Prognostic Score Research Articles

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.

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 <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.

A new prognostic scoring system for newly diagnosed multiple myeloma in the era of new drugs.

We developed a new predictive staging system to explore the heterogeneity of survival in newly diagnosed multiple myeloma (NDMM) patients in the real world. In this retrospective study, we evaluated the predictive value of cytogenetic abnormal and clinical data in 375 patients with NDMM at our center. Established a weighted MM prognostic scoring system risk model and validated its predicted PFS and OS by external cohort. Elevated lactate dehydrogenase levels (1 point), international staging system stage II/III (1 point), 1q21+ ≥ 52.75% (0.5 point), del (17p) ≥ 3.5% (0.5 point), and t (14;16) ≥ 35.25% (1 point) had independent prognostic significance. Patients were further divided into three risk groups: low (I) (score 0-0.5, 16.5%), intermediate (II) (score 1, 46.7%), and high (III) (score 1.5-3, 36.8%). In the training cohort, the 3-year PFS was 79.5% vs. 65.3% vs. 40.3% (p < 0.001), and the 3-year OS was 87.7% vs.70.1% vs. 55% (p < 0.001) for the three risk groups. In the external validation cohort, the 3-year PFS was 85.5% vs.61.2% vs. 43.1% (p < 0.001) and the 3-year OS was 91.4% vs.83.5% vs. 56.9% (p < 0.001) for the three risk groups. The risk stratification of this model shows good discrimination and calibration, and its application in clinical practice can improve the risk assessment of patients with NDMM and guide personalized treatment strategies.

Role of artificial intelligence in predicting neurological outcomes in post cardiac resuscitation

Being an extremely high mortality rate condition, cardiac arrest cases have rightfully been evaluated via various studies and scoring factors for effective resuscitative practices and neurological outcomes post resuscitation. This narrative review aims to explore the role of Artificial Intelligence (AI) in predicting neurological outcomes post cardiac resuscitation. Methodology involved detailed review of all relevant recent studies of AI, different machine learning algorithms, prediction tools and assessing their benefit in predicting neurological outcomes in post cardiac resuscitation cases as compared to more traditional prognostic scoring systems and tools. Previously, outcome determining clinical, blood and radiological factors were prone to other influencing factors like limited accuracy and time constraints. Studies conducted also emphasized that to predict poor neurological outcomes, a more multimodal approach helped adjust for confounding factors, interpret diverse datasets and provide a reliable prognosis which only demonstrates need for AI to help overcome challenges faced. Advanced machine learning algorithms like Artificial Neural Networks (ANN) using supervised learning by AI have improved accuracy of prognostic models outperforming conventional models. Several real-world cases of effective AI powered algorithm models have been cited here. Studies comparing machine learning tools like XGBoost, AI Watson, hyperspectral imaging, ChatGPT-4 and AI based gradient boosting have noted their beneficial uses. AI could help reduce workload, healthcare costs and help personalize care, process vast genetic and lifestyle data and help reduce side effects from treatments. Limitations of AI have been covered extensively in this article including data quality, bias, privacy issues and transparency. Our objectives should be to use more diverse data sources, use interpretable data output giving process explanation, validation method and implement policies to safeguard patient data. Despite the limitations, the advancements already made by AI and its potential in predicting neurological outcomes in post cardiac resuscitation cases has been quite promising and boosts a continually improving system, albeit requiring close human supervision with training and improving models, with plans to educate clinicians, the public and sharing collected data.

