Natriuretic peptides are established biomarkers related to the prognosis of heart failure. New biomarkers have emerged in the field of cardiovascular disease. The prognostic value of these biomarkers in heart failure with reduced left ventricular ejection fraction is not well-established. We conducted a prospective, single-centre study, including (July 2019 to March 2023) 104 patients being consecutively admitted with a diagnosis of acute heart failure with reduced ejection fraction decompensation. The median follow-up was 23.5 months, during which 20 deaths (19.4%) and 21 readmissions for heart failure (20.2%) were recorded. Plasma biomarkers, such as NT-proBNP, GDF-15, sST2, suPAR, and FGF-23, were associated with an increased risk of all-cause mortality. However, a Cox regression analysis showed that the strongest predictors of mortality were an estimated glomerular filtration rate (HR 0.96 [0.93-0.98]), GDF-15 (HR 1.3 [1.16-1.45]), and sST2 (HR 1.2 [1.11-1.35]). The strongest predictive model was formed by the combination of the glomerular filtration rate and sST2 (C-index 0.758). In conclusion, in patients with acute decompensated heart failure with reduced ejection fraction, GDF-15 and sST2 showed the highest predictive power for all-cause mortality, which was superior to other established biomarkers such as natriuretic peptides. GDF-15 and sST2 may provide additional prognostic information to improve the prognostic assessment.

Fatigue, cognitive impairment, anxiety, depression, and sleep disturbance are cancer-related behavioral symptoms that may persist years after early-stage breast cancer, affecting quality of life. We aimed to generate a predictive model of long-term cancer-related behavioral symptoms clusters among breast cancer survivors 4 years after diagnosis.

Patients with early-stage breast cancer were included from the CANcer TOxicity trial (ClinicalTrials.gov identifier NCT01993498). Our outcome was the proportion of patients reporting cancer-related behavioral symptoms clusters 4 years after diagnosis (≥3 severe symptoms). Predictors, including clinical, behavioral, and treatment-related characteristics; Behavioral Symptoms Score (BSS; 1 point per severe cancer-related behavioral symptom at diagnosis); and a proinflammatory cytokine (interleukin 1b; interleukin 6; tumor necrosis factor α) genetic risk score were tested using multivariable logistic regression, implementing bootstrapped augmented backwards elimination. A 2-sided P less than .05 defined statistical significance.

In the development cohort (n = 3555), 642 patients (19.1%) reported a cluster of cancer-related behavioral symptoms at diagnosis, and 755 (21.2%) did so 4 years after diagnosis. Younger age (adjusted odds ratio for 1-year decrement = 1.012, 95% confidence interval [CI] = 1.003 to 1.020), previous psychiatric disorders (adjusted odds ratio vs no = 1.27, 95% CI = 1.01 to 1.60), and BSS (adjusted odds ratio ranged from 2.17 [95% CI = 1.66 to 2.85] for BSS = 1 vs 0 to 12.3 [95% CI = 7.33 to 20.87] for BSS = 5 vs 0) were predictors of reporting a cluster of cancer-related behavioral symptoms (area under the curve = 0.73, 95% CI = 0.71 to 0.75). Genetic risk score was not predictive of these symptoms. Results were confirmed in the validation cohort (n = 1533).

Younger patients with previous psychiatric disorders and higher baseline symptom burden have greater risk of long-term clusters of cancer-related behavioral symptoms. Our model might be implemented in clinical pathways to improve management and test the effectiveness of risk-mitigation interventions among breast cancer survivors.

To easily detect frailty in a timely fashion, enabling targeted interventions and appropriate monitoring, will be a major worldwide public health and economic challenge as the proportion of older people increases in the population. Based on a review and meta-analysis showing that C-reactive protein (CRP), haemoglobin, albumin and vitamin D are associated with frailty, we aimed to develop and validate a biological score using these biomarkers for the detection of frailty.

We conducted a retrospective, cross-sectional, monocentric study using the electronic healthcare database of Lille University Hospital, France.

Inclusion criteria were patients aged 50 and over, being hospitalised at Lille University Hospital between 1 January 2008 and 31 December 2021. We identified patients whose CRP, haemoglobin, albumin and vitamin D levels were measured. We selected patients whose assays fell within normal thresholds, outside acute clinical situations.

To assess frailty, we used a scale adapted to electronic healthcare database, called the Hospital Frailty Risk Score. To develop and validate the predictive frailty score, the whole population was divided into a development and a validation cohort.

26 554 patients were included, of which 17 702 were in the development cohort and 8852 in the validation cohort. Based on the results of the multivariate analysis, we developed an equation combining CRP, haemoglobin, albumin and vitamin D with age and sex to obtain a score referred to as the bFRAil (biological FRAilty) score. Within the validation cohort, the area under the curve for this score is 0.78 (0.77-0.80) and the negative predictive value is 83.7%.

This study has made it possible, for the first time, to develop and validate in a hospital setting a biological score called bFRAil score based on simple, easily measurable biomarkers for identifying frail patients in daily medical practice. Further studies are needed to validate its use.

Postoperative complications are prone to occur in patients after radical pancreaticoduodenectomy (PD). This study aimed to construct and validate a model for predicting postoperative major complications in patients after PD.

The clinical data of 360 patients who underwent PD were retrospectively collected from two centers between January 2019 and December 2023. Visceral adipose volume (VAV) and subcutaneous adipose volume (SAV) were measured using three-dimensional (3D) computed tomography (CT) reconstruction. According to the Clavien-Dindo classification system, the postoperative complications were graded. Subsequently, a predictive model was constructed based on the results of least absolute shrinkage and selection operator (LASSO) multivariate logistic regression analysis and stepwise (stepAIC) selection. The nomogram was internally validated by the training and test cohort. The discriminatory ability and clinical utility of the nomogram were evaluated by area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve analysis (DCA).

The major complications occurred in 13.3% (n = 48) of patients after PD. The nomogram revealed that high VAV/SAV, high system inflammation response index (SIRI), high triglyceride glucose-body mass index (TyG-BMI), low prognostic nutritional index (PNI) and CA199 ≥ 37 were independent risk factors for major complications. The C-index of this model was 0.854 (95%CI [0.800-0.907]), showing excellent discrimination. The calibration curve demonstrated satisfactory concordance between nomogram predictions and actual observations. The DCA curve indicated the substantial clinical utility of the nomogram.

