Prognostic Score Research Articles

AI-enhanced detection of coronary inflammation predicts 10-year risk for cardiac mortality and MACE in individuals with no or minimal coronary atherosclerosis on routine CCTA

Abstract Background Coronary CT angiogram (CCTA) is one of the first line investigations for patients with chest pain suspected of coronary artery disease (CAD). Current CCTA interpretations mainly focus on luminal obstruction, which is identified in the minority of patients, whereas the majority who have no or minimal atherosclerotic plaques visible on CCTA are often provided with reassurance. However, residual risk from inflammation that drives atherosclerosis and plaque vulnerability are not captured with luminal stenoses alone. Fat attenuation index (FAI) Score quantifies coronary inflammation on routine CCTA, and integrate with clinical risk factors/plaque burden in an artificial intelligence enabled risk algorithm (AI-Risk) that could help to risk stratify this patient group. Purpose To evaluate the prognostic value or FAI Score and AI-Risk in the risk stratification of patients with no or minimal atherosclerosis. Methods In a nested cohort within the Oxford Risk Factors and Non-invasive imaging (ORFAN) study, consecutive patients (n=3,393) who underwent routine clinical CCTA were followed up over a median(IQR) 7.7(6.4-9.1) years for cardiac mortality and major adverse cardiac events (MACE, including myocardial infarction, new onset heart failure, cardiac mortality). CADRADS 2.0 classification 0 or 1 were used to define no or minimal luminal stenoses. FAI Score was calculated for each of the 3 epicardial coronary arteries. Results A total of 1,678 patients had no or minimal atherosclerosis on CCTA [median (IQR) age 53(45-63), 49.8% male]. Cardiac mortality occurred in 3.1% and MACE occurred in 8.8% of the patients during the follow-up period. Patients at the lowest quartile of FAI Score or low/medium AI-Risk classification showed very low event rate during follow up. Patients with the highest quartile of LAD FAI Score showed 9 times higher risk of cardiac mortality (Panel A) and 5 times higher risk of MACE (Panel B) compared to those at lowest quartile. Similar findings were observed for LCX and RCA. Finally, integrating coronary inflammation with clinical risk factors and plaque burden, patients at very high AI-Risk classification showed 5 times higher risk of cardiac mortality (Panel C), and 4 times higher risk of MACE (Panel D) compared to those with low/medium risk classification. Conclusion Coronary artery inflammation measured by FAI Score could stratify the risk of patients with no or minimal coronary atherosclerosis on CCTA, and could help to guide early preventative treatments.

Clinical correlates and prognostic impact of cognitive decline in patients with heart failure and preserved ejection fraction: insights from PARAGON-HF

Abstract Background Cognitive decline is common in patients with heart failure (HF), but its clinical correlates and prognostic impact are not well understood in this population. Purpose To examine the baseline variables associated with cognitive decline and the associations between cognitive decline and HF outcomes in patients with heart failure and preserved ejection fraction (HFpEF). Methods We analyzed cognitive function, as measured by the Mini-Mental State Examination (MMSE), in 2895 patients with HFpEF enrolled in a pre-specified sub-study of PARAGON-HF. Logistic regression analysis was performed to determine the factors associated with cognitive decline i.e., a decrease in MMSE score of ≥3 points by 48 weeks. Time-updated Cox proportional hazards regressions and semiparametric proportional rates models were used to examine the subsequent risk of clinical outcomes after a decline in MMSE scores of ≥1-, 2- and 3-points during follow-up respectively, adjusted for known prognostic variables including NT-proBNP and baseline MMSE. Results A total of 450 (15.5% of the population) patients experienced a decrease in MMSE score of ≥3 points from baseline, at any follow-up visit. The corresponding number for a decrease in MMSE score of ≥2 points and ≥1 point was 755 (26.1%) and 1231 (42.5%), respectively. Independent predictors of cognitive decline at 48 weeks included older age, living in Latin America or the Asia/Pacific region, ischemic etiology, prior HF hospitalization, history of stroke/TIA, and lower serum albumin level (Figure 1). There was a graded relationship between the size of the decrease in MMSE score from baseline and the subsequent risk of mortality. Notably, a decrease of ≥3 points was associated with a 50% increase in the subsequent risk of death from any cause (adjusted HR 1.53, 95% CI 1.10-2.11) and a 90% elevation in risk of death from cardiovascular causes (1.89, 1.25-2.86), respectively, after extensive adjustment for known prognostic variables and baseline MMSE score. By contrast, cognitive decline during follow-up was not associated with a higher risk of HF hospitalization or the composite of HF hospitalization or CV death (Figure 2). Conclusions In patients with HFpEF, a decline in MMSE score during follow-up is independently associated with a graded increase in the risk for mortality, but not of HF hospitalization.The predictors for cognitive declineRisk of outcomes after a decline in MMSE

