Image guidance using 3D-ultrasound (3D-US) for daily positioning of lumpectomy cavity for boost irradiation

<p>Abstract</p> <p>Purpose</p> <p>The goal of this study was to evaluate the use of 3D ultrasound (3DUS) breast IGRT for electron and photon lumpectomy site boost treatments.</p> <p>Materials and methods</p> <p>20 patients with a prescribed photon or electron boost were enrolled in this study. 3DUS images were acquired both at time of simulation, to form a coregistered CT/3DUS dataset, and at the time of daily treatment delivery. Intrafractional motion between treatment and simulation 3DUS datasets were calculated to determine IGRT shifts. Photon shifts were evaluated isocentrically, while electron shifts were evaluated in the beam's-eye-view. Volume differences between simulation and first boost fraction were calculated. Further, to control for the effect of change in seroma/cavity volume due to time lapse between the 2 sets of images, interfraction IGRT shifts using the first boost fraction as reference for all subsequent treatment fractions were also calculated.</p> <p>Results</p> <p>For photon boosts, IGRT shifts were 1.1 ± 0.5 cm and 50% of fractions required a shift >1.0 cm. Volume change between simulation and boost was 49 ± 31%. Shifts when using the first boost fraction as reference were 0.8 ± 0.4 cm and 24% required a shift >1.0 cm. For electron boosts, shifts were 1.0 ± 0.5 cm and 52% fell outside the dosimetric penumbra. Interfraction analysis relative to the first fraction noted the shifts to be 0.8 ± 0.4 cm and 36% fell outside the penumbra.</p> <p>Conclusion</p> <p>The lumpectomy cavity can shift significantly during fractionated radiation therapy. 3DUS can be used to image the cavity and correct for interfractional motion. Further studies to better define the protocol for clinical application of IGRT in breast cancer is needed.</p

Patient perception of physician compassion after a more optimistic vs a less optimistic message: A randomized clinical trial

IMPORTANCE: Information regarding treatment options and prognosis is essential for patient decision making. Patient perception of physicians as being less compassionate when they deliver bad news might be a contributor to physicians' reluctance in delivering these types of communication. OBJECTIVE: To compare patients' perception of physician compassion after watching video vignettes of 2 physicians conveying a more optimistic vs a less optimistic message, determine patients' physician preference after watching both videos, and establish demographic and clinical predictors of compassion. DESIGN, SETTING, AND PARTICIPANTS: Randomized clinical trial at an outpatient supportive care center in a cancer center in Houston, Texas, including English-speaking adult patients with advanced cancer who were able to understand the nature of the study and complete the consent process. Actors and patients were blinded to the purpose of the study. Investigators were blinded to the videos observed by the patient. INTERVENTION: One hundred patients were randomized to observe 2 standardized, roughly 4-minute videos depicting a physician discussing treatment information (more optimistic message vs less optimistic message) with a patient with advanced cancer. Both physicians made an identical number of empathetic statements (5) and displayed identical posture. After viewing each video, patients completed assessments including the Physician Compassion Questionnaire (0 = best, 50 = worst). MAIN OUTCOMES AND MEASURES: Patients' perception of physician compassion after being exposed to a more optimistic vs an equally empathetic but less optimistic message. RESULTS: Patients reported significantly better compassion scores after watching the more optimistic video as compared with the less optimistic video (median [interquartile range], 15 [5-23] vs 23 [10-31]; P &lt; .001). There was a sequence effect favoring the second video on both compassion scores (P &lt; .001) and physician preference (P &lt; .001). Higher perception of compassion was found to be associated with greater trust in the medical profession independent of message type: 63 patients observing the more optimistic message ranked the physician as trustworthy vs 39 after the less optimistic message (P = .03). CONCLUSIONS AND RELEVANCE: Patients perceived a higher level of compassion and preferred physicians who provided a more optimistic message. More research is needed in structuring less optimistic message content to support health care professionals in delivering less optimistic news. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT02357108

Identification of Long Bone Fractures in Radiology Reports Using Natural Language Processing to support Healthcare Quality Improvement.

BACKGROUND: Important information to support healthcare quality improvement is often recorded in free text documents such as radiology reports. Natural language processing (NLP) methods may help extract this information, but these methods have rarely been applied outside the research laboratories where they were developed. OBJECTIVE: To implement and validate NLP tools to identify long bone fractures for pediatric emergency medicine quality improvement. METHODS: Using freely available statistical software packages, we implemented NLP methods to identify long bone fractures from radiology reports. A sample of 1,000 radiology reports was used to construct three candidate classification models. A test set of 500 reports was used to validate the model performance. Blinded manual review of radiology reports by two independent physicians provided the reference standard. Each radiology report was segmented and word stem and bigram features were constructed. Common English “stop words” and rare features were excluded. We used 10-fold cross-validation to select optimal configuration parameters for each model. Accuracy, recall, precision and the F1 score were calculated. The final model was compared to the use of diagnosis codes for the identification of patients with long bone fractures. RESULTS: There were 329 unique word stems and 344 bigrams in the training documents. A support vector machine classifier with Gaussian kernel performed best on the test set with accuracy=0.958, recall=0.969, precision=0.940, and F1 score=0.954. Optimal parameters for this model were cost=4 and gamma=0.005. The three classification models that we tested all performed better than diagnosis codes in terms of accuracy, precision, and F1 score (diagnosis code accuracy=0.932, recall=0.960, precision=0.896, and F1 score=0.927). CONCLUSIONS: NLP methods using a corpus of 1,000 training documents accurately identified acute long bone fractures from radiology reports. Strategic use of straightforward NLP methods, implemented with freely available software, offers quality improvement teams new opportunities to extract information from narrative documents

