Updates on coding in dermatology.

Correct coding is an important component of effective dermatology practice management. Over the past several years there have been updates to many commonly used codes within dermatology. This review highlights many of these updates, such as: the skin biopsy codes have been subdivided to reflect the different biopsy techniques. The definition of complex linear repairs has been updated and clarified. Outpatient and inpatient evaluation and management visits have new coding guidelines to determine level of care. Dermatopathology consultation codes have been updated and category III codes related to digital pathology have been created. Understanding the details and nuances of each of these categories of codes is vital to ensuring appropriate coding is performed.

Acoustic regularities in infant-directed speech and song across cultures.

When interacting with infants, humans often alter their speech and song in ways thought to support communication. Theories of human child-rearing, informed by data on vocal signalling across species, predict that such alterations should appear globally. Here, we show acoustic differences between infant-directed and adult-directed vocalizations across cultures. We collected 1,615 recordings of infant- and adult-directed speech and song produced by 410 people in 21 urban, rural and small-scale societies. Infant-directedness was reliably classified from acoustic features only, with acoustic profiles of infant-directedness differing across language and music but in consistent fashions. We then studied listener sensitivity to these acoustic features. We played the recordings to 51,065 people from 187 countries, recruited via an English-language website, who guessed whether each vocalization was infant-directed. Their intuitions were more accurate than chance, predictable in part by common sets of acoustic features and robust to the effects of linguistic relatedness between vocalizer and listener. These findings inform hypotheses of the psychological functions and evolution of human communication.

A hierarchical anatomical framework and workflow for organizing stereotactic encephalography in epilepsy.

Stereotactic electroencephalography (SEEG) is an increasingly utilized method for invasive monitoring in patients with medically intractable epilepsy. Yet, the lack of standardization for labeling electrodes hinders communication among clinicians. A rational clustering of contacts based on anatomy rather than arbitrary physical leads may help clinical neurophysiologists interpret seizure networks. We identified SEEG electrodes on post-implant CTs and registered them to preoperative MRIs segmented according to an anatomical atlas. Individual contacts were automatically assigned to anatomical areas independent of lead. These contacts were then organized using a hierarchical anatomical schema for display and interpretation. Bipolar-referenced signal cross-correlations were used to compare the similarity of grouped signals within a conventional montage versus this anatomical montage. As a result, we developed a hierarchical organization for SEEG contacts using well-accepted, free software that is based solely on their post-implant anatomical location. When applied to three example SEEG cases for epilepsy, clusters of contacts that were anatomically related collapsed into standardized groups. Qualitatively, seizure events organized using this framework were better visually clustered compared to conventional schemes. Quantitatively, signals grouped by anatomical region were more similar to each other than electrode-based groups as measured by Pearson correlation. Further, we uploaded visualizations of SEEG reconstructions into the electronic medical record, rendering them durably useful given the interpretable electrode labels. In conclusion, we demonstrate a standardized, anatomically grounded approach to the organization of SEEG neuroimaging and electrophysiology data that may enable improved communication among and across surgical epilepsy teams and promote a clearer view of individual seizure networks.

Development of a core outcome set for basal cell carcinoma.

BACKGROUND: There is variation in the outcomes reported in clinical studies of basal cell carcinoma. This can prevent effective meta-analyses from answering important clinical questions. OBJECTIVE: To identify a recommended minimum set of core outcomes for basal cell carcinoma clinical trials. METHODS: Patient and professional Delphi process to cull a long list, culminating in a consensus meeting. To be provisionally accepted, outcomes needed to be deemed important (score, 7-9, with 9 being the maximum) by 70% of each stakeholder group. RESULTS: Two hundred thirty-five candidate outcomes identified via a systematic literature review and survey of key stakeholders were reduced to 74 that were rated by 100 health care professionals and patients in 2 Delphi rounds. Twenty-seven outcomes were provisionally accepted. The final core set of 5 agreed-upon outcomes after the consensus meeting included complete response; persistent or serious adverse events; recurrence-free survival; quality of life; and patient satisfaction, including cosmetic outcome. LIMITATIONS: English-speaking patients and professionals rated outcomes extracted from English language studies. CONCLUSION: A core outcome set for basal cell carcinoma has been developed. The use of relevant measures may improve the utility of clinical research and the quality of therapeutic guidance available to clinicians.

