ACR Appropriateness Criteria® Routine Chest Radiography

AI-assisted Analysis to Facilitate Detection of Humeral Lesions on Chest Radiographs

Purpose To develop an artificial intelligence (AI) system for humeral tumor detection on chest radiographs (CRs) and evaluate the impact on reader performance. Materials and Methods In this retrospective study, 14 709 CRs (January 2000 to December 2021) were collected from 13 468 patients, including CT-proven normal (n = 13 116) and humeral tumor (n = 1593) cases. The data were divided into training and test groups. A novel training method called false-positive activation area reduction (FPAR) was introduced to enhance the diagnostic performance by focusing on the humeral region. The AI program and 10 radiologists were assessed using holdout test set 1, wherein the radiologists were tested twice (with and without AI test results). The performance of the AI system was evaluated using holdout test set 2, comprising 10 497 normal images. Receiver operating characteristic analyses were conducted for evaluating model performance. Results FPAR application in the AI program improved its performance compared with a conventional model based on the area under the receiver operating characteristic curve (0.87 vs 0.82, P = .04). The proposed AI system also demonstrated improved tumor localization accuracy (80% vs 57%, P < .001). In holdout test set 2, the proposed AI system exhibited a false-positive rate of 2%. AI assistance improved the radiologists' sensitivity, specificity, and accuracy by 8.9%, 1.2%, and 3.5%, respectively (P < .05 for all). Conclusion The proposed AI tool incorporating FPAR improved humeral tumor detection on CRs and reduced false-positive results in tumor visualization. It may serve as a supportive diagnostic tool to alert radiologists about humeral abnormalities. Keywords: Artificial Intelligence, Conventional Radiography, Humerus, Machine Learning, Shoulder, Tumor Supplemental material is available for this article. © RSNA, 2024.

Deep Learning-Based Computer-Aided Detection System for Preoperative Chest Radiographs to Predict Postoperative Pneumonia

RATIONALE AND OBJECTIVES

The role of preoperative chest radiography (CR) for prediction of postoperative pneumonia remains uncertain. We aimed to develop and validate a prediction model for postoperative pneumonia incorporating findings of preoperative CRs evaluated by a deep learning-based computer-aided detection (DL-CAD) system MATERIALS AND METHODS: This retrospective study included consecutive patients who underwent surgery between January 2019 and March 2020 and divided into development (surgery in 2019) and validation (surgery between January and March 2020) cohorts. Preoperative CRs obtained within 1-month before surgery were analyzed with a commercialized DL-CAD that provided probability values for the presence of 10 different abnormalities in CRs. Logistic regression models to predict postoperative pneumonia were built using clinical variables (clinical model), and both clinical variables and DL-CAD results for preoperative CRs (DL-CAD model). The discriminative performances of the models were evaluated by area under the receiver operating characteristic curves.

RESULTS

In development cohort (n = 19,349; mean age, 57 years; 11,392 men), DL-CAD results for pulmonary nodules (odds ratio [OR, for 1% increase in probability value], 1.007; p = 0.021), consolidation (OR, 1.019; p < 0.001), and cardiomegaly (OR, 1.013; p < 0.001) were independent predictors of postoperative pneumonia and were included in the DL-CAD model. In validation cohort (n = 4957; mean age, 56 years; 2848 men), the DL-CAD model exhibited a higher AUROC than the clinical model (0.843 vs. 0.815; p = 0.012).

CONCLUSION

Abnormalities in preoperative CRs evaluated by a DL-CAD were independent risk factors for postoperative pneumonia. Using DL-CAD results for preoperative CRs led to an improved prediction of postoperative pneumonia.

Analysis of Line and Tube Detection Performance of a Chest X-ray Deep Learning Model to Evaluate Hidden Stratification

This retrospective case-control study evaluated the diagnostic performance of a commercially available chest radiography deep convolutional neural network (DCNN) in identifying the presence and position of central venous catheters, enteric tubes, and endotracheal tubes, in addition to a subgroup analysis of different types of lines/tubes. A held-out test dataset of 2568 studies was sourced from community radiology clinics and hospitals in Australia and the USA, and was then ground-truth labelled for the presence, position, and type of line or tube from the consensus of a thoracic specialist radiologist and an intensive care clinician. DCNN model performance for identifying and assessing the positioning of central venous catheters, enteric tubes, and endotracheal tubes over the entire dataset, as well as within each subgroup, was evaluated. The area under the receiver operating characteristic curve (AUC) was assessed. The DCNN algorithm displayed high performance in detecting the presence of lines and tubes in the test dataset with AUCs > 0.99, and good position classification performance over a subpopulation of ground truth positive cases with AUCs of 0.86-0.91. The subgroup analysis showed that model performance was robust across the various subtypes of lines or tubes, although position classification performance of peripherally inserted central catheters was relatively lower. Our findings indicated that the DCNN algorithm performed well in the detection and position classification of lines and tubes, supporting its use as an assistant for clinicians. Further work is required to evaluate performance in rarer scenarios, as well as in less common subgroups.

