In conclusion, the ML models have excellent performance in predicting in-ICU mortality in patients with severe pneumonia. Moreover, this study highlights the potential advantages of selecting individual ML models for predicting in-ICU mortality in different subgroups.Conventional severity-of-illness scoring systems have shown suboptimal performance for predicting in-intensive care unit (ICU) mortality in patients with severe pneumonia. This study aimed to develop and validate machine learning (ML) models for mortality prediction in patients with severe pneumonia. This retrospective study evaluated patients admitted to the ICU for severe pneumonia between January 2016 and December 2021. The predictive performance was analyzed by comparing the area under the receiver operating characteristic curve (AU-ROC) of ML models to that of conventional severity-of-illness scoring systems. Three ML models were evaluated: (1) logistic regression with L2 regularization, (2) gradient-boosted decision tree (LightGBM), and (3) multilayer perceptron (MLP). Among the 816 pneumonia patients included, 223 (27.3%) patients died. All ML models significantly outperformed the Simplified Acute Physiology Score II . In the analysis for NRI, the LightGBM and MLP models showed superior reclassification compared with the logistic regression model in predicting in-ICU mortality in all length of stay in the ICU subgroups; all age subgroups; all subgroups with any APACHE II score, PaO Approximately half of the patients with respiratory failure in the ICU have pneumonia, and a quarter of them are re-admitted to the ICU4. Approximately 20–50% of patients with pneumonia who are admitted to the ICU die7. Clinical scoring systems to predict in-ICU mortality in critically ill patients have been introduced, but none of these have been specifically developed for patients admitted to the ICU with severe pneumonia. Therefore, there is an urgent need for severity assessment tools for predicting in-ICU mortality in patients with severe pneumonia8.Pneumonia is among the leading causes of death globally11; laboratory markers such as lactate, thrombocytopenia, troponin, and bicarbonate levels14; and intervention strategies such as the use of vasopressors, MV, and continuous renal replacement therapy have been identified to be closely related to the prognoses of ICU patients16. However, previously validated predictive models including CURB-65, CRB-65, Pneumonia Severity Index (PSI), Quick Sequential Organ Failure Assessment (qSOFA), Simplified Acute Physiology Score 3 (SAPS-3), and Acute Physiology and Chronic Health Evaluation II (APACHE II) had suboptimal predictive performance in patients with severe pneumonia at ICU admission or emergency room department visits19. Therefore, a new prediction model for in-ICU mortality of patients admitted to the ICU for severe pneumonia needs to be developed.There has been recent interest in identifying the factors related to the outcomes of ICU patients. Vital signs20, cardiology22 (early detection of heart failure)23, cancer research25, and intensive care medicine 28. Although the integration of ML into the ICU setting is still in its early stages, several studies have already explored its application in managing critically ill patients26. Some studies have used large population datasets to predict length of stay (LOS)29, ICU readmission30, mortality rates33, and the risks of sepsis34 or acute respiratory distress syndrome35. Particular, there is increasing evidence that ML models are superior to conventional severity-of-illness scoring systems for predicting mortality in ICU patients37 or patients with pneumonia38. In addition, ML models may have good predictive performance for mortality, especially in patients with severe pneumonia41. One study demonstrated that the artificial neural network model had strong predictive performance for 14-day hospital readmission in patients with pneumonia42. Another study demonstrated that ML model has good performance for predicting 30-day mortality in sepsis patients28. However, evidence on whether ML models can accurately predict in-ICU mortality in patients with severe pneumonia who require ICU admission is rare.Machine learning (ML) has demonstrated great potential in various medical fields, including surgeryThus, the present study aimed to elucidate the performance of ML models in predicting mortality in patients with severe pneumonia who require ICU admission, using the information at the time of ICU admission. Our findings will aid in selecting individual ML models for predicting in-ICU mortality in different subgroups.43. This study was approved by the IRB Committee of Seoul National University Seoul Metropolitan Government (SNU-SMG) Boramae Medical Center (IRB Number: 10-2021-110) on November 16, 2022 and was conducted in accordance with the 1975 ethical guidelines of the Declaration of Helsinki. The requirement for informed consent from the study subjects was waived by the IRB Committee of Seoul National University Seoul Metropolitan Government (SNU-SMG) Boramae Medical Center due to the retrospective study design.This study complied with the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) reporting guidelinesThis observational study retrospectively evaluated patients admitted to the ICU for pneumonia at the SNU-SMG Boramae Medical Center between January 2016 and December 2021. The inclusion criteria were: (1) age ≥ 18 years; (2) ICU admission; (3) International Classification of Disease 10th edition code for pneumonia as a major diagnosis or detection of pneumonia on chest computed tomography within 1 week of ICU admission; (4) C-reactive protein (CRP) level ≥ 4 mg/dL; and (5) use of antibiotics for pneumonia. The exclusion criteria were: (1) no oxygen requirement; (2) transfer to the general ward within 3 days; and (3) ICU admission due to more serious medical conditions other than pneumonia.Baseline data including age, sex, body mass index, smoking history, previous underlying disease, and respiratory comorbidities and clinical features were collected. Clinical features included prognostic scores, vital signs, laboratory examination results, and treatment with antibiotics or steroids.3.The primary outcome measure was the prognostic accuracy of ML models compared with that of conventional severity-of-illness scoring systems for predicting in-ICU mortality in patients who required ICU admission for severe pneumonia. The secondary outcome measures were as follows: (1) the prognostic accuracy of the ML models; (2) the prognostic accuracy of the ML models in different subgroups; (3) the clinical factors contributing to the prediction of in-ICU mortality in patients admitted to the ICU for severe pneumonia. ICU admission due to severe pneumonia was determined as the presence of at least one major criterion or three minor criteria of the Infectious Disease Society of America/American Thoracic Society guidelinesThe conventional severity-of-illness scoring systems included the SOFA, SAPS II, and APACHE II scores. Among the scoring systems, the best model that showed the strongest performance was used as the baseline comparator. For the statistical and ML models, we tested three popular models: (1) logistic regression with L2 regularization, (2) gradient-boosted decision tree (LightGBM), and (3) multilayer perceptron (MLP).Variables with more than a 20% missing rate were excluded to generate the available dataset. Approximately 40% of the data were randomly separated with stratification by the outcome and subgrouping variables. The held-out data were used as a test set only for internal validation of the models. The remaining data were used to develop the models as a training set in a tenfold cross-validation scheme.44. Outliers were detected using an isolation forest45 and subsequently replaced with the closest normal value of the training set. All the variables included in the analysis and their missing rates are listed in Supplementary Table Missing values were imputed using multivariate imputation by chained equations46. To rank the variables, the mean absolute SHAP values were calculated as the relative importance of the variables. A LightGBM was used for the SHAP evaluation47. The guiding metric for cross-validation performance was the area under the receiver operating characteristic curve (AU-ROC).The influence of each variable on the predictive ability of the model was evaluated using the SHapley Additive exPlanations (SHAP) methodSupplementary Fig. 48. Subgroup analyses were performed according to (1) the period from hospital admission to ICU admission, (2) age, (3) APACHE II scores, (4) PaO2/FiO2 ratio, (5) history of chronic respiratory disease, (6) history of cerebrovascular accident (CVA) or dementia, (7) MV, and (8) use of vasopressors.The model performance in the different patient subgroups was evaluated using the test set. We prespecified subgroups based on the clinically important phenotypes of pneumonia in the ICUt-test or Mann–Whitney U test. We evaluated the AU-ROC as an overall performance measure and compared the models using the Delong method49. Owing to an imbalance in outcome prevalence, the area under the precision-recall curve (AU-PRC) was also evaluated as another overall performance measure50. Furthermore, the performance of the models was evaluated in detail according to sensitivity, positive predictive value, negative predictive value, diagnostic odds ratio, and net reclassification improvement (NRI)51 at three low false-positive rates (FPR) levels of 10%, 20%, and 30%. The recalibration effects were also evaluated using decision curves, which presented a net benefit against different decision thresholds52. The sensitivity at fixed FPR levels was evaluated in the subgroups using the best-performing model for overall performance. Calibration errors were evaluated before and after calibration using the Brier score and calibration curves. The Brier score was calculated as the mean squared error of the predicted probabilities53. P values were adjusted using the Benjamini–Hochberg method for multiple comparisons, and the significance level was set at P < 0.05.Categorical variables were analyzed using the chi-squared test, while continuous variables were analyzed using the independent In total, 816 patients with pneumonia admitted to the ICU were included in the analysis  or carbon dioxide (HCO3−), lower levels of oxygen saturation (SpO2), and a lower ratio of PaO2/FiO2. For laboratory findings, the levels of urea nitrogen, creatinine, alanine aminotransferase, total bilirubin, and alkaline phosphatase (ALP) and the prothrombin time and