Balance check before
library("cobalt")
## Warning: package 'cobalt' was built under R version 4.3.3
## cobalt (Version 4.5.5, Build Date: 2024-04-02)
bal.tab(master %>% select("Age","Race_Group" ,"Gender","CAD","HTN","DM","HF_score","Dialysis_Vintage"), treat = master$treated,disp=c("means","sds"))
## Note: `s.d.denom` not specified; assuming "pooled".
## Balance Measures
## Type M.0.Un SD.0.Un M.1.Un SD.1.Un Diff.Un
## Age Contin. 68.0508 13.2277 70.7312 11.3590 0.2174
## Race_Group_White Binary 0.5593 . 0.6688 . 0.1094
## Race_Group_Asian Binary 0.0424 . 0.0688 . 0.0264
## Race_Group_Black Binary 0.2373 . 0.1562 . -0.0810
## Race_Group_Other Binary 0.1610 . 0.1062 . -0.0548
## Gender_Male Binary 0.5424 . 0.6062 . 0.0639
## CAD_Y Binary 0.8729 . 0.8750 . 0.0021
## HTN_Y Binary 0.9831 . 0.9688 . -0.0143
## DM_Y Binary 0.7966 . 0.7438 . -0.0529
## HF_score_1 Binary 0.2797 . 0.3750 . 0.0953
## HF_score_2 Binary 0.2797 . 0.3375 . 0.0578
## HF_score_3 Binary 0.4407 . 0.2875 . -0.1532
## Dialysis_Vintage Contin. 3.3475 4.5524 3.3937 3.3983 0.0115
##
## Sample sizes
## Control Treated
## All 118 160
Balance check after
bal.tab(master %>% select("Age","Race_Group" ,"Gender","CAD","HTN","DM","HF_score","Dialysis_Vintage"), treat = master$treated, weights = master$weight.ATE,disp=c("means","sds"))
## Note: `s.d.denom` not specified; assuming "pooled".
## Balance Measures
## Type M.0.Adj SD.0.Adj M.1.Adj SD.1.Adj Diff.Adj
## Age Contin. 69.5203 11.9377 69.0118 12.9739 -0.0412
## Race_Group_White Binary 0.6429 . 0.6106 . -0.0323
## Race_Group_Asian Binary 0.0407 . 0.0569 . 0.0162
## Race_Group_Black Binary 0.1879 . 0.1984 . 0.0104
## Race_Group_Other Binary 0.1285 . 0.1341 . 0.0056
## Gender_Male Binary 0.5951 . 0.5749 . -0.0201
## CAD_Y Binary 0.8897 . 0.8682 . -0.0215
## HTN_Y Binary 0.9851 . 0.9728 . -0.0123
## DM_Y Binary 0.7706 . 0.7794 . 0.0089
## HF_score_1 Binary 0.3044 . 0.3446 . 0.0402
## HF_score_2 Binary 0.3119 . 0.2914 . -0.0205
## HF_score_3 Binary 0.3837 . 0.3641 . -0.0196
## Dialysis_Vintage Contin. 4.0389 6.5613 3.3245 3.3107 -0.1778
##
## Effective sample sizes
## Control Treated
## Unadjusted 118. 160.
## Adjusted 102.2 138.43
Univariate linear regression outcome los
| Characteristic |
N |
Beta |
95% CI |
p-value |
| group |
549 |
|
|
|
| BWH |
|
— |
— |
|
| MGH |
|
-2.3 |
-4.3, -0.38 |
0.020 |
| Age |
549 |
-0.01 |
-0.08, 0.07 |
0.893 |
| Gender |
549 |
|
|
|
| Female |
|
— |
— |
|
| Male |
|
1.1 |
-0.88, 3.1 |
0.278 |
| Race_Group |
549 |
|
|
|
| White |
|
— |
— |
|
| Asian |
|
-4.5 |
-9.1, 0.01 |
0.051 |
| Black |
|
-3.2 |
-5.7, -0.66 |
0.014 |
| Other |
|
-2.9 |
-5.9, 0.00 |
0.051 |
| HTN |
549 |
|
|
|
| N |
|
— |
— |
|
| Y |
|
-4.6 |
-11, 2.2 |
0.187 |
| DM |
549 |
|
|
|
| N |
|
— |
— |
|
| Y |
|
1.5 |
-0.82, 3.9 |
0.205 |
| CAD |
549 |
|
|
|
| N |
|
— |
— |
|
| Y |
|
