Preoperative Care Assessment of Need Scores Are Associated With Postoperative Mortality and Length of Stay in Veterans Undergoing Knee Replacement
Background: Care Assessment of Need (CAN) scores predicting 90-day mortality and hospitalization are automatically computed each week for patients receiving care at Veterans Health Administration facilities. While currently used only by primary care teams for care coordination, we explored their value as a perioperative risk stratification tool before major elective surgery.
Methods: We collected relevant demographic and perioperative data along with perioperative CAN scores for veterans who underwent total knee replacement between July 2014 and December 2015. We examined score distribution, relationships of preoperative CAN 1-year mortality scores with 1-year postoperative mortality and index hospital length of stay (LOS), and patterns of mortality.
Results: Among 8206 patients, 1-year mortality was 1.4% (110 patients), and CAN scores exhibited near-normal distribution. Median scores among survivors were significantly higher than those of in nonsurvivors (45 vs 75; P < .001). The Kaplan-Meier curves showed an approximately 4-fold higher rate of death at 1 year in the highest tercile for 1-year mortality CAN scores compared with those with lower scores (2.0% vs 0.5% respectively; P < .001). Locally estimated scatterplot smoothing curves revealed a significant and nonlinear increase in hospital LOS across preoperative CAN scores.
Conclusions: Although designed for ambulatory care use, CAN scores can identify patients at high risk for mortality and extended hospital LOS in an elective surgery population. The CAN scores may prove valuable in supporting informed decision making and preoperative planning in high-risk and vulnerable populations. Further study is needed to confirm the validity of CAN scores and compare them to other more widely used surgical risk calculators.
For the 1-year survival outcome, the primary dependent variable, we queried the vital status files in the CDW for the date of death if applicable. We confirmed survival beyond 1 year by examining vital signs in the CDW for a minimum of 2 independent encounters beyond 1 year after the date of discharge. To compute the index LOS, the secondary outcome, we computed the difference between the date of admission and date of hospital discharge.
Statistical Methods
The parameters and performance of the multivariable logistic regression models developed to compute the various CAN mortality and hospitalization risk scores have been previously described.12 Briefly, Wang and colleagues created parsimonious regression models using backward selection. Model discrimination was evaluated using C (concordance)-statistic. Model calibration was assessed by comparing predicted vs observed event rates by risk deciles and performing Cox proportional hazards regression.
We plotted histograms to display preoperative CAN scores as a simple measure of distribution (Figure 1). We also examined the cumulative proportion of patients at each preoperative CAN 1-year mortality score.
Using a conventional t test, we compared means of preoperative CAN 1-year mortality scores in patients who survived vs those who died within 1 year. We also constructed a plot of the proportion of patients who had died within 1 year vs preoperative CAN 1-year mortality scores. Kaplan-Meier curves were then constructed examining 1-year survival by CAN 1-year mortality score by terciles.
Finally, we examined the relationship between preoperative CAN 1-year mortality scores and index LOS in 2 ways: We plotted LOS across CAN scores, and we constructed a
Results
We identified 8206 patients who had undergone a TKR over the 18-month study period. The overall mean (SD) for age was 65 (8.41) years; 93% were male, and 78% were White veterans. Patient demographics are well described in a previous publication.16,17
In terms of model parameters for the CAN score models, C-statistics for the 90-day outcome models were as follows: 0.833 for the model predicting hospitalization (95% CI, 0.832-0.834); 0.865 for the model predicting death (95% CI, 0.863-0.876); and 0.811 for the model predicting either event (95% CI, 0.810-0.812). C-statistics for the 1-year outcome models were 0.809 for the model predicting hospitalization (95% CI, 0.808-0.810); 0.851 for the model predicting death (95% CI, 0.849-0.852); and 0.787 for the model predicting either event (95% CI, 0.786-0.787). Models were well calibrated with α = 0 and β = 1, demonstrating strong agreement between observed and predicted event rates.
The distribution of preoperative CAN 1-year mortality scores was close to normal (median, 50; interquartile range, 40; mean [SD] 48 [25.6]) (eTable). The original CAN score models were developed having an equal number of patients in each strata and as such, are normally distributed.12 Our cohort was similar in pattern of distribution. Distributions of the remaining preoperative CAN scores (90-day mortality, 1-year hospitalization, 90-day hospitalization) are shown in Figures 2, 3, and 4. Not surprisingly, histograms for both 90-day and 1-year hospitalization were skewed toward higher scores, indicating that these patients were expected to be hospitalized in the near future.
Overall, 1.4% (110/8096) of patients died within 1 year of surgery. Comparing 1-year mortality CAN scores in survivors vs nonsurvivors, we found statistically significant differences in means (47 vs 66 respectively, P < .001) and medians (45 vs 75 respectively, P < .001) (Table 2). In the plot examining the relationship between preoperative 1-year mortality CAN scores and 1-year mortality, the percentage who died within 1 year increased initially for patients with CAN scores > 60 and again exponentially for patients with CAN scores > 80. Examining Kaplan-Meier curves, we found that survivors and nonsurvivors separated early after surgery, and the differences between the top tercile and the middle/lower terciles were statistically significant (P < .001). Mortality rates were about 0.5% in the lower and middle terciles but about 2% in the upper tercile (Figure 5).
In the plot examining the relationship between CAN scores and index LOS, the LOS rose significantly beyond a CAN score of 60 and dramatically beyond a CAN score of 80 (Figure 6). LOESS curves also showed 2 inflection points suggesting an incremental and sequential rise in the LOS with increasing CAN scores (Figure 7). Mean (SD) LOS in days for the lowest to highest terciles was 2.6 (1.7), 2.8 (2.1), and 3.6 (2.2), respectively.
