Project presentation

class: center, middle, inverse, title-slide

# Project presentation
## 探討ICU住院老人衰弱狀態之趨勢
### 蘇睿寧
### 2022-01-02

---

## Overviews
###- 背景描述及資料收集
###- 資料整理
###- 表格製作
###- 圖片製作
---

## 背景描述及資料收集
<img src="ncku1.png" width="55%" style="display: block; margin: auto;" />

---

## 重要性及動機
###-衰弱(Frailty)是預測老年人預後重要的多面向衡量指標
###-To investigate the impact of frailty on the risk of  mortality of elderly patients in medical ICUs.
###- To investigate the trajectory of frailty of elderly patients in medical ICUs.

---
## 收案條件
###-Convenient sampling
###-Inclusion criteria
* **Age ≥ 65 with ICU admission**

###- Exclusion criteria
* **The expectation of  ICU stay or survival was less than 24 hours**
* **Had previously been enrolled**
* **Those whose surrogate was unable to provide baseline information about frailty**

---
## 資料收集內容
### **- Data collection by using questionnaires** 
###- Questionnaires for demographic data
###- Clinical Frailty Scale, CFS
###- Acute Physiology and Chronic Health Evaluation Score II, APACHE II score

---
## 重要興趣議題---衰弱

* **- Ranking frailty phenotype into 9 scores,  1 to 9, higher score means more frailer**
* **- Defined CFS> 4 as frail**

---
## 結果
###- 共收案106人
###- 追蹤至出院後1個月
<img src="datacollect.png" width="85%" style="display: block; margin: auto;" />

</style>

---
## Packages

```r
library(tidyr)
library(dplyr)
library(tidyverse)
library(memisc)
library(lattice)
library(ggplot2)
library(naniar)
library(gmodels)
library(gtsummary)
```

---
## 資料整理
<img src="ncku2.png" width="55%" style="display: block; margin: auto;" />

---
## 資料載入及變項了解

```r
dta<-read.csv("D:\\frail coding1.csv", head=T,fileEncoding = "UTF-8-BOM")   
dim(dta) 
```

```
## [1] 106  34
```

```r
names(dta) 
```

```
##  [1] "id"                "chart_number"      "Admission_date"   
##  [4] "ICU_in_date"       "ICU_out_date"      "DC_date"          
##  [7] "death_status"      "birthday"          "age"              
## [10] "sex"               "education"         "marriage"         
## [13] "Living_situation"  "HTN"               "DM"               
## [16] "Heart"             "Respiratory"       "stroke"           
## [19] "Dementia"          "Renal_failure"     "Cirrhosis"        
## [22] "Cancer"            "comorbidity"       "drug_number"      
## [25] "Fall"              "MDSADL_score"      "MUST_score"       
## [28] "APACHEII_score_in" "CFS_bf_ICU"        "CFS_in_ICU"       
## [31] "ICU_day"           "CFS_DC"            "hospital_day"     
## [34] "CFS_af_DC_1m"
```

---

## 資料檢查

```r
dta<- dta[-c(2)]
DT::datatable(dta, rownames=FALSE, options=list(scrollY = 300, pageLength=10), fillContainer =TRUE)
```

<div id="htmlwidget-931637180f07f1136539" style="width:100%;height:auto;" class="datatables html-widget"></div>
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<\/thead>\n<\/table>","options":{"scrollY":300,"pageLength":10,"columnDefs":[{"className":"dt-right","targets":[0,5,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,31]}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
---

## 資料結構檢視

```r
str(dta)  
```

### CFS_DC和CSF_af_DC_1m判定為character

---

## 發現4個時間點的CFS資料遺漏值Coding方法不同

.pull-left[

```r
table(dta$CFS_bf_ICU)
```

```
## 
##  1  2  3  4  5  6  7 
##  4  5 29 11 14 19 24
```

```r
table(dta$CFS_in_ICU)
```

```
## 
##  6  7  8  9 
## 11 92  2  1
```
]

