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血红蛋白波动对腹膜透析患者心血管预后的影响
杨文娟, 田娜, 张倩, 王艳, 王丽, 宋淑华, 马小琴, 赵彩萍, 陈孟华
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血红蛋白波动对腹膜透析患者心血管预后的影响
Effect of hemoglobin volatility on cardiovascular prognosis in peritoneal dialysis patients
目的 探讨腹膜透析（peritoneal dialysis，PD）患者血红蛋白（hemoglobin，Hb）波动与心血管预后的关系。方法 回顾性分析2003年5月1日至2014年10月31日宁夏医科大学总医院PD中心稳定透析>3个月并规律随访至少1年的PD患者资料，使用Hb周期性法根据透析后1个月、3个月、6个月、12个月Hb较基线变化绝对值的平均值分为低波动组（≤10 g/L）、中波动组（>10~20 g/L）及高波动组（>20 g/L），比较各组之间的基线资料。采用Kaplan-Meier生存分析和Cox回归方程分析3组患者Hb波动与心血管相关死亡、全因死亡之间的关系。根据改善全球肾脏病预后组织（KDIGO）指南及相关文献，将患者按研究终点（心血管死亡和全因死亡）时的Hb水平分为达标组（Hb≥110 g/L）和未达标组（Hb<110 g/L），采用Cox回归分析法比较两组Hb平均波动水平与心血管相关死亡的关系。采用多因素线性回归法分析PD患者Hb波动的相关因素。结果 共纳入267例PD患者，男性160例（59.93%），年龄（52.66±13.72）岁，中位透析龄37（21，61）个月。患者基线Hb（透析前）为（80.16±14.89）g/L，研究结束时Hb为（105.34±22.08）g/L。与低、中波动组比较，高波动组体重指数、基线Hb水平均较低（均P<0.05）。与低波动组相比，中、高波动组估计肾小球滤过率水平均较低，高波动组尿素氮水平较高（均P<0.05）。与中波动组相比较，高波动组促红细胞生成素用量较多（P<0.05）。Kaplan-Meier生存分析结果显示，三组患者以心血管相关死亡为研究终点事件的生存率（Log-rank χ2=2.961，P=0.228）及整体生存率（Log-rank χ2=0.735，P=0.693）差异均无统计学意义。Cox回归分析结果显示，在校正了年龄、性别、血肌酐、血白蛋白后，Hb平均波动越大，心血管相关死亡风险越低（HR=0.972，95%CI 0.947~0.999，P=0.040）；在未达标组人群中，校正相关混杂因素后，Hb波动大仍是心血管相关死亡风险低的保护因素（HR=0.946，95%CI 0.903~0.992，P=0.022），但与全因死亡均无明显相关性。多因素线性回归分析结果显示，Hb波动与尿素清除指数（B=4.682，95%CI 2.480~6.884，P<0.001）及促红细胞生成素用量（B=0.001，95%CI 0~0.001，P=0.003）呈正相关，与基线Hb水平呈负相关（B=-0.554，95%CI -0.651~-0.457，P<0.001）。结论 在较低的Hb水平（Hb未达标）PD患者人群中，高Hb波动度是心血管相关死亡的保护因素。在贫血治疗中，相比于Hb波动因素，采取合理的方案及时纠正贫血至达标水平，对降低PD患者心血管相关死亡的影响更大。
Objective To investigate the effect of hemoglobin (Hb) volatility on cardiovascular prognosis in peritoneal dialysis (PD) patients. Methods Retrospective cohort study was designed. Patients undergoing stable PD for more than 3 months and followed up regularly for at least 1 year were enrolled from May 1, 2013 to October 31, 2014 in the General Hospital of Ningxia Medical University. According to the Hb variation based on the mean changes in Hb standard deviation at 1 month, 3 months, 6 months, 12 months over baseline Hb, all patients were divided into low volatility group (≤10 g/L), moderate volatility group (>10-20 g/L) and high volatility group (>20 g/L), and baseline information were compared among these groups. Kaplan-Meier survival analysis and Cox regression equation were used to analyze the relationship between Hb variation and cardiovascular mortality and all-cause mortality. Besides, the patients were divided into qualified group (Hb≥110 g/L) and substandard group (Hb<110 g/L) by the Hb level at the study endpoint (cardiovascular death and all-cause death) according to KDIGO guidelines and relevant literature. Cox regression analysis was used to analyze the relationship between Hb variation and cardiovascular death in qualified group or substandard group. Multivariate linear regression analysis was used to analyze the related factors of Hb fluctuation in PD patients. Results A total of 267 patients were enrolled. There were 160 males (59.93%) in this study. The age was (52.66±13.72) years old, and the median dialysis age was 37(21, 61) months. The patients' baseline Hb (before dialysis) was (80.16±14.89) g/L and at the end of the study Hb