BACKGROUND: Acute liver failure (ALF) may be the first and most dramatic presentation of Wilson\'s disease (WD). ALF due to WD (WD-ALF) is difficult to distinguish from other causes of liver disease and is a clear indication for liver transplantation. There is no firm recommendation on specific and supportive medical treatment for this condition.
OBJECTIVE: To critically evaluate the diagnostic and therapeutic management of WD-ALF patients in order to improve their survival with native liver.
METHODS: A retrospective analysis of patients with WD-ALF was conducted in two pediatric liver units from 2018 to 2023.
RESULTS: During the study period, 16 children (9 males) received a diagnosis of WD and 2 of them presented with ALF. The first was successfully treated with an unconventional combination of low doses of D-penicillamine and zinc plus steroids, and survived without liver transplant. The second, exclusively treated with supportive therapy, needed a hepatotransplant to overcome ALF.
CONCLUSIONS: Successful treatment of 1 WD-ALF patient with low-dose D-penicillamine and zinc plus steroids may provide new perspectives for management of this condition, which is currently only treated with liver transplantation.

Majority of gestational trophoblastic neoplasia (GTN) follows molar pregnancies where diagnosis is mostly based on persistent or rising serum human chorionic gonadotrophin (hCG). Diagnosis of GTN could be based on clinical presentation, serum hCG measurement, imaging, histology, and genotyping. A high index of suspicion in women of reproductive age presenting with abnormal vaginal bleeding or unusual systematic presentation is important. An accurate staging and classification system for GTN is crucial to evaluate the risk and the prognosis of patients, and to optimize treatment. GTN is staged using the International Federation of Gynecology and Obstetrics 2000 staging and the modified World Health Organization prognostic scoring system.

UNASSIGNED: Merkel cell carcinoma (MCC) is an aggressive skin cancer with a poor prognosis, which only improved with the introduction of immunotherapies. An MCC prediction model with high diagnostic accuracy is lacking. The aim was to develop an MCC prognostic score (MCC-PS) based on combinations of previously proposed risk factors.
UNASSIGNED: A multicentric, retrospective study was conducted to develop MCC-PS, which included age, neuron-specific enolase (NSE), C-reactive protein (CRP), creatinine, bilirubin, and international normalized ratio (INR). Creatinine, bilirubin, and INR were used to calculate the model of end-stage liver disease (MELD) score. A total of 98 patients were included in the study, including 36.7% with stage I according to American Joint Committee on Cancer 2018 (n = 36), 30.6% with stage II (n = 30), 25.5% with stage III (n = 25), and 7.1% with stage IV (n = 7). Survival data of MCC patients were correlated with selected laboratory parameters and risk factors. Primary endpoint was MCC-specific survival (MSS) and the secondary endpoint was progression-free survival. Several statistical methods were used to develop the prognostic score, including correlation analysis, Kaplan-Meier curves, Cox regression, and time-dependent receiver operating characteristic analysis.
UNASSIGNED: The MCC-PS is based on the sum of the following baseline variables: elevated CRP (≥5.5 mg/l), elevated NSE (≥22.8 µg/l), MELD score ≥ 11, and age ≥ 75 years. An MELD score ≥ 11 was scored as 4 points, elevated NSE level as 3 points, elevated CRP level as 2 points, and age ≥ 75 years as 1 point. A high-risk group according to the MCC-PS was characterized by a score of 4 or more points. The high-risk group was associated with a worse prognosis than the low-risk group (1-year MSS 62%, 2-year 43.1%, 5-year 17.6% as compared to 1-year MSS 79.5%, 3-year 75%, 5-year 72%). Notably, the developed MCC-PS predicts MCC outcome measures with high accuracy (3-year MSS: area under the curve (AUC) 0.934, sensitivity 87.5% and specificity 82.2%; 5-year MSS: AUC 0.93, sensitivity 89% and specificity 82%).
UNASSIGNED: MCC-PS is the first prognostic score predicting MCC outcome with a high accuracy based on five easily available laboratory parameters and patient\'s age. An MCC-PS of 4 or more indicates a high-risk patient associated with a poor prognosis.

