Prognostic System Research Articles

Prescreening bank failures with K-means clustering: Pros and cons

To study the merits of the popular K-means clustering technique while predicting failures of commercial banks, we contrast hereafter two forecasting systems. The first one contains two complementary stages, with unsupervised K-means clustering followed by logistic regression deployed over the three most hazardous clusters (out of five) formed. The second system incorporates logistic regression over the entire sample of banks. We find that the first prognostic system is relatively strict. It better identifies bank failures beforehand, but it also projects more potential failures among the solvent banks. The second system is more lenient. It does not identify in advance actual bank failures in many cases, yet it does not speculate failures for many solvent banks either. The second system achieves a slightly higher overall predictive power than the first system. The minor statistical disadvantage of the K-means clustering is observed mainly because of the scarcity of bank failures in practice. The K-means clustering prescreening stage intensifies the systemic costs associated with type-II errors (predicting failures for solvent banks), but it simultaneously reduces the systemic costs linked to type-I errors (not predicting failures for eventually failed banks). Overall, the K-means clustering prescreening technique has prospective economic advantages, as it assists in slashing the total simulated systemic costs.

Pulmonary function as a continuum of risk: critical care utilization and survival after allogeneic hematopoietic stem cell transplantation - a multicenter cohort study.

Abnormal pre-transplant pulmonary function tests (PFTs) are associated with reduced survival after allogeneic HCT. Existing scoring systems consider risk dichotomously, attributing risk only to those with abnormal lung function. In a multicenter cohort of 1717 allo-HCT recipients, we examined the association between pre-transplant PFT measures and need for ICU admission (120d), frequency of mechanical ventilation (120d) and overall survival (5 y). Predictive models were developed and validated using Cox proportional hazards, incorporating age, FEV1 (forced expiratory volume in 1-second) and diffusing capacity (DLCO). In univariate analysis, hazard ratios for each outcome (95% CI) were: mechanical ventilation (FEV1: 0.60 [0.52-0.69], DLCO: 0.69 [0.61-0.77], p < 0.001), ICU admission (FEV1: 0.74 [0.67-0.82], DLCO: 0.79 [0.72-0.86], p < 0.001) and overall survival (FEV1: HR 0.87 [0.81-0.94], DLCO: 0.83 [0.77-0.89], p < 0.001). A multivariable Cox model was developed and compared to the HCT-CI Pulmonary score in a validation cohort. This model was better at predicting need for ICU admission and mechanical ventilation, while both models predicted overall survival (p < 0.001). In conclusion, the risk conferred by pre-transplant pulmonary function should be considered in a continuous rather than dichotomous manner. A more granular prognostication system can better inform risk of critical care utilization in the early post-HCT period.

Comparison of Manchester, qSOFA, emergency severity index, and national early warning scores for prognostic estimation and effective triage system in geriatric patients.

Triage refers to classifying and prioritizing patients based on the severity of their injuries or illnesses in the health care setting. The increasing number of elderly patients seeking care in emergency departments (EDs) highlights the need for special attention to the unique needs of this patient population. We aimed to compare the qSOFA, Emergency Severity Index (ESI), National Early Warning Score (NEWS), and Manchester Triage System (MTS) scores to assist ED physicians in assessing the severity of elderly patients' clinical conditions and triaging them appropriately. This cross-sectional study included 1066 patients aged 65 and over who presented to our ED as outpatients or by ambulance between September 1, 2022, and August 30, 2023. Scoring systems at the time of admission to the ED were recorded separately for outpatients and arriving by ambulance. According to the qSOFA, patients with a score of 0 were 0.976 times less likely to arrive by ambulance compared to those scoring 1 and above (OR = 0.976, p = .934). According to the NEWS, patients in the moderate-risk category were 0.447 times less likely to arrive by ambulance (OR = 0.447, p = .054). According to the ESI score, patients requiring high resource use with normal vital signs were 146.758 times more likely to arrive by ambulance (OR = 146.758, p = .001). Significant differences in patients' methods of presentation to the ED were observed based on the MTS, qSOFA, NEWS, and ESI scores.

