023; 17:1207-1219. [PMID: 38270524 DOI: 10.1080/17476348.2024.2302940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 01/04/2024] [Indexed: 01/26/2024]
Abstract
INTRODUCTION Asthma and chronic obstructive pulmonary disease (COPD) are leading causes of morbidity and mortality worldwide. Despite all available diagnostics and treatments, these conditions pose a significant individual, economic and social burden. Artificial intelligence (AI) promises to support clinical decision-making processes by optimizing diagnosis and treatment strategies of these heterogeneous and complex chronic respiratory diseases. Its capabilities extend to predicting exacerbation risk, disease progression and mortality, providing healthcare professionals with valuable insights for more effective care. Nevertheless, the knowledge gap between respiratory clinicians and data scientists remains a major constraint for wide application of AI and may hinder future progress. This narrative review aims to bridge this gap and encourage AI deployment by explaining its methodology and added value in asthma and COPD diagnosis and treatment. AREAS COVERED This review offers an overview of the fundamental concepts of AI and machine learning, outlines the key steps in building a model, provides examples of their applicability in asthma and COPD care, and discusses barriers to their implementation. EXPERT OPINION Machine learning can advance our understanding of asthma and COPD, enabling personalized therapy and better outcomes. Further research and validation are needed to ensure the development of clinically meaningful and generalizable models.
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Affiliation(s)
- Joana Antão
- Lab3R - Respiratory Research and Rehabilitation Laboratory, School of Health Sciences, University of Aveiro (ESSUA), Aveiro, Portugal
- iBiMED - Institute of Biomedicine, Department of Medical Sciences, University of Aveiro, Aveiro, Portugal
- Department of Research and Development, Ciro, Horn, The Netherlands
- Department of Respiratory Medicine, Maastricht University Medical Centre, NUTRIM School of Nutrition and Translational Research in Metabolism, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Jeroen de Mast
- Economics and Business, University of Amsterdam, Amsterdam, The Netherlands
| | - Alda Marques
- Lab3R - Respiratory Research and Rehabilitation Laboratory, School of Health Sciences, University of Aveiro (ESSUA), Aveiro, Portugal
- iBiMED - Institute of Biomedicine, Department of Medical Sciences, University of Aveiro, Aveiro, Portugal
| | - Frits M E Franssen
- Department of Research and Development, Ciro, Horn, The Netherlands
- Department of Respiratory Medicine, Maastricht University Medical Centre, NUTRIM School of Nutrition and Translational Research in Metabolism, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Martijn A Spruit
- Department of Research and Development, Ciro, Horn, The Netherlands
- Department of Respiratory Medicine, Maastricht University Medical Centre, NUTRIM School of Nutrition and Translational Research in Metabolism, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Qichen Deng
- Department of Research and Development, Ciro, Horn, The Netherlands
- Department of Respiratory Medicine, Maastricht University Medical Centre, NUTRIM School of Nutrition and Translational Research in Metabolism, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
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15
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Juarez-Orozco LE, Niemi M, Yeung MW, Benjamins JW, Maaniitty T, Teuho J, Saraste A, Knuuti J, van der Harst P, Klén R. Hybridizing machine learning in survival analysis of cardiac PET/CT imaging. J Nucl Cardiol 2023; 30:2750-2759. [PMID: 37656345 PMCID: PMC10682215 DOI: 10.1007/s12350-023-03359-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 07/07/2023] [Indexed: 09/02/2023]
Abstract
BACKGROUND Machine Learning (ML) allows integration of the numerous variables delivered by cardiac PET/CT, while traditional survival analysis can provide explainable prognostic estimates from a restricted number of input variables. We implemented a hybrid ML-and-survival analysis of multimodal PET/CT data to identify patients who developed myocardial infarction (MI) or death in long-term follow up. METHODS Data from 739 intermediate risk patients who underwent coronary CT and selectively stress 15O-water-PET perfusion were analyzed for the occurrence of MI and all-cause mortality. Images were evaluated segmentally for atherosclerosis and absolute myocardial perfusion through 75 variables that were integrated through ML into an ML-CCTA and an ML-PET score. These scores were then modeled along with clinical variables through Cox regression. This hybridized model was compared against an expert interpretation-based and a calcium score-based model. RESULTS Compared with expert- and calcium score-based models, the hybridized ML-survival model showed the highest performance (CI .81 vs .71 and .64). The strongest predictor for outcomes was the ML-CCTA score. CONCLUSION Prognostic modeling of PET/CT data for the long-term occurrence of adverse events may be improved through ML imaging score integration and subsequent traditional survival analysis with clinical variables. This hybridization of methods offers an alternative to traditional survival modeling of conventional expert image scoring and interpretation.
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Affiliation(s)
- Luis Eduardo Juarez-Orozco
- Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
- Turku PET Centre, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-8, 20520, Turku, Finland.
| | - Mikael Niemi
- Turku PET Centre, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-8, 20520, Turku, Finland
| | - Ming Wai Yeung
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Jan Walter Benjamins
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Teemu Maaniitty
- Turku PET Centre, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-8, 20520, Turku, Finland
| | - Jarmo Teuho
- Turku PET Centre, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-8, 20520, Turku, Finland
| | - Antti Saraste
- Turku PET Centre, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-8, 20520, Turku, Finland
- Heart Center, Turku University Hospital, Turku, Finland
| | - Juhani Knuuti
- Turku PET Centre, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-8, 20520, Turku, Finland
| | - Pim van der Harst
- Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Riku Klén
- Turku PET Centre, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-8, 20520, Turku, Finland
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Zhang Z, Yu H, Wang Q, Ding Y, Wang Z, Zhao S, Bian T. A Macrophage-Related Gene Signature for Identifying COPD Based on Bioinformatics and ex vivo Experiments. J Inflamm Res 2023; 16:5647-5665. [PMID: 38050560 PMCID: PMC10693783 DOI: 10.2147/jir.s438308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 11/21/2023] [Indexed: 12/06/2023] Open
Abstract
Background This study aims to investigate the association between immune cells and the development of COPD, while providing a new method for the diagnosis of COPD according to the changes in immune microenvironment. Methods In this study, the "CIBERSORT" algorithm was used to estimate the tissue infiltration of 22 types of immune cells in GSE20257 and GSE10006. The "limma" package was used for differentially expressed analysis. The key modules associated with vital immune cells were identified using WGCNA. GO and KEGG enrichment analysis revealed the biological functions of the candidate genes. Ultimately, a novel diagnostic prediction model was constructed via machine learning methods and multivariate logistic regression analysis based on GSE20257. Furthermore, we examined the stability of the model on one internal test set (GSE10006), three external test sets (GSE8545, GSE57148 and GSE76925), one single-cell transcriptome dataset (GSE167295), macrophages (THP-M cells) and lung tissue from COPD patients. Results M0 macrophages (AUC > 0.7 in GSE20257 and GSE10006) were considered as the most important immune cells through exploring the immune microenvironment landscapes in COPD patients and healthy controls. The differentially expressed genes from GSE20257 and GSE10006 were divided into six and five modules via WGCNA, respectively. The green module in GSE20257 (cor = 0.41, P < 0.001) and the brown module in GSE10006 (cor = 0.67, P < 0.001) were highly correlated with M0 macrophages and were selected as key modules. Forty-one intersected genes obtained from two modules were primarily involved in regulation of cytokine production, regulation of innate immune response, specific granule, phagosome, lysosome, ferroptosis, and other biological processes. On the basis of the candidate genetic markers further characterized via the "Boruta" and "LASSO" algorithm for COPD, a diagnostic model comprising CLEC5A, FTL and SLC2A3 was constructed, which could accurately distinguish COPD patients from healthy controls in multiple datasets. GSE20257 as the training set has an AUC of 0.916. The AUCs of the internal test set and three external test sets were 0.873, 0.932, 0.675 and 0.688, respectively. Single-cell sequencing analysis suggested that CLEC5A, FTL and SLC2A3 were expressed in macrophages from COPD patients. The expressions of CLEC5A, FTL and SLC2A3 were up-regulated in THP-M cells and lung tissue from COPD patients. Conclusion According to the variations of immune microenvironment in COPD patients, we constructed and validated a novel macrophage M0-associated diagnostic model with satisfactory predictive value. CLEC5A, FTL and SLC2A3 are expected to be promising targets of immunotherapy in COPD.
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Affiliation(s)
- Zheming Zhang
- Wuxi Medical Center of Nanjing Medical University, Wuxi, People’s Republic of China
- Department of Respiratory Medicine, Wuxi People’s Hospital, Nanjing Medical University, Wuxi, People’s Republic of China
| | - Haoda Yu
- Wuxi Medical Center of Nanjing Medical University, Wuxi, People’s Republic of China
- Department of Respiratory Medicine, Wuxi People’s Hospital, Nanjing Medical University, Wuxi, People’s Republic of China
| | - Qi Wang
- Department of Gastroenterology, Affiliated Hospital of Jiangsu University, Zhenjiang, People’s Republic of China
| | - Yu Ding
- Wuxi Medical Center of Nanjing Medical University, Wuxi, People’s Republic of China
- Department of Respiratory Medicine, Wuxi People’s Hospital, Nanjing Medical University, Wuxi, People’s Republic of China
| | - Ziteng Wang
- Wuxi Medical Center of Nanjing Medical University, Wuxi, People’s Republic of China
- Department of Respiratory Medicine, Wuxi People’s Hospital, Nanjing Medical University, Wuxi, People’s Republic of China
| | - Songyun Zhao
- Wuxi Medical Center of Nanjing Medical University, Wuxi, People’s Republic of China
| | - Tao Bian
- Wuxi Medical Center of Nanjing Medical University, Wuxi, People’s Republic of China
- Department of Respiratory Medicine, Wuxi People’s Hospital, Nanjing Medical University, Wuxi, People’s Republic of China
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Dai Q, Zhu X, Zhang J, Dong Z, Pompeo E, Zheng J, Shi J. The utility of quantitative computed tomography in cohort studies of chronic obstructive pulmonary disease: a narrative review. J Thorac Dis 2023; 15:5784-5800. [PMID: 37969311 PMCID: PMC10636446 DOI: 10.21037/jtd-23-1421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 09/27/2023] [Indexed: 11/17/2023]
Abstract
Background and Objective Chronic obstructive pulmonary disease (COPD) is a significant contributor to global morbidity and mortality. Quantitative computed tomography (QCT), a non-invasive imaging modality, offers the potential to assess lung structure and function in COPD patients. Amidst the coronavirus disease 2019 (COVID-19) pandemic, chest computed tomography (CT) scans have emerged as a viable alternative for assessing pulmonary function (e.g., spirometry), minimizing the risk of aerosolized virus transmission. However, the clinical application of QCT measurements is not yet widespread enough, necessitating broader validation to determine its usefulness in COPD management. Methods We conducted a search in the PubMed database in English from January 1, 2013 to April 20, 2023, using keywords and controlled vocabulary related to QCT, COPD, and cohort studies. Key Content and Findings Existing studies have demonstrated the potential of QCT in providing valuable information on lung volume, airway geometry, airway wall thickness, emphysema, and lung tissue density in COPD patients. Moreover, QCT values have shown robust correlations with pulmonary function tests, and can predict exacerbation risk and mortality in patients with COPD. QCT can even discern COPD subtypes based on phenotypic characteristics such as emphysema predominance, supporting targeted management and interventions. Conclusions QCT has shown promise in cohort studies related to COPD, since it can provide critical insights into the pathogenesis and progression of the disease. Further research is necessary to determine the clinical significance of QCT measurements for COPD management.