Construction of an inflammatory, nutritional, and metabolic prognostic risk score in patients with heart failure with preserved ejection fraction: a machine learning-based approach

Abstract Background Chronic inflammation, malnutrition, and metabolic syndrome contribute to the underlying mechanisms of heart failure with preserved ejection fraction (HFpEF). Inflammatory, nutritional, and metabolic biomarkers often interact with each other and affect the prognosis of patients. Purpose We aimed to identify the most predictive indicators from a pool of 21 biomarkers encompassing inflammation, nutritional status, as well as lipid, glucose, thyroid, and uric acid metabolism. Subsequently, predictive biomarkers were used to develop a risk score to enhance risk prediction for patients with HFpEF. Methods We included patients diagnosed with HFpEF and without infective or systemic disease. The primary outcome was all-cause mortality. We employed the Least Absolute Shrinkage and Selection Operator (LASSO) regression to screen the biomarkers. A machine learning-based approach was utilized to fine-tune the optimal model parameters. Additionally, we generated 1000 bootstrapping datasets to identify biomarkers recurring above a 95% frequency in repetitions, which were then employed to formulate the biomarker risk score. The discrimination of incorporating the risk score into the basic model was evaluated using 100 iterations of 5-fold cross-validation to assess the predictive efficacy. Results Among the 1311 included patients (with a median age of 64 years and 42.6% female), 363 patients (27.7%) experienced mortality over a median follow-up period of 2.7 years. The final risk score model was derived from 5 biomarkers—albumin (ALB), red blood cell distribution width-standard deviation (RDW-SD), lymphocytes, triiodothyronine (T3), and uric acid—each selected in over 95% of 1000 bootstrapping iterations. Integration of this risk score into the basic model notably enhanced discrimination (∆C-index=0.015, 95% CI 0.005-0.023) and reclassification (IDI: 3.3%, 95% CI 1.7%-5.8%; NRI: 15.2%, 95% CI 6.5%-22.5%) for risk stratification. In time-dependent receiver operating characteristic (ROC) analysis, the mean area under the curve (AUC) of the risk score was 0.688, 0.710, and 0.738 at 1, 3, and 5 years post-discharge, respectively, in the cross-validation sets. Incorporating the risk score into the basic model significantly improved the AUC at 1, 3, and 5 years (DeLong test P-value<0.05). Furthermore, in multivariable Cox regression, the risk score demonstrated an independent association with all-cause mortality (HR: 1.81, 95% CI 1.51-2.16, P<0.001 per 1 score increase). Conclusions A risk score derived from 5 commonly used inflammatory, nutritional, thyroid and uric acid metabolic biomarkers can effectively identify high-risk patients with HFpEF. This approach supports the development of potential individualized management strategies for patients with HFpEF.Study population and model constructionPredictive performance of the risk score

Improving prognostic accuracy in ischemic cardiomyopathy: the CMR-LGE score

Abstract Background There is a need for accurate prognostic stratification tools in patients with ischemic cardiomyopathy (ICM). Several studies have shown the considerable impact of cardiac magnetic resonance (CMR) imaging findings notably late gadolinium enhancement (LGE) but an easy interpretable score to guide clinical practice is lacking. Purpose To determine the CMR parameters most predictive of mortality in a cohort of ICM patients with left ventricular ejection fraction (LVEF) less than 50%, and then to develop a readily interpretable score based on these parameters. Methods Between 2008 and 2022, all consecutive patients with a history of ICM with LVEF<50% and presence of ischemic LGE on viability CMR examination, were recruited by two independent centers. The primary outcome was all-cause death using French National Registry of Death. The first center (N=2,900) was designated as the derivation cohort for variable selection and score development, and the second center (N=691) was used as the validation cohort to evaluate the performance of the final score. Thirty-two variables (17 clinical and 15 CMR) were initially assessed. Automatic feature selection was performed using a machine-learning approach with a Random Survival Forest (RSF) algorithm. Using the selected variables, our proposed score, the CMR-LGE score, was derived from coefficients of the Cox regression. Performance evaluation on the validation cohort was conducted using Harrel’s C index compared with traditional prognostic factors associated with poor outcomes in ICM. Categories for the prognostic score were identified using a survival conditional inference tree analysis on the derivation cohort, aiming to maximize the log-rank score. Results Among the 3,591 patients included (mean age 65±12 years; 75% male; mean LVEF 44±6%), 549 (15.3%) died over a median follow-up of 9 (interquartile range: 7–12) years. Feature selection with RSF highlighted that the most important variables to predict death were LGE variables: the extent, the location (septal and anterior) and the transmurality of ischemic LGE as well as the extent of additional midwall LGE (Figure 1). Following these findings, we developed the CMR-LGE score by rounding the coefficient of a Cox regression (Figure 2A). Harrel’s C index of CMR-LGE score outperformed traditional prognostic factors, including LVEF (0.88 vs 0.69). Finally, based on our CMR-LGE score, we identified a low-risk population (CMR-LGE score below 6) and a high-risk population (CMR-LGE above 8), validated using survival curves in the validation cohort (Figure 2B). Conclusion Using RSF, we identified that the 5 most important CMR variables to predict mortality in ICM were all LGE features. Our CMR-LGE score showed excellent performance to stratify patient risk compared to traditional prognostic factors including LVEF.Variable selectionModel building and evaluation

