Tricuspid valve flow measurement using a deep learning framework for automated valve-tracking 2D phase contrast.

PURPOSE: Tricuspid valve flow velocities are challenging to measure with cardiovascular MR, as the rapidly moving valvular plane prohibits direct flow evaluation, but they are vitally important to diastolic function evaluation. We developed an automated valve-tracking 2D method for measuring flow through the dynamic tricuspid valve. METHODS: Nine healthy subjects and 2 patients were imaged. The approach uses a previously trained deep learning network, TVnet, to automatically track the tricuspid valve plane from long-axis cine images. Subsequently, the tracking information is used to acquire 2D phase contrast (PC) with a dynamic (moving) acquisition plane that tracks the valve. Direct diastolic net flows evaluated from the dynamic PC sequence were compared with flows from 2D-PC scans acquired in a static slice localized at the end-systolic valve position, and also ventricular stroke volumes (SVs) using both planimetry and 2D PC of the great vessels. RESULTS: The mean tricuspid valve systolic excursion was 17.8 ± 2.5 mm. The 2D valve-tracking PC net diastolic flow showed excellent correlation with SV by right-ventricle planimetry (bias ± 1.96 SD = -0.2 ± 10.4 mL, intraclass correlation coefficient [ICC] = 0.92) and aortic PC (-1.0 ± 13.8 mL, ICC = 0.87). In comparison, static tricuspid valve 2D PC also showed a strong correlation but had greater bias (p = 0.01) versus the right-ventricle SV (10.6 ± 16.1 mL, ICC = 0.61). In most (8 of 9) healthy subjects, trace regurgitation was measured at begin-systole. In one patient, valve-tracking PC displayed a high-velocity jet (380 cm/s) with maximal velocity agreeing with echocardiography. CONCLUSION: Automated valve-tracking 2D PC is a feasible route toward evaluation of tricuspid regurgitant velocities, potentially solving a major clinical challenge.

Artificial Intelligence for Contrast-Free MRI: Scar Assessment in Myocardial Infarction Using Deep Learning-Based Virtual Native Enhancement.

BACKGROUND: Myocardial scars are assessed noninvasively using cardiovascular magnetic resonance late gadolinium enhancement (LGE) as an imaging gold standard. A contrast-free approach would provide many advantages, including a faster and cheaper scan without contrast-associated problems. METHODS: Virtual native enhancement (VNE) is a novel technology that can produce virtual LGE-like images without the need for contrast. VNE combines cine imaging and native T1 maps to produce LGE-like images using artificial intelligence. VNE was developed for patients with previous myocardial infarction from 4271 data sets (912 patients); each data set comprises slice position-matched cine, T1 maps, and LGE images. After quality control, 3002 data sets (775 patients) were used for development and 291 data sets (68 patients) for testing. The VNE generator was trained using generative adversarial networks, using 2 adversarial discriminators to improve the image quality. The left ventricle was contoured semiautomatically. Myocardial scar volume was quantified using the full width at half maximum method. Scar transmurality was measured using the centerline chord method and visualized on bull's-eye plots. Lesion quantification by VNE and LGE was compared using linear regression, Pearson correlation (R), and intraclass correlation coefficients. Proof-of-principle histopathologic comparison of VNE in a porcine model of myocardial infarction also was performed. RESULTS: VNE provided significantly better image quality than LGE on blinded analysis by 5 independent operators on 291 data sets (all P<0.001). VNE correlated strongly with LGE in quantifying scar size (R, 0.89; intraclass correlation coefficient, 0.94) and transmurality (R, 0.84; intraclass correlation coefficient, 0.90) in 66 patients (277 test data sets). Two cardiovascular magnetic resonance experts reviewed all test image slices and reported an overall accuracy of 84% for VNE in detecting scars when compared with LGE, with specificity of 100% and sensitivity of 77%. VNE also showed excellent visuospatial agreement with histopathology in 2 cases of a porcine model of myocardial infarction. CONCLUSIONS: VNE demonstrated high agreement with LGE cardiovascular magnetic resonance for myocardial scar assessment in patients with previous myocardial infarction in visuospatial distribution and lesion quantification with superior image quality. VNE is a potentially transformative artificial intelligence-based technology with promise in reducing scan times and costs, increasing clinical throughput, and improving the accessibility of cardiovascular magnetic resonance in the near future.

