Citations

ABSTRACT Background Nab ‐sirolimus (ABI‐009, nab ‐rapamycin; Aadi Bioscience Inc. [Aadi]) is a human albumin‐bound form of sirolimus nanoparticles, a potent mTOR inhibitor. This phase I trial was conducted to define dose‐limiting toxicities (DLT), maximum tolerated or recommended phase II dose (MTD/RP2D), and pharmacokinetics of Nab ‐sirolimus in combination with temozolomide and irinotecan. Methods Using a rolling 6 design, Nab ‐sirolimus was administered intravenously (IV) on days (D) 1 and 8 of cycle (C) 1. In subsequent cycles, Nab ‐sirolimus was administered D1 and D8 in combination with temozolomide (125 mg/m 2 /dose, maximum 250 mg/dose) and irinotecan (90 mg/m 2 /dose) orally, daily on D1–5. Cycle duration was 21 days. Three dose levels (DL) of Nab ‐sirolimus were investigated (DL1: 35 mg/m 2 /dose, DL‐1: 20 mg/m 2 /dose, and DL‐2: 15 mg/m 2 /dose). The observation period for estimating the MTD/RP2D was defined by cycles 1 and 2. Results Thirty‐three patients were enrolled, 32 were eligible. Dose determination included 17 evaluable patients, median (range) age 12 (2–20) years and six additional patients were enrolled (four evaluable for toxicity) on a pharmacokinetic cohort. C1 or C2 DLTs were primarily thrombocytopenia including 2/5 patients at DL1, 2/6 patients at DL‐1, and 1/6 patients at DL‐2. One patient (DL1) with Ewing Sarcoma had a partial response and remained on study for 35 cycles. Rapamycin clearance was dose dependent. Irinotecan clearance and its active metabolite SN‐38 exposure were not affected by coadministration with Nab ‐sirolimus. Conclusion The MTD for Nab ‐sirolimus was 15 mg/m 2 /dose IV on D1 and D8 in combination with temozolomide 125 mg/m 2 /dose and oral irinotecan 90 mg/m 2 /dose daily for 5 days during 21D cycles. Trial Registration ClinicalTrials.gov identifier NCT02975882

Abstract Background Delirium, an acute brain dysfunction, is proposed to be highly prevalent in clinical care and shown to significantly increase the risk of mortality and dementia. Objectives To report on the global prevalence of clinically documented delirium and delirium-related clinical practices in wards caring for paediatric and adult patients in healthcare facilities. Design A prospective, cross-sectional, 39-question survey completed on World Delirium Awareness Day, 15 March 2023. Participants Clinicians or researchers with access to clinical data. Main Outcome and Measure The primary outcome was the prevalence of clinically documented delirium at 8:00 a.m. (4 h) and 8:00 p.m. (±4 h). Secondary outcomes included delirium-related care practices and barriers to use. Descriptive statistics were calculated and multilevel modelling was completed. Results 1664 wards submitted surveys from 44 countries, reporting on delirium assessments at 8:00 a.m. (n = 36 048) and 8:00 p.m. (n = 32 867); 61% reported use of validated delirium assessment tools. At 8:00 a.m., 18% (n = 2788/15 458) and at 8:00 p.m., 17.7% (n = 2454/13 860) were delirium positive. Top prevention measures were pain management (86.7%), mobilisation (81.4%) and adequate fluids (80.4%). Frequently reported pharmacologic interventions were benzodiazepines (52.7%) and haloperidol (46.2%). Top barriers included the shortage of staff (54.3%), lack of time to educate staff (48.6%) and missing knowledge about delirium (38%). Conclusion and Relevance In this study, approximately one out of five patients were reported as delirious. The reported high use of benzodiazepines needs further evaluation as it is not aligned with best-practice recommendations. Findings provide a benchmark for future quality improvement projects and research.

