Pain trajectories after bilateral orthotopic lung transplantation surgery performed via a clamshell incision.

INTRODUCTION: The nature, intensity, and progression of acute pain after bilateral orthotopic lung transplantation (BOLT) performed via a clamshell incision has not been well investigated. We aimed to describe acute pain after clamshell incisions using pain trajectories for the study cohort, in addition to stratifying patients into separate pain trajectory groups and investigating their association with donor and recipient perioperative variables. METHODS: After obtaining IRB approval, we retrospectively included all patients ≥18 years old who underwent primary BOLT via clamshell incision at a single center between January 1, 2017, and June 30, 2022. We modeled the overall pain trajectory using pain scores collected over the first seven postoperative days and identified separate pain trajectory classes via latent class analysis. RESULTS: Three hundred one adult patients were included in the final analysis. Three separate pain trajectory groups were identified, with most patients (72.8%) belonging to a well-controlled, stable pain trajectory. Uncontrolled pain was either observed in the early postoperative period (10%), or in the late postoperative period (17.3%). Late postoperative peaking trajectory patients were younger (p = .008), and sicker with a higher lung allocation score (p = .005), receiving preoperative mechanical ventilation (p < .001), or VV-ECMO support (p < .001). CONCLUSION: Despite the extensive nature of a clamshell incision, most pain trajectories in BOLT patients had a well-controlled stable pain profile. The benign nature of pain profiles in our patient population may be attributed to the routine institutional practice of early thoracic epidural analgesia for BOLT patients unless contraindicated.

The pandemic's unseen wounds: COVID-19's profound effects on mental health.

Objective: This review aims to explore the impact of the COVID-19 pandemic on mental health, with a focus on the physiological and psychological consequences, including comorbidities. The goal is to understand the direct and indirect populations affected by mental distress and identify potential interventions. Methodology: A comprehensive literature search was conducted using various databases, including Google Scholar, ResearchGate, ScienceDirect, PubMed, PLoS One, and Web of Science. The search utilized relevant keywords to investigate the direct and indirect impacts of COVID-19 on mental health. The selected articles were critically evaluated and analyzed to identify key findings and insights. Main findings: Mental health, being an intrinsic component of overall well-being, plays a vital role in physiological functioning. The COVID-19 pandemic, caused by the emergence of the novel SARS-CoV-2 virus, has had a devastating global impact. Beyond the respiratory symptoms, individuals recovering from COVID-19 commonly experience additional ailments, such as arrhythmia, depression, anxiety, and fatigue. Healthcare professionals on the frontlines face an elevated risk of mental illness. However, it is crucial to recognize that the general population also grapples with comparable levels of mental distress. Conclusion: The COVID-19 pandemic has underscored the significance of addressing mental health concerns. Various strategies can help mitigate the impact, including counselling, fostering open lines of communication, providing mental support, ensuring comprehensive patient care, and administering appropriate medications. In severe cases, treatment may involve the supplementation of essential vitamins and antidepressant therapy. By understanding the direct and indirect impacts of COVID-19 on mental health, healthcare providers and policymakers can develop targeted interventions to support individuals and communities affected by the pandemic. Continued research and collaborative efforts are essential to address this pervasive issue effectively.

End-to-End diagnosis of breast biopsy images with transformers.

Diagnostic disagreements among pathologists occur throughout the spectrum of benign to malignant lesions. A computer-aided diagnostic system capable of reducing uncertainties would have important clinical impact. To develop a computer-aided diagnosis method for classifying breast biopsy images into a range of diagnostic categories (benign, atypia, ductal carcinoma in situ, and invasive breast cancer), we introduce a transformer-based hollistic attention network called HATNet. Unlike state-of-the-art histopathological image classification systems that use a two pronged approach, i.e., they first learn local representations using a multi-instance learning framework and then combine these local representations to produce image-level decisions, HATNet streamlines the histopathological image classification pipeline and shows how to learn representations from gigapixel size images end-to-end. HATNet extends the bag-of-words approach and uses self-attention to encode global information, allowing it to learn representations from clinically relevant tissue structures without any explicit supervision. It outperforms the previous best network Y-Net, which uses supervision in the form of tissue-level segmentation masks, by 8%. Importantly, our analysis reveals that HATNet learns representations from clinically relevant structures, and it matches the classification accuracy of 87 U.S. pathologists for this challenging test set.

