Measuring Burden of Unhealthy Behaviours Using a Multivariable Predictive Approach: Life Expectancy Lost in Canada Attributable to Smoking,Alcohol, Physical Inactivity,and Diet

BackgroundBehaviours such as smoking, poor diet, physical inactivity, and unhealthy alcohol consumption are leading risk factors for death. We assessed the Canadian burden attributable to these behaviours by developing, validating, and applying a multivariable predictive model for risk of all-cause death.MethodsA predictive algorithm for 5 y risk of death—the Mortality Population Risk Tool (MPoRT)—was developed and validated using the 2001 to 2008 Canadian Community Health Surveys. There were approximately 1 million person-years of follow-up and 9,900 deaths in the development and validation datasets. After validation, MPoRT was used to predict future mortality and estimate the burden of smoking, alcohol, physical inactivity, and poor diet in the presence of sociodemographic and other risk factors using the 2010 national survey (approximately 90,000 respondents). Canadian period life tables were generated using predicted risk of death from MPoRT. The burden of behavioural risk factors attributable to life expectancy was estimated using hazard ratios from the MPoRT risk model.FindingsThe MPoRT 5 y mortality risk algorithms were discriminating (C-statistic: males 0.874 [95% CI: 0.867–0.881]; females 0.875 [0.868–0.882]) and well calibrated in all 58 predefined subgroups. Discrimination was maintained or improved in the validation cohorts. For the 2010 Canadian population, unhealthy behaviour attributable life expectancy lost was 6.0 years for both men and women (for men 95% CI: 5.8 to 6.3 for women 5.8 to 6.2). The Canadian life expectancy associated with health behaviour recommendations was 17.9 years (95% CI: 17.7 to 18.1) greater for people with the most favourable risk profile compared to those with the least favourable risk profile (88.2 years versus 70.3 years). Smoking, by itself, was associated with 32% to 39% of the difference in life expectancy across social groups (by education achieved or neighbourhood deprivation).ConclusionsMultivariable predictive algorithms such as MPoRT can be used to assess health burdens for sociodemographic groups or for small changes in population exposure to risks, thereby addressing some limitations of more commonly used measurement approaches. Unhealthy behaviours have a substantial collective burden on the life expectancy of the Canadian population.     

Classification and regression tree (CART) model to predict pulmonary tuberculosis in hospitalized patients

ABSTRACT: BACKGROUND: Tuberculosis (TB) remains a public health issue worldwide. The lack of specific clinical symptoms to diagnose TB makes the correct decision to admit patients to respiratory isolation a difficult task for the clinician. Isolation of patients without the disease is common and increases health costs. Decision models for the diagnosis of TB in patients attending hospitals can increase the quality of care and decrease costs, without the risk of hospital transmission. We present a predictive model for predicting pulmonary TB in hospitalized patients in a high prevalence area in order to contribute to a more rational use of isolation rooms without increasing the risk of transmission. METHODS: Cross sectional study of patients admitted to CFFH from March 2003 to December 2004. A classification and regression tree (CART) model was generated and validated. The area under the ROC curve (AUC), sensitivity, specificity, positive and negative predictive values were used to evaluate the performance of model. Validation of the model was performed with a different sample of patients admitted to the same hospital from January to December 2005. RESULTS: We studied 290 patients admitted with clinical suspicion of TB. Diagnosis was confirmed in 26.5% of them. Pulmonary TB was present in 83.7% of the patients with TB (62.3% with positive sputum smear) and HIV/AIDS was present in 56.9% of patients. The validated CART model showed sensitivity, specificity, positive predictive value and negative predictive value of 60.00%, 76.16%, 33.33%, and 90.55%, respectively. The AUC was 79.70%. CONCLUSIONS: The CART model developed for these hospitalized patients with clinical suspicion of TB had fair to good predictive performance for pulmonary TB. The most important variable for prediction of TB diagnosis was chest radiograph results. Prospective validation is still necessary, but our model offer an alternative for decision making in whether to isolate patients with clinical suspicion of TB in tertiary health facilities in countries with limited resources.     