international normalized ratio (PT-INR) were higher in the non-survivor group. Meanwhile, the survivor group was more likely to be treated with steroids and vasopressors. The comparison results between the training and test sets for the baseline characteristics and clinical features are presented in Supplementary Tables The clinical characteristics are presented in Table The prognostic performance and accuracy of the prediction models for in-ICU mortality are summarized in Fig. P < 0.001). The MLP model showed better performance than SAPS II, with an AU-ROC of 0.838 [0.791–0.884]), although there was no significant difference compared with the logistic regression (0.820 [0.771–0.869]) and LightGBM (0.827 [0.777–0.876]) models. Furthermore, all tested ML models showed numerically higher AU-PRC values for predicting in-ICU mortality than the SAPS II. The MLP model showed the highest AU-PRC value of 0.670 , which was numerically higher than that of the logistic regression (0.640 [0.522–0.739]) and LightGBM (0.653 [0.552–0.770]) models, while SAPS II dropped around 0.406. The cross-validation performance results are presented in Supplementary Table All the proposed ML models significantly outperformed all scoring systems with respect to AU-ROC (P = 0.023), APACHE II score ≤ 19 subgroup , and MV treatment subgroup . There was no significant difference in the performance of the ML models in other subgroups.Figure 2/FiO2 ratio < 200; all subgroups with or without history of respiratory disease; with or without history of CVA or dementia; treatment with MV, and use of inotropic agents , white blood cell (WBC) count. Among these variables, the partial SHAP dependence plots for the top six variables with the mean absolute SHAP values are illustrated in Supplementary Fig. The selected predictors of in-ICU mortality are shown in Fig. 2/FiO2 ratio < 200; all subgroups with or without history of respiratory disease; with or without history of CVA or dementia; treatment with MV, and use of inotropic agents. Furthermore, the LightGBM model showed higher NRI values than the MLP in most of the above subgroups, except for the subgroups aged 65–74 years, with an APACHE II score of ≤ 19, with history of CVA or dementia. Therefore, ML models have the potential to improve in-ICU mortality prediction in patients with severe pneumonia admitted to the ICU. Moreover, this study shows the potential advantages of individual ML models for predicting in-ICU mortality in different subgroups of patients with severe pneumonia admitted to the ICU.Our study evaluated the prognostic accuracy of ML models compared with that of conventional severity-of-illness scoring systems for predicting in-ICU mortality in patients with severe pneumonia. All ML models showed excellent performance in predicting in-ICU mortality and were superior to SAPS II. In addition, when the ML models were applied in the different subgroups, the LightGBM and MLP models showed superior reclassification compared with the logistic regression model in predicting in-ICU mortality in all LOS in the ICU subgroups; all age subgroups; all subgroups with any APACHE II score, PaO54. The Clinical Pulmonary Infection Score is a well-validated prediction model for the development of ventilator-associated pneumonia. However, its predictive performance for mortality is inferior to that of the APACHE II score55. Given the limitations of clinical severity-of-illness scoring systems, the usefulness of ML models has recently been studied to predict mortality in patients with pneumonia. The results show that various ML models outperform CURB-6556 and PSI57 for predicting mortality in patients with severe community- or hospital-acquired pneumonia. However, few previous studies have used ML models to predict the prognosis of patients with severe pneumonia admitted to the ICU. One small study reported that an ML approach had better performance than APACHE II and PSI for predicting mortality in critically ill influenza patients58. In our study, SAPS II showed a numerically higher AU-ROC than APACHE II and SOFA for predicting in-ICU mortality on the first day of ICU admission. Importantly, the ML models outperformed SAPS II, which suggested that the ML model can provide more accurate information for optimal decision-making based on the estimated probability of mortality.CURB-65 and PSI are the most commonly used clinical severity-of-illness scoring systems for patients with community-acquired pneumonia. However, CURB-65 and PSI have a limitation in that CURB-65 has low sensitivity, while PSI has low specificity for mortality61. LightGBM is a gradient-boosted decision tree algorithm that has gained popularity for its high performance and efficiency62. Several studies have demonstrated the favorable predictive value of LightGBM in the field of medicine65. Another study found that LightGBM had the best predictive ability among other ML models including XGBoost, logistic regression, and naïve Bayes66. MLP-based models are effective in capturing nonlinear relationships, making them ideal candidates for complex and multifactorial disease classification including in stroke68 when compared to conventional statistical modeling. Moreover, both LightGBM and MLP have been used in many clinical studies71, demonstrating their extensive applicability and promising predictive performance. In the present study, the ML models outperformed conventional severity-of-illness systems. Scoring systems use a limited number of variables, which might restrict their predictive power in individual patients72. ML models are capable of utilizing high-dimensional data, and this could account for their superior performance to conventional scoring systems.We selected logistic regression, LightGBM, and MLP as the ML predictive models for mortality in patients with severe pneumonia based on several considerations. Logistic regression is a well-established and widely used ML model for binary classification tasks. It offers a simple and interpretable approach to modeling the relationship between predictor variables and the outcome as well as performance of ML2, WBC count, pH, initial pulse rate, lymphocyte, HCO3−, and PaCO2. This could be the reason for the lower performance of the logistic regression model, a generalized linear model, in predicting the in-ICU mortality of patients with severe pneumonia. Although cross-validation results were not specified to the FPR level, the MLP model had the largest difference in the AU-ROC value between internal validation and cross-validation. This indicates that compared with the other models, the MLP model might be more relatively overfitted to the training set.In this study, the LightGBM model showed the highest predictive performance with respect to NRI at 10% FPR. This result supports that decision-tree-based models could be more beneficial than logistic regression models for predicting in-ICU mortality in pneumonia patients at a high cut-off point of 90% specificity. ML models have a strength in capturing the nonlinear relations between the features and the predicted outcomes. We found notable non-linear relationships between in-ICU mortality and several selected variables, including PaO2/FiO2 ratio, urine output, and initial SBP. This might be the reason for the lack of statistical significance in these differences. The SHAP model was used to determine the important influencing factors of in-ICU mortality in the ML models, and the results were similar to previous studies: PaO2/FiO274, CRP levels75, urine output76, initial SBP73, PaO277, and leukopenia79 or leukocytosis81.Contrary to the performance in different subgroups at the 10% FPR level, our results in the entire test set demonstrated no significant difference in AU-ROC between the ML models and the logistic regression model. In partial dependence plots of the variables that contributed the most to model predictions, linear relationships with in-ICU mortality were observed, especially in those of the PaO82. In community-acquired pneumonia, elevated ALP levels were not associated with mortality83. However, in critically ill patients with septic acute kidney injury (AKI), elevated ALP levels are associated with mortality84. In our study, septic shock was found in > 40% of the critically ill patients with pneumonia, and AKI was a common condition. It appears that ALP is related to the severity of impaired renal function through systemic inflammation caused by pneumonia rather than the severity of pneumonia itself. With respect to prolonged PT-INR, substantial coagulation abnormalities are commonly observed in patients with sepsis or pneumonia85. The excessive production of thrombogenic tissue factors in sepsis pneumonia compared with low levels of tissue factors under normal conditions86 leads to the development of systemic coagulopathy during the period of pneumonia87.In addition, using the SHAP model, we found that higher ALP levels and prolonged PT-INR were associated with a higher risk of in-ICU mortality. ALP can be elevated as an acute-phase reaction in acute infectionsOur study had some limitations. First, as the models were developed using data retrospectively collected in a single center and were not externally validated, the results had limited generalizability. Although our study provides valuable insights into the performance of the ML models, further studies are needed to assess the generalizability and real-time applicability of these ML models in predicting in-ICU mortality in patients with severe pneumonia. Furthermore, these studies should include robust external validation using independent datasets and evaluation of the model performance in prospective clinical practice. Second, owing to the small sample size and the inclusion of patients admitted within a long study period of over 6 years, there is a possibility of heterogeneity with respect to patient characteristics, treatment measures, and potential biases. Thus, it might be challenging to clearly establish the usefulness of ML, which is a non-parametric algorithm. Furthermore, six subgrouping variables were adopted. A large number of stratification variables with a small sample size could lead to optimistic results in the internal validation of the models.Compared to conventional severity-of-illness scoring systems, the ML models of LightGBM, MLP, and logistic regression have better predictive performance for in-ICU mortality in patients with severe pneumonia. Moreover, this study shows the potential advantages of selecting individual ML models for predicting in-ICU mortality in different subgroups of patients with severe pneumonia.Supplementary Information.


