Citations

ABSTRACTEpidermolysis bullosa (EB) is a rare genetic disorder requiring intensive and costly care, often diagnosed in infancy when families are unprepared for the financial burden. We analyzed 160 U.S.‐based crowdfunding campaigns for living EB patients, extracting data on demographics and financial needs. 33.8% of patients requested financial support for wound care supplies, 36.9% of patients requested support for travel costs, and 9.4% of patients requested support with special clothing or bedding. These findings highlight persistent, unresolved financial needs for EB‐related care.

Background: Estimating operative time with low accuracy and precision leads to suboptimal scheduling of available operating rooms (ORs) and waste of healthcare resources. We aimed to develop a predictive machine learning (ML) model for operative time using our institutional data to improve the scheduling of neurosurgical ORs, minimize waste, and maximize resource utilization. Methods: We developed a predictive model using our institution’s multicenter registry, which included diverse neurosurgical cases across three tertiary hospitals. We applied multiple ML techniques, including linear regression, support vector regression, deep neural networks, and extreme gradient boosting regression (XGBR). The best model was combined with the first fit decreasing bin-packing algorithm to create a practical OR scheduling system based on prior data. Results: The XGBR model exhibited the best performance, with lower mean absolute errors (MAEs) (root mean squared error: 52.24, MAE: 37.25) and higher R2 (0.76) values than other models. Implementing this model in a simulated OR scheduling scenario using institutional data, we successfully scheduled 30 random procedures with minimal residual time, such that the model ran out of procedures for the week with 86 and 171 min remaining in the cranial and spinal ORs, respectively, demonstrating potential efficiency gains. The cranial procedures were scheduled over 4 days and spinal procedures over 6 days, optimizing available OR time. Conclusion: Integrating ML models into operative time prediction can significantly improve the accuracy and efficiency of surgical schedules. This approach minimizes wasted OR time and enhances resource allocation, potentially improving patient outcomes and satisfaction by reducing waiting times.

Ventricular tachycardia (VT) is a life-threatening arrhythmia that requires both relatively rapid and accurate detection in intensive care units (ICUs). Continuous monitoring systems play a crucial role in detecting them. However, previous studies have reported that nearly 9 out of 10 arrhythmia alarms in ICUs tend to be false positives, which usually transpire to a well-documented phenomenon called “alarm fatigue” that leads to desensitization, delayed responses and increased cognitive burden on healthcare providers. We developed a deep learning based, one-dimensional convolutional neural network (1D-CNN) to classify VT alarms using multiple raw waveform inputs, including two electrocardiogram (ECG) leads, photoplethysmogram (PPG) and arterial blood pressure (ABP) signals. The model was trained using the publicly available VTaC Arrhythmia Benchmark Dataset. Ten-second waveform segments preceding each alarm were pre-processed and used to train the model to correctly classify the VT alarm. On the test set, the model achieved an area under the receiver operating characteristic curve (AUC-ROC) of 0.901, overall accuracy of 83.22%, F1-score of 73.3%, sensitivity of 77.53%, specificity of 85.63%, and positive predictive value (PPV) of 69.57%. The model successfully detected over three-quarters of them while significantly reducing false positive rates for the detection of VT. This study demonstrates that a deep learning based 1D-CNN model using short segments of raw waveform data can achieve robust performance in distinguishing true and false VT alarms.

Background: Prophylactic antimicrobials for pediatric patients requiring extracorporeal membrane oxygenation (ECMO) support are not well delineated; there are no consistent guidelines nationally or internationally. We analyzed antimicrobial use before and after implementing a narrow-spectra ECMO antimicrobial prophylaxis protocol intended to standardize prophylaxis by risk related to ECMO type and configuration. Methods: This retrospective, single-center study included children under 18 years old requiring ECMO between May 2018 and December 2021. Medical and cardiac surgical patients requiring open-chested (central) or peripheral veno-venous or veno-arterial ECMO were included. We evaluated the impact of an ECMO antimicrobial prophylaxis protocol by comparing the appropriateness of antimicrobial use as well as nosocomial infections in the pre versus post protocol. Results: Our study included 55 children with 58 individual ECMO episodes. More than 50% of the patients were open-chested during ECMO support, and the majority of ECMO support was veno-arterial. In the post protocol period, patients were 4 times as likely to have appropriate antimicrobial prescribing (Odds ratio 4.4; 95% confidence interval: 1.1–19; P = 0.036), including both prophylactic (per protocol) and therapeutic (chart documentation of an infection with appropriate therapeutic antimicrobials, including drug and duration). There was no difference in the odds of nosocomial infection occurring during ECMO pre protocol versus post protocol (pre vs. post protocol, P = 0.71 for invasive infections, P = 0.73 for all infections including potential colonization). Conclusions: After implementation of a standardized narrow-spectra ECMO antimicrobial prophylaxis protocol, we observed an increase in appropriate and standardized antimicrobial prescribing without concomitant increase in nosocomial infections.