Development and Validation of a Clinical Score to Predict Epilepsy After Cerebral Venous Thrombosis

One of 10 patients develop epilepsy in the late phase after cerebral venous thrombosis (CVT) diagnosis but predicting the individual risk is difficult. To develop and externally validate a prognostic score to estimate the individual risk of post-CVT epilepsy. This observational cohort study included both retrospective and prospective patients enrolled from 1994 through 2022. For development of the DIAS3 score, data from the International CVT Consortium (n = 1128), a large international hospital-based multicenter CVT cohort, were used. For validation, data from 2 independent multicenter cohorts, the ACTION-CVT (n = 543) and the Israel CVT study (n = 556), were used. Of 2937 eligible, consecutively enrolled adult patients with radiologically verified CVT, 710 patients with a history of epilepsy prior to CVT, follow-up less than 8 days, and missing late seizure status were excluded. The prediction score (DIAS3) was developed based on available literature and clinical plausibility and consisted of 6 readily available clinical variables collected during the acute phase: decompressive hemicraniectomy, intracerebral hemorrhage at presentation, age, seizure(s) in the acute phase (excluding status epilepticus), status epilepticus in the acute phase, and subdural hematoma at presentation. Time to a first late seizure, defined as occurring more than 7 days after diagnosis of CVT. Of 1128 patients included in the derivation cohort (median age, 41 [IQR, 30-53] years; 805 women [71%]), 128 (11%) developed post-CVT epilepsy during a median follow-up of 12 (IQR, 3-26) months. According to the DIAS3 score, the predicted 1-year and 3-year risk of epilepsy in individual patients ranged from 7% to 68% and 10% to 83%, respectively. Internal and external validation showed adequate discrimination in the derivation cohort (1 year and 3 years: C statistic, 0.74; 95% CI, 0.70-0.79) and the 2 independent validation cohorts, (ACTION-CVT) 1 year: C statistic, 0.76; 95% CI, 0.67-0.84; 3 years: C statistic, 0.77; 95% CI, 0.66-0.84; and Israel CVT study 1 year: C statistic, 0.80; 95% CI, 0.75-0.86. Calibration plots indicated adequate agreement between predicted and observed risks. The DIAS3 score (freely available online) is a simple tool that can help predict the risk of post-CVT epilepsy in individual patients. The model can improve opportunities for personalized medicine and may aid in decision-making regarding antiseizure medication, patient counseling, and facilitation of research on epileptogenesis in CVT.

Predicting outcomes in patients with exacerbation of COPD requiring mechanical ventilation

BackgroundAcute exacerbations of COPD (AECOPD) are common and significantly contribute to mortality in patients with COPD. Prognostic scores can assist clinicians in making tailored decisions to manage AECOPD. In the current study, we therefore aimed to evaluate the performance of the Noninvasive Ventilation Outcomes (NIVO) score, originally designed to assess in-ICU mortality, in predicting 1 year mortality and NIV failure in AECOPD.MethodsThis retrospective study analyzed data from patients hospitalized for AECOPD requiring mechanical ventilation between January 1st, 2018, and December 31st, 2022. Mortality was assessed at the end of ICU stay and 1 year after admission, while NIV failure was defined as intubation or death without intubation.ResultsAmong 302 ICU admissions of COPD patients, 190 patients with AECOPD requiring mechanical ventilation were included. Of these, 44 (23%) died in the ICU, 62 out of 184 (34%) failed NIV, and 78 (41%) died within 1 year of admission. Patients who died in ICU or experienced NIV failure had more severe COPD and more impaired blood gas parameters at admission. The NIVO score demonstrated an AUC of 0.68 in predicting 1-year mortality and an AUC of 0.85 in predicting NIV failure. A NIVO score over 7 was associated with higher 1-year mortality and NIV failure (HR of 4.4 [1.8–10.9] and 41.6 [5.6–307.9], respectively).ConclusionBeyond predicting in-ICU mortality, the NIVO-score is a reliable tool in predicting 1-year mortality and NIV failure in AECOPD.