Examining prognostic scores for short-term major adverse cardiovascular events prediction following primary PCI: a comparative analysis

Abstract Background Accurately predicting short-term MACE (major adverse cardiac events) following primary PCI remains a clinical challenge. This study aims to assess the effectiveness of four established risk scores in predicting short-term MACE after primary PCI (percutaneous coronary intervention). Methodology We enrolled a cohort of consecutive adult patients diagnosed with STEMI (ST elevation myocardial infarction) undergoing primary PCI in this prospective observational study. Patients were followed at the interval of 3 months up for up to 12 months, and MACE events were recorded. Additionally, we obtained TIMI, PAMI, CADILLAC, and GRACE scores. Results A total of 2839 patients (79.3% male, mean age 55.6 ± 11.2 years) were included. Over a median follow-up of 244 days, the composite MACE rate was 18.3% (519). All-cause mortality was 13.5% (384), re-infarction requiring revascularization was 4.3% (121), heart failure-related re-hospitalization was 2.6% (74), stent thrombosis occurred in 5.6% (160), and CVA (cerebrovascular accident) events were documented in 1% (28). The AUC (area under the curve) for TIMI, PAMI, CADILLAC, and GRACE scores were 0.658 [0.630-0.685], 0.658 [0.632-0.684], 0.669 [0.644-0.695], and 0.675 [0.649-0.701], respectively, for the prediction of MACE. On multivariable Cox regression, medium and high-risk categories based on GRACE score were independent predictors of MACE with adjusted HR (hazard ratio) of 1.52 [1.21-1.92]; p<0.001 and 1.92 [1.34-2.75]; p<0.001, respectively. Conclusion A significant proportion of patients experienced short-term MACE after primary PCI. While none of the assessed scores demonstrated significant predictive power, the GRACE score exhibited comparatively better predictive ability than TIMI, PAMI, and CADILLAC scores

Development and validation of a prognostic score integrating remote heart failure symptoms and clinical variables for mortality risk prediction after myocardial infarction. The PragueMi score

Abstract Background While heart failure (HF) symptoms are associated with adverse prognosis after myocardial infarction (MI), they are not routinely used for patients’ stratification. Purpose The primary objective of this study was to develop and validate a score to predict mortality risk after MI, combining remotely recorded HF symptoms and clinical risk factors, and to compare it against the guideline-recommended GRACE score. Methods A cohort study design using prospectively collected data from consecutive patients hospitalized for MI at a large tertiary heart centre between June 2017 and September 2022 was used. Results Data from 1,135 patients (aged 64±12 years, 26.7% women), were split into derivation (70%) and validation cohort (30%). Components of the 23-item Kansas City Cardiomyopathy Questionnaire (KCCQ) questionnaire and clinical variables were used as possible predictors. The best model included the following variables – age, heart failure history, admission creatinine and heart rate, ejection fraction at hospital discharge, and HF symptoms 1 month after discharge including walking impairment, leg swelling, and change in HF symptoms. Based on these variables, the PragueMi score was developed. In the validation cohort, the PragueMi score showed superior discrimination to the GRACE score for 6 months (AUC 90.1, 95% CI 81.8-98.4 vs. 77.4, 95% CI 62.2-92.5, p=0.04) and 1-year risk prediction (AUC 89.7, 95% CI 83.5-96.0 vs. 76.2, 95% CI 64.7-87.7, p=0.004). Conclusion The PragueMi score combining heart failure symptoms and clinical variables performs better than the currently recommended GRACE score.

