Citations

As machine learning (ML)-based decision support tools proliferate in clinical practice, understanding how clinicians integrate personalized ML predictions alongside randomized controlled trial (RCT) evidence is critical. We designed a web-based clinical decision support system (CDSS) presenting survival and adverse event data from a simulated RCT and ML model across 12 synthetic multiple myeloma scenarios. In a within-subjects study with 32 physicians, we evaluated how clinicians synthesize competing evidence sources to make treatment decisions. When ML and RCT outputs were concordant, physicians reported greater confidence than with RCT data alone. When results were discordant, most physicians shifted toward the ML-supported treatment, often before reviewing any information about model training or validation, suggesting a tendency toward automation bias rather than algorithm avoidance. Despite reporting higher perceived reliability after viewing model quality disclosures, physicians were largely unable to describe the validation procedures they had reviewed. Taken together, these findings reveal that clinicians may over-rely on ML recommendations even when equipped with tools designed to support critical appraisal. We discuss implications for CDSS design, clinician training, and the institutional safeguards needed before ML-based systems are deployed in high-stakes oncology settings.

e23223 Background: Early-stage lung cancer is potentially curable with surgery, yet emergency or nonelective presentation represents a care-delivery phenotype reflecting failures in early diagnosis or access to care rather than tumor biology alone. The national impact of emergency presentation among surgically treated, presumed early-stage lung cancer patients is poorly defined. This study evaluates emergency versus elective presentation as a care-delivery phenotype and its association with perioperative morbidity, failure-to-rescue, and inpatient outcomes. Methods: A survey-weighted analysis of the National Inpatient Sample (2016–2023) was conducted. Adult lung cancer hospitalizations were identified using ICD-10-CM C34* in any diagnosis position. A presumed early-stage surgical cohort was defined by curative-intent lung resection without metastatic disease, malignant pleural effusion, or palliative care coding. The primary exposure was nonelective versus elective admission. Outcomes included in-hospital mortality; ICU-level care, defined by invasive mechanical ventilation, shock, or respiratory failure; major complications; failure-to-rescue; length of stay; and hospitalization cost and charges. Multivariable survey-weighted models adjusted for patient demographics, payer, neighborhood income, hospital characteristics, and year. Results: In the weighted cohort of 422,925 hospitalizations, nonelective admission occurred in 27.3%. After multivariable adjustment, nonelective admission was independently associated with higher odds of in-hospital mortality (adjusted odds ratio [aOR] 2.59, 95% CI 2.22–3.03), ICU-level care (aOR 4.53, 95% CI 4.32–4.76), and major complications (aOR 5.00, 95% CI 4.78–5.23), as well as longer length of stay (+2.42 days). Notably, failure-to-rescue among patients with major complications did not differ by admission type (aOR 0.90, 95% CI 0.77–1.05), indicating that excess mortality associated with nonelective presentation is driven by presentation severity and care pathway rather than differences in rescue quality. In unadjusted analyses, nonelective presentation was associated with higher mortality (2.21% vs 0.68%), greater ICU-level care use (33.8% vs 8.9%), more frequent major complications (45.0% vs 12.4%), and longer length of stay (7.84 vs 4.99 days) compared with elective admissions. Conclusions: In a procedure-defined, presumed early-stage lung cancer surgical cohort, nonelective admission identifies a common care-delivery failure phenotype associated with higher perioperative morbidity, ICU-level care, in-hospital mortality, and longer hospitalization. Emergency presentation represents a systems-level vulnerability in curative-intent lung cancer care and serves as a scalable and actionable marker for quality improvement and disparity-focused analyses.