UNASSIGNED: Acute Myeloid Leukemia (AML) exhibits a wide array of phenotypic manifestations, progression patterns, and heterogeneous responses to immunotherapies, suggesting involvement of complex immunobiological mechanisms. This investigation aimed to develop an integrated prognostic model for AML by incorporating cancer driver genes, along with clinical and phenotypic characteristics of the disease, and to assess its implications for immunotherapy responsiveness.
UNASSIGNED: Critical oncogenic driver genes linked to survival were identified by screening primary effector and corresponding gene pairs using data from The Cancer Genome Atlas (TCGA), through univariate Cox proportional hazard regression analysis. This was independently verified using dataset GSE37642. Primary effector genes were further refined using LASSO regression. Transcriptomic profiling was quantified using multivariate Cox regression, and the derived prognostic score was subsequently validated. Finally, a multivariate Cox regression model was developed, incorporating the transcriptomic score along with clinical parameters such as age, gender, and French-American-British (FAB) classification subtype. The \'Accurate Prediction Model of AML Overall Survival Score\' (APMAO) was developed and subsequently validated. Investigations were conducted into functional pathway enrichment, alterations in the gene mutational landscape, and the extent of immune cell infiltration associated with varying APMAO scores. To further investigate the potential of APMAO scores as a predictive biomarker for responsiveness to cancer immunotherapy, we conducted a series of analyses. These included examining the expression profiles of genes related to immune checkpoints, the interferon-gamma signaling pathway, and m6A regulation. Additionally, we explored the relationship between these gene expression patterns and the Tumor Immune Dysfunction and Exclusion (TIDE) dysfunction scores.
UNASSIGNED: Through the screening of 95 cancer genes associated with survival and 313 interacting gene pairs, seven genes (ACSL6, MAP3K1, CHIC2, HIP1, PTPN6, TFEB, and DAXX) were identified, leading to the derivation of a transcriptional score. Age and the transcriptional score were significant predictors in Cox regression analysis and were integral to the development of the final APMAO model, which exhibited an AUC greater than 0.75 and was successfully validated. Notable differences were observed in the distribution of the transcriptional score, age, cytogenetic risk categories, and French-American-British (FAB) classification between high and low APMAO groups. Samples with high APMAO scores demonstrated significantly higher mutation rates and pathway enrichments in NFKB, TNF, JAK-STAT, and NOTCH signaling. Additionally, variations in immune cell infiltration and immune checkpoint expression, activation of the interferon-γ pathway, and expression of m6A regulators were noted, including a negative correlation between CD160, m6A expression, and APMAO scores.
UNASSIGNED: The combined APMAO score integrating transcriptional and clinical parameters demonstrated robust prognostic performance in predicting AML survival outcomes. It was linked to unique phenotypic characteristics, distinctive immune and mutational profiles, and patterns of expression for markers related to immunotherapy sensitivity. These observations suggest the potential for facilitating precision immunotherapy and advocate for its exploration in upcoming clinical trials.

Early prognostic assessment of patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) is important for guiding clinical management and reducing mortality. The aim of this study was to dynamically monitor the clinical characteristics of HBV-ACLF patients, thereby allowing the construction of a novel prognostic scoring model to predict the outcome of HBV-ACLF patients. Clinical data was prospectively collected for 518 patients with HBV-ACLF and randomly divided into training and validation sets. We constructed day-1, day-2, and day-(1 + 3) prognostic score models based on dynamic time points. The prognostic risk score constructed for day-3 was found to have the best predictive ability. The factors included in this scoring system, referred to as DSM-ACLF-D3, were age, hepatic encephalopathy, alkaline phosphatase, total bilirubin, triglycerides, very low-density lipoprotein, blood glucose, neutrophil count, fibrin, and INR. ROC analysis revealed the area under the curve predicted by DSM-ACLF-D3 for 28-day and 90-day mortality (0.901 and 0.889, respectively) was significantly better than those of five other scoring systems: COSSH-ACLF IIs (0.882 and 0.836), COSSH-ACLFs (0.863 and 0.832), CLIF-C ACLF (0.838 and 0.766), MELD (0.782 and 0.762) and MELD-Na (0.756 and 0.731). Dynamic monitoring of the changes in clinical factors can therefore significantly improve the accuracy of scoring models. Evaluation of the probability density function and risk stratification by DSM-ACLF-D3 also resulted in the best predictive values for mortality. The novel DSM-ACLF-D3 prognostic scoring model based on dynamic data can improve early warning, prediction and clinical management of HBV-ACLF patients.

