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ORIGINAL RESEARCH

Electrocardiographic Differences between COPD Patients Evaluated for Lung Transplantation With and without Pulmonary Hypertension

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Figures & data

Table 1.   ECG findings described in patients with emphysema

Table 2.   Patient characteristics

Figure 1.  Electrocardiographic characteristics of COPD patients without and with PH.Panel A corresponds to the ECG of a COPD patient without PH (mean PAP 17 mm Hg, heart rate 81 bpm, PR interval 124 ms, QRS complex duration 76 ms, QTc interval 401 ms, QRS axis + 37° and T wave axis + 70°). Panel B shows the ECG of a COPD patient with PH (mean PAP 65 mmHg, heart rate 63 bpm, PR interval 216 ms, QRS complex duration 84 ms, QTc interval 409 ms, QRS axis + 72° and T wave axis – 22°). In the ECG of the COPD patient with PH (panel B) the PR interval (PR) is longer, the P wave (P) in V1 and the S wave in lead I are of larger amplitude. In addition, there are negative T waves in the inferior leads (*).

Figure 1.  Electrocardiographic characteristics of COPD patients without and with PH.Panel A corresponds to the ECG of a COPD patient without PH (mean PAP 17 mm Hg, heart rate 81 bpm, PR interval 124 ms, QRS complex duration 76 ms, QTc interval 401 ms, QRS axis + 37° and T wave axis + 70°). Panel B shows the ECG of a COPD patient with PH (mean PAP 65 mmHg, heart rate 63 bpm, PR interval 216 ms, QRS complex duration 84 ms, QTc interval 409 ms, QRS axis + 72° and T wave axis – 22°). In the ECG of the COPD patient with PH (panel B) the PR interval (PR) is longer, the P wave (P) in V1 and the S wave in lead I are of larger amplitude. In addition, there are negative T waves in the inferior leads (*).

Table 3.   Electrocardiographic variables

Figure 2.  Hierarchical binary recursive partitioning algorithm to predict the presence of PH in COPD patients.The model correctly classified 64 out of 90 (71.1%) PH patients, and 39 out of 52 (75%) patients without PH. The model precision was 83.1% with an AUC by ROC of 0.74.

Figure 2.  Hierarchical binary recursive partitioning algorithm to predict the presence of PH in COPD patients.The model correctly classified 64 out of 90 (71.1%) PH patients, and 39 out of 52 (75%) patients without PH. The model precision was 83.1% with an AUC by ROC of 0.74.

Table 4.   Electrocardiographic variables in COPD patients with mean PAP < 40 or ≥ 40 mmHg

Figure 3.  Hierarchical binary recursive partitioning algorithm to predict mean PAP ≥ 40 mmHg in COPD patients.The model correctly classified the 16 COPD patients (100%) with mean PAP ≥ 40 mmHg and 75 out of 126 (59.5%) patients who had a mean PAP < 40 mmHg. The model precision was 24% with an AUC by ROC of 0.80. Abbreviations: mPAP: mean pulmonary artery pressure.

Figure 3.  Hierarchical binary recursive partitioning algorithm to predict mean PAP ≥ 40 mmHg in COPD patients.The model correctly classified the 16 COPD patients (100%) with mean PAP ≥ 40 mmHg and 75 out of 126 (59.5%) patients who had a mean PAP < 40 mmHg. The model precision was 24% with an AUC by ROC of 0.80. Abbreviations: mPAP: mean pulmonary artery pressure.

Table 5.   Electrocardiographic variables in COPD patients with PH and a PVR < 3 or ≥ 3 Wood Units

Figure 4.  Hierarchical binary recursive partitioning algorithm to predict PAOP > 15 mmHg in COPD patients.The model correctly classified 21 out of 42 COPD patients (50%) with a PAOP > 15 mmHg and 77 out of 97 (79.4%) patients who had a PAOP ≤ 15 mmHg. The model precision was 51.2% with an AUC by ROC of 0.65. Abbreviations: PAOP: pulmonary artery occlusion pressure.

Figure 4.  Hierarchical binary recursive partitioning algorithm to predict PAOP > 15 mmHg in COPD patients.The model correctly classified 21 out of 42 COPD patients (50%) with a PAOP > 15 mmHg and 77 out of 97 (79.4%) patients who had a PAOP ≤ 15 mmHg. The model precision was 51.2% with an AUC by ROC of 0.65. Abbreviations: PAOP: pulmonary artery occlusion pressure.

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