Comparison of Different Prognostic Scoring Systems in Predicting the Severity and Outcome in Alcoholic Hepatitis in Indian Patients

Abstract Introduction: Alcohol abuse causes a variety of liver diseases including alcoholic steatosis, alcoholic hepatitis (AH), liver fibrosis, cirrhosis, and hepatocellular carcinoma. AH is a clinical syndrome which typically occurs after the harmful use of alcohol. There are mild forms that are likely to improve with conservative management, whereas severe cases are associated with a high mortality rate and have a high risk of death despite treatment. The identification of severe AH is important, as this can be associated with 30%–50% short-term mortality. Existing prognostic scores have been validated in the western population, and the data of application in the Indian population are scant. Materials and Methods: It is a prospective observational study conducted for 2 years at Tertiary Care Hospital Army Hospital (Research and Referral) Delhi. The sample size was kept to be 45. For each patient, clinical and laboratory values were obtained on the day of admission and used to calculate the prognostic models according to their formulas. The various scores – Maddrey’s discriminant function (mDF), Glasgow AH score (GAHS), age, bilirubin, international normalized ratio (INR), creatinine score, and model of end-stage liver disease (MELD) including its modification MELD-sodium were calculated. For patients started on steroids, early changes in bilirubin level and Lille score were calculated. Results: All the prognostic scores were found to be good predictors of mortality. When compared with each other, MELD was most indicative of mortality with maximum area under the receiver operating characteristic curve. All the scores were significantly higher in the nonsurvivors than the survivors (P &lt; 0.0001). Conclusion: AH is a clinical syndrome that occurs after the harmful use of alcohol and confers a high mortality. Effective treatment and support for patients with AH is dependent on a proper assessment of the disease severity and estimation of the risk of death as early in the course of the disease as possible.

The clinical prognostic risk stratification system for HIV infected hepatocellular carcinoma

BackgroundPatients with human immunodeficiency virus (HIV) are more susceptible to liver cancer because of their compromised immune system. There is no specific prognostic model for HIV-infected hepatocellular carcinoma (HCC) patients. MethodsClinical data of 85 patients with HIV-infected HCC was divided into a 7:3 ratio for training and internal validation sets, while the data of 23 patients with HIV-infected HCC was served as the external validation set. Data of 275 HIV-negative HCC patients was considered as external HIV-negative validation set. Variables associated with overall survival (OS) in the training set were used to develop the HIV-infected HCC prognosis (HIHP) model. The model was tested in the internal and external validation sets. The predictive accuracy of the model was assessed with conventional HIV-negative HCC prognostic scoring systems. ResultsIn the training set, variables independently associated with OS in multivariable analysis were organ involvement and tumor number. The HIHP model demonstrated a significant association with OS in the training set, with a median OS of 13 months for low risk, 7 months for medium risk, and 3 months for high risk (p < 0.001). The HIHP model showed a significant association with OS, and exhibited greater discriminative abilities compared to conventional HIV-negative HCC prognostic models both in the internal and external validation sets. In the external HIV-negative validation set, the HIHP model did not show better discrimination than conventional HIV-negative HCC scores. ConclusionThe new model presented in the work provided a more accurate prognostic prediction of OS in HIV-infected HCC patients. However, the model is not applicable to patients with HIV-negative HCC.

Prognostic Value of Myosteatosis and Albumin-Bilirubin Grade for Survival in Hepatocellular Carcinoma Post Chemoembolization.