e23193 Background: Small cell lung cancer (SCLC) is typically treated with outpatient systemic therapy; inpatient chemotherapy during acute hospitalization represents a high-risk care-delivery phenotype driven by delayed diagnosis, access barriers, or acute clinical decompensation rather than tumor biology alone. National data describing this care pathway and its inpatient outcomes remain limited. Methods: The U.S. National Inpatient Sample (2016–2023) was analyzed using survey weights. Adult hospitalizations meeting an ICD-10–based proxy definition for small cell lung cancer, defined by lung cancer diagnoses combined with neuroendocrine malignancy codes and excluding palliative care admissions, were identified. Inpatient chemotherapy was defined using ICD-10-PCS administration codes with prespecified sensitivity definitions. Outcomes included in-hospital mortality (primary), ICU-level care, sepsis, acute kidney injury, length of stay, hospitalization costs, and routine discharge. Multivariable survey-weighted logistic regression adjusted for patient demographics, payer, admission characteristics, and hospital factors. Results: The analytic cohort included 2,507 unweighted hospitalizations, representing 12,535 nationally weighted admissions. In multivariable analyses adjusting for demographics, payer, admission characteristics, and hospital factors, inpatient chemotherapy was independently associated with higher in-hospital mortality (adjusted odds ratio [aOR] 2.31, 95% CI 1.48–3.60) and greater ICU-level care (aOR 2.80, 95% CI 1.86–4.23). These associations were consistent across prespecified sensitivity analyses. In unadjusted analyses, inpatient chemotherapy occurred in 12.3%–19.3% of admissions annually from 2016–2023 and was associated with higher mortality (8.7% vs 5.2%), greater ICU-level care (11.8% vs 4.9%), longer length of stay (11.6 vs 7.0 days), higher costs ($38,446 vs $23,746), and lower routine discharge rates (42.8% vs 48.4%). Among 400 admissions with procedure-day data, chemotherapy was administered a mean of 3–5 days after admission. Late inpatient chemotherapy (day ≥2) was associated with higher mortality (10.6% vs 5.6%), greater ICU-level care (14.8% vs 9.2%), longer hospitalization (14.1 vs 8.0 days), and higher costs ($46,203 vs $28,025) compared with early chemotherapy (day 0–1), supporting inpatient chemotherapy as a marker of late care escalation rather than planned inpatient treatment. Conclusions: Inpatient chemotherapy identifies a high-risk care-delivery phenotype in hospitalized small cell lung cancer, with higher mortality, greater ICU-level care, and increased resource use, supporting its role as a marker of late care escalation or system-level barriers and a scalable quality indicator.

10610 Background: The role for genetic testing (GT) for prostate cancer (PCa) patients (pts) has expanded to find pts with germline pathogenic variants (PV) for therapeutic opportunities given FDA-approvals for PARP inhibitors in metastatic PCa as well as understanding familial cancer risk and opportunities for tailored screening and risk reduction. The PROMISE Registry (NCT04995198) was set up as a genetic registry to partner with PCa pts for rapid access to a new standard of care given insurance coverage and clinical implementation were initially major barriers. A second goal is to characterize disease features and treatment outcomes to inform management and provide updates about novel therapeutic clinical trials, both of which remain important to rare genetic variant carriers. Methods: PROMISE is a US decentralized prospective PCa genetic registry initiated in 2021 with initial phase to enroll and provide clinical-grade germline genetic testing (30 gene cancer-specific panel) to 5,000 PCa pts to identify ~500 PCa pts with variants of interest (pathogenic variants, PV, and variants of uncertain significance, VUS) in DNA repair genes for long-term clinical follow-up of 15 years (LTFU). Here, we report the initial phase testing results and identification of the LTFU cohort. Study design and enrollment strategy have been reported separately. Results: Between 2021-2026, 7,275 pts with any stage PCa across 50 US states self-consented to PROMISE, of which 5,674 (78%) of pts completed genetic testing, indicating feasibility of patient-directed, decentralized enrollment. We have identified 495 (8.7%) pts for LTFU. Baseline data is reported for n=367 (Table 1, will be updated at time of presentation). The most frequently observed PV/LPV variants being in CHEK2 (25%) , BRCA2 (23%) , ATM (12%) , and BRCA1 (4.6%). At the time of PCa diagnosis, the majority of LTFU pts had non-metastatic disease, with 47% of pts with Gleason>7, median PSA of 5 ng/mL, and median time from diagnosis to PROMISE GT of 25 months. Conclusions: PROMISE is a nationwide decentralized PCa genetic registry that has completed initial goal to recruit 5,000 PCa pts to offer germline genetic testing and recruit a cohort of 500 for LTFU. Data about diagnosis characteristics are reported, and analysis of ongoing disease course, treatment response/outcomes and overall survival is being collected. Clinical trial information: NCT04995198 . Long-term follow-up participant demographics. Characteristic Overall, N = 367 Age at Diagnosis (yrs) 63 (57, 69) Race N = 361 (6, nr) Asian, American Indian or Alaska Native, Multiracial, Other 22 (6.0%) Black or African American 22 (6.0%) White 317 (86%) Disease State at Diagnosis N = 348 (19, nr) Metastatic 46 (13%) Non-metastatic 302 (87%) Time from Diagnosis to PROMISE GT (months) 25 (4, 82) Gleason Score N = 280 (87, nr) </= 7 148 (53%) >7 132 (47%) PSA At Diagnosis (ng/mL) 5 (0, 10) (88, nr) Subjects with baseline data entered. nr=not reported. Median (Q1, Q3); n (%).

