The standard procedure of kidney transplantation is a retroperitoneal graft-placement into the right or left
It is known that perioperative success or failure in kidney transplantation is related to donor organ quality [
Nevertheless, there are two significant and time related hazards, cold ischemic time and
This is a single-center retrospective database analysis of all
All consecutive standard kidney transplants defined as implantation of primary deceased single donor kidney transplantations into a pristine iliac fossa of an adult recipient (age > 18 years) were included. Living related transplantations, kidney retransplantations, and transplantations of organs with variant anatomy, such as double ureter or horseshoe kidneys, were excluded. Kidney transplants with synchronous procedures such as lung, heart, liver, or pancreas transplantation, ileum conduit, ureter-to-ureter anastomosis, nephrectomy, dialysis catheter removal, appendectomy, cholecystectomy, or inguinal hernia repair were excluded as well. After applying these inclusion and exclusion criteria a total number of
Data was collected on donor organs, recipients, surgery, and adverse events (Tables
Univariable binary regression of circadian risk development for binary output variables. Shown are the results of univariable regression analyses of the circadian risk development per 3-hour day and night shift intervals for the investigated endpoints
3 h interval | No | Yes | % | Univariable binary regression | | Circadian risk development | |||
| | | OR | 95% CI | |||||
| |||||||||
Reoperation | 12 AM–3 AM | 81 | 15 | 15.6 | 0.657 | 0.878 | 0.496–1.555 | 0.653 | |
| | | | | | | | ||
6 AM–9 AM | 117 | 17 | 12.7 | 0.140 | 0.670 | 0.394–1.140 | 0.124 | ||
9 AM–12 PM ( | 229 | 38 | 14.2 | 0.140 | 0.751 | 0.514–1.098 | 0.131 | ||
12 AM–3 PM | 226 | 48 | 17.5 | 0.904 | 1.022 | 0.718–1.454 | 0.904 | ||
3 PM–6 PM | 136 | 33 | 19.5 | 0.406 | 1.191 | 0.789–1.798 | 0.412 | ||
6 PM–9 PM | 133 | 24 | 15.3 | 0.482 | 0.847 | 0.534–1.344 | 0.475 | ||
| | | | | | | | ||
| |||||||||
3 h interval | No | Yes | % | Univariable binary regression | | Circadian risk development | |||
| | | OR | 95% CI | |||||
| |||||||||
Perioperative graft loss | 12 AM–3 AM | 94 | 2 | 2.1 | 0.269 | 0.447 | 0.107–1.862 | 0.211 | |
| | | | | | | | ||
6 AM–9 AM | 129 | 5 | 3.7 | 0.707 | 0.836 | 0.327–2.133 | 0.701 | ||
9 AM–12 PM | 259 | 8 | 3.0 | 0.224 | 0.623 | 0.291–1.335 | 0.200 | ||
12 PM–3 PM | 262 | 12 | 4.4 | 0.984 | 1.007 | 0.523–1.937 | 0.984 | ||
3 PM–6 PM | 159 | 10 | 5.9 | 0.289 | 1.465 | 0.724–2.965 | 0.307 | ||
6 PM–9 PM | 151 | 6 | 3.8 | 0.725 | 0.856 | 0.361–2.033 | 0.720 | ||
9 PM–12 AM | 132 | 8 | 5.7 | 0.407 | 1.386 | 0.641–2.997 | 0.423 | ||
| |||||||||
3 h interval | No | Yes | % | Univariable binary regression | | Circadian risk development | |||
| | | OR | 95% CI | |||||