Artificial intelligence for prediction of COVID-19 progression using CT imaging and clinical data.

OBJECTIVES: Early recognition of coronavirus disease 2019 (COVID-19) severity can guide patient management. However, it is challenging to predict when COVID-19 patients will progress to critical illness. This study aimed to develop an artificial intelligence system to predict future deterioration to critical illness in COVID-19 patients. METHODS: An artificial intelligence (AI) system in a time-to-event analysis framework was developed to integrate chest CT and clinical data for risk prediction of future deterioration to critical illness in patients with COVID-19. RESULTS: A multi-institutional international cohort of 1,051 patients with RT-PCR confirmed COVID-19 and chest CT was included in this study. Of them, 282 patients developed critical illness, which was defined as requiring ICU admission and/or mechanical ventilation and/or reaching death during their hospital stay. The AI system achieved a C-index of 0.80 for predicting individual COVID-19 patients' to critical illness. The AI system successfully stratified the patients into high-risk and low-risk groups with distinct progression risks (p < 0.0001). CONCLUSIONS: Using CT imaging and clinical data, the AI system successfully predicted time to critical illness for individual patients and identified patients with high risk. AI has the potential to accurately triage patients and facilitate personalized treatment. KEY POINT: • AI system can predict time to critical illness for patients with COVID-19 by using CT imaging and clinical data.

Prognostication of patients with COVID-19 using artificial intelligence based on chest x-rays and clinical data: a retrospective study.

BACKGROUND: Chest x-ray is a relatively accessible, inexpensive, fast imaging modality that might be valuable in the prognostication of patients with COVID-19. We aimed to develop and evaluate an artificial intelligence system using chest x-rays and clinical data to predict disease severity and progression in patients with COVID-19. METHODS: We did a retrospective study in multiple hospitals in the University of Pennsylvania Health System in Philadelphia, PA, USA, and Brown University affiliated hospitals in Providence, RI, USA. Patients who presented to a hospital in the University of Pennsylvania Health System via the emergency department, with a diagnosis of COVID-19 confirmed by RT-PCR and with an available chest x-ray from their initial presentation or admission, were retrospectively identified and randomly divided into training, validation, and test sets (7:1:2). Using the chest x-rays as input to an EfficientNet deep neural network and clinical data, models were trained to predict the binary outcome of disease severity (ie, critical or non-critical). The deep-learning features extracted from the model and clinical data were used to build time-to-event models to predict the risk of disease progression. The models were externally tested on patients who presented to an independent multicentre institution, Brown University affiliated hospitals, and compared with severity scores provided by radiologists. FINDINGS: 1834 patients who presented via the University of Pennsylvania Health System between March 9 and July 20, 2020, were identified and assigned to the model training (n=1285), validation (n=183), or testing (n=366) sets. 475 patients who presented via the Brown University affiliated hospitals between March 1 and July 18, 2020, were identified for external testing of the models. When chest x-rays were added to clinical data for severity prediction, area under the receiver operating characteristic curve (ROC-AUC) increased from 0·821 (95% CI 0·796-0·828) to 0·846 (0·815-0·852; p<0·0001) on internal testing and 0·731 (0·712-0·738) to 0·792 (0·780-0 ·803; p<0·0001) on external testing. When deep-learning features were added to clinical data for progression prediction, the concordance index (C-index) increased from 0·769 (0·755-0·786) to 0·805 (0·800-0·820; p<0·0001) on internal testing and 0·707 (0·695-0·729) to 0·752 (0·739-0·764; p<0·0001) on external testing. The image and clinical data combined model had significantly better prognostic performance than combined severity scores and clinical data on internal testing (C-index 0·805 vs 0·781; p=0·0002) and external testing (C-index 0·752 vs 0·715; p<0·0001). INTERPRETATION: In patients with COVID-19, artificial intelligence based on chest x-rays had better prognostic performance than clinical data or radiologist-derived severity scores. Using artificial intelligence, chest x-rays can augment clinical data in predicting the risk of progression to critical illness in patients with COVID-19. FUNDING: Brown University, Amazon Web Services Diagnostic Development Initiative, Radiological Society of North America, National Cancer Institute and National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health.

Ablation Therapy for Advanced Stage Non-Small Cell Lung Cancer: A National Cancer Database Study.