Lipid remodeling regulators are now being investigated as potential therapeutic targets for cancer therapy as a result of their involvement, which includes promoting cancer cells’ adaptation to the restricted environment. Lysophosphatidylcholine acyltransferases  are enzymes that regulate the remodeling of bio-membranes. The functions of these enzymes in cancer are largely unknown. In the current study, we found that genes belonging to the LPCAT family participated in tumor advancement and were strongly linked to dismal prognosis in many different malignancies. We constructed the LPCATs scores model and explored this model in pan-cancer. Malignant pathways in pan-cancer were positively related to LPCATs scores, and all pathways had strong links to the tumor microenvironment (TME). Multiple immune-associated features of the TME in pan-cancer were likewise associated with higher LPCATs scores. In addition, the LPCATs score functioned as a prognostic marker for immune checkpoint inhibitor (ICI) therapies in patients with cancer. LPCAT4 enhanced cell growth and cholesterol biosynthesis by up-regulating ACSL3 in hepatocellular carcinoma (HCC). WNT/β-catenin/c-JUN signaling pathway mediated LPCAT4’s regulation on ACSL3. These findings demonstrated that genes in the LPCAT family might be used as cancer immunotherapy and prognosis-related biomarkers. Specifically, LPCAT4 could be a treatment target of HCC. Lysophosphatidylcholine acyltransferases (LPCATs) are a family of enzymes consisting of four isoforms LPCAT1-4) in humans that catalyze the conversion of lysophosphatidylcholine (LPC) to phosphatidylcholine (PC) -4 in hum. The abiIn this research, we systematically analyzed the LPCAT family of genes across 32 different tumor types for alterations , expression, clinical characteristics, and prognostic significance. We also specifically explored LPCAT4’s biological function in LIHC and uncovered the underlying mechanism.We analyzed LPCATs family genes (LPCAT1-4) expression levels in pan-cancer. It was found that LPCAT1-4 expression levels were elevated in most kinds of cancer types .We studied the genetic alterations that occurred in the LPCAT1-4 genes and how those alterations were linked to mRNA levels. We discussed the top10 mutant genes, variant classification, single-nucleotide variant (SNV) class, variant type, variants per sample, and variant classification . For theAdditional research on LPCAT family gene copy number variants (CNV) was conducted. The proportion of different types of CNV  of each gene in pan-cancer was shown . The CNVTo approximate the LPCATs score in the TCGA cohort, we used ssGSEA. HNSC had the greatest and LUSC had the least LPCATs score . Also, tWe further analyzed the association between LPCATs score and survival rate. The findings of the uniCox analysis are as follows: (1) In terms of OS, LPCATs score served as a risk factor in LIHC, LGG, KIRC, HNSC, and CESC, and a protective factor in ESCA and STAD; (2) For PFS, LPCATs score was a risk factor in LIHC, CESC, KIRC, HNSC, LGG, and PAAD; (3) In terms of DSS, LPCATs score functioned as a risk factor in LIHC, LGG, KIRC, and HNSC, but a protective factor in ESCA and STAD; (4) In terms of DFI, LPCATs score functioned as a risk factor in THCA, whereas a protective factor in ACC .We carried out a GSVA premised on HALLMARK pathways to determine the prospective pathways that were influenced by the LPCATs score. We evaluated the link between LPCATs Score and immune infiltrating score in pan-cancer. It was found that a high LPCATs Score predicated a high level of immune infiltrating in SARC, GBM, and UVM, which ranked top three in pan-cancer. Additionally, the LPCATs score had a strong link to the majority of immune cells, suggesting that the TME was immune-activated .We examined the significance of the LPCATs Score as a biomarker by making a comparison of it with standard biological markers premised on their ability to accurately predict response outcomes and OS in immune checkpoint blockade (ICB) subcohorts. We determined that the LPCAT Score alone had an area under the receiver operating characteristic curve (AUC) of more than 0.5 in 17 of the 20 ICB subcohorts . The LPCWe searched for powerful anticancer medications by searching databases called GDSC (Genomics of Drug Sensitivity in Cancer) and CTRP . These databases included information on pharmaceuticals whose unique mechanism of action involves the suppression of LPCATs family genes. In pan-cancer, the link between gene expression and the sensitivity of GDSC pharmaceuticals (top 30) was represented in The above findings revealed that LPCATs score was a risk factor in the OS of LIHC, we thus further explored the LPCATs family members’ function in LIHC. Our PCR assay revealed that LPCAT4 mRNA expression level was significantly elevated in LIHC samples . In paraA total of 2731 genes were differentially expressed between the groups with high and low expression levels of LPCAT4  [We assessed the levels of cholesterol in LIHC cells and observed a reduction in cholesterol synthesis when LPCAT4 was down-regulated . A mass rid.org)  showed tGSEA was applied between the high- and low-LPCAT4 expression groups, and WNT signaling pathway was found to be significantly enriched in the high LPCAT4 expression group . The wesIn addition, down-regulation of LPCAT4 could decrease ACSL3 expression level, while overexpression of c-JUN dismissed this effect . SimilarTaken together, these data implicated that the Wnt-β-catenin-c-JUN pathway may be involved in mediating LPCAT4 regulation on ACSL3 expression.The lysophosphatidylcholine acyltransferases (LPCATs) family contains four members, namely LPCAT1-4 . LPCATs With the aid of the TCGA and GTEx datasets, we evaluated the differential expression and prognostic significance of genes belonging to the LPCATs family across 32 distinct kinds of tumors. We discovered that genes belonging to the LPCAT family were remarkably engaged in the growth of tumors and acted as risk factors for the majority of cancers. To get a deeper understanding of the possible carcinogenic mechanisms of genes belonging to the LPCATs family, we provided a detailed description of many gene alterations  in LPCATs family genes. The results showed that these genetic alterations together contributed to the abnormal expression of LPCATs family genes.In addition, we constructed a LPCATs score using the ssGSEA approach in pan-cancer. The levels of LPCATs were shown to be elevated in tumor tissue compared to surrounding normal tissue in the cases of BLCA, ESCA, HNSC, KICH, KIRC, KIRP, LIHC, STAD, and THCA, whereas they were found to be lowered in the cases of BRCA, LUAD, LUSC, and PRAD. Additionally, a high score on the LPCAT might indicate a grim prognosis for patients with many different malignancies. After conducting extensive research, we concluded that the LPCATs scores had a favorable correlation with several cancerous pathways in pan-cancer, including PI3K/AKT, epithelial-mesenchymal transition, apoptosis, and the cell cycle.The structure and function of the tumor microenvironment (TME) are intricately linked to the advancement and metastasis of tumors . In partIn addition, our analysis indicated that LPCATs score was a good indicator for patients who received ICB treatment. High tumor mutation burden (TMB-H) has been proposed as a predictive biomarker for response to immune checkpoint blockade, largely due to the potential for tumor mutations to generate immunogenic neoantigens . T.ClonaTaken together, the construction of LPCATs scores has important predictive significance for cancer research and prediagnosis of cancer. High LPCATs scores indicate a grim prognosis for patients with many different malignancies and have a favorable correlation with several cancerous pathways. LPCATs scores can also predicate sensitivity to pharmaceuticals and ICB treatment. Thus, with the use of LPCATs scores, we can evaluate the risk of cancer patients and treatment efficiency.To further illustrate the biological function of LPCATs family genes, we focused on LPCAT4’s function in LIHC. Abnormal expression of LPCAT4 was involved in the progression of colorectal cancer and prostate cancer. LPCAT4 participated in transforming growth factor-β mediated alteration in phospholipid metabolism in HepG2 cells . HoweverIn summary, our research showed that genes belonging to the LPCAT family are risk factors for developing tumors and perform key roles in the onset and advancement of cancers. The higher LPCATs scores were shown to have a strong association with a variety of immune-related characteristics of TIME in pan-cancer. Moreover, the LPCATs score was a probable biological marker for treatment effectiveness in patients with tumors undergoing ICI therapy. In addition, we identified LPCAT4 as a new treatment target for LIHC.http://vip.sangerbox.com/) is a web-based platform for analyzing gene expression in TCGA. GSCALite (http://bioinfo.life.hust.edu.cn/web/GSCALite/) is a web-based program that integrated immunogenomic and genomic data of 33 different tumor types from TCGA, as well as pharmacological responsiveness from the Genomics of Drug Sensitivity in Cancer (GDSC). In addition, GSCALite was used for analyzing the correlation between LPCATs score and overall survival (OS) rate, pathway activity, and immune infiltration. TIDE  is a web-based database used for analyzing OS rates of patients who received immune checkpoint blockade.The SangerBox  . The mycoplasma-free status of all cell lines was verified, after which they were cultured at 37° C in a humidified air environment containing 5% CO2.LIHC cells were transiently transfected at a seeding density of 2000 cells/well in 96-well flat-bottomed plates with 100 μl of growth medium. After adding 10 μl of CCK-8 solution to each well, the plates were then placed in an incubator at 37° C for 2-, 24, 48, and 72-hours, respectively. An automated microplate reader  was employed to determine each well’s absorbance at 450 nm, and the results were recorded.To conduct the clonogenic assay, 500 cells were plated in a 6-well plate and left to grow for 14 days. In addition, 0.1% of crystal violet was used to stain the cell colonies after they had been fixed with paraformaldehyde at a concentration of 4 %. Thereafter, following the imaging process, the stained colonies were counted manually.The design and synthesis of siRNAs for LPCAT4 and c-JUN were done by RiboBio Inc. . LPCAT4 and c-JUN plasmids were obtained from Biosense Technologies . PI3K inhibitor LY294002 was procured from Sigma. The transfection was carried out following the specifications provided by the manufacturer of the Reagent . After 48–72 hours, the cells were harvested for use in subsequent studies.We utilized the TRIzol reagent kit  to isolate total RNA from the cell or tissue cultures before reverse-transcribing it into complementary DNA (cDNA) with the aid of reverse transcription reagents  following the procedures outlined by the manufacturer. For the reverse transcription polymerase chain reaction (RT-PCR) procedure, the Bio-Rad T100 equipment was used. Quantitative Real-Time PCR (qRT-PCR) was done with the SYBR Premix ExTaq . The complementary DNA was employed as a template in an amplification process using specified primers .Cholesterol was extracted from cells and fragments using a