Evaluation of naples prognostic score to predict long-term mortality in patients with pulmonary embolism

Abstract Background/Introduction APE mortality rates are still not satisfactory despite new diagnosis and treatment methods (1). Thus, risk classification is crucial due to the wide range of clinical presentations. Several risk stratifications have been developed such as simplified Pulmonary Embolism Severity Index (sPESI) (2). Nonetheless, there remains a need for an optimal indicator that can be conveniently measured with a high degree of precision to predict clinical outcomes. Naples prognostic score (NPS) is a newly defined index that is developed to determine the prognosis of cancer patients and reflects the inflammatory and nutritional status of the patients (3). Purpose The aim of our study was to determine the long-term prognostic value of NPS in APE patients. Methods Two hundred ninety-three patients diagnosed with APE between November 2016 and February 2022 were included in the study. NPS consists of 4 parameters: serum albumin, total cholesterol (TC) concentration, neutrophil-to-lymphocyte ratio (NLR) and lymphocyte-to-monocyte ratio (LMR). Naples scores of all patients were calculated and the patients were divided into 2 groups according to their NPS and their long-term mortality was compared. The primary endpoint of the study was long-term mortality. Results The long-term mortality was observed in 38 patients out of 293 patients in the mean follow-up of 24 (16-42) months. In the low-Naples risk group (group 1), there were 215 patients, while the high-Naples risk group (group 2) had 78 patients. Multivariate analysis showed that NPS as a categorical parameter (HR: 3.458, 95%CI:1.734-6.896, p<0.001), NPS as a numerical parameter, (HR:1.651, 95%CI:1.217-2.241, p=0.001) and simplified pulmonary embolism severity index (sPESI) (HR:1.887, 95%CI:1.223-2.911), p=0.004) score were independent predictors of long-term mortality. The receiver operating characteristic curve analysis showed that the model 2 with NPS as continuous had higher discriminative ability than the baseline model for detecting patients with mortality (Area under curve (AUC) values = 0.791 vs. 0.723, respectively, p=0.04) (Figure 1A). The long-term predictive capability of NPS was non-inferior than the sPESI score (Figure 1B). Conclusions The current study highlights that NPS, which reflects systemic inflammation and nutritional status may have potential to predict long-term mortality in APE patients. To our knowledge, this is the first study to show the relationship between long-term mortality and NPS in APE patients.

The Prognostic Value of a Naples Score in Determining in-Hospital Mortality in Patients with Acute Ischemic Stroke Undergoing Endovascular Treatment.

Background/Objectives: The Naples prognostic score (NPS), reflecting inflammation and nutritional status, has prognostic value, especially in cancer. This study evaluated its ability to predict in-hospital mortality in acute ischemic stroke (AIS) patients undergoing endovascular treatment (EVT). Methods: We retrospectively studied 244 patients with AIS who were admitted between April 2020 and December 2023. Patients were included if they presented within 6 h of symptom onset with evidence of intracranial proximal arterial occlusion. The EVT was performed using aspiration catheters, stent retrievers, or both. The NPS was calculated based on the neutrophil-lymphocyte ratio, lymphocyte-monocyte ratio, and albumin and total cholesterol levels. Results: We found a significant association between higher NPS scores and in-hospital mortality. Patients with a high NPS (3 or 4) had a mortality rate of 41.6% compared to 21.0% in the low-NPS group (0, 1, or 2). The full model incorporating NPS showed superior predictive ability for in-hospital mortality compared with the baseline model (areas under the curve 0.881 vs. 0.808). A receiver-operating characteristic analysis at a cutoff of >2.5 for the NPS showed a sensitivity of 86.6% and specificity of 41.9%. This study demonstrated that incorporating the NPS into the predictive model improved the accuracy and calibration for predicting in-hospital mortality. A decision curve analysis showed the net benefit of using the full model incorporating NPS over the baseline model, emphasizing its potential clinical application in prognostication. Conclusions: NPS is a reliable predictor of in-hospital mortality in AIS patients undergoing EVT. Incorporating NPS into clinical practice could help to identify high-risk patients and improve outcomes through tailored interventions.

Predictors of irreversible renal dysfunction in patients with idiopathic retroperitoneal fibrosis.

Idiopathic retroperitoneal fibrosis (iRPF) can lead to irreversible kidney damage. This study aimed to investigate predictors of irreversible renal dysfunction in patients with iRPF. Eighty-three patients with newly diagnosed iRPF were enrolled between January 2010 and Sep 2022 at Zhongshan Hospital of Fudan University, including 60 in the training set and 23 in the validation set. They were regularly contacted or followed up via outpatient examinations by specialist doctors, who documented their condition and treatment progress. Predictors of irreversible renal dysfunction were identified using univariate and multivariate regression, logistic model, and receiver operating curve analyses. In the training set, over a median follow-up of 29 months, 16.7% of patients had an estimated glomerular filtration rate (eGFR) of < 60 ml/min/1.73m2 at the last follow-up, and 25% had hydronephrosis or required prolonged double-J stents. A prognostic score was developed by assigning 1, 1, and 2 points for peripheral CD19+ B cells <9.3%, serum creatinine (sCr) ≥120 μmol/l, and no response at 6 months, respectively. A score of ≥ 2 for predicting irreversible renal dysfunction had sensitivity and specificity of 100% and 92%, respectively. In the validation set, 21.7% of patients suffered from irreversible renal dysfunction. The sensitivity and specificity for predicting irreversible renal dysfunction were 100% and 94.4%, respectively. A prognostic score based on factors including CD19+ B cells <9.3% and sCr ≥120 μmol/l at baseline, and no response at 6 months, is suitable for predicting irreversible renal dysfunction in iRPF.

