Citations

Introduction: This study evaluated the impact of parallel-level patient transfers on bed utilization efficiency within the Mayo Clinic Health System in Southern Minnesota, focusing on optimizing resources across tertiary and critical access hospitals. Methods: A retrospective analysis of 179,066 Emergency Department visits (2018–2022) was conducted, with ~2% involving parallel-level transfers for observation or admission. Machine learning was utilized to identify patients suitable for parallel transfers based on demographics, comorbidities, and clinical factors. A Random Forest model with an AUROC of 0.87 guided transfer decisions. Saved patient days were calculated as the difference between the actual LOS and the benchmark LOS based on Diagnosis-Related Groups (DRGs). Generalized estimating equations analyzed length of stay (LOS) differences, adjusted for confounders, with 95% confidence intervals (CI). Statistical analyses were conducted using SPSS (v.26). Results: The mean patient age was 56 years (SD = 17.2), with 51.4% being female. Saved patient days increased from ~600 to 5200 days over the study period. Transferred patients had a 5.7% longer unadjusted LOS compared to non-transferred patients (95% CI: 2.9–8.6%, p < 0.001). After adjustment for demographics and comorbidities, the LOS difference was not significant (adjusted mean difference: 0.4%, 95% CI: −1.7–2.5%, p = 0.51). Conclusions: Parallel-level transfers increased saved patient days, reflecting enhanced resource utilization. However, the adjusted LOS differences were not significant, highlighting the need for robust transfer protocols and controlled studies to confirm these findings.

Objectives/Goals: This study aims to evaluate the performance of a third-party artificial intelligence (AI) product in predicting diagnosis-related groups (DRGs) in a community healthcare system. We highlight a use case illustrating how clinical practice leverages AI-predicted information in unexpected yet advantageous ways and assess the AI predictions accuracy and practical application. Methods/Study Population: DRGs are crucial for hospital reimbursement under the prospective payment model. The Mayo Clinic Health System (MCHS), a network of clinics and hospitals serving a substantial rural population in Minnesota and Wisconsin, has recently adopted an AI algorithm developed by Xsolis (an AI-focused healthcare solution provider). This algorithm, a 1D convolutional neural network, predicts DRGs based on clinical documentation. To assess the accuracy of AI-generated DRG predictions for inpatient discharges, we analyzed data from 930 patients hospitalized at MCHS Mankato between March 2 and May 13, 2024. The Xsolis platform provided the top three DRG predictions for the first 48 hours of each inpatient stay. The accuracy of these predictions was then compared against the final billed DRG codes from the hospital’s records. Results/Anticipated Results: In our validation set, Xsolis achieved a top-3 DRG prediction accuracy of 71% at 24 hours and 81% at 48 hours, which is lower than the originally reported accuracy of 81.1% and 83.3%, respectively. Interestingly, discussions with clinical practice leaders revealed that the most valuable information derived from the AI predictions was the expected geometric mean length of stay (GMLOS), which Xsolis was perceived to predict accurately. In the Medicare system, each DRG is associated with an expected GMLOS, a critical factor for efficient hospital flow planning. A subsequent analysis comparing predicted GMLOS with the actual length of stay showed variances of -0.10 days on day 1 and 0.14 days on day 2, indicating a high degree of accuracy and aligning with clinical practice perceptions. Discussion/Significance of Impact: Our research underscores that clinical practice can leverage AI predictions in unexpected yet beneficial ways. While initially focused on DRG prediction, the associated GMLOS emerged as more significant. This suggests that AI algorithm validation should be tailored to specific clinical needs rather than relying solely on generalized benchmarks.

Blood transfusion is a common therapeutic intervention in hospitalized patients. There are numerous indications for transfusion, including anemia and coagulopathy with deficiency of single or multiple coagulation components such as platelets or coagulation factors. Nevertheless, the practice of transfusion in critically ill patients has been controversial mainly due to a lack of evidence and the need to consider the appropriate clinical context for transfusion. Further, transfusion carries many risk factors that must be balanced with benefits. Therefore, transfusion practice in ICU patients has constantly evolved, and we endeavor to present a contemporary review of transfusion practices in this population guided by clinical trials and expert guidelines.

Necrotizing fasciitis is a rare soft tissue infection characterized by a rapidly spreading infection of the subcutaneous tissue. Early diagnosis is important as it requires immediate and complete debridement of infected tissues and antibiotic therapy. Necrotizing fasciitis usually involves the extremities, abdomen, and groin, but rarely involves the head and neck. Necrotizing fasciitis has an aggressive course; however, in rare cases, it can present in a subacute indolent form which can be misdiagnosed as other cutaneous diseases. Our case is a unique presentation of subacute necrotizing fasciitis of the posterior neck, which was initially diagnosed as a herpes zoster infection, in a patient with undiagnosed diabetes mellitus, which was complicated with diabetic ketoacidosis and sepsis.

Multisystem inflammatory syndrome is a life-threatening condition associated with elevated inflammatory markers and multiple organ injury. A diagnosis of exclusion, it has been reported after severe acute respiratory syndrome coronavirus 2 infection (SARS-CoV-2) in children and adults; recently it has been described in some post-COVID-19 vaccinated individuals. The prognosis with supportive care and immunomodulatory therapy is good, although some individuals may require treatment in the intensive care unit (ICU). Here we report a case of a 58-year-old man who developed multi-organ failure after receiving the second dose of the Moderna mRNA-1273 COVID-19 vaccine. He required critical organ support in the ICU. An extensive workup was done to rule out alternative infectious and inflammatory processes. Following a period of gradual in-hospital convalescence, our patient made a full recovery. To our knowledge, this is the first comprehensively described case of multisystem inflammatory syndrome associated with Moderna mRNA-1273 COVID-19 vaccine in an adult over 50 years of age.

A 68-year-old man presented to the Emergency Department with undifferentiated shock. During the three days prior, he experienced a non-specific viral-like illness. On examination his blood pressure was 70/40 mm Hg with cool, clammy, and mottled extremities and flat neck veins. Laboratory investigations revealed a positive influenza B screen alongside elevated hemoglobin and hematocrit. Following aggressive fluid resuscitation his blood pressure had marginally improved and he was transferred to the intensive care unit (ICU). Vasopressor support with cautious fluid resuscitation continued and at 7- and 10-hours following presentation, serum albumin levels were extremely low. Idiopathic systemic capillary leak syndrome triggered by influenza B infection was diagnosed. Following a 9-day ICU stay the patient made a complete recovery and remains stable on intravenous immunoglobulin therapy. This case highlights the importance judicious fluid resuscitation and serum albumin levels when confronted with refractory shock.