Abstract Background Existing clinical preoperative risk assessment tools such as the Revised Cardiac Risk Index (RCRI) have been used extensively to predict perioperative major adverse cardiovascular events (MACE). Electrocardiogram (ECG) analysis using Deep Learning can identify hidden features that human eyes cannot identify and might help further risk stratify patients undergoing noncardiac surgery. Objective We aimed to develop a Deep Learning model that predicts perioperative MACE in patients undergoing elective noncardiac surgeries. Methods We included patients ≥ 18-year-old who underwent elective non-cardiac surgery between 2000-2021. MACE was validated in duplicate and were defined as a composite of in-hospital myocardial infarction, cardiac arrest or mortality. A convolutional neural network was developed using ECGs within 30 days before surgery. We randomly split subjects into training, internal validation, and testing datasets in a 7:1:2 ratio. Performance was evaluated using the area under the receiver operator characteristics curve (AUC) values on the testing dataset. Models trained included AI-ECG models to predict in-hospital MACE and to predict in-hospital mortality, and AI-ECG+RCRI models to predict in-hospital MACE and to predict in-hospital mortality. All models were validated using 30-day validated outcomes (MACE or all-cause mortality) in a community-based cohort and were compared with the Revised Cardiac Risk Index (RCRI) score using AUC values. Findings: We included 195,214 patients who underwent a total of 241,999 surgeries. 169,214 ECGs were used in the training dataset, 24,234 in the internal validation dataset, and 48,551 in the testing dataset. In the test cohort, the AI-ECG algorithm discriminated risk for in-hospital MACE with an AUC of 0.82 (95% CI 0.79-0.85). The algorithm similarly discriminated 30-day MACE with an AUC value of 0.79 (95% CI 0.77-0.81) surpassing the discrimination of the RCRI score AUC of 0.69 (95% CI 0.66-0.72). All models’ performances are shown in Figure1A. The AI-ECG model to predict in-hospital mortality had an AUC of 0.83 (95% CI 0.80-0.86) and predicted 30-day mortality with an AUC of 0.80 (95% CI 0.77-0.82). Other models’ performances are shown Figure1B. Training the AI-ECG models including RCRI data did not improve the AI-ECG models’ performance. Conclusion AI-ECG models with no additional clinical data can improve perioperative risk prediction in non-cardiac surgery, outperforming a conventional and widely used risk stratification tool such as the RCRI.

AbstractIntroductionAcute myeloid leukemia (AML) remains one of the deadliest hematopoietic malignancies. A better understanding of the molecular biology governing AML may lead to improved risk stratification and facilitate the development of novel therapies. Proteins are responsible for much of the biology of cells. Several studies have examined the global proteome in bulk mononuclear cells (MNCs) from AML specimens, which are comprised a heterogenous population of cells at various stages of differentiation.MethodsGiven the potential impact of the nonleukemic cells on protein expression profiles, we applied an integrative proteogenomic approach utilizing next‐generation sequencing and mass spectrometry‐based proteomics to identify novel protein biomarkers in unsorted MNCs and viable leukemic blasts (VLBs) isolated from blood and bone marrow specimens obtained at the time of AML diagnosis.ResultsWe identified significant differences in protein expression between VLBs and MNCs. Subsequent studies (N = 27) focused on proteomic profiling of VLBs that identified novel candidate biomarkers associated with mutational genotypes and clinical outcome, some of which were recapitulated in an independent cohort of patients. Using mass spectrometry, we also detected mutated protein products, some of which were predicted via in silico analyses to be potential neoantigens amenable to adoptive immunotherapy. As previously described, analyses comparing transcript and protein expression showed an overall modest correlation between mRNA and protein dataset, but enriching for genes associated with mutations significantly improved the protein–RNA correlation.ConclusionTogether, the results provide insight into the biology of VLBs and demonstrate the gains derived from examining the proteome in addition to genome and transcriptome.