MVnet: automated time-resolved tracking of the mitral valve plane in CMR long-axis cine images with residual neural networks: a multi-center, multi-vendor study.

BACKGROUND: Mitral annular plane systolic excursion (MAPSE) and left ventricular (LV) early diastolic velocity (e') are key metrics of systolic and diastolic function, but not often measured by cardiovascular magnetic resonance (CMR). Its derivation is possible with manual, precise annotation of the mitral valve (MV) insertion points along the cardiac cycle in both two and four-chamber long-axis cines, but this process is highly time-consuming, laborious, and prone to errors. A fully automated, consistent, fast, and accurate method for MV plane tracking is lacking. In this study, we propose MVnet, a deep learning approach for MV point localization and tracking capable of deriving such clinical metrics comparable to human expert-level performance, and validated it in a multi-vendor, multi-center clinical population. METHODS: The proposed pipeline first performs a coarse MV point annotation in a given cine accurately enough to apply an automated linear transformation task, which standardizes the size, cropping, resolution, and heart orientation, and second, tracks the MV points with high accuracy. The model was trained and evaluated on 38,854 cine images from 703 patients with diverse cardiovascular conditions, scanned on equipment from 3 main vendors, 16 centers, and 7 countries, and manually annotated by 10 observers. Agreement was assessed by the intra-class correlation coefficient (ICC) for both clinical metrics and by the distance error in the MV plane displacement. For inter-observer variability analysis, an additional pair of observers performed manual annotations in a randomly chosen set of 50 patients. RESULTS: MVnet achieved a fast segmentation (<1 s/cine) with excellent ICCs of 0.94 (MAPSE) and 0.93 (LV e') and a MV plane tracking error of -0.10 ± 0.97 mm. In a similar manner, the inter-observer variability analysis yielded ICCs of 0.95 and 0.89 and a tracking error of -0.15 ± 1.18 mm, respectively. CONCLUSION: A dual-stage deep learning approach for automated annotation of MV points for systolic and diastolic evaluation in CMR long-axis cine images was developed. The method is able to carefully track these points with high accuracy and in a timely manner. This will improve the feasibility of CMR methods which rely on valve tracking and increase their utility in a clinical setting.

Automated left atrial time-resolved segmentation in MRI long-axis cine images using active contours.

BACKGROUND: Segmentation of the left atrium (LA) is required to evaluate atrial size and function, which are important imaging biomarkers for a wide range of cardiovascular conditions, such as atrial fibrillation, stroke, and diastolic dysfunction. LA segmentations are currently being performed manually, which is time-consuming and observer-dependent. METHODS: This study presents an automated image processing algorithm for time-resolved LA segmentation in cardiac magnetic resonance imaging (MRI) long-axis cine images of the 2-chamber (2ch) and 4-chamber (4ch) views using active contours. The proposed algorithm combines mitral valve tracking, automated threshold calculation, edge detection on a radially resampled image, edge tracking based on Dijkstra's algorithm, and post-processing involving smoothing and interpolation. The algorithm was evaluated in 37 patients diagnosed mainly with paroxysmal atrial fibrillation. Segmentation accuracy was assessed using the Dice similarity coefficient (DSC) and Hausdorff distance (HD), with manual segmentations in all time frames as the reference standard. For inter-observer variability analysis, a second observer performed manual segmentations at end-diastole and end-systole on all subjects. RESULTS: The proposed automated method achieved high performance in segmenting the LA in long-axis cine sequences, with a DSC of 0.96 for 2ch and 0.95 for 4ch, and an HD of 5.5 mm for 2ch and 6.4 mm for 4ch. The manual inter-observer variability analysis had an average DSC of 0.95 and an average HD of 4.9 mm. CONCLUSION: The proposed automated method achieved performance on par with human experts analyzing MRI images for evaluation of atrial size and function. Video Abstract.

Valvular imaging in the era of feature-tracking: A slice-following cardiac MR sequence to measure mitral flow.

