Prognostic significance of combined pulmonary fibrosis and emphysema in patients with resected non-small-cell lung cancer: a retrospective cohort study
Shogo KumagaiSatoshi MarumoKeiji YamanashiJunko TokunoYuichiro UedaT. ShojiTakafumi NishimuraChenglong HuangMotonari Fukui
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Abstract:
Combined pulmonary fibrosis and emphysema (CPFE) is a unique disorder that is usually diagnosed on the basis of high resolution computed tomography (HRCT) findings. It is unclear whether CPFE is an independent prognostic factor in patients with non-small-cell lung cancer (NSCLC). Therefore, we conducted a retrospective analysis to assess the impact of CPFE on the prognosis of patients with completely resected NSCLC. We retrospectively reviewed 365 patients diagnosed with NSCLC who underwent complete resection at the Tazuke Kofukai Medical Research Institute, Kitano Hospital between January 2007 and December 2012. Patients were classified into four groups according to chest HRCT findings: those with CPFE, those with fibrosis, those with emphysema or those with a normal lung except for the presence of a tumour. We evaluated disease-free survival (DFS) and overall survival (OS) using the two-tailed log-rank test and the Cox proportional hazards model. The two-tailed log-rank test demonstrated that the four groups had significantly different DFS and OS (P < 0.01). In the multivariate analysis, CPFE was found to be an independent prognostic factor for DFS and OS compared with a normal lung [hazard ratio (HR): 2.52; 95% confidence interval (CI): 1.24−5.13; P = 0.01 and HR: 4.53; 95% CI: 1.91−10.7; P < 0.01, respectively]. CPFE is a significant, unfavourable prognostic factor for NSCLC patients after curative resection.Keywords:
High-resolution computed tomography
Log-rank test
Log-rank test
Cancer Survival
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Related Article, see p 971KEY POINT: Kaplan-Meier curves, log-rank-test, and Cox proportional hazards regression are common examples of “survival analysis” techniques, which are used to analyze the time until an event of interest occurs.In this issue of Anesthesia & Analgesia, Song et al1 report results of a randomized trial in which they studied the onset of labor analgesia with 3 different epidural puncture and maintenance techniques. These authors compared the techniques on the primary outcome of time until adequate analgesia was reached—defined as a visual analog scale (VAS) score of ≤30 mm—with Kaplan-Meier curves, log-rank tests, and Cox proportional hazards regression. In studies addressing the time until an event of interest occurs, some but not all patients will typically have experienced the event at the end of the follow-up period. Patients in whom the even has not occurred—or who are lost to follow-up during the observation period—are said to be “censored.” It is unknown when and, depending on the event, if the event will occur.2 Simply excluding censored patients from the analysis would bias the analysis results. Specific statistical methods are thus needed that can appropriately account for such censored patient observations. Since the event of interest is often death, these analyses are traditionally termed “survival analyses,” and the time until the event occurs is referred to as the “survival time.” However, as done by Song et al,1 these techniques can also be used for the analysis of the time to any other well-defined event. Among the many available survival analysis methods, Kaplan-Meier curves, log-rank tests to compare these curves, and Cox proportional hazards regression are most commonly used. The Kaplan-Meier method estimates the survival function, which is the probability of “surviving” (ie, the probability that the event has not yet occurred) beyond a certain time point. The corresponding Kaplan-Meier curve is a plot of probability (y-axis) against time (x-axis) (Figure). This curve is a step function in which the estimated survival probability drops vertically whenever one or more outcome events occurred with a horizontal time interval between events. Plotting several Kaplan-Meier curves in 1 figure allows for a visual comparison of estimated survival probabilities between treatment or exposure groups; the curves can formally be compared with a log-rank test. The null hypothesis tested by the log-rank test is that the survival curves are identical over time; it thus compares the entire curves rather than the survival probability at a specific time point.Figure.: Kaplan-Meier plot of the percentage of patients without adequate analgesia, redrawn from Figure 2 in Song et al.1 Note that the original figure plotted the probability of adequate analgesia, as this is easily interpretable for readers in the context of the study research aim. In contrast, we present the figure as conventionally done in a Kaplan-Meier curve or plot, with the estimated probability (here expressed as percentage) of “survival” plotted on the y-axis. Vertical drops in the plot indicate that one or more patients reached the end point of experiencing adequate analgesia at the respective time point. CEI indicates continuous epidural infusion; DPE, dural puncture