Prediction of mortality in very premature infants: a systematic review of prediction models

Context

Being born very preterm is associated with elevated risk for neonatal mortality. The aim of this review is to give an overview of prediction models for mortality in very premature infants, assess their quality, identify important predictor variables, and provide recommendations for development of future models.

Methods

Studies were included which reported the predictive performance of a model for mortality in a very preterm or very low birth weight population, and classified as development, validation, or impact studies. For each development study, we recorded the population, variables, aim, predictive performance of the model, and the number of times each model had been validated. Reporting quality criteria and minimum methodological criteria were established and assessed for development studies.

Results

We identified 41 development studies and 18 validation studies. In addition to gestational age and birth weight, eight variables frequently predicted survival: being of average size for gestational age, female gender, non-white ethnicity, absence of serious congenital malformations, use of antenatal steroids, higher 5-minute Apgar score, normal temperature on admission, and better respiratory status. Twelve studies met our methodological criteria, three of which have been externally validated. Low reporting scores were seen in reporting of performance measures, internal and external validation, and handling of missing data.

Conclusions

Multivariate models can predict mortality better than birth weight or gestational age alone in very preterm infants. There are validated prediction models for classification and case-mix adjustment. Additional research is needed in validation and impact studies of existing models, and in prediction of mortality in the clinically important subgroup of infants where age and weight alone give only an equivocal prognosis.     

Predicting 30-day mortality of aortic valve replacement by the AVR score

Objectives

The objective of this study is to develop a simple risk score to predict 30-day mortality of aortic valve replacement (AVR).

Methods

In a development set of 673 consecutive patients who underwent AVR between 1990 and 1993, four independent predictors for 30-day mortality were identified: body mass index (BMI) ≥30, BMI <20, previous coronary artery bypass grafting (CABG) and recent myocardial infarction. Based on these predictors, a 30-day mortality risk score—the AVR score—was developed. The AVR score was validated on a validation set of 673 consecutive patients who underwent AVR almost two decennia later in the same hospital.

Results

Thirty-day mortality in the development set was ≤2% in the absence of any predictor (class I, low risk), 2–5% in the solitary presence of BMI ≥30 (class II, mild risk), 5–15% in the solitary presence of previous CABG or recent myocardial infarction (class III, moderate risk), and >15% in the solitary presence of BMI <20, or any combination of BMI ≥30, previous CABG or recent myocardial infarction (class IV, high risk). The AVR score correctly predicted 30-day mortality in the validation set: observed 30-day mortality in the validation set was 2.3% in 487 class I patients, 4.4% in 137 class II patients, 13.3% in 30 class III patients and 15.8% in 19 class IV patients.

Conclusions

The AVR score is a simple risk score validated to predict 30-day mortality of AVR.     

Using matrix assisted laser desorption ionisation mass spectrometry (MALDI-MS) profiling in order to predict clinical outcomes of patients with heart failure

Background

Current risk prediction models in heart failure (HF) including clinical characteristics and biomarkers only have moderate predictive value. The aim of this study was to use matrix assisted laser desorption ionisation mass spectrometry (MALDI-MS) profiling to determine if a combination of peptides identified with MALDI-MS will better predict clinical outcomes of patients with HF.

Methods

A cohort of 100 patients with HF were recruited in the biomarker discovery phase (50 patients who died or had a HF hospital admission vs. 50 patients who did not have an event). The peptide extraction from plasma samples was performed using reversed phase C18. Then samples were analysed using MALDI-MS. A multiple peptide biomarker model was discovered that was able to predict clinical outcomes for patients with HF. Finally, this model was validated in an independent cohort with 100 patients with HF.