Cholesterol Assay Kit (Abcam) according to the manufacturer’s instruction, and the cholesterol content was normalized by the corresponding protein content.A PCR-amplified 1300 base pair (bp) segment comprising the three c-JUN binding domains was introduced into a psiCHECK-2 luciferase reporter vector to construct an ACSL3 promoter vector. As a further step, vectors with mutated c-JUN binding domains were developed. Lipofectamine 2,000 Reagent (Invitrogen) was used for the purpose ofco-transfecting a psiCHECK-2 derived vector with a c-JUN expressing vector into LIHC cells.ChIP assay was done to determine whether or not c-JUN combined with the ACSL3 promoter using a CHIP assay kit as per the directions provided by the manufacturer . Initially, 1% formaldehyde was used to fix LIHC cells with or without ectopic c-JUN expression so that proteins could be covalently cross-linked to DNA. Next, chromatin was extracted from the LIHC cells. Sonication was done to reduce the length of the cross-linked DNA to between 200 and 1,000 bp, and it was subsequently subjected to an immunoselection procedure, which involved the use of an antibody against c-JUN . Premised on the selected primers, PCR was subsequently utilized to determine the enrichment of DNA fragments in the potential c-JUN-binding domains located in the ACSL3 promoter .SDS-PAGE (Sodium Dodecyl Sulphate - PolyAcrylamide Gel Electrophoresis) analysis was conducted on cell lysates and tumor tissues, each containing an equivalent amount of protein before being transferred to a Polyvinylidene fluoride (PVDF) membrane . The concentration of antibodies used in the study was: LPCAT4, ACSL3, β-catenin , c-jun  and GAPDH .All the data generated for this study are available on request to the corresponding author.Supplementary Figure 1Supplementary Table 1Supplementary Table 2Supplementary Table 3


Non-curative resection (non-CR) after endoscopic submucosal dissection (ESD) requires additional surgery due to the possibility of lymph node metastasis (LNM). Therefore, it is important to accurately predict the risk of non-CR to avoid unnecessary preoperative procedures. Thus, we aimed to develop and verify a nomogram to predict the risk of non-CR prior to ESD.Patients who underwent ESD for early gastric cancer (EGC) were divided into CR and non-CR groups based on the present ESD criteria. The pre-procedural factors, such as endoscopic features, radiologic findings, and pathology of the lesion, were compared between the groups to identify the risk factors associated with non-CR. A nomogram was developed using multivariate analysis, and its predictive value was assessed using an external validation group.Among 824 patients, 682 were curative (82.7%) and 142 were non-curative (17.3%). By comparing two groups, endoscopic features including redness, whitish mucosal change, fold convergence, and large lesion size; histologic features such as moderately or poorly differentiated or signet ring cell carcinoma; and abnormal CT findings including non-specific lymph node enlargement and fold thickening were identified as significant predictors of non-CR. The nomogram was developed based on these predictors and showed good predictive performance in the external validation, with an area under the curve of 0.87.We developed a nomogram to predict the risk of non-CR prior to ESD. These predictive factors in addition to the existing ESD criteria can help provide the best treatment option for patients with EGC.The online version contains supplementary material available at 10.1007/s00464-023-09949-0. For patients with early gastric cancer (EGC), endoscopic submucosal dissection (ESD) is an attractive treatment option to curatively remove tumors in a minimally invasive manner. There are well-established absolute and expanded indications for ESD set by the Japanese gastric cancer treatment guidelines to effectively treat cancer with minimal risk of lymph node metastasis (LNM) .Physicians undertake various pre-procedural evaluations, such as endoscopy, abdominal computed tomography (CT), and endoscopic ultrasonography, to select only suitable cases for successful ESD. Despite these efforts, it is reported that approximately 16.1–21.4% of patients who receive ESD do not meet the indication of curative resection (CR) later and are considered non-curatively resected , 6, patiSeveral previous studies have evaluated the predictive factors associated with non-CR and increased risk of LNM based on post-procedural histologic and surgical data , 7. Kim Thus, we aimed to investigate the pre-procedural features for predicting the risk of non-CR before ESD. We focused on endoscopic, radiologic, and pathologic features of the tumor, so as to depict the real-world clinical situation, by including the most frequently used pre-procedural diagnostic modalities.For our study, we retrospectively reviewed patients with EGC from two different hospitals: a group of patients for the development of the nomogram (“development set)” and another group for the external validation of our nomogram .To develop the nomogram, we reviewed the data of 824 patients from Gangnam Severance Hospital who underwent ESD for EGC between 2010 and 2019. Patients aged > 19 years who underwent ESD for primary gastric cancer were included in the study. The patients were subsequently categorized into a CR group of 682 patients and a non-CR group of 142 patients based on their post-procedural histologic report. The criteria for the CR group were decided based on the Japanese gastric cancer treatment guidelines [The patients’ demographic information, including age and sex, and clinicopathologic information about tumor size, tumor location, gross appearance, depth of invasion, histology of the lesion, existence of LNM after additive surgery, endoscopic features of the lesions, and abnormal CT findings was collected.For the validation set, we collected data from 129 patients at Soonchunhyang Hospital who met our study’s inclusion criteria. When classified according to our definition of CR and non-CR, 48 and 81 patients were categorized into the non-CR and CR groups, respectively. An identical set of data regarding patients’ demographic and clinicopathologic information was collected for this study.Our study was approved by the Institutional Review Board of Yonsei University , and all patient information was anonymized and de-identified before analysis.For the analysis of pre-procedural features, we mainly focused on endoscopic, radiologic, and pathologic data.For endoscopic analysis, all the patients’ pre-procedural endoscopic images were reviewed by two expert endoscopists from the Gangnam Severance Hospital Fig. . Images For radiologic analysis, patients’ abdominopelvic CT images taken as part of the pre-procedural evaluation were used. They were carefully reviewed by an experienced radiologist from Gangnam Severance Hospital. We focused on abnormal CT findings of non-specific lymph node (LN) enlargement, mucosal thickening, or enhancement . Since LNs larger than 1 cm are generally considered markedly enlarged LNs with a high potential for malignancies, we only included cases with benign features, like LNs that were < 1 cm in size and had an oval or elongated shape , 12.For pathological analysis, we classified the histology of the gastric tumors according to the World Health Organization classification system . For thep value < 0.05 was considered statistically significant. SPSS  and R package version 4.0.5 were used for analysis.The continuous variables are described as mean values with standard deviations, and categorical variables are presented as numbers with percentages. We identified pre-procedural factors associated with non-CR primarily using univariate analysis and analyzed them using the chi-square test or Fisher’s exact test for categorical variables and Student’s t test for continuous variables. Variables that were significant in the univariate analysis were subsequently included in the multivariate analysis. Multivariate analysis was performed using multiple logistic regression, and nomograms were constructed based on the prediction model built using multivariate analysis. Later, the predictive abilities of our models were evaluated using the receiver operating characteristic (ROC) curve and calibration plot. For all our studies, a The comprehensive baseline characteristics of the development and validation sets are presented in Table Of the 824 patients who underwent ESD, 142 were later classified into the non-CR group (17.2%). The reasons for non-CR were sub-analyzed: 35 (4.2%), 53 (6.4%), 40 (4.4%), and 84 (10.1%) patients had vertical margin involvement, LVI, submucosal invasion in undifferentiated-type EGC, and submucosal invasion > 500 μm, respectively. Among patients with non-CR who received additive surgery later, 11 (1.3%) were confirmed to have LNM by surgical pathology.The endoscopic and radiologic features of these patients were reviewed. Abnormal CT findings were noted in 193 (23.4%) patients. Mucosal thickening was found in 79 patients (9.6%); LN enlargement, in 99 patients (12.0%); and both mucosal thickening and LN enlargement, in 15 patients (0.2%).The clinicopathological features of the validation set were analyzed and are listed in Table Univariate and multivariate analyses of pre-procedural features were performed on data from the development set to identify the variables associated with non-CR Table . In univSubsequently, we sub-analyzed the data based on lesion histology. Patients were grouped based on differentiation of gastric tumors as differentiated and undifferentiated Univariate and multivariate analyses were performed for both groups. The results of the comprehensive analyses for both groups are listed in Tables In multivariate analysis of the patients with differentiated-type histology Table , endoscoThere were fewer variables associated with non-CR in the multivariate analysis of undifferentiated-type histology Table . Only enBased on the predictive variables obtained from the multivariate analysis, we developed a nomogram to predict the risk of non-CR using a logistic regression model Fig. . Points An ROC curve was constructed to assess the performance of our nomogram. The area under the curve (AUC) was 0.8036 (95% CI 0.7583–0.8489), establishing the good predictive ability of our model . This indicated the good predictive ability of our nomogram for non-CR , we were not able to achieve satisfactory AUC results for our external validation for the undifferentiated-type histology group. Further evaluations using a larger study size will provide more consistent results in future. Second, because not all of the patients’ primary diagnoses were confirmed in a single center, we analyzed the histology of the final resected specimens instead of the initial forceps biopsy data. Since the histologic discrepancy between forceps biopsy and endoscopic resection specimen is not reported to be high, (approximately 4.4–10.7%) –24, we bIn conclusion, we developed a nomogram and externally validated it to predict non-CR in patients with EGC using various pre-procedural data. We believe that our model can help physicians and patients to choose the best treatment options and avoid unnecessary procedures before surgery.Supplementary file1 (PDF 220 KB)Below is the link to the electronic supplementary material.