OBJECTIVE: Pediatric adrenocortical carcinoma (pACC) is rare, and prognostic stratification remains challenging. We aimed to confirm the prognostic value of the previously published pediatric scoring system (pS-GRAS) in an international multicenter cohort.
METHODS: Analysis of pS-GRAS items of pACC from 6 countries in collaboration of ENSAT-PACT, GPOH-MET, and IC-PACT.
METHODS: We received patient data of the pS-GRAS items including survival information from 9 centers. PS-GRAS score was calculated as a sum of tumor stage (1 = 0; 2-3 = 1; 4 = 2 points), grade (Ki67 index: 0%-9% = 0; 10%-19% = 1; ≥20% = 2 points), resection status (R0 = 0; RX/R1/R2 = 1 point), age (<4 years = 0; ≥4 years = 1 point), and hormone production (androgen production = 0; glucocorticoid-/mixed-/no-hormone production = 1 point) generating 8 scores and 4 groups (1: 0-2, 2: 3-4, 3: 5, 4: 6-7). Primary endpoint was overall survival (OS).
RESULTS: We included 268 patients with median age of 4 years. The analysis of the pS-GRAS score showed a significantly favorable prognosis in patients with a lower scoring compared to higher scoring groups (5-year OS: Group 1 98%; group 2 87% [hazard ratio {HR} of death 3.6, 95% CI of HR 1.6-8.2]; group 3 43% [HR of death 2.8, 95% CI 1.9-4.4]; group 4: OS 18% [HR of death 2.1, 95% CI 1.7-2.7]). In the multivariable analysis, age (HR of death 3.5, 95% CI 1.8-7.0), resection status (HR of death 5.5, 95% CI 2.7-11.1), tumor stage (HR of death 1.9, 95% CI of HR 1.2-3.0), and Ki67 index (HR of death 1.7, 95% CI 1.2-2.4) remained strong independent outcome predictors. Especially infants < 4 years showed more often low-risk constellations with a better OS for all tumor stages.
CONCLUSIONS: In an international multicenter study, we confirmed that the pS-GRAS score is strongly associated with overall survival among patients with pACC. Age, resection status, stage, and Ki67 index are important parameters for risk stratification.

Observational studies are frequently used to estimate the effect of an exposure or treatment on an outcome. To obtain an unbiased estimate of the treatment effect, it is crucial to measure the exposure accurately. A common type of exposure misclassification is recall bias, which occurs in retrospective cohort studies when study subjects may inaccurately recall their past exposure. Particularly challenging is differential recall bias in the context of self-reported binary exposures, where the bias may be directional rather than random and its extent varies according to the outcomes experienced. This paper makes several contributions: (1) it establishes bounds for the average treatment effect even when a validation study is not available; (2) it proposes multiple estimation methods across various strategies predicated on different assumptions; and (3) it suggests a sensitivity analysis technique to assess the robustness of the causal conclusion, incorporating insights from prior research. The effectiveness of these methods is demonstrated through simulation studies that explore various model misspecification scenarios. These approaches are then applied to investigate the effect of childhood physical abuse on mental health in adulthood.

OBJECTIVE: Atherosclerosis and cancer may progress through common pathological factors. This study was performed to investigate the association between the abdominal aortic calcification (AAC) volume and outcomes following surgical treatment for pancreatic cancer.
METHODS: The subjects of this retrospective study were 194 patients who underwent pancreatic cancer surgery between 2007 and 2020. The AAC volume was assessed through routine preoperative computed tomography. Univariate and multivariate analyses were performed to evaluate the impact of the AAC volume on oncological outcomes.
RESULTS: A higher AAC volume (≥ 312 mm3) was identified in 66 (34%) patients, who were significantly older and had a higher prevalence of diabetes and sarcopenia. Univariate analysis revealed several risk factors for overall survival (OS), including male sex, an AAC volume ≥ 312 mm3, elevated carbohydrate antigen 19-9, prolonged operation time, increased intraoperative bleeding, lymph node metastasis, poor differentiation, and absence of adjuvant chemotherapy. Multivariate analysis identified an AAC volume ≥ 312 mm3, prolonged operation time, lymph node metastasis, poor differentiation, and absence of adjuvant chemotherapy as independent OS risk factors. The OS rate was significantly lower in the high AAC group than in the low AAC group.
CONCLUSIONS: The AAC volume may serve as a preoperative prognostic indicator for patients with pancreatic cancer.