Clinical and chest radiographic features of missed lung cancer and their association with patient outcomes

PURPOSE

To examine clinical and chest radiographic features of missed lung cancer (MLC) and explore their association with patient outcomes.

METHODS

We retrospectively reviewed chest radiographs obtained at least six months before lung cancer (LC) diagnosis in 95 patients to identify the first positive chest radiograph showing MLC. We assessed chest radiographic features of MLC and their association with patient outcomes.

RESULTS

Seventy-five (78.9%) patients (39 men, 36 women; mean age, 64.5 ± 10.5 years) had MLC. The median diagnostic delay was 31.3 months (6.6-128.0 months). The median MLC size was 16 mm (5-57 mm), and 54.7%, 68.0%, and 74.7% of MLC were in the left lung, the middle/lower zones, and the outer two-thirds of the lung, respectively. MLC exhibited a round/oval shape, partly/poorly defined margin, irregular/spiculated border, a density less than the aortic knob, and anatomical superimposition in 57.3%, 77.3%, 61.3%, 85.3%, and 88.0% of cases, respectively. Thirty-five (46.7%) patients had stage III + IV LC at diagnosis. Thirty-one (41.3%) patients died. MLC in the inner one-third of the lung, exhibiting a density equal to/greater than the aortic knob, or superimposed by midline structures was significantly associated with stage III + IV LC at diagnosis. The 3-year all-cause mortality significantly increased when MLC was in the upper zone, superimposed by pulmonary vessels, superimposed by pulmonary vessels plus ribs, or superimposed by pulmonary vessels plus in the inner one-third of the lung.

CONCLUSION

MLC with some radiographic features pertaining to their location, density, and superimposed structures was found to portend a worse outcome.

Preoperative investigations for elective surgical patients in a resource limited setting: Systematic review

Background

Methods

Results

Conclusion

•

Ordering preoperative investigation is a common practice.

•

Routine laboratory tests has significant burden on health care costs.

•

Preoperative tests should be guided by the patient's clinical history, co-morbidities, and physical examination.

•

Ordering preoperative investigations based on recommendation of guidelines is very essential.

Association of Artificial Intelligence-Aided Chest Radiograph Interpretation With Reader Performance and Efficiency

IMPORTANCE

The efficient and accurate interpretation of radiologic images is paramount.

OBJECTIVE

To evaluate whether a deep learning-based artificial intelligence (AI) engine used concurrently can improve reader performance and efficiency in interpreting chest radiograph abnormalities.

DESIGN, SETTING, AND PARTICIPANTS

This multicenter cohort study was conducted from April to November 2021 and involved radiologists, including attending radiologists, thoracic radiology fellows, and residents, who independently participated in 2 observer performance test sessions. The sessions included a reading session with AI and a session without AI, in a randomized crossover manner with a 4-week washout period in between. The AI produced a heat map and the image-level probability of the presence of the referrable lesion. The data used were collected at 2 quaternary academic hospitals in Boston, Massachusetts: Beth Israel Deaconess Medical Center (The Medical Information Mart for Intensive Care Chest X-Ray [MIMIC-CXR]) and Massachusetts General Hospital (MGH).

MAIN OUTCOMES AND MEASURES

The ground truths for the labels were created via consensual reading by 2 thoracic radiologists. Each reader documented their findings in a customized report template, in which the 4 target chest radiograph findings and the reader confidence of the presence of each finding was recorded. The time taken for reporting each chest radiograph was also recorded. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) were calculated for each target finding.