.pull-right[

```r
table(dta$CFS_DC) 
```

```
## 
##    3    5    6    7    8    9 9999    N 
##    1   11   46   31    1    2    1   13
```

]

```r
table(dta$CFS_af_DC_1m ) 
```

```
## 
##         2    3    4    5    6    7    8    9 9999    M    N 
##    9    3    4    3    7   40   23    1    2    1    1   12
```

---
## 用{naniar}替代奇怪的遺漏值

```r
library(naniar) 
```

```r
dta_1 <- replace_with_na_all( dta,  
                         condition = ~.x %in% c("N", "9999", "M", "") )
```

.pull-left[

```r
table(dta_1$CFS_bf_ICU)
```

```
## 
##  1  2  3  4  5  6  7 
##  4  5 29 11 14 19 24
```

```r
table(dta_1$CFS_in_ICU)
```

```
## 
##  6  7  8  9 
## 11 92  2  1
```

]
.pull-right[

```r
table(dta_1$CFS_DC)
```

```
## 
##  3  5  6  7  8  9 
##  1 11 46 31  1  2
```

```r
table(dta_1$CFS_af_DC_1m )
```

```
## 
##  2  3  4  5  6  7  8  9 
##  3  4  3  7 40 23  1  2
```
]
---

```r
dta_1$CFS_DC<-as.numeric(as.character(dta_1$CFS_DC))
dta_1$CFS_af_DC_1m<-as.numeric(as.character(dta_1$CFS_af_DC_1m))
str(dta_1)
```

---
## 表格製作 
<img src="ncku3.png" width="55%" style="display: block; margin: auto;" />

---

## 基本資料表(衰弱、死亡與否、婚姻、居住狀況分組)

.pull-left[

```r
table(dta_1$death_status)
```

```
## 
##  0  1  2  3  5 
## 84  7  7  6  2
```

```r
table(dta_1$marriage)
```

```
## 
##  0  1  3  4 
## 61  3  5 37
```

```r
table(dta_1$Living_situation)
```

```
## 
##  0  1  2  3  4  5  6 
##  4 21 44  6 11 18  2
```

]

.pull-right[

```r
dta_1 =mutate(dta_1, frail= 
  cases("frail" = (CFS_bf_ICU>4),
        "non-frail" = (CFS_bf_ICU<=4 ) ))

dta_1 = mutate(dta_1, death= 
  cases("death" = (death_status>=1),
        "survive" = (death_status<1 ) ))

dta_1 = mutate(dta_1, live= 
  cases("Live alone" = (Living_situation<1), 
        "Not live alone" = (Living_situation>=1)))

dta_1 = mutate(dta_1, marry= 
  cases("Married" = (marriage<1), 
        "Widowed or Unmarried" = (marriage>=1)))

dta_1 = mutate(dta_1, gender= 
  cases("Male" = (sex>=1), 
        "Female" = (sex<1)))
```
]

---
## 依據住院前CFS分組
###- CFS>4為frail group
###- CFS<=4為 non-frail group
.pull-left[

```r
library(gmodels)
#以資料做一個交叉表，包括個數和佔比
with(dta_1, CrossTable(frail))
```

]

.pull-right[

```r
library(gtsummary)
Rowtable<-dta_1 |>
  select(c(frail)) |>
  tbl_summary()
Rowtable
```

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  <thead class="gt_col_headings">
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      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1"><strong>Characteristic</strong></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>N = 106</strong><sup class="gt_footnote_marks">1</sup></th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr><td class="gt_row gt_left">frail</td>
<td class="gt_row gt_center"></td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">frail</td>
<td class="gt_row gt_center">57 (54%)</td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">non-frail</td>
<td class="gt_row gt_center">49 (46%)</td></tr>
  </tbody>
  
  <tfoot>
    <tr class="gt_footnotes">
      <td colspan="2">
        <p class="gt_footnote">
          <sup class="gt_footnote_marks">
            <em>1</em>
          </sup>
           
          n (%)
          <br />
        </p>
      </td>
    </tr>
  </tfoot>
</table>
</div>
]

---
## 比較兩種表格 (mutate with cases...)