was (105.34±22.08) g/L. Body mass index and baseline Hb levels in the high volatility group were lower than those in low volatility group and moderate volatility group (all P<0.05). Both moderate and high volatility groups had lower estimated glomerular filtration rate than that in low volatility group, and high volatility group had higher urea nitrogen level than that in low volatility group (all P<0.05). The amount of erythropoietin usage in the high volatility group was higher than that in moderate volatility group (P<0.05). The Kaplan-Meier survival analysis results showed that there was no significant difference in survival rate for all-cause death (Log-rank χ2=0.735, P=0.693) and cardiovascular death (Log-rank χ2=2.961, P=0.228) in different Hb volatility groups. Cox regression analysis showed that after adjusting for age, sex, serum creatinine, and blood albumin, higher Hb volatility was associated with a lower risk of cardiovascular death (HR=0.972, 95%CI 0.947-0.999, P=0.040). After adjusting for related confounding factors, higher Hb volatility was still a protective factor for cardiovascular death in the substandard group (HR=0.946, 95%CI 0.903-0.992, P=0.022), but there was no significant correlation between Hb fluctuation and all-cause death. Multivariate linear regression analysis results showed that the fluctuation level of Hb was positively correlated with Kt/V (B=4.682, 95%CI 2.480-6.884, P<0.001) and erythropoietin dosages (B=0.001, 95%CI 0-0.001, P=0.003), and negatively correlated with baseline Hb (B=-0.554, 95%CI -0.651- -0.457, P<0.001). Conclusions High Hb variability is a protective factor for cardiovascular death in PD patients with lower Hb level (substandard Hb). Adopting a reasonable program to correct anemia timely to reach the standard level has a greater impact on reducing risk of cardiovascular death in PD patients than Hb variation in anemia treatment.
腹膜透析 / 心血管疾病 / 预后 / 血红蛋白波动 {{custom_keyword}} /
Peritoneal dialysis / Cardiovascular diseases / Prognosis / Hemoglobin volatility {{custom_keyword}} /
彭苗 , {{custom_editor}}
表1 不同血红蛋白波动组患者基线资料的比较 |
| 项目 | 低波动组 | 中波动组 | 高波动组 | F/χ2 | P值 |
|---|---|---|---|---|---|
| (≤10 g/L，n=22) | (>10~20 g/L，n=66) | (>20 g/L，n=179) | |||
| 性别(男/女，例) | 16/6 | 44/22 | 100/79 | 3.861 | 0.152 |
| 年龄(岁) | 55.27±14.21 | 53.83±14.36 | 51.91±13.42 | 0.907 | 0.405 |
| 透析龄(月) | 43.5(24.5，53.8) | 36.0(20.0，74.0) | 37.0(22.0，59.8) | 0.437 | 0.652 |
| 体重指数(kg/m2) | 24.14±0.66 | 23.76±0.44 | 22.37±0.23ab | 6.366 | 0.002 |
| eGFR[ml·min-1·(1.73 m2)-1] | 7.08±2.82 | 5.40±3.42a | 5.04±2.51a | 5.289 | 0.006 |
| Kt/V | 1.76±0.57 | 1.76±0.42 | 2.00±0.63b | 3.921 | 0.021 |
| 血肌酐(μmol/L) | 811.16±619.20 | 940.84±460.39a | 916.28±402.50a | 7.973 | 0.024 |
| 尿素氮(mmol/L) | 24.21±9.75 | 27.07±14.29 | 30.76±12.93a | 7.614 | 0.023 |
| 血清白蛋白(g/L) | 32.38±6.93 | 31.49±6.61 | 31.96±6.06 | 0.609 | 0.552 |
| 血钙(mmol/L) | 2.02±0.21 | 2.32±2.41 | 1.95±0.33 | 2.748 | 0.250 |
| 血磷(mmol/L) | 1.73±0.61 | 2.01±0.92 | 2.06±0.77 | 4.346 | 0.112 |
| iPTH(ng/L) | 167.50(97.60，371.00) | 291.00(115.25，424.25) | 272.00(137.00，470.00) | 1.372 | 0.261 |
| 血清铁(μmol/L) | 11.52±4.40 | 12.40±6.07 | 12.41±6.70 | 0.001 | 0.823 |
