Constructing a Risk Predictive Model of Fractures by Falls for Elderly: A Retrospective Study Focus on Elderly Hospital Inpatient

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Methods The data of elderly patients who had sustained a fall were accessed from the hospital's adverse event reporting system and electronic patient records between January 2022 and April 2024. The collected data included general information, clinical data, laboratory examination results, and imaging findings. The Least Absolute Shrinkage and Selection Operator (LASSO) regression model and multivariate logistic regression analysis were conducted to develop a risk-predictive model for fractures. The C-index was used for the internal validation of the model. Results 103 patients > 55 years who had sustained a fall were identified and their mean age was 76.98 ± 7.917 years. The occurrence of fractures was 21.4% (22 of 103). The risk prediction nomogram for fractures was developed with 4 prognostic factors which included fall time (00:01–08:00, P = 0.04), gender (female, P = 0.02), serum potassium (> 5.5mmol/L, P = 0.003), serum calcium (1.97-2.11mmol/L, P = 0.001). The calibration results and the C-index values (0.87; 95% confidence interval: 0.82296–0.91704) showed that the nomogram was very reliable. Conclusion The prediction nomogram we developed is a simple and accurate tool for the early prediction risk of fractures by falls in elderly hospital inpatients, allowing for the timely initiation of appropriate preventive measures. Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Health sciences/Risk factors Predictive model Fractures Falls Elderly Inpatient Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The World Health Organization (WHO) identified a fall as an event forcing a person to rest inadvertently on the ground, floor, or other lower level(1). Falls are the second leading cause of unintentional injury deaths worldwide, 684,000 individuals succumb to fall-related injuries every year(2, 3). Falling is considered one of the major problems that may affect the elderly, leading to multiple health issues(4). Falls and their consequent complications pose a major burden for the elderly, their caregivers, and the healthcare system. In Saudi Arabia, previous literature demonstrated that 31.6 to 57.7% of older individuals experienced at least one fall incident in the past year (5). Significant complications in the elderly accompany falls, such as soft tissue injuries, 5% suffer fractures and 1–2% have a hip fracture which is the one with the greatest functional impact, mortality, and hospital costs(6). The annual cost of falls in the United States is approximately $31 billion(7). The implications of falls are wide-ranging, from fractures and head injuries to prolonged hospitalizations(8). Hospital inpatient falls have been a major area of concern in the healthcare setting, especially for the elderly, as elderly patients are at increased risk of harm and significant morbidity secondary to inpatient falls(9). Orthopedic injuries play a central role in harm to patients following inpatient falls, with apparent higher mortality compared to community falls, with features of increased dependence at discharge(9). Falls and their consequent complications pose a major burden for the elderly, their caregivers, and the healthcare system. Falls typically arise from the complex interplay of various factors rather than a singular cause. Walking dysfunction is the most common problem in post-stroke patients and involves an inability to use the ankle dorsiflexor, abnormal gait, and an increased risk of falls due to foot drop(10). Hemiparesis and physical deconditioning following a stroke leads to many individuals cannot get up after falling (11). Poor health and physiologic decreases in function as the major contributors to fall risk in older adults(12). As an important public health problem, osteoporosis is associated with high costs for the health system due to the several thousand fractures each year(13). There is consistent evidence that females have 1.3 times higher fall rates than males, and are more likely to develop bone fractures after falls (6). Other factors were also considered as potential risk factors for the fall which included and were not limited to hypoglycemia, loss