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(Chest. 2005;128:3828-3837.)
© 2005 American College of Chest Physicians

Pattern of Variables Describing Desaturator COPD Patients, as Revealed by Cluster Analysis*

Domenico Maurizio Toraldo, MD; Giuseppe Nicolardi, MD; Francesco De Nuccio, PhD; Rosario Lorenzo, MD and Nicolino Ambrosino, MD, FCCP

* From the "A. Galateo" Lung Disease Hospital (Drs. Toraldo and Lorenzo), Third Division ASL/LE/1, San Cesario di Lecce, Lecce, Italy; the Laboratory of Human Anatomy (Drs. Nicolardi and De Nuccio), Department of Biological and Environmental Sciences and Technologies, University of Lecce, Lecce, Italy; and Pulmonary Unit (Dr. Ambrosino), Cardio-Thoracic Department. University Hospital, Pisa, Italy.

Correspondence to: Domenico Maurizio Toraldo, MD, Via A.C. Casetti 73100 Lecce, Italy; e-mail: d.torald{at}tin.it


   Abstract
TOP
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Study objectives: The aims of this study were to define, by cluster analysis, a pattern of clinical variables that differentiate desaturator (D) from nondesaturator (ND) patients affected by COPD, and to identify daytime variables that are predictive of nocturnal desaturation.

Patients: Fifty-one random, consecutive COPD outpatients (20 women; mean [± SD] age, 69.6 ± 4.0 years) with mild daytime hypoxemia (PaO2, 60 to 70 mm Hg) were enrolled into the study. Obstructive sleep apnea syndrome patients were excluded.

Measurements and results: Lung volumes, arterial blood gas levels, and mean pulmonary artery pressure (MPAP) were measured, and nocturnal desaturation was evaluated with nighttime polygraphy. With least squares simple linear regression, the percentage of total recording time was highly correlated with a total nocturnal recording time of arterial oxygen saturation of < 90 mm Hg (T90) and MPAP (R = 0.84; R2 = 71.20%); T90 was also highly correlated with daytime PaCO2 (R = 0.70; R2 = 48.96%). Multiple regression showed that T90 was highly correlated with both MPAP and PaCO2 (R2 = 97.75%). Hierarchical cluster analysis conducted with these three variables showed that D and ND patients differed in both nocturnal and daytime variables. The mean T90 was 30 ± 3.5% in 19.2% and 8%, respectively, of the D and ND groups. Moreover, two D subgroups differing in MPAP and two ND subgroups differing in PaCO2 were identified.

Conclusions: D patients may be identified by a pattern of T90, MPAP, and PaCO2 values, rather than by T90 alone, with the latter two variables being predictors of nocturnal desaturation severity.

Key Words: arterial oxygen saturation • cluster analysis • COPD • hypoxemia • pulmonary hypertension • sleep-related hypoxemia


   Introduction
TOP
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 References
 
Patients with COPD and mild daytime hypoxemia (PaO2 between 60 and 70 mm Hg) may experience severe episodes of reduced arterial oxygen saturation (SaO2) during sleep1234567 that physicians may not be aware of when evaluating daytime blood gas values.8 Moreover, transient nocturnal hypoxemia may be associated with elevated pulmonary artery pressure (PAP).67910 It has also been implicated in right ventricular hypertrophy,11 although this has not been supported by subsequent data.12

A COPD patient showing a percentage of total nocturnal recording time spent with an SaO2 of < 90% (T90) of ≥ 30% and a nadir nocturnal SaO2 (NSaO2) of ≤ 85% was defined as a desaturator (D), and other patients were defined as being nondesaturators (NDs).613 Awake D patients have lower PaO2 and higher PaCO2 values than awake ND patients.13 Moreover, daytime hypercapnia is a risk factor for nocturnal hypoxemia in COPD patients with mild daytime hypoxemia.6

At present, COPD patients are classified D or ND based on a T90 of 30%. The aim of this study was to try to identify, using cluster analysis, a pattern of daytime clinical variables that distinguishes D from ND COPD patients.


   Materials and Methods
TOP
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 References
 
The study protocol was approved by the Ethics Committee of the "V. Fazzi" Hospital of Lecce (Italy). All patients gave their informed consent for participating in the study.