| |||||||||
Perioperative graft loss due to surgical reasons | 12 AM–3 AM | 95 | 1 | 1 | 0.616 | 0.597 | 0.079–4.496 | 0.588 | |
| | | | | | | | ||
6 AM–9 AM | 131 | 3 | 2.2 | 0.583 | 1.413 | 0.411–4.863 | 0.598 | ||
9 AM–12 PM | 265 | 2 | 0.7 | 0.205 | 0.388 | 0.090–1.677 | 0.151 | ||
12 PM–3 PM | 269 | 5 | 1.8 | 0.813 | 1.130 | 0.410–3.113 | 0.815 | ||
3 PM–6 PM | 166 | 3 | 1.8 | 0.902 | 1.080 | 0.315–3.708 | 0.903 | ||
6 PM–9 PM | 157 | 0 | 0 | 0.996 | 0.000 | 0.000 | 0.018 | ||
9 PM–12 AM | 136 | 4 | 2.9 | 0.249 | 1.913 | 0.635–5.769 | 0.281 | ||
| |||||||||
3 h interval | No | Yes | % | Univariable binary regression | | Circadian risk development | |||
| | | OR | 95% CI | |||||
| |||||||||
Delayed graft function | 12 AM–3 AM | 48 | 25 | 34.2 | 0.441 | 1.220 | 0.735–2.026 | 0.445 | |
3 AM–6 AM | 12 | 3 | 20.0 | 0.388 | 0.571 | 0.160–2.040 | 0.364 | ||
6 AM–9 AM | 68 | 31 | 31.3 | 0.812 | 1.056 | 0.672–1.660 | 0.812 | ||
9 AM–12 PM | 137 | 49 | 26.3 | 0.188 | 0.783 | 0.544–1.127 | 0.183 | ||
12 PM–3 PM | 121 | 60 | 33.1 | 0.345 | 1.184 | 0.834–1.681 | 0.347 | ||
3 PM–6 PM | 82 | 30 | 26.8 | 0.389 | 0.822 | 0.526–1.284 | 0.384 | ||
6 PM–9 PM | 76 | 29 | 27.6 | 0.527 | 0.863 | 0.548–1.361 | 0.524 | ||
9 PM–12 AM | 57 | 34 | 37.4 | 0.121 | 1.429 | 0.910–2.246 | 0.126 | ||
| |||||||||
3 h interval | No | Yes | % | Univariable binary regression | | Circadian risk development | |||
| | | OR | 95% CI | |||||
| |||||||||
Discharge on dialysis | 12 AM–3 AM | 73 | 4 | 5.1 | 0.119 | 0.441 | 0.158–1.233 | 0.081 | |
3 AM–6 AM | 15 | 4 | 21.1 | 0.145 | 2.305 | 0.750–7.082 | 0.178 | ||
6 AM–9 AM | 99 | 15 | 13.2 | 0.342 | 1.329 | 0.739–2.389 | 0.354 | ||
9 AM–12 PM | 186 | 21 | 10.1 | 0.818 | 0.942 | 0.568–1.564 | 0.817 | ||
12 PM–3 PM | 181 | 19 | 9.5 | 0.577 | 0.861 | 0.510–1.456 | 0.572 | ||
3 PM–6 PM | 112 | 15 | 11.8 | 0.629 | 1.155 | 0.645–2.068 | 0.633 | ||
6 PM–9 PM | 105 | 9 | 7.9 | 0.323 | 0.698 | 0.342–1.425 | 0.303 | ||
9 PM–12 AM | 91 | 15 | 14.2 | 0.208 | 1.461 | 0.810–2.634 | 0.223 |
Proportions of
3 h time interval | Teaching operation | CUSUM | |||||||
---|---|---|---|---|---|---|---|---|---|
Expected ( | Counted ( | % | 95% CI | Mean | Max | Min | Med | SD | |
12 AM–3 AM ( | 65 | 62 | 65 | 54.16; 74.08 | 54.5 | 345 | 1 | 37.5 | 52.3 |
3 AM–6 AM ( | 17 | 17 | 68 | 46.50; 85.05 | 56.8 | 235 | 4 | 39.0 | 52.0 |
6 AM–9 AM ( | 90 | 103 | 77 | 68.80; 83.71 | 52.9 | 341 | 1 | 29.5 | 60.1 |
9 AM–12 PM ( | 180 | 194 | 73 | 66.89; 77.91 | 45.0 | 330 | 1 | 29.0 | 55.5 |
12 PM–3 PM ( | 185 | 191 | 70 | 63.89; 75.09 | 39.6 | 361 | 1 | 22.0 | 51.9 |
3 PM–6 PM ( | 114 | 112 | 66 | 58.61; 73.35 | 39.7 | 350 | 1 | 26.0 | 46.5 |
6 PM–9 PM ( | 106 | 96 | 61 | 53.05; 68.81 | 49.8 | 347 | 1 | 33.0 | 51.9 |
9 PM–12 AM ( | 94 | 76 | 54 | 45.66; 62.72 | 50.9 | 346 | 1 | 38.0 | 54.5 |
Regression analysis of the risks for