Development and Methodological Validation of a Modified Staging System for de Novo Metastatic Breast Cancer

Validation of a new method for prognostication of de novo metastatic breast cancer (dnMBC) to better reflect the heterogenecity of the disease. To perform external methodological validation of the Plichta staging system, a novel prognostic system for de novo metastatic breast cancer (dnMBC). This retrospective cohort study used a multicenter, nationwide, population-based Danish Breast Cancer Group database to validate the new method. Participants were patients with dnMBC diagnosed between 2010 and 2019. Data were analyzed from April to June 2023. A recursive partitioning analysis (RPA) was performed, as demonstrated by Plichta and colleagues, to group patients with similar overall survival (OS) based on clinical factors. The main outcome was to group patients into 4 prognostic groups based on 3-year OS as stage IVa, greater than 70%; stage IVb, 50% to 70%; stage IVc, 25% to less than 50%; or stage IVd, less than 25%. Bootstrapping was applied for 1000 iterations, with final stage assignments based on the most commonly occurring assignment. A total of 1859 women were included with a median (IQR) age of 69 (57-77) years. With a median potential follow-up of 89.9 (95% CI, 86.4-95.1) months and a median OS of 31.7 (95% CI, 29.5-34.1) months, the RPA stratified patients into 10 groups, with organ sites, estrogen receptor status, and human epidermal growth factor receptor 2 status as the key clinical factors. Three-year survival rates ranged from 62% (95% CI, 56%-69%) to 8% (95% CI, 3%-21%), which were further combined into 3 stage groups: IVb, 59.4% (95% CI, 56.2%-62.8%); IVc, 39.4% (95% CI, 36.2%-43.0%); and IVd, 15.4% (95% CI, 11.2%-21.3%) (P < .001). Following bootstrapping, an IVa group emerged, resulting in 4 stage groups with separate 3-year OS rates identified as IVa, 75.8% (95% CI, 67.8%-84.7%); IVb, 58.8% (95% CI, 55.5%-62.3%); IVc, 39.2% (95% CI, 35.8%-43.0%); and IVd, 14.4% (95% CI, 10.8%-19.4%) (P < .001). These findings provide external and independent validation of the methods applied in the novel Plichta staging system for dnMBC. This could guide future revisions of the current American Joint Committee on Cancer staging guidelines and may be incorporated as a stratification factor in clinical trials.

An Integrated Monitoring, Diagnostics, and Prognostics System for Aero-Engines under Long-Term Performance Deterioration

In the field of aircraft engine diagnostics, many advanced algorithms have been proposed over the last few years. However, there is still wide room for improvement, especially in the development of more integrated and complete engine health management systems to detect, identify, and forecast complex faults in a short time. Furthermore, it is necessary to ensure that these systems preserve their capabilities over time despite engine deterioration. This paper addresses these necessities by proposing an integrated system that considers the joint operation of feature extraction, anomaly detection, fault identification, and prognostic algorithms for engines with long operation times. To effectively reveal the actual engine condition, light adaptive degraded engine models are computed along with different health indicators that are used as inputs to train and test recognition and prediction models. The system is developed and evaluated using a specialized NASA platform which provides data from a turbofan engine fleet simultaneously experiencing long-term performance deterioration and faults. Contrary to other compared solutions, our results show that the proposed system is robust against the effects of engine deterioration, maintaining its level of detection, recognition, and prediction accuracy over a total engine service life. The low computational cost algorithms has generally fast performance in all stages, making the system suitable for online applications.

