正常血流动力学参数表

流动力监测各指标及临床意义血流动力学监测的每个参数都有他的临床意义，怎样结合其它参数或临床等等都是我们应该掌握和思考的，而且只有在临床中不断运用、思考才能真正理解这些参数。

本文介绍了直接测量所得指标动脉血压、心率、中心静脉压、右心房压、右心室压、肺动脉压、肺毛细血管嵌顿压、心输出量。

测量指标所派生的指标：心脏排血指数、心脏搏出量、肺血管阻力、心室做功指数和PICCO参数：主要监测指标1～Hg)低于75mmHg2心率(HR)[3 3中心静脉压(CVP) 正常值：0.49～1.18kPa(5～12cmH20)。

体循环血容量改变、右心室射血功能静脉回流障碍均可使CVP发生变化，胸腔、腹腔内压变化亦可影响CVP测定结果。

在无条件测定PC CVP对血容量的估计及输液的监测有一定价值。

一般CVP增高见于右心衰，严重三尖瓣返流，心包CVP低反映血容量不足，但补液时需考虑左心功能（LVEDP）[4]。

4右心房压(RAP) 正常值：0～1.07kPa(0～8mmHg)。

反映循环容量负荷或右心室前负荷变化，比C 准确。

心包积液及右心衰竭时可造成相对性右室前负荷增加，右室流入道狭窄(如三尖瓣狭窄)时右能完全代表右室前负荷。

??超过10mmHg升高，升高见于右心衰竭（包括右心室梗死）、三尖瓣狭窄闭不全，缩窄性心包炎、心包积液、心肌病、肺动脉高压或肺动脉口狭窄。

当血容量不足时，降低5右心室压(RVP) 正常值：收缩压2.00～3.33kPa(15～25mmHg)，舒张压0～1.07kPa(0～8mmHg)。

收缩压>30mmHg压意义同RAP6肺动脉压～14mmHg 压1.33～肺动脉狭窄或瓣下狭窄，7LVEDP。

容量不足时，则降低。

监测的目的在于，给左心室以一个最适宜的前负荷，使之保持在不足以引起的范围内，同时又要根据原理，用足够的前负荷使心肌纤维适当的伸长以达到最大的心排血量。

临心脏病病人，一般以PCWP略高（即14~18mmHg）为正常值，这样可充分发挥Frank—Starling定律机制，维持心输出量的要求，又不至于发生肺郁血。

简明常用血流动力学参数意义对照表1. LSI 左心搏指数2. RSI 右心搏指数3. LCI 左心排指数4. RCI 右心排指数以上四个指数代表心脏的功能指数，其中左心排指数最重要，等同于心脏指数（CI），一般来说，CI<1.5=预后极差；1.5—2.0= 心源性休克；2.0—2.2=前向性心功能不全。

5. CWT 心脏总功率：反映心脏的负荷，一般运动时，功率会增大，如果正常情况总功率偏大，则代表心脏负荷偏大；偏小则视情况而定，有身体强健者，心脏功率不必很大，但器质性偏小，则有可能造成供血不足，头晕眼花等等。

6. LWE 左心室有效功率7. LTPF 左心室总泵力8. LWT 左心室功率9. LEWK 左心室机械效率10. JP 左心室喷血压力：该指数与血压有关，如果该指数偏大，则需要小心高血压了。

11. VP 左心室有效泵力12. EF 喷血分数：非常重要的指标，EF值长期偏小，则有很大可能性是心衰。

13. AWK 动脉机械效率14. EPE 射流压力15. LCRI 左室等容指数16. RCRI 右室等容指数15/16两个参数代表心脏的容血量，其意义不如有效循环容量重要。

17. LVDV 左室舒张末血量18. LVDP 左室舒末期压力19. CR 左室喷血阻抗20. PDM 平均舒张压：高血压的判断指标之一21. PSM 平均收缩压：高血压的判断指标之一22. PPM 平均脉压：高血压的判断指标之一23. MAP 平均动脉压：高血压的判断指标之一24. HR 心率25. CVPS 中心静脉收缩压26. CVPM 中心静脉平均压：非常重要的指标严重升高：1.静脉充盈过量（循环超负荷）2.静脉充血（心脏压塞、PEEP右心衰、左心衰晚期）3.左向右分流，严重二尖瓣狭窄右室收缩力下降4.肺血管壁阻力增高（肺水肿、COPD)严重降低：容量不足、血管过度扩张27. PAWPS 肺毛收缩压28. PAWPM 肺毛平均压（肺毛细血管嵌顿压）：抽烟过量或者感冒或肺部病变可能导致该值不正常。

肝脏是人体内唯一的双重供血器官，正常人肝窦每分钟平均接受来自门静脉和肝动脉的血液为1.5L，其中2/3来自门静脉，1/3来自肝动脉。

肝脏的分流指数是肝动脉血流量或门静脉的血流量占总肝血流量的比值。

当肝组织发生病变时，肝血流各成分的变化与肝内不同病理改变密切相关。

慢性迁延性肝炎肝动脉血流量增加，门静脉血流量正常或轻度降低，这是因为肝动脉充血、门静脉淤血所致。

肝硬化时，由于肝动脉、静脉短路导致肝动脉血流量增加，肝小叶结构破坏，门静脉压升高使血流受阻，导致门静脉血流量降低。

肝硬化晚期，肝内血管大部分闭锁，血流量减少，结果肝动脉和门静脉血流量均降低。

原发性肝癌时，病变部位动脉血供增加，肝血流各成分亦出现相应的变化，结合其它影像检查有助于对肝内病变的鉴别。

许多学者根据门静脉高压症的血液动力学改变选择术式、估计预后，并对术式进行评估和改进，以求增进疗效。

有文献报道饮食对门静脉血流量有明显影响［2］。

由此可见，对肝血流各成分指数的测定有着十分重要的意义。

对人体器官的放射性核素动态显像可获得器官组织功能性改变的信息，并能定量地分析器官组织的动态活动以及组织的生理生化现象，对正确诊断病变组织的性质具有十分重要的意义。

用肝脏的放射性核素动态显像可测定肝脏内部的星状细胞(Kupffer cells)对放射性核素的清除率［4］以及肝动脉与门静脉的分流指数，同时可根据显像对肝内病变的进行鉴别［1］。

目前，分流指数的测定方法如下：用带计算机的γ照相机同时绘出肝、肾、脾以及心脏的时间-放射性曲线。

根据肾、脾以及心脏的时间-放射性曲线的峰值确定肝动脉与门静脉血流相的分界点测定分流指数，在数学处理上采用斜率法、面积法、计数法［1，4，5］。

本文根据肝脏血供的特殊性以及放射性药物在血管中的输运情况，建立了肝血流的动力学模型，用高斯函数对肝的时间-放射性曲线进行拟合，计算出肝动脉和门静脉的分流指数，结合总肝血流量的测定可得出肝动流和门静脉血流量。

PICCO监测关键词：血流动力学监测，参数，运用，PICCOPICCO是英文pulse indicator continuous cardiac output 或Pulse index continuous cardiac output的缩写，即脉波指示剂连续心排血量监测。

