Borah, Bijan J.

ImportanceImplementing multidisciplinary teams for treatment of complex brain tumors needing awake craniotomies is associated with significant costs. To date, there is a paucity of analysis on the cost utility of introducing advanced multidisciplinary standardized teams to enable awake craniotomies.ObjectiveTo assess the cost utility of introducing a standardized program of awake craniotomies.Design, Setting, and ParticipantsA retrospective economic evaluation was conducted at Mayo Clinic Florida. All patients with single, unilateral lesions who underwent elective awake craniotomies between January 2016 and December 2021 were considered eligible for inclusion. The economic perspective of the health care institution and a time horizon of 1 year were considered. Data were analyzed from October 2022 to May 2023.ExposureTreatment with an awake craniotomy before standardization (2016-2018) compared with treatment with awake craniotomy after standardization (2018-2021).Main Outcomes and MeasuresPatient demographics, perioperative, and postoperative outcomes, including length of stay, intensive care (ICU) admission, extent of resection, readmission rates, and 1-year mortality were compared between patients undergoing surgery before and after standardization. Direct medical costs were estimated from Medicare reimbursement rates for all billed procedures. A cost-utility analysis was performed considering differences in direct medical costs and in 1-year mortality within the periods before and after standardization of procedures. Uncertainty was explored in probability sensitivity analysis.ResultsA total of 164 patients (mean [SD] age, 49.9 [15.7] years; 98 [60%] male patients) were included in the study. Of those, 56 underwent surgery before and 108 after implementation of procedure standardization. Procedure standardization was associated with reductions in length of stay from a mean (SD) of 3.34 (1.79) to 2.46 (1.61) days (difference, 0.88 days; 95% CI, 0.33-1.42 days; P = .002), length of stay in ICU from a mean (SD) of 1.32 (0.69) to 0.99 (0.90) nights (difference, 0.33 nights; 95% CI, 0.06-0.60 nights; P = .02), 30-day readmission rate from 14% (8 patients) in the prestandardization cohort to 5% (5 patients) (difference, 9%; 95% CI, 19.6%-0.3%; P = .03), while extent of resection and intraoperative complication rates were similar between both cohorts. The standardized protocol was associated with mean (SD) savings of $7088.80 ($12 389.50) and decreases in 1-year mortality (dominant intervention). This protocol was found to be cost saving in 75.5% of all simulations in probability sensitivity analysis.Conclusions and RelevanceIn this economic evaluation of standardization of awake craniotomy, there was a generalized reduction in length of stay, ICU admission time, and direct medical costs with implementation of an optimized protocol. This was achieved without compromising patient outcomes and with similar extent of resection, complication rates, and reduced readmission rates.