.pull-left[

```r
Rowtable<-dta_1 |>
  select(c(frail,age, sex, Living_situation, marriage)) |>
  tbl_summary(by=frail, missing = "no",
              type = Living_situation ~"categorical",marriage ~ "categorical")
Rowtable
```

<img src="tb1.png" width="60%" style="display: block; margin: auto;" />
]
.pull-right[

```r
Rowtable<-dta_1 |>
  select(c(frail,age, gender, live, marry)) |>
  tbl_summary(by=frail, missing = "no",
              type = live ~ "categorical", marry ~ "categorical")
Rowtable
```

]

---
## 基本資料表格
.pull-left[

```r
Table1<-dta_1 |>
  select(c(frail, age, comorbidity, drug_number, MDSADL_score, APACHEII_score_in, gender, marry, live)) |>
  tbl_summary(by=frail,
    type = list(gender ~ "categorical",marry ~ "categorical", live ~ "categorical"),
    label = list(age ~ "Age",comorbidity ~ "num of Dz",
                 drug_number ~ "num of medication",
                 MDSADL_score ~ "MDSADL", 
                 APACHEII_score_in ~ "APACHE II", 
                 gender ~ "Gender", 
                 marry ~ "Marriage", 
                 live ~"Livine status"),
    statistic = list(all_continuous() ~ "{mean} ({sd})",
                     all_categorical() ~ "{n} ({p}%)"),
    digits = list(all_continuous() ~ c(1, 2),
                  all_categorical() ~ c(0, 2)),
    missing = "no",percent = "row",
    include = everything())|>
  add_p(test = all_continuous() ~ "t.test") |>
  add_n()
#Default is "chisq.test" when expected cell counts >=5 and "fisher.test" when expected cell counts <5
Table1
```

]

.pull-right[
<img src="tb3.png" width="90%" style="display: block; margin: auto;" />
]
---
## 更精緻的表格

.pull-left[

```r
Table1%>% 
  modify_header(label ~ "**Variable**") %>%
  modify_spanning_header(c("stat_1", "stat_2") ~ "**Initial Frailty Status**") %>%
  modify_footnote(
    all_stat_cols() ~ "Mean (SD) or n (%)"
  ) %>%
  modify_caption("**Table 1. Patient Characteristics**") %>%
  bold_labels()
```
]

.pull-right[
<img src="tb4.png" width="80%" style="display: block; margin: auto;" />
]
---
## Logistic regression of mortality
### 先整理資料agian...
.pull-left[

```r
#列出個案死亡狀態編碼
table(dta_1$death_status)
```

```
## 
##  0  1  2  3  5 
## 84  7  7  6  2
```
* **coding 0=存活**
* **coding 1=ICU死亡**
* **coding 2=病房死亡**
* **coding 3=出院後追蹤死**
* **coding 5=臨終出院**
]
.pull-right[

```r
#利用mutate+cases進行分類
dta_1 = mutate(dta_1, Hdeath= 
  cases("survive" = (death_status>2 & death_status<4),
        "survive"=(death_status<1), 
        "mortality in hospital"=(death_status>=1 &death_status<=2),
        "mortality in hospital"=death_status>=5))
table(dta_1$Hdeath)
```

```
## 
##               survive mortality in hospital 
##                    90                    16
```
]
---
### Logistic regression of mortality(Univariance)

.pull-left[

```r
table.a<-glm(Hdeath~ APACHEII_score_in, data=dta_1, family=binomial) |> sjPlot::tab_model()
table.a
```