PURPOSE The likelihood of finding a human leukocyte antigen (HLA)–matched unrelated donor (MUD) for hematopoietic cell transplantation can be predicted using a donor search prognosis score. Patients without a MUD may use alternative donors (haploidentical related, mismatched unrelated, or umbilical cord blood). METHODS This multicenter biological assignment trial was conducted by the Blood and Marrow Transplant Clinical Trials Network (BMT CTN 1702). Eligibility criteria were broad to mirror clinical practice. The primary end point was 2-year survival from evaluability and compared between those Very Likely (>90%) and Very Unlikely (<10%) to find a MUD. All other patients, Less Likely to find a MUD, were enrolled in an observational arm. Transplant outcomes were compared for all three groups. RESULTS A total of 1,751 evaluable patients at 47 centers were Very Likely (54.7%), Less Likely (29.5%), and Very Unlikely (15.8%) to identify a MUD. Survival did not differ in univariate (hazard ratio [HR], 1.00 [95% CI, 0.82 to 1.21]; P = .98) or multivariate (HR, 1.07 [95% CI, 0.86 to 1.33]; P = .56) analyses between the Very Unlikely and Very Likely groups, measured through 2 years from the beginning of a search for a MUD or alternative donor. Of the transplanted patients (n = 1,179), MUD was used for 94% of the Very Likely, 38% of Less Likely, and 9% of Very Unlikely patients. Multivariate analyses showed no differences in relapse, treatment-related mortality, disease-free survival, and acute and chronic graft-versus-host diseases for the three search prognosis groups after transplantation. CONCLUSION Using a donor search prognosis strategy to prioritize an alternative donor for patients Very Unlikely to find a MUD resulted in survival and transplant outcomes that were not statistically different compared with those Very Likely to find a MUD.

Abstract Although follicular lymphoma (FL) typically follows an indolent course, patients with FL who experience early events, such as transformation or progression, have increased risk of death related to lymphoma. The FL24Cx is an algorithm based on a 45-target gene expression profiling (GEP) assay, which was developed and trained using 265 formalin-fixed, paraffin-embedded tissue samples on a reliable platform to predict, at the time of diagnosis, whether a patient will experience an event within 24 months. The modeling also confirmed and relied upon previously reported synergy between immune response (IR) gene expression signatures IR1 and IR2. Once locked, the 5-factor logistic regression FL24Cx model was independently validated in a retrospectively assessed cohort of 232 patients from 2 immunochemotherapy-treated arms of SWOG Cancer Research Network S0016 phase 3 clinical trial, in which it assigned 169 patients to the low-risk group with 29 events before 24 months (17.2%) and 63 patients to the high-risk group with 24 events before 24 months (38.1%). The relative risk of an event within 24 months after registration among patients who were classified into the high-risk group relative to patients who were classified into the low-risk group was 2.2 (95% confidence interval, 1.41 to 3.51). An up-front GEP biomarker, such as the FL24Cx, rigorously validated in a clinical laboratory and with a clinically relevant turnaround time, could identify and steer enrollment of patients at high risk for early events in clinical trials, thus enabling timely interpretation of such trials and increasing the pace of innovation.

