Hemoglobin Concentration and Post-Operative Delirium in Elderly Patients Undergoing Femoral Neck Fracture Surgery

This study aimed to determine the relationship between hemoglobin (Hb) concentration and post-operative delirium (POD) in elderly patients undergoing femoral neck fracture (FNF) surgery and to investigate whether the change in Hb concentration is associated with POD and the risk factors for POD. A total of 889 patients admitted with FNF between January 2016 and December 2020 were enrolled in this single-center, retrospective, case–control study. Hb concentrations were determined at admission and post-operative day 1 and the change in Hb concentration was defined as the absolute value of difference in pre-operative and post-operative Hb concentration. POD was assessed using the Confusion Assessment Method for the Intensive Care Unit (ICU) or the Confusion Assessment Method once a daily after surgery. The logistic regression analysis was performed for statistical analysis. In total, 172 (19.3%) patients developed POD and 151 (87.8%) patients developed POD within post-operative 3 days. Low pre-operative Hb concentration [p = 0.026, odds ratio (OR) = 0.978] and significant change in Hb concentration (p = 0.006, OR = 1.033) were significantly associated with POD. After excluding change in Hb concentration or pre-operative Hb concentration, neither of them was significantly associated with POD (p > 0.05). The interaction analysis of change in Hb concentration and pre-operative Hb concentration in the logistic regression model was negative. There was no significant relationship between post-operative Hb concentration and POD. Age (p < 0.001, OR = 1.072), stroke history (p = 0.003, OR = 2.489), post-operative ICU transfer (p = 0.007, OR = 1.981), and visual analog scale score within post-operative 2 days (p1 = 0.016 and p2 = 0.006) were independently associated with POD in the logistic regression analysis. Patients with low pre-operative Hb concentrations and high changes in Hb concentration seem to have an increased risk of POD and should receive more attention. Old age, stroke history, post-operative ICU transfer, and pain within post-operative 2 days were significantly associated with POD.

Association of COVID-19 with Hepatic Injury Prevalence and Associated Factors

Background: Novel Coronavirus Disease 19 (COVID-19) was reported by the WHO as a pandemic in March 2020. It was associated with liver injury in up to 50% of patients. This retrospective cohort study investigated the prevalence and associated factors of liver injury among COVID-19 patients. Methods: We include 2319 consecutive COVID-19 patients from April 2020 to November 2020. Liver function tests were performed at baseline, 24–48 h after admission, and before mortality/discharge. We compared Saudis and non-Saudis, in admission rate, serum ALT level, morbidity, and mortality. Serum ALT was compared between sexes, admitted and non-admitted patients, and the deceased and survivors. Results: Men (1356; 58.5%) and non-Saudis (1328; 57.3%) were predominant. The mean (SD) age was 41.67 ± 18.3 years (18 - 100). One-third of the patients had comorbidities, and 1022 (44.1%) required hospital admission. Intensive Care Unit (ICU) transfer was required in 185/1022 (18%). Male and non-Saudis were most likely to be transferred to the ICU (P < 0.001). Hepatocellular liver injury was found in 797 (34.4%) patients. Male and admitted patients were more likely to have a hepatic injury (P = 0.001). Conclusions: The mortality rate among admitted patients was 17.8% (182/1022). Mortality was associated with older age and hepatic injury (P < 0.001 and P = 0.004, respectively).

Machine Learning-based Derivation and External Validation of a Tool to Predict Death and Development of Organ Failure in Hospitalized Patients with COVID-19

BackgroundCOVID-19 mortality risk stratification tools could improve care, inform accurate and rapid triage decisions, and guide family discussions regarding goals of care. A minority of COVID-19 prognostic tools have been tested in external cohorts. Our objective was to compare machine learning algorithms and develop a tool for predicting subsequent clinical outcomes in COVID-19. MethodsWe conducted a retrospective cohort study that included hospitalized patients with COVID-19 from March 2020 to March 2021. 712 consecutive patients from University of Washington (UW) and 345 patients from Tongji Hospital in China were included. We applied three different machine learning algorithms to clinical and laboratory data collected within the initial 24 hours of hospital admission to determine the risk of in-hospital mortality, transfer to the intensive care unit (ICU), shock requiring vasopressors, and receipt of renal replacement therapy (RRT). Mortality risk models were derived, internally validated in UW and externally validated in Tongji Hospital. The risk models for ICU transfer, shock and RRT were derived and internally validated in the UW dataset. ResultsAmong the UW dataset, 122 patients died (17%) during hospitalization and the mean days to hospital mortality was 15.7 +/- 21.5 (mean +/- SD). Elastic net logistic regression resulted in a C-statistic for in-hospital mortality of 0.72 (95% CI, 0.64 to 0.81) in the internal validation and 0.85 (95% CI, 0.81 to 0.89) in the external validation set. Age, platelet count, and white blood cell count were the most important predictors of mortality. In the sub-group of patients > 50 years of age, the mortality prediction model continued to perform with a C-statistic of 0.82 (95% CI:0.76,0.87). Mortality prediction models also performed well for shock and RRT in the UW dataset but functioned with lower accuracy for ICU transfer. ConclusionsWe trained, internally and externally validated a prediction model using data collected within 24 hours of hospital admission to predict in-hospital mortality on average two weeks prior to death. We also developed models to predict RRT and shock with high accuracy. These models could be used to improve triage decisions, resource allocation, and support clinical trial enrichment.