A novel prognostic risk-scoring system based on m5C methylation regulator-mediated patterns for glioma patients

N5-methylcytosine (m5C) methylation modification plays a crucial role in the epigenetic mechanisms underlying tumorigenesis, aggressiveness, and malignancy in diffuse glioma. Our study aimed to develop a novel prognostic risk-scoring system to assess the impact of m5C modification in glioma patients. Initially, we identified two distinct m5C clusters based on the expression level of m5C regulators in The Cancer Genome Atlas glioblastoma (TCGA-GBM) dataset. Differentially expressed genes (DEGs) between the two m5C cluster groups were determined. Utilizing these m5C regulation-related DEGs, we classified glioma patients into three gene cluster groups: A, B, and C. Subsequently, an m5C scoring system was developed through a univariate Cox regression model, quantifying the m5C modification patterns utilizing six DEGs associated with disease prognosis. The resulting scoring system allowed us to categorize patients into high- or low-risk groups based on their m5C scores. In test (TCGA-GBM) and validation (Chinese Glioma Genome Atlas [CGGA]-1018 and CGGA-301) datasets, glioma patients with a higher m5C score consistently exhibited shorter survival durations, fewer isocitrate dehydrogenase (IDH) mutations, less 1p/19q codeletion and higher World Health Organization (WHO) grades. Additionally, distinct immune cell infiltration characteristics were observed among different m5C cluster groups and risk groups. Our study developed a novel prognostic scoring system based on m5C modification patterns for glioma patients, complementing existing molecular classifications and providing valuable insights into prognosis for glioma patients.

Prognostic mortality factors in advanced light chain cardiac amyloidosis: A prospective cohort study.

Predicting mortality in severe AL cardiac amyloidosis is challenging due to elevated biomarker levels and limited thresholds for stratifying severe cardiac damage. This prospective, observational, cohort study included de novo, confirmed cardiac AL amyloidosis patients at the Henri Mondor National Reference Centre. The goal was to identify predictors of mortality to enhance prognostic stratification and improve informed decision-making regarding therapy. Over the 12-year study period, among the 233 patients included, 133 were NYHA III-IV and 179 Mayo 2004 III. The independent predictors for mortality identified were hsTnT, NT-proBNP, cardiac output, and conjugated bilirubin. A novel prognostic, conditional stratification, Mondor amyloidosis cardiac staging (MACS) was developed with biomarker cut-off values for Stage 1: hsTnT≤107ng/L and NT-proBNP≤3867ng/L (n=77; 33%); for stage 2 NT-proBNP>3867ng/L (n=72; 30%). For stage 3, if troponin >107ng/L, regardless of NT-proBNP then CB 4μmol/L, was added (n=41; 17.5%) and stage 4: CB>4μmol/L (n=43; 18.5%). The median overall survival was 8months 95% CI [2-24]. At 1year, 102 (44%) patients died and the Kaplan-Meier median survival with MACS Stage 1 was not reached, while stage 2 was 15.2months (95% CI [11-18]) and stage 3, 6.6months (95% CI [1-13]). Notably, among European stage II patients, 17.1%, n=8 were MACS stage 3 and European stage IIIb 21.4% (n=23) were MACS stage 4. Importantly, among European stage IIIb patients 42.2% (n=29) were classified MACS stage 4 and 12.5% n=9 were only MACS stage 2. The Mondor prognostic staging system, including conjugate bilirubin may significantly improve prognostic stratification for patients with severe cardiac amyloidosis.

Dynamic Survival Risk Prognostic Model and Genomic Landscape for Atypical Teratoid/Rhabdoid Tumors: A Population-Based, Real-World Study.

