I-MOVE: Inpatient Pre-Discharge Mobility Score As a Predictor of Post-Discharge Mortality

Results

Patient Characteristics

The initial search returned 452 records, of which a total of 26 (5.7%) were excluded for either being duplicates or not meeting the inclusion/exclusion criteria. Patient characteristics are shown in Table 1.

For the final dataset of 426 patients, 30-day mortality rate, readmission rate, and rate of the combined death/readmission outcome were 6.1% (26 patients), 15% (64 patients) and 19.7% (84 patients), respectively. A total of 6 patients were readmitted and died within 30 days after the initial discharge. The number of patients that had an I-MOVE score greater than or equal to 8 was 232 (54.4%). Table 2 presents the mean, standard deviation, and median I-MOVE score by patient discharge destination. Patients discharged home had an average I-MOVE score of 11.98, versus 7.24 for patients discharge to a skilled nursing facility (P = 0.2).

 

Analysis

Table 3 presents the odds ratios and coefficient estimate of the models. In the univariate analysis, an I-MOVE score greater than or equal to 8 was significantly correlated with 30-day mortality (P < 0.001), and the combined outcome (P = 0.044) but not with 30-day readmission (P = 0.76). After controlling for confounding variables, I-MOVE greater than or equal to 8 was a significant predictor of 30-day mortality (P < 0.01) but not 30-day readmission (P = 0.75) 

or the combined outcome death/readmission (P = 0.17).

Discussion

An I-MOVE score of less than 8 (inability to transfer from bed to a chair unassisted) is a statistically significant predictor of 30-day post-discharge mortality but not readmission or the combined outcome of death/readmission.

A recent review that evaluated published models that attempted to predict readmissions concluded that most current readmission risk prediction models designed for either comparative or clinical purposes perform poorly and that efforts are needed to improve their performance as use becomes more widespread [8]. Health care providers’ ability to predict which patients would be readmitted within 30 days was also shown by a recent study to be very poor, with C-statistics around 0.60 [9]. This inability of both experts and statistical methods to accurately predict readmissions may reflect some inherent randomness or unpredictability of readmissions, or the fact that a paradigm shift is still needed in the identification of the most important risk factors for readmissions. Along the same line, a recent evaluation of interventions aimed at reducing readmissions found that none of those identified in the literature managed to consistently reduce readmission rates long-term [10]. In addition, hospitals with greater adherence to recommended care processes did not achieve meaningfully better 30-day hospital readmission rates compared to those with lower levels of performance.

Conceptually, readmissions are an example of what is called “complexity science,” where many agents or factors (including the patient’s underlying illness, quality of care delivered, continuity and coordination of care, and resources available in the patient’s environment) and their interactions all play a role in the outcome [11,12]. Since I-MOVE primarily evaluates the physical capacity of the patient, and not any of the other variables that strongly affect readmission, it is perhaps not surprising that it did not predict readmission. It can be argued, on the other hand, that short term (30-day) mortality is more dependent on the patient’s physical and functional status [13] and so more likely to correlate with a measure such as I-MOVE. Inouye et al [13] found that pre-hospital, self-reported need for assistance in 7 basic “activities of daily living” (among which are transfers and ambulation) correlated with 90-day, and 2-year, post-hospital mortality.

The study has the advantage of a relatively large sample size, and the fact that the I-MOVE score was assessed before discharge eliminates the possibility of assessor bias. However, it has some limitations. We used a convenience sample, which may have introduced selection bias. Although we have no data on how providers selected patients for I-MOVE assessment, it would be reasonable to assume that patients were selected from among those whose activity level was, in terms of independence, doubtful or uncertain. That is, those who were not clearly vigorous (up and walking easily), nor clearly debilitated (in need of great assistance) may have been more likely to be assessed using I-MOVE. A more systematic selection of subjects might increase or decrease the predictive performance of the I-MOVE assessment. In addition, although we attempted to control for potential confounders, it is possible that additional confounders were left out of our analysis.

In summary, although the predictive performance of I-MOVE still needs to be confirmed by prospective studies with a comprehensive selection of subjects, the I-MOVE score at discharge appears to be associated with 30-day post-discharge mortality.

Acknowledgments: We thank the Department of Medicine’s clinical research office for their help in study design, data acquisition, and statistical analysis. 

Corresponding author: Santiago Romero-Brufau, MD, Mayo Clinic Center for Innovation,  200 First St. SW, Rochester, MN 55905,  romerobrufau.santiago@mayo.edu.

Funding/support: This publication was supported by grant number UL1 TR000135 from the National Center for Advancing Translational Sciences (NCATS). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.