Imaging of non-cardiac, non-traumatic causes of acute chest pain

Development and external validation of a novel modality for rapid recognition of aortic dissection based on peripheral pulse oximetry waveforms

BACKGROUND

Aortic dissection (AD) is a life-threatening cardiovascular emergency that is often misdiagnosed as other chest pain conditions. Physiologically, AD may cause abnormalities in peripheral blood flow, which can be detected using pulse oximetry waveforms.

PURPOSE

This study aimed to assess the feasibility of identifying AD based on pulse oximetry waveforms and to highlight the key waveform features that play a crucial role in this diagnostic method.

METHODS

This prospective study employed high-risk chest pain cohorts from two emergency departments. The initial cohort was enriched with AD patients (n = 258, 47% AD) for model development, while the second cohort consisted of chest pain patients awaiting angiography (n = 71, 25% AD) and was used for external validation. Pulse oximetry waveforms from the four extremities were collected for each patient. After data preprocessing, a recognition model based on the random forest algorithm was trained using patients' gender, age, and waveform difference features extracted from the pulse oximetry waveforms. The performance of the model was evaluated using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA). The importance of features was also assessed using Shapley Value and Gini importance.

RESULTS

The model demonstrated strong performance in identifying AD in both the training and external validation sets. In the training set, the model achieved an area under the ROC curve of 0.979 (95% CI: 0.961-0.990), sensitivity of 0.918 (95% CI: 0.873-0.955), specificity of 0.949 (95% CI: 0.912-0.985), and accuracy of 0.933 (95% CI: 0.904-0.959). In the external validation set, the model attained an area under the ROC curve of 0.855 (95% CI: 0.720-0.965), sensitivity of 0.889 (95% CI: 0.722-1.000), specificity of 0.698 (95% CI: 0.566-0.812), and accuracy of 0.794 (95% CI: 0.672-0.878). Decision curve analysis (DCA) further showed that the model provided a substantial net benefit for identifying AD. The median mean and median variance of the four limbs' signals were the most influential features in the recognition model.

CONCLUSIONS

This study demonstrated the feasibility and strong performance of identifying AD based on peripheral pulse oximetry waveforms in high-risk chest pain populations in the emergency setting. The findings also provided valuable insights for future human fluid dynamics simulations to elucidate the impact of AD on blood flow in greater detail.

Nonradiology Health Care Professionals Significantly Benefit From AI Assistance in Emergency-Related Chest Radiography Interpretation

BACKGROUND

Chest radiographs (CXRs) are still of crucial importance in primary diagnostics, but their interpretation poses difficulties at times.

RESEARCH QUESTION

Can a convolutional neural network-based artificial intelligence (AI) system that interprets CXRs add value in an emergency unit setting?

STUDY DESIGN AND METHODS

A total of 563 CXRs acquired in the emergency unit of a major university hospital were retrospectively assessed twice by three board-certified radiologists, three radiology residents, and three emergency unit-experienced nonradiology residents (NRRs). They used a two-step reading process: (1) without AI support; and (2) with AI support providing additional images with AI overlays. Suspicion of four suspected pathologies (pleural effusion, pneumothorax, consolidations suspicious for pneumonia, and nodules) was reported on a five-point confidence scale. Confidence scores of the board-certified radiologists were converted into four binary reference standards of different sensitivities. Performance by radiology residents and NRRs without AI support/with AI support were statistically compared by using receiver-operating characteristics (ROCs), Youden statistics, and operating point metrics derived from fitted ROC curves.

RESULTS

NRRs could significantly improve performance, sensitivity, and accuracy with AI support in all four pathologies tested. In the most sensitive reference standard (reference standard IV), NRR consensus improved the area under the ROC curve (mean, 95% CI) in the detection of the time-critical pathology pneumothorax from 0.846 (0.785-0.907) without AI support to 0.974 (0.947-1.000) with AI support (P < .001), which represented a gain of 30% in sensitivity and 2% in accuracy (while maintaining an optimized specificity). The most pronounced effect was observed in nodule detection, with NRR with AI support improving sensitivity by 53% and accuracy by 7% (area under the ROC curve without AI support, 0.723 [0.661-0.785]; with AI support, 0.890 [0.848-0.931]; P < .001). Radiology residents had smaller, mostly nonsignificant gains in performance, sensitivity, and accuracy with AI support.

INTERPRETATION

We found that in an emergency unit setting without 24/7 radiology coverage, the presented AI solution features an excellent clinical support tool to nonradiologists, similar to a second reader, and allows for a more accurate primary diagnosis and thus earlier therapy initiation.