| 透析剂量(L/d) | 6.53±0.90 | 5.90±1.23 | 6.23±0.77 | 2.202 | 0.045 |
| 促红细胞生成素用量(IU/周) | 9 105.26±1 940.64 | 7 378.69±3 499.67 | 9 004.58±3 033.60b | 13.652 | 0.001 |
| 铁剂用量(mg/周) | 200(100，473) | 100(0，473) | 200(100，945) | 5.567 | 0.056 |
| 基线血红蛋白(g/L) | 96.18±12.63 | 88.18±12.09a | 75.23±13.38ab | 42.042 | <0.001 |
| 血红蛋白平均值(g/L) | 99.45±12.98 | 99.97±14.43 | 109.50±13.15ab | 15.238 | <0.001 |
| 血红蛋白平均波动值(g/L) | 7.80±1.40 | 15.86±2.72a | 34.68±10.92ab | 181.803 | <0.001 |
| 注：eGFR：估计肾小球滤过率；Kt/V：尿素清除指数；iPTH：全段甲状旁腺素；数据形式除已注明外，其余以$\bar{x}±s$或M（P25，P75）形式表示；与低波动组比较，aP<0.05；与中波动组比较，bP<0.05 |
表2 生存患者与死亡患者血红蛋白水平的比较（$\bar{x}±s$） |
| 项目 | 生存组(n=175) | 心血管相关死亡组(n=40) | t值a | P值a | 全因死亡组(n=92) | t值b | P值b |
|---|---|---|---|---|---|---|---|
| 基线血红蛋白(g/L) | 80.40±15.50 | 81.73±11.99 | 0.512 | 0.467 | 79.70±13.72 | -0.367 | 0.713 |
| 血红蛋白平均值(g/L) | 108.08±14.61 | 101.30±14.20 | -2.664 | 0.008 | 102.99±12.69 | -2.832 | 0.005 |
| 血红蛋白平均波动值(g/L) | 29.62±13.92 | 22.78±10.49 | -3.481 | 0.001 | 25.59±12.08 | -2.350 | 0.019 |
| 注：a：心血管相关死亡组与生存组比较；b：全因死亡组与生存组比较 |
表3 血红蛋白平均波动与心血管相关死亡、全因死亡的关系（Cox回归分析，n=267） |
| 项目 | 心血管相关死亡 | 全因死亡 | ||
|---|---|---|---|---|
| HR(95%CI) | P值 | HR(95%CI) | P值 | |
| 未校正模型 | 0.970(0.944~0.996) | 0.026 | 0.989(0.973~1.005) | 0.186 |
| 模型1 | 0.972(0.946~0.999) | 0.041 | 0.993(0.978~1.009) | 0.397 |
| 模型2 | 0.972(0.947~0.999) | 0.040 | 0.993(0.978~1.009) | 0.386 |
| 模型3 | 0.972(0.946~1.000) | 0.048 | 0.997(0.980~1.014) | 0.722 |
| 模型4 | 0.977(0.943~1.013) | 0.204 | 1.003(0.980~1.027) | 0.777 |
| 注：HR：风险比；CI：置信区间；模型1：校正年龄、性别；模型2：校正血肌酐、血白蛋白及模型1中的影响因素；模型3：校正血红蛋白是否达标及模型2中的影响因素；模型4：校正原发病（如糖尿病肾病、原发性肾小球肾炎、高血压肾损伤等）、体重指数、尿素清除指数、血清铁、促红细胞生成素剂量、铁剂剂量及模型3中的影响因素 |
表4 血红蛋白平均波动与达标组、未达标组患者心血管相关死亡的关系（Cox回归分析） |
| 项目 | 达标组(n=112) | 未达标组(n=155) | ||
|---|---|---|---|---|
| HR(95%CI) | P值 | HR(95%CI) | P值 | |
| 未校正模型 | 0.993(0.957~1.030) | 0.696 | 0.949(0.911~0.989) | 0.012 |
| 模型1 | 0.993(0.956~1.031) | 0.702 | 0.954(0.915~0.995) | 0.028 |
| 模型2 | 0.973(0.928~1.021) | 0.267 | 0.946(0.903~0.992) | 0.022 |
| 模型3 | 0.974(0.916~1.036) | 0.405 | 0.970(0.915~1.028) | 0.308 |
| 注：HR：风险比；CI：置信区间；模型1：校正年龄、性别、糖尿病；模型2：校正血肌酐、血白蛋白、血清钙、血清铁、血全段甲状旁腺素及模型1中的影响因素；模型3：校正体重指数、尿素清除指数及模型2中的影响因素 |
表5 腹膜透析患者血红蛋白波动的相关因素（线性回归分析，n=267） |
| 项目 | 单因素 | 多因素 | ||||
|---|---|---|---|---|---|---|
| B | B的95%CI | P值 | B | B的95%CI | P值 | |
| 年龄(岁) | -0.062 | -0.176~0.061 | 0.338 | |||
| 性别(女/男) | 3.487 | 0.206~6.767 | 0.037 | |||
| 透析龄(月) | -0.029 | -0.088~0.300 | 0.335 | |||
| 体重指数(kg/m2) | -0.788 | -1.273~-0.303 | 0.002 | |||
| eGFR | -0.365 | -0.938~0.207 | 0.210 | |||
| Kt/V | 4.433 | 1.466~7.399 | 0.004 | 4.682 | 2.480~6.884 | <0.001 |
| 基线血红蛋白(g/L) | -0.546 | -0.633~-0.459 | <0.001 | -0.554 | -0.651~-0.457 | <0.001 |
| 促红细胞生成素用量(IU/周) | 0.001 | 0.001~0.002 | <0.001 | 0.001 | 0~0.001 | 0.003 |
| 铁剂用量(mg/周) | -0.001 | -0.005~0.003 | 0.552 | |||
| 透析剂量(L/d) | -0.032 | -1.819~1.754 | 0.971 | |||
| 血清铁(μmol/L) | 0.003 | -0.252~0.255 | 0.979 | |||
| 血肌酐(μmol/L) | 0 | -0.004~0.004 | 0.933 | |||
| iPTH(ng/L) | 0.002 | -0.003~0.007 | 0.425 | |||
| 血清白蛋白(g/L) | -0.111 | -0.371~0.148 | 0.399 | |||
| 注：eGFR：估计肾小球滤过率，单位为ml·min-1·(1.73 m2)-1；Kt/V：尿素清除指数；iPTH：全段甲状旁腺素；CI：置信区间 |