RESULTS

A total of 6 radiologists (2 attending radiologists, 2 thoracic radiology fellows, and 2 residents) participated in the study. The study involved a total of 497 frontal chest radiographs-247 from the MIMIC-CXR data set (demographic data for patients were not available) and 250 chest radiographs from MGH (mean [SD] age, 63 [16] years; 133 men [53.2%])-from adult patients with and without 4 target findings (pneumonia, nodule, pneumothorax, and pleural effusion). The target findings were found in 351 of 497 chest radiographs. The AI was associated with higher sensitivity for all findings compared with the readers (nodule, 0.816 [95% CI, 0.732-0.882] vs 0.567 [95% CI, 0.524-0.611]; pneumonia, 0.887 [95% CI, 0.834-0.928] vs 0.673 [95% CI, 0.632-0.714]; pleural effusion, 0.872 [95% CI, 0.808-0.921] vs 0.889 [95% CI, 0.862-0.917]; pneumothorax, 0.988 [95% CI, 0.932-1.000] vs 0.792 [95% CI, 0.756-0.827]). AI-aided interpretation was associated with significantly improved reader sensitivities for all target findings, without negative impacts on the specificity. Overall, the AUROCs of readers improved for all 4 target findings, with significant improvements in detection of pneumothorax and nodule. The reporting time with AI was 10% lower than without AI (40.8 vs 36.9 seconds; difference, 3.9 seconds; 95% CI, 2.9-5.2 seconds; P < .001).

CONCLUSIONS AND RELEVANCE

These findings suggest that AI-aided interpretation was associated with improved reader performance and efficiency for identifying major thoracic findings on a chest radiograph.

Perioperative care in open aortic vascular surgery: A Consensus Statement by the Enhanced Recovery after Surgery (ERAS®) Society and Society for Vascular Surgery

The Society for Vascular Surgery and the Enhanced Recovery After Surgery (ERAS®) Society formally collaborated and elected an international, multi-disciplinary panel of experts to review the literature and provide evidence-based recommendations related to all of the health care received in the perioperative period for patients undergoing open abdominal aortic operations (both transabdominal and retroperitoneal approaches, including supraceliac, suprarenal, and infrarenal clamp sites, for aortic aneurysm and aortoiliac occlusive disease). Structured around the ERAS® core elements, 36 recommendations were made and organized into preadmission, preoperative, intraoperative, and postoperative recommendations.

Effect of deep learning-based assistive technology use on chest radiograph interpretation by emergency department physicians: a prospective interventional simulation-based study

BACKGROUND

Interpretation of chest radiographs (CRs) by emergency department (ED) physicians is inferior to that by radiologists. Recent studies have investigated the effect of deep learning-based assistive technology on CR interpretation (DLCR), although its relevance to ED physicians remains unclear. This study aimed to investigate whether DLCR supports CR interpretation and the clinical decision-making of ED physicians.

METHODS

We conducted a prospective interventional study using a web-based performance assessment system. Study participants were recruited through the official notice targeting board for certified emergency physicians and residents working at the present ED. Of the eight ED physicians who volunteered to participate in the study, seven ED physicians were included, while one participant declared withdrawal during performance assessment. Seven physicians' CR interpretations and clinical decision-making were assessed based on the clinical data from 388 patients, including detecting the target lesion with DLCR. Participant performance was evaluated by area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and accuracy analyses; decision-making consistency was measured by kappa statistics. ED physicians with < 24 months of experience were defined as 'inexperienced'.

RESULTS

Among the 388 simulated cases, 259 (66.8%) had CR abnormality. Their median value of abnormality score measured by DLCR was 59.3 (31.77, 76.25) compared to a score of 3.35 (1.57, 8.89) for cases of normal CR. There was a difference in performance between ED physicians working with and without DLCR (AUROC: 0.801, P < 0.001). The diagnostic sensitivity and accuracy of CR were higher for all ED physicians working with DLCR than for those working without it. The overall kappa value for decision-making consistency was 0.902 (95% confidence interval [CI] 0.884-0.920); concurrently, the kappa value for the experienced group was 0.956 (95% CI 0.934-0.979), and that for the inexperienced group was 0.862 (95% CI 0.835-0.889).

CONCLUSIONS

This study presents preliminary evidence that ED physicians using DLCR in a clinical setting perform better at CR interpretation than their counterparts who do not use this technology. DLCR use influenced the clinical decision-making of inexperienced physicians more strongly than that of experienced physicians. These findings require prospective validation before DLCR can be recommended for use in routine clinical practice.

AI-based improvement in lung cancer detection on chest radiographs: results of a multi-reader study in NLST dataset

OBJECTIVE

Assess if deep learning-based artificial intelligence (AI) algorithm improves reader performance for lung cancer detection on chest X-rays (CXRs).

METHODS

This reader study included 173 images from cancer-positive patients (n = 98) and 346 images from cancer-negative patients (n = 196) selected from National Lung Screening Trial (NLST). Eight readers, including three radiology residents, and five board-certified radiologists, participated in the observer performance test. AI algorithm provided image-level probability of pulmonary nodule or mass on CXRs and a heatmap of detected lesions. Reader performance was compared with AUC, sensitivity, specificity, false-positives per image (FPPI), and rates of chest CT recommendations.