Patients
Fifty-one consecutive stable COPD outpatients attending the "A. Galateo" Lung Disease Hospital (San Cesario, Lecce, Italy) between June 2000 and January 2001 were enrolled into the study. They had been free from acute exacerbations for at least 4 weeks and had daytime PaO2 values between 60 and 70 mm Hg (ie, mild hypoxemia). COPD was diagnosed according to American Thoracic Society criteria.14 Patients with a history of loud snoring and excessive daytime sleepiness, as evaluated by the Epworth sleepiness scale (ESS) [range, 0 to 10],15 were excluded from the study because of suspected obstructive sleep apnea syndrome.16 We also excluded subjects in whom the mean PAP (MPAP) could not be evaluated by color Doppler echocardiography. Other exclusion criteria were treatment with anxiolytic agents, nocturnal long-term oxygen therapy, or analeptic drugs, and hepatic cirrhosis or chronic kidney failure.

Lung and Heart Function Measurements
Static lung volumes were measured by body plethysmographs (6200 Autobox DL; SensorMedics; Yorba Linda, CA) and dynamic lung volumes by mass flow sensors (Vmax229; SensorMedics) with the patients seated according to standard procedures. The normal values were those reported by the European Respiratory Society.17 Arterial blood gases were measured at the radial artery using microelectrodes (ABL 520; Radiometer; Copenhagen, Denmark), with the patient seated and spontaneously breathing air. Resting daytime MPAP was measured with a color Doppler echocardiograph (Vingmed Ultrasound; GE; Horten, Norway) by means of the subcostal approach.18 The mean of three measurements was considered.

Nocturnal desaturation was evaluated with polygraphic recording (Poly-Mesam; MAP; Martinsried, Germany) of oxygen saturation, snoring, air flow, thoracic and abdominal respiratory movements, heart rate, including ECG in real-time mode, and body position. Polygraphic recordings were performed between 10:00 PM and 6:00 AM. The signals, which were stored in a digital recorder, were computer-analyzed, and manually validated by a physician on the morning after the recording. COPD patients with an apnea-hypopnea index (AHI) score of >5 events per hour were excluded from the study. Body mass index was calculated as the body weight/body height ratio (in kilograms per square meter). Obesity was diagnosed when BMI > 29.9. kg/m2

Definitions
Nocturnal hypoxemia was defined as an SaO2 of < 90% for at least 5 min with a NSaO2 of ≤ 85%. Time in bed was defined as the time from the start to the end of the recording. The percentage of total recording time (TRT) was defined as the time spent in bed – sleep latency + intrasleep wakefulness. The TRT spent in bed with an SaO2 of < 90% was defined as the T90. The minimal TRT required for a satisfactory analysis of nocturnal recordings was 2 h. COPD patients with a T90 of >30% and a NSaO2 of ≤ 85% were defined as D and the others as ND.613

Statistical Analysis
Statistical analyses were carried out by Drs. Nicolardi and De Nuccio with a statistical software package (Statgraphics plus, version 2.1; Statistical Graphics Corp; Cambridge, MA). All variables first underwent descriptive statistical analysis to obtain the distribution fitting and to calculate average, variance, SD, range, standard skewness and standard kurtosis. To evaluate whether the data variables were normally distributed, we calculated the {chi}2 goodness-of-fit, the Shapiro-Wilks W test, the z-score for skewness, and the z-score for kurtosis. We also obtained a table of 95.0% confidence intervals (CI) with the mean of each variable to bind the sampling error in the estimates of these means. Patients were subdivided by gender, and the variables of men and women in the N and ND groups were analyzed by t test for unpaired data with comparison of means. The {alpha} probability of type I error was minimized by the F-test of the Fisher-Snedecor test for comparison of variance, by the Mann-Whitney W test for comparison of medians, and with the Kolmogorov-Smirnov test, all of which were performed at a minimum 95% confidence level.

The relationships among the patients’ variables were first evaluated using a table of Pearson product-moment correlations of pairs of variables followed by simple and multiple regressions. The latter were carried out with the least-squares method so as to obtain linear prediction equations. Prediction accuracy referred to bias and precision, bias being the mean difference between predicted and measured values, and precision the 95% CI for the difference.

