Development Of Prognostic Models Research Articles

The Role of m6A Methylation in Tumor Immunity and Immune-Associated Disorder.

N6-methyladenosine (m6A) represents the most prevalent and significant internal modification in mRNA, with its critical role in gene expression regulation and cell fate determination increasingly recognized in recent research. The immune system, essential for defense against infections and maintaining internal stability through interactions with other bodily systems, is significantly influenced by m6A modification. This modification acts as a key post-transcriptional regulator of immune responses, though its effects on different immune cells vary across diseases. This review delineates the impact of m6A modification across major system-related cancers-including those of the respiratory, digestive, endocrine, nervous, urinary reproductive, musculoskeletal system malignancies, as well as acute myeloid leukemia and autoimmune diseases. We explore the pathogenic roles of m6A RNA modifications within the tumor immune microenvironment and the broader immune system, highlighting how RNA modification regulators interact with immune pathways during disease progression. Furthermore, we discuss how the expression patterns of these regulators can influence disease susceptibility to immunotherapy, facilitating the development of diagnostic and prognostic models and pioneering new therapeutic approaches. Overall, this review emphasizes the challenges and prospective directions of m6A-related immune regulation in various systemic diseases throughout the body.

Construction and validation of a TTN mutation associated immune prognostic model for evaluating immune microenvironment and outcomes of gastric cancer: An observational study.

Gastric cancer (GC) is a prevalent form of cancer worldwide, and TTN (titin) mutations are frequently observed in GC. However, the association between TTN mutations and immunotherapy for GC remains unclear, necessitating the development of novel prognostic models. The prognostic value and potential mechanisms of TTN in stomach adenocarcinoma were evaluated by TCGA (The Cancer Genome Atlas)-stomach adenocarcinoma cohort analysis, and an immune prognostic model was constructed based on TTN status. We validated it using the GSE84433 dataset. We performed Gene Set Enrichment Analysis and screened for differentially expressed genes, and used lasso (least absolute shrinkage and selection operator) regression analysis to screen for survival genes to construct a multifactorial survival model. In addition, we evaluated the relative proportions of 22 immune cells using the CIBERSORT algorithm for immunogenicity analysis. Finally, we constructed the nomogram integrating immune prognostic model and other clinical factors. GESA showed enrichment of immune-related phenotypes in patients with TTN mutations. We constructed an immune prognostic model based on 16 genes could identify gastric cancer patients with higher risk of poor prognosis. Immuno-microenvironmental analysis showed increased infiltration of naive B cells, plasma cells, and monocyte in high-risk patients. In addition, Nomo plots predicted the probability of 1-year, 3-year, and 5-year OS (overall survival) in GC patients, showing good predictive performance. In this study, we identified that TTN gene may be a potential clinical biomarker for GC and TTN mutations may be a predictor of immunotherapy in patients. We constructed and validated a new model for prognosis of GC patients based on immune characteristics associated with TTN mutations. This study may provide potential therapeutic strategies for gastric cancer.

Factors Associated with High-Risk Plaque Characteristics Among Patients with Medium to Severe Carotid Artery Stenosis

ObjectiveThere has been a large discussion in literature regarding the proper management of asymptomatic patients with significant carotid artery stenosis (CAS). This study aims to identify potential risk factors associated with high-risk carotid plaques. MethodsThis is a retrospective study based on a prospective database. Eligible patients had medium to severe symptomatic or asymptomatic carotid stenosis (≥50%, NASCET criteria). This study will analyze patients recruited by our institution as part of the multicenter TAXINOMISIS project (NCT03495830). According to protocol, all patients underwent a colored Duplex ultrasound examination and a magnetic resonance angiography (MRA) at baseline. Carotid plaques were classified according to Gray-Weale ultrasonographic criteria (Types I-V). Main outcomes included the occurrence of symptoms, the high/low echogenicity of the plaque, the existence of intraplaque hemorrhage (IPH) and the existence of lipidic/necrotic core. Secondary, risk factors associated with the aforementioned outcomes were evaluated. ResultsA total of 62 patients (mean age: 68.7+/-9.3 years, 66.1% males, 24.2% symptomatic) were recruited by our department. Mean carotid stenosis was 70.81%±13.53%. In multivariate regression analysis, CRP > 2mg/l was strongly associated with symptomatic stenosis (OR=9.92 [1.12-88.178]; P=0.039), and low HDL levels (<1200mmol/l) were associated with lipidic/necrotic plaque core (OR=16.88 [1.10-259.30]; P=0.043). Low HDL levels (OR=7.22 [1.00-51.95], P=0.049) and HbA1c >7% (OR=0.08 [0.01-0.93], P=0.044) were associated with type III/IV plaques whereas HgAbc1 >7% (OR=14.26 [1.21-168.34], P=0.035) was associated with Type V plaques. ConclusionsThis preliminary study has revealed some potential risk factors associated with unstable carotid plaques. This data could help the future development of prognostic models in order to early detect patients that could benefit from further intervention.

