Hypothetical Patient Research Articles

A cost effectiveness analysis of a quality improvement intervention to increase preferential balanced fluid prescribing

BackgroundThe higher cost of balanced fluids has historically contributed to preferential normal saline (NS) use. Recent clinical trials demonstrated that balanced fluids reduced the incidence of major adverse kidney events among hospitalized patients and also reduced mortality among critically ill patients. This study sought to conduct an economic evaluation of a recently published quality improvement (QI) intervention to increase preferential balanced fluid prescribing, which significantly increased the relative proportion of balanced fluid orders.MethodsUsing clinical trial data and real-world data from our prior QI intervention in conjunction with local and regional cost data, we constructed a decision tree to examine the economic cost and clinical consequences of using NS, lactated ringers (LR), and plasma-lyte for 30,000 hypothetical adult patients and 1,290 hypothetical critically ill adult patients with sepsis admitted to our institution over one year. Clinical outcomes (hospitalization to day 30) included development of acute kidney injury (AKI), new requirement for renal replacement therapy (RRT), and in-hospital mortality. The primary economic outcome was the difference in cost between the pre- and post-QI intervention periods. Sensitivity analyses and breakeven analyses were performed. Evaluation was conducted from South Carolina hospitals’ perspective.ResultsFor the overall cohort, simulation predicted that 53 AKIs, 2 instances of RRT, and 62 deaths were avoided in the post-intervention year with a net cost savings of $704,507. For the critically ill patients with sepsis, simulation predicted that 32 AKIs, 4 instances of RRT, and 12 deaths were avoided with a net cost savings of $474,676. Sensitivity analyses using outcome data from additional clinical trials also estimated annual cost savings ($77,677-$414,872).ConclusionsThe QI intervention was predicted to improve patient outcomes while yielding significant cost-savings. The marginally increased cost of balanced fluids should not discourage implementation of interventions aimed at increasing evidence-supported balanced fluid prescribing.

The 2023 British Thoracic Society Guideline for Pleural Disease Update on Malignant Pleural Effusion

The management of malignant pleural effusions (MPEs) has developed hugely since the publication of the management of an MPE: British Thoracic Society (BTS) Pleural Disease Guideline 2010. The presence of an MPE reflects advanced or metastatic disease (excluding malignant pleural mesothelioma) and thus the management of MPE often focuses on symptomatic relief of symptoms and improving quality of life. We provide a review of the 2023 BTS guidance in relation to malignant pleural effusions and specific points on imaging and systemic anti-cancer treatment by concentrating on a hypothetical patient vignette.

Real-world practitioner perceptions of CARTITUDE-4 results for patients with previously treated multiple myeloma.

Initially approved for the fifth-line or later therapeutic setting, the chimeric antigen receptor (CAR) T-cell regimen ciltacabtagene autoleucel (cilta-cel) was recently approved for second-line (2L) treatment in relapsed/refractory multiple myeloma (RRMM). Oncology practitioners use clinical trials to inform treatment, but real-world impressions and impact on practice are lacking. We aimed to determine whether presenting CARTITUDE-4 clinical trial data would impact real-world preferences/perceptions around CAR T-cell therapy. Recruiting from the Cardinal Health Oncology Provider Extended Network (OPEN), we surveyed hematologists/oncologists to investigate fourth-line (4L) preferences in a hypothetical patient with triple-class-refractory MM. We posed the same questions and answers before and after the trial presentation and compared pre-/post-preferences toward cilta-cel and sequencing relative to bispecific antibodies (BsAbs). Using the same methodology as described above, we also performed a secondary analysis comparing pre-/post-perceptions on the use of CAR T-cell therapy in earlier lines for patients with triple-class-refractory MM. Among 50 respondents, decision-making factors before the trial presentation included CAR T-cell center availability (58%), comorbidities (52%), and center locations (34%). Additionally, 48% of 46 respondents chose 4L cilta-cel. Among 47, 40% wanted more real-world/long-term CAR T-cell therapy outcomes in any line, 38% wanted more 2L data, and 34% favored 2L/third-line (3L) use. After the presentation, the preference for cilta-cel doubled from 48% to 88% (p<0.001) among 50 respondents and rose from 34% to 55% (p=0.001) for earlier-line CAR T-cell therapy among 49. Moreover, 55% of 49 respondents preferred CAR T-cell therapy prior to BsAbs. We have shown that making oncology practitioners aware of trials precipitated decision-making factors and led to notable, significant shifts in future intended practice patterns. Being aware of trial data enables practitioners to make more informed decisions, tailor therapies to individual patients, and ultimately improve outcomes.