<table style="border-collapse:collapse; border:none;">
<tr>
<th style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm;  text-align:left; ">&nbsp;</th>
<th colspan="3" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">Hdeath</th>
</tr>
<tr>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  text-align:left; ">Predictors</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">Odds Ratios</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">CI</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">p</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">(Intercept)</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">0.02</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">0.00&nbsp;&ndash;&nbsp;0.15</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  "><strong>&lt;0.001</strong></td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">APACHEII score in</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">1.09</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">1.01&nbsp;&ndash;&nbsp;1.19</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  "><strong>0.031</strong></td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm; border-top:1px solid;">Observations</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="3">106</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm;">R<sup>2</sup> Tjur</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="3">0.060</td>
</tr>

</table>

]
.pull-right[

```r
table.c<-glm(Hdeath~ CFS_bf_ICU, data=dta_1, family=binomial) |> sjPlot::tab_model()
table.c
```

<table style="border-collapse:collapse; border:none;">
<tr>
<th style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm;  text-align:left; ">&nbsp;</th>
<th colspan="3" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">Hdeath</th>
</tr>
<tr>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  text-align:left; ">Predictors</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">Odds Ratios</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">CI</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">p</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">(Intercept)</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">0.08</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">0.01&nbsp;&ndash;&nbsp;0.36</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  "><strong>0.003</strong></td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">CFS bf ICU</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">1.18</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">0.87&nbsp;&ndash;&nbsp;1.63</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">0.294</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm; border-top:1px solid;">Observations</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="3">106</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm;">R<sup>2</sup> Tjur</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="3">0.009</td>
</tr>

</table>
]

---
### Logistic regression of mortality

```r
glm(Hdeath~ age+CFS_bf_ICU+APACHEII_score_in+gender, data=dta_1, family=binomial)  |> 
  tbl_regression(
    label = list(gender ~ "Male", CFS_bf_ICU ~ "Initial CFS",APACHEII_score_in ~ "APACHE II" ),
    exponentiate = TRUE,
    pvalue_fun = ~style_pvalue(.x, digits = 2),
    show_single_row= c("gender")) |> 
  add_significance_stars(pattern =  "{estimate} ({conf.low}- {conf.high}){stars}",
                         hide_ci = F, hide_se = F, hide_p = F)|>
  modify_caption("**Table 2. Logistic Regression of Mortality in the Hospital**")
```

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<table class="gt_table">
  <caption><strong>Table 2. Logistic Regression of Mortality in the Hospital</strong></caption>
  
  <thead class="gt_col_headings">
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1"><strong>Characteristic</strong></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>OR</strong><sup class="gt_footnote_marks">1,2</sup></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>SE</strong><sup class="gt_footnote_marks">2</sup></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>95% CI</strong><sup class="gt_footnote_marks">2</sup></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>p-value</strong></th>
    </tr>
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  <tbody class="gt_table_body">
    <tr><td class="gt_row gt_left">age</td>
<td class="gt_row gt_center">0.97 (0.90- 1.04)</td>
<td class="gt_row gt_center">0.038</td>
<td class="gt_row gt_center">0.90, 1.04</td>
<td class="gt_row gt_center">0.40</td></tr>
    <tr><td class="gt_row gt_left">Initial CFS</td>
<td class="gt_row gt_center">1.15 (0.83- 1.63)</td>
<td class="gt_row gt_center">0.172</td>
<td class="gt_row gt_center">0.83, 1.63</td>
<td class="gt_row gt_center">0.42</td></tr>
    <tr><td class="gt_row gt_left">APACHE II</td>
<td class="gt_row gt_center">1.09 (1.00- 1.19)</td>
<td class="gt_row gt_center">0.042</td>
<td class="gt_row gt_center">1.00, 1.19</td>
<td class="gt_row gt_center">0.052</td></tr>
    <tr><td class="gt_row gt_left">Male</td>
<td class="gt_row gt_center">1.62 (0.52- 5.15)</td>
<td class="gt_row gt_center">0.579</td>
<td class="gt_row gt_center">0.52, 5.15</td>
<td class="gt_row gt_center">0.41</td></tr>
  </tbody>
  