Extensive Type A Aortic Arterial Dissection Presenting With Stroke Symptoms: A Case Report

Aortic dissection (AD) is a rare but often lethal condition if not properly and urgently treated. Most often, patients arrive with acute hemodynamic instability and ripping chest agony. The patient's life depends critically on a correct diagnosis made as soon as possible. We describe a 60-year-old man who arrived at the emergency room with symptoms of a brain stroke, including poor consciousness, left-sided weakness, and speech disturbance associated with hemodynamic instability, and chest pain. Thoracic aortic arch dissection was observed on CT angiography (CTA). In addition, CTA revealed that the dissection extends proximally into the left common carotid artery, left subclavian artery, brachiocephalic trunk, and right common carotid artery and distally to the left common iliac artery, coupled with significant stenosis of the left common iliac artery. Proper management of blood pressure (BP) parameters is life-saving for the patient. Since our hospital did not offer cardiothoracic surgery services, the patient was transferred to a different institution, where he received medical care immediately from an expert team and had surgery.

The diagnostic performance and utility of chest XR in relation to chest CT in nontraumatic respiratory emergency patients

Background and Aim

This study aimed to determine the diagnostic performance and utility of chest radiography in relation to chest computed tomography (CT) in nontraumatic respiratory emergency patients.

Patients and Methods

Patients presenting to the emergency department with respiratory complaints due to nontraumatic pathologies and who had consecutive chest XR and chest CT assessments with an interval of fewer than 6 hours were enrolled in the study (n = 561).

Results

The two methods were determined to be consistent with moderate agreement in detecting pleural effusion (κ = 0.576, P < 0.001), pneumothorax (κ = 0.567, P < 0.001), increased cardiothoracic ratio (κ =0.472, P < 0.001), and pneumonic consolidation (κ = 0.465, P < 0.001). The consistency rate was significantly higher in patients aged <40 years (95.5% in ≤30 years and 90.9% in 31-40 years) as compared to older patients (81.8%, 68.2%, and 72.7% in 41-60 years, 61-80 years, and >80 years, respectively; P < 0.001 for each). The consistency rate was also higher for posteroanterior (PA) chest XR views than for anteroposterior (AP) chest XR views (72.7% vs. 68.2%, P = 0.005) and for high- and moderate-quality chest XR views than for poor-quality views (72.7% and 77.3% vs. 70.5%, P = 0.001).

Conclusion

The consistency between the chest XR and CT was more likely in patients aged <40 years and for PA and moderate-to-high quality chest XR views, as compared to older patients and AP and poor-quality views, respectively. We suggest that an upright position PA chest X-ray with high imaging quality may be the first choice, especially in patients aged <40 years admitted to the emergency department with respiratory symptoms.

Clinically focused multi-cohort benchmarking as a tool for external validation of artificial intelligence algorithm performance in basic chest radiography analysis

Artificial intelligence (AI) algorithms evaluating [supine] chest radiographs ([S]CXRs) have remarkably increased in number recently. Since training and validation are often performed on subsets of the same overall dataset, external validation is mandatory to reproduce results and reveal potential training errors. We applied a multicohort benchmarking to the publicly accessible (S)CXR analyzing AI algorithm CheXNet, comprising three clinically relevant study cohorts which differ in patient positioning ([S]CXRs), the applied reference standards (CT-/[S]CXR-based) and the possibility to also compare algorithm classification with different medical experts’ reading performance. The study cohorts include [1] a cohort, characterized by 563 CXRs acquired in the emergency unit that were evaluated by 9 readers (radiologists and non-radiologists) in terms of 4 common pathologies, [2] a collection of 6,248 SCXRs annotated by radiologists in terms of pneumothorax presence, its size and presence of inserted thoracic tube material which allowed for subgroup and confounding bias analysis and [3] a cohort consisting of 166 patients with SCXRs that were evaluated by radiologists for underlying causes of basal lung opacities, all of those cases having been correlated to a timely acquired computed tomography scan (SCXR and CT within < 90 min). CheXNet non-significantly exceeded the radiology resident (RR) consensus in the detection of suspicious lung nodules (cohort [1], AUC AI/RR: 0.851/0.839, p = 0.793) and the radiological readers in the detection of basal pneumonia (cohort [3], AUC AI/reader consensus: 0.825/0.782, p = 0.390) and basal pleural effusion (cohort [3], AUC AI/reader consensus: 0.762/0.710, p = 0.336) in SCXR, partly with AUC values higher than originally published (“Nodule”: 0.780, “Infiltration”: 0.735, “Effusion”: 0.864). The classifier “Infiltration” turned out to be very dependent on patient positioning (best in CXR, worst in SCXR). The pneumothorax SCXR cohort [2] revealed poor algorithm performance in CXRs without inserted thoracic material and in the detection of small pneumothoraces, which can be explained by a known systematic confounding error in the algorithm training process. The benefit of clinically relevant external validation is demonstrated by the differences in algorithm performance as compared to the original publication. Our multi-cohort benchmarking finally enables the consideration of confounders, different reference standards and patient positioning as well as the AI performance comparison with differentially qualified medical readers.