Segmenting Skin Biopsy Images with Coarse and Sparse Annotations using U-Net.

The number of melanoma diagnoses has increased dramatically over the past three decades, outpacing almost all other cancers. Nearly 1 in 4 skin biopsies is of melanocytic lesions, highlighting the clinical and public health importance of correct diagnosis. Deep learning image analysis methods may improve and complement current diagnostic and prognostic capabilities. The histologic evaluation of melanocytic lesions, including melanoma and its precursors, involves determining whether the melanocytic population involves the epidermis, dermis, or both. Semantic segmentation of clinically important structures in skin biopsies is a crucial step towards an accurate diagnosis. While training a segmentation model requires ground-truth labels, annotation of large images is a labor-intensive task. This issue becomes especially pronounced in a medical image dataset in which expert annotation is the gold standard. In this paper, we propose a two-stage segmentation pipeline using coarse and sparse annotations on a small region of the whole slide image as the training set. Segmentation results on whole slide images show promising performance for the proposed pipeline.

Scale-Aware Transformers for Diagnosing Melanocytic Lesions.

Diagnosing melanocytic lesions is one of the most challenging areas of pathology with extensive intra- and inter-observer variability. The gold standard for a diagnosis of invasive melanoma is the examination of histopathological whole slide skin biopsy images by an experienced dermatopathologist. Digitized whole slide images offer novel opportunities for computer programs to improve the diagnostic performance of pathologists. In order to automatically classify such images, representations that reflect the content and context of the input images are needed. In this paper, we introduce a novel self-attention-based network to learn representations from digital whole slide images of melanocytic skin lesions at multiple scales. Our model softly weighs representations from multiple scales, allowing it to discriminate between diagnosis-relevant and -irrelevant information automatically. Our experiments show that our method outperforms five other state-of-the-art whole slide image classification methods by a significant margin. Our method also achieves comparable performance to 187 practicing U.S. pathologists who interpreted the same cases in an independent study. To facilitate relevant research, full training and inference code is made publicly available at https://github.com/meredith-wenjunwu/ScATNet.

Analysis of Regions of Interest and Distractor Regions in Breast Biopsy Images.

This paper studies why pathologists can misdiagnose diagnostically challenging breast biopsy cases, using a data set of 240 whole slide images (WSIs). Three experienced pathologists agreed on a consensus reference ground-truth diagnosis for each slide and also a consensus region of interest (ROI) from which the diagnosis could best be made. A study group of 87 other pathologists then diagnosed test sets (60 slides each) and marked their own regions of interest. Diagnoses and ROIs were categorized such that if on a given slide, their ROI differed from the consensus ROI and their diagnosis was incorrect, that ROI was called a distractor. We used the HATNet transformer-based deep learning classifier to evaluate the visual similarities and differences between the true (consensus) ROIs and the distractors. Results showed high accuracy for both the similarity and difference networks, showcasing the challenging nature of feature classification with breast biopsy images. This study is important in the potential use of its results for teaching pathologists how to diagnose breast biopsy slides.

Classifying Breast Histopathology Images with a Ductal Instance-Oriented Pipeline.

In this study, we propose the Ductal Instance-Oriented Pipeline (DIOP) that contains a duct-level instance segmentation model, a tissue-level semantic segmentation model, and three-levels of features for diagnostic classification. Based on recent advancements in instance segmentation and the Mask RCNN model, our duct-level segmenter tries to identify each ductal individual inside a microscopic image; then, it extracts tissue-level information from the identified ductal instances. Leveraging three levels of information obtained from these ductal instances and also the histopathology image, the proposed DIOP outperforms previous approaches (both feature-based and CNN-based) in all diagnostic tasks; for the four-way classification task, the DIOP achieves comparable performance to general pathologists in this unique dataset. The proposed DIOP only takes a few seconds to run in the inference time, which could be used interactively on most modern computers. More clinical explorations are needed to study the robustness and generalizability of this system in the future.