Results

After normalisation and alignment of all the processed spectra, a total of 11,389 peptides (m/z) were detected using MALDI-MS. A multiple biomarker model was developed from 14 plasma peptides that was able to predict clinical outcomes in HF patients with an area under the receiver operating characteristic curve (AUC) of 1.000 (p?=?0.0005). This model was validated in an independent cohort with 100 HF patients that yielded an AUC of 0.817 (p?=?0.0005) in the biomarker validation phase. Addition of this model to the BIOSTAT risk prediction model increased the predictive probability for clinical outcomes of HF from an AUC value of 0.643 to an AUC of 0.823 (p?=?0.0021). Moreover, using the prediction model of fourteen peptides and the composite model of the multiple biomarker of fourteen peptides with the BIOSTAT risk prediction model achieved a better predictive probability of time-to-event in prediction of clinical events in patients with HF (p?=?0.0005).

Conclusions

The results obtained in this study suggest that a cluster of plasma peptides using MALDI-MS can reliably predict clinical outcomes in HF that may help enable precision medicine in HF.

     

A Prognostic Model for Triple-Negative Breast Cancer Patients Based on Node Status,Cathepsin-D and Ki-67 Index

Objective

The aim of this study was to evaluate clinicopathologic factors that could possibly affect the outcome of patients with triple negative breast cancer and subsequently build a prognostic model to predict patients’ outcome.

Methods

We retrospectively analyzed clinicopathologic characteristics and outcome of 504 patients diagnosed with triple-negative invasive ductal breast cancer. 185 patients enrolled between 2000 and 2002 were designated to the training set. The variables that had statistically significant correlation with prognosis were combined to build a model. The prognostic value of the model was further validated in the separate validation set containing 319 patients enrolled between 2003 and 2006.

Results

The median follow-up duration was 66 months. 174 patients experienced recurrence, and 111 patients died. Positivity for ≥4 lymph nodes, Cathepsin-D positivity, and Ki-67 index ≥20% were independent factors for DFS, while the lymph nodes status and Ki-67 index were the prognostic factors for OS. The prognostic model was established based on the sum of all three factors, where positivity for ≥4 lymph nodes, Cathepsin-D and Ki-67 index ≥20% would individually contribute 1 point to the risk score. The patients in the validation set were assigned to a low-risk group (0 and 1 point) and a high-risk group (2 and 3 points). The external validation analysis also demonstrated that our prognostic model provided the independent high predictive accuracy of recurrence.

Conclusion

This model has a considerable clinical value in predicting recurrence, and will help clinicians to design an appropriate level of adjuvant treatment and schedule adequate appointments of surveillance visits.     

A Predictive Model for Assessing Surgery-Related Acute Kidney Injury Risk in Hypertensive Patients: A Retrospective Cohort Study

BackgroundAcute kidney injury (AKI) is a serious post-surgery complication; however, few preoperative risk models for AKI have been developed for hypertensive patients undergoing general surgery. Thus, in this study involving a large Chinese cohort, we developed and validated a risk model for surgery-related AKI using preoperative risk factors.ResultsSurgery-related AKI developed in 1994 hospitalized patients (8.2%). The predictors identified by our Xiang-ya Model were age, gender, eGFR, NLR, pulmonary infection, prothrombin time, thrombin time, hemoglobin, uric acid, serum potassium, serum albumin, total cholesterol, and aspartate amino transferase. The area under the receiver-operating characteristic curve (AUC) for the validation set and cross validation set were 0.87 (95% CI 0.86–0.89) and (0.89; 95% CI 0.88–0.90), respectively, and was therefore similar to the AUC for the training set (0.89; 95% CI 0.88–0.90). The optimal cutoff value was 0.09. Our model outperformed that developed by Kate et al., which exhibited an NRI of 31.38% (95% CI 25.7%-37.1%) and an IDI of 8% (95% CI 5.52%-10.50%) for patients who underwent cardiac surgery (n = 2101).Conclusions/SignificanceWe developed an AKI risk model based on preoperative risk factors and biomarkers that demonstrated good performance when predicting events in a large cohort of hypertensive patients who underwent general surgery.     

