score model
Recently Published Documents

Backgrounds: A plug-and-play standardized algorithm to identify the ischemic risk in patients with coronary artery disease (CAD) undergoing percutaneous coronary intervention (PCI) could play a valuable step to help a wide spectrum of clinic workers. This study intended to investigate the ability to use the accumulation of multiple clinical routine risk scores to predict long-term ischemic events in patients with CAD undergoing PCI.Methods: This was a secondary analysis of the I-LOVE-IT 2 (Evaluate Safety and Effectiveness of the Tivoli drug-eluting stent (DES) and the Firebird DES for Treatment of Coronary Revascularization) trial, which was a prospective, multicenter, and randomized study. The Global Registry for Acute Coronary Events (GRACE), baseline Synergy Between Percutaneous Coronary Intervention with Taxus and Cardiac Surgery (SYNTAX), residual SYNTAX, and age, creatinine, and ejection fraction (ACEF) score were calculated in all patients. Risk stratification was based on the number of these four scores that met the established thresholds for the ischemic risk. The primary end point was ischemic events at 48 months, defined as the composite of cardiac death, nonfatal myocardial infarction, stroke, or definite/probable stent thrombosis (ST).Results: The 48-month ischemic events had a significant trend for higher event rates (from 6.61 to 16.93%) with an incremental number of risk scores presenting the higher ischemic risk from 0 to ≥3 (p trend < 0.001). In addition, the categories were associated with increased risk for all components of ischemic events, including cardiac death (from 1.36 to 3.15%), myocardial infarction (MI) (from 3.31 to 9.84%), stroke (3.31 to 6.10%), definite/probable ST (from 0.58 to 1.97%), and all-cause mortality (from 2.14 to 6.30%) (all p trend < 0.05). The net reclassification index after combined with four risk scores was 12.5% (5.3–20.0%), 9.4% (2.0–16.8%), 12.1% (4.5–19.7%), and 10.7% (3.3–18.1%), which offered statistically significant improvement in the performance, compared with SYNTAX, residual SYNTAX, ACEF, and GRACE score, respectively.Conclusion: The novel multiple risk score model was significantly associated with the risk of long-term ischemic events in these patients with an increment of scores. A meaningful improvement to predict adverse outcomes when multiple risk scores were applied to risk stratification.

This study aims to investigate the effect of corporate social responsibility disclosure (CSRD) on financial distressed risk (FDR) among firms listed on the Tehran Stock Exchange (TSE). This paper also examines whether there is a negative linkage between institutional ownership as a corporate governance mechanism and corporate bankruptcy. The final research purpose is to analyze if there is a moderating effect of institutional owners on the relationship between CSRD and FDR too. The study sample consists of 200 firms listed on the TSE between 2013 and 2018, and the statistical model is logistic regression. When FDR is assessed under both Article 141 of Iran’s business law and the Altman Z-score model, our results on the main research hypotheses are quite similar. Considering the social and cultural conditions and economic situation of the Iranian market, the results show that firms with a high level of CSR disclosure are not able to make themselves more creditworthy and do not have better access to financing, resulting in more financial insolvency. Our findings confirm institutional shareholders play a vital role in facilitating a firm’s emergence from bankruptcy. The results also demonstrate financial distress risk is less seen among companies with more institutional owners that disclose more CSR information. In other words, since the goals related to CSR are long-term and Iranian institutional investors have a long-term horizon towards the company, the presence of more institutional owners within a firm push managers to provide additional voluntary CSR disclosure so firms can maintain the trust of their shareholders at the highest possible level and prevent financial distress. Our additional analysis indicates there is a positive association between financial leverage and firm failure, whereas the current ratio and ROA are negatively connected with corporate bankruptcy. Finally, when FDR is assessed on the Altman Z-score model, our evidence supports a negative relation between purchase and sale-related party transactions and bankruptcy risk, which is consistent with the efficient transaction hypothesis.

This study aims to analyze the effect of the fraud pentagon theory consisting of external pressure, effective monitoring, rationalization, capability, and arrogance on fraudulent financial reporting. This study uses the F-score model to see the potential for fraudulent financial reporting. The data used in this study are secondary data from the company's annual reports. The population of this research is state-owned companies listed on the IDX (Indonesia Stock Exchange) during 2015-2019. The sampling technique used purposive sampling so that the sample obtained is 180 samples. The analysis technique used is logistic regression analysis with S.P.S.S. versions 20.0. The findings show that external pressure and rationalization have a significant effect on fraudulent financial reporting. Meanwhile, effective monitoring, capability, and arrogance have no considerable impact on fraudulent financial reporting. The results of this study indicate the occurrence of fraudulent financial reporting in state-owned companies listed on the IDX if the related state-owned companies experience external pressure and have rationalizations to commit fraud.