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Affiliation(s)
- Qi Dai
- School of Medicine, Tongji University, Shanghai, China
- Department of Radiology, Ningbo No.2 Hospitall, Ningbo, China
| | - Xiaoxiao Zhu
- Department of Respiratory and Critical Care Medicine, Ningbo No.2 Hospital, Ningbo, China
| | - Jingfeng Zhang
- Department of Radiology, Ningbo No.2 Hospitall, Ningbo, China
| | - Zhaoxing Dong
- Department of Respiratory and Critical Care Medicine, Ningbo No.2 Hospital, Ningbo, China
| | - Eugenio Pompeo
- Department of Thoracic Surgery, Policlinico Tor Vergata University, Rome, Italy
| | - Jianjun Zheng
- Department of Radiology, Ningbo No.2 Hospitall, Ningbo, China
| | - Jingyun Shi
- School of Medicine, Tongji University, Shanghai, China
- Department of Radiology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
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Park JE, Kim DY, Park JW, Jung YJ, Lee KS, Park JH, Sheen SS, Park KJ, Sunwoo MH, Chung WY. Development of a Machine Learning Model for Predicting Weaning Outcomes Based Solely on Continuous Ventilator Parameters during Spontaneous Breathing Trials. Bioengineering (Basel) 2023; 10:1163. [PMID: 37892893 PMCID: PMC10604888 DOI: 10.3390/bioengineering10101163] [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: 09/07/2023] [Revised: 09/30/2023] [Accepted: 10/02/2023] [Indexed: 10/29/2023] Open
Abstract
Discontinuing mechanical ventilation remains challenging. We developed a machine learning model to predict weaning outcomes using only continuous monitoring parameters obtained from ventilators during spontaneous breathing trials (SBTs). Patients who received mechanical ventilation in the medical intensive care unit at a tertiary university hospital from 2019-2021 were included in this study. During the SBTs, three waveforms and 25 numerical data were collected as input variables. The proposed convolutional neural network (CNN)-based weaning prediction model extracts features from input data with diverse lengths. Among 138 enrolled patients, 35 (25.4%) experienced weaning failure. The dataset was randomly divided into training and test sets (8:2 ratio). The area under the receiver operating characteristic curve for weaning success by the prediction model was 0.912 (95% confidence interval [CI], 0.795-1.000), with an area under the precision-recall curve of 0.767 (95% CI, 0.434-0.983). Furthermore, we used gradient-weighted class activation mapping technology to provide visual explanations of the model's prediction, highlighting influential features. This tool can assist medical staff by providing intuitive information regarding readiness for extubation without requiring any additional data collection other than SBT data. The proposed predictive model can assist clinicians in making ventilator weaning decisions in real time, thereby improving patient outcomes.
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Affiliation(s)
- Ji Eun Park
- Department of Pulmonary and Critical Care Medicine, Ajou University School of Medicine, Suwon 16499, Republic of Korea; (J.E.P.)
| | - Do Young Kim
- Land Combat System Center, Hanwha Systems, Sungnam 13524, Republic of Korea;
| | - Ji Won Park
- Department of Pulmonary and Critical Care Medicine, Ajou University School of Medicine, Suwon 16499, Republic of Korea; (J.E.P.)
| | - Yun Jung Jung
- Department of Pulmonary and Critical Care Medicine, Ajou University School of Medicine, Suwon 16499, Republic of Korea; (J.E.P.)
| | - Keu Sung Lee
- Department of Pulmonary and Critical Care Medicine, Ajou University School of Medicine, Suwon 16499, Republic of Korea; (J.E.P.)
| | - Joo Hun Park
- Department of Pulmonary and Critical Care Medicine, Ajou University School of Medicine, Suwon 16499, Republic of Korea; (J.E.P.)
| | - Seung Soo Sheen
- Department of Pulmonary and Critical Care Medicine, Ajou University School of Medicine, Suwon 16499, Republic of Korea; (J.E.P.)
| | - Kwang Joo Park
- Department of Pulmonary and Critical Care Medicine, Ajou University School of Medicine, Suwon 16499, Republic of Korea; (J.E.P.)
| | - Myung Hoon Sunwoo
- Department of Electrical and Computer Engineering, Ajou University, Suwon 16499, Republic of Korea;
| | - Wou Young Chung
- Department of Pulmonary and Critical Care Medicine, Ajou University School of Medicine, Suwon 16499, Republic of Korea; (J.E.P.)
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19
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Liu M, Xiao W, Du L, Yu Y, Chen X, Mao B, Fu J. Effectiveness and safety of opioids on breathlessness and exercise endurance in patients with chronic obstructive pulmonary disease: A systematic review and meta-analysis of randomised controlled trials. Palliat Med 2023; 37:1365-1378. [PMID: 37710987 DOI: 10.1177/02692163231194838] [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] [Indexed: 09/16/2023]
Abstract
BACKGROUND Opioids are recommended to treat advanced refractory dyspnoea despite optimal therapy by the American Thoracic Society clinical practice guidelines, while newly published randomised controlled trials of opioids in chronic obstructive pulmonary disease yield conflicting results. AIM This study aimed to evaluate the effectiveness and safety of opioids for patients with chronic obstructive pulmonary disease. DESIGN Systematic review and meta-analysis (PROSPERO CRD42021272556). DATA SOURCES Databases of PubMed, EMBASE and CENTRAL were searched from inception to 2022 for eligible randomised controlled trials. RESULTS Twenty-four studies including 975 patients, were included. In cross-over studies, opioids improved breathlessness (standardised mean difference, -0.43; 95% CI, -0.55 to -0.30; I2 = 18%) and exercise endurance (standardised mean difference, 0.22; 95% CI, 0.02-0.41; I2 = 70%). However, opioids failed to improve dyspnoea (standardised mean difference, -0.02; 95% CI, -0.22 to 0.19; I2 = 39%) and exercise endurance (standardised mean difference, 0.00; 95% CI, -0.27 to 0.27; I2 = 0%) in parallel control studies that administered sustained-release opioids for more than 1 week. The opioids used in most crossover studies were short-acting and rarely associated with serious adverse effects. Only minor side effects such as dizziness, nausea, constipation and vomiting were identified for short-acting opioids. CONCLUSIONS Sustained-release opioids did not improve dyspnoea and exercise endurance. Short-acting opioids appeared to be safe, have potential to lessen dyspnoea and improve exercise endurance, supporting benefit in managing episodes of breathlessness and providing prophylactic treatment for exertional dyspnoea.
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Affiliation(s)
- Meilu Liu
- Division of Pulmonary Medicine, Department of Internal Medicine, Institute of Integrated Traditional Chinese and Western Medicine, Sichuan University, Chengdu, Sichuan, China
| | - Wei Xiao
- Division of Pulmonary Medicine, Department of Internal Medicine, Institute of Integrated Traditional Chinese and Western Medicine, Sichuan University, Chengdu, Sichuan, China
| | - Longyi Du
- Division of Pulmonary Medicine, Department of Internal Medicine, Institute of Integrated Traditional Chinese and Western Medicine, Sichuan University, Chengdu, Sichuan, China
| | - Yan Yu
- Division of Pulmonary Medicine, Department of Internal Medicine, Institute of Integrated Traditional Chinese and Western Medicine, Sichuan University, Chengdu, Sichuan, China
| | - Xugui Chen
- Division of Pulmonary Medicine, Department of Internal Medicine, Institute of Integrated Traditional Chinese and Western Medicine, Sichuan University, Chengdu, Sichuan, China
| | - Bing Mao
- Division of Pulmonary Medicine, Department of Internal Medicine, Institute of Integrated Traditional Chinese and Western Medicine, Sichuan University, Chengdu, Sichuan, China
| | - Juanjuan Fu
- Division of Pulmonary Medicine, Department of Internal Medicine, Institute of Integrated Traditional Chinese and Western Medicine, Sichuan University, Chengdu, Sichuan, China
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20
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Liu J, Wu P, Lai S, Wang J, Hou H, Zhang Y. Prognostic models for upper urinary tract urothelial carcinoma patients after radical nephroureterectomy based on a novel systemic immune-inflammation score with machine learning. BMC Cancer 2023; 23:574. [PMID: 37349696 DOI: 10.1186/s12885-023-11058-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Accepted: 06/11/2023] [Indexed: 06/24/2023] Open
Abstract
PURPOSE This study aimed to evaluate the clinical significance of a novel systemic immune-inflammation score (SIIS) to predict oncological outcomes in upper urinary tract urothelial carcinoma(UTUC) after radical nephroureterectomy(RNU). METHOD The clinical data of 483 patients with nonmetastatic UTUC underwent surgery in our center were analyzed. Five inflammation-related biomarkers were screened in the Lasso-Cox model and then aggregated to generate the SIIS based on the regression coefficients. Overall survival (OS) was assessed using Kaplan-Meier analyses. The Cox proportional hazards regression and random survival forest model were adopted to build the prognostic model. Then we established an effective nomogram for UTUC after RNU based on SIIS. The discrimination and calibration of the nomogram were evaluated using the concordance index (C-index), area under the time-dependent receiver operating characteristic curve (time-dependent AUC), and calibration curves. Decision curve analysis (DCA) was used to assess the net benefits of the nomogram at different threshold probabilities. RESULT According to the median value SIIS computed by the lasso Cox model, the high-risk group had worse OS (p<0.0001) than low risk-group. Variables with a minimum depth greater than the depth threshold or negative variable importance were excluded, and the remaining six variables were included in the model. The area under the ROC curve (AUROC) of the Cox and random survival forest models were 0.801 and 0.872 for OS at five years, respectively. Multivariate Cox analysis showed that elevated SIIS was significantly associated with poorer OS (p<0.001). In terms of predicting overall survival, a nomogram that considered the SIIS and clinical prognostic factors performed better than the AJCC staging. CONCLUSION The pretreatment levels of SIIS were an independent predictor of prognosis in upper urinary tract urothelial carcinoma after RNU. Therefore, incorporating SIIS into currently available clinical parameters helps predict the long-term survival of UTUC.
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Affiliation(s)
- Jianyong Liu
- Department of Urology, Beijing Hospital, National Center of Gerontology, Institute of the Geriatric Medicine, Chinese Academy of Medical Sciences, No. 1 DaHua Road, Dong Dan, Beijing, China
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
- Beijing Hospital Continence Center, Beijing, China
| | - Pengjie Wu
- Department of Urology, Beijing Hospital, National Center of Gerontology, Institute of the Geriatric Medicine, Chinese Academy of Medical Sciences, No. 1 DaHua Road, Dong Dan, Beijing, China
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
- Beijing Hospital Continence Center, Beijing, China
| | - Shicong Lai
- Department of Urology, Peking University People's Hospital, 100044, Beijing, China
| | - Jianye Wang
- Department of Urology, Beijing Hospital, National Center of Gerontology, Institute of the Geriatric Medicine, Chinese Academy of Medical Sciences, No. 1 DaHua Road, Dong Dan, Beijing, China.
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
- Beijing Hospital Continence Center, Beijing, China.
| | - Huimin Hou
- Department of Urology, Beijing Hospital, National Center of Gerontology, Institute of the Geriatric Medicine, Chinese Academy of Medical Sciences, No. 1 DaHua Road, Dong Dan, Beijing, China.
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
- Beijing Hospital Continence Center, Beijing, China.
| | - Yaoguang Zhang
- Department of Urology, Beijing Hospital, National Center of Gerontology, Institute of the Geriatric Medicine, Chinese Academy of Medical Sciences, No. 1 DaHua Road, Dong Dan, Beijing, China.
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
- Beijing Hospital Continence Center, Beijing, China.
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21
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Long H, Li S, Chen Y. Digital health in chronic obstructive pulmonary disease. Chronic Dis Transl Med 2023; 9:90-103. [PMID: 37305103 PMCID: PMC10249197 DOI: 10.1002/cdt3.68] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 02/11/2023] [Accepted: 04/03/2023] [Indexed: 06/13/2023] Open
Abstract
Chronic obstructive pulmonary disease (COPD) can be prevented and treated through effective care, reducing exacerbations and hospitalizations. Early identification of individuals at high risk of COPD exacerbation is an opportunity for preventive measures. However, many patients struggle to follow their treatment plans because of a lack of knowledge about the disease, limited access to resources, and insufficient clinical support. The growth of digital health-which encompasses advancements in health information technology, artificial intelligence, telehealth, the Internet of Things, mobile health, wearable technology, and digital therapeutics-offers opportunities for improving the early diagnosis and management of COPD. This study reviewed the field of digital health in terms of COPD. The findings showed that despite significant advances in digital health, there are still obstacles impeding its effectiveness. Finally, we highlighted some of the major challenges and possibilities for developing and integrating digital health in COPD management.