of independence, ACS, arrhythmias, and greater fear of falling(14). Fractures of the laryngohyoid complex are associated with fatal falls(15). Most studies have focused on fall prevention and risk assessments, however, there is little research on the risk factors for fractures in elder hospitalized patients. Fall is one of the main causes of injury in old individuals leading to fractures, that might lead to prompt demise (15). Determining the risk factors for fractures of elderly hospital inpatients by falls is essential to help healthcare authorities develop effective prevention strategies and help enhance the quality of life among the elderly. Furthermore, there is no risk prediction model for fracture by falls for elderly hospital inpatients. Contemporary modeling techniques may be used to facilitate an unbiased assessment of the association between the risk of fracture and the predictor variables. Therefore, this study aimed to develop a new simple but accurate nomogram to predict elderly hospital inpatients’ risk of fracture by fall. Methods Patients A retrospective review was performed on the patients who had sustained a fall during hospitalization. Patients admitted to a certain tertiary hospital from January 2022 to April 2024. The inclusion criterion was all patients above the age of 55 years who had sustained a fall during hospitalization. The exclusion criteria were:1.Patients sustaining falls while attending outpatient appointments or attending the accident and emergency department;2. Incomplete medical record. The patients were assigned into two groups: the case group of patients with fractures and the control group of patients without fractures. Ethical review The full name of the ethics committee that reviewed my study is Nanchong Central Hospital,and approved all methodologies utilized in this study.All methods were performed in accordance with the relevant guidelines and regulations of Scientific Reports.Due to the retrospective nature of the study, Nanchong Central Hospital waived the need of obtaining informed consent. The datasets generated and/or analysed during the current study are not publicly available due patient privacy. Data collection All patients who had sustained a fall were identified using the hospital’s adverse event reporting system. Outcomes of these falls were recorded according to the definitions of fracture by Imaging and Clinical Diagnosis. Data for these patients were collated from electronic patient records, adverse event reporting systems, discharge summaries, and electronic care flow records, including their general information, clinical data, laboratory examination, and imaging findings. If patients underwent more than one laboratory examination, we used the result of the first examination during this hospitalization. Statistical analysis The features of both the case and control groups were analyzed using SPSS software (Version 22.0, IBM, USA) and R software. Categorical variables were reported as frequencies and percentages, while grade data were also expressed as frequencies and percentages. Continuous variables with a normal distribution were presented as mean and standard deviation. Predictors were identified using the least absolute shrinkage and selection operator (LASSO) regression implemented in R software. A multivariable logistic regression analysis was conducted to construct a predictive model for fracture risk among elderly hospital inpatients, utilizing the predictors selected by LASSO. The odds ratio (OR) indicated the association between various factors and fracture risk. P-values below 0.05 were deemed statistically significant. A nomogram was developed based on the outcomes of the multivariable analyses, incorporating only those predictors with P-values less than 0.05. The C-index was utilized to assess the discriminative ability of the nomogram. Calibration curves were constructed to evaluate the concordance between the observed outcomes and the predicted probabilities of fracture, employing R software. A diagonal line with a 45-degree angle signifies a well-calibrated model. Bootstrapping with 1,000 resamples was conducted to obtain a more precise C-index for validation purposes.Decision curve analysis (DCA) was conducted to assess the clinical use of the fracture-risk predictive nomogram