Citations

Abstract Background Time spent awaiting discharge after the acute need for hospitalization has resolved is an important potential contributor to hospital length of stay (LOS). Objective To measure the prevalence, impact, and context of patients who remain hospitalized for prolonged periods after completion of acute care needs. Design, Setting, and Participants We conducted a cross‐sectional “point‐in‐time” survey at each of 15 academic US hospitals using a structured data collection tool with on‐service acute care medicine attending physicians in fall 2022. Main Outcomes and Measures Primary outcomes were number and percentage of patients considered “medically ready for discharge” with emphasis on those who had experienced a “major barrier to discharge” (medically ready for discharge for ≥1 week). Estimated LOS attributable to major discharge barriers, contributory discharge needs, and associated hospital characteristics were measured. Results Of 1928 patients sampled, 35.0% ( n = 674) were medically ready for discharge including 9.8% ( n = 189) with major discharge barriers. Many patients with major discharge barriers (44.4%; 84/189) had spent a month or longer medically ready for discharge and commonly (84.1%; 159/189) required some form of skilled therapy or daily living support services for discharge. Higher proportions of patients experiencing major discharge barriers were found in public versus private, nonprofit hospitals (12.0% vs. 7.2%; p = .001) and county versus noncounty hospitals (14.5% vs. 8.8%; p = .002). Conclusions Patients experience major discharge barriers in many US hospitals and spend prolonged time awaiting discharge, often for support needs that may be outside of clinician control.

AbstractThis study explores the utility of the large language models (LLMs), specifically ChatGPT and Google Bard, in predicting neuropathologic diagnoses from clinical summaries. A total of 25 cases of neurodegenerative disorders presented at Mayo Clinic brain bank Clinico‐Pathological Conferences were analyzed. The LLMs provided multiple pathologic diagnoses and their rationales, which were compared with the final clinical diagnoses made by physicians. ChatGPT‐3.5, ChatGPT‐4, and Google Bard correctly made primary diagnoses in 32%, 52%, and 40% of cases, respectively, while correct diagnoses were included in 76%, 84%, and 76% of cases, respectively. These findings highlight the potential of artificial intelligence tools like ChatGPT in neuropathology, suggesting they may facilitate more comprehensive discussions in clinicopathological conferences.

Abstract Background Cyclin D has been shown to play an essential role in acute lymphoblastic leukemia (ALL) initiation and progression, providing rationale for targeting the CDK4/6‐cyclin D complex that regulates cell cycle progression. Procedure The Children's Oncology Group AINV18P1 phase 1 trial evaluated the CDK4/6 inhibitor, palbociclib, in combination with standard four‐drug re‐induction chemotherapy in children and young adults with relapsed/refractory B‐ and T‐cell lymphoblastic leukemia (ALL) and lymphoma. Palbociclib (50 mg/m 2 /dose) was administered orally once daily for 21 consecutive days, first as a single agent (Days 1–3) and subsequently combined with re‐induction chemotherapy. This two‐part study was designed to determine the maximum tolerated dose (MTD) or recommended phase 2 dose (RP2D), followed by an expansion pharmacokinetic cohort. Results Twelve heavily pretreated patients enrolled, all of whom were evaluable for toxicity. One dose‐limiting hematologic toxicity (DLT) occurred at the starting dose of 50 mg/m 2 /dose orally for 21 days. No additional DLTs were observed in the dose determination or pharmacokinetic expansion cohorts, and overall rates of grade 3/4 nonhematologic toxicities were comparable to those observed with the chemotherapy platform alone. Five complete responses were observed, two among four patients with T‐ALL and three among seven patients with B‐ALL. Pharmacokinetic studies showed similar profiles with both liquid and capsule formulations of palbociclib. Conclusions Palbociclib in combination with re‐induction chemotherapy was well tolerated with a RP2D of 50 mg/m 2 /day for 21 days. Complete responses were observed among heavily pretreated patients.

Abstract Primary aldosteronism (PA) is the most common cause of secondary hypertension and is associated with increased morbidity and mortality when compared with blood pressure–matched cases of primary hypertension. Current limitations in patient care stem from delayed recognition of the condition, limited access to key diagnostic procedures, and lack of a definitive therapy option for nonsurgical candidates. However, several recent advances have the potential to address these barriers to optimal care. From a diagnostic perspective, machine-learning algorithms have shown promise in the prediction of PA subtypes, while the development of noninvasive alternatives to adrenal vein sampling (including molecular positron emission tomography imaging) has made accurate localization of functioning adrenal nodules possible. In parallel, more selective approaches to targeting the causative aldosterone-producing adrenal adenoma/nodule (APA/APN) have emerged with the advent of partial adrenalectomy or precision ablation. Additionally, the development of novel pharmacological agents may help to mitigate off-target effects of aldosterone and improve clinical efficacy and outcomes. Here, we consider how each of these innovations might change our approach to the patient with PA, to allow more tailored investigation and treatment plans, with corresponding improvement in clinical outcomes and resource utilization, for this highly prevalent disorder.

Abstract Despite recent methodology advancements in clinical natural language processing (NLP), the adoption of clinical NLP models within the translational research community remains hindered by process heterogeneity and human factor variations. Concurrently, these factors also dramatically increase the difficulty in developing NLP models in multi-site settings, which is necessary for algorithm robustness and generalizability. Here, we reported on our experience developing an NLP solution for Coronavirus Disease 2019 (COVID-19) signs and symptom extraction in an open NLP framework from a subset of sites participating in the National COVID Cohort (N3C). We then empirically highlight the benefits of multi-site data for both symbolic and statistical methods, as well as highlight the need for federated annotation and evaluation to resolve several pitfalls encountered in the course of these efforts.