RESEARCH/Original article
Changes in telemonitored physiological variables and symptoms prior to exacerbations of chronic obstructive pulmonary disease
Journal of Telemedicine and Telecare 2015, Vol. 21(1) 29–36 ! The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/1357633X14562733 jtt.sagepub.com
Christopher Burton1, Hilary Pinnock2 and Brian McKinstry3
Summary We examined symptom scores and physiological measurements from patients who were using a pilot COPD telemonitoring service. Of 33 patients recruited to the study, 19 were monitored for longer than 200 days. We identified three patterns of exacerbation, which we termed discrete (n ¼ 5), rolling (n ¼ 9) and over-ridden (n ¼ 4). The association between FEV1, pulse and SpO2 and total symptom score was examined using multilevel logistic regression. The intraclass correlation coefficient for the model was high (0.36) indicating that much of the variance was due to differences between individuals, rather than within individuals. Compared to baseline, at the onset of exacerbations (n ¼ 172) the mean pulse rate increased from 87 to 94 /min and the mean SpO2 fell from 93.6 to 92.4%. However, physiological variables did not differentiate between exacerbations and isolated bad days (n ¼ 150). Few patient records displayed clear patterns of normality and exacerbation. Clinicians selecting patients for telemonitoring should assess the patient’s perception of variation in their symptoms and provide careful training and support whilst patients are learning to monitor their condition. Accepted: 25 September 2014
Introduction Exacerbations of chronic obstructive pulmonary disease (COPD) are a major cause of hospital admissions and death.1 Prompt intervention with antibiotics and steroids may prevent admissions and improve quality of life,2 although difficulties in recognising early symptoms of deterioration3 and delays in accessing care4 may result in late presentation. While many patients find telemonitoring in COPD to be reassuring and perceive it to have prevented hospital admissions,4,5 the evidence for reduced hospitalisation for exacerbations of COPD is unconvincing.6 A recent trial of telemonitoring in COPD showed no significant benefit in delaying time to admission.7 One reason for this lack of benefit was considered to be the absence of algorithms that accurately predict exacerbations. Algorithms for detecting exacerbations have been derived from studies of paper-based symptom diaries,3 based on international definitions of exacerbations.8 However, paper diaries have significant limitations, being susceptible to selective and retrospective recording. Although new symptom-based algorithms with improved predictive values are being developed, their performance in clinical practice remains insufficiently sensitive and specific.9 Similarly, physiological measures such as spirometry, pulse oximetry and heart rate have poorly understood day-to-day variation, which may provoke
unnecessary alerts and, alone, have limited predictive validity for COPD admissions.10 A composite measure that combines pulse oximetry with symptoms in predicting a deterioration requiring treatment with antibiotics or steroids may be useful, but it is not clear how best these should be combined.10 In a pilot telemonitoring study we used algorithms that generated frequent clinically unnecessary alerts,4 suggesting poor discrimination for exacerbations. Although it is well recognised that people with COPD under-report exacerbations,3,4 there is concern that such high levels of alerting may represent over-detection of exacerbations resulting in alert fatigue,11 and/or over-treatment.4 We therefore examined the daily data from COPD patients using a pilot telemonitoring service,4 to describe exacerbation patterns, examine relationships between symptoms and physiological variables and identify any 1
Centre of Academic Primary Care, University of Aberdeen, UK Allergy and Respiratory Research Group, Centre for Population Health Sciences, University of Edinburgh, UK 3 E-health Research Group, Centre for Population Health Sciences, University of Edinburgh, UK 2
Corresponding author: Hilary Pinnock, Allergy and Respiratory Research Group, Centre for Population Health Sciences, University of Edinburgh, Doorway 3, Medical School, Teviot Place, Edinburgh EH8 9AG, UK. Email: [email protected]
30
Journal of Telemedicine and Telecare 21(1)
changes in physiological variables at the onset of exacerbation.