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Hispanics are the largest racial/ethnic minority group in the United States, and they experience a substantial burden of kidney disease. Although the prevalence of chronic kidney disease (CKD) is similar or slightly lower in Hispanics than non-Hispanic whites, the age- and sex-adjusted prevalence rate of end-stage renal disease is almost 50% higher in Hispanics compared with non-Hispanic whites. This has been attributed in part to faster CKD progression among Hispanics. Furthermore, Hispanic ethnicity has been associated with a greater prevalence of cardiovascular disease risk factors, including obesity and diabetes, as well as CKD-related complications. Despite their less favorable socioeconomic status, which often leads to limited access to quality health care, and their high comorbid condition burden, the risk for mortality among Hispanics appears to be lower than for non-Hispanic whites. This survival paradox has been attributed to a complex interplay between sociocultural and psychosocial factors, as well as other factors. Future research should focus on evaluating the long-term impact of these factors on patient-centered and clinical outcomes. National policies are needed to improve access to and quality of health care among Hispanics with CKD.Copyright © 2018 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.
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Chronic kidney disease is a known risk factor for end-stage renal and cardiovascular diseases. However, data are limited on the causes of hospitalization in patients with chronic kidney disease of maintenance period. This study aimed to aggregate hospitalization data of CKD patients and to determine the high-risk population. In addition, we compared CKD population to general population.We conducted a post hoc analysis of the chronic kidney disease-Japan cohort study, a multicenter prospective cohort study of 2966 patients with chronic kidney disease with a median 3.9 years of follow-up. We examined the hospitalization reasons and analyzed the risk factors.We found 2897 all-cause hospitalization events (252.3 events/1000 person-years), a hospitalization incidence 17.1-fold higher than that in an age- and sex-matched cohort from the general Japanese population. Kidney, eye and adnexa, and heart-related hospital admissions were the most common. All-cause hospitalization increased with chronic kidney disease stage and with the presence of diabetes. Patients with diabetes at enrollment had 345.7 hospitalization events/1000 person-years, which is considerably higher than 196.8 events/1000 person-years for those without diabetes. Survival analysis, using hospitalization as an event, showed earlier all-cause hospitalization with the progression of chronic kidney disease stage and diabetes. Cardiovascular disease hospitalizations were more strongly influenced by diabetes than chronic kidney disease stage.Patients with chronic kidney disease and diabetes are highly vulnerable to hospitalization for a variety of diseases. These descriptive data can be valuable in predicting the prognosis of patients with chronic kidney disease.
