Balance check after
## Balance Measures
## Type M.0.Adj SD.0.Adj M.1.Adj SD.1.Adj
## Age Contin. 71.2874 10.6759 66.6335 13.9670
## Race_Group_White Binary 0.5643 . 0.4904 .
## Race_Group_Black Binary 0.3242 . 0.2775 .
## Race_Group_Others Binary 0.1116 . 0.2322 .
## Gender_Male Binary 0.5654 . 0.5543 .
## HTN_Y Binary 0.9636 . 1.0000 .
## DM_Y Binary 0.6706 . 0.8070 .
## HF_score_Total Contin. 43.0592 7.4632 44.2057 8.2481
## Dialysis_Vintage Contin. 2.6643 1.8776 2.3082 4.0551
## Access_type_cat_2_TDC Binary 0.2374 . 0.4420 .
## Location_HH Binary 0.2325 . 0.4452 .
## EF Contin. 51.7821 14.2818 47.5666 15.4262
## NT_proBNP Contin. 49487.5956 47450.5060 37004.0344 25157.3080
## Diff.Adj
## Age -0.3775
## Race_Group_White -0.0739
## Race_Group_Black -0.0467
## Race_Group_Others 0.1206
## Gender_Male -0.0112
## HTN_Y 0.0364
## DM_Y 0.1364
## HF_score_Total 0.1441
## Dialysis_Vintage -0.1001
## Access_type_cat_2_TDC 0.2046
## Location_HH 0.2126
## EF -0.2879
## NT_proBNP -0.3194
##
## Effective sample sizes
## 1 2
## Unadjusted 84. 93.
## Adjusted 76.59 81.99
Table 1
Univariate negative‐binomial regression outcome los
Univariate negative‐binomial regression for LOS
| Characteristic |
N |
IRR |
95% CI |
p-value |
| time |
488 |
|
|
|
| 1 |
|
— |
— |
|
| 2 |
|
1.41 |
1.21, 1.64 |
<0.001 |
| Age |
488 |
1.00 |
1.00, 1.01 |
0.155 |
| Gender |
488 |
|
|
|
| Female |
|
— |
— |
|
| Male |
|
1.14 |
0.98, 1.33 |
0.090 |
| Race_Group |
488 |
|
|
|
| White |
|
— |
— |
|
| Black |
|
0.60 |
0.50, 0.71 |
<0.001 |
| Others |
|
0.66 |
0.54, 0.81 |
<0.001 |
| UF_rate |
488 |
0.95 |
0.94, 0.97 |
<0.001 |
| Dialysis_Vintage |
488 |
0.99 |
0.97, 1.01 |
0.465 |
| HF_score_Total |
488 |
1.03 |
1.02, 1.04 |
<0.001 |
| Access_type |
488 |
|
|
|
| AVF |
|
— |
— |
|
| AVG |
|
0.56 |
0.37, 0.87 |
0.008 |
| TDC |
|
2.18 |
1.89, 2.52 |
<0.001 |
| weight.ATE.trunc |
488 |
1.17 |
1.09, 1.25 |
<0.001 |
Multivariate negative‐binomial regression los: age gender race
glm.nb(
formula = LOS ~ time +Age+ Gender + Race_Group,
data = all_bwh,
weights = weight.ATE.trunc
) %>%
tbl_regression(
exponentiate = TRUE,
pvalue_fun = ~style_pvalue(.x, digits = 3)
) %>%
bold_p() %>%
bold_labels() %>%
add_n(location = "level") %>%
modify_caption("**Multivariable NB regression for LOS**")
Multivariable NB regression for LOS
| Characteristic |
N |
IRR |
95% CI |
p-value |
| time |
|
|
|
|
| 1 |
215 |
— |
— |
|
| 2 |
272 |
1.47 |
1.27, 1.71 |
<0.001 |
| Age |
488 |
1.00 |
1.00, 1.01 |
0.696 |
| Gender |
|
|
|
|
| Female |
215 |
— |
— |
|
| Male |
273 |
1.07 |
0.92, 1.24 |
0.395 |
| Race_Group |
|
|
|
|
| White |
255 |
— |
— |
|
| Black |
145 |
0.60 |
0.50, 0.72 |
<0.001 |
| Others |
87 |
0.63 |
0.51, 0.77 |
