3.1 Source of data: medical records, questionnaires, app/devices, COPDGene

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

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

3.7 Statistical method(s)

Interleukin-6 is a Strong Predictor of the Frequency of COPD Exacerbation Within 1 Year

Affiliations

• 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 collection (Download Detect.pdf)

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.

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.