Frailty Index based on laboratory tests and in-hospital falls among older adults

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Methods We conducted a retrospective cohort study using electronic medical records from patients aged ≥ 60 years who were admitted to Nagoya University Hospital in 2020. We assessed fall risk using the St. Thomas's Risk Assessment Tool in Falling Elderly Inpatients (STRATIFY). We calculated FI-lab based on 35 common laboratory parameters tested on admission. Each fall was reported prospectively by nurses through computer-based incident report forms. The relationship between FI-lab and in-hospital falls was analyzed using multivariate binomial logistic regression. Predictive performance was compared using the area under the receiver operating characteristic curve (AUROC) and net reclassification improvement (NRI). Results Data for 5984 patients were included (mean age 73 years, 63.5% male). The mean FI-lab score was 0.31 ± 0.16. Falls occurred in 175 patients (2.9%) during a median hospital stay of 9 days. FI-lab was associated with falls independently of STRATIFY. Adding FI-lab to STRATIFY significantly improved its predictive accuracy, increasing AUROC from 0.674 to 0.715 (p = 0.018), with NRI of 0.413 (p < 0.001). Conclusions FI-lab on admission was independently associated with in-hospital fall risk and improved the predictive ability of STRATIFY. FI-lab could be a valuable component in more accurate fall prediction. Fall screening falls FI-lab Frailty Index-laboratory STRATIFY Figures Figure 1 Figure 2 Introduction Falls are one of the most common accidents occurring in hospitals [ 1 ]. Falls are associated with trauma that ranges from minor scratches to death and can impose a financial burden as a result of the additional medical care required and prolonged hospital stays [ 2 , 3 ]. The risk of falls and potentially fatal fall-related injuries increases with age [ 4 ]. Furthermore, the problem of falls is escalating worldwide with population aging [ 5 ]. Various fall prediction tools have been developed [ 6 , 7 ], but their predictive ability seems to be inadequate [ 6 , 8 ]. Therefore, the search continues for more accurate prediction tools that are less invasive, time-consuming, and costly to use [ 9 , 10 ]. Several abnormalities in laboratory tests have been reported to be associated with falls, including hyponatremia, anemia, and hypoalbuminemia [ 9 , 11 – 13 ]. However, the existing fall prediction tools do not include laboratory test results [ 7 ]. Addition of these results to the existing tools may improve their ability to predict falls. A previous study found that addition of comorbidities, polypharmacy, number of drugs that increase the risk of falls, and laboratory test results improved the ability of the Morse Fall Scale to predict falls [ 9 ]. However, it is unclear whether adding only laboratory tests to existing tools would improve their predictive ability. The Frailty Index based on laboratory tests (FI-lab), proposed in 2014 as one of the indicators of frailty, essentially counts the number of laboratory test results that deviate from the normal range [ 14 ]. FI-lab is simple with high potential for automation [ 15 ] and is associated with mortality in various settings, including inpatient [ 16 – 18 ], home-based [ 19 ], nursing home [ 20 ], and community-based care [ 16 , 21 ]. A previous study reported that FI-lab was associated with falls in community-dwelling older adults [ 21 ]. However, there are no reports on FI-lab and falls in the hospital setting nor on the effects of adding FI-lab to existing fall prediction tools. The aims of this study were to determine whether FI-lab is associated with falls in hospitalized older adults and whether addition of the FI-lab score can improve the predictive ability of a traditional fall prediction tool. Methods Study design This retrospective cohort study analyzed data extracted from the Nagoya University Hospital electronic medical records system. The study protocol was approved by the Ethics Committee of Nagoya University Hospital (approval number 2020 − 0357) and conducted in accordance with the principles of the Declaration of Helsinki and its amendments. The need for informed consent was waived in view of the analysis being based on patient data extracted from medical records. However, patients could withdraw from the study via the opt-out method by accessing the Nagoya University Hospital website in accordance with the national ethical guidelines for medical and health research involving human subjects [ 22 ]. The recommendations in the Strengthening the Reporting of Observational Studies in Epidemiology statement were followed [ 23 ]. Study population Data for patients who met the following criteria were extracted from the medical records: admission between January 1, 2020 and December 31, 2020; age 60 years or older on admission; and hospitalization using the Diagnosis Procedure Combination (DPC)/Per-Diem Payment System (PDPS) [ 24 ]. The DPC/PDPS is a Japanese reimbursement system used in acute care hospitals in Japan, and excludes most psychiatric hospitalizations. For patients who were readmitted within 30 days of discharge, their readmission was excluded. Also, those who did not have sufficient laboratory data available to calculate an FI-lab score were excluded. Data collection Demographic and anthropometric data were obtained from the medical records. Ability to perform activities of daily living on admission was evaluated by nurses using the Barthel Index [ 25 ], which is routinely recorded in the DPC/PDPS. The comorbidity burden was evaluated using the Charlson Comorbidity Index (CCI) [ 26 ]. The CCI score was calculated using the International Classification of Diseases, Tenth Revision (ICD-10) codes [ 27 ] extracted from the DPC/PDPS. Symptoms and signs on admission were recorded by nurses as part of routine clinical practice. Data on the use of medications on admission were also extracted, and the polypharmacy score [ 28 ] was calculated based on whether six medication categories were used (antihypertensive drugs, antidiabetics, antithrombotic drugs, sleep drugs, antipsychotics, and non-steroidal anti-inflammatory drugs). The polypharmacy score ranges from 0 to 6. Assessment of fall risk Fall risk was assessed and recorded routinely on admission using the assessment tool originally developed by our hospital. This tool consists of 40 items and is used at our hospital to identify patients at risk of falling and intervene on this risk in routine clinical practice [ 28 ] but has not been adequately validated in the literature. In the present study, we calculated the St. Thomas's Risk Assessment Tool in Falling Elderly Inpatients (STRATIFY) [ 29 ] score using the extracted data. STRATIFY is a widely used and well-studied fall prediction tool [ 6 ] and consists of 5 items, namely, history of falls, mental status, vision, toileting, and mobility. Each item has a score of 1, and the total STRATIFY score ranges from 0 to 5. We modified two items in STRATIFY to accommodate our hospital database and actual practice (Supplementary Table 1). We assessed the “history of falls” item by history-taking over a 1-year period. The “mental status” item in the original STRATIFY assesses agitation only. However, the frequency of agitation on admission was very low (0.6%) in our study population. Therefore, we used the modified criterion suggested by Papaioannou et al. [ 30 ], which includes confusion, disorientation, and agitation. Frailty Indices FI-lab contains 35 commonly measured laboratory parameters (Supplementary Table 2). The laboratory test data obtained on the day of admission or the day following admission were used. If the laboratory data were recorded more than once, the results of the first test were used. An FI-lab score was calculated for each patient by dividing the number of abnormal test results by the number of laboratory tests performed [ 14 , 17 ]. The FI-lab score ranges from 0 to 1. We calculated the FI-lab score for patients who had at least 70% of laboratory results available. The measured ratio [ 31 ] was calculated by dividing the number of measured laboratory tests by 35 (the total number of laboratory tests). We also generated a 40-item standard non-laboratory Frailty Index (FI-clinical) (Supplementary Table 3), using the method proposed by Theou et al [ 32 ]. Domains of FI-clinical include symptoms and signs, body mass index (BMI), comorbidities, functional ability, cognitive status, and sensory function. FI-clinical was calculated as the sum of the deficit values divided by the total number of items measured. FI-clinical ranges from 0 to 1, with a higher score indicating worse frailty. We calculated FI-clinical for only patients with data available for at least 80% of items (in fact, FI-clinical could be calculated for all participants). Outcome The primary outcome was falls during hospitalization. A fall was defined as an unexpected event in which the patient came to rest on the ground, floor, or a lower level without known loss of consciousness. At our hospital, each fall is reported by a nurse using a standardized computer-based incident report form and confirmed by responsible staff (physicians, nurses, and a pharmacist) in the Department of Patient Safety. The secondary outcomes were injurious falls during hospitalization, in-hospital mortality, length of hospital stay, and the rate of discharge to home. Injurious falls were defined as falls that resulted in abrasions, contusions, lacerations, bruises, sprains, pain, head injuries, other unspecified injuries, or any other serious injury. Statistical analysis The patient data are summarized using descriptive statistics. We divided the study population into three groups based on previously reported FI-lab cut-off points ( 0.4) [ 18 ]. Difference among groups were evaluated using Cochran-Armitage or Jonckheere-Terpstra tests for trends. We also explored the correlations between the FI-lab score and various clinical parameters, including age, BMI, the Barthel Index, CCI, STRATIFY score, and FI-clinical using Spearman’s rank correlation tests. Additionally, we explored the correlations between STRATIFY and FI-clinical. We calculated odds ratios (ORs) and 95% confidence intervals (95% CIs) using a logistic regression model to assess the associations of in-hospital falls as the dependent variable with age, sex, STRATIFY, FI-clinical, and FI-lab scores as independent variables. In addition to analysis as a continuous variable, we also analyzed the FI-lab score as a binary variable with a cut-off point of 0.4 (≤ 0.4 or > 0.4). This cut-off point was set based on a previous report [ 18 ] and because the in-hospital fall rate in this study appeared to be higher in patients with an FI-lab score of > 0.4. We calculated the ORs when the binary FI-lab variable was added to the five STRATIFY items, giving a total of six items in view of enhancing the clinical feasibility. The ability of the STRATIFY, FI-lab, and FI-clinical used individually and in combination to predict falls was calculated by receiver operating characteristic (ROC) curve analysis. An area under the ROC curve (AUROC) of ≥ 0.5 to < 0.6 indicates no discrimination ability, ≥ 0.6 to < 0.7 indicates poor discrimination ability, ≥ 0.7 to < 0.8 indicates fair discrimination ability, ≥ 0.8 to < 0.9 indicates good discrimination ability, and ≥ 0.9 to ≤ 1.0 indicates excellent discrimination ability [ 33 ]. The AUROCs were compared using the DeLong method [ 34 ]. The risk reclassification ability of FI-lab and FI-clinical score was evaluated by the net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indices. We also performed several subgroup analyses by dividing the patients by age (stratified at 75 years) and sex to determine whether these factors affect the association between the FI-lab score and the risk of falls. We also performed three sensitivity analyses. The first was an analysis with polypharmacy score as a covariate. The second was an analysis using ROC curve analysis by the Delong method to calculate a modified FI-lab cut-off point for in-hospital falls. The third was an analysis using our hospital’s fall risk assessment tool [ 28 ] instead of STRATIFY. Our assessment tool consists of seven categories, including history of falls, activities of daily living, cognitive function, sensory function, and medication (Supplementary Table 4). Each category contains 2 to 7 items, and the total number of items is 40. A score of 1 is assigned to a category if any item in the category is checked “yes.” The sum of the scores for each category is defined as the fall risk score and ranges from 0 to 7. All statistical analyses were performed using EZR version 1.61 (Saitama Medical Center, Jichi Medical University, Saitama, Japan). EZR is a graphical user interface for R version 4.2.2 (The R Foundation for Statistical Computing, Vienna, Austria) [ 35 ]. A sample size calculation was not conducted a priori for this study, and all available data were used. The analyses were performed without imputation of missing values. A p-value of < 0.05 was considered statistically significant. Results A total of 7661 patients aged ≥ 60 years were admitted to our hospital via the DPC/PDPS during the study period. After exclusion of 1677 patients because of readmission within 30 days (n = 1468), insufficient laboratory data (n = 176), or both (n = 33), data for 5984 patients were analyzed. The mean age was 73 ± 7.3 years (range 60–99) and 63.5% of the patients were male (Table 1). The median STRATIFY score was 0 (IQR 0, 1), the mean FI-lab score was 0.31 ± 016, and the mean FI-clinical score was 0.11 ± 0.09. The frequency distributions for the STRATIFY, FI-lab, and FI-clinical scores are shown in Supplementary Fig. 1. The most common primary diagnoses prompting admission are shown in Supplementary Table 5. Comparisons among the three FI-lab levels are shown in Supplementary Table 6. The groups with higher FI-lab scores were significantly older, had lower BMI, lower Barthel Index, higher CCI, higher STRATIFY score, and higher FI-clinical score. However, correlations between FI-lab and these parameters were weak (age, r = 0.17; BMI, r=–0.13; Barthel Index, r=–0.29; CCI, r = 0.20; STRATIFY, r = 0.17; FI-clinical, r = 0.37; all p < 0.001). On the other hand, there was a strong correlation between STRATIFY and FI-clinical (r = 0.66, p < 0.001). A total of 175 patients (2.9%) sustained falls during hospitalization (Table 1). Patients with a higher FI-lab score had a higher fall rate (Fig. 1). The results of the multiple regression analyses for in-hospital falls are shown in Table 2. FI-lab was associated with in-hospital falls independently of STRATIFY (model 2, FI-lab per 0.1 unit, adjusted OR 1.28, 95% CI 1.16–1.40). Similarly, FI-clinical was associated with in-hospital falls independently of STRATIFY (model 4, FI-clinical per 0.1 unit, adjusted OR 1.40, 95% CI 1.19–1.65). When STRATIFY, FI-lab and FI-clinical were simultaneously included as covariates, the two frailty indices were independently associated with in-hospital falls (model 6). Table 1 Baseline characteristics of the study population and outcomes Characteristics and outcomes value Participants, n (%) 5984 (100.0) Age, years 73.0 ± 7.3 Male sex 3798 (63.5) Emergency hospitalization 1551 (25.9) Body mass index, kg/m 2 22.3 ± 3.8 Barthel Index a , median (IQR) 100 (100–100) Barthel Index a , mean ± SD b 94.1 ± 15.5 CCI c 2 (1–2) Polypharmacy score d 1 (0–2) STRATIFY score e , median (IQR) 0 (0–1) STRATIFY score e , mean ± SD b 0.42 ± 0.72 History of falls 807 (13.5) Mental state abnormality 353 (5.9) Vision impairment 442 (7.4) Frequent urination 419 (7.0) Transferring and/or mobility impairment 504 (8.4) FI-lab f 0.31 ± 0.16 Measured ratio 0.87 ± 0.08 FI-clinical f 0.11 ± 0.09 Outcomes In-hospital falls 175 (2.9) In-hospital injurious falls 32 (0.5) In-hospital mortality 95 (1.6) Length of hospital stay 9 (5–16) Discharge home 5382 (89.9) Notes : Data are presented as the mean ± standard deviation, median (interquartile range), or number (percentage). CCI = Charlson Comorbidity Index; FI-clinical = standard