Veterans with a history of traumatic brain injury (TBI) and/or posttraumatic stress disorder (PTSD) may be at increased risk of suicide attempts and other forms of intentional self-harm as compared to veterans without TBI or PTSD.Using administrative data from the US Veterans Health Administration (VHA), we studied associations between TBI and PTSD diagnoses, and subsequent diagnoses of intentional self-harm among US veterans who used VHA health care between 2008 and 2017.All veterans with encounters or hospitalizations for intentional self-harm were assigned “index dates” corresponding to the date of the first related visit; among those without intentional self-harm, we randomly selected a date from among the veteran’s health care encounters to match the distribution of case index dates over the 10-year period. We then examined the prevalence of TBI and PTSD diagnoses within the 5-year period prior to veterans’ index dates. TBI, PTSD, and intentional self-harm were identified using International Classification of Diseases diagnosis and external cause of injury codes from inpatient and outpatient VHA encounters. We stratified analyses by veterans’ average yearly VHA utilization in the 5-year period before their index date . Variations in prevalence and odds of intentional self-harm diagnoses were compared by veterans’ prior TBI and PTSD diagnosis status  for each VHA utilization stratum. Multivariable models adjusted for age, sex, race, ethnicity, marital status, Department of Veterans Affairs service-connection status, and Charlson Comorbidity Index scores.About 6.7 million veterans with at least two VHA visits in the 5-year period before their index dates were included in the analyses; 86,644 had at least one intentional self-harm diagnosis during the study period. During the periods prior to veterans’ index dates, 93,866 were diagnosed with TBI only; 892,420 with PTSD only; and 102,549 with comorbid TBI/PTSD. Across all three VHA utilization strata, the prevalence of intentional self-harm diagnoses was higher among veterans diagnosed with TBI, PTSD, or TBI/PTSD than among veterans with neither diagnosis. The observed difference was most pronounced among veterans in the high VHA utilization stratum. The prevalence of intentional self-harm was six times higher among those with comorbid TBI/PTSD  than among veterans with neither TBI nor PTSD . Adjusted odds ratios suggested that, after accounting for potential confounders, veterans with TBI, PTSD, or comorbid TBI/PTSD had higher odds of self-harm compared to veterans without these diagnoses. Among veterans with high VHA utilization, those with comorbid TBI/PTSD were 4.26 (95% CI 4.15-4.38) times more likely to receive diagnoses for intentional self-harm than veterans with neither diagnosis. This pattern was similar for veterans with low and medium VHA utilization.Veterans with TBI and/or PTSD diagnoses, compared to those with neither diagnosis, were substantially more likely to be subsequently diagnosed with intentional self-harm between 2008 and 2017. These associations were most pronounced among veterans who used VHA health care most frequently. These findings suggest a need for suicide prevention efforts targeted at veterans with these diagnoses. For decades, suicide has been one of the leading causes of death ,2 and a Suicide prevention is a top clinical priority for the VA and its nationally integrated health care system, overseen by the Veterans Health Administration (VHA) . CentralOther important risk factors for suicidal behavior include sociodemographic, psychological, and physical health characteristics -17. In pPrevious studies examining associations between TBI and PTSD diagnoses and suicidal behavior have mostly been conducted with relatively small or select samples or with veterans from a single war era ,18,19. TIn this study, we analyzed data from approximately 6.7 million veterans who received VHA health care between 2008 and 2017 to examine population-level variations in the prevalence of intentional self-harm among those with or without prior TBI and PTSD diagnosis. We focused on suicide attempts as well as other intentional self-harm events because previous research ,12 has sThis study was approved by the VA Bedford Healthcare System Institutional Review Board. We identified 6,693,275 veterans who visited a VHA facility at least twice during the 10-year period between January 1, 2008, and December 31, 2017. We extracted veterans’ demographic and health care data from the VHA Corporate Data Warehouse (CDW) database. All preprocessing and analyses were conducted using the VA Informatics and Computing Infrastructure (VINCI) accessed from Bedford, Virginia.and injury or poisoning assigned for the same encounter, or presence of codes identifying injury or poisoning and diagnoses for a mental disorder assigned for the same encounter. Patrick et al [International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) manual. For this study, we derived a set of International Statistical Classification of Diseases, Tenth Revision (ICD-10) codes that corresponded with the ICD-9 codes used by Patrick et al [This study defined intentional self-harm as suicide attempts or other self-harm using diagnosis codes specified for those behaviors supplemented with additional evidence that an injury incurred by a veteran was due to self-harm. Suicide attempt or intentional self-harm was defined as a health care encounter  that met any of three conditions: presence of a code that explicitly indicated suicide attempt or intentional self-harm, presence of codes identifying suicidal ideation ck et al  used thick et al . The algFor veterans with one or more intentional self-harm events, we chose the first coded incident of intentional self-harm as the “index date.” For veterans with no intentional self-harm events, we randomly selected a date from among the veterans’ encounters to match the distribution of dates among those with self-harm events in the 10-year period. Each veteran was classified into one of four mutually exclusive groups based on the presence or absence of TBI and PTSD diagnoses during the 5 years prior to the veteran’s index date:No TBI or PTSD diagnosesTBI diagnosis onlyPTSD diagnosis onlyComorbid TBI/PTSD diagnosesThe codes used to identify both TBI and PTSD are included in Table S2 in low VHA utilization group included those at the 50th percentile and below (≤6.6 visits per year), the medium utilization group included those above the 50th but below and at the 75th percentiles (>6.6 and ≤15.6 visits), and the high utilization group included those above the 75th percentile (>15.6 visits). Analyses were stratified by VHA utilization group.To account for “informed presence” bias based on the varying opportunity to receive the diagnosis codes of interest arising from differential health care utilization , we clasVeterans’ age, sex, race, ethnicity, marital status, and VA service-connection status—a measure of military service–related disability assigned by the Veterans Benefits Administration—were extracted from the CDW. Age was categorized as <30, 30-44, 45-69, or ≥70 years. Sex was categorized as a binary variable, while race was categorized as White, Black or African American, Asian, Native Hawaiian or other Pacific Islander, American Indian or Alaska Native, or other and ethnicity as Hispanic versus non-Hispanic. Marital status was categorized as single, married, divorced, or widowed, and VA service-connection as no service-connection, service-connected <50%, or service-connected ≥50%. We also computed the Charlson Comorbidity Index (CCI) scores using diagnoses from inpatient and outpatient visits, and categorized veterans as having low (<5), medium (5-15), or high (>15) Charlson Comorbidity scores .We calculated the prevalence of veterans’ intentional self-harm (yes/no) according to TBI and PTSD status for each VHA health care utilization group. We modeled the likelihood of intentional self-harm among veterans by TBI and PTSD status using logistic regression to estimate odds ratios (ORs) and their corresponding 95% CIs. Adjusted ORs (AORs) were calculated using two multivariable logistic regression models: minimally adjusted models that controlled for age, sex, and race, and fully adjusted models that controlled for age, sex, race, ethnicity, marital status, VA service-connection status, and CCI scores.low ; medium , and high . Overall, we identified 86,644 (1.29%) veterans with one or more encounters involving intentional self-harm. TBI and PTSD were relatively prevalent among VHA users, with 1.4%  having been diagnosed with TBI only, 13.33%  with PTSD only, and 1.53%  with comorbid TBI/PTSD.Among the 6,693,275 veterans who used VHA health care between 2008 and 2017 at least twice, 93.1%  were coded as male and 6.9%  as female; 76.74%  were coded as being White, 15.85%  Black or African American, 0.98%  Asian, 0.84%  American Indian or Alaska Native, 0.96%  Native Hawaiian or other Pacific Islander, and 4.64%  as other . More thlow, medium, and high VHA utilization groups.The prevalence of intentional self-harm by TBI and PTSD diagnosis status is presented in high VHA utilization group . In this stratum, the prevalence of intentional self-harm was highest for veterans in the comorbid TBI/PTSD diagnosis group  and lowest among those with no TBI or PTSD diagnosis . Those in the TBI only and PTSD only diagnosis groups also had elevated prevalence levels, at 5.21%  and 5.91% , respectively. This pattern of elevated prevalence was similar for veterans in the medium and low VHA utilization groups; however, the disparity between diagnosis groups in the medium and low VHA utilization groups was considerably lower than in the high VHA utilization group. Across all three VHA utilization groups, the prevalence of self-harm diagnosis among veterans with PTSD only was consistently higher by about 0.7% than those in the TBI only diagnosis group.Between 2008 and 2017, the overall prevalence of intentional self-harm was highest for veterans in the high VHA utilization group, and compared to veterans with no TBI or PTSD diagnoses, those with comorbid TBI/PTSD had the highest odds of intentional self-harm , followed by those in the PTSD only group  and then those in the TBI only group . These patterns were similar in the fully adjusted model, where the AORs for comorbid TBI/PTSD group were the highest , followed by the PTSD only group  and then by the TBI only group .Among veterans in the medium VHA utilization group, the odds of intentional self-harm were highest among those with comorbid TBI/PTSD , followed by the PTSD only group  and then the TBI only group . This pattern changed slightly when potential confounders were added to the models: AORs using the fully adjusted model were highest among those with PTSD only  and lowest among those with TBI only . The AOR for the comorbid TBI/PTSD group decreased from 7.42 (95% CI 7.01-7.85) to 3.17 (95% CI 2.99-3.36) after accounting for all available confounding variables.Among veterans in the low VHA utilization group, the odds of intentional self-harm were also highest among those with comorbid TBI/PTSD , followed by the PTSD only group  and then the TBI only group . Similar to the medium VHA utilization group, the pattern changed when potential confounders were added to the fully adjusted models: AORs for the PTSD only group  were highest, closely followed by the comorbid TBI/PTSD