Participants and study design
This was a retrospective cohort study of patients aged ≥20 years admitted to Shizuoka General Hospital from April 2019 to September 2020. We excluded inpatients with inpatient dental surgery, inpatients with obstetrics and gynaecology during pregnancy, childbirth, puerperium, and inpatients with unknown bedridden status, which are not subject to DPC (Figure 1).
Variables at hospitalisation
Variables were age, sex, height, weight, body mass index (BMI), emergency admission [4], ambulance transport, history of dementia, Parkinson’s disease, stroke, and visual impairment (diagnosis of glaucoma or cataract) [5], use of care insurance, presence of cognitive function scores, inpatient ward (internal medicine, department of surgery, or emergency department) [4], disturbance of consciousness. Additionally, the variables for ADLs [4] were eating [4], transferring [5,6], dressing, moving and using the toilet, bathing, walking on the level ground [6], stair climbing, changing clothes, defecation management, and urination management [5]. Furthermore, there were good sleep condition, use of sleeping pills [4], medication management status (myself or others), fall assessment items at admission (history of falls within one year [4–6]), inability to stand without holding, impaired judgment and comprehension, and toileting assistance, and use of portable toilet, and level of independence in the daily life of an individual with disabilities (bedriddenness rank).
The Japanese MHLW bedriddenness rank
Bedriddenness rank [10, 11] by the Ministry of Health, Labor, and Welfare is an official assessment tool in Japan’s long-term care insurance system that evaluates the degree to which a person’s daily life is limited. The degree of bedriddenness can be easily assessed by monitoring the person’s movements in everyday life, such as whether the patient is independent, in a wheelchair, or bed. Further, it is ranked into four levels, with particular attention to the state of mobility rather than ability. As for the degree of bedriddenness by rank, rank J is defined as independence/autonomy, rank A as house-bound, rank B as chair-bound, and rank C as bed-bound. We ranked the degree of bedriddenness based on evaluation by medical personnel and reports from family members. The detailed evaluation procedure and its reliability have already been reported by Tago et al. [4, 11]. Bedriddenness. Thus, the bedriddenness rank is easy to evaluate and is commonly used in Japan’s medical and nursing care settings.
Falls within 28 days of hospitalisation as an outcome
The primary outcome was the time from admission to the date of a fall incident level 2 or higher requiring medical resources. Moreover, for patients who died in the hospital, the death date was used as a censoring date. For patients without falls, the discharge date was used as a censoring date. The classification of fall incident or accident levels [12] is shown in Supplementary Table 1. Based on the 1987 Kellogg International Work Group on Fall Prevention in the Elderly [13], the definition of a fall was an unintentional landing of any part of the body other than the sole on the same or lower surface. It also includes falls from wheelchairs and beds.
For the inpatients at the Shizuoka General Hospital in 2019, the average length of stay was 11.4 days, and that for the DPC-specific hospital group in the same year was 11.3 days [14]. In this study, the average length of stay for inpatients was 12.9 days, and 90.8 % of the patients were discharged within 28 days of admission (Figure 2). Therefore, we examined a model to predict falls within 28 days of admission.
Statistical analysis
Demographic data and the distribution of candidate predictors at hospitalisation were summarised as mean ± standard deviation or maximum (range) for continuous variables; distribution type and frequency (%) were considered for categorical variables. Additionally, t-tests for continuous variables and chi-square tests for categorical variables were performed to compare any differences between groups. Finally, the fall rate was estimated by the Kaplan-Meier method.
We explored the risk factors that affect the time from hospital admission to fall. Further, we entered candidate predictors and known predictors that were significant (p < 0.001) in comparing patient background tables by fall status into a multivariable Cox proportional hazards model. Factors that were significant in the multivariable model were identified as risk factors. The hazard ratio, 95% confidence interval (CI), and p-value were calculated in the Cox model. Spearman’s correlation coefficient was used to confirm independence between the covariates with an absolute value > 0.3. Further, one of the two items was selected based on its clinical importance and the possibility of collecting data reliably at admission.
In this study, we explored risk factors by performing a one-step stratification on essential variables. This operation improves the predictive performance of the prediction model by accounting for the interactions among the covariates and treating their relationships as non-linear. First, for each variable of interest, the number of other variables for which the absolute value of Spearman’s correlation coefficient exceeds 0.3 was counted. Then, from a group of candidate stratification factors with high numbers, clinically meaningful items were selected as stratification factors.
The prediction model was constructed using validation set (2/3 of the inpatients were randomly selected). The remaining test set (1/3 of the inpatients) compared the predicted and measured fall values at 28 days after admission. The prediction performance index of the prediction model was calculated as a c-index. In addition, to compare the prediction model constructed by stratifying essential stratification items with the prediction model without stratification, the percentage of falls when the difference between the predictions is greater than 0 (good stratification model), and less than 0 (wrong stratification model) were compared and evaluated by the chi-square test.
The significance level of the two-tailed test was set at 0.05. All analyses were performed using R version 4.1.0 (R Development Core Team, Vienna, Austria) and SPSS statistical software version 27 (IBM Corp).