non-laboratory Frailty Index; FI-lab = Frailty Index based on laboratory tests; IQR = interquartile range; SD = standard deviation; STRATIFY = St. Thomas's Risk Assessment Tool in Falling Elderly Inpatients. a Barthel Index score ranges from 0 to 100, with a higher score indicating higher function. b The mean and SD are shown for reference. c The CCI score ranges from 0 to 37, with a higher score indicating more comorbidities. d The polypharmacy score ranges from 0 to 6, with a higher score indicating more medications in use. e The STRATIFY score ranges from 0 to 5, with a higher score indicating a higher fall risk. f The FI-lab and FI-clinical scores range from 0 to 1, with a higher score indicating worse frailty. Table 3 shows the predictive performance of STRATIFY, FI-lab, and FI-clinical when used individually and in combination. Adding FI-lab to STRATIFY (model 2) significantly increased the AUROC from 0.674 to 0.715 (p=0.018), with a significant NRI of 0.413 (95% CI 0.265–0.560, p<0.001), indicating that the new model improved the risk reclassification for 41.3% of patients. The IDI was 0.005 (95% CI 0.002–0.008, p<0.001), indicating a small but significant improvement in the overall discriminative ability of the model. The results were similar when the FI-lab score was converted to a binary variable and added to STRATIFY (model 3, AUROC = 0.718, comparison with STRATIFY alone, p<0.001) (Fig. 2). Adding FI-clinical increased the predictive performance of STRATIFY (model 4) (AUROC 0.707, 95% CI 0.667–0.747, difference p<0.001; NRI 0.173, 95% CI 0.023–0.323, p=0.024) although IDI was not statistically significant (IDI 0.002, 95% CI -0.0002–0.005, p=0.08). Table 3 Predictive performance of STRATIFY, FI-lab, and FI-clinical for in-hospital falls (n = 5984) Model Variable a AUC 95% CI p NRI 95% CI p IDI 95% CI p Model 1 STRATIFY 0.674 (0.63–0.72) ref ref ref ref ref Model 2 STRATIFY 0.715 (0.68–0.75) 0.018 0.413 (0.27–0.56) < 0.001 0.005 (0.002–0.008) < 0.001 + FI-lab Model 3 STRATIFY 0.718 (0.68–0.76) < 0.001 0.519 (0.37–0.67) < 0.001 0.005 (0.003–0.008) < 0.001 Including FI-lab b Model 4 STRATIFY 0.707 (0.67–0.75) 0.003 0.173 (0.02–0.32) 0.024 0.002 (-0.0002–0.005) 0.08 + FI-clinical Model 5 FI-lab 0.726 (0.69–0.76) 0.011 n/a n/a + FI-clinical Model 6 STRATIFY 0.733 (0.70–0.77) < 0.001 0.400 (0.25–0.55) < 0.001 0.006 (0.002–0.009) 0.002 + FI-lab + FI-clinical Notes : AUC = area under the receiver-operating characteristic curve; CI = confidence interval; FI-clinical = standard non-laboratory Frailty Index; FI-lab = Frailty Index based on laboratory tests; IDI = integrated discrimination improvement; NRI = net reclassification improvement; STRATIFY = St. Thomas's Risk Assessment Tool in Falling Elderly Inpatients. a All models include age and sex as covariates. b Total of 6 items: 5 STRATIFY items and 1 item based on whether the FI-lab score was higher than 0.4. The results of the subgroup and sensitivity analyses are shown in Supplementary Tables 7 and 8. Adding FI-lab to STRATIFY significantly improved the AUROC regardless of age (stratified at 75 years) and sex. Adding polypharmacy score did not impact the results. Analysis of the AUROCs revealed that an FI-lab cut-off point of 0.345 had the best ability to predict in-hospital falls. However, changing the FI-lab cut-off point from 0.40 to 0.345 did not significantly improve the AUROC whether the binary FI-lab variable was used alone or included in STRATIFY. Finally, the fall risk assessment tool used at our hospital predicted in-hospital falls with accuracy similar to that of STRATIFY, and addition of the binary FI-lab variable (cut-off 0.40) to the fall risk assessment tool significantly improved the predictive ability. The results for the secondary outcomes are shown in Table 1 and Supplementary Table 6. Patients with a higher FI-lab score more frequently sustained injurious falls, but this finding was not statistically significant. Discussion In this retrospective cohort study, the FI-lab score on admission was associated with the risk of in-hospital falls independently of the STRATIFY score. Moreover, addition of the FI-lab score to the STRATIFY score improved the predictive ability further. We found that the correlation between the FI-lab score and the Barthel Index was weak, which is consistent with previous findings in older adults receiving inpatient [ 17 ] or home-based medical care [ 19 ]. This weak association is not surprising given that FI-lab does not assess the patient’s functional status. In view of these results and the nature of FI-lab, it appears that the FI-lab evaluates the accumulated acute or chronic organ dysfunction reflected in laboratory tests. Therefore, the FI-lab score may serve as an indicator of multimorbidity, which is a known risk factor for falls in older adults [ 36 ]. STRATIFY, which does not account for comorbidity, was used as the traditional fall prediction tool in the present study. FI-lab may have improved the predictive performance by compensating for the inability of STRATIFY to evaluate multimorbidity. The correlation between the FI-lab score and the CCI was weak in this study, which is consistent with previous findings [ 17 , 19 ], and may reflect the fact that the CCI includes a number of conditions that cannot be detected by blood tests (e.g., stroke). We found only one previous study that evaluated the association between the FI-lab score and falls [ 21 ]. In that study, falls were assessed as a secondary outcome in 2933 community-dwelling adults with a mean age of 60.2 years. The FI-lab score was calculated based on 23 common blood tests and vital signs and found to have an average value of 0.28. The FI-lab score was associated with falls (FI-lab per 0.01 unit, adjusted for age, OR 1.01, 95% CI 1.00–1.02). Despite differences in the study populations and the items included in FI-lab tool, the results of that study are consistent with our present findings. Furthermore, in the present study, the FI-lab score was associated with falls independent of a traditional fall prediction tool. FI-clinical was associated with in-hospital falls, consistent with previous reports [ 37 ]. Adding FI-clinical to STRATIFY significantly improved AUROC and NRI, but not IDI (model 4). Considering that AUROC, NRI, and IDI were all significantly improved when FI-lab was added (model 2), and the lower limit of CI for NRI with FI-lab was (model 2, 0.265) higher than with FI-clinical (model 4, 0.023), the degree of improvement in predictive ability with the addition of FI-lab might be greater than that with FI-clinical. This may be due to the overlap of the items that constitute FI-clinical and STRATIFY. Actually, their correlation was high (r = 0.66). Rather, the alternative may be to use FI-clinical instead of STRATIFY, and to use a combination of FI-clinical and FI-lab. Some places have a system to automatically calculate FI-clinical using information in electronic medical records [ 38 ]. In such situations, the combined use of FI-lab and FI-clinical may be relatively easy. One implication of this study is that, in hospitals that use STRATIFY as a fall prediction tool, adding FI-lab may improve its predictive ability. The predictive ability of other fall prediction tools might also be improved by addition of FI-lab. The FI-lab score could also improve predictive tools for conditions other than falls. In our study, the predictive ability was unaffected by whether the FI-lab score was treated as a continuous variable or as a binary variable. FI-lab may be easier to implement in clinical practice if used as a binary variable. Although adding the FI-lab score to the STRATIFY score significantly improved the ability to predict falls, the resulting AUROC of 0.715 (model 2) was still relatively low. Both the present results and existing fall prediction tools generally show relatively low accuracy [ 6 , 8 ]. Consequently, recent guidelines recommend against using a specific fall prediction scoring system [ 8 ]. However, adding new components to fall prediction tools could improve their predictive ability enough to be of practical use. In the present study, we incorporated information on multimorbidity. Other potential components include more refined indicators on polypharmacy [ 39 ] and direct observations of mobility [ 28 ]. Further research is needed to develop more accurate prediction models. Even if accurate fall prediction tools are developed, patient education and detailed assessment and intervention for high-risk patients will remain important. Nevertheless, this study is the first to evaluate the usefulness of including the FI-lab score in a clinical prediction tool. The strengths of this study include its relatively large sample size, its use of STRATIFY, which is one of the fall prediction tools most widely used in acute care settings [ 6 ], and the fact that the results obtained using STRATIFY were similar to those obtained using our hospital's fall prediction tool. This study also has some limitations. First, it had a retrospective cohort design, although the falls data were collected prospectively. Second, the analysis was based on data from a university hospital. Therefore, caution is needed when generalizing its findings to other institutions and regions. Third, although this study showed that a high FI-lab score may be a risk factor for falls, a high FI-lab score may not translate to a specific fall prevention strategy for the patient (unlike, for example, providing visual aids for a patient with vision impairment). Conclusion The FI-lab score on admission was associated with the risk of in-hospital falls independently of the STRATIFY score, a widely used fall prediction tool. Addition of the FI-lab score to the STRATIFY score improved the predictive ability for in-hospital falls. Further studies in other settings are needed. Declarations Acknowledgments We thank all the Nagoya University Hospital staff for their support. Funding This work was supported by the Hori Sciences and Arts Foundation (Grant No. 31–1-031), and the Open Access funding provided by Nagoya University. Competing interests The authors have no conflict of interest to declare. The funding sources (the Hori Sciences and Arts Foundation and Nagoya University) had no role in the design and conduct of the study; in the collection, analysis, and interpretation of data; in the preparation of the manuscript; or the review or approval of the manuscript. Ethics approval The study protocol was approved by the Ethics Committee of Nagoya University Graduate School of Medicine (approval number 2020-0357) and conducted according to the principles of the Declaration of Helsinki and its amendments. Consent to participate The need for informed consent was waived in view of the analysis being based on patient data extracted from medical records. However, patients could withdraw from the study via the opt-out method by accessing the Nagoya University Hospital website in accordance with the national ethical guidelines for medical and health research involving human subjects. Data availability Participants of this study did not agree for their data to be shared publicly, so supporting data is not available. Author contributions Hirotaka Nakashima: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Software, Visualization, Writing – original draft Takahiro Imaizumi: Data curation, Investigation, Methodology, Resources, Software, Validation, Writing – review & editing Hitoshi Komiya: Investigation, Methodology, Resources, Writing – review & editing Akemi Morohashi: Data curation, Investigation, Resources, Software, Writing – review & editing Kazuhisa Watanabe: Methodology, Writing – review & editing Chisato Fujisawa: Methodology, Writing – review & editing Yosuke Yamada: Methodology, Writing – review & editing Yoshimasa Nagao: Investigation, Resources, Writing – review & editing Hiroyuki Umegaki: Methodology, Supervision, Writing – review & editing This work was supported by the Hori Sciences and Arts Foundation (Grant No. 31–1-031), and the Open Access funding provided by Nagoya University. 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Aging Clin Exp Res 35:1927-1935. https://doi.org/10.1007/s40520-023-02477-w Ysea-Hill O, Gomez CJ, Mansour N et al (2022) The association of a frailty index from laboratory tests and vital signs with clinical outcomes in hospitalized older adults. J Am Geriatr Soc 70:3163-3175. https://doi.org/10.1111/jgs.17977 Nakashima H, Watanabe K, Komiya H et al (2024) Frailty Index Based on Common Laboratory Tests for Patients Starting Home-Based Medical Care. J Am Med Dir Assoc 25:105114. https://doi.org/10.1016/j.jamda.2024.105114 Yang M, Zhuo Y, Hu X, Xie L (2018) Predictive validity of two frailty tools for mortality in Chinese nursing home residents: frailty index based on common laboratory tests (FI-Lab) versus FRAIL-NH. Aging Clin Exp Res 30:1445-1452. https://doi.org/10.1007/s40520-018-1041-7 Blodgett JM, Theou O, Howlett SE, Wu FC, Rockwood K (2016) A frailty index based on laboratory deficits in community-dwelling men predicted their risk of adverse health outcomes. Age Ageing 45:463-468. https://doi.org/10.1093/ageing/afw054 The Ministry of Education, Culture, Sports, Science and Technology, the Ministry of Health, Labour and Welfare, and the Ministry of Economy, Trade and Industry, Ethical Guidelines for Medical and Health Research Involving Human Subjects 2021 (Guidance) (2022 revised) (Japanese language only) (2022) https://www.mhlw.go.jp/content/000946358.pdf von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP (2007) Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Bmj 335:806-808. https://doi.org/10.1136/bmj.39335.541782.AD Hayashida K, Murakami G, Matsuda S, Fushimi K (2021) History and Profile of Diagnosis Procedure Combination (DPC): Development of a Real Data Collection System for Acute Inpatient Care in Japan. J Epidemiol 31:1-11. https://doi.org/10.2188/jea.JE20200288 Mahoney FI, Barthel DW (1965) FUNCTIONAL EVALUATION: THE BARTHEL INDEX. Md State Med J 14:61-65 Charlson ME, Pompei P, Ales KL, MacKenzie CR (1987) A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 40:373-383 Quan H, Sundararajan V, Halfon P et al (2005) Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care 43:1130-1139. https://doi.org/10.1097/01.mlr.0000182534.19832.83 Tanaka S, Imaizumi T, Morohashi A et al (2023) In-Hospital Fall Risk Prediction by Objective Measurement of Lower Extremity Function in a High-Risk Population. J Am Med Dir Assoc 24:1861-1867.e1862. https://doi.org/10.1016/j.jamda.2023.07.020 Oliver D, Britton M, Seed P, Martin FC, Hopper AH (1997) Development and evaluation of evidence based risk assessment tool (STRATIFY) to predict which elderly inpatients will fall: case-control and cohort studies. Bmj 315:1049-1053. https://doi.org/10.1136/bmj.315.7115.1049 Papaioannou A, Parkinson W, Cook R, Ferko N, Coker E, Adachi JD (2004) Prediction of falls using a risk assessment tool in the acute care setting. BMC Med 2:1. https://doi.org/10.1186/1741-7015-2-1 Soh CH, Guan L, Reijnierse EM, Lim WK, Maier AB (2022) Comparison of the modified Frailty-Index based on laboratory tests and the Clinical Frailty Scale in predicting mortality among geriatric rehabilitation inpatients: RESORT. Arch Gerontol Geriatr 100:104667. https://doi.org/10.1016/j.archger.2022.104667 Theou O, Haviva C, Wallace L, Searle SD, Rockwood K (2023) How to construct a frailty index from an existing dataset in 10 steps. Age Ageing 52. https://doi.org/10.1093/ageing/afad221 Safari S, Baratloo A, Elfil M, Negida A (2016) Evidence Based Emergency Medicine; Part 5 Receiver Operating Curve and Area under the Curve. Emerg (Tehran) 4:111-113 DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837-845 Kanda Y (2013) Investigation of the freely available easy-to-use software 'EZR' for medical statistics. Bone Marrow Transplant 48:452-458. https://doi.org/10.1038/bmt.2012.244 Huberty S, Freystätter G, Wieczorek M et al (2023) Association Between Multimorbidity and Rate of Falls: A 3-Year 5-Country Prospective Study in Generally Healthy and Active Community-Dwelling Adults Aged ≥70 Years. J Am Med Dir Assoc 