RESULTS

With AI, the average sensitivity of readers for the detection of visible lung cancer increased for residents, but was similar for radiologists compared to that without AI (0.61 [95% CI, 0.55-0.67] vs. 0.72 [95% CI, 0.66-0.77], p = 0.016 for residents, and 0.76 [95% CI, 0.72-0.81] vs. 0.76 [95% CI, 0.72-0.81, p = 1.00 for radiologists), while false-positive findings per image (FPPI) was similar for residents, but decreased for radiologists (0.15 [95% CI, 0.11-0.18] vs. 0.12 [95% CI, 0.09-0.16], p = 0.13 for residents, and 0.24 [95% CI, 0.20-0.29] vs. 0.17 [95% CI, 0.13-0.20], p < 0.001 for radiologists). With AI, the average rate of chest CT recommendation in patients positive for visible cancer increased for residents, but was similar for radiologists (54.7% [95% CI, 48.2-61.2%] vs. 70.2% [95% CI, 64.2-76.2%], p < 0.001 for residents and 72.5% [95% CI, 68.0-77.1%] vs. 73.9% [95% CI, 69.4-78.3%], p = 0.68 for radiologists), while that in cancer-negative patients was similar for residents, but decreased for radiologists (11.2% [95% CI, 9.6-13.1%] vs. 9.8% [95% CI, 8.0-11.6%], p = 0.32 for residents and 16.4% [95% CI, 14.7-18.2%] vs. 11.7% [95% CI, 10.2-13.3%], p < 0.001 for radiologists).

CONCLUSIONS

AI algorithm can enhance the performance of readers for the detection of lung cancers on chest radiographs when used as second reader.

KEY POINTS

• Reader study in the NLST dataset shows that AI algorithm had sensitivity benefit for residents and specificity benefit for radiologists for the detection of visible lung cancer. • With AI, radiology residents were able to recommend more chest CT examinations (54.7% vs 70.2%, p < 0.001) for patients with visible lung cancer. • With AI, radiologists recommended significantly less proportion of unnecessary chest CT examinations (16.4% vs. 11.7%, p < 0.001) in cancer-negative patients.

Prescription practice and clinical utility of chest radiographs in a pediatric intensive care unit: a prospective observational study

Background

Chest radiograph (CXR) prescribing pattern and practice vary widely among pediatric intensive care units (PICU). ‘On demand’ approach is increasingly recommended as against daily ‘routine’ CXRs; however, the real-world practice is largely unknown.

Methods

This was a prospective observational study performed in children younger than 12 years admitted to PICU of a tertiary care teaching hospital in India. Data were collected on all consecutive CXRs performed between December 2016 and April 2017. The primary outcome was to assess the factors that were associated with higher chest radiograph prescriptions in PICU. Secondary outcomes were to study the indications, association with mechanical ventilation, image quality and avoidable radiation exposure.

Results

Of 303 children admitted during the study period, 159 underwent a total of 524 CXRs in PICU. Median (IQR) age of the study cohort was 2 (0.6–5) years. More than two thirds [n = 115, 72.3%] were mechanically ventilated. Most CXRs (n = 449, 85.7%) were performed on mechanically ventilated patients, amounting to a median (IQR) of 3 (2–5) radiographs per ventilated patient. With increasing duration of ventilation, the number of CXRs proportionately increased in the first two weeks of mechanical ventilation. In non-ventilated children, about two thirds (68%) underwent only one CXR. Majority of the prescriptions were on demand (n = 461, 88%). Most common indications were peri-procedure prescriptions (37%) followed by evaluation for respiratory disease status (24%). About 40% CXRs resulted in interventions; adjustment in ventilator settings (13.5%) was the most frequent intervention. In 26% (n = 138) of radiographs, image quality required improvement. One or more additional body part exposure other than chest and upper abdomen were noted 336 (64%) images. Children with > 3 CXR had higher PRISM III score, more often mechanically ventilated, had higher number of indwelling devices [mean (SD) 2.6 (1.2) vs. 1.7 (1.0)] and stayed longer in PICU [median (IQR) 11(7.5–18.5) vs. 6 (3–9)].

Conclusion

On demand prescription was the prevalent practice in our PICU. Most non-ventilated children underwent only one CXR while duration of PICU stay and the number of devices determined the number of CXRs in mechanically ventilated children. Quality improvement strategies should concentrate on the process of acquisition of images and limiting the radiation exposure to unwanted body parts.

Chest CT findings of coronavirus disease 2019 (COVID-19): A comprehensive meta-analysis of 9907 confirmed patients

OBJECTIVES

We performed a systematic review and meta-analysis of the prevalence of chest CT findings in patients with confirmed COVID-19 infection.