Relationship between hearing loss and Glasgow prognostic score in patients with cancer

ObjectiveOxidative stress damages cochlear hair cells in vitro. However, the effect of systemic inflammation on hearing loss remains unclear. Growing evidence suggests that malnutrition influences the development of hearing loss. In this study, we aimed to investigate the influence of the Glasgow prognostic score (GPS), which is calculated based on systemic inflammatory responses and malnutrition, on auditory threshold increases in patients with cancer. MethodsThis single-center retrospective cohort study included patients with cancer who underwent standard pure-tone audiometry (PTA) between November 2014 and May 2023. Patients with complete data in their electronic medical records within 90 days before undergoing standard PTA were included. Multivariate analysis was performed using auditory threshold as the response variable. Covariates, including GPS, were obtained from blood data and physical data before standard PTA. The GPS was classified into three levels based on serum albumin and C-reactive protein levels. ResultsStandard PTA was performed 14,868 times in 5,462 patients. Of these, 742 had cancer and 384 met the inclusion criteria. Multivariate analysis revealed that older age, creatinine clearance <60 mL/min, and high GPS significantly increased the auditory threshold at frequencies of 500–8,000 Hz. A history of platinum drug use and male sex increased the auditory threshold at frequencies >4,000 and >2,000 Hz, respectively. ConclusionThe GPS was independently associated with elevated standard PTA thresholds in patients with cancer. These results suggest an association between malnutrition/chronic inflammation and hearing loss and provide new information for planning clinical research on hearing loss prevention.

Scoring System to Personalize Management of Emphysematous Pyelonephritis.

Emphysematous pyelonephritis (EPN) is a life-threatening condition that requires prompt diagnosis and treatment. The prognosis of EPN is variable, and there is no single treatment that is universally effective. In this study, we developed a scoring system to predict the prognosis of EPN and to guide management. The scoring system was developed based on a retrospective analysis of 91 patients with EPN. Nineteen risk factors for emphysematous pyelonephritis were assessed with univariate and multivariate analysis. Seven factors were found significant on analysis. The scoring system was developed by including these 7 risk factors: renal stone disease, leukocytosis, raised creatinine, EPN grade, and septic shock. The score ranged from 1 to 18, with a higher score indicating a worse prognosis. The scoring system was able to stratify patients into three risk groups: good risk, intermediate risk, and poor risk. The scoring system can be used to personalize the management of EPN. Patients in the good-risk group may be managed with conservative treatment, while patients in the intermediate-risk and poor-risk groups may require intervention, such as DJ stenting, percutaneous nephrolithotomy or nephrectomy. The scoring system is a valuable tool for predicting the prognosis of EPN and guiding management. It can help clinicians to tailor treatment to the individual patient and to improve outcomes. The prognostic score helps identify patients who are at high risk. This score helps in the selection of appropriate management options.

A novel scoring system proposal to guide surgical treatment indications for high grade gliomas in elderly patients: DAK-75.

High-grade gliomas are the most prevalent neurooncological desease in adults, their incidence increases with age, peaking in the seventh decade. This paper aims to address how to select patients for surgical resection by identifying pre-surgical predictors of 12-month mortality in newly diagnosed HGG patients aged ≥ 75years. A prognostic score will be proposed to guide surgical decisions based on expected survival. Retrospective observational single-center cohort study was carried out at the "Città della Salute e della Scienza-Molinette" University Hospital in Turin, Italy. All consecutive patients aged ≥ 75years newly diagnosed with HGG were included, regardless of whether they underwent surgical resection. Clinical, radiological, histological and molecular data were collected.Variables potentially available at the time of diagnosis were considered to develop a multivariable logistic regression predictive model, with 12-months overall survival as the dependent variable. 102 patients aged 75years or older received a new diagnosis of high-grade glioma, of whom 68 underwent surgical resection. Patients undergoing surgery were slightly younger (76.9 vs 79.0years, p = 0.007) and had better performance status (median KPS 80 vs 70). Most tumors undergoing surgery were localized in cortical or subcortical non-motor areas (p < 0.001) and less frequently deep-seated (p = 0.023) or multifocal (p < 0.001). A predictive model, the DAK-75 score, was developed: the AUROC of the final model was 0.822 (95% CI 0.741-0.902). The score includes clinical presentation, tumor location, and KPS, ranging from 0 to 20, categorizing risk scores into low-risk and high-risk groups (< or > 8). Higher scores corresponded to fewer surgical patients and higher one-year mortality rates (92.2% vs 47.1%, p < 0.001). DAK-75 score may represent a valuable tool in the decision-making process for neurosurgical intervention in elderly patients diagnosed with HGG. Further studies are needed to externally and prospectively validate the scoring system.