BACKGROUND: In mitral valve dysfunction, noninvasive measurement of transmitral blood flow is an important clinical examination. Flow imaging of the mitral valve, however, is challenging, since it moves in and out of the image plane during the cardiac cycle. PURPOSE: To more accurately measure mitral flow, a slice-following MRI phase contrast sequence is proposed. This study aimed to implement such a sequence, validate its slice-following functionality in a phantom and healthy subjects, and test its feasibility in patients with mitral valve dysfunction. STUDY TYPE: Prospective. PHANTOM AND SUBJECTS: The slice-following functionality was validated in a cone-shaped phantom by measuring the depicted slice radius. Sixteen healthy subjects and 10 mitral valve dysfunction patients were enrolled at two sites. FIELD STRENGTH/SEQUENCE: 1.5T and 3T gradient echo cine phase contrast. ASSESSMENT: A single breath-hold retrospectively gated sequence using offline feature-tracking of the mitral valve was developed. Valve displacements were measured and imported to the scanner, allowing the slice position to change dynamically based on the cardiac phase. Mitral valve imaging was performed with slice-following and static imaging planes. Validation was performed by comparing mitral stroke volume with planimetric and aortic stroke volume. STATISTICAL TESTS: Measurements were compared using linear regression, Pearson's R, parametric paired t-tests, Bland-Altman analysis, and intraclass correlation coefficient (ICC). RESULTS: Phantom experiments confirmed accurate slice displacements. Slice-following was feasible in all subjects, yielding physiologically accurate mitral flow patterns. In healthy subjects, mitral and aortic stroke volumes agreed, with ICC = 0.72 and 0.90 for static and slice-following planes; with bias ±1 SDs 23.2 ± 13.2 mls and 8.4 ± 10.8 mls, respectively. Agreement with planimetry was stronger, with ICC = 0.84 and 0.96; bias ±1 SDs 13.7 ± 13.7 mls and -2.0 ± 8.8 mls for static and slice-following planes, respectively. DATA CONCLUSION: Slice-following outperformed the conventional sequence and improved the accuracy of transmitral flow, which is important for assessment of diastolic function and mitral regurgitation. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:1412-1421.

Quality control-driven deep ensemble for accountable automated segmentation of cardiac magnetic resonance LGE and VNE images.

Background: Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging is the gold standard for non-invasive myocardial tissue characterisation. However, accurate segmentation of the left ventricular (LV) myocardium remains a challenge due to limited training data and lack of quality control. This study addresses these issues by leveraging generative adversarial networks (GAN)-generated virtual native enhancement (VNE) images to expand the training set and incorporating an automated quality control-driven (QCD) framework to improve segmentation reliability. Methods: A dataset comprising 4,716 LGE images (from 1,363 patients with hypertrophic cardiomyopathy and myocardial infarction) was used for development. To generate additional clinically validated data, LGE data were augmented with a GAN-based generator to produce VNE images. LV was contoured on these images manually by clinical observers. To create diverse candidate segmentations, the QCD framework involved multiple U-Nets, which were combined using statistical rank filters. The framework predicted the Dice Similarity Coefficient (DSC) for each candidate segmentation, with the highest predicted DSC indicating the most accurate and reliable result. The performance of the QCD ensemble framework was evaluated on both LGE and VNE test datasets (309 LGE/VNE images from 103 patients), assessing segmentation accuracy (DSC) and quality prediction (mean absolute error (MAE) and binary classification accuracy). Results: The QCD framework effectively and rapidly segmented the LV myocardium (<1 s per image) on both LGE and VNE images, demonstrating robust performance on both test datasets with similar mean DSC (LGE: 0.845±0.075; VNE: 0.845±0.071; p=ns). Incorporating GAN-generated VNE data into the training process consistently led to enhanced performance for both individual models and the overall framework. The quality control mechanism yielded a high performance (MAE=0.043, accuracy=0.951) emphasising the accuracy of the quality control-driven strategy in predicting segmentation quality in clinical settings. Overall, no statistical difference (p=ns) was found when comparing the LGE and VNE test sets across all experiments. Conclusions: The QCD ensemble framework, leveraging GAN-generated VNE data and an automated quality control mechanism, significantly improved the accuracy and reliability of LGE segmentation, paving the way for enhanced and accountable diagnostic imaging in routine clinical use.

