applying of risk-adjusted cusum control chart monitoring of medical information zi-hsuan chen...

Applying of risk-adjusted CUSUM control chart monitoring

of medical information

Zi-Hsuan Chen

Advisor: Jing-Er Chiu, Ph.D.

Contents

2

Reason &Purposes

Literature Review

Research Methods

Expected Results

Research Reason

3

Reduce determine error,

and quickly the failure occurs.

Time conditions

Risk adjustment

factor

Patient's heterogeneity

Reason&

PurposesLiterature Review Research Methods Expected Results

Research Purposes

4

Construct risk-adjusted CUSUM control chart.

Calculation of risk-adjusted CUSUM control chart ARL values.

Added time conditions, the early detection of surgical failure.

Reason&


Research Framework

5

Shoulder surgery

Logistic Model

ARLCUSUM

Cox Model

ARL

CUSUM

Time conditions

Reason&


6

Limitations of Research

The data include information from May

2010 to May 2013

Part of simulation data

Biswas ed.(2008)

Reason&


CUSUM Control Chart

Proposed by Page(1954) Small process shifts

Sample

Cum

ulative Sum

28252219161310741

4

3

2

1

0

-1

-2

-3

0

UCL=2.542

LCL=-2.542

CUSUM Chart of C1

Source: Quality Management Shih Hui , Hsu

7

Reason&


Author Result

Steiner et al.(2000)

Since there individual differences between the patients.To find a risk-adjusted weight and standardize the basis of the risk of patients .

Woodall (2006)

The medical risks may come from the physicians surgical skills 、 Anesthesia practices 、 Patient physical condition The risk factor, and so on.

J.S., Yu(2007)

Explore of the heterogeneity patients before and after risk adjustment to the Fall rates.

Applying Of CUSUM Control Chart In Medical

8

Reason&


Logistic Regression

Observed outcome can have only two possible

9

Reason&


𝑙𝑜𝑔 [𝑝𝑡 ]=𝑙𝑜𝑔[ 𝑝1− p ]=𝛽0 +β1𝑥1 𝑡+β2 𝑥2 𝑡+⋯+β𝑘𝑥𝑘𝑡

Success

Failure

Source : Wikipedia

Author Result

Steiner et al.

(2000)

Explore to the interns and experienced doctors of cardiac surgery performance ,using logistic regression backward elimination) which filter out the main factor affecting the cardiac surgery.

Chen Yuzhi et al.(2002)

Logistic regression analysis to explore the inpatient fall over event occurred easily cause injury or complications.

Biswas ed.(2008)

Apply logistic regression analysis to explore the impact of kidney transplant surgeon factor is the age of the recipient, age of the donor, the donor and recipient weight ratio, donor age, disease status.

Applying Of Logistic Regression In Medical

10

Reason&


Cox PH Model

Proposed by Cox(1972)

Author Result

Biswas ed.(2008)

Apply of Cox Model in Kidney transplantation data. The primary outcome of interest was graft failure, including death with a functioning graft during the one-year period post-transplant.Note that in both risk-adjustment models, factors such as recipient age, donor to recipient weight ratio, donor age and presence of other diseases, among several other factors, are highly predictive of transplant failure.

Use survival time

Applying Of Cox Model In Medical

11

Reason&


Use the Logistic Model Method and Cox Model Method to compare the result , which can quickly detect the patient has a shoulder joint shift or death situation.

Research Process

12

Collection and Simulation of patient information

Notation and Assumptions

Calculate and Simulation

CUSUM control chart

Calculate ARL

Comparative analysis

Conclusion

EstablishCox Model

EstablishLogistic Model

Reason&


Object Of Study And Assumptions

Logistic Model

Cox Model

13

𝑙𝑜𝑔 [𝐻𝑅 ( 𝑋 ) ]=𝑙𝑜𝑔[ h (𝑡 ,𝑥 )h0 (𝑡 ) ]=𝛽1𝑥1+𝛽2𝑥2+⋯ 𝛽𝑝 𝑥𝑝

𝑙𝑜𝑔 [𝑝𝑡 ]=𝑙𝑜𝑔[ 𝑝1− p ]=𝛽0 +β1𝑥1 𝑡+β2 𝑥2 𝑡+⋯+β𝑘𝑥𝑘𝑡

Reason&


Logistic Model

Cox Model

Variable Risk factor Explain

Yt Surgery Status

Shoulder surgery status ： Success(0) 、 Failure (1)