The model based on clinical and CT indices demonstrates good predictive performance and clinical benefit for major complications in patients undergoing PD.

In Thailand, the assessment of vitamin D status by measuring 25-hydroxyvitamin D[25(OH)D] levels in individuals at risk for osteoporosis is constrained by limited facilities and high costs. This study aimed to create a clinical model for predicting vitamin D deficiency in women with osteoporosis or risk factors for osteoporosis.

This was a cross-sectional study of 490 women. All participants had 25(OH)D levels measured. A questionnaire was used to assess factors related to vitamin D status. Vitamin D deficiency was defined as 25(OH)D levels < 30 ng/mL. Logistic regression analyses were conducted to investigate predictors of vitamin D deficiency. In the model, odds ratios (ORs) were converted into simple scores. The optimal cutoff for women at a high risk of vitamin D deficiency was established. Internal validation was assessed using a Bootstrap.

Sixty percent had vitamin D deficiency. The final model for predicting vitamin D deficiency consisted of a body mass index ≥ 25 kg/m2 (OR:1.15), lack of exercise (OR:1.59), exercise 1-2 times/week (OR:1.40), sunlight exposure < 15 min/day (OR:1.70), no vitamin D supplementation (OR:8.76), and vitamin D supplementation of 1-20,000 IU/week (OR:2.31). The area under the curve was 0.747. At a cutoff of 6.6 in total risk score (range 4-13.6), the model predicted vitamin D deficiency with a sensitivity of 71.9 % and a specificity of 65.3 %. The internal validation by Bootstrap revealed a ROC of 0.737.

In women at risk of osteoporosis, a simple risk score can identify individuals with a high risk of vitamin D deficiency. These women could benefit from vitamin D supplementation without requiring 25(OH)D measurements.

Introduction: Prediction models for periprosthetic joint infections (PJIs) are gaining interest due to their potential to improve clinical decision-making. However, their external validity across various settings remains uncertain. This study aimed to externally validate promising preoperative PJI prediction models in a recent multinational European cohort. Methods: Three preoperative PJI prediction models - by Tan et al. (2018), Del Toro et al. (2019), and Bülow et al. (2022) - that have previously demonstrated high levels of accuracy were selected for validation. A retrospective observational analysis of patients undergoing total hip arthroplasty (THA) and total knee arthroplasty (TKA) at centers in the Netherlands, Portugal, and Spain between January 2020 and December 2021 was conducted. Patient characteristics were compared between our cohort and those used to develop the models. Performance was assessed through discrimination and calibration. Results: The study included 2684 patients, 60 of whom developed a PJI (2.2 %). Our cohort differed from the models' original cohorts with respect to demographic variables, procedural variables, and comorbidity prevalence. The overall accuracies of the models, measured with the c  statistic, were 0.72, 0.69, and 0.72 for the Tan, Del Toro, and Bülow models, respectively. Calibration was reasonable, but the PJI risk estimates were most accurate for predicted infection risks below 3 %-4 %. The Tan model overestimated PJI risk above 4 %, whereas the Del Toro model underestimated PJI risk above 3 %. Conclusions: The Tan, Del Toro, and Bülow PJI prediction models were externally validated in this multinational cohort, demonstrating potential for clinical application in identifying high-risk patients and enhancing preoperative counseling and prevention strategies.

The objective was to explore dosimetric predictors of brain necrosis (BN) in fractionated stereotactic radiotherapy (SRT).

After excluding collinearities carefully, multivariate logistic models were developed for comprehensive analyses of dosimetric predictors in patients who received first-line fractionated SRT for brain metastases (BMs). The normal brain volume receiving an xx Gy biological dose in 2 Gy fractions (VxxEQD2) was calculated from the retrieved dose-volume parameters.

Thirty Gy/3 fractions (fr) SRT was delivered to 34 patients with 75 BMs (median target volume, 3.2 cc), 35 Gy/5 fr to 30 patients with 57 BMs (6.4 cc), 37.5 Gy/5 fr to 28 patients with 47 BMs (20.2 cc), and 40 Gy/10 fr to 20 patients with 37 BMs (24.3 cc), according to protocols, depending on the total target volume (p < 0.001). After excluding the three-fraction groups, the incidence of symptomatic BN was significantly higher in patients with a larger V50EQD2 (adjusted odds ratio: 1.07, p < 0.02), V55EQD2 (1.08, p < 0.01), or V60EQD2 (1.09, p < 0.01) in the remaining five- and ten-fraction groups. The incidence of BN was also significantly higher in cases with V55EQD2 > 30 cc or V60EQD2 > 20 cc (p < 0.05). These doses correspond to 28 or 30 Gy/5 fr and 37 or 40 Gy/10 fr, respectively.

In five- or ten-fraction SRT, larger V55EQD2 or V60EQD2 are BN risk predictors. These biologically high doses may affect BN incidence. Thus, the planning target volume margin should be minimized as much as possible.

Clinical utility data on pulmonary nodule (PN) risk stratification biomarkers are lacking. We aimed to determine the incremental predictive value and clinical utility of using an artificial intelligence (AI) radiomics-based computer-aided diagnosis (CAD) tool in addition to routine clinical information to risk stratify PNs among real-world patients.

We performed a retrospective cohort study of patients with PNs who underwent lung biopsy. We collected clinical data and used a commercially available AI radiomics-based CAD tool to calculate a Lung Cancer Prediction (LCP) score. We developed logistic regression models to evaluate a well-validated clinical risk prediction model (the Mayo Clinic model) with and without the LCP score (Mayo vs Mayo + LCP) using area under the curve (AUC), risk stratification table, and standardized net benefit analyses.

Among the 134 patients undergoing PN biopsy, cancer prevalence was 61%. Addition of the radiomics-based LCP score to the Mayo model was associated with increased predictive accuracy (likelihood ratio test, P = .012). The AUCs for the Mayo and Mayo + LCP models were 0.58 (95% CI = 0.48 to 0.69) and 0.65 (95% CI = 0.56 to 0.75), respectively. At the 65% risk threshold, the Mayo + LCP model was associated with increased sensitivity (56% vs 38%; P = .019), similar false positive rate (33% vs 35%; P = .8), and increased standardized net benefit (18% vs -3.3%) compared with the Mayo model.

