Juvenile Dermatomyositis Magnetic Resonance Imaging Score (JIS) does not correlate with criteria for clinically inactive disease: a single-centre retrospective evaluation

The Paediatric Rheumatology International Trials Organisation (PRINTO) criteria for clinically inactive disease (CID) and their proposal for glucocorticoid tapering do not consider MRI findings, despite the growing use of MRI and development of reliable MRI scoring tools. We aim to evaluate how CID correlates with MRI scores and physician decision making. We retrospectively used the Juvenile Dermatomyositis Imaging Score (JIS) to score MRIs of all children with JDM over a 10-year period. Demographic, diagnosis, treatment and core set measures data were collected. Correlation between CID and JIS was assessed as well as correlation with the physician treatment decision. There were 25 patients with 59 follow-up episodes to analyse correlation between physician treatment decision and JIS; and 50 episodes for the CID category and JIS correlation. JIS was not significantly associated with the CID category but did correlate with the physician decision. No significant association was found between clinical decision and CID category. The JIS area under the ROC curve (AUC) was 0.80 (95% CI 0.62–0.99) with a score ≥ 8 to predict an escalation. JIS sensitivity and specificity were both 78% with accuracy of 78%, compared to only 67%, 46% and 49%, respectively, for the CID criteria. Clinical criteria alone are not sufficient to assess disease activity status. Clinical decision trends correlated to MRI findings but not PRINTO CID criteria. Multi centre prospective studies are needed to replicate our findings and establish how to best use MRI as a biomarker of disease activity.

Emergency Ocular Telehealth Services

The availability of ocular telehealth services in the emergency department and urgent care center settings can facilitate the diagnosis and management of ocular complaints. While the start-up costs involving ophthalmic equipment and training modules may seem high, it is well worth the investment knowing that these services will increase patient satisfaction, improve overall clinical outcomes, and aid in the physician decision-making process in regard to patient transfers for eye emergencies. Although the majority of ophthalmic urgencies can be managed as outpatient, true ophthalmic emergencies may require more immediate evaluation in order to preserve good visual outcome. Gaps in the availability of ophthalmologists needed for the immediate evaluation of eye emergencies can be filled by the implementation of ocular telehealth.

Defensive Medicine Practices: Scale Development and Validation

Aim Defensive medicine refers to practices with low marginal benefit to patients that doctors may undertake to protect themselves from legal liability. We aimed to develop a scale to measure the practice of defensive medicine. Method We identified aspects of defensive medicine previously reported in the literature and conducted and analyzed semi-structured interviews with 21 physicians in Sakarya to augment and clarify these aspects between May 15, 2018, and June 15, 2018. Informed by these results, we developed, pilot tested, refined, and fielded a 10-item survey to 1724 doctors in Turkey between April 1, 2019, and July 16, 2019. We examined the psychometric properties of the scale using exploratory (EFA) and confirmatory factor analyses (CFA). Results The 10-item scale provided measures of 2 factors: positive defensive medicine (assurance) and negative defensive medicine (avoidance), with Cronbach’s alpha >0.8 for the scale and both subscales in both the EFA and CFA subsamples and excellent goodness-of-fit measures. Conclusions We developed a highly reliable scale to measure positive and negative defensive medicine practice that may be suitable for future research on physician decision making.

Physician decision-making on use of treatments for relapsed/refractory multiple myeloma (RRMM) in later lines: What drives preferences?

