A Study to Investigate the Association of Real-world Sensor-derived Biometric Data With Clinical Parameters and Patient-reported Outcomes for Monitoring Disease Activity in Patients With Chronic Obstructive Pulmonary Disease (COPD)
概览
- 阶段
- 不适用
- 干预措施
- 未指定
- 疾病 / 适应症
- Pulmonary Disease, Chronic Obstructive
- 发起方
- Merck Healthcare KGaA, Darmstadt, Germany, an affiliate of Merck KGaA, Darmstadt, Germany
- 入组人数
- 77
- 试验地点
- 11
- 主要终点
- Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Physical Activity
- 状态
- 已完成
- 最后更新
- 3个月前
概览
简要总结
The purpose of this multicenter, prospective cohort study is to investigate the correlation of real-world sensor-derived biometric data obtained via a wearable device with clinical parameters and patient-reported outcomes (PROs) for monitoring disease activity and predicting exacerbations for participants with Chronic Obstructive Pulmonary Disease (COPD). The cohort of participants with COPD will be followed for 3 months. A calibration cohort with non-COPD participants will be included and followed for 2 weeks.
研究者
入排标准
入选标准
- •For participants with COPD:
- •Participants ≥40 and ≤80 years at baseline
- •Diagnosis of COPD stage II to IV
- •History of moderate or severe exacerbations (≥2 moderate exacerbations or ≥1 severe exacerbations in any 12-month time window during last 3 years prior to inclusion and ≥1 moderate or severe exacerbations in the last 12 months prior to inclusion, considering that the last 12 months may reflect lower exacerbation rate due to Covid-19 measures)
- •For participants in the calibration cohort:
- •Participants ≥40 and ≤80 years at baseline
排除标准
- •For participants with COPD:
- •Clinically relevant and/or serious concurrent medical conditions including, but not limited to visual problems, severe mental illness or cognitive impairment, musculoskeletal or movement disorders, cardiac disease (e.g., heart failure, arrythmia \[esp. atrial fibrillation and conduction blocks\]), lung cancer (currently treated) that in the opinion of the Investigator, would interfere with participant's ability to participate in the study or draw meaningful conclusions from the study
- •Participants with a cardiac pacemaker, defibrillators, or other implanted electronic devices
- •Participants with known allergies or sensitivity to silicon or hydrogel
- •Less than 6 weeks since previous moderate/severe exacerbation
- •For participants in the calibration cohort:
- •Participants with a cardiac pacemaker, defibrillators, or other implanted electronic devices
- •Participants with known allergies or sensitivity to silicon or hydrogel
- •Diagnosis of pulmonary disease including, but not limited to COPD, asthma, pulmonary fibrosis, with impact on the lung function and exercise capacity
结局指标
主要结局
Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Physical Activity
时间窗: Day 0(Baseline) and Day 8 to Day 14
An activity flag is extracted from the accelerometer by Vivalink, by using a predefined threshold for adult movement. For stair climbing, first periodic movement was determined, by using frequency analysis on specific time windows, and generating a ratio to the total spectrum indicating periodic activity over a certain threshold.
Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Heart Rate
时间窗: Day 0(Baseline) and Day 8 to Day 14
Heart rate is provided by Vivalink.
Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Heart Rate Variability (SDRR, SDNN, SDNNI, RMSSD, ln(RMSSD))
时间窗: Day 0(Baseline) and Day 8 to Day 14
Heart rate variability reflecting differences in time intervals between 2 R-waves in the ECG (milliseconds) SDRR (Standard Deviation of Intervals between Heartbeats), SDNN (Standard Deviation of Intervals between Heartbeats, after removing abnormal Beats), SDNNI (Mean of the Standard Deviations of all the NN intervals for each 5 min Segment of a 24-h HRV Recording), and RMSSD (Mean of the Standard Deviations of all the NN intervals for each 5 min Segment of a 24-hour HRV Recording) and In(RMSDD)
Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Heart Rate Variability (pNN50)
时间窗: Day 0(Baseline) and Day 8 to Day 14
pNN50 is the percentage of adjacent NN intervals that differ from each other by more than 50 milliseconds.
Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Heart Rate Variability (Stress Index)
时间窗: Day 0(Baseline) and Day 8 to Day 14
Baevsky's Stress Index is a heart rate variability (HRV) measure used to assess autonomic nervous system activity and physiological stress, especially in monitoring chronic obstructive pulmonary disease (COPD) exacerbations. It is calculated as: amplitude of the mode (AMo) divided by two times the mode (Mo) multiplied by the difference between the maximum and minimum RR intervals (MxDMn). AMo is the percentage of RR intervals at the most frequent value, Mo is the most common RR interval, and MxDMn is the range of RR intervals. The index typically ranges from 50 to over 900. Lower values (50-150) indicate low stress and better autonomic balance, while higher values (above 500) reflect increased stress and sympathetic activity. Values above 900 are considered very high stress. This is a single composite score with no subscales; higher scores represent worse outcomes.
Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Heart Rate Variability (LF and HF)
时间窗: Day 0(Baseline) and Day 8 to Day 14
Applying a Fast Fourier Transformation (FFT) or autoregressive (AR) modeling one can separate Heart rate variability (HRV) into its component ultra-low-frequency (ULF), very low frequency (VLF), Low-Frequency power (LF), and High-Frequency power (HF) rhythms that operate within different frequency ranges. Given in absolute values of power (milliseconds squared). LF power, low frequency power (0.04-0.15 Hz). HF power, high frequency power (0.15-0.40 Hz). LF/HF Ratio, spectral HRV index computed as (LF/HF).
Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Heart Rate Variability (LF/HF)
时间窗: Day 0(Baseline) and Day 8 to Day 14
Applying a Fast Fourier Transformation (FFT) or autoregressive (AR) modeling one can separate Heart rate variability (HRV) into its component ultra-low-frequency (ULF), very low frequency (VLF), Low-Frequency power (LF), and High-Frequency power (HF) rhythms that operate within different frequency ranges. Given in absolute values of power (milliseconds squared). LF power, low frequency power (0.04-0.15 Hz). HF power, high frequency power (0.15-0.40 Hz). LF/HF Ratio, spectral HRV index computed as (LF/HF).
Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Temperature
时间窗: Day 0(Baseline) and Day 8 to Day 14
Temperature is provided by Vivalink. The value for temperature is derived by Vivalink from the display temperature and then calibrated using initial calibration values, in an IP protected process. The sensor temperature is considered only as a relative value to evaluate changes in the temperature, and not as an objective human body temperature value, meaning no thresholds relative to normal human body temperature are considered, and it will not be used as a marker for fever or hypothermia.
Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Respiratory Rate
时间窗: Day 0(Baseline) and Day 8 to Day 14
Respiration rate is provided by Vivalink.
Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Cough Frequency
时间窗: Day 0(Baseline) and Day 8 to Day 14
Cough Frequency was provided by vivalink.
Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Sleep Patterns
时间窗: Day 0(Baseline) and Day 8 to Day 14
The basis of the sleep pattern calculations is the self-reported bedtimes. With the same technique as the cough frequency prediction, inactivity signals can be predicted from the labeled data to improve the bedtime accuracy, and the changes in accelerometer (step detection algorithms) can be used to quantify the number of clear breaks in the sleep (standing up, strong cough, etc.).
Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Resting Heart Rate
时间窗: Day 0(Baseline) and Day 8 to Day 14
Resting Heart Rate is provided by Vivalink.
Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Inspiration vs Expiration Time Ratio
时间窗: Day 0(Baseline) and Day 8 to Day 14
Using the breathing signal one can determine the inspiration and expiration peaks. The difference between said peaks in milliseconds can be used to determine the ratio of inspiration (distance from lower point to next peak) vs expiration (distance from peak to next lower point).
Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Frequency of Additional Medication
时间窗: Day 0(Basseline) and Day 8 to Day 14
Count of the number of times the use of additional medication as a log activity is reported per day.
Prediction of Moderate or Severe COPD Exacerbations by Building a Statistical Model Employing Sensor-Derived Data and Demographic and Medical Covariates - Accuracy
时间窗: Up to 3 months
Accuracy was calculated as (True Positives + True Negatives) / Total Population. True Positives (TP) are events correctly predicted as exacerbations. True Negatives (TN) are events correctly predicted as non-exacerbations. Total Population refers to the total number of events evaluated. Accuracy scores reflect XGBoost algorithm performance using random and time-based 70/30 data splits. The values were calculated in form of percentage where 100% is the ideal scenario for perfect predictability.
Prediction of Moderate or Severe COPD Exacerbations by Building a Statistical Model Employing Sensor-Derived Data and Demographic and Medical Covariates - Precision
时间窗: Up to 3 months
Precision was calculated as True Positives / (True Positives + False Positives). True Positives (TP) are events correctly predicted as exacerbations. False Positives (FP) are events incorrectly predicted as exacerbations. Total Population refers to the total number of events evaluated. Precision scores reflect XGBoost algorithm performance using random and time-based 70/30 data splits. The values were calculated in form of percentage where 100% is the ideal scenario for perfect predictability.