Methods The pilot study took place in 2008, with ethics approval from the appropriate committees. A full description of the study methodology with qualitative and quantitative outcomes has been published elsewhere.4 A total of 33 commercially available tele-monitoring systems were installed in the homes of patients. The patients were selected by their general practitioners as having moderate/severe COPD and being at risk of a hospital admission. There were four participating practices situated in relatively deprived areas of Lothian. The only exclusion criterion was moderate/severe dementia. Patients used a touch screen computer (Intel Corporation, Santa Clara, California) to record a validated symptom score,2 comprising three questions asking about the cardinal symptoms of an exacerbation,12 and five which aimed to detect possible infective triggers. Physiological measurements were oxygen saturation (SpO2), pulse rate and forced expiratory volume in one second (FEV1). Daily readings were transmitted via a broadband link to a call centre which referred the patients to their usual primary care service for clinical care. Patients were provided with an action plan and an emergency supply of antibiotics and steroids which they were encouraged to commence as soon as an exacerbation was recognised.
Data processing The daily monitoring data available for analysis are summarised in Table 1. In addition to the symptom score
and physiological measurements, patients also noted whether they were taking antibiotics or steroids that day. All monitoring data were anonymous, identified only by a study identity number and were date/time stamped. Prior to analysis, the data were grouped by day. Where more than one reading for the day was identified we took the most abnormal value (lowest SpO2 and FEV1, highest pulse rate) on the grounds that deterioration may lead to repeated readings and also a concern that in some cases unusually healthy readings observed in the data may have indicated that someone other than the user had ‘‘tested’’ the device. Extreme values for pulse rate 170 were trimmed to these levels to minimise outlier effects and recordings with FEV1 more than 3 SDs above the individual mean were also excluded. From the symptom data we generated a total symptom score (one point for each item present, range 0-8).2 In line with global guidelines,8 we used two indicators of exacerbations: the first was whether the patient met Anthonisen’s et al. criteria (three or more symptoms including at least one of increased breathlessness, sputum amount or sputum colour for at least two days12), which has been widely applied in studies using the validated symptom score2,3,10 and the second was whether the patient was taking an antibiotic on that day. For both of these indicators of exacerbations we defined the onset of an exacerbation as when two consecutive days meeting the criteria immediately followed two consecutive days which did not meet the criteria. All data series contained missing data points. This was especially the case for FEV1. Missing data could not be assumed to be missing at random, especially as a sequence of missed days might indicate admission to hospital because of deterioration or a holiday during a period of
Table 1. Daily monitoring data available for analysis. Symptom score
4
Please record any WORSENING of symptoms from your usual daily level. I am more breathless than usual* My sputum has increased in colour* My sputum has increased in amount* I have a cold (such as runny or blocked nose) I have increased wheeze or chest tightness I have a sore throat I have an increased cough I have a fever Medication Please indicate if you are taking: Antibiotics Steroids Physiological measurements Oxygen saturation FEV1 and peak flow *symptoms indicated with an asterisk score 2: other symptoms score 1.
Frequency Normally completed daily
Normally completed daily
Normally completed daily Completed approximately weekly
Burton et al.
31
comparatively good health. We therefore did not attempt imputation of missing data.
Table 2. Characteristics of the participants. ID
Age, years
Sex
Severity*
Exacerbation patterns
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 26 27 29
77 76 75 77 60 67 60 72 48 No No 65 64 No 69 63 68 59 No
M F M M F M M M M
Mild Very severe Moderate Severe Moderate Moderate Very severe Severe Very severe
M M
Moderate Moderate
M F M M
Severe Very severe Very severe Severe
Time series plots were constructed for each participant who collected more than 200 days of data. These presented total symptom score, whether the patient met the criteria of Anthonisen et al. for exacerbation 12 and whether they reported taking antibiotics that day. Missing days were left blank. Two researchers independently inspected and grouped the plots according to the apparent pattern of symptoms and response. There was no attempt at a priori classification. After initial independent inspection the two researchers discussed their decisions before agreeing a classification of exacerbation patterns based on the data.