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Affiliation(s)
- Huanyu Long
- Department of Pulmonary and Critical Care MedicinePeking University Third HospitalBeijingChina
| | - Shurun Li
- Peking University Health Science CenterBeijingChina
| | - Yahong Chen
- Department of Pulmonary and Critical Care MedicinePeking University Third HospitalBeijingChina
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22
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van der Molen MC, Slebos DJ, Augustijn SWS, Kerstjens HAM, Hartman JE. The minimal important difference of the constant work rate cycle test in severe COPD. Respir Med 2023; 215:107265. [PMID: 37224889 DOI: 10.1016/j.rmed.2023.107265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 04/21/2023] [Accepted: 04/30/2023] [Indexed: 05/26/2023]
Abstract
BACKGROUND The Constant Work Rate Cycle Test (CWRT) is a commonly used and sensitive test to detect treatment success in patients with Chronic Obstructive Pulmonary Disease (COPD). Earlier, the Minimal Important Difference (MID) of the CWRT was estimated at 101 s (or 34%) change from baseline based on one well executed study. However, this study was performed in a population of patients with mild-to-moderate COPD, and we have learned that MIDs might be quite different in patients with severe COPD. Therefore, we aimed to establish the MID of the CWRT in patients with severe COPD. METHODS We included 141 patients with severe COPD, who underwent either pulmonary rehabilitation, bronchoscopic lung volume reduction with endobronchial valves, or a sham bronchoscopy as a control group. CWRT workload was set at 75% of the peak work capacity, as determined by an incremental cycle test. We used the change in 6-min walking test (6-MWT), forced expiratory volume in 1s (FEV1), residual volume (RV), and St. George's Respiratory Questionnaire (SGRQ) total score as anchors to calculate the MID. RESULTS All anchors had an association of ≥0.41 with change in CWRT. The MID estimates for the different anchors were: 6-MWT 278 s (95%), FEV1 273 s (90%), RV 240 s (84%), and SGRQ 208 s (71%). The average of these four MID estimates resulted in an MID of 250 s (or 85%). CONCLUSION We established the MID for CWRT at 250 s (or 85%) change from baseline in patients with severe COPD.
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Affiliation(s)
- Marieke C van der Molen
- University of Groningen, University Medical Center Groningen, Department of Pulmonary Diseases, Groningen, the Netherlands; University of Groningen, University Medical Center Groningen, Groningen Research Institute for Asthma and COPD, Groningen, the Netherlands.
| | - Dirk-Jan Slebos
- University of Groningen, University Medical Center Groningen, Department of Pulmonary Diseases, Groningen, the Netherlands; University of Groningen, University Medical Center Groningen, Groningen Research Institute for Asthma and COPD, Groningen, the Netherlands
| | - Sonja W S Augustijn
- University of Groningen, University Medical Center Groningen, Department of Pulmonary Diseases, Groningen, the Netherlands
| | - Huib A M Kerstjens
- University of Groningen, University Medical Center Groningen, Department of Pulmonary Diseases, Groningen, the Netherlands; University of Groningen, University Medical Center Groningen, Groningen Research Institute for Asthma and COPD, Groningen, the Netherlands
| | - Jorine E Hartman
- University of Groningen, University Medical Center Groningen, Department of Pulmonary Diseases, Groningen, the Netherlands; University of Groningen, University Medical Center Groningen, Groningen Research Institute for Asthma and COPD, Groningen, the Netherlands
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23
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Andonov DI, Ulm B, Graessner M, Podtschaske A, Blobner M, Jungwirth B, Kagerbauer SM. Impact of the Covid-19 pandemic on the performance of machine learning algorithms for predicting perioperative mortality. BMC Med Inform Decis Mak 2023; 23:67. [PMID: 37046259 PMCID: PMC10092913 DOI: 10.1186/s12911-023-02151-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 03/15/2023] [Indexed: 04/14/2023] Open
Abstract
BACKGROUND Machine-learning models are susceptible to external influences which can result in performance deterioration. The aim of our study was to elucidate the impact of a sudden shift in covariates, like the one caused by the Covid-19 pandemic, on model performance. METHODS After ethical approval and registration in Clinical Trials (NCT04092933, initial release 17/09/2019), we developed different models for the prediction of perioperative mortality based on preoperative data: one for the pre-pandemic data period until March 2020, one including data before the pandemic and from the first wave until May 2020, and one that covers the complete period before and during the pandemic until October 2021. We applied XGBoost as well as a Deep Learning neural network (DL). Performance metrics of each model during the different pandemic phases were determined, and XGBoost models were analysed for changes in feature importance. RESULTS XGBoost and DL provided similar performance on the pre-pandemic data with respect to area under receiver operating characteristic (AUROC, 0.951 vs. 0.942) and area under precision-recall curve (AUPR, 0.144 vs. 0.187). Validation in patient cohorts of the different pandemic waves showed high fluctuations in performance from both AUROC and AUPR for DL, whereas the XGBoost models seemed more stable. Change in variable frequencies with onset of the pandemic were visible in age, ASA score, and the higher proportion of emergency operations, among others. Age consistently showed the highest information gain. Models based on pre-pandemic data performed worse during the first pandemic wave (AUROC 0.914 for XGBoost and DL) whereas models augmented with data from the first wave lacked performance after the first wave (AUROC 0.907 for XGBoost and 0.747 for DL). The deterioration was also visible in AUPR, which worsened by over 50% in both XGBoost and DL in the first phase after re-training. CONCLUSIONS A sudden shift in data impacts model performance. Re-training the model with updated data may cause degradation in predictive accuracy if the changes are only transient. Too early re-training should therefore be avoided, and close model surveillance is necessary.
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Affiliation(s)
- D I Andonov
- Department of Anaesthesiology and Intensive Care Medicine, School of Medicine, Technical University of Munich, Munich, Germany
| | - B Ulm
- Department of Anaesthesiology and Intensive Care Medicine, School of Medicine, Technical University of Munich, Munich, Germany
- Department of Anaesthesiology and Intensive Care Medicine, School of Medicine, University Hospital Ulm, University of Ulm, Albert-Einstein-Allee 23, Ulm, 89081, Germany
| | - M Graessner
- Department of Anaesthesiology and Intensive Care Medicine, School of Medicine, University Hospital Ulm, University of Ulm, Albert-Einstein-Allee 23, Ulm, 89081, Germany
| | - A Podtschaske
- Department of Anaesthesiology and Intensive Care Medicine, School of Medicine, Technical University of Munich, Munich, Germany
| | - M Blobner
- Department of Anaesthesiology and Intensive Care Medicine, School of Medicine, Technical University of Munich, Munich, Germany
- Department of Anaesthesiology and Intensive Care Medicine, School of Medicine, University Hospital Ulm, University of Ulm, Albert-Einstein-Allee 23, Ulm, 89081, Germany
| | - B Jungwirth
- Department of Anaesthesiology and Intensive Care Medicine, School of Medicine, University Hospital Ulm, University of Ulm, Albert-Einstein-Allee 23, Ulm, 89081, Germany
| | - S M Kagerbauer
- Department of Anaesthesiology and Intensive Care Medicine, School of Medicine, Technical University of Munich, Munich, Germany.
- Department of Anaesthesiology and Intensive Care Medicine, School of Medicine, University Hospital Ulm, University of Ulm, Albert-Einstein-Allee 23, Ulm, 89081, Germany.
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24
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Zhao Y, Cao LY, Zhao YX, Wang F, Xie LL, Xing HY, Wang Q. Medical record data-enabled machine learning can enhance prediction of left atrial appendage thrombosis in nonvalvular atrial fibrillation. Thromb Res 2023; 223:174-183. [PMID: 36764084 DOI: 10.1016/j.thromres.2023.01.001] [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: 10/11/2022] [Revised: 11/16/2022] [Accepted: 01/03/2023] [Indexed: 01/09/2023]
Abstract
BACKGROUND As a major complication of non-valvular atrial fibrillation (NVAF), left atrial appendage (LAA) thrombosis is associated with cerebral ischemic strokes, as well as high morbidity. Due to insufficient incorporation of risk factors, most current scoring methods are limited to the analysis of relationships between clinical characteristics and LAA thrombosis rather than detecting potential risk. Therefore, this study proposes a clinical data-driven machine learning method to predict LAA thrombosis of NVAF. METHODS Patients with NVAF from January 2014 to June 2022 were enrolled from Southwest Hospital. We selected 40 variables for analysis, including demographic data, medical history records, laboratory results, and the structure of LAA. Three machine learning algorithms were adopted to construct classifiers for the prediction of LAA thrombosis risk. The most important variables related to LAA thrombosis and their influences were recognized by SHapley Addictive exPlanations method. In addition, we compared our model with CHADS2 and CHADS2-VASc scoring methods. RESULTS A total of 713 participants were recruited, including 127 patients with LAA thrombosis and 586 patients with no obvious thrombosis. The consensus models based on Random Forest and eXtreme Gradient Boosting LAA thrombosis prediction (RXTP) achieved the best accuracy of 0.865, significantly outperforming CHADS2 score and CHA2DS2-VASc score (0.757 and 0.754, respectively). The SHAP results showed that B-type natriuretic peptide, left atrial appendage width, C-reactive protein, Fibrinogen and estimated glomerular filtration rate are closely related to the risk of LAA thrombosis in nonvalvular atrial fibrillation. CONCLUSIONS The RXTP-NVAF model is the most effective model with the greatest ROC value and recall rate. The summarized risk factors obtained from SHAP enable the optimization of the treatment strategy, thereby preventing thromboembolism events and the occurrence of cardiogenic ischemic stroke.
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Affiliation(s)
- Yue Zhao
- Department of Pharmacy, the First Affiliated Hospital of Army Medical University (Third Military Medical University),Chongqing, China
| | - Li-Ya Cao
- Department of Pharmacy, the First Affiliated Hospital of Army Medical University (Third Military Medical University),Chongqing, China
| | - Ying-Xin Zhao
- Department of Pharmacy, Army Medical Center, Army Medical University (Third Military Medical University),Chongqing, China
| | - Fei Wang
- Medical Big Data and Artificial Intelligence Center, the First Affiliated Hospital of Army Medical University (Third Military Medical University), Chongqing, China
| | - Lin-Li Xie
- Department of Pharmacy, the First Affiliated Hospital of Army Medical University (Third Military Medical University),Chongqing, China
| | - Hai-Yan Xing
- Department of Pharmacy, Army Medical Center, Army Medical University (Third Military Medical University),Chongqing, China.
| | - Qian Wang
- Department of Pharmacy, the First Affiliated Hospital of Army Medical University (Third Military Medical University),Chongqing, China.
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25
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Balasubramanian A, Putcha N, MacIntyre NR, Jensen RL, Kinney G, Stringer WW, Hersh CP, Bowler RP, Casaburi R, Han MK, Porszasz J, Barr RG, Regan E, Make BJ, Hansel NN, Wise RA, McCormack MC. Diffusing Capacity and Mortality in Chronic Obstructive Pulmonary Disease. Ann Am Thorac Soc 2023; 20:38-46. [PMID: 35969416 PMCID: PMC9819262 DOI: 10.1513/annalsats.202203-226oc] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Accepted: 08/15/2022] [Indexed: 02/05/2023] Open
Abstract
Rationale: Chronic obstructive pulmonary disease (COPD) mortality risk is often estimated using the BODE (body mass index, obstruction, dyspnea, exercise capacity) index, including body mass index, forced expiratory volume in 1 second, dyspnea score, and 6-minute walk distance. Diffusing capacity of the lung for carbon monoxide (DlCO) is a potential predictor of mortality that reflects physiology distinct from that in the BODE index. Objectives: This study evaluated DlCO as a predictor of mortality using participants from the COPDGene study. Methods: We performed time-to-event analyses of individuals with COPD (former or current smokers with forced expiratory volume in 1 second/forced vital capacity < 0.7) and DlCO measurements from the COPDGene phase 2 visit. Cox proportional hazard methods were used to model survival, adjusting for age, sex, pack-years, smoking status, BODE index, computed tomography (CT) percent emphysema (low attenuation areas below -950 Hounsfield units), CT airway wall thickness, and history of cardiovascular or kidney diseases. C statistics for models with DlCO and BODE scores were used to compare discriminative accuracy. Results: Of 2,329 participants, 393 (16.8%) died during the follow-up period (median = 4.9 yr). In adjusted analyses, for every 10% decrease in DlCO percent predicted, mortality increased by 28% (hazard ratio = 1.28; 95% confidence interval, 1.17-1.41, P < 0.001). When compared with other clinical predictors, DlCO percent predicted performed similarly to BODE (C statistic DlCO = 0.68; BODE = 0.70), and the addition of DlCO to BODE improved its discriminative accuracy (C statistic = 0.71). Conclusions: Diffusing capacity, a measure of gas transfer, strongly predicted all-cause mortality in individuals with COPD, independent of BODE index and CT evidence of emphysema and airway wall thickness. These findings support inclusion of DlCO in prognostic models for COPD.
