class: center, middle, inverse, title-slide .title[ # ECOPD Literature Review ] .author[ ### Lin Yu ] .date[ ### 2023-02-28 ] --- class: inverse, middle # The Workflow -- ## Step 1: Identify eligible papers - ### 1.1 Search database (Pubmed) - ### 1.2 Manually review (Title/Abstract) -- ## Step 2: Data extraction - ### 2.1 Create data extraction template - ### 2.2 Full text review -- ## Step 3: Summarize findings --- class: inverse, center, middle # __Step 1__ ## _Identify eligible papers_ --- class: inverse ### Pubmed: (COPD) AND (exacerbation) AND (prediction) -- - Search 1: **Pulmonary Disease, Chronic Obstructive[MeSH Term]**: - *Chronic Obstructive Lung Disease* - *Chronic Obstructive Pulmonary Diseases* - *Chronic Obstructive Airway Disease* - *Chronic Obstructive Pulmonary Disease * - *Airflow Obstruction(s), Chronic* - *Chronic Airflow Obstruction(s)* - *COAD/COPD* -- - Search 2:**Disease exacerbation[MeSH Term]**: - *Disease exacerbation* - *Progression, Disease* - *Clinical Course* - *Clinical Progression* - *Progression, Clinical* - *Disease Exacerbation* - *Exacerbation, Disease* -- - Search 3:**Prediction[Title/Abstract]**: *Forecast* (prediction model/risk predicition) --- class: center, middle,inverse # Search Query # ![](data:image/png;base64,#figure/search_key_words.PNG) # **~1400** items returned --- class: inverse ### PRISMA flow diagram of studies through the review process ![](data:image/png;base64,#figure/flowchart.png) --- class: inverse, center, middle # __Step 2__ ## _Data Extraction_ ---
--- class: inverse, center, middle # __Step 3__ ## _Summarize Findings_ --- ### 3.1 Source of data: medical records, questionnaires, app/devices, COPDGene ![](data:image/png;base64,#figure/vitalsign0.PNG) ![](data:image/png;base64,#figure/vitalsign.PNG) --- ![](data:image/png;base64,#figure/mycopd.PNG) ![](data:image/png;base64,#figure/mycopd1.PNG) --- ### 3.2 Participants mild/severe copd, non-copd patients -- ### 3.3 Study design ``` ## Setting Freq Prop(%) ## 1 CASE-CONTROL 1 2.27 ## 2 CASE-CROSSOVER 1 2.27 ## 3 CROSS-SECTIONAL 5 11.36 ## 4 PROSPECTIVE 28 63.64 ## 5 RETROSPECTIVE 9 20.45 ``` the average follow-up time in prospective studies:1.35 yrs -- ### 3.4 Outcome(s) AECOPD occurrence/severity/COPD hospitalization/AECOPD readmission --- ### 3.5 Predictors .pull-left[ 3.5.1 Demographic characteristics - Age; sex; BMI; smoking; etc 3.5.2 Sympton(s)/vital sign(s) - Blood pressure; dyspnea; cough 3.5.3 Lab report - Eosinophil; imaging; neutrophil/lymphocyte, eosinophil/neutrophil Creatinine/Serum Cystatin C; platelet/lymphocyte 3.5.4 Spirometry - FEV1%pred; inspiratory capacity/total lung capacity 3.5.5 Medication ] -- .pull-right[ 3.5.6 Air pollution/ behavior - PM2.5; six-minute walk 3.5.7 Biomarker(s) - Soluble urokinase-type plasminogen activator receptor 3.5.8 Indices - **GOLD** /**CAT**/**mMRC** - **BCSS**:breathlessness, cough and sputum scale - **BODEX**:BMI, airflow obstruction, dyspnea, previous exacerbation - **BODE**:BMI, airflow obstruction, dyspnea, exercise - **ADO**: age, dyspnea, and airflowobstruction - **CODEXS** : comorbidity, obstructive dyspnea, exacerbation - **DOSE**: Dyspnoea, Obstruction, Smoking, Exacerbation ] --- ## 3.6 Sample size ``` ## [1] 327 68139 1066 60 24 162 345 1609 340 121 153 44 ## [13] 22053 403 144 45722 73 212 9 6075 493 553 471 1711 ## [25] 62 35 164 61 149 1033 127 62 98 206 95 168 ## [37] 129 44 160 292 246 178 247 28 125 526 412 2380 ``` ``` ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 9.00 97.25 173.00 3277.83 476.50 68139.00 ``` <img src="data:image/png;base64,#AECOPD_files/figure-html/unnamed-chunk-4-1.png" width="2000" height="200" /> --- ## 3.7 Statistical method(s) .pull-left[ ### Heuristic - **logistic regression** - **cox regression** - generalized estimation equation - mixed-effect model - negative binomial regression - roc-threshold ] -- .pull-right[ ### Machine learning - random forest - bootstrap - classification tree - decision tree (dt) - naive bayes classifier - k-nearest neighbors (knn) - linear discrimint classifier - naive bayes (nb) - support vector machine - cart ] --- class: center ## 3.8 Performance evaluation ![](data:image/png;base64,#AECOPD_files/figure-html/unnamed-chunk-6-1.png)<!