Accounting calendar and cyclic ageing factors in diagnostic and prognostic models of second-life EV batteries application in energy storage systems

The rapid expansion of the electric vehicle market has significantly increased the demand for lithium-ion batteries, posing challenges for manufacturers and policymakers regarding efficient use and recycling. When these batteries reach the end of their primary lifecycle, their repurposing for secondary applications such as energy storage becomes critical to addressing environmental and resource management issues. This paper focuses on applying second-life batteries in energy storage systems, emphasizing the importance of accounting for calendar and cyclic aging factors to optimize battery performance and longevity. Calendar aging refers to the degradation that occurs over time due to chemical reactions within the battery, even when it is not in use. This type of aging is influenced by temperature, state of charge (SOC), and storage conditions. Cyclic aging, on the other hand, results from repeated charging and discharging cycles, which cause mechanical and chemical changes within the battery, leading to capacity fade and increased internal resistance. The combined effects of these aging processes necessitate the development of high-precision diagnostic and prognostic models to manage the performance and longevity of second-life batteries effectively. In Ukraine, the adoption of electric vehicles is accelerating, leading to an influx of used electric vehicles. This situation necessitates the prompt development of strategies for repurposing these batteries for energy storage applications. The complexities associated with final recycling processes make secondary use an attractive interim solution. By repurposing used EV batteries, Ukraine can mitigate immediate challenges related to battery waste and resource scarcity while supporting the transition to renewable energy sources. This paper highlights the need for an integral degradation index (DI) that combines calendar and cyclic aging factors with stochastic influences to provide a comprehensive measure of battery health. Such an index is essential for optimizing battery management practices, including the scheduling of charging and discharging cycles, to extend the operational life of secondary batteries. The study also presents practical recommendations for implementing these models in various energy storage scenarios, ranging from residential solar energy systems to industrial grid support and electric vehicle charging stations. By adopting optimized battery management strategies, the potential for extending the lifespan of secondary batteries and reducing operational costs is significant. This approach supports sustainable energy practices and aligns with global efforts to promote renewable energy sources and circular economy principles. Keywords: Lithium-Ion Battery, Electric Vehicle, Energy Storage, Battery Degradation, Calendar Ageing, Cyclic Ageing, Integral Degradation Index, Remaining Useful Life, State of Health.

Identification of subgroups and development of prognostic risk models along the glycolysis–cholesterol synthesis axis in lung adenocarcinoma

Lung cancer is one of the most dangerous malignant tumors affecting human health. Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer. Both glycolytic and cholesterogenic pathways play critical roles in metabolic adaptation to cancer. A dataset of 585 LUAD samples was downloaded from The Cancer Genome Atlas database. We obtained co-expressed glycolysis and cholesterogenesis genes by selecting and clustering genes from Molecular Signatures Database v7.5. We compared the prognosis of different subtypes and identified differentially expressed genes between subtypes. Predictive outcome events were modeled using machine learning, and the top 9 most important prognostic genes were selected by Shapley additive explanation analysis. A risk score model was built based on multivariate Cox analysis. LUAD patients were categorized into four metabolic subgroups: cholesterogenic, glycolytic, quiescent, and mixed. The worst prognosis was the mixed subtype. The prognostic model had great predictive performance in the test set. Patients with LUAD were effectively typed by glycolytic and cholesterogenic genes and were identified as having the worst prognosis in the glycolytic and cholesterogenic enriched gene groups. The prognostic model can provide an essential basis for clinicians to predict clinical outcomes for patients. The model was robust on the training and test datasets and had a great predictive performance.