Cost-Utility Analysis of Multigene Assays to Guide Treatment Decisions for Node-Negative Early Breast Cancer.

Clinicopathologic and patient factors, such as tumor grade, size, age, and menopausal status, provide limited prognostic and predictive information in hormone receptor positive (HR +), human epidermal growth receptor 2 negative (HER2-), node-negative early-stage breast cancer, leading to potential over- or under-treatment. Multigene expression profile tests used in clinical practice in the USA, including the 21-gene assay, 70-gene assay, 12-gene assay, and 50-gene assay, offer prognostic information beyond traditional clinicopathologic features to improve treatment decisions. This study aimed to estimate the cost-effectiveness of these four multigene assays compared with clinicopathologic risk assessment alone. A decision tree categorized hypothetical patients with HR + /HER2- early-stage invasive breast cancer according to clinical and genomic risk, and integrated clinical expert insights for chemotherapy allocation with literature inputs. A Markov model simulated lifetime costs and outcomes of chemotherapy decisions over a patient's lifetime. The probability of distant breast cancer recurrence was derived from TAILORx (21-gene assay), MINDACT (70-gene assay), and TransATAC (12-gene assay, 50-gene assay) studies. Costs were calculated from a US societal perspective in 2021 US dollars, considering healthcare costs, lost productivity, and patient out-of-pocket expenses. The 21-gene assay and 50-gene assay were less costly ( -$12,189 and -$2410, respectively) and more effective [0.23 and 0.07 quality-adjusted life years (QALYs), respectively] compared with clinicopathologic risk alone. Similarly, the 70-gene assay and 12-gene assay are also cost-effective alternatives [incremental cost-effectiveness ratio (ICER): 27,760 and 7942, respectively]. All four multigene assays were cost-effective from a societal perspective, offering low net lifetime costs or savings with improved outcomes compared with clinicopathologic risk assessment alone. These assays can help refine treatment decisions by providing prognostic risk estimates. In the case of the 21-gene assay, it can also predict chemotherapy benefit leading to the highest lifetime cost savings and greatest QALY gain.

Understanding decision making for the use of atraumatic restorative approach based on non-clinical factors by Indian pedodontists - a conjoint analysis.

Atraumatic Restorative Treatment (ART) approach is considered as one of the minimally invasive interventions. The success of restoration depends on various clinical factors like material, patient and operator factor. Conjoint analysis is a technique for measuring individual's preference structures via systematical variations of products attributes in an experimental design. Assess Pedodontists' perception of non-clinical factors and investigate influence of child's age, level of cooperation and vulnerability status on their decisions to perform Atraumatic Restorative Approach (ART) approach using hypothetical patient scenario's and conjoint design. A cross-sectional exploratory survey using a web-based questionnaire was performed among Pedodontists registered as life members under Indian Association of Pedodontic and Preventive Dentistry. Self-designed structured scenario-based questionnaire was prepared and validated. Three non-clinical factors Age of child, Cooperation level and Vulnerability status were considered. Using orthogonal design, SPSS software nine profiles were created randomly along with two holdouts. Final study proforma consisted of three sections with description of eleven clinical scenarios. It was administered to participants using Google forms. Using SPSS conjoint software relative utilities for each factor on decision for using ART was estimated. Four hundred and thirty-two pedodontists had completed the survey (35.9%). Pedodontists considered vulnerability status of the child as the most important factor for their decision to use ART treatment. For the vulnerability status, the factor vulnerable had the greatest utility (-0.364) compared to non-vulnerable factor (0.364). For cooperation level, factor uncooperative had greater utility (-0.343), as compared to moderately cooperative (0.066) and uncooperative (0.277). For the age factor, the age of 4 years had the greatest utility (-0.175) compared with age 6 (-0.013) and age 12 (0.19). The most preferred scenario by pedodontists to consider ART as treatment of choice was child belonging to vulnerable section, being very young and uncooperative.