239 Background: Physicians consider many factors when selecting third line or greater (3L+) treatments for RRMM, such as patient age and treatment- and disease-specific factors. A greater understanding of preferences that drive treatment decision-making in RRMM in later lines is important. This study assessed treatment preferences of US hematologists/oncologists and oncologists (HemOnc/Oncs) for RRMM patients in 3L+. Methods: A targeted literature review informed a discrete choice experiment (DCE) survey aimed at identifying treatment preferences. Qualitative interviews with HemOnc/Oncs and a pilot DCE survey were then administered to test the validity of the study attributes. The three versions of the final survey varied in presented patient profiles (3L; 4L; 5L). Participating HemOnc/Oncs were presented with 20 DCE choice tasks; in each task, they were asked to choose between pairs of hypothetical treatments including varying levels for each attribute: overall survival (OS), overall response rate (ORR), progression free survival (PFS), keratopathy (corneal epithelium changes with/without symptoms), thrombocytopenia, neutropenia, steroids, preparations, mode of administration, and drug regimen frequency of administration. DCE data were analyzed using multinomial logit regression (MLR) to estimate treatment preferences for each profile; 4L and 5L data were combined (4L+) because patient profiles were comparable. Mean relative importance of the attributes were estimated. Results: The DCE survey was completed by 227 HemOnc/Oncs (N=227); 3L (n=83), 4L (n=73), or 5L (n=71). For 3L and 4L+, as predicted, OS had the highest mean relative importance of the attributes relative to other attributes (3L: 38.1%; 4L+: 36.5%) (Table); the lowest relative importance was whether the treatment required additional preparations for administration (3L: 1.3%; 4L+: 2.1%). Results of the MLR showed that physicians preferred treatments with decreased Grade 3/4 keratopathy risk of 0% compared to 25% (3L: odds ratio [OR] 1.22, p < 0.0001; 4L+: OR 1.12, p < 0.01); physicians also preferred treatments with decreased Grade 3/4 thrombocytopenia risk of 21% compared to 60% (3L: OR 1.16, p < 0.05; 4L+: OR 1.11, p < 0.05). For 4L+, physicians preferred a subcutaneously administered treatment (OR 1.10, p < 0.05). Mean importance of attributes relative to other attributes when selecting patient treatments. Conclusions: In assessing treatment preferences, HemOnc/Oncs placed higher relative importance on OS, PFS, ORR, and preferred a decreased risk of Grade 3/4 keratopathy and thrombocytopenia when choosing later line therapies for RRMM.[Table: see text]

Artificial Intelligence and Machine Learning in Precision and Genomic Medicine

The advancement of precision medicine in medical care has led behind the conventional symptom-driven treatment process by allowing early risk prediction of disease through improved diagnostics and customization of more effective treatments. It is necessary to scrutinize overall patient data alongside broad factors to observe and differentiate between ill and relatively healthy people to take the most appropriate path toward precision medicine, resulting in an improved vision of biological indicators that can signal health changes. Precision and genomic medicine combined with artificial intelligence have the potential to improve patient healthcare. Patients with less common therapeutic responses or unique healthcare demands are using genomic medicine technologies. AI provides insights through advanced computation and inference, enabling the system to reason and learn while enhancing physician decision-making. Many cell characteristics, including gene up-regulation, proteins binding to nucleic acids, and splicing, can be measured at high throughput and used as training objectives for predictive models. Researchers can create a new era of effective genomic medicine with the improved availability of a broad range of data sets and modern computer techniques such as machine learning. This review article has elucidated the contributions of ML algorithms in precision and genome medicine.

Factors Affecting Primary Care Physician Decision-Making for Patients with Complex Multimorbidity: A Qualitative Study

Background Patients with multiple chronic conditions (multimorbidity) and additional psychosocial complexity are at higher risk of adverse outcomes. Establishing treatment or care plans for these patients must account for their disease interactions, finite self-management abilities, and even conflicting treatment recommendations from clinical practice guidelines. Despite existing insight into how primary care physicians (PCPs) approach care decisions for their patients in general, less is known about how PCPs make care planning decisions for more complex populations. We therefore sought to describe factors affecting physician decision-making when care planning for complex patients with multimorbidity Methods This was a qualitative study involving semi-structured telephone interviews with PCPs working ≥ 40% time in a team-based, patient-centered medical home setting in the integrated healthcare system of the U.S. Department of Veterans Affairs, the Veterans Health Administration (VHA). Interviews were conducted from April to July, 2020. Content was analyzed with inductive thematic analysis. Results 25 physicians participated in interviews; most were MDs (n = 21) and worked in hospital-affiliated clinics (n = 14) across all regions of the VHA’s national clinic network. Seven major themes emerged for factors affecting decision-making for complex patients with multimorbidity. Physicians described collaborating on care plans with their care team; considering impacts from patient access and resources on care plans; the boundaries provided by organizational structures; tailoring decisions to individual patients; making decisions in keeping with an underlying internal style or habit; working towards an overarching goal for care; and impacts on decisions from their own emotions and relationship with patient. Conclusions PCPs described individual, relationship-based, and environmental factors affecting their care planning for high-risk and complex patients with multimorbidity in the VHA. Findings offer useful strategies employed by physicians to effectively conduct care planning for complex patients, such as delegation of follow-up within care teams, optimizing visit time vs frequency, and deliberate investment in patient relationship building to gain buy-in to care plans.