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Hemodialysis patients with larger hemoglobin level fluctuations have higher mortality rates. We describe facility-level interpatient hemoglobin variability, its relation to patient mortality, and factors associated with facility-level hemoglobin variability or achieving hemoglobin levels of 10.5-12.0 g/dL. Facility-level hemoglobin variability may reflect within-patient hemoglobin variability and facility-level anemia-control practices.Prospective cohort study.Data from the Dialysis Outcomes and Practice Patterns Study (DOPPS; 26,510 hemodialysis patients, 930 facilities, 12 countries, 1996-2008) and from the Centers for Medicare & Medicaid Services (CMS; 193,291 hemodialysis patients, 3,741 US facilities, 2002).Standard deviation (SD) in single-measurement hemoglobin levels in hemodialysis patients in facility cross-sections (facility-level hemoglobin SD); patient characteristics; facility practices.Patient-level mortality; additionally, facility practices correlated with facility-level hemoglobin SD or patient hemoglobin levels of 10.5-12.0 g/dL.Facility-level hemoglobin SD varied more than 5-fold across DOPPS facilities (range, 0.5-2.7 g/dL; mean, 1.3 g/dL) and by country (range, 1.1 in Japan-DOPPS [2005/2006] to 1.7 g/dL in Spain-DOPPS [1998/1999]), with substantial decreases seen in many countries from 1998 to 2007. Facility-level hemoglobin SD was related inversely to patient age, but was associated minimally with more than 30 other patient characteristics and facility mean hemoglobin levels. Several anemia management practices were associated strongly with facility-level hemoglobin SD and having a hemoglobin level of 10.5-12.0 g/dL. When examined in CMS data, facility-level hemoglobin SD was positively associated with within-patient hemoglobin SD during the prior 6 months. Patient mortality rates were higher with greater facility-level hemoglobin SD (DOPPS: HR, 1.08 per 0.5-g/dL greater facility-level hemoglobin SD [95% CI, 1.02-1.15; P = 0.006]; CMS: HR, 1.16 per 0.5-g/dL greater facility-level hemoglobin SD [95% CI, 1.11-1.21; P < 0. 001]).Residual confounding.Facility-level hemoglobin SD was associated strongly and positively with patient mortality, not tightly linked to numerous patient characteristics, but related strongly to facility anemia management practices. Facility-level hemoglobin variability may be modifiable and its optimization may improve hemodialysis patient survival.Copyright © 2011 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.
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Most hemodialysis patients show hemoglobin fluctuations between low-normal and high levels. This hemoglobin variability may cause left ventricle hypertrophy and may increase mortality as well. Recently, many studies were designed to evaluate the effect of hemoglobin variability on mortality but results were conflicting. We aimed to investigate the effect of hemoglobin variability on mortality and some cardiovascular parameters in hemodialysis population.Hundred and seventy-five prevalent hemodialysis patients classified into three hemoglobin variability groups according to their hemoglobin levels throughout 24 month observation period: Low-Normal, Low-High, Normal-High. Groups were compared in terms of laboratory, demographical data and mortality rates, initial and the end of 24 month echocardiographic data. Initial and last echocardiographic data were compared within groups in terms of left ventricle mass index increase.Mortality rates and cardiovascular risk factors such as coronary heart disease, diabetes mellitus and hypertension that may affect mortality were same between three groups. There was no significant difference between three groups in terms of echocardiographic and laboratory parameters. Only Low-High group showed significant increase on left ventricle mass index when initial and last echocardiographic parameters were compared.Consistent with previous studies, we found that most of the patients exhibited hemoglobin variability and our study is consistent with some of the studies that did not find any relationship between hemoglobin variability and mortality. Firstly, in this study based on objective data, it was shown that hemoglobin variability has adverse effect on left ventricle geometry independent from anemia.