This study aimed to investigate the prognostic value of preoperative myosteatosis and the albumin-bilirubin (ALBI) grade in patients with hepatocellular carcinoma (HCC) undergoing transarterial chemoembolization (TACE) and develop a robust prognostic score based on these factors. Patients with HCC who underwent TACE between January 2009 and December 2020 were included. Multivariate Cox regression analysis identified prognostic factors. CT-based body composition parameters were acquired from baseline abdominal CT images at the level of the third lumbar vertebra. A prognostic score (Myo-ALBI) was developed based on the presence of preoperative myosteatosis and the ALBI grade, and its prognostic value was evaluated. Of 446 patients, 63% were male, and the mean age was 62.4 years. Preoperative myosteatosis was present in 41.5% of patients. The BCLC stages were mostly B (67.9%). Multivariate analysis shows that preoperative myosteatosis, ALBI grade 2, and ALBI grade 3 were independent prognostic factors. The Myo-ALBI grade was incorporated into a prognostic model, including alpha-fetoprotein and up-to-seven criteria, to generate a nomogram. The C-index of the nomogram based on the Myo-ALBI grade (0.743) was significantly higher than the non-Myo-ALBI nomogram (0.677), the up-to-seven criteria (0.653), the ALBI grade (0.616), and the Child-Pugh class (0.573) (all p < 0.05). The t-ROC curve for the nomogram was consistently superior to the other models throughout the observation period in all patients and the BCLC-B subgroup. The combination of preoperative CT-derived myosteatosis and ALBI grade enhances prognostication for patients with unresectable HCC undergoing TACE. The Myo-ALBI nomogram constructed in this study could support individualized prognosis prediction, assisting in treatment decision-making for HCC patients.

P17.01.A ANAMNESTIC RADIOLOGICAL METASTASES OUTCOME SURGICAL SCORE (ARMO-S). A PURPOSE OF A PREDICTIVE SCORING SYSTEM FOR SURGICAL BRAIN METASTASES

Abstract BACKGROUND Several risk stratification scores have been suggested to aid prognostication and guide treatment strategies for brain metastases (BMs). However, the current scores do not focus on the specific neurosurgical population, therefore not predicting short-term mortality and postoperative performance status. MATERIAL AND METHODS This retrospective observational study of 362 consecutive patients treated with surgery for BMs aims to identify the factors associated with post-surgical outcomes and propose a surgery-specific prognostic score for patients with BMs candidate for open surgery. RESULTS Factors significantly associated with OS and performance status in multivariate analysis were age, KPS, surgical site, synchronous debut of BM, number, tumor volume, seizure, extra-cranial metastases, and deep-seated location. The variables were incorporated into the Anamnestic Radiological Metastases Outcome Surgical score (ARMO-S). The values range between 0 and 10. Patients were divided into two groups (low-risk and high-risk) based on each significant subgroup’s median survival and performance status with an optimal cutoff value determined as 4. The two groups have significant differences in OS (9.6 versus 14 months, p = 0.0048) postoperative KPS (90 versus 70, p=0.012) and KPS at last follow-up evaluation (75 versus 30, p&amp;lt;0.001) CONCLUSION ARMO-S is a simple and comprehensive score for BM patients selected for neurosurgery, as it incorporates the main factors of the most important prognostic scores, implementing them with more surgery-specific predictive elements such as tumor location and volume, presence of seizures at onset, and involvement of eloquent brain areas.

Explore the expression of mitochondria-related genes to construct prognostic risk model for ovarian cancer and validate it, so as to provide optimized treatment for ovarian cancer.

The use of gene development data from public database has become a new starting point to explore mitochondrial related gene expression and construct a prognostic prediction model of ovarian cancer. Data were obtained from the TCGA and ICGC databases, and the intersection with mitochondrial genes was used to obtain the differentially expressed genes. q-PCR, Cox proportional risk regression, minimal absolute contraction and selection operator regression analysis were performed to construct the prognostic risk model, and ROC curve was used to evaluate the model for centralized verification. The association between risk scores and clinical features, tumor mutation load, immune cell infiltration, macrophage activation analysis, immunotherapy, and chemosensitivity was further evaluated. A prognostic risk score model for ovarian cancer patients was constructed based on 12 differentially expressed genes. The score was highly correlated with ovarian cancer macrophage infiltration and was a good predictor of the response to immunotherapy. M1 and M2 macrophages in the ovarian tissue in the OV group were significantly activated, providing a reference for the study of the polarity change of tumor-related macrophages for the prognosis and treatment of ovarian cancer. In terms of drug sensitivity, the high-risk group was more sensitive to vinblastine, Acetalax, VX-11e, and PD-0325901, while the low-risk group was more sensitive to Sabutoclax, SB-505124, cisplatin, and erlotinib. The prognostic risk model of ovarian cancer associated to mitochondrial genes built on the basis of public database better evaluated the prognosis of ovarian cancer patients and guided individual treatment.