group  and then the TBI only group .Among veterans in the Our analysis of approximately 6.7 million VHA-using veterans found that those with a documented diagnosis of TBI or PTSD were significantly more likely to have subsequent VHA documentation of a suicide attempt or other form of intentional self-harm relative to those with neither diagnosis between 2008 and 2017. For all three VHA utilization groups, the odds of intentional self-harm for veterans in the comorbid TBI/PTSD group were more than three times as high as those with neither condition, even after controlling for multiple potential confounders. For those in the PTSD only group, the adjusted odds of intentional self-harm were approximately three times that of those with neither condition. For the TBI only group, the adjusted odds of intentional self-harm were nearly double relative to those with neither condition. For all three VHA utilization groups, the prevalence of intentional self-harm for veterans in the comorbid TBI/PTSD group was higher than in the TBI only and PTSD only groups.medium and low VHA utilization groups, adjusted odds were highest for the PTSD only group closely followed by the comorbid TBI/PTSD group. However, for the high VHA utilization group, adjusted odds for the comorbid TBI/PTSD group were consistently higher than those of the PTSD only group. Previous studies have suggested that symptom severity may play an important role in this association [For the ociation ,36. Our Psychological and functional impairments can be common for veterans with co-occurring PTSD and TBI . PTSD isSimilarly, veterans with TBI, regardless of severity, may have a variety of co-occurring psychological and physical health challenges for years following injury , includiVeterans with TBI or PTSD also face different challenges in seeking treatment such as problems with access, sociocultural environment, past medical experiences, and illness burden ,45,52. AA study performed by McCarthy et al  suggestshigh VHA utilization group, while comprising only 25.19%  of the study population, comprised 65.77%  of those with detected suicide attempts and other forms of intentional self-harm, 47.76%  of the TBI only group, 49.07%  of the PTSD only group, and 56.85%  of the comorbid TBI/PTSD group. It is likely that underdiagnosis was present [low and medium VHA utilization groups may have experienced TBI, PTSD, or both but received care for them outside the VHA or not at all. Such misclassification of true exposure or outcomes status can bias results. However, we observed similar patterns across all three levels of VHA health care utilization, suggesting that results may not be explained entirely by informed presence bias. Third, our study used a broad range of diagnosis codes for intentional self-harm, per Patrick et al [There are several limitations to take into account in the context of these results. First, this study only included veterans who received health care in VHA settings. Motives for veterans not seeking VHA care are complex, and thus these veterans likely have different TBI-, PTSD-, and intentional self-harm–related profiles. Second, this study relied on diagnoses in VHA administrative data to identify both TBI and PTSD diagnosis status and intentional self-harm outcomes. The ck et al , to poteOur study adds to the literature on associations of TBI and PTSD diagnoses with intentional self-harm. It provides generalized results across a large veteran population of varied demographics and VHA utilization. Our findings indicate that veterans with a TBI and/or PTSD diagnosis are more likely to have an intentional self-harm event than veterans without either of these diagnoses. These results may contribute to VHA clinical decision-making regarding the frequency and intensity of follow-up or treatment for veterans with PTSD and TBI diagnoses.Future research on the risk of intentional self-harm among veterans with TBI and/or PTSD can benefit from both qualitative and quantitative efforts. Qualitatively exploring veterans’ experiences and perceptions of health and functional status, as well as facilitators and barriers to health care services, can help to better identify health and access needs. Quantitatively exploring TBI and PTSD severity based on the nature of trauma exposure, current or previous treatment, and the intensity of posttraumatic symptoms can provide more granular associations between TBI and PTSD diagnosis and intentional self-harm. These efforts may help to inform clinicians in targeting preventative and treatment strategies toward a high-risk veteran cohort.This study examined 6.7 million veteran VHA users and found that those with TBI and/or PTSD diagnoses were more likely to have documented intentional self-harm compared to veterans with neither diagnosis. Veterans with TBI, PTSD, or comorbid TBI/PTSD should be considered high-priority recipients of VA suicide prevention services and may need special engagement efforts given various access and treatment barriers experienced among those with these conditions.


Other conditions included the presence of rheumatic diseases , tumors  and infections . LVV-GCA patients with predominant extracranial GCA phenotype were younger  and had a longer delay to the diagnosis , but had polymyalgia rheumatica symptoms more frequently than those with predominantly cranial GCA phenotype . When FDG uptake was compared according to the two different disease patterns, no statistically significant differences were observed. However, patients with extracranial LVV-GCA showed a non-significantly higher frequency of vasculitic involvement of lower-extremity arteries. (4) Conclusions: Regardless of the predominant phenotype, LVV identified by PET-CT is more commonly due to GCA in the Spanish population. In these GCA patients, younger age, PMR, and a higher frequency of lower-extremity artery vasculitis suggest the presence of LVV.(1) Objective:To assess the spectrum of PET-CT-related large vessel vasculitis (LVV) in a Spanish tertiary center and to determine whether FDG uptake by PET-CT differs between giant cell arteritis (GCA) with predominant cranial or extracranial phenotypes. (2) Methods: The spectrum of patients diagnosed with LVV by PET-CT in a tertiary referral hospital that cares for 450,000 people over a period of two years was reviewed. Moreover, differences in FDG uptake between LVV-GCA with predominantly cranial and extracranial phenotype were analyzed. (3) Results: Eighty patients were diagnosed with LVV by PET-CT. Most were due to systemic vasculitis ( Large vessel vasculitis (LVV) in adults covers a wide spectrum of conditions, with giant cell arteritis (GCA) being the most common entity . AlthougPositron emission tomography-computed tomography (PET-CT) with 18F-fluorodeoxyglucose (18F-FDG) is one of the imaging techniques that has been shown to be useful in identifying patients who present with a predominantly extracranial pattern of GCA . 18F-FDGPET-CT is an expensive technique, and this fact constitutes a limitation for its use in typical cases of GCA associated with cranial ischemic manifestation. In these patients presenting with headache or other cranial ischemic manifestations, Doppler ultrasonography and/or biopsy of the temporal arteries are the most commonly used tools for making a diagnosis of GCA. For this reason, in clinical practice, PET-CT is performed when GCA is suspected and cranial ischemic manifestations are not relevant.Taking into account these considerations, the purpose of the present study was to assess the spectrum of PET-CT-related LVV in a Spanish tertiary center and to determine whether FDG uptake by PET-CT differs between GCA with a predominant cranial or extracranial phenotype.This is a retrospective study conducted at the Fundación Jiménez Díaz University Hospital . Patients who underwent FDG-PET-CT between April 2021 and March 2023 were evaluated. Those with LVV were assessed.The study procedures acted in accordance with the Helsinki Declaration of 1975, as revised in 2000. Although it was a retrospective study, ethical committee approval was obtained (PIC034-23).First, we analyzed the spectrum of LVV. In a second step, we evaluated the differences of FDG-PET-CT between GCA patients according to the predominant disease pattern, that is, predominantly cranial or extracranial LVV phenotype.Fundación Jiménez Díaz University Hospital provides medical care to 450,000 people. During the study period, 1302 patients underwent PET-CT.The Big Data Department of the Jiménez Díaz Foundation Hospital was asked to carry out an exhaustive search of the patients who underwent PET-CT during the two years of the study. Then PET-CT of patients that included any of the following keywords: “vasculitis”, “large vessels”, “medium vessels”, “vascular wall”, “vascular”, “aortitis”, “vessel inflammation”, “increased FDG vessel uptake”, “polymyalgia rheumatica”, “giant cell arteritis”, “Takayasu arteritis” were reviewed by members of the Divisions of Rheumatology and Nuclear Medicine. Those patients in whom agreement on the presence of LVV was confirmed were included in the study. Subsequently, clinical information was obtained by reviewing medical records. Additionally, rheumatologists evaluated disease diagnosis and demographic and clinical characteristics, as well as laboratory data.18F-FDG-PET-CT examinations were performed on an integrated digital PET-CT system . Patients were administered 175–350 mbq (2.5–5.0 MBq/kg) of 18F-FDG after at least a 4h fast. The postinjection rest time was 60min. PET-CT was performed in the supine position, with arms stretched above the head, and scans were acquired from the base of the skull to the femur.Low-dose CT was performed for PET co-registration  followed by PET imaging (1.45 min per bed). Blood glucose levels before tracer injection were <200 mg/mL in all cases.All PET-CT scans were reviewed by a nuclear medicine physician with experience in LVV. The nuclear medicine physician visually evaluated the characteristics of the distribution of the radiopharmaceutical in four segments of the aorta  and in five arterial branches  and used a visual uptake classification scale. Additionally, information on involvement of the axillary, vertebral, humeral, iliac, and femoral arteries was evaluated. The standardized grading system from 0 to 3 (vascular to hepatic uptake): 0  =  no uptake (≤mediastinum); 1  =  low-grade uptake (<liver); 2  =  intermediate-grade uptake (=liver), 3  =  high-grade uptake (>liver), with grade 2 indicative and grade 3 considered strongly positive for LVV. In all cases, positive PET-CT scans were re-evaluated for confirmation.t-test, the Mann–Whitney U-test, Chi-squared test or Fisher’s exact test according to the normal distribution or the number of subjects. All analyses were conducted using Stata software, version 17/SE , with a two-sided significance level set at 5%. A p-value less than 0.05 was considered statistically significant.Demographic and clinical characteristics of patients with LVV associated with GCA were presented as mean (standard deviation) or percentages for categorical variables. For continuous variables that did not follow a normal distribution, data were reported as median and interquartile range (IQR). Univariable differences between groups were assessed through Student’s During the period of study, 80 patients were diagnosed with LVV by PET-CT.n = 64; 80%), especially GCA . In this