Abstract Background Remdesivir is FDA-approved for the treatment of hospitalized patients with severe COVID-19. Many patients improve clinically to allow for hospital dismissal before completing the 5-day course. In prior work, those who continued remdesivir in an outpatient setting had better outcomes. Here, we assessed patients’ perspectives and the economic impact of this outpatient practice. Methods During a one-year period, 3,029 patients received remdesivir for COVID-19 in the hospital; of them, 542 were dismissed to continue remdesivir in the outpatient setting and were surveyed. The cost of care was compared between those who remained in the hospital vs. those who were dismissed. Results 470 of 542 patients were surveyed, and 93 (19.8%) responded. Responders were older than non-responders (median, 63 vs. 58 years). The majority (70.5%) had symptoms resolved by the time of the survey. Ten patients (11.4%) had persistent symptoms attributed to long COVID-19, including fatigue (30.1%), brain fog (23.7%), and insomnia (18.3%). 73.8% were excited to learn about the outpatient remdesivir program. The majority (81.0%) felt that receiving therapy at home was the right decision; 79.5% had support and felt the benefits of completing treatment at home. The majority were satisfied with communication before (75.6%) and during (80.5%) the outpatient appointment, information on benefits (80.3%) and risks (58.5%), time spent at the infusion center (88.6%), quality of care (82.3%) and overall experience (76.0%). The benefits of treatment were high-value (82.6%), while the infusions' cost, inconvenience, and discomfort were unimportant for most responders. After adjusting for gender, comorbidity score, and WHO scale, the predicted costs for the groups were $16,544 (inpatient) and $9,097 (outpatient) per patient (difference of $7,447; p< .01). During this study, we estimated a total of 1,077 hospital room-days were made available to other patients. Conclusion An outpatient remdesivir program that allowed for early dismissal was perceived favorably by patients hospitalized for COVID-19. The program resulted in significant cost and resource savings, the latter in terms of the availability of hospital rooms for other patients needing critical services. Disclosures Christina G. Rivera (O'Connor), Pharm.D, Gilead Sciences: Advisor/Consultant|Gilead Sciences: Board Member|Gilead Sciences: Grant/Research Support|Gilead Sciences: Honoraria Bijan J. Borah, Ph.D., Boehringer Ingelheim: Advisor/Consultant|Exact Sciences: Advisor/Consultant Ravindra Ganesh, M.B.B.S., M.D., Alpaca Health: Advisor/Consultant Raymund R. Razonable, MD, Allovir: Endpoint Adjudication Committee|American Society of Transplantation: Board Member|Gilead: Grant/Research Support|Novartis: DSMB|Regeneron: Grant/Research Support|Roche: Grant/Research Support

AbstractBackgroundAppropriate time for ejection fraction (EF) reassessment after revascularization in patients with left ventricular dysfunction has not been investigated comprehensively, although 3 months after revascularization is recommended to stratify the risk of sudden cardiac death (SCD).HypothesisEF reassessed within different timeframe after revascularization may have incosistent contribution for risk stratification of SCD.MethodsPatients who had EF ≤ 40% before revascularization and had EF reassessment at least once during follow‐up were included. The role of early (<3 months) versus late (3–12 months) EF measurements in prediction of all‐cause mortality and SCD were compared.ResultsA total of 1589 patients were identified. EF reassessed <3 months was lower than EF reassessed within 3–12 months (42.1 ± 9.7% vs. 45.8 ± 10.8%; p < .01). Among 1069 patients who had EF reassessed <3 months, EF ≤ 35% was associated with a higher risk of all‐cause mortality (hazard ratio [HR], 1.67; 95% confidence interval [CI], 1.22–2.29; p < .01), but had no association with the risk of SCD (HR, 1.44; 95% CI, 0.84–2.48; p = .18). By contrast, among 595 patients who had EF reassessed within 3–12 months, EF ≤ 35% was associated with higher risks of both all‐cause death (HR, 1.81; 95% CI, 1.06–3.10; p = .03) and SCD (HR, 2.71; 95% CI, 1.31–5.61; p < .01). The relative contribution of SCD to all‐cause death was higher in patients with EF ≤ 35% than patients with EF > 35% when EF was reassessed within 3–12 months (p = .04). However, when EF was reassessed <3 months, the mode of death was similar in patients with EF ≤ 35% versus >35% (p = .85).Conclusions3 to 12 months after revascularization may be appropriate for cardiac function reassessment and SCD risk stratification.