Biologics and oral systemic treatment preferences in patients and physicians for moderate-to-severe atopic dermatitis: a discrete choice experiment in the United Kingdom and Germany

Background: As the available treatments for moderate-to-severe atopic dermatitis (AD) expand, understanding patient and physician preferences becomes crucial for informed decision-making. Objective: To quantify patient and physician preferences for biologics and oral systemic AD treatment attributes. Materials and methods: We conducted a cross-sectional, online discrete choice experiment (DCE) involving 306 AD patients and 206 physicians throughout the United Kingdom and Germany. Qualitative interviews identified the key attributes for inclusion in the DCE. Each choice task comprised two hypothetical patient profiles. Data were analyzed using a random-parameters logit model. Results: Results indicated a significant emphasis on efficacy, with reducing sleep disturbance and itch ranking first and second among patients, and the reverse for physicians. Time to itch relief was the third most important efficacy attribute for both groups, but relatively more important for patients than for physicians. For both groups, the risk of eye problems was the most important safety concern of those included. Mode of administration was not of great importance compared to efficacy and safety attributes. Conclusions: Our findings suggest patients prioritize sleep disturbance, an attribute not captured in prior preference studies in AD, time to itch relief and itch. These findings emphasize the importance of addressing sleep-related issues, whilst also targeting fast itch control, to enhance patients’ well-being.

Cost-utility of nelarabine for the first-line treatment of newly diagnosed pediatric T-cell acute lymphoblastic leukemia in Canada.

The Children's Oncology Group (COG)-AALL0434 trial investigated the addition of nelarabine to the augmented Berlin-Frankfurt-Münster (aBFM) protocol in patients (1.0-30.99years) with newly diagnosed T-cell acute lymphoblastic leukemia (T-ALL). Despite demonstrating superior outcomes, nelarabine is not currently funded by many health systems, in part due to a lack of cost-effectiveness data. We estimated the cost-utility of nelarabine for this indication from a Canadian public healthcare payer perspective. We developed a microsimulation model that followed hypothetical patients with newly diagnosed T-ALL from post-induction therapy to death. Three health states were modeled: relapse-free, post-relapse, and death. Efficacy was estimated using AALL0434 and retrospective data from Ontario, Canada. Costs were obtained from Canadian sources. Utility estimates and long-term mortality risks were sourced from literature. Total healthcare costs, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratio (ICER) were reported. Probabilistic and scenario analyses were conducted. Incorporating nelarabine in the aBFM protocol increased costs by $51,670 Canadian dollars per patient, but resulted in 1.97 more QALYs and an ICER of $26,184/QALY. Most of the identified cost and benefit were accrued within the AALL0434 trial period (first 11years post diagnosis) and while patients were in the relapse-free health state. Across multiple scenarios, the ICER was stable under an assumed $50,000/QALY threshold. Incorporating nelarabine into aBFM was cost-effective across different scenarios and assumptions. These results support its funding by public and private payers.