H1N1 is the most common subtype of influenza virus circulating worldwide and can cause severe disease in some populations. Early prediction and intervention for patients who develop severe influenza will greatly reduce their mortality. In this study, we conducted a comprehensive analysis of 180 PBMC samples from three published datasets from the GEO DataSets. Differentially expressed gene (DEG) analysis and weighted correlation network analysis (WGCNA) were performed to provide candidate DEGs for model building. Functional enrichment and CIBERSORT analyses were also performed to evaluate the differences in composition and function of PBMCs between patients with severe and mild disease. Finally, a risk score model was built using lasso regression analysis, with six genes (CX3CR1, KLRD1, MMP8, PRTN3, RETN and SCD) involved. The model performed moderately in the early identification of patients that develop severe H1N1 disease.

Financial distress merupakan perusahaan berada dalam kondisi kesulitan keuangan untuk melunasi semua kewajibannya. Salah satu faktor yang mempengaruhi kondisi financial distress adalah kegagalan dalam mengelola keuangan perusahaan.  Penelitian ini bertujuan menemukan bukti empiris adanya pengaruh rasio leverage, pertumbuhan perusahaan, profitabilitas dan likuiditas terhadap financial distress serta memprediksi  financial distress menggunakan 4 analisis yaitu Altman Z-score, Springate, Zmijewski dan Grover. Objek penelitian ini adalah perusahaan infastruktur, utilitas dan transportasi pada tahun 2015-2019. Penelitian ini menggunakan teknik purposive sampling dengan sampel 19 perusahaan dan 95 data perusahaan. Hasil penelitian secara bersama-sama leverage, pertumbuhan perusahaan, profitabilitas, dan likuiditas dengan menggunakan 4 analisis berpengaruh terhadap financial distress. Secara parsial Rasio leverage berpengaruh negatif terhadap financial distress menggunakan model Altman Z-score. Model Grover, Springate dan Zmijewski menghasilkan bahwa rasio leverage berpengaruh positif terhadap financial distress. Rasio pertumbuhan perusahaan tidak berpengaruh terhadap financial distress menggunakan pengukuran financial distress model Altman Z-Score, Springate, Zmijewski dan Grover. Rasio profitabilitas tidak berpengaruh terhadap financial distress menggunakan model Altman Z-Score. Model Springate menunjukan bahwa rasio profitabilitas berpengaruh positif terhadap financial distress namun berpengaruh negatif melalui model Zmijewski dan Grover. Rasio likuiditas berpengaruh positif terhadap financial distress menggunakan model Altman Z-score dan Grover namun berpengaruh negatif jika melalui analisa Springate dan Zmijewski.

Breast cancer is the most common cancer, with the highest mortality rate and the most diagnosed cancer type in women worldwide. To identify the effect innate immune checkpoint for breast cancer immunotherapy, the innate immune prognostic biomarkers were selected through the ICI score model and the risk model in breast cancer patients. Moreover, the reliability and accuracy of the ICI score model and the risk model were further examined through the analysis of breast cancer prognosis and immune cell infiltration. The pan cancer analysis further confirmed and selected CXCL9 as the key innate immune checkpoint for breast cancer immunotherapy and identified three small molecular drugs for target CXCL9 through molecular docking analysis. In summary, CXCL9 significantly correlated with the prognostic of breast cancer and immune cell infiltration and could be innate immune checkpoint for breast cancer immunotherapy.

This study aims to examine the potential for bankruptcy of companies with three analytical models, namely Altman Z-Score, Springate S-Score, and Zmijewski X-Score, and assess the level of accuracy of the three models. Each model uses ratio analysis with the elements of assets, debt, capital, and company profits. This study uses a sample of coal mining companies listed on the Indonesia Stock Exchange (IDX) during the 2014-2018 period. The sampling technique in this study used purposive sampling and obtained 24 sample companies. This study uses secondary data, namely the company's financial statements obtained from IDX's official website. This study calculates financial ratios, compares the scores of the three bankruptcy prediction models, and tests the model's accuracy. The results of this study show that of the three models, the Springate S-Score model is the most accurate in predicting bankruptcy, with an accuracy rate of 83.33%, as evidenced by two companies that were delisted from the IDX. This study can be used as a reference and as material for consideration in making investment decisions for companies and investors.