Use of a commercially available AI radiomics-based CAD tool as a supplement to clinical information improved PN cancer risk prediction and may result in clinically meaningful changes in risk stratification.

Since the mid-1980s, there has been little progress in improving survival of patients diagnosed with osteosarcoma. Survival prediction models play a key role in clinical decision-making, guiding healthcare professionals in tailoring treatment strategies based on individual patient risks. The increasing interest of the medical community in using machine learning (ML) for predicting survival has sparked an ongoing debate on the value of ML techniques versus more traditional statistical modelling (SM) approaches. This study investigates the use of SM versus ML methods in predicting overall survival (OS) using osteosarcoma data from the EURAMOS-1 clinical trial (NCT00134030). The well-established Cox proportional hazard model is compared to the extended Cox model that includes time-varying effects, and to the ML methods random survival forests and survival neural networks. The impact of eight variables on OS predictions is explored. Results are compared on different model performance metrics, variable importance, and patient-specific predictions. The article provides comprehensive insights to aid healthcare researchers in evaluating diverse survival prediction models for low-dimensional clinical data.

To quantify the potential advantages of using 10 year risk prediction models for cardiovascular disease, in combination with risk thresholds specific to both age and sex, to identify individuals at high risk of cardiovascular disease for allocation of statin treatment.

Prospective open cohort study.

Primary care data from the UK Clinical Practice Research Datalink GOLD, linked with hospital admissions from Hospital Episode Statistics and national mortality records from the Office for National Statistics in England, 1 January 2006 to 31 May 2019.

1 046 736 individuals (aged 40-85 years) with no cardiovascular disease, diabetes, or a history of statin treatment at baseline using data from electronic health records.

10 year risk of cardiovascular disease, calculated with version 2 of the QRISK cardiovascular disease risk algorithm (QRISK2), with two main strategies to identify individuals at high risk: in strategy A, estimated risk was a fixed cut-off value of ≥10% (ie, as per the UK National Institute for Health and Care Excellence guidelines); in strategy B, estimated risk was ≥10% or ≥90th centile of age and sex specific risk distributions.

Compared with strategy A, strategy B stratified 20 241 (149.8%) more women aged ≤53 years and 9832 (150.2%) more men aged ≤47 years as having a high risk of cardiovascular disease; for all other ages the strategies were the same. Assuming that treatment with statins would be initiated in those identified as high risk, differences in the estimated gain in cardiovascular disease-free life years from statin treatment for strategy B versus strategy A were 0.14 and 0.16 years for women and men aged 40 years, respectively; among individuals aged 40-49 years, the numbers needed to treat to prevent one cardiovascular disease event for strategy B versus strategy A were 39 versus 21 in women and 19 versus 15 in men, respectively.

This study quantified the potential gains in cardiovascular disease-free life years when implementing prevention strategies based on age and sex specific risk thresholds instead of a fixed risk threshold for allocation of statin treatment. Such gains should be weighed against the costs of treating more younger people with statins for longer.

Associations between markers of impaired kidney function and adverse outcomes among South Asians is understudied and could differ from existing data derived mostly from North American or European cohorts.

We conducted a prospective analysis of 9797 participants from the ongoing cardiometabolic risk reduction study in South Asia, India. We examined the associations between baseline spot urine albumin-to-creatinine (UACR) ratio and creatinine-based estimated glomerular filtration rate (eGFR) estimating equations with all-cause mortality using Cox proportional hazards regression, adjusting for baseline age, sex, diabetes, systolic blood pressure, tobacco, history of cardiovascular disease, and cholesterol. Additionally, we calculated population attributable fraction (PAF) for both markers.

Over a median 7-year follow-up, with 66,909 person-years, 791 deaths occurred. At baseline, the weighted prevalence of UACR ≥ 30 mg/g and eGFRCKD-EPI 2009 <60 ml/min per 1.73 m2 was 6.6% and 1.6%, respectively. The risk for mortality was increased with higher UACR (10-30 hazard ratio [HR]: 1.6 [1.2-2.1]), 30-300 HR: 2.4 [1.8-3.1]), and ≥300 (HR: 6.0 [3.8-9.4] relative to UACR <10 mg/g). Risk for mortality was also higher with lower eGFRCKD-EPI 2009 (44-30; HR: 4.5 [2.5-8.3] and <30 HR: 7.0 [3.7-13.0], relative to 90-104 ml/min per 1.73 m2). PAF for mortality because of UACR ≥30 mg/g and eGFRCKD-EPI 2009 <45 ml/min per 1.73 m2 were 24.4% and 13.4%, respectively.

Single-time point assessment of UACR ≥30 mg/g or eGFRCKD-EPI 2009 <45 ml/min per 1.73 m2 portends higher mortality risk among urban South Asians. Because albuminuria is common and associated with accelerated decline in GFR, screening and targeted efforts to reduce albuminuria are warranted.

Breast cancer is most commonly faced with form of cancer amongst women worldwide. In spite of the fact that the breast cancer research and awareness have gained considerable momentum, there is still no one treatment due to disease heterogeneity. Survival data may be of specific interest in breast cancer studies to understand its dynamic and complex trajectories. This study copes with the most important covariates affecting the disease progression. The study utilizes the German Breast Cancer Study Group 2 (GBSG2) and the Molecular Taxonomy of Breast Cancer International Consortium dataset (METABRIC) datasets. In both datasets, interests lie in relapse of the disease and the time when the relapse happens. The three models, namely the Cox proportional hazards (PH) model, random survival forest (RSF) and conditional inference forest (Cforest) were employed to analyse the breast cancer datasets. The goal of this study is to apply these methods in prediction of breast cancer progression and compare their performances based on two different estimation methods: the bootstrap estimation and the bootstrap .632 estimation. The model performance was evaluated in concordance index (C-index) and prediction error curves (pec) for discrimination. The Cox PH model has a lower C-index and bigger prediction error compared to the RSF and the Cforest approach for both datasets. The analysis results of GBSG2 and METABRIC datasets reveal that the RSF and the Cforest algorithms provide non-parametric alternatives to Cox PH model for estimation of the survival probability of breast cancer patients.

Stage classification for Siewert II adenocarcinoma of the esophagogastric junction (AEG) treated with neoadjuvant chemotherapy (NAC) has not been established.

To investigate the optimal stage classification for Siewert II AEG with NAC.

