骨折的危险评分以及骨折的绝对风险

目的结合以前报道的骨折危险评分(FRISK),报道5年和10年内骨折的绝对风险。材料与方法所有的参与者均要求签署知情同意,Barwon健康人类研究伦理委员会(Barwon Health Human Research Ethics Committee)批准了该研     

DWI 双指数模型参数与前列腺癌 Gleason 评分及临床侵袭性相关性研究

目的：研究多 b值DWI双指数模型参数预测前列腺癌恶性程度的应用价值。方法回顾性分析57例经穿刺或手术证实为前列腺癌患者的多 b 值 DWI(b 值0~800 s/ mm2)图像,逐层勾画全肿瘤感兴趣区,利用体素不相干运动(IVIM)计算肿瘤扩散系数 Dt、灌注系数 Dp 及灌注分数 f。根据 Gleason 评分(GS)及 D’Amico 分级分组,采用 ANOVA 方差分析及 Spearman 相关分析,低侵袭及中-高侵袭组间行受试者工作特征(ROC)分析。结果癌灶 Dt 在 GS 及 D’Amico 组间存在显著差异且呈负相关。Dp及 f 组间无统计学意义。对 Dt 进行 ROC 分析,敏感性、特异性分别为82.2%,100%;85.1%,90.0%;AUC=0.928,0.838;界值分别为1.073×10-3 mm2/s,1.117×10-3 mm2/s。结论Dt 可预测前列腺癌恶性程度。Dp 及 f 与前列腺癌恶性度无相关性。     

创伤评分在伤情评估和风险预测中的研究进展

创伤评分是判断伤情严重程度的标准,对多发伤患者正确诊断指导治疗及判断预后具有重要的现实意义.本文就创伤评分的发展过程以及在临床中的应用进展作一综述.     

Validation of BMI genetic risk score and DNA methylation in a Korean population

International Journal of Legal Medicine - When DNA profiles obtained from biological evidence at a crime scene fail to match suspects or anyone in the database, forensic DNA phenotyping, which is...     

The prognostic value of the hamstring outcome score to predict the risk of hamstring injuries

ObjectivesHamstring injuries are common among soccer players. The hamstring outcome score (HaOS) might be useful to identify amateur players at risk of hamstring injury. Therefore the aims of this study were: To determine the association between the HaOS and prior and new hamstring injuries in amateur soccer players, and to determine the prognostic value of the HaOS for identifying players with or without previous hamstring injuries at risk of future injury.DesignCohort study.MethodsHaOS scores and information about previous injuries were collected at baseline and new injuries were prospectively registered during a cluster-randomized controlled trial involving 400 amateur soccer players. Analysis of variance and t-tests were used to determine the association between the HaOS and previous and new hamstring injury, respectively. Logistic regression analysis indicated the prognostic value of the HaOS for predicting new hamstring injuries.ResultsAnalysis of data of 356 players indicated that lower HaOS scores were associated with more previous hamstring injuries (F = 17.4; p = 0.000) and that players with lower HaOS scores sustained more new hamstring injuries (T = 3.59, df = 67.23, p = 0.001). With a conventional HaOS score cut-off of 80%, logistic regression models yielded a probability of hamstring injuries of 11%, 18%, and 28% for players with 0,1, or 2 hamstring injuries in the previous season, respectively.ConclusionsThe HaOS is associated with previous and future hamstring injury and might be a useful tool to provide players with insight into their risk of sustaining a new hamstring injury risk when used in combination with previous injuries.     