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To investigate the effect of hemoglobin fluctuations on cardiovascular prognosis of patients on peritoneal dialysis (PD).Retrospective descriptive study, with sample composed of 333 patients treated with PD at the Renal Unit. Two indicators were adopted to indicate hemoglobin fluctuations of PD patients: absolute value of hemoglobin variability (HV) and HV trend. Moreover, the new cardiovascular events and recurrent rate of cardiovascular events within 3 months after PD were recorded and were compared in groups of PD patients classified by hemoglobin fluctuation indicators.Patients whose HV value is less than 10 g/l have an increased risk of new cardiovascular events than patients with HV value > 10 g/l (27.2 vs. 12.2%, p < 0.05) during 3 months after PD. Patients who kept high hemoglobin values (≥ 110 g/l) 3 months after PD are prone to develop recurrent cardiovascular events than patients with relatively low hemoglobin values (< 110 g/l; p < 0.05).Hemoglobin fluctuations were associated with the cardiovascular prognosis in patients on PD. A limitation of our study is its retrospective design. However, the results in this research indicated anemia in PD patients without cardiovascular events history should be timely treated. Instead, hemoglobin should be kept within a relatively low level in patients with history of cardiovascular events. Additional clinical researches are needed to verify and improve our findings.
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唐政. 血红蛋白稳定性的评价方法[J]. 肾脏病与透析肾移植杂志, 2014, 23(2): 161-162.
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No authors listed. Summary of recommendation statements[J]. Kidney Int Suppl (2011), 2013, 3(3): 263-265. DOI: 10.1038/kisup.2013.31.
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Equations to estimate glomerular filtration rate (GFR) are routinely used to assess kidney function. Current equations have limited precision and systematically underestimate measured GFR at higher values.To develop a new estimating equation for GFR: the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation.Cross-sectional analysis with separate pooled data sets for equation development and validation and a representative sample of the U.S. population for prevalence estimates.Research studies and clinical populations ("studies") with measured GFR and NHANES (National Health and Nutrition Examination Survey), 1999 to 2006.8254 participants in 10 studies (equation development data set) and 3896 participants in 16 studies (validation data set). Prevalence estimates were based on 16,032 participants in NHANES.GFR, measured as the clearance of exogenous filtration markers (iothalamate in the development data set; iothalamate and other markers in the validation data set), and linear regression to estimate the logarithm of measured GFR from standardized creatinine levels, sex, race, and age.In the validation data set, the CKD-EPI equation performed better than the Modification of Diet in Renal Disease Study equation, especially at higher GFR (P < 0.001 for all subsequent comparisons), with less bias (median difference between measured and estimated GFR, 2.5 vs. 5.5 mL/min per 1.73 m(2)), improved precision (interquartile range [IQR] of the differences, 16.6 vs. 18.3 mL/min per 1.73 m(2)), and greater accuracy (percentage of estimated GFR within 30% of measured GFR, 84.1% vs. 80.6%). In NHANES, the median estimated GFR was 94.5 mL/min per 1.73 m(2) (IQR, 79.7 to 108.1) vs. 85.0 (IQR, 72.9 to 98.5) mL/min per 1.73 m(2), and the prevalence of chronic kidney disease was 11.5% (95% CI, 10.6% to 12.4%) versus 13.1% (CI, 12.1% to 14.0%).The sample contained a limited number of elderly people and racial and ethnic minorities with measured GFR.The CKD-EPI creatinine equation is more accurate than the Modification of Diet in Renal Disease Study equation and could replace it for routine clinical use.National Institute of Diabetes and Digestive and Kidney Diseases.