4.3 |
1.4, 7.3 |
0.004 |
| Dialysis_Vintage |
549 |
0.20 |
0.01, 0.38 |
0.041 |
| HF_score |
549 |
|
|
|
| 1 |
|
— |
— |
|
| 2 |
|
0.22 |
-2.2, 2.7 |
0.859 |
| 3 |
|
2.6 |
0.27, 4.9 |
0.029 |
| NT_proBNP |
499 |
0.00 |
0.00, 0.00 |
0.732 |
| weight.ATE |
549 |
0.17 |
-0.72, 1.1 |
0.710 |
Multivariate linear regression los: age gender race
| Characteristic |
N |
Beta |
95% CI |
p-value |
| group |
|
|
|
|
| BWH |
272 |
— |
— |
|
| MGH |
276 |
-2.2 |
-4.1, -0.29 |
0.025 |
| Age |
549 |
-0.03 |
-0.11, 0.05 |
0.440 |
| Gender |
|
|
|
|
| Female |
228 |
— |
— |
|
| Male |
320 |
0.73 |
-1.3, 2.7 |
0.474 |
| Race_Group |
|
|
|
|
| White |
343 |
— |
— |
|
| Asian |
26 |
-4.1 |
-8.7, 0.39 |
0.075 |
| Black |
106 |
-3.4 |
-6.1, -0.81 |
0.011 |
| Other |
72 |
-2.7 |
-5.7, 0.27 |
0.076 |
Multivariate linear regression los
| Characteristic |
N |
Beta |
95% CI |
p-value |
| group |
|
|
|
|
| BWH |
272 |
— |
— |
|
| MGH |
276 |
-2.0 |
-3.9, -0.13 |
0.037 |
| Age |
549 |
-0.05 |
-0.14, 0.03 |
0.207 |
| Gender |
|
|
|
|
| Female |
228 |
— |
— |
|
| Male |
320 |
0.00 |
-2.0, 2.0 |
0.998 |
| Race_Group |
|
|
|
|
| White |
343 |
— |
— |
|
| Asian |
26 |
-3.7 |
-8.1, 0.74 |
0.104 |
| Black |
106 |
-2.9 |
-5.5, -0.27 |
0.031 |
| Other |
72 |
-2.6 |
-5.5, 0.42 |
0.093 |
| HTN |
|
|
|
|
| N |
11 |
— |
— |
|
| Y |
537 |
-3.8 |
-10, 2.8 |
0.266 |
| DM |
|
|
|
|
| N |
124 |
— |
— |
|
| Y |
425 |
1.6 |
-0.84, 4.1 |
0.198 |
| CAD |
|
|
|
|
| N |
66 |
— |
— |
|
| Y |
482 |
3.3 |
0.21, 6.5 |
0.038 |
| Dialysis_Vintage |
549 |
0.25 |
0.06, 0.44 |
0.012 |
| HF_score |
|
|
|
|
| 1 |
178 |
— |
— |
|
| 2 |
166 |
0.09 |
-2.4, 2.5 |
0.943 |
| 3 |
204 |
2.6 |
0.29, 5.0 |
0.028 |
Univariate time to 1st_readmission 180
| Characteristic |
N |
HR |
95% CI |
p-value |
| group |
551 |
|
|
|
| BWH |
|
— |
— |
|
| MGH |
|
0.74 |
0.51, 1.06 |
0.101 |
| Age |
551 |
0.99 |
0.98, 1.01 |
0.226 |
| Gender |
551 |
|
|
|
| Female |
|
— |
— |
|
| Male |
|
1.06 |
0.74, 1.53 |
0.750 |
| Race_Group |
551 |
|
|
|
| White |
|
— |
— |
|
| Asian |
|
1.25 |
0.61, 2.56 |
0.546 |
| Black |
|
1.40 |
0.88, 2.22 |
0.157 |
| Other |
|
1.06 |
0.60, 1.88 |
0.839 |
| HTN |
551 |
|
|
|
| N |
|
— |
— |
|
| Y |
|
0.68 |
0.31, 1.45 |
0.315 |
| DM |
551 |
|
|
|
| N |
|
— |
— |
|
| Y |
|
1.22 |
0.77, 1.92 |
0.403 |
| CAD |
551 |
|
|
|
| N |
|
— |
— |
|
| Y |
|
0.88 |
0.53, 1.48 |
0.641 |
| Dialysis_Vintage |
551 |
0.94 |
0.90, 0.99 |
0.011 |
| HF_score |
551 |
|
|
|
| 1 |
|
— |
— |
|
| 2 |
|
0.80 |
0.50, 1.29 |
0.369 |
| 3 |
|
1.05 |
0.69, 1.62 |
0.815 |
| NT_proBNP |
501 |
1.00 |
1.00, 1.00 |
0.395 |
| LOS |
549 |
0.99 |
0.97, 1.01 |
0.225 |
| HD_Unit |
551 |
|
|
|
| No |
|
— |
— |
|
| Yes |
|
1.04 |
0.62, 1.75 |
0.887 |
| weight.ATE |
551 |
0.93 |
0.74, 1.15 |
0.500 |
Multivariate time to 1st_readmission 180
| Characteristic |
HR |
95% CI |
p-value |