Cluster analysis is used in such diverse fields as artificial intelligence, biology, medicine, psychology, and business. It entails grouping similar objects into distinct, mutually exclusive subsets referred to as clusters. Elements within a cluster share a high degree of "natural association," whereas the clusters are relatively distinct from one another. This procedure has recently been used in clinical medicine to classify patients and their data.1920

To differentiate between patients, we used a hierarchical cluster analysis performed by Dr. Nicolardi with the nearest-neighbor method, and using combinations of the variables that showed the highest correlations in the simple and multiple regressions. The number of clusters was progressively increased by reducing the squared Euclidean method-calculated distance between clusters. Clusters had centroid values of T90, MPAP rest, and PaCO2, and were represented by a two-dimensional scatterplot with boundary drawing and centroid positions. The values of variables in the clusters were graphically represented by box-and-whisker plots, and were analyzed by analysis of variance. We used the Fisher least significant differences method, or the Bonferroni method, at 95% CI, to evaluate significant differences among the means with minimal {alpha} probability of type I error, so as to assess significant differences between clusters.


   Results
TOP
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 References
 
Fifteen of the 66 patients (27%) were excluded from the study because chest impedance caused by pulmonary emphysema precluded measurement of resting daytime MPAP with subcostal color Doppler echocardiography. This group did not differ significantly from the remaining 51 subjects. Considering all patients, T90, MPAP at rest, PaCO2, NSaO2, mean nocturnal SaO2 (MSaO2) predicted total lung capacity (TLC), baseline SaO2 (BSaO2) awake, predicted vital capacity (VC), and BMI had a bimodal distribution. The variables examined in this study did not differ between men and women (Table 1 ).


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  		Table 1.. Mean ± SD and Ranges of the Clinical Variables Studied in the 51 COPD Patients, Divided by Gender

 
Predictors of Nocturnal Desaturation
Table 2 shows the significant linear correlations identified by the least-squares method. The highest correlation was between T90 and MPAP (R = 0.84); this model explained 71.20% (R2) of the T90 variability. In addition, T90 was highly correlated with daytime PaCO2 (R = 0.70), but this model explained only 48.96% of T90 variability. Finally, T90 was correlated with the FEV1/VC ratio, predicted TLC, BMI, and awake BSaO2, but with a lower coefficient vs the regression between T90 and MPAP. Also the nocturnal variables MSaO2, and NSaO2 were related with several daytime parameters. In particular, MSaO2 was significantly correlated, in decreasing order of correlation coefficient, with MPAP, BSaO2, PaCO2, FEV1/VC ratio, FEV1, and predicted TLC, whereas NSaO2 was correlated, in decreasing order of correlation coefficient, with BSaO2, MPAP, PaCO2, FEV1/VC ratio, and predicted TLC. Last, MPAP was closely correlated with PaCO2 – 52.87% of MPAP variability being explained by this model.


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  		Table 2.. Least-Squares Method Simple Linear Regressions Between T90 as the Dependent Variable and Daytime Parameters as Independent Variables*

 
Multiple linear regression with a constant between T90 vs MPAP and PaCO2 revealed significant relationships among these variables, but explained 71.64% (R2) of the T90 variability. This corresponds to an increase of only 0.44% with respect to the R2 value of T90 vs MPAP linear regression. The same model without a constant resulted in a very high R2, which shows that this model explains 97.75% of the T90 variability. Similar R2 values were obtained by substituting PaCO2 with BMI or by adding BMI to MPAP and PaCO2 with or without a constant (Table 3 ).