PICCO血流动力学监测已经广泛应用于危重症，但是有些医护对它理解得仍不够透彻。

本文较全面的综述了PICCO的操作、护理、优点、缺点以及其参数的意义等等，以指导临床。

一．简介德国Pulsion医疗系统公司将肺热稀释法与动脉脉搏波形(pulse contour)分析技术结合起来制造出PICCO系统,该系统同时具备了CO连续监测功能和容量指标,并可监测血管阻力的变化。

经肺热稀释法测心输出量CO、心脏指数 CI、胸内容量指数ITBI、全舒张末容积指数 GEDI、血管外肺水指数 ELWI 、肺血管通透性指数PVPI。

通过经肺热稀释法对动脉脉搏轮廓法初次校正后，可以连续监测脉搏轮廓心输出量pulse contour cardiac output ,PCCO 、心率HR、每搏输出量 SV 、平均动脉压MAP 、容量反应（每搏输出量变异性SVV，脉搏压力变异性 PPV）、系统性血管阻力指数SVRI 、左心室收缩力指数dPmax。

临床参数及其意义:CI 3.5-5.5L/min/m2 CI低于2.50l/min/m2时可出现心衰，低于1.8l/min/m2并伴有微循环障碍时为心源性休克；ITBI 850-1000ml/ m2:小于低值为前负荷不足,大于高值为前负荷过重.GEDI 680-800ml/ m2:小于低值为前负荷不足,大于高值为前负荷过重.ELWI 3-7ml/kg :大于高值为肺水过多,将出现肺水肿.PVPI 1-3:反映右心室后负荷大小.SVV<=10%, PPV <=10%:反映液体复苏的反应性.SVRI 1200-2000dyn.s.cm-5.m2 : 反映左心室后负荷大小；体循环中小动脉病变，或因神经体液等因素所致的血管收缩与舒张状态，均可影响结果。

血流动力监测各指标及临床意义血流动力学监测的每个参数都有他的临床意义，怎样结合其它参数或临床等等都是我们应该掌握和经常思考的，而且只有在临床中不断运用、思考才能真正理解这些参数。

本文介绍了直接测量所得指标：上肢动脉血压、心率、中心静脉压、右心房压、右心室压、肺动脉压、肺毛细血管嵌顿压、心输出量。

由直接测量指标所派生的指标：心脏排血指数、心脏搏出量、肺血管阻力、心室做功指数和PICCO参数：血管外肺水、胸血容量。

介绍了临床应用于判断左心功能、疾病的鉴别、心功能状态的治疗原则、指导疾病的治疗等。

供大家参考。

1、主要监测指标1.1直接测量所得指标1.1.1上肢动脉血压(AP) 正常值：收缩压12.0～18.7kPa(90～140mmHg)，舒压8.0～12.0kPa(60～90mmHg)。