313. Host Protein Biomarkers Predicting Severity of Lung Damage due to COVID-19

Background Biomarkers to predict the severity of lung damage due to COVID-19 are urgently needed to inform management and treatment decisions. Our objective was to investigate the predictive value of host proteins for worsening respiratory failure in one of the by COVID-19 most affected and diverse patient populations in the US. Methods We performed a prospective single-center cross-sectional study of 34 adult patients admitted to Montefiore Medical Center in the Bronx, New York, for respiratory symptoms due to PCR-confirmed COVID-19. Exclusion criteria were age &lt; 21, history of prior SARS-CoV-2 infection, and/or underlying severe chronic lung diseases requiring home O2 and/or high dose steroids. We stratified and compared patients by whether they developed worsening respiratory failure, necessitating transfer to the intensive care unit (ICU) during their hospital stay. Using a custom Luminex Assay, we measured hospital admission serum concentrations of 8 host proteins, representing respiratory-associated epithelial (RAGE, SP-D, CC16), endothelial (Ang-2, vWF), and immune pathways (S100A12, ICAM-1, VCAM-1). Results Except for race and WHO COVID-19 scores, demographics, co-morbidities, symptoms, and symptom duration were not statistically significantly different between patients requiring transfer to the ICU (n=15) and non-ICU patients (n=19). Higher log-transformed levels for 5/8 proteins (S100A12, ICAM-1, Ang-2, RAGE, SP-D) showed significant or marginally significant increased cause-specific hazard for ICU transfer (n=15). Estimated cumulative incidence functions further showed a significantly or near significantly increased risk for ICU transfer for patients with above the median values of S100A12 or ICAM-1 (p=0.013), Ang-2 (p=0.056) or RAGE (p=0.077), respectively (Figure 1). Host proteins predicting need for ICU transfer did not correlate strongly with other clinical laboratory markers for COVID-19 severity (CRP, LDH, D-Dimer, Fibrinogen, Ferritin). Figure 1. Patients with above median levels of host protein markers S100A12, ICAM-1, Ang-2, and RAGE have a significantly or near significantly increased risk for severe respiratory failure requiring transfer to the ICU. Comparison of estimated cumulative incidence at 7 days post admission for host protein markers above and below median levels for (A) S10012 (median 96,675 pg/ml); (B) ICAM-1 (median (1,192,277 pg/ml); (C) Ang-2 (median 3463 pg/ml); (D) RAGE (median 6356 pg/ml); and (E) SP-D (median 11,832 pg/ml). Conclusion These results suggest that host proteins have additional predictive value for the severity of COVID-19-associated lung damage at time of presentation to the hospital. Disclosures Inessa Gendlina, Nothing to disclose

A physician-led medical emergency team increases the rate of medical interventions: A multicenter study in Korea

Background According to the rapid response system’s team composition, responding teams were named as rapid response team (RRT), medical emergency team (MET), and critical care outreach. A RRT is often a nurse-led team, whereas a MET is a physician-led team that mainly plays the role of an efferent limb. As few multicenter studies have focused on physician-led METs, we comprehensively analyzed cases for which physician-led METs were activated. Methods We retrospectively analyzed cases for which METs were activated. The study population consisted of subjects over 18 years of age who were admitted in the general ward from January 2016 to December 2017 in 9 tertiary teaching hospitals in Korea. The data on subjects’ characteristics, activation causes, activation methods, performed interventions, in-hospital mortality, and intensive care unit (ICU) transfer after MET activation were collected and analyzed. Results In this study, 12,767 cases were analyzed, excluding those without in-hospital mortality data. The subjects’ median age was 67 years, and 70.4% of them were admitted to the medical department. The most common cause of MET activation was respiratory distress (35.1%), followed by shock (11.8%), and the most common underlying disease was solid cancer (39%). In 7,561 subjects (59.2%), the MET was activated using the screening system. The commonly performed procedures were arterial line insertion (17.9%), intubation (13.3%), and portable ultrasonography (13.0%). Subsequently, 29.4% of the subjects were transferred to the ICU, and 27.2% died during hospitalization. Conclusions This physician-led MET cohort showed relatively high rates of intervention, including arterial line insertion and portable ultrasonography, and low ICU transfer rates. We presume that MET detects deteriorating patients earlier using a screening system and begins ICU-level management at the patient’s bedside without delay, eventually preventing the patient’s condition from worsening and transfer to the ICU.