| group |
|
|
|
| BWH |
— |
— |
|
| MGH |
0.69 |
0.47, 1.01 |
0.058 |
| Age |
0.99 |
0.98, 1.01 |
0.407 |
| Gender |
|
|
|
| Female |
— |
— |
|
| Male |
1.11 |
0.74, 1.66 |
0.612 |
| Race_Group |
|
|
|
| White |
— |
— |
|
| Asian |
1.28 |
0.57, 2.85 |
0.552 |
| Black |
1.35 |
0.85, 2.14 |
0.201 |
| Other |
1.07 |
0.59, 1.96 |
0.814 |
| HTN |
|
|
|
| N |
— |
— |
|
| Y |
0.62 |
0.27, 1.43 |
0.267 |
| DM |
|
|
|
| N |
— |
— |
|
| Y |
1.09 |
0.67, 1.77 |
0.725 |
| CAD |
|
|
|
| N |
— |
— |
|
| Y |
0.86 |
0.50, 1.50 |
0.602 |
| Dialysis_Vintage |
0.94 |
0.90, 0.99 |
0.011 |
| HF_score |
|
|
|
| 1 |
— |
— |
|
| 2 |
0.82 |
0.48, 1.38 |
0.448 |
| 3 |
0.98 |
0.60, 1.59 |
0.926 |
| HD_Unit |
|
|
|
| No |
— |
— |
|
| Yes |
0.94 |
0.52, 1.69 |
0.841 |
Univariate logistic regression outcome Binary Outcome is 1 or more
versus 0: more_vs_zero_90days
| Characteristic |
N |
OR |
95% CI |
p-value |
| group |
551 |
|
|
|
| BWH |
|
— |
— |
|
| MGH |
|
0.56 |
0.40, 0.79 |
<0.001 |
| Age |
551 |
0.99 |
0.97, 1.00 |
0.045 |
| Gender |
551 |
|
|
|
| Female |
|
— |
— |
|
| Male |
|
1.20 |
0.85, 1.69 |
0.292 |
| Race_Group |
551 |
|
|
|
| White |
|
— |
— |
|
| Asian |
|
0.85 |
0.37, 1.87 |
0.689 |
| Black |
|
1.51 |
0.97, 2.34 |
0.066 |
| Other |
|
0.94 |
0.56, 1.57 |
0.821 |
| HTN |
551 |
|
|
|
| N |
|
— |
— |
|
| Y |
|
1.14 |
0.35, 3.94 |
0.824 |
| DM |
551 |
|
|
|
| N |
|
— |
— |
|
| Y |
|
1.15 |
0.77, 1.73 |
0.489 |
| CAD |
551 |
|
|
|
| N |
|
— |
— |
|
| Y |
|
0.90 |
0.54, 1.50 |
0.680 |
| Dialysis_Vintage |
551 |
0.91 |
0.86, 0.96 |
<0.001 |
| HF_score |
551 |
|
|
|
| 1 |
|
— |
— |
|
| 2 |
|
0.80 |
0.52, 1.22 |
0.308 |
| 3 |
|
0.90 |
0.60, 1.35 |
0.614 |
| NT_proBNP |
501 |
1.00 |
1.00, 1.00 |
0.592 |
| HD_Unit |
551 |
|
|
|
| No |
|
— |
— |
|
| Yes |
|
1.16 |
0.65, 2.07 |
0.606 |
| weight.ATE |
551 |
0.90 |
0.77, 1.05 |
0.182 |
Multivariate logistic regression outcome Binary Outcome is 1 or more
versus: 0 more_vs_zero_90days
| Characteristic |
N |
OR |
95% CI |
p-value |
| group |
|
|
|
|
| BWH |
241 |
— |
— |
|
| MGH |
259 |
0.49 |
0.34, 0.71 |
<0.001 |
| Age |
501 |
0.98 |
0.97, 1.00 |
0.057 |
| Gender |
|
|
|
|
| Female |
205 |
— |
— |
|
| Male |
295 |
1.19 |
0.81, 1.77 |
0.379 |
| Race_Group |
|
|
|
|
| White |
313 |
— |
— |
|
| Asian |
20 |
1.04 |
0.38, 2.74 |
0.932 |
| Black |
99 |
1.24 |
0.74, 2.08 |
0.410 |
| Other |
68 |
0.90 |
0.51, 1.59 |
0.719 |
| HTN |
|
|
|
|
| N |
11 |
— |
— |
|
| Y |
489 |
1.20 |
0.36, 4.26 |
0.770 |
| DM |
|
|
|
|
| N |
116 |
— |
— |
|
| Y |
385 |
1.04 |
0.64, 1.68 |
0.887 |
| CAD |
|
|
|
|
| N |
63 |
— |
— |
|
| Y |
437 |
0.90 |
0.49, 1.66 |
0.730 |
| Dialysis_Vintage |
501 |
0.90 |
0.85, 0.95 |
<0.001 |
| HF_score |
|
|
|
|
| 1 |
168 |
— |
— |
|
| 2 |
153 |
0.83 |
0.51, 1.34 |
0.444 |
| 3 |
180 |
0.91 |
0.57, 1.43 |
0.671 |
| NT_proBNP |
501 |
1.00 |
1.00, 1.00 |
0.630 |