Association between symptoms and physiological variables We inspected the association between symptom score, FEV1, pulse and SpO2 by constructing scatter plots for each pair of variables and each patient. We then tested the association of FEV1, pulse and SpO2 with total symptom score using multilevel regression, nested by individual and adjusted for the autocorrelation present in the data by specifying an AR(1) correlation structure to the models.
data data
data
data
Four people declined the pilot study baseline assessment but used the telemonitoring service under the arrangements with the health service. They provided anonymous data and we have no baseline information on these four patients. *Severity was based on FEV1 and classified according to the Global initiative for Obstructive Lung Disease.8
Changes associated with exacerbation In order to capture changes in monitored values around the onset of an exacerbation, we took the worst value (lowest SpO2, highest pulse rate) from the first day in which criteria were met and the one preceding it. We took this approach because there was liberal antibiotic use in the study with treatment commencing at an early change of symptoms and it was likely that participants starting antibiotics on a given day would already have completed their telemonitoring data for the day before commencing, or being instructed to commence treatment. We analysed this using three approaches. First we summarised the changes in monitoring values from two days before the exacerbation with the worst value at the onset. Second we compared the worst day at the start of antibiotic treatment with the worst of two days during a period of non-treatment using multilevel logistic regression. Third we compared the onset of an exacerbation with a single ‘‘bad day’’ in which the clinical criterion for exacerbation was met on one day only and then reverted to sub-threshold level. Multilevel regression analyses were conducted using the glmmPQL function from the MASS package for R.
Results Of the 33 patients recruited to the telemonitoring service, 19 participants (mean age 67 years, 3 female) collected
data on more than 200 days and were included in the analysis. The characteristics of the participants are summarised in Table 2.
Classification of exacerbation patterns We identified three patterns of exacerbation, which we termed discrete exacerbations, rolling exacerbations and over-ridden exacerbations: 1. Discrete exacerbations were seen in five patients who had long spells of minimal symptoms with occasional flare-up of symptoms, most of which were treated with antibiotic. 2. A less distinct rolling exacerbation pattern was seen in nine patients. These patients frequently had high levels of symptoms and took courses of antibiotics. In some cases they rarely returned to normal levels of symptoms or stayed off antibiotic treatment for more than a few days. Three of these patients had more than 20 antibiotic courses in a year, each course lasting between five days and 2 weeks. 3. A pattern of over-ridden exacerbations was seen in four patients who frequently had symptom levels which indicated an exacerbation but who only rarely took antibiotics. We regarded this as overriding the alarm signal from the symptoms.
32 One patient could not be categorised using this system because they did not have a change in symptom scores/ exacerbations throughout the monitoring period. In general, these patterns remained constant for an individual patient over the course of the telemonitoring. Example plots are shown in Figure 1.
Association between symptoms and physiological variables The correlation between symptom score and SpO2 is shown in Figure 2. Plots of symptom score against FEV1 or pulse rate showed similarly weak association. There was a strong correlation between FEV1 and PEFR (data not shown). The results of the multilevel linear regression model of total symptom score predicted by FEV1, SpO2 and pulse rate are summarised in Table 3. The intraclass correlation coefficient for the model was high (0.36) indicating that much of the variance was
Journal of Telemedicine and Telecare 21(1) accounted for between individuals rather than within individuals.
Association between physiological variables and exacerbations There were 172 treated exacerbation episodes suitable for analysis. The median number of exacerbations was 7 per patient (interquartile range 2 to 14). The analysis required sequences of data for consecutive days and was restricted to symptom score, pulse rate and SpO2 because FEV1 was only collected intermittently. The mean pulse rate before exacerbation was 87.4 per minute (95% CI: 85.1 to 89.7) and at the start of exacerbation rose slightly to 93.7 per minute (91 to 96.3). Mean SpO2 before exacerbation was 93.6% (93.2 to 94.1), falling to 92.4% (91.9 to 92.9) around the onset of exacerbation. The distributions of changes for each exacerbation are shown in Figure 3. In addition to the 172 treated
Figure 1. Classification of exacerbation patterns. (a) Discrete pattern (n ¼ 5) exhibiting long spells of minimal symptoms with occasional flares of symptoms, most of which were treated with antibiotic (b) Rolling pattern (n ¼ 9) showing frequent, almost continuous, high levels of symptoms with frequent courses of antibiotics (c) Over-ridden pattern (n ¼ 4) with frequent symptoms, few of which triggered antibiotic prescription.
Burton et al.
33
Figure 2. Correlation between symptom score and SpO2.
Table 3. Multilevel linear regression model of total symptom score predicted by FEV1, SpO2 and pulse rate.