心排量、全身血管阻力、大动脉壁弹性、循环容量及血液粘度等均可影响动脉血压。

一般用袖带血压计测量。

在休克或体循环直视心脏手术时，应以桡动脉穿刺直接测量为准[1]。

血压是反应心排量水平和保证器官有效灌注的基础，过高时增大左室后负荷和心肌耗氧，过低不能保证重要器官有效灌注。

当MAP低于75mmHg 时，心肌供血曲线变陡下降，因此，MAP75~80mmHg，是保证心肌供血大致正常的最低限度[2]。

对原有高血压病人，合理的MAP应略高于此。

1.1.2心率(HR)正常值：60～100次/min。

反映心泵对代改变、应激反应、容量改变、心功能改变的代偿能力。

心率适当加快有助于心输出量的增加，<50次/min或>160次/min，心输出量会明显下降[3]。

1.1.3中心静脉压(CVP)正常值：0.49～1.18kPa(5～12cmH20)。

体循环血容量改变、右心室射血功能异常或静脉回流障碍均可使CVP发生变化，胸腔、腹腔压变化亦可影响CVP测定结果。

在无条件测定PCWP时，CVP对血容量的估计及输液的监测有一定价值。

一般CVP增高见于右心衰，严重三尖瓣返流，心包填塞。

血流动力学监测 2011.11.30 血流动力学 ← 研究血液在心血管系统中流动的一系列物理学问题，即流量、阻力、压力之间的关系 ← 依据物理学定律，结合生理学和病理生理学的概念，对循环中血液运动的规律进行定量的、动态的、连续的测量分析，用于了解病情、指导治疗 ← 随着对疾病理解的深入和治疗要求的提高，临床上需要更多的参数来精确的反映病情的变化 ← 重症患者的治疗离开了监测会变的盲目；而监测方法离开了对治疗的反馈指导将变得无用 血流动力学监测的常规内容 ← 体循环: 心率、血压、CVP、CO、SVR← 肺循环: PAP、PAWP、PVR← 氧动力学参数: 氧输送(DO2)、氧消耗(VO2) ← 氧代谢参数: 血乳酸、SaO2、SvO2、ScvO2临床血流动力学监测 ← 容量评估及容量反应性 ← 细化体循环血压监测及指导治疗 ← 体循环氧动力学监测 ← 微循环监测 ← 线粒体功能的监测 临床血流动力学监测的核心内容 ← 评价体循环：容量复苏和药物治疗效果 ← 监测、评估微循环：组织灌注与氧代谢状况 容量评估及容量反应性 ← 容量治疗是重症患者治疗的基础措施，通常在临床治疗的最初阶段就已经开始 ← 合理的容量治疗取决于对患者容量状态的评估 ← 容量评估是临床治疗的基石，是血流动力学监测的关键 容量评估的指标 ← 静态前负荷指标压力负荷指标---CVP、PAWP心脏容积负荷指标---RVEDVI、GEDVI、ITBVI← 心肺相互作用相关的动态前负荷指标---SVV、SPV、PPV ← 容量负荷试验← 被动抬腿试验静态压力负荷指标----CVP、PAWP← 根据心室压力-容积曲线，由心腔压力间接反应前负荷← CVP近似右房压，PAWP反映左心舒张末压，是目前最常用的容量评估指标← 但其评估容量的临床价值存在争议心室顺应性正常 心室顺应性下降 心室顺应性增强 静态压力负荷指标----CVP、PAWP ← 压力负荷受到测量、胸腔内压、心率、心肌顺应性等多种因素影响，对前负荷的评估上有局限性← 静态或基础CVP和PAWP难以准确预测容量反应性← 将CVP8 -12mmHg、PAWP12 -15mmHg作为严重感染和感染性休克早期治疗的液体复苏目标，尚缺乏大规模临床试验证实，存在争议← CVP的价值体现在动态的变化和观察中，而不是仅仅某一孤立的数值心脏容积负荷指标 ← RVEDVI、GEDVI、ITBVI在压力变化过程中保持相对独立，不受胸腔内压或腹腔内压变化的影响← 能更准确的反应心脏容量负荷← 临床可通过PiCCO经肺热稀释技术测量得到 多个研究表明RVEDVI、GEDVI、ITBVI数值在正常范围低限时数值越低，液体反应性越好；数值越高，则液体反应性越差；中间数值不能预测液体反应性 心肺相互作用相关的动态前负荷指标← SVV、SPV、PPV是动态前负荷指标← SVV可以通过PiCCO或NICOM技术动态监测获得← 更准确的反应心室SV的变化← 机械通气时动脉压的波形和压力值随吸气、呼气相应发生升高、降低的周期性改变；血容量不足时，这种变化尤为显著← 机械通气时SV的变化幅度大，提示左、右心室均处于心功能曲线的上升支，此时容量反应性好；反之，提示至少一个心室处于心功能曲线的平台支，容量反应性差← 大量研究证实SVV、PPV、SPV预测容量反应的敏感性和特异性均明显优于静态前负荷← SVV、PPV、SPV是目前容量评估的重点 机械通气患者SVV正常值<10-15%SVV、PPV、SPV的临床使用受下列条件限制：容量控制通气潮气量恒定(8 -12ml/kg)窦性心律、无心律失常 对于非机械通气或存在心律失常的患者 如何评估容量？ 容量负荷试验或被动抬腿试验Frank-Starling定律 ← 只有在左、右心室均处于心功能曲线上升支时，增加心脏前负荷才能显著提高心排量，即容量反应性好 ← 心室处于心功能曲线平台支时，即使增加心脏前负荷也难以进一步增加心排量，即容量反应性差，且可导致肺水肿等容量过度的危害 ← A:收缩力正常← B:收缩力增加← C:收缩力下降容量反应性 ← 目前无法评估机体的绝对容量值，主要通过容量治疗后机体反应，间接评估容量的需求← 容量反应性好是容量治疗的基本前提← 根据Frank-Starling定律，容量治疗后CO或SV较前增加≥12-15%，被认为是容量治疗有效容量负荷试验 ← 方法：在30分钟内输入晶体液500-1000ml或胶体液300-500ml，判断容量反应性及耐受性，从而决定是否继续容量治疗← 可通过监测CVP的动态变化，遵循“2-5”法则指导容量负荷试验← 容量负荷试验特点：要求加快输液速度！ 被动抬腿试验(PLRT) ← PLRT相当于自体模拟的容量负荷试验，但受自身神经系统的调节，作用一般维持10分钟左右← 对于前负荷有反应的患者，通常在30-90秒内能见到最大反应，SV增加可达到10% -15%← 如果PLRT能够引起SV增加明显超过10%，那么容量负荷治疗可引起SV增加明显超过15%PLRTPLRT1. 患者位于半坐位（头抬高呈45度）或仰卧位2. 观察T1时间SV的数值3. 同时放平头部和/或升高脚的位置（脚抬高呈45度）4. 等待1分钟5. 观察T2时间SV的数值6. SV%增加>10-15%=前负荷有反应7. SV%增加<10-15%=前负荷无反应8. 必要时可重复上述操作← 可食道心脏超声同步监测PLRT期间主动脉流速的变化来预测容量反应性← PLRT后主动脉流速增加≥10-13%，预测容量治疗有反应，敏感性和特异性均大于80%← 近年PLRT的趋势，儿童可经胸超声获得主动脉流速的变化、成人经外周动脉流速来预测容量反应性 容量挑衅 对于非机械通气或存在心律失常的患儿，没有CVP和超声，如何评估容量反应？ ← 容量负荷试验以20ml/kg晶体液在30分钟内输入← 或PLRT← 评估容量治疗后CO或SV较前是否增加≥12 -15% ← 是，前负荷有反应，可继续容量挑衅← 否，前负荷无反应，停止输液 体循环血压监测 ← MAP=舒张压＋1/3脉压差MAP=CO*SVR=SV*HR*SVR← MAP是2001、2005版EGDT的主要治疗目标← 在NICOM或PiCCO指导下，动态监测SV和SVR有助于指导容量复苏，合理使用正性肌力药物、血管活性药物体循环氧动力学监测 ← 氧供(DO2)← 氧耗(VO2)← 氧债DO2← 氧供是每分钟内转运供应到组织的氧量，由血氧含量和心排量组成 ← 合适的氧供依赖于有效的肺气体交换、血红蛋白水平、足够的血氧饱和度和心排量 DO2------呼吸循环殊途同归← DaO2=[ (CO)×动脉氧含量(CaO2)] ← CO=SV×HR← CaO2=(1.38×Hgb×SaO2)+(0.0031×PaO2)← DaO2=SV×HR×[(1.38×Hgb×SaO2)+(0.0031×PaO2)]VO2← 氧耗是指组织所消耗的氧量，例如系统的气体交换 ← 此参数不能直接测得，可以通过动脉和静脉的氧供差值计算得出 ← VO2=DaO2-DvO2← VO2=CO×(CaO2-CvO2)×10← VO2=CO×Hgb×13.8×(SaO2-SvO2) ← VO2=5×15×13.8×(0.99-0.75)← 正常值=200-250ml O2/minVO2/DO2的关系← 正常情况下氧供大约为氧耗的四倍，所以氧需求量并不依赖于氧供，即曲线上的氧供非依赖区；此时如果氧供减少，细胞可以摄取更多的氧以维持氧耗的正常水平 ← 一旦这种代偿机制被耗竭，氧耗量就开始依赖于氧供，这段曲线被称为氧供依赖区 氧债 ← 当氧供不足以满足机体的需求时，则出现氧债 ← 一旦氧债出现，必须提供额外的氧供以偿还氧的欠缺 ← 氧需＞氧耗＝氧债 ← 影响氧债积蓄的因素： 氧供减少 细胞氧摄取减少 氧需求增加 微循环监测 ← 微循环障碍---严重全身性感染的早期事件 ← 微循环障碍意味着随之而来的细胞氧摄取障碍和微循环窘迫 微循环监测 ← SvO2/ScvO2← 血乳酸← Pcv-aCO2← 侧流暗视野视频显微镜技术(SDF)SvO2← VO2=C(a-v)O2×CO×10← 若SaO2=1.0 SvO2=1-[VO2/(CO×10×CaO2)] ← SvO2与氧供、氧耗有关SvO2与容量复苏← SvO2>65%、ScvO2>70%是2001、2005版EGDT 的治疗目标← 2009年哈佛医学院牵头的急诊医学休克协作组研究结果表明，SvO2<70%或>90%均导致死亡率增加，以SvO2达标或过高作为复苏目标存在片面性← SvO2异常升高提示组织氧利用障碍，此时需要观察微循环功能以及线粒体功能← SaO2↓← SvO2↓← CaO2-CvO2 —← SaO2—← SvO2↓← CaO2-CvO2↑← SaO2↓← SvO2↓← CaO2-CvO2↑← SaO2↑← SvO2—/↑← CaO2-CvO2—← SaO2—← SvO2↑← CaO2-CvO2↓← 肺氧合功能障碍 ← 周围组织循环不良 ← 组织代谢增加 ← 肺氧合功能下降伴心功能不全 (机械通气对循环的抑制) ← 吸氧或MV使肺氧合功能改善 ← 组织氧耗量降低（低温、镇静、肌松） ← 组织摄氧功能下降 （败血症、氰化物中毒、硝普钠应用） ← 肺外分流 不能单纯将高SvO2水平作为容量复苏的目标 乳 酸 ← 血乳酸是评价危重症严重程度及预后的指标← 血乳酸持续升高与APACHEII密切相关，感染性休克血乳酸>4mmol/L，病死率达80%← 目前多采用乳酸清除率和高乳酸(>2mmol/L)时间来作为评估指标 血乳酸水平与组织灌注、细胞缺氧，以及肝脏糖异生能力有关 Pcv-aCO2← 理论上，组织缺氧状态下组织PO2将下降，组织PCO2将升高，但实际并非如此← 内毒素血症组织PO2可能并不低---细胞病性缺氧← 低氧性缺氧时，组织PCO2没有升高；仅在缺血性缺氧时组织PCO2才明显升高← PCO2的上升与组织的灌注不足密切相关Pcv-aCO2← 微循环血流灌注不足即休克存在时，组织PO2将降低，组织PCO2将升高，反映的本质是组织局部DO2减少和缺氧代谢增加← 更高的组织PO2和更低的组织PCO2可能才是休克复苏的理想目标。