Machine learning techniques for mitoses classification.

BACKGROUND: Pathologists analyze biopsy material at both the cellular and structural level to determine diagnosis and cancer stage. Mitotic figures are surrogate biomarkers of cellular proliferation that can provide prognostic information; thus, their precise detection is an important factor for clinical care. Convolutional Neural Networks (CNNs) have shown remarkable performance on several recognition tasks. Utilizing CNNs for mitosis classification may aid pathologists to improve the detection accuracy. METHODS: We studied two state-of-the-art CNN-based models, ESPNet and DenseNet, for mitosis classification on six whole slide images of skin biopsies and compared their quantitative performance in terms of sensitivity, specificity, and F-score. We used raw RGB images of mitosis and non-mitosis samples with their corresponding labels as training input. In order to compare with other work, we studied the performance of these classifiers and two other architectures, ResNet and ShuffleNet, on the publicly available MITOS breast biopsy dataset and compared the performance of all four in terms of precision, recall, and F-score (which are standard for this data set), architecture, training time and inference time. RESULTS: The ESPNet and DenseNet results on our primary melanoma dataset had a sensitivity of 0.976 and 0.968, and a specificity of 0.987 and 0.995, respectively, with F-scores of .968 and .976, respectively. On the MITOS dataset, ESPNet and DenseNet showed a sensitivity of 0.866 and 0.916, and a specificity of 0.973 and 0.980, respectively. The MITOS results using DenseNet had a precision of 0.939, recall of 0.916, and F-score of 0.927. The best published result on MITOS (Saha et al. 2018) reported precision of 0.92, recall of 0.88, and F-score of 0.90. In our architecture comparisons on MITOS, we found that DenseNet beats the others in terms of F-Score (DenseNet 0.927, ESPNet 0.890, ResNet 0.865, ShuffleNet 0.847) and especially Recall (DenseNet 0.916, ESPNet 0.866, ResNet 0.807, ShuffleNet 0.753), while ResNet and ESPNet have much faster inference times (ResNet 6 s, ESPNet 8 s, DenseNet 31 s). ResNet is faster than ESPNet, but ESPNet has a higher F-Score and Recall than ResNet, making it a good compromise solution. CONCLUSION: We studied several state-of-the-art CNNs for detecting mitotic figures in whole slide biopsy images. We evaluated two CNNs on a melanoma cancer dataset and then compared four CNNs on a public breast cancer data set, using the same methodology on both. Our methodology and architecture for mitosis finding in both melanoma and breast cancer whole slide images has been thoroughly tested and is likely to be useful for finding mitoses in any whole slide biopsy images.

Does Intolerance of Single-Lung Ventilation Preclude Robotic Off-Pump Totally Endoscopic Coronary Bypass Surgery?

OBJECTIVE: Robotic off-pump totally endoscopic coronary artery bypass (TECAB) usually requires isolated single (right) lung ventilation to adequately expose the surgical site. However, in some patients, persistent oxygen desaturation may occur and conversion to cardiopulmonary bypass (CPB) or sternotomy may be necessary. We reviewed the characteristics and clinical outcomes in patients who did not tolerate single-lung ventilation during TECAB surgery. METHODS: After Institutional Review Board approval we reviewed 440 patients undergoing robotic TECAB at our institution between July 2013 and April 2019. Patients were separated into 2 groups based on their ability to tolerate single-lung ventilation during the procedure. Group 1 included patients able to tolerate single-lung ventilation and Group 2 were patients who required double-lung ventilation to tolerate the procedure. Early and mid-term outcomes were compared. RESULTS: Group 2 (121 patients) had higher Society of Thoracic Surgeons scores, higher body mass index, and more triple-vessel disease than Group 1 (319 patients). Group 2 had more bilateral internal mammary artery use, multivessel grafting, and longer operative times. One patient underwent conversion to sternotomy and 5 required CPB (all in Group 1). Intensive care unit and hospital length of stay were longer in Group 2. Observed/expected mortality did not differ between groups (1.06% in Group 2 vs 0.4% in Group 1; P = 0.215). At mid-term follow-up, cardiac-related/overall mortality and freedom from major adverse cardiac events were similar. CONCLUSIONS: In our cohort, intolerance of single-lung ventilation did not preclude robotic off-pump TECAB. Double-lung ventilation is feasible during the procedure and may prevent conversions to sternotomy or use of CPB, resulting in excellent early and mid-term outcomes.