An atypical teratoid/rhabdoid tumor (AT/RT) is an uncommon and aggressive pediatric central nervous system neoplasm. However, a universal clinical consensus or reliable prognostic evaluation system for this malignancy is lacking. Our study aimed to develop a risk model based on comprehensive clinical data to assist in clinical decision-making. We conducted a retrospective study by examining data from the Surveillance, Epidemiology, and End Results (SEER) repository, spanning 2000 to 2019. The external validation cohort was sourced from the Children's Hospital Affiliated to Chongqing Medical University, China. To discern independent factors affecting overall survival (OS) and cancer-specific survival (CSS), we applied Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF) regression analyses. Based on these factors, we structured nomogram survival predictions and initiated a dynamic online risk-evaluation system. To contrast survival outcomes among diverse treatments, we used propensity score matching (PSM) methodology. Molecular data with the most common mutations in AT/RT were extracted from the Catalogue of Somatic Mutations in Cancer (COSMIC) database. The annual incidence of AT/RT showed an increasing trend (APC, 2.86%; 95% CI:0.75-5.01). Our prognostic study included 316 SEER database participants and 27 external validation patients. The entire group had a median OS of 18 months (range 11.5 to 24 months) and median CSS of 21 months (range 11.7 to 29.2). Evaluations involving C-statistics, DCA, and ROC analysis underscored the distinctive capabilities of our prediction model. An analysis via PSM highlighted that individuals undergoing triple therapy (integrating surgery, radiotherapy, and chemotherapy) had discernibly enhanced OS and CSS. The most common mutations of AT/RT identified in the COSMIC database were SMARCB1, BRAF, SMARCA4, NF2, and NRAS. In this study, we devised a predictive model that effectively gauges the prognosis of AT/RT and briefly analyzed its genomic features, which might offer a valuable tool to address existing clinical challenges.

Machine Learning to Allocate Palliative Care Consultations During Cancer Treatment.

For patients with advanced cancer, early consultations with palliative care (PC) specialists reduce costs, improve quality of life, and prolong survival. However, capacity limitations prevent all patients from receiving PC shortly after diagnosis. We evaluated whether a prognostic machine learning system could promote early PC, given existing capacity. Using population-level administrative data in Ontario, Canada, we assembled a cohort of patients with incurable cancer who received palliative-intent systemic therapy between July 1, 2014, and December 30, 2019. We developed a machine learning system that predicted death within 1 year of each treatment using demographics, cancer characteristics, treatments, symptoms, laboratory values, and history of acute care admissions. We trained the system in patients who started treatment before July 1, 2017, and evaluated the potential impact of the system on PC in subsequent patients. Among 560,210 treatments received by 54,628 patients, death occurred within 1 year of 45.2% of treatments. The machine learning system recommended the same number of PC consultations observed with usual care at the 60.0% 1-year risk of death, with a first-alarm positive predictive value of 69.7% and an outcome-level sensitivity of 74.9%. Compared with usual care, system-guided care could increase early PC by 8.5% overall (95% CI, 7.5 to 9.5; P < .001) and by 15.3% (95% CI, 13.9 to 16.6; P < .001) among patients who live 6 months beyond their first treatment, without requiring more PC consultations in total or substantially increasing PC among patients with a prognosis exceeding 2 years. Prognostic machine learning systems could increase early PC despite existing resource constraints. These results demonstrate an urgent need to deploy and evaluate prognostic systems in real-time clinical practice to increase access to early PC.

Factors affecting prognosis in myelodysplastic syndrome: an 11 years’ experience from a tertiary care center