epidural; EP, conventional epidural; PIEB, programmed intermittent epidural bolus.The log-rank test assesses statistical significance but does not estimate an effect size. Moreover, while there is a stratified log-rank test that can adjust the analysis for a few categorical variables, the log-rank test is essentially not useful to simultaneously analyze the relationships of multiple variables on the survival time. Thus, when researchers either desire (a) to estimate an effect size3 (ie, the magnitude of the difference between groups)—as done in the study by Song et al1—or (b) to test or control for effects of several independent variables on survival time (eg, to adjust for confounding in observational research),4 a Cox proportional hazards model is typically used. The Cox proportional hazards regression5 technique does not actually model the survival time or probability but the so-called hazard function. This function can be thought of as the instantaneous risk of experiencing the event of interest at a certain time point (ie, the probability of experiencing the event during an infinitesimally small time period). The event risk is inversely related to the survival function; thus, “survival” rapidly declines when the hazard rate is high and vice versa. The exponentiated regression coefficients in Cox proportional hazards regression can conveniently be interpreted in terms of a hazard ratio (HR) for a 1-unit increase in the independent variable, for continuous independent variables, or versus a reference category, for categorical independent variables. While the HR is not the same as a relative risk, it can for all practical purposes be interpreted as such by researchers who are not familiar with the intricacies of survival analysis techniques. For those wishing to delve deeper into the details and learn more about survival analysis—including but not limited to the topics that we briefly touch on here—we refer to our tutorial on this topic previously published in Anesthesia & Analgesia.2 Importantly, even though the techniques discussed here do not make assumptions on the distribution of the survival times or survival probabilities, these analysis methods have other important assumptions that must be met for valid inferences, as also discussed in more detail in the previous tutorial.2
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Objective: The 2017 American College of Cardiology/American Heart Association (ACC/AHA) hypertension guidelines decreased the definition of diastolic hypertension to 80 mmHg from 90 mmHg. However, this action may not be appropriate in the elderly, considering the limited value of increased DBP in predicting future cardiovascular risk and the worse prognosis associated with significant lower DBP in the elderly. Design and method: A total of 1178 participants with SBP<130 mmHg derived from the Northern Shanghai Study, a prospective study focusing on the cardiovascular risk of the community-dwelling elderly Chinese in the northern Shanghai, China, were included. Study participants were divided into Group 1 (DBP<80 mmHg) and Group 2 (80<DBP<90 mmHg) according to DBP level. Survival analysis and Cox proportional hazard regression were used to study the future cardiovascular risk of the two groups. Results: After 5.8 years of follow-up, a total of 172 events were observed. Survival analysis showed no significant difference in the rates of the primary endpoints between two groups (14.9%vs14.0%, P = 0.80). In multiple COX proportional hazard regression, after adjusted for covariates, participants in Group 2 showed similar future cardiovascular risk (Hazard Ratio 1.05, 95% Confidence Interval 0.75-1.48, P = 0.78), as compared with Group 1. Conclusions: No increased cardiovascular risk observed in the elderly with SBP<130 mmHg and DBP between 80-90 mmHg, compared with those with DBP<80 mmHg. Therefore, it is maybe unreasonable to downregulate the definition of the diastolic hypertension in the elderly.
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Background: Recently, microRNA-133b (miR-133b) dysregulation has been shown to play a key role in several human cancers, as well as glioma. In this study, we aimed to investigate the clinical significance and prognostic value of miR-133b in glioma.Methods: Real-time quantitative PCR was employed to measure the expression level of miR-133b in tissues. Survival analysis was carried out by using the log-rank test and Kaplan–Meier method. Prognostic factors for overall survival were identified by univariate and multivariate analyses using the Cox proportional hazards regression model.Results: The expression level of miR-133b was significantly lower in glioma tissues compared with matched non-cancerous brain tissues (p < .05). Its level was strongly correlated with Karnofsky Performance Scale score (p < .001) and WHO grade (p < .001). Kaplan–Meier survival and log-rank analysis indicated that the decreased expression of miR-133b was strongly correlated with shorter overall survival of patients with glioma (log-rank test, p = .03).Conclusions: The current investigation demonstrated that miR-133b level is useful for predicting the prognosis of patients with glioma.