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Affiliation(s)
- Aparna Balasubramanian
- Division of Pulmonary & Critical Care Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Nirupama Putcha
- Division of Pulmonary & Critical Care Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Neil R MacIntyre
- Division of Pulmonary & Critical Care Medicine, Duke University, Durham, North Carolina
| | - Robert L Jensen
- Division of Pulmonary & Critical Care Medicine, University of Utah, Salt Lake City, Utah
| | - Gregory Kinney
- Department of Epidemiology, Colorado School of Public Health, University of Colorado, Denver, Colorado
| | - William W Stringer
- Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, California
| | - Craig P Hersh
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | | | - Richard Casaburi
- Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, California
| | - MeiLan K Han
- Division of Pulmonary & Critical Care Medicine, University of Michigan, Ann Arbor, Michigan; and
| | - Janos Porszasz
- Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, California
| | - R Graham Barr
- Department of Epidemiology, Columbia University, New York, New York
| | - Elizabeth Regan
- Department of Epidemiology, Colorado School of Public Health, University of Colorado, Denver, Colorado
- Division of Rheumatology, National Jewish Health, National Jewish Health, Denver, Colorado
| | - Barry J Make
- Department of Epidemiology, Colorado School of Public Health, University of Colorado, Denver, Colorado
| | - Nadia N Hansel
- Division of Pulmonary & Critical Care Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Robert A Wise
- Division of Pulmonary & Critical Care Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Meredith C McCormack
- Division of Pulmonary & Critical Care Medicine, Johns Hopkins University, Baltimore, Maryland
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Zhang S, Li X, Ma H, Zhu M, Zhou Y, Zhang Q, Peng H. Relationship between Antithrombin III Activity and Mortality in Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease. COPD 2022; 19:353-364. [PMID: 36469629 DOI: 10.1080/15412555.2022.2106200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
We aimed to explore the role of antithrombin III (AT-III) activity in diagnosing patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) and chronic bronchitis, and its relationship with all-cause mortality of AECOPD patients. We performed univariate and multivariate Cox regression analyses of the factors determining all-cause mortality. We recruited 279 patients with AECOPD and 91 with chronic bronchitis. On admission, patients with AECOPD had lower AT-III activity (80.7 vs. 86.35%, p = 0.002) and higher neutrophil percentages (70.12 vs. 66.40%, p = 0.02) than those with chronic bronchitis. The patients who died were older (78 vs. 73 years, p < 0.001); had higher CRP (39.05 vs. 5.65 mg/L, p < 0.001), D-dimer (1.72 vs. 0.46 mg/L, p < 0.001), FIB (3.56 vs. 3.05 g/L, p = 0.01) levels; and exhibited lower AT-III activity (71.29 vs. 82.94%, p < 0.001) than the survivors. The AT-III area under the receiver operating characteristic curve for predicting COPD all-cause mortality was 0.75 (p < 0.001), optimal cutoff point 79.75%, sensitivity 86.8%, and specificity 57.1%. Multivariate Cox regression analyses showed that increased levels of CRP (HR = 1.005, p = 0.02), D-dimer (HR = 1.17, p = 0.01), WBC count (HR = 1.11, p = 0.002), and reduced AT-III activity (HR = 0.97, p = 0.02) were independent prognostic factors for all-cause mortality. Patients with AT-III ≤ 79.75% were 4.52 times (p = 0.001) more likely to die than those with AT-III > 79.75%. AT-III activity was lower in patients with AECOPD than in those with chronic bronchitis and is potentially useful as an independent predictor of all-cause mortality in patients with AECOPD: reduced AT-III activity and increased CRP and D-dimer levels indicate a higher risk of all-cause mortality.
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Affiliation(s)
- Shuling Zhang
- Department of Pulmonary and Critical Care Medicine, Liyuan Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Xiaoguang Li
- Department of Cardiology, Hubei No.3 People's Hospital of Jianghan University, Wuhan, People's Republic of China
| | - Haili Ma
- Department of Pulmonary and Critical Care Medicine, Liyuan Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Mengpei Zhu
- Department of Geriatrics Medicine, Liyuan Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Yuequan Zhou
- Department of Pulmonary and Critical Care Medicine, Liyuan Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Qianqian Zhang
- Department of Pulmonary and Critical Care Medicine, Liyuan Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Hongxing Peng
- Department of Pulmonary and Critical Care Medicine, Liyuan Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, People's Republic of China
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Zeng Z, Ke X, Gong S, Huang X, Liu Q, Huang X, Cheng J, Li Y, Wei L. Blood urea nitrogen to serum albumin ratio: a good predictor of in-hospital and 90-day all-cause mortality in patients with acute exacerbations of chronic obstructive pulmonary disease. BMC Pulm Med 2022; 22:476. [PMID: 36522751 PMCID: PMC9753245 DOI: 10.1186/s12890-022-02258-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 11/23/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Previous studies on acute exacerbation of chronic obstructive pulmonary disease (AECOPD) have found that those who died in hospital had higher blood urea nitrogen levels and a worse nutritional status compared to survivors. However, the association between the blood urea nitrogen to serum albumin ratio (BUN/ALB ratio) and in-hospital and short-term prognosis in patients with AECOPD remains unclear. The aim of this study was to explore the usefulness of BUN/ALB ratio in AECOPD as an objective predictor for in-hospital and 90-day all-cause mortality. METHODS We recorded the laboratory and clinical data in patients with AECOPD on admission. By drawing the ROC curve for the patients, we obtained the cut-off point for the BUN/ALB ratio for in-hospital death. Multivariate logistic regression was used for analyses of the factors of in-hospital mortality and multivariate Cox regression was used to analyze the factors of 90-day all-cause mortality. RESULTS A total of 362 patients were recruited and 319 patients were finally analyzed. Twenty-three patients died during hospitalization and the fatality rate was 7.2%. Furthermore, 14 patients died by the 90-day follow-up. Compared with in-hospital survivors, patients who died in hospital were older (80.78 ± 6.58 vs. 75.09 ± 9.73 years old, P = 0.001), had a higher prevalence of congestive heart failure(69.6% vs. 27.4%, P < 0.001), had a higher BUN/ALB ratio [0.329 (0.250-0.399) vs. 0.145 (0.111-0.210), P < 0.001], had higher neutrophil counts [10.27 (7.21-14.04) vs. 6.58 (4.58-9.04), P < 0.001], higher blood urea nitrogen levels [10.86 (7.10-12.25) vs. 5.35 (4.14-7.40), P < 0.001], a lower albumin level (32.58 ± 3.72 vs. 36.26 ± 4.53, P < 0.001) and a lower lymphocyte count [0.85 (0.58-1.21) vs. 1.22 (0.86-1.72), P = 0.001]. The ROC curve showed that the area under the curve (AUC) of BUN/ALB ratio for in-hospital death was 0.87, (95%CI 0.81-0.93, P < 0.001), the best cut-off point value to discriminate survivors from non-survivors in hospital was 0.249, the sensitivity was 78.3%, the specificity was 86.5%, and Youden's index was 0.648. Having a BUN/ALB ratio ≥ 0.249 was an independent risk factor for both in-hospital and 90-day all-cause mortality after adjustment for relative risk (RR; RR = 15.08, 95% CI 3.80-59.78, P < 0.001 for a multivariate logistic regression analysis) and hazard ratio (HR; HR = 5.34, 95% CI 1.62-17.57, P = 0.006 for a multivariate Cox regression analysis). CONCLUSION An elevated BUN/ALB ratio was a strong and independent predictor of in-hospital and 90-day all-cause mortality in patients with AECOPD.
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Affiliation(s)
- Zixiong Zeng
- grid.417009.b0000 0004 1758 4591Department of Respiratory Medicine, Guangdong Provincial Key Laboratory of Major Obstetric Diseases, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong China
| | - Xiaocui Ke
- grid.417009.b0000 0004 1758 4591Department of Respiratory Medicine, Guangdong Provincial Key Laboratory of Major Obstetric Diseases, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong China
| | - Shan Gong
- grid.417009.b0000 0004 1758 4591Department of Respiratory Medicine, Guangdong Provincial Key Laboratory of Major Obstetric Diseases, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong China
| | - Xin Huang
- grid.417009.b0000 0004 1758 4591Department of Respiratory Medicine, Guangdong Provincial Key Laboratory of Major Obstetric Diseases, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong China
| | - Qin Liu
- grid.417009.b0000 0004 1758 4591Department of Respiratory Medicine, Guangdong Provincial Key Laboratory of Major Obstetric Diseases, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong China
| | - Xiaoying Huang
- grid.417009.b0000 0004 1758 4591Department of Respiratory Medicine, Guangdong Provincial Key Laboratory of Major Obstetric Diseases, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong China
| | - Juan Cheng
- grid.417009.b0000 0004 1758 4591Department of Respiratory Medicine, Guangdong Provincial Key Laboratory of Major Obstetric Diseases, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong China
| | - Yuqun Li
- grid.417009.b0000 0004 1758 4591Department of Respiratory Medicine, Guangdong Provincial Key Laboratory of Major Obstetric Diseases, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong China
| | - Liping Wei
- grid.417009.b0000 0004 1758 4591Department of Respiratory Medicine, Guangdong Provincial Key Laboratory of Major Obstetric Diseases, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong China
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Machine Learning Model Development and Validation for Predicting Outcome in Stage 4 Solid Cancer Patients with Septic Shock Visiting the Emergency Department: A Multi-Center, Prospective Cohort Study. J Clin Med 2022; 11:jcm11237231. [PMID: 36498805 PMCID: PMC9737041 DOI: 10.3390/jcm11237231] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 11/12/2022] [Accepted: 11/30/2022] [Indexed: 12/12/2022] Open
Abstract
A reliable prognostic score for minimizing futile treatments in advanced cancer patients with septic shock is rare. A machine learning (ML) model to classify the risk of advanced cancer patients with septic shock is proposed and compared with the existing scoring systems. A multi-center, retrospective, observational study of the septic shock registry in patients with stage 4 cancer was divided into a training set and a test set in a 7:3 ratio. The primary outcome was 28-day mortality. The best ML model was determined using a stratified 10-fold cross-validation in the training set. A total of 897 patients were included, and the 28-day mortality was 26.4%. The best ML model in the training set was balanced random forest (BRF), with an area under the curve (AUC) of 0.821 to predict 28-day mortality. The AUC of the BRF to predict the 28-day mortality in the test set was 0.859. The AUC of the BRF was significantly higher than those of the Sequential Organ Failure Assessment score and the Acute Physiology and Chronic Health Evaluation II score (both p < 0.001). The ML model outperformed the existing scores for predicting 28-day mortality in stage 4 cancer patients with septic shock. However, further studies are needed to improve the prediction algorithm and to validate it in various countries. This model might support clinicians in real-time to adopt appropriate levels of care.