AML typical mutations (CEBPA, FLT3, NPM1) identify a high-risk chronic myelomonocytic leukemia independent of CPSS molecular

AbstractMutations commonly associated with acute myeloid leukemia (AML), such as CEBPA, FLT3, IDH1/2, and NPM1, are rarely found in chronic myelomonocytic leukemia (CMML), and their prognostic significance in CMML has not been clearly identified. In 127 patients with CMML, we have retrospectively analyzed next-generation sequencing and polymerase chain reaction data from bone marrow samples collected at the time of CMML diagnosis. Seven patients harbored CEBPA mutations, 8 FLT3 mutations, 12 IDH1 mutations, 26 IDH2 mutations, and 11 NPM1 mutations. Patients with CMML harboring CEBPA, FLT3, and/or NPM1 mutations (mutCFN) more frequently had the myeloproliferative subtype, a high prevalence of severe cytopenia, and elevated blast counts. Regardless of their CMML Prognostic Scoring System molecular classification, mutCFN patients with CMML had a poor prognosis, and the multivariate analysis identified mutCFN as an independent marker of overall survival. The genetic profile of these mutCFN patients with CMML closely resembled that of patients with AML, with higher-risk clinical characteristics. Our findings lead us to suggest including the assessment of these mutations in CMML prognostic models and treating these patients with AML-type therapies, including intensive chemotherapy and allogeneic stem cell transplantation, whenever feasible. Furthermore, certain targeted therapies approved for use in AML should be considered.

Global Leadership Initiative on Malnutrition Criteria and Immunonutritional Status Predict Chemoadherence and Survival in Stage II/III Gastric Cancer Treated with XELOX Chemotherapy.

Adjuvant chemotherapy is crucial for the treatment of advanced gastric cancer. However, various factors negatively impact chemoadherence, with malnutrition after gastrectomy being a critical determinant. This study aims to analyze the impact of malnutrition, assessed through the Global Leadership Initiative on Malnutrition (GLIM) and other immunonutritional indices, on chemoadherence and its subsequent effect on survival. This retrospective study included 116 patients who underwent curative gastrectomy and received oxaliplatin and capecitabine (XELOX). Preoperative nutritional status was assessed using the GLIM criteria along with other immunonutritional indices, such as the prognostic nutritional index (PNI), C-reactive protein-to-albumin ratio (CAR), neutrophil-lymphocyte ratio (NLR), controlling nutritional status (CONUT) score, and modified Glasgow Prognostic Score (mGPS). Chemotherapy adherence was measured using relative dose intensity (RDI). Statistical analyses included least absolute shrinkage and selection operator (LASSO) regression to identify the key predictors of RDI and Cox proportional hazards models and assess the impact on survival. Overall, 116 patients were included in this analysis. In the multivariate analysis using LASSO regression, higher GLIM severity was independently associated with a lower RDI (coefficient = -0.0216; p < 0.01). Other significant factors influencing RDI included older age (p < 0.01), female sex (p = 0.02), higher mGPS (p = 0.03), higher CONUT score (p = 0.04), and higher CAR (p = 0.05), all of which were associated with a lower RDI. The Cox proportional hazards analysis revealed that higher RDI was significantly associated with better survival (hazard ratio [HR] = 0.06; p < 0.005). This study highlights the critical role of immunonutritional status, particularly as measured using the GLIM criteria, in maintaining adherence to chemotherapy and improving survival outcomes in patients with gastric cancer. Routine preoperative nutritional assessments using GLIM can help identify high-risk patients, and early nutritional interventions may improve chemotherapy adherence and outcomes. These findings support the integration of nutritional strategies, specifically targeting those identified by the GLIM, into standard care to enhance the efficacy and survival of chemotherapy.