Cardiothoracic Applications of 3-dimensional Printing

Medical 3-dimensional (3D) printing is emerging as a clinically relevant imaging tool in directing preoperative and intraoperative planning in many surgical specialties and will therefore likely lead to interdisciplinary collaboration between engineers, radiologists, and surgeons. Data from standard imaging modalities such as computed tomography, magnetic resonance imaging, echocardiography, and rotational angiography can be used to fabricate life-sized models of human anatomy and pathology, as well as patient-specific implants and surgical guides. Cardiovascular 3D-printed models can improve diagnosis and allow for advanced preoperative planning. The majority of applications reported involve congenital heart diseases and valvular and great vessels pathologies. Printed models are suitable for planning both surgical and minimally invasive procedures. Added value has been reported toward improving outcomes, minimizing perioperative risk, and developing new procedures such as transcatheter mitral valve replacements. Similarly, thoracic surgeons are using 3D printing to assess invasion of vital structures by tumors and to assist in diagnosis and treatment of upper and lower airway diseases. Anatomic models enable surgeons to assimilate information more quickly than image review, choose the optimal surgical approach, and achieve surgery in a shorter time. Patient-specific 3D-printed implants are beginning to appear and may have significant impact on cosmetic and life-saving procedures in the future. In summary, cardiothoracic 3D printing is rapidly evolving and may be a potential game-changer for surgeons. The imager who is equipped with the tools to apply this new imaging science to cardiothoracic care is thus ideally positioned to innovate in this new emerging imaging modality

Malignancy and acute pulmonary embolism: risk stratification including the right to left ventricle diameter ratio in 1596 subjects

PURPOSE: To test the hypothesis that subjects with a known malignancy at the time of acute pulmonary embolism (PE) have different clinical characteristics and predictors of 30-day all-cause mortality when compared with subjects with no known malignancy. MATERIALS AND METHODS: A retrospective (August 2003 to March 2010) cohort of 1596 consecutive positive (for acute PE) computed tomography pulmonary angiograms (CTPAs) performed at a single, large, urban teaching hospital was separated into those from subjects with (n=835) and those from subjects without (n=761) a known malignancy. Clinical characteristics were compared between groups, and a logistic regression model determined predictors of 30-day all-cause mortality for each group. RESULTS: Subjects with malignancy were older (60.8+/-13.9 vs. 54.5+/-18.8 y, P1.0) had a higher risk of 30-day death only among subjects with no known malignancy at the time of the CTPA (odds ratio=4.08, 95% confidence interval: 1.67-9.96). CONCLUSIONS: Among subjects who present with acute PE, those with a malignancy had different clinical characteristics and predictors of mortality when compared with the cohort of subjects with no known malignancy. A computed tomography-derived right to left ventricular diameter ratio predicts 30-day all-cause mortality only for those subjects who do not have a malignancy

Association Between Confidence Level of Acute Pulmonary Embolism Diagnosis on CTPA images and Clinical Outcomes

RATIONALE AND OBJECTIVES: The purpose was to evaluate clinical characteristics associated with low confidence in diagnosis of acute pulmonary embolism (PE) as expressed in computed tomography pulmonary angiography (CTPA) reports and to evaluate the effect of confidence level in PE diagnosis on patient clinical outcomes. MATERIALS AND METHODS: This study included radiology reports from 1664 consecutive CTPA considered positive for acute PE (8/2003-5/2010). All reports were retrospectively assessed for the level of confidence in diagnosis. Baseline characteristics and outcomes (therapies related to PE and short-term mortality) were compared between high and low confidence groups. Multivariable logistic and Cox regression analyses were used to analyze the relationship between the confidence level and outcomes. RESULTS: One-hundred sixty of 1664 (9.6%) reports had language that reflected a low confidence in PE diagnosis. The low confidence group had smaller (segmental and subsegmental) suspected emboli (prevalence, 72.5% vs. 50.7%; P \u3c .001) and more comorbidities. The low confidence group had a lower likelihood of receiving PE-related therapies (adjusted odds ratio [OR], 0.18; 95% confidence interval, 0.10-031, P \u3c .001), but there was no change in the all-cause and PE-related 30-day and/or 90-day mortality (OR of death for low confidence, 0.81-1.13, P values \u3e .5). CONCLUSIONS: Roughly 10% of positive CTPA reports had uncertainty in PE findings, and patients with reports categorized as low confidence had smaller emboli and more comorbidities. Although the low confidence group was less likely to receive PE-related therapies, patients in this group were not associated with higher probability of short-term mortality

Developing Validated Tools to Identify Pulmonary Embolism in Electronic Databases: Rationale and Design of the PE-EHR+ Study

BACKGROUND: Contemporary pulmonary embolism (PE) research, in many cases, relies on data from electronic health records (EHRs) and administrative databases that use International Classification of Diseases (ICD) codes. Natural language processing (NLP) tools can be used for automated chart review and patient identification. However, there remains uncertainty with the validity of ICD-10 codes or NLP algorithms for patient identification. METHODS: The PE-EHR+ study has been designed to validate ICD-10 codes as Principal Discharge Diagnosis, or Secondary Discharge Diagnoses, as well as NLP tools set out in prior studies to identify patients with PE within EHRs. Manual chart review by two independent abstractors by predefined criteria will be the reference standard. Sensitivity, specificity, and positive and negative predictive values will be determined. We will assess the discriminatory function of code subgroups for intermediate- and high-risk PE. In addition, accuracy of NLP algorithms to identify PE from radiology reports will be assessed. RESULTS: A total of 1,734 patients from the Mass General Brigham health system have been identified. These include 578 with ICD-10 Principal Discharge Diagnosis codes for PE, 578 with codes in the secondary position, and 578 without PE codes during the index hospitalization. Patients within each group were selected randomly from the entire pool of patients at the Mass General Brigham health system. A smaller subset of patients will also be identified from the Yale-New Haven Health System. Data validation and analyses will be forthcoming. CONCLUSIONS: The PE-EHR+ study will help validate efficient tools for identification of patients with PE in EHRs, improving the reliability of efficient observational studies or randomized trials of patients with PE using electronic databases