Continuous variables | Descriptive statistics | Univariable binary regression | Risk-adjusted multivariable binary regression | |||||||
---|---|---|---|---|---|---|---|---|---|---|
Med | Mean | Range | MV | | OR | 95% CI | | OR | 95% CI | |
| ||||||||||
| | | | | | | | | | |
| | | | | | | | | | |
Donor creatinine [ | 70 | 91 | 0–8840 | 0 | 0.756 | 1.000 | 0.999–1.001 | Not selected | ||
Recipient age [yr.] | 55 | 54 | 18–77 | 0 | 0.802 | 1.001 | 0.990–1.013 | |||
Recipient BMI [kg/m2] | 25 | 25 | 15–38 | 35/2.8 | 0.521 | 1.013 | 0.975–1.052 | |||
CIT [min.] | 858 | 916 | 125–2458 | 65/5.2 | 0.030 | 1.000 | 1.000–1.001 | 0.055 | Not calculated | |
1st surgeon’s CUSUM | 29 | 46 | 1–361 | 12/1.0 | 0.761 | 1.000 | 0.998–1.003 | Not selected | ||
| ||||||||||
Categorical variables | | MV | | OR | 95% CI | | OR | 95% CI | ||
| ||||||||||
| ||||||||||
| | | | | | | | | ||
Recipient’s right fossa | 740 | 0 | 0.279 | 1.180 | 0.875–1.593 | Not selected | ||||
Right donor kidney | 611 | 0 | 0.062 | 0.755 | 0.563–1.014 | 0.109 | Not calculated | |||
Number of arteries | ||||||||||
One | 947 | 0 | Reference | Collinearity with number of arterial anastomoses | ||||||
>one | 315 | 0.258 | 1.208 | 0.871–1.677 | ||||||
Numbers of arterial anastomoses | ||||||||||
One | 1140 | 0 | Reference | Reference | ||||||
>one | 122 | 0.083 | 1.489 | 0.950–2.335 | 0.142 | Not calculated | ||||
Number of veins | ||||||||||
One | 1220 | 0 | Reference | Not selected | ||||||
>one | 42 | 0.258 | 1.520 | 0.736–3.141 | ||||||
Numbers of venous anastomoses | ||||||||||
One | 1261 | 0 | Reference | |||||||
>one | 1 | 1.000 | 0.000 | 0.000 | ||||||
| ||||||||||
Nonstented | 380 | 5/0.4 | Reference | Reference | ||||||
| | | | | | | |
Regression analysis of the risks for
Continuous variables | Descriptive statistics | Univariable binary regression | Risk-adjusted multivariable binary regression | |||||||
---|---|---|---|---|---|---|---|---|---|---|
Med | Mean | Range | | | OR | 95% CI | | OR | 95% CI | |
| ||||||||||
| | | | | | | | | | |
Donor BMI [kg/m2] | 25 | 25 | 15–38 | 4/0.3 | 0.086 | 1.051 | 0.993–1.112 | 0.276 | Not calculated | |
Donor creatinine [ | 70 | 91 | 0–8840 | 0 | 0.835 | 1.000 | 0.998–1.002 | Not selected | ||
Recipient age [yr.] | 55 | 54 | 18–77 | 0 | 0.580 | 1.006 | 0.985–1.028 | |||
| | | | | | | | | | |
CIT [min.] | 858 | 916 | 125–2458 | 65/5.2 | 0.059 | 1.001 | 1.000–1.001 | 0.481 | Not calculated | |
1st surgeon’s CUSUM | 29 | 46 | 1–361 | 12/1.0 | 0.615 | 1.001 | 0.997–1.006 | Not selected | ||
| ||||||||||
Categorical variables | | | | OR | 95% CI | | OR | 95% CI | ||
| ||||||||||
| ||||||||||
| | | | | | | | | ||
Recipient’s right fossa | 740 | 0 | 0.500 | 0.844 | 5.15–1.381 | Not selected | ||||
Right donor kidney | 611 | 0 | 0.715 | 0.913 | 0.559–1.489 | |||||
Number of arteries | ||||||||||