by quantifying the net benefits of different threshold probabilities in the data( 16 ). Results The general information on these patients is presented in Table 1 . The 103 patients who had sustained a fall aged from 56 to 92 years old and their mean age was 76.98 ± 7.917 years old. Among them, 22 (21.4%) patients have suffered fractures, aged from 66 to 92 years old and their mean age was 78.18 ± 5.086 years old, one patient had fractures in three parts, such as distal radius fracture, ulnar styloid process fracture, surgical neck fracture of the humerus; two patients had fractures in two parts, and other patients have fractures in one part (Table 2 ). The longest hospitalization time for patients with fractures was 98 days, the mean hospitalization time was 23.09 ± 22.74 days. The longest hospitalization time for patients without fractures was 41 days, the mean hospitalization time was 15.38 ± 9.12 days. Table 1 Characteristics of Elderly Hospital Inpatient Who Falls Characteristic control group(n = 81) case group (n = 22) Age (year) 76.65 ± 8.522 78.18 ± 5.086 55–64 6(7.4%) 0(0.0%) 65–74 21(25.9%) 3(13.6%) >74 54(66.7%) 19(86.4%) Gender male 48(59.3%) 8(36.4%) female 33(40.7%) 14(63.6%) Fall time 08:01 AM-12:00 AM 7(8.6%) 3(13.6%) 12:01 PM-18:00 19(23.5%) 10(45.5%) 18:01 − 00:00AM 20(24.7%) 6(27.3%) 00:01 AM-08:00 AM 35(43.2%) 3(13.6%) BMI 23.9 26(32.1%) 5(22.7%) Indwelling catheterization NO 57(70.4%) 16(72.7%) YES 24(29.6%) 6(27.3%) Diuretics NO 45(55.6%) 13(59.1%) YES 36(44.4%) 9(40.9%) Glucocorticoids NO 49(60. 5%) 14(63.6%) YES 32(39.5%) 8(36.4%) Sedatives NO 58(71.6%) 19(86.4%) YES 23(28.4%) 3(13.6%) Osteoporosis NO 71(87.7%) 20(90.9%) YES 10(12.3%) 2(9.1%) Parkinson NO 77(95.1%) 22(100.0%) YES 4(4.9%) 0(0.0%) Cerebral infarction NO 51(63.0%) 16(72.7%) YES 30(37.0%) 6(27.3%) Hypertension NO 40(49.4%) 11(50.0%) YES 41 (50.6%) 11(50.0%) Diabetes NO 63(77.8%) 14(63.6%) YES 18(22.2%) 8(36.4%) Arrhythmias NO 67(82.7%) 18(81.8%) YES 14(17.3%) 4(18.2%) Coronary heart disease NO 64(79.0%) 17(77.3%) YES 17(21.0%) 5(22.7%) Hypotension NO 80(98.8%) 22(100.0%) YES 1(1.2%) 0(0.0%) Sleep disorders NO 76(93.8%) 20(90.9%) YES 5(6.2%) 2(9.1%) COPD NO 53(65.4%) 14(63.6%) YES 28(34.6%) 8(36.4%) Cataract NO 80(98.8%) 22(100.0%) YES 1(1.2%) 0(0.0%) Heart failure NO 52(64.2%) 15(68.2%) YES 29(35.8%) 7(31.8%) Malnutrition NO 73(90.1%) 18(81.8%) YES 8(9.9%) 4(18.2%) Anemia NO 53(65.4%) 14(63.6%) YES 28(34.6%) 8(36.4%) Serum potassium (mmol/L) 5.5 2(2.5%) 4(18.2%) Serum calcium (mmol/L) ≤ 1.96 5(6.2%) 5(22.7%) 1.97–2.11 32(39.5%) 5(22.7%) 2.12–2.24 26(32.1%) 8(36.4%) >2.24 18(22.2%) 4(18.2%) BMI = Body Mass Index; COPD = chronic obstructive pulmonary disease Table 2 Characteristics of Fractures by Falls on Elderly Hospital Inpatient patients Fracture site Patient 1 Skull base fracture Patient 2 Femoral neck fracture Patient 3 Temporal bone fracture Patient 4 Femoral neck fracture Patient 5 Fracture of parietal bone、Nasal bone fracture Patient 6 Femoral subtrochanteric fracture Patient 7 Femoral neck fracture Patient 8 Humerus fracture Patient 9 Zygomatic fracture Patient 10 Intertrochanteric fracture of femur Patient 11 proximal humeral fractures Patient 12 Intertrochanteric fracture of femur Patient 13 Radial fracture Patient 14 Clavicle fracture Patient 15 Radial fracture, Ulnar styloid process fracture Patient 16 Intertrochanteric fracture of femur Patient 17 Distal radius fracture, Ulnar styloid process fracture, Surgical neck fracture of humerus Patient 18 Femoral tuberosity fracture Patient 19 Periprosthetic fracture of the femur Patient 20 Ulnar olecranon fracture Patient 21 Humerus fracture Patient 22 Femoral neck fracture According to LASSO regression by R software, 5 of 25 factors (Fig. 1 ) were considered as the potential predictors namely fall time, gender, diabetes, serum potassium (K+), and serum calcium (Ca+). Multivariate logistic regression analysis of these predictors indicated significant differences in fall time (00:01–08:00, P = 0.04), gender (female, P = 0.02), K+ (> 5.5mmol/L, P = 0.003), Ca+ (1.97-2.11mmol/L, P = 0.001) between the two groups (Table 3 ). When assessing fall time in patients who had sustained a fall, more falls occurred between 00:01 AM and 08:00 AM (35 patients, 33.98%), and assessing fall time in patients whose fall led