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  		Table 3.. Least-Squares Method Multiple Regressions Between T90 as the Nocturnal Variable and Some Daytime Parameters*

 
Cluster Analysis
Cluster analysis with T90, MPAP, and PaCO2 showed that the number of patients was relatively equal in the D group (26 of 51 patients; 51%) and the ND group (25 of 51 patients; 49%) [Table 4 ]. In both groups, all variables had a normal but predominantly asymmetrical distribution (Fig 1 ). Moreover, most patients in group D were male (18/26 = 69%), whereas the numbers of male patients (13 of 25 patients; 52%) and female patients (12 of 25 patients; 48%) were similar in group ND. Sixty percent of women (12 of 20) were in group ND, and 58% of men (18 of 31) were in group D. Using multiple range test tables (data not shown) and box-and-whisker plots, we found differences between D and ND patients but not between the men and women of each group in the following variables: T90; resting MPAP; PaCO2; NSaO2; MSaO2; predicted TLC; awake BSaO2; predicted VC; and BMI (Fig 1). Moreover, the FEV1/VC ratio, PaO2, predicted FEV1, age, AHI; and ESS score did not differ between D and ND patients. All D patients showed an MPAP ≥ 28 mm Hg, and among them 73% had an MPAP of > 30 mm Hg. Last, in 19.2% and 8% of subjects, respectively, in the D and ND groups, the mean (± SD) T90 was in the range of 30 ± 3.5%.


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  		Table 4.. Variable Mean Values ± SE and Range of Both D and ND Groups also Subdivided by Gender*

 


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  		Figure 1.. Box-and-whisker plots of all clinical variables. M = male; F = female; D = desaturators; ND = non-desaturators; ns = not significant.

 
By reducing the distance between the elements of clusters, we obtained more clusters, or rather, subdivisions of groups D and ND (Fig 2 ). Two subgroups, involving 76% of patients, resulted from the ND group, with the remaining 24% (6 of 25 patients) being constituted by scattered single patients. The subgroup ND1 was constituted by 5 of 25 patients (20%; 1 of 5 women [20% of the subgroup]; 4 of 5 men [80% of the subgroup]). The centroids were as follows: T90, 22.0%; MPAP, 19.2 mm Hg; and PaCO2, 46.5 mm Hg. The subgroup ND2 was constituted by 14 of 25 patients (56%; 8 of 14 women [57.1% of the subgroup]; 6 of 14 men [42.9% of the subgroup]). The centroids were as follows: T90, 20.1%; MPAP, 18.5 mm Hg; PaCO2, 33.3 mm Hg.



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  		Figure 2.. Two-dimensional scatterplots of the clusters. ND1 and ND2 are subgroups from ND patients (clusters 4 and 7, respectively); D1 and D2 are subgroups from D patients (clusters 1 and 2, respectively); the other clusters are of one patient. See Figure 1 for abbreviations not used in the text.

 
Similarly, group D was divided into two subgroups that involved 88.4% of D patients, with the remaining 11.5% (3 of 26 patients) consisting of scattered single patients (Fig 2). The subgroup D1 was constituted by 7 of 26 patients (26.9%; 3 of 7 women [42.8% of the subgroup]; 4 of 7 men [57.2% of the subgroup]). The centroids were as follows: T90, 38.8%; MPAP, 38.0 mm Hg; PaCO2, 52.0 mm Hg. The subgroup D2 was constituted by 16 of 26 patients (61.5%; 5 of 16 women [31.2% of the subgroup]; 11 of 16 men [68.8% of the subgroup]). The centroids were as follows: T90, 37.0%; MPAP, 31.5 mm Hg; PaCO2, 49.9 mm Hg. These data show that PaCO2 was higher in ND1 patients than in ND2 patients, and that MPAP was higher in D1 patients than in D2 patients.


   Discussion
TOP
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
References
 
Using linear regression analysis, the most significant correlations were between T90 and MPAP, between T90 and PaCO2, between MPAP and PaCO2 (Table 2), and between T90 and both MPAP and PaCO2 (Table 3), with the latter correlation being the best linear multiple model. Less statistically significant relationships were identified between the nocturnal variable T90 and the daytime parameters FEV1/VC ratio, predicted TLC, BMI, and BSaO2, which are listed according to decreasing order of R and adjusted R2 values. These data indicate a weak association between T90 and BMI (R = 0.44; R2 = 19.31), and hence that BMI has little effect on T90.

Also, the nocturnal parameters MSaO2 and NSaO2 were correlated with MPAP, PaCO2, FEV1/VC ratio, predicted TLC, and BSaO2, and the correlation was more significant than with T90. Like T90, neither MSaO2 nor NSaO2 correlated with PaO2 or BMI. There was only a slight correlation between BMI and T90, and between MSaO2 and FEV1.

