[Development of a risk prediction model and a sample risk chart for long-term care certification based on the functional health of older adults].

Objectives　The first aim of this study was to develop risk prediction models based on age, sex, and functional health to estimate the absolute risk of the 3-year incidence of long-term care certification and to evaluate its performance. The second aim was to produce risk charts showing the probability of the incident long-term care certification as a tool for prompting older adults to engage in healthy behaviors.Methods　This study's data was obtained from older adults, aged ≥65 years, without any disability (i.e., they did not certify≥care level 1) and residing in Yabu, Hyogo Prefecture, Japan (n=5,964). A risk prediction model was developed using a logistic regression model that incorporated age and the Kihon Checklist (KCL) score or the Kaigo-Yobo Checklist (KYCL) score for each sex. The 3-year absolute risk of incidence of the long-term care certification (here defined as≥care level 1) was then calculated. We evaluated the model's discrimination and calibration abilities using the area under the receiver operating characteristic curves (AUC) and the Hosmer-Lemeshow goodness-of-fit test, respectively. For internal validity, the mean AUC was calculated using a 5-fold cross-validation method.Results　After excluding participants with missing KCL (n=4) or KYCL (n=1,516) data, we included 5,960 for the KCL analysis and 4,448 for the KYCL analysis. We identified incident long-term care certification for men and women during the follow-up period: 207 (8.2%) and 390 (11.3%) for KCL analysis and 128 (6.6%) and 256 (10.2%) for KYCL analysis, respectively. For calibration, the χ2 statistic for the risk prediction model using KCL and KYCL was: P=0.26 and P=0.44 in men and P=0.75 and P=0.20 in women, respectively. The AUC (mean AUC) in the KCL model was 0.86 (0.86) in men and 0.83 (0.83) in women. In the KYCL model, the AUC was 0.86 (0.85) in men and 0.85 (0.85) in women. The risk charts had six different colors, suggesting the predicted probability of incident long-term care certification.Conclusions　The risk prediction model demonstrated good discrimination, calibration, and internal validity. The risk charts proposed in our study are easy to use and may help older adults in recognizing their disability risk. These charts may also support health promotion activities by facilitating the assessment and modification of the daily behaviors of older adults in community settings. Further studies with larger sample size and external validity verification are needed to promote the widespread use of risk charts.

Preoperative frailty is associated with progression of postoperative cardiac rehabilitation in patients undergoing cardiovascular surgery.

OBJECTIVE: Preoperative frailty affects the progression of cardiac rehabilitation (CR) after cardiovascular surgery. Different frailty assessment measures are available. However, it remains unclear which tool most likely predicts the progress of CR. Our aim was to evaluate preoperative frailty using different methods and to identify the predictors in the progress of postoperative CR. METHODS: Eighty-nine patients underwent elective cardiovascular surgery at our institution between May 2016 and April 2018. Mortality cases and patients without evaluation of preoperative frailty were excluded. This study included the remaining 78 patients. We divided the patients into two groups: 47 patients who achieved 100 m walking within 7 days after surgery (successful CR group) and 31 patients who achieved 100 m walking later than 8 days after surgery (delayed CR group). Preoperative frailty was assessed using the Kaigo-Yobo Check-List, Cardiovascular Health Study, Short Physical Performance Battery, and Clinical Frailty Scale. RESULTS: The prevalence of frailty defined by these four measures was higher in the delayed CR group. The delayed CR group had lower nutritional status, serum hemoglobin level, serum albumin level, and psoas muscle index. Multivariable analysis demonstrated the Kaigo-Yobo Check-List score as an independent predictor for delayed CR (odds ratio 1.53, 95% confidence interval 1.18-1.98, p = 0.001) and Clinical Frailty Scale as an independent predictor for discharge to a health care facility (odds ratio 3.70, 95% confidence interval 1.30-10.51, p = 0.014). CONCLUSIONS: Among the various tools for assessing frailty, the Kaigo-Yobo Check-List was most likely to predict the progress of postoperative CR after elective cardiovascular surgery.

Are the Kihon Checklist and the Kaigo-Yobo Checklist Compatible With the Frailty Index?

OBJECTIVES: To explore comparability of Kihon Checklist (KCL) and Kaigo-Yobo Checklist (KYCL) to Frailty Index (FI) in predicting risks of long-term care insurance (LTCI) certification and/or mortality over 3 years. DESIGN: Prospective cohort study. SETTING AND PARTICIPANTS: 1023 Japanese community-dwelling older adults from the Kusatsu Longitudinal Study of Aging and Health. MEASURES: Frailty status was quantified at baseline using KCL, KYCL, and 32-deficit and 68-deficit FI. Relationships of the measures were examined using Spearman rank correlation coefficients. Cox regression models examined the risk of new certification of LTCI or mortality according to KCL, KYCL, and FI. Predictive abilities of KCL and KYCL were compared with FI using area under the receiver operating characteristic curve (AUC), C statistics, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). RESULTS: Mean age was 74.7 years and 57.6% were women. KCL and KYCL were significantly correlated to 32-FI (r = 0.60 and 0.36, respectively) and to 68-FI (r = 0.88 and 0.61, respectively). During the follow-up period, 92 participants (9%) were newly certified for LTCI or died. Fully adjusted Cox models showed that higher KCL, KYCL, 32-FI, and 68-FI were all significantly associated with elevated risks [hazard ratio (HR) = 1.03, 95% CI = 1.01-1.04, P < .001; HR = 1.04, 95% CI = 1.02-1.05, P < .001; HR = 1.03, 95% CI = 1.01-1.05, P = .001; HR = 1.04, 95% CI = 1.02-1.06, P < .001, respectively, per 1/100 increase of max score]. AUC and C-statistics of KCL and KYCL were not different statistically from those of 32-FI and 68-FI. Predictive abilities of KCL were superior to 32-FI in NRI and IDI but inferior to 68-FI in category-free NRI, and those of KYCL were superior to 32-FI in IDI but inferior to 68-FI in NRI. CONCLUSIONS: Although KCL and KYCL include smaller numbers of items than standard FI, both tools were shown to be highly correlated with FI, significant predictors of LTCI certification and/or mortality, and compatible to FI in the risk prediction.