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Unsupervised Learning Identifies Computed Tomographic Measurements as Primary Drivers of Progression, Exacerbation, and Mortality in Chronic Obstructive Pulmonary Disease. Ann Am Thorac Soc 2022; 19:1993-2002. [PMID: 35830591 DOI: 10.1513/annalsats.202110-1127oc] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Rationale: Chronic obstructive pulmonary disease (COPD) is a heterogeneous syndrome with phenotypic manifestations that tend to be distributed along a continuum. Unsupervised machine learning based on broad selection of imaging and clinical phenotypes may be used to identify primary variables that define disease axes and stratify patients with COPD. Objectives: To identify primary variables driving COPD heterogeneity using principal component analysis and to define disease axes and assess the prognostic value of these axes across three outcomes: progression, exacerbation, and mortality. Methods: We included 7,331 patients between 39 and 85 years old, of whom 40.3% were Black and 45.8% were female smokers with a mean of 44.6 pack-years, from the COPDGene (Genetic Epidemiology of COPD) phase I cohort (2008-2011) in our analysis. Out of a total of 916 phenotypes, 147 continuous clinical, spirometric, and computed tomography (CT) features were selected. For each principal component (PC), we computed a PC score based on feature weights. We used PC score distributions to define disease axes along which we divided the patients into quartiles. To assess the prognostic value of these axes, we applied logistic regression analyses to estimate 5-year (n = 4,159) and 10-year (n = 1,487) odds of progression. Cox regression and Kaplan-Meier analyses were performed to estimate 5-year and 10-year risk of exacerbation (n = 6,532) and all-cause mortality (n = 7,331). Results: The first PC, accounting for 43.7% of variance, was defined by CT measures of air trapping and emphysema. The second PC, accounting for 13.7% of variance, was defined by spirometric and CT measures of vital capacity and lung volume. The third PC, accounting for 7.9% of the variance, was defined by CT measures of lung mass, airway thickening, and body habitus. Stratification of patients across each disease axis revealed up to 3.2-fold (95% confidence interval [CI] 2.4, 4.3) greater odds of 5-year progression, 5.4-fold (95% CI 4.6, 6.3) greater risk of 5-year exacerbation, and 5.0-fold (95% CI 4.2, 6.0) greater risk of 10-year mortality between the highest and lowest quartiles. Conclusions: Unsupervised learning analysis of the COPDGene cohort reveals that CT measurements may bolster patient stratification along the continuum of COPD phenotypes. Each of the disease axes also individually demonstrate prognostic potential, predictive of future forced expiratory volume in 1 second decline, exacerbation, and mortality.
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Cai D, Xiao T, Zou A, Mao L, Chi B, Wang Y, Wang Q, Ji Y, Sun L. Predicting acute kidney injury risk in acute myocardial infarction patients: An artificial intelligence model using medical information mart for intensive care databases. Front Cardiovasc Med 2022; 9:964894. [PMID: 36158815 PMCID: PMC9489917 DOI: 10.3389/fcvm.2022.964894] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 08/16/2022] [Indexed: 11/29/2022] Open
Abstract
Background Predictive models based on machine learning have been widely used in clinical practice. Patients with acute myocardial infarction (AMI) are prone to the risk of acute kidney injury (AKI), which results in a poor prognosis for the patient. The aim of this study was to develop a machine learning predictive model for the identification of AKI in AMI patients. Methods Patients with AMI who had been registered in the Medical Information Mart for Intensive Care (MIMIC) III and IV database were enrolled. The primary outcome was the occurrence of AKI during hospitalization. We developed Random Forests (RF) model, Naive Bayes (NB) model, Support Vector Machine (SVM) model, eXtreme Gradient Boosting (xGBoost) model, Decision Trees (DT) model, and Logistic Regression (LR) models with AMI patients in MIMIC-IV database. The importance ranking of all variables was obtained by the SHapley Additive exPlanations (SHAP) method. AMI patients in MIMIC-III databases were used for model evaluation. The area under the receiver operating characteristic curve (AUC) was used to compare the performance of each model. Results A total of 3,882 subjects with AMI were enrolled through screening of the MIMIC database, of which 1,098 patients (28.2%) developed AKI. We randomly assigned 70% of the patients in the MIMIC-IV data to the training cohort, which is used to develop models in the training cohort. The remaining 30% is allocated to the testing cohort. Meanwhile, MIMIC-III patient data performs the external validation function of the model. 3,882 patients and 37 predictors were included in the analysis for model construction. The top 5 predictors were serum creatinine, activated partial prothrombin time, blood glucose concentration, platelets, and atrial fibrillation, (SHAP values are 0.670, 0.444, 0.398, 0.389, and 0.381, respectively). In the testing cohort, using top 20 important features, the models of RF, NB, SVM, xGBoost, DT model, and LR obtained AUC of 0.733, 0.739, 0.687, 0.689, 0.663, and 0.677, respectively. Placing RF models of number of different variables on the external validation cohort yielded their AUC of 0.711, 0.754, 0.778, 0.781, and 0.777, respectively. Conclusion Machine learning algorithms, particularly the random forest algorithm, have improved the accuracy of risk stratification for AKI in AMI patients and are applied to accurately identify the risk of AKI in AMI patients.
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Affiliation(s)
- Dabei Cai
- Department of Cardiology, The Affiliated Changzhou No. 2 People’s Hospital of Nanjing Medical University, Changzhou, China
- Graduate School of Dalian Medical University, Dalian Medical University, Dalian, China
| | - Tingting Xiao
- Department of Cardiology, The Affiliated Changzhou No. 2 People’s Hospital of Nanjing Medical University, Changzhou, China
| | - Ailin Zou
- Department of Cardiology, The Affiliated Changzhou No. 2 People’s Hospital of Nanjing Medical University, Changzhou, China
| | - Lipeng Mao
- Department of Cardiology, The Affiliated Changzhou No. 2 People’s Hospital of Nanjing Medical University, Changzhou, China
- Graduate School of Dalian Medical University, Dalian Medical University, Dalian, China
| | - Boyu Chi
- Department of Cardiology, The Affiliated Changzhou No. 2 People’s Hospital of Nanjing Medical University, Changzhou, China
- Graduate School of Dalian Medical University, Dalian Medical University, Dalian, China
| | - Yu Wang
- Department of Cardiology, The Affiliated Changzhou No. 2 People’s Hospital of Nanjing Medical University, Changzhou, China
| | - Qingjie Wang
- Department of Cardiology, The Affiliated Changzhou No. 2 People’s Hospital of Nanjing Medical University, Changzhou, China
- Graduate School of Dalian Medical University, Dalian Medical University, Dalian, China
- *Correspondence: Qingjie Wang,
| | - Yuan Ji
- Department of Cardiology, The Affiliated Changzhou No. 2 People’s Hospital of Nanjing Medical University, Changzhou, China
- Yuan Ji,
| | - Ling Sun
- Department of Cardiology, The Affiliated Changzhou No. 2 People’s Hospital of Nanjing Medical University, Changzhou, China
- Graduate School of Dalian Medical University, Dalian Medical University, Dalian, China
- Ling Sun,
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Gregory A, Xu Z, Pratte K, Lee S, Liu C, Chase R, Yun J, Saferali A, Hersh CP, Bowler R, Silverman E, Castaldi PJ, Boueiz A. Clustering-based COPD subtypes have distinct longitudinal outcomes and multi-omics biomarkers. BMJ Open Respir Res 2022; 9:9/1/e001182. [PMID: 35999035 PMCID: PMC9403129 DOI: 10.1136/bmjresp-2021-001182] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 07/31/2022] [Indexed: 11/25/2022] Open
Abstract
Introduction Chronic obstructive pulmonary disease (COPD) can progress across several domains, complicating the identification of the determinants of disease progression. In our previous work, we applied k-means clustering to spirometric and chest radiological measures to identify four COPD-related subtypes: ‘relatively resistant smokers (RRS)’, ‘mild upper lobe-predominant emphysema (ULE)’, ‘airway-predominant disease (AD)’ and ‘severe emphysema (SE)’. In the current study, we examined the associations of these subtypes to longitudinal COPD-related health measures as well as blood transcriptomic and plasma proteomic biomarkers. Methods We included 8266 non-Hispanic white and African-American smokers from the COPDGene study. We used linear regression to investigate cluster associations to 5-year prospective changes in spirometric and radiological measures and to gene expression and protein levels. We used Cox-proportional hazard test to test for cluster associations to prospective exacerbations, comorbidities and mortality. Results The RRS, ULE, AD and SE clusters represented 39%, 15%, 26% and 20% of the studied cohort at baseline, respectively. The SE cluster had the greatest 5-year FEV1 (forced expiratory volume in 1 s) and emphysema progression, and the highest risks of exacerbations, cardiovascular disease and mortality. The AD cluster had the highest diabetes risk. After adjustments, only the SE cluster had an elevated respiratory mortality risk, while the ULE, AD and SE clusters had elevated all-cause mortality risks. These clusters also demonstrated differential protein and gene expression biomarker associations, mostly related to inflammatory and immune processes. Conclusion COPD k-means subtypes demonstrate varying rates of disease progression, prospective comorbidities, mortality and associations to transcriptomic and proteomic biomarkers. These findings emphasise the clinical and biological relevance of these subtypes, which call for more study for translation into clinical practice. Trail registration number NCT00608764.
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Affiliation(s)
- Andrew Gregory
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Zhonghui Xu
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Katherine Pratte
- Department of Biostatistics, National Jewish Health, Denver, Colorado, USA
| | - Sool Lee
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Congjian Liu
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Robert Chase
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Jeong Yun
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA.,Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Aabida Saferali
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Craig P Hersh
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA.,Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Russell Bowler
- Division of Pulmonary, Critical Care and Sleep Medicine, National Jewish Health, Denver, Colorado, USA
| | - Edwin Silverman
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA.,Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Peter J Castaldi
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA.,General Medicine and Primary Care, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Adel Boueiz
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA .,Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
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Dritsas E, Trigka M. Machine Learning Methods for Hypercholesterolemia Long-Term Risk Prediction. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22145365. [PMID: 35891045 PMCID: PMC9322993 DOI: 10.3390/s22145365] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 07/12/2022] [Accepted: 07/16/2022] [Indexed: 06/12/2023]
Abstract
Cholesterol is a waxy substance found in blood lipids. Its role in the human body is helpful in the process of producing new cells as long as it is at a healthy level. When cholesterol exceeds the permissible limits, it works the opposite, causing serious heart health problems. When a person has high cholesterol (hypercholesterolemia), the blood vessels are blocked by fats, and thus, circulation through the arteries becomes difficult. The heart does not receive the oxygen it needs, and the risk of heart attack increases. Nowadays, machine learning (ML) has gained special interest from physicians, medical centers and healthcare providers due to its key capabilities in health-related issues, such as risk prediction, prognosis, treatment and management of various conditions. In this article, a supervised ML methodology is outlined whose main objective is to create risk prediction tools with high efficiency for hypercholesterolemia occurrence. Specifically, a data understanding analysis is conducted to explore the features association and importance to hypercholesterolemia. These factors are utilized to train and test several ML models to find the most efficient for our purpose. For the evaluation of the ML models, precision, recall, accuracy, F-measure, and AUC metrics have been taken into consideration. The derived results highlighted Soft Voting with Rotation and Random Forest trees as base models, which achieved better performance in comparison to the other models with an AUC of 94.5%, precision of 92%, recall of 91.8%, F-measure of 91.7% and an accuracy equal to 91.75%.
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Dritsas E, Trigka M. Data-Driven Machine-Learning Methods for Diabetes Risk Prediction. SENSORS 2022; 22:s22145304. [PMID: 35890983 PMCID: PMC9318204 DOI: 10.3390/s22145304] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 07/10/2022] [Accepted: 07/13/2022] [Indexed: 01/11/2023]
Abstract
Diabetes mellitus is a chronic condition characterized by a disturbance in the metabolism of carbohydrates, fats and proteins. The most characteristic disorder in all forms of diabetes is hyperglycemia, i.e., elevated blood sugar levels. The modern way of life has significantly increased the incidence of diabetes. Therefore, early diagnosis of the disease is a necessity. Machine Learning (ML) has gained great popularity among healthcare providers and physicians due to its high potential in developing efficient tools for risk prediction, prognosis, treatment and the management of various conditions. In this study, a supervised learning methodology is described that aims to create risk prediction tools with high efficiency for type 2 diabetes occurrence. A features analysis is conducted to evaluate their importance and explore their association with diabetes. These features are the most common symptoms that often develop slowly with diabetes, and they are utilized to train and test several ML models. Various ML models are evaluated in terms of the Precision, Recall, F-Measure, Accuracy and AUC metrics and compared under 10-fold cross-validation and data splitting. Both validation methods highlighted Random Forest and K-NN as the best performing models in comparison to the other models.