Aims: Myelodysplastic syndrome (MDS) is a clonal bone marrow neoplasia characterized by morphological findings of dysplasia in hematopoietic cells, peripheral cytopenia(s), ineffective hematopoiesis, recurrent genetic abnormalities, and an increased risk of conversion to acute myeloid leukemia (AML). The International Prognostic Scoring System (IPSS) is the most commonly used prognostic classification system for MDS. Classification was made by a combination of morphology, cytopenia, and genetic studies. In this study, we aimed to examine the parameters that affect prognosis in MDS patients, show their effects on mortality, and evaluate their positive or negative effects on the course of the disease. Methods: Two hundred twenty-nine patients who applied to Erciyes University Faculty of Medicine, Department of Hematology, and were diagnosed with MDS according to WHO classification between 2010 and 2020 were included in this retrospective study. Age, gender, additional disease status, laboratory parameters, bone marrow biopsy materials, and genetic mutation analysis data were available. The bone marrow aspiration and biopsy examinations of each patient were evaluated and categorized according to the WHO classification. The prognosis was evaluated according to the data of the patients, survival-exitus, and survival after MDS-AML conversion. Risk scoring was analyzed with three different scoring systems (IPSS, WPSS, and R-IPSS). Results: Of the 229 MDS patients included in the study, 57% (131) were male. The mean age of the patients was 67 years. Age, MDS-AML conversion times, disease duration, cellularity, and pathology blast rate were found to be statistically significant between the groups (p&lt;0.05). Leukocyte, neutrophil, platelet, hematocrit, lymphocyte, monocyte, CRP, erythropoietin, ferritin, and LDH data were found to be statistically significant regarding survival (p&lt;0.05). Age, IPSS risk status 3, and W-PSS risk status 3 were found to be independent risk factors affecting survival. Conclusion: Age, IPSS high risk, and WPSS high risk status were found to be independent risk factors affecting survival. Although our study revealed important data in the analysis of MDS patients, single-center analysis of patients and retrospective analysis revealed the need for further studies.

Evaluation of the correlation of neutrophil/lymphocyte ratio with prognostic classification systems in papillary thyroid carcinoma patients.

We aimed to evaluate the use of cost-effective NLR (Neutrophil Lymphocyte Ratio) in determining the prognosis and recurrence risk of thyroid papillary carcinoma patients. This retrospective, cross-sectional and single-center study was carried out in the Department of General Surgery, Istanbul Gaziosmanpaşa Training and Research Hospital. Between 2018 and 2021, who were diagnosed with papillary thyroid cancer and underwent total thyroidectomy, and patients who underwent total thyroidectomy due to multinodular goiter were analyzed. For patients in the malignancy group, the NLR cutoff value was determined as 1.73, the sensitivity was calculated as 51.77% and the specificity as 86.15%. NLR in the malignant group was found to be 9.5 times higher than the NLR in the control group (Odds Ratio: 9.5). A statistically significant difference was found between NLR and papillary thyroid carcinoma prognostic classification systems (AJCC/TNM, AMES, and MACIS). NLR medians differ according to ATA recurrence risk classification (P = .020). According to the results we obtained in our study, we believe that cost-effective NLR can be a useful indicator in terms of predicting malignancy in a patient with thyroid nodule and in determining the prognosis and risk of recurrence in patients with thyroid papillary carcinoma.

Granulocyte-monocyte/macrophage apheresis for steroid-nonresponsive or steroid-intolerant severe alcohol-associated hepatitis: A pilot study.

Patients with severe alcohol-associated hepatitis (SAH) have a high short-term mortality rate. Unmet needs exist in patients who are refractory to corticosteroids (CS) or are ineligible for early liver transplantation. This was a prospective, open-label, nonrandomized pilot study conducted at a liver transplant center in Tokyo, Japan, starting in October 2015. Lille model and Model for End-stage Liver Disease (MELD) score-defined CS nonresponsive or CS-intolerant patients with SAH who fulfilled the inclusion criteria (leukocytosis over 10,000/μL, etc.) were considered for enrollment. The median duration from admission to enrollment was 23 days (IQR, 14-31 days), after standard of care. Granulocyte-monocyte/macrophage apheresis (GMA) performed with Adacolumn twice per week, up to 10 times per treatment course, was evaluated. 13 GMA treatments were conducted through December 2021. Maddrey Discriminant Function was 53.217.7 at admission. The overall survival rate was 90.9% at 90 and 180 days. MELD scores significantly improved, from median (IQRs) of 23 (20-25) to 15 (13-21) after GMA (p<0.0001). Estimated mortality risks using the Lille model and MELD scores significantly improved from 20.9%±16.5% to 7.4%±7.3% at 2 months and from 30.4%±21.3% to 11.6%±10.8% at 6 months, respectively (both p<0.01), and were internally validated. The cumulative rate of alcohol relapse was 35.9% per year. No severe adverse events were observed. In exploratory analysis, granulocyte colony-stimulating factor levels were significantly correlated with prognostic systems such as MELD-Sodium scores after GMA (correlation coefficient= -0.9943, p<0.0001) but not before GMA (p=0.62). Compared to published studies, GMA is associated with a lower-than-expected 90- and 180-day mortality in patients with CS-nonresponsive or CS-intolerant SAH. GMA may meet the needs as a salvage anti-inflammatory therapy for SAH. (Trial registration: UMIN000019351 and jRCTs No.032180221) (274 words).