regard, besides five patients diagnosed with Takayasu arteritis, another five patients who showed increased FDG uptake in large vessels were diagnosed with classic Polyarteritis or ANCA-associated vasculitis. Other conditions included the presence of rheumatic diseases , tumors  and infections   .Fifty-four of the 80 patients with LVV were diagnosed with GCA. Since FDG-PET was generally not performed in patients presenting with the classic cranial GCA pattern of the disease, the majority of them, 41 (75.9%) of 54 patients, met the definition of GCA with predominant extracranial phenotype. Most patients with LVV-GCA without cranial ischemic manifestations had not received glucocorticoids before PET-CT examination. However, some of the patients with cranial ischemic manifestations were started on glucocorticoid treatment within the week before PET-CT evaluation.The differences between patients with a predominantly cranial or extracranial pattern of the disease are shown in p = 0.017). In contrast, patients with extracranial LVV-GCA had a longer delay in diagnosis from symptom onset compared to those with the classic cranial GCA phenotype (median [IQR]: 12 [p = 0.006). All patients with the predominant cranial phenotype had a diagnosis confirmed by a positive biopsy and/or ultrasound of the temporal artery, while none of the 22 with extracranial LVV-GCA in whom one of these tests was performed had positive results. Despite differences in the presence of cranial ischemic manifestations, as only 1 of 41 patients with predominantly extracranial LVV-GCA complained of headache at diagnosis, patients with extracranial LVV-GCA more commonly had PMR than those with predominant cranial ischemic manifestations . Similarly, patients with extracranial LVV-GCA more frequently had fever >38 °C at diagnosis, but the difference was not statistically significant. No differences in the presence of constitutional syndrome were observed between patients with predominant cranial or extracranial features. In this regard, it is possible that the high frequency of constitutional characteristics in patients with cranial GCA may have been one of the reasons for requesting PET-CT in these patients. Finally, patients with a predominantly extracranial LVV-GCA pattern had a milder inflammatory response manifested by lower ESR, CRP and platelet levels and higher hemoglobin values.In this sense, patients with predominantly cranial characteristics were older at the time of diagnosis of the disease  had increased FDG uptake involving predominantly the shoulders, hip and greater trochanter. The frequency of increased of FDG uptake in these sites was significantly more common in patients with a clinical diagnosis of PMR. In this regard, in those without a clinical diagnosis of PMR (n = 33), the frequency of increased FDG uptake in these extravascular sites was significantly reduced  .Regardless of the predominant LVV phenotype, patients with a clinical diagnosis of PMR  evaluated prospective cohort and cross-sectional studies related to diagnosis, follow-up, outcome prediction, and technical aspects of LVV imaging . The diaIncreased vascular FDG uptake has been reported in more than two-thirds of GCA patients. As observed in our series, more than 50% of patients with GCA have vasculitis involvement in the thoracic and abdominal aorta and the subclavian arteries . In keepPMR may be the initial manifestation of GCA, and imaging techniques, particularly PET-CT, can reveal LVV in at least one-third of patients presenting with isolated PMR . EULAR eAlthough PET-CT is not generally used in patients with suspected GCA in whom cranial manifestations are clinically evident, recent studies indicate that PET-CT may also be useful in making the diagnosis of GCA in patients who present with predominant cranial manifestations ,19. In tAnother question we wanted to address in this study was whether the clinical spectrum of LVV-GCA in our series differed from that in other southern European populations. To do so, we used data published in Reggio-Emilia, Italy, since twenty years ago we reported that the clinical spectrum of biopsy-proven GCA in this Italian region did not differ from that found in a population in Spain . With reIn the Italian series, involvement of the carotid, axillary and brachial arteries was significantly more frequent than in our series. In contrast, brachiocephalic involvement was reported less frequently. However, LVV involvement of the thoracic and abdominal aorta and the iliac and femoral arteries was similar .A possible explanation for the differences between these series may be due to the use of different imaging techniques to make the diagnosis of LVV in Reggio Emilia. Furthermore, as previously described, an analysis of the clinical manifestations confirmed that in our series the indication for performing PET-CT in search of GCA was more focused on patients without typical cranial manifestations of GCA. In this sense, we had a longer delay in the diagnosis of GCA than in Reggio-Emilia (10.1 vs. 5.3 weeks). In addition, classic cranial ischemic manifestations of GCA, such as headache (36.2% vs. 24.1%), scalp tenderness (15.7% vs. 3.7%), abnormal temporal artery on physical examination (34.4% vs. 3.7%) or jaw claudication (17.3% vs. 3.7%) was more frequently reported in patients from the Reggio-Emilia series of LVV-GCA.Consistent with our findings, in a series of 60 GCA patients who underwent PET-CT at the onset of GCA, Malich et al. demonstrated that the most commonly involved arteries were the ascending aorta (72%), followed by the brachiocephalic trunk (62%), aortic arch (60%) and descending aorta (60%) .Although patients with GCA often present with cranial manifestations and extracranial involvement of the large vessels, in some cases, GCA presents with only extracranial involvement of the disease without any classic cranial features of this vasculitis. At this point, we are tempted to establish two well-differentiated phenotypes of the disease. However, we are aware that we must be very cautious in saying that there are two disease phenotypes, as experts may be reluctant to accept this point. However, it has been established that patients with predominantly extracranial GCA, manifested by large vessel involvement of arteries other than the cranial ones, have clinical differences compared to those found in patients with GCA who present the classic cranial manifestations of the disease ,23. In tAt this point, the experts’ opinion is that a multimodal imaging approach in individuals with LVV that includes the role of ultrasound and MRI is needed to achieve a comprehensive evaluation of vascular disease in patients with LVV. This need forthecombined use of different imaging tests to increase diagnosis accuracy of GCA is supported by a recent review that showed that combined cranial and large vessel ultrasound and PET-CT yielded accuracy for the diagnosis of GCA .On the other hand, correlation of imaging findings with clinical assessment is required . In thisIn conclusion, regardless of the clinical expression of the disease, PET-CT is an excellent tool to identify the presence of LVV. This technique is especially useful in individuals in whom the classic cranial ischemic manifestations of the disease are not clinically apparent.-FDG-PET-CT may be an important diagnostic tool in patients with suspected LVV, in particular of GCA patients without cranial ischemic manifestations of the disease.-No significant differences in the PET-CT large vessel involvement were found between the patients with GCA who were considered to have a predominantly extracranial phenotype when compared with those who also had cranial ischemic manifestations of GCA.


An elevated heart rate at admission or discharge is known to be associated with poor cardiovascular outcomes in patients with acute myocardial infarction (AMI). The association between post-discharge average office-visit heart rate and cardiovascular outcomes in patients with AMI has rarely been studied. We analyzed data for 7840 patients from the COREA-AMI registry who had their heart rates measured at least three times after hospital discharge. The office-visit heart rates were averaged and categorized into four groups by quartiles . The primary end point was a composite of cardiovascular death, myocardial infarction, and ischemic stroke. During a median of 5.7 years of follow-up, major adverse cardiovascular events (MACE) affected 1357 (17.3%) patients. An average heart rate higher than 80 bpm was associated with an increased incidence of MACE compared to the reference average heart rate of 68–74 bpm. When dichotomized into <74 or ≥74 bpm, a lower average heart rate was not associated with MACE in patients with LV systolic dysfunction, in contrast to those without LV systolic dysfunction. An elevated average heart rate at office visits after AMI was associated with an increased risk of cardiovascular outcomes. Heart rate monitoring at office visits after discharge provides an important predictor related to cardiovascular events. Heart rate was determined to be a risk factor for mortality and morbidity in various cardiovascular diseases, including hypertension , heart fGuidelines recommend that beta-blocker treatment should be considered for patients with AMI, unless the patients are in Killip class III or higher, and have cardiogenic shock ,9. Beta-Thus, we sought to assess the association between heart rate during follow-up and cardiovascular outcomes in a real-world AMI registry to determine the optimal heart rate after acute myocardial infarction.n = 2325), and those who died in the hospital (n = 554) were excluded. Thus, 7840 patients who had their heart rate measured at least 3 times after discharge were analyzed. Patients were categorized into four groups according to quartiles of average heart rate values.Between January 2004 and April 2015, a total of 10,719 patients with AMI were registered in the Convergent Registry of Catholic and Chonnam University for Acute MI (COREA-AMI), which was designed to investigate real-world outcomes in all-comers with AMI. According to electrocardiography findings, the clinical diagnosis was divided into ST-segment elevation myocardial infarction (STEMI) and non-ST-segment elevation myocardial infarction (NSTEMI). The COREA-AMI I registry has been previously reported on, and has included patients with AMI who underwent PCI from January 2004 to December 2009. The COREA-AMI II registry added additional patients from January 2010 to August 2014. The current registry comprised updated new clinical and angiographic parameters and evaluated long-term clinical data for as long as possible until 2019. Patients were followed at 6 months after enrollment (PCI date), and every year thereafter. Their heart rates were collected from medical records of office visits, and were measured by automated blood pressure devices. The timing of the first office-visit heart rate measurement was 6 months after the index procedure (PCI), and every year thereafter. Coronary angiography and primary percutaneous coronary intervention (PCI) were performed according to the current standard guidelines. Antiplatelet therapy and peri-procedural anticoagulation were administered according to standard regimens. All patients were prescribed aspirin (loading dose 200 mg) plus P2Y12 inhibitors, such as clopidogrel (loading dose 300 or 600 mg), ticagrelor (loading