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Jo YS. Long-term outcome of chronic obstructive pulmonary disease: A review. Tuberc Respir Dis (Seoul) 2022; 85:289-301. [PMID: 35822318 PMCID: PMC9537656 DOI: 10.4046/trd.2022.0074] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 07/06/2022] [Indexed: 11/24/2022] Open
Abstract
Chronic obstructive pulmonary disease (COPD) is a chronic airway inflammation characterized by fixed airflow limitation and chronic respiratory symptoms, such as cough, sputum, and dyspnea. COPD is a progressive disease characterized by a decline in lung function. During the natural course of the disease, acute deterioration of symptoms leading to hospital visits can occur and influence further disease progression and subsequent exacerbation. Moreover, COPD is not only restricted to pulmonary manifestations but can present with other systemic diseases as comorbidities or systemic manifestations, including lung cancer, cardiovascular disease, pulmonary hypertension, sarcopenia, and metabolic abnormalities. These pulmonary and extrapulmonary conditions lead to the aggravation of dyspnea, physical inactivity, decreased exercise capacity, functional decline, reduced quality of life, and increased mortality. In addition, pneumonia, which is attributed to both COPD itself and an adverse effect of treatment (especially the use of inhaled and/or systemic steroids), can occur and lead to further deterioration in the prognosis of COPD. This review summarizes the long-term outcomes of patients with COPD. In addition, recent studies on the prediction of adverse outcomes are summarized in the last part of the review.
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Affiliation(s)
- Yong Suk Jo
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul St Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
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35
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Tang H, Jin Z, Deng J, She Y, Zhong Y, Sun W, Ren Y, Cao N, Chen C. Development and validation of a deep learning model to predict the survival of patients in ICU. J Am Med Inform Assoc 2022; 29:1567-1576. [PMID: 35751440 DOI: 10.1093/jamia/ocac098] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 05/23/2022] [Accepted: 06/06/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Patients in the intensive care unit (ICU) are often in critical condition and have a high mortality rate. Accurately predicting the survival probability of ICU patients is beneficial to timely care and prioritizing medical resources to improve the overall patient population survival. Models developed by deep learning (DL) algorithms show good performance on many models. However, few DL algorithms have been validated in the dimension of survival time or compared with traditional algorithms. METHODS Variables from the Early Warning Score, Sequential Organ Failure Assessment Score, Simplified Acute Physiology Score II, Acute Physiology and Chronic Health Evaluation (APACHE) II, and APACHE IV models were selected for model development. The Cox regression, random survival forest (RSF), and DL methods were used to develop prediction models for the survival probability of ICU patients. The prediction performance was independently evaluated in the MIMIC-III Clinical Database (MIMIC-III), the eICU Collaborative Research Database (eICU), and Shanghai Pulmonary Hospital Database (SPH). RESULTS Forty variables were collected in total for model development. 83 943 participants from 3 databases were included in the study. The New-DL model accurately stratified patients into different survival probability groups with a C-index of >0.7 in the MIMIC-III, eICU, and SPH, performing better than the other models. The calibration curves of the models at 3 and 10 days indicated that the prediction performance was good. A user-friendly interface was developed to enable the model's convenience. CONCLUSIONS Compared with traditional algorithms, DL algorithms are more accurate in predicting the survival probability during ICU hospitalization. This novel model can provide reliable, individualized survival probability prediction.
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Affiliation(s)
- Hai Tang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.,Shanghai Engineering Research Center of Lung Transplantation, Shanghai, China
| | - Zhuochen Jin
- College of Design and Innovation, Tongji University, Shanghai, China
| | - Jiajun Deng
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.,Shanghai Engineering Research Center of Lung Transplantation, Shanghai, China
| | - Yunlang She
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.,Shanghai Engineering Research Center of Lung Transplantation, Shanghai, China
| | - Yifan Zhong
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.,Shanghai Engineering Research Center of Lung Transplantation, Shanghai, China
| | - Weiyan Sun
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.,Shanghai Engineering Research Center of Lung Transplantation, Shanghai, China
| | - Yijiu Ren
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.,Shanghai Engineering Research Center of Lung Transplantation, Shanghai, China
| | - Nan Cao
- College of Design and Innovation, Tongji University, Shanghai, China
| | - Chang Chen
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.,Shanghai Engineering Research Center of Lung Transplantation, Shanghai, China
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Stroke Risk Prediction with Machine Learning Techniques. SENSORS 2022; 22:s22134670. [PMID: 35808172 PMCID: PMC9268898 DOI: 10.3390/s22134670] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 06/16/2022] [Accepted: 06/20/2022] [Indexed: 01/25/2023]
Abstract
A stroke is caused when blood flow to a part of the brain is stopped abruptly. Without the blood supply, the brain cells gradually die, and disability occurs depending on the area of the brain affected. Early recognition of symptoms can significantly carry valuable information for the prediction of stroke and promoting a healthy life. In this research work, with the aid of machine learning (ML), several models are developed and evaluated to design a robust framework for the long-term risk prediction of stroke occurrence. The main contribution of this study is a stacking method that achieves a high performance that is validated by various metrics, such as AUC, precision, recall, F-measure and accuracy. The experiment results showed that the stacking classification outperforms the other methods, with an AUC of 98.9%, F-measure, precision and recall of 97.4% and an accuracy of 98%.
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Olsson M, Currow DC, Ekström MP. Exploring the most important factors related to self-perceived health among older men in Sweden: a cross-sectional study using machine learning. BMJ Open 2022; 12:e061242. [PMID: 35728903 PMCID: PMC9214374 DOI: 10.1136/bmjopen-2022-061242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
OBJECTIVE To evaluate which factors are the most strongly related to self-perceived health among older men and describe the shape of the association between the related factors and self-perceived health using machine learning. DESIGN AND SETTING This is a cross-sectional study within the population-based VAScular and Chronic Obstructive Lung disease study (VASCOL) conducted in southern Sweden in 2019. PARTICIPANTS A total of 475 older men aged 73 years from the VASCOL dataset. MEASURES Self-perceived health was measured using the first item of the Short Form 12. An extreme gradient-boosting model was trained to classify self-perceived health as better (rated: excellent or very good) or worse (rated: fair or poor) using self-reported data on 19 prevalent physician-diagnosed health conditions, intensity of 9 symptoms and 9 demographic and lifestyle factors. Importance of factors was measured in SHapley Additive exPlanations absolute mean and higher scores correspond to greater importance. RESULTS The most important factors for classifying self-perceived health were: pain (0.629), sleep quality (0.595), breathlessness (0.549), fatigue (0.542) and depression (0.526). Health conditions ranked well below symptoms and lifestyle variables. Low levels of symptoms, good sleep quality, regular exercise, alcohol consumption and a body mass index between 22 and 28 were associated with better self-perceived health. CONCLUSIONS Symptoms are more strongly related to self-perceived health than health conditions, which suggests that the impacts of health conditions are mediated through symptoms, which could be important targets to improve self-perceived health. Machine learning offers a new way to assess composite constructs such as well-being or quality of life.
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Affiliation(s)
- Max Olsson
- Department of Clinical Sciences Lund, Respiratory Medicine and Allergology, Lunds University Faculty of Medicine, Lund, Sweden
| | - David C Currow
- Faculty of Science, Medicine and Health, University of Wollongong, Wollongong, New South Wales, Australia
| | - Magnus Per Ekström
- Department of Clinical Sciences Lund, Respiratory Medicine and Allergology, Lunds University Faculty of Medicine, Lund, Sweden
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Machine learning-based modeling of acute respiratory failure following emergency general surgery operations. PLoS One 2022; 17:e0267733. [PMID: 35482751 PMCID: PMC9049563 DOI: 10.1371/journal.pone.0267733] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 04/13/2022] [Indexed: 11/19/2022] Open
Abstract
Background Emergency general surgery (EGS) operations are associated with substantial risk of morbidity including postoperative respiratory failure (PRF). While existing risk models are not widely utilized and rely on traditional statistical methods, application of machine learning (ML) in prediction of PRF following EGS remains unexplored. Objective The present study aimed to develop ML-based prediction models for respiratory failure following EGS and compare their performance to traditional regression models using a nationally-representative cohort. Methods Non-elective hospitalizations for EGS (appendectomy, cholecystectomy, repair of perforated ulcer, large or small bowel resection, lysis of adhesions) were identified in the 2016–18 Nationwide Readmissions Database. Factors associated with PRF were identified using ML techniques and logistic regression. The performance of XGBoost and logistic regression was evaluated using the receiver operating characteristic curve and coefficient of determination (R2). The impact of PRF on mortality, length of stay (LOS) and hospitalization costs was secondarily assessed using generalized linear models. Results Of 1,003,703 hospitalizations, 8.8% developed PRF. The XGBoost model exhibited slightly superior discrimination compared to logistic regression (0.900, 95% CI 0.899–0.901 vs 0.894, 95% CI 0.862–0.896). Compared to logistic regression, XGBoost demonstrated excellent calibration across all risk levels (R2: 0.998 vs 0.962). Congestive heart failure, neurologic disorders, and coagulopathy were significantly associated with increased risk of PRF. After risk-adjustment, PRF was associated with 10-fold greater odds (95% confidence interval (CI) 9.8–11.1) of mortality and incremental increases in LOS by 3.1 days (95% CI 3.0–3.2) and $11,900 (95% CI 11,600–12,300) in costs. Conclusions Logistic regression and XGBoost perform similarly in overall classification of PRF risk. However, due to superior calibration at extremes of risk, ML-based models may prove more useful in the clinical setting, where probabilities rather than classifications are desired.
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Utility of Noncancerous Chest CT Features for Predicting Overall Survival and Noncancer Death in Patients With Stage I Lung Cancer Treated With Stereotactic Body Radiotherapy. AJR Am J Roentgenol 2022; 219:579-589. [PMID: 35416054 DOI: 10.2214/ajr.22.27484] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Background: Noncancerous imaging markers can be readily derived from pretreatment diagnostic and radiotherapy planning chest CT examinations. Objective: To explore the ability of noncancerous features on chest CT to predict overall survival (OS) and noncancer-related death in patients with stage I lung cancer treated with stereotactic body radiation therapy (SBRT). Methods: This retrospective study included 282 patients (168 female, 114 male; median age, 75 years) with stage I lung cancer treated with SBRT between January 2009 and June 2017. Pretreatment chest CT was used to quantify coronary artery calcium (CAC) score, pulmonary artery (PA)-to-aorta ratio, emphysema, and body composition in terms of the cross-sectional area and attenuation of skeletal muscle and subcutaneous adipose tissue at the T5, T8, and T10 vertebral levels. Associations of clinical and imaging features with OS were quantified using a multivariable Cox proportional hazards (PH) model. Penalized multivariable Cox PH models to predict OS were constructed using clinical features only and using both clinical and imaging features. Models' discriminatory ability was assessed by constructing time-varying ROC curves and computing AUC at prespecified times. Results: After a median OS of 60.8 months (95% CI 55.8-68.9), 148 (52.5%) patients died, including 83 (56.1%) with noncancer deaths. Higher CAC score (11-399: hazard ratio [HR] 1.83 [95% CI 1.15-2.91], P=.01; ≥400: HR 1.63 [95% CI 1.01-2.63], P=.04), higher PA-to-aorta ratio (HR 1.33 [95% CI 1.16-1.52], P<.001, per 0.1-unit increase), and lower thoracic skeletal muscle index (HR 0.88 [95% CI 0.79-0.98], P=.02, per 10 cm2/m2 increase) were independently associated with shorter OS. Discriminatory ability for 5-year OS was greater for the model including clinical and imaging features than for the model including clinical features only (AUC, 0.75 [95% CI 0.68-0.83] versus 0.61 [95% CI 0.53-0.70], p < .01). The model's most important clinical or imaging feature based on mean standardized regression coefficients was the PA-to-aorta ratio. Conclusions: In patients undergoing SBRT for stage I lung cancer, higher CAC score, higher PA-to-aorta ratio, and lower thoracic skeletal muscle index independently predicted worse OS. Clinical Impact: Noncancerous imaging features on chest CT performed before SBRT improve survival prediction compared with clinical features alone.
