Can oncologists predict survival for patients with progressive disease after standard chemotherapies?

Background

Prediction of prognosis is important for patients so that they can make the most of the rest of their lives. Oncologists could predict survival, but the accuracy of such predictions is unclear.

Methods

In this observational prospective cohort study, 14 oncologists treating 9 major adult solid malignancies were asked to complete questionnaires predicting survival based on performance status, oral intake, and other clinical factors when patients experienced progressive disease after standard chemotherapies. Clinically predicted survival (cps) was calculated by the oncologists from the date of progressive disease to the predicted date of death. Actual survival (as) was compared with cps using Kaplan–Meier survival curves, and factors affecting inaccurate prediction were determined by logistic regression analysis. The prediction of survival time was considered accurate when the cps/as ratio was between 0.67 and 1.33.

Results

The study cohort consisted of 75 patients. Median cps was 120 days (interquartile range: 60–180 days), and median as was 121 days (interquartile range: 40–234 days). The participating oncologists accurately predicted as within a 33% range 36% of the time; the survival time was overestimated 36% of time and underestimated 28% of the time. The factors affecting the accuracy of the survival estimate were the experience of the oncologist, patient age, and information given about the palliative care unit.

Conclusions

Prediction of cps was accurate for just slightly more than one third of all patients in this study. Additional investigation of putative prognostic factors with a larger sample size is warranted.     

结直肠癌术后肝转移的危险因素分析及预测模型构建

目的:分析结直肠癌术后肝转移的危险因素并构建预测模型.方法:选取2011年1月至2015年12月于苏州市第九人民医院行根治性手术切除的247例结直肠癌患者作为观察对象,依据随访是否出现肝转移分为转移组、非转移组.对两组各项指标进行单因素筛选,再行Logistic多因素回归分析,并建立预测模型,应用受试者工作特征(rec...     

预测年轻乳腺癌患者生存率列线图的建立和验证

目的:构建预测年轻乳腺癌患者生存情况的列线图,以期帮助临床诊疗。方法:收集SEER数据库中5 525例年轻乳腺癌患者的临床信息,通过单因素Log-rank检验和多因素Cox生存分析筛选出独立预后因素,用于构建预测患者3、5年总生存率(overall survival,OS)和癌症特异性生存率（cancer special survival,CSS)的列线图,将我院就诊的147例年轻乳腺癌患者作为验证集进行外部验证。结果:单因素和多因素分析结果显示,种族、病理类型、组织学分级、T分期、N分期、M分期、ER状态、HER-2状态、手术方式是与患者OS和CSS相关的独立危险因素,将这些因素纳入并建立预测患者OS和CSS的列线图模型。内部和外部验证结果显示模型具有良好的预测性能。基于建立的OS和CSS列线图模型对患者进行了风险分层,能够准确地将年轻乳腺癌患者分成预后有显著差异的三个风险亚组。结论:本研究构建的预测模型能较为准确的预测年轻乳腺癌患者的预后情况,为临床的诊疗提供科学依据。     

Ultrasound-determined fetal subcutaneous tissue thickness for a birthweight prediction model

AIM: The aim of this study was to explore a birthweight prediction model using ultrasound determined tissue thickness (SCTT) parameters. METHODS: We measured routine ultrasonographic biometric parameters and in addition, fetal SCTT in 201 healthy singleton pregnancies. Mid-arm fat and lean mass, mid-thigh fat and lean mass, subscapular fat mass and abdominal fat mass (AFM) were measured in order to calculate a birthweight prediction model. Ultrasound measurements were analyzed using an 'anovarepeated measures model'. The growth rate (beta-slope) of the selected parameters was computed and the correlation coefficient with the birthweight and the Kendall rank correlation tau, were calculated. RESULTS: From the ultrasound determined SCTT parameters, only abdominal circumference (AC), AFM, and MTLM showed a statistically significant trend. The beta-slope of mid-thigh lean mass was excluded since it exhibited significant correlation with the beta-slope of AFM. The final regression model could be calculated as: birthweight (gr.) = intercept +alpha(1)(AFM beta-slope) + alpha(2)(AC beta-slope), where alpha(1), alpha(2) represent regression coefficients. CONCLUSIONS: We provide a graphical birthweight prediction model for clinical practice using conventional and specific ultrasound measurements of fetal subcutaneous tissue thickness. This model is based upon an overall analysis of the ultrasound estimated body components.     