Sang-Wook Lee Biomedical Simulation Laboratory,University of Toronto,5King’s College Road Toronto,Toronto,ON M5S3G8Canada;School of Mechanical and AutomotiveEngineering,University of Ulsan,Ulsan680-749,South KoreaLuca AntigaDepartment of Bioengineering, Mario Negri Institute for PharmacologicalResearch,24020Ranica(BG),Italy David A.Steinman1Biomedical Simulation Laboratory,University of Toronto,5King’s College Road Toronto,Toronto,ON M5S3G8Canada e-mail:steinman@mie.utoronto.ca Correlations Among Indicators of Disturbed Flow at the Normal Carotid BifurcationA variety of hemodynamic wall parameters(HWP)has been proposed over the years to quantify hemodynamic disturbances as potential predictors or indicators of vascular wall dysfunction.The aim of this study was to determine whether some of these might,for practical purposes,be considered redundant.Image-based computationalfluid dynamics simulations were carried out for Nϭ50normal carotid bifurcations reconstructed from magnetic resonance imaging.Pairwise Spearman correlation analysis was performed for HWP quantifying wall shear stress magnitudes,spatial and temporal gradients,and harmonic contents.These were based on the spatial distributions of each HWP and,harmonic(DH)parameter were found to depend on how the wall shear stress magnitude was defined in the presence offlow reversals.Many of the proposed HWP were found to provide essentially the same information about disturbedflow at the normal carotid bifurcation.RRT is recommended as a robust single metric of low and oscillating shear. On the other hand,gradient-based HWP may be of limited utility in light of possible redundancies with other HWP,and practical challenges in their measurement.Further investigations are encouraged before thesefindings should be extrapolated to other vas-cular territories.͓DOI:10.1115/1.3127252͔Keywords:wall shear stress,atherosclerosis,hemodynamic wall parameter,carotid bifurcation1IntroductionThere is much evidence suggesting that initiation and progres-sion of atherosclerotic disease is influenced by“disturbedflow”͓1͔.Notwithstanding the imprecise nature of this term͓2͔,variousmetrics have been proposed over the years to quantifyflow dis-turbances.Originally focused on the magnitudes of wall shear stress͑WSS͓͒3,4͔these hemodynamic wall parameters͑HWP͒have since incorporated spatial and temporal gradients of WSS ͓5–8͔and,more recently,the harmonic content of time-varying WSS waveforms͓2,9͔.In a recent computationalfluid dynamics͑CFD͒study of the relationship between geometry and disturbedflow at the carotid bifurcations of young adults͓10͔,we noted that ourfindings were relatively insensitive to the choice of either time-averaged wall shear stress magnitude͑TAWSS͒or oscillatory shear index͑OSI͒as metrics of disturbedflow.This was found to be explained by a strong and significant inverse correlation between these two quan-tities.Such correlations among HWP are not unexpected,as rec-ognized early by Friedman and Deters͓11͔;however,they have been little-investigated in light of the growth in the number and complexity of candidate HWP.With this in mind,the objective of the present study was to use a representative sample of normal carotid bifurcation geometries to comprehensively test for correlations among established and recently-proposed HWP.Especially in the context of large-scalestudies of so-called geometric and hemodynamic risk factors inatherosclerosis,we aimed to determine whether a subset of HWP,or even a single HWP,might serve as a sufficiently robust markerof disturbedflow.2Materials and Methods2.1Computational Fluid Dynamics.N=50anatomically re-alistic carotid bifurcation geometries were digitally reconstructedfrom black blood magnetic resonance imaging͑MRI͒of25osten-sibly healthy young adults,as described previously͓12͔.CFDsimulations were carried out using a well-validated in-housefinite-element-based CFD solver͓13–15͔.Quadratic tetrahedral-element meshes were generated by a commercial mesh generator ͑ICEM-CFD;ANSYS,Berkeley,CA͒using a nominally uniform node spacing of0.2mm,previously shown to be sufficient forresolving wall shear stresses to within10%accuracy͓16͔.Rigidwalls and Newtonian rheology were assumed.Pulsatileflowboundary conditions were prescribed based on representativewaveform shapes and allometrically-scaled inlet and outletflowrates.Further details of the CFD simulations are provided else-where͓10͔.For each tetrahedral element the vector WSS,␶w,was calcu-lated as the projection of the stress tensor onto the element’s sur-face at each node,using the element’s quadratic shape functions. As nodes are connected to multiple elements,contributions to each nodal␶w were averaged together.From these time-varying nodal WSS vectors,a variety of HWP were computed,as summa-rized in Table1,and detailed below.1Corresponding author.Contributed by the Bioengineering Division of ASME for publication in the J OUR-NAL OF B IOMECHANICAL E NGINEERING.Manuscript received August12,2008;finalmanuscript received January1,2009;published online May11,2009.Review con-ducted by Fumihiko Kajiya.Paper presented at the2008Summer Bioengineering Conference͑SBC2008͒,Marco Island,FL,June25–29,2008.2.2Magnitude-Based HWP.Time-averaged wall shear stress was calculated by integrating each nodal WSS magnitude over the cardiac cycle.For each CFD model,the nodal TAWSS were normalized by the fully-developed ͑i.e.,Poiseuille ͒value,based on the model’s imposed cycle-averaged flow rate,the as-sumed viscosity,and the mean diameter ofthe model’s commoncarotid artery ͑CCA ͒at a location three radii upstream of the bifurcation ͑i.e.,CCA3,as defined in Ref.͓10͔͒.Oscillatory shear index,a dimensionless metric of changes in the WSS direction,originally introduced by Ku et al.͓6͔,was malized with respect to its fully-developed value at CCA3.2.3Gradient-Based HWP.Originally proposed by Lei et al.͓19͔,the wall shear stress spatial gradient ͑WSSG ͒may be con-sidered a marker of endothelial cell tension.As shown in Table 1,it is calculated from the WSS gradient tensor components parallel and perpendicular to the time-averaged WSS vector ͑m and n ,respectively ͒.Here the WSS gradients were calculated directly from the velocities of each element,taking advantage of their quadratic shape functions.As with WSS itself,elemental contri-butions to nodal WSSG values were averaged together.Since the WSSG of fully-developed flow in a straight uniform pipe is zero,here it is normalized by the fully-developed TAWSS divided by the mean diameter at CCA3.Subsequently,Longest and Kleinstreuer ͓20͔proposed the WSS angle gradient ͑WSSAG ͒to highlight regions exposed to large changes in WSS direction,irrespective of magnitude.As indicated by the formula in Table 1,this was done by calculating,for each element’s node ͑index j ͒,its direction relative to some reference WSS vector ͑index i ͒,here chosen to be that at the element’s centroid.The WSS temporal gradient ͑WSST ͒,originally suggested by Ojha ͓7͔as a factor in distal anastomotic intimal hyperplasia,is simply the maximum absolute rate of change in WSS magnitude over the cardiac cycle.Here it is normalized to WSST at location CCA3,determined from Womersley’s solution of fully-developed pulsatile flow based on the CCA3diameter,the heart rate,and the imposed flow rate Fourier coefficients.2.4Harmonic-Based HWP.Recently,Himburg and Fried-man ͓2͔suggested the harmonic content of the WSS waveform as a possible metric of disturbed flow,subsequently linking this to the frequency-dependent responses of endothelial cells ͓21͔.Fol-lowing those authors,the time-varying WSS magnitude at each node was Fourier-decomposed,with the dominant harmonic ͑DH ͒simply defined as the harmonic with the highest amplitude.Around the same time,Gelfand et al.