The finding that T90, MPAP, and PaCO2 were required to identify D and ND patients by cluster analysis demonstrates that these variables play a predictive role. D patients had higher mean values of MPAP, PaCO2 and, obviously, T90 than ND patients (Table 4). Interestingly, cluster analysis identified the following subpopulations of D and ND patients: two D subgroups divided according to MPAP values; and two ND subgroups divided according to PaCO2 values (Fig 2). The small subgroup of hypercapnic ND1 patients (5 of 25 patients) consisted mostly of men (80% of the subgroup). Also, subgroup D2 (16 of 26 patients), which had a lower degree of pulmonary hypertension, was constituted mainly by men (68.8% of the subgroup).

The Relationship Between Daytime and Nocturnal Variables
There was a significant linear correlation between the daytime variables PaCO2, predicted TLC, and MPAP, and the nocturnal variables MSaO2, NSaO2, and T90 (Table 2). MSaO2 was significantly lower in group D than in group ND and was closely related to the increase in daytime PaCO2 values. It is generally acknowledged that predicted TLC and the FEV1/VC ratio are related to COPD severity, although in our models only 24.49% of T90 variability was explained by the predicted TLC

In group D, 26.9% of patients (D1) had higher T90 and MPAP values, whereas 61.5% of patients (D2) had lower T90 and MPAP values. These differences may reflect different degrees of disease severity.

It was not possible to measure daytime MPAP using color Doppler echocardiography in our COPD patients with hyperinflation because of chest impedance caused by pulmonary emphysema. This coincides with the results of the report by Tramarin et al,21 who obtained estimates of pulmonary hypertension by Doppler echocardiography in only 30% of 100 COPD patients. Similarly, Bach et al22 had a success rate of 26% among 207 patients with advanced COPD who were undergoing evaluation for lung volume reduction surgery. In contrast, Laaban and colleagues23 were able to measure pulmonary hypertension in 66% of 41 patients with severe COPD. Last, Arcasoy et al24 measured systolic PAP by Doppler echocardiography in 166 of 374 candidates (44%) for lung transplantation.

We performed a hierarchical cluster analysis using the three variables that showed the highest correlation in the least-squares method multiple linear regressions (ie, T90, MPAP, and PaCO2). First, we identified two major clusters of patients designated as D and ND, the mean values for which coincided with those of the cluster centroids. By reducing the intercluster distance, we identified the D1, D2, ND1, and ND1 subgroups from among 13 clusters (Fig 2), with the remaining clusters consisting of one patient each. This illustrates that many subgroups differ in one variable only. The D1 and D2 subgroups differed only in MPAP values, and the ND1 and ND2 subgroups differed only in PaCO2 values.

Cluster analysis indicated that the COPD patients in group D could be identified not only by T90 values, but also, and even better, by a pattern of the daytime parameters MPAP and PaCO2 (which are also predictors of nocturnal desaturation), and nocturnal T90.

Mechanisms of Nocturnal Oxygen Desaturation in COPD
Acute alveolar hypoxia, which is frequently observed during sleep in COPD patients, causes pulmonary vasoconstriction, which results in elevated PAP. Accordingly, episodes of nocturnal hypoxemia, especially when severe and prolonged, may bring about "peaks" of pulmonary hypertension.25 Fletcher et al13 demonstrated that about 25% of COPD patients with daytime PaO2 values of > 60 mm Hg experienced nocturnal desaturation. In another study, D patients had higher PAP both at rest and during exercise.8 During a 3-year follow-up, PAP decreased in D patients who had been treated with oxygen during sleep and increased in D control patients,26 who also had a lower survival rate.27 O’Donohue and Bowman28 found that approximately 45% of COPD patients had significant oxyhemoglobin desaturation during sleep, and that most had evidence of pulmonary artery hypertension. Chaouat et al12 reported that nocturnal desaturation correlated with daytime PaCO2 in COPD patients with a daytime PaO2 of > 55 mm Hg, but not with PAP at rest measured by cardiac catheterization. According to McKeon et al,29 nocturnal desaturation was not predictive of nocturnal oxygen desaturation. In another study,30 daytime levels of SaO2 correlated well with nocturnal desaturation, whereas according to Mohsenin et al31 awake SaO2 is not a good predictor of nocturnal oxygen desaturation in COPD patients. We found that awake PaCO2 and BSaO2, but not awake PaO2, were linked to MSaO2, NSaO2, and T90 (Table 2). In addition, BSaO2, but not awake PaO2, differed between the D and ND patients. Moreover, PaO2 was not a good predictor of nocturnal oxygen desaturation.