AI Survival Prediction Modeling: The Importance of Considering Treatments and Changes in Health Status over Time

Background and objectives: Deep learning (DL)-based models for predicting the survival of patients with local stages of breast cancer only use time-fixed covariates, i.e., patient and cancer data at the time of diagnosis. These predictions are inherently error-prone because they do not consider time-varying events that occur after initial diagnosis. Our objective is to improve the predictive modeling of survival of patients with localized breast cancer to consider both time-fixed and time-varying events; thus, we take into account the progression of a patient’s health status over time. Methods: We extended four DL-based predictive survival models (DeepSurv, DeepHit, Nnet-survival, and Cox-Time) that deal with right-censored time-to-event data to consider not only a patient’s time-fixed covariates (patient and cancer data at diagnosis) but also a patient’s time-varying covariates (e.g., treatments, comorbidities, progressive age, frailty index, adverse events from treatment). We utilized, as our study data, the SEER-Medicare linked dataset from 1991 to 2016 to study a population of women diagnosed with stage I–III breast cancer (BC) enrolled in Medicare at 65 years or older as qualified by age. We delineated time-fixed variables recorded at the time of diagnosis, including age, race, marital status, breast cancer stage, tumor grade, laterality, estrogen receptor (ER), progesterone receptor (PR), and human epidermal receptor 2 (HER2) status, and comorbidity index. We analyzed six distinct prognostic categories, cancer stages I–III BC, and each stage’s ER/PR+ or ER/PR− status. At each visit, we delineated the time-varying covariates of administered treatments, induced adverse events, comorbidity index, and age. We predicted the survival of three hypothetical patients to demonstrate the model’s utility. Main Outcomes and Measures: The primary outcomes of the modeling were the measures of the model’s prediction error, as measured by the concordance index, the most commonly applied evaluation metric in survival analysis, and the integrated Brier score, a metric of the model’s discrimination and calibration. Results: The proposed extended patients’ covariates that include both time-fixed and time-varying covariates significantly improved the deep learning models’ prediction error and the discrimination and calibration of a model’s estimates. The prediction of the four DL models using time-fixed covariates in six different prognostic categories all resulted in approximately a 30% error in all six categories. When applying the proposed extension to include time-varying covariates, the accuracy of all four predictive models improved significantly, with the error decreasing to approximately 10%. The models’ predictive accuracy was independent of the differing published survival predictions from time-fixed covariates in the six prognostic categories. We demonstrate the utility of the model in three hypothetical patients with unique patient, cancer, and treatment variables. The model predicted survival based on the patient’s individual time-fixed and time-varying features, which varied considerably from Social Security age-based, and stage and race-based breast cancer survival predictions. Conclusions: The predictive modeling of the survival of patients with early-stage breast cancer using DL models has a prediction error of around 30% when considering only time-fixed covariates at the time of diagnosis and decreases to values under 10% when time-varying covariates are added as input to the models, regardless of the prognostic category of the patient groups. These models can be used to predict individual patients’ survival probabilities based on their unique repertoire of time-fixed and time-varying features. They will provide guidance for patients and their caregivers to assist in decision making.

Assessing the Performance of ChatGPT in Answering Patients' Questions Regarding Congenital Bicuspid Aortic Valve.

Artificial intelligence (AI) models, such as ChatGPT, are widely being used in academia as well as by the common public. In the field of medicine, the information obtained by the professionals as well as by the patients from the AI tools has significant advantages while at the same time posing valid concerns regarding the validity and adequacy of information regarding healthcare delivery and utilization. Therefore, it is important to vet these AI tools through the prism of practicing physicians. To demonstrate the immense utility as well as potential concerns of using ChatGPT to gather medical information, a set of questions were posed to the chatbot regarding a hypothetical patient with a congenital bicuspid aortic valve (BAV), and the answers were recorded and reviewed based on three criteria: (i) readability/technicality; (ii) adequacy/completeness; and (iii) accuracy/authenticity. While the ChatGPT provided detailed information about clinical pictures, treatment, and outcomes regarding BAV, the information was generic and brief, and the utility was limited due to a lack of specific information based on an individual patient's clinical status. The authenticity of the information could not be verified due to a lack of citations. Further, human aspects that would normally emerge in nuanced doctor-patient communication were missing in the ChatGPT output. Although the performance of AI in medical care is expected to grow, imperfections and ethical concerns may remain a huge challenge in utilizing information from the chatbots alone without adequate communications with health providers, despite having numerous advantages of this technology to society in many walks of human life.