Deep neural network ensemble for on-the-fly quality control-driven segmentation of cardiac MRI T1 mapping.

Recent developments in artificial intelligence have generated increasing interest to deploy automated image analysis for diagnostic imaging and large-scale clinical applications. However, inaccuracy from automated methods could lead to incorrect conclusions, diagnoses or even harm to patients. Manual inspection for potential inaccuracies is labor-intensive and time-consuming, hampering progress towards fast and accurate clinical reporting in high volumes. To promote reliable fully-automated image analysis, we propose a quality control-driven (QCD) segmentation framework. It is an ensemble of neural networks that integrate image analysis and quality control. The novelty of this framework is the selection of the most optimal segmentation based on predicted segmentation accuracy, on-the-fly. Additionally, this framework visualizes segmentation agreement to provide traceability of the quality control process. In this work, we demonstrated the utility of the framework in cardiovascular magnetic resonance T1-mapping - a quantitative technique for myocardial tissue characterization. The framework achieved near-perfect agreement with expert image analysts in estimating myocardial T1 value (r=0.987,p<.0005; mean absolute error (MAE)=11.3ms), with accurate segmentation quality prediction (Dice coefficient prediction MAE=0.0339) and classification (accuracy=0.99), and a fast average processing time of 0.39 second/image. In summary, the QCD framework can generate high-throughput automated image analysis with speed and accuracy that is highly desirable for large-scale clinical applications.

MOCOnet: Robust Motion Correction of Cardiovascular Magnetic Resonance T1 Mapping Using Convolutional Neural Networks.

Background: Quantitative cardiovascular magnetic resonance (CMR) T1 mapping has shown promise for advanced tissue characterisation in routine clinical practise. However, T1 mapping is prone to motion artefacts, which affects its robustness and clinical interpretation. Current methods for motion correction on T1 mapping are model-driven with no guarantee on generalisability, limiting its widespread use. In contrast, emerging data-driven deep learning approaches have shown good performance in general image registration tasks. We propose MOCOnet, a convolutional neural network solution, for generalisable motion artefact correction in T1 maps. Methods: The network architecture employs U-Net for producing distance vector fields and utilises warping layers to apply deformation to the feature maps in a coarse-to-fine manner. Using the UK Biobank imaging dataset scanned at 1.5T, MOCOnet was trained on 1,536 mid-ventricular T1 maps (acquired using the ShMOLLI method) with motion artefacts, generated by a customised deformation procedure, and tested on a different set of 200 samples with a diverse range of motion. MOCOnet was compared to a well-validated baseline multi-modal image registration method. Motion reduction was visually assessed by 3 human experts, with motion scores ranging from 0% (strictly no motion) to 100% (very severe motion). Results: MOCOnet achieved fast image registration (<1 second per T1 map) and successfully suppressed a wide range of motion artefacts. MOCOnet significantly reduced motion scores from 37.1±21.5 to 13.3±10.5 (p < 0.001), whereas the baseline method reduced it to 15.8±15.6 (p < 0.001). MOCOnet was significantly better than the baseline method in suppressing motion artefacts and more consistently (p = 0.007). Conclusion: MOCOnet demonstrated significantly better motion correction performance compared to a traditional image registration approach. Salvaging data affected by motion with robustness and in a time-efficient manner may enable better image quality and reliable images for immediate clinical interpretation.

Minimum 5-year results in 96 consecutive hips treated with a tapered titanium stem system.

We performed total hip arthroplasty with single titanium stem in 96 consecutive, nonselected hips. All patients, regardless of bone type and shape, neck shaft angulation, or age, received the same implant. Patients were followed for a minimum of 5 years, and an independent radiographer evaluated the hips for ingrowth, subsidence, leg-length discrepancy, and remodeling. The average Harris Hip score was 96 points (range, 73-100 points) at final follow-up. Radiographically, all stems were ingrown. No stem had more than 3 mm of subsidence, and there were no leg-length discrepancies more than 5 mm. We concluded that this titanium stem is a versatile option for total hip arthroplasty in patients with a wide variety of demographic and femur characteristics.