One | 947 | 0 | Reference | Collinearity with number of arterial anastomoses | ||||||
>one | 315 | 0.043 | 1.716 | 1.017–2.893 | ||||||
Number of arterial anastomoses | ||||||||||
One | 1140 | 0 | Reference | Reference | ||||||
>one | 122 | 0.060 | 1.853 | 0.974–3.525 | 0.069 | Not calculated | ||||
Number of veins | ||||||||||
One | 1220 | 0 | Reference | Not selected | ||||||
>one | 42 | 0.963 | 0.967 | 0.231–4.053 | ||||||
Numbers of venous anastomoses | ||||||||||
One | 1261 | 0 | Reference | |||||||
>one | 1 | 0.999 | 0.000 | 0.000 | ||||||
Stenting of ureter anastomosis | ||||||||||
Nonstented | 380 | 5/0.4 | Reference | |||||||
Stented | 877 | 0.309 | 1.348 | 0.759–2.395 |
Regression analysis of the risks of
Continuous variables | Descriptive statistics | Univariable binary regression | Risk-adjusted multivariable binary regression | |||||||
---|---|---|---|---|---|---|---|---|---|---|
Med | Mean | Range | | | OR | 95% CI | | OR | 95% CI | |
| ||||||||||
Donor age [yr.] | 53 | 52 | 5–88 | 4/0.3 | 0.270 | 1.016 | 0.988–1.045 | Not selected | ||
Donor BMI [kg/m2] | 25 | 25 | 15–38 | 4/0.3 | 0.466 | 1.035 | 0.943–1.136 | |||
Donor creatinine [ | 70 | 91 | 0–8840 | 0 | 0.646 | 0.998 | 0.992–1.005 | |||
Recipient age [yr.] | 55 | 54 | 18–77 | 0 | 0.823 | 0.996 | 0.963–1.030 | |||
| | | | | | | | | | |
CIT [min.] | 858 | 916 | 125–2458 | 65/5.2 | 0.681 | 1.00 | 0.999–1.001 | Not selected | ||
1st surgeon’s CUSUM | 29 | 46 | 1–361 | 12/1.0 | 0.350 | 0.995 | 0.984–1.006 | |||
| ||||||||||
Categorical variables | | | | OR | 95% CI | | OR | 95% CI | ||
| ||||||||||
| ||||||||||
| | | | | | | | | ||
Recipient’s right fossa | 740 | 0 | 0.558 | 0.773 | 0.326–1.833 | Not selected | ||||
Right donor kidney | 611 | 0 | 0.218 | 1.747 | 0.719–4.245 | |||||
Number of arteries | ||||||||||
One | 947 | 0 | Reference | |||||||
>one | 315 | 0.375 | 1.515 | 0.606–3.787 | ||||||
| ||||||||||
One | 1140 | 0 | Reference | Reference | ||||||
| | | | | | | | |||
Number of veins | ||||||||||
One | 1220 | 0 | Reference | Not selected | ||||||
>one | 42 | 0.713 | 1.463 | 0.192–11.169 | ||||||
Numbers of venous anastomoses | ||||||||||
One | 1261 | 0 | Reference | |||||||
>one | 1 | 1.000 | 0.000 | 0.000 | ||||||
Stenting of ureter anastomosis | ||||||||||
Nonstented | 380 | 5/0.4 | Reference | |||||||
Stented | 877 | 0.609 | 1.305 | 0.471–3.617 |
Regression analysis of the risks for
Continuous variables | Descriptive statistics | Univariable binary regression | Risk-adjusted multivariable binary regression | |||||||
---|---|---|---|---|---|---|---|---|---|---|
Med | Mean | Range | | | OR | 95% CI | | OR | 95% CI | |
| ||||||||||
| | | | | | | | | | |
Donor BMI [kg/m2] | 26 | 26 | 12–52 | 3/0.3 | 0.510 | 1.011 | 0.978–1.046 | Not selected | ||
Donor creatinine [ | 68 | 95 | 0–8408 | 0 | 0.776 | 1.000 | 1.000-1.000 | |||