<0.001 |
Multivariate negative‐binomial regression los ：
time+Gender+Race_Group+Dialysis_Vintage+HF_score_Total+UF_rate
Multivariable NB regression for LOS
| Characteristic |
N |
IRR |
95% CI |
p-value |
| time |
|
|
|
|
| 1 |
215 |
— |
— |
|
| 2 |
272 |
1.29 |
1.11, 1.49 |
<0.001 |
| Gender |
|
|
|
|
| Female |
215 |
— |
— |
|
| Male |
273 |
0.94 |
0.81, 1.09 |
0.439 |
| Race_Group |
|
|
|
|
| White |
255 |
— |
— |
|
| Black |
145 |
0.71 |
0.59, 0.85 |
<0.001 |
| Others |
87 |
0.68 |
0.56, 0.83 |
<0.001 |
| Dialysis_Vintage |
488 |
1.00 |
0.98, 1.02 |
0.891 |
| HF_score_Total |
488 |
1.02 |
1.01, 1.03 |
<0.001 |
| UF_rate |
488 |
0.97 |
0.95, 0.98 |
<0.001 |
90 days poisson regression: time
| Characteristic |
N |
IRR |
95% CI |
p-value |
| time |
|
|
|
|
| 1 |
215 |
— |
— |
|
| 2 |
272 |
0.96 |
0.79, 1.17 |
0.715 |
90 days poisson regression: time Age Gender Race_Group
| Characteristic |
N |
IRR |
95% CI |
p-value |
| time |
|
|
|
|
| 1 |
215 |
— |
— |
|
| 2 |
272 |
1.00 |
0.81, 1.22 |
0.965 |
| Age |
488 |
1.00 |
1.00, 1.01 |
0.384 |
| Gender |
|
|
|
|
| Female |
215 |
— |
— |
|
| Male |
273 |
1.24 |
1.01, 1.52 |
0.041 |
| Race_Group |
|
|
|
|
| White |
255 |
— |
— |
|
| Black |
145 |
1.20 |
0.96, 1.51 |
0.111 |
| Others |
87 |
0.97 |
0.72, 1.29 |
0.838 |
90 days poisson
regression：time+Gender+Race_Group+Dialysis_Vintage+HF_score_Total+UF_rate
| Characteristic |
N |
IRR |
95% CI |
p-value |
| time |
|
|
|
|
| 1 |
215 |
— |
— |
|
| 2 |
272 |
0.96 |
0.78, 1.17 |
0.670 |
| Gender |
|
|
|
|
| Female |
215 |
— |
— |
|
| Male |
273 |
1.24 |
1.00, 1.53 |
0.049 |
| Race_Group |
|
|
|
|
| White |
255 |
— |
— |
|
| Black |
145 |
1.14 |
0.90, 1.44 |
0.280 |
| Others |
87 |
0.95 |
0.70, 1.26 |
0.718 |
| Dialysis_Vintage |
488 |
0.97 |
0.92, 1.00 |
0.110 |
| HF_score_Total |
488 |
1.00 |
0.98, 1.01 |
0.571 |
| UF_rate |
488 |
0.99 |
0.97, 1.01 |
0.569 |
180 days poisson regression: time
| Characteristic |
N |
IRR |
95% CI |
p-value |
| time |
|
|
|
|
| 1 |
215 |
— |
— |
|
| 2 |
272 |
0.88 |
0.75, 1.02 |
0.095 |
180 days poisson regression： Age Gender Race_Group
| Characteristic |
N |
IRR |
95% CI |
p-value |
| time |
|
|
|
|
| 1 |
215 |
— |
— |
|
| 2 |
272 |
0.86 |
0.73, 1.01 |
0.058 |
| Age |
488 |
1.00 |
0.99, 1.00 |
0.491 |
| Gender |
|
|
|
|
| Female |
215 |
— |
— |
|
| Male |
273 |
1.12 |
0.96, 1.32 |
0.159 |
| Race_Group |
|
|
|
|
| White |
255 |
— |
— |
|
| Black |
145 |
1.49 |
1.24, 1.78 |
<0.001 |
| Others |
87 |
1.31 |
1.05, 1.64 |
0.016 |
180 days poisson regression：
time+Gender+Race_Group+Dialysis_Vintage+HF_score_Total+UF_rate
| Characteristic |
N |
IRR |
95% CI |
p-value |
| time |
|
|
|
|
| 1 |
215 |
— |
— |
|
| 2 |
272 |
0.83 |
0.71, 0.98 |
0.027 |
| Gender |