-- --> --- [Interleukin-6 is a Strong Predictor of the Frequency of COPD Exacerbation Within 1 Year](https://pubmed.ncbi.nlm.nih.gov/34737559/) ![](data:image/png;base64,#figure/ROC097.PNG) --- class: center, inverse,middle # Case study 1 --- ![](data:image/png;base64,#figure/casestudy1.PNG) ### [Affiliations](https://pubmed.ncbi.nlm.nih.gov/36092968/) - The First Hospital of China Medical University, Shenyang, Liaoning - Shanghai Pulmonary Hospital, Institute of Respiratory Medicine, School of Medicine, Tongji University, Shanghai - Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong - Peking University Third Hospital, Beijing - The Second Xiangya Hospital of Central South University, Changsha, Hunan - The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi - The First Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan --- ![](data:image/png;base64,#figure/casestudy2.PNG) --- ### Data collection (
Download Detect.pdf
) ![](data:image/png;base64,#figure/casestudy3.PNG) ### Statistical method - Complete-case analysis strategy - AIC, BIC, adjusted R2, AUC - Stepwise logistic regression - Age,sex, exacerbation history over the past year were forced in the model - Overfitting issue: bootstrap ### Results GOLD classification, most recent exacerbation in the past 12 months, number of exacerbations that resulted in hospitalization in the past 12 months, phlegm color, respiratory rate, and chest tightness. --- class: center, inverse,middle # Case study 2 --- ![](data:image/png;base64,#figure/casestudy4.PNG) ![](data:image/png;base64,#figure/casestudy5.PNG) --- class: center, inverse,middle
Download Accept.pdf
--- class: center, inverse,middle # THANKS --- #backup slides ##Duration An overview of time duration ``` ## [1] 2.9972603 0.7479452 1.0000000 0.7452055 3.1671233 1.8356164 ## [7] 1.4986301 7.8356164 3.0000000 1.3260274 2.3369863 12.0054795 ## [13] 7.0027397 2.4958904 0.4958904 1.9205479 2.1671233 0.4109589 ## [19] 2.0000000 1.2465753 3.8328767 0.7424658 2.0000000 1.5780822 ## [25] 1.9205479 ``` Summary statistics: ``` ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 0.411 1.247 1.921 2.652 2.997 12.005 ``` --- # References #### [1] https://www.ncbi.nlm.nih.gov/mesh/ #### [2] Chmiel FP, Burns DK, Pickering JB, Blythin A, Wilkinson TM, Boniface MJ. Prediction of Chronic Obstructive Pulmonary Disease Exacerbation Events by Using Patient Self-reported Data in a Digital Health App: Statistical Evaluation and Machine Learning Approach. JMIR Med Inform. 2022 Mar 21;10(3):e26499. doi: 10.2196/26499. PMID: 35311685; PMCID: PMC8981014. #### [3] https://mymhealth.com/mycopd #### [4] Hawthorne G, Richardson M, Greening NJ, Esliger D, Briggs-Price S, Chaplin EJ, Clinch L, Steiner MC, Singh SJ, Orme MW. A proof of concept for continuous, non-invasive, free-living vital signs monitoring to predict readmission following an acute exacerbation of COPD: a prospective cohort study. Respir Res. 2022 Apr 26;23(1):102. doi: 10.1186/s12931-022-02018-5. PMID: 35473718; PMCID: PMC9044843. --- #### [5] Huang H, Huang X, Zeng K, Deng F, Lin C, Huang W. Interleukin-6 is a Strong Predictor of the Frequency of COPD Exacerbation Within 1 Year. Int J Chron Obstruct Pulmon Dis. 2021 Oct 28;16:2945-2951. doi: 10.2147/COPD.S332505. PMID: 34737559; PMCID: PMC8560075. #### [6] Adibi A, Sin DD, Safari A, Johnson KM, Aaron SD, FitzGerald JM, Sadatsafavi M. The Acute COPD Exacerbation Prediction Tool (ACCEPT): a modelling study. Lancet Respir Med. 2020 Oct;8(10):1013-1021. doi: 10.1016/S2213-2600(19)30397-2. Epub 2020 Mar 13. PMID: 32178776.