Table 2.
Weight processing by algorithm and type of algorithm.
| Item | Patients retained, n (% of raw weights) | Weight measurements retained, n (% of raw weights) | Weight (kg), mean (SD; range) | Weight (kg), median (IQR) | |
| Raw weights | 99,958 (100) | 1,175,995 (100) | 94.3 (22.0; 0-674.0) | 91.8 (27.4) | |
| Algorithms that used all data | |||||
|
|
Buta et al [18] | 90,159 (90.2) | 1,131,996 (96.3) | 94.3 (21.9; 12.3-111.1) | 91.9 (27.3) |
|
|
Chan and Raffa [19] | 96,132 (96.2) | 1,170,114 (99.5) | 94.3 (21.9; 24.5-330.0) | 91.8 (27.4) |
|
|
Maguen et al [26] | 98,352 (98.4) | 1,037,293 (88.2) | 93.3 (21.0; 31.9-245.4) | 91.0 (26.4) |
|
|
Breland et al [17] | 99,958 (100) | 1,175,177 (99.9) | 94.3 (21.9; 34.0-315.0) | 91.8 (27.4) |
|
|
Maciejewski et al [25] | 99,958 (100) | 1,146,995 (97.5) | 94.4 (21.8; 28.1-247.7) | 91.9 (27.2) |
|
|
Littman et al [24] | 96,130 (96.2) | 1,161,661 (98.8) | 94.3 (21.8; 34.0-247.7) | 91.9 (27.2) |
| Period-specific algorithms | |||||
|
|
Rosenberger et al [28] | 63,405 (63.4) | 227,215 (19.3) | 94.3 (21.0; 0-596.2) | 92.0 (26.3) |
|
|
Kazerooni and Lim [23] | 23,987 (24) | 71,961 (6.1) | 94.8 (21.8; 0-559.6) | 92.5 (27.2) |
|
|
Goodrich et al [20] | 95,748 (95.8) | 199,830 (17) | 93.5 (20.6; 36.3-226.8) | 91.2 (25.7) |
|
|
Janney et al [22] | 95,742 (95.8) | 199,830 (17) | 93.5 (20.6; 35.6-247.7) | 91.2 (25.7) |
|
|
Jackson et al [21]a | 96,559 (96.6) | 251,501 (21.4) | 93.6 (20.6; 27.4-259.0) | 91.2 (25.9) |
|
|
Noël et al [27]a | 99,958 (100) | 683,008 (58.1) | 94.0 (20.9; 31.8-267.1) | 91.6 (26.1) |
aThese algorithms differ from the other period-specific algorithms as they first use all available data and then proceed to aggregate measures by the mean or median within select periods.