Daily Anesthesiology Research Analysis

BACKGROUND: Diversification of the healthcare workforce is promoted to address racial and ethnic disparities in obstetric anesthesia care; however, the supporting evidence remains insufficient. We assessed the association of the nurse workforce diversity with neuraxial labor analgesia (NLA) use and disparities in NLA use. METHODS: We analyzed 2019-2022 US birth certificate data for vaginal and intrapartum cesarean deliveries. The exposure was the registered nurse (RN) diversity index in the hospital county, calculated as the proportion of RNs identifying with minoritized racial and ethnic groups divided by the proportion of the county's total population identifying with those groups. The index was categorized into quartiles with the first quartile indicating the lowest diversity. The outcome was NLA use. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) of NLA associated with the diversity index were estimated using mixed-effects logistic regression models. RESULTS: We analyzed 10,979,988 birth certificates. Overall, 80.0% recorded NLA use. Relative to women who gave birth in hospitals in the first quartile of the RN diversity index (low diversity), the odds of receiving NLA increased 10% for women in the fourth quartile (high diversity) of the index (aOR: 1.10; 95% CI: 1.06, 1.14). Compared with White women, increased odds of receiving NLA associated with higher RN workforce diversity were more pronounced among Hispanic, Black, Asian, American Indian or Alaskan Native, and Native Hawaiian or Other Pacific Islander women. CONCLUSIONS: RN workforce diversity was positively associated with NLA use and reduced disparities in NLA use.

BACKGROUND: Immunocompromise is common in the intensive care unit (ICU) and is strongly associated with adverse outcomes. However, robust quantitative tools for assessing the severity of immunocompromise are lacking. We aimed to develop and validate a machine learning-powered immunocompromise score and its risk stratification based on common immunocompromise conditions and biomarkers in the ICU. METHODS: A single-centre retrospective study was carried out in two ICUs of an academic tertiary care center in China. Adult patients who were admitted to the ICU for at least three days were enrolled. Feature selection was performed via the Boruta algorithm. The primary endpoint was 28-day all-cause mortality, whereas secondary endpoints included septic shock, the use of special antimicrobial agents, the peak levels of interleukin-6 (IL-6), etc. Seven machine learning models were developed via 10-fold cross-validation. The predicted probabilities from the optimal model were used to define the Immunocompromise and Severity (ICS) Score. To rapidly obtain patient immunocompromise information, a simplified ICS score (SICS score) was developed based on LASSO-selected features from day 1. Additionally, secondary endpoints were used to validate the rationale of ICS and SICS scores, and 216 patients were included for temporal validation. RESULTS: A total of 1863 patients were included for Algorithm derivation, with 679 deaths (36.9%). Among the seven machine learning models, the XGBoost model using data from the first 3 ICU days showed the highest performance, defined as ICS score (AUC 0.887; sensitivity 0.896; specificity 0.723; accuracy 0.787), and its performance was superior to that of the APACHE II and SOFA (P < 0.001). The LASSO algorithm selected 5 key variables (age, organ failure, immune impairing diseases and treatments, and IL-6 >100 pg/mL with lymphopenia <0.8×10 CONCLUSIONS: Derived from common immunocompromising conditions and biomarkers, the ICS and SICS scores and their risk stratification system provide an accurate and timely solution for identifying and stratifying immunocompromise in critically ill patients. This framework may facilitate early clinical decision-making and resource allocation.

BACKGROUND: Viscoelastic coagulation testing (VET) is a key tool for the early diagnosis and treatment of trauma-induced coagulopathy. However, differences exist between VET technologies. Our aim was to identify viscoelastic test thresholds for the ClotPro® analyser, and propose an approach to establishing a VET algorithm for the early management of trauma patients that aligns with current guidelines. METHODS: ClotPro® data were collected from trauma patients upon arrival, during surgical care, and throughout their ICU stay. Standard coagulation tests including prothrombin time index <70% or international normalised ratio >1.2, fibrinogen concentration <150 mg dl RESULTS: We included 375 patients (67.8% male) with a median injury severity score of 16 (interquartile range, 9-27). Receiver operating characteristic curve analysis for detecting an international normalised ratio >1.2 using EX-Test clotting time yielded an area under the receiver operating characteristic curve (AUC) of 0.71. Detection of fibrinogen <150 mg dl CONCLUSIONS: These ClotPro® cut-off values allow for early and reliable detection of hypofibrinogenaemia and thrombocytopenia. Only after correction of fibrinogen and platelet deficits should prolonged CT be considered a weak marker for the augmentation of coagulation factors.