͓9͔defined the harmonic index ͑HI ͒as the relative fraction of the harmonic amplitude spec-trum arising from the pulsatile flow components.Table 1Definitions of hemodynamic wall parameters …HWP …2.5Data Analysis.Correlations among the various HWP were evaluated in two ways:locally,whereby spatial distributionsof HWP were compared;and globally,whereby overall“burdens”of disturbedflow were compared.Local correlations were tested byfirst discretizing each model’ssurface into afinite number of contiguous patches,within each ofwhich the respective HWP was averaged͑for the ordinal DH,themedian was used͒.This amounted to mapping each distribution ofHWP onto an objective template plane,fixed with respect to abifurcation-specific coordinate system͓22͔.Remembering thatmodel dimensions have previously been normalized with respectto their respective CCA3radius,patches were nominally0.5unitsin length along the direction of the vessel axis,with12patchesdistributed circumferentially at each axial level.To facilitate pool-ing of patches from all cases,and in light of differences in eachCFD model’s relative length,only those patch locations presentfor all CFD models were included,resulting in288patches permodel.Although absolute patch sizes may have varied betweenand within models,this mapping procedure ensured that all sur-faces were spatially discretized in a consistent way͓22͔.Global comparisons were made by calculating,for each CFDmodel,the fraction of its surface area exposed to“abnormal”val-ues of a particular HWP.As detailed in Ref.͓10͔,“abnormal”wasdefined,for each HWP,as the upper quintile͑for TAWSS,thelower quintile͒of the HWP data pooled from all CFD models,thus providing an objective threshold between normal and abnor-mal.To ensure a consistent spatial extent across all cases,thesurfaces were clipped at planes three andfive maximally inscribedsphere radii along the common and internal carotid arteries ͑CCA3and ICA5,respectively͓10͔͒.Then,for each case,the area of the surface experiencing HWP above͑for TAWSS,below͒thethreshold was calculated.To factor out the influence of vesselsize—for the same shape,a larger vessel will experience a largerarea of disturbedflow—this absolute surface area was divided bythe total͑clipped͒surface area.In this way,for each HWP,eachCFD model was assigned a single value characterizing how muchof its surface was exposed to disturbedflow.For both local and global comparisons,a Spearman rank corre-lation coefficient͑r͒and significance͑p-value͒were computed for each of the28unique pairs of HWP using PRISM version4͑GraphPad Software,San Diego,CA͒.Correlations having p Ͻ0.05were deemed strong for͉r͉Ͼ0.8,weak for͉r͉Ͻ0.5,and moderate in between.Spearman correlation analysis was chosen in part because HWP distributions are unlikely to be normal.Also, in assessing these correlations based on rank,we sought to iden-tify,in the local correlations,whether the sites of extrema for one HWP would be reflected in the sites of extrema for another HWP. Global correlations sought to identify whether the ranking of cases from low to high burden of“disturbedflow,”based on the threshold of a given HWP,would be the same as that obtained based on another HWP.3ResultsAs depicted in Fig.1for a representative case,disturbedflow based on WSS magnitude quantities͑TAWSS,OSI,and RRT͒was concentrated around the outer walls of the bifurcation,consistent with many previous observations.For gradient-based HWP ͑WSSG,WSSAG,and WSST͒elevated values were concentrated around the bifurcation apex and,to a lesser but more variable extent,around the external and internal carotid artery͑ECA and ICA͒branches.Distributions of harmonic HWP͑DH and HI͒were more distinctive:higher DH was concentrated away from the outer walls of the bifurcation,whereas elevated HI reflected the general locations,if not the specific spatial extents,of the magnitude-based HWP.Overall,these observations hint at the correlations among the patched HWP distributions,detailed in Table2and depicted graphically for selected HWP pairs in Fig.2.Strong correlations were seen between TAWSS and both RRT͑r=−0.99͒and WSSG ͑r=0.86͒,albeit for different reasons:regions of elevated RRT correlated well with those experiencing low TAWSS͑r=−0.99͒, whereas elevated WSSG correlated with elevated TAWSS͑r =0.86͒.Moderate inverse correlations were found between TAWSS and both OSI and HI͑r=−0.66and r=−0.72,respec-tively͒,whereas TAWSS was positively correlated with WSST ͑r=0.63͒.Although many of the correlations were weak,all were statistically significant.Of all HWP,only DH was neither stronglynor moderately correlated with any other HWP.It is also worthnoting that the correlations identified by pooling the cases werefairly consistent across the50cases analyzed individually,as evi-denced by the relatively narrow confidence intervals shown in thesame table.According to the global correlations summarized in Table3,TAWSS,OSI,or RRT would rank vessels in similar order fromlow to high burdens of disturbedflow,as indicated by the strongpositive global correlation coefficients.Conversely,if disturbedflow was defined as elevated WSSG,vessels would be ranked inreverse order to this,as indicated by the moderate negative corre-lations with TAWSS,OSI,and RRT.As with the local correla-tions,HI was moderately correlated with the trio of magnitude-based HWP,while DH was only weakly correlated with otherHWP.Overall,corresponding local and global correlations wereof similar strength.A notable exception was OSI versus WSSAG, for which the local correlation was moderate͑r=0.73͒,while the global correlation was weak͑r=0.05͒and not significant.Reasons for this are given in Sec.4.It is worth noting that the above results were found to be rela-tively insensitive to the choice of data analysis method.For ex-ample,the Spearman correlation analysis of the continuous distri-butions of HWP͑i.e.,the CFD nodal values prior to patching͒revealed correlations similar to those obtained after patching.Glo-bal correlations based on a90th percentile threshold for disturbedflow were similar to those reported here using the80th percentilethreshold,afinding consistent with Ref.