|
|
|
|
| Female |
215 |
— |
— |
|
| Male |
273 |
1.10 |
0.93, 1.30 |
0.265 |
| Race_Group |
|
|
|
|
| White |
255 |
— |
— |
|
| Black |
145 |
1.57 |
1.30, 1.89 |
<0.001 |
| Others |
87 |
1.33 |
1.06, 1.65 |
0.012 |
| Dialysis_Vintage |
488 |
0.96 |
0.92, 1.0 |
0.038 |
| HF_score_Total |
488 |
1.01 |
1.00, 1.02 |
0.271 |
| UF_rate |
488 |
0.99 |
0.98, 1.01 |
0.351 |
Univariate coxph decongestion_time_days
| Characteristic |
N |
HR |
95% CI |
p-value |
| time |
488 |
|
|
|
| 1 |
|
— |
— |
|
| 2 |
|
0.83 |
0.62, 1.12 |
0.225 |
| Age |
488 |
0.99 |
0.98, 1.01 |
0.378 |
| Gender |
488 |
|
|
|
| Female |
|
— |
— |
|
| Male |
|
0.80 |
0.58, 1.12 |
0.192 |
| Race_Group |
488 |
|
|
|
| White |
|
— |
— |
|
| Black |
|
1.68 |
1.06, 2.67 |
0.026 |
| Others |
|
1.72 |
1.22, 2.43 |
0.002 |
| Dialysis_Vintage |
488 |
1.00 |
0.97, 1.03 |
0.903 |
| HF_score_Total |
488 |
0.97 |
0.95, 0.99 |
0.003 |
| Access_type |
488 |
|
|
|
| AVF |
|
— |
— |
|
| AVG |
|
1.78 |
1.03, 3.08 |
0.040 |
| TDC |
|
0.57 |
0.40, 0.80 |
0.001 |
| UF_rate |
488 |
1.04 |
1.02, 1.07 |
0.001 |
| weight.ATE.trunc |
488 |
0.91 |
0.79, 1.05 |
0.192 |
coxph decongestion_time_days :Age gender race
| Characteristic |
HR |
95% CI |
p-value |
| time |
|
|
|
| 1 |
— |
— |
|
| 2 |
0.77 |
0.57, 1.03 |
0.081 |
| Age |
1.00 |
0.98, 1.01 |
0.719 |
| Gender |
|
|
|
| Female |
— |
— |
|
| Male |
0.85 |
0.62, 1.16 |
0.310 |
| Race_Group |
|
|
|
| White |
— |
— |
|
| Black |
1.64 |
1.06, 2.56 |
0.027 |
| Others |
1.76 |
1.21, 2.56 |
0.003 |
coxph decongestion_time_days
:time+Gender+Race_Group+DM+Dialysis_Vintage+HF_score_Total
| Characteristic |
HR |
95% CI |
p-value |
| time |
|
|
|
| 1 |
— |
— |
|
| 2 |
0.86 |
0.62, 1.18 |
0.340 |
| Gender |
|
|
|
| Female |
— |
— |
|
| Male |
0.98 |
0.69, 1.38 |
0.893 |
| Race_Group |
|
|
|
| White |
— |
— |
|
| Black |
1.42 |
0.92, 2.19 |
0.110 |
| Others |
1.67 |
1.14, 2.45 |
0.009 |
| Dialysis_Vintage |
0.99 |
0.96, 1.02 |
0.474 |
| HF_score_Total |
0.97 |
0.95, 1.00 |
0.020 |
| UF_rate |
1.03 |
1.01, 1.06 |
0.005 |
decongestion_time_days,n_90,n_180,los
|
1
(N=84) |
2
(N=93) |
Overall
(N=177) |
| decongestion_time_days |
|
|
|
| Mean (SD) |
5.24 (3.80) |
6.18 (7.72) |
5.73 (6.18) |
| Median [Q1,Q3] |
4.00 [2.00,7.00] |
4.00 [2.00,6.00] |
4.00 [2.00,7.00] |
| n_90 |
|
|
|
| Mean (SD) |
0.869 (1.03) |
0.882 (1.10) |
0.876 (1.06) |
| Median [Q1,Q3] |
1.00 [0,1.00] |
1.00 [0,1.00] |
1.00 [0,1.00] |
| n_180 |
|
|
|
| Mean (SD) |
1.46 (1.72) |
1.31 (1.59) |
1.38 (1.65) |
| Median [Q1,Q3] |
1.00 [0,2.00] |
1.00 [0,2.00] |
1.00 [0,2.00] |
| LOS |
|
|
|
| Mean (SD) |
8.99 (6.98) |
10.6 (11.1) |
9.85 (9.42) |
| Median [Q1,Q3] |
7.00 [3.00,14.0] |
6.00 [4.00,12.0] |
7.00 [4.00,13.0] |