Building CT Radiomics Based Nomogram for Preoperative Esophageal Cancer Patients Lymph Node Metastasis Prediction

PURPOSE: To build and validate a radiomics-based nomogram for the prediction of pre-operation lymph node (LN) metastasis in esophageal cancer. PATIENTS AND METHODS: A total of 197 esophageal cancer patients were enrolled in this study, and their LN metastases have been pathologically confirmed. The data were collected from January 2016 to May 2016; patients in the first three months were set in the training cohort, and patients in April 2016 were set in the validation cohort. About 788 radiomics features were extracted from computed tomography (CT) images of the patients. The elastic-net approach was exploited for dimension reduction and selection of the feature space. The multivariable logistic regression analysis was adopted to build the radiomics signature and another predictive nomogram model. The predictive nomogram model was composed of three factors with the radiomics signature, where CT reported the LN number and position risk level. The performance and usefulness of the built model were assessed by the calibration and decision curve analysis. RESULTS: Thirteen radiomics features were selected to build the radiomics signature. The radiomics signature was significantly associated with the LN metastasis (P<0.001). The area under the curve (AUC) of the radiomics signature performance in the training cohort was 0.806 (95% CI: 0.732-0.881), and in the validation cohort it was 0.771 (95% CI: 0.632-0.910). The model showed good discrimination, with a Harrell’s Concordance Index of 0.768 (0.672 to 0.864, 95% CI) in the training cohort and 0.754 (0.603 to 0.895, 95% CI) in the validation cohort. Decision curve analysis showed our model will receive benefit when the threshold probability was larger than 0.15. CONCLUSION: The present study proposed a radiomics-based nomogram involving the radiomics signature, so the CT reported the status of the suspected LN and the dummy variable of the tumor position. It can be potentially applied in the individual preoperative prediction of the LN metastasis status in esophageal cancer patients.     

Mobile Application-Based Seoul National University Prostate Cancer Risk Calculator: Development,Validation, and Comparative Analysis with Two Western Risk Calculators in Korean Men

Objectives

We developed a mobile application-based Seoul National University Prostate Cancer Risk Calculator (SNUPC-RC) that predicts the probability of prostate cancer (PC) at the initial prostate biopsy in a Korean cohort. Additionally, the application was validated and subjected to head-to-head comparisons with internet-based Western risk calculators in a validation cohort. Here, we describe its development and validation.

Patients and Methods

As a retrospective study, consecutive men who underwent initial prostate biopsy with more than 12 cores at a tertiary center were included. In the development stage, 3,482 cases from May 2003 through November 2010 were analyzed. Clinical variables were evaluated, and the final prediction model was developed using the logistic regression model. In the validation stage, 1,112 cases from December 2010 through June 2012 were used. SNUPC-RC was compared with the European Randomized Study of Screening for PC Risk Calculator (ERSPC-RC) and the Prostate Cancer Prevention Trial Risk Calculator (PCPT-RC). The predictive accuracy was assessed using the area under the receiver operating characteristic curve (AUC). The clinical value was evaluated using decision curve analysis.

Results

PC was diagnosed in 1,240 (35.6%) and 417 (37.5%) men in the development and validation cohorts, respectively. Age, prostate-specific antigen level, prostate size, and abnormality on digital rectal examination or transrectal ultrasonography were significant factors of PC and were included in the final model. The predictive accuracy in the development cohort was 0.786. In the validation cohort, AUC was significantly higher for the SNUPC-RC (0.811) than for ERSPC-RC (0.768, p<0.001) and PCPT-RC (0.704, p<0.001). Decision curve analysis also showed higher net benefits with SNUPC-RC than with the other calculators.

Conclusions

SNUPC-RC has a higher predictive accuracy and clinical benefit than Western risk calculators. Furthermore, it is easy to use because it is available as a mobile application for smart devices.     