A nomogram was established based on Cox regression model that analyzed variables associated with overall survival (OS) and disease-specific survival (DSS). The nomogram performance in terms of discrimination and calibration ability was evaluated using the likelihood-ratio test, Akaike information criterion, Harrell concordance index, time-receiver operating characteristic curve, and decision curve analysis.

Data from 725 patients with Siewert type II AEG who underwent neoadjuvant therapy and gastrectomy were obtained from the Surveillance, Epidemiology, and End Results database. Univariate and multivariate analyses revealed that sex, marital status, race, ypT stage, and ypN stage were independent prognostic factors of OS, whereas sex, race, ypT stage, and ypN stage were independent prognostic factors for DSS. These factors were incorporated into the OS and DSS nomograms. Our novel nomogram model performed better in terms of OS and DSS prediction compared to the 8th American Joint Committee of Cancer pathological staging system for esophageal and gastric cancer. Finally, a user-friendly web application was developed for clinical use.

The nomogram established specifically for patients with Siewert type II AEG receiving NAC demonstrated good prognostic performance. Validation using external data is warranted before its widespread clinical application.

BACKGROUNDPreclinical studies suggest that cholesterol accumulation leads to insulin resistance. We previously reported that alterations in a monocyte cholesterol metabolism transcriptional network (CMTN) - suggestive of cellular cholesterol accumulation - were cross-sectionally associated with obesity and type 2 diabetes (T2D). Here, we sought to determine whether the CMTN alterations independently predict incident prediabetes/T2D risk, and correlate with cellular cholesterol accumulation.METHODSMonocyte mRNA expression of 11 CMTN genes was quantified among 934 Multi-Ethnic Study of Atherosclerosis (MESA) participants free of prediabetes/T2D; cellular cholesterol was measured in a subset of 24 monocyte samples.RESULTSDuring a median 6-year follow-up, lower expression of 3 highly correlated LXR target genes - ABCG1 and ABCA1 (cholesterol efflux) and MYLIP (cholesterol uptake suppression) - and not other CMTN genes, was significantly associated with higher risk of incident prediabetes/T2D. Lower expression of the LXR target genes correlated with higher cellular cholesterol levels (e.g., 47% of variance in cellular total cholesterol explained by ABCG1 expression). Further, adding the LXR target genes to overweight/obesity and other known predictors significantly improved prediction of incident prediabetes/T2D.CONCLUSIONThese data suggest that the aberrant LXR/ABCG1-ABCA1-MYLIP pathway (LAAMP) is a major T2D risk factor and support a potential role for aberrant LAAMP and cellular cholesterol accumulation in diabetogenesis.FUNDINGThe MESA Epigenomics and Transcriptomics Studies were funded by NIH grants 1R01HL101250, 1RF1AG054474, R01HL126477, R01DK101921, and R01HL135009. This work was supported by funding from NIDDK R01DK103531 and NHLBI R01HL119962.

Backgroud: Congenital heart defects (CHDs) are the most frequent group of major congenital anomalies, accounting for almost 1% of all births. They comprise a very heterogeneous group of birth defects in terms of their severity, clinical management, epidemiology, and embryologic origins. Taking this heterogeneity into account is an important imperative to provide reliable prognostic information to patients and their caregivers, as well as to compare results between centers or to assess alternative diagnostic and treatment strategies. The Anatomic and Clinical Classification of CHD (ACC-CHD) aims to facilitate both the CHD coding process and data analysis in clinical and epidemiological studies. The objectives of the study were to (1) Describe the long-term childhood survival of newborns with CHD, and (2) Develop and validate predictive models of infant mortality based on the ACC-CHD. Methods: This study wasbased on data from a population-based, prospective cohort study: Epidemiological Study of Children with Congenital Heart Defects (EPICARD). The final study population comprised 1881 newborns with CHDs after excluding cases that were associated with chromosomal and other anomalies. Statistical analysis included non-parametric survival analysis and flexible parametric survival models. The predictive performance of models was assessed by Harrell's C index and the Royston-Sauerbrei RD2, with internal validation by bootstrap. Results: The overall 8-year survival rate for newborns with isolated CHDs was 0.96 [0.93-0.95]. There was a substantial difference between the survival rate of the categories of ACC-CHD. The highest and lowest 8-year survival rates were 0.995 [0.989-0.997] and 0.34 [0.21-0.50] for "interatrial communication abnormalities and ventricular septal defects" and "functionally univentricular heart", respectively. Model discrimination, as measured by Harrell's C, was 87% and 89% for the model with ACC-CHD alone and the full model, which included other known predictors of infant mortality, respectively. The predictive performance, as measured by RD2, was 45% and 50% for the ACC-CHD alone and the full model. These measures were essentially the same after internal validation by bootstrap. Conclusions: The ACC-CHD classification provided the basis of a highly discriminant survival model with good predictive ability for the 8-year survival of newborns with CHDs. Prediction of individual outcomes remains an important clinical and statistical challenge.

Autologous stem cell transplantation (ASCT) has been a mainstay in myeloma treatment for over three decades, but patient prognosis post-ASCT varies significantly. In a retrospective study of 5259 patients with multiple myeloma (MM) at the University of Arkansas for Medical Sciences undergoing ASCT with a median 57-month follow-up, we divided the dataset into training (70%) and validation (30%) subsets. Employing univariable and multivariable Cox analyses, we systematically assessed 29 clinical variables, identifying crucial adverse prognostic factors, such as extended duration between MM diagnosis and ASCT, elevated serum ferritin, and reduced transferrin levels. These factors could enhance existing prognostic models. Additionally, we pinpointed significant poor prognosis markers like high serum calcium and low platelet counts, though they are applicable to a smaller patient population. Utilizing seven easily accessible high-risk variables, we devised a four-stage system (ATM4S) with primary stage borders determined through K-adaptive partitioning. This staging system underwent validation in both the training dataset and an independent cohort of 514 ASCT-treated MM patients from the University of Iowa. We also explored cytogenetic risk factors within this staging system, emphasizing its potential clinical utility for refining prognostic assessments and guiding personalized treatment approaches.

