CT pulmonary angiography-based scoring system to predict the prognosis of acute pulmonary embolism

Kanako K. Kumamaru, Sachin S. Saboo, Ayaz Aghayev, Phoebe Cai, Carlos Gonzalez Quesada, Elizabeth George, Zoha Hussain, Tianrun Cai, Frank J. Rybicki

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

Background The purpose is to develop a comprehensive risk-scoring system based on CT findings for predicting 30-day mortality after acute pulmonary embolism (PE), and to compare it with PE Severity Index (PESI). Materials and methods The study included consecutive 1698 CT pulmonary angiograms (CTPA) positive for acute PE performed at a single institution (2003–2010). Two radiologists independently assessed each study regarding clinically relevant findings and then performed adjudication. These variables plus patient clinical information were included to build a LASSO logistic regression model to predict 30-day mortality. A point score for each significant variable was generated based on the final model. PESI score was calculated in 568 patients who visited the hospital after 2007. Results Inter-reader agreements of interpretations were >95% except for septal bowing (92%). The final prediction model showed superior ability over PESI (AUC = 0.822 vs 0.745) for predicting all-cause 30-day mortality (12.4%). The scoring system based on the significant variables (age (years), pleural effusion (+20), pericardial effusion (+20), lung/liver/bone lesions suggesting malignancy (+60), chronic interstitial lung disease (+20), enlarged lymph node in thorax (+20), and ascites (+40)) stratified patients into 4 severity categories, with mortality rates of 0.008% in class-I (≤50 pt), 3.8% in class-II (51-100 pt), 17.6% in class-III (101-150 pt), and 40.9% in class-IV (>150 pt). The mortality rate in the CTPA-high risk category (class-IV) was higher than those in the PESI's high risk (27.4%) and very high risk (25.2%) categories. Conclusion The CTPA-based model was superior to PESI in predicting 30-day mortality. Incorporating the CTPA-based scoring system into image interpretation workflows may help physicians to select the most appropriate management approach for individual patients.

Original languageEnglish (US)
Pages (from-to)473-479
Number of pages7
JournalJournal of Cardiovascular Computed Tomography
Volume10
Issue number6
DOIs
StatePublished - Nov 1 2016

Fingerprint

Pulmonary Embolism
Lung
Mortality
Angiography
Logistic Models
Pericardial Effusion
Workflow
Interstitial Lung Diseases
Pleural Effusion
Ascites
Area Under Curve
Computed Tomography Angiography
Thorax
Lymph Nodes
Physicians
Bone and Bones
Liver
Neoplasms

Keywords

  • Computed tomography
  • Mortality
  • Prediction
  • Pulmonary embolism
  • Risk assessment

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Cardiology and Cardiovascular Medicine

Cite this

Kumamaru, K. K., Saboo, S. S., Aghayev, A., Cai, P., Quesada, C. G., George, E., ... Rybicki, F. J. (2016). CT pulmonary angiography-based scoring system to predict the prognosis of acute pulmonary embolism. Journal of Cardiovascular Computed Tomography, 10(6), 473-479. https://doi.org/10.1016/j.jcct.2016.08.007

CT pulmonary angiography-based scoring system to predict the prognosis of acute pulmonary embolism. / Kumamaru, Kanako K.; Saboo, Sachin S.; Aghayev, Ayaz; Cai, Phoebe; Quesada, Carlos Gonzalez; George, Elizabeth; Hussain, Zoha; Cai, Tianrun; Rybicki, Frank J.

In: Journal of Cardiovascular Computed Tomography, Vol. 10, No. 6, 01.11.2016, p. 473-479.