Integration of transcriptomics and machine learning for insights into breast cancer: exploring lipid metabolism and immune interactions.

Breast cancer (BRCA) represents a substantial global health challenge marked by inadequate early detection rates. The complex interplay between the tumor immune microenvironment and fatty acid metabolism in BRCA requires further investigation to elucidate the specific role of lipid metabolism in this disease. We systematically integrated nine machine learning algorithms into 184 unique combinations to develop a consensus model for lipid metabolism-related prognostic genes (LMPGS). Additionally, transcriptomics analysis provided a comprehensive understanding of this prognostic signature. Using the ESTIMATE method, we evaluated immune infiltration among different risk subgroups and assessed their responsiveness to immunotherapy. Tailored treatments were screened for specific risk subgroups. Finally, we verified the expression of key genes through in vitro experiments. We identified 259 differentially expressed genes (DEGs) related to lipid metabolism through analysis of the cancer genome atlas program (TCGA) database. Subsequently, via univariate Cox regression analysis and C-index analysis, we developed an optimal machine learning algorithm to construct a 21-gene LMPGS model. We used optimal cutoff values to divide the lipid metabolism prognostic gene scores into two groups according to high and low scores. Our study revealed distinct biological functions and mutation landscapes between high-scoring and low-scoring patients. The low-scoring group presented a greater immune score, whereas the high-scoring group presented enhanced responses to both immunotherapy and chemotherapy drugs. Single-cell analysis highlighted significant upregulation of CPNE3 in epithelial cells. Moreover, by employing molecular docking, we identified niclosamide as a potential targeted therapeutic drug. Finally, our experiments demonstrated high expression of MTMR9 and CPNE3 in BRCA and their significant correlation with prognosis. By employing bioinformatics and diverse machine learning algorithms, we successfully identified genes associated with lipid metabolism in BRCA and uncovered potential therapeutic agents, thereby offering novel insights into the mechanisms and treatment strategies for BRCA.

Visceral Obesity and a High Glasgow Prognostic Score Are Key Prognostic Factors for Metastatic Colorectal Cancer Treated with First Line Chemotherapy.

The prognostic significance of a high visceral fat area (VFA) in metastatic colorectal cancer (mCRC) remains unclear. We evaluated the prognostic impact of high-VFA on the long-term outcomes of patients with mCRC who underwent chemotherapy. Ninety patients with metastatic CRC who underwent chemotherapy were included. VFA measurement was performed by pre-treatment computed tomography using image analysis system. Overall survival (OS) and progression-free survival (PFS) rates were analyzed using the Cox proportional hazards model and Kaplan-Meier curves with the log-rank test. High-VFA was identified in 39 patients. The OS (2-year OS rates: 51.6% vs 33.3%, p=0.0023) and PFS rates (2-year PFS rates: 18.0% vs 2.7%, p=0.012) were significantly lower in the high-VFA group than in the low-VFA group. In multivariate analysis, the independent significant predictors of OS were carbohydrate antigen 19-9 (CA19-9) ≥37.0 U/mL (HR: 1.99, 95%CI [1.20-3.31], p=0.007), Glasgow prognostic score (GPS) of 1 or 2 (HR: 2.65, 95%CI [1.53-4.58], p<0.001), and high-VFA (HR: 3.09, 95%CI [1.81-5.25], p<0.001). Similarly, the independent significant predictors of PFS were CA19-9 ≥37.0 U/mL (HR: 2.02, 95%CI [1.21-3.38], p=0.007), GPS of 1 or 2 (HR: 1.87, 95%CI [1.17-2.99], p=0.008), and high-VFA (HR: 2.65, 95% CI [1.61-4.35], p<0.001). We demonstrated that pre-treatment high-VFA and high-GPS were significantly associated with worse OS and PFS rates in patients with mCRC who underwent chemotherapy.

Usefulness of Nutritional Assessment Indicators in Predicting Treatment Discontinuation Due to Adverse Events from PARP Inhibitors in Ovarian Cancer Patients.