24:804-810.e804. https://doi.org/10.1016/j.jamda.2022.12.011 Zhu Y, Liu Z, Wang Y et al (2016) Agreement between the frailty index and phenotype and their associations with falls and overnight hospitalizations. Arch Gerontol Geriatr 66:161-165. https://doi.org/10.1016/j.archger.2016.06.004 Clegg A, Bates C, Young J et al (2016) Development and validation of an electronic frailty index using routine primary care electronic health record data. Age Ageing 45:353-360. https://doi.org/10.1093/ageing/afw039 Zia A, Kamaruzzaman SB, Tan MP (2015) Polypharmacy and falls in older people: Balancing evidence-based medicine against falls risk. Postgrad Med 127:330-337. https://doi.org/10.1080/00325481.2014.996112 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6290898","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":443257398,"identity":"d6f6f4b3-c903-4bf1-b01d-89266873ac3a","order_by":0,"name":"Hirotaka Nakashima","email":"data:image/png;base64,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","orcid":"","institution":"Nagoya University Hospital","correspondingAuthor":true,"prefix":"","firstName":"Hirotaka","middleName":"","lastName":"Nakashima","suffix":""},{"id":443257400,"identity":"044cd837-c28c-46bb-845d-8291ccbdfbd3","order_by":1,"name":"Takahiro Imaizumi","email":"","orcid":"","institution":"Nagoya University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Takahiro","middleName":"","lastName":"Imaizumi","suffix":""},{"id":443257402,"identity":"b7ddc871-0910-43e7-9df7-b84297bcfbca","order_by":2,"name":"Hitoshi Komiya","email":"","orcid":"","institution":"Nagoya University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hitoshi","middleName":"","lastName":"Komiya","suffix":""},{"id":443257403,"identity":"ca492136-ff85-4962-9137-ca5753f17b31","order_by":3,"name":"Akemi Morohashi","email":"","orcid":"","institution":"Nagoya University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Akemi","middleName":"","lastName":"Morohashi","suffix":""},{"id":443257404,"identity":"5392d2b0-0b8b-4887-b069-7be87859a460","order_by":4,"name":"Kazuhisa Watanabe","email":"","orcid":"","institution":"Nagoya University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kazuhisa","middleName":"","lastName":"Watanabe","suffix":""},{"id":443257405,"identity":"d9a56fb3-d7b3-450e-a831-687071f8976e","order_by":5,"name":"Chisato Fujisawa","email":"","orcid":"","institution":"Nagoya University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chisato","middleName":"","lastName":"Fujisawa","suffix":""},{"id":443257406,"identity":"e0ac1bb3-6878-41f7-b838-bf4b359d5e3b","order_by":6,"name":"Yosuke Yamada","email":"","orcid":"","institution":"Nagoya University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yosuke","middleName":"","lastName":"Yamada","suffix":""},{"id":443257407,"identity":"0eeae2b5-70dc-459d-bf10-28d53b552002","order_by":7,"name":"Yoshimasa Nagao","email":"","orcid":"","institution":"Nagoya University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yoshimasa","middleName":"","lastName":"Nagao","suffix":""},{"id":443257408,"identity":"6b719816-82c4-4e52-b3e9-8488c32d6068","order_by":8,"name":"Hiroyuki Umegaki","email":"","orcid":"","institution":"Nagoya University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hiroyuki","middleName":"","lastName":"Umegaki","suffix":""}],"badges":[],"createdAt":"2025-03-24 02:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6290898/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6290898/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s40520-025-03090-9","type":"published","date":"2025-06-05T15:57:02+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82011148,"identity":"f9e02a6e-ec8a-4332-8cc1-f90905dd1eeb","added_by":"auto","created_at":"2025-05-06 01:39:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":63692,"visible":true,"origin":"","legend":"\u003cp\u003eRate of falls by FI-lab score (n = 5984). Patients with a higher Frailty Index-laboratory (FI-lab) score had a higher fall rate. FI-lab score ranges from 0 to 1, with a higher score indicating worse frailty. The bar for FI-lab between 0.9 and 1.0 (indicated by *) is not drawn because the number of patients was too small (n=2, with one fall)\u003c/p\u003e\n\u003cp\u003e(Alt text: Bar chart showing the relationship between the Frailty Index laboratory and fall rates during hospitalization. As the Frailty Index laboratory score increases, the fall rate also increases.)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6290898/v1/46260c9782f5e43ebbfa04c8.png"},{"id":82010483,"identity":"96925456-5044-4bb0-a5fd-33cf0a0780b6","added_by":"auto","created_at":"2025-05-06 01:31:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":75449,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic curves for the STRATIFY score alone and the STRATIFY score including FI-lab for in-hospital falls (n=5984). The STRATIFY (St. Thomas's Risk Assessment Tool in Falling Elderly Inpatients) score is a traditional fall prediction tool composed of five items and ranges from 0 to 5. “STRATIFY including FI-lab” includes the five STRATIFY items and one binary item based on whether Frailty Index-laboratory (FI-lab) was higher than 0.4. Area under the receiver-operating characteristic curve of STRATIFY including FI-lab (0.718) was significantly higher than that of STRATIFY alone (0.674) (p\u0026lt;0.001)\u003c/p\u003e\n\u003cp\u003e(Alt text: The figure shows two receiver operating characteristic curves representing the performance of predicting falls in hospitalized patients. One curve demonstrates the performance of the STRATIFY, a traditional fall prediction tool, while the other shows the improved performance when the Frailty Index laboratory is added.)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6290898/v1/00e1b53b8cf663e97079265f.png"},{"id":84242590,"identity":"06db2cdc-f8a7-4c57-b078-094902a159ce","added_by":"auto","created_at":"2025-06-09 16:10:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":777396,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6290898/v1/ecf77a56-ab79-4da3-8222-8cff9b56acee.pdf"},{"id":82010481,"identity":"f4d77572-ee83-4b19-9ccb-4f6e6b317f5e","added_by":"auto","created_at":"2025-05-06 01:31:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":501818,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials250321.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6290898/v1/bd0b5644bf7c304219f66d88.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Frailty Index based on laboratory tests and in-hospital falls among older adults","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFalls are one of the most common accidents occurring in hospitals [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Falls are associated with trauma that ranges from minor scratches to death and can impose a financial burden as a result of the additional medical care required and prolonged hospital stays [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The risk of falls and potentially fatal fall-related injuries increases with age [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Furthermore, the problem of falls is escalating worldwide with population aging [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eVarious fall prediction tools have been developed [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], but their predictive ability seems to be inadequate [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Therefore, the search continues for more accurate prediction tools that are less invasive, time-consuming, and costly to use [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral abnormalities in laboratory tests have been reported to be associated with falls, including hyponatremia, anemia, and hypoalbuminemia [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, the existing fall prediction tools do not include laboratory test results [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Addition of these results to the existing tools may improve their ability to predict falls. A previous study found that addition of comorbidities, polypharmacy, number of drugs that increase the risk of falls, and laboratory test results improved the ability of the Morse Fall Scale to predict falls [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, it is unclear whether adding only laboratory tests to existing tools would improve their predictive ability.\u003c/p\u003e \u003cp\u003eThe Frailty Index based on laboratory tests (FI-lab), proposed in 2014 as one of the indicators of frailty, essentially counts the number of laboratory test results that deviate from the normal range [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. FI-lab is simple with high potential for automation [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and is associated with mortality in various settings, including inpatient [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], home-based [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], nursing home [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and community-based care [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. A previous study reported that FI-lab was associated with falls in community-dwelling older adults [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, there are no reports on FI-lab and falls in the hospital setting nor on the effects of adding FI-lab to existing fall prediction tools.\u003c/p\u003e \u003cp\u003eThe aims of this study were to determine whether FI-lab is associated with falls in hospitalized older adults and whether addition of the FI-lab score can improve the predictive ability of a traditional fall prediction tool.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy design\u003c/p\u003e \u003cp\u003eThis retrospective cohort study analyzed data extracted from the Nagoya University Hospital electronic medical records system. The study protocol was approved by the Ethics Committee of Nagoya University Hospital (approval number 2020\u0026thinsp;\u0026minus;\u0026thinsp;0357) and conducted in accordance with the principles of the Declaration of Helsinki and its amendments. The need for informed consent was waived in view of the analysis being based on patient data extracted from medical records. However, patients could withdraw from the study via the opt-out method by accessing the Nagoya University Hospital website in accordance with the national ethical guidelines for medical and health research involving human subjects [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The recommendations in the Strengthening the Reporting of Observational Studies in Epidemiology statement were followed [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStudy population\u003c/p\u003e \u003cp\u003eData for patients who met the following criteria were extracted from the medical records: admission between January 1, 2020 and December 31, 2020; age 60 years or older on admission; and hospitalization using the Diagnosis Procedure Combination (DPC)/Per-Diem Payment System (PDPS) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The DPC/PDPS is a Japanese reimbursement system used in acute care hospitals in Japan, and excludes most psychiatric hospitalizations. For patients who were readmitted within 30 days of discharge, their readmission was excluded. Also, those who did not have sufficient laboratory data available to calculate an FI-lab score were excluded.\u003c/p\u003e \u003cp\u003eData collection\u003c/p\u003e \u003cp\u003eDemographic and anthropometric data were obtained from the medical records. Ability to perform activities of daily living on admission was evaluated by nurses using the Barthel Index [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], which is routinely recorded in the DPC/PDPS. The comorbidity burden was evaluated using the Charlson Comorbidity Index (CCI) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The CCI score was calculated using the International Classification of Diseases, Tenth Revision (ICD-10) codes [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] extracted from the DPC/PDPS. Symptoms and signs on admission were recorded by nurses as part of routine clinical practice. Data on the use of medications on admission were also extracted, and the polypharmacy score [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] was calculated based on whether six medication categories were used (antihypertensive drugs, antidiabetics, antithrombotic drugs, sleep drugs, antipsychotics, and non-steroidal anti-inflammatory drugs). The polypharmacy score ranges from 0 to 6.\u003c/p\u003e \u003cp\u003eAssessment of fall risk\u003c/p\u003e \u003cp\u003eFall risk was assessed and recorded routinely on admission using the assessment tool originally developed by our hospital. This tool consists of 40 items and is used at our hospital to identify patients at risk of falling and intervene on this risk in routine clinical practice [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] but has not been adequately validated in the literature. In the present study, we calculated the St. Thomas's Risk Assessment Tool in Falling Elderly Inpatients (STRATIFY) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] score using the extracted data. STRATIFY is a widely used and well-studied fall prediction tool [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and consists of 5 items, namely, history of falls, mental status, vision, toileting, and mobility. Each item has a score of 1, and the total STRATIFY score ranges from 0 to 5. We modified two items in STRATIFY to accommodate our hospital database and actual practice (Supplementary Table\u0026nbsp;1). We assessed the \u0026ldquo;history of falls\u0026rdquo; item by history-taking over a 1-year period. The \u0026ldquo;mental status\u0026rdquo; item in the original STRATIFY assesses agitation only. However, the frequency of agitation on admission was very low (0.6%) in our study population. Therefore, we used the modified criterion suggested by Papaioannou et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], which includes confusion, disorientation, and agitation.\u003c/p\u003e \u003cp\u003eFrailty Indices\u003c/p\u003e \u003cp\u003eFI-lab contains 35 commonly measured laboratory parameters (Supplementary Table\u0026nbsp;2). The laboratory test data obtained on the day of admission or the day following admission were used. If the laboratory data were recorded more than once, the results of the first test were used. An FI-lab score was calculated for each patient by dividing the number of abnormal test results by the number of laboratory tests performed [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The FI-lab score ranges from 0 to 1. We calculated the FI-lab score for patients who had at least 70% of laboratory results available. The measured ratio [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] was calculated by dividing the number of measured laboratory tests by 35 (the total number of laboratory tests).\u003c/p\u003e \u003cp\u003eWe also generated a 40-item standard non-laboratory Frailty Index (FI-clinical) (Supplementary Table\u0026nbsp;3), using the method proposed by Theou et al [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Domains of FI-clinical include symptoms and signs, body mass index (BMI), comorbidities, functional ability, cognitive status, and sensory function. FI-clinical was calculated as the sum of the deficit values divided by the total number of items measured. FI-clinical ranges from 0 to 1, with a higher score indicating worse frailty. We calculated FI-clinical for only patients with data available for at least 80% of items (in fact, FI-clinical could be calculated for all participants).\u003c/p\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003cp\u003eThe primary outcome was falls during hospitalization. A fall was defined as an unexpected event in which the patient came to rest on the ground, floor, or a lower level without known loss of consciousness. At our hospital, each fall is reported by a nurse using a standardized computer-based incident report form and confirmed by responsible staff (physicians, nurses, and a pharmacist) in the Department of Patient Safety.