to fracture, more occurred between 12:01 PM and 18:00 (10 patients, 9.7%). These factors include fall time (00:01–08:00, P = 0.04), gender (female, P = 0.02), K+ (> 5.5mmol/L, P = 0.003), Ca+ (1.97-2.11mmol/L, P = 0.001) were developed a fracture risk prediction nomogram which caused by falls in elderly hospital inpatient (Fig. 2 ). The scores in the nomogram are presented in Table 4 . The calibration results (Fig. 3 ) and the C-index values (0.87; 95% confidence interval: 0.82296–0.91704) showed that the nomogram was very reliable. DCA results showed that within the range of 0.01–0.67, the net benefit rate of the prediction nomogram was higher than that of those for “all” or “none” patients (Fig. 4 ). The C-index value of internal cross-validation was 0.7953076. Table 3 Potentially Prediction Factors of Fractures by Falls on Elderly Hospital Inpatient Variable multivariate analysis β Odds ratio (95% CI) P Intercept -1.168 0.31 (0.006–9.722) 0.52 Fall time (12:01 PM-18:00) 0.59 1.804 (0.255–15.88) 0.565 Fall time (18:01 − 00:00 AM) -0.789 0.454 (0.047–4.416) 0.482 Fall time (00:01 AM-08:00 AM) -2.535 0.079 (0.004–0.851) 0.045 Gender (female) 1.442 4.233 (1.24–16.58) 0.026 Diabetes (YES) 0.471 1.602 (0.386–6.372) 0.502 Serum potassium (3.5–5.5 mmol/L) 2.65 14.159 (1.027–718.28) 0.106 Serum potassium (>5.5 mmol/L) 6.305 547.53 (14.505-90731.46) 0.003 Serum calcium (1.97–2.11 mmol/L) -4.243 0.014 (0.0007–0.141) 0.001 Serum calcium (2.12–2.24 mmol/L) -3.085 0.045 (0.003–0.378) 0.008 Serum calcium (>2.24 mmol/L) -3.691 0.024(0.001–0.251) 0.004 β is regression coefficient. Table 4 Risk Scores for Fractures by Falls on Elderly Hospital Inpatient Risk factor Score Fall time 08:01 AM-12:00 AM 39 12:01 PM-18:00 49 18:01 − 00:00 AM 29 00:01 AM-08:00 AM 0 Gender male 0 female 21 Serum potassium (mmol/L) 5.5 100 Serum calcium (mmol/L) ≤ 1.96 64 1.97–2.11 0 2.12–2.24 17 >2.24 10 Discussion An increasing interest has recently been noticed regarding the problem of elderly falls and their consequences because of the increasing proportion of the elderly population. Elderly inpatient falls remain a considerable patient safety issue, with fractures playing a central role in harm to patients following these falls ( 4 ). Fractures carried a variety of financial burdens, these combined with additional length of stay led to a notable pressure on hospital beds and additional medical costs. In this study, the longest hospitalization time for fracture patients was 98 days, the mean hospitalization time was 23.09 ± 22.74 days, and the mean hospitalization time for patients with fractures was longer than patients without fractures. To some extent, it has increased the suffering of patients and the burden of medical insurance. Understanding the risk factors of fractures caused by falls in elderly inpatients might help establish evidence-based interventions and effective prevention strategies to reduce the fractures caused by falls. Available Mendelian randomization studies found no conclusive effects of serum calcium levels on bone mineral density and fracture( 17 ). It was reported that serum calcium levels < 2.11 mmol/L were common in older patients with hip fractures( 18 ). In this study, the fracture risk prediction nomogram caused by falls in elderly hospital inpatients we constructed shows serum calcium levels 2.24mmol/L gets the risk score of graded 10 points(Fig. 2 ). The important discovery is that serum calcium levels between 1.97mmol/L and 2.11mmol/L are a protective factor in this study, and get the risk score of graded 0 points (Table 3 , Fig. 2 ). Literature reported that ingestion of potassium (K+)-rich foods reduced the incidence of osteoporosis( 19 ). Another study found that hyperkalemia at admission is associated with increased 30-day mortality after a hip fracture( 20 ). However, there is currently no research on the association between hyperkalemia and fractures. In our research, as the serum potassium increases, the probability of fracture increases. The serum potassium levels>5.5mmol/L is a risk factor for fractures caused by falls in elderly inpatients(P = 0.003, Table 3 ), and the fracture risk prediction nomogram we constructed shows serum potassium levels>5.5mmol/L gets the risk score of graded 100 points(Fig. 2 ). In the future, research on the mechanisms