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To examine the long-term follow-up of patients with that previously underwent risk stratification based on multicolour FISH testing.On 81 patients with intermediate-risk urothelial carcinoma, a multicolour-FISH was performed. Patients were sub-divided into low- and high-risk groups based on chromosomal patterns. Univariate analysis, using Mantel-Cox log-rank test for disease-free, progression-free survival and overall survival, was employed to determine the prognostic significance of FISH analysis. Survival times were calculated according to the Kaplan-Meier product-limit method and multivariate analysis using Cox proportional hazards regression model.The univariate Mantel-Cox log-rank test showed significant differences between the low-risk and the high-risk group for disease-free survival (p=0.005) and overall survival (p=0.038), but not for progression-free survival (p=0.129).Our long-term follow-up data appear to be able to divide tumors into low and high risk groups for recurrence based on molecular/genetic changes observed with FISH.
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癌症在近數十年來，一直在全國十大死因中位居高位，自1982年起更躍居國人十大死因的第一位，且大多數癌症的死亡率，隨著年齡增加而上升，若與其它國家相較，台灣是肝癌、鼻咽癌、子宮頸癌的高危險區。因此，可以預見的是，隨著人口結構的老化，癌症的發生率與死亡率還將持續增加。在我們參考過國內外所做關於癌症存活分析的研究中，對於臨床分期、治療方法、淋巴結是否轉移、病人確定診斷時的年齡及組織病理型態都被認為是影響癌症預後的重要因子﹔因此本研究之設計主要用以比較三種不同女性癌症(子宮頸癌、卵巢癌、子宮內膜癌)之平均存活時間的長短，即探討其平均存活時間有無統計上的顯著差異，排列出存活時間的序位，找出影響女性癌症存活時間之重要預測因子，提供給臨床醫療照顧者正視女性癌症防治之重要性，並給女性同胞一個警訊，以期早期發現，早期治療，促進國人健康。
本研究為探討婦女三種癌症從診斷確定後開始接受治療的存活時間，研究所採用之方法為存活分析最常採用的Kaplan-Meier estimator，以估計每一種癌症的存活時間,再進而利用logrank test或Breslow test以比較三種癌症的存活函數(survival function)；同時為進一步評估影響每一種癌症存活時間長短的重要預測因子,以time-dependent covariates先行檢驗所收集的存活資料是否有符合 Cox regression model 的假設，最後再進行time -constant Cox proportional hazard model的統計分析,以找出發病並確定診斷時的年齡、The International Federation Gynecology and Obstetrics(FIGO)臨床分期、淋巴結是否轉移、組織病理切片的型態、接受治療方式等是否可有效預測這三種婦女癌症之存活時間的長短。
在單變項分析結果方面，以Kaplan-Meier estimator估計每一種癌症的存活時間, 進而利用logrank test檢定出預測因子於三種癌症中有存活差異的：在子宮頸癌為年齡、疾病分期、淋巴結是否轉移、治療方式；在卵巢癌為年齡、疾病分期、淋巴結是否轉移、組織病理切片的型態以及治療方式；在子宮內膜癌為年齡、疾病分期、淋巴結是否轉移、分化級數以及治療方式。
在多變項分析結果方面，在Cox proportional hazard model中，可有效預測這三種婦女癌症之存活時間的長短，在子宮頸癌為淋巴結是否轉移、診斷年齡、疾病分期；在卵巢癌為淋巴結是否轉移和疾病分期；在子宮內膜癌為分化級數以及淋巴結是否轉移。
根據我們對三種癌症初步所做的檢定看來，疾病分期的早晚與淋巴結是否轉移的確對存活時間有相當顯著的影響，不論是在國內或國外報告，均相當認同這點。三種女性癌症之平均存活時間序位分別為子宮頸癌99個月、子宮內膜癌83個月、卵巢癌62個月：五年存活率分別為子宮內膜癌66.94%、子宮頸癌63.16%、卵巢癌34.91%，不同之平均存活時間亦具有統計上的顯著差異，可見雖同樣為女性癌症，但一旦罹患對婦女同胞造成的傷害仍不容忽視，希望本篇研究能提供臨床相關醫療人有參考之價值；而新政府在防治癌症工作上能更加落實，以造福社會大眾，促進國人健康。
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Background Although most people with relapsing onset multiple sclerosis (R-MS) eventually transition to secondary progressive multiple sclerosis (SPMS), little is known about disability progression in SPMS. Methods All R-MS patients in the Cardiff MS registry were included. Cox proportional hazards regression was used to examine a) hazard of converting to SPMS and b) hazard of attaining EDSS 6.0 and 8.0 in SPMS. Results 1611 R-MS patients were included. Older