\u003c/p\u003e \u003cp\u003eThe secondary outcomes were injurious falls during hospitalization, in-hospital mortality, length of hospital stay, and the rate of discharge to home. Injurious falls were defined as falls that resulted in abrasions, contusions, lacerations, bruises, sprains, pain, head injuries, other unspecified injuries, or any other serious injury.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe patient data are summarized using descriptive statistics. We divided the study population into three groups based on previously reported FI-lab cut-off points (\u0026lt;\u0026thinsp;0.25, 0.25\u0026ndash;0.4, and \u0026gt;\u0026thinsp;0.4) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Difference among groups were evaluated using Cochran-Armitage or Jonckheere-Terpstra tests for trends. We also explored the correlations between the FI-lab score and various clinical parameters, including age, BMI, the Barthel Index, CCI, STRATIFY score, and FI-clinical using Spearman\u0026rsquo;s rank correlation tests. Additionally, we explored the correlations between STRATIFY and FI-clinical.\u003c/p\u003e \u003cp\u003eWe calculated odds ratios (ORs) and 95% confidence intervals (95% CIs) using a logistic regression model to assess the associations of in-hospital falls as the dependent variable with age, sex, STRATIFY, FI-clinical, and FI-lab scores as independent variables. In addition to analysis as a continuous variable, we also analyzed the FI-lab score as a binary variable with a cut-off point of 0.4 (\u0026le;\u0026thinsp;0.4 or \u0026gt;\u0026thinsp;0.4). This cut-off point was set based on a previous report [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and because the in-hospital fall rate in this study appeared to be higher in patients with an FI-lab score of \u0026gt;\u0026thinsp;0.4. We calculated the ORs when the binary FI-lab variable was added to the five STRATIFY items, giving a total of six items in view of enhancing the clinical feasibility.\u003c/p\u003e \u003cp\u003eThe ability of the STRATIFY, FI-lab, and FI-clinical used individually and in combination to predict falls was calculated by receiver operating characteristic (ROC) curve analysis. An area under the ROC curve (AUROC) of \u0026ge;\u0026thinsp;0.5 to \u0026lt;\u0026thinsp;0.6 indicates no discrimination ability, \u0026ge;\u0026thinsp;0.6 to \u0026lt;\u0026thinsp;0.7 indicates poor discrimination ability, \u0026ge;\u0026thinsp;0.7 to \u0026lt;\u0026thinsp;0.8 indicates fair discrimination ability, \u0026ge;\u0026thinsp;0.8 to \u0026lt;\u0026thinsp;0.9 indicates good discrimination ability, and \u0026ge;\u0026thinsp;0.9 to \u0026le;\u0026thinsp;1.0 indicates excellent discrimination ability [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The AUROCs were compared using the DeLong method [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The risk reclassification ability of FI-lab and FI-clinical score was evaluated by the net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indices.\u003c/p\u003e \u003cp\u003eWe also performed several subgroup analyses by dividing the patients by age (stratified at 75 years) and sex to determine whether these factors affect the association between the FI-lab score and the risk of falls. We also performed three sensitivity analyses. The first was an analysis with polypharmacy score as a covariate. The second was an analysis using ROC curve analysis by the Delong method to calculate a modified FI-lab cut-off point for in-hospital falls. The third was an analysis using our hospital\u0026rsquo;s fall risk assessment tool [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] instead of STRATIFY. Our assessment tool consists of seven categories, including history of falls, activities of daily living, cognitive function, sensory function, and medication (Supplementary Table\u0026nbsp;4). Each category contains 2 to 7 items, and the total number of items is 40. A score of 1 is assigned to a category if any item in the category is checked \u0026ldquo;yes.\u0026rdquo; The sum of the scores for each category is defined as the fall risk score and ranges from 0 to 7.\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed using EZR version 1.61 (Saitama Medical Center, Jichi Medical University, Saitama, Japan). EZR is a graphical user interface for R version 4.2.2 (The R Foundation for Statistical Computing, Vienna, Austria) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. A sample size calculation was not conducted a priori for this study, and all available data were used. The analyses were performed without imputation of missing values. A p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 7661 patients aged ≥ 60 years were admitted to our hospital via the DPC/PDPS during the study period. After exclusion of 1677 patients because of readmission within 30 days (n = 1468), insufficient laboratory data (n = 176), or both (n = 33), data for 5984 patients were analyzed. The mean age was 73 ± 7.3 years (range 60–99) and 63.5% of the patients were male (Table 1). The median STRATIFY score was 0 (IQR 0, 1), the mean FI-lab score was 0.31 ± 016, and the mean FI-clinical score was 0.11 ± 0.09. The frequency distributions for the STRATIFY, FI-lab, and FI-clinical scores are shown in Supplementary Fig. 1. The most common primary diagnoses prompting admission are shown in Supplementary Table 5.\u003c/p\u003e\n\u003cp\u003eComparisons among the three FI-lab levels are shown in Supplementary Table\u0026nbsp;6. The groups with higher FI-lab scores were significantly older, had lower BMI, lower Barthel Index, higher CCI, higher STRATIFY score, and higher FI-clinical score. However, correlations between FI-lab and these parameters were weak (age, r = 0.17; BMI, r=–0.13; Barthel Index, r=–0.29; CCI, r = 0.20; STRATIFY, r = 0.17; FI-clinical, r = 0.37; all p \u0026lt; 0.001). On the other hand, there was a strong correlation between STRATIFY and FI-clinical (r = 0.66, p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eA total of 175 patients (2.9%) sustained falls during hospitalization (Table 1). Patients with a higher FI-lab score had a higher fall rate (Fig. 1). The results of the multiple regression analyses for in-hospital falls are shown in Table\u0026nbsp;2. FI-lab was associated with in-hospital falls independently of STRATIFY (model 2, FI-lab per 0.1 unit, adjusted OR 1.28, 95% CI 1.16–1.40). Similarly, FI-clinical was associated with in-hospital falls independently of STRATIFY (model 4, FI-clinical per 0.1 unit, adjusted OR 1.40, 95% CI 1.19–1.65). When STRATIFY, FI-lab and FI-clinical were simultaneously included as covariates, the two frailty indices were independently associated with in-hospital falls (model 6).\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTable\u0026nbsp;1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eBaseline characteristics of the study population and outcomes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCharacteristics and outcomes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParticipants, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e± 7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(63.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmergency hospitalization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(25.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBody mass index, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e± 3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBarthel Index\u003csup\u003ea\u003c/sup\u003e, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(100–100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBarthel Index\u003csup\u003ea\u003c/sup\u003e, mean ± SD\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e± 15.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCCI\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1–2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePolypharmacy score\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0–2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSTRATIFY score\u003csup\u003ee\u003c/sup\u003e, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0–1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSTRATIFY score\u003csup\u003ee\u003c/sup\u003e, mean ± SD\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e± 0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHistory of falls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(13.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMental state abnormality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVision impairment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFrequent urination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTransferring and/or mobility impairment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFI-lab\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e± 0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMeasured ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e± 0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFI-clinical\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e± 0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOutcomes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIn-hospital falls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIn-hospital injurious falls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIn-hospital mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLength of hospital stay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(5–16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDischarge home\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(89.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003eNotes\u003c/em\u003e: Data are presented as the mean ± standard deviation, median (interquartile range), or number (percentage). CCI = Charlson Comorbidity Index; FI-clinical = standard non-laboratory Frailty Index; FI-lab = Frailty Index based on laboratory tests; IQR = interquartile range; SD = standard deviation; STRATIFY = St. Thomas's Risk Assessment Tool in Falling Elderly Inpatients.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003eBarthel Index score ranges from 0 to 100, with a higher score indicating higher function.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003csup\u003eb\u003c/sup\u003eThe mean and SD are shown for reference.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003csup\u003ec\u003c/sup\u003eThe CCI score ranges from 0 to 37, with a higher score indicating more comorbidities.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003csup\u003ed\u003c/sup\u003eThe polypharmacy score ranges from 0 to 6, with a higher score indicating more medications in use.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003csup\u003ee\u003c/sup\u003eThe STRATIFY score ranges from 0 to 5, with a higher score indicating a higher fall risk.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003csup\u003ef\u003c/sup\u003eThe FI-lab and FI-clinical scores range from 0 to 1, with a higher score indicating worse frailty.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cimg 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VdtT8E8mcAe2OpTyJv2y2RahVdoa+gojaVhBLmWMGVbLsQer46wwX758eRqzHLKtoI3dLmoX2pNwVs5Z+VYYbU2bq2+U22n4fwW+vojNfR+8Qz+gbqSRbOpJXtqVco9SW4UQQgiHQSboYWk0oWMixnYIfUJbWzX4x0CfOM6DCT37tJnQdbG/v9+k0cTvqMBkmIkv+8ffvXu31k+O8yBEm9A2Z86cacK0bQb29vaaYx+0dQV7+8S9hIm+2gU372EwhBBCCAfJBP2EwqotK89M1oT2Gi/CzZs3f/iHxadPnzYTwCETNO1Z9l+XqcEEUXuewc+PAvxj5bx/etV+bSbxoP36Q7h27VpT1vv372dvQl68eNE8MM37dZYS9OWhqIsrV640bzaE9tGHEEIIYQBfw4lFe5blJpPeacz3/cc4T0ca9hDL7+DXvmTwfMrjeX0Ps8JKv85BYdpjXZM1VlxX7dcmjP3e2qstR/08DJtzBG8X0riN/dztQX7yCcpUOtdLTmnLstyvfIRLP/UfT+flhhBCCKEfp/gzuZGGEEIIIYQQRkC2uIQQQgghhDAiMkEPIYQQQghhRGSCHkIIIYQQwojIBD2EEEIIIYQRkQl6CBtAn8/nWEO/TY7Tp/f7wE8vzkvPT1B2pSF+kZ9wHCtjtbX0wunnRI86Y7U1cSr3uPzM51htLYgfUu5YGbOdVS7uKI8hY+/LyBla9lpofsslhLB29POD/DRhSVdcG/pZRf3sZI15aVSufiLxuDA2W1MmP28JSndcGJut+YlR/cwo8cepb4/N1kI/3dqV5igxRjsfF9uKMdpY/Zjyx0BW0EPYIJMLv/lK6irgQ0OTycfUV6f8/H8Jn/OfDFZT3/FiTLbGzvpglz4QdVxWdmFMtj5//vzs67wXLlxoPp51nBiTrQUfqZuX5qgxJjszVvBhQVZ1l/mg4NgYW1++evVq8yVsxusxkAl6CBvkzp07W69evTrwepIB98aNG1Pfd/SKD1emJ6x8PeivBXlFd9IZu62HfGF37IzJ1m7X58+fNzfl48TY+nVb2Uedsdn569evzZec+VLzoW+9WBFjsjFy4NmzZ73zrJtM0EPYIEweeIJn4iDevn07+wy/YHBgMGZQ3tvb27p06VIz4DAw8el+wl+/ft0MbmJ7e3uWB47LIL4oY7U1co/bW4ux2Rp53GSfPHnS7Dk9TozN1rWyjwNjsjO66Eg+VtOPA2OyMeWygi55yBoyrq+DTNBD2DCsDjBx0ABz+fLlacw3CP/w4cNsUGawYhB7//59M5DdunWrCSder/SQAwxKTEwYXD5+/NiEnWTGaGvkHrdVXRiTrZHNTZYHIel0nBiLrXn4Gct2gHUwxvEDWZRxXPr0WGxMOZSNfGDsGNIu6yAT9BA2DAMAAwn7Nt+9ezfbL+vw5D8UBiMmJXLH+cbZl7HZmpUgXuseR8bYr3kQIv9xYyy2ZmLFBAjHOSu79PHjwpjHak1YjzpjsTH2/PTp09Q3DjJBD+EQ4DUaN7QaDBQMLv56jdd3DFznzp078HqTlQH5GcQ8z3F7tb8oY7E1adBFN9bj2D5j69espLE3VTY/TozB1j4BYlWTVceXL19OY48HY+zTx40x2JiVfJf19OnTJuxQmVxYIYQ1Mxlg2AjXuL3pT0cRNhlEGr/icJObXBPvYaQTOzs7s3DOlZ40ZR7i5NdPzzkej6zjwBht7TrJSbejzBhtXco5LozR1s5kgj6Tc5QZo5097jj06bH25Xnxm+YUfybKhBBCCCGEEEZAtriEEEIIIYQwIjJBDyGEEEIIYURki8sxh/+uDyGEEEIIR4dM0EMIIYQQQhgR2eISQgghhBDCiMgEPYQQQgghhBGRCfoJhg8e6CtwOP8CHD/q73Fyx/EjCZskNt8csfXmiK03R2y9GWLn9RMbz4E96OHkoR/kd/iRfz4WIPihfv8oAh+iII9/JCD0JzbfHLH15oitN0dsvRli5/UTG88nK+gnkL/++qv5pO3e3t405Bv6RLN/Hte5c+dOc/zy5UtzDP2JzTdHbL05YuvNEVtvhth5/cTG/cgE/QTy/v375njx4sXm6Fy9enXrzZs3U99BeOU0ebqt5gvdxOabI7beHLH15oitN0PsvH5i435kgn4C+fTp0/SszocPH6ZnW1uvXr2a7f26efPmgbjQn9h8c8TWmyO23hyx9WaInddPbNyPTNBPIGfPnp2e1fnb3/42Pdva2tnZYZNYc5x3UYV2YvPNEVtvjth6c8TWmyF2Xj+xcT8yQT+BnD9/vjn+/vvvzdF5/fr11pUrV6a+77A3jD1j+S/1xYjNN0dsvTli680RW2+G2Hn9xMb9yAT9BPLTTz9tPXz4cOv69evNP2sI/cTR3bt3m2MJ/9Bx6dKlA3lCP2LzzRFbb47YenPE1pshdl4/sXFPvoYTCz9hRBeQ4yeMhH7OSE54eBhObL45YuvNEVtvjth6M8TO6yc27uYUfyYVDSGEEEIIIYyAbHEJIYQQQghhRGSCHkIIIYQQwojIBD2EEEIIIYQRkQl6CCGEEEIII2LwBJ0fkNdXnfSTOMDvWSp8yO9UktZ/lL7GP//5z+YTrzXQofZbmqEfbnu1X609FOfti+3x19rG+0YNZLhMfjYJORw9nLB5/eOoEZtvjth6c8TWmyO23gyx8/qJjTtofstlIPzMzc7OztT3nYcPH37d29ub+laDfobHf35H6Od2SBOGg+3UXtvb27Pzsn1p1xLSKo3nBdLv7+9PfT9CPGV7GmTgBOdqV9KV6Y8qsfnmiK03R2y9OWLrzRA7r5/YuJuFJuhStkSVWTUYkwarQQOtq9zjDPb0zucd0zujznFuZ851cXgbcJHULianlCX8gvSLA/xiPKrE5psjtt4csfXmiK03Q+y8fmLj+Sz8S+8o6kbAiF4Z4mRUrxR+GkYGwpXxchgEkEUe0hHu6Tn3cjGq8oc66vACO5f2wq+LA5RHbU4eXVAc1VbeNjVoqz5tU14cgGyVc9SIzTdHbL05YuvNEVtvhth5/cTG/Vj4n0Rv3ry59fTp06lva+vFixdbv/zyy9S3tXXv3r2tiUGxwtarV6+afT3ax3Pu3Lkm/MyZM82nXgV7zScT8SZu0ghbz58/n8ZsbT158mTr/v37M3m1fefsOXrw4MEs/7w9SCeV9+/fb0062tS3tXXx4sXmWLMpn+TVkfakXYE8tCd7tK5evdr42av18uXLA/+PUPLp06fp2XCuXLmy9e7du6nvaBGbb47YenPE1psjtt4MsfP6iY37sfAEnck4xtJm/Y8fPzZHwST5y5cvBwz04cOH5njt2rXmiEEnTyjNOdy9e3fr8ePHzcRajSCYuKsRmXy/ffu2OXeYuDPhp0zyq7xwEDqoOr2gHWQ73OTp8Yc0+GkHHraAC4F2ps24IC5fvtyEI4e+QVoeupyzZ89Ozxaj7GdHhdh8c8TWmyO23hyx9WaInddPbNyPpX5mkYkyK9ZM0mUYwZONVrP7goFpGFbKkd0Fq/A1aBTKxGWC3h8euGQ3nmxpuzbKi4aLhQcmZOjCIQ1t9ObNm8Yvzp8/3xxp6xKefpX/JBCbb47YenPE1psjtt4MsfP6iY1/ZKkJOivhrFpjSAwjtKrO080Qnj171jxFaaW8DVbHtQrv0KgYWPh5+A5PkG2dkAcrXuN4ewq1a8mjR4+aJ1jQhYN8njTLCwk/D1884bo8nnK5mMr0JaQ5isTmmyO23hyx9eaIrTdD7Lx+YuOeTJ5WlmIyKW7+gbNke/v7P2vq3MNA/xiAQ8Zkcj7zK+3EkE1aylGcNumTpwzzMpAX6mAfsT/95wu3o8D+iqMNSggr83g7tuFpcGpn1wXnfatW1lGC+ojYfL1QDxFbrxfqIWLr9UI9RGy9PqiDiJ3XA/qL2LhOu/bhWEOno4MeFbhoahfnUSI23xyx9eaIrTdHbL0ZYuf1ExvP5xR/Jk8J4QTCqyS2Fc3bUjQG+N+EyQUy9/XT2InNN0dsvTli680RW2+G2Hn9xMbdZIJ+wuECGfs/0x4FHYcQm2+O2HpzxNabI7beDLHz+omN2+mcoPPEEEIIIYQQQtgcWUEPIYQQQghhRCz1M4shhBBCCCGE1ZIJegghhBBCCCNioQk6P+7O/nQcX2PSB4E4V3jpSMNG+1pcG/xwPHlKfv7556oc3B9//HHAT7nud4f8UpbwcJd5nD5+pPbqwu2HvYT3geNkk3WzjM3bwkOdPrYWGpvII3wMiL3DWOjTr73vlqRfh7GwTF8u55OkO3awB30oZNvf32/O+S1LieGH3vUj7pwTB6TVOUf9IDzwYSFcDX5zEtnlD8P7b1GS18vUOeH+G5vkKX9z0/XzOonyNy+RuenfwVwX2II6q+1qkEZt4zbSuSCNt2mos4zNCccBfZzw8roI3+lja5Aty/5LeOwbxkaffu33Qe5xus9B+nUYC8v0ZcK4Lwr8x3EO0n33qlBOzsAHAIGxauGEuSHVSCUYnDgmxDU5gslMbcAhnPyiNkF30EkTIyjLREfqflwm6IDdui4O2V/IBoS7ffC77UI7i9q8ZF5/DvNtDcS3jR/EdY09IRwG8/q1jxeMET5OpF+HMbFoXy7viczfavfJo87gLS78SPvkIm9eKbDNAR4/ftwcF+H69etbk8Fi6vvO8+fPt3755Zetmzdvbr1+/Xoauhp4rSLdxd27d5sjr/yIu3z5cuMH/JMOcGQ/bLEo2P/Vq1eNvXCywadPn7b++uuvaaqtrTNnzkzPwrK02bxG7L4cXOuTB51mjGE8861ajDmT8bE5HstXp+HY4uPF27dvmzFFpF+Ho0RbXy7viW/evGm9Tx5lFtqDzg+2c2O7dOlSc2PzyVof7t27N9s3NHnqqU7wz5071xxpENKUE+pF4GGAMjnWePDgQaObHg7E6dOnZzpr8nRSwPbYi4tDDzEXLlxo7DC03UM/ajavcRS+vjZmNKgzWcHmT548mV3bGuwZ63CrGH9C2BT0Y+5X9GnfZ55+HY4abX1ZMA+5cuXK1He8WPhXXF6+fNk8ibOajhtyobMqSF748uVLc3SQ5ZN4YNK8LLu7u025HGswKefBw1fPgUGNfHI+eT/uPHr0qKmzr7gwMeSth96k8KB29erVJi4sT83mDiu9XH9hOZig3Lhxo7m+cfRp3g6V8OD++fPnqS+E8cM9ijGEPs29tEb6dTgKzOvLL168aBYNjyODJ+g8rfirYG5yGO7du3fTkP7s7e01k7sSZNEgckyoeXpaFTR4Vh/n40+rWnHRCiNvPdQ+cOfOneYYlqPL5kB8+QAZFoNfAeg7Qcl2onAUmbf9NP06HBXa+vLHjx+P7XxuoRV0Jsu+Ys5K39mzZ6e+/mBUVtP9pxRr2ya0Yu0TlbAZaFvHB3TaihV0HrT06jQsT5vN1f91PfiDchgOe8/v378/9X0b165duzb1fYc0eaAPRxHGDBbQaqRfh6NErS8f+222Xweyv7/f/McsvyJBdhx+x+P4r3HBucJJIzxcTv+tC5NGmYUrH3p4eulQhnte+dvom+44oP+elhPY19vG21I2pm3KfGE+y9i87MceF36kr63druSBtrwhHDZ9+rXfT/0+ln4dxsQyfVlwD9S4fRw5xZ9J5UMIIYQQQggjYOF/Eg0hhBBCCCGsnkzQQwghhBBCGBGZoIcQQgghhDAiMkEPIYQQQghhRGSCHkIIIYQQwojIBP2EU/4GPb9r3uersPwGN2lrv8WNzD6/T8pHd5DRJschvvZVTeVXPSi3lm5MLGJz8qiu+j10t5/cPLsva3PKVn7Fjdnmi/Zv6lmzD/ll/z5QFnm62sXbxGVLX5x0GbOtw+YY2q+9L5X918eWPtcG9OnXgv5KWvo50JdVnlz69cllkTGavuT5HPKvcoz2a0dOfRnUv9V/V9mXM0E/wdCpnj171pzTqba3t5vzeZCP9PxCZ/l1Lzru/v7+1NeOLkBkkJ6PxLRdVAzotS/JcoHu7e01Mq5evdpcFHwwiQ/QtF28h80iNscufLyIeuL0oSI+DqYw3M7OTucHo5a1Ofq+fft2Vt6rV6+a9h6rzRft33D9+vXp2XeQNwTsXftSsoP937x5M2sTylU70QayNW1B+Nj7d1g/i/Rr0tC/6Et8oIh8QB9j7CScL3bTt+bRp18DZaArfRb5d+/ebcL5ErL6NU7jVvr1yWORvsw9p/bJf1jHGP3+/fsD/fXhw4dbFy5caOK4fq5cudKEc1z5/XAiOJxA+NF//xiUoEt0/fA/HxBo+0DO5AbQxCGD8yGgT9eHd4jTxwsA+ZOLeer7/oEqlUv6LnmHwaI2Jx5X1sdtzPnQ+g61eQnx/vEI/EN1WBeL2hpkF6+boM/V5LZR9suSUk/8KtfzlO0wJluHzVH2F9HVr0nv/cf7Nkf1I/L7mNrFvH4NtWulTE+Znib9+uSwSF8W5Gvrq6seo8twXTvg+SRHrKIvZwX9BMKTKityWontC0+brJp+/PixeVItnxAfPXo0WyVZBFaE+/Lly5cDK/U8tTro0faUfRgsanPyTa7TxlEfntiF1/nFixezp/ohDLF5jXPnzk3PxmPzRW0N9HFW+DYFun769GnqO2hPtS+r7Kx6OmPr32H9LNqveevlYwXXPLLgxo0bTT/C//z589lq5rLozRzyuFfolX85TlPm+fPnp77065PCMmP0pvE+q+sGuFeA4nVUmlX05UzQTyC8stnZ2Zn6+vPu3butydPj7BUlaMLIgMxgvyh06iEX68WLF5tj2xYNoI66iA6bRW3ugwM2Z1DzQULw0CSb9GWozUvYmsEg5IzB5ovaGpgwbPKmwWvRp0+fTn0/wsSGbS+8hi3bfUz9O6yfZfq1P/g5jBm7u7vN1gLG9aFjSBs8FLB1hgdLxi1th6tRTtrTr48/y/TlwwS9fZ7DddPFsn05E/QTCCt25aDYByaBDPSawDx48GA2aWBALgd3Js+sntScT6wZuMu97H3gxsLkRTK5WMp6ff78eXp2uCxq85KHDx82bw8cn7it2+YCWeWqrjhsmy9qa2xy586dqW8+fW3dhR5wlI8VF1/Bf/ny5exhmDdUJWPp32H9rGoMKWHs5m0kYynXwCr6NWOST/gZt7h/OExc2h4c0q+PN+vqyyWr6MsOb4SGPsQu05czQQ+9YTAtB1mgo7Oyq44PerJkclFzmuTrIlnkYkWG5PGkygPDSeD06dPTs2/4U73bpHSrsDlwY609kB11WMmm39KHmSjTp7v+0aePrfvw4cOHJs/e3l5Tfi0vD6MhLAITZd52CSZHvLkB3tAQz1jAJJ1+z5aTsj/L9e3XyKOcLngje+3atakvhNWzqjFa+D2T+x/XjBbIONYWCZchE/QTiO9BHAKDqW+x4GmS/1YuLwKg4867ADTR0yqi768eApMobji18s6cOTM9O1wWtblDfm605QAw5Kl+WZujAw9CWn1nxa2s12HbfFFba6KMY8WP7VyEbQIemtjG0lYebVzbQjaW/h3Wz6L9mnGRLSbKyyTcJ8aMB84qJhja2y54+C37b20sE+nXx5tV3A83DWO0v90E7hH8/xfwhvPWrVvNubNUX57cjMIJpNb0hMnpv6j5b2j8kwn3AT+u7T+UPX0b5JUcOf13tOKEp92Z/hoB8hXW9h/fLmMM1PRRHbwebnO39+TpvIl3SOP/Vd7FsjZ3Xco4QdgYqOnherf1b0H9S7tif+Un3zy8j3oZbmvK4Ly0I/p53lofl4xwcqi1ea2flP3a+1PZdxWOq/Wzkj79GnwMKcukHOJruIxwfKm1s/oLrq0vy48r74nrGKNFOUYLlVmLL2UM5RR/JkLCCYOVU54GF3nNcxTgaZdXrOU/MR4msfnmiK3DcST9OhwX0pfnkwn6CYatIYv808PYYRsHW282tT1hCLH55oitw3Ek/TocF9KXu8kE/YTDBXKcBkT2tfFkzq9fjJXYfHPE1uE4kn4djgvpy+1kgh5CCCGEEMKIOFK/4sIvRvC0FUIIIYQQwnHlyK2gH7fXISGEEEIIITiDV9CZIOuDNO7Yc4PzMPbi1OiKE11p+LhCHxmhG/7LGDt2IVu3peOtBnGkC2FM9Onf6ts4H0/4Jx+F561dCCePZcYP5cVxHurExt0MnqCzen379u3mQx4svssBHy/Z3d1tvqZEWO0jBH1udl1p9vf3m0+MU45+ID4Mhw7Np527YPKNrWlL2pwHMId24gMUxOefe8KY6NO/eajU+EX/vnr16jRmq/lo0N7eXhNHP+daCCGcDJYZP5hE8vEnwhlD5sk5qcTG81nZHnR9WXAefbandKVh8s9P8vA54tpn50M/+O1ROnYXfG1OP3/E75XiBJN1LpZsNwpjpE//9ofKO3fuNA//zunTp5vjKj/dHEIYP8uOH5oPcf/c2dlpzsNBYuP5LD1B51XwMq8XtEUCV26TeP/+/SyOchzifMIYVo9PTHha1QcFeHrlk/+g9gnhqMPDv/A3Rq9fv86HU0IInWj88PsmY0jeLq+Ok2bjhSfo9+7dayZmvApeBuTwVMSrilevXh3YY8QP2BPOdhp+9B0ePHjQlEucJoxhffDwhb2ZkOs1Pw9HPLHygET7cJ496OEoQ59mbBFanaHv5y1RCKGLcvwA3TfzPyyr4STaeOEJuvagz3tFMQ9kfPnypTF0iW6SrF4xiWfyzqScPHkq3QyyNyuKPEwBn6/lCVYPSOxTzyQmHGX8DRHwYEq/ZpxjbPKFgxBCcMrxAzQ/Yu6yzC6D8I2TaOOlt7iw/6c0muCmxs1NrtymAjz58FSEoeeRvaCHh/+PwdmzZzNhCccGxiDv3/Rt/umIsY3FASbp5T9IhxAClOOHwxjC4lZYjpNq45X9k2gNJtRMvOUwpKMJ+7zVcLZWpJMfLjydqg14IGM7ktrv+fPnW7du3WrOQzhKsDWLPebAxNx/rcUXFLI4EEIo6Ro/BFsw2hYxw3xOtI0nE+dBbG9vs9TduJ2dnWnoNyYTuFkcbn9/fxrzHc9f+nUOe3t7s/CynLA8bl/ZHLC17O1tQ9s6nr+MC+Gw6dO/PV5OY9bu7u4sjOsghHByWGb8ePjw4YGwUCc2ns+R+5JoCCGEEEIIx5m1bnEJIYQQQgghDCMT9BBCCCGEEEZEJughhBBCCCGMiEzQQwghhBBCGBGZoIcQQgghhDAiMkE/4fgncvmN0bYPSgl+k1Qfniq/3IVfcS63C8oiPWXPg98/LX8DtSwPOegYAgzt30Afa+u/ktG3zy7bv3W9qU+nf4ewOVY1fmgcwLWNLTX6jB98RE2yy3ToojjGjTGOH0fdxuBlr9LGmaCfYOhMz549a87pVNvb2815G1wUfP6cX+bc3d1tvrbonfXTp09NHK7Pp//p1JcuXZr6uqGce/fuTX3f4CLc29tryrt69WpzUfBBmZs3bw66QMPxZGj/Bvp42c8EcchQn5v38aJl+zc3hStXrjRlcaT89O8QNsMqxw99LR0Hfb5M3Gf8YFHs8uXLjdy2Lx6rXD4IObbx4zjYGD8y9vf3mzQrtfFEYDiB8HEhPsZSQpfgAwJ9mFxMs7T+4YAh8NEB8ugDMW2gL45ygPSUL0o5pFPacPJYpn+Tz/sWkGdo34ZF+zd4PskR6d8hrI9Vjx8O8foQzzzmjR8ejl4+JlAH8tZ0GcP4cRxs3KXHKmycFfQTCE+qq/o07unTp5vjtWvXmqfHSYecveZZFTzB3rhxY+r7xpcvX5onVlGuZt69e7d1JTQcb1bZvwWrM5MbStO3ceX2rmWo9W9WdkD9WkddV+nfIayHdYwfJeX9alFczvPnz5txQdy5c6e5J/N2mTHLOezx47jYmB0Fsi9O4zaswsaZoJ9A3r9/vzV5upz6FoMLjI6pzqsjnZJJ+qNHjxr/Knj79u3WxYsXp75vyN81UaKOfsGEk8Eq+nfJq1evts6dO9fc8HYr27uWoda/YXvO69707xBWzzrGD4frncnzqmAMYHLIhNe3X+ie/Pjx42Zxga0hzmGOH8fBxoz/LBL69pdyu8yyNs4E/QTCXvFlny6ZgHPh12CSTudl8qwny9L1XYHkYmgrRxMlyWRCU9br8+fP07NwUlhF/3Y0EdfKCas+9LX//b//94E+7W4V/bsP6d8hrJZVjx8OkzUmdMhfxf0ReLjXBJEJZG3hgMnqx48fp77vHNb4cRxszFt87gN6C6D7Q2n/ZWycCXoYDJOK8pV8CRcHHZdOXXN9X21xMeiC4pxXRvoPaZfPkyrbEEJYNbqRlAPv//gf/+NAn3a3bP/mhsDqjMrkWHsADSEcDbiGuUdpbFjF/dFhgtj11o03gMedTdqY7b2+zXYdZIJ+Ajl79uwPk42+8JqMp1O9kvdXaqLPBL4vfkHxmo4nWP4b3eG/pfmVi9oFd+bMmelZOCks07/boO9p25ZeWda2pQylq3/jf/HiRXNO2bdu3WrOnfTvEFbLOsYPYEuorm1Wb5fZ+lADedwLaw/x3JP5P7GSwxo/joONcSwMausQ5eEv7b+UjSc3hnACqTU9YXL6L2r+Sxk//8k86XwH0uCIh8lT5Q9h80Cmy8IPk0lK4y+ZTFhm/xXtedv+47smI5wMam2v/uJ9xvu3+3H0acf7eB+W6d9CZXLtldRkhBCWp3ZtESY3ZPwgrcLKuC76jB9+Ty7HCIXjavdIwg+TWvk1ncdsY1BcrTzJWJRT/JkICScMnqhZCV/kNc9RgKdZ9rlpX1g4WaR/hxAWJePH+omN55MJ+gmGVzV8JGAVr+rHBK+h+FBAn48lheNL+ncIYVEyfqyf2LibTNBPOFwgx+lGz742nszLferhZJL+HUJYlIwf6yc2bicT9BBCCCGEEEZEfsUlhBBCCCGEEZEJegghhBBCCCNioQk6G+D1cQ3+U5X9NsC5wktHGvYa1eLa4PclyVPChzxqcnB//PHHAT/lut8d8ktZwsOp73FE7dWF26/8VDAQRpw+HhRCOHksO5b0yR9CCCeJhSboly5dar6gxPb1t2/fNl/AA35SZm9vrwl/+PBh86ENzkkL/COAPsZBOG57e7s6CYc3b940eWsTZM/vZfJ1J4Xv7u42n9HGv7Oz0/iVD8enb9nIL/10BMLJQ7rj9h/GwA2Rz+R3QZrXr183NsA2fOXQPy5Auz19+rSJzz+thXAyWXYs6ZM/hBBOGoMn6BpU9bUkJsBMuoHfe6xNZklLuhp8ltUnxkI/U8Mk+fnz59PQb7RNBtvKb0M6oR+Te75CJVjtOc6TTn57lAebeejhqfw6FvbBXsfpv69DCMNZdizpmz+EEE4SgyfoDK6sTvM6UivbbZPvPrByogm+w6ScgZtJOisvq4QVm3JVXj8mz6tX4vgB/ZMO9n/16lVjLxwPMbQ/D2l6a6JX1iGE0EbbWBJCCKHOQltcWDVlZZutLkzOfNtDH3i9qYkdq+e1Cf65c+eaIwM7aWrbXIbCwwBltr1OZTUf3fRwEL5t+8FebGXSQ8z79++b9uchhlfWnGcPegihi9pYEkIIoc5CE3Rg+weTM1bTcUMm0NqDDl++fGmODrJ8Eg/lNpdF0B50jjWYlGviGb7x6NGjxma8xdAknP81YPVLDzH379/PVpcQQie1sSSEEEKdhfags/9Y6B8/3717Nw3pD/sOWYUvQRYDuRwTam2pWAVMLI/jP36uGv+lBdoZx+vps2fPDn5rEkI4ubSNJSGEEOostILOZNlXzFkRYdI2FCbJrKb7r7jUJn5aqc2AvnnK/f9nzpxp2oP9pOoDvN24detWcx5CCDVqY0kIIYQWvg5kf3//62RS/XVnZ4c9Ko3D73jc9vb2NPRrc65w0ggPl9vd3Z3Gfv16+/btWbjyoYenlw5luOeVv42+6Y4De3t7B+orsK+3jbelt7PnP+62CiG0s8qxxPOHEMJJ5hR/JoNiCCGEEEIIYQQs/E+iIYQQQgghhNWTCXoIIYQQQggjIhP0EEIIIYQQRkQm6CGEEEIIIYyITNBDCCGEEEIYESudoPMb5vry5yLwMQv/CFIfyONfpaP8VXxEB5n6kqk7winTw9q+osrvu8/7wir5a/p6Geuk/A36rvo4tBNpvb34nXrp7HLb8Dp2tTv6KF2pm8JVHnXIVwpD2DyrHEsU1mccgVWMJS6DMSRjSQjhMFnpBJ3Pvy/6q41M7vi8/xAYYMs8lI8ey/Ly5cvmI0q3b99uZMoh++7du81XUIGw2ldJGdj39/envjpdN583b97MylwX3IiePXvWnHMz2t7ebs7nQT7So9vjx4+noVtbnz59munMlwK70M2RtNiJj1/VPkRFOXxtVnL9y7PYj3Yg/OrVq43NaZ+bN2/2vrGHEJZnlWMJE2Ug7MGDB50TbljFWCIUx/ifsSSEcKhMBqPRwMcrhn70hjz+MYxV0qWPPq7RBR9gIl0XyODjSg551v3xH+T7x6AE+nTpjK39IyOCMPIu2qXQp02u20LpsBn2FfjdlqSpyQshrJZVjyXluOnXdR+GjiU6pxwfUwRpavJCCGGdrHQFnZUMX23Q6ghHnL9SVBiuhHSE6/UiMj2/y/z48WMTBgoXnPcpHzdvlQY5WtlZhK7tH+/fv5/FoePp06d/+Cz2KqEurDLxyf4hoBuf+MfmZT2uXbvWrDxNbmRNHGUM5ezZs9Oz7/Am4dy5c1PfVnNO+V++fDnwhqJ8a8JbjqFvZEIIw1j1WII8rmvGQMFqPNf7EIaMJXDnzp1m/OJNHPo4GUtCCIfByiboDKzl9gNgcGXgu3379ta7d++aMOL2plsTdnZ2fpj4smVkd3d36tv6YbsEMhnEye8TWcJFV/lMxplIEk456OBbNRxuPgzYLnsR7t+/P6sz+MMCr4YJRydeqTLZvHXrVlPuopPdLnggoM5DwX7Y8fLly7N66MFGE2RuZtTj0aNHjb8v1LHtJl+72WpbUe1VtqCObucQwmpZx1gC5QP3EIaOJaDyuA+gV3lPylgSQtg0K5ugM8AxARWaVPsqJ6sVmmxqgsVePyZ1fWFCxmCpAZWJrPCy2soHXzmBrv3SDNbcQFzOIlAGq0JMuEtZejjADsRhI84pF7fMzaoGe8UXkYnNsJ1ufuwPrT08oDvh/tagdD6x5mbY9oDUBQ9X169fn8nkIaqs1+fPn6dnIYRVs+6xRPyf//N/Dowf7lYxljispvv9QWQsCSFskpVucenLspPdZWECyco7gzsTvD7bSbgJdT1ItN0wBNt1eH3KhHveavyqJ+Sronyw6YI6cPPVQ0bpdGOWrdrqfOXKla23b99Ofd9u7Ky6gcvnoY2bfAhh/LSNJYwDjI++Ws394n/+z/95YPxwt4qxpAT9QgjhMNn4BF0DsL9CLF8nCq1sa7BmC82ZM2eavYsKY18h/jYZNUirbSW4tgF9CLUbhmBlCB27VuoBvVixXze85u1arWqDfeZs+VFebMiWnBJeVd+4cWPqa4c25IapB5/a/wGoTKDc2n5XtjNx8y3Dgf4SQlgP6xhLeCv6/Pnz5rzvmLiqsQTIS9qSjCUhhI0ymVCuhP3pr2jgdnZ2mv+Gl5//gPdzT4vjP/b5FQBPA/IjT+nA5RGHA4VRdlf57pcrf4VAZeKQ5ZT5pZfj+alvKU/h5FW46rEJKK9EeuBUJ7ULurofhx2E27u0ZY1aG0xuxAfihJcpvbwP1ewPLiOEsB5q15muTb8++44loPGyHHtrLDuWgMLKcEF4CCFsklP8mQw+JwpWZcrtKrWw4wyrRLzera0gHQd43c3+2JPUpiEcBhlLQghh9Zy4CTqvNre3v/2yi+D1KP8AdFxvMG2wNYRXy7UPLR1laE9el8/bUhRCWA0ZS0IIYbWc2BV0/13bnZ2d5tdkTiLcWI/TzYcHMFb0Tmp7hnBYZCwJIYTVcSIn6CGEEEIIIYyVQ/mZxbHBHkN+HjGEEEIIIYTDJivoU47b69kQQgghhHA0GbyCzkTWP8ojx149nIfpN277wD/jIFuwTxx58yANaReBjwfp4xYg/dHlJDDkzYHaXfbCRmpnb7cQQgjrY8i4zT3N73GgcXvR++ZJYRk75/7Yjz42lh1x5ZySOZzijmV/ZgV9KPzGbPm7tfrdWX5nts9v1zr6Teuh+ZYBfSkTfYGyOec3cFWX44z/HnAX2IM0ZXsTpt8L5jeLy/gQQgirpe+4DRq7dY8D7nM+bus8HGRZO+PP/bGbPjbGdoJ5mc8Rse9x778r24P++PHj6dlw+JLnxNBT32ZAX369xeFnFvmZsCEr/0cV6trH5ny9lXS13wA+ffp0c1zFl1hDCCF003fcBr7G6vc4vRnWT2Hy85EPHjxozsNBlrGzyP2xmz429l9QunPnztb+/v7U963/Mj/ps9PiqLL0BJ2LvnyFNg9/LdG2ncS3rmi7C0fykF+U21R8C44m2spX5q2BPnw2PnyzGwMPFwK28wth8jQ7879+/Tof8QghhJHA2M2Exnn37t2B7RZnzpzJ/10tSc3OkPvjetje3p6efbPr169fm+O8ed1RZeEJOr8jzqSNJ5gh0GmZAGPYhw8fVp/g6fRPnjxpzpl8U5b8PEG9evWqOUeWzoFGQh6yuUAePXrUhJOffISTvrZCzkc2qA+T0VxM33jz5k3z9M8FgP1oAz0M6Y0JNssgH0II40D3t9rKbVZzV0eXnXN/XD3v378/MF+U3bEvrm2x9yiz8ASdyTUT3r6vgQSTvWvXrjXnTIRrH4EgnAk28BqEsvD7xJmLo9ymwuSb9ECcLhL0/PLlS3OxtMFrP9LlYvoOtrhx40ZzIeBoAz55DUzU79+/37QNdq099IQQQtgsLExlkWn9dNk598fV8/bt29n8roSJO1+DP24svcWFiW2b0eiUdE45PeEwWV4XtQuB13paWQ/9wW61To+Nr1+/3rQ9AxSDkG9/CSGEsHkYm3nTqXsui1aM1YzPFy5caBbIBGP71atXp74whC475/64epiLaMG1DbZsHTdW9k+iNVh1ZVIsR4dlQPDXFGxnWRWspmtbCyBbDwX5XPNw2O7DKoBgQNLbD/BXSrXXfCGEEDZHec/lnri7u9tMbrj/gsZtxnbekIbhdNlZ5P64Gti6rAdLHn5qc0b6svr3sWLSuQbBz9yQDTfplNPQb9ye/nShHD+fWMNlTJ4uZz+ziEMmYfL/53/+5+zcw3Fe3t7053Y8Xj975OXpvJb3JEGdVX+cwP7erm128p9IwqYhhBDWS99xWxCm+yD4vZb7aaizrJ1zf5xPHxt7vBx9uC3vcSNfEg0hhBBCCGFErHWLSwghhBBCCGEYmaCHEEIIIYQwIjJBDyGEEEIIYURkgh5CCCGEEMKIyAQ9hBBCCCGEEZEJegghhBBCCCMiE/QTDl/oEnwEwL/42gZ59AU1Pmns1MK6oCzyUHYbfJhA5fkX2ThXuBxy+LBBCCEcVxYZtxlHPZ/w8bwvfcZtwXhMWv/AjI/pxI913B5qZ69XLR3hq74/ipqdpYtkjNHOQ22sNLjSlvgVV+vrNYbYGNu6faEsb5U2zgT9BEOnevbsWXNOp9re3m7Ou+AC4Kte/Hw+7pdffpnGfJM3BC6MS5cuTX11SHPv3r1ZeXzNVBfv5cuXZ+G4nZ2d5ottfAG178UZQghHiUXGbSYVjKMlTCT4sjfjJ5+k7zNu9hm3QRMpfXWTz947Grf5yvcYx+2hdtZ9iTrt7u429XHWcX+ENjvTtrQpYRxZ0BqbnRfpy6TZ399v6sUXRMknPn361ITjPnz4MA1tp6+NgXLKawg77u3tNeXxlXxsvlIbTwSHEwhfCPUvnwm6BF/paoN4XNtX6CYXT1VuG/qyHcca6IJMwTlhZXrCvFz0a9MxhBCOIouO20A+H0uBfE4fOTBv3Ia2e4G+Tl3qAmMZtxexs9uCc2SUtNmkjWXs7F835aivc8IY7LyIjWv1kJ05Jy9uCH1sDJSDk91I7324lLMKG2eCfgJRR6pBeJ8BiHSbHIDo6OhVKxMIL2W01TGEEI4ai47bgnG5a0IBxM+TA7W8jspiMkU6n1QpD2N2rT61sE2iutUgvI992u5Tq74/dtmZONWD8FJGWx03waI2xq5uW+qoOqt+mqi32axEunSlpxzNPzTpxl/WoZRTxg8lW1xOIO/fv2+2gwyFVzdi0nea7Sb+emld8KqKV0u8in38+PE09EdcP6COeu0YQghHmUXH7TYYLyeTu60XL15MQ1bH27dvm1f+bEHgXvHq1avZ3l2N04zlkwnPD3t6D3vcXsbO2m7CvXETe7277Mz208lkstGH+DHdH5ex8blz56ZnB1H92OJDvR89etT4VwF2vnjx4tT3Dfm7/qdgWRtngn4CYZ9WebEuAhfBly9fpr4f8X/YKF1Xpy5h7xyDD9T2dXEBtF20nz9/np6FEMLRZVXjtsP+XxY/NC7v7+83Y6aP1e76jttMVPkfIU1iuFd8/PixOXfu3LlTDT/McXsZO5OPe9Xu7m4zWe6anK3i/thlZy2e7e3tNfusazIPy87r6MsOk3TqvwobM/9oWxikna9fvz6TyQNvWa9lbJwJeliK06dPT89+hCd4Bqua838u7YKLSAMN/0zEBL1ccXn37t3WtWvXpr4QQgh9YGKnMZnVbCZ4qxi3maQwCetD2+LKUQY7zVshXredWVm/cOFC08ZM0plIHnV4GHnz5s3U922if+XKlanvINhmFTbmbYgm4JzzQKu3Iy6f9uYt/yrJBP0Ecvbs2dmkd1HIz4WyzqdgwUqEg/5Olx5nzpyZnoUQwtFlFeN2G5pwlL+0sig3btw48IsXT58+bcJKWJ2sLa4c5ri9qvsjWzO1sr0u5tmZxSvB6m7JYdl5URszIWY+oLzUvdZ/6Fe1/rYIPqnXQyyLhQ4Lhzwo1Cb9S9l4Umg4gdSanjA5/ZOG/tGEf3zQOY5/TCkhTPGknYf+OUNO/1wxuQAav+AfM5SGcwc9SV/DZYQQwlGnNqZpbMTVxm3343zsVlif8Vr0Hbflx7l8heGkr0P4YVPToaa329nvU5u8P7bZuS2/IOwwqZXv+rb1ZcKVxus71L7Q18aCNiYOPG+tH0NNxhBO8WciJJwweMLkdVHf1zxHDbbG8PprVStCIYRw2GTc3gyx8/qJjeeTCfoJhtcy/JPQul/DbRr+MYcPBfT5UEEIIRwlMm5vhth5/cTG3XRO0NkUH0IIIYQQQtgcWUEPIYQQQghhRORXXEIIIYQQQhgRmaCHEEIIIYQwIjJBDyGEEEIIYURkgh5CCCGEEMKIyAQ9hBBCCCGEEbHQBJ3PrPITjOXPMP7zn/+chfMj7ZvGy+f3NYEfw/dwHL9RORb4xLL0QldcjcPSG/0Ooy1rUH+1axukkT1LV8bp89Z9UV8S6NKnTfqmK9F1Nmak4yKfbRa0g9t1U5TtWWNM/X8I3s/dDbUz6X1MQsaibY0dXZchdu0aGxeFurk+7sqxos1um+5DXToPhTxqyz716ML1GDKurmL8cLDzvPKp56r70mFS9glvR2zhcXKrsncfuP/V+j96Dukri8B1PG/OMGr4mcVF4dOq5SdV+RRq+UnZGuUn25dlZ2fnwOd19RlWlaPPw44JPhkr/dBfn5AtIQ7d2z4nOwZW3Z4las/aJ5RrkNb7IbZTX+2ydRv69O/QfMtAebh1sKr20ueV+1zzNQ7DrtCnXGxEmr6fjR4KZS9qtz7Qz2vj86KUbY3soWOSruMh9aYc8qxjjEH/ckzxdqHctjoeVh+ap/NQavVYxNaLjKvLjh8OdkEWerSxzr4E1GOoDZaBupb1LeuHPu4nPWk2QVv/79NWi7Kutj0MFt7iwhPY1atXm69Alfz000/Tszo8OfGJ11XBU9KrV6+2Xr9+PQ35psOkfht9UhzKx48ft86dO9ecv3z5svWTsMSNGZ6O121n2nNyUU99w+FLZct8Upi2mVz4U99mmAz207PVwvXi18oyLCsHu04G6alvc/Rpz8ePH69Vt3v37k3PNgd1WpTyq3j379+fnq0XrttNXnv0jXn3MBhDHxJ9da5R1mMT47lY1TgEjPGTieDUV4e+NJmwTn2r59GjR9Oz9UM7Me8p5wfcN548edL61lbX7SbamP4/eQib+r5DW62jHVZ5bxsDC0/QX7x40RjfB22Mo4k3ja/XKf6KAT83Jh/cy3SeFyd4HVJ7NfX8+fNmEKwNUH0mt16Wu9qrkbZ6+Wsmf22jV4cciZP+pOEiwhaEI1dpRZlHcGGqLKUnDJmKc91oF6UvdVN428WMHMVJPkfyUDb+69evNwMFYdTDbeTl4VeZf//732dpSC8diff8ii9RGsqfR2m/Pnj5bbhushPHUi9P19YWylfmrUG80pbO+w/ITtgAR/mXLl1qBnHC8bs87zf4lV9phfpAeUOq1cP7v+SrTC+vRPk4ktbtpbAynHNkS7/SHm14u+BKFO7yVEYZTp1UX8Jr9uUcuHkpb5s8/JInV9rR09cgnbdfHx1B9i8hDX2IvsS58iJH8fjnQXq1WVm2t0mJlwluO89H/5VsXYNdUN9F8WulVp7C2+zped3Nw3VGttpVtnRZ6gPStQRb+Xguh30B+fjVzl1QPuWpbp5H7VWOH5Kv9N6WxClf33Zyu5Y6S5bqBqRRetkPKE92JX2trYnXPV15vT5ejstzp/bhXDI4R07J27dvqw9/zIMYV969ezcNOQifoG+bL7m9SlfS1d9Vb68zeB4WKIXszhG7lKgfqc2Qo3Mc5ZGmdm9zHdrag3P3j4ZvC+nD0WucyZP37PWFwvT6YmKoxk8aj5t0juaceE+n13akVfo+IE8y25BOy1LqS92RTR0FuqjOpMcpneoIbheldb9kkpc48pcyCP/tt99m5SgP5+gpGwvOkeHy8XsaQT2UnrScy49OsrmfA2lkI8IpC50JV/2AfO5HLrhd/Jx4r3sX0lXOkU5tUIZ08bSui+pDPUmjcvCTRrb1dG1t0WbLMv0ioAdykCtZpR29vjqX3srT1iYcSaMyynqoXMkBL191lDzh+YhzW5Bf9gXKwY9THtK4PjW8HtK1du7lco4u5FMaIFzlce761ewLkgVt8tQObh9ke1qPc6SLnMrtqyNyy7LxS2fPQxh+0aaT2pEjeTl3XRSndMLTePtQDn4gXjoobB6uA67MR5jqWIMyVde2c9kbVB71W5Q2nbGJwgQ6yG5+XqZzfZHjduBcNkBv73sO6co648jjZXtZHJXGy1X7c9Q58aTvag+X0XauMiVHsilHaQA98Su992+vQ9s58vELZCm+lEdaL1syuiC953GQrbJVnlyX/Ybg+mM/ZOtcelEW4YQB59gZyK90OqJzWSe1v+vOUeV3nZPHdam1x5hZ+J9Ez54925zfuHHjh20uDx48aF416QlN2ziApzqe4IAnpYnRmnQ8Hd26dasJ55XbmzdvmvM+1J4Eh6AnqtLpCVbwRDZp1Fl5vD3glRkr+L5lhzcLvGbRq8PJBTLbXjHpbM2xpHzN+PTp08a24FszsB8ypCP8t//23xp7o5u/wv7y5cvW+/fvD8idtHnzegn78rSPDJ46wZ+AgbcPk87enOtVLeWQH8pX3qAVAPIhm5UYnpSV9tq1a80Rzp8/f6CdP3/+3BwpC3tgf3RcFOxEfb3+85ANVMe2rUded9lJfRkkx9O1tQXtiwyuB1av5uGrPKUrV1rol2pDyquBjqdPn27yq39Kb++vWvGgTdSO3p61ehBG//d6c73oWsde9KsSz+f2x65t11vfPloDvSkTG9BnHeQBMpFPO3J90n8FaVjRUl/QtQs1+5a0yZP+buc7d+7M3kDSFzyuBDm0u+oAfXUsx6QuaEe2PKJPX7AnetXGLd7Qetl+TvvQv9CV1UrZCBlaAaSOfcEW2Ai3zL2ka9yS/b0POdK75mrUdK5da+iDXqwO+paqtn5Yg77GPR1oF8qZh/pYbUxsGz/o71q5Jy/QF8iPHI0purbngZ7oURtXsZHkYDPKpm5XrlxpwgC7oY+uA41Z0NXWYt68wOVJF90/+zCvr/q8i/LoK9hV99kaffshepJWYGv6NeHMBzXHo15qS8YG0khvrz9hlMHYVl67an/kyE4cue6532n+UkIaH/fa2mPMLDRB94tUhsL4TNq5CPH7RczAcOHCheZcNyLS0fkxLg2jjis0qPQBoyNLA4DTR4YGutJp4HdqYfDp06fp2frRxSY3b8Cs2QV085abd8H3hQvJ5dKWNVQe+tF/VA+9hqJPUNdlaSsfKFcDEA6G3LyGUmsLBjXKph/7gNIGdnL7uvNrCJSWGxBltF0jTK5Ip8F0EYbWYxmGXm/UUW1ce4VKGHHYgJtIF2fOnGmOTB760Ne+feVx3XBzxN7Yoc91Sz/Qza3GKvqAFmvQS+P9uuAaRVecj8nYAv1rbdyHrrFiXh8aMm6pDwnqoPqUbh59xjcm2UxEF0H3+Lb7yCpBR6+7+iyTSmzKXEJ6+HYFnyyKIeORJrO+7aKLvm09ZJxiUsskkjbreugWbfMe/FwftWsQfXlYaWvLIf2wbS60CPRhymAMqrVlCfpjf2Dy3pdNztNWwcJ70B2ehGh0TbB8gsOgRgfmQuOCwfh+Q1HjM+ln0KPjY3zS9B0UKJebanljoWzf674s1IG6cQEJ9OVC8dUJVkf86XARsJNWLbAb8DDDRccTu8I4uj4lPAxxESs99iQ9KwW+qiC7L4tshDzRdbNkUCr3IfIQ5ysv66Sc7OoG7/r7+TK0tQWDMjcmXT+rBNtTHgMg12G5cocO6DRksMU+ajPJY2DtWw9uhn69UD5+2WUei1xvrMqojWsTGq4pH7dqoB92oo9TX12fwAOQr0iLvvbtK08wrmmValkW6QM1NJlCL50vCos9tInGJCYW+LleGOt9TNE54z0rcNSD9F3jziLM60N9xi3vQ5uAByYmp8uWR9+kj+rN+TK0jR+6rtXmtDX2wnE9YHPqQn7SsBih9qj13b7jEWUyIda9VeVTbtvku09bDx2n0JN+S5v1ufeRnmsB2znYR3OuEuTKhsuA7HIupH5NGZp3YUvSMdf4j//4jwP3P96e4+f+qkm52lFt0IbesJULUl2sY562diaduzeThm329OA4F5OOemAvz+SimKXjXCh80jkaP3nKdJMONwtzmfgppwuX5+ldHw9fBPK6rEnna8K9DOntYX5O/b2e+D1etpVfaaW3twN5XSfKJkx+9PP0OOH2clsL1/Hvf//77Nx1JY2Xzzllyo/D7zqVEOb9qbQVx19//XUWRpkqo9S7bB+XCx6HnBql/sgs28fr43bydGVbQK0tXGel97YhbFGQI/0kx+uHPq6/6yvndeLc8ysvYbV6uCOv8DI59zhwO3n5OPAw9QG3mccjv8TjKau0N0fqAwrHOV4HZJRh2KQMk2zidC79a/LcjiXEqYwS5cFJvuir4+RmNgtDn1IX5eEoZMsaxCk/7n/9r/81OyePy0cvbxPKcLmeVmXq3MshXH6OTtmv3JblOELakrIPuV/6qUyF45ahS2evt/T1MOlU6lnWw+uuawDwt6H0ONrK+5PLpz3QWX6lUz08LXFKi57g7e51B9ebctyvfOpDrp/X0e1FGijtU4ZJNnEeXqZT2TV5gjicQzrpUgO5klfK9HrKhqBwDxuKtyPOkU044mp2QwfVy+sgO4myv5RhKos4Dy/HF6i1B8geY+MUfyaKhXAk4el7yFN0CMcFVohrK7mHCStqbN/Y1ArxEDJWLMcY+9txgz7Kan65gp6+u34Yu3jL3eftxaZYyRaXEDYNr8DYg7buva4hjBFeE/PKdmywFWFsk3NuvIwVmeAsDjbs2nIVVgN74MsJIn13FVuLQjtsj2PsGtPkHLKCHkIIRwRW0thHubOz88OvHRwm/g9bY1w9D4vBqjn7sm/fvp3V8zXBYtP2dE/4/v7+6CaJ4fDIBD2EEEIIIYQRkS0uIYQQQgghjIhM0EMIIYQQQhgRC03Q+Qcl9hzi+OcR/d6s/hmn5kjDb13W4tpgv2XtR+v9gxGl++OPPw74Kdf97pBfyhIert/tXIQ2W/Vlnk31z5KHDTrM++1S6NsHCOuSh5wh7aJ26KPjccHtW7pF4Zpp68NdcTXarmPCu6AMyoKy/3O9zMvfB8mtOdWxLNvR2DKELnltuF6rqHcXq7JtCCGE+Sw0QeeDOfwzA9vX+c9X/okE+EoT/yRE+MOH3z45zDlpgR+hJ4w4wnH8c0RtEg78kD15axMxz+9l8rlqhe/u7jb/2IKff6rCr3w4/mOaf7SSfjoC4eQh3TL/9NRmK+CGN2+SOc+m+ueSMdDnn1v69gHC2+QxSfC26gMfraAcPnBw3KhNipnslX1F9qZfLwL91T/0UMKvZAz5RzKuMe/Tcl39iImvX0Nl/+fjHav450l0QBfk6/qTE23XHtd0l53aaJPXBfrQnthxHf806n1rWdsOeXgLIYSTzuAJOjd+0E2UGzI3WOAGXZvMkrbtxs0XymqTLW5yfJGOmw+TK6ftJtFWfhvSCf24wfkXubiZLHvD67IV9PnK6TybDp2orotFJ33Q1gfaoF0Wmczw1TC+AHec4Drh63MlXdfcov2aCRrXybpp0xu4HvwaOoz+L/3ayuZ6XcROY7mWhd5SrIK2fhpCCKHO4Ak6N34mR7xS1epv1w11HnwC1m+4gkk5EwIm6ase2Gsr19z4gZsScW2/MUycv1Z2V64QddmKFWNuyKywr2JlyfXSgwEozHXgqC0iigPCOMc+QvE45Sde6aS7HkL0mp7wvvUq+4BkCFbMVb7rBkorfWoQzm/48gEIbO72AeqNI53qWAvjXGVp8iIb4ld9a2HkpR6SgWyQPJUBqi9OuiJH8gjnSB69oSll9EXllPlVDk51mId0BOnKERl9tkZQV+UH5e2bH1QutNkcvC3l+tivyxbeF3k751C24gS6IY+wUq7Sq96yBXrPg3Sui+ql9pBsL1NplZ50vAXgbQVh4LYFdFOeof1U8SorhBBCwdcF2Zl+GhU3GXinod95+PDhD59sBcKUry0vkF+Qbs8+x+tMJsDVOMLbPnnbJo/0xNX0XoY2W7Xp3kbNpshDLmUAR9Wbc5VHOaT797//PdNFedBDulGGwr08P1c8fp0LwpAjW9YgjXRQuY7CAb3VFwhT3WQ7/PNsqPyAvu4nP052lMwyDEeZQuFuK8WXYaQjPU42lB8IUzhH6YcO5Mev9IS5LqVeNVSPEvSU7aUjfspwG0kH4ChdHenIkfzSF39b+aB8nl7gl35+7jZy2SqX+Dab1+RhB/LWoO6S47LAywaXST71A5eP3viRQ3qlgVIecdQDiGvTkXSyh+sLshVO4chSWcgnTvVqO3fbguQCceojKoMw75t+TtmSo3whhBAOsvCvuPCafJK/WSHGaZWmD5MBuckLX758aY4Osli90eoMlNtcFmFy02jK5ViDFfvJza7zC33oJr1K56tLzjK26otvW2DfOita7PfWyjav3Sc3xa3/9//+X7OnFj08DzYhrX+xjLcKrPizUuZ7aklHfe/cufPDdglsh2xQG9fo6gOTG/j07JvelI0OpKeNBH2iz5cLvS+9evXqwDYXPu3LCv6jR48a+ciqhb179262AoiDz58/b125cqXx80VT7A1lGPmxL33L3zbRDnDu3LnmCLwtkr7oQJna1oHNVH+30SK09Y/3799vPXv27MAXWvHz/yBd+NYTbYXBrzdToFXWEtJh57JOhNE3ZO95qFxos7n6Gn3U+x3XTBu0E7rg2mBlm7JkT7ZTCfocbUk9aFvsjk6k5w1hG2yBY/sX8L8T3vfbUB90W7Ka730IHXH46ddt17njtoVl+illk5+VfPT1PhJCCOEbC+1B94koA6wG+qFw4+PVZwmydEPEcaP1fwxbFm4e3MAXgXyumzufCMAqbbUIy07itA2AiYLfnKkn9WXPvm8bAGxLHBNn8rZNykRbH3CQhw7I89fh3OTnbX+iDthBbcQ5Tg9JTBYI58EC+aSvhQFtJzk46srkgnMmUqSFWtgQfEKIWxdd/YOHj02D3X2yRt/ChuuwAQ8d9Dtvsz6U13hfvA9qEj0PxhrSzruGlqXtOp/HMv2UenHtUm7bwkYIIZxkFlpBZ7Lsq8AMtL7y2hduQNwQfJJXuxlpNUYTpaPEqmw1FCY7rBKWE1rZsg9MYng4Kh9m1F6aaHibcbOlvkxkmNCyIttFrQ84yEIm6ZgQ+Eou+jGB67rB86CALQTnrFzqjQx9ChthF+pK+loYK8relhxJx6oj9edNAnIJr4X1hYceX1Vd1+Slq3/wBsD/gZkHyq6V3nUgm5VvaFaFJv64oWWojztnzpxpVsoVTj/Fj33pA96OQ9oUPekTqxoz/PqhP/N/GW3XeRfL9FOuDa4R+iAPLvS7EEIIBZMb1CAmA2qzZ3By02HJpHH4HY+bTAKmoQf3R5JGeLjc5IYxjf2231Hhyocenl46lOGeV/42+qbryzxbKZwj4Zy34TLcph7mdZX95Mehj9sHmS7X83OODPnVRuhZpnPwS6brKbytSScUXtZzMik/EFbz69x18Xp6uNcJh9/lka8WVuZV3ZDtutfCSh0VJ7/O1Te8bMLUN+TXOXK8nuhXUtYXXRyPUz2hppfLUphwvf7zP//zQDqd4xyvp+zpuJ107vLK9nC/61/anHq6X85xu5ZO7Sw/ssB1I43SgZeHnq6f2kR+yROEtaE8OMrzclwfzinTbY4eUNpR6T38119/nZ0TBy6LsLI8nSPT7Ymsmh6kQ8cQQghfv57iz2SQDCGEEwMr276tRSvfQ1aRNwWr04turXGQw/879N3OE0II4fBY+J9EQwjhKMLkvPwZRLY8jXFyzjYqfiI0hBDCySIr6CGEEEIIIYyIrKCHEEIIIYQwIjJBDyGEEEIIYURkgh5CCCGEEMKIyAQ9hBBCCCGEEZEJegghhBBCCCNi8ASdnyjj88w4fjuYL8LJXzri9bXFRUGG5CFLX6zj3MtyR5paeNvXKoF61eK76vfHH38c8LeVi0N+KUt4uH6PObQjW3W156rwflb7yu0yeN/GrYo2XZe9Fh3kIK8G7TKvH69Sl8NmaN8gredh3GB8WBbJ3RRd5fXVZVV1d3wcbuuH3n9XYTfyqz1rzItvw8cfHHVrQ/eYGuRd972la0w4SqyiP6wb9FukP4UjBj+zOBR9GZEvwJVfy9NX8fiS3G+//dak8zRDIb++cKiv7wHyvSx9gY60OqdcfaUOunTRl+3KLy121U/nhHs5Zbng+nmdRJte4SBu21p7rRJk07ZAe+m8hHRle/eBPrRu0Av9cF39fwi6DhepM6xSl6MI/ag2BiyCxpXDgDrgFoX+T/5VXgf0SfUrbFOzT9l/l63HPBZtb/Qsxxz8teum65oijDjSrBKNLScV2nMTY3jJKsePMG4Gr6DzhPzgwYOtycXZ+F++fNkcS/haHU/7SrcIekL86aefmiNf05sMWs058msfFiFt21f3JhfT1ocPH6a+77CycPPmza3JQNZ8sMTpqt+QD5tIJ/RDj6tXrzZ+wE5t5YSD0H5nzpxpzif9d60fl6Ev3Lp1qzmn3VgZrq0QcT0MBTn37t1b+0rI/fv3myN2WuZadOjLXCuLskpdjiKvX7+eni0H/ebJkydT3+aZTBCmZ4vBGKrxfFV8+vTpwP2idi8o+++y9ZhH7Z4zD+5JtG3ZV5D16tWrH8YhrinuKzXWdW/R2HJSefTo0fRssyzSn8LRZPAE/e3bt9VBbx68fmMy4tsSGGQIw9VeczLQTp4Wm3i9nlukbOBmxoRIEy6Hidgvv/zSTNJXdfMU1LF8tahPbVNn4i5fvtz4a8hGHP31JnaU7TTBQ5bCcIK8vH5UHlB74IgvQRbpyUcaytA5Tu2ldF62kO7uSAcuS/rr1TRpav2BOG6mly5dmpVZ6uhlEieQh3zVmzykV1rp4NAXzp49O/V9649MABzkcMO8fv16cw5uW4WVnD9/vnnAYKJKH5dd2pBtkOe2pr6qB2l0DtRf9vK+I/3cPk4f/QUySEcegV/2dF3lvL/VdPE2lNxaWzu1viNb4Fx+W/04lxzZS+XhyjLB7Q1KpzzerpJV3tgJd5vIppIF0gvHOeH0GyCM/LKRUH7lky4eJuftJ7zutXiBXKWTvqUurr+co7Jkd/A80p0whZPHIZwxnomt8rgMzvuA3jjZtIRyJZej7Cm68uOXPjXZ4t27d0376mHD4eGC+zB4W5Zfx3UdnXn1I055aXfXGac05dhCmOSVfUz9QuHkKfUCxaNbmRcURlkCv/Qln4P+xKl/eZ9pGweog+RLHx11jkz6Gf3NdRHSB6d6Sq50QY7Xs+0aIw+ubK8vX77M8nq4whTuuuNX+dJbutbq6nLDIdCsow+AVzpkk3N49VK+8ppMQJp0ehXE+eTCbtKRXhDe9rpMr+iUtwTZtVeZng/XhnQD0rXpUasfEE49RVluLY/sUtPbQRaQTuccVR7hkuH2pE7SV3rIduSVLMI8DhSGk+4cJV/nnk7yVCa43FJn2ZwwyfJ6eJs4kl/TkTDpAbKZbI1DrvIiC0hTK8/rAq6343UjveoBxLm/hrdHF8hROdRB+oPCVU/hdZAdVAfOkVPi4W1p0Fdxrj/leR4v3+3XpgtpvV6E/+tf/2qOOG8PQT7ZmLy1+hFOmW3tgyO9t0Opr+slpBcQ737P7+ccSSO9OUc+UL7OicORTjLnnUvHLl28DMKIK/F2KNtEqEzZTHJLXWptShpQ/Unj6Vwvwkkju9V0EZ6vZh/hNvA4wrwOlO1IX9UZ8M/Lzzlh6MdxHpTTVk/KVvmSC6RXuNsBfaTLvPqRn3DlBcLIU55TnvIrn/yuu8oH5FK2hzmEefkcVScvj3AcYaSnviUqA6d8nBOOX2UAZeCXXaS/8svv9Sd9rVzJ8HMdcdKFo9vJ4wRl4ZTf66G8rgf6kRZISzr8OC+LPKoH6RRPehy01S9sjh+vkCWggWsdTBcYqJPR8OoMcuowbagDlWUgi85UootJnVsd11EndleTBbX6AeGuu1/EHGt5wNO1gS6l7vhLkFPqjV7kI87bQDLdlTriJ3+J2k1xZTq3BeeSS/m62Akvy1e4y6rhMsuyka8yhGR7Hyn7A+FlPvCyoC2dtyNpvE0pY16dwNunDW9HzqWf2hhUN+F18PxAOq+fQzjxbifH6yxdhOcpbaM2aNMF+6pcOdIS12VH4jye9C5fdLWP6wqEl7qUlPbmXHX3+nq48sjv5dbKEKQhXmnKsksbeRmuC0f1Y441Owm1h8sVZfnIkVzXpUzHOfHguhCmdMji3B1yPH0N4lRPUdoN3Oaun85LGY7nhT75Fdalu0O6ms2B8pGFDpwL8shPXtkYKB9/n/qVeQX5cKp7mU5+nOsF+AlXW7j9HLclyK+jO5Wh+Bplf5Bf9hPkl73RU+elPpSpNkSGzmuorsrvcoG8ipcr7aLyy/YiTHVWfbwOwuup9gHqIft5HWpyw+FxqD+zOGl8eu7Msc3E0askwd4r8vD6bwi8Jpx00gP7vgWyXAfSrXJfJ3VaZp80W3rQC939VRq2KamFtTG5KA/Ue56Oeu0Fk4u8Oc7j2bNnzStQ5dPWHkCGlw+0L9tKSO/tPoTyNe8yYHPva9jgwoULU1875TaYPtReZZfQl9hOA5TBdi22Z71//77ZMrMq6Gfsq1e7LAP9l+0/tClt22eL2rxxoUat77Tt1RzSPrV+um5q1zH1YlvD5OY5DVkc2kD//8Cxtkd5keu9Dfo24w3ycJz3GRMZi932fa6RkqF2owzKYtshecstE/Poyk//fPPmzdTXDeMMOte2GDAGdG2L7GKR+mkLBPkmk7ppaDdt1x7XMnJoE2QOuWcBeeWW3Vu/yDjdF+rWp9/NG+uGtldXedwvuJ/Rp/j/AfrRUPuHzXJoE3QGICbCGoA41jqfp4FyX3Bf6PhMuHyvWa1z6gIZOjCvC03KNeChM4Ok72Nl7xoTNC442Yp05K3d1LjYuTGr/tTVbVzjxYsXzWDik+x5aJKH88GUdmC/v+AGgC60DfoyyNDOQ1GfUr2oEzovyo0bN7aePn3anCOLNpg3sZBtBZPn2v89OLRf3xsubU96roFr1641duJGU2vnRVA/WNU/ltG26gNtN22n77jg1PoO7cS555Wd+7ZPrZ8uyvb29uyapUxAfknt2qYOhC/6/zcl1APbqF1qLHK9t0H78OCs8vrIvHLlSvNgJ7CDruu+LGI38lAW9wEeELTXuy9d+emX1Iv4edB/sT8LHA5jOnVCPv8s72M+k3/8yNdDNigeWYvUj+uxa+JXUrv2NHbS99CHNqF+uhZKpLP6Idc215Bfg32vR7+XMJ4zri8yTvelb7/rM9YNaS/ZyPsXdScvUB71Z5Ku/oV9F5lPhQ0xGTCXZnIxNq9G5PTKhNcpCpt0huYVi/zkmXS4mZ+4EtIga9LZZ+kkW3icy1AYjrKFh8uhhyCtwpENbfUrwz2v/G30TecyPZ3nl/5ub9mibANBHRSuegqvV80GakdvT9J5W1Cux8sJT4suyPcwbxOhOJyn5Vx4n1K4h3m9cW7fWpmeHh1rSIZs7nnc5k5Zf6G82K+G6iKQ4/klU7p4OXLo5G1T1svjdO64zbyulOV5oVa+7ymv6eLtRZz3PeSVtPUd7/s41bPWPl4nt73LdTsLxaGn18PL4LysA0fCauXKj1Nd5FcZHD18MtmYnSO/SxcvU67sb66vZJW293iX+euvv87OyeOyPLzUS+eqm8vk3NPUrtWyXqBz1aHU1fsIcchV++DUZ0SZF+Rvy6+ycV6e2oL4NrzOyuN4POXihIdzpOx59avFeZ1VF2QpLccyH05+nECW0tbqrXxtaSQPhw5u2xrYx3Vz+7nt0Atcb89HOW4H7Oj5SxTu+skhVyBH4TV7zOtProPqJj+O9A55lU59UXTJnddPw3o4xZ9JI4SwcniS99UyrRTw9B7aYaWQFR2tfBxlyj5wnOp2VCnbBGphq6LW5ussLxxd6CuTiSCzxmnIctDPeHuzqrdPIWySQ92DHo4vGhgd9ktnct4NrzTZ+nAcJrDUpdxzyyvVVe6XD8PgIVnbtgTtxOvvdVHbirDK/xUJIYTjSCboYS2wOsb+N/6xRa7vPuuTDBPzPnu1jwJ6yPA+AKvaLx+GwwMyD4DeJuxrXeeDM6uX+kdhuTt37kxjQ/gOq+eg/71aBh482WfOPm8WjEI4amSLSwghhBBCCCMiK+ghhBBCCCGMiEzQQwghhBBCGBGZoIcQQgghhDAiMkEPIYQQQghhRGSCHkIIIYQQwojIBD2EEEIIIYQRkQl6CCGEEEIIIyIT9BBCCCGEEEbE4Ak6X/jKV7lCCCGEEEJYD4Mn6A8ePJiehRBCCCGEEFbNQltcPn78uHXq1Kmtn3/+eevPP/9szmuOeGDVnXRD+f3336tycf/3//7fA/5//OMf01wHYbW/LU4Qv+63AsiXPcRff/11oA7uqDt2k5/zIZT1pmxkzmPRtgL0pE6hG2/3sk+sA/qCylsHyO3Tt4aAzFpfopxV2ayrr7eV7/QZW44C6o9D0Lgv+o4v81hEl9AP7Iobci/xdl3ltedIL7k21Ddq1yXX4bqvxXXVf1lkF9y8a5C2X/VcZyzj4DJzl9HydSC7u7tft7e3m/OdnZ2vv/76axMGHAkTnOMoZm9vbxo6jP39/Sa/QzmShy4qv4Rw8t6+fXsa8iMPHz5s0nBcF+hKGW4bhzq4faiz9EH3Lv1r9Kn3qqEOlInuoRu1t/r2Om3m1yRltvVBv6aGgP64tmtwFXAtYKN519EQlh2XDuMaGxNq91VwUm24SejvukYX6fe1a2/ZdpNM10X349qY2HaPWee1eBT6ptsEG7XdT2S/dc514DBstux4PlYWWkG/detWc7xy5crWf//v/33rl19+afwlL1++bNykY0xDVgPlXbx4ceprh3STzjj11bl79+7WpENNfesBXefp4fz000+NXovSp96r5sOHD9OzMI/JALp1+vTppp0n12BzXBfPnj3bunnzZnNOP6SdaqsM9+/fn54NA/1XfX2X3Lt3rzmi/+Rm3Jwvy7Lj0mFcY2NiciOcni0HffH169dTX1gXXPdnzpxpzrlm+9w/nfLaY9X2yZMnU99iMC5xDbkuuh/XVmTb+gnX4jru4ateaV4HrJhPJqezewhzs0ePHjXnJfQB0q6Tw7LZOuaZY2CpX3F58+bNoAu99hqG1xIK78Oyr1JUFq6cqEiXtjJ4vaW86ojI0KsVxQl/9cS2oD4gt/YKrwuV4WWX+OsfvbZUffyiwq/yXX/yC2+zea/UAPmkpXy3rWTghNvRw5FBXsKki/w41Q198HOstaPqrTpArV2VTvK8/gpzp/LdNiWyJzCYeDu4Pi7fy6U+bgeObq8ar169mt2YAXmfP3+e+r5BGA8Nly5dmtlMZaicVSCZ1Nf1pr6yDWncTjpiL++n6lNuH0fxOMrrQunQA7x86Gpv8HoJz1P2KW9rhzjJli5uJ5ff1j6kUZzCVR6uBmW4HUnn177XVbKeP38+DfkG4Z5OeXGi1Jn09Dn6HmH4XRfp4LpwDqRTmJxsJtryy861/oQr7Sy9Fa78OOnjqO05qg2gbAcvEzmqk/LUyiFM+nSlcwjX9a124qh8bhOc26VsVyC9JkPKIwdlPWogA50uXLgwDfnO5cuXm3FLSLdy4ul9wJFdia/pULsOvW2IRwYLAzyEKA1hLq/NZgqn/LJPAvHqI2Veta3yK0zh5HXevn17YIHn7Nmz1T7Qh1JvL09151jqoXBcaTNHecv8+FU/ykX/Wjqn1NVRXg9Xehx6gNpcuiue8lV/juTV0fNvBJbRh6LXCXplJvD7KzAxuZib9DB5Yp69AiGtZBBeyzu5iJu8cshy8Jd6OF5e2zlHZFOWyitfleBX2Tp33aQ74cormUB4rX5AnOR4HkA36VnDy0M+9QKvH+HIJZ3qKr+3mfRQ+X5OHGk9vZ+Dp3eUhqN0atObcEGYypXOwuuHHMWpLOJcN1A615MwlalzT6cyPI+fUwb6lefoV5YvlL+mD0fPp3ogT2kpQ3mVVnYqcdngOjqqNxCv9gDi3F/SVnYN6qO06EVeoXDVU3gdSINf+pT1Ex7eloayiQPkeVsrHDy/209tgp945VE/EoT/9ttvzdFlOeTxOpUygXPSEeftofZBf9J4/yn19ThQGdJXNlG5yFRZLktlAeGcq/8gQ+fEkd/r0marUhfO3e/lexmuo1PLj182VDh+tT2QTjI9P3hZLsMhP5BW5647+RVOmOteq5PKIQx9lBf66AOEI1s2lh2Ao/L5OeV4OuSrbG9DQAevh5/XQKbLdiiHOMrw+nFUeFk+56QF6chRYULlSg54vfyc8pRfOslPHGGCOPzIl74Kc8iv8kH6oIuXp3DVua1dSa/ywPWvQZynL1E/AdKRXnWXPm576SeZHFWHEtLJ5jqnPM8Pnq6mb5uNJQtcD869Hco8Kosw111+0uMol7Au+66a7z18BbQpX2t0UMXlSFfinQHKxiePjF3DG0pQPjIVzlENDqXfIdx1pV6ut/QpbeH1LiGP20cdBtCl1F+og9dAjufzMtBDNuPoMqgbcknbpi8gm7SeRnlLSEOcaNMbXcq6ko70ZX0k0x3ppFdNDyjbRZTtWqYjTvYrbUlekB5ytToCcdKvLAdZkidID9RNcZSvcEAGskq8LGhLV9ZJ58C561hC3prMGl5fzlUuOkoG5163LnsR57o6hBNf2kB4nbGr5Jbll7ZRG3BUn1QejoSrXDn0LnUvIZ3Hk768HoA0XmfOlY+j7AilHtSlhPweTjrZi/JxpU3I435vx1oZAt2kC5Rlu78sk7rJ9l5P6VjSld/LIa/bzOtAnPIA4dJfroQ8hCNHlHm8jmo7kB5t5ZTt20cfIB11htLmQv3W4zwf5UrX0rYgP+mVpw3lr6WjHMniKDsqD0d09TbnXH50VP4aXg9H7aa4sgz3YxfpBchUnMuoQZy3ofwcyeuOMpDrfdAp49rqJohrkwXe3qSTLK+7twO4Dp6uBrK9buDnHi9Xk0d4Wc823bvaqtQduUAa5UGO2osj8jbFUltcVsHECFikcX32MT9+/Hh69iN6TYKrvRrRawv2204aYxpah1dFjl67wKQTNMd1wB68tj3J0l8OsN+6qLUHr5Qom9eQk846De2G/WG0L/nULm16D3k9R/nqOzjsRv/g/OrVq80rrHks0q7s6+Z1sfL5/wsM7c81+m6H6sNkcNl6//791PetTc+fPz/1tVNug1kV7BfV6+tPnz41eybZLoGOffTqC23PT8LSDsvS1d5tTAb+WT/AUe95kI6xiXL0GrXtehjSPqvok0Nou7apF6/kVzFmcZ1fv369kcne5K77Qh/oi31hnJA9cSVtY1CtHTTW67W695N55Yi+6dpY1b2NPk8d3r17N3frK/WeTHR+2CoF9BHGrUWp3W+6WOSeBl++fJmeHYSyy+u4L7V7WhfozFZjQT/mfwPHSN8xmX7hNqhd24QPsXFbW924ceOA/eh39GHsOM/2m+BQJ+gYg0m18PNF0MCI4yIt4UbLBdA1eDBYsYeqvKG+ePGiGYD6/vMm+36ZiGgfGZ0A/zL7l9BJ9cNpkHOZy8h3sBE3FDqrQDYDKg83fSYcQjcpdMYGbXozQXOb0RbkrV0oDELcoAX5lR50AySsi6HtChpkcN7PVtGf2ZNJ/5Pe2AL9FoV/xKLfA23ZZk8H2/o/jXLD1T+argLsRHvxEHzt2rVmgrXKAVH9pzYGLEJbe7ehNpQeHP06qkEa+gvXHZMkxova9YCcIe2zqjFWbaNrljKhnAC1XdvojS7LTqQF9VCbLPvQwSRH/4gMPCzqhxBKmHS7rWv2rI1BXe2AvHJ/dZ9yoG+6LhYZA2vcuXOn6Zd9FxgYl7hOynsMYeon9CXZRgsN1Jmxw8dJxhDJKu8381jknoYO/k0YHiqY7NWu4xqkB/RHR8puu6d1QT7yKx39mDF1GfTwv4o5i9AYNm/81LzDyyz7dF8bi7a2AmSojtiNa0ltMwomnXglTC5wHotmTkwGplmYXiHgSA+TC3AWNpk8N2ECv+Jwk8aYxnx/zSIneY7np2z3q1zCwfVAdomXp7Seh3p6XdHV66t4p6yDdBEun/MapQzKLevtev39738/EKdz0nh5gCz5cZTlYUqP7cu8jpevOtb0hlI+lPUR3ufU/rUw4bIVV2tXd6TzcNLX0gmPQ2+nrLPbxXX1+qrPeFv5OeW5HNnRcZu0IRkqz/O4zUtq9VVe6ltD9ROU6WVIHrJBZXhZtXZxPE7njtvM7en1Jh+4LLmyT3oceDz5a33PIb7Wji4HJ2rt42HeD1x/tZHwPkn5ntbrxbnXQbqC6428sp9LF/lVBkdPy0/26txlks7rRh08Xq7sAwov8/s5cqDsA2WY283Llu2dsizhti3boRZWluNyvX3n6aM4nKdV3b0NpCNxnvZf//rX7Fx1kt/LJI/rpj7jYU7ZVyjf8Xjpo3Z2exAnvVzv0h7SB6f0Hqb6E0d7KNz7pmR6OWo7ZHl4rd7Et6XxOnFOWfKX/UO4/p5G/V502UV4edLT7eDxpZ90nra8Hr3/69zDRNknSjk1G5d107nq7/GlHUlDvCCN2sTbo7SN6lrqt0pO8WdSSAihJzxt+2qTVgd4Gg/fOE42SXuPj7JNWEFkhXXIKmhYPaxsruotyXGFt06s1Kavhnkc+h70EI4STAzKV7i8Is1k7TvYiFeKx2VynvYeF2xjKF9rs01jlf/DEIbDQ9K5c+emvhDCsmSCHsIAWLVjvyP/nCLHHtbwHWy0qv3fh03ae3xo5dHbBFb1PwxhGDww0Qbs9fW3GuFHeMPAnmf2mutNXAhtZItLCCGEEEIIIyIr6CGEEEIIIYyITNBDCCGEEEIYEZmghxBCCCGEMCIyQQ8hhBBCCGFEZIIeQgghhBDCiMgEPYQQQgghhBGRCXoIIYQQQggjIhP0EEIIIYQQRkQm6CGEEEIIIYyITNBDCCGEEEIYEZmghxBCCCGEMCIyQQ8hhBBCCGFEZIIeQgghhBDCiMgEPYQQQgghhBGRCXoIIYQQQggjIhP0EEIIIYQQRkQm6CGEEEIIIYyIhSbop06datw///nPrZ9//nnmL92ff/45zbE4yPjb3/429Q1DerTlR3/iqUMN8q2iDqtAunbp+/vvv8/SoPc//vGPJtzzlu6//uu/quFtZQBya/HYqyYL98cffxzwt5WLox4uy9vPw//6669p6DfwE37UkN5lfQRt2RUPtAd2WyfLXItDmGePdcP1omtnEbDRusaNRdtZfajmyri2a79P+6Nb19hxnOmqe1+7YP919ftF+04f1He4dubBtTVEDx/z59mH+Nq1h17r7pd9ro+jAm3Upy0Pi2XH6CPD14E8fPjw697e3tT39evOzs707OvX7e3tWVyZrgZp9vf3p74fIQ4VkTsU9Nrd3W3OkVHqgswhcpE1rz7rBFt1IVsJ6q/63b59e2ZnziWL+ujc206ySFuDtMSXbed9weO9/LItvFwgnevndQLi2/Qq6ZvusGmzJ8gGbfE1yKO+vyooGx3Up9ZJlz3WDXaj7KF9R+m5BsjvfXpZJJsjspdp29Ku3lfQvW2c6dP+6qs+DpwUVlH3dfT7VfadNtBb/Z36d/V92amPHupznpZzwmpltF17KnMd/VL2PakcRv3VB06C7QdP0OnkbQOIX6h9wMjzBiPkdd0U2ujShYYd2rhD67ZK0BVbddmhZqdaHQmr3YTL+pGuNqBxcaistps5tLUt4V7OPLuWetTqVEOD+1FAurZdC/PiS7BXnxvgUGp9bB0Mre+qoV/37WeArb2PzuvTQyhtvmzbdtkV2V3XdJ/2L21xklhF3VfZ70t9lu07Nco+Mc8GGs/76EG62nVIH23rh9ivdu2Rp0uvRVhFex9lNE4fBkPH6KPK4C0uP/3009b79++nvn7wakmvp/TahHOYXGhNmF5ry/V5zVe+mhWcTzrP1qVLl3545YTcJ0+eNOdlvhLiSI8MydNrFY7Kr1dqeu3CkXDq7fVSnRRPPslTWI07d+5wFWxdvXq1Nc3FixcbHb2cx48fN8ehoBc2unnz5jTkO2/fvm3KunXr1tabN2+moatB/cChDq9evWpeieJu3LgxjfkRvWJEBv0KsIdep/qrUkGY8imtt59QHyZt7fUsYZKNLIHf+wD5hWQ+evRoGrI4qgcysdf169dn+qs+OOlOWJ++WsPlCb8e1KcBP7LJI5k1faCPPaij8uIEcsivdvA2ENKRdC6HPKq76+7pRak7Dltjc8Lcbp5OqI6eljCV5eWjI2OOrmvvO+qv6CMko0zbhpe1CNIB53UE6VJrB1A8eqoOaoMyn9vc26Jmt5p9BToqDnSuvJSpchWHky2Vn6Py1JC+ZR08D3Fehtfry5cvs/Bam+MEYbKB9wV0bOuXklOzu6dFrvQmvFbnd+/eHajnmTNntj58+DD1HQRZ3Mv6gu6XL1+e+r5z4cKF5ppwPWv6eb0+fvw4DZ3fjticOqkvIcf7FfWo2Zcwb0fSKY+HS1/v+w7xhKudXEeXSXkKk0xvC6G0ytfWpyQP0Fd+6aMyOFLf2j1WuO1x+IG8kiObuG2VzpHuHMnrlLLAbaRwhVFf141wUFrK0FHpD5Vv8/RhkA1XPsHwVFs+vfKk40/L5JOf88mF1pwjS6s3ft61auPhpHd/TRfQU6904Lz2FEx+18/l+dMbYaoTRxzx5ONcOlGG6qTyOA59CnTb1JDeuBpt+T2fdK6hvKqf7FPSFke4t4uX25aHMknXZavS3vILbK02Rx5+nJdLOOcclZ8j+qrehEmOQ7mql869buC293OVS1k1XJcaqofK97pyxA+S8+uvvzZHnNdVtiO9dHOQrzxAOuqh/ODnpCOP8hFX04djX3tIRyCdbE16nPpIW37iZRviXZ7CVT5+6Qhtuns4qN0BWdLJ60ge0hFGWs/vUDfXkXS1cr0c2aMG4e4cZEm/Gq6Ll12eI5cjkL6Uie7S1ePJRxwQjgzKVFqgHPw1uxFW2reEcJdHGpUpnd2Wfq6y8Hu5gvzoVNZH4TWZZfuSTn7SSBbluX74cS63hPSup9KXfcfL0TmOtDjSlnoK0nn5bekoU2VQrurSBulVdgllEMfRyyetwoFztS06qb5eb50LletyvE5+7jYknDzud7sQjh/5CkeObCLc7qSVPsjz8hTuY3kNyiBO+ZAv/dEDueDlEM455bk++IlXfuWpgQzVTeeSpfzg6ZDvcUK6k9brIZ1cjza7g3QQrrvqRhhOeThH/mFRt+6CYFw1uCDMK4ghuipPesJlSO8QjssRXlZNF0Cu52uTD66fy6PRiXNHOpeN3/MTrjop/yIgr6x3DdfLcT0c1Q/Xphs2l1y5miwgriwbCPd2cbsiq5YH2trTIV5tiRzKEtJXTunK8jUAAOmkD+ceV0Nl4iTTZWB7tZ2HK5/8JfPiweuBnrQVUB553ZGOukqXUn5bH3H7Qun3/gHEc+6y2vThqPLb6tt1zRNXth1ySzwd57KbZECbbdp0L8v2tvA+RXiZH7y9SpDjNva00h8IL2WX9gMPL22JjFq7i1IXkE1Ux9IWpV8gx2Uhu5YO+W4bdPc6exzhXn9cidpT5+RRncu6I5+0spPqWrMrlHUlrfcDyfF0xLueLp/0ykO4O9mAo8ooKfVxe3GUDCH7yQ6UrfNST+H1AtKVcsHTuB5dUF4tnXRR+3n9Ccdf1h095UcX5a/RVgdkkE9xZRnupwzXXboCx5p84XYH+aW3O3Qt26CEdF5X+Tk6yFG53kauj2wPNRkl1JM0yl/WTfHuSlRvrwMyVGfpwZGwNrt7u+pcaaWTtyEgl7SHxUZ+ZpFXdvPQK4jXr19vTYw8De2m9jpkHmfPnl0oX8mkIelJM8fWn768fPmyyaPXKkM5d+7c9Ow75esy5E862uDtSGxfmXTWql6fPn06UGfSPX36dBq7PHfv3h1kx6FMLtaZ7m2vYdsgz/379w+8FnOwF1uQSDe58Keh44B2Ur1xtPGq0CtA7ML2J2wsKIfygHi9Wl1Gn2Wv3V9++aV5LQ30Z7ZqPX/+vLlOzp8/34R3sawtJ4P9gfyrZOiY1LUFTuMxrvaal7Ykji0IlNtFTQ+uP8Z6ZOi1dds1STv1ZZ590YVxEf1fvHix9ezZs2YMo19xbwC93uZ6p70F9kIm1/kyr77pg0AZbGFC5z4sM351sci9V7DdhHzi8+fPjX0cbMuWScrAcf2xPaTcrlBCO9E+JWyrYYxd9F4xtB3RH72hb1tBW7+t9f2+rHosX8VcqMaQPjXvmh3aXm12l62oM33owYMHTf9iLKAfj5G1T9AxKoYQ3MRr+4gZJGmovhcdN1MudN30MTqNNy+/btDKx82Zm/QQrly50gwwgs44pKOrk9HpNFnoCxf0tWvXpr6D3Lt374AeyGZP4FCYKIMPHthLNzCBHtw0ZMsxw2Dv9RkyMFI/0nOB00fLvffYHFsPuWlyg9Feaz1ElTe2VcAEyvsFk49VtRfXNf+ngEzsW074uC6Ioz9xY2FQbNOnjz0WveZL0BXd6M/0YW4iDOrz5CxrS+rj/9cxpA/OY9kxqYQ20w2z1q8ZN2lTTTbbYJJbjvfoxQMt9mb8wP5cW5xjU0EdZHNB32gbr/vaF1uhP+imzWRdD2jctHnoKCc/Grdlj0XtSx3RU/Yty6mxzPg1j6H3Xke66zqotTdyVVccdcG+XQ+IQDzXO/1AYDv6gybutInmF9KBhx7uez5WMGZL1tB2pG8wydR9sQ9t/bbW92v4PYaHG8apZcYfjankwf6Uz9H/34cy2+YWQ+nbp/pcs0Paa954QXm0J2guyH2pzzV4KEwumKWZdLTmVYDcZOCexnxj0hFmcZMLcxr6/fUGryXIozQK9/1VyCiZdIADeYTCcMgu8Xy1eJAOOFAdpAf5XAb1kt/ronidk87tIVspTw2lxaF7DcKR7Xq7rcHjVI+2tivD5bx8r4fs6LbFSYcyfHIRHfCr3JJ5fUt4OtWtzFPapmwHnXt74dDd07oNhMd7OXIun/NSX46ElZT1r6XxsomX/ugBXjZpqbv8Hocr+2pJWQ+hMNWdI/Gumyj1gb728H6k+nkY+rv9azJUf0F5qkuXbaCme9m35Tytrg+3B/Fu71q/cru4q+nhslSeKHUs29bjJM8p26dsB44q0/Wo9SFkuR2UptRRbVezo5fhdivtW0N1UT7SeZ29D6huZVtJD+G6E+d9sPyfD5fv4Z4Hv5+DxyPD6ypbObU2wrlsZLhf6drSE17ifUO6ArLdroIwtbnyyl9DsuVKFC57eLt6nHTpakevi9J7mOxCnNv3t99+m51LppevMGRJT1yt3qRtS1PWiTj53faO0iqd4+0su5X20bmXrX7gcU6Z1o84r5PrVqtDqU9ZZ53jwMNI76jNaAcgXmm8PQlzff/97383x1p7rZNT/JkUHEIIIYQ1wyomq/W+usjK7pAV2nB8YRWZbayr6g9sNZlMSOeuZofxkU/9hxBCCBvCtyLBoltlQgjHm6yghxBCCCGEMCKygh5CCCGEEMKIyAQ9hBBCCCGEEZEJegghhBBCCCMiE/QQQgghhBBGRCboIYQQQgghjIhM0EMIIYQQQhgRmaCHEEIIIYQwIjJBDyGEEEIIYUQsPEHn08R/+9vfpr4QQgghhBDCKljqS6I///zz1suXL6e+EEIIIYQQwrIstILOyjmTc/jHP/6xderUqar766+/mjRDYGUemZsEPfvou2idlmXTNqF91YbhG9i/rQ2w159//jn1jQ/0pg/VWJfum7QJ/XSdZTHW/f777815ly3n0XecCSGEEAZP0Lk5PXv2bOv+/ftbHz582Hr8+PHW7u7u1s7OzhaL8XIPHz6c5ugPN8F79+5NfZvj6tWr07N2mHAcFnfv3m3s3MWqJvDIuXXrVtOG29vbs4nJSQYbPHnyZOr7Ea6DixcvNudMFMdmM/oOfUiorzDx3N/fb85Xybrk1tBCwTrhLeEvv/zSjH1d/WAefcaZEEIIAQZP0D9+/Lh1+vTpZkLSNWllQvDTTz9Nff3gJrjIxH5ZXr9+PT1rh0nYWFl0Ra8Gq3tnz55tzqkzbXLSwQa3b9+e+rp58ODB9GyceF9h4slD2KpZl9wam9xix5jWtx/U6DPOhBBCCLDQFpf37983x7ZJK5MAf43Lip22TPhKL+kUXk4yladthUyv0DmSjlVLyfM8pFEZOId0hD169Gga8g1kKX2pVxtKjxNtZSOT+tX0pT44dNBre6UF6cZR4bx1YGVPZahebau5etUup7ai3FevXm1dv359JstRm+A4Fx4uB15OWzs6sgd6u3zJQD9Q/ZRW597naqiv4GQXjsiTXVUGuOwuiKds5Mh+qq/bRu1Za3+3leohnTi6PaCst+qmcjni1zkyan1FSBbpShQnHSSbcr1+pY6gOnL0OgrPL/t01VsoXxnvdlGcysXv6SUDp3qrbQhTX+Ao3YTs3SbP08te5TgTQgghtPJ1IPv7+/xTaeO2t7e/Pnz48Ovu7u4sTI50nr48J8/Ozk5zvre3NwtHHufEe3qHfITjSKM8HJWHI6CjIF7+27dvN35QfvKgi+chnDCdS65DeqVBN8mtle22Ikz6AnFeb2RKN8mUzdBf58ThB+UB8iGvxPWSPgKZtTyup5/X7CUb+bnbpQ3Vh6Pq47YlTOEcVa7ydUGdlE7nyEVHnORKZ68jeBoHHZQHkCv7le1COtkbR7zK8brITtIXGTp3kOnhnMtW0oG8KhdcJ5D+UMY50g9kH7eRnwPpvd1UJ9kBvDwPn1dvzyd7kr+mA/GkJU5poZShtMS7DMqXfCAPfq8/cW11IUx156i8IYQQQhff70RLwI3Nb6TciMqbUHnz48ZFWInf6HQDrN3QfAJAHi9feZAvWYJ8pVz36ybqTnoqjYMfmSVdZbfVUedlPvzk0Xmph8sD4msTG8BmZRx+2ZLzWrsI4pCPA+ksOEeWJinuynqVUDbphGS7c93L9H2Q/SSH+rhM4tC9tCnnbfqTR+2BLNlP+rkjXVv7A+FdbV3i8fQvyZUMcJ3KenVdRw55lK7sH2WfAJfrdVK/gDb7zKu3ywbS4nc95ChX4a43ZbsMUdoH2uoC8rfVRUco/SGEEEIbC/8Oehfl/nNe7759+/aHfxz79OnT9Gx9aLvAECY3XWYQMzdvH3ZZLzG0bGxGeZcvX25sVttuwD/8kYZ/OPPtGA7x/BMvMnhlX7LofvpaO6LzZILSxOE41z9MTiY2Mxvi5v2jK/uJSYccbdUAl+F7jimbMnybQRva+oBtJxO1aej6oSzXf8j/ZfRpa/oqW87oK/zz9jL/xNjFjRs3tp4/fz71fafWJ4ZQs0+fercxmSgfkMdYxPXLOXqir67Lz58/N8dl0f9sLNPWIYQQgrOWCTpwE9TeV26a5eSMiZL/YgvpFplMd3H+/PlmT7D2gyKfG74mdtoTqj31TAguXLjQTHKUh2NtoiwkyyfCnHeV3QblkJcJBTd7JhQlmrBokl3ajPKYsDJJ3tvb23rz5s005huEM5nyOiFLk+o22tqR8vnHYU1K9GshKsftMm8irbohB9vJtuU+X6Bv3blzp9Gdf76bJ5vJJQ8Pff/plUkX/UD2pQz8XX2h5MqVK81+dIEtyvbqYl5bA9cRE3MedrE5bUQ59ONVgmxsQP3p29DWJ2rQR+Ddu3fNkfZrs8+8ehOvf8bV9XXp0qXZNac2Ih8y6RukQ0890FA2D7HC+2lfkE+foE+11aVtnAkhhBA6mUyGloJXvIipOb0Wln9ys5odwfNObvQHXlHz2ljnOIe0tXSlLoAO8qtcmEweZ+GSRxi4Hsoj3XElLgunetfK7qojcV435Hoa4r2OnJcy//Wvfx2QIV2cUl/hdURGieKUjmMpC6e8ZRx+6cqxxPWm3kJhOOojGyDD6044OsnWTtkWfvS88qOr2xrdZG/H84DySAeXwbnrW7a/pyVdmbcNpQeOKhtcBjbw8l338jqqQRqXDcojWRy9HbGj215xolbHWliJ4iVP/dzLwgEylM71dz1JU7ZNmYa6gNtNYVDTm3iFSZbbRHqHEEIIzlJfEg2BVUJWBn1lmtVDraS30SfNoqxTdgghhBDCulnbFpdwMqhtLdF2hhpsNWAf8Lom0L53PYQQQgjhKJIV9LAU2mfr7O/v5x/kQgghhBAWYmvr/wPA/1nbx/QGCAAAAABJRU5ErkJggg==\"\u003e\u003c/p\u003e\n\u003cp\u003eTable 3 shows the predictive performance of STRATIFY, FI-lab, and FI-clinical when used individually and in combination. Adding FI-lab to STRATIFY (model 2) significantly increased the AUROC from 0.674 to 0.715 (p=0.018), with a significant NRI of 0.413 (95% CI 0.265–0.560, p\u0026lt;0.001), indicating that the new model improved the risk reclassification for 41.3% of patients. The IDI was 0.005 (95% CI 0.002–0.008, p\u0026lt;0.001), indicating a small but significant improvement in the overall discriminative ability of the model. The results were similar when the FI-lab score was converted to a binary variable and added to STRATIFY (model 3, AUROC = 0.718, comparison with STRATIFY alone, p\u0026lt;0.001) (Fig. 2). Adding FI-clinical increased the predictive performance of STRATIFY (model 4) (AUROC 0.707, 95% CI 0.667–0.747, difference p\u0026lt;0.001; NRI 0.173, 95% CI 0.023–0.323, p=0.024) although IDI was not statistically significant (IDI 0.002, 95% CI -0.0002–0.005, p=0.08).\u003c/p\u003e\n\u003cdiv\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tabb\" border=\"1\"\u003e\n \u003ccolgroup cols=\"11\"\u003e\u003c/colgroup\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"11\"\u003e\n \u003cp\u003eTable 3 Predictive performance of STRATIFY, FI-lab, and FI-clinical for in-hospital falls (n = 5984)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariable\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSTRATIFY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.63–0.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSTRATIFY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.68–0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.27–0.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.002–0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+ FI-lab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSTRATIFY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.68–0.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.37–0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.003–0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncluding FI-lab\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSTRATIFY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.67–0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.02–0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.0002–0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+ FI-clinical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFI-lab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.726\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.69–0.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+ FI-clinical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSTRATIFY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.70–0.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.25–0.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.002–0.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+ FI-lab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+ FI-clinical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"11\"\u003e\n \u003cp\u003e\u003cem\u003eNotes\u003c/em\u003e: AUC = area under the receiver-operating characteristic curve; CI = confidence interval; FI-clinical = standard non-laboratory Frailty Index; FI-lab = Frailty Index based on laboratory tests; IDI = integrated discrimination improvement; NRI = net reclassification improvement; STRATIFY = St. Thomas's Risk Assessment Tool in Falling Elderly Inpatients.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"11\"\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003eAll models include age and sex as covariates.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"11\"\u003e\n \u003cp\u003e\u003csup\u003eb\u003c/sup\u003eTotal of 6 items: 5 STRATIFY items and 1 item based on whether the FI-lab score was higher than 0.4.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe results of the subgroup and sensitivity analyses are shown in Supplementary Tables\u0026nbsp;7 and 8. Adding FI-lab to STRATIFY significantly improved the AUROC regardless of age (stratified at 75 years) and sex. Adding polypharmacy score did not impact the results. Analysis of the AUROCs revealed that an FI-lab cut-off point of 0.345 had the best ability to predict in-hospital falls. However, changing the FI-lab cut-off point from 0.40 to 0.345 did not significantly improve the AUROC whether the binary FI-lab variable was used alone or included in STRATIFY. Finally, the fall risk assessment tool used at our hospital predicted in-hospital falls with accuracy similar to that of STRATIFY, and addition of the binary FI-lab variable (cut-off 0.40) to the fall risk assessment tool significantly improved the predictive ability.\u003c/p\u003e\n\u003cp\u003eThe results for the secondary outcomes are shown in Table\u0026nbsp;1 and Supplementary Table\u0026nbsp;6. Patients with a higher FI-lab score more frequently sustained injurious falls, but this finding was not statistically significant.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this retrospective cohort study, the FI-lab score on admission was associated with the risk of in-hospital falls independently of the STRATIFY score. Moreover, addition of the FI-lab score to the STRATIFY score improved the predictive ability further.\u003c/p\u003e \u003cp\u003eWe found that the correlation between the FI-lab score and the Barthel Index was weak, which is consistent with previous findings in older adults receiving inpatient [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] or home-based medical care [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This weak association is not surprising given that FI-lab does not assess the patient\u0026rsquo;s functional status. In view of these results and the nature of FI-lab, it appears that the FI-lab evaluates the accumulated acute or chronic organ dysfunction reflected in laboratory tests. Therefore, the FI-lab score may serve as an indicator of multimorbidity, which is a known risk factor for falls in older adults [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. STRATIFY, which does not account for comorbidity, was used as the traditional fall prediction tool in the present study. FI-lab may have improved the predictive performance by compensating for the inability of STRATIFY to evaluate multimorbidity. The correlation between the FI-lab score and the CCI was weak in this study, which is consistent with previous findings [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], and may reflect the fact that the CCI includes a number of conditions that cannot be detected by blood tests (e.g., stroke).\u003c/p\u003e \u003cp\u003eWe found only one previous study that evaluated the association between the FI-lab score and falls [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In that study, falls were assessed as a secondary outcome in 2933 community-dwelling adults with a mean age of 60.2 years. The FI-lab score was calculated based on 23 common blood tests and vital signs and found to have an average value of 0.28. The FI-lab score was associated with falls (FI-lab per 0.01 unit, adjusted for age, OR 1.01, 95% CI 1.00\u0026ndash;1.02). Despite differences in the study populations and the items included in FI-lab tool, the results of that study are consistent with our present findings. Furthermore, in the present study, the FI-lab score was associated with falls independent of a traditional fall prediction tool.\u003c/p\u003e \u003cp\u003eFI-clinical was associated with in-hospital falls, consistent with previous reports [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdding FI-clinical to STRATIFY significantly improved AUROC and NRI, but not IDI (model 4). Considering that AUROC, NRI, and IDI were all significantly improved when FI-lab was added (model 2), and the lower limit of CI for NRI with FI-lab was (model 2, 0.265) higher than with FI-clinical (model 4, 0.023), the degree of improvement in predictive ability with the addition of FI-lab might be greater than that with FI-clinical. This may be due to the overlap of the items that constitute FI-clinical and STRATIFY. Actually, their correlation was high (r\u0026thinsp;=\u0026thinsp;0.66). Rather, the alternative may be to use FI-clinical instead of STRATIFY, and to use a combination of FI-clinical and FI-lab. Some places have a system to automatically calculate FI-clinical using information in electronic medical records [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In such situations, the combined use of FI-lab and FI-clinical may be relatively easy.\u003c/p\u003e \u003cp\u003eOne implication of this study is that, in hospitals that use STRATIFY as a fall prediction tool, adding FI-lab may improve its predictive ability. The predictive ability of other fall prediction tools might also be improved by addition of FI-lab. The FI-lab score could also improve predictive tools for conditions other than falls. In our study, the predictive ability was unaffected by whether the FI-lab score was treated as a continuous variable or as a binary variable. FI-lab may be easier to implement in clinical practice if used as a binary variable.\u003c/p\u003e \u003cp\u003eAlthough adding the FI-lab score to the STRATIFY score significantly improved the ability to predict falls, the resulting AUROC of 0.715 (model 2) was still relatively low. Both the present results and existing fall prediction tools generally show relatively low accuracy [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Consequently, recent guidelines recommend against using a specific fall prediction scoring system [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, adding new components to fall prediction tools could improve their predictive ability enough to be of practical use. In the present study, we incorporated information on multimorbidity. Other potential components include more refined indicators on polypharmacy [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] and direct observations of mobility [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Further research is needed to develop more accurate prediction models. Even if accurate fall prediction tools are developed, patient education and detailed assessment and intervention for high-risk patients will remain important. Nevertheless, this study is the first to evaluate the usefulness of including the FI-lab score in a clinical prediction tool.\u003c/p\u003e \u003cp\u003eThe strengths of this study include its relatively large sample size, its use of STRATIFY, which is one of the fall prediction tools most widely used in acute care settings [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], and the fact that the results obtained using STRATIFY were similar to those obtained using our hospital's fall prediction tool. This study also has some limitations. First, it had a retrospective cohort design, although the falls data were collected prospectively. Second, the analysis was based on data from a university hospital. Therefore, caution is needed when generalizing its findings to other institutions and regions. Third, although this study showed that a high FI-lab score may be a risk factor for falls, a high FI-lab score may not translate to a specific fall prevention strategy for the patient (unlike, for example, providing visual aids for a patient with vision impairment).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe FI-lab score on admission was associated with the risk of in-hospital falls independently of the STRATIFY score, a widely used fall prediction tool. Addition of the FI-lab score to the STRATIFY score improved the predictive ability for in-hospital falls. Further studies in other settings are needed.\u003c/p\u003e"},{"header":"Declarations","content":"\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all the Nagoya University Hospital staff for their support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Hori Sciences and Arts Foundation (Grant No. 31\u0026ndash;1-031), and the Open Access funding provided by Nagoya University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflict of interest to declare.