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Although treating anemia of chronic kidney disease by erythropoiesis-stimulating agents (ESA) may improve survival, most studies have examined associations between baseline hemoglobin values and survival and ignored variations in clinical and laboratory measures over time. It is not clear whether longitudinal changes in hemoglobin or administered ESA have meaningful associations with survival after adjustment for time-varying confounders. With the use of time-dependent Cox regression models, longitudinal associations were examined between survival and quarterly (13-wk averaged) hemoglobin values and administered ESA dose in a 2-yr (July 2001 to June 2003) cohort of 58,058 maintenance hemodialysis patients from a large dialysis organization (DaVita) in the United States. After time-dependent and multivariate adjustment for case mix, quarterly varying administered intravenous iron and ESA doses, iron markers, and nutritional status, hemoglobin levels between 12 and 13 g/dl were associated with the greatest survival. Among prevalent patients, the lower range of the recommended Kidney Disease Quality Outcomes Initiative hemoglobin target (11 to 11.5 g/dl) was associated with a higher death risk compared with the 11.5- to 12-g/dl range. A decrease or increase in hemoglobin over time was associated with higher or lower death risk, respectively, independent of baseline hemoglobin. Administration of any dose of ESA was associated with better survival, whereas among those who received ESA, requiring higher doses were surrogates of higher death risk. In this observational study, greater survival was associated with a baseline hemoglobin between 12 and 13 g/dl, treatment with ESA, and rising hemoglobin. Falling hemoglobin and requiring higher ESA doses were associated with decreased survival. Randomized clinical trials are required to examine these associations.
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中华医学会肾脏病学分会肾性贫血诊断和治疗共识专家组. 肾性贫血诊断与治疗中国专家共识(2018修订版)[J]. 中华肾脏病杂志, 2018, 34(11): 860-866. DOI: 10.3760/cma.j.issn.1001-7097.2018.11.012.
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Hemoglobin levels vary substantially over time in hemodialysis patients, and this variability may portend poor outcomes. For a given patient, hemoglobin concentration over time can be described by absolute levels, rate of change, or by the difference between observed level and expected level based on the preceding trend (i.e., seemingly random variability). We investigated the independent associations of these different methods of describing hemoglobin over time with mortality in a retrospective cohort of 34,963 hemodialysis patients. Hemoglobin concentration over time was modeled with linear regression for each subject, and the model was then used to define the subject's absolute level of hemoglobin (intercept), temporal trend in hemoglobin (slope), and hemoglobin variability (residual standard deviation). Survival analyses indicated that each 1g/dl increase in the residual standard deviation was associated with a 33% increase in rate of death, even after adjusting for multiple covariates. Patient characteristics accounted for very little of the variation in our hemoglobin variability metric (R2 = 0.019). We conclude that greater hemoglobin variability is independently associated with higher mortality.
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Hemoglobin (Hgb) levels fluctuate in patients with end-stage renal disease over time. This study quantified Hgb level variability and the likelihood of falling within the Hgb level goal range of 11 to 12 g/dL. Implications on the percentage of patients exceeding 3-month rolling average Hgb levels of 12, 12.5, and 13 g/dL were determined.Phase I (n = 65,009) tracked patients with Hgb values initially outside the goal range (<11 or >12 g/dL) during 2000. Correlation with facility-specific thresholds also was evaluated. Phase II (n = 48,133) quantified variation in 3-month rolling average Hgb levels in a subset with greater than 10 months of data (mean Hgb, 11.4 +/- 1.3 g/dL).A total of 24,948 patients (38.4%) had Hgb levels between 11 and 12 g/dL. In only 8% did Hgb levels consistently remain less than 11 g/dL, and in 18%, greater than 12 g/dL all year. Twenty-nine percent (18,633 patients) moved from below to above target range or vice versa. Greater mean facility Hgb level correlated with a greater percentage of patients with Hgb levels greater than 10 g/dL (R2 = 0.49) and greater than 12.5 g/dL (R2 = 0.61). For facilities to have 90% or greater of patients with 3-month rolling average Hgb levels greater than 10 g/dL, 13% to 31% of patients will have 3-month rolling average Hgb values greater than 12.5 g/dL. The average individual patient is expected to have a +/-1.4-g/dL fluctuation in 3-month rolling average Hgb levels per year. Despite increased mean Hgb levels and erythropoietin (EPO) and iron use, the spread of the Hgb distribution curve remained unchanged in the last 6 years.Variability caused by laboratory assays, biological factors, and therapeutic response determines patient Hgb level variability. Improving factors that can be manipulated (eg, standardizing EPO and iron algorithms) and adjustment of the target Hgb level range, specifically, by increasing the upper bound, likely will decrease the observed variability and further enhance the quality of anemia management.Copyright 2003 by the National Kidney Foundation, Inc.