Recipient age [yr.] | 56 | 54 | 18–77 | 0 | 0.013 | 1.015 | 1.003–1.027 | 0.742 | Not calculated | |
| | | | | | | | | | |
| | | | | | | | | | |
1st surgeon’s CUSUM | 29 | 46 | 1–361 | 5/0.6 | 0.770 | 1.000 | 0.997–1.002 | Not selected | ||
| ||||||||||
Categorical variables | | | | OR | 95% CI | | OR | 95% CI | ||
| ||||||||||
| ||||||||||
Night shift surgery 3 AM–6 AM | 16 | 0 | 0.303 | 0.515 | 0.146–1.822 | Not selected | ||||
Recipient’s right fossa | 555 | 0 | 0.013 | 0.689 | 0.514–0.924 | 0.065 | Not calculated2 | |||
Right donor kidney | 425 | 0 | 0.618 | 1.076 | 0.808–1.432 | Not selected | ||||
Number of arteries | ||||||||||
One | 667 | 0 | Reference | |||||||
>one | 216 | 0.432 | 1.146 | 0.822–1.599 | ||||||
Number of arterial anastomoses | ||||||||||
One | 802 | 0 | Reference | Reference | ||||||
>one | 81 | 0.006 | 1.927 | 1.212–3.064 | 0.087 | Not calculated | ||||
Number of veins | ||||||||||
One | 852 | 0 | Reference | Not selected | ||||||
>one | 31 | 0.835 | 0.920 | 0.418–2.025 | ||||||
Number of venous anastomoses | ||||||||||
One | 882 | 0 | Reference | |||||||
>one | 1 | 1.000 | 0.000 | 0.000 | ||||||
Stenting of ureter anastomosis | ||||||||||
Nonstented | 214 | 0 | Reference | |||||||
Stented | 669 | 0.161 | 0.791 | 0.571–1.098 |
The investigated study endpoints were
For the analysis of a possible risk development during day- or nighttime surgery the circadian 24 hours was analyzed using different permutations of defined time intervals with the goal of identifying time blocks of day- and nighttime surgery that are associated with the most significant risk increments for the investigated study endpoints. The start-times of surgery (skin incision times) determined the day or night shift intervals each kidney transplantation was assigned to.
Deceased donor kidney transplants were performed on 24 hours a day, seven days a week basis by a team of either two or three or seldom four surgeons. All ureter-to-bladder anastomoses were performed using the Gregoir-Lich antireflux technique [
Regular working hours at our institution are from 07:30 AM to 4:30 PM with a 30 minutes’ rest-time. Surgeons and staff who are assigned for
(a) Shown is the distribution of night- and daytime shifts over 24 hours at our institution. Regular working hours at our institution are 07:30 AM to 4:30 PM. Included are two hand-over periods of 30–45 minutes for each shift change
A surgeons’ experience with kidney transplantation was estimated by the number of performed transplants he accumulated until the date of each transplant (labeled here as CUSUM). This measure of surgical experience was examined for significant differences in distribution between investigated time intervals. Teaching transplants were defined as operations performed by a primary surgeon with less surgical seniority as compared to the assisting surgeon.