Abstract Measurable residual disease (MRD) assessment by flow cytometry (FC) plays an essential role in prognosis and therapy escalation of B-cell acute lymphoblastic leukemia (B-ALL). However, the high degree of expertise and manual analysis time required limits the availability of this assay. To overcome this limitation, we developed a data-enhancing artificial intelligence (AI) pipeline that accelerates and simplifies MRD analysis. Unaltered FC files from 171 B-ALL MRD-positive and 89 MRD-negative cases were processed through an AI pipeline trained with 31 expert-gated negative controls. Cluster-informed downsampling reduced FC files from 1.2 million to 155 884 cells per case, on average, (87% cellularity reduction), whereas preserving small MRD populations (median, 100% retention for MRD of &lt;1%) and allowing for true percentage MRD estimates using a correction factor. A deep neural network cell classifier automatically identified normal hematopoietic subsets (macro-averaged F1 score of 0.86); and an AI measure of anomaly discriminated B-ALL from benign mononuclear (area under the curve [AUC] of 0.98) or B-lymphoid cells (AUC of 0.94). Manual analysis of AI-enhanced files was completed in only 1.01 minutes per case, on average (standard deviation of ±0.57); with 100% positive agreement with conventional analysis (for MRD of ≥0.01%), 100% negative agreement, and excellent quantitative correlation (R2 = 0.92). Our cloud-based AI-enhancement solution accelerates B-ALL MRD identification without compromising test performance and has the potential of facilitating B-ALL MRD analysis by more clinical laboratories.

Abstract Background: Alaska Native people are committed to increasing colorectal cancer (CRC) screening to reduce the high rate of CRC in this population. We conducted a community-level cluster randomized controlled trial (RCT) to evaluate the impact of multi-target stool DNA test introduction (mt-sDNA; Cologuard® Exact Sciences, Madison WI) on CRC screening in southwest Alaska Native communities using a mt-sDNA pre-processing lab in Anchorage, Alaska. Methods: A total of 32 rural/remote Alaska Native communities were evenly randomized to either high intensity patient navigation or medium intensity mailed health education compared to 14 communities receiving standard of care (screening recommendations at health care visits). The intervention was conducted from April 2021-February 2023. Outcome measures included CRC screening completion within 12 months and method used (mt-sDNA vs colonoscopy). Results: A total of 2032 Alaska Native participants (mean [SD] age, 56 [8.4] years; 817 women [40%]) were enrolled: 587 participants randomized to usual care, 696 participants randomized to the mailed education arm, and 749 participants randomized to the patient navigation arm. The mt-sDNA test was significantly preferred over colonoscopy by both mailed education participants (68%) and patient navigation participants (71%) (p&lt;0.01). Mt-sDNA was somewhat more preferred by older participants ages 65-75 (79%) vs younger participants ages 45-54 (64%). Both men (71%) and women (68%) significantly preferred mt-sDNA compared with colonoscopy (p&lt;0.01). Overall, 38% of those in the patient navigation intervention completed screening compared with 33% in the mailed education intervention and 10% in the usual care arm. No significant differences were observed in mt-sDNA completion between the patient navigation arm (154 participants [40%]) and the mailed education arm (100 participants [36%]) (p= 0.34). Colonoscopy completion (not including follow-up colonoscopies after abnormal stool tests) was higher in both the patient navigation arm (112 participants [44%]) and the mailed education arm (87 participants [43%]) compared with usual care (38 participants [14%]). Over 61% of kits sent to participants were completed and returned, however, 38% of the kits were not able to be processed, primarily because of mail transit time exceeding the manufacturer’s collection-to-processing limitation of 72 hrs. Conclusions: In this RCT among Alaska Native peoples who were not up to date with CRC screening recommendations, both a mailed education intervention and a patient navigator facilitated intervention were effective at increasing CRC screening compared with usual care. Participants overwhelmingly preferred mt-sDNA for screening but limitations in getting samples to the lab in time for analysis meant that about one-third of mt-sDNA samples were unable to be processed. These findings show that screening outreach and mt-sDNA use can substantially increase CRC screening, but shipping and weather issues remain to be addressed for expanding use of mt-sDNA in rural/remote Alaskan communities. Citation Format: Diana Redwood, Christie Flanagan, Lauren Jeffries, Peter Holck, Danika Bailie, Christine Desnoyers, Joseph Klejka, Lila Rutten, John Kisiel. Increase in colorectal cancer screening among rural and remote Alaska Native peoples using the multi-target stool DNA: A randomized controlled trial [abstract]. In: Proceedings of the 18th AACR Conference on the Science of Cancer Health Disparities; 2025 Sep 18-21; Baltimore, MD. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2025;34(9 Suppl):Abstract nr A148.