An online international comparison of palliative care identification in primary care using the Surprise Question

Background: The Surprise Question (‘Would I be surprised if this patient died within 12 months?’) identifies patients in the last year of life. It is unclear if ‘surprised’ means the same for each clinician, and whether their responses are internally consistent. Aim: To determine the consistency with which the Surprise Question is used. Design: A cross-sectional online study of participants located in Belgium, Germany, Italy, The Netherlands, Switzerland and UK. Participants completed 20 hypothetical patient summaries (‘vignettes’). Primary outcome measure: continuous estimate of probability of death within 12 months (0% [certain survival]–100% [certain death]). A threshold (probability estimate above which Surprise Question responses were consistently ‘no’) and an inconsistency range (range of probability estimates where respondents vacillated between responses) were calculated. Univariable and multivariable linear regression explored differences in consistency. Trial registration: NCT03697213. Setting/participants: Registered General Practitioners (GPs). Of the 307 GPs who started the study, 250 completed 15 or more vignettes. Results: Participants had a consistency threshold of 49.8% (SD 22.7) and inconsistency range of 17% (SD 22.4). Italy had a significantly higher threshold than other countries ( p = 0.002). There was also a difference in threshold levels depending on age of clinician, for every yearly increase, participants had a higher threshold. There was no difference in inconsistency between countries ( p = 0.53). Conclusions: There is variation between clinicians regarding the use of the Surprise Question. Over half of GPs were not internally consistent in their responses to the Surprise Question. Future research with standardised terms and real patients is warranted.

An Update on Pediatric Immune Thrombocytopenia (ITP): Differentiating Primary ITP, IPD, and PID

Immune thrombocytopenia (ITP) is the most common acquired thrombocytopenia in children and is caused by both immune-mediated decreased platelet production and increased platelet destruction. In the absence of a diagnostic test, ITP must be differentiated from other thrombocytopenic disorders, including inherited platelet disorders (IPD). In addition, a diagnosis of secondary ITP due to a primary immune deficiency (PID) with immune dysregulation may not be apparent at diagnosis but can alter management and should be considered in an expanding number of clinical scenarios. The diagnostic evaluation of children with thrombocytopenia will vary based on the clinical history and laboratory features. Access to genotyping has broadened the ability to specify the etiology of thrombocytopenia, while increasing access to immunophenotyping, functional immunologic and platelet assays, and biochemical markers has allowed for more in-depth evaluation of patients. With this greater availability of testing, diagnostic algorithms in patients with thrombocytopenia have become complex. In this article, we highlight the diagnostic evaluation of thrombocytopenia in children with a focus on ITP, including consideration of underlying genetic and immune disorders, and utilize hypothetical patient cases to describe disease manifestations and strategies for treatment of pediatric ITP.

Optimal triage for COVID-19 patients under limited healthcare resources: Development of a parsimonious machine learning prediction model and threshold optimization using discrete-event simulation (Preprint)

BACKGROUND The coronavirus disease 2019 (COVID-19) pandemic has caused an unprecedented burden on healthcare systems. OBJECTIVE To effectively triage COVID-19 patients within situations of limited data availability and to explore optimal thresholds to minimize mortality rates while maintaining the healthcare system capacity. METHODS A nationwide sample of 5601 patients confirmed for COVID-19 up until April 2020 was retrospectively reviewed. XGBoost and logistic regression analysis were used to develop prediction models for the patients’ maximum clinical severity during hospitalization, classified according to the WHO Ordinal Scale for Clinical Improvement (OSCI). The recursive feature elimination technique was used to evaluate the extent of the model performance’s maintenance when clinical and laboratory variables are eliminated. Using populations based on hypothetical patient influx scenarios, discrete-event simulation was performed to find the optimal threshold within limited resource environments that minimizes mortality rates. RESULTS The cross-validated area under the receiver operating characteristics (AUROC) of the baseline XGBoost model that utilized all 37 variables was 0.965 for OSCI ≥ 6. Compared to the baseline model’s performance, the AUROC of the feature-eliminated model that utilized 17 variables was maintained at 0.963 with statistical insignificance. Our prediction model was provided online for clinical implementation. Optimal thresholds were found to minimize mortality rates in a hypothetical patient influx scenario. The benefit of utilizing an optimal triage threshold was clear, reducing mortality up to 18.1% compared to the conventional Youden Index. CONCLUSIONS Our adaptive triage model and its threshold optimization capability reveal that COVID-19 management can be integrated using both medical and healthcare management sectors to guarantee maximum treatment efficacy.