Midterm results of all-polyethylene tibial components in primary total knee arthroplasty.

Our goal was to determine the 7-year survivorship of a total knee arthroplasty system with all-polyethylene tibial components. From June 1996 to December 1997, 129 consecutive patients (177 knees) (>/=70 years old) underwent such primary arthroplasty at 2 centers in the United States. The 88 patients (120 knees) with 7 years or more of follow-up were evaluated radiographically and functionally. Average results included: Knee Society Score, 93.7 points (SD, 8.1); Knee Society function score, 68.9 points (SD, 27.6); range of motion, 119 degrees (SD, 10.9); osteolysis, 0%; stress shielding, 6.6%; progressive radiolucencies, 2.5%; Kaplan-Meier survivorship (revision), 99.4% (95% confidence intervals, 98.2%-100.0%). At midterm follow-up, an all-polyethylene tibial component proved to be an excellent surgical option for total knee arthroplasty in an elderly patient population.

Assessment of diastolic function and atrial remodeling by MRI - validation and correlation with echocardiography and filling pressure.

Atrial fibrosis can be estimated noninvasively by magnetic resonance imaging (MRI) using late gadolinium enhancement (LGE), but diastolic dysfunction is clinically assessed by transthoracic echocardiography (TTE), and rarely by MRI. This study aimed to evaluate well-established diastolic parameters using MRI, and validate them with TTE and left ventricular (LV) filling pressures, and to study the relationship between left atrial (LA) remodeling and parameters of diastolic function. The study retrospectively included 105 patients (53 ± 16 years, 39 females) who underwent 3D LGE MRI between 2012 and 2016. Medical charts were reviewed for the echocardiographic diastolic parameters E, A, and e' by TTE, and pressure catheterizations. E and A were measured from in-plane phase-contrast cardiac MRI images, and e' by feature-tracking, and validated with TTE. Interobserver and intraobserver variability was examined. Furthermore, LA volumes, function, and atrial LGE was correlated with diastolic parameters. Evaluation of e' in MRI had strong agreement with TTE (r = 0.75, P < 0.0001), and low interobserver and intraobserver variability. E and A by TTE showed strong agreement to MRI (r = 0.77, P = 0.001; r = 0.73, P = 0.003, for E and A, respectively). Agreement between E/e' by TTE and MRI was strong (r = 0.85, P = 0.0004), and E/e' by TTE correlated moderately to invasive pressures (r = 0.59, P = 0.03). There was a strong relationship between LA LGE and pulmonary capillary wedge pressure (r = 0.81, P = 0.01). In conclusion, diastolic parameters can be measured with good reproducibility by cardiovascular MRI. LA LGE exhibited a strong relationship with pulmonary capillary wedge pressure, an indicator of diastolic function.

Use of magnetic resonance imaging in differentiating compartmental location of spinal tumors.

Magnetic resonance imaging (MRI) is the best imaging modality for evaluating most spinal tumors. In this review, we describe the MRI appearance of extradural, intradural-extramedullary, and intradural-intramedullary tumors as well as the image interpretation techniques that permit identification of the compartment in which a lesion is located. In addition, we review the literature and our experience in using an anatomic classification system to narrow the differential diagnosis for patients with spine tumors. This method permits successful identification of a narrow differential diagnosis, which can be used to guide additional evaluation and treatment.

Unicompartmental knee arthroplasty compares favorably to total knee arthroplasty in the same patient.