MLCD: A Unified Software Package for Cancer Diagnosis.

PURPOSE: Machine Learning Package for Cancer Diagnosis (MLCD) is the result of a National Institutes of Health/National Cancer Institute (NIH/NCI)-sponsored project for developing a unified software package from state-of-the-art breast cancer biopsy diagnosis and machine learning algorithms that can improve the quality of both clinical practice and ongoing research. METHODS: Whole-slide images of 240 well-characterized breast biopsy cases, initially assembled under R01 CA140560, were used for developing the algorithms and training the machine learning models. This software package is based on the methodology developed and published under our recent NIH/NCI-sponsored research grant (R01 CA172343) for finding regions of interest (ROIs) in whole-slide breast biopsy images, for segmenting ROIs into histopathologic tissue types and for using this segmentation in classifiers that can suggest final diagnoses. RESULT: The package provides an ROI detector for whole-slide images and modules for semantic segmentation into tissue classes and diagnostic classification into 4 classes (benign, atypia, ductal carcinoma in situ, invasive cancer) of the ROIs. It is available through the GitHub repository under the Massachusetts Institute of Technology license and will later be distributed with the Pathology Image Informatics Platform system. A Web page provides instructions for use. CONCLUSION: Our tools have the potential to provide help to other cancer researchers and, ultimately, to practicing physicians and will motivate future research in this field. This article describes the methodology behind the software development and gives sample outputs to guide those interested in using this package.

Assessment of Machine Learning of Breast Pathology Structures for Automated Differentiation of Breast Cancer and High-Risk Proliferative Lesions.

Importance: Following recent US Food and Drug Administration approval, adoption of whole slide imaging in clinical settings may be imminent, and diagnostic accuracy, particularly among challenging breast biopsy specimens, may benefit from computerized diagnostic support tools. Objective: To develop and evaluate computer vision methods to assist pathologists in diagnosing the full spectrum of breast biopsy samples, from benign to invasive cancer. Design, Setting, and Participants: In this diagnostic study, 240 breast biopsies from Breast Cancer Surveillance Consortium registries that varied by breast density, diagnosis, patient age, and biopsy type were selected, reviewed, and categorized by 3 expert pathologists as benign, atypia, ductal carcinoma in situ (DCIS), and invasive cancer. The atypia and DCIS cases were oversampled to increase statistical power. High-resolution digital slide images were obtained, and 2 automated image features (tissue distribution feature and structure feature) were developed and evaluated according to the consensus diagnosis of the expert panel. The performance of the automated image analysis methods was compared with independent interpretations from 87 practicing US pathologists. Data analysis was performed between February 2017 and February 2019. Main Outcomes and Measures: Diagnostic accuracy defined by consensus reference standard of 3 experienced breast pathologists. Results: The accuracy of machine learning tissue distribution features, structure features, and pathologists for classification of invasive cancer vs noninvasive cancer was 0.94, 0.91, and 0.98, respectively; the accuracy of classification of atypia and DCIS vs benign tissue was 0.70, 0.70, and 0.81, respectively; and the accuracy of classification of DCIS vs atypia was 0.83, 0.85, and 0.80, respectively. The sensitivity of both machine learning features was lower than that of the pathologists for the invasive vs noninvasive classification (tissue distribution feature, 0.70; structure feature, 0.49; pathologists, 0.84) but higher for the classification of atypia and DCIS vs benign cases (tissue distribution feature, 0.79; structure feature, 0.85; pathologists, 0.72) and the classification of DCIS vs atypia (tissue distribution feature, 0.88; structure feature, 0.89; pathologists, 0.70). For the DCIS vs atypia classification, the specificity of the machine learning feature classification was similar to that of the pathologists (tissue distribution feature, 0.78; structure feature, 0.80; pathologists, 0.82). Conclusion and Relevance: The computer-based automated approach to interpreting breast pathology showed promise, especially as a diagnostic aid in differentiating DCIS from atypical hyperplasia.