Variable Risk factor Explain

YtSurgical

failure point in time

Time of surgery from the start time until relapseUnit ： month

14

Object Of Study And Assumptions

Reason&

PurposesLiterature Review Research Methods Expected ResultsVariable Risk factor Explain

Sex Female (0) and Male (1)

Age 34 – 94 year old to quantify the variables

Artificial bone Devices bone before surgery ： NO(0) 、 YES(1)

The degree of injury Minor (0), Medium (1), More serious(2)

Bone plates at their

own expenseNHI payment ： NO(0) 、 YES(1)

Cancellous bone

drugsEdible medicine ： NO(0) 、 YES(1)

Smoking Smoking behavior ： NO(0) 、 YES(1)

MenopauseFemale patient's menstrual status ：

Normal (0) 、 Menopause (1)

Vegetarian food Non-vegetarian(0) 、 Vegetarian (1)

DisplandAfter surgery the patient‘s degree of deviation ：0mm ~ 19mm (quantify the variables)

Logistic Model in CUSUM

Monitoring probability of Yt

Binary data is transformed into probability

Establish a Logistic Regression Model Find the significant factor

Calculate the weight value

Draw CUSUM control chartSteiner et al.(2000)

15

Reason&


𝑙𝑜𝑔 [𝑝𝑡 ]=𝑙𝑜𝑔[ 𝑝1− p ]=𝛽1𝑥1+𝛽2𝑥2+⋯ 𝛽𝑝 𝑥𝑝

𝑊 𝑡={[ (1−𝑝𝑡+𝑅0𝑝𝑡 )𝑅𝐴

(1−𝑝𝑡+𝑅𝐴𝑝𝑡 )𝑅0 ]𝑖𝑓 𝑦 𝑡=1

[ 1−𝑝𝑡+𝑅0𝑝𝑡

1−𝑝𝑡+𝑅𝐴𝑝𝑡]𝑖𝑓 𝑦𝑡=0

𝑋 𝑡=max [0 ,𝑋 𝑡 −1+𝑊 𝑡 ] ,𝑡=1,2,3….

Cox Model in CUSUM

Monitoring probability of Yt

Yt of time conditions convert the probability of occurrence

Establish a Cox Regression Model Find the significant factor

Calculate the weight value

Draw CUSUM control chart

Biswas ed.(2008)

16

Reason&


log [HR (X ) ]=log [ h (t , x )h0 (t ) ]=β1 x1+β2 x2+⋯ β p x p

𝐺𝑡+𝑑𝑡=max (0 ,𝐺𝑡+𝑑𝑈 𝑡 ) t>0

Simulation ARL steps (Logistic Model )

Simulate number of samples N

calculation the patient t the surgery the probability of

failure

In control Out –of-control ⇒ARL1

Randomly generated patient after surgery results

To draw CUSUM control chart ， and monitoring control

chart

is true

is true

is true is true

17

Steiner et al.(2000)

Reason&


Simulation ARL steps(Cox Model )

W

Simulate number of samples N , added time's condition

In control Out –of-control ⇒ARL1

Randomly generated patient after surgery results

Calculate significant factor and Hazard ratio 。

To draw CUSUM control chart ， and monitoring control

chart

is true

is true

is true is true

18Biswas ed.(2008)

Reason&


In CUSUM control chart, Cox model method can quickly detect the patient has a shoulder joint shift or death situation.

In simulation ARL, use the Cox model to find the failure in a more timely manner.

19

Expected Results

Reason&


Collecting more variables of time, and apply to Cox model analysis of shoulder surgery.

Input data in simulate method, then compare the result between simulation and actual.

20

Future Research

Reason&


- The end -

21

applying of risk-adjusted cusum control chart monitoring of medical information zi-hsuan chen...

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