Incidental Finding of an Extensive Type B Aortic Dissection Extending to the Iliac Arteries

An aortic dissection is a life-threatening event that requires urgent evaluation. A dissection is defined as a tear in the innermost layer of the aortic wall forming a true and false lumen. This is normally diagnosed with a CT with contrast when clinical suspicion is present. Deciding whether urgent surgical intervention is required is key, as it may determine the survival of the patient. The treatment of type A aortic dissection involves emergent open-heart surgery. Medical treatment and clinical follow-up are recommended for uncomplicated type B dissections. In this report, we present a case of an extensive type B aortic dissection in an asymptomatic patient who required immediate surgical intervention.

How is type D personality associated with the major psychological outcomes in noncardiac chest pain patients?

Noncardiac chest pain (NCCP) may lead many problems on the health-care system. Having type D personality has been shown to adversely affect NCCP patients. This study aimed to determine the psychological comorbidities that type D personality is associated with, in patients with NCCP. The participants of this cross-sectional study were 360 patients diagnosed with NCCP. Patients filled out questionnaires about sociodemographic, behavioral, and clinical factors (severity of pain, somatization, cardiac anxiety, fear of body sensations, depression, and type D personality). Type D personality was more prevalent among female (p < 0.005), and those people having this personality showed lower sleep quality (p = 0.001) and sexual life satisfaction (p < 0.001) and more likely to be smoker (p < 0.001). Type D personality is strongly associated with fear of body sensations (β = 5.92, SE = 1.95, p = 0.003), pain intensity (β = 3.53, SE = 0.98, p < 0.001), depression (β = 2.91, SE = 0.62, p < 0.001), and somatization (β = 1.75, SE = 0.55, p < 0.001). Type D personality and major psychological comorbidities were strongly associated. Physicians should consider that having type D personality can be linked to NCCP in an effort to help patients receive effective psychological consultations.

ACR Appropriateness Criteria® Nontraumatic Chest Wall Pain

Chest pain is a common reason that patients may present for evaluation in both ambulatory and emergency department settings, and is often of musculoskeletal origin in the former. Chest wall syndrome collectively describes the various entities that can contribute to chest wall pain of musculoskeletal origin and may affect any chest wall structure. Various imaging modalities may be employed for the diagnosis of nontraumatic chest wall conditions, each with variable utility depending on the clinical scenario. We review the evidence for or against use of various imaging modalities for the diagnosis of nontraumatic chest wall pain. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

A practical biphasic contrast media injection protocol strongly enhances the aorta and pulmonary artery simultaneously using a single CT angiography scan

Background

Enhancement profiles of the pulmonary artery (PA) and aorta differ when using computed tomography (CT) angiography. Our aim was to determine the optimal CT protocol for a one-time CT scan that assesses both blood vessels.

Methods

We prospectively enrolled 101 cases of CT angiography in patients with suspected pulmonary embolism or aortic dissection from our center between 2018 and 2020. We also retrospectively collected the data of 40 patients who underwent traditional two-time CT scans between 2015 and 2018. Patients were divided into four groups: test bolus (TB) I, TB II, bolus-tracking (BT) I, and BT II. The enhancement of the PA and aorta, and the radiation doses used in the four groups were collected. Those who underwent two-time scans were classified into the traditional PA or aorta scan groups. Data were compared between the BT and traditional groups.

Results

The aortic enhancement was highest in BT II (294.78 ± 64.48 HU) followed BT I (285.18 ± 64.99 HU), TB II (186.58 ± 57.53 HU), and TB I (173.62 ± 69.70 HU). The radiation dose used was lowest in BT I (11.85 ± 5.55 mSv) and BT II (9.07 ± 3.44 mSv) compared with that used in the traditional groups (20.07 ± 7.78 mSv) and accounted for half of the traditional group (45.17–59.02%). The aortic enhancement was also highest in BT II (294.78 ± 64.48 HU) followed by BT I (285.18 ± 64.99 HU) when compared with that in the traditional aorta scan group (234.95 ± 94.18 HU).

Conclusion

Our CT protocol with a BT technique allows for a lower radiation dose and better image quality of the PA and aorta than those obtained using traditional CT scans.

Trial registration: NCT04832633, retrospectively registered in April 2021 to the clinical trial registry.