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Substantial variability in hemoglobin levels has been associated with increased mortality risk in hemodialysis patients. Variability also has been associated with concurrent comorbid conditions and hospitalization. Adequate adjustment for confounding by disease severity is needed to estimate the association of hemoglobin level variability with mortality risk.Retrospective cohort study.Medicare hemodialysis patients in 3 groups: prevalent on July 1, 2006 (n = 133,246), prevalent on July 1, 1996 (n = 78,602), and incident between January 1, 2005, and June 30, 2006 (n = 24,999).Hemoglobin level variability estimated using the residual deviation around the linear trend in hemoglobin levels during a 6-month entry period.Time to death.We fit Cox models of 1-year mortality with and without adjustment for disease severity (comorbid conditions, hospitalization days, and months with hemoglobin level <10 g/dL), measured concurrently with hemoglobin level variability.Disease severity was associated positively with hemoglobin level variability in all groups. Before adjustment for disease severity, HRs for hemoglobin level variability were 1.27 (95% CI, 1.24-1.31) per 1 g/dL for patients prevalent in 2006, 1.32 (95% CI, 1.27-1.38) for patients prevalent in 1996, and 1.08 (95% CI, 1.03-1.13) for patients incident in 2005-2006. After adjustment, HRs for hemoglobin level variability were 1.02 (95% CI, 0.99-1.05), 1.07 (95% CI, 1.03-1.12), and 1.01 (95% CI, 0.95-1.06), respectively.We did not adjust for time-varying confounding of hemoglobin level; an inclusion requirement introduces potential selection bias; our findings may not apply to incident hemodialysis patients younger than 65 years; assessment of comorbid conditions from claims is subject to misclassification, with possible residual confounding attributable to comorbid conditions; this observational study cannot prove causality.After adjustment for concurrent disease severity, evidence supporting an association between hemoglobin level variability and mortality risk was weak and inconsistent. The clinical utility of hemoglobin level variability may be limited.Copyright © 2011 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.
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Hemoglobin levels in individuals with chronic kidney disease fluctuate frequently above or below the recommended target levels within short periods of time even though the calculated mean hemoglobin remains within the target range of 11 to 12 g/dl. Both pharmacologic features and dosing of erythropoiesis-stimulating agents may lead to cyclic pattern of hemoglobin levels within the recommended range. Several longitudinal studies highlight the complexity of maintaining stable hemoglobin levels over time. As a consequence, patients may risk increased hospitalization and mortality, because both low and high hemoglobin levels are associated with increased cardiovascular events and death. The duration of time that hemoglobin remains higher or lower than the target thresholds may be important to adverse outcomes. It is not clear whether adverse effects of hemoglobin variability are because of the therapy with erythropoiesis-stimulating agents and/or iron or despite such a therapy. Several factors affect hemoglobin variability, including those that are drug related, such as pharmacokinetic parameters, patient-related differences in demographic characteristics, and factors affecting clinical status, as well as clinical practice guidelines, treatment protocols, and reimbursement policies. Strategies that consider each of these factors and reduce hemoglobin variability may be associated with improved clinical outcomes.
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赵朗. TG-G指数、TG-G-BMI指数与冠状动脉粥样硬化病变程度相关性研究[D]. 济南: 山东大学, 2019.
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王东昕, 崔占前, 郭星梅, 等. BMI对行急诊PCI的超急期ST段抬高型心肌梗死患者预后的影响[J]. 天津医科大学学报, 2016, 22(3): 234-237.
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杨宏伟, 张幽幽, 王丽君. 不同BMI的2型糖尿病患者心血管等靶器官功能损伤的风险评估[J]. 重庆医学, 2017, 46(16): 2218-2220, 2223. DOI: 10.3969/j.issn.1671-8348.2017.16.018.
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表1 不同血红蛋白波动组患者基线资料的比较表2 生存患者与死亡患者血红蛋白水平的比较（$\bar{x}±s$）
图1 各血红蛋白波动组以心血管相关死亡为终点事件的生存率比较（Kaplan-Meier生存曲线）图2 各血红蛋白波动组以全因死亡为终点事件的生存率比较（Kaplan-Meier生存曲线）表3 血红蛋白平均波动与心血管相关死亡、全因死亡的关系（Cox回归分析，n=267）表4 血红蛋白平均波动与达标组、未达标组患者心血管相关死亡的关系（Cox回归分析）表5 腹膜透析患者血红蛋白波动的相关因素（线性回归分析，n=267）/
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