METHODS

Systematic review of the literature was performed using PubMed, Scopus, Embase, and Google Scholar to retrieve original studies on chest CT findings of patients with confirmed COVID-19, available up to 10 May 2020. Data on frequency and distribution of chest CT findings were extracted from eligible studies, pooled and meta-analyzed using random-effects model to calculate the prevalence of chest CT findings.

RESULTS

Overall, 103 studies (pooled population: 9907 confirmed COVID-19 patients) were meta-analyzed. The most common CT findings were ground-glass opacities (GGOs) (77.18%, 95%CI = 72.23-81.47), reticulations (46.24%, 95%CI = 38.51-54.14), and air bronchogram (41.61%, 95%CI = 32.78-51.01). Pleural thickening (33.35%, 95%CI = 21.89-47.18) and bronchial wall thickening (15.48%, 95%CI = 8.54-26.43) were major atypical and airway findings. Lesions were predominantly distributed bilaterally (75.72%, 95%CI = 70.79-80.06) and peripherally (65.64%, 95%CI = 58.21-72.36), while 8.20% (95%CI = 6.30-10.61) of patients had no abnormal findings and pre-existing lung diseases were present in 6.01% (95%CI = 4.37-8.23).

CONCLUSIONS

The most common CT findings in COVID-19 are GGOs with/without consolidation, reticulations, and air bronchogram, which often involve both lungs with peripheral distribution. However, COVID-19 might present with atypical manifestations or no abnormal findings in chest CT, which deserve clinicians' notice.

Choosing Wisely: Determining performance of unjustified imaging in a large healthcare system

AIMS

The Choosing Wisely Campaign identifies procedures and treatments that lack clinical justification for routine use according to expert opinion and evidence-based medicine. This study describes the rates and features of two such examples over a 10-year period.

METHODS

This is a cross-sectional rolling cohort study between 2008 and 2017 in Clalit Health Services, the largest healthcare delivery system in Israel, with seven main hospitals and over 4.5 million members nationwide. All adult members who visited a Clalit Emergency Department (ED), and all children members who visited a Clalit ED for abdominal pain or appendicitis were eligible to be included in this study. Our measures were routine chest radiograph (CXR) in the context of pre-admission assessment for adults and abdominal computed tomography (CT) to rule out appendicitis for children.

RESULTS

Of the 3 689 869 adult visits without a clinical indication for a CXR, 9.1% or 337 058 of them received a chest radiograph. Of the 35 973 children visits for presumed appendicitis, 7.2% of them had no imaging performed, 82.3% had an ultrasound (US), 6.9% had an US followed by a CT, and 3.6% or 1293 of them received a CT. There were several independent risk factors such as BMI, hospital, sex, year and diagnosis that are associated with having imaging that is not clinically indicated.

CONCLUSIONS

Overall, this study found that diagnostic imaging practices are applied inconsistently by hospital and by population. Intervention efforts should be focused on subpopulations at greatest risk to further reduce exposure to such imaging.

Preoperative laboratory testing: Implications of "Choosing Wisely" guidelines

Preoperative laboratory testing is often necessary and can be invaluable for diagnosis, assessment, and treatment. However, performing routine laboratory tests for patients who are considered otherwise healthy is not usually beneficial and is costly. It is estimated that $18 billion (U.S.) is spent annually on preoperative testing, although how much is wasteful remains unknown. Ideally, a targeted and comprehensive patient history and physical exam should largely determine whether preprocedure laboratory studies should be obtained. Healthcare providers, primarily anesthesiologists, should remain cost-conscious when ordering specific laboratory or imaging tests prior to surgery based on available literature. We review the overall evidence and key points from the Choosing Wisely guidelines, the identification of potential wasteful practices, possible harms of testing, and key clinical findings associated with preoperative laboratory testing.

Impact of an ultrasound-driven diagnostic protocol at early intensive-care stay: a randomized-controlled trial

Background

Point-of-care ultrasound (POCUS) is a tool in increasing use, but there is still a lack of basics for its routine use and evidence of its impact in intensive care.

Objective

To measure the impact of POCUS on resource utilization, diagnostic accuracy, and clinical management in medical-surgical intensive care units (ICUs).

Methods

Prospective, controlled study, in two polyvalent ICUs. The patients were randomly assigned to POCUS or control group.

Interventions

POCUS patients received systematic ultrasound examination of optic nerve, lung/pleura, heart, abdomen, and venous system, performed at the bedside by trained intensivists. Control patients were treated by critical care specialists who do not perform ultrasound in their clinical practice.