Patterns of missing data were analyzed by Little’s test for Missing Data Completely at Random (MCAR) using SPSS Version 22 (PASW Statistics Inc., IBM, Somers, NY, USA). Missing data had a verified MCAR pattern, if significance level was
Binary univariable regression analysis was used to determine the odds ratios and the significance level of risk factors for the investigated study endpoints. Risk factors with significant
As a result of the multivariable regression analysis of the investigated study endpoints we found that cold ischemic time (CIT) was only relevant for the study endpoint of
Using JMP® Pro Version 11.2.0 (SAS Institute Inc., Cary, NC, USA) we finally identified a nonlinear regression function of the type
(a) Increments of odds ratios (OR) for
First and second derivate of that function were implemented to the two-dimensional
Missing value (MV) analysis by Little’s MCAR verified a pattern of data completely missing at random with
Regression analysis of the risks for
Continuous variables | Descriptive statistics | Univariable binary regression | Risk-adjusted multivariable binary regression | |||||||
---|---|---|---|---|---|---|---|---|---|---|
Med | Mean | Range | | | OR | 95% CI | | OR | 95% CI | |
| ||||||||||
| | | | | | | | | | |
Donor BMI [kg/m2] | 25 | 26 | 12–52 | 3/0.3 | 0.275 | 1.026 | 0.980–0.1075 | Not selected | ||
Donor creatinine [ | 70 | 91 | 0–8408 | 0 | 0.552 | 0.999 | 0.996–1.002 | |||
Recipient age [yr.] | 56 | 54 | 18–77 | 0 | 0.181 | 1.012 | 0.995–1.029 | 0.418 | Not calculated | |
Recipient BMI [kg/m2] | 25 | 25 | 15–38 | 33/3.4 | 0.129 | 1.041 | 0.988–1.097 | 0.178 | Not calculated | |
CIT [min.] | 858 | 917 | 125–2458 | 6/0.6 | 0.289 | 1.000 | 1.000–1.001 | Not selected | ||
1st surgeon’s CUSUM | 29 | 46 | 1–361 | 17/1.8 | 0.975 | 1.000 | 0.996–1.004 | |||
| ||||||||||
Categorical variables | | | | OR | 95% CI | | OR | 95% CI | ||
| ||||||||||
| ||||||||||
Night shift surgery 3 AM–6 AM | 20 | 0 | 0.163 | 2.212 | 0.725–6.747 | 0.214 | Not calculated | |||
Right recipient’s fossa | 614 | 0 | 0.229 | 0.775 | 0.512–1.174 | Not selected | ||||
Right donor kidney | 474 | 0 | 0.986 | 0.996 | 0.661–1.502 | |||||
Number of arteries | ||||||||||
One | 735 | 0 | Reference | Collinearity with number of arterial anastomoses | ||||||
>one | 250 | 0.053 | 1.544 | 0.995–2.396 | ||||||
Number of arterial anastomoses | ||||||||||
One | 888 | 0 | Reference | Reference | ||||||
>one | 97 | 0.039 | 1.842 | 1.031–3.292 | 0.066 | Not calculated | ||||
Number of veins | ||||||||||
One | 950 | 0 | Reference | Not selected | ||||||
>one | 35 | 0.832 | 1.122 | 0.388–3.245 | ||||||
Number of venous anastomosis | ||||||||||
One | 984 | 0 | Reference | |||||||
>one | 1 | 1.000 | 0.000 | 0.000 | ||||||
Stenting of ureter anastomosis | ||||||||||
Nonstented | 242 | 3/0.3 | Reference | |||||||
Stented | 740 | | 0.811 | 0.510–1.290 |
After several permutations (data not shown) the following 3-hour interval division of 24 h was identified as the time blocks that provide the best resolution with the highest significance levels and the highest calculable hazard ratios for the investigated study endpoints: 12 PM–3 AM, 3 AM–6 AM, 6 AM–9 AM, 9 AM–12 AM, 12 AM–3 PM, 3 PM–6 PM, 6 PM–9 PM, and 9 PM–12 PM (Table
There were no significant differences between the frequencies of individual surgeons’ postoperative complications that caused subsequent reoperations in each investigated three-hour interval (
In the next step we analyzed how big the risk was for any of the binary endpoints within each of the 3 h daytime intervals. Univariable binary regression analysis showed a substantial increase in the risk for
Risk-adjusted multivariable regression analyses revealed that donor age, donor BMI, and
Donor age,
Donor age, recipient BMI, and cold ischemic time were significant independent risk factors for delayed graft function (Table
Donor age was the only independent significant risk factor for hospital discharge on hemodialysis (Table
Multivariable regression analyses revealed that CIT was relevant only for the endpoint of
This study reports for the first time that transplantation in the early morning hours between 3 AM and 6 AM is an independent significant risk factor for early outcome after kidney transplantation (Tables
Variables that reflect the surgical complexity such as the number of renal arteries and veins [
Despite the small number of patients who underwent transplanting within the 3 AM to 6 AM interval it must be noted that initiation of transplantation during that interval significantly increased the risk of
