基于体检指标建立的脑动脉硬化预测评分系统

目的探索联合多个体检指标建立脑动脉硬化预测模型及评分系统,并评价该预测模型及评分系统。方法根据经颅多普勒超声(TCD)结果将1 325例健康体检人群分为非脑动脉硬化组767例和脑动脉硬化组558例。对其进行问卷调查及收集体检指标,使用多因素Logistic回归模型建立脑动脉硬化的预测模型,进一步探索建立脑动脉硬化的预测评分系统。结果选出P<0.05的指标6个(包括性别、年龄、脉压、同型半胱氨酸、腰围、吸烟)进行多因素逐步Logistic回归,建立数学模型:Logitistic(PI)=0.232×性别+0.155×吸烟+0.026×年龄+0.005×脉压+0.005×同型半胱氨酸+0.004×腰围-1.029。结合临床资料,选出性别、高血压、颈动脉硬化、糖尿病、吸烟、年龄、同型半胱氨酸、高敏C反应蛋白、脉压和体重指数10项指标建立脑动脉硬化的预测评分系统,总分为10分。结论基于常规体检指标成功建立了脑动脉硬化预测模型和评分系统,值得扩展研究。     

社区获得性肺炎患者并发胸腔积液临床预测模型的构建与验证

目的 建立并验证社区获得性肺炎(CAP)患者并发胸腔积液的临床预测模型。方法 研究纳入NPHDC数据库中的305例CAP患者,依据是否并发胸腔积液,将患者分为CAP组和CAP伴胸腔积液组;分析与比较CAP患者伴胸腔积液可能相关的29项危险因素。采用Lasso回归与多因素Logistic回归分析筛选最佳预测变量并构建临床预测模型。模型进一步基于500次重复抽样的Boots trap法分别通过受试者工作特征(ROC)曲线下的AUC面积及Hosmer-Lemeshow检验下的校准曲线评估其区分度及校准度,并绘制临床决策(DCA)曲线评估模型的临床适用性。结果 Lasso回归筛选出5个预测因子,更进一步通过Logistic回归分析筛选出最终的3个预测因子(心功能不全、血清白蛋白、PSI评分),并据此构建Nomogram列线图。经模型评估,该模型ROC曲线下AUC面积为0.790(95%CI:0.734～0.838),表明疾病区分度较好;校准曲线显示预测概率与实际发生概率具有较好的一致性(Chi-square:5.497,P=0.789>0.05);DCA则提示良好的临床适用度(范围约0....     

中老年健康体检人群眼底动脉硬化与高血压相关性分析

目的：了解中老年人群眼底动脉硬化分级与高血压分级的关系。方法：从2010年在华东疗养院进行健康体检的45岁及以上人群中随机抽取1827人的体检资料进行统计分析。结果：检出眼底动脉硬化753人,其中患有高血压者501人,与Ⅰ级眼底动脉硬化患者比较,Ⅱ级眼底动脉硬化患者高血压2、3级比例明显增大（2级：19.8%比31.0%,3级：14.5%比53.2%,P均〈0.05）。结论：高血压是眼底动脉硬化的危险因素,定期体检是发现和防治眼动脉硬化的关键。     

肝硬化并发门静脉血栓形成患者列线图预测模型分析及外部验证

目的 探讨建立肝硬化患者门静脉血栓形成(PVT)列线图预测模型及外部验证情况。方法 2010年1月～2021年10月我院收治的肝硬化伴或不伴PVT患者,收集一般资料、血清学指标、超声等影像学资料。对建模组数据应用Lasso回归和多因素Logistic回归模型筛选出肝硬化并发PVT的独立危险因素并建立列线图预测模型,应用验证组数据对该模型进行外部验证。结果 肝硬化并发PVT患者独立危险因素为脾切除史、门静脉主干内径和脾静脉直径(P＜0.05);在验证人群,对列线图预测模型进行外部验证,其ROC曲线下面积为0.751(95%CI:0.674～0.827),预测模型区分度良好,经Hosmer-Lemeshow检验,P值为0.170,提示预测模型的校准度较高。结论 我们开发了一种易于使用的肝硬化并发PVT形成的列线图预测模型,可以协助临床医护人员筛选高PVT风险人群,制定及时、个性化的诊治方案,希望最终改善患者预后。     