Development and Validation of Prognostic Models Using Radiomic Features from Pre-Treatment Positron Emission Tomography (PET) Images in Head and Neck Squamous Cell Carcinoma (HNSCC) Patients.

High-dimensional radiomics features derived from pre-treatment positron emission tomography (PET) images offer prognostic insights for patients with head and neck squamous cell carcinoma (HNSCC). Using 124 PET radiomics features and clinical variables (age, sex, stage of cancer, site of cancer) from a cohort of 232 patients, we evaluated four survival models-penalized Cox model, random forest, gradient boosted model and support vector machine-to predict all-cause mortality (ACM), locoregional recurrence/residual disease (LR) and distant metastasis (DM) probability during 36, 24 and 24 months of follow-up, respectively. We developed models with five-fold cross-validation, selected the best-performing model for each outcome based on the concordance index (C-statistic) and the integrated Brier score (IBS) and validated them in an independent cohort of 102 patients. The penalized Cox model demonstrated better performance for ACM (C-statistic = 0.70, IBS = 0.12) and DM (C-statistic = 0.70, IBS = 0.08) while the random forest model displayed better performance for LR (C-statistic = 0.76, IBS = 0.07). We conclude that the ML-based prognostic model can aid clinicians in quantifying prognosis and determining effective treatment strategies, thereby improving favorable outcomes in HNSCC patients.

Immune cell-related prognostic risk model and tumor immune environment modulation in esophageal carcinoma based on single-cell and bulk RNA sequencing.

Immune cells play a pivotal role in the tumor microenvironment, exerting significant influence on tumor progression and patient outcomes, but the current biomarkers are insufficient to fully capture the complex and diverse tumor immune microenvironment and the impact of immunotherapy. The advent of single-cell sequencing allows us to explore the tumor microenvironment at an unprecedented resolution, enabling the identification and characterization of distinct subsets of immune cells, thereby paving the way for the development of prognostic models using immune cells. Leveraging single-cell data, our study deeply investigated the intricacies of immune microenvironment heterogeneity in esophageal carcinoma. We elucidated the composition, functionality, evolution, and intercellular communication patterns of immune cells, culminating in the construction of an independent prognostic model at the single-cell level. Furthermore, we conducted a comprehensive analysis of disparities in immune infiltration and immune checkpoint expression between patients categorized into high- and low-risk groups, which may impact patient prognosis. In summary, our study harnessed multiomics data to delineate the immune profile of esophageal carcinoma patients, provide a method for leveraging molecular signatures of immune cells to identify potential biomarkers, while concurrently providing evidence for the potential benefits of immunotherapy.

Body composition and late-occurring chronic health conditions after autologous stem cell transplantation for lymphoma.

Autologous peripheral blood stem cell transplantation (aPBSCT) is the standard of care for adults with relapsed lymphoma, yet recipients remain at risk of developing chronic health conditions (CHCs). It was hypothesized that body composition measurements of skeletal muscle and fat are associated with late-onset CHCs and nonrelapse mortality after aPBSCT. Leveraging the Blood or Marrow Transplant Survivor Study, we examined association between pre-aPBSCT body composition and new-onset grade 3-5 CHCs among 187 adults with lymphoma treated with aPBSCT (2011-2014) surviving ≥2 years after aPBSCT. Using computed tomography scans at the L3 level, skeletal muscle mass (skeletal muscle area and skeletal muscle density [SMD]) and body fat (subcutaneous adipose tissue and visceral adipose tissue) were measured and quantified as sex-specific z-scores. Competing risk models were built to study the impact of body composition on incident grade 3 through 5 CHCs and nonrelapse mortality (NRM) adjusting for confounders. The study cohort had a median age at aPBSCT of 57 years with 63% males, 77% non-Hispanic Whites and 81% with non-Hodgkin lymphoma. The 5-year cumulative incidence of grade 3 through 5 CHCs was 47% (95% Confidence Interval, CI, 38%-56%). Each SD increase in SMD was associated with 30% reduced risk of grade 3 through 5 CHCs (95% CI,0.50-0.96). The 10-year cumulative incidence of NRM was 16% (95% CI, 10-22). No body composition measure was associated with NRM. The association between SMD and grade 3 through 5 CHCs following aPBSCT could inform development of prognostic models to identify adults with lymphoma at greatest risk of morbidity following aPBSCT.