Public Acceptance of Living Donor Liver Transplant for Colorectal Liver Metastases: A Web-Based Survey.

Recent advancements in cancer treatment and post-transplant management have expanded the population of living donor liver transplant (LDLT) candidates. We aimed to examine variations in public acceptance of LDLT based on patient diagnosis, including unresectable colorectal liver metastases (uCRLM). A web-based survey collected demographic information and general perceptions about organ donation in different settings. Respondents indicated their likelihood of being a living liver donor for a family member with genetic liver disease, alcohol-related liver disease (ALD), and uCRLM. Differences in the likelihood of donation between scenarios were compared. There were 491 survey respondents (female [76.5%], Caucasians [87.4%], and had at least a college degree [98.2%]). Most (82.4%) were aware of the option of living liver donation before the study and 95% supported living organ donation in general. Over 80% were registered as organ donors. Ninety percent indicated that they would be likely to donate to a family member with a genetic liver disease if they qualified as a living donor; significantly more than ALD (59%) and uCRLM (71%) (p < 0.001). Willingness to donate to patients with uCRLM was significantly higher (p < 0.001) than the hypothetical patient with ALD with a clinically accepted recovery period of 6 months. This study is the first of its kind to assess thepublic acceptance of living liver donation for uCRLM. Respondents were as or more supportive of donating to uCRLM as they were of generally accepted indications for LT. Further surveys with a broader respondent pool are warranted.

Identifying an optimal cancer risk threshold for resection of pancreatic intraductal papillary mucinous neoplasms

BackgroundIPMN consensus guidelines make implicit judgments on what cancer risk level should prompt surgery. We used decision modeling to estimate this cancer risk threshold (CRT) for BD-IPMN patients. MethodsWe created a decision model to compare quality-adjusted life years (QALYs) following surgery or surveillance for BD-IPMNs. We simulated treatment decisions for hypothetical patients, varying age, comorbidities and lesion location (pancreatic head/tail). The base case was a 60-year-old patient with mild comorbidities and pancreatic head IPMN. Probabilities, life expectancies, and utilities were incorporated from literature/public datasets. CRT was defined as the level of cancer risk at which the expected value of QALYs for surgery first exceeded that of surveillance. ResultsIn the base case, surgery was preferred over surveillance, yielding 21.90 vs. 21.88 QALYs. The optimal CRT for a BD-IPMN patient depended on age, comorbidities, and location. CRT in the base case was 20 % and 3 % for an IPMN in the head and tail of the pancreas, respectively. Other drivers of preferred treatment were age and likelihood of postoperative mortality. ConclusionFor BD-IPMNs, the optimal CRT varies depending on patient age and risk of surgical complications. Personalized risk threshold values could guide treatment decisions and inform future treatment consensus guidelines.

Fairness in AI-Driven Oncology: Investigating Racial and Gender Biases in Large Language Models.

Large language model (LLM) chatbots have many applications in medical settings. However, these tools can potentially perpetuate racial and gender biases through their responses, worsening disparities in healthcare. With the ongoing discussion of LLM chatbots in oncology and the widespread goal of addressing cancer disparities, this study focuses on biases propagated by LLM chatbots in oncology. Chat Generative Pre-trained Transformer (Chat GPT; OpenAI, San Francisco, CA, USA) was asked to determine what occupation a generic description of "assesses cancer patients" would correspond to for different demographics. Chat GPT, Gemini (Alphabet Inc., Mountain View, CA, USA), and Bing Chat (Microsoft Corp., Redmond, WA, USA) were prompted to provide oncologist recommendations in the top U.S. cities and demographic information (race, gender) of recommendations was compared against national distributions. Chat GPT was also asked to generate a job description for oncologists with different demographic backgrounds. Finally, Chat GPT, Gemini, and Bing Chat were asked to generate hypothetical cancer patients with race, smoking, and drinking histories. LLM chatbots are about two times more likely to predict Blacksand Native Americans as oncology nurses than oncologists, compared to Asians (p < 0.01 and < 0.001, respectively). Similarly, they are also significantly more likely to predict females than males as oncology nurses (p < 0.001). Chat GPT's real-world oncologist recommendations overrepresent Asians by almost double and underrepresent Blacks by double and Hispanics by seven times. Chatbots also generate different job descriptions based on demographics, including cultural competency and advocacy and excluding treatment administration for underrepresented backgrounds. AI-generated cancer cases are not fully representative of real-world demographic distributions and encode stereotypes on substance abuse, such as Hispanics having a greater proportion of smokers than Whites by about 20% in Chat GPT breast cancer cases. To our knowledge, this is the first study of its kind to investigate racial and gender biases of such a diverse set of AI chatbots, and that too, within oncology. The methodology presented in this study provides a framework for targeted bias evaluation of LLMs in various fields across medicine.