Pars plana vitrectomy versus combined pars plana vitrectomy and scleral buckle for primary repair of rhegmatogenous retinal detachment.

OBJECTIVE: To compare pars plana vitrectomy (PPV) with PPV and scleral buckle (PPV/SB) for repair of rhegmatogenous retinal detachment (RRD). DESIGN: A retrospective chart review. PARTICIPANTS: Patients who underwent PPV or PPV/SB for RRD repair at a single institution. METHODS: A retrospective chart review of patients in two different treatment groups and analysis of the anatomic and functional results. RESULTS: Single-surgery anatomic success was achieved in 31 of 37 (83.8%) phakic eyes that underwent PPV and in 66 of 68 (97.1%) phakic eyes that underwent PPV/SB (p = 0.0216). Among pseudophakic eyes, 42 of 48 (87.5%) in the PPV group and 62 of 66 (93.9%) in the PPV/SB group achieved single-surgery reattachment (p = 0.3175). Visual acuity improvement was marginally greater in the PPV group among phakic (p = 0.4898) and pseudophakic (p = 0.2465) eyes. CONCLUSIONS: PPV/SB may be associated with a decreased risk for retinal redetachment when compared to PPV for repair of phakic RRD. In pseudophakic eyes, the anatomic success rate between the two techniques appears to be similar.

Long-term potency, sterility, and stability of vancomycin, ceftazidime, and moxifloxacin for treatment of bacterial endophthalmitis.

PURPOSE: To determine the long-term potency, sterility, and stability of vancomycin, ceftazidime, and moxifloxacin prepared in single-use polypropylene syringes for intravitreal injection. METHODS: Experimental study. Vancomycin 1 mg/0.1 mL, ceftazidime 2 mg/0.1 mL, and moxifloxacin 160 µg/0.1 mL were compounded and prepared in 1-mL polypropylene syringes and stored at 4 °C, -20 °C, and -80 °C. Antibiotic potency, sterility, pH, osmolality, and concentration were tested at baseline and at 1, 2, 4, 8, 12, and 24 weeks after preparation. RESULTS: Potency, sterility, and stability were preserved for all 3 antibiotics at all temperatures out to 24 weeks, although there was a trend toward reduced potency at Week 24 for vancomycin and ceftazidime stored at 4°C. The largest zones of inhibition for Staphylococcus epidermidis and S. aureus were consistently demonstrated by moxifloxacin. CONCLUSION: Vancomycin, ceftazidime, and moxifloxacin prepared in single-use polypropylene syringes retain potency, sterility, and stability out to 24 weeks when stored at -20 °C or -80 °C. The results of this study may have important implications for the current management of endophthalmitis.

Metastatic liposarcoma masquerading as an inflammatory granuloma.

PURPOSE: The purpose of this study was to report a case of retroperitoneal liposarcoma metastatic to the choroid. METHODS: A case report is presented of a 67-year-old woman who presented with decreased vision from an extramacular lesion resembling an inflammatory granuloma in her left eye. The lesion showed rapid growth and was associated with extensive fibrovascular proliferation. RESULTS: Diagnostic vitrectomy showed malignant cells in the vitreous. Histopathologic evaluation of the enucleation specimen showed metastatic, dedifferentiated liposarcoma to the choroid with retinal penetration and vitreous invasion. CONCLUSION: Metastatic sarcoma to the choroid is very rare. In this case, the tumor was quite aggressive and penetrated through the retina, ultimately leading to enucleation.