Prognostic outcomes in patients with metastatic renal cell carcinoma receiving second-line treatment with tyrosine kinase inhibitor following first-line immune-oncology combination therapy.

This study aimed to assess the prognostic outcomes in mRCC patients receiving second-line TKI following first-line IO combination therapy. This study retrospectively included 243 mRCC patients receiving second-line TKI after first-line IO combination therapy: nivolumab plus ipilimumab (n = 189, IO-IO group) and either pembrolizumab plus axitinib or avelumab plus axitinib (n = 54, IO-TKI group). Oncological outcomes between the two groups were compared, and prognostication systems were developed for these patients. In the IO-IO and IO-TKI groups, the objective response rates to second-line TKI were 34.4% and 25.9% (p = 0.26), the median PFS periods were 9.7 and 7.1 months (p = 0.79), and the median OS periods after the introduction of second-line TKI were 23.1 and 33.5 months (p = 0.93), respectively. Among the several factors examined, non-CCRCC, high CRP, and low albumin levels were identified as independent predictors of both poor PFS and OS by multivariate analyses. It was possible to precisely classify the patients into 3 risk groups regarding both PFS and OS according to the positive numbers of the independent prognostic factors. Furthermore, the c-indices of this study were superior to those of previous systems as follows: 0.75, 0.64, and 0.61 for PFS prediction and 0.76, 0.70, and 0.65 for OS prediction by the present, IMDC, and MSKCC systems, respectively. There were no significant differences in the prognostic outcomes after introducing second-line TKI between the IO-IO and IO-TKI groups, and the histopathology, CRP and albumin levels had independent impacts on the prognosis in mRCC patients receiving second-line TKI, irrespective of first-line IO combination therapies.

LASSO-derived prognostic model predicts cancer-specific survival in advanced pancreatic ductal adenocarcinoma over 50 years of age: a retrospective study of SEER database research.

This study aimed to develop a prognostic model for patients with advanced ductal adenocarcinoma aged ≥50 years. Patient information was extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to screen the model variables. Cases from Nanchang Central Hospital were collected for external validation. The new nomogram and the American Joint Committee on Cancer (AJCC) criteria were evaluated using integrated discrimination improvement (IDI) and net reclassification index (NRI) indicators. Survival curves presented the prognosis of the new classification system and AJCC criteria. In total, 17,621 eligible patients were included. Lasso Cox regression selected 4 variables including age, chemotherapy, radiotherapy and AJCC stage. The C-index of the training cohort was 0.721. The C-index value of the validation cohort was 0.729. The AUCs for the training cohorts at 1, 2, and 3 years were 0.749, 0.729, and 0.715, respectively. The calibration curves showed that the predicted and actual probabilities at 1, 2, and 3 years matched. External validation confirmed the model's outstanding predictive power. Decision curve analysis indicated that the clinical benefit of the nomogram was higher than that of the AJCC staging system. The model evaluation indices preceded the AJCC staging with NRI (1-year: 0.88, 2-year: 0.94, 3-year: 0.72) and IDI (1-year: 0.24, 2-year: 0.23, 3-year: 0.22). The Kaplan-Meier curves implied that the new classification system was more capable of distinguishing between patients at different risks. This study established a prognostic nomogram and risk classification system for advanced pancreatic cancer in patients aged ≥50 years to provide a practical tool for the clinical management of patients with pancreatic ductal adenocarcinoma.