Results

We included 80 patients, 40 per group. There were no significant differences in age, sex, APACHE II score, or admission diagnosis. POCUS group used fewer resources per patient in the first 5 days of hospitalization: chest radiography (2.6 ± 2.0 vs 4.1 ± 3.5, P = 0.01), additional ultrasound evaluations performed by a radiology specialist (0.6 ± 0.7 vs 1.1 ± 0.7, P = 0.002), and computed tomography studies (0.5 ± 0.6 vs 0.9 ± 0.7, P = 0.007). Time to perform any requested ultrasound evaluation after ICU admission was 2.1 ± 1.6 h versus 7.7 ± 6.7 h (P = 0.001). Systematic ultrasound evaluation led to better characterization of ICU admission diagnosis in 14 (35%) patients and change in clinical management in 24 (60%). POCUS group had lower fluid balance at 48 and 96 h after admission (P = 0.01) and spent less time mechanically ventilated (5.1 ± 5.7 days vs 8.8 ± 9.4, P = 0.03).

Conclusions

Systematic application of POCUS may decrease utilization of conventional diagnostic imaging resources and time of mechanical ventilation, and facilitate meticulous intravenous fluid administration in critically ill patients during the first week of stay in the ICU.

Trial registration ClinicalTrials.gov Identifier: NCT03608202.

Preoperative Pulmonary Evaluation

The preanesthesia evaluation is an opportunity to elucidate a patient's underlying medical disease, determine if the patient is optimized, treat modifiable conditions, screen for potentially unrecognized disorders, and present the clear picture of the patient's overall risk for perioperative complications. This article presents the preoperative assessment of pulmonary patients in 2 sections. First, the components of a thorough assessment of patients presenting for preanesthesia evaluation, which should occur for all patients, regardless of the presence of pulmonary pathology, are discussed. Then, the considerations unique to patients with pulmonary diseases commonly encountered are described.

Development and Validation of a Deep Learning-Based Automated Detection Algorithm for Major Thoracic Diseases on Chest Radiographs

IMPORTANCE

Interpretation of chest radiographs is a challenging task prone to errors, requiring expert readers. An automated system that can accurately classify chest radiographs may help streamline the clinical workflow.

OBJECTIVES

To develop a deep learning-based algorithm that can classify normal and abnormal results from chest radiographs with major thoracic diseases including pulmonary malignant neoplasm, active tuberculosis, pneumonia, and pneumothorax and to validate the algorithm's performance using independent data sets.

DESIGN, SETTING, AND PARTICIPANTS

This diagnostic study developed a deep learning-based algorithm using single-center data collected between November 1, 2016, and January 31, 2017. The algorithm was externally validated with multicenter data collected between May 1 and July 31, 2018. A total of 54 221 chest radiographs with normal findings from 47 917 individuals (21 556 men and 26 361 women; mean [SD] age, 51 [16] years) and 35 613 chest radiographs with abnormal findings from 14 102 individuals (8373 men and 5729 women; mean [SD] age, 62 [15] years) were used to develop the algorithm. A total of 486 chest radiographs with normal results and 529 with abnormal results (1 from each participant; 628 men and 387 women; mean [SD] age, 53 [18] years) from 5 institutions were used for external validation. Fifteen physicians, including nonradiology physicians, board-certified radiologists, and thoracic radiologists, participated in observer performance testing. Data were analyzed in August 2018.

EXPOSURES

Deep learning-based algorithm.

MAIN OUTCOMES AND MEASURES

Image-wise classification performances measured by area under the receiver operating characteristic curve; lesion-wise localization performances measured by area under the alternative free-response receiver operating characteristic curve.

RESULTS

The algorithm demonstrated a median (range) area under the curve of 0.979 (0.973-1.000) for image-wise classification and 0.972 (0.923-0.985) for lesion-wise localization; the algorithm demonstrated significantly higher performance than all 3 physician groups in both image-wise classification (0.983 vs 0.814-0.932; all P < .005) and lesion-wise localization (0.985 vs 0.781-0.907; all P < .001). Significant improvements in both image-wise classification (0.814-0.932 to 0.904-0.958; all P < .005) and lesion-wise localization (0.781-0.907 to 0.873-0.938; all P < .001) were observed in all 3 physician groups with assistance of the algorithm.

CONCLUSIONS AND RELEVANCE

The algorithm consistently outperformed physicians, including thoracic radiologists, in the discrimination of chest radiographs with major thoracic diseases, demonstrating its potential to improve the quality and efficiency of clinical practice.