Intensive care doctors and nurses personal preferences for Intensive Care, as compared to the general population: a discrete choice experiment

Background To test the hypothesis that Intensive Care Unit (ICU) doctors and nurses differ in their personal preferences for treatment from the general population, and whether doctors and nurses make different choices when thinking about themselves, as compared to when they are treating a patient. Methods Cross sectional, observational study conducted in 13 ICUs in Australia in 2017 using a discrete choice experiment survey. Respondents completed a series of choice sets, based on hypothetical situations which varied in the severity or likelihood of: death, cognitive impairment, need for prolonged treatment, need for assistance with care or requiring residential care. Results A total of 980 ICU staff (233 doctors and 747 nurses) participated in the study. ICU staff place the highest value on avoiding ending up in a dependent state. The ICU staff were more likely to choose to discontinue therapy when the prognosis was worse, compared with the general population. There was consensus between ICU staff personal views and the treatment pathway likely to be followed in 69% of the choices considered by nurses and 70% of those faced by doctors. In 27% (1614/5945 responses) of the nurses and 23% of the doctors (435/1870 responses), they felt that aggressive treatment would be continued for the hypothetical patient but they would not want that for themselves. Conclusion The likelihood of returning to independence (or not requiring care assistance) was reported as the most important factor for ICU staff (and the general population) in deciding whether to receive ongoing treatments. Goals of care discussions should focus on this, over likelihood of survival.

Optimal Triage for COVID-19 Patients Under Limited Intensive Care Unit Capacity: Development of a Parsimonious Machine Learning Prediction Model and Threshold Optimization Using Discrete-Event Simulation

Background: The coronavirus disease 2019 (COVID-19) pandemic has caused an unprecedented burden on healthcare systems. To effectively triage COVID-19 patients within situations of limited data availability and to explore optimal thresholds to minimize mortality rates while maintaining the healthcare system capacity.Methods: A nationwide sample of 5601 patients confirmed for COVID-19 up until April 2020 was retrospectively reviewed. XGBoost and logistic regression analysis were used to develop prediction models for the patients’ maximum clinical severity during hospitalization, classified according to the WHO Ordinal Scale for Clinical Improvement (OSCI). The recursive feature elimination technique was used to evaluate the extent of the model performance’s maintenance when clinical and laboratory variables are eliminated. Using populations based on hypothetical patient influx scenarios, discrete-event simulation was performed to find the optimal threshold within limited resource environments that minimizes mortality rates.Results: The cross-validated area under the receiver operating characteristics (AUROC) of the baseline XGBoost model that utilized all 37 variables was 0.965 for OSCI ≥ 6. Compared to the baseline model’s performance, the AUROC of the reduced model that utilized 17 variables was maintained at 0.963 with statistical insignificance. Optimal thresholds were found to minimize mortality rates in a hypothetical patient influx scenario. The benefit of utilizing an optimal triage threshold was clear, reducing mortality up to 18.1% compared to the conventional Youden Index.Conclusions: Our adaptive triage model and its threshold optimization capability reveal that COVID-19 management can be integrated using both medical and healthcare management sectors to guarantee maximum treatment efficacy.

The Screening and COnsensus Based on Practices and Evidence (SCOPE) Program–Results of a Survey on Daily Practice Patterns for Patients with mCRC

The SCOPE project aimed to better understand practice patterns, identify drivers for treatment goals, and determine third- and fourth-line treatment choices for patients with metastatic colorectal cancer (mCRC). The survey was developed by an expert panel of gastrointestinal oncologists. Questions concerned general practice patterns, and treatment decisions for three hypothetical patient case scenarios. Participants had to routinely manage patients with mCRC. We present results from 629 participants who provided input on patient treatment scenarios (data cutoff: 17/01/2020). Prolonging overall survival (OS; 51%) was the main aim in first line. In third line, quality of life (QOL) was the primary goal (34%). Forty-three percent also cited efficacy-focused goals; 18% and 13% noted prolonging OS and improving progression-free survival as main aims, respectively. For fit and active patients, 89% of respondents considered trifluridine-tipiracil an appropriate third-line treatment; regorafenib (31%) or clinical trial enrollment (29%) were the fourth-line options. For patients with comorbidities and limited caregiver support, trifluridine-tipiracil was the preferred third-line treatment (70%). For KRAS-mutated patients with comorbidities and adverse events who received prior oxaliplatin, 90% considered oxaliplatin rechallenge an unsuitable third-line treatment, mainly due to the risk of cumulative toxicity (75%). In the third/fourth-line settings, trifluridine-tipiracil followed by regorafenib was the most common option (54%); 17% chose regorafenib followed by trifluridine-tipiracil. Efficacy coupled with QOL are important goals in third-line treatment. Daily practice patterns reflect the guideline recommendations in third- and fourth-line settings, with a trend toward using trifluridine-tipiracil versus regorafenib in KRAS-wildtype and KRAS-mutant tumors.

Differences in Preferences Between Clinicians and Patients for the Use and Dosing of Direct Oral Anticoagulants for Atrial Fibrillation