͓10͔.Finally,in light ofthe concentrations of HWP extrema around the bifurcation region,we repeated the local correlation analysis using patches extendingaxially only halfway along each branch͑i.e.144patches permodel͒to exclude the distal parts of the branches.Again thetrends were the same,although some of the moderate correlationsactually increased in strength,such as TAWSS versus OSI͑from r=−0.66to r=−0.77͒and TAWSS versus WSST͑from r=0.62to r=0.75͒.The detailed results are omitted in the interest of space, and because they do not affect the implications and conclusions discussed below.4Discussion4.1Summary and Implications of Findings.This compre-hensive evaluation of correlations among HWP at the normal ca-rotid bifurcation clearly demonstrates that some of these param-eters may,for practical purposes,be considered redundant.By virtue of their definitions in Table1,correlations among WSS, OSI,and RRT were expected,although not necessarily at the strengths observed.While WSS and OSI were moderately corre-lated,Fig.1suggests the OSI captures apparentflow disturbances at the ECA branch.Notwithstanding whether these are significant in the context of atherosclerosis—plaques do tend to occur at the ICA—our results would suggest that RRT can replace WSS and OSI as a single marker of“low and oscillatory”shear.In fact,as noted earlier,RRT is,by definition,the inverse of the magnitude of the time-averaged WSS vector.This explains its near-perfect correlation with TAWSS,which,remember,is the time-average of the WSS magnitude.In other words,RRT is simply another type of time-averaged WSS,but inverted and with a more tangible connection to the biological mechanisms underlying atherosclero-sis͓18͔.To appreciate the practical implications of replacing TAWSS and OSI with RRT,consider our recent study in which exposure todisturbed flow was found to be significantly predicted by a com-bination of bifurcation area ratio and tortuosity ͓10͔.There,the findings were shown to be independent of the choice of TAWSS or OSI as the metric of disturbed flow.Here,repeating the multiple regressions using RRT above the 80th percentile as the criterion for disturbed flow,we found near-identical—if anything,slightly stronger—coefficients:R adj 2=0.341͑p =0.0001͒,␤tortuosity =−0.498͑p =0.0001͒,and ␤AR1=0.459͑p =0.0007͒.In other words,exposure of the vessels to “disturbed flow”is the same,whether defined by extrema of TAWSS,OSI,or RRT.The strong positive local correlations ͑and consequent strong negative global correlations ͒between TAWSS and its spatial and temporal gradients likely reflect the fact that all of these quantities are highest around the apex of the bifurcation.As pointed out by Ojha ͓7͔,the use of WSS spatial gradients as risk indicators for intimal thickening is questionable in light of their concentration about the bifurcation apex,a region usually spared of plaques.As suggested recently by Goubergrits et al.͓23͔,regions elsewhere experiencing elevated WSSG may represent a consequence of ath-erosclerosis rather than a cause.Either way,for the normal carotid bifurcation at least,our findings would suggest that TAWSS could be used instead of WSSG,which is anyway more susceptible to measurement uncertainty ͓24,25͔,owing to its reliance on spatial gradients.A similar conclusion may be drawn from the moderate correla-tions between TAWSS and WSST,although it is worth remember-ing that in this study all CFD models were exposed to the same waveform shape .Intersubject variations in waveform shape are reported to be on the order of 10%͓26,27͔.Such variations in flow rate dynamics have been found to have a relatively minor influence on variations in the distributions of a variety of HWP,at least relative to the influence of uncertainty in the reconstructed geometry ͓25͔.Thus,it is reasonable to conclude that our findings are robust to our assumptions about waveform shape.It was also observed that the spatial distributions of OSI and WSSAG were moderately correlated ͑r =0.73͒,consistent with previous qualitative observations for carotid bifurcations ͓25͔and coronary arteries ͓23͔.As pointed out by Goubergrits et al.͓23͔,WSSAG may be thought of as an extension of OSI;however,being based on differential versus integrated quantities,WSSAG distributions tend to be noisier and more sensitive to uncertainty,something evident here and also in Ref.͓25͔.Nevertheless,for the representative case presented in Fig.1,this correlation is less obvious.Elevated values of WSSAG do coincide with the periph-ery of those regions exposed to elevated OSI;however,the core region of elevated OSI at the carotid bulb is characterized by low WSSAG and,as with the other gradient-based HWP,elevated WSSAG is concentrated at the bifurcation apex,a regiontypicallyFig.1HWP distributions for a representative case.Except for the ordinal DH,contour levels de-picted in each frame’s legend correspond to the 80th,85th,90th,and 95th percentile values based on the HWP distribution pooled over all cases.Note identification of CCA3and ICA5clip planes in the upper left …TAWSS …panel.spared of atherosclerosis.Moreover,the global correlation of these quantities was much weaker ͑r =0.05͒.This may be ex-plained in reference to the respective scatter plot in Fig.2,which clearly shows that the local ͑i.e.,patchwise ͒Spearman rank cor-relation was biased by the preponderance of patches having low OSI and WSSAG values.Focusing only on those patches having OSI Ͼ0.1,it can be seen that there is no obvious correlation with WSSAG.Because the global correlations focused only on those regions exposed to the upper quintile of HWP values,they better reflect the correlation,or lack thereof,of these extrema.Having said this,it is worth noting that,for most of the other pairwise comparisons,global and local correlation coefficients were in much closer agreement.Of all HWP,only DH was found to be essentially independent of the other HWP.This was somewhat surprising,since Himburg and Friedman ͓2͔,in introducing the use of WSS harmonics as metrics of disturbed flow,reported an inverse correlation between DH and TAWSS ͑Pearson r =−0.62͒.By way of explaining this,we note that their study was carried out on porcine iliac arteries,which are nominally straight vessels experiencing largely axial flows.On the other hand,flow at the carotid bifurcation is decid-edly nonaxial,and likely subject to more reverse flow.In such regions,rectification of the time-varying WSS vector—remember that DH was derived here from the WSS vector magnitude,per its original definition ͓28͔—could alter its harmonic content.To appreciate the impact of this,we recomputed DH and HI using instead the time-varying “axial”WSS,namely,the compo-nent of the instantaneous WSS vector projected onto a unit vector defined by the direction of its time-averaged value.In this way,flow reversals relative to the nominal axial direction are pre-Table 2Spearman rank correlation coefficients for pairwise local comparisons of the pooled HWP distributions …N =14,400patches total ….Shown in brackets are 95%confidence intervals,drawn from pairwise comparisons of the 50datasets