Nutritional status is an important factor influencing toxicity of treatment. Nutritional assessment indicators such as the Prognostic Nutritional Index (PNI), Controlling Nutritional Status (CONUT) score and modified Glasgow Prognostic Score (mGPS) have been reported to be associated with treatment-related adverse events (AEs) for various malignancies. However, there are no reports investigating the relationship between nutritional status and AEs from poly-(ADP-ribose) polymerase (PARP) inhibitors (PARPi), which are widely used in recent years as maintenance therapy for ovarian cancer. The primary objective was to investigate the usefulness of nutritional assessment indicators in predicting treatment discontinuation due to AEs from PARPi. This multicenter retrospective study included patients diagnosed with ovarian cancer who received maintenance therapy with PARPi from January 2018 to December 2023. PNI, CONUT score, and mGPS were calculated based on hematological parameters measured within 7 days before the start of PARPi therapy. A total of 272 patients received maintenance therapy with PARPi during the period, but due to the absence of the blood collection of albumin levels within one week or other exclusion criteria, 71 patients were finally included in this analysis. AEs were seen in 59 patients (83.1%), including 25 (35.2%) severe events (grade ≥3 in Common Terminology Criteria for Adverse Events v5.0). Eighteen patients (25.4%) discontinued treatment due to PARPi-related AEs. Low PNI (<48.44) and high mGPS (≥1) were predictors of treatment discontinuation in both univariate and multivariate analyses. CONUT was not a significant predictor in this study. Our study suggested that PNI and mGPS can predict the risk of treatment discontinuation due to PARPi-related AEs before starting maintenance therapy. This insight opens avenues for more personalized treatment plans, potentially improving patient outcomes.

Outpatient intensive nutrition therapy improves survival and frailty in males with Alcohol related ACLF - Randomised controlled trial

Background and AimsImprovement in the nutritional status of Acute on Chronic Liver Failure(ACLF) patients may lead to reduction in morbidity and mortality. This study assessed the impact of dietician supported outpatient intensive nutrition therapy(OINT) on survival and frailty in patients with alcohol related ACLF Methods70 patients with alcohol related ACLF(Asia Pacific Association for the Study of the Liver, APASL-criteria) and frailty were randomized 1:1 to receive standard medical therapy(SMT) plus OINT(intervention) versus SMT(control) alone. The primary outcome was an improvement in survival at 3 months. Secondary outcome measures included improvement in frailty, prognostic scores and hospitalization. ResultsThere was a significant improvement in overall survival in the OINT group as compared to SMT after 3 months of follow up, 91.4% (Standard error (SE): 4.7%) vs. 57.1% (SE: 8.4%), P<.00). On cox regression model, inclusion in the intervention arm, baseline Skeletal Muscle Index(SMI), and Asia Pacific Association for the Study of the Liver ACLF Research Consortium(AARC score) were independent predictors of survival (P<.05). The Liver frailty index(LFI) score also significantly improved in the OINT as compared to SMT, Δ-0.93 -(0.71-1.13) vs. Δ -0.33 -(0.44-0.72)(P<.00). The disease severity including MELD, MELD-Na, and AARC score showed a significant improvement in the OINT group as compared to the SMT group(P<.05). The patients in OINT group had lesser number of hospitalizations 6(17%) versus 16(45.7%)(P=.01) as compared to SMT group. ConclusionOutpatient intensive nutrition therapy significantly improves survival, frailty and disease severity with a reduction in number of hospitalizations and supports the key role of nutrition in treatment of alcohol related ACLF patients.

Genome sequencing in the management of myelodysplastic syndromes and related disorders.

Myeloid neoplasms originate from the clonal proliferation of hematopoietic stem cells, which is driven by the acquisition of somatic genetic mutations. Within these disorders, myelodysplastic syndromes (MDS) are specifically characterized by morphologic abnormalities (dysplasia) and impaired maturation of myeloid precursors (ineffective hematopoiesis), resulting in peripheral blood cytopenia. Several studies have advanced the field of MDS, with a few landmark papers leading to a paradigm shift, opening new avenues of research and enabling a molecular revolution. These seminal papers include the first description of the 5q- syndrome, the identification of somatic mutations of TET2 in myeloid neoplasms, the detection of common pathway mutations in the splicing machinery, and the discovery of clonal hematopoiesis. The somatic genomic landscape of MDS is now well-defined. Genes that are recurrently mutated include epigenetic regulators, as well as genes of RNA splicing machinery, transcription regulation, DNA repair control, cohesin complex, and signal transduction. Furthermore, several disorders with a germline genetic predisposition to MDS have been identified, collectively accounting for up to 15% of all MDS cases. Genomic profiling can significantly improve the diagnostic approach to MDS, allowing the identification of distinct nosologic entities such as SF3B1-mutant or TP53-mutant MDS. The Molecular International Prognostic Scoring System for MDS (IPSS-M) has already proven to be a valuable tool for individualized risk assessment and treatment decisions. In addition, the recently developed molecular taxonomy of MDS will likely facilitate the implementation of precision medicine approaches for these disorders. This will necessitate the establishment of specialized infrastructures within public health systems, involving close collaboration between healthcare institutions, academia, and the life sciences industry.