Deimplementation of Routine Chest X-rays in Adult Intensive Care Units

BACKGROUND

Choosing Wisely® is a national initiative to deimplement or reduce low-value care. However, there is limited evidence on the effectiveness of strategies to influence ordering patterns.

OBJECTIVE

We aimed to describe the effectiveness of an intervention to reduce daily chest X-ray (CXR) ordering in two intensive care units (ICUs) and evaluate deimplementation strategies.

DESIGN

We aimed to describe the effectiveness of an intervention to reduce daily chest X-ray (CXR) ordering in two intensive care units (ICUs) and evaluate deimplementation strategies.

SETTING

The study was performed in the medical intensive care unit (MICU) and cardiovascular intensive care unit (CVICU) of an academic medical center in the United States from October 2015 to June 2016.

PARTICIPANTS

The initiative included the staff of the MICU and CVICU (physicians, surgeons, nurse practitioners, fellows, residents, medical students, and X-ray technologists).

INTERVENTION COMPONENTS

We utilized provider education, peer champions, and weekly data feedback of CXR ordering rates.

MEASUREMENTS

We analyzed the CXR ordering rates and factors facilitating or inhibiting deimplementation.

RESULTS

Segmented linear time-series analysis suggested a small but statistically significant decrease in CXR ordering rates in the CVICU (P < .001) but not in the MICU. Facilitators of deimplementation, which were more prominent in the CVICU, included engagement of peer champions, stable staffing, and regular data feedback. Barriers included the need to establish goal CXR ordering rates, insufficient intervention visibility, and waning investment among medical residents in the MICU due to frequent rotation and competing priorities.

CONCLUSIONS

Intervention modestly reduced CXRs ordered in one of two ICUs evaluated. Understanding why adoption differed between the two units may inform future interventions to deimplement low-value diagnostic tests.

Routine use of chest x-ray for low-risk patients undergoing a periodic health examination: a retrospective cohort study

BACKGROUND

Many evidence-based recommendations advocate against the use of routine chest x-rays for asymptomatic, low-risk outpatients; however, it is unclear how regularly chest x-rays are ordered in primary care. Our study aims to describe the frequency of, and variation in, routine chest x-ray use in low-risk outpatients among primary care physicians.

METHODS

In this retrospective cohort study, Ontario residents aged 18 years and older with a periodic health examination (PHE) between Apr. 1, 2010, and Mar. 31, 2015, were identified via administrative claims data. Patients with a recent history (last 3 years) of any of the following were excluded: cardiac or pulmonary disease, high-risk comorbidity (e.g., diabetes), consultations/visits or procedures involving cardiac or pulmonary specialists, cancer and severe chest trauma. The primary outcome, a routine chest x-ray, was defined as at least 1 chest x-ray claim within 7 days after a PHE.

RESULTS

While a routine chest x-ray followed only 2.42% of 2 847 508 PHEs, one-quarter of family physicians (499/2031) ordered chest x-rays for more than 5.0% of their PHEs (interquartile range 1.5%-5.0%) and accounted for 62.9% of all tests observed. Routine chest x-ray use declined by 2.0% per quarter (adjusted rate ratio 0.98, 95% confidence interval [CI] 0.97-0.98). Older age (45-64 yr v. 18-44 yr, adjusted odds ratio [OR] 1.82, 95% CI 1.78-1.86; ≥ 65 yr v. 18-44 yr, adjusted OR 2.48, 95% CI 2.39-2.58) and male sex of the patient (OR 2.19, 95% CI 2.14-2.24) and male sex of the provider (OR 1.55, 95% CI 1.51-1.59) were significantly associated with increased odds of a routine chest x-ray being ordered.

INTERPRETATION

It is relatively uncommon for a chest x-ray to be ordered as part of a PHE in Ontario; however, the substantial variation observed among physicians suggests potential for interventions targeted at the most frequent users.

Daily routine versus on-demand chest radiograph policy and practice in adult ICU patients- clinicians' perspective

Background

Chest radiographs are taken daily as a part of routine investigations in Intensive care unit (ICU) patients. They are less effective and unlikely to alter the management of the majority of these patients compared to the radiographs obtained when indicated. According to the American College of Radiology (ACR) Appropriateness criteria, only selective ordering of chest radiographs is recommended, including elderly or high risk patients. The aim of this study was to identify and assess the clinician’s perspective in abandoning the current practice of daily routine chest radiograph and replacing with the on-demand radiograph in Saudi hospitals.