Azacitidine (AZA) has been one of the standard treatments for transplantation-ineligible patients with myelodysplastic syndrome (MDS); however, hematological toxicities frequently cause treatment interruption in the early phase of the therapy. The present study conducted a multicenter retrospective study to investigate the prognostic impacts of various factors, including factors included in the Revised International Prognostic Scoring System (IPSS-R) and severe cytopenia in the early phase of AZA monotherapy in 212 patients with MDS. Severe cytopenia was evaluated after the initiation of therapy by absolute neutrophil counts on the 29th day after AZA (ANC29) initiation, and red cell concentrates (RCC) and platelet concentrate (PC) transfusion units required within 28 days from the start of AZA, designated in the present study as RCC28 and PC28, respectively. The survival period was determined from the 29th day of AZA treatment to death from any cause as the conditional survival period after the first cycle of AZA (CS-AZA1). Multivariate analysis demonstrated that severe thrombocytopenia defined by >30 units of PC28 and very poor risk cytogenetics according to IPSS-R were independent prognostic factors for CS-AZA1. The Kyoto Conditional Survival Scoring System was subsequently developed by incorporating severe thrombocytopenia defined by PC28 and very poor risk cytogenetics, which successfully stratified the risks of the patients in CS-AZA1. In conclusion, extreme PC transfusion dependency during the first cycle of AZA and very poor risk cytogenetics are important prognostic factors in AZA monotherapy for MDS.

This study addresses the limited non-invasive tools for Oral Cavity Squamous Cell Carcinoma (OSCC) survival prediction by identifying Computed Tomography (CT)-based biomarkers to improve prognosis prediction. A retrospective analysis was conducted on data from 149 OSCC patients, including CT radiomics and clinical information. An ensemble approach involving correlation analysis, score screening, and the Sparse-L1 algorithm was used to select functional features, which were then used to build Cox Proportional Hazards models (CPH). Our CPH achieved a 0.70 concordance index in testing. The model identified two CT-based radiomics features, Gradient-Neighboring-Gray-Tone-Difference-Matrix-Strength (GNS) and normalized-Wavelet-LLL-Gray-Level-Dependence-Matrix-Large-Dependence-High-Gray-Level-Emphasis (HLE), as well as stage and alcohol usage, as survival biomarkers. The GNS group with values above 14 showed a hazard ratio of 0.12 and a 3-year survival rate of about 90%. Conversely, the GNS group with values less than or equal to 14 had a 49% survival rate. For normalized HLE, the high-end group (HLE > - 0.415) had a hazard ratio of 2.41, resulting in a 3-year survival rate of 70%, while the low-end group (HLE ≤ - 0.415) had a 36% survival rate. These findings contribute to our knowledge of how radiomics can be used to predict the outcome so that treatment plans can be tailored for patients people with OSCC to improve their survival.

Postoperative distant metastasis is the main cause of death in breast cancer patients. We aimed to construct a nomogram to predict the risk of metastasis of luminal B type invasive ductal carcinoma.

We applied the data of 364 luminal B type breast cancer patients between 2008 and 2013. Patients were categorized into modeling group and validation group randomly (1:1). The breast cancer metastasis nomogram was developed from the logistic regression model using clinicopathological variables. The area under the receiver-operating characteristic curve (AUC) was calculated in modeling group and validation group to evaluate the predictive accuracy of the nomogram.

The multivariate logistic regression analysis showed that tumor size, No. of the positive level 1 axillary lymph nodes, human epidermal growth factor receptor 2 (HER2) status and Ki67 index were the independent predictors of the breast cancer metastasis. The AUC values of the modeling group and the validation group were 0.855 and 0.818, respectively. The nomogram had a well-fitted calibration curve. The positive and negative predictive values were 49.3% and 92.7% in the modeling group, and 47.9% and 91.0% in the validation group. Patients who had a score of 60 or more were thought to have a high risk of breast cancer metastasis.

The nomogram has a great predictive accuracy of predicting the risk of breast cancer metastasis. If patients had a score of 60 or more, necessary measures, like more standard treatment methods and higher treatment adherence of patients, are needed to take to lower the risk of metastasis and improve the prognosis.

To validate prognostic performance of the China liver cancer (CNLC) staging system as well as to compare these parameters with those of the Barcelona Clinic Liver Cancer (BCLC) staging system for Chinese hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE).

This multicenter retrospective study included 1,124 patients with HCC between January 2012 and December 2020 from six Chinese hospitals. Based on overall survival (OS), the prognostic performance outcomes for the CNLC and BCLC staging systems were compared by model discrimination [C statistic and Akaike information criterion (AIC)], monotonicity of the gradient (linear trend chi-square test), homogeneity (likelihood ratio chi-square test), and calibration (calibration plots). A prospective cohort of 44 patients receiving TACE-based therapy included between January 2021 and December 2022 was used to prospectively validate the outcomes.

Median OS was 19.1 (18.2-20.0) months, with significant differences in OS between stages defined by the CNLC and BCLC observed (p<0.001). The CNLC performed better than the BCLC regarding model discrimination (C-index: 0.661 vs. 0.644; AIC: 10,583.28 vs. 10,583.72), model monotonicity of the gradient (linear trend chi-square test: 66.107 vs. 57.418; p<0.001), model homogeneity (159.2 vs. 158.7; p<0.001). Both staging systems had good model calibration. Similar results were observed in the prospective cohort.

Combining model discrimination, gradient monotonicity, homogeneity, and calibration, the CNLC performed better than the BCLC for Chinese HCC patients receiving TACE.

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex and debilitating illness with a significant global prevalence, affecting over 65 million individuals. It affects various systems, including the immune, neurological, gastrointestinal, and circulatory systems. Studies have shown abnormalities in immune cell types, increased inflammatory cytokines, and brain abnormalities. Further research is needed to identify consistent biomarkers and develop targeted therapies. This study uses explainable artificial intelligence and machine learning techniques to identify discriminative metabolites for ME/CFS.

The model investigates a metabolomics dataset of CFS patients and healthy controls, including 26 healthy controls and 26 ME/CFS patients aged 22-72. The dataset encapsulated 768 metabolites into nine metabolic super-pathways: amino acids, carbohydrates, cofactors, vitamins, energy, lipids, nucleotides, peptides, and xenobiotics. Random forest methods together with other classifiers were applied to the data to classify individuals as ME/CFS patients and healthy individuals. The classification learning algorithms' performance in the validation step was evaluated using a variety of methods, including the traditional hold-out validation method, as well as the more modern cross-validation and bootstrap methods. Explainable artificial intelligence approaches were applied to clinically explain the optimum model's prediction decisions.