572 Background: The DEFeNDR (Decreasing the burden of Febrile Neutropenia through Dynamic RPM) program leverages in-home technology and centralized nurse monitoring to facilitate ambulatory management as an alternative to hospital-based care for cancer patients with neutropenic fever (NF). The objective of this study is to assess 30-day care utilization outcomes and patient satisfaction at 12 months after program implementation. Methods: Adult cancer patients hospitalized with NF were assessed daily for DEFeNDR program eligibility including MASCC score ≥21 (per ASCO guidelines) and no cellular therapy (CAR-T, BMT) in the prior 100 days. Patients agreeing to participate in the 30-day program received a cellular-enabled tablet with pre-connected devices for twice-daily vital signs and symptom assessments. Patient generated data were EHR-integrated in real-time. Embedded decision trees facilitated alerts for adverse trends. Virtual RPM nurses responded to alerts and utilized predefined care pathways. The primary endpoint was the mean proportion of evaluable days spent inpatient (mPEDI) within 30 days of eligibility comparing those who participated in the program (RPM) with those who declined (no RPM). Results: Of 85 eligible patients, 62 were offered DEFeNDR participation. Of those, 34 (54.8%) participated and 28 (45.2%) declined. Baseline characteristics and care utilization outcomes are summarized in Table 1. RPM patients experienced significantly lower mPEDI compared with unmonitored patients [5.1% (Standard Error [SE], 1.9%) vs 12.4% (SE, 2.1%); Wilcoxon Rank Sums p-value, 0.032]. RPM patients also experienced a trend toward fewer total hospital days and lower 30-day readmission rate, as well as shorter mean length of stay and lower rate of ICU utilization when readmitted. Non-visit care activities (EHR clinical communications, documentation, and patient messaging) were decreased from pre-hospital baseline for 58.8% of RPM patients versus 46.6% of those unmonitored. All RPM patients completing a patient satisfaction survey (n=9) indicated that they would recommend the program to patients with a similar condition. Conclusions: Cancer patients with NF participating in the DEFeNDR program experienced a significant reduction in mPEDI and care utilization compared with those who declined. Further studies are warranted to assess comparative effectiveness of this model vs usual care.[Table: see text]

Abstract Background The Surgery in Early Metastatic Seminoma (SEMS) trial examined retroperitoneal lymph node dissection as first-line treatment for patients with isolated 1-3 cm retroperitoneal lymphadenopathy. To date, the standard of care for these patients has been either chemotherapy or radiotherapy. Herein, we evaluated the relative cost-effectiveness of these management strategies. Methods A microsimulation model assessed the cost-effectiveness of retroperitoneal lymph node dissection, chemotherapy, and radiotherapy for stage IIA seminoma. Sensitivity analyses were performed to evaluate model robustness. Retroperitoneal lymph node dissection recurrence probabilities were obtained from the SEMS trial. All other probability and utility values were obtained from published literature. Primary outcomes included costs from a commercial insurer’s perspective, effectiveness (quality adjusted life-years [QALYs]), and incremental cost-effectiveness ratios using a willingness-to-pay threshold of $100 000/QALY. Results At a lifetime horizon, the mean costs per patient for retroperitoneal lymph node dissection, radiotherapy, and chemotherapy were $58 469, $98 783, and $104 096, and the mean QALYs were 40.61, 40.70, and 39.15, respectively. Retroperitoneal lymph node dissection was found to be the most cost-effective approach because of high costs and accrued disutility of chronic toxicities associated with radiotherapy (cost-effectiveness ratios = $433 845/QALY) and chemotherapy (dominated). On 1-way sensitivity analyses, retroperitoneal lymph node dissection was no longer cost-effective if the probabilities of infertility and cardiovascular toxicity after radiotherapy were less than 13% and 16%, respectively, or if the 2-year probability of progression after retroperitoneal lymph node dissection was more than 26%. Conclusions Retroperitoneal lymph node dissection was the most cost-effective treatment approach for stage IIA seminoma. These findings support clinical guideline consideration of including retroperitoneal lymph node dissection as a treatment option for well-selected patients with stage IIA seminoma.