Single variable FEV1b SpO2c Pulse rated Multivariable FEV1b SpO2c Pulse rated
Coefficienta
SE
n
P-value
0.009 -0.002 0.017
0.009 0.005 0.005
1709 8612 8612
0.32 0.77
Changes in telemonitored physiological variables and symptoms prior to exacerbations of chronic obstructive pulmonary disease
Journal of Telemedicine and Telecare 2015, Vol. 21(1) 29–36 ! The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/1357633X14562733 jtt.sagepub.com
Christopher Burton1, Hilary Pinnock2 and Brian McKinstry3
Summary We examined symptom scores and physiological measurements from patients who were using a pilot COPD telemonitoring service. Of 33 patients recruited to the study, 19 were monitored for longer than 200 days. We identified three patterns of exacerbation, which we termed discrete (n ¼ 5), rolling (n ¼ 9) and over-ridden (n ¼ 4). The association between FEV1, pulse and SpO2 and total symptom score was examined using multilevel logistic regression. The intraclass correlation coefficient for the model was high (0.36) indicating that much of the variance was due to differences between individuals, rather than within individuals. Compared to baseline, at the onset of exacerbations (n ¼ 172) the mean pulse rate increased from 87 to 94 /min and the mean SpO2 fell from 93.6 to 92.4%. However, physiological variables did not differentiate between exacerbations and isolated bad days (n ¼ 150). Few patient records displayed clear patterns of normality and exacerbation. Clinicians selecting patients for telemonitoring should assess the patient’s perception of variation in their symptoms and provide careful training and support whilst patients are learning to monitor their condition. Accepted: 25 September 2014
Introduction Exacerbations of chronic obstructive pulmonary disease (COPD) are a major cause of hospital admissions and death.1 Prompt intervention with antibiotics and steroids may prevent admissions and improve quality of life,2 although difficulties in recognising early symptoms of deterioration3 and delays in accessing care4 may result in late presentation. While many patients find telemonitoring in COPD to be reassuring and perceive it to have prevented hospital admissions,4,5 the evidence for reduced hospitalisation for exacerbations of COPD is unconvincing.6 A recent trial of telemonitoring in COPD showed no significant benefit in delaying time to admission.7 One reason for this lack of benefit was considered to be the absence of algorithms that accurately predict exacerbations. Algorithms for detecting exacerbations have been derived from studies of paper-based symptom diaries,3 based on international definitions of exacerbations.8 However, paper diaries have significant limitations, being susceptible to selective and retrospective recording. Although new symptom-based algorithms with improved predictive values are being developed, their performance in clinical practice remains insufficiently sensitive and specific.9 Similarly, physiological measures such as spirometry, pulse oximetry and heart rate have poorly understood day-to-day variation, which may provoke
unnecessary alerts and, alone, have limited predictive validity for COPD admissions.10 A composite measure that combines pulse oximetry with symptoms in predicting a deterioration requiring treatment with antibiotics or steroids may be useful, but it is not clear how best these should be combined.10 In a pilot telemonitoring study we used algorithms that generated frequent clinically unnecessary alerts,4 suggesting poor discrimination for exacerbations. Although it is well recognised that people with COPD under-report exacerbations,3,4 there is concern that such high levels of alerting may represent over-detection of exacerbations resulting in alert fatigue,11 and/or over-treatment.4 We therefore examined the daily data from COPD patients using a pilot telemonitoring service,4 to describe exacerbation patterns, examine relationships between symptoms and physiological variables and identify any 1
Centre of Academic Primary Care, University of Aberdeen, UK Allergy and Respiratory Research Group, Centre for Population Health Sciences, University of Edinburgh, UK 3 E-health Research Group, Centre for Population Health Sciences, University of Edinburgh, UK 2
Corresponding author: Hilary Pinnock, Allergy and Respiratory Research Group, Centre for Population Health Sciences, University of Edinburgh, Doorway 3, Medical School, Teviot Place, Edinburgh EH8 9AG, UK. Email: [email protected]
30
Journal of Telemedicine and Telecare 21(1)
changes in physiological variables at the onset of exacerbation.