Prediction of Moderate or Severe COPD Exacerbations by Building a Statistical Model Employing Sensor-Derived Data and Demographic and Medical Covariates - Recall
时间窗: Up to 3 months
Recall was calculated as True Positives / (True Positives + False Negatives). True Positives (TP) are events correctly predicted as exacerbations. False Negatives (FN) are events incorrectly predicted as non-exacerbations. Total Population refers to the total number of events evaluated. Recall scores reflect XGBoost algorithm performance using random and time-based 70/30 data splits. The values were calculated in form of percentage where 100% is the ideal scenario for perfect predictability.
Prediction of Moderate or Severe COPD Exacerbations by Building a Statistical Model Employing Sensor-Derived Data and Demographic and Medical Covariates - Specificity
时间窗: Up to 3 months
Specificity was calculated as True Negatives / (True Negatives + False Positives). True Negatives (TN) are events correctly predicted as non-exacerbations. False Positives (FP) are events incorrectly predicted as exacerbations. Total Population refers to the total number of events evaluated. Specificity scores reflect XGBoost algorithm performance using random and time-based 70/30 data splits. The values were calculated in form of percentage where 100% is the ideal scenario for perfect predictability.
次要结局
- Correlation of Sensor-Collected Data With COPD Assessment Test (CAT) Questionnaire: Participants Health Status and Symptoms at Baseline and Study End(Baseline (Day 0) and at 3 months)
- Correlation of Sensor-Collected Data With Lung Function (FEV1) at Baseline and Study End(Baseline (Day 0) and at 3 months)
- Correlation of Sensor-Collected Data With Lung Function (FVC) at Baseline and Study End(Baseline (Day 0) and at 3 months)
- Correlation of Sensor-Collected Data With Lung Function (FEV1/FVC) at Baseline and Study End(Baseline (Day 0) and at 3 months)
- Correlation of Sensor-Collected Data With COPD Assessment Test (CAT) Questionnaire: Lung Function and Lab Values at Baseline and Study End (White Blood Cells Count)(Baseline (Day 0) and at 3 months)
- Correlation of Sensor-Collected Data With COPD Assessment Test (CAT) Questionnaire: Lung Function and Lab Values at Baseline and Study End (Erythrocytes Count)(Baseline (Day 0) and at 3 months)
- Correlation of Sensor-Collected Data With COPD Assessment Test (CAT) Questionnaire: Lung Function and Lab Values at Baseline and Study End (Partial Pressure of Oxygen (pO2))(Baseline (Day 0) and at 3 months)
- Correlation of Sensor-Collected Data With COPD Assessment Test (CAT) Questionnaire: Lung Function and Lab Values at Baseline and Study End (Partial Pressure of Carbon Dioxide (pCO2))(Baseline (Day 0) and at 3 months)
- Correlation of Sensor-Collected Data With COPD Assessment Test (CAT) Questionnaire: Lung Function and Lab Values at Baseline and Study End (O2 Saturation)(Baseline (Day 0) and at 3 months)
- Correlation of Sensor-Collected Data With Number, Date of Onset, and Duration of Mild, Moderate, and Severe Exacerbations(Up to 3 months)
- Association Between Sensor Parameters (Heart Rate and Resting Heart Rate) and CAT Score(7 days before Severe/Moderate Excarbations(S/M E) (7-day window period))
- Association Between Sensor Parameters (Respiration Rate) and CAT Score(7 days before Severe/Moderate Excarbations(S/M E) (7-day window period) and 14 days before S/M E (1 day window period))
- Association Between Sensor Parameters (SDRR, SDNN, SDNNI, RMSSD, In(RMSSD)) and CAT Score(7 days before Severe/Moderate Excarbations(S/M E) (7-day window period) and 14 days before S/M E (1 day window period))
- Association Between Sensor Parameters (Stress Index, LF/HF) and CAT Score(7 days before Severe/Moderate Excarbations(S/M E) (7-day window period) and 14 days before S/M E (1 day window period))
- Association Between Sensor Parameters (pNN50) and CAT Score(7 days before Severe/Moderate Excarbations(S/M E) (7-day window period) and 14 days before S/M E (1 day window period))
- Association Between Sensor Parameters (Temperature) and CAT Score(14 days before S/M E (1 day window period))
- Association Between Sensor Parameters (Physical Activity) and CAT Score(14 days before S/M E (1 day window period))
- Association Between Sensor Parameters (Sleep Pattern) and CAT Score(14 days before S/M E (1 day window period))
- Predicting the CAT Score by Building a Statistical Model Employing Sensor-Derived Data and Demographic and Medical Covariates(Up to 3 months)