AI对非门控胸部LDCT平扫冠状动脉钙化积分危险分层的预测价值

目的 探讨人工智能（AI）在非门控胸部低剂量CT（LDCT）平扫冠状动脉钙化积分（CACS）危险分层中的预测价值。 方法 回顾性收集接受冠状动脉CT血管成像（CCTA）检查及非门控LDCT平扫的病人152例（训练集与测试集比例为2∶1）,训练集为上海长征医院收集的102例病人;测试集为武汉同济医院收集的50例病人。分别由AI模型和2名中年资影像医师在所有病人影像上勾画钙化,获得CACS后进行CACS危险分层（低危、中危和高危）,使用手动标注非门控LDCT的训练集数据,构建非门控LDCT的CACS及其危险度分层的AI模型。将测试集数据导入AI模型进行验证,与心电门控CT平扫获得的标准CACS及其危险分层进行对比分析,分别记录放射科医师手动评估及AI模型自动评估测试集CACS所需时间。采用分类准确度、组内相关系数（ICC）、Kappa检验和Bland-Altman一致性分析评估AI模型的性能,并采用Wilcoxon符号秩检验比较AI模型与标准CACS危险分层间的差异。采用配对t 检验比较AI、影像医师评估CACS危险分层所需时间。 结果 在训练集和测试集中,标准CACS的中位数分别为165.89（36.04,425.76）、96.50（25.75,346.75）,AI模型测得CACS的中位数分别为167.07（43.17,449.11）、75.51（24.30,250.74）,两者一致性均较好[ICC分别为0.977（0.965, 0.984）、0.989（0.980, 0.994）]。在测试集中进行Bland-Altman一致性分析,结果显示AI模型评估的CACS与标准CACS差值在95%一致性界限内的病例有48例,界限外的只有2例。在训练集和测试集中,AI模型预测的CACS危险度分层与标准CACS危险度分层的一致性均较好（κ值分别为0.895、0.899,均P<0.001）。AI模型预测训练集CACS危险分层的分类准确度为97.1%,其中对高危、中危、低危的分类准确度分别为96.9%、95.1%、100%。AI模型预测测试集CACS危险分层的分类准确度为94.0%,其中对高危、中危、低危的分类准确度分别为100.00%、82.40%、100.00%。AI模型预测测试集CACS危险分层与标准CACS危险分层的差异无统计学意义（Z=2.00,P=0.564）。采用AI模型评估不同CACS危险分层所需时间均较放射科医师少（P<0.001）。 结论 AI模型能够较为准确地评估LDCT平扫的CACS及其危险分层,明显提高工作效率,具有一定的临床应用价值。     

A total atherosclerotic score for whole-body MRA and its relation to traditional cardiovascular risk factors

The aim of this study was to create a scoring system for whole-body magnetic resonance angiography (WBMRA) that allows estimation of atherosclerotic induced luminal narrowing, and determine whether the traditional cardiovascular (CV) risk factors included in the Framingham risk score (FRS) were related to this total atherosclerotic score (TAS) in an elderly population. A group of 306 subjects, aged 70, were recruited from the general population and underwent WBMRA in a 1.5-T scanner. Three-dimensional sequences were acquired after administration of one i.v. injection of 40 ml gadodiamide. The arterial tree was divided into five territories (carotid, aorta, renal, upper and lower leg) comprising 26 vessel segments, and assessed according to its degree of stenosis or occlusion. FRS correlated to TAS (r = 0.30, P < 0.0001), as well as to the atherosclerotic score for the five individual territories. Of the parameters included in the FRS, male gender (P < 0.0001), systolic blood pressure (P = 0.0002), cigarette pack-years (P = 0.0008) and HDL cholesterol (P = 0.008) contributed to the significance. A scoring system for WBMRA was created. The significant relation towards traditional CV risk factors indicates that the proposed scoring system could be of value for assessing atherosclerotically induced luminal narrowing.     