Methods The pilot study took place in 2008, with ethics approval from the appropriate committees. A full description of the study methodology with qualitative and quantitative outcomes has been published elsewhere.4 A total of 33 commercially available tele-monitoring systems were installed in the homes of patients. The patients were selected by their general practitioners as having moderate/severe COPD and being at risk of a hospital admission. There were four participating practices situated in relatively deprived areas of Lothian. The only exclusion criterion was moderate/severe dementia. Patients used a touch screen computer (Intel Corporation, Santa Clara, California) to record a validated symptom score,2 comprising three questions asking about the cardinal symptoms of an exacerbation,12 and five which aimed to detect possible infective triggers. Physiological measurements were oxygen saturation (SpO2), pulse rate and forced expiratory volume in one second (FEV1). Daily readings were transmitted via a broadband link to a call centre which referred the patients to their usual primary care service for clinical care. Patients were provided with an action plan and an emergency supply of antibiotics and steroids which they were encouraged to commence as soon as an exacerbation was recognised.
Data processing The daily monitoring data available for analysis are summarised in Table 1. In addition to the symptom score
and physiological measurements, patients also noted whether they were taking antibiotics or steroids that day. All monitoring data were anonymous, identified only by a study identity number and were date/time stamped. Prior to analysis, the data were grouped by day. Where more than one reading for the day was identified we took the most abnormal value (lowest SpO2 and FEV1, highest pulse rate) on the grounds that deterioration may lead to repeated readings and also a concern that in some cases unusually healthy readings observed in the data may have indicated that someone other than the user had ‘‘tested’’ the device. Extreme values for pulse rate 170 were trimmed to these levels to minimise outlier effects and recordings with FEV1 more than 3 SDs above the individual mean were also excluded. From the symptom data we generated a total symptom score (one point for each item present, range 0-8).2 In line with global guidelines,8 we used two indicators of exacerbations: the first was whether the patient met Anthonisen’s et al. criteria (three or more symptoms including at least one of increased breathlessness, sputum amount or sputum colour for at least two days12), which has been widely applied in studies using the validated symptom score2,3,10 and the second was whether the patient was taking an antibiotic on that day. For both of these indicators of exacerbations we defined the onset of an exacerbation as when two consecutive days meeting the criteria immediately followed two consecutive days which did not meet the criteria. All data series contained missing data points. This was especially the case for FEV1. Missing data could not be assumed to be missing at random, especially as a sequence of missed days might indicate admission to hospital because of deterioration or a holiday during a period of
Table 1. Daily monitoring data available for analysis. Symptom score
4
Please record any WORSENING of symptoms from your usual daily level. I am more breathless than usual* My sputum has increased in colour* My sputum has increased in amount* I have a cold (such as runny or blocked nose) I have increased wheeze or chest tightness I have a sore throat I have an increased cough I have a fever Medication Please indicate if you are taking: Antibiotics Steroids Physiological measurements Oxygen saturation FEV1 and peak flow *symptoms indicated with an asterisk score 2: other symptoms score 1.
Frequency Normally completed daily
Normally completed daily
Normally completed daily Completed approximately weekly
Burton et al.
31
comparatively good health. We therefore did not attempt imputation of missing data.
Table 2. Characteristics of the participants. ID
Age, years
Sex
Severity*
Exacerbation patterns
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 26 27 29
77 76 75 77 60 67 60 72 48 No No 65 64 No 69 63 68 59 No
M F M M F M M M M
Mild Very severe Moderate Severe Moderate Moderate Very severe Severe Very severe
M M
Moderate Moderate
M F M M
Severe Very severe Very severe Severe
Time series plots were constructed for each participant who collected more than 200 days of data. These presented total symptom score, whether the patient met the criteria of Anthonisen et al. for exacerbation 12 and whether they reported taking antibiotics that day. Missing days were left blank. Two researchers independently inspected and grouped the plots according to the apparent pattern of symptoms and response. There was no attempt at a priori classification. After initial independent inspection the two researchers discussed their decisions before agreeing a classification of exacerbation patterns based on the data.