肝细胞癌患者肝切除术后预后列线图的建立与验证

目的旨在建立和验证预测肝细胞癌肝切除术后患者总生存期(OS)的列线图。方法选取广西医科大学附属肿瘤医院2004年2月-2013年10月肝细胞癌肝切除术患者1013例。随机分为训练队列(n=710)和验证队列(n=303),在训练队列中,通过Cox比例风险模型确定独立危险因素,并构建训练队列的列线图预测1、3、5年的生存率。通过训练队列内部验证与验证队列的外部验证,并采用C指数、受试者工作特征曲线(ROC曲线)以及校准曲线对模型的性能进行评价。连续变量2组间比较采用独立样本t检验。分类变量2组间比较采用χ2检验或Fisher检验。通过Cox比例风险模型进行单因素和多因素分析。结果训练队列中1、3、5年总生存率分别为0. 72、0. 48、0. 34;验证队列中1、3、5年总生存率分别为0. 66、0. 45、0. 32。单因素和多因素分析确定影响肝细胞癌肝切除术后患者OS的危险因素为年龄、肿瘤数目、肿瘤大小、肿瘤包膜、血管侵犯、微卫星灶、AST、AFP(P值均0. 05),并将其构建列线图模型。训练队列中,预测OS的C指数为0. 748[95%可信区间(95%CI):0. 712～0. 784]。1、3、5年生存率的校准曲线显示列线图的预测值和实际观察值结果一致; 1、3、5年生存率ROC曲线下面积分别为0. 81(95%CI:0. 76～0. 87)、0. 82(95%CI:0. 77～0. 88)、0. 79(95%CI:0. 71～0. 88)。在验证队列中,C指数为0. 712 (95%CI:0. 685～0. 739)。1、3、5年生存率的校准曲线显示列线图预测值与实际观察值结果一致。1、3、5年生存率的ROC曲线下面积分别是0. 75(95%CI:0. 71～0. 79)、0. 77(95%CI:0. 73～0. 81)、0. 74(95%CI:0. 68～0. 80)。结论建立的列线图可以有效的预测肝细胞癌肝切除术后患者的OS。     

西北燥证对新疆地区冠心病病人MACE发生率的预测研究

目的:构建并验证冠心病病人中远期主要不良心血管事件(MACE)的临床预测模型,探讨西北燥证等因素对新疆地区冠心病病人预后的影响。方法:收集2015—2020年在新疆维吾尔自治区中医医院心内科就诊的冠心病病人的临床资料,构建数据库(654例),并随访1年内MACE发生情况,统计分析病人一般资料和西北燥证罹患情况。将所有病人随机分为训练组(457例,70%)和验证组(197例,30%),采用Lasso回归优化筛选出冠心病病人不良预后的潜在影响因素,并运用多因素Logistic回归分析构建预测模型,绘制列线图。在验证组中采用受试者工作特征(ROC)曲线下面积(AUC)评价模型的鉴别能力,Calibration校准曲线评价其准确性及决策曲线(DCA)分析评价其临床实用性。结果:Lasso回归与多因素Logistic回归分析结果显示：吸烟、糖尿病史、心血管疾病家族史以及西北燥证是冠心病病人发生MACE的危险因素。将上述4个变量纳入构建列线图预测模型,训练组和验证组的AUC分别为0.736[95%CI(0.685,0.788)]和0.713[95%CI(0.631,0.794)],校准曲线提示该模...     