| HD_Unit |
|
|
|
|
| No |
454 |
— |
— |
|
| Yes |
47 |
1.31 |
0.68, 2.54 |
0.415 |
Univariate logistic regression outcome Binary Outcome is more versus
0 or 1: more_vs_one_zero_90days
| Characteristic |
N |
OR |
95% CI |
p-value |
| group |
551 |
|
|
|
| BWH |
|
— |
— |
|
| MGH |
|
0.53 |
0.32, 0.87 |
0.013 |
| Age |
551 |
0.98 |
0.97, 1.00 |
0.105 |
| Gender |
551 |
|
|
|
| Female |
|
— |
— |
|
| Male |
|
1.42 |
0.86, 2.39 |
0.179 |
| Race_Group |
551 |
|
|
|
| White |
|
— |
— |
|
| Asian |
|
1.17 |
0.27, 3.57 |
0.807 |
| Black |
|
2.82 |
1.58, 4.99 |
<0.001 |
| Other |
|
2.14 |
1.04, 4.19 |
0.031 |
| HTN |
551 |
|
|
|
| N |
|
— |
— |
|
| Y |
|
0.64 |
0.17, 3.68 |
0.547 |
| DM |
551 |
|
|
|
| N |
|
— |
— |
|
| Y |
|
1.25 |
0.70, 2.39 |
0.469 |
| CAD |
551 |
|
|
|
| N |
|
— |
— |
|
| Y |
|
0.66 |
0.35, 1.35 |
0.230 |
| Dialysis_Vintage |
551 |
0.92 |
0.84, 0.99 |
0.070 |
| HF_score |
551 |
|
|
|
| 1 |
|
— |
— |
|
| 2 |
|
0.50 |
0.25, 0.98 |
0.049 |
| 3 |
|
1.07 |
0.62, 1.86 |
0.812 |
| NT_proBNP |
501 |
1.00 |
1.00, 1.00 |
0.007 |
| HD_Unit |
551 |
|
|
|
| No |
|
— |
— |
|
| Yes |
|
0.18 |
0.02, 0.67 |
0.038 |
| weight.ATE |
551 |
1.09 |
0.87, 1.33 |
0.443 |
Multivariate logistic regression outcome Binary Outcome is more
versus 0 or 1: more_vs_one_zero_90days
| Characteristic |
N |
OR |
95% CI |
p-value |
| group |
|
|
|
|
| BWH |
241 |
— |
— |
|
| MGH |
259 |
0.41 |
0.23, 0.70 |
0.001 |
| Age |
501 |
0.99 |
0.97, 1.01 |
0.326 |
| Gender |
|
|
|
|
| Female |
205 |
— |
— |
|
| Male |
295 |
1.71 |
0.95, 3.14 |
0.077 |
| Race_Group |
|
|
|
|
| White |
313 |
— |
— |
|
| Asian |
20 |
3.00 |
0.64, 10.7 |
0.114 |
| Black |
99 |
2.65 |
1.34, 5.24 |
0.005 |
| Other |
68 |
2.29 |
1.03, 4.96 |
0.038 |
| HTN |
|
|
|
|
| N |
11 |
— |
— |
|
| Y |
489 |
0.51 |
0.12, 3.22 |
0.408 |
| DM |
|
|
|
|
| N |
116 |
— |
— |
|
| Y |
385 |
1.14 |
0.57, 2.41 |
0.725 |
| CAD |
|
|
|
|
| N |
63 |
— |
— |
|
| Y |
437 |
0.72 |
0.32, 1.68 |
0.434 |
| Dialysis_Vintage |
501 |
0.93 |
0.84, 1.01 |
0.144 |
| HF_score |
|
|
|
|
| 1 |
168 |
— |
— |
|
| 2 |
153 |
0.55 |
0.25, 1.14 |
0.112 |
| 3 |
180 |
1.18 |
0.64, 2.22 |
0.595 |
| NT_proBNP |
501 |
1.00 |
1.00, 1.00 |
0.015 |
| HD_Unit |
|
|
|
|
| No |
454 |
— |
— |
|
| Yes |
47 |
0.18 |
0.02, 0.75 |
0.050 |
Univariate logistic regression outcome Binary Outcome is more versus
0 or 1: more_vs_zero_180days
| Characteristic |
N |
OR |
95% CI |
p-value |
| group |
551 |
|
|
|
| BWH |
|
— |
— |
|
| MGH |
|
0.68 |
0.49, 0.96 |
0.026 |
| Age |
551 |
0.99 |
0.98, 1.00 |
0.196 |
| Gender |
551 |
|
|
|
| Female |
|
— |
— |
|
| Male |
|
0.90 |
0.64, 1.26 |
0.524 |
| Race_Group |
551 |
|
|
|
| White |
|
— |
— |
|
| Asian |
|
1.18 |
0.54, 2.63 |
0.684 |
| Black |
|
1.42 |
0.92, 2.22 |
0.117 |
| Other |
|
0.75 |
0.45, 1.25 |
0.272 |
| HTN |
551 |
|
|
|
| N |
|
— |
— |
|
| Y |
|