individually …N =288patches each ….All correlations significant to p <0.001,except WSSAG versus DH,p =0.03.OSIRRT WSSG WSSAG WSST DH HI TAWSS Ϫ0.66͓Ϫ0.76,Ϫ0.53͔Ϫ0.99͓Ϫ0.99,Ϫ0.98͔0.86͓0.80,0.91͔Ϫ0.29͓Ϫ0.41,Ϫ0.16͔0.63͓0.46,0.79͔Ϫ0.33͓Ϫ0.45,Ϫ0.21͔Ϫ0.72͓Ϫ0.81,Ϫ0.60͔OSI 0.72͓0.59,0.82͔Ϫ0.38͓Ϫ0.62,Ϫ0.16͔0.73͓0.68,0.78͔Ϫ0.27͓Ϫ0.47,Ϫ0.04͔0.06͓Ϫ0.08,0.16͔0.53͓0.39,0.66͔RRT Ϫ0.81͓Ϫ0.88,Ϫ0.72͔0.38͓0.25,0.52͔Ϫ0.57͓Ϫ0.74,Ϫ0.41͔0.30͓0.19,0.43͔0.74͓0.65,0.82͔WSSG 0.09͓Ϫ0.11,0.26͔0.67͓0.52,0.82͔Ϫ0.31͓Ϫ0.47,Ϫ0.14͔Ϫ0.51͓Ϫ0.67,Ϫ0.26͔WSSAG 0.07͓Ϫ0.13,0.24͔Ϫ0.02͓Ϫ0.15,0.10͔0.45͓Ϫ0.32,Ϫ0.55͔WSST Ϫ0.14͓Ϫ0.26,Ϫ0.04͔Ϫ0.08͓Ϫ0.38,0.15͔DH0.27͓0.14,0.41͔Fig.2Scatter plots for selected pairwise comparisons of HWP .Note that the local …patched …data are plotted using a log-log scale to better depict the full dynamic range of data.served.As can be seen by comparing Fig.3to Fig.1,this had a marked effect on the spatial distributions of DH and HI.The rea-son for this is also given in Fig.3:rectification of the instanta-neous WSS served to break up the clear fifth harmonic oscillation of the time-varying axial WSS in favor of the lower frequencies.As summarized in Table 4,this served to strengthen the correla-tions between the harmonic and other HWP.Of note is the local correlation coefficient for TAWSS versus DH ͑r =−0.69͒,now close to that originally reported by Himburg and Friedman.Whether DH and HI should be defined based on magnitude or axial WSS cannot be answered by the present study.It is also not clear whether DH should be considered a monotonic HWP in light of possible limits to the temporal response of endothelial cells to shear ͓21͔.Nevertheless,our findings do bring to attention a heretofore-underappreciated issue in the harmonic analysis of WSS in the presence of strongly nonaxial flow.4.2Potential Limitations.This study has made the custom-ary assumptions of rigid walls,Newtonian rheology,and fully-developed inlet boundary conditions,previously shown to be of relatively minor influence on the distribution of WSS ͓16,29,30͔.The use of allometrically-scaled flow rates was recently shown to have little impact on the relative burdens of disturbed flow among the 50cases considered here ͓10͔;however,as noted earlier,theassumption of a constant waveform shape might have served to underestimate variations in WSST,as well as the harmonic HWP.An obvious limitation of this study is the relatively narrow scope of vascular configurations considered,namely,normal ca-rotid bifurcations.We do note,however,that recent work from Goubergrits et al.͓23͔similarly reported possible redundancies among gradient and magnitude-based HWP for the case of a nor-mal coronary artery.Huo et al.͓31͔also noted a significant power-law relationship between TAWSS and OSI on the outer walls of the common carotid and celiac arteries,based on a CFD model of flow along the length of the mouse aorta.While a power-law relationship is expected based on the definition of OSI,those au-thors did note a difference in the power-law coefficients derived from the carotid and celiac sites,an observation consistent with the branch-specific clustering of data points in the OSI versus TAWSS scatter plot in Fig.2.Those authors also noted a corre-spondence between elevated TAWSS and elevated WSSG,al-though no quantitative relationship was found.Nevertheless,we encourage further investigation before our findings should be ex-trapolated to other vascular territories.It is also important to appreciate that our study has made no attempt to prioritize any of these HWP in terms of their purportedTable 3Spearman rank correlation coefficients for each pair-wise global comparison of the relative surface area exposed to the HWP beyond its 80th percentile value …N =50cases ….OSIRRT WSSG WSSAG WSST DH HI TAWSS 0.80a0.97aϪ0.63a Ϫ0.16Ϫ0.250.040.68a OSI 0.88aϪ0.50a 0.05Ϫ0.260.040.66a RRT Ϫ0.63aϪ0.10Ϫ0.250.020.74a WSSG 0.58a0.54a Ϫ0.33b Ϫ0.27WSSAG 0.43cϪ0.240.20WSST Ϫ0.20Ϫ0.04DH0.01ap Ͻ0.05.bp Ͻ0.01.cp Ͻ0.001.Fig.3Distributions of DH and HI based on the axial WSS component rather than WSS magnitude,shown for same case depicted in Fig.1.The arrows indicate the site of the time-varying WSS waveforms and corresponding spectra shown to the right.Table 4Spearman rank correlation coefficients for local and global comparisons of DH and HI,as derived from the time-varying axial WSS rather than the WSS magnitude.Local GlobalDHHI DH HI TAWSS Ϫ0.69a Ϫ0.77a 0.76a 0.83a OSI 0.55a 0.65a 0.78a 0.89a RRT 0.70a 0.81a 0.78a 0.89a WSSG Ϫ0.58a Ϫ0.54a Ϫ0.54b Ϫ0.46a WSSAG 0.31a 0.51a Ϫ0.180.12WSST Ϫ0.36aϪ0.14a Ϫ0.34cϪ0.15DH0.63a0.64aap Ͻ0.05.bp Ͻ0.01.cp Ͻ0.001.links to the underlying biological mechanisms.Thus,some of the HWP we regard here as redundant might be shown to have closer mechanistic links in studies of individual biological responses to the local hemodynamic environment.Rather,we suggest that our findings are most applicable to large-scale studies of hemody-namic factors in atherosclerosis,which are more concerned with quantifying overall burdens or identifying patterns of localization of disturbedflow,whatever this vague term may prove to mean precisely.5ConclusionsFor the normal carotid bifurcation at least,many of the pur-ported indicators of disturbedflow are significantly correlated. Based on thesefindings we recommend the use of relative resi-dence time͑RRT͒as a robust single metric of low and oscillatory shear.In light of possible redundancies,any perceived benefits of gradient-based HWP are likely outweighed by practical challenges with their measurement.Dominant harmonic͑DH͒is a promising new HWP,but issues related to its definition in nonaxialflow need to be resolved.AcknowledgmentThe authors thank Dr.Mort Friedman for helpful discussions. The authors also thank the anonymous reviewers for their valu-able suggestions.DAS acknowledges the support of Grant No. MOP-62934from the Canadian Institutes of Health Research. SWL and DAS were supported by,respectively,a postdoctoral fellowship and a career investigator award from the Heart and Stroke Foundation.References͓1͔Chien,S.,2008,“Effects of Disturbed Flow on Endothelial Cells,”Ann.Biomed.Eng.,36͑4͒,pp.554–562.͓2͔Himburg,H.A.,and Friedman,M.H.,2006,“Correspondence of Low Mean Shear and High Harmonic Content in the Porcine Iliac Arteries,”ASME J.Biomech.Eng.,128͑6͒,pp.852–856.͓3͔Caro,C.G.,Fitz-Gerald,J.M.,and Schroter,R.C.,1971,“Atheroma and Arterial Wall Shear.Observation,Correlation and Proposal of a Shear Depen-dent Mass Transfer Mechanism for Atherogenesis,”Proc.R.Soc.London,Ser.B,177͑1046͒,pp.109–159.͓4͔Fry,D.L.,1968,“Acute Vascular Endothelial Changes Associated With In-creased Blood Velocity Gradients,”Circ.Res.,22͑2͒,pp.165–197.͓5͔Friedman,M.H.,and Ehrlich,L.W.,1975,“Effect of Spatial Variations in Shear on Diffusion at the Wall of an Arterial Branch,”Circ.Res.,37͑4͒,pp.446–454.͓6͔Ku,D.N.,Giddens,D.P.,Zarins,C.K.,and Glagov,S.,1985,“Pulsatile 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血流动力学监测参数PAdP：肺动脉舒张压*CVP与BP、血容量关系：1、CVP低，BP低，表示血容量严重不足，需大量补液。