Research output: Contribution to journalArticle

Kumamaru, KK, Saboo, SS, Aghayev, A, Cai, P, Quesada, CG, George, E, Hussain, Z, Cai, T & Rybicki, FJ 2016, 'CT pulmonary angiography-based scoring system to predict the prognosis of acute pulmonary embolism', Journal of Cardiovascular Computed Tomography, vol. 10, no. 6, pp. 473-479. https://doi.org/10.1016/j.jcct.2016.08.007
Kumamaru, Kanako K. ; Saboo, Sachin S. ; Aghayev, Ayaz ; Cai, Phoebe ; Quesada, Carlos Gonzalez ; George, Elizabeth ; Hussain, Zoha ; Cai, Tianrun ; Rybicki, Frank J. / CT pulmonary angiography-based scoring system to predict the prognosis of acute pulmonary embolism. In: Journal of Cardiovascular Computed Tomography. 2016 ; Vol. 10, No. 6. pp. 473-479.
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abstract = "Background The purpose is to develop a comprehensive risk-scoring system based on CT findings for predicting 30-day mortality after acute pulmonary embolism (PE), and to compare it with PE Severity Index (PESI). Materials and methods The study included consecutive 1698 CT pulmonary angiograms (CTPA) positive for acute PE performed at a single institution (2003–2010). Two radiologists independently assessed each study regarding clinically relevant findings and then performed adjudication. These variables plus patient clinical information were included to build a LASSO logistic regression model to predict 30-day mortality. A point score for each significant variable was generated based on the final model. PESI score was calculated in 568 patients who visited the hospital after 2007. Results Inter-reader agreements of interpretations were >95{\%} except for septal bowing (92{\%}). The final prediction model showed superior ability over PESI (AUC = 0.822 vs 0.745) for predicting all-cause 30-day mortality (12.4{\%}). The scoring system based on the significant variables (age (years), pleural effusion (+20), pericardial effusion (+20), lung/liver/bone lesions suggesting malignancy (+60), chronic interstitial lung disease (+20), enlarged lymph node in thorax (+20), and ascites (+40)) stratified patients into 4 severity categories, with mortality rates of 0.008{\%} in class-I (≤50 pt), 3.8{\%} in class-II (51-100 pt), 17.6{\%} in class-III (101-150 pt), and 40.9{\%} in class-IV (>150 pt). The mortality rate in the CTPA-high risk category (class-IV) was higher than those in the PESI's high risk (27.4{\%}) and very high risk (25.2{\%}) categories. Conclusion The CTPA-based model was superior to PESI in predicting 30-day mortality. Incorporating the CTPA-based scoring system into image interpretation workflows may help physicians to select the most appropriate management approach for individual patients.",
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AU - Saboo, Sachin S.

AU - Aghayev, Ayaz

AU - Cai, Phoebe

AU - Quesada, Carlos Gonzalez

AU - George, Elizabeth

AU - Hussain, Zoha

AU - Cai, Tianrun

AU - Rybicki, Frank J.

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N2 - Background The purpose is to develop a comprehensive risk-scoring system based on CT findings for predicting 30-day mortality after acute pulmonary embolism (PE), and to compare it with PE Severity Index (PESI). Materials and methods The study included consecutive 1698 CT pulmonary angiograms (CTPA) positive for acute PE performed at a single institution (2003–2010). Two radiologists independently assessed each study regarding clinically relevant findings and then performed adjudication. These variables plus patient clinical information were included to build a LASSO logistic regression model to predict 30-day mortality. A point score for each significant variable was generated based on the final model. PESI score was calculated in 568 patients who visited the hospital after 2007. Results Inter-reader agreements of interpretations were >95% except for septal bowing (92%). The final prediction model showed superior ability over PESI (AUC = 0.822 vs 0.745) for predicting all-cause 30-day mortality (12.4%). The scoring system based on the significant variables (age (years), pleural effusion (+20), pericardial effusion (+20), lung/liver/bone lesions suggesting malignancy (+60), chronic interstitial lung disease (+20), enlarged lymph node in thorax (+20), and ascites (+40)) stratified patients into 4 severity categories, with mortality rates of 0.008% in class-I (≤50 pt), 3.8% in class-II (51-100 pt), 17.6% in class-III (101-150 pt), and 40.9% in class-IV (>150 pt). The mortality rate in the CTPA-high risk category (class-IV) was higher than those in the PESI's high risk (27.4%) and very high risk (25.2%) categories. Conclusion The CTPA-based model was superior to PESI in predicting 30-day mortality. Incorporating the CTPA-based scoring system into image interpretation workflows may help physicians to select the most appropriate management approach for individual patients.

AB - Background The purpose is to develop a comprehensive risk-scoring system based on CT findings for predicting 30-day mortality after acute pulmonary embolism (PE), and to compare it with PE Severity Index (PESI). Materials and methods The study included consecutive 1698 CT pulmonary angiograms (CTPA) positive for acute PE performed at a single institution (2003–2010). Two radiologists independently assessed each study regarding clinically relevant findings and then performed adjudication. These variables plus patient clinical information were included to build a LASSO logistic regression model to predict 30-day mortality. A point score for each significant variable was generated based on the final model. PESI score was calculated in 568 patients who visited the hospital after 2007. Results Inter-reader agreements of interpretations were >95% except for septal bowing (92%). The final prediction model showed superior ability over PESI (AUC = 0.822 vs 0.745) for predicting all-cause 30-day mortality (12.4%). The scoring system based on the significant variables (age (years), pleural effusion (+20), pericardial effusion (+20), lung/liver/bone lesions suggesting malignancy (+60), chronic interstitial lung disease (+20), enlarged lymph node in thorax (+20), and ascites (+40)) stratified patients into 4 severity categories, with mortality rates of 0.008% in class-I (≤50 pt), 3.8% in class-II (51-100 pt), 17.6% in class-III (101-150 pt), and 40.9% in class-IV (>150 pt). The mortality rate in the CTPA-high risk category (class-IV) was higher than those in the PESI's high risk (27.4%) and very high risk (25.2%) categories. Conclusion The CTPA-based model was superior to PESI in predicting 30-day mortality. Incorporating the CTPA-based scoring system into image interpretation workflows may help physicians to select the most appropriate management approach for individual patients.

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