  <tfoot>
    <tr class="gt_footnotes">
      <td colspan="5">
        <p class="gt_footnote">
          <sup class="gt_footnote_marks">
            <em>1</em>
          </sup>
           
          *p<0.05; **p<0.01; ***p<0.001
          <br />
        </p>
        <p class="gt_footnote">
          <sup class="gt_footnote_marks">
            <em>2</em>
          </sup>
           
          OR = Odds Ratio, SE = Standard Error, CI = Confidence Interval
          <br />
        </p>
      </td>
    </tr>
  </tfoot>
</table>
</div>

---
## 圖片製作
<img src="ncku4.png" width="55%" style="display: block; margin: auto;" />

---
## 依據是否死亡進行分組

```r
library(gmodels)
with(dta_1, CrossTable(frail, death, prop.t=FALSE, prop.c=FALSE, prop.chisq=FALSE))
```

<img src="tabledeath.png" width="50%" />
---

## 衰弱與總死亡與否的關聯

.pull-left[

```r
with(dta_1, CrossTable(frail, death, chisq=TRUE))
```
<img src="chisq.png" width="100%" />

]
.pull-right[

```r
ggplot(dta_1, 
       aes(x = frail, fill = death)) + 
  geom_bar(position = "dodge")
```

]

---
## 衰弱與年齡、MDSALD、APACHE2的關聯

```r
library(GGally) # Extension to 'ggplot2'
ggparcoord(data = dta_1,  columns = c(8,25,27), groupColumn = 34,showPoints = TRUE, boxplot=TRUE)
ggparcoord(data = dta_1,  columns = c(8,25,27), groupColumn = 35,showPoints = TRUE, boxplot=TRUE)
```
.pull-left[
<img src="ggparcoord.png" width="100%" style="display: block; margin: auto;" />
]
.pull-right[
<img src="ggparcoord2.png" width="100%" style="display: block; margin: auto;" />
]

---

## 存活、衰弱及APACHE score

```r
dotplot(death ~ APACHEII_score_in | frail, 
       data=dta_1,pch=1,cex=0.8,alpha=1,layout=c(2, 1),
       xlab="APACHE II", ylab='survive or not ',  auto.key=list(space="top", columns=2),par.settings=standard.theme(color=FALSE))
```

---
## 做matplot

```r
dta_1$frail<-as.integer(dta_1$frail)
dta_1$frail2 <- dta_1$frail
dta_1$frail2 [dta_1$frail == 1] <- 1
dta_1$frail2 [dta_1$frail == 2] <- 16
head(dta_1$frail2)
```

```
## [1]  1  1  1 16  1 16
```

```r
matplot(x=c(1,2,3,4), 
        y=t(dta_1[, c(29, 30, 32, 34)]), 
        type='b',
        pch = dta_1$frail2,
        cex=0.5,
        col=dta_1$frail2,
        bty='n',
        xlim=c(1,4),
        ylim=c(1,10),
        main="不同時間CFS的變化",
        xlab="測量次數",ylab="CFS score")
abline(h=4, col="red")
```

---
## matplot結果
<img src="matplot.png" width="80%" style="display: block; margin: auto;" />

---
## 結論
### -研究部份
* **APACHE II會影響ICU老人的住院期間死亡狀，但衰弱不會**
* **ICU住院的衝擊會造成老人衰弱狀態加劇，即使住院前功能獨立未衰弱者，即使復原到可出院時，仍會處於需要被照顧的衰弱狀態**

### -資料分析部分
* **package眾多，可以選擇比較習慣或上手的使用**
* **收集資料時應該先想清楚可能的分析方法，以免造成資料處理的困擾(包括那些資料要收集、編碼方式)**