PURPOSE: To compare overall survival (OS) of ablation with no treatment for patients with advanced stage non-small cell lung cancer. METHODS: Patients with clinical stage IIIB (T1-4N3M0, T4N2M0) and stage IV (T1-4N0-3M1) non-small cell lung cancer, in accordance with the American Joint Committee on Cancer, 7th edition, who did not receive treatment or who received ablation as their sole primary treatment besides chemotherapy from 2004 to 2014, were identified from the National Cancer Data Base. OS was estimated using the Kaplan-Meier method and evaluated by log-rank test, univariate and multivariate Cox proportional hazard regression, and propensity score-matched analysis. Relative survival analyses comparing age- and sex-matched United States populations were performed. RESULTS: A total of 140,819 patients were included. The 1-, 2-, 3- and 5-year survival rates relative to age- and sex-matched United States population were 28%, 18%, 12%, and 10%, respectively, for ablation (n = 249); and 30%, 15%, 9%, and 5%, respectively for no treatment (n = 140,570). Propensity score matching resulted in 249 patients in the ablation group versus 498 patients in the no-treatment group. After matching, ablation was associated with longer OS than that in the no-treatment group (median, 5.9 vs 4.7 months, respectively; hazard ratio, 0.844; 95% confidence interval, 0.719-0.990; P = .037). These results persisted in patients with an initial tumor size of ≤3 cm. CONCLUSIONS: Preliminary results suggest ablation may be associated with longer OS in patients with late-stage non-small cell lung cancer than survival in those who received no treatment.

Facial De-identification of Head CT Scans.

Online supplemental material is available for this article.

Exploratory Application of Augmented Reality/Mixed Reality Devices for Acute Care Procedure Training.

INTRODUCTION: Augmented reality (AR), mixed reality (MR), and virtual reality devices are enabling technologies that may facilitate effective communication in healthcare between those with information and knowledge (clinician/specialist; expert; educator) and those seeking understanding and insight (patient/family; non-expert; learner). Investigators initiated an exploratory program to enable the study of AR/MR use-cases in acute care clinical and instructional settings. METHODS: Academic clinician educators, computer scientists, and diagnostic imaging specialists conducted a proof-of-concept project to 1) implement a core holoimaging pipeline infrastructure and open-access repository at the study institution, and 2) use novel AR/MR techniques on off-the-shelf devices with holoimages generated by the infrastructure to demonstrate their potential role in the instructive communication of complex medical information. RESULTS: The study team successfully developed a medical holoimaging infrastructure methodology to identify, retrieve, and manipulate real patients' de-identified computed tomography and magnetic resonance imagesets for rendering, packaging, transfer, and display of modular holoimages onto AR/MR headset devices and connected displays. Holoimages containing key segmentations of cervical and thoracic anatomic structures and pathology were overlaid and registered onto physical task trainers for simulation-based "blind insertion" invasive procedural training. During the session, learners experienced and used task-relevant anatomic holoimages for central venous catheter and tube thoracostomy insertion training with enhanced visual cues and haptic feedback. Direct instructor access into the learner's AR/MR headset view of the task trainer was achieved for visual-axis interactive instructional guidance. CONCLUSION: Investigators implemented a core holoimaging pipeline infrastructure and modular open-access repository to generate and enable access to modular holoimages during exploratory pilot stage applications for invasive procedure training that featured innovative AR/MR techniques on off-the-shelf headset devices.

Future considerations for clinical dermatology in the setting of 21st century American policy reform: The Relative Value Scale Update Committee.

The American Medical Association-Specialty Society Relative Value Scale Update Committee, also known as the RUC, plays a critical role in assessing the relative value of physician services and procedures. This committee provides access for all physicians, including dermatologists, to the reimbursement process. Since the introduction of the Resource-Based Relative Value Scale by Medicare, the RUC has done important work to evaluate and refine reimbursement for physician services. The RUC recommendations have also led the Current Procedural Terminology (CPT) Editorial Panel to develop additional reimbursement codes as new procedures and services are developed. In this article (from the series Future Considerations for Clinical Dermatology in the Setting of 21st Century American Policy Reform), we will review the RUC, including its history and membership, the RUC update process, and a brief discussion of a few issues of particular importance to dermatologists.

A risk adjustment approach to estimating the burden of skin disease in the United States.