Development of novel prognostic models based on dynamic changes in risk factors for hepatitis B associated acute-on-chronic liver failure:a 10-year retrospective study

Background/AimsAcute-on-chronic liver failure (ACLF) is associated with high short-term mortality, and early prediction is critical to reduce the deaths of ACLF patients. To date, however, the prognostic accuracy of current models for ACLF is unsatisfactory, particularly, in patients with hepatitis B virus (HBV) infection. This study aims to develop novel prognostic models based on the dynamic changes in variables to predict the short-term mortality of HBV-associated ACLF (HBV-ACLF). MethodsA retrospective cohort study was conducted, with the population comprised in whom ACLF was confirmed.319 patients were enrolled and their clinical data were collected on Days 1 and 7 following hospital admission. Univariate and multivariate analyses were performed to identify risk factors for 28 and 90-day mortality. The dynamic alterations in the risk factors were further analyzed, and Days 1 and 7 prognostic models were constructed. Receiver operating characteristic (ROC) analysis were used to identify and compared the predictors of prognosis among our model. ResultsUnivariate and multivariate analyses revealed significant risk factors at Days 1 and 7, which when combined with the clinically important parameters, were used to establish the Days 1 and 7 prognostic models. For 28-day mortality, the predictive accuracy of the Day 1 prognostic model was significantly higher than that of the albumin-bilirubin (ALBI) model. For 90-day mortality, the predictive accuracy of the Days 1 and 7 prognostic models was significantly higher than that of the Model of End-Stage Liver Disease (MELD), MELD-sodium (MELD-Na), and ALBI prognostic models. ConclusionsThe prognostic models established in this study were superior to the existing prognostic scoring systems to accurately predict short-term mortality, and therefore, could be potential novel prognostic tools for HBV-ACLF.

Predicting effects of a digital stress intervention for patients with depressive symptoms: Development and validation of meta-analytic prognostic models using individual participant data.

Digital stress interventions could be helpful as an "indirect" treatment for depression, but it remains unclear for whom this is a viable option. In this study, we developed models predicting individualized benefits of a digital stress intervention on depressive symptoms at 6-month follow-up. Data of N = 1,525 patients with depressive symptoms (Center for Epidemiological Studies' Depression Scale, CES-D ≥ 16) from k = 6 randomized trials (digital stress intervention vs. waitlist) were collected. Prognostic models were developed using multilevel least absolute shrinkage and selection operator and boosting algorithms, and were validated using bootstrap bias correction and internal-external cross-validation. Subsequently, expected effects among those with and without a treatment recommendation were estimated based on clinically derived treatment assignment cut points. Performances ranged from R² = 21.0%-23.4%, decreasing only slightly after model optimism correction (R² = 17.0%-19.6%). Predictions were greatly improved by including an interim assessment of depressive symptoms (optimism-corrected R2 = 32.6%-35.6%). Using a minimally important difference of d = -0.24 as assignment cut point, approximately 84.6%-93.3% of patients are helped by this type of intervention, while the remaining 6.7%-15.4% would experience clinically negligible benefits (δ^ = -0.02 to -0.19). Using reliable change as cut point, a smaller subset (39.3%-46.2%) with substantial expected benefits (δ^ = -0.68) receives a treatment recommendation. Meta-analytic prognostic models applied to individual participant data can be used to predict differential benefits of a digital stress intervention as an indirect treatment for depression. While most patients seem to benefit, the developed models could be helpful as a screening tool to identify those for whom a more intensive depression treatment is needed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Transfer learning-based deep learning models for proton exchange membrane fuel remaining useful life prediction