Risk stratification of spontaneous bacterial peritonitis in cirrhosis with ascites based on classification and regression tree analysis

Risk stratification for spontaneous bacterial peritonitis (SBP) in patients with cirrhosis and ascites helps guide care. Existing prediction models, such as end-stage liver disease (MELD) score, are accurate but controversial in clinical practice. We developed and validated a practical user-friendly bedside tool for SBP risk stratification of patients with cirrhosis and ascites. Using classification and regression tree (CART) analysis, a model was developed for prediction of SBP in cirrhosis with ascites. The CART model was derived on data collected from 676 patients admitted from January 2007 to December 2009 retrospectively, and then was prospectively tested in another independent 198 inpatients between January 2010 and December 2010. The accuracy of CART model was evaluated using the area under the receiver operating characteristic curve. The performance of the model was further validated by comparing its predictive accuracy with that of the MELD score. Furthermore, the model was used to stratify SBP among patients with MELD scores under 15. CART analysis identified four variables for prediction of SBP: creatinine, total bilirubin, prothrombin time and white blood cell count, and three risk groups: low (2.0%), intermediate (27.5–33.3%) and high (60.6–86.4%) risk. The accuracy of CART model (0.881) exceeded that of MELD (0.791). Subjects in the intermediate risk and high risk groups had 22.21-fold (95% confident interval (CI), 9.98–49.45) and 173.50-fold (95% CI, 77.68–634.33) increased risk of SBP, respectively, comparing with the low risk group. Similar results were found when this risk stratification was applied to the validation cohort. Cirrhotic patients with ascites at low, intermediate, and high risk for SBP can be easily identified using CART model, which provides clinicians with a validated, practical bedside tool for SBP risk stratification.     

A Novel Model to Predict Esophageal Varices in Patients with Compensated Cirrhosis Using Acoustic Radiation Force Impulse Elastography

Background & Aims

Few noninvasive methods can accurately identify esophageal varices (EVs) in patients with compensated cirrhosis. We developed and validated a novel, acoustic radiation force impulse (ARFI) elastography-based prediction model for high-risk EVs (HEVs) in patients with compensated cirrhosis.

Methods

A total of 143 patients with compensated cirrhosis between February, 2010 and February, 2013 (training set) and 148 between June, 2010 and May, 2013 (validation set) who underwent ARFI elastography and endoscopy were prospectively recruited. Independent predictors of HEVs were used to construct a prediction model.

Results

Based on multivariate analysis, we developed two new statistical models, a varices risk score and ARFI-spleen diameter-to-platelet ratio score (ASPS), the latter of which was calculated as ARFI velocity × spleen diameter/platelet count. The area under receiver operating characteristic curve (AUROC) of the varices risk score and ASPS to predict HEVs were 0.935 (95% confidence interval [CI] 0.882–0.970) and 0.946 (95% CI 0.895–0.977), respectively. When ASPS, a simpler model with a higher AUROC, was applied in the validation set, acceptable diagnostic accuracy for HEVs was observed (AUROC = 0.814 [95% CI 0.743–0.885]). To detect HEVs, a negative predictive value of 98.3% was achieved at ASPS <2.83, whereas a positive predictive value of 100% was achieved at ASPS >5.28.

Conclusions

ASPS, a novel noninvasive ARFI-based prediction model, can accurately identify HEVs in patients with compensated cirrhosis. ASPS <2.83 may safely rule out the presence of HEVs, whereas patients with ASPS >5.28 should be considered for endoscopic examinations or appropriate prophylactic treatment.     

Nomogram to Assess the Risk of Central Cervical Lymph Node Metastasis in Patients With Clinical N0 Papillary Thyroid Carcinoma

ObjectiveTo develop and validate an individualized risk prediction model for the need for central cervical lymph node dissection in patients with clinical N0 papillary thyroid carcinoma (PTC) diagnosed using ultrasound.MethodsUpon retrospective review, derivation and internal validation cohorts comprised 1585 consecutive patients with PTC treated from January 2017 to December 2019 at hospital A. The external validation cohort consisted of 406 consecutive patients treated at hospital B from January 2016 to June 2020. Independent risk factors for central cervical lymph node metastasis (CLNM) were determined through univariable and multivariable logistic regression analysis. An individualized risk prediction model was constructed and illustrated as a nomogram, which was internally and externally validated.ResultsThe following risk factors of CLNM were established: a solitary primary thyroid nodule’s diameter, shape, calcification, and capsular abutment-to-lesion perimeter ratio. The areas under the receiver operating characteristic curves of the risk prediction model for the internal and external validation cohorts were 0.921 and 0.923, respectively. The calibration curve showed good agreement between the nomogram-estimated probability of CLNM and the actual CLNM rates in the 3 cohorts. The decision curve analysis confirmed the clinical usefulness of the nomogram.ConclusionThis study developed and validated a model for predicting the risk of CLNM in individual patients with clinical N0 PTC, which should be an efficient tool for guiding clinical treatment.     