缺血性卒中复发风险预测模型的系统评价

目的 系统评价缺血性卒中(IS)复发风险预测模型。方法 计算机检索中国知网、万方数据知识服务平台、维普网、PubMed、Ovid、Cochrane Library、Web of Science发表的IS复发风险预测模型的相关文献。由2名研究者参考CHARMS清单提取纳入文献的基本特征,采用PROBAST独立评价纳入文献的风险偏倚和适用性。结果 共纳入20项研究。12项研究采用Logistic回归模型建模,7项研究采用Cox回归模型建模,1项研究采用其他模型建模;7项研究仅进行内部验证,6项研究仅进行外部验证,2项研究进行内部研究和外部验证,5项研究未进行模型验证;IS复发率为2%～48%。20项研究纳入的预测因子为4～20个,最常见的预测因子是年龄、短暂性脑缺血发作(TIA)/卒中史、高血压、糖尿病、心血管疾病、外周动脉疾病。16项研究报道了模型的AUC,为0.55～0.933,其中10项研究模型的AUC≥0.7;3项研究报道了一致性指数(CI),为0.630～0.68;1项研究未报告模型的区分度。仅6项研究进行了模型校准。结论 基于现有文献,IS复发风险预测模型预测IS复发的AUC为...     

骨质疏松性胸腰椎骨折PVP术后新发骨折的列线图预测模型构建与验证

目的 探究骨质疏松性胸腰椎骨折(OTF)病人行经皮椎体成形术(PVP)后新发骨折的影响因素，建立列线图预警模型，并验证模型的预测效能。方法 回顾性选取2017～2018年我院收治的193例行PVP的OTF病人作为建模组，2019年1～12月收治的63例行PVP的OTF病人作为验证组。以术后2年或诊断为术后新发骨折为随访终点，统计建模组术后骨折发生率。通过单因素与多因素Cox比例风险模型筛选影响术后新发骨折的独立危险因素，采用R软件建立列线图模型预测术后新发骨折风险，并对模型的预测效果进行内部及外部验证。结果 建模组PVP术后新发骨折发生率为21.76%(42/193)。多因素Cox回归分析显示，腰椎T值、椎体内裂隙样变、伤椎前缘高度恢复率、骨水泥注入量、骨水泥渗漏是OTF病人PVP术后新发骨折的独立危险因素。基于多因素Cox回归分析结果建立预测术后新发骨折的列线图模型，预测模型中建模组的C-index为0.796(95%CI:0.724～0.836),验证组的C-index为0.730(95%CI:0.706～0.784);建模组预测术后1年及2年新发骨折发生率ROC的AUC分别为0....     

儿童法洛四联症根治术后机械通气时间延长风险的预测模型建立与内部验证

目的 建立并内部验证预测法洛四联症(tetralogy of Fallot,TOF)根治术后机械通气时间延长(prolonged mechanical ventilation, PMV)风险的列线图模型。 方法 连续入选2019年6月至12月在我院行TOF根治术的6月龄到6岁患儿，并回顾性分析其临床数据。PMV定义为术后机械通气持续时间超过48h。基于入选的患儿做为训练集开发预测PMV风险的列线图模型。采用最小绝对收缩与选择算子(The least absolute shrinkage and selection operator, LASSO)回归模型用于列线图模型的变量选择；应用多因素logistic回归分析来建立预测模型，该模型纳入由LASSO回归模型所选择的所有变量。采用C指数，校准图和决策曲线分析(Decision curve analysis, DCA)评估预测模型的准确性，一致性和临床实用性。采用Bootstrap重复抽样的方法对模型进行内部验证。 结果 入选的109名患儿，分为机械通气延长组（PMV组）（n=32，占29.4%）与非机械通气延长组（非PMV组）（n=77，占70.6%）。PMV组患儿术后机械通气时间显著长于非PMV组（P<0.01）。多因素logistic回归分析显示术前McGoon比<1.5（OR=3.564，95%CI:1.078-11.782，P<0.05），术中较长的体外循环时间（OR=1.020，95%CI:1.007-1.032，P<0.01）和术后较低的左室射血分数（OR=0.885，95%CI:0.792-0.988，P<0.05）为术后PMV的独立预测因素。并且，该模型具有良好的一致性和区分能力，C指数为0.774。模型经过内部验证后，校正曲线表现良好，C指数较高，等于0.756。DCA表明，当阈概率在大于2%且小于76%的范围内，ICU医师做出改变通气策略的干预决定，列线图模型具有很好的临床效果。 结论 我们开发并内部验证一种高精度的列线图模型，以协助ICU医生进行与术后PMV相关的临床决策。然而，在推荐用于临床实践之前，该模型需要进行外部验证。     