dose 180 mg) or prasugrel (loading dose 60 mg), before or during PCI. Predilation, direct stenting, postadditional balloon inflation, thrombus aspiration and administration of glycoprotein IIb/IIIa receptor blockers were performed at the discretion of individual interventional cardiologists. After the procedure, dual antiplatelet therapy was maintained for 1 year, and after that, the antiplatelet regimen was selected according to the balance of ischemic and bleeding risk. The necessity of beta-blocker treatment was decided by the physicians in each case. The dosage or the continuation of the use of beta-blocker treatment was left at the discretion of physicians. The post-intervention medications also included statins and renin-angiotensin-aldosterone blockers.The primary endpoint was a composite of cardiovascular death, myocardial infarction, and ischemic stroke. The secondary endpoints were individual components of the primary endpoint. All deaths were considered to be cardiovascular deaths unless a definite non-cardiac cause could be clearly identified. Myocardial infarction was defined as an elevated cardiac marker(s) with clinical evidence of ischemia, such as symptoms of myocardial ischemia, new ischemic electrocardiographic changes, and development of pathologic Q waves. Ischemic stroke was defined as the presence of a new focal neurologic deficit thought to be vascular in origin, with signs or symptoms lasting more than 24 h. For all composite outcomes, we analyzed the number of patients with at least one event from the composite outcomes. Patients with more than one contributing event were only counted once. The study protocol was approved by the ethics committee at each participating center, and was followed according to the principles of the Declaration of Helsinki. All patients provided written informed consent.Baseline characteristics are described as the mean ± standard deviation for continuous variables, and as the absolute number and percentage for categorical variables. Heart rates are expressed as a continuous variable, or averaged and categorized into four groups by quartiles ). Differences in baseline characteristics between the different quartiles of average heart rate were evaluated using a one-way analysis of variance for continuous variables, and chi-square tests for categorical variables. Major adverse cardiovascular outcomes (MACE) were defined as cardiovascular death, myocardial infarction, and ischemic stroke. Multivariable analysis was performed to assess the prognostic value of an average heart rate and MACE after adjusting for sex, age, BMI, Killip class, hypertension, diabetes, previous MI, previous PCI, peripheral artery disease, end-stage renal disease, chronic lung disease, and left-ventricular systolic dysfunction (defined as a left-ventricular ejection fraction of less than 40%). Hazard ratios (HRs) were estimated with multivariable adjusted Cox proportional hazards models using the group with heart rates of 68–74 bpm as a reference. Survival curves were constructed with Kaplan–Meier estimates by groups for the primary composite end points and individual components of MACE. The groups were compared using the log-rank test for time-to-event data with respect to cardiovascular death, myocardial infarction, and ischemic stroke. Time-to-event was the duration between the index procedure (PCI) and the occurrence of the clinical events. The unadjusted and adjusted relationships between heart rate and primary outcomes were also assessed using a time-dependent Cox PH model with restricted cubic splines. We calculated hazard ratios for average heart rate (a heart rate of 74 bpm or greater) within each subgroup using Cox PH models. The cutoff of 74 bpm was selected as the median average heart rate in the study population.p value of 0.05 was considered to be statistically significant. All statistical analyses were performed with R version 4.0.2 .All statistical tests were two-sided, and a p < 0.001). The mean average heart rate at the office visit was 74 ± 8.42 bpm (interquartile range 68.09–79.29). When patients were categorized into four groups according to the quartiles of average heart rate, there was a significant trend toward a higher average heart rate with diabetes, chronic lung disease, high heart rate at admission and discharge, decreased LV systolic function, and low prescription rate of beta-blockers and renin-angiotensin-aldosterone blockers at discharge. There were no significant differences among the heart rate quartiles with regard to body mass index, hypertension, dyslipidemia, smoking status, end-stage renal disease, and peripheral artery disease. The prescription rate of beta-blockers decreased as the quartile of average heart rate increased .Among the 10,719 patients included in the COREA-AMI registry, data for 7840 patients with heart rate measured at least 3 times after hospital discharge were available. The baseline characteristics of the total patient population are shown in During a median follow-up period of 5.7 years (IQR 3.8–7.6), the primary outcome, which is a composite of cardiovascular death, myocardial infarction, and ischemic stroke, occurred in 1357 patients (17.3%). For the secondary outcomes, cardiovascular death, myocardial infarction, and ischemic stroke occurred in 781 (10.0%), 474 (6.0%), and 272 (3.5%) patients, respectively .Kaplan–Meier curves for average heart rate divided by quartiles and primary outcomes during the follow-up period are shown in p < 0.001). Discharge heart rate was not associated with the risk of MACE in univariate analysis . Compared with the reference group (heart rate 68–74 bpm), the adjusted hazard ratio for the primary outcomes was 0.91  for the heart rate < 68 bpm group, 1.12  for the heart rate of 74–80 bpm group, and 1.39  for the heart rate higher than 80 bpm group. (p < 0.001). In addition, age, BMI, Killip class, hypertension, diabetes, and LV systolic dysfunction were all independently associated with the risk of MACE in patients with AMI who were treated with PCI. Even after applying a restricted cubic spline Cox regression analysis, average heart rate showed a positive linear relationship with the risk of primary outcomes , and the prescription rate of β-blockers was lower. In addition, a higher average heart rate was associated with MACE in patients with heart rate measurements recorded on more than six occasions. The survival period was longer in the group of patients with heart rate measurements recorded on more than six occasions .The average heart rate for the office visits showed significant interactions with LV systolic dysfunction and the number of heart rate measurements . Lower aThe major findings of this study regarding average heart rate and clinical outcomes of acute myocardial infarctions undergoing PCI were as follows: (1) A higher average office-visit heart rate was associated with an increased risk of cardiovascular events in the post-MI period. (2) Cardiovascular mortality and myocardial infarction increased in patients with a higher average heart rate. (3) Average office-visit heart rate had a linear relationship with cardiovascular events. The lower the average heart rate was, the better the clinical outcomes experienced during the post-MI period.In patients with AMI, there were results showing a link to prognosis depending on the heart rate at admission and discharge. Perne et al. suggested that a heart rate >90 bpm at admission reduced survival at 3 months in patients presenting with myocardial infarction in a large German registry . Antoni The oldest biomarker in cardiovascular medicine is resting heart rate. Heart rate is a major determinant of myocardial oxygen demand, and it affects coronary blood flow through diastolic filling time. The prognostic value of heart rate in patients with various heart diseases, including myocardial infarction, hypertension, and heart failure, is well established ,18,19. TIn heart failure, elevated heart rate is considered a physiological and beneficial compensatory response up to a certain point, whereas there might be a direct causative effect on outcomes in coronary disease. Heart rate (measured at rest and during sinus rhythm) has prognostic importance in patients with left-ventricular systolic dysfunction, and changes in heart rate are predictive of outcomes . In contOur study had several limitations. First, the possibility of unobserved confounders and selection bias should be considered because our study was retrospective, observational, and nonrandomized. Second, we did not analyze the type, dose, and achievement rate of the recommended dose of β-blockers. The results may be impacted by these factors. However, in the OBTAIN and KAMIR studies, there was no correlation between the dose of β-blockers and the survival benefit ,14. GiveThese large, real-world data show that elevated average office-visit heart rate is associated with poor clinical outcomes in patients with acute myocardial infarction. Heart rate monitoring at post-discharge office visits provides an important predictor related to cardiovascular events. A higher office heart rate was a risk factor associated with cardiovascular death and recurrent myocardial infarction. Therefore, the thorough tracking of heart-rate changes in outpatients can aid the treatment of patients in the post-MI period.


Background: Critical-access hospitals (CAHs) are required to meet the CDC 7 Core Elements of antimicrobial stewardship programs (ASPs). CAHs have lower adherence to the core elements than larger acute-care hospitals, and literature defining which core-element deficiencies exist within CAHs as well as barriers to adherence is lacking. Methods: We evaluated 21 CAH ASPs (5 in Nebraska and 15 in Iowa) that self-identified as potentially deficient in the Core Elements, via self-assessment followed by in-depth interviews with local ASP team members to assess adherence to the CDC Core Elements for ASPs. Core-element compliance was rated as either full (1 point), partial (0.5), or deficient (0), with a maximum score of 7 per ASP. High-priority recommendations to ensure core-element compliance were provided to facilities as written feedback. Self-reported barriers to implementation were thematically categorized. Results: Among the 21 CAH ASPs, none fully met all 7 core elements , with a median of 5 full core elements met . Only 6 ASPs (28.6%) had at least partial adherence to each of the 7 core elements. Action  and leadership commitment  were the core elements with the highest adherence, and accountability  and education  were the lowest. The most frequent high-priority recommendations were to provide physician and pharmacist leader ASP training , to track antimicrobial stewardship interventions , and to provide or track educational activities  . One-third of programs were recommended to establish a physician leader. The most commonly self-identified barriers to establishing and maintaining an ASP were a lack of dedicated resources such as time of personnel , lack of infectious diseases expertise and training , and electronic medical record limitations  . Conclusions: CAH ASPs demonstrate several critical gaps in achieving adherence to the CDC Core Elements, primarily in training for physician and pharmacist leaders and providing stewardship-focused education. Further resources and training customized to the issues present in CAH ASPs should be developed.Disclosures: None