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Liu W, Zhang L, Xin Z, Zhang H, You L, Bai L, Zhou J, Ying B. A Promising Preoperative Prediction Model for Microvascular Invasion in Hepatocellular Carcinoma Based on an Extreme Gradient Boosting Algorithm. Front Oncol 2022; 12:852736. [PMID: 35311094 PMCID: PMC8931027 DOI: 10.3389/fonc.2022.852736] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 02/11/2022] [Indexed: 01/27/2023] Open
Abstract
BackgroundThe non-invasive preoperative diagnosis of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is vital for precise surgical decision-making and patient prognosis. Herein, we aimed to develop an MVI prediction model with valid performance and clinical interpretability.MethodsA total of 2160 patients with HCC without macroscopic invasion who underwent hepatectomy for the first time in West China Hospital from January 2015 to June 2019 were retrospectively included, and randomly divided into training and a validation cohort at a ratio of 8:2. Preoperative demographic features, imaging characteristics, and laboratory indexes of the patients were collected. Five machine learning algorithms were used: logistic regression, random forest, support vector machine, extreme gradient boosting (XGBoost), and multilayer perception. Performance was evaluated using the area under the receiver operating characteristic curve (AUC). We also determined the Shapley Additive exPlanation value to explain the influence of each feature on the MVI prediction model.ResultsThe top six important preoperative factors associated with MVI were the maximum image diameter, protein induced by vitamin K absence or antagonist-II, α-fetoprotein level, satellite nodules, alanine aminotransferase (AST)/aspartate aminotransferase (ALT) ratio, and AST level, according to the XGBoost model. The XGBoost model for preoperative prediction of MVI exhibited a better AUC (0.8, 95% confidence interval: 0.74–0.83) than the other prediction models. Furthermore, to facilitate use of the model in clinical settings, we developed a user-friendly online calculator for MVI risk prediction based on the XGBoost model.ConclusionsThe XGBoost model achieved outstanding performance for non-invasive preoperative prediction of MVI based on big data. Moreover, the MVI risk calculator would assist clinicians in conveniently determining the optimal therapeutic remedy and ameliorating the prognosis of patients with HCC.
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Affiliation(s)
- Weiwei Liu
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Lifan Zhang
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, China
| | - Zhaodan Xin
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Haili Zhang
- Department of Liver Surgery & Liver Transplantation Center, West China Hospital, Sichuan University, Chengdu, China
| | - Liting You
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Ling Bai
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Juan Zhou
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China
- *Correspondence: Juan Zhou, ; Binwu Ying,
| | - Binwu Ying
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China
- *Correspondence: Juan Zhou, ; Binwu Ying,
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Wang JM, Han MK, Labaki WW. Chronic obstructive pulmonary disease risk assessment tools: is one better than the others? Curr Opin Pulm Med 2022; 28:99-108. [PMID: 34652295 PMCID: PMC8799486 DOI: 10.1097/mcp.0000000000000833] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW Risk assessment tools are essential in COPD care to help clinicians identify patients at higher risk of accelerated lung function decline, respiratory exacerbations, hospitalizations, and death. RECENT FINDINGS Conventional methods of assessing risk have focused on spirometry, patient-reported symptoms, functional status, and a combination of these tools in composite indices. More recently, qualitatively and quantitatively assessed chest imaging findings, such as emphysema, large and small airways disease, and pulmonary vascular abnormalities have been associated with poor long-term outcomes in COPD patients. Although several blood and sputum biomarkers have been investigated for risk assessment in COPD, most still warrant further validation. Finally, novel remote digital monitoring technologies may be valuable to predict exacerbations but their large-scale performance, ease of implementation, and cost effectiveness remain to be determined. SUMMARY Given the complex heterogeneity of COPD, any single metric is unlikely to fully capture the risk of poor long-term outcomes. Therefore, clinicians should review all available clinical data, including spirometry, symptom severity, functional status, chest imaging, and bloodwork, to guide personalized preventive care of COPD patients. The potential of machine learning tools and remote monitoring technologies to refine COPD risk assessment is promising but remains largely untapped pending further investigation.
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Affiliation(s)
- Jennifer M Wang
- Division of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, Michigan, USA
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Choudhury S, Chohan A, Dadhwal R, Vakil AP, Franco R, Taweesedt PT. Applications of artificial intelligence in common pulmonary diseases. Artif Intell Med Imaging 2022; 3:1-7. [DOI: 10.35711/aimi.v3.i1.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 02/14/2022] [Accepted: 02/23/2022] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) is a branch of computer science where machines are trained to imitate human-level intelligence and perform well-defined tasks. AI can provide accurate results as well as analyze vast amounts of data that cannot be analyzed via conventional statistical methods. AI has been utilized in pulmonary medicine for almost two decades and its utilization continues to expand. AI can help in making diagnoses and predicting outcomes in pulmonary diseases based on clinical data, chest imaging, lung pathology, and pulmonary function testing. AI-based applications enable physicians to use enormous amounts of data and improve their precision in the treatment of pulmonary diseases. Given the growing role of AI in pulmonary medicine, it is important for practitioners caring for patients with pulmonary diseases to understand how AI can work in order to implement it into clinical practices and improve patient care. The goal of this mini-review is to discuss the use of AI in pulmonary medicine and imaging in cases of obstructive lung disease, interstitial lung disease, infections, nodules, and lung cancer.
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Affiliation(s)
- Saiara Choudhury
- Department of Pulmonary Medicine, Corpus Christi Medical Center, Corpus Christi, TX 78411, United States
| | - Asad Chohan
- Department of Pulmonary Medicine, Corpus Christi Medical Center, Corpus Christi, TX 78411, United States
| | - Rahul Dadhwal
- Department of Pulmonary Medicine, Corpus Christi Medical Center, Corpus Christi, TX 78411, United States
| | - Abhay P Vakil
- Department of Pulmonary Medicine, Corpus Christi Medical Center, Corpus Christi, TX 78411, United States
| | - Rene Franco
- Department of Pulmonary Medicine, Corpus Christi Medical Center, Corpus Christi, TX 78411, United States
| | - Pahnwat Tonya Taweesedt
- Department of Pulmonary Medicine, Corpus Christi Medical Center, Corpus Christi, TX 78411, United States
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Wu F, Zhou Y, Peng J, Deng Z, Wen X, Wang Z, Zheng Y, Tian H, Yang H, Huang P, Zhao N, Sun R, Chen R, Ran P. Rationale and design of the Early Chronic Obstructive Pulmonary Disease (ECOPD) study in Guangdong, China: a prospective observational cohort study. J Thorac Dis 2022; 13:6924-6935. [PMID: 35070376 PMCID: PMC8743397 DOI: 10.21037/jtd-21-1379] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 11/17/2021] [Indexed: 01/04/2023]
Abstract
Background Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease and its clinically relevant subtypes are not well understood. Which clinical characteristics are more likely to be present among individuals who develop COPD remains to be studied in depth. Therefore, we designed a prospective observational cohort study, entitled the Early Chronic Obstructive Pulmonary Disease (ECOPD) study, to fill this evidence gap. The ECOPD study has four specific aims: (I) identification of characteristics, parameters, and biomarkers that may predict the development of airflow obstruction and annual decline in lung function with normal spirometry; (II) identification of clinically relevant early COPD subtypes; (III) identification of characteristics, parameters, and biomarkers that may predict disease progression in these early COPD subtypes; (IV) development and validation of machine learning models to predict development of airflow obstruction and disease progression. Methods We will recruit approximately 2,000 participants aged 40–80 years, including approximately 1,000 with COPD [post-bronchodilator forced expiratory volume in 1 second (FEV1)/forced vital capacity (FVC) <0.7] and approximately 1,000 without COPD, using a population-based survey for COPD. We will assess all participants using standard respiratory epidemiological questionnaires, pulmonary function tests [pre-bronchodilator and post-bronchodilator spirometry, and impulse oscillometry (IOS)], health outcomes [modified British Medical Research Council (mMRC) dyspnea scale, COPD assessment test (CAT), COPD clinical questionnaire (CCQ)], inspiratory and expiratory chest computed tomography (CT), and biomarker measurements (blood and urine), as well as satellite remote sensing pollutant exposure measures. Subgroup will additionally complete induced sputum, exercise capacity tests [6-minute walk test (6MWT) and cardiopulmonary exercise testing (CPET)] and home monitoring/personal sampling as pollutant exposure measures. Study procedures will be performed at baseline and every 1 year thereafter. Discussion The ECOPD study will provide insight into many aspects of early COPD and improve our understanding of COPD development, which may facilitate therapeutic interventions with the potential to modify the course of disease. Trial Registration Chinese Clinical Trial Registry, ChiCTR1900024643. Registered on 19 July, 2019.
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Affiliation(s)
- Fan Wu
- National Center for Respiratory Medicine, 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, China.,Guangzhou Laboratory, Bio-Island, Guangzhou, China
| | - Yumin Zhou
- National Center for Respiratory Medicine, 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, China.,Guangzhou Laboratory, Bio-Island, Guangzhou, China
| | - Jieqi Peng
- National Center for Respiratory Medicine, 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, China
| | - Zhishan Deng
- National Center for Respiratory Medicine, 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, China
| | - Xiang Wen
- National Center for Respiratory Medicine, 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, China
| | - Zihui Wang
- National Center for Respiratory Medicine, 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, China
| | - Youlan Zheng
- National Center for Respiratory Medicine, 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, China
| | - Heshen Tian
- National Center for Respiratory Medicine, 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, China
| | - Huajing Yang
- National Center for Respiratory Medicine, 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, China
| | - Peiyu Huang
- National Center for Respiratory Medicine, 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, China
| | - Ningning Zhao
- National Center for Respiratory Medicine, 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, China
| | - Ruiting Sun
- National Center for Respiratory Medicine, 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, China
| | - Rongchang Chen
- Shenzhen Institute of Respiratory Disease, Shenzhen People's Hospital, Shenzhen, China
| | - Pixin Ran
- National Center for Respiratory Medicine, 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, China.,Guangzhou Laboratory, Bio-Island, Guangzhou, China
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Moll M, Boueiz A, Ghosh AJ, Saferali A, Lee S, Xu Z, Yun JH, Hobbs BD, Hersh CP, Sin DD, Tal-Singer R, Silverman EK, Cho MH, Castaldi PJ. Development of a Blood-based Transcriptional Risk Score for Chronic Obstructive Pulmonary Disease. Am J Respir Crit Care Med 2022; 205:161-170. [PMID: 34739356 PMCID: PMC8787248 DOI: 10.1164/rccm.202107-1584oc] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 11/03/2021] [Indexed: 01/17/2023] Open
Abstract
Rationale: The ability of peripheral blood biomarkers to assess chronic obstructive pulmonary disease (COPD) risk and progression is unknown. Genetics and gene expression may capture important aspects of COPD-related biology that predict disease activity. Objectives: Develop a transcriptional risk score (TRS) for COPD and assess the contribution of the TRS and a polygenic risk score (PRS) for disease susceptibility and progression. Methods: We randomly split 2,569 COPDGene (Genetic Epidemiology of COPD) participants with whole-blood RNA sequencing into training (n = 1,945) and testing (n = 624) samples and used 468 ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points) COPD cases with microarray data for replication. We developed a TRS using penalized regression (least absolute shrinkage and selection operator) to model FEV1/FVC and studied the predictive value of TRS for COPD (Global Initiative for Chronic Obstructive Lung Disease 2-4), prospective FEV1 change (ml/yr), and additional COPD-related traits. We adjusted for potential confounders, including age and smoking. We evaluated the predictive performance of the TRS in the context of a previously derived PRS and clinical factors. Measurements and Main Results: The TRS included 147 transcripts and was associated with COPD (odds ratio, 3.3; 95% confidence interval [CI], 2.4-4.5; P < 0.001), FEV1 change (β, -17 ml/yr; 95% CI, -28 to -6.6; P = 0.002), and other COPD-related traits. In ECLIPSE cases, we replicated the association with FEV1 change (β, -8.2; 95% CI, -15 to -1; P = 0.025) and the majority of other COPD-related traits. Models including PRS, TRS, and clinical factors were more predictive of COPD (area under the receiver operator characteristic curve, 0.84) and annualized FEV1 change compared with models with one risk score or clinical factors alone. Conclusions: Blood transcriptomics can improve prediction of COPD and lung function decline when added to a PRS and clinical risk factors.