Estimates of fibromyalgia prevalence vary widely worldwide due to differences in diagnostic criteria, study methodology, and population characteristics. Accurate estimates are essential to inform population-level surveillance, guide resource allocation, and highlight disparities in disease recognition and access to care. The objective of this systematic review and meta-analysis was to determine the global prevalence of fibromyalgia in the general population, and secondarily to identify factors moderating prevalence variation and to assess prevalence in defined at-risk subpopulations. Pooled prevalence estimates with 95% confidence intervals (CIs) were calculated using the inverse variance heterogeneity model with Freeman–Tukey transformation. Subgroup analyses were performed by sex, diagnostic criteria, country, and other study characteristics, and meta-regression assessed moderators including publication year and Human Development Index (HDI). Of 21,645 records identified, 1,728 underwent full-text review and 882 studies were included in the final analysis. Across 38 countries and 30,070,032 individuals (188 studies), the pooled fibromyalgia prevalence in the general population was 1.40% (95% CI, 0.49–2.47), with substantial heterogeneity (I 2 = 100%) and very low certainty of evidence. Among at-risk subpopulations (9,771,123 participants; 768 studies), prevalence was markedly higher in autoimmune and inflammatory conditions, such as chronic urticaria (37.1%), interstitial cystitis (30.8%), systemic sclerosis (20.6%), celiac disease (20.5%), and inflammatory bowel disease (19.9%), and elevated in chronic pain, rheumatologic, and post-infection cohorts. Meta-regression identified publication year (β = 0.008, P < .001) and HDI (β = 0.725, P = .002) as significant moderators of prevalence. In conclusion, fibromyalgia affects 1% to 2% of the global population, with substantially higher rates in at-risk subpopulations. Temporal and socioeconomic trends emphasize diagnostic inconsistency and global disparities in care.

Importance Chronic pain is a leading cause of disability, and many affected patients rely on long-term opioid therapy despite its unfavorable adverse effect profile. Spinal cord stimulation is an established therapy for chronic pain that may secondarily reduce opioid consumption by providing sustained analgesia. Objective To quantify changes in opioid use following spinal cord stimulation implantation for the treatment of chronic pain through a systematic review and meta-analysis. Data sources We conducted a comprehensive search of MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, and Scopus from each database’s inception to July 1, 2024. Study selection We included studies describing patients who underwent spinal cord stimulator implantation for the treatment of chronic pain. A total of 2,784 potential studies were identified and screened by multiple independent reviewers, yielding a final total of 43 studies included in the systematic review. Data extraction and synthesis Data extraction was completed by multiple independent reviewers following Preferred Reporting Items for Systematic Reviews and Meta-Analysis and Meta-analysis Of Observational Studies in Epidemiology guidelines. A random effects model with Hartung-Knapp-Sidik-Jonkman adjustment was used to estimate pooled estimates. Certainty assessment was performed following the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) framework. Main outcome(s) and measure(s) The primary outcomes were change in continuous opioid use (ie, morphine milligram equivalent (MME)) and categorical opioid use (yes/no) from preimplantation to 12 months following spinal cord stimulator implantation. Results Meta-analysis of 12 month continuous MME use comprising 22 study comparisons (1,666 individuals) revealed a weighted mean difference of −18.06 MME (95% CI −24.41 to −11.70; 95% prediction interval −38.29 to 2.17; p<0.001). Meta-analysis of categorical opioid use at 12 months comprising 24 study comparisons (1,527 individuals) revealed an OR of 0.47 (95% CI 0.30 to 0.75; 95% prediction interval 0.10 to 2.11; p=0.003), signifying lower odds of opioid continuation. Conclusions and relevance Spinal cord stimulation is associated with reduced opioid dose consumption and increased odds of opioid cessation at 12 months postimplantation compared with baseline in patients with chronic pain; however, both of these conclusions are supported by evidence of very low GRADE certainty. PROSPERO registration number CRD42024574921.

