Nomogram to Predict Intensive Care Following Gastrectomy for Gastric Cancer: A Useful Clinical Tool to Guide the Decision-Making of Intensive Care Unit Admission

BackgroundWe aimed to generate and validate a nomogram to predict patients most likely to require intensive care unit (ICU) admission following gastric cancer surgery to improve postoperative outcomes and optimize the allocation of medical resources.MethodsWe retrospectively analyzed 3,468 patients who underwent gastrectomy for gastric cancer from January 2009 to June 2018. Here, 70.0% of the patients were randomly assigned to the training cohort, and 30.0% were assigned to the validation cohort. Least absolute shrinkage and selection operator (LASSO) method was performed to screen out risk factors for ICU-specific care using the training cohort. Then, based on the results of LASSO regression analysis, multivariable logistic regression analysis was performed to establish the prediction nomogram. The calibration and discrimination of the nomogram were evaluated in the training cohort and validated in the validation cohort. Finally, the clinical usefulness was determined by decision curve analysis (DCA).ResultsAge, the American Society of Anesthesiologists (ASA) score, chronic pulmonary disease, heart disease, hypertension, combined organ resection, and preoperative and/or intraoperative blood transfusions were selected for the model. The concordance index (C-index) of the model was 0.843 in the training cohort and 0.831 in the validation cohort. The calibration curves of the ICU-specific care risk nomogram suggested great agreement in both training and validation cohorts. The DCA showed that the nomogram was clinically useful.ConclusionsAge, ASA score, chronic pulmonary disease, heart disease, hypertension, combined organ resection, and preoperative and/or intraoperative blood transfusions were identified as risk factors for ICU-specific care after gastric surgery. A clinically friendly model was generated to identify those most likely to require intensive care.

Quanzhenyiqitang Reverses LPS-Induced Inflammation via Inhibiting PYK2/p38MAPK/HDAC2/CK2 Signaling Pathway in Rat Alveolar Macrophage

Chronic obstructive pulmonary disease (COPD) is a common chronic pulmonary disease with multiple etiologies and pathological changes. PYK2 expression is significantly increased in lipopolysaccharide-induced lung injury, but it mediates chronic lung inflammation. The mechanism of its occurrence remains unclear. Quanzhenyiqitang is often used in clinical treatment of COPD, so this study explored the mechanism of its treatment of lipopolysaccharide-induced lung injury. In this study, transfection, flow cytometry, QRT-PCR, and Western blotting methods were used to study the mechanism of Quanzhenyiqitang lipopolysaccharide-induced lung injury. The results showed that the mechanism of occurrence remains unclear. Our novel observations imply that the PYK2/p38MAPK/HDAC2/CK2 pathway is one of the fundamental underlying mechanisms that mediate the pathogenic progression of COPD, and Quanzhenyiqitang may be the therapeutic drug to prevent chronic inflammation and delay the progression of COPD by inhibiting PYK2 signaling pathways.

Impact of Comorbidities on Beneficial Effect of Lactated Ringers vs. Saline in Sepsis Patients

Background: Lactated Ringers reduced mortality more than saline in sepsis patients but increased mortality more than saline in traumatic brain injury patients.Method: This prospective cohort study was conducted in a medical intensive care unit (ICU) in central Taiwan. We applied standard sepsis evaluation protocol and identified heart, lung, liver, kidney, and endocrine comorbidities. We also evaluated resuscitation response with central venous pressure, central venous oxygen saturation, and serum lactate level simultaneously. Propensity-score matching and Cox regression were used to estimate mortality. The competing risk model compared the lengths of hospital stays with the subdistribution hazard ratio (SHR).Results: Overall, 938 patients were included in the analysis. The lactated Ringers group had a lower mortality rate (adjusted hazard ratio, 0.59; 95% CI 0.43-0.81) and shorter lengths of hospital stay (SHR, 1.39; 95% C.I. 1.15-1.67) than the saline group; the differences were greater in patients with chronic pulmonary disease and small and non-significant in those with chronic kidney disease, moderate to severe liver disease and cerebral vascular disease. The resuscitation efficacy was the same between fluid types, but serum lactate levels were significantly higher in the lactated Ringers group than in the saline group (0.12 mg/dl/h; 95% C.I.: 0.03, 0.21), especially in chronic liver disease patients. Compared to the saline group, the lactated Ringers group achieved target glucose level earlier in both diabetes and non-diabetes patients.Conclusion: Lactate Ringer's solution provides greater benefits to patients with chronic pulmonary disease than to those with chronic kidney disease, or with moderate to severe liver disease. Comorbidities are important in choosing resuscitation fluid types.