基于6个代谢基因预测肝癌预后评分系统的构建

[目的]利用生物信息学方法构建一个基于代谢基因的风险评分系统来预测肝癌预后.[方法]利用R语言limma包分析从TCGA数据库下载的原始数据,识别肝癌标本与癌旁组织间差异表达的代谢基因.进行单Cox回归分析和Lasso回归分析,以获得预后相关的代谢基因,并建立预后风险评分系统.采用Kaplan-Meier曲线、受试者工...     

External validation of the CACE-HF risk score for mortality in patients with heart failure

AimsTo validate externally the CACE-HF clinical prediction rule, which predicts 1-year mortality in patients with heart failure (HF).MethodsWe performed an external validation of the CACE-HF risk score in patients included in the RICA heart failure registry who had completed 1 year of follow-up, comparing the characteristics of the derivation and validation cohorts. The performance of the risk score was evaluated in terms of calibration, using calibration-in-the-large (a), calibration slope (b), and the Hosmer-Lemeshow test, and in terms of discrimination, using the area under the ROC curve.ResultsIn total, 3337 patients were included in the validation cohort. There were no significant differences between the derivation and validation cohorts in 1-year mortality (24.63% vs. 22.98%) or in the risk score and risk classes. The discrimination capacity in the validation cohort was slightly lower, 0.67 (95% CI: 0.65, 0.69), compared to that of the derivation cohort. Calibration results were a −0.05 (95% CI: −0.14, 0.03), indicating that the average predictions did not differ from the average outcome frequency, and b = 0.75 (95% CI: 0.64, 0.86), indicating a modest inconsistency in predictor effects. Observed mortality versus predicted mortality according to the deciles and risk classes were very similar in both cases, indicating good calibration.ConclusionsThe results of the external validation of the CACE-HF risk score show that although the capacity for discrimination was slightly lower than in the derivation cohort, the calibration was excellent. This tool, therefore, can assist in decision-making in the management of these patients.     

A novel online calculator to predict perioperative blood transfusion in patients undergoing liver resection for hepatocellular carcinoma: an international multicenter study

BackgroundTo develop an easy-to-use model to predict the probability of perioperative blood transfusion (PBT) in patients undergoing liver resection for hepatocellular carcinoma (HCC).Method878 patients from Eastern Hepatobiliary Surgery Hospital of Shanghai were enrolled in the training cohort, while 691 patients from Tongji Hospital of Wuhan and 364 patients from two hospitals from Europe and America served as the Eastern and Western external validation cohorts, respectively. Independent predictors of PBT were identified and used for the nomogram construction. The predictive performance of the model was assessed using the concordance index (C-index) and calibration plot, and externally validated using the two independent cohorts. This model was compared with four currently available prediction risk scores.ResultsEight preoperative variables were identified as independent predictors of PBT, which were incorporated into the new nomogram model, with a C-index of 0.833 and a well-fitted calibration plot. The nomogram performed well on the externally Eastern and Western validation cohorts (C-indexes: 0.786 and 0.777). The discriminatory ability of the nomogram was superior to the four currently available prediction scores (C-indexes: 0.833 vs. 0.671–0.770). The nomogram was programmed into an online calculator, which is available at http://www.asapcalculate.top/Cal3_en.html.ConclusionA nomogram model, using an easy-to-access website, can be used to calculate the PBT risk and identify which patients undergoing HCC resection are at high risks of PBT and can benefit most by using blood conservation techniques.     