The Penn Classification Predicts Hospital Mortality in Acute Stanford Type A and Type B Aortic Dissections

OBJECTIVES

Mortality in acute aortic dissection varies depending on anatomic location, extent, and associated complications. The Stanford classification guides surgical versus medical management. The Penn classification stratifies mortality risk in patients with Stanford type A aortic dissections undergoing surgery. The objective of the present study was to determine whether the Penn classification can predict hospital mortality in patients with acute Stanford type A and type B aortic dissections undergoing surgical or medical management.

DESIGN

Retrospective, observational study.

SETTING

Tertiary care, university hospital.

PARTICIPANTS

Patients with acute aortic dissection between January 2008 and December 2017.

INTERVENTIONS

Examination of hospital mortality after surgical or medical management.

MEASUREMENTS AND MAIN RESULTS

Three hundred fifty-two patients had confirmed dissections (186 type A, 166 type B). The overall mortality was 18.8% for type A and 13.3% for type B. Penn class A patients with type A or type B dissections undergoing surgical repair had the lowest mortality (both 3.1%). Penn class B, C, or B+C patients with type A dissections and Penn class B+C patients with type B dissections undergoing medical management had the greatest incidence of mortality (50.0%-57.1%). All others had intermediate mortality (6.7%-39.3%). Logistic regression analysis demonstrated that Penn class B, C, and B+C patients had a greater odds of mortality and predicted mortality than did Penn class A patients.

CONCLUSIONS

The Penn classification predicts hospital mortality in patients with acute Stanford type A or type B aortic dissections undergoing surgical or medical management. Early endovascular repair may confer lower risk of mortality in patients with type B dissections presenting without ischemia.

"Singultus" uncloaking potentially fatal vascular dissections

Aortic dissection (AD) is a serious condition in which the intimal layer of aorta tears and blood surges in between the intimal and medial layers of aorta causing it to separate (dissect). It usually presents with excruciating pain radiating to the back. Here we present a unique presentation of AD where an old-aged Caucasian male presented with a chronic history of intractable hiccups. His computed tomography (CAT scan) revealed the dissection of the descending thoracic aorta. He was managed conservatively and was discharged home in stable condition. The purpose of this report is to highlight this unusual presentation of AD and unmask the possible etiology of hiccups in such cases.

Extracardiac findings on coronary computed tomography angiography in patients without significant coronary artery disease

OBJECTIVE

To analyse extracardiac findings in patients without significant coronary artery disease (CAD) in general and in symptomatic patients in particular.

METHODS

We searched the Radiology Information System database for coronary computed tomography angiographies (CTA) performed from 2000-2014 and retrospectively enrolled 3,898 patients without significant CAD (coronary stenosis < 50%) in CTA. In 2,330 symptomatic patients, we analysed the spectrum of extracardiac findings and identified pathologies potentially explaining chest pain. Finally, we investigated variables affecting the number of extracardiac findings detected in CTA.

RESULTS

Overall extracardiac findings were found in 1,177 patients (30.2%; 95%CI, 28.8-31.7%). 94 patients (2.4%; 95%CI, 2.0-2.9%) had extracardiac findings with a recommendation for follow-up, sixteen patients (0.4%; 95%CI, 0.3-0.7%) had incidental urgent, and another three patients (0.1%; 95%CI, 0.1-0.2%) had incidental malignant extracardiac findings. 185 of 2,330 symptomatic patients (7.9%; 95%CI, 6.9-9.1%) revealed extracardiac findings potentially explaining chest pain after exclusion of significant CAD. The number of extracardiac findings increased significantly with patient age (p < 0.001) and the cumulative experience of the CT reader (p < 0.001).

CONCLUSION

30.2% of patients undergoing CTA for exclusion of CAD had ECF, and 7.9% of symptomatic patients without significant CAD on their examination had findings that could potentially explain their symptoms.

KEY POINTS

• Of patients undergoing CTA, 2.8% have relevant incidental extracardiac findings. • CTA could identify the differential diagnosis of chest pain when excluding significant CAD. • Patient age and reader's professional experience influence the number of detected ECFs.

Clinically integrated multi-organ point-of-care ultrasound for undifferentiated respiratory difficulty, chest pain, or shock: a critical analytic review

Rapid and accurate diagnosis and treatment are paramount in the management of the critically ill. Critical care ultrasound has been widely used as an adjunct to standard clinical examination, an invaluable extension of physical examination to guide clinical decision-making at bedside. Recently, there is growing interest in the use of multi-organ point-of-care ultrasound (MOPOCUS) for the management of the critically ill, especially in the early phase of resuscitation. This article will review the role and utility of symptom-based and sign-oriented MOPOCUS in patients with undifferentiated respiratory difficulty, chest pain, or shock and how it can be performed in a timely, effective, and efficient manner.