PURPOSE The continuous development of new imaging approaches, molecular phenotyping, genetic subtypes, prognosis assessments, and effective therapies across a range of disease states has created a need to redefine terminology and best practices for clinical trial conduct in patients with advanced prostate cancer. Methods We convened an international expert committee of diverse working groups, the Prostate Cancer Working Group 4 (PCWG4), between 2016 and 2025. Our objective was to formulate updated criteria based on emerging evidence and clinical trial data in a biomarker context to provide guidance for clinical trial design, eligibility, and end point assessments for patients with advanced prostate cancer. RESULTS PCWG4 redefines terminology around the disease state and previous therapies in a patient-centric context and terminology focused on androgen pathway modulation. We consider imaging, with a particular focus on positron emission tomography (PET)–defined disease. New recommendations are provided for disease state terminology, defining eligibility criteria, response and delay/prevent end points, intervals for reassessments including imaging, and patient-reported outcome determination. We provide recommendations in a biomarker-based context of use for the intended indication, reflective of patient benefit for specific interventions. We emphasize the need for development of validated PET imaging and molecular and phenotypic criteria as well as trial designs to appropriately risk stratify patients, predict and assess benefit, and measure post-treatment outcomes reliably in a trial framework. CONCLUSION PCWG4 updates recommendations on patient and tumor characterization, therapy development, and imaging criteria and extends guidance into earlier androgen pathway modulator–naïve/sensitive disease states to reflect an evolving, heterogeneous, and diverse patient population to optimize treatment benefits for all patients.

BACKGROUND: Recent years have seen considerable growth and significant changes in the anesthesiology residency application process. Although the National Residency Match Program provides data on attributes of successfully matching applicants, it does not provide detailed characteristics of applicants’ research portfolio. This single-program retrospective analysis aims to characterize granular aspects of applicant research experiences to determine whether research involvement is linked to higher odds of matching. METHODS: Applications from interviewed United States allopathic (US-MD) and osteopathic (US-DO) applicants at a single anesthesiology residency program were analyzed for research experience. The primary outcome assessed was the association of any research experience with odds of ranked-to-match and the association of specific types of research experience with ranked-to-match. Secondary outcomes included the association of medical degree type (US-MD vs US-DO) or affiliation of medical school with an anesthesiology residency program to the number of research experiences, and the association of research experiences with the United States Medical Licensing Examination (USMLE) Step 1 score type (Pass/Fail vs Scored). RESULTS: Applicants with at least one research experience were more likely to be ranked-to-match than applicants without research experience (odds ratio [OR]: 2.16, 95% confidence interval [CI], [1.55–3.02], P < .001). Similar odds were demonstrated for applicants with at least one peer-reviewed publication compared to no peer-reviewed publications (OR: 2.25, 95% CI, [1.79–2.82], P < .001), and at least one anesthesiology-related research experience compared to no anesthesiology-related research experience (OR: 1.76, 95% CI, [1.36–2.26], P < .001. Applicants from US-MD schools were more likely to have research experience than those from US-DO schools ( P < .001); applicants from a medical school affiliated with an anesthesiology residency were more likely to have research experience than applicants from unaffiliated medical schools ( P < .001); and applicants with Pass/Fail USMLE Step 1 examinations were more likely than their Scored counterparts to report at least one research experience ( P < .001). CONCLUSIONS: Applicant research is associated with greater odds of being ranked-to-match in anesthesiology residency. Applicants from US-MD schools and schools affiliated with anesthesiology residency programs have significantly greater rates of reporting at least one research experience. There is a trend toward a higher incidence of reporting at least one research experience with the change of USMLE Step 1 policy toward a Pass/Fail examination.