Direct insurance claims tabulation and risk adjustment statistical methods can be used to estimate health care costs associated with various diseases. In this third manuscript derived from the new national Burden of Skin Disease Report from the American Academy of Dermatology, a risk adjustment method that was based on modeling the average annual costs of individuals with or without specific diseases, and specifically tailored for 24 skin disease categories, was used to estimate the economic burden of skin disease. The results were compared with the claims tabulation method used in the first 2 parts of this project. The risk adjustment method estimated the direct health care costs of skin diseases to be $46 billion in 2013, approximately $15 billion less than estimates using claims tabulation. For individual skin diseases, the risk adjustment cost estimates ranged from 11% to 297% of those obtained using claims tabulation for the 10 most costly skin disease categories. Although either method may be used for purposes of estimating the costs of skin disease, the choice of method will affect the end result. These findings serve as an important reference for future discussions about the method chosen in health care payment models to estimate both the cost of skin disease and the potential cost impact of care changes.

Contribution of health care factors to the burden of skin disease in the United States.

The American Academy of Dermatology has developed an up-to-date national Burden of Skin Disease Report on the impact of skin disease on patients and on the US population. In this second of 3 manuscripts, data are presented on specific health care dimensions that contribute to the overall burden of skin disease. Through the use of data derived from medical claims in 2013 for 24 skin disease categories, these results indicate that skin disease health care is delivered most frequently to the aging US population, who are afflicted with more skin diseases than other age groups. Furthermore, the overall cost of skin disease is highest within the commercially insured population, and skin disease treatment primarily occurs in the outpatient setting. Dermatologists provided approximately 30% of office visit care and performed nearly 50% of cutaneous surgeries. These findings serve as a critical foundation for future discussions on the clinical importance of skin disease and the value of dermatologic care across the population.

The burden of skin disease in the United States.

Since the publication of the last US national burden of skin disease report in 2006, there have been substantial changes in the practice of dermatology and the US health care system. These include the development of new treatment modalities, marked increases in the cost of medications, increasingly complex payer rules and regulations, and an aging of the US population. Recognizing the need for up-to-date data to inform researchers, policy makers, public stakeholders, and health care providers about the impact of skin disease on patients and US society, the American Academy of Dermatology produced a new national burden of skin disease report. Using 2013 claims data from private and governmental insurance providers, this report analyzed the prevalence, cost, and mortality attributable to 24 skin disease categories in the US population. In this first of 3 articles, the presented data demonstrate that nearly 85 million Americans were seen by a physician for at least 1 skin disease in 2013. This led to an estimated direct health care cost of $75 billion and an indirect lost opportunity cost of $11 billion. Further, mortality was noted in half of the 24 skin disease categories.

Effect of patient size on mean sterile water attenuation during multiphase CT examinations.

OBJECTIVE: The purpose of this study was to determine the variability of attenuation measurements in a water phantom included in the FOV during multiphase 64-MDCT. SUBJECTS AND METHODS: Ninety-seven consecutively registered patients undergoing multiphase kidney and liver protocol CT of the abdomen on the same 64-MDCT scanner had a sealed water bottle placed on their anterior abdomen during the examination. Region of interest (ROI) measurements of the mean attenuation (in HU) of the water bottle were made during the unenhanced, dynamic, and delayed phases of contrast enhancement. Generalized estimating equations were used to model mean attenuation in the ROI as a function of cross-sectional patient area, contrast phase, and protocol. Day of month and time of day were covariates. A phantom was created to model the patient study. RESULTS: The mean attenuation values in the water bottle ROIs were -13.1 HU for the kidney protocol and -9.1 HU for the liver protocol in the unenhanced phase, -11.7 HU for the kidney and -9.5 HU for the liver protocol in the dynamic phase, and -11.9 HU for the kidney and 11.0 HU for the liver protocol in the delayed phase. Kidney protocol water bottle ROI attenuation values were lower than the liver values (p = 0.04). In all phases with both protocols, the values differed from 0 HU (all p < 0.0001). Water bottle ROI attenuation decreased as patient cross-sectional area increased (-0.01 HU/cm(2), p < 0.0001). Three patients had absolute water bottle attenuation changes greater than 20 HU between phases. Day of the month (p = 0.08) and time of day (p = 0.93) were not significant factors. Phantom data supported the study findings. CONCLUSION: The mean attenuation of water decreased as patient diameter increased. Both artifactual enhancement and decrease in enhancement greater than 20 HU were found in three larger patients.