There are several surgical management options for medial knee arthritis, and it is often difficult to decide whether a unicompartmental knee arthroplasty (UKA) or total knee arthroplasty (TKA) is appropriate. Few studies have compared UKAs and TKAs in the same patient. We identified 23 patients with osteoarthritis who had a TKA on one side and a UKA on the other. The preoperative values of the 2 knees were not statistically different. Strict criteria were used to determine whether a UKA could be performed (functional anterior cruciate ligament, minimal deformity, and minimum changes in other compartments). Preoperative radiographs were reviewed to confirm that the knee undergoing TKA had medial compartment disease only. Knee Society scores, radiographic analysis, and patient preferences were recorded for all patients. Average follow-up was 46 months (range, 7.2-148 months) and 42 months (range, 11.5-59.8 months) for TKA and UKA, respectively. We found little or no difference in outcomes between the 2 procedures, except for an improved range of motion with the UKA compared with the TKA (123 degrees +/-9 degrees vs 119.8 degrees +/-7 degrees, respectively). Knee Society scores improved from 45.9 to 89.7 in UKA and from 42.4 to 90.3 in TKA. Of the 23 patients, 11 expressed no preference between either knee and 12 preferred the unicompartmental knee; no patient preferred the total knee. With appropriate patient selection, UKAs are a successful option for patients with osteoarthritis.

Midterm results with the PFC Sigma total knee arthroplasty system.

We prospectively studied 207 consecutive patients (284 knees) undergoing total knee arthroplasty (June 1996 to December 1997) with a cemented tricompartmental Sigma PFC (DePuy Orthopaedics, Warsaw, Ind) total knee arthroplasty via a standard procedure (median follow-up, 87 months). Cruciate-retaining (272 knees, 96%) and cruciate-substituting (12, 4%) implants were used. There was one revision secondary to a ligament disruption after a fall. No implants were radiographically loose or at risk for loosening. Radiolucencies (none>2 mm or progressive) were shown on anteroposterior (7% of medial tibias) and lateral (17% of posterior femora) radiographs. Knee Society pain scores improved significantly (preoperative median, 20 points; postoperative median, 50 points [P<.001]). The PFC Sigma Knee system has excellent midterm durability.

Short-term mortality and its association with independent risk factors in adult spinal deformity surgery.

STUDY DESIGN: Retrospective review. OBJECTIVES: To determine postoperative mortality after adult spinal deformity surgery. To determine whether independent risk factors can predict mortality. SUMMARY OF BACKGROUND DATA: Although mortality after adult spinal surgery is reported to range from 0.03% to 3.52%, there is a general paucity of data on mortality and its associated risk factors after adult spinal deformity surgery. METHODS: Three hundred sixty-one adults with spinal deformity underwent 407 corrective procedures. For patients who died within 30 days of the procedure, the following risk factors were examined to determine if each could independently predict mortality: demographic information, American Society of Anesthesiologists' (ASA) classification, operative time, surgical approach, number of fusion levels, primary versus revision surgery, and intraoperative blood loss. RESULTS: Ten of the 407 procedures resulted in death (2.4% mortality): 1 intraoperatively secondary to cardiac ischemia, 3 secondary to sepsis/multiple organ failure, 2 each secondary to pulmonary embolus, uncal herniation/cerebral edema, and shock. The average preoperative ASA levels for patients who died and patients who survived were 3.0 and 2.3, respectively (P < 0.0001). Age, gender, operative time, surgical approach, number of fusion levels, revision status, and estimated blood loss did not have an independently significant correlation to mortality. CONCLUSION: There was a strong association (P < 0.0001) between increasing ASA class and increasing mortality. The other risk factors could not independently predict postoperative mortality within 30 days after adult spinal deformity surgery.

Pulmonary embolism after adult spinal deformity surgery.