Clinical prognostic rules for severe acute respiratory syndrome in low- and high-resource settings

BACKGROUND: An accurate prognostic model for patients with severe acute respiratory syndrome (SARS) could provide a practical clinical decision aid. We developed and validated prognostic rules for both high- and low-resource settings based on data available at the time of admission. METHODS: We analyzed data on all 1755 and 291 patients with SARS in Hong Kong (derivation cohort) and Toronto (validation cohort), respectively, using a multivariable logistic scoring method with internal and external validation. Scores were assigned on the basis of patient history in a basic model, and a full model additionally incorporated radiological and laboratory results. The main outcome measure was death. RESULTS: Predictors for mortality in the basic model included older age, male sex, and the presence of comorbid conditions. Additional predictors in the full model included haziness or infiltrates on chest radiography, less than 95% oxygen saturation on room air, high lactate dehydrogenase level, and high neutrophil and low platelet counts. The basic model had an area under the receiver operating characteristic (ROC) curve of 0.860 in the derivation cohort, which was maintained on external validation with an area under the ROC curve of 0.882. The full model improved discrimination with areas under the ROC curve of 0.877 and 0.892 in the derivation and validation cohorts, respectively. CONCLUSION: The model performs well and could be useful in assessing prognosis for patients who are infected with re-emergent SARS.     

Risk Factors for Death in Elderly Emergency Department Patients with Suspected Infection

OBJECTIVES: To identify independent risk factors for death in elderly emergency department (ED) patients admitted for infection and to derive and validate a mortality‐prediction rule for such patients. DESIGN: Prospective cohort study. SETTING: Tertiary hospital ED with 55,000 annual visits. PARTICIPANTS: ED patients aged 65 and older admitted for infection between December 2003 and September 2004 in the derivation cohort and October 2005 and October 2006 in the validation cohort. MEASUREMENTS: Primary outcome: 28‐day in‐hospital mortality. Data were extracted from charts, and multivariate logistic regression were performed to identify independent mortality predictors. A prediction model was constructed and then validated in a second cohort. RESULTS: Nine hundred thirty‐five patients were included in the derivation cohort and 2,015 in the validation cohort. Mortality was 6% in the derivation cohort and 7% in the validation cohort. In the derivation cohort, logistic regression revealed five independent mortality predictors: respiratory compromise (respiratory rate >20 breaths per minute or hypoxemia) (odds ratio (OR)=4.0, 95% confidence interval (CI)=1.7–9.4), tachycardia (heart rate ≥120 betas per minute; OR=3.2, 95% CI=1.6–6.3), cardiovascular failure (systolic blood pressure <90 mmHg despite fluid challenge or lactate ≥4.0; OR=9.0, 95% CI=4.7–17), preexisting terminal illness (OR=5.7, 95% CI=2.2–15), and platelet count less than 150,000/mm³ (OR=2.7, 95% CI=1.3–5.6). Mortality increased with the number of factors: 0.51% for no factors, 3.1% for one factor, 14% for two factors, 47% for three or more risk factors. The c‐statistic was 0.87 for the derivation model and 0.74 for the validation model. Almost 80% of patients in both cohorts were in low‐risk groups (0 or 1 factor). CONCLUSION: A rule derived from five readily available variables predicts mortality in infected elderly ED patients and allows identification of a large low‐risk subgroup.     

Development and validation of a prediction model for functional decline in older medical inpatients

ObjectiveTo prevent functional decline in older inpatients, identification of high-risk patients is crucial. The aim of this study was to develop and validate a prediction model to assess the risk of functional decline in older medical inpatients.MethodsIn this retrospective cohort study, patients ≥65 years admitted acutely to medical wards were included. The healthcare database of 246 acute care hospitals (n = 229,913) was used for derivation, and two acute care hospitals (n = 1767 and 5443, respectively) were used for validation. Data were collected using a national administrative claims and discharge database. Functional decline was defined as a decline of the Katz score at discharge compared with on admission.ResultsAbout 6% of patients in the derivation cohort and 9% and 2% in each validation cohort developed functional decline. A model with 7 items, age, body mass index, living in a nursing home, ambulance use, need for assistance in walking, dementia, and bedsore, was developed. On internal validation, it demonstrated a c-statistic of 0.77 (95% confidence interval (CI) = 0.767–0.771) and good fit on the calibration plot. On external validation, the c-statistics were 0.79 (95% CI = 0.77–0.81) and 0.75 (95% CI = 0.73–0.77) for each cohort, respectively. Calibration plots showed good fit in one cohort and overestimation in the other one.ConclusionsA prediction model for functional decline in older medical inpatients was derived and validated. It is expected that use of the model would lead to early identification of high-risk patients and introducing early intervention.     