Introduction: Using a digital process that leverages electronic health records (EHRs) can ease many of the challenges presented by the traditional enrollment process for clinical trials. We tested if automated batch enrollment using a technology-enabled subject recruitment system (TESRS) enhances recruitment while preserving representation of research subjects for the study population in our study setting. Methods: An ongoing community-based prospective adult cohort study was used to randomize 600 subjects who were eligible by age and residential address to TESRS (n = 300) and standard mailing method (n = 300), respectively, for 3 months. Then, TESRS was initiated and included automatic identification of patients’ preference for being contacted (online patient portal vs postal mail) from EHRs and automatic sending out of invitation letters followed by completion of a short online survey for checking eligibility and the digital consent process if eligible. We compared (1) median time to consent from invitation sent out per subject and total subjects recruited after a 3-month recruitment period, (2) the estimated study staff’s time, and (3) representation of sociodemographic characteristics (e.g., age, sex, race, SES measured by HOUSES index, and rural residence) between subjects recruited via TESRS and those via traditional mailing methods. Results: Median age of randomized subjects (n = 600) was 63 years with 52.0% female and 89.2% non-Hispanic White. Over a 3-month period, results showed consent rate via TESRS was 13% (39/297) similar to 11% (31/295) via standard mailing. However, recruitment was significantly faster with the TESRS approach (median 7 vs 26 days) given the study staff’s effort. Study staff’s time saved by using TESRS compared to standard mailing approach was estimated at 40 min per subject (equivalent to 200 h for 300 subjects). No significant differences in characteristics of research subjects from the study population were found. Conclusion: Our study demonstrated the utility of TESRS as a subject recruitment digital technology which significantly enhanced the recruitment effort while reducing the study staff burden of recruitment while maintaining the consistency of characteristics of recruited subjects. The strategy and support for implementing and testing TESRS in other study settings should be considered.

Health insurance literacy (HIL) constructs are related to financial toxicity in patients with cancer.

Objectives Machine learning (ML)–based emulators improve the calibration of decision-analytical models, but their performance in complex microsimulation models is yet to be determined. Methods We demonstrated the use of an ML-based emulator with the Colorectal Cancer (CRC)-Adenoma Incidence and Mortality (CRC-AIM) model, which includes 23 unknown natural history input parameters to replicate the CRC epidemiology in the United States. We first generated 15,000 input combinations and ran the CRC-AIM model to evaluate CRC incidence, adenoma size distribution, and the percentage of small adenoma detected by colonoscopy. We then used this data set to train several ML algorithms, including deep neural network (DNN), random forest, and several gradient boosting variants (i.e., XGBoost, LightGBM, CatBoost) and compared their performance. We evaluated 10 million potential input combinations using the selected emulator and examined input combinations that best estimated observed calibration targets. Furthermore, we cross-validated outcomes generated by the CRC-AIM model with those made by CISNET models. The calibrated CRC-AIM model was externally validated using the United Kingdom Flexible Sigmoidoscopy Screening Trial (UKFSST). Results The DNN with proper preprocessing outperformed other tested ML algorithms and successfully predicted all 8 outcomes for different input combinations. It took 473 s for the trained DNN to predict outcomes for 10 million inputs, which would have required 190 CPU-years without our DNN. The overall calibration process took 104 CPU-days, which included building the data set, training, selecting, and hyperparameter tuning of the ML algorithms. While 7 input combinations had acceptable fit to the targets, a combination that best fits all outcomes was selected as the best vector. Almost all of the predictions made by the best vector laid within those from the CISNET models, demonstrating CRC-AIM’s cross-model validity. Similarly, CRC-AIM accurately predicted the hazard ratios of CRC incidence and mortality as reported by UKFSST, demonstrating its external validity. Examination of the impact of calibration targets suggested that the selection of the calibration target had a substantial impact on model outcomes in terms of life-year gains with screening. Conclusions Emulators such as a DNN that is meticulously selected and trained can substantially reduce the computational burden of calibrating complex microsimulation models. Highlights Calibrating a microsimulation model, a process to find unobservable parameters so that the model fits observed data, is computationally complex. We used a deep neural network model, a popular machine learning algorithm, to calibrate the Colorectal Cancer Adenoma Incidence and Mortality (CRC-AIM) model. We demonstrated that our approach provides an efficient and accurate method to significantly speed up calibration in microsimulation models. The calibration process successfully provided cross-model validation of CRC-AIM against 3 established CISNET models and also externally validated against a randomized controlled trial.