Proton exchange membrane fuel cells (PEMFCs) offer power generation capabilities for diverse applications including commercial enterprises, industrial sectors, and residential technologies. Nevertheless, the comprehensive integration of PEMFC applications could be improved by challenges related to degradation and durability. The imperative development of efficient performance prognostic models assumes a pivotal role in the prognosis of remaining useful life (RUL), health monitoring, and effective utilization of PEMFCs. This paper centers on the prognostication of critical components within PEMFCs and introduces a transfer learning approach based on variational autoencoder and bi-directional long short-term memory with an attention mechanism (Bi-LSTM-AM) model. This approach combines feature fusion, knee-point detection, and a sophisticated deep-learning-based predictive model. Notably, incorporating the variational autoencoder as the framework for feature fusion introduces a novel perspective previously unexplored. Identifying the knee point and knowing the start point on the training data, facilitates optimized parameter computation. The application of transfer learning facilitates the transfer of optimal model parameters and weights from a source to a target dataset. Conclusively, the estimation of stack voltage degradation and real-time RUL prediction based on the test dataset is executed by implementing our proposed method. The stack voltage prediction findings showcase the Bi-LSTM-AM model’s superior performance relative to comparison models. The proposed online rolling prediction model, utilizing a sliding window technique for RUL prediction, yields significantly enhanced accuracy, culminating in a relative error margin ranging from approximately 1.69% to 5.04%.

Development and validation of prognostic machine learning models for short- and long-term mortality among acutely admitted patients based on blood tests

Several scores predicting mortality at the emergency department have been developed. However, all with shortcomings either simple and applicable in a clinical setting, with poor performance, or advanced, with high performance, but clinically difficult to implement. This study aimed to explore if machine learning algorithms could predict all-cause short- and long-term mortality based on the routine blood test collected at admission. Methods: We analyzed data from a retrospective cohort study, including patients > 18 years admitted to the Emergency Department (ED) of Copenhagen University Hospital Hvidovre, Denmark between November 2013 and March 2017. The primary outcomes were 3-, 10-, 30-, and 365-day mortality after admission. PyCaret, an automated machine learning library, was used to evaluate the predictive performance of fifteen machine learning algorithms using the area under the receiver operating characteristic curve (AUC). Results: Data from 48,841 admissions were analyzed, of these 34,190 (70%) were randomly divided into training data, and 14,651 (30%) were in test data. Eight machine learning algorithms achieved very good to excellent results of AUC on test data in a of range 0.85–0.93. In prediction of short-term mortality, lactate dehydrogenase (LDH), leukocyte counts and differentials, Blood urea nitrogen (BUN) and mean corpuscular hemoglobin concentration (MCHC) were the best predictors, whereas prediction of long-term mortality was favored by age, LDH, soluble urokinase plasminogen activator receptor (suPAR), albumin, and blood urea nitrogen (BUN). Conclusion: The findings suggest that measures of biomarkers taken from one blood sample during admission to the ED can identify patients at high risk of short-and long-term mortality following emergency admissions.

Identification of ferroptosis-related subtypes, characteristics of TME infiltration and development of prognostic models in gastric cancer

BackgroundFerroptosis is a distinct form of cell death characterized by unique morphology, biochemistry, and genetics, playing a crucial role in the initiation, progression, prognosis, and therapeutic strategies of tumors. However, the impact of ferroptosis-related genes (FRGs) on the tumor microenvironment (TME) remains unclear. This study may advance the existing knowledge of FRGs in gastric cancer, and push ahead with more effective prognostic assessment and the development of more effective immunotherapy approaches. MethodsFRGs were acquired from the FerrDb database and a consensus clustering technique was adopted to categorize patients with GC into groups in line with the expression profiles of 44 FRGs in order to further investigate the expression properties of these proteins. Assessment of the immune status, microsatellite instability (MSI) and cancer stem cell (CSC) index between the high- and low- risk groups to assess the proportion of TIICs in the TME, ssGSVA was adopted to detect the abundance of infiltrating immune cells from the low-risk and high-risk groups. Expression levels of eight ferroptosis-related genes of prognostic signature in GC tissues and adjacent normal tissues was detected by RT-PCR. ResultsIn the GC cohort, TP53 has the highest mutation frequency (44 %), and was shown to be highly linked with the expression levels of 11 FRGs. In accordance with the Kaplan-Meier curve, the overall survival time of patients with subtype A (Low FRG-score) discernibly exceeded that of patients with subtype B (High FRG-score).In addition, there is a significant difference in the infiltration of most immune cells between subtype A and subtype B, and some important immune checkpoints (CTLA4, PDCD1, CD274, LAG3, PDCD1LG2, and HAVCR2) have higher expression in cluster A. Finally, low FRG-scores were significantly associated with MSI-H status, while high FRG-scores were significantly associated with microsatellite stable status (MSS). FRG-score is negatively related to the cancer stem cell (CSC). ConclusionLow FRG-score, due to its high microsatellite instability (MSI-H), high mutational load and immune activation, indicates the possible advantage of OS. In addition, the FRG-score was closely related to the cancer stem cell (CSC) index and the sensitive degree of chemotherapeutic drug.