Intravitreal bevacizumab for the treatment of refractory diabetic macular edema.

BACKGROUND AND OBJECTIVE: To report the effect of intravitreal bevacizumab on visual acuity and central retinal thickness (CRT) in refractory diabetic macular edema. PATIENTS AND METHODS: Records of 60 eyes of 54 consecutive patients who underwent intravitreal bevacizumab therapy for refractory diabetic macular edema were reviewed. All eyes received intravitreal bevacizumab 1.25 mg/0.05 mL, and 36 eyes underwent pretreatment and post-treatment optical coherence tomography. Mean follow-up was 7.4 months. RESULTS: Pretreatment mean visual acuity plus or minus standard deviation was 0.71 +/- 0.28 logarithm of the minimum angle of resolution (LogMAR) Snellen letters. At final follow-up, mean visual acuity had improved to 0.66 +/- 0.30 LogMAR (P = .0543). Mean baseline CRT was 440 +/- 106 microm, and follow-up mean CRT was 386 +/- 129 microm (P = .008). Vitrectomized eyes had worse visual acuity and CRT outcomes (P = .002 and P = .028, respectively) compared with nonvitrectomized eyes. CONCLUSION: Intravitreal bevacizumab may provide a functional and anatomic benefit in eyes with persistent diabetic macular edema despite previous treatments.

Optical coherence tomography of traumatic maculopathy following blunt trauma with sugarcane.

An unusual case of traumatic maculopathy following blunt trauma with a sugarcane stick is reported in a 12-year-old boy. An area of parafoveal exudates associated with decrease in visual acuity was observed in the affected eye of the patient. Baseline parameters related to the best-corrected visual acuity, visual fields, fundus photographs, fluorescein angiography, and macular optical coherence tomography were performed and the patient was observed for 6 months with conservative management. There was complete resolution of the exudates with commensurate increase in the best-corrected visual acuity over this period. This unusual presentation of traumatic maculopathy is discussed along with the role of optical coherence tomography in such cases.

Bilateral infraorbital nerve blocks decrease postoperative pain but do not reduce time to discharge following outpatient nasal surgery.

PURPOSE: While infraorbital nerve blocks have demonstrated analgesic benefits for pediatric nasal and facial plastic surgery, no studies to date have explored the effect of this regional anesthetic technique on adult postoperative recovery. We designed this study to test the hypothesis that infraorbital nerve blocks combined with a standardized general anesthetic decrease the duration of recovery following outpatient nasal surgery. METHODS: At a tertiary care university hospital, healthy adult subjects scheduled for outpatient nasal surgery were randomly assigned to receive bilateral infraorbital injections with either 0.5% bupivacaine (Group IOB) or normal saline (Group NS) using an intraoral technique immediately following induction of general anesthesia. All subjects underwent a standardized general anesthetic regimen and were transported to the recovery room following tracheal extubation. The primary outcome was the duration of recovery (minutes) from recovery room admission until actual discharge to home. Secondary outcomes included average and worst pain scores, nausea and vomiting, and supplemental opioid requirements. RESULTS: Forty patients were enrolled. A statistically significant difference in mean [SD] recovery room duration was not observed between Groups IOB and NS (131 [61] min vs 133 [58] min, respectively; P = 0.77). Subjects in Group IOB did experience a reduction in average pain on a 0-100 mm scale (mean [95% confidence interval]) compared to Group NS (-11 [-21 to 0], P = 0.047), but no other comparison of secondary outcomes was statistically significant. CONCLUSIONS: When added to a standardized general anesthetic, bilateral IOB do not decrease actual time to discharge following outpatient nasal surgery despite a beneficial effect on postoperative pain.