STUDY DESIGN: Retrospective review. OBJECTIVE: To determine the incidence and identify the associated risk factors of pulmonary embolism (PE) in patients who receive pharmacologic thromboprophylaxis after adult spinal deformity surgery. SUMMARY OF BACKGROUND DATA: The risk of PE after adult spinal deformity surgery is reported to be as high as 2.2%. However, the incidence and associated risks of PE in the same patient population who receive postoperative pharmacologic thromboprophylaxis is unknown. METHODS: The study included 361 adult patients with spinal deformity who underwent 407 corrective spinal procedures for scoliosis, kyphosis, or kyphoscoliosis. The incidence of PE was determined and compared with a study (historical control) of similar patients undergoing similar surgery but without postoperative pharmacologic thromboprophylaxis. Their demographic information, American Society of Anesthesiologists score, operative time, surgical approach, surgical complexity, and intraoperative blood loss were also analyzed to determine the presence of associated risk factors. RESULTS: Despite universal pharmacologic thromboprophylaxis, 10 pulmonary emboli (2.4%) were diagnosed. Patients undergoing anterior spinal surgery were at a significantly higher risk than those undergoing posterior spinal surgery (P = 0.024). The right-side anterior approach was also associated with a significantly higher incidence of PE compared with the left-sided anterior approach (P = 0.018). Although the rate of PE after posterior spinal surgery did not differ from the historical control, the rate of PE after anterior surgery was reduced by 50% compared with the historical control. Age, gender, estimated blood loss, operative time, revision status, and the number of fusion levels were not significant variables for PE. There were 2 epidural hematomas requiring decompression (0.48%) and 1 wound hematoma (0.24%). CONCLUSION: Although pharmacologic thromboprophylaxis probably does not have a role after posterior spinal surgery, the data in this study suggest that it does lower the incidence of PE after anterior spinal surgery.

The histopathology of fatal untreated human respiratory syncytial virus infection.

The pathology of respiratory syncytial virus (RSV) infection was evaluated 1 day after an outpatient diagnosis of RSV in a child who died in a motor vehicle accident. We then identified 11 children with bronchiolitis from the Vanderbilt University autopsy log between 1925 and 1959 who met criteria for possible RSV infection in the preintensivist era. Their tissue was re-embedded and evaluated by routine hematoxylin and eosin and PAS staining and immunostaining with RSV-specific antibodies. Tissue from three cases was immunostain-positive for RSV antigen and was examined in detail. Small bronchiole epithelium was circumferentially infected, but basal cells were spared. Both type 1 and 2 alveolar pneumocytes were also infected. Although, not possible for archival cases, tissue from the index case was evaluated by immunostaining with antibodies to define the cellular components of the inflammatory response. Inflammatory infiltrates were centered on bronchial and pulmonary arterioles and consisted of primarily CD69+ monocytes, CD3+ double-negative T cells, CD8+ T cells, and neutrophils. The neutrophil distribution was predominantly between arterioles and airways, while the mononuclear cell distribution was in both airways and lung parenchyma. Most inflammatory cells were concentrated submuscular to the airway, but many cells traversed the smooth muscle into the airway epithelium and lumen. Airway obstruction was a prominent feature in all cases attributed to epithelial and inflammatory cell debris mixed with fibrin, mucus, and edema, and compounded by compression from hyperplastic lymphoid follicles. These findings inform our understanding of RSV pathogenesis and may facilitate the development of new approaches for prevention and treatment.

Growth of respiratory syncytial virus in primary epithelial cells from the human respiratory tract.

Respiratory syncytial virus (RSV) is the most important cause of lower respiratory tract disease in infants and children. To study RSV replication, we have developed an in vitro model of human nasopharyngeal mucosa, human airway epithelium (HAE). RSV grows to moderate titers in HAE, though they are significantly lower than those in a continuous epithelial cell line, HEp-2. In HAE, RSV spreads over time to form focal collections of infected cells causing minimal cytopathic effect. Unlike HEp-2 cells, in which wild-type and live-attenuated vaccine candidate viruses grow equally well, the vaccine candidates exhibit growth in HAE that parallels their level of attenuation in children.

Sacral insufficiency fracture surgically treated by fibular allograft.

Sacral insufficiency fractures have been known to occur distally after long instrumentation to the sacrum. Most such fractures are treated nonoperatively, but surgery is indicated for patients who have nonunions, persistent pain, neurologic deficits, or gross displacement. The current report elucidates the potential complication of sacral fracture after long lumbar arthrodesis, reviews the pertinent literature, presents three patients with sacral fractures after long instrumented lumbar spinal arthrodesis to the sacrum, and describes a new surgical technique for stabilizing such fractures. One patient was treated nonoperatively, and two patients were treated with arthrodesis from a posterior approach and augmentation with a strut fibular allograft. All three patients were followed to radiographic and clinical union. The authors conclude that sacral fracture is a potential complication after a long lumbar arthrodesis. Nonoperative techniques are often successful, but when they are not, a new technique using fibular allografts can be successful.