When putting these two findings together the logical consequence is to avoid transplantations between 3 AM and 6 AM in the morning, at least as long as CIT is not prolonged above the turning point of 23.5 hours. Following this approach would replace the so far undisputed transplant dogma of
Not only is it a judgment of common sense that working at late night hours inevitably induces increased error and defect rates, but also it has been shown extensively that sleep deprivation and mental fatigue negatively impact on key cognitive functions such as attention [
The surgeon usually has to organize and schedule the transplantation procedure. This involves informing involved personnel (intensive care unit, anesthesiologist, scrub nurses, and surgical team), carrying out the recipient examination and repetitive communication with the transplant coordinator. Further, the donor organ needs to be inspected and prepared ahead of the actual start of anesthesia. Consequently, the transplant surgeon usually is up on his feet at least one hour before the actual start of the operating procedure. The critical time period of 3 AM to 6 AM, which reflects the start-time of the operation, usually requires a wake-up call at least 1-2 hours earlier. Moreover, if the transplant center has no specialized transplant team in stand-by, who could be called in to perform an organ transplantation at any time, then the in-house on-call surgeons, who are often involved in other emergency procedures and consultations, will have to perform the transplantation in between all other emergency procedures, as is the case in our center. This implies that the on-call surgeons frequently have no time to rest before the onset of the transplant procedure, which possibly explains the high risk increment during the last hours of a 24-hour night shift. There were no differences in neither age, seniority, experience, nor training, when comparing daytime shifts and night shifts. Respectively, there were no differences in seniority and transplant surgery experience: for neither the surgeons, nor anesthesiologists, nor nursing staff or any other caregivers. This may be different from institution to institution and may thus lead to different results.
Theoretically there is a possible impact of surgical support staff’s fatigue on surgical outcome as well, because the surgical support staff has similar day- and nighttime shifts. But the staff is not responsible for and not involved in the management process of emergencies, such as interdisciplinary telephone conferences, ER visits, patient examinations, discussion of CT scan results, and other time consuming nightly events, which do keep the surgeons from sleeping. Because the staff is not involved in processing of nonsurgical emergencies and because the staff of the general/transplant surgery department is exclusively assigned to the general/transplant surgery team, their workload during night shifts is significantly less.
Another possible influence might be the experience level of the supporting surgical staff. But the staff’s team always consisted of one senior scrub nurse and one learning scrub nurse, who were randomly assigned to the surgeons’ night shift team. There was no systematic hazardous team-bias that possibly could have altered the quality of surgery during night shifts or certain night shift hours.
Fechner et al. [
In our current analysis
We found that more than one arterial anastomosis was a significant risk for
One consequence of intentional shifting of the start-time of a transplant operation from the 3 AM–6 AM interval to a day shift interval after 6 AM is the likely collisions with scheduled subsequent elective surgeries. Our data though justifies the postponing of the elective surgery schedule in order to avoid likely higher complication rates of night shift kidney transplantations as in our opinion these aspects outweigh the negative consequences of a delayed elective surgeries schedule, because higher rates of complications not only affect each transplanted patient, but also have substantial negative economic consequences for the hospital as well.
Pulsatile perfusion preservation could be a means to avoid delayed graft function caused by prolonged CIT [
The conventional approach to odds ratio calculations for CIT-associated risks usually is a comparison of a predefined CIT interval against the mean risk that lies outside this predefined interval (two-sided; before and after) [
Literature about CIT-impact on kidney transplantation outcome.