2、CVP低，BP正常，表示血容量不足，需适当补液。

3、CVP正常，BP低，表示血容量不足或心功能不全，可进行补液试验：5～10分钟内静脉滴入等渗盐水250ml→①中心静脉压不变，血压升高，提示血容量不足，根据情况适当补液；②中心静脉压升高3～5mmHg，血压不变，提示心功能不全，根据情况给予强心药物治疗。

4、CVP高，血压低，表示心功能不全或血容量相对过多，需给予强心药物，纠正酸中毒，舒张血管治疗。

5、CVP高，血压正常，表示容量血管过度收缩，需舒张血管治疗。

CVP指导扩容的“5－2法则”：低血容量病人应连续监测CVP，当CVP<8cmH2O，10分钟内输液200ml；CVP为8～13cmH2O 时输液100ml；CVP>14cmH2O时输液50ml。

输液期间观察CVP的变化：若CVP升高5 cmH2O，应停止输液；当CVP升高2～5 cmH2O 时，可暂停输液10分钟，再观察CVP变化，这时CVP仍升高2 cmH2O以上则应停止输液；若CVP升高不超过2cmH2O，按上述标准输液，直到CVP升高超过5 cmH2O，或暂停10分钟后仍升高2 cmH2O以上为止。

@PCWP指导输液的“7－3”法则：危重病人或合并心脏病者应监测PCWP。

当PCWP<10mmHg，10分钟内输液200ml；PCWP 为11～18 mmHg时输液100ml；PCWP>18mmHg时输液50ml。

输液期间观察PCWP的变化：若PCWP升高7mmHg，应停止输液；当PCWP升高3～7mmHg 时，可暂停输液10分钟，再观察PCWP变化，这时PCWP仍升高3mmHg以上则应停止输液；若PCWP升高不超过3mmHg，按上述标准输液，直到PCWP升高超过7mmHg，或暂停10分钟后仍升高3mmHg以上为止。

血流动力学监测一、血流动力学的基础理论二、有创肺动脉压监测三、有创动脉血压监测四、中心静脉压监测五、脉波指示剂连续心排血量监测六、心阻抗血流图七、超声多普勒技术八、肺水测定血流动力学（hemodynamics）是血液在循环系统中运动的物理学，通过对作用力、流量和容积三方面因素的分析，观察并研究血液在循环系统中的运动情况。

血流动力学监测（hemodynamics monitoring）是指依据物理学的定律，结合生理和病理生理学概念，对循环系统中血液运动的规律性进行定量地、动态地、连续地测量和分析，并将这些数据反馈性用于对病情发展的了解和对临床治疗的指导。

血流动力学监测应用于临床已经有数十年的历史。

可以说，从根据血压来了解循环系统的功能变化就已经开始了应用血流动力学的原理对病情的变化进行监测。

随着医学的发展，临床治疗水平的提高，危重患者的存活时间也逐渐延长。

对于这些危重患者的临床评估，越来越需要定量的、可在短时间内重复的监测方法。

1929年，一位名叫Forssman的住院医师对着镜子经自己的左肘前静脉插入导管，测量右心房压力。

之后，右心导管的技术逐步发展。

临床上开展了中心静脉压力及心内压力的测定和“中心静脉血氧饱和度”的测定。

应用Fick法测量心输出量也从实验室走向临床。

在血流动力学的发展史上具有里程碑意义的是应用热稀释法测量心输出量的肺动脉漂浮导管（Swan-Ganz catheter）的出现，从而使得血流动力学指标更加系统化和具有对治疗的反馈指导性。

近年来，血流动力学监测方法正在向无创性监测发展。

虽然，目前绝大多数无创性血流动力学监测方法尚欠成熟，但随着这些方法的准确性和可重复性的增强，无创性的监测正在被越来越多的临床工作者所接受。

心脏超声检查可以越来越准确地反映心室功能的变化，并可提供动态的监测性参数，在很大程度上弥补了应用肺动脉飘浮导管在容积监测方面上的不足。

血流动力学监测是危重病学医师实施临床工作的一项重要内容。

血流动力监测各指标及临床意义血流动力学监测的每个参数都有他的临床意义，怎样结合其它参数或临床等等都是我们应该掌握和经常思考的，而且只有在临床中不断运用、思考才能真正理解这些参数。

本文介绍了直接测量所得指标：上肢动脉血压、心率、中心静脉压、右心房压、右心室压、肺动脉压、肺毛细血管嵌顿压、心输出量。

由直接测量指标所派生的指标：心脏排血指数、心脏搏出量、肺血管阻力、心室做功指数和PICCO参数：血管外肺水、胸内血容量。

介绍了临床使用于判断左心功能、疾病的鉴别、心功能状态的治疗原则、指导疾病的治疗等。

供大家参考。

1、主要监测指标1.1直接测量所得指标1.1.1上肢动脉血压(AP) 正常值：收缩压12.0～18.7kPa(90～140mmHg)，舒张压8.0～12.0kPa(60～90mmHg)。

心排量、全身血管阻力、大动脉壁弹性、循环容量及血液粘度等均可影响动脉血压。

一般用袖带血压计测量。

在休克或体循环直视心脏手术时，应以桡动脉穿刺直接测量为准[1]。

血压是反应心排量水平和保证器官有效灌注的基础，过高时增大左室后负荷和心肌耗氧，过低不能保证重要器官有效灌注。

当MAP低于75mmHg 时，心肌供血曲线变陡下降，因此，MAP75~80mmHg，是保证心肌供血大致正常的最低限度[2]。

对原有高血压病人，合理的MAP应略高于此。

1.1.2心率(HR)正常值：60～100次/min。

反映心泵对代谢改变、应激反应、容量改变、心功能改变的代偿能力。

心率适当加快有助于心输出量的增加，<50次/min或>160次/min，心输出量会明显下降[3]。

1.1.3中心静脉压(CVP)正常值：0.49～1.18kPa(5～12cmH20)。

体循环血容量改变、右心室射血功能异常或静脉回流障碍均可使CVP发生变化，胸腔、腹腔内压变化亦可影响CVP测定结果。

在无条件测定PCWP时，CVP对血容量的估计及输液的监测有一定价值。