BACKGROUND:External comparisons of hospital antimicrobial use (AU), risk-adjusted using encounter characteristics, may better inform antimicrobial stewardship program strategy. Barriers to encounter-level modeling include feasibility of data collection and defining optimal methods for selecting input variables for risk-adjustment purposes. METHODS:We measured achievements in sharing validated, encounter-level AU data among a multisystem hospital collaborative. Then, we performed retrospective analyses to compare variable selection strategies for AU risk-adjustment models. Electronic health record data from 50 US hospitals from 2020 to 2021 were split for model training and testing. Four input variable strategies were compared: (1) diagnosis-related group categories, (2) adjudicated Elixhauser comorbidity categories, (3) agnostic strategy including all diagnosis and procedure categories from AHRQ's Clinical Classification Software Refined (CCSR), and (4) adjudicated strategy where CCSR categories not appropriate for risk-adjustment were excluded by expert consensus. Gradient-boosted machine tree-based models estimated antibacterial days of therapy (DOT). Accuracy was measured for each strategy using mean absolute error (MAE); correlation plots compared model estimates and observed DOT among testing encounters. The top 20 most influential variables were defined using model variable importance. RESULTS:Fifty of 76 hospitals successfully shared validated datasets using local resources. MAE was lowest for modeling strategies with larger numbers of CCSR inputs. Agnostic and adjudicated strategies had highly correlated estimates and similar influential variables. CONCLUSIONS:Expert adjudication required personnel effort and potentially introduced biases, yet did not produce results different from an agnostic approach. Risk-adjustment incorporating large encounter-level data and machine learning may prove feasible and meaningful in future hospital AU assessments.
Regulatory T cells (Treg) act as a powerful barrier to effective antitumor immunity. Although manipulating Treg is a promising anticancer strategy, doing so while sparing general immune tolerance has been a challenge. Identifying factors specifically expressed in tumor-infiltrating Treg is therefore important for better understanding cancer pathogenesis and identifying novel therapeutic targets that enhance antitumor immunity. We show that T cell Immunoglobulin and Mucin 3 (Tim-3) expression on tumor Treg is required for the function and survival of these cells, in part through Akt and FOXO1 signaling. Deleting Tim-3 in Treg leads to delayed tumor-specific T-cell exhaustion and lower tumor burden, without altering peripheral homeostasis. Similar effects were noted when Tim-3 was only deleted from half of the Treg or when deletion was delayed until after tumor inoculation. Moreover, Treg-specific deletion of Tim-3 cooperated with PD-1 checkpoint blockade to sensitize an immunotherapy-resistant tumor model. In addition, a decrease in Tim-3+ tumor Treg correlated with responsiveness to PD-1/LAG-3 combination checkpoint blockade in a human clinical trial. Overall, our data provide evidence that Tim3-expressing Treg are a promising target to modulate tumor-specific immune responses.
Project HOPE (Helping Overcome Pressures in Eldercare) involved implementing routine caregiver burden screening and a standardized support intervention in an outpatient geriatric medicine clinic.
BACKGROUND:Mpox, Nipah virus, and Andes virus are emerging infectious diseases that cause outbreaks associated with nosocomial transmission. METHODS:This narrative review summarizes the Mpox, Nipah virus, and Andes virus epidemiology, clinical manifestations, diagnosis, and treatment with a focus on environmental survival, susceptibility to antiseptics and disinfectants, and infection prevention. RESULTS:Mpox, Nipah virus, and Andes virus all survive in the environment and are capable of indirect transmission. All these viruses are enveloped and susceptible to commonly used antiseptics and surface disinfectants. CONCLUSIONS:Rapid identification of patients with known or suspected infections of these emerging viruses coupled with adherence to infection prevention recommendations will prevent healthcare-associated transmission.
Background Before 1970, US hospitals conducted scheduled culturing of the air and environmental surfaces. Currently, routine environmental culturing is not recommended. Targeted microbiologic sampling is only recommended for research purposes, outbreak investigations, and when there are validated culture methods and action level based on culture results. Methods The paper is a commentary. The literature was reviewed to find published guidelines that provide recommendations for appropriate microbiologic sampling of the hospital environment based on validated culture methods and provide action levels based on culture results. Results Microbiologic sampling of the hospital environment is recommended in the following circumstances: Biological monitoring of sterilization processes, cultures of water/dialysate used for hemodialysis, dental waterlines, specified locations in pharmacies performing sterile compounding, surveillance for Legionella, and sampling of duodenoscopes. Conclusions Health care facilities should only perform routine microbiological sampling of the environment when recommended by guidelines.