Multimarker Proteomic Profiling for the Prediction of Cardiovascular Mortality in Patients with Chronic Heart Failure

Risk stratification of patients with systolic chronic heart failure (HF) is critical to better identify those who may benefit from invasive therapeutic strategies such as cardiac transplantation. Proteomics has been used to provide prognostic information in various diseases. Our aim was to investigate the potential value of plasma proteomic profiling for risk stratification in HF. A proteomic profiling using surface enhanced laser desorption ionization - time of flight - mass spectrometry was performed in a case/control discovery population of 198 patients with systolic HF (left ventricular ejection fraction <45%): 99 patients who died from cardiovascular cause within 3 years and 99 patients alive at 3 years. Proteomic scores predicting cardiovascular death were developed using 3 regression methods: support vector machine, sparse partial least square discriminant analysis, and lasso logistic regression. Forty two ion m/z peaks were differentially intense between cases and controls in the discovery population and were used to develop proteomic scores. In the validation population, score levels were higher in patients who subsequently died within 3 years. Similar areas under the curves (0.66 – 0.68) were observed for the 3 methods. After adjustment on confounders, proteomic scores remained significantly associated with cardiovascular mortality. Use of the proteomic scores allowed a significant improvement in discrimination of HF patients as determined by integrated discrimination improvement and net reclassification improvement indexes. In conclusion, proteomic analysis of plasma proteins may help to improve risk prediction in HF patients.     

Critical Serum Creatinine Values in Very Preterm Newborns

Background

Renal failure in neonates is associated with an increased risk of mortality and morbidity. But critical values are not known.

Objective

To define critical values for serum creatinine levels by gestational age in preterm infants, as a predictive factor for mortality and morbidity.

Study Design

This was a retrospective study of all preterm infants born before 33 weeks of gestational age, hospitalized in Nantes University Hospital NICU between 2003 and 2009, with serum creatinine levels measured between postnatal days 3 to 30. Children were retrospectively randomized into either training or validation set. Critical creatinine values were defined within the training set as the 90^th percentile values of highest serum creatinine (HSCr) in infants with optimal neurodevelopmental at two years of age. The relationship between these critical creatinine values and neonatal mortality, and non-optimal neural development at two years, was then assessed in the validation set.

Results and Conclusion

The analysis involved a total of 1,461 infants (gestational ages of 24-27 weeks (n=322), 28-29 weeks (n=336), and 30-32 weeks (803)), and 14,721 creatinine assessments. The critical values determined in the training set (n=485) were 1.6, 1.1 and 1.0 mg/dL for each gestational age group, respectively. In the validation set (n=976), a serum creatinine level above the critical value was significantly associated with neonatal mortality (Odds ratio: 8.55 (95% confidence interval: 4.23-17.28); p<0.01) after adjusting for known renal failure risk factors, and with non-optimal neurodevelopmental outcome at two years (odds ratio: 2.06 (95% confidence interval: 1.26-3.36); p=0.004) before adjustment. Creatinine values greater than 1.6, 1.1 and 1.0 mg/dL respectively at 24-27, 28-29, 30-32 weeks of gestation were associated with mortality before and after adjustment for risk factors, and with non-optimal neurodevelopmental outcome, before adjustment.     

Assessing Discriminative Performance at External Validation of Clinical Prediction Models

Introduction

External validation studies are essential to study the generalizability of prediction models. Recently a permutation test, focusing on discrimination as quantified by the c-statistic, was proposed to judge whether a prediction model is transportable to a new setting. We aimed to evaluate this test and compare it to previously proposed procedures to judge any changes in c-statistic from development to external validation setting.