Authors | Year | Endpoint | Number of CIT intervals | CIT interval details | Resolution [hours] | OR calculation method [stepwise forward/blockwise two-sided] |
---|---|---|---|---|---|---|
Debout et al. [ | 2015 | Graft failure, death | 4 | 6–16 h, 16–24 h, 24–36 h, >36 h | 8 and 12 | Blockwise two-sided |
| ||||||
Gill et al. [ | 2014 | DGF | 7 | 0–6 h, 6–12 h, 12–18 h, 18–24 h, 24–30 h, 30–36 h, >36 h | 6 | Blockwise two-sided |
| ||||||
Sert et al. [ | 2014 | DGF | 3 | 0–10 h, 10–20 h, 20–30 h, >30 h | 10 | Blockwise two-sided |
| ||||||
van der Vliet et al. [ | 2011 | DGF, 5 yr graft survival | 5 | 0–16 h, 16–20 h, 21–25 h, 26–30 h, >30 h | 4 and 16 | Blockwise two-sided |
| ||||||
Quiroga et al. [ | 2006 | DGF, AR | 5 | 5–17 h, 18–20 h, 21–24 h, 25–31 h, >32 h | 3, 4, 5, 7, 13 | Blockwise two-sided |
| ||||||
Su et al. [ | 2004 | Graft failure | 6 | 0–8 h, 9–16 h, 17–24 h, 25–36 h, 37–48 h, >48 h | 8 and 12 | Blockwise two-sided |
| ||||||
Opelz [ | 2004 | Graft failure | 5 | 0–6 h, 7–12 h, 13–24 h, 25–36 h, >36 h | 6 and 12 | Blockwise two-sided |
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Smits et al. [ | 2000 | Graft failure | 4 | 0–18 h, 19–24 h, 25–36 h, >37 h | 5 and 18 | Blockwise two-sided |
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Ojo et al. [ | 1997 | DGF | 4 | 0–12 h, 13–24 h, 25–36 h, | 12 | Blockwise two-sided |
Furthermore, when converting a continuous variable to a categorical variable it is necessary to generate categorical steps that are small enough. Otherwise, significant risk increments or any nonlinear risk development is artificially concealed due to lack of resolution. Most published analyses though are based upon CIT categorizations with resolutions ranging from 6 to 12 hours [
This study demonstrates a new mathematical method for calculating the cut-off value for the largest CIT-mediated risk increment for adverse early outcomes such as delayed graft function. The proposed method for calculating time related risk increments and cut-offs utilizes a cumulative stepwise forward categorization of CIT. We believe that this approach is appropriate when the mathematical relation between a continuous variable such as time and the odds ratio for an adverse event is unknown. In detail, this method allowed the deduction of a nonlinear regression function with the highest SSE and
Furthermore, we demonstrate that utilizing the
Acute rejection
Confidence interval
Cold ischemic time
Cumulative summation
Delayed graft function
Initial nonfunction
Odds ratio
Residual sum of squares
Reoperation.
All authors declare that there is no conflict of interests that could be perceived as prejudicing the impartiality of the research reported.
Nikos Emmanouilidis and Julius Boeckler participated in research design. Nikos Emmanouilidis, Julius Boeckler, Bastian P. Ringe, Alexander Kaltenborn, and Harald Schrem participated in the writing of the paper. Nikos Emmanouilidis, Julius Boeckler, Bastian P. Ringe, Frank Lehner, Jürgen Klempnauer, and Harald Schrem participated in the performance of the research. Nikos Emmanouilidis, Julius Boeckler, and Bastian P. Ringe participated in data acquisition. Nikos Emmanouilidis, Julius Boeckler, Alexander Kaltenborn, Hans Friedrich Koch, and Harald Schrem participated in data analysis. Nikos Emmanouilidis and Julius Boeckler contributed equally.
The work of the authors Alexander Kaltenborn and Harald Schrem was supported by a grant from the German Federal Ministry of Education and Research (Reference no. 01EO1302).