Clinical utility of N-terminal pro-B-type natriuretic peptide for risk stratification of patients with acute decompensated heart failure. Derivation and validation of the ADHF/NT-proBNP risk score

Background

NT-proBNP has been associated with prognosis in acute decompensated heart failure (ADHF). Whether NT-proBNP provides additional prognostic information beyond that obtained from standard clinical variables is uncertain. We sought to assess whether N-terminal pro-B-type natriuretic peptide (NT-proBNP) determination improves risk reclassification of patients with ADHF and to develop and validate a point-based NT-proBNP risk score.

Methods

This study included 824 patients with ADHF (453 in the derivation cohort, 371 in the validation cohort). We compared two multivariable models predicting 1-year all-cause mortality, including clinical variables and clinical variables plus NT-proBNP. We calculated the net reclassification improvement (NRI) and the integrated discrimination improvement (IDI). Then, we developed and externally validated the NT-proBNP risk score.

Results

One-year mortalities for the derivation and validation cohorts were 28.3% and 23.4%, respectively. Multivariable predictors of mortality included chronic obstructive pulmonary disease, estimated glomerular filtration rate, sodium, hemoglobin, left ventricular ejection fraction, and moderate to severe tricuspid regurgitation. Adding NT-proBNP to the clinical variables only model significantly improved the NRI (0.129; p = 0.0027) and the IDI (0.037; p = 0.0005). In the derivation cohort, the NT-proBNP risk score had a C index of 0.839 (95% CI: 0.798–0.880) and the Hosmer–Lemeshow statistic was 1.23 (p = 0.542), indicating good calibration. In the validation cohort, the risk score had a C index of 0.768 (95% CI: 0.711–0.817); the Hosmer–Lemeshow statistic was 2.76 (p = 0.251), after recalibration.

Conclusions

The NT-proBNP risk score provides clinicians with a contemporary, accurate, easy-to-use, and validated predictive tool. Further validation in other datasets is advisable.     

Validating and updating a prediction rule for neurological sequelae after childhood bacterial meningitis

Recently, a prediction rule for developing neurological sequelae after childhood bacterial meningitis was developed on a small derivation set. Before implementing in practice a prediction rule must first be tested in new patients (external validation). Our aim was to study the external validity of this rule and, if necessary, to update the rule. The prediction rule was tested on newly available data (validation set) by assessing the rule's calibration and discrimination. We updated the prediction rule by adding extra predictors and re-estimating the regression coefficients of the original predictors in the combined datasets. The rule showed poor agreement between predicted risks and observed frequencies. The ROC area was 0.65 (95% CI 0.57-0.72), which was statistically significantly lower than in the derivation set (0.87 (0.78-0.96)), p-value<0.01. The updated prediction rule showed adequate performance in the combined data sets; the ROC area was 0.77 (95% CI 0.72-0.82). Further study of the generalizability of this updated rule may stimulate application in clinical practice.     

Development and validation of a prognostic index for 2‐year mortality in Chinese older residents living in nursing homes

Aim: There is no mortality prediction index for Chinese nursing home older residents. The objective of this study was to derive and validate a 2‐year mortality prognostic index for them. Methods: We carried out a prospective cohort study on 1120 older residents from 12 nursing homes of Hong Kong. We obtained potential predictors of mortality and carried out updated functional assessment. Each risk factor associated independently with 2‐year mortality in a derivation cohort was assigned a score based on the odds ratio, and risk scores were calculated for each participant by adding the points of risk factors present. Similar analysis was carried out on the validation cohort. Results: Independent predictors of mortality included: aged 86–90 years (3 points); aged ≥91 years (4 points); Charlson comorbidity index ≥4 (6 points); Barthel Index 5–60 (5 points); Barthel Index 0 (10 points); number of hospitalizations in the preceding year (Adbefore) 1 (4 points); Adbefore 2 (5 points) and Adbefore ≥3 (6 points). In the derivation cohort, 2‐year mortality was 10.8% in the low‐risk group (≤4 points) and 59.9% in the high‐risk group (≥14 points). In the validation cohort, 2‐year mortality was 11.8% in the low‐risk group and 60.4% in the high‐risk group. The receiver–operator characteristic curve area was 0.761 for the derivation cohort and 0.742 for the validation cohort. Conclusions: Our prognostic index had satisfactory discrimination and calibration in an independent sample of Chinese nursing home older residents. It can be used to identify older residents with a high risk for poor outcomes, who need a different level of care. Geriatr Gerontol Int 2012; 12: 555–562.     