Background Direct oral anticoagulants (DOACs) are effective in reducing the stroke risk for patients with nonvalvular atrial fibrillation if prescribed at the labeled dose, yet underdosing is frequent. Little is known about clinician knowledge and patient or clinician preferences for DOAC dosing. Methods and Results From April 2019 to March 2020, 240 clinicians and 343 patients with atrial fibrillation completed an assessment of anticoagulation knowledge/preferences. Clinician knowledge of DOAC dosing was tested with 4 hypothetical patient scenarios. Patients and clinicians were asked to grade the importance of 25 factors in anticoagulation decision making. Among clinicians, the median age was 55 years, and 23% were primary care clinicians. In scenarios of a patient indicated for full‐dose DOAC, 41.2% of clinicians underdosed apixaban and 17.6% underdosed rivaroxaban. In scenarios of a patient indicated for reduced‐dose DOAC, 64.6% and 71.7% of clinicians chose to use reduced‐dose apixaban and rivaroxaban, respectively. Only 35.0% of clinicians correctly answered all 4 scenarios with the label‐indicated dose; this knowledge gap was similar between clinicians who did and did not underdose. Among patients with atrial fibrillation, the median age was 65 years, and 89% were currently anticoagulated. Patients and clinicians ranked stroke prevention and avoiding severe bleeding as very important to anticoagulation decision making. Patients were more likely than clinicians to rank the ability to reduce anticoagulation dose if needed as very important (70.5% versus 43.6%; P <0.001). Conclusions There are considerable knowledge gaps regarding DOAC dosing in clinicians treating patients with atrial fibrillation, as well as significant differences in treatment dosing preferences between clinicians and patients.

484 Solriamfetol Titration & AdministRaTion (START): Physician titration strategies in a hypothetical patient with narcolepsy

Introduction Solriamfetol (Sunosi®), a dopamine/norepinephrine reuptake inhibitor, is approved (US and EU) to treat excessive daytime sleepiness (EDS) in adults with narcolepsy (75–150 mg/day) or obstructive sleep apnea (OSA) (37.5–150 mg/day). Patient characteristics, comorbidities, and other EDS medications can influence treatment strategies. To understand factors physicians consider when initiating solriamfetol, this study analyzed titration strategies for a hypothetical patient. Methods This virtual, descriptive, cross-sectional, qualitative survey enrolled US-based physicians treating patients with EDS due to OSA and/or narcolepsy. Responses to 4 open-ended questions regarding a hypothetical patient were recorded. Patient scenario: 32-year-old woman with narcolepsy (Epworth Sleepiness Scale score=8) using an amphetamine stimulant (35 mg/day) and sodium oxybate (6 g/night) for 6 months and occasionally experiencing non-use-limiting but bothersome adverse events (AEs) with the stimulant. Content analysis of the recordings identified themes in the responses; a trained linguist captured language choices/patterns. Results Twenty-six physicians (neurologists, n=7 [27%]; internists/family practitioners, n=7 [27%]; pulmonologists, n=6 [23%]; psychiatrists, n=5 [19%]; otolaryngologists, n=1 [4%]) representing 781 patients on stable solriamfetol doses participated; 19 (73%) were board-certified in sleep disorders. Physicians had been treating narcolepsy a mean 15.7±6.6 years. Most (21 [81%]) thought the patient appropriate for solriamfetol, 3 (12%) thought not appropriate, and 2 (8%) thought appropriateness depended on other factors. Sixteen physicians (62%) suggested adjusting her stimulant, 3 (12%) the stimulant and sodium oxybate, and 1 (4%) neither. Nineteen (73%) would titrate solriamfetol per the label, with 13 (50%) aiming for 75 mg/day and 8 (31%) for 150 mg/day. Physicians emphasized stopping the stimulant before starting solriamfetol: 10 (39%) would taper down before starting solriamfetol, 7 (27%) while starting solriamfetol, and 1 (4%) while aiming to eventually switch; 8 (31%) would discontinue abruptly. Nineteen physicians (73%) would not change their approach if the stimulant dose were 60 mg/day. Most clinicians would change their approach if AEs occurred while starting solriamfetol by taking a slower or more gradual approach, while some would titrate off the stimulant more aggressively. Conclusion Physicians considered existing medications and potential AEs in their titration strategy when initiating solriamfetol. Support (if any) Jazz Pharmaceuticals

Neuro-Oncology Clinicians’ Attitudes and Perspectives on Medical Assistance in Dying

IntroductionMedical assistance in dying (MAiD), also known as physician-assisted death, is currently legal in several locations across the globe. Even more jurisdictions are considering legalizing MAiD. Brain cancer or its treatments can lead to cognitive impairment, which can impact decision-making capacity. In most jurisdictions patients are assessed for mental capacity to inform MAiD eligibility. We sought to explore worldwide neuro-oncology clinicians’ attitudes and perspectives on MAiD, including interpretation of decision-making capacity for patient MAiD eligibility.MethodsAn online survey was distributed to members of national and international neuro-oncology societies. We asked questions about decision-making capacity and MAiD, in part using hypothetical patient scenarios. Multiple choice and free-text responses were captured.ResultsThere were 125 survey respondents. Impaired cognition was identified as the most important factor that would signal a decline in patient capacity. At least 26% of survey respondents had moral objections to MAiD. Fewer clinicians were willing to support a MAiD decision for patients with lower-grade tumors and better performance status.Conclusionshile there are differing opinions on the moral permissibility of MAiD in general and for neuro-oncology patients, most clinicians agree that capacity must be assessed carefully before a decision is made. Further, some patients with brain tumors are not generally thought to be MAiD-eligible due to the nature of their specific diseases. These results can inform assessments of patient capacity in neuro-oncological practice in jurisdictions where MAiD is legal.