The metabolomics of C-glycosyltryptophan, oleoylcholine, cortisone, and 3-hydroxydecanoate were determined to be crucial for ME/CFS diagnosis. The random forest model outperformed the other classifiers in ME/CFS prediction using the 1000-iteration bootstrapping method, achieving 98% accuracy, precision, recall, F1 score, 0.01 Brier score, and 99% AUC. According to the obtained results, the bootstrap validation approach demonstrated the highest classification outcomes.

The proposed model accurately classifies ME/CFS patients based on the selected biomarker candidate metabolites. It offers a clear interpretation of risk estimation for ME/CFS, aiding physicians in comprehending the significance of key metabolomic features within the model.

Veteran populations are frequently diagnosed with mental health conditions such as substance use disorder and PTSD. These conditions are associated with adverse outcomes including a higher risk of suicide. The Veterans Health Administration (VHA) has designed a robust mental health system to address these concerns. Veterans can access mental health treatment in acute inpatient, residential, and outpatient settings. Residential programs play an important role in meeting the needs of veterans who need more structure and support. Residential specialty types in the VHA include general mental health, substance use disorder, PTSD, and homeless/work programs. These programs are affiliated with a DVA facility (i.e., medical center). Although residential care can improve outcomes, there is evidence that some patients are discharged from these settings before achieving the program endpoint. These unplanned discharges are referred to using language such as against medical advice, self-discharge, or irregular discharge. Concerningly, unplanned discharges are associated with patient harm including death by suicide. Although there is some initial evidence to locate factors that predict irregular discharge in VHA residential programs, no work has been done to examine features associated with irregular discharge in each residential specialty.

We conducted a retrospective cohort study of patients who were discharged from VHA residential treatment programs between January 2018 and September 2022. We included the following covariates: Principal diagnosis, gender, age, race/ethnicity, number of physical health conditions, number of mental health diagnostic categories, marital status, risk of homelessness, urbanicity, and service connection. We considered two discharge types: Regular and irregular. We used logistic regression to determine the odds of irregular discharge using models stratified by bed specialty as well as combined odds ratios and 95% CIs across program specialties. The primary purposes are to identify factors that predict irregular discharge and to determine if the factors are consistent across bed specialties. In a secondary analysis, we calculated facility-level adjusted rates of irregular discharge, limiting to facilities with at least 50 discharges. We identified the amount of residual variation that exists between facilities after adjusting for patient factors.

A total of 279 residential programs (78,588 patients representing 124,632 discharges) were included in the analysis. Substance use disorder and homeless/work programs were the most common specialty types. Both in the overall and stratified analyses, the number of mental health diagnostic categories and younger age were predictors of irregular discharge. In the facility analysis, there was substantial variation in irregular discharge rates across residential specialties even after adjusting for all patient factors. For example, PTSD programs had a mean adjusted irregular discharge rate of 15.3% (SD: 7.4; range: 2.1-31.2; coefficient of variation: 48.4%).

Irregular discharge is a key concern in VHA residential care. Patient characteristics do not account for all of the observed variation in rates across residential specialty types. There is a need to develop specialty-specific measures of irregular discharge to learn about system-level factors that contribute to irregular discharge. These data can inform strategies to avoid harms associated with irregular discharge.

Quantifying the risk of progression to Alzheimer's disease (AD) could help identify persons who could benefit from early interventions. We used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 544, discovery cohort) and the National Alzheimer's Coordinating Center (NACC, n = 508, validation cohort), subdividing individuals with mild cognitive impairment (MCI) into risk groups based on cerebrospinal fluid amyloid-β levels and identifying differential gray matter patterns. We then created models that fused neural networks with survival analysis, trained using non-parcellated T1-weighted brain MRIs from ADNI data, to predict the trajectories of MCI to AD conversion within the NACC cohort (integrated Brier score: 0.192 [discovery], and 0.108 [validation]). Using modern interpretability techniques, we verified that regions important for model prediction are classically associated with AD. We confirmed AD diagnosis labels using postmortem data. We conclude that our framework provides a strategy for risk-based stratification of individuals with MCI and for identifying regions key for disease prognosis.

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system that affects millions of people worldwide. The disease course varies greatly across individuals and many disease-modifying treatments with different safety and efficacy profiles have been developed recently. Prognostic models evaluated and shown to be valid in different settings have the potential to support people with MS and their physicians during the decision-making process for treatment or disease/life management, allow stratified and more precise interpretation of interventional trials, and provide insights into disease mechanisms. Many researchers have turned to prognostic models to help predict clinical outcomes in people with MS; however, to our knowledge, no widely accepted prognostic model for MS is being used in clinical practice yet.

To identify and summarise multivariable prognostic models, and their validation studies for quantifying the risk of clinical disease progression, worsening, and activity in adults with MS.

We searched MEDLINE, Embase, and the Cochrane Database of Systematic Reviews from January 1996 until July 2021. We also screened the reference lists of included studies and relevant reviews, and references citing the included studies.

We included all statistically developed multivariable prognostic models aiming to predict clinical disease progression, worsening, and activity, as measured by disability, relapse, conversion to definite MS, conversion to progressive MS, or a composite of these in adult individuals with MS. We also included any studies evaluating the performance of (i.e. validating) these models. There were no restrictions based on language, data source, timing of prognostication, or timing of outcome.

Pairs of review authors independently screened titles/abstracts and full texts, extracted data using a piloted form based on the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS), assessed risk of bias using the Prediction Model Risk Of Bias Assessment Tool (PROBAST), and assessed reporting deficiencies based on the checklist items in Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD). The characteristics of the included models and their validations are described narratively. We planned to meta-analyse the discrimination and calibration of models with at least three external validations outside the model development study but no model met this criterion. We summarised between-study heterogeneity narratively but again could not perform the planned meta-regression.