0.45 |
0.11, 1.48 |
0.211 |
| DM |
551 |
|
|
|
| N |
|
— |
— |
|
| Y |
|
1.31 |
0.88, 1.96 |
0.184 |
| CAD |
551 |
|
|
|
| N |
|
— |
— |
|
| Y |
|
1.01 |
0.60, 1.69 |
0.966 |
| Dialysis_Vintage |
551 |
0.91 |
0.86, 0.95 |
<0.001 |
| HF_score |
551 |
|
|
|
| 1 |
|
— |
— |
|
| 2 |
|
0.79 |
0.52, 1.20 |
0.270 |
| 3 |
|
1.00 |
0.67, 1.50 |
0.989 |
| NT_proBNP |
501 |
1.00 |
1.00, 1.00 |
0.338 |
| HD_Unit |
551 |
|
|
|
| No |
|
— |
— |
|
| Yes |
|
1.09 |
0.61, 1.95 |
0.764 |
| weight.ATE |
551 |
0.84 |
0.72, 0.98 |
0.033 |
Multivariate logistic regression outcome Binary Outcome is more
versus 0 or 1: more_vs_zero_180days
| Characteristic |
N |
OR |
95% CI |
p-value |
| group |
|
|
|
|
| BWH |
241 |
— |
— |
|
| MGH |
259 |
0.57 |
0.39, 0.82 |
0.003 |
| Age |
501 |
0.99 |
0.97, 1.00 |
0.081 |
| Gender |
|
|
|
|
| Female |
205 |
— |
— |
|
| Male |
295 |
0.81 |
0.54, 1.20 |
0.289 |
| Race_Group |
|
|
|
|
| White |
313 |
— |
— |
|
| Asian |
20 |
1.87 |
0.71, 5.27 |
0.214 |
| Black |
99 |
1.30 |
0.77, 2.20 |
0.325 |
| Other |
68 |
0.64 |
0.36, 1.12 |
0.121 |
| HTN |
|
|
|
|
| N |
11 |
— |
— |
|
| Y |
489 |
0.49 |
0.12, 1.69 |
0.283 |
| DM |
|
|
|
|
| N |
116 |
— |
— |
|
| Y |
385 |
1.25 |
0.77, 2.02 |
0.371 |
| CAD |
|
|
|
|
| N |
63 |
— |
— |
|
| Y |
437 |
1.00 |
0.54, 1.85 |
>0.999 |
| Dialysis_Vintage |
501 |
0.90 |
0.85, 0.95 |
<0.001 |
| HF_score |
|
|
|
|
| 1 |
168 |
— |
— |
|
| 2 |
153 |
0.86 |
0.53, 1.38 |
0.534 |
| 3 |
180 |
1.01 |
0.64, 1.60 |
0.976 |
| NT_proBNP |
501 |
1.00 |
1.00, 1.00 |
0.402 |
| HD_Unit |
|
|
|
|
| No |
454 |
— |
— |
|
| Yes |
47 |
1.14 |
0.60, 2.22 |
0.689 |
Univariate logistic regression outcome Binary Outcome is more versus
0 or 1: more_vs_one_zero_180days
| Characteristic |
N |
OR |
95% CI |
p-value |
| group |
551 |
|
|
|
| BWH |
|
— |
— |
|
| MGH |
|
0.51 |
0.34, 0.75 |
<0.001 |
| Age |
551 |
0.98 |
0.97, 1.00 |
0.051 |
| Gender |
551 |
|
|
|
| Female |
|
— |
— |
|
| Male |
|
1.18 |
0.80, 1.74 |
0.414 |
| Race_Group |
551 |
|
|
|
| White |
|
— |
— |
|
| Asian |
|
1.29 |
0.50, 2.99 |
0.572 |
| Black |
|
1.83 |
1.14, 2.91 |
0.012 |
| Other |
|
0.99 |
0.53, 1.77 |
0.966 |
| HTN |
551 |
|
|
|
| N |
|
— |
— |
|
| Y |
|
0.74 |
0.22, 2.93 |
0.631 |
| DM |
551 |
|
|
|
| N |
|
— |
— |
|
| Y |
|
1.06 |
0.68, 1.70 |
0.796 |
| CAD |
551 |
|
|
|
| N |
|
— |
— |
|
| Y |
|
1.00 |
0.57, 1.84 |
0.993 |
| Dialysis_Vintage |
551 |
0.90 |
0.84, 0.96 |
0.004 |
| HF_score |
551 |
|
|
|
| 1 |
|
— |
— |
|
| 2 |
|
0.66 |
0.40, 1.08 |
0.103 |
| 3 |
|
0.93 |
0.60, 1.46 |
0.763 |
| NT_proBNP |
501 |
1.00 |
1.00, 1.00 |
0.013 |
| HD_Unit |
551 |
|
|
|
| No |
|
— |
— |
|
| Yes |
|
0.28 |
0.09, 0.66 |
0.008 |
| weight.ATE |
551 |
1.09 |
0.92, 1.29 |
0.298 |
Multivariate logistic regression outcome Binary Outcome is more
versus 0 or 1:more_vs_one_zero_180days
| Characteristic |
N |
OR |