related to hyperkalemia and fractures can be conducted. In this study, more falls occurred between 00:01 AM and 08:00 AM (35 patients, 33.98%), the reason may be that the patient went to the bathroom alone at night without turning on the lights and was unwilling to wake up the accompanying personnel. Based on this, our hospital ward is preparing to install induction night lights. In our research, more fractures by fall occurred between 12:01 PM and 18:00 (10 patients, 9.7%), and the fracture risk prediction nomogram we constructed shows fall time between 12:01 PM and 18:00 gets the risk score of graded 100 points (Fig. 2 ). During this period, we can increase the number of nurses and nursing staff to provide better care for patients. There is consistent evidence that females have 1.3 times higher fall rates than males, and are more likely to develop bone fractures after falls( 5 ). The reason may be that hormonal changes experienced by women as they age or after menopause may be linked to a more rapid decline in bone mass compared to men. Estrogen may regulate the metabolism and function of bone and skeletal muscle via estrogen receptors( 21 ). Most postmenopausal females have bone loss related to estrogen deficiency( 22 ).In this study, the incidence of fractures in elderly females after falls is higher than that in males (Table 1 ), and female is a risk factor for fractures caused by falls in elderly inpatients (P = 0.02, Table 3 ), gets the risk score of graded 21 points by our predictive nomogram in prediction of fractures, while male gets the risk score of graded 0 points. Therefore, we can encourage elderly females to enhance bone function through the implementation of a proper diet and inculcation of regular exercise through evaluating physical condition, to prevent fractures after falling. Older individuals with hypertension and diabetes mellitus are more vulnerable to falls( 1 ). It was found that the prevalence of hypertension was not associated with fracture( 23 ). In this study, hypertension was not a potential predictor for fractures. It was reported that type 2 diabetes mellitus was associated with increased fracture risk, which resulted in an increased risk of disability in women( 13 ). According to LASSO regression by R software, diabetes was considered as the potential predictor for fractures in this study, but, multivariate logistic regression analysis has no significant differences in diabetes (P = 0.5). Osteoporosis in patients with diabetes may be associated with fractures after falling. Next, we will expand the sample size to further study the correlation between diabetes, osteoporosis, and fall fractures. Osteoporosis is one of the leading causes of morbidity and mortality in older people, and its prevalence ranks the highest among non-communicable diseases worldwide, at about 0.83%( 24 ). Osteoporosis is contributing to an increasing number of osteoporotic vertebral fractures( 25 ). Fractures of the hip, spine, and distal forearm are regarded as typical osteoporotic fractures( 26 ). In this study, out of 103 elderly people who fell, 40 elderly people with osteoporosis had 8 fractures after falling, of which 6 were females. Osteoporosis was not a potential predictor for fractures in this study, however, the incidence of fractures after falls in female osteoporosis patients was significantly higher than that in males. Therefore, for elderly female patients, regular physical activities, a sufficient intake of calcium, and a normal vitamin D level are important for bone health which is an important measure to prevent a fracture. Conclusion Falls and their consequent complications such as fractures pose a major burden for the elderly, their caregivers, and the healthcare system. Prevention of falls and their consequent complications have been becoming increasingly challenging. A risk prediction model of fracture by falls for elderly hospital inpatient has been developed in this study and it boasts a relatively high accuracy in early identification of patients who have a high risk of fracture. It may help clinicians develop strategies to prevent fractures and improve care quality. In addition, the prediction model may help elderly hospital inpatients identify the risk factors of fracture and have timely prevention. Declarations