We included 57 studies, from which we identified 75 model developments, 15 external validations corresponding to only 12 (16%) of the models, and six author-reported validations. Only two models were externally validated multiple times. None of the identified external validations were performed by researchers independent of those that developed the model. The outcome was related to disease progression in 39 (41%), relapses in 8 (8%), conversion to definite MS in 17 (18%), and conversion to progressive MS in 27 (28%) of the 96 models or validations. The disease and treatment-related characteristics of included participants, and definitions of considered predictors and outcome, were highly heterogeneous amongst the studies. Based on the publication year, we observed an increase in the percent of participants on treatment, diversification of the diagnostic criteria used, an increase in consideration of biomarkers or treatment as predictors, and increased use of machine learning methods over time. Usability and reproducibility All identified models contained at least one predictor requiring the skills of a medical specialist for measurement or assessment. Most of the models (44; 59%) contained predictors that require specialist equipment likely to be absent from primary care or standard hospital settings. Over half (52%) of the developed models were not accompanied by model coefficients, tools, or instructions, which hinders their application, independent validation or reproduction. The data used in model developments were made publicly available or reported to be available on request only in a few studies (two and six, respectively). Risk of bias We rated all but one of the model developments or validations as having high overall risk of bias. The main reason for this was the statistical methods used for the development or evaluation of prognostic models; we rated all but two of the included model developments or validations as having high risk of bias in the analysis domain. None of the model developments that were externally validated or these models' external validations had low risk of bias. There were concerns related to applicability of the models to our research question in over one-third (38%) of the models or their validations. Reporting deficiencies Reporting was poor overall and there was no observable increase in the quality of reporting over time. The items that were unclearly reported or not reported at all for most of the included models or validations were related to sample size justification, blinding of outcome assessors, details of the full model or how to obtain predictions from it, amount of missing data, and treatments received by the participants. Reporting of preferred model performance measures of discrimination and calibration was suboptimal.

The current evidence is not sufficient for recommending the use of any of the published prognostic prediction models for people with MS in clinical routine today due to lack of independent external validations. The MS prognostic research community should adhere to the current reporting and methodological guidelines and conduct many more state-of-the-art external validation studies for the existing or newly developed models.

One of the main concerns of researchers and institutions is how to assess the future performance of scholars and identify their potential to become successful scientists. In this study, we model scholarly success in terms of the probability of a scholar belonging to a group of highly impactful scholars as determined by their citation trajectory structures. To this end, we developed a new set of impact measures based on a scholar's citation trajectory structure (rather than on absolute citation or h-index rates), that show a stable trend and scale for highly impactful scholars, independent of their field of study, seniority and citation index. These measures were then incorporated as influence factors into the logistic regression models and used as features for probabilistic classifiers based on these models to identify the successful scholars in the heterogeneous corpus of 400 of most and least cited professors from two Israeli universities. From the practical point of view, the study may yield useful insights and serve as an aid in making promotion decisions by institutions, as well as a self-assessment tool for researchers who strive to increase their academic influence and become leaders in their field.

ICU patients with SARS-CoV-2-related pneumonia are at risk to develop a central dysautonomia which can contribute to mortality and respiratory failure. The pupillary size and its reactivity to light are controlled by the autonomic nervous system. Pupillometry parameters (PP) allow to predict outcomes in various acute brain injuries. We aim at assessing the most predictive PP of in-hospital mortality and the need for invasive mechanical ventilation (IV).

We led a prospective, two centers, observational study. We recruited adult patients admitted to ICU for a severe SARS-CoV-2 related pneumonia between April and August 2020. The pupillometry was performed at admission including the measurement of baseline pupillary diameter (PD), PD variations (PDV), pupillary constriction velocity (PCV) and latency (PDL).

Fifty patients, 90 % males, aged 66 (60-70) years were included. Seven (14 %) patients died in hospital. The baseline PD (4.1 mm [3.5; 4.8] vs 2.6 mm [2.4; 4.0], P = 0.009), PDV (33 % [27; 39] vs 25 % [15; 36], P = 0.03) and PCV (3.5 mm.s^-1 [2.8; 4.4] vs 2.0 mm.s^-1 [1.9; 3.8], P = 0.02) were significantly lower in patients who will die. A PD value <2.75 mm was the most predictive parameter of in-hospital mortality, with an AUC = 0.81, CI 95 % [0.63; 0.99]. Twenty-four (48 %) patients required IV. PD and PDV were significantly lower in patients who were intubated (3.5 mm [2.8; 4.4] vs 4.2 mm [3.9; 5.2], P = 0.03; 28 % [25; 36 %] vs 35 % [32; 40], P = 0.049, respectively).

A reduced baseline PD is associated with bad outcomes in COVID-19 patients admitted in ICU. It is likely to reflect a brainstem autonomic dysfunction.

Metabolic associated fatty liver disease (MAFLD) is a serious condition, and a simple method is needed for practitioners to identify patients with the disease and have a high risk of disease progression.

We developed and validated a nomogram for fatty liver disease and reclassified the risk factors for MAFLD. The development cohort had 335 patients who received bioelectrical impedance analysis and liver ultrasound attenuation measurements at Shenzhen People's Hospital between September 2020 and June 2021. The validation cohort had 200 patients from other hospitals who received the same evaluation. A random forest procedure and binary logistic analysis were used to screen for risk factors, establish a fatty liver disease predictive model, and forecast the risk of MAFLD. The performance of the nomogram was evaluated by measurement of discrimination, calibration, and clinical usefulness.

The nomogram provided good predictions in a model that included body mass index (BMI) and waist circumference. The areas under the curve of the nomogram were 0.793 in the development cohort and 0.774 in the validation cohort. The nomogram performed well for calibration, category-free net reclassification improvement, and integrated discrimination improvement. Decision curve analysis indicated the nomogram performed better than BMI for predicting net outcome.

The nomogram was an effective screening tool for fatty liver disease, and for those overweight individuals, may help physicians make appropriate decisions regarding treatment of MAFLD.

Maintenance in small hydroelectric plants (SHPs) is essential for securing the expansion of clean energy sources and supplying the energy estimated to be required for the coming years. Identifying failures in SHPs before they happen is crucial for allowing better management of asset maintenance, lowering operating costs, and enabling the expansion of renewable energy sources. Most fault prognosis models proposed thus far for hydroelectric generating units are based on signal decomposition and regression models. In the specific case of SHPs, there is a high occurrence of data being censored, since the operation is not consistently steady and can be repeatedly interrupted due to transmission problems or scarcity of water resources. To overcome this, we propose a two-step, data-driven framework for SHP prognosis based on time series feature engineering and survival modeling. We compared two different strategies for feature engineering: one using higher-order statistics and the other using the Tsfresh algorithm. We adjusted three machine learning survival models-CoxNet, survival random forests, and gradient boosting survival analysis-for estimating the concordance index of these approaches. The best model presented a significant concordance index of 77.44%. We further investigated and discussed the importance of the monitored sensors and the feature extraction aggregations. The kurtosis and variance were the most relevant aggregations in the higher-order statistics domain, while the fast Fourier transform and continuous wavelet transform were the most frequent transformations when using Tsfresh. The most important sensors were related to the temperature at several points, such as the bearing generator, oil hydraulic unit, and turbine radial bushing.