age at MS onset (hazard ratio [HR] 1.02, 95%CI 1.01–1.03), male sex (HR 1.71, 95%CI 1.41–2.08), and residual disability after onset (HR 1.38, 95%CI 1.11–1.71) were asso- ciated with increased hazard of SPMS. Male sex (EDSS 6.0 HR 1.41 [1.04–1.90], EDSS 8.0 HR 1.75 [1.14–2.69]) and higher EDSS at SPMS onset (EDSS 6.0 HR 1.31 [1.17–1.46]; EDSS 8.0 HR 1.38 [1.19–1.61]) were associated with increased hazard of reaching disability milestones, while older age at SPMS was associated with a lower hazard of progression (EDSS 6.0 HR 0.94 [0.92–0.96]; EDSS 8.0: HR 0.92 [0.90–0.95]). Conclusions Different factors are associated with hazard of SPMS compared to hazard of disability progres- sion after SPMS onset. These data may be used to plan services, and provide a baseline for comparison for future interventional studies and has relevance for new treatments for SPMS RobertsonNP@cardiff.ac.uk
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Objective This study aimed to investigate the effect of Licartin on prevention of recurrence of hepatocelluar carcinoma( HCC). Methods Retrospectively collected 55 HCC patients after operation treated by Licartin as trail group,another 55 patients received none treatment as control group,multivariable COX regression,Kaplan-Meier curve and log-rank test were used to assess the difference of prognosis between the two groups. Results Multivariable COX regression showed that Licartin can significantly decrease the recurrence rate( HR,0. 604; 95% CI,0. 424-0. 860,P = 0. 005) and extend the survival time( HR,0. 586; 95% CI 0. 358-0. 960,P = 0. 034) of HCC. Kaplan-Meier curve and log-rank test also showed the same results. Conclusion Transarterial Licartin infusion can prevent recurrence of HCC and extend the survival time. However,the results should be confirmed by random control trail.
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The hazard ratio and median survival time are the routine indicators in survival analysis. We briefly introduced the relationship between hazard ratio and median survival time and the role of proportional hazard assumption. We compared 110 pairs of hazard ratio and median survival time ratio in 58 articles and demonstrated the reasons for the difference by examples. The results showed that the hazard ratio estimated by the Cox regression model is unreasonable and not equivalent to median survival time ratio when the proportional hazard assumption is not met. Therefore, before performing the Cox regression model, the proportional hazard assumption should be tested first. If proportional hazard assumption is met, Cox regression model can be used; if proportional hazard assumption is not met, restricted mean survival times is suggested.风险比（hazard ratio，HR）和中位生存时间是生存分析时的常规分析和报告指标。本文简要介绍了HR和中位生存时间的关系以及比例风险假定在这两者之间的作用，分析了检索出的58篇文献中的110对风险比和中位生存时间比的差异，并通过实例阐明了产生这种差异的原因。结果表明，在不满足比例风险假定时，Cox回归模型计算得到的风险比是不合理的，且与中位生存时间之比不等价。因此，在使用Cox回归模型前，应先进行比例风险假定的检验，只有符合比例风险假定时才能使用该模型；当不符合比例风险假定时，建议使用限制性平均生存时间。.
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