Development and Validation of a Prognostic Model for One-year Survival of Cirrhosis Patients with First-ever Spontaneous Bacterial Peritonitis

Background and AimsSpontaneous bacterial peritonitis (SBP) is one of the leading causes of death in patients with liver cirrhosis. We aimed to establish a prognostic model to evaluate the 1-year survival of cirrhosis patients after the first episode of SBP.MethodsA prognostic model was developed based on a retrospective derivation cohort of 309 cirrhosis patients with first-ever SBP and was validated in a separate validation cohort of 141 patients. We used Uno’s concordance, calibration curve, and decision curve (DCA) analysis to evaluate the discrimination, calibration, and clinical net benefit of the model.ResultsA total of 59 (19.1%) patients in the derivation cohort and 42 (29.8%) patients in the validation cohort died over the course of 1 year. A prognostic model in nomogram form was developed with predictors including age [hazard ratio (HR): 1.25; 95% confidence interval (CI): 0.92–1.71], total serum bilirubin (HR: 1.66; 95% CI: 1.28–2.14), serum sodium (HR: 0.94; 95% CI: 0.90–0.98), history of hypertension (HR: 2.52; 95% CI: 1.44–4.41) and hepatic encephalopathy (HR: 2.06; 95% CI: 1.13–3.73). The nomogram had a higher concordance (0.79) compared with the model end-stage liver disease (0.67) or Child-Turcotte-Pugh (0.71) score. The nomogram also showed acceptable calibration (calibration slope, 1.12; Bier score, 0.15±0.21) and optimal clinical net benefit in the validation cohort.ConclusionsThis prediction model developed based on characteristics of first-ever SBP patients may benefit the prediction of patients’ 1-year survival.     

Development and validation of a clinical prediction rule for major adverse outcomes in coronary bypass grafting.

In this study, we develop and internally validate a clinical prediction rule for in-hospital major adverse outcomes, defined as death, renal failure, reinfarction, cardiac arrest, cerebrovascular accident, or coma, in patients who underwent coronary artery bypass grafting (CABG). All adult patients (n = 9,498) who underwent a CABG and no other concomitant surgery at 12 academic medical centers from August 1993 to October 1995 were included in the study. We assessed in-hospital major adverse outcomes and their predictors using information on admission, coronary angiography, and postoperative hospital course. Predictor variables were limited to information available before the procedure, and outcome variables were represented only by events that occurred postoperatively. We developed and internally validated a clinical prediction rule for any major adverse outcome after CABG. The rule's ability to discriminate outcomes and its calibration were assessed using receiver-operating characteristic analysis and the Hosmer-Lemeshow goodness-of-fit statistic, respectively. A major adverse outcome occurred in 6.5% of patients in the derivation set and 7.2% in the validation set. Death occurred in 2.5% of patients in the derivation set and 2.2% in the validation set. Sixteen variables were independently correlated with major adverse outcomes, with the risk score value attributed to each risk factor ranging from 2 to 12 points. The rule stratified patients into 6 levels of risk based on the total risk score. The spread in probability between the lowest and highest risk groups of having a major adverse outcome was 1.7% to 32.3% in the derivation set and 2.2% to 22.3% in the validation set. The prediction model performed well in both outcome discrimination and calibration. Thus, this clinical prediction rule allows accurate stratification of potential CABG candidates before surgery according to the risk of experiencing a major adverse outcome postoperatively.