Postoperative pain remains a challenging medical condition impacting the quality of life of every patient. Although several predictive factors for postoperative pain have been identified, an adequate prediction of postoperative pain in patients at risk has not been achieved yet.The primary objective of this study is to identify specific genetic risk factors for the development of acute and chronic postoperative pain to construct a prediction model facilitating a more personalised postoperative pain management for each individual. The secondary objectives are to build a databank enabling researchers to identify other risk factors for postoperative pain, for instance, demographic and clinical outcome indicators; provide insight into (genetic) factors that predict pharmacological pain relief; investigate the relationship between acute and chronic postoperative pain.

In this prospective, observational study, patients who undergo elective surgery will be recruited to a sample size of approximately 10 000 patients. Postoperative acute and chronic pain outcomes will be collected through questionnaires at different time points after surgery in the follow-up of 6 months. Potential genetic, demographic and clinical risk factors for prediction model construction will be collected through blood, questionnaires and electronic health records, respectively.Genetic factors associated with acute and/or chronic postoperative pain will be identified using a genome-wide association analysis. Clinical risk factors as stated in the secondary objectives will be assessed by multivariable regression. A clinical easy-to-use prediction model will be created for postoperative pain to allow clinical use for the stratification of patients.

The Institutional Review Board of the Radboud university medical centre approved the study (authorisation number: 2012/117). The results of this study will be made available through peer-reviewed scientific journals and presentations at relevant conferences, which will finally contribute to personalised postoperative pain management.

NCT02383342.

Aims: The role of inflammation markers in myocarditis is unclear. We assessed the diagnostic and prognostic correlates of C-reactive protein (CRP) at diagnosis in patients with myocarditis. Methods and results: We retrospectively enrolled patients with clinically suspected (CS) or biopsy-proven (BP) myocarditis, with available CRP at diagnosis. Clinical, laboratory and imaging data were collected at diagnosis and at follow-up visits. To evaluate predictors of death/heart transplant (Htx), a machine-learning approach based on random forest for survival data was employed. We included 409 patients (74% males, aged 37 ± 15, median follow-up 2.9 years). Abnormal CRP was reported in 288 patients, mainly with CS myocarditis (p < 0.001), recent viral infection, shorter symptoms duration (p = 0.001), chest pain (p < 0.001), better functional class at diagnosis (p = 0.018) and higher troponin I values (p < 0.001). Death/Htx was reported in 13 patients, of whom 10 had BP myocarditis (overall 10-year survival 94%). Survival rates did not differ according to CRP levels (p = 0.23). The strongest survival predictor was LVEF, followed by anti-nuclear auto-antibodies (ANA) and BP status. Conclusions: Raised CRP at diagnosis identifies patients with CS myocarditis and less severe clinical features, but does not contribute to predicting survival. Main death/Htx predictors are reduced LVEF, BP diagnosis and positive ANA.

Serum C-reactive protein (CRP) is known to be a biomarker for systemic inflammatory reactions. In the present study, we sought to measure the predictive value of serum CRP level for metastatic renal cell carcinoma (mRCC) treated with first-line ipilimumab and nivolumab using our real-world clinical dataset including non-clear cell RCC (nccRCC). The clinical record of patients who underwent the first-line ipilimumab plus nivolumab treatment for mRCC including ccRCC and nccRCC from 2018 to 2021 was retrospectively analyzed. All patients were diagnosed with either intermediate or poor-risk group defined by IMCD (international metastatic RCC database consortium). In total, 74 patients were involved. The median age was 68 years and 24 (32.4%) patients deceased during the follow-up. Forty-five (61%) and 29 (39%) patients were classified into intermediate and poor-risk groups. The one-year overall survival (OS) rate and objective response rate were 65% and 41% for all 74 mRCC patients, respectively. The receiver operating characteristic curve identified 1.0 mg/dL of serum CRP level as an ideal cut-off for predicting overall survival (OS). Serum CRP > 1.0 mg/dL and nccRCC were the independent predictors for OS in 74 mRCC patients. OS for patients with CRP > 1 mg/dL was significantly shorter than those with CRP < 1 mg/dL in both ccRCC (58 patient: p = 0.009) and nccRCC (16 patients: p = 0.008). The present study indicated that serum CRP level is a prognostic indicator for OS in both ccRCC and nccRCC patients treated with the first-line ipilimumab plus nivolumab treatment.

We aimed to investigate whether the age-adjusted Charlson comorbidity index (ACCI) can predict the postoperative overall survival (OS) and cancer-specific survival (CSS) of esophageal squamous cell carcinoma (ESCC) patients. Between 1 July 2015 and 31 July 2021, a retrospective cohort study was conducted among patients with primary ESCC who underwent radical esophagectomy. A total of 352 patients were included, with median age of 63.00 (IQR (interquartile range) 56.00-68.00). The patients were divided into low (n = 300) and high (n = 52) ACCI groups based on the optimal cut-off value of 5 points. Chronic pulmonary disease (38.4%) was the most common comorbidity. The results of the multivariate Cox regression showed that the ACCI (HR = 1.63, 95%CI: 1.04-2.56), tumor size (HR = 1.67, 95%CI: 1.05-2.66), pTNM (II vs. I, HR = 4.74, 95%CI: 1.82-12.32; III vs. I, HR = 6.08, 95%CI: 2.37-15.60), and postoperative chemotherapy (HR = 0.60, 95%CI: 0.40-0.91) were significantly associated with the OS. Furthermore, the ACCI, tumor size, pTNM, and postoperative chemotherapy were also significantly associated with the CSS. Interactions were identified between the ACCI and postoperative chemotherapy, pTNM stage, and tumor size in relation to the OS and CSS. In conclusion, the ACCI may be an independent prognostic factor affecting the long-term prognosis of patients after radical esophagectomy.