International Analysis of Electronic Health Records of Children and Youth Hospitalized With COVID-19 Infection in 6 Countries

International audienceAdditional sources of pediatric epidemiological and clinical data are needed to efficiently study COVID-19 in children and youth and inform infection prevention and clinical treatment of pediatric patients

Evolving phenotypes of non-hospitalized patients that indicate long COVID

International audienceAbstract Background For some SARS-CoV-2 survivors, recovery from the acute phase of the infection has been grueling with lingering effects. Many of the symptoms characterized as the post-acute sequelae of COVID-19 (PASC) could have multiple causes or are similarly seen in non-COVID patients. Accurate identification of PASC phenotypes will be important to guide future research and help the healthcare system focus its efforts and resources on adequately controlled age- and gender-specific sequelae of a COVID-19 infection. Methods In this retrospective electronic health record (EHR) cohort study, we applied a computational framework for knowledge discovery from clinical data, MLHO, to identify phenotypes that positively associate with a past positive reverse transcription-polymerase chain reaction (RT-PCR) test for COVID-19. We evaluated the post-test phenotypes in two temporal windows at 3–6 and 6–9 months after the test and by age and gender. Data from longitudinal diagnosis records stored in EHRs from Mass General Brigham in the Boston Metropolitan Area was used for the analyses. Statistical analyses were performed on data from March 2020 to June 2021. Study participants included over 96 thousand patients who had tested positive or negative for COVID-19 and were not hospitalized. Results We identified 33 phenotypes among different age/gender cohorts or time windows that were positively associated with past SARS-CoV-2 infection. All identified phenotypes were newly recorded in patients’ medical records 2 months or longer after a COVID-19 RT-PCR test in non-hospitalized patients regardless of the test result. Among these phenotypes, a new diagnosis record for anosmia and dysgeusia (OR 2.60, 95% CI [1.94–3.46]), alopecia (OR 3.09, 95% CI [2.53–3.76]), chest pain (OR 1.27, 95% CI [1.09–1.48]), chronic fatigue syndrome (OR 2.60, 95% CI [1.22–2.10]), shortness of breath (OR 1.41, 95% CI [1.22–1.64]), pneumonia (OR 1.66, 95% CI [1.28–2.16]), and type 2 diabetes mellitus (OR 1.41, 95% CI [1.22–1.64]) is one of the most significant indicators of a past COVID-19 infection. Additionally, more new phenotypes were found with increased confidence among the cohorts who were younger than 65. Conclusions The findings of this study confirm many of the post-COVID-19 symptoms and suggest that a variety of new diagnoses, including new diabetes mellitus and neurological disorder diagnoses, are more common among those with a history of COVID-19 than those without the infection. Additionally, more than 63% of PASC phenotypes were observed in patients under 65 years of age, pointing out the importance of vaccination to minimize the risk of debilitating post-acute sequelae of COVID-19 among younger adults

Multinational characterization of neurological phenotypes in patients hospitalized with COVID-19

International audienceAbstract Neurological complications worsen outcomes in COVID-19. To define the prevalence of neurological conditions among hospitalized patients with a positive SARS-CoV-2 reverse transcription polymerase chain reaction test in geographically diverse multinational populations during early pandemic, we used electronic health records (EHR) from 338 participating hospitals across 6 countries and 3 continents (January–September 2020) for a cross-sectional analysis. We assessed the frequency of International Classification of Disease code of neurological conditions by countries, healthcare systems, time before and after admission for COVID-19 and COVID-19 severity. Among 35,177 hospitalized patients with SARS-CoV-2 infection, there was an increase in the proportion with disorders of consciousness (5.8%, 95% confidence interval [CI] 3.7–7.8%, p FDR < 0.001) and unspecified disorders of the brain (8.1%, 5.7–10.5%, p FDR < 0.001) when compared to the pre-admission proportion. During hospitalization, the relative risk of disorders of consciousness (22%, 19–25%), cerebrovascular diseases (24%, 13–35%), nontraumatic intracranial hemorrhage (34%, 20–50%), encephalitis and/or myelitis (37%, 17–60%) and myopathy (72%, 67–77%) were higher for patients with severe COVID-19 when compared to those who never experienced severe COVID-19. Leveraging a multinational network to capture standardized EHR data, we highlighted the increased prevalence of central and peripheral neurological phenotypes in patients hospitalized with COVID-19, particularly among those with severe disease

International comparisons of laboratory values from the 4CE collaborative to predict COVID-19 mortality

International audienceAbstract Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size largely retain good transportability performance when porting to different sites. The combination of routine laboratory test values at admission along with basic demographic features can predict mortality in patients hospitalized with COVID-19. Importantly, this potentially deployable model differs from prior work by demonstrating not only consistent performance but also reliable transportability across healthcare systems in the US and Europe, highlighting the generalizability of this model and the overall approach