As a treatment for the widely spread cardiovascular diseases (CVD), bypass vascular grafts have room for improvement in terms of mechanical property match with native arteries. A 3D‐printed nozzle is presented, featuring unique internal structures, to extrude artificial vascular grafts with a flower‐mimicking geometry. The multilayer‐structured graft wall allows the inner and outer layers to interfere sequentially during lateral expansion, replicating the nonlinear elasticity of native vessels. Both experiment and simulation results verify the necessity and benefit of the flower‐mimicking structure in obtaining the self‐toughening behavior. The gelation study of natural polymers and the utilization of sacrificial phase enables the smooth extrusion of the multiphase conduit, where computer‐assisted image analysis is employed to quantify manufacturing fidelity. The cell viability tests demonstrate the cytocompatibility of the gelatin methacryloyl (GelMA)/sodium alginate grafts, suggesting potential for further clinical research with further developments. This study presents a feasible approach for fabricating bypass vascular grafts and inspires future treatments for CVD.

The NCCN Guidelines for Ovarian Cancer/Fallopian Tube Cancer/Primary Peritoneal Cancer provide multidisciplinary diagnostic workup, staging, and treatment recommendations for this disease. These NCCN Guidelines Insights detail how the evolution of the use of PARP inhibitors as maintenance and single-agent regimens for the treatment of ovarian cancer informed panel recommendations in the guidelines.

Problem definition: Coordinated care network (CCN) is a burgeoning paradigm where patients’ diagnosis and treatment plans are developed based on collaboration between multiple, colocated medical specialties to holistically address patients’ health needs. A primary performance metric for CCNs is how quickly patients can complete their itinerary of appointments at multiple medical services in the network. Rapid completion is critical to care delivery but also presents a major operational challenge. Because information about a patient’s condition and treatment options evolves over the course of the itinerary, care paths are not known a priori. Thus, appointments (except for the first one) cannot be reserved in advance, which may result in significant delays if capacity is not allocated properly. Methodology/results: We study capacity allocation for the patient’s first (root) appointment as the primary operational lever to achieve rapid itinerary completion in CCNs. We develop a novel queueing-based analytical framework to optimize this root appointment allocation, maximizing the proportion of patients completing care by prespecified deadlines. Our framework accounts for the complex interactions among all patients in the network through the blocking process, which contrasts with conventional siloed planning. We provide an exact characterization of the itinerary time and develop a mean-field approximation with convergence guarantees that permits tractable solutions for large-scale network problems. In a simulation case study of Mayo Clinic, our solution improves on-time completion from 60% under the current plan to more than 93%. Managerial implications: We demonstrate that root appointment allocation is a multifaceted problem and that ignoring any of those facets can lead to poor performance. Simultaneously accounting for all of these complexities makes manual template design or traditional optimization methods inadequate, highlighting the significance of our integrated approach. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0649 .

AbstractBackgroundThe clinical relevance of cardiac troponin (cTn) elevation in takotsubo syndrome (TTS) remains uncertain. The present study sought to investigate the role of cardiac troponin (cTn) elevations in mortality prediction of patients with Takotsubo syndrome (TTS).MethodsPatients enrolled in the International Takotsubo (InterTAK) Registry from January 2011 to February 2020 with available data on peak cTn levels were included in the analysis. Peak cTn levels during the index hospitalization were used to define clinically relevant myocardial injury. The threshold at which clinically relevant myocardial injury drives mortality at 1 year was identified using restricted cubic spline analysis.ResultsOut of 2′938 patients, 222 (7.6%) patients died during 1‐year follow‐up. A more than 28.8‐fold increase of cTn above the upper reference limit was identified as threshold for clinically relevant myocardial injury. The presence of clinically relevant myocardial injury was significantly associated with an increased risk of mortality at 5 years (adjusted HR 1.58, 95% CI 1.18–2.12, p =.002). Clinically relevant myocardial injury was related to an increased 5‐year mortality risk in patients with apical TTS (adjusted HR 1.57, 95% CI 1.21–2.03, p =.001), in presence of physical stressors (adjusted HR 1.60, 95% CI 1.22–2.11, p =.001), and in absence of emotional stressors (adjusted HR 1.49, 95% CI, 1.17–1.89, p =.001).ConclusionThis study for the first time determined a troponin threshold for the identification of TTS patients at excess risk of mortality. These findings advance risk stratification in TTS and assist in identifying patients in need for close monitoring and follow‐up.