Machine learning based risk prediction model for asymptomatic individuals who underwent coronary artery calcium score: Comparison with traditional risk prediction approaches

BackgroundMachine learning (ML) is a computer algorithm used to identify patterns for prediction in various tasks, and ML methods have been beneficial for developing prediction models when applied to heterogeneous and large datasets. We aim to examine the prognostic ability of a ML-based prediction algorithm utilizing routine health checkup data to predict all-cause mortality (ACM) compared to established risk prediction approaches.MethodsA total 86155 patients with seventy available parameters (35 clinical, 32 laboratory, and 3 coronary artery calcium score [CACS] parameters) were analyzed. ML involved feature selection, splitting data randomly into a training (70%) and test set (30%), and model building with a boosted ensemble algorithm. The developed ML model was validated in a separate cohort of 4915 patients. The performance of ML for predicting ACM was compared with the following models: (i) the Framingham risk score (FRS) + CACS, (ii) atherosclerotic cardiovascular disease (ASCVD) + CACS, with (iii) logistic regression (LR) model.ResultsIn the derivation dataset, 690 patients died during the median 4.6-year follow-up (interquartile range, 3.0–6.6 years). The AUC value in the ML model was significantly higher than the other models in test set (ML: 0.82, FRS + CACS: 0.70, ASCVD + CACS: 0.74; LR model: 0.79, p < 0.05 for all), but not statistically significantly higher in validation set (ML: 0.78, FRS + CACS: 0.62, ASCVD + CACS: 0.72; LR model: 0.74, p: 0.572 and 0.625 for ASCVD + CACS and LR model, respectively). The ML model improved reclassification over the other models in low to intermediate risk patients (p < 0.001 for all).ConclusionThe prediction algorithm derived by ML methods showed a robust ability to predict ACM and improved reclassification over established conventional risk prediction approaches in asymptomatic population undergoing a health checkup.     

Comparison of the treadmill exercise score and single-photon emission computed tomographic thallium imaging in risk assessment

Background  

This study compared the prognostic value of exercise single-photon emission computed tomographic (SPECT) thallium imaging with that of treadmill exercise score in medically treated patients with coronary artery disease (CAD)     

心肌灌注显像与冠状动脉钙化积分对冠心病风险评估的研究现状及相互关系

冠心病是严重威胁人类健康的常见病和多发病.早期诊断冠心病,无创、准确地进行心脏不良事件的风险评估并及时干预,是亟待解决的临床问题.风险评估模型对于心脏风险评估的有效性虽已被众多临床试验所证实,但仍有局限性.心肌灌注显像及冠状动脉钙化积分以及两者结合,为冠心病诊断及心脏风险的评估提供了新的思路.     

冠心病炎症因子表达及其与冠状动脉病变危险分数的相关性

目的探讨炎症在动脉粥样硬化发展中的作用和临床意义及其与冠状动脉病变危险分数的相关性。方法选择急性冠脉综合征患者56例,包括急性心肌梗死（AMI）组26例,不稳定心绞痛（UAP）组30例,以同期住院的冠状动脉造影检查正常的30例患者作为对照（C）组,进行对比研究。采用酶联免疫吸附法测定血清sICAM-1和hs-CRP水平,并对UAP患者冠脉病变的结果进行危险评分。结果血清sICAM-1浓度AMI组明显高于UAP组和对照组,而UAP组明显高于对照组;血清hs CRP浓度AMI组明显高于UAP组和对照组,但UAP组与对照组比较无显著差异。UAP患者血清sICAM-1浓度与冠状动脉危险分数相关（r=0．445,P〈0．05）,而hs CRP浓度与冠状动脉危险分数无明显相关性。结论炎症参与了急性冠脉综合征的发生发展过程,血清sICAM-1浓度与冠状动脉危险分数相关,可作为冠心病病情监测的指标之一。     

Higher coronary artery calcium score is associated with increased risk of atrial fibrillation recurrence after catheter ablation

