哮喘患者疲劳症状的临床特征及影响因素分析

目的 探讨哮喘患者疲劳症状的临床特点及影响因素。方法 选取哮喘患者198例,根据疲劳严重度量 表（FSS）评分将患者分为疲劳组（≥4分,126例）和非疲劳组（＜4分,72例）。分析2组临床基本特征、肺功能、呼出气 一氧化氮（FeNO）、呼吸困难分级（mMRC）、哮喘控制测试评分（ACT）、日常生活能力（MBI）评分、6 min 步行测试 （6MWT）、汉密尔顿抑郁量表17项（HAMD-17）、匹兹堡睡眠质量指数量表（PSQI）、过去1年急性发作次数等方面的 差异。二分类Logistic回归分析哮喘患者疲劳症状的影响因素。根据筛选后的指标构建列线图预测模型,通过受试 者工作特征（ROC）曲线和校准曲线评价模型的预测价值。结果 疲劳组 ACT 评分低于非疲劳组,mMRC 分级、 HAMD-17评分、PSQI评分及上1年急性发作次数高于非疲劳组（P＜0.01）,2组余指标差异无统计学意义（P＞0.05）。 二分类 Logistic 回归分析显示,ACT 评分（OR=0.644,95%CI：0.508~0.816）、mMRC 分级（OR=2.313,95%CI：1.349～ 3.966）、HAMD-17评分（OR=1.561,95%CI：1.273~1.913）及PSQI评分（OR=1.932,95%CI：1.506～2.479）是哮喘患者发 生疲劳的影响因素。基于4项影响因素构建列线图预测模型,预测模型的ROC曲线下面积为0.935（95%CI：0.902~ 0.967）。内部验证显示,C-index 为 0.929,校准曲线表明列线图的预测结果与实际的观测结果一致性良好。结论 哮喘患者中疲劳的发生率较高,哮喘控制不佳、高mMRC分级、负性情绪和睡眠障碍是发生疲劳的重要影响因素,根 据影响因素构建的列线图具有较高的预测价值。     

A post‐radical‐prostatectomy nomogram incorporating new pathological variables and interaction terms for improved prognosis

Study Type – Prognosis (systematic review)
Level of Evidence 2a What’s known on the subject? and What does the study add? Nomograms are commonly used by urologists to assess a patient’s risk of developing biochemical failure (PSA recurrence) after radical prostatectomy. The nomograms currently available on websites and in the published literature do not take into account recent developments in pathological reporting which may improve our ability to more accurately predict patient prognosis. Furthermore, currently available nomograms treat all clinical predictors as independent variables without considering the possibility that these factors may be interlinked, which may alter their predictive value. Our study assesses the predictive value of several new pathological variables and demonstrates that the per cent of Gleason patterns 4 and/or 5 (% 4/5), intraductal prostatic carcinoma and prostate weight significantly improve the predictive value of a model based on established pathological variables. We also show that consideration of interactions between % 4/5, surgical margin status and extracapsular extension further improves our accuracy in predicting patient PSA recurrence. Finally, we find that published nomograms based on PSA recurrence defined as ≥0.4 ng/mL provide over‐optimistic predictions for prostate cancer patients where PSA recurrence was defined as ≥0.2 ng/mL.

OBJECTIVE

• ? To evaluate new variables in prostate pathology reporting including, the per cent of Gleason patterns 4 and/or 5 (% 4/5), presence or absence of intraductal carcinoma of the prostate (IDCP), tumour volume and the prostatic zone of tumour origin as predictors of post‐radical‐prostatectomy (RP) biochemical recurrence (BCR).
• ? To develop an optimal postoperative nomogram for patients with prostate cancer.

PATIENTS AND METHODS

• ? Our study cohort was 1939 eligible patients from the Abbott West Australian Prostatectomy Database.
• ? Multivariate Cox proportional hazard regression models were developed to predict BCR which was defined as prostate‐specific antigen (PSA) ≥0.2 ng/mL.
• ? Our models and the 2009 Kattan postoperative nomogram were compared in terms of discrimination and calibration, with internal validation of our final model performed using bootstrapping methods. Our final model is presented as a nomogram.

RESULTS

• ? The Kattan nomogram was accurate in discriminating our patients according to risk (concordance index: 0.791) but calibration analysis indicated underestimation of patient risk, particularly for high‐risk disease.
• ? Our nomogram incorporates % 4/5, IDCP and prostate weight plus interaction terms between % 4/5, positive surgical margins and extracapsular extension, giving improved predictive accuracy (concordance index: 0.828) and calibration.

CONCLUSIONS

• ? Nomograms that predict risk of BCR defined as PSA ≥0.4 ng/mL may not be optimal for patient cohorts where BCR is defined as PSA ≥0.2 ng/mL.
• ? If our findings are validated in other populations, current post‐RP nomograms may be improved to a modest degree by incorporating the new variables prostate weight, IDCP and % 4/5, and by considering interactions between predictive variables.

     

The Karakiewicz nomogram is the most useful clinical predictor for survival outcomes in patients with localized renal cell carcinoma

BACKGROUND:

Outcomes after surgical removal of localized renal cell carcinoma (RCC) are variable. There have been multiple prognostic nomograms and risk groups developed for estimation of survival outcomes, with different models in use for evaluating patient eligibility in ongoing trials of adjuvant therapy. The authors aimed to establish the most useful prognostic model for patients with localized RCC to guide trial design, biomarker research, and clinical counseling.

METHODS:

A total of 390 consecutive patients who underwent nephrectomy for sporadic localized RCC in a tertiary institution (1990‐2006) with 65 months median follow‐up were retrospectively evaluated. The Karakiewicz nomogram, the Kattan nomogram, the Sorbellini nomogram, and the Leibovich model were compared in predicting survival outcomes (overall survival, cancer‐specific survival, and freedom from recurrence) using likelihood analysis, adequacy indices, decision curve analysis, calibration, and concordance indices.

RESULTS:

Overall, the Karakiewicz nomogram outperformed the Kattan nomogram, the Sorbellini nomogram, and the Leibovich model. Highly improved accuracy was seen using the Karakiewicz nomogram in survival prediction, using likelihood ratio analysis in bivariate models including the competing prognostic models. The Karakiewicz nomogram showed higher adequacy and concordance indices and improved clinical benefit relative to all other nomograms. All 4 models were reasonably calibrated. Exploratory comparisons of prespecified discretized Karakiewicz nomograms and the SORCE trial recruitment criteria (a discretized Leibovich model) of high‐risk patients favored the discretized Karakiewicz nomograms.

CONCLUSIONS:

The Karakiewicz nomogram was shown to be superior to the other tested nomograms and risk groups in predicting survival outcomes in localized RCC. Routine integration of this model into trial design and biomarker research should be considered. Cancer 2011;. © 2011 American Cancer Society.