Methods

This was a cross-sectional study. A valid self-administered questionnaire was distributed to all clinical staff members working in ICUs in the major tertiary hospitals in Saudi Arabia. The study population was primarily the ICU intensivists (physicians), nurses and respiratory therapists (RT). The data collected were statistically processed using SPSS version 20.0; descriptive and inferential analyses were done.

Results

Out of 730 questionnaires sent, we received only 495 completed questionnaires with a response rate of 67.8%. Majority of them (n = 351) are working at academic hospitals. About half of the respondents (n = 247) are working in an open-format ICUs. Findings showed that the daily routine chest X-ray was performed in almost 96.8% of ICUs patients, which the majority of the clinical staff members (73%) thought that this current daily routine CXR protocol in the ICUs should be replaced with the on-demand CXR policy. Interestingly, the differences in demographic and work-related characteristics had no significant impact on the clinician’s view and supported moving to on-demand CXR policy and practice.

Conclusions

The daily routine CXR is still a common practice in most of the Saudi hospitals ICUs although enough empirical evidence shows that it can be avoided. We observed that intensivists support the change of the current practice and recommend an on-demand CXR policy likely to be followed in intensive care management.

Electronic supplementary material

The online version of this article (10.1186/s12880-018-0248-6) contains supplementary material, which is available to authorized users.

Is Routine Chest X-Ray After Ultrasound-Guided Central Venous Catheter Insertion Choosing Wisely?: A Population-Based Retrospective Study of 6,875 Patients

BACKGROUND

A routine chest radiograph (CXR) is recommended as a screening test after central venous catheter (CVC) insertion. The goal of this study was to assess the value of a routine postprocedural CXR in the era of ultrasound-guided CVC insertion.

METHODS

This population-based retrospective cohort study was performed to review the records of all adult patients who had a CVC inserted in the operating room in a tertiary institution between July 1, 2008, and December 31, 2015. We determined the incidence of pneumothorax and catheter misplacement after ultrasound-guided CVC insertion. A logistic regression analysis was performed to examine the potential risk factors associated with these complications, and a cost analysis was conducted to evaluate the economic impact.

RESULTS

Of 18,274 patients who had a CVC inserted, 6,875 patients were included. The overall incidence of pneumothorax and catheter misplacement was 0.33% (95% CI, 0.22-0.5) (23 patients) and 1.91% (95% CI, 1.61-2.26) (131 patients), respectively. The site of catheterization was the major determinant of pneumothorax and catheter misplacement; left subclavian vein catheterization was the site at a higher risk for pneumothorax (OR, 6.69 [95% CI, 2.45-18.28]; P < .001), and catheterization sites other than the right internal jugular vein were at a higher risk for catheter misplacement. Expenditures on routine postprocedural CXR were US $105,000 to $183,000 per year at our institution.

CONCLUSIONS

This study found that pneumothorax and catheter misplacement after ultrasound-guided CVC insertion were rare, and the costs of a postprocedural CXR were exceedingly high. We concluded that a routine postprocedural CXR is unnecessary and not a wise choice in our setting.

Chest X-ray Prior to Thyroidectomy: Is It Really Needed?

BACKGROUND

Chest X-ray (CXR) prior to thyroid surgery continues to be routinely obtained at some institutions despite the lack of evidence for its utility. This study aimed to determine the utility of preoperative CXR in patients undergoing thyroidectomy at a single institution.

METHODS

In total, 594 thyroidectomy patients were included in the study. Demographics, CXR findings, anesthesia records and pathologic data were assessed. We investigated whether difficult intubation or cancer stage correlated with the presence of CXR abnormalities.

RESULTS

Of the total cohort, 83% had a preoperative CXR and 67% had cancer on surgical pathology. In total, 56% had at least one CXR abnormality, the most frequent being skeletal abnormalities (25%), followed by tracheal deviation (16%). Of 78 patients (15.8%) with tracheal deviation on CXR, only 5% had a difficult intubation. Tracheal deviation was more commonly seen in non-cancer cases compared to cancer cases (27 vs. 12%, p < 0.001). CXR impacted management in 4%. Among patients with cancer, a higher T-stage (>2) was associated with higher rate of tracheal deviation compared to T1 (17 vs. 8%, p < 0.001). While patients with non-metastatic cancer (n = 277) compared to metastatic cancer patients had a higher proportion of any abnormality on CXR (57 vs. 44%, p = 0.045), there was no significant difference for tracheal deviation, skeletal abnormalities or lung nodules. Of patients with nodules on CXR (n = 29), only 14% were found to have metastatic disease.

CONCLUSION

The utility of preoperative CXR in patients undergoing thyroidectomy is very limited. In the climate of value-based care, routine use of this modality may be redundant and should only be ordered if clinically indicated.