比较ABCD2-I与ABCD2评分预测短暂性脑缺血性发作患者卒中复发的价值

目的比较ABCD2评分与ABCD2-I评分在短暂性脑缺血发作（TransientIschemicAttack,TIA）患者中预测卒中复发的效度。方法前瞻性、连续纳入发病7d内且完成核磁弥散加权成像（DiffusionWeightedImaging,DWI）的TIA患者,对其进行1年随访,评价指标是卒中复发。将ABCD2评分与AB—CD2-I评分分为低危、中危、高危三组,评价二种评分对TIA患者进行卒中复发风险分层的能力;采用曲线下面积（AreaUndertheCurve,AUC）比较二种评分预测卒中复发的效度。结果纳入符合入排标准的患者共410例,其中110例患者（27．07％）1年卒中复发。ABCI~评分低、中、高危卒中复发风险分别为21．88％,27．64％,41．18％,ABCD2-I评分低、中、高危卒中复发风险分别为4．71％,21．94％,49．61％。ABCD。评分的预测效度AUC=0．59,ABCD2-I评分的预测效度AUC=0．77。结论ABCD2-I评分较ABCD2评分能更好地预测TIA患者1年卒中复发。     

A robust approach to t linear mixed models applied to multiple sclerosis data

We discuss a robust extension of linear mixed models based on the multivariate t distribution. Since longitudinal data are successively collected over time and typically tend to be auto-correlated, we employ a parsimonious first-order autoregressive dependence structure for the within-subject errors. A score test statistic for testing the existence of autocorrelation among the within-subject errors is derived. Moreover, we develop an explicit scoring procedure for the maximum likelihood estimation with standard errors as a by-product. The technique for predicting future responses of a subject given past measurements is also investigated. Results are illustrated with real data from a multiple sclerosis clinical trial.     

福建同安主要恶性肿瘤死亡率分析与趋势预测

[目的]分析福建省同安恶性肿瘤死因及死亡率变化趋势，为预防控制提供依据。[方法]计算标化死亡率、早死所致生命损失年，并采用灰色模型预测，对1990—2001年同安恶性肿瘤死亡资料统计分析。[结果]1990—2001年同安恶性肿瘤死亡率呈上升趋势。恶性肿瘤标化死亡率126．72／10万，食管癌、肝癌、胃癌和肺癌标化死亡率分别为39．12／10万、36．59／10万、12．82／10万和12．60／10万。肺癌死亡率上升趋势明显，女性肺癌标化生命损失年YLLs率上升18．18％。以灰色模型GM(1，1)预测2005年同安恶性肿瘤死亡率为142．06／10万。[结论]肝癌、食管癌、肺癌、胃癌是同安肿瘤研究与防治的重点。     

Application of Machine Learning Approaches to Predict Postnatal Growth Failure in Very Low Birth Weight Infants

PurposeThe aims of the study were to develop and evaluate a machine learning model with which to predict postnatal growth failure (PGF) among very low birth weight (VLBW) infants.Materials and MethodsOf 10425 VLBW infants registered in the Korean Neonatal Network between 2013 and 2017, 7954 infants were included. PGF was defined as a decrease in Z score >1.28 at discharge, compared to that at birth. Six metrics [area under the receiver operating characteristic curve (AUROC), accuracy, precision, sensitivity, specificity, and F1 score] were obtained at five time points (at birth, 7 days, 14 days, 28 days after birth, and at discharge). Machine learning models were built using four different techniques [extreme gradient boosting (XGB), random forest, support vector machine, and convolutional neural network] to compare against the conventional multiple logistic regression (MLR) model.ResultsThe XGB algorithm showed the best performance with all six metrics across the board. When compared with MLR, XGB showed a significantly higher AUROC (p=0.03) for Day 7, which was the primary performance metric. Using optimal cut-off points, for Day 7, XGB still showed better performances in terms of AUROC (0.74), accuracy (0.68), and F1 score (0.67). AUROC values seemed to increase slightly from birth to 7 days after birth with significance, almost reaching a plateau after 7 days after birth.ConclusionWe have shown the possibility of predicting PGF through machine learning algorithms, especially XGB. Such models may help neonatologists in the early diagnosis of high-risk infants for PGF for early intervention.     

Numerical Simulation and Experimental Validation of Squeeze Casting of AlSi9Mg Aluminum Alloy Component with a Large Size

The squeeze casting process for an AlSi9Mg aluminum alloy flywheel housing component was numerically simulated using the ProCAST software, and orthogonal simulation tests were designed according to the L16 (4) ⁵ orthogonal test table to investigate the alloy melt flow rule under four factors and four levels each of the pouring temperature, mold temperature, pressure holding time and specific pressure, as well as the distributions of the temperature fields, stress fields and defects. The results showed that the flywheel housing castings in all 16 test groups were fully filled, and the thinner regions solidified more quickly than the thicker regions. Hot spots were predicted at the mounting ports and the convex platform, which could be relieved by adding a local loading device. Due to the different constraints on the cylinder surface and the lower end surface, the solidification was inconsistent, the equivalent stress at the corner junction was larger, and the castings with longer pressure holding time and lower mold temperature had larger average equivalent stress. Shrinkage cavities were mainly predicted at mounting ports, the cylindrical convex platform, the peripheral overflow groove and the corner junctions, and there was also a small defect region at the edge of the upper end face in some test groups.