pride系列文献解读之二十四

Importance

of

Biomarkers

for

Long-Term

Mortality

Prediction

in

AcuteDyspneic

Patient生物标志物对急性呼吸困难患者长期死亡率预测的重要性儿童保健科刘萍2018年9月PRIDE系列文献解读之二十四Clinical

Chemistry2010年影响因子3.406ABSTRACTABSTRACTABSTRACT-bacgroundAlthough

numerous

biomarkers

may

be

prognostically

meaningfulin

patients

with

acute

dyspnea,

few

comparative

analyses

haveaddressed

possible

associations

between

a

wide

range

of

candidatebiomarkers

and

clinical

variables.尽管有大量的生物标志物可能对呼吸困难患者的预后有意义，但很少有比较分析处理大量候选生物标志物与临床变量之间的可能联系。ABSTRACT-methods·Vitalstatuswasobtainedfor517acutelydyspneic

patients

at

4

years

after

emergency

departmentpresentation.

A

wide

array

of

biomarkers

was

measured

in

this

cohort,

including

natriureticpeptides,

necrosis

markers,

inflammatory

markers,

hematologic

markers,

and

renal

markers.·We

performed

statistical

evaluation

by

using

minimization

of

the

Bayesian

information

criterionto

evaluate

predictors

of

4-yearmortality.·Cox

proportional

hazards

analysis

wasused

to

confirm

results

from

the

Bayesian

informationcriterion.

A

final

risk

model

wasderived,

and

this

modelwas

then

validated

by

applying

it

topatients

from

a

separate

cohort

of

acutely

dyspneic

patients.·对517例急性呼吸困难的患者的预后进行分析，检测了大量的生物标志物，如心肌坏死标志物、炎性标志物、血液标志物和肾标志物。·利用最小化贝叶斯信息准则进行统计分析4年的死亡率预测因子。·利用COX比例风险模型验证了贝叶斯信息的正确性。ABSTRACT-result·By

4

years,

there

were

186

deaths

(36%).

In

addition

to

several

clinical

variables,several

biomarkers

were

significant

predictors

of

death,

including

log

transformedconcentrations

of

hemoglobin(HR=0.77;

P＜0.001),

soluble

ST2

(HR=1.38;

P

＜0.001),

and

amino-terminal

pro-B–type

natriuretic

peptide

(HR=1.19;P

＜

0.001).·Risk

models

that

used

these

significant

variables

were

accurate

in

predicting

4-year

mortality

in

both

the

training

and

validation

sets.·

4年内，有186(36%)人死亡，除了一些临床变量，生物标志物如Hb(HR=0.77,P＜0.01)，可溶性ST2(HR=1.38,P＜0.01),NT-proBNP(HR=1.19,P＜0.01)是重要的预测因子。·无论是在建模队列或是在验证队列，利用这些重要的变量用来预测4年死亡率的风险模型时精确的。ABSTRACT-conclusion·When

added

to

traditional

clinical

variables,

selected

biomarkers

addedsignificant

value

for

long-term

prognostication

in

acute

dyspnea.·当临床变量选择性的加入生物标志物后，对急性呼吸困难的长期预后能显著增加其预测价值。前言方法方法-统计学方法结果心肌梗死血管紧张素转换酶抑制剂螺内酯结果肺部啰音下肢水肿房颤X线肺水肿BUNCrCCrRBC压积

Hb失代偿结果·变量的筛选：选择BIC值按顺序最小值的计算方法筛选出来的变量依次是:NT-proBNP,sST2,age,

hemoglobin,prevalent

spironolactone

use,

andtobaccouse(Table

2).·其中没有被筛选出来的变量有leftventricular

ejection

fraction,

diagnosis

ofADHF,or

measures

of

renal

function.结果·预测死亡率的候选变量随后建立COX比例风险模型如Table

3·并验证了所选变量的统计重要性。结果Supplemental

Table

1:

Method

for

calculating

PRIDE

risk

scoreVariableTo

CalculatePointsPointsLn(NT-proBNP)Multiply

by

5=

pointsLn(ST2)Multiply

by

10=

pointsAge1

year

=

1

point=

pointsHemoglobinMultiply

by

(-4)=

pointsNever

Smoker0points=

pointsFormerSmoker6points=

pointsCurrentSmoker20points=

pointsSpironolactone

use

prior

topresentation28points=

pointsSum

total=

points·从原始变量中计算风险得分的方法如补充数据表1所示结果Supplemental

Table

2:

Distribution

of

PRIDE

Risk

Scores

in

derivation

and

validationcohortsPRIDELinzPRIDE

Risk

ScoreAll(N=561)All(N=251)Mean

(SD)37.9

(37.8)50.8

(33.4)Median

(IQR)40.6(9.2,64.9)51.8(30.0,74.5)Range-61.1

-

144.7-39.1

-

139.0SD

denotes:

standard

deviation;IQR

denotes:

interquartile

range·我们的预测模型包括了所有的有意义的变量，由于有些因素与生存率呈负相关，因此得分范围从-61.1到144.7

(see

online

Supplemental

Table2),

且Q1的得分均值为-16.4(对照组).·在验证队列得分为-39.1到139.0.Q1的得分均值为-13.9(对照组).结果Supplemental

Table

3:

Four-year

mortalityrates

byquintiles

of

risk

(using

PRIDEcutpoints)

in

PRIDE

and

Linz

cohorts4-Year

All-Cause

MortalityRisk

QuintileRange

of

PointsPRIDE

(N=561)Linz

(N=251)1≤2.21.8%19.1%22.3

to

29.012.5%20.0%329.1

to

50.826.8%39.3%450.9

to

69.944.6%58.5%5≥70.077.7%80.3%·用五分位数来描述4年死亡预测风险模型的性能(seeonline

Supplemental

Tables

3

and4).结果·ROC曲线分析的曲线下面积0.84(P<0.001)在整个呼吸困难的群体。·

四年死亡率持续增加，五分之一分数增加。每个五分之一的HR和95%CI

可以说明风险预测的连续性(Table

4).结果·最高的五分之一人群(Q5)的4年粗死亡率超过75%·而最低的五分之一人群(Q1)的死亡率低于5%。·高五分之一人群相关的风险出现得较早，并且从随访开始持续了4年(图1)。结果·与建模相似，验证队列中观察到的死亡率随着五分位数的增加而增加，在Q5中死亡率最高，在Q1中死亡率明显降低

(seeonlineSupplemental

Fig.

2).讨论讨论结论We

have

shown

that

with

the

use

of

clinical

and

biochemical

variables,

patients

atlower

vs

higher

short

term,

intermediate,

and

longer-term

risk

maybe

identifiedwith

relative

ease.

Although

adding

extra

biomarkers

to

the

conventional

onescurrently

in

use(such

as

natriuretic

peptides)may

increase

the

cost,

the

enhancedknowledge

regarding

risk

may

be

incrementally

useful

for

triaging

as

well

as

fortherapeutic

decision-making.我们已经证明，随着临床和生物标志物的使用，患者的风险可以相对容易确定，虽然在目前使用常规的生物标志物（如BNP）的基础上添加额外的生物标志物可能会增加成本，但关于风险知识的增强对疾病分类（诊断）和治疗决策非常有用。结论In

light

of

emerging