"Hospice Care Could Be a Compassionate Choice": ChatGPT Responses to Questions About Decision Making in Advanced Cancer.

Background: Patients with cancer use the internet to inform medical decision making. Objective: To examine the content of ChatGPT responses to a hypothetical patient question about decision making in advanced cancer. Design: We developed a medical advice-seeking vignette in English about a patient with metastatic melanoma. When inputting this vignette, we varied five characteristics (patient age, race, ethnicity, insurance status, and preexisting recommendation of hospice/the opinion of an adult daughter regarding the recommendation). ChatGPT responses (N = 96) were coded for mentions of: hospice care, palliative care, financial implications of treatment, second opinions, clinical trials, discussing the decision with loved ones, and discussing the decision with care providers. We conducted additional analyses to understand how ChatGPT described hospice and referenced the adult daughter. Data were analyzed using descriptive statistics and chi-square analysis. Results: Responses more frequently mentioned clinical trials for vignettes describing 45-year-old patients compared with 65- and 85-year-old patients. When vignettes mentioned a preexisting recommendation for hospice, responses more frequently mentioned seeking a second opinion and hospice care. ChatGPT's descriptions of hospice focused primarily on its ability to provide comfort and support. When vignettes referenced the daughter's opinion on the hospice recommendation, approximately one third of responses also referenced this, stating the importance of talking to her about treatment preferences and values. Conclusion: ChatGPT responses to questions about advanced cancer decision making can be heterogeneous based on demographic and clinical characteristics. Findings underscore the possible impact of this heterogeneity on treatment decision making in patients with cancer.

The journey to LDL-C goal in the era of combination lipid lowering therapy and why it matters for ASCVD patients: a UK perspective

Objective We aim to explore the concept of distance and journey to goal, and consideration of these 2 elements a priori when choosing LLT. Methods Modelling of expected % LDL-C reductions was carried out on a range of hypothetical patients’ baseline LDL-C values prior to any LLT being commenced. Therapies were then added in a stepwise manner based on the pathway demonstrated in current national guidance and compared with goal achievement on a novel LLT optimization pathway implemented in Morecambe Bay NHS Trust. Results Modelling of a stepwise lipid management pathway shows that high-intensity statin monotherapy is not sufficient in most modelled baseline LDL-C scenarios to achieve guideline-recommended goals. Furthermore, ezetimibe second line may preclude 3rd line injectable prescribing and lead to “ezetimibe limbo” where the patient is now below the reimbursement threshold for injectable prescribing but still not achieving their LDL-C target. Overall goal achievement is poor across the spectrum of modelled LDL-C levels. In contrast, by following the Morecambe Bay pathway all patients on statin for the range of hypothetical baseline LDL-C levels can reach an LDL-C target of < 1.8 mmol/L. Conclusions This study identifies a therapeutic gap when following a stepwise approach highlighted by recent national guidance. Our proposal of a novel pathway highlights that the order in which drugs are added is important in the context of national reimbursement thresholds and allows LDL-C goal to be reached in a timely manner, regardless of the starting baseline LDL-C level.