Methods

We compared the use of the permutation test to the use of benchmark values of the c-statistic following from a previously proposed framework to judge transportability of a prediction model. In a simulation study we developed a prediction model with logistic regression on a development set and validated them in the validation set. We concentrated on two scenarios: 1) the case-mix was more heterogeneous and predictor effects were weaker in the validation set compared to the development set, and 2) the case-mix was less heterogeneous in the validation set and predictor effects were identical in the validation and development set. Furthermore we illustrated the methods in a case study using 15 datasets of patients suffering from traumatic brain injury.

Results

The permutation test indicated that the validation and development set were homogenous in scenario 1 (in almost all simulated samples) and heterogeneous in scenario 2 (in 17%-39% of simulated samples). Previously proposed benchmark values of the c-statistic and the standard deviation of the linear predictors correctly pointed at the more heterogeneous case-mix in scenario 1 and the less heterogeneous case-mix in scenario 2.

Conclusion

The recently proposed permutation test may provide misleading results when externally validating prediction models in the presence of case-mix differences between the development and validation population. To correctly interpret the c-statistic found at external validation it is crucial to disentangle case-mix differences from incorrect regression coefficients.     

MicroRNA-29a-5p Is a Novel Predictor for Early Recurrence of Hepatitis B Virus-Related Hepatocellular Carcinoma after Surgical Resection

It is still difficult to predict the probability of tumor recurrence after resection of hepatocellular carcinoma (HCC). In this study, we set out to identify specific microRNA (miRNA) in microdissected hepatitis B virus (HBV)-related HCC tissue from formalin-fixed paraffin-embedded (FFPE) samples which might be used in predicting early recurrence after HCC resection. Taqman low density arrays were used to detect the 667 miRNA profiles in both the microdissected tumorous and adjacent non-tumorous liver tissues from 20 HCC patients (discovery set) including 10 patients with early tumor recurrence and 10 without early tumor recurrence and to identify the differentially expressed miRNAs related to HCC recurrence. Then quantitative real-time PCR (qRT-PCR) was used to verify the findings in 106 patients (training set), and to develop a predictive assay. The identified miRNAs were further validated in an independent cohort of 112 patients (validation set). Thirty seven miRNAs were identified from 20 HCC patients and validated in 106 HCC patients using qRT-PCR. A significant association was found between miR-29a-5p level in HCC tissues and early tumor recurrence (P = 0.0002). This association was further confirmed in the independent validation set of 112 patients (P = 0.0154). MiR-29a-5p level was significantly associated with both time to tumor recurrence (TTR) (P = 0.0015) and overall survival (OS) (P = 0.0079) in validation set. In the multivariate analyses, miR-29a-5p was identified as an independent factor for TTR, particularly for those patients with early stage of HCC. The sensitivity and specificity of miR-29a-5p for the prediction of early HCC recurrence of BCLC 0/A stage HCC were 74.2% and 68.2%, respectively. These suggest that miR-29a-5p might be a useful marker for the prediction of early tumor recurrence after HCC resection, especially in BCLC 0/A stage HCCs.     