95% CI |
p-value |
| group |
|
|
|
|
| BWH |
241 |
— |
— |
|
| MGH |
259 |
0.41 |
0.27, 0.63 |
<0.001 |
| Age |
501 |
0.98 |
0.96, 1.00 |
0.021 |
| Gender |
|
|
|
|
| Female |
205 |
— |
— |
|
| Male |
295 |
1.04 |
0.67, 1.63 |
0.864 |
| Race_Group |
|
|
|
|
| White |
313 |
— |
— |
|
| Asian |
20 |
1.70 |
0.52, 4.96 |
0.345 |
| Black |
99 |
1.64 |
0.94, 2.88 |
0.081 |
| Other |
68 |
0.86 |
0.43, 1.65 |
0.659 |
| HTN |
|
|
|
|
| N |
11 |
— |
— |
|
| Y |
489 |
0.71 |
0.19, 3.03 |
0.616 |
| DM |
|
|
|
|
| N |
116 |
— |
— |
|
| Y |
385 |
0.98 |
0.56, 1.73 |
0.943 |
| CAD |
|
|
|
|
| N |
63 |
— |
— |
|
| Y |
437 |
1.27 |
0.64, 2.65 |
0.503 |
| Dialysis_Vintage |
501 |
0.91 |
0.84, 0.97 |
0.008 |
| HF_score |
|
|
|
|
| 1 |
168 |
— |
— |
|
| 2 |
153 |
0.69 |
0.40, 1.19 |
0.187 |
| 3 |
180 |
0.92 |
0.56, 1.54 |
0.762 |
| NT_proBNP |
501 |
1.00 |
1.00, 1.00 |
0.038 |
| HD_Unit |
|
|
|
|
| No |
454 |
— |
— |
|
| Yes |
47 |
0.28 |
0.09, 0.71 |
0.014 |
Univariate linear regression outcome decongestion_time_days
| Characteristic |
N |
Beta |
95% CI |
p-value |
| group |
551 |
|
|
|
| BWH |
|
— |
— |
|
| MGH |
|
-1.0 |
-1.6, -0.48 |
<0.001 |
| Age |
551 |
-0.01 |
-0.03, 0.02 |
0.486 |
| Gender |
551 |
|
|
|
| Female |
|
— |
— |
|
| Male |
|
-0.02 |
-0.61, 0.57 |
0.955 |
| Race_Group |
551 |
|
|
|
| White |
|
— |
— |
|
| Asian |
|
-1.0 |
-2.4, 0.36 |
0.152 |
| Black |
|
-0.65 |
-1.4, 0.10 |
0.092 |
| Other |
|
-0.71 |
-1.6, 0.17 |
0.115 |
| HTN |
551 |
|
|
|
| N |
|
— |
— |
|
| Y |
|
-0.37 |
-2.4, 1.7 |
0.722 |
| DM |
551 |
|
|
|
| N |
|
— |
— |
|
| Y |
|
0.60 |
-0.09, 1.3 |
0.090 |
| CAD |
551 |
|
|
|
| N |
|
— |
— |
|
| Y |
|
0.65 |
-0.24, 1.5 |
0.155 |
| Dialysis_Vintage |
551 |
-0.05 |
-0.11, 0.00 |
0.056 |
| HF_score |
551 |
|
|
|
| 1 |
|
— |
— |
|
| 2 |
|
-0.09 |
-0.82, 0.65 |
0.818 |
| 3 |
|
0.64 |
-0.06, 1.3 |
0.074 |
| NT_proBNP |
501 |
0.00 |
0.00, 0.00 |
0.306 |
| HD_Unit |
551 |
|
|
|
| No |
|
— |
— |
|
| Yes |
|
0.27 |
-0.73, 1.3 |
0.597 |
| weight.ATE |
551 |
-0.14 |
-0.41, 0.12 |
0.290 |
decongestion_time_days Multivariate linear regression los: age
gender race
| Characteristic |
N |
Beta |
95% CI |
p-value |
| group |
|
|
|
|
| BWH |
274 |
— |
— |
|
| MGH |
276 |
-1.0 |
-1.6, -0.47 |
<0.001 |
| Age |
551 |
-0.02 |
-0.04, 0.01 |
0.200 |
| Gender |
|
|
|
|
| Female |
228 |
— |
— |
|
| Male |
322 |
-0.15 |
-0.74, 0.44 |
0.628 |
| Race_Group |
|
|
|
|
| White |
345 |
— |
— |
|
| Asian |
26 |
-0.83 |
-2.2, 0.51 |
0.227 |
| Black |
106 |
-0.78 |
-1.6, -0.01 |
0.049 |
| Other |
72 |
-0.78 |
-1.7, 0.11 |
0.087 |
decongestion_time_days Multivariate linear regression los
| Characteristic |
N |
Beta |
95% CI |
p-value |
| group |
|
|
|
|
| BWH |
274 |
— |
— |
|
| MGH |
276 |
-1.1 |
-1.6, -0.50 |
<0.001 |
| Age |
551 |
-0.02 |
-0.04, 0.01 |
0.140 |
| Gender |
|
|
|
|
| Female |
228 |
— |
— |