Post Hoc Bias in Treatment Decisions

A goal of health care is to reduce symptoms and improve health status, whereas continuing dubious treatments can contribute to complacency, discourage the search for alternatives, and lead to shortfalls in care. To test a potential bias in intuitive reasoning following a marginal improvement in symptoms after a dubious treatment (post hoc bias). Surveys eliciting treatment recommendations for hypothetical patients were sent to community members throughout North America recruited via an online survey platform in the early winter months of 2023 and 2024 and presented to health care professionals (pharmacists who were approached in person using a secret shopper technique) in the summer months of 2023. Respondents received randomized versions of surveys that differed according to whether vague symptoms improved or remained unchanged after a dubious treatment. The primary outcome was a recommendation to continue treatment. In total, 1497 community members (mean [SD] age, 38.1 [12.5] years; 663 female [55.3%]) and 100 health care professionals were contacted. The first scenario described a patient with a sore throat who took unprescribed antibiotics; respondents were more likely to continue antibiotics after initial treatment if there was a marginal improvement in symptoms vs when symptoms remained unchanged (67 of 150 respondents [45%] vs 25 of respondents [17%]; odds ratio [OR], 3.98 [95% CI, 2.33-6.78]; P < .001). Another scenario described a patient with wrist pain who wore a copper bracelet; respondents were more likely to continue wearing the copper bracelet after initial care was followed by a marginal improvement in symptoms vs when symptoms remained unchanged (78 of 100 respondents [78%] vs 25 of 99 respondents [25%]; OR, 16.19 [95% CI, 5.32-19.52]; P < .001). A third scenario described a patient with fatigue who took unprescribed vitamin B12; respondents were more likely to continue taking vitamin B12 when initial treatment was followed by a marginal improvement in symptoms vs when symptoms remained unchanged (80 of 100 respondents [80%] vs 33 of 100 respondents [33%]; OR, 7.91 [95% CI, 4.18-14.97]; P < .001). Four further scenarios involving dubious treatments found similar results, including when tested on health care professionals. In this study of clinical scenarios, a marginal improvement in symptoms led patients to continue a dubious and sometimes costly treatment, suggesting that clinicians should caution patients against post hoc bias.

Pharmacokinetic model-based assessment of factor IX prophylaxis treatment regimens in severe hemophilia B

An important aspect of improving care for people with hemophilia B (HB) is developing optimal treatment strategies. Here we aimed to provide in-silico evidence, comparing the estimated optimal posology of factor IX (FIX) products to support the patient-physician decision-making process. A population pharmacokinetic (popPK) model-based assessment comparing the performance of FIX products (rFIX, rIX-FP, rFIXFc, N9-GP) was developed. PopPK analyses were used to determine a product’s optimal posology to target predefined steady-state FIX activity trough levels in a hypothetical population of 10,000 people with severe HB. Model-derived optimal posologies were compared across several parameters including trough levels, proportion of patients per regimen and consumption, considering 64 hypothetical patient scenarios of different FIX trough level targets and ages. Results indicated a marked difference between FIX products estimated to achieve target trough levels, consumption and dosing frequencies. rIX-FP was associated with higher trough levels than rFIX and rFIXFc, at a lower weekly dose and administration frequency, across all age groups. N9-GP use in adolescents and adults was associated with lower consumption compared with rIX-FP. Insights from this study may be utilized by clinicians to inform decision-making, by considering the model-generated estimated optimal posologies alongside multiple clinical factors and patient preferences.

Venous thromboembolism prophylaxis prescribing patterns for patients with orthopedic trauma: a clinical vignette survey

BackgroundA recent clinical trial suggested aspirin is a viable alternative to enoxaparin for venous thromboembolism (VTE) prophylaxis in patients after orthopedic trauma. The initial impact of these findings on VTE...

Danger, Danger, Gaston Labat! Does zero-shot artificial intelligence correlate with anticoagulation guidelines recommendations for neuraxial anesthesia?

IntroductionArtificial intelligence and large language models (LLMs) have emerged as potentially disruptive technologies in healthcare. In this study GPT-3.5, an accessible LLM, was assessed for its accuracy and reliability in...