Association between symptoms and physiological variables We inspected the association between symptom score, FEV1, pulse and SpO2 by constructing scatter plots for each pair of variables and each patient. We then tested the association of FEV1, pulse and SpO2 with total symptom score using multilevel regression, nested by individual and adjusted for the autocorrelation present in the data by specifying an AR(1) correlation structure to the models.
data data
data
data
Four people declined the pilot study baseline assessment but used the telemonitoring service under the arrangements with the health service. They provided anonymous data and we have no baseline information on these four patients. *Severity was based on FEV1 and classified according to the Global initiative for Obstructive Lung Disease.8
Changes associated with exacerbation In order to capture changes in monitored values around the onset of an exacerbation, we took the worst value (lowest SpO2, highest pulse rate) from the first day in which criteria were met and the one preceding it. We took this approach because there was liberal antibiotic use in the study with treatment commencing at an early change of symptoms and it was likely that participants starting antibiotics on a given day would already have completed their telemonitoring data for the day before commencing, or being instructed to commence treatment. We analysed this using three approaches. First we summarised the changes in monitoring values from two days before the exacerbation with the worst value at the onset. Second we compared the worst day at the start of antibiotic treatment with the worst of two days during a period of non-treatment using multilevel logistic regression. Third we compared the onset of an exacerbation with a single ‘‘bad day’’ in which the clinical criterion for exacerbation was met on one day only and then reverted to sub-threshold level. Multilevel regression analyses were conducted using the glmmPQL function from the MASS package for R.
Results Of the 33 patients recruited to the telemonitoring service, 19 participants (mean age 67 years, 3 female) collected
data on more than 200 days and were included in the analysis. The characteristics of the participants are summarised in Table 2.
Classification of exacerbation patterns We identified three patterns of exacerbation, which we termed discrete exacerbations, rolling exacerbations and over-ridden exacerbations: 1. Discrete exacerbations were seen in five patients who had long spells of minimal symptoms with occasional flare-up of symptoms, most of which were treated with antibiotic. 2. A less distinct rolling exacerbation pattern was seen in nine patients. These patients frequently had high levels of symptoms and took courses of antibiotics. In some cases they rarely returned to normal levels of symptoms or stayed off antibiotic treatment for more than a few days. Three of these patients had more than 20 antibiotic courses in a year, each course lasting between five days and 2 weeks. 3. A pattern of over-ridden exacerbations was seen in four patients who frequently had symptom levels which indicated an exacerbation but who only rarely took antibiotics. We regarded this as overriding the alarm signal from the symptoms.
32 One patient could not be categorised using this system because they did not have a change in symptom scores/ exacerbations throughout the monitoring period. In general, these patterns remained constant for an individual patient over the course of the telemonitoring. Example plots are shown in Figure 1.
Association between symptoms and physiological variables The correlation between symptom score and SpO2 is shown in Figure 2. Plots of symptom score against FEV1 or pulse rate showed similarly weak association. There was a strong correlation between FEV1 and PEFR (data not shown). The results of the multilevel linear regression model of total symptom score predicted by FEV1, SpO2 and pulse rate are summarised in Table 3. The intraclass correlation coefficient for the model was high (0.36) indicating that much of the variance was
Journal of Telemedicine and Telecare 21(1) accounted for between individuals rather than within individuals.
Association between physiological variables and exacerbations There were 172 treated exacerbation episodes suitable for analysis. The median number of exacerbations was 7 per patient (interquartile range 2 to 14). The analysis required sequences of data for consecutive days and was restricted to symptom score, pulse rate and SpO2 because FEV1 was only collected intermittently. The mean pulse rate before exacerbation was 87.4 per minute (95% CI: 85.1 to 89.7) and at the start of exacerbation rose slightly to 93.7 per minute (91 to 96.3). Mean SpO2 before exacerbation was 93.6% (93.2 to 94.1), falling to 92.4% (91.9 to 92.9) around the onset of exacerbation. The distributions of changes for each exacerbation are shown in Figure 3. In addition to the 172 treated
Figure 1. Classification of exacerbation patterns. (a) Discrete pattern (n ¼ 5) exhibiting long spells of minimal symptoms with occasional flares of symptoms, most of which were treated with antibiotic (b) Rolling pattern (n ¼ 9) showing frequent, almost continuous, high levels of symptoms with frequent courses of antibiotics (c) Over-ridden pattern (n ¼ 4) with frequent symptoms, few of which triggered antibiotic prescription.
Burton et al.
33
Figure 2. Correlation between symptom score and SpO2.
Table 3. Multilevel linear regression model of total symptom score predicted by FEV1, SpO2 and pulse rate.
Single variable FEV1b SpO2c Pulse rated Multivariable FEV1b SpO2c Pulse rated
Coefficienta
SE
n
P-value
0.009 -0.002 0.017
0.009 0.005 0.005
1709 8612 8612
0.32 0.77