An integrated mRNA-lncRNA signature for relapse prediction in laryngeal cancer

Patients with laryngeal cancer with early relapse usually have a poor prognosis. In this study, we aimed to identify a multi-gene signature to improve the relapse prediction in laryngeal cancer. One microarray data set GSE27020 (training set, N = 109) and one RNA-sequencing data set (validation set, N = 85) were included into the analysis. In the training set, the microarray expression profile was re-annotated into an mRNA-long noncoding RNA (lncRNA) biphasic profile. Then, LASSO Cox regression model identified nine relapse-related RNA (eight mRNA and one lncRNA), and a risk score was calculated for each sample according to the model coefficients. Patients with high-risk showed poorer relapse-free survival than patients with low risk (hazard ratios (HR): 6.189, 95% confidence interval (CI): 3.075-12.460, P < 0.0001). The risk score demonstrated good accuracy in predicting the relapse (area under time-dependent receiver-operating characteristic (AUC): 0.859 at 1 year, 0.822 at 3 years, and 0.815 at 5 years). The results were validated in the validation set (HR: 3.762, 95% CI: 1.594-8.877, P = 0.011; AUC: 0.770 at 1 year, 0.769 at 3 years, and 0.728 at 5 years). The multivariate analysis reached consistent results after adjustment by multiple confounders. When compared with a 27-gene signature, a 2-lncRNA signature, and Tumor-Node-Metastasis stage, the risk score also showed better performance (P < 0.05). In conclusion, we successfully developed a robust mRNA-lncRNA signature that can accurately predict the relapse in laryngeal cancer.     

Identification of a three-miRNA signature as a novel potential prognostic biomarker in patients with clear cell renal cell carcinoma

Current studies suggest that some microRNAs (miRNAs) are associated with prognosis in clear cell renal cell carcinoma (ccRCC). In this paper, we aimed to identify a miRNAs signature to improve prognostic prediction for ccRCC patients. Using ccRCC RNA-Seq data of The Cancer Genome Atlas (TCGA) database, we identified 177 differentially expressed miRNAs between ccRCC and paracancerous tissue. Then all the ccRCC tumor samples were divided into training set and validation set randomly. Three-miRNA signature including miR130b, miR-18a, and miR-223 were constructed by the least absolute shrinkage and selection operator (LASSO) Cox regression model in training set. According to optimal cut-off value of three-miRNA signature risk score, all the patients could be classified into high-risk group and low-risk group significantly. Survival of patients was significantly different between two groups (hazard ratio, 5.58, 95% confidence interval, 3.17-9.80; P < 0.0001), and three-miRNA signature performed favorably prognostic and predictive accuracy. The results were further validated in the validation set and total set. Multivariate Cox regression analyses and subgroup analyses showed that three-miRNA signature was an independent prognostic factor. Two nomograms that integrated three-miRNA signature and three clinicopathological risk factors were constructed to predict overall survival and disease-free survival after surgery for ccRCC patients. Functional enrichment analysis showed the possible roles of three-miRNA signature in some cancer-associated biological processes and pathways. In conclusion, we developed a novel three-miRNA signature that performed reliable prognostic for patient survival with ccRCC, it might facilitate ccRCC patients counseling and individualize management.     

Development and Internal Validation of a Predictive Model for Individual Cancer Risk Assessment for Thyroid Nodules

Objective: The objective of this study was to develop and validate a predictive model for the assessment of the individual risk of malignancy of thyroid nodules based on clinical, ultrasound, and analytic variables.Methods: A retrospective case-control study was carried out with 542 patients whose thyroid nodules were analyzed at our endocrinology department between 2013 and 2018 while undergoing treatment for thyroidectomy. Starting with a multivariate logistic regression analysis, which included clinical, analytic, and ultrasound variables, a predictive model for thyroid cancer (TC) risk was devised. This was then subjected to a cross-validation process, using resampling techniques.Results: In the final model, the independent predictors of the risk of malignancy were: being male, age of the extremes, family history of TC, thyroid-stimulating hormone level >4.7 μU/L, presence of autoimmune thyroiditis, solid consistency, hypoechogenicity, irregular or microlobed borders, nodules that are taller than they are wide, microcalcifications, and suspicious adenopathy. With a cut-off point of 50% probability of thyroid cancer, the predictive model had an area under the receiver operating characteristic curve of 0.925 (95% confidence interval 0.898 to 0.952). Finally, using the 10-fold cross-validation method, the accuracy of the model was found to be 88.46%, with a kappa correlation coefficient of 0.62.Conclusion: A predictive model for the individual risk of malignancy of thyroid nodules was developed and validated using clinical, analytic, and ultrasound variables. An online calculator was developed from this model to be used by clinicians to improve decision-making in patients with thyroid nodules.