Insurance Status, Language, and Access to Care for Pediatric Anterior Cruciate Ligament Injury.

Patients with public insurance (PUBs) face more difficulty obtaining orthopaedic appointments and have longer wait times than privately insured patients (PVTs). These delays are associated with greater injury severity at the time of surgery, which affects sports injuries such as anterior cruciate ligament tears where early surgical stabilization leads to better outcomes. Additionally, previous evidence showed that patients with limited English proficiency often must rely on informal translation services, such as family members or friends, to communicate with their orthopaedic surgeons, which may represent a disparity in the care provided. It was hypothesized that PUBs would be less likely to obtain an appointment compared with PVTs and that most providers would not offer professional translation services to Spanish-speaking patients. Cross-sectional study. The authors called 50 randomly selected orthopaedic surgeons' offices in California specializing in sports medicine to request an appointment. Each office was called 4 times in random order for the hypothetical patient having either private or public insurance and speaking either Spanish or English. The hypothetical PUB had significantly decreased access to an appointment (19% of offices offered an appointment) when compared to the PVT (73.8% offered an appointment). Independent private practice (IPP) offices were less likely to accept public insurance (13.3%) compared with offices at academic medical centers (57.1%). There was no difference in access to an appointment for the Spanish- versus English-speaking patient. Translation services were offered at 73.8% of the orthopaedic offices. Overall, the data illustrated disparities in access to pediatric orthopaedic care for PUBs compared to those with private insurance. Disparities were most prominent in IPP settings, which were less likely than academic offices to accept public insurance. Additionally, it was found that 73.8% of the offices the authors contacted offered Spanish translation services. Interventions should focus on increasing acceptance of public insurance and translation services in IPP settings. Future studies should expand this analysis to other languages and investigate the potential impacts of language on the quality of care provided.

Cost-effectiveness of early vs delayed use of abemaciclib combination therapy for patients with high-risk hormone receptor-positive/human epidermal growth factor receptor 2-negative early breast cancer.

Abemaciclib was newly approved for hormone receptor-positive (HR+), human epidermal growth factor receptor 2-negative (HER2-) high-risk early breast cancer (EBC). Clinical guidelines recommended abemaciclib as the first-line treatment for HR+/ HER2- EBC (early use) or HR+/ HER2- metastatic breast cancer (MBC) (delayed use). To compare the cost-effectiveness of early vs delayed use of abemaciclib for treatment of HR+/HER2- high-risk EBC. Early use was defined as combined abemaciclib and endocrine therapy as first-line therapy for EBC, followed by treatment with fulvestrant for MBC. Delayed use was defined as endocrine therapy for EBC, followed by combined abemaciclib and fulvestrant therapy for MBC. A 5-state model was developed to estimate lifetime costs, life-years (LYs), and quality-adjusted life-years (QALYs) of hypothetical patients with HR+/ HER2- EBC from a third-party US payer's perspective. Key clinical and safety data were derived from the monarchE and MONARCH 2 clinical trials. Costs, utilities, and disutility values of adverse events were obtained from the literature. We calculated the incremental cost-effectiveness ratio (ICER) of early vs delayed abemaciclib use and compared it with a willingness-to-pay (WTP) threshold of $100,000 per LY or QALY. Deterministic and probabilistic sensitivity analyses (PSAs) were performed to test the robustness of the base-case model. Base-case analysis showed early use yielded 21.08 LYs and 17.93 QALYs for $586,213 and delayed use yielded 11.14 LYs and 9.38 QALYs for $157,576. The ICER of early vs delayed use was $43,136/LY and $50,104/QALY, which was cost-effective at the WTP threshold of $100,000. The PSA result indicated that a 94.6% likelihood of early use (vs delayed use) was cost-effective at the WTP threshold of $100,000 per QALY. This study suggests that giving abemaciclib in the early stage rather than waiting until patients develop metastatic disease (current standard of care in MBC) is a cost-effective strategy.