Vos et al32 distinguished nocturnal hypoxemic patients from normoxemic patients according to the hypercapnic ventilatory response. They found that patients with nocturnal hypoxemia had significantly lower hypercapnic ventilatory responses, lower PaO2, and more complaints of sleepiness, which suggests that other physiopathologic factors are involved in these patients.

Pattern of Variables Describing D COPD Patients and Clinical Applicability
The aim of this study was to determine whether, by using cluster analysis, we could identify a pattern of clinical variables that would distinguish between D and ND COPD patients more effectively than a cutoff value of T90. We have shown that D patients had a mean T90 of 37.2, with a range of 30.8 to 45.5, whereas ND patients had a mean T90 of 21.2, with a range of 15.2 to 28.4. Moreover, 19.2% and 8%, respectively, of the D and ND groups had a mean T90 value of approximately 30 ± 3.5% (calculated from single-patient clusters) [Fig 2], which suggests that the cutoff value was not effective in identifying D patients. Lewis et al33 reported that nighttime desaturation assessed by T90 in COPD patients has a considerable night-to-night variability when measured by pulse oximetry, and moreover that a single home pulse oximetry recording may not be an accurate measure of nocturnal desaturation.

Mean PaCO2, T90, and BMI values were higher in our D patients than in the patients reported on by Chaouat et al,34 and their MPAP values were higher than those reported in another study by Chaouat et al.12 Moreover, D COPD patients have been reported to have higher BMI values than ND patients, and a PACO2 of >50 mm Hg, but did not differ in FEV1 percent predicted, FEV1/VC ratio, AHI, and ESS score values.35 These findings implicate higher body weight in those patients with nocturnal oxygen desaturation, at least in those subjects.

Finally, cluster analysis showed that in the small ND1 subgroup most of the subjects with higher PaCO2 values were men. This applies also to the D2 subgroup, but those patients had lower levels of MPAP compared to women. Further studies are required to understand the meaning of these findings.

In conclusion, this cluster analysis showed that D patients can be identified not by the T90 value alone, but by a pattern of T90, MPAP, and PaCO2 values, and that the latter two variables are predictors of the severity of nocturnal desaturation. In any event, a T90 cutoff value does not appear to describe adequately the D patients or to assess correctly the severity of nocturnal desaturation. Moreover, cluster analysis identified subgroups of D and ND patients that differed in the degree of disease severity.


   Acknowledgements
 
The authors thank the cardiologists, Drs. Clemente Salerno, Gaetano Manca, Silvia Spedicato, and Maria Rosaria Greco, for performing the Doppler echocardiographic tests. We are indebted to Jean Gilder for revising and editing the text.


   Footnotes
 
Abbreviations: AHI = apnea-hypopnea index; BMI = body mass index; BSaO2 = baseline arterial oxygen saturation awake; CI = confidence interval; D = desaturator; ESS = Epworth sleepiness scale; MPAP = mean pulmonary artery pressure; MSaO2 = mean nocturnal arterial oxygen saturation; ND = nondesaturator; NSaO2 = nadir nocturnal arterial oxygen saturation; PAP = pulmonary artery pressure; PH = pulmonary hypertension; SaO2 = arterial oxygen saturation; TLC = total lung capacity; T90 = percentage of total nocturnal recording time spent with arterial oxygen saturation of < 90%; TRT = total recording time; VC = vital capacity

Received for publication April 8, 2005. Accepted for publication May 31, 2005.


   References
TOP
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 

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S. Kostianev, B. Marinov, D. Iluchev, D. Toraldo, G. Nicolardi, F. De Nuccio, and N. Ambrosino
Is the more (intricate) the better?
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