|
| Male |
322 |
-0.20 |
-0.80, 0.40 |
0.514 |
| Race_Group |
|
|
|
|
| White |
345 |
— |
— |
|
| Asian |
26 |
-0.84 |
-2.2, 0.49 |
0.218 |
| Black |
106 |
-0.75 |
-1.5, 0.03 |
0.062 |
| Other |
72 |
-0.86 |
-1.7, 0.03 |
0.060 |
| HTN |
|
|
|
|
| N |
11 |
— |
— |
|
| Y |
539 |
-0.08 |
-2.1, 1.9 |
0.938 |
| DM |
|
|
|
|
| N |
124 |
— |
— |
|
| Y |
427 |
0.38 |
-0.36, 1.1 |
0.314 |
| CAD |
|
|
|
|
| N |
66 |
— |
— |
|
| Y |
484 |
0.41 |
-0.53, 1.3 |
0.398 |
| Dialysis_Vintage |
551 |
-0.05 |
-0.11, 0.01 |
0.094 |
| HF_score |
|
|
|
|
| 1 |
178 |
— |
— |
|
| 2 |
166 |
-0.16 |
-0.90, 0.57 |
0.665 |
| 3 |
206 |
0.51 |
-0.19, 1.2 |
0.158 |
Univariate linear regression n_90
| Characteristic |
N |
Beta |
95% CI |
p-value |
| group |
551 |
|
|
|
| BWH |
|
— |
— |
|
| MGH |
|
-0.24 |
-0.45, -0.03 |
0.027 |
| Age |
551 |
-0.01 |
-0.02, 0.00 |
0.022 |
| Gender |
551 |
|
|
|
| Female |
|
— |
— |
|
| Male |
|
0.13 |
-0.09, 0.35 |
0.252 |
| Race_Group |
551 |
|
|
|
| White |
|
— |
— |
|
| Asian |
|
-0.05 |
-0.55, 0.45 |
0.842 |
| Black |
|
0.39 |
0.12, 0.67 |
0.006 |
| Other |
|
0.13 |
-0.20, 0.45 |
0.439 |
| HTN |
551 |
|
|
|
| N |
|
— |
— |
|
| Y |
|
0.03 |
-0.71, 0.78 |
0.930 |
| DM |
551 |
|
|
|
| N |
|
— |
— |
|
| Y |
|
0.08 |
-0.17, 0.34 |
0.520 |
| CAD |
551 |
|
|
|
| N |
|
— |
— |
|
| Y |
|
-0.10 |
-0.43, 0.23 |
0.547 |
| Dialysis_Vintage |
551 |
-0.02 |
-0.04, 0.00 |
0.040 |
| HF_score |
551 |
|
|
|
| 1 |
|
— |
— |
|
| 2 |
|
-0.19 |
-0.46, 0.08 |
0.168 |
| 3 |
|
-0.04 |
-0.30, 0.21 |
0.740 |
| NT_proBNP |
501 |
0.00 |
0.00, 0.00 |
0.369 |
| HD_Unit |
551 |
|
|
|
| No |
|
— |
— |
|
| Yes |
|
-0.15 |
-0.52, 0.22 |
0.437 |
| weight.ATE |
551 |
-0.02 |
-0.12, 0.08 |
0.726 |
decongestion_time_days Multivariate linear regression n_90: age
gender race
| Characteristic |
N |
Beta |
95% CI |
p-value |
| group |
|
|
|
|
| BWH |
274 |
— |
— |
|
| MGH |
276 |
-0.25 |
-0.46, -0.04 |
0.022 |
| Age |
551 |
-0.01 |
-0.02, 0.00 |
0.146 |
| Gender |
|
|
|
|
| Female |
228 |
— |
— |
|
| Male |
322 |
0.13 |
-0.09, 0.35 |
0.249 |
| Race_Group |
|
|
|
|
| White |
345 |
— |
— |
|
| Asian |
26 |
0.01 |
-0.49, 0.51 |
0.967 |
| Black |
106 |
0.33 |
0.04, 0.62 |
0.025 |
| Other |
72 |
0.16 |
-0.17, 0.49 |
0.346 |
decongestion_time_days Multivariate linear regression n_90
| Characteristic |
N |
Beta |
95% CI |
p-value |
| group |
|
|
|
|
| BWH |
274 |
— |
— |
|
| MGH |
276 |
-0.27 |
-0.48, -0.06 |
0.013 |
| Age |
551 |
-0.01 |
-0.01, 0.00 |
0.224 |
| Gender |
|
|
|
|
| Female |
228 |
— |
— |
|
| Male |
322 |
0.17 |
-0.06, 0.39 |
0.150 |
| Race_Group |
|
|
|
|
| White |
345 |
— |
— |
|
| Asian |
26 |
-0.01 |
-0.51, 0.48 |
0.954 |
| Black |
106 |
0.32 |
0.03, 0.61 |
0.033 |
| Other |
72 |
0.14 |
-0.19, 0.48 |
0.403 |
| HTN |
|
|
|
|
| N |
11 |
— |
— |
|
| Y |
539 |
-0.06 |
-0.80, 0.68 |
0.874 |
| DM |
|
|
|
|
| N |