BackgroundCoronary artery calcium score (CACS) is associated with an increased risk of atrial fibrillation (AF) development, but scarce data are available regarding the impact on AF recurrence. This study aims to assess the impact of CACS on AF recurrence following catheter ablation.MethodsRetrospective study of patients with AF undergoing cardiac computed tomography (CCT) before ablation (2017–2019). Patients with coronary artery disease (CAD), significant valvular heart disease and previous catheter ablation were excluded. A cut-off of CACS ≥ 100 was used according to literature.ResultsA total of 311 patients were included (median age 57 [48, 64] years, 65% men and 21% with persistent AF). More than half of the patients had a CACS > 0 (52%) and 18% a CACS ≥ 100. Patients with CACS ≥ 100 were older (64 [59, 69] vs 55 [46, 63] years, p ?< ?0.001), had more frequently hypertension (68% vs 42%, p ?< ?0.001) and diabetes mellitus (21% vs 10%, p ?= ?0.020). During a median follow-up of 34 months (12–57 months), 98 patients (32%) had AF recurrence. CACS ≥ 100 was associated with increased risk of AF recurrence (unadjusted Cox regression: hazard ratio [HR] 2.0; 95% confidence interval [CI], 1.3–3.1, p ?= ?0.002). After covariate adjustment, CACS ≥ 100 and persistent AF remained independent predictors of AF recurrence (HR, 1.7; 95% CI, 1.0–2.8, p ?= ?0.039 and HR, 2.0; 95% CI, 1.3–3.2, p ?= ?0.004, respectively).ConclusionAn opportunistic evaluation of CACS could be an important tool to improve clinical care considering that CACS ≥ 100 was independently associated with a 69% increase in the risk of AF recurrence after first catheter ablation.     

Development of a risk score to guide brain imaging in older patients admitted with falls and confusion

Objectives

CT scanning of the brain is commonly performed in older people admitted to hospital with a fall, but the yield of positive findings is low. We used audit data to develop a risk-stratification score to guide more efficient use of CT scanning.

Methods

12 potential predictors of positive CT findings were derived from a literature review. Case notes of consecutive patients presenting with falls and confusion who had undergone brain imaging were reviewed as part of an ongoing audit. Correlation of each factor with positive CT findings was undertaken and a final risk score was developed. Receiver-operating characteristic analysis was undertaken, an optimum cut-off identified, and positive and negative predictive values were calculated.

Results

66 patients with a mean age of 74.8 years were included. 13 of the 66 (20%) brain imaging studies revealed a new pathology. Previous history of falls, atrial fibrillation, head or face trauma, focal neurological signs, warfarin use and a Glasgow coma score of <14 were significant univariate positive predictors. Antecedent dementia was included as a negative predictor. The final weighted score (range –1 to 8 points) gave an area under the curve of 0.83 (95% confidence interval 0.70 to 0.96, p<0.001). When using a cut-off of 3 points, sensitivity for significant new pathology on brain imaging was 83%, specificity was 89%, positive predictive value was 63% and negative predictive value was 96%.

Conclusion

A simple weighted risk score may be able to guide the need for brain imaging in older people presenting to hospital with falls. The score requires validation in a larger, prospectively collected cohort.Patients who fall and present to medical admissions units with confusion pose a diagnostic problem because of their inability to give a coherent history. A pre-existing diagnosis of dementia often further hampers the clinical assessment; because of these reasons, older people with falls and confusion often under go CT of the head. The diagnostic yield of these investigations is often low. A study of 294 patients with acute confusion found a diagnostic yield of only 14% if clinical suspicion was the sole reason for referral [1]. A large number of these investigations could therefore, in theory, be avoided, enabling better use of resources, reducing healthcare costs and minimising patient exposure to unnecessary radiation. To better target the use of brain imaging in this patient group, a risk scoring system is required. Such scoring systems have been developed for use in general [2] and paediatric patients [3] presenting with head trauma, but old age is usually included as part of the indication for scanning. As such, existing risk scoring systems may lack discrimination when used in older patients. Pre-existing falls risk scoring systems that are widely used, such as the Tinetti score [4], are mainly designed to calculate risk of falls rather than to help preselect patients that might have significant intracranial pathology as a cause (or result) of their fall.We therefore developed a risk scoring system specifically for use in older patients admitted to hospital with a fall who are also confused.