Hemophagocytosis, hyper-inflammatory responses, and multiple organ damages in COVID-19-associated hyperferritinemia

Hyperferritinemia comes to light frequently in general practice. However, the characteristics of COVID-19-associated hyperferritinemia and the relationship with the prognosis were not well described. The retrospective study included 268 documented COVID-19 patients. They were divided into the hyperferritinemia group (≥ 500 µg/L) and the non-hyperferritinemia group (< 500 µg/L). The prevalence of fever and thrombocytopenia and the proportion of patients with mechanical ventilator support and in-hospital death were much higher in the hyperferritinemia group (P < 0.001). The hyperferritinemia patients showed higher median IL-6, D-dimer, and hsCRP (P < 0.001) and lowered FIB level (P = 0.036). The hyperferritinemia group had a higher proportion of patients with AKI, ARDS, and CSAC (P < 0.001). According to the multivariate analysis, age, chronic pulmonary disease, and hyperferritinemia were found to be significant independent predictors for in-hospital mortality [HR 1.041 (95% CI 1.015–1.068), P = 0.002; HR 0.427 (95% CI 0.206–0.882), P = 0.022; HR 6.176 (95% CI 2.447–15.587), P < 0.001, respectively]. The AUROC curve was 0.88, with a cut-off value of ≥ 971 µg/L. COVID-19 patients with hyperferritinemia had a high proportion of organ dysfunction, were more likely to show hyper-inflammation, progressed to hemophagocytic lymphohistiocytosis, and indicated a higher proportion of death.

Emergency department and hospital utilisation and expenditures in the last year of life: retrospective chronic diseases cohort study

ObjectivesWe aimed to examine the influence of chronic diseases in emergency department (ED) and inpatient utilisation and expenditures in the 12 months before death.MethodsRetrospective cohort study of ED and inpatient database. Adults deceased at a hospital in Portugal in 2013 were included. We tested the influence of chronic diseases on the number of ED visits, hospital admissions and expenditures using generalised linear models.ResultsThe study included 484 patients (81.8% ≥65 years, median two chronic diseases). Nearly all (91.3%) attended the ED in the 12 months before death. The median number of admissions was 1, median expenditure was €6159. Adjusting for confounders, chronic pulmonary disease increased ED and inpatient utilisation (1.49; 95% CI: 1.22 to 1.83; 95% CI 1.29, 1.09 to 1.51). Increased ED utilisation was observed for patients with renal disease, dementia and metastatic solid tumour (1.40, 95% CI 1.15 to 1.71; 1.39, 95% CI 1.11 to 1.75; 1.31, 95% CI 1.07 to 1.60). Other malignancies showed increased inpatient utilisation (1.24, 95% CI 1.09 to 1.42). The number of chronic conditions had a considerable effect on expenditures (3: 2.08, 95% CI 1.44 to 2.99; ≥4: 4.02, 95% CI 2.51 to 6.45).ConclusionWe found a high use of hospitals at the end of life, particularly EDs. Our findings suggest that people with cancer, renal disease, chronic pulmonary disease and dementia are relevant when developing cost-effective alternatives to hospital care.

A weighted patient network-based framework for predicting chronic diseases using graph neural networks

Chronic disease prediction is a critical task in healthcare. Existing studies fulfil this requirement by employing machine learning techniques based on patient features, but they suffer from high dimensional data problems and a high level of bias. We propose a framework for predicting chronic disease based on Graph Neural Networks (GNNs) to address these issues. We begin by projecting a patient-disease bipartite graph to create a weighted patient network (WPN) that extracts the latent relationship among patients. We then use GNN-based techniques to build prediction models. These models use features extracted from WPN to create robust patient representations for chronic disease prediction. We compare the output of GNN-based models to machine learning methods by using cardiovascular disease and chronic pulmonary disease. The results show that our framework enhances the accuracy of chronic disease prediction. The model with attention mechanisms achieves an accuracy of 93.49% for cardiovascular disease prediction and 89.15% for chronic pulmonary disease prediction. Furthermore, the visualisation of the last hidden layers of GNN-based models shows the pattern for the two cohorts, demonstrating the discriminative strength of the framework. The proposed framework can help stakeholders improve health management systems for patients at risk of developing chronic diseases and conditions.

438. Phenotypic Differences Between Distinct Immune Biomarker Clusters During the ‘Hyperinflammatory’ Middle-Phase of COVID-19

Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections peak during an inflammatory ‘middle’ phase and lead to severe illness predominately among those with certain comorbid noncommunicable diseases (NCDs). We used network machine learning to identify inflammation biomarker patterns associated with COVID-19 among those with NCDs. Methods SARS-CoV-2 RT-PCR positive subjects who had specimens available within 15-28 days post-symptom onset were selected from the DoD/USU EPICC COVID-19 cohort study. Plasma levels of 15 inflammation protein biomarkers were measured using a broad dynamic range immunoassay on samples collected from individuals with COVID-19 at 8 military hospitals across the United States. A network machine learning algorithm, topological data analysis (TDA), was performed using results from the ‘hyperinflammatory’ middle phase. Backward selection stepwise logistic regression was used to identify analytes associated with each cluster. NCDs with a significant association (0.05 significance level) across clusters using Fisher’s exact test were further evaluated comparing the NCD frequency in each cluster against all other clusters using a Kruskal-Wallis test. A sensitivity analysis excluding mild disease was also performed. Results The analysis population (n=129, 33.3% female, median 41.3 years of age) included 77 ambulatory, 31 inpatient, 16 ICU-level, and 5 fatal cases. TDA identified 5 unique clusters (Figure 1). Stepwise regression with a Bonferroni-corrected cutoff adjusted for severity identified representative analytes for each cluster (Table 1). The frequency of diabetes (p=0.01), obesity (p&lt; 0.001), and chronic pulmonary disease (p&lt; 0.001) differed among clusters. When restricting to hospitalized patients, obesity (8 of 11), chronic pulmonary disease (6 of 11), and diabetes (6 of 11) were more prevalent in cluster C than all other clusters. Cluster differences in comorbid diseases and severity by cluster. 1A: bar plot of diabetes prevalence; 1B: bar plot of chronic lung disease ; 1C: bar plot of obesity prevalence; 1D: prevalence of steroid treatment ; 1E: Topologic data analysis network with clusters labeled; 1F: Bar plot of ordinal levels of severity. Conclusion Machine learning clustering methods are promising analytical tools for identifying inflammation marker patterns associated with baseline risk factors and severe illness due to COVID-19. These approaches may offer new insights for COVID19 prognosis, therapy, and prevention. Disclosures Simon Pollett, MBBS, Astra Zeneca (Other Financial or Material Support, HJF, in support of USU IDCRP, funded under a CRADA to augment the conduct of an unrelated Phase III COVID-19 vaccine trial sponsored by AstraZeneca as part of USG response (unrelated work))

Characteristics and outcomes of acute COVID-19 infection in paediatric and young adult patients with underlying cardiac disease

Objective To describe outcomes of acute coronavirus disease 2019 in paediatric and young adult patients with underlying cardiac disease and evaluate the association between cardiac risk factors and hospitalisation. Study design We conducted a retrospective single-institution review of patients with known cardiac disease and positive severe acute respiratory syndrome coronavirus 2 RT-PCR from 1 March, 2020 to 30 November, 2020. Extracardiac comorbidities and cardiac risk factors were compared between those admitted for coronavirus disease 2019 illness and the rest of the cohort using univariate analysis. Results Forty-two patients with a mean age of 7.7 ± 6.7 years were identified. Six were 18 years of age or more with the oldest being 22 years of age. Seventy-six percent were Hispanic. The most common cardiac diagnoses were repaired cyanotic (n = 7, 16.6%) and palliated single ventricle (n = 7, 16.6%) congenital heart disease. Fourteen patients (33.3%) had underlying syndromes or chromosomal anomalies, nine (21%) had chronic pulmonary disease and eight (19%) were immunosuppressed. Nineteen patients (47.6%) reported no symptoms. Sixteen (38.1%) reported only mild symptoms. Six patients (14.3%) were admitted to the hospital for acute coronavirus disease 2019 illness. Noncardiac comorbidities were associated with an increased risk of hospitalisation (p = 0.02), particularly chronic pulmonary disease (p = 0.01) and baseline supplemental oxygen requirement (p = 0.007). None of the single ventricle patients who tested positive required admission. Conclusions Hospitalisations for coronavirus disease 2019 were rare among children and young adults with underlying cardiac disease. Extracardiac comorbidities like pulmonary disease were associated with increased risk of hospitalisation while cardiac risk factors were not.

Der Blick auf die Komorbiditäten zeigt auch die Versorgungsqualität

<b>Background:</b> In contrast to other chronic rheumatic musculoskeletal diseases such as rheumatoid arthritis, comorbidities in axial spondyloarthritis (axSpA) and their impact on disease outcomes are less well studied. The aim of this study was to investigate the prevalence of comorbidities and their association with disease activity and functional impairment in a large population-based cohort of patients with axSpA. <b>Methods:</b> A random sample of patients with axSpA, stratified by age and sex, was drawn from health insurance data. Patients in the sample received a survey on demographic, socioeconomic, and disease-related parameters. Comorbidities were defined using the Elixhauser coding algorithms excluding rheumatoid arthritis/collagen vascular diseases and including osteoporosis and fibromyalgia, resulting in a set of 32 comorbidities. The prevalence of comorbidities in the axSpA patients and their pharmacological treatment were examined. Multivariable linear regression models were calculated to determine the association of comorbidities with disease activity and functional status. <b>Results:</b> A total of 1776 axSpA patients were included in the ana­lyses (response, 47%; mean age, 56 years; 46% female). The most prevalent comorbidities were hypertension, depression, and chronic pulmonary disorders. The number of comorbidities was significantly associated with both the BASDAI and BASFI: β (95% CI) = 0.17 (0.09–0.24) and 0.24 (0.15–0.32), respectively. When analysed separately, hypertension, depression, and chronic pulmonary disease were comorbidities with a significant and independent association with BASFI, while for BASDAI, such an association was found for depression and chronic pulmonary disease only. <b>Conclusions:</b> Comorbidities are common in axSpA patients and are associated with higher disease activity and higher levels of functional impairment. Higher disease activity and higher levels of functional impairment might be indicators of severe disease resulting in the development of comorbidities.