Identifying CTH and MAP1LC3B as ferroptosis biomarkers for prognostic indication in gastric cancer decoding

Gastric cancer (GC), known for its high incidence and poor prognosis, urgently necessitates the identification of reliable prognostic biomarkers to enhance patient outcomes. We scrutinized data from 375 GC patients alongside 32 non-cancer controls, sourced from the TCGA database. A univariate Cox Proportional Hazards Model (COX) regression was employed to evaluate expressions of ferroptosis-related genes. This was followed by the application of Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate COX regression for the development of prognostic models. The composition of immune cell subtypes was quantified utilizing CIBERSORT, with their distribution in GC versus control samples being comparatively analyzed. Furthermore, the correlation between the expressions of Cystathionine Gamma-Lyase (CTH) and Microtubule Associated Protein 1 Light Chain 3 Beta (MAP1LC3B) and the abundance of immune cell subtypes was explored. Our bioinformatics findings underwent validation through immunohistochemical analysis. Our prognostic models integrated CTH and MAP1LC3B. Survival analysis indicated that patients categorized as high-risk, as defined by the model, exhibited significantly lower survival rates compared to their low-risk counterparts. Notably, CTH expression inversely correlated with monocyte levels, while MAP1LC3B expression showed an inverse relationship with the abundance of M2 macrophages. Immunohistochemical validation corroborated lower expressions of CTH and MAP1LC3B in GC tissues relative to control samples, in concordance with our bioinformatics predictions. Our study suggests that the dysregulation of CTH, MAP1LC3B, and the accompanying monocyte-macrophage dynamics could be pivotal in the prognosis of GC. These elements present potential targets for prognostic assessment and therapeutic intervention.

Development of prognostic models for predicting 90-day neurological function and mortality after cardiac arrest

BackgroundThe survivors of cardiac arrest experienced vary extent of hypoxic ischemic brain injury causing mortality and long-term neurologic disability. However, there is still a need to develop robust and reliable prognostic models that can accurately predict these outcomes. ObjectivesTo establish reliable models for predicting 90-day neurological function and mortality in adult ICU patients recovering from cardiac arrest. MethodsWe enrolled patients who had recovered from cardiac arrest at Binhaiwan Central Hospital of Dongguan, from January 2018 to July 2021. The study's primary outcome was 90-day neurological function, assessed and divided into two categories using the Cerebral Performance Category (CPC) scale: either good (CPC 1–2) or poor (CPC 3–5). The secondary outcome was 90-day mortality. We analyzed the relationships between risk factors and outcomes individually. A total of four models were developed: two multivariable logistic regression models (models 1 and 2) for predicting neurological function, and two Cox regression models (models 3 and 4) for predicting mortality. Models 2 and 4 included new neurological biomarkers as predictor variables, while models 1 and 3 excluded. We evaluated calibration, discrimination, clinical utility, and relative performance to establish superiority between the models. ResultsModel 1 incorporates variables such as gender, site of cardiopulmonary resuscitation (CPR), total CPR time, and acute physiology and chronic health evaluation II (APACHE II) score, while model 2 includes gender, site of CPR, APACHE II score, and serum level of ubiquitin carboxy-terminal hydrolase L1 (UCH-L1). Model 2 outperforms model 1, showcasing a superior area under the receiver operating characteristic curve (AUC) of 0.97 compared to 0.83. Additionally, model 2 exhibits improved accuracy, sensitivity, and specificity. The decision curve analysis confirms the net benefit of model 2. Similarly, models 3 and 4 are designed to predict 90-day mortality. Model 3 incorporates the variables such as site of CPR, total CPR time, and APACHE II score, while model 4 includes APACHE II score, total CPR time, and serum level of UCH-L1. Model 4 outperforms model 3, showcasing an AUC of 0.926 and a C-index of 0.830. The clinical decision curve analysis also confirms the net benefit of model 4. ConclusionsBy integrating new neurological biomarkers, we have successfully developed enhanced models that can predict 90-day neurological function and mortality outcomes more accurately.