Predicting the Disease Outcome in COVID-19 Positive Patients Through Machine Learning: A Retrospective Cohort Study With Brazilian Data

The first officially registered case of COVID-19 in Brazil was on February 26, 2020. Since then, the situation has worsened with more than 672, 000 confirmed cases and at least 36, 000 reported deaths by June 2020. Accurate diagnosis of patients with COVID-19 is extremely important to offer adequate treatment, and avoid overloading the healthcare system. Characteristics of patients such as age, comorbidities and varied clinical symptoms can help in classifying the level of infection severity, predict the disease outcome and the need for hospitalization. Here, we present a study to predict a poor prognosis in positive COVID-19 patients and possible outcomes using machine learning. The study dataset comprises information of 8, 443 patients concerning closed cases due to cure or death. Our experimental results show the disease outcome can be predicted with a Receiver Operating Characteristic AUC of 0.92, Sensitivity of 0.88 and Specificity of 0.82 for the best prediction model. This is a preliminary retrospective study which can be improved with the inclusion of further data. Conclusion: Machine learning techniques fed with demographic and clinical data along with comorbidities of the patients can assist in the prognostic prediction and physician decision-making, allowing a faster response and contributing to the non-overload of healthcare systems.

Factors Influencing Physician Decision Making to Attempt Advanced Resuscitation in Asystolic Out-of-Hospital Cardiac Arrest

The objective of this study was to identify the key elements used by prehospital emergency physicians (EP) to decide whether or not to attempt advanced life support (ALS) in asystolic out-of-hospital cardiac arrest (OHCA). From 1 January 2009 to 1 January 2017, all adult victims of asystolic OHCA in Geneva, Switzerland, were retrospectively included. Patients with signs of “obvious death” or with a Do-Not-Attempt-Resuscitation order were excluded. Patients were categorized as having received ALS if this was mentioned in the medical record, or, failing that, if at least one dose of adrenaline had been administered during cardiopulmonary resuscitation (CPR). Prognostic factors known at the time of EP’s decision were included in a multivariable logistic regression model. Included were 784 patients. Factors favourably influencing the decision to provide ALS were witnessed OHCA (OR = 2.14, 95% CI: 1.43–3.20) and bystander CPR (OR = 4.10, 95% CI: 2.28–7.39). Traumatic aetiology (OR = 0.04, 95% CI: 0.02–0.08), age > 80 years (OR = 0.14, 95% CI: 0.09–0.24) and a Charlson comorbidity index greater than 5 (OR = 0.12, 95% CI: 0.06–0.27) were the factors most strongly associated with the decision not to attempt ALS. Factors influencing the EP’s decision to attempt ALS in asystolic OHCA are the relatively young age of the patients, few comorbidities, presumed medical aetiology, witnessed OHCA and bystander CPR.

Physicians’ decreased tendency to choose palliative care for patients with advanced dementia between 1999 and 2015

Background Physicians’ decision-making for seriously ill patients with advanced dementia is of high importance, especially as the prevalence of dementia is rising rapidly, and includes many challenging ethical, medical and juridical aspects. We assessed the change in this decision-making over 16 years (from 1999 to 2015) and several background factors influencing physicians’ decision. Methods A postal survey including a hypothetical patient-scenario representing a patient with an advanced dementia and a life-threatening gastrointestinal bleeding was sent to 1182 and 1258 Finnish physicians in 1999 and 2015, respectively. The target groups were general practitioners (GPs), surgeons, internists and oncologists. The respondents were asked to choose between several life-prolonging and palliative care approaches. The influence of physicians’ background factors and attitudes on their decision were assessed. Results The response rate was 56%. A palliative care approach was chosen by 57 and 50% of the physicians in 1999 and 2015, respectively (p = 0.01). This change was statistically significant among GPs (50 vs 40%, p = 0.018) and oncologists (77 vs 56%, p = 0.011). GPs chose a palliative care approach less often than other responders in both years (50 vs. 63% in 1999 and 40 vs. 56% in 2015, p < 0.001). In logistic regression analysis, responding in 2015 and being a GP remained explanatory factors for a lower tendency to choose palliative care. The impact of family’s benefit on the decision-making decreased, whereas the influence of the patient’s benefit and ethical values as well as the patient’s or physician’s legal protection increased from 1999 to 2015. Conclusions Physicians chose a palliative care approach for a patient with advanced dementia and life-threatening bleeding less often in 2015 than in 1999. Specialty, attitudes and other background factors influenced significantly physician decision-making. Education on the identification and palliative care of the patients with late-stage dementia are needed to make these decisions more consistent.