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Affiliation(s)
- Matthew Moll
- Channing Division of Network Medicine
- Division of Pulmonary and Critical Care Medicine, and
| | - Adel Boueiz
- Channing Division of Network Medicine
- Division of Pulmonary and Critical Care Medicine, and
| | - Auyon J. Ghosh
- Channing Division of Network Medicine
- Division of Pulmonary and Critical Care Medicine, and
| | | | - Sool Lee
- Channing Division of Network Medicine
- Department of Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, North Carolina
| | | | - Jeong H. Yun
- Channing Division of Network Medicine
- Division of Pulmonary and Critical Care Medicine, and
| | - Brian D. Hobbs
- Channing Division of Network Medicine
- Division of Pulmonary and Critical Care Medicine, and
| | - Craig P. Hersh
- Channing Division of Network Medicine
- Division of Pulmonary and Critical Care Medicine, and
| | - Don D. Sin
- Centre for Heart Lung Innovation, St. Paul’s Hospital, Vancouver, British Columbia, Canada
- Respiratory Division, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada; and
| | | | - Edwin K. Silverman
- Channing Division of Network Medicine
- Division of Pulmonary and Critical Care Medicine, and
| | - Michael H. Cho
- Channing Division of Network Medicine
- Division of Pulmonary and Critical Care Medicine, and
| | - Peter J. Castaldi
- Channing Division of Network Medicine
- Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
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Tedesco S, Andrulli M, Larsson MÅ, Kelly D, Alamäki A, Timmons S, Barton J, Condell J, O’Flynn B, Nordström A. Comparison of Machine Learning Techniques for Mortality Prediction in a Prospective Cohort of Older Adults. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:12806. [PMID: 34886532 PMCID: PMC8657506 DOI: 10.3390/ijerph182312806] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 12/01/2021] [Accepted: 12/02/2021] [Indexed: 12/16/2022]
Abstract
As global demographics change, ageing is a global phenomenon which is increasingly of interest in our modern and rapidly changing society. Thus, the application of proper prognostic indices in clinical decisions regarding mortality prediction has assumed a significant importance for personalized risk management (i.e., identifying patients who are at high or low risk of death) and to help ensure effective healthcare services to patients. Consequently, prognostic modelling expressed as all-cause mortality prediction is an important step for effective patient management. Machine learning has the potential to transform prognostic modelling. In this paper, results on the development of machine learning models for all-cause mortality prediction in a cohort of healthy older adults are reported. The models are based on features covering anthropometric variables, physical and lab examinations, questionnaires, and lifestyles, as well as wearable data collected in free-living settings, obtained for the "Healthy Ageing Initiative" study conducted on 2291 recruited participants. Several machine learning techniques including feature engineering, feature selection, data augmentation and resampling were investigated for this purpose. A detailed empirical comparison of the impact of the different techniques is presented and discussed. The achieved performances were also compared with a standard epidemiological model. This investigation showed that, for the dataset under consideration, the best results were achieved with Random UnderSampling in conjunction with Random Forest (either with or without probability calibration). However, while including probability calibration slightly reduced the average performance, it increased the model robustness, as indicated by the lower 95% confidence intervals. The analysis showed that machine learning models could provide comparable results to standard epidemiological models while being completely data-driven and disease-agnostic, thus demonstrating the opportunity for building machine learning models on health records data for research and clinical practice. However, further testing is required to significantly improve the model performance and its robustness.
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Affiliation(s)
- Salvatore Tedesco
- Tyndall National Institute, University College Cork, Lee Maltings Complex, Dyke Parade, T12R5CP Cork, Ireland; (M.A.); (J.B.); (B.O.)
| | - Martina Andrulli
- Tyndall National Institute, University College Cork, Lee Maltings Complex, Dyke Parade, T12R5CP Cork, Ireland; (M.A.); (J.B.); (B.O.)
| | - Markus Åkerlund Larsson
- Department of Public Health and Clinical Medicine, Section of Sustainable Health, Umeå University, SE-901 87 Umeå, Sweden; (M.Å.L.); (A.N.)
| | - Daniel Kelly
- School of Computing, Engineering and Intelligent Systems, Ulster University, Londonderry BT48 7JL, UK; (D.K.); (J.C.)
| | - Antti Alamäki
- Department of Physiotherapy, Karelia University of Applied Sciences, Tikkarinne 9, FI-80200 Joensuu, Finland;
| | - Suzanne Timmons
- Centre for Gerontology and Rehabilitation, University College Cork, T12XH60 Cork, Ireland;
| | - John Barton
- Tyndall National Institute, University College Cork, Lee Maltings Complex, Dyke Parade, T12R5CP Cork, Ireland; (M.A.); (J.B.); (B.O.)
| | - Joan Condell
- School of Computing, Engineering and Intelligent Systems, Ulster University, Londonderry BT48 7JL, UK; (D.K.); (J.C.)
| | - Brendan O’Flynn
- Tyndall National Institute, University College Cork, Lee Maltings Complex, Dyke Parade, T12R5CP Cork, Ireland; (M.A.); (J.B.); (B.O.)
| | - Anna Nordström
- Department of Public Health and Clinical Medicine, Section of Sustainable Health, Umeå University, SE-901 87 Umeå, Sweden; (M.Å.L.); (A.N.)
- School of Sport Sciences, UiT the Arctic University of Norway, 9037 Tromsø, Norway
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De Ramón Fernández A, Ruiz Fernández D, Gilart Iglesias V, Marcos Jorquera D. Analyzing the use of artificial intelligence for the management of chronic obstructive pulmonary disease (COPD). Int J Med Inform 2021; 158:104640. [PMID: 34890934 DOI: 10.1016/j.ijmedinf.2021.104640] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 10/21/2021] [Accepted: 11/03/2021] [Indexed: 12/31/2022]
Abstract
OBJECTIVE Chronic obstructive pulmonary disease (COPD) is a disease that causes airflow limitation to the lungs and has a high morbidity around the world. The objective of this study was to evaluate how artificial intelligence (AI) is being applied for the management of the disease, analyzing the objectives that are raised, the algorithms that are used and what results they offer. METHODS We conducted a scoping review following the Arksey and O'Malley (2005) and Levac et al. (2010) guidelines. Two reviewers independently searched, analyzed and extracted data from papers of five databases: Web of Science, PubMed, Scopus, Cinahl and Cochrane. To be included, the studies had to apply some AI techniques for the management of at least one stage of the COPD clinical process. In the event of any discrepancy between both reviewers, the criterion of a third reviewer prevailed. RESULTS 380 papers were identified through database searches. After applying the exclusion criteria, 67 papers were included in the study. The studies were of a different nature and pursued a wide range of objectives, highlighting mainly those focused on the identification, classification and prevention of the disease. Neural nets, support vector machines and decision trees were the AI algorithms most commonly used. The mean and median values of all the performance metrics evaluated were between 80% and 90%. CONCLUSIONS The results obtained show a growing interest in the development of medical applications that manage the different phases of the COPD clinical process, especially predictive models. According to the performance shown, these models could be a useful complementary tool in the decision-making by health specialists, although more high-quality ML studies are needed to endorse the findings of this study.
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Deep radiomics-based survival prediction in patients with chronic obstructive pulmonary disease. Sci Rep 2021; 11:15144. [PMID: 34312450 PMCID: PMC8313653 DOI: 10.1038/s41598-021-94535-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 07/05/2021] [Indexed: 12/16/2022] Open
Abstract
Heterogeneous clinical manifestations and progression of chronic obstructive pulmonary disease (COPD) affect patient health risk assessment, stratification, and management. Pulmonary function tests are used to diagnose and classify the severity of COPD, but they cannot fully represent the type or range of pathophysiologic abnormalities of the disease. To evaluate whether deep radiomics from chest computed tomography (CT) images can predict mortality in patients with COPD, we designed a convolutional neural network (CNN) model for extracting representative features from CT images and then performed random survival forest to predict survival in COPD patients. We trained CNN-based binary classifier based on six-minute walk distance results (> 440 m or not) and extracted high-throughput image features (i.e., deep radiomics) directly from the last fully connected layer of it. The various sizes of fully connected layers and combinations of deep features were experimented using a discovery cohort with 344 patients from the Korean Obstructive Lung Disease cohort and an external validation cohort with 102 patients from Penang General Hospital in Malaysia. In the integrative analysis of discovery and external validation cohorts, with combining 256 deep features from the coronal slice of the vertebral body and two sagittal slices of the left/right lung, deep radiomics for survival prediction achieved concordance indices of 0.8008 (95% CI, 0.7642–0.8373) and 0.7156 (95% CI, 0.7024–0.7288), respectively. Deep radiomics from CT images could be used to predict mortality in COPD patients.
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Exarchos KP, Kostikas K. Artificial intelligence in COPD: Possible applications and future prospects. Respirology 2021; 26:641-642. [PMID: 33851496 DOI: 10.1111/resp.14061] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 03/29/2021] [Indexed: 11/27/2022]
Affiliation(s)
- Konstantinos P Exarchos
- Respiratory Medicine Department, University of Ioannina School of Medicine, Ioannina, Greece
| | - Konstantinos Kostikas
- Respiratory Medicine Department, University of Ioannina School of Medicine, Ioannina, Greece
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Feng Y, Wang Y, Zeng C, Mao H. Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease. Int J Med Sci 2021; 18:2871-2889. [PMID: 34220314 PMCID: PMC8241767 DOI: 10.7150/ijms.58191] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 05/20/2021] [Indexed: 02/05/2023] Open
Abstract
Chronic airway diseases are characterized by airway inflammation, obstruction, and remodeling and show high prevalence, especially in developing countries. Among them, asthma and chronic obstructive pulmonary disease (COPD) show the highest morbidity and socioeconomic burden worldwide. Although there are extensive guidelines for the prevention, early diagnosis, and rational treatment of these lifelong diseases, their value in precision medicine is very limited. Artificial intelligence (AI) and machine learning (ML) techniques have emerged as effective methods for mining and integrating large-scale, heterogeneous medical data for clinical practice, and several AI and ML methods have recently been applied to asthma and COPD. However, very few methods have significantly contributed to clinical practice. Here, we review four aspects of AI and ML implementation in asthma and COPD to summarize existing knowledge and indicate future steps required for the safe and effective application of AI and ML tools by clinicians.
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Affiliation(s)
- Yinhe Feng
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China.,Department of Respiratory and Critical Care Medicine, People's Hospital of Deyang City, Affiliated Hospital of Chengdu College of Medicine, Deyang, Sichuan Province, China
| | - Yubin Wang
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Chunfang Zeng
- Department of Respiratory and Critical Care Medicine, People's Hospital of Deyang City, Affiliated Hospital of Chengdu College of Medicine, Deyang, Sichuan Province, China
| | - Hui Mao
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
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Matheson AM, Parraga G. Machine Learning Predictions of COPD Mortality: Computational Hide and Seek. Chest 2020; 158:846-847. [PMID: 32892878 DOI: 10.1016/j.chest.2020.04.038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Accepted: 04/28/2020] [Indexed: 10/23/2022] Open
Affiliation(s)
- Alexander M Matheson
- Department of Medical Biophysics and Robarts Research Institute, Western University, London, Canada
| | - Grace Parraga
- Department of Medical Biophysics and Robarts Research Institute, Western University, London, Canada.
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