Driver mutation zygosity is a critical factor in predicting clonal hematopoiesis transformation risk

Clonal hematopoiesis (CH) can be caused by either single gene mutations (eg point mutations in JAK2 causing CHIP) or mosaic chromosomal alterations (e.g., loss of heterozygosity at chromosome 9p). CH is associated with a significantly increased risk of hematologic malignancies. However, the absolute rate of transformation on an annualized basis is low. Improved prognostication of transformation risk is urgently needed for routine clinical practice. We hypothesized that the co-occurrence of CHIP and mCAs at the same locus (e.g., transforming a heterozygous JAK2 CHIP mutation into a homozygous mutation through concomitant loss of heterozygosity at chromosome 9) might have important prognostic implications for malignancy transformation risk. We tested this hypothesis using our discovery cohort, the UK Biobank (n = 451,180), and subsequently validated it in the BioVU cohort (n = 91,335). We find that individuals with a concurrent somatic mutation and mCA were at significantly increased risk of hematologic malignancy (for example, In BioVU cohort incidence of hematologic malignancies is higher in individuals with co-occurring JAK2 V617F and 9p CN-LOH; HR = 54.76, 95% CI = 33.92–88.41, P < 0.001 vs. JAK2 V617F alone; HR = 44.05, 95% CI = 35.06–55.35, P < 0.001). Currently, the ‘zygosity’ of the CHIP mutation is not routinely reported in clinical assays or considered in prognosticating CHIP transformation risk. Based on these observations, we propose that clinical reports should include ‘zygosity’ status of CHIP mutations and that future prognostication systems should take mutation ‘zygosity’ into account.

Clinical outcomes of allogeneic hematopoietic stem cell transplantation for chronic myelomonocytic leukemia

Chronic myelomonocytic leukemia (CMML) is a clonal disease derived from bone marrow hematopoietic stem cells, with a poor prognosis. Allogeneic hematopoietic stem cell transplantation (allo- HSCT) is one of the curable methods for CMML. The outcome of patient transplantation is influenced by various factors such as disease characteristics and comorbidities. Based on the existing prognostic stratification system, screening suitable CMML patients for transplantation and early transplantation is beneficial for their long-term survival. Doctors can evaluate the survival status of CMML patients after transplantation based on the newly developed transplant prognosis model and make targeted medical decisions.

Consensus for the diagnosis and management of extramedullary plasmacytoma in China(2024)

Extramedullary plasmacytoma (EMP) is a special type of malignant plasmacytosis, which is complex and heterogeneous. Most EMP patients have poor prognosis and lack a stratified prognostic system or ideal treatment strategy supported by evidence-based medical evidence, which cannot meet clinical needs. In order to improve the understanding of this disease entity, Plasma Cell Disease Group, Chinese Society of Hematology, Chinese Medical Association and Chinese Myeloma Committee-Chinese Hematology Association developed the "Chinese Expert Consensus on the diagnosis and treatment of extramedullary plasmacytoma", which aims to standardize the clinical diagnosis and treatment of EMP and ultimately improve the overall survival of patients with plasmacytoma.

Impact of Sarcopenia on Prognosis in Primary Hepatocellular Carcinoma Patients Treated with Transcatheter Arterial Chemoembolization: A Single Center Retrospective Study.