BACKGROUND: Although machine learning is a promising tool for making prognoses, the performance of machine learning in predicting outcomes after stroke remains to be examined.
OBJECTIVE: This study aims to examine how much data-driven models with machine learning improve predictive performance for poststroke outcomes compared with conventional stroke prognostic scores and to elucidate how explanatory variables in machine learning-based models differ from the items of the stroke prognostic scores.
METHODS: We used data from 10,513 patients who were registered in a multicenter prospective stroke registry in Japan between 2007 and 2017. The outcomes were poor functional outcome (modified Rankin Scale score >2) and death at 3 months after stroke. Machine learning-based models were developed using all variables with regularization methods, random forests, or boosted trees. We selected 3 stroke prognostic scores, namely, ASTRAL (Acute Stroke Registry and Analysis of Lausanne), PLAN (preadmission comorbidities, level of consciousness, age, neurologic deficit), and iScore (Ischemic Stroke Predictive Risk Score) for comparison. Item-based regression models were developed using the items of these 3 scores. The model performance was assessed in terms of discrimination and calibration. To compare the predictive performance of the data-driven model with that of the item-based model, we performed internal validation after random splits of identical populations into 80% of patients as a training set and 20% of patients as a test set; the models were developed in the training set and were validated in the test set. We evaluated the contribution of each variable to the models and compared the predictors used in the machine learning-based models with the items of the stroke prognostic scores.
RESULTS: The mean age of the study patients was 73.0 (SD 12.5) years, and 59.1% (6209/10,513) of them were men. The area under the receiver operating characteristic curves and the area under the precision-recall curves for predicting poststroke outcomes were higher for machine learning-based models than for item-based models in identical populations after random splits. Machine learning-based models also performed better than item-based models in terms of the Brier score. Machine learning-based models used different explanatory variables, such as laboratory data, from the items of the conventional stroke prognostic scores. Including these data in the machine learning-based models as explanatory variables improved performance in predicting outcomes after stroke, especially poststroke death.
CONCLUSIONS: Machine learning-based models performed better in predicting poststroke outcomes than regression models using the items of conventional stroke prognostic scores, although they required additional variables, such as laboratory data, to attain improved performance. Further studies are warranted to validate the usefulness of machine learning in clinical settings.

BACKGROUND: Type C hepatitis B-related acute-on-chronic liver failure (HBV-ACLF), which is based on decompensated cirrhosis, has different laboratory tests, precipitating events, organ failure and clinical outcomes. The predictors of prognosis for type C HBV-ACLF patients are different from those for other subgroups. This study aimed to construct a novel, short-term prognostic score that applied serological indicators of hepatic regeneration and noninvasive assessment of liver fibrosis to predict outcomes in patients with type C HBV-ACLF.
METHODS: Patients with type C HBV-ACLF were observed for 90 days. Demographic information, clinical examination, and laboratory test results of the enrolled patients were collected. Univariate and multivariate logistic regression were performed to identify independent prognostic factors and develop a novel prognostic scoring system. A receiver operating characteristic (ROC) curve was used to analyse the performance of the model.
RESULTS: A total of 224 patients with type C HBV-ACLF were finally included. The overall survival rate within 90 days was 47.77%. Age, total bilirubin (TBil), international normalized ratio (INR), alpha-fetoprotein (AFP), white blood cell (WBC), serum sodium (Na), and aspartate aminotransferase/platelet ratio index (APRI) were found to be independent prognostic factors. According to the results of the logistic regression analysis, a new prognostic model (named the A3Twin score) was established. The area under the curve (AUC) of the receiver operating characteristic curve (ROC) was 0.851 [95% CI (0.801-0.901)], the sensitivity was 78.8%, and the specificity was 71.8%, which were significantly higher than those of the MELD, IMELD, MELD-Na, TACIA and COSSH-ACLF II scores (all P < 0.001). Patients with lower A3Twin scores (<-9.07) survived longer.
CONCLUSIONS: A new prognostic scoring system for patients with type C HBV-ACLF based on seven routine indices was established in our study and can accurately predict short-term mortality and might be used to guide clinical management.