\u003c/p\u003e\n\u003cp\u003eThe funding sources (the Hori Sciences and Arts Foundation and Nagoya University) had no role in the design and conduct of the study; in the collection, analysis, and interpretation of data; in the preparation of the manuscript; or the review or approval of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Ethics Committee of Nagoya University Graduate School of Medicine (approval number 2020-0357) and conducted according to the principles of the Declaration of Helsinki and its amendments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe need for informed consent was waived in view of the analysis being based on patient data extracted from medical records. However, patients could withdraw from the study via the opt-out method by accessing the Nagoya University Hospital website in accordance with the national ethical guidelines for medical and health research involving human subjects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants of this study did not agree for their data to be shared publicly, so supporting data is not available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHirotaka Nakashima: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Software, Visualization, Writing \u0026ndash; original draft\u003c/p\u003e\n\u003cp\u003eTakahiro Imaizumi: Data curation, Investigation, Methodology, Resources, Software, Validation, Writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eHitoshi Komiya: Investigation, Methodology, Resources, Writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eAkemi Morohashi: Data curation, Investigation, Resources, Software, Writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eKazuhisa Watanabe: Methodology, Writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eChisato Fujisawa: Methodology, Writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eYosuke Yamada: Methodology, Writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eYoshimasa Nagao: Investigation, Resources, Writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eHiroyuki Umegaki: Methodology, Supervision, Writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Hori Sciences and Arts Foundation (Grant No. 31\u0026ndash;1-031), and the Open Access funding provided by Nagoya University.\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to declare that are relevant to the content of this article.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSutton JC, Standen PJ, Wallace WA (1994) Patient accidents in hospital: incidence, documentation and significance. 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Step Safely. https://www.who.int/teams/social-determinants-of-health/safety-and-mobility/step-safely (Accessed on April 26, 2024)\u003c/li\u003e\n\u003cli\u003ePark SH (2018) Tools for assessing fall risk in the elderly: a systematic review and meta-analysis. Aging Clin Exp Res 30:1-16. https://doi.org/10.1007/s40520-017-0749-0\u003c/li\u003e\n\u003cli\u003eParsons R, Blythe RD, Cramb SM, McPhail SM (2023) Inpatient Fall Prediction Models: A Scoping Review. Gerontology 69:14-29. https://doi.org/10.1159/000525727\u003c/li\u003e\n\u003cli\u003eMontero-Odasso M, van der Velde N, Martin FC et al (2022) World guidelines for falls prevention and management for older adults: a global initiative. Age Ageing 51. https://doi.org/10.1093/ageing/afac205\u003c/li\u003e\n\u003cli\u003eNoh HM, Song HJ, Park YS, Han J, Roh YK (2021) Fall predictors beyond fall risk assessment tool items for acute hospitalized older adults: a matched case-control study. Sci Rep 11:1503. https://doi.org/10.1038/s41598-021-81034-9\u003c/li\u003e\n\u003cli\u003eKwon E, Chang SJ, Kwon M (2023) A Clinical Data Warehouse Analysis of Risk Factors for Inpatient Falls in a Tertiary Hospital: A Case-Control Study. J Patient Saf 19:501-507. https://doi.org/10.1097/pts.0000000000001163\u003c/li\u003e\n\u003cli\u003eDharmarajan TS, Avula S, Norkus EP (2006) Anemia increases risk for falls in hospitalized older adults: an evaluation of falls in 362 hospitalized, ambulatory, long-term care, and community patients. J Am Med Dir Assoc 7:287-293. https://doi.org/10.1016/j.jamda.2005.10.010\u003c/li\u003e\n\u003cli\u003eFehlberg EA, Lucero RJ, Weaver MT et al (2017) Associations between hyponatraemia, volume depletion and the risk of falls in US hospitalised patients: a case-control study. BMJ Open 7:e017045. https://doi.org/10.1136/bmjopen-2017-017045\u003c/li\u003e\n\u003cli\u003eZhao M, Li S, Xu Y, Su X, Jiang H (2020) Developing a Scoring Model to Predict the Risk of Injurious Falls in Elderly Patients: A Retrospective Case-Control Study in Multicenter Acute Hospitals. Clin Interv Aging 15:1767-1778. https://doi.org/10.2147/cia.S258171\u003c/li\u003e\n\u003cli\u003eHowlett SE, Rockwood MR, Mitnitski A, Rockwood K (2014) Standard laboratory tests to identify older adults at increased risk of death. BMC Med 12:171. https://doi.org/10.1186/s12916-014-0171-9\u003c/li\u003e\n\u003cli\u003eSapp DG, Cormier BM, Rockwood K, Howlett SE, Heinze SS (2023) The frailty index based on laboratory test data as a tool to investigate the impact of frailty on health outcomes: a systematic review and meta-analysis. Age Ageing 52. https://doi.org/10.1093/ageing/afac309\u003c/li\u003e\n\u003cli\u003eHakeem FF, Maharani A, Todd C, O\u0026apos;Neill TW (2023) Development, validation and performance of laboratory frailty indices: A scoping review. Arch Gerontol Geriatr 111:104995. https://doi.org/10.1016/j.archger.2023.104995\u003c/li\u003e\n\u003cli\u003eNakashima H, Nagae M, Komiya H et al (2023) Combined use of the Clinical Frailty Scale and laboratory tests in acutely hospitalized older patients. Aging Clin Exp Res 35:1927-1935. https://doi.org/10.1007/s40520-023-02477-w\u003c/li\u003e\n\u003cli\u003eYsea-Hill O, Gomez CJ, Mansour N et al (2022) The association of a frailty index from laboratory tests and vital signs with clinical outcomes in hospitalized older adults. J Am Geriatr Soc 70:3163-3175. https://doi.org/10.1111/jgs.17977\u003c/li\u003e\n\u003cli\u003eNakashima H, Watanabe K, Komiya H et al (2024) Frailty Index Based on Common Laboratory Tests for Patients Starting Home-Based Medical Care. J Am Med Dir Assoc 25:105114. https://doi.org/10.1016/j.jamda.2024.105114\u003c/li\u003e\n\u003cli\u003eYang M, Zhuo Y, Hu X, Xie L (2018) Predictive validity of two frailty tools for mortality in Chinese nursing home residents: frailty index based on common laboratory tests (FI-Lab) versus FRAIL-NH. Aging Clin Exp Res 30:1445-1452. https://doi.org/10.1007/s40520-018-1041-7\u003c/li\u003e\n\u003cli\u003eBlodgett JM, Theou O, Howlett SE, Wu FC, Rockwood K (2016) A frailty index based on laboratory deficits in community-dwelling men predicted their risk of adverse health outcomes. Age Ageing 45:463-468. https://doi.org/10.1093/ageing/afw054\u003c/li\u003e\n\u003cli\u003eThe Ministry of Education, Culture, Sports, Science and Technology, the Ministry of Health, Labour and Welfare, and the Ministry of Economy, Trade and Industry, Ethical Guidelines for Medical and Health Research Involving Human Subjects 2021 (Guidance) (2022 revised) (Japanese language only) (2022) https://www.mhlw.go.jp/content/000946358.pdf\u003c/li\u003e\n\u003cli\u003evon Elm E, Altman DG, Egger M, Pocock SJ, G\u0026oslash;tzsche PC, Vandenbroucke JP (2007) Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Bmj 335:806-808. https://doi.org/10.1136/bmj.39335.541782.AD\u003c/li\u003e\n\u003cli\u003eHayashida K, Murakami G, Matsuda S, Fushimi K (2021) History and Profile of Diagnosis Procedure Combination (DPC): Development of a Real Data Collection System for Acute Inpatient Care in Japan. J Epidemiol 31:1-11. https://doi.org/10.2188/jea.JE20200288\u003c/li\u003e\n\u003cli\u003eMahoney FI, Barthel DW (1965) FUNCTIONAL EVALUATION: THE BARTHEL INDEX. Md State Med J 14:61-65\u003c/li\u003e\n\u003cli\u003eCharlson ME, Pompei P, Ales KL, MacKenzie CR (1987) A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 40:373-383\u003c/li\u003e\n\u003cli\u003eQuan H, Sundararajan V, Halfon P et al (2005) Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care 43:1130-1139. https://doi.org/10.1097/01.mlr.0000182534.19832.83\u003c/li\u003e\n\u003cli\u003eTanaka S, Imaizumi T, Morohashi A et al (2023) In-Hospital Fall Risk Prediction by Objective Measurement of Lower Extremity Function in a High-Risk Population. J Am Med Dir Assoc 24:1861-1867.e1862. https://doi.org/10.1016/j.jamda.2023.07.020\u003c/li\u003e\n\u003cli\u003eOliver D, Britton M, Seed P, Martin FC, Hopper AH (1997) Development and evaluation of evidence based risk assessment tool (STRATIFY) to predict which elderly inpatients will fall: case-control and cohort studies. Bmj 315:1049-1053. https://doi.org/10.1136/bmj.315.7115.1049\u003c/li\u003e\n\u003cli\u003ePapaioannou A, Parkinson W, Cook R, Ferko N, Coker E, Adachi JD (2004) Prediction of falls using a risk assessment tool in the acute care setting. BMC Med 2:1. https://doi.org/10.1186/1741-7015-2-1\u003c/li\u003e\n\u003cli\u003eSoh CH, Guan L, Reijnierse EM, Lim WK, Maier AB (2022) Comparison of the modified Frailty-Index based on laboratory tests and the Clinical Frailty Scale in predicting mortality among geriatric rehabilitation inpatients: RESORT. Arch Gerontol Geriatr 100:104667. https://doi.org/10.1016/j.archger.2022.104667\u003c/li\u003e\n\u003cli\u003eTheou O, Haviva C, Wallace L, Searle SD, Rockwood K (2023) How to construct a frailty index from an existing dataset in 10 steps. Age Ageing 52. https://doi.org/10.1093/ageing/afad221\u003c/li\u003e\n\u003cli\u003eSafari S, Baratloo A, Elfil M, Negida A (2016) Evidence Based Emergency Medicine; Part 5 Receiver Operating Curve and Area under the Curve. Emerg (Tehran) 4:111-113\u003c/li\u003e\n\u003cli\u003eDeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837-845\u003c/li\u003e\n\u003cli\u003eKanda Y (2013) Investigation of the freely available easy-to-use software \u0026apos;EZR\u0026apos; for medical statistics. Bone Marrow Transplant 48:452-458. https://doi.org/10.1038/bmt.2012.244\u003c/li\u003e\n\u003cli\u003eHuberty S, Freyst\u0026auml;tter G, Wieczorek M et al (2023) Association Between Multimorbidity and Rate of Falls: A 3-Year 5-Country Prospective Study in Generally Healthy and Active Community-Dwelling Adults Aged \u0026ge;70 Years. J Am Med Dir Assoc 24:804-810.e804. https://doi.org/10.1016/j.jamda.2022.12.011\u003c/li\u003e\n\u003cli\u003eZhu Y, Liu Z, Wang Y et al (2016) Agreement between the frailty index and phenotype and their associations with falls and overnight hospitalizations. Arch Gerontol Geriatr 66:161-165. https://doi.org/10.1016/j.archger.2016.06.004\u003c/li\u003e\n\u003cli\u003eClegg A, Bates C, Young J et al (2016) Development and validation of an electronic frailty index using routine primary care electronic health record data. Age Ageing 45:353-360. https://doi.org/10.1093/ageing/afw039\u003c/li\u003e\n\u003cli\u003eZia A, Kamaruzzaman SB, Tan MP (2015) Polypharmacy and falls in older people: Balancing evidence-based medicine against falls risk. Postgrad Med 127:330-337. https://doi.org/10.1080/00325481.2014.996112\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"aging-clinical-and-experimental-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"acer","sideBox":"Learn more about [Aging Clinical and Experimental Research](http://link.springer.com/journal/40520)","snPcode":"40520","submissionUrl":"https://submission.nature.com/new-submission/40520/3","title":"Aging Clinical and Experimental Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Fall screening, falls, FI-lab, Frailty Index-laboratory, STRATIFY","lastPublishedDoi":"10.21203/rs.3.rs-6290898/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6290898/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eAims\u003c/h2\u003e \u003cp\u003eTo explore the association between Frailty Index based on laboratory tests (FI-lab) and in-hospital fall risk in older adults, and to explore whether incorporating FI-lab improves the predictive accuracy of a traditional fall risk prediction tool.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective cohort study using electronic medical records from patients aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years who were admitted to Nagoya University Hospital in 2020. We assessed fall risk using the St. Thomas's Risk Assessment Tool in Falling Elderly Inpatients (STRATIFY). We calculated FI-lab based on 35 common laboratory parameters tested on admission. Each fall was reported prospectively by nurses through computer-based incident report forms. The relationship between FI-lab and in-hospital falls was analyzed using multivariate binomial logistic regression. Predictive performance was compared using the area under the receiver operating characteristic curve (AUROC) and net reclassification improvement (NRI).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eData for 5984 patients were included (mean age 73 years, 63.5% male). The mean FI-lab score was 0.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16. Falls occurred in 175 patients (2.9%) during a median hospital stay of 9 days. FI-lab was associated with falls independently of STRATIFY. Adding FI-lab to STRATIFY significantly improved its predictive accuracy, increasing AUROC from 0.674 to 0.715 (p\u0026thinsp;=\u0026thinsp;0.018), with NRI of 0.413 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eFI-lab on admission was independently associated with in-hospital fall risk and improved the predictive ability of STRATIFY. FI-lab could be a valuable component in more accurate fall prediction.\u003c/p\u003e","manuscriptTitle":"Frailty Index based on laboratory tests and in-hospital falls among older adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-06 01:31:06","doi":"10.21203/rs.3.rs-6290898/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-15T12:00:39+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-14T12:54:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-13T09:30:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-11T13:18:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"68332976366142773597691603374058109439","date":"2025-04-08T18:23:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"216834171541212184401559335996164497081","date":"2025-04-06T06:36:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-06T01:01:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"275201565697353448306727078627094611695","date":"2025-04-03T15:12:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"125161414947264548621798123679297223471","date":"2025-04-02T08:31:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"298581851020874047412346708244964690849","date":"2025-04-01T09:15:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"139876822237188029692473049345499483911","date":"2025-04-01T07:52:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"48491119845666266703251698682368237605","date":"2025-04-01T06:59:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-01T06:28:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-28T18:57:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-25T01:49:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"Aging Clinical and Experimental Research","date":"2025-03-24T02:08:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"aging-clinical-and-experimental-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"acer","sideBox":"Learn more about [Aging Clinical and Experimental Research](http://link.springer.com/journal/40520)","snPcode":"40520","submissionUrl":"https://submission.nature.com/new-submission/40520/3","title":"Aging Clinical and Experimental Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"adfac463-3a0d-4c2b-a0b2-b9479ee6b0ca","owner":[],"postedDate":"May 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-09T16:03:13+00:00","versionOfRecord":{"articleIdentity":"rs-6290898","link":"https://doi.org/10.1007/s40520-025-03090-9","journal":{"identity":"aging-clinical-and-experimental-research","isVorOnly":false,"title":"Aging Clinical and Experimental Research"},"publishedOn":"2025-06-05 15:57:02","publishedOnDateReadable":"June 5th, 2025"},"versionCreatedAt":"2025-05-06 01:31:06","video":"","vorDoi":"10.1007/s40520-025-03090-9","vorDoiUrl":"https://doi.org/10.1007/s40520-025-03090-9","workflowStages":[]},"version":"v1","identity":"rs-6290898","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6290898","identity":"rs-6290898","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}