Transfer Learning for Mortality Prediction in Non-Small Cell Lung Cancer with Low-Resolution Histopathology Slide Snapshots.

High-resolution whole slide image scans of histopathology slides have been widely used in recent years for prediction in cancer. However, in some cases, clinical informatics practitioners may only have access to low-resolution snapshots of histopathology slides, not high-resolution scans. We evaluated strategies for training neural network prognostic models in non-small cell lung cancer (NSCLC) based on low-resolution snapshots, using data from the Veterans Affairs Precision Oncology Data Repository. We compared strategies without transfer learning, with transfer learning from general domain images, and with transfer learning from publicly available high-resolution histopathology scans. We found transfer learning from high-resolution scans achieved significantly better performance than other strategies. Our contribution provides a foundation for future development of prognostic models in NSCLC that incorporate data from low-resolution pathology slide snapshots alongside known clinical predictors.

Development and validation of survival prognostic models for head and neck cancer patients using machine learning and dosiomics and CT radiomics features: a multicentric study

BackgroundThis study aimed to investigate the value of clinical, radiomic features extracted from gross tumor volumes (GTVs) delineated on CT images, dose distributions (Dosiomics), and fusion of CT and dose distributions to predict outcomes in head and neck cancer (HNC) patients.MethodsA cohort of 240 HNC patients from five different centers was obtained from The Cancer Imaging Archive. Seven strategies, including four non-fusion (Clinical, CT, Dose, DualCT-Dose), and three fusion algorithms (latent low-rank representation referred (LLRR),Wavelet, weighted least square (WLS)) were applied. The fusion algorithms were used to fuse the pre-treatment CT images and 3-dimensional dose maps. Overall, 215 radiomics and Dosiomics features were extracted from the GTVs, alongside with seven clinical features incorporated. Five feature selection (FS) methods in combination with six machine learning (ML) models were implemented. The performance of the models was quantified using the concordance index (CI) in one-center-leave-out 5-fold cross-validation for overall survival (OS) prediction considering the time-to-event.ResultsThe mean CI and Kaplan-Meier curves were used for further comparisons. The CoxBoost ML model using the Minimal Depth (MD) FS method and the glmnet model using the Variable hunting (VH) FS method showed the best performance with CI = 0.73 ± 0.15 for features extracted from LLRR fused images. In addition, both glmnet-Cindex and Coxph-Cindex classifiers achieved a CI of 0.72 ± 0.14 by employing the dose images (+ incorporated clinical features) only.ConclusionOur results demonstrated that clinical features, Dosiomics and fusion of dose and CT images by specific ML-FS models could predict the overall survival of HNC patients with acceptable accuracy. Besides, the performance of ML methods among the three different strategies was almost comparable.

Effect of financial deprivation on the quality of life of patients with traumatic brain injury

BACKGROUND: Traumatic brain injury (TBI) is a global public health problem and one of the leading causes of mortality and disability. The prevalence and increase in the number of cases of TBI, high percentage of complications, disability, uncertain medical and social prognosis determine the relevance of studying this medical and social problem, the most significant in which are the consequences of craniocerebral trauma, often acquiring a chronic course, affecting the quality of life of the patient and his family.&#x0D; AIM: To study the influence of financial deprivation on the quality of life of patients with post-TBI.&#x0D; MATERIALS AND METHODS: The study of quality of life in patients with a post-TBI event was conducted using the WHOQOL-100 questionnaire. The study randomly sampled 414 respondents of working age (93.6% men). 54% of the respondents had suffered TBI within 1 to 5 years. The control group included clinically healthy subjects. The Dembo–Rubinstein test was used to diagnose the adequacy of respondents’ self-assessment with TBI. In the study of the impact of financial deprivation on the quality of life of the respondents, 4 degrees of deprivation correlated with the level of income per patient’s family member were considered.&#x0D; RESULTS: The study of the impact of financial deprivation on the quality of life of patients with TBI identified 4 deprivation groups of respondents. The study obtained evidence of the effect of deprivation on the quality of life of respondents with TBI. The indicators of quality of life of patients included in the study significantly increase when moving from the group with degree 4 of deprivation to the group with degree 1 of deprivation. The greatest decrease in quality of life indicators was observed in respondents with degrees 2–4 of deprivation.&#x0D; CONCLUSION: A multicomponent assessment of the results of TBI takes into account quality of life, a somatic and socially determined criterion of a patient’s social functioning that has prognostic value. The definition and typology of predictors of quality of life will allow the development of prognostic models of quality of life after TBI of different severity.