124 |
— |
— |
|
| Y |
427 |
-0.01 |
-0.29, 0.26 |
0.922 |
| CAD |
|
|
|
|
| N |
66 |
— |
— |
|
| Y |
484 |
-0.02 |
-0.37, 0.33 |
0.902 |
| Dialysis_Vintage |
551 |
-0.03 |
-0.05, 0.00 |
0.021 |
| HF_score |
|
|
|
|
| 1 |
178 |
— |
— |
|
| 2 |
166 |
-0.18 |
-0.46, 0.09 |
0.190 |
| 3 |
206 |
-0.07 |
-0.33, 0.19 |
0.590 |
Univariate linear regression n_180
| Characteristic |
N |
Beta |
95% CI |
p-value |
| group |
551 |
|
|
|
| BWH |
|
— |
— |
|
| MGH |
|
-0.42 |
-0.75, -0.09 |
0.012 |
| Age |
551 |
-0.02 |
-0.03, -0.01 |
0.006 |
| Gender |
551 |
|
|
|
| Female |
|
— |
— |
|
| Male |
|
0.02 |
-0.32, 0.35 |
0.909 |
| Race_Group |
551 |
|
|
|
| White |
|
— |
— |
|
| Asian |
|
0.04 |
-0.73, 0.81 |
0.916 |
| Black |
|
0.59 |
0.17, 1.0 |
0.007 |
| Other |
|
0.16 |
-0.34, 0.65 |
0.537 |
| HTN |
551 |
|
|
|
| N |
|
— |
— |
|
| Y |
|
-0.23 |
-1.4, 0.92 |
0.697 |
| DM |
551 |
|
|
|
| N |
|
— |
— |
|
| Y |
|
0.07 |
-0.33, 0.47 |
0.730 |
| CAD |
551 |
|
|
|
| N |
|
— |
— |
|
| Y |
|
-0.14 |
-0.65, 0.36 |
0.584 |
| Dialysis_Vintage |
551 |
-0.04 |
-0.07, -0.01 |
0.020 |
| HF_score |
551 |
|
|
|
| 1 |
|
— |
— |
|
| 2 |
|
-0.25 |
-0.66, 0.17 |
0.249 |
| 3 |
|
-0.06 |
-0.46, 0.33 |
0.748 |
| NT_proBNP |
501 |
0.00 |
0.00, 0.00 |
0.147 |
| HD_Unit |
551 |
|
|
|
| No |
|
— |
— |
|
| Yes |
|
-0.39 |
-0.95, 0.18 |
0.182 |
| weight.ATE |
551 |
0.02 |
-0.13, 0.18 |
0.745 |
decongestion_time_days Multivariate linear regression n_180: age
gender race
| Characteristic |
N |
Beta |
95% CI |
p-value |
| group |
|
|
|
|
| BWH |
274 |
— |
— |
|
| MGH |
276 |
-0.44 |
-0.76, -0.11 |
0.009 |
| Age |
551 |
-0.01 |
-0.03, 0.00 |
0.042 |
| Gender |
|
|
|
|
| Female |
228 |
— |
— |
|
| Male |
322 |
0.00 |
-0.34, 0.34 |
0.994 |
| Race_Group |
|
|
|
|
| White |
345 |
— |
— |
|
| Asian |
26 |
0.15 |
-0.61, 0.91 |
0.695 |
| Black |
106 |
0.46 |
0.02, 0.90 |
0.043 |
| Other |
72 |
0.13 |
-0.38, 0.63 |
0.617 |
decongestion_time_days Multivariate linear regression n_180
| Characteristic |
N |
Beta |
95% CI |
p-value |
| group |
|
|
|
|
| BWH |
274 |
— |
— |
|
| MGH |
276 |
-0.47 |
-0.80, -0.15 |
0.004 |
| Age |
551 |
-0.01 |
-0.03, 0.00 |
0.059 |
| Gender |
|
|
|
|
| Female |
228 |
— |
— |
|
| Male |
322 |
0.05 |
-0.30, 0.39 |
0.790 |
| Race_Group |
|
|
|
|
| White |
345 |
— |
— |
|
| Asian |
26 |
0.12 |
-0.64, 0.88 |
0.758 |
| Black |
106 |
0.46 |
0.02, 0.91 |
0.043 |
| Other |
72 |
0.13 |
-0.38, 0.64 |
0.621 |
| HTN |
|
|
|
|
| N |
11 |
— |
— |
|
| Y |
539 |
-0.37 |
-1.5, 0.76 |
0.523 |
| DM |
|
|
|
|
| N |
124 |
— |
— |
|
| Y |
427 |
-0.10 |
-0.52, 0.33 |
0.658 |
| CAD |
|
|
|
|
| N |
66 |
— |
— |
|
| Y |
484 |
0.06 |
-0.48, 0.60 |
0.829 |
| Dialysis_Vintage |
551 |
-0.04 |
-0.08, -0.01 |
0.010 |
| HF_score |
|
|
|
|
| 1 |
178 |
— |
— |
|
| 2 |
166 |
-0.20 |
-0.62, 0.22 |
0.358 |
| 3 |
206 |
-0.08 |
-0.48, 0.32 |
0.702 |