Predicting postoperative atrial fibrillation after cardiac surgery using the Naples prognostic score

Introduction The Naples prognostic score (NPS) is a novel indicator of nutritional and inflammatory statuses in cancer patients. Development of atrial fibrillation after cardiac surgery (POAF) is a common complication that increases the incidence of adverse events. Numerous studies have investigated predictors of POAF. Yet, this study is the first to evaluate the prognostic value of NPS in predicting the development of POAF. Materials and methods The population of this retrospective single-center case–control study consisted of all consecutive patients who underwent cardiac surgery between January 2021 and December 2023. The patients included in the study sample were divided into two groups according to whether they had POAF (group POAF) or remained in sinus rhythm (group RSR). Univariate and multivariate analyses were conducted to identify the variables that significantly predicted the development of POAF. Results This study consisted of 860 patients with a mean age of 61.77 ± 9.13 years and 77.5% (n = 667) were male. The incidence of POAF in the sample was 24.8% (n = 214). NPS was significantly higher in group POAF than in group RSR (2.18 ± 0.99 vs. 1.96 ± 1.02, P = 0.008). Multivariate analysis revealed age [odds ratio (OR): 1.242, 95% confidence interval (CI): 1.020–1.304, P &lt; 0.001] and high NPS (OR: 1.698, 95% CI: 1.121–1.930, P &lt; 0.010) as independent predictors of POAF. Conclusion High NPS values, along with advanced age, were found to be strongly associated with an increased risk of developing POAF. Therefore, it is concluded that NPS is a significant and independent predictor of POAF in patients undergoing cardiac surgery.

Treatment of lower-risk myelodysplastic syndromes.

Myelodysplastic neoplasms (MDS) involve clonal hematopoiesis and cellular dysplasia, driven by genetic and epigenetic alterations. Spliceosome mutations and epigenetic dysregulation underscore the intricate pathogenesis of MDS. The bone marrow microenvironment, stromal dysfunction, chronic inflammation, and immune dysregulation contribute to disease progression. This complex pathogenesis underscores the necessity for targeted therapies, offering a personalized medicine approach, particularly in lower-risk patients. The development of risk scores like the International Prognostic Scoring System (PISS), its revision IPSS-R, and the incorporation of molecular genetics in IPSS-M have refined the diagnostic and prognostic framework of MDS. These scoring systems facilitate tailored treatment strategies and better prognostication, especially for lower-risk MDS patients. The progression from IPSS to IPSS-R and now to IPSS-M epitomizes the shift towards personalized medicine in MDS management. In this review we discuss recent developments and positive phase III studies in lower-risk MDS. The review concludes by proposing a treatment algorithm for LR-MDS and highlighting ongoing trials in this heterogeneous patient population.

Monitoring Immune Dysfunction in Critically Ill Patients with Carbapenem-Resistant Acinetobacter baumannii Sepsis Treated with Regimens Including Cefiderocol: A Pilot Study to Identify Accessible Biomarkers to Stratify Patients’ Prognosis

Background: Multidrug-resistant Acinetobacter baumannii (CRAB) infections are a serious problem in critical care. This study aims to develop an early prognostic score for immune paralysis, using practical and cost-effective parameters, to predict ICU mortality in patients with CRAB infections being treated with Cefiderocol. Methods: We carried out an observational pilot study on consecutive patients hospitalized in the ICU with ensuing septic Acinetobacter baumannii infections treated with Cefiderocol monotherapy or Cefiderocol including combinations. We investigated the predictive power of lymphocyte counts, lymphocyte subpopulations, serum cholinesterase levels, and reactivation of herpes viruses. Results: Overall, 36 of 39 patients entered in our analysis: 20 survivors and 16 deceased. A total of 12 patients developed bacteremia, 19 patients had HAP/VAP, and 5 patients had a soft tissue infection. Univariate analyses of factors associated with unfavorable outcome revealed a significant association for age (OR: 1.5, CI: 1.11–2.02), SAPS II (OR: 1.05, CI: 1.01–1.1), SOFA score (OR: 1.37, CI: 1.06–1.76), lymphocytopenia (OR: 32.5, CI: 3.45–306.4), viral reactivation (OR: 9.75, CI: 1.72–55.4), and cholinesterase drop &lt;1600 U/L (OR: 39.7, CI: 5.8–271.6). At variance, monotherapy or associations with Cefiderocol were not associated. In the final multivariable model, the only independent predictors of death were age (OR: 1.42, CI: 0.98–2.05), lymphocytopenia (OR: 18.2, CI: 0.87–371), and cholinesterase drop to below 1600 U/L (OR: 9.7, CI: 0.77–123.7). Conclusions: Age, lymphocytopenia, and serum cholinesterase drops, which were nearly significantly associated with an unfavorable outcome, may help pinpoint patients with acute immune paralysis during sepsis. Knowledge of such an immune state may in turn directly influence patients’ care.