Development and validation of prognostic models for small cell lung cancer patients with liver metastasis: a SEER population-based study

BackgroundThis study was to establish and validate prediction models to predict the cancer-specific survival (CSS) and overall survival (OS) of small-cell lung cancer (SCLC) patients with liver metastasis.MethodsIn the retrospective cohort study, SCLC patients with liver metastasis between 2010 and 2015 were retrospectively retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly divided into the training group and testing group (3: 1 ratio). The Cox proportional hazards model was used to determine the predictive factors for CSS and OS in SCLC with liver metastasis. The prediction models were conducted based on the predictive factors. The performances of the prediction models were evaluated by concordance indexes (C-index), and calibration plots. The clinical value of the models was evaluated by decision curve analysis (DCA).ResultsIn total, 8,587 patients were included, with 154 patients experiencing CSS and 154 patients experiencing OS. The median follow-up was 3 months. Age, gender, marital status, N stage, lung metastases, multiple metastases surgery of metastatic site, chemotherapy, and radiotherapy were independent predictive factors for the CSS and OS of SCLC patients with liver metastasis. The prediction models presented good performances of CSS and OS among patients with liver metastasis, with the C-index for CSS being 0.724, whereas the C-index for OS was 0.732, in the training set. The calibration curve showed a high degree of consistency between the actual and predicted CSS and OS. DCA suggested that the prediction models provided greater net clinical benefit to these patients.ConclusionOur prediction models showed good predictive performance for the CSS and OS among SCLC patients with liver metastasis. Our developed nomograms may help clinicians predict CSS and OS in SCLC patients with liver metastasis.

Investigation of statistical methods used in prognostic prediction models for obstetric care: A 10 year-span cross-sectional study.

Obstetric care is a highly active area in the development and application of prognostic prediction models. The development and validation of these models often require the utilization of advanced statistical techniques. However, failure to adhere to rigorous methodological standards could greatly undermine the reliability and trustworthiness of the resultant models. Consequently, the aim of our study was to examine the current statistical practices employed in obstetric care and offer recommendations to enhance the utilization of statistical methods in the development of prognostic prediction models. We conducted a cross-sectional survey using a sample of studies developing or validating prognostic prediction models for obstetric care published in a 10-year span (2011-2020). A structured questionnaire was developed to investigate the statistical issues in five domains, including model derivation (predictor selection and algorithm development), model validation (internal and external), model performance, model presentation, and risk threshold setting. On the ground of survey results and existing guidelines, a list of recommendations for statistical methods in prognostic models was developed. A total of 112 eligible studies were included, with 107 reporting model development and five exclusively reporting external validation. During model development, 58.9% of the studies did not include any form of validation. Of these, 46.4% used stepwise regression in a crude manner for predictor selection, while two-thirds made decisions on retaining or dropping candidate predictors solely based on p-values. Additionally, 26.2% transformed continuous predictors into categorical variables, and 80.4% did not consider nonlinear relationships between predictors and outcomes. Surprisingly, 94.4% of the studies did not examine the correlation between predictors. Moreover, 47.1% of the studies did not compare population characteristics between the development and external validation datasets, and only one-fifth evaluated both discrimination and calibration. Furthermore, 53.6% of the studies did not clearly present the model, and less than half established a risk threshold to define risk categories. In light of these findings, 10 recommendations were formulated to promote the appropriate use of statistical methods. The use of statistical methods is not yet optimal. Ten recommendations were offered to assist the statistical methods of prognostic prediction models in obstetric care.