Objective: This study aimed to investigate the prognostic effect of sarcopenia on primary hepatocellular carcinoma (HCC) patients after transcatheter arterial chemoembolization (TACE). Methods: This retrospective study enrolled 265 patients diagnosed with HCC who underwent TACE between April 2014 and February 2021. The patients were divided into two groups: the sarcopenia group (n=133) and the non-sarcopenia group (n=132). The study analyzed the differences in overall survival (OS) and progression-free survival (PFS) using Kaplan-Meier curves. The independent risk factors for OS and PFS were determined using univariate and multivariate Cox regression analysis. Based on these factors, the study constructed a prognostic risk grading system. Results: At 3 and 6 months post-TACE, the prognoses of the sarcopenia group were worse than that of the non-sarcopenia group according to the mRECIST criteria. Kaplan-Meier curves showed that the cumulative OS and PFS rate in the non-sarcopenia group were significantly higher compared to the sarcopenia group (HR=3.319, 95%CI: 2.283-4.824, Log-rank P < 0.001; HR=0.631, 95%CI: 0.486-0.820, Log-rank P < 0.001). Sarcopenia, maximal tumor diameter, and AFP ≥ 200 ng/mL were independent risk factors for OS and PFS. The prognostic risk grading system based on sarcopenia, AFP ≥ 200 ng/mL, and maximal tumor diameter≥8.9 cm showed significant differences in prognosis between risk groups. Conclusion: Sarcopenia had excellent predictive value for OS and PFS in patients after TACE, and AFP ≥ 200 ng/mL and maximal tumor diameter were also independent risk factors for a poor prognosis. The prognostic risk grading system based on sarcopenia, AFP, and maximal tumor diameter had good guiding value for the prognosis of patients.

Spar Cap/Shear Web Debonding Under Fatigue Loading Studied On The DTU 12.6m Wind Turbine Blade

Understanding fatigue damage initiation and propagation of composite wind turbine blades is an essential step towards the design of reliable prognostic and health management systems (PHM) and to improve lifetime predictions. This study presents a numerical investigation of the debonding process in the spar cap/shear web region of a full-scale composite wind turbine blade under fatigue loading and presents the manufacturing approach for creating initial debondings. For this purpose, numerical simulations have been performed to design and specify the size and locations of artificial defects to provoke steady, controllable damage growth in the adhesive bondline between the rear shear web and cap region of the DTU 12.6m wind turbine blade under fatigue loading. Subsequently, these artificial defects are embedded into the manufacturing process of the composite wind turbine blade with the help of slip foils and partially perforated slip foils to create regions with no bonding, 30% and 50% bonding of the contact surfaces of the adhesive joint, respectively.The numerical predictions as well as the manufactured and instrumented blade sets the framework to subsequently execute experimental tests of damage growth in the adhesive joint of the bondline in order to compare numerical and experimental data. The experimental demonstration will allow to better understand the complexity of damage growth of composite structures under fatigue loading and the necessity of accurate numerical prediction tools for reliable prognostic health management systems.

Precision prognosis of colorectal cancer: a multi-tiered model integrating microsatellite instability genes and clinical parameters.

Prognostic assessment for colorectal cancer (CRC) displays substantial heterogeneity, as reliance solely on traditional TNM staging falls short of achieving precise individualized predictions. The integration of diverse biological information sources holds the potential to enhance prognostic accuracy. To establish a comprehensive multi-tiered precision prognostic evaluation system for CRC by amalgamating gene expression profiles, clinical characteristics, and tumor microsatellite instability (MSI) status in CRC patients. We integrated genomic data, clinical information, and survival follow-up data from 483 CRC patients obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. MSI-related gene modules were identified using differential expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA). Three prognostic models were constructed: MSI-Related Gene Prognostic Model (Model I), Clinical Prognostic Model (Model II), and Integrated Multi-Layered Prognostic Model (Model III) by combining clinical features. Model performance was assessed and compared using Receiver Operating Characteristic (ROC) curves, Kaplan-Meier analysis, and other methods. Six MSI-related genes were selected for constructing Model I (AUC = 0.724); Model II used two clinical features (AUC = 0.684). Compared to individual models, the integrated Model III exhibited superior performance (AUC = 0.825) and demonstrated good stability in an independent dataset (AUC = 0.767). This study successfully developed and validated a comprehensive multi-tiered precision prognostic assessment model for CRC, providing an effective tool for personalized medical management of CRC.