Table 2.
Multivariate model coefficients and predicted probability of falling according to risk category for validation sample and development sample.
| Predictor variable | Validation sample
|
Development sample
|
||
|---|---|---|---|---|
| Regression coefficient | OR (95% CI) | Regression coefficient | OR (95% CI) | |
| Fell in previous 12 mo, yes/no | 2.83 (1.84, 3.81) | 16.90 (6.30, 45.00) | 1.76 (1.10, 2.42) | 5.80 (3.00, 11.22) |
| FOG in past mo, yes/no | 0.05 (30.76, 0.88) | 1.06 (0.47, 2.40) | 0.87 (0.17, 1.60) | 2.39 (1.19, 4.80) |
| Self-selected gait speed <1.1 m/s, yes/no | 0.87 (0.07, 1.67) | 2.38 (1.07, 5.31) | 0.62 (30.04, 1.27) | 1.86 (0.96, 3.58) |
| Risk categorya | Number of participants who fell/total number of participants scoring in this category | Predicted (actual) probability of falling, % | Number of participants who fell/total number of participants scoring in this category | Predicted (actual) probability of falling, % |
| Low (0) | 4/47 | 1 (9) | 8/43 | 17 (19) |
| Med (>0 to <8) | 15/53 | 30 (28) | 36/73 | 51 (49) |
| High (>8) | 47/71 | 66 (66) | 76/89 | 85 (85) |
a
Weights are assigned to each of the three predictor variables based on the regression coefficients of the 3-predictor regression (ie, falling in past month = 6, freezing of gait in the past month = 3, gait speed <1.1 m per second = 2). The sum of the predictor weights give a maximum score of 11 and a minimum score of 0. Probability of falling in the next 6 months is then classified (based on the sum of the predictor weights) as low (0), moderate (>0 to < 8) or high (≥8) [7].