Conflicts of interest The authors have stated explicitly that there are no conflicts of interest in connection with this article. We thank all the people who have provided us with support and help during the writing of this article. Funding This work was supported by Sichuan Provincial Nursing Research Project Plan (No. H23025) and This work was supported by Sichuan Provincial Grassroots Health Development Research Center(No.SWFZ22-C-92). Author Contribution Wenqiang Wang collected data and drafted the manuscript. Zonghan Du provided research ideas. Peng Xie guided the research process and manuscript revisions. References Alanazi, A. & Salih, S. Fall Prevalence and Associated Risk Factors Among the Elderly Population in Tabuk City, Saudi Arabia: A Cross-Sectional Study 2023. Cureus 15 (9), e45317 (2023). Vaishya, R. & Vaish, A. Falls in Older Adults are Serious. Indian J. Orthop. 54 (1), 69–74 (2020). Aleixo, P. & Abrantes, J. 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Nutrients ; 15 (19). (2023). Prost, S., Pesenti, S., Fuentes, S., Tropiano, P. & Blondel, B. Treatment of osteoporotic vertebral fractures. Orthopaedics & traumatology, surgery & research: OTSR. ;107(1s):102779. (2021). Imamudeen, N. et al. Management of Osteoporosis and Spinal Fractures: Contemporary Guidelines and Evolving Paradigms. Clin. Med. Res. 20 (2), 95–106 (2022). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-7634641","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":542477855,"identity":"28ca03a6-3767-49e6-badc-26e79a9fa75f","order_by":0,"name":"Wenqiang Wang","email":"","orcid":"","institution":"Beijing Anzhen Nanchong Hospital of Capital Medical University \u0026 Nanchong Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wenqiang","middleName":"","lastName":"Wang","suffix":""},{"id":542477856,"identity":"ae12f549-ceb0-4ea6-b5f9-ff8fb9d37c3e","order_by":1,"name":"Zonghan Du","email":"","orcid":"","institution":"Beijing 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Hospital","correspondingAuthor":true,"prefix":"","firstName":"Peng","middleName":"","lastName":"Xie","suffix":""}],"badges":[],"createdAt":"2025-09-17 02:08:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7634641/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7634641/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96022732,"identity":"3e7f33d2-dc8f-4fd7-9c80-42e34076f090","added_by":"auto","created_at":"2025-11-16 16:30:27","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":15407,"visible":true,"origin":"","legend":"","description":"","filename":"Figurelegends2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7634641/v1/cd0bf78c6d388b1c7803db2f.docx"},{"id":96246757,"identity":"2acf4ebb-ae63-46cc-ad7b-58aa8b3bdb10","added_by":"auto","created_at":"2025-11-19 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16:30:29","extension":"html","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":98333,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7634641/v1/215b889e9ed983832826016e.html"},{"id":96245595,"identity":"ef42240f-6ea2-4e10-a600-052c5e2885fa","added_by":"auto","created_at":"2025-11-19 07:21:09","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":249112,"visible":true,"origin":"","legend":"\u003cp\u003eFeature selection using the LASSO regression model. LASSO coefficient profiles of the 25 features, 5 features with nonzero coefficients were selected.\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7634641/v1/4e0ed36e168f95cfce3546db.jpg"},{"id":96022747,"identity":"85cc4c49-1b5a-4d5e-8533-27c99fbae2d9","added_by":"auto","created_at":"2025-11-16 16:30:28","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":66163,"visible":true,"origin":"","legend":"\u003cp\u003eDeveloped fractures risk predictive nomogram by Falls on Elderly Hospital Inpatient.\u003c/p\u003e\n\u003cp\u003eNote: The probability of fractures is calculated by drawing a line to the point on the axis for each of the following features. The points for each feature are summed and located on the total point line. Next, a vertical line is projected from the total point line to the predicted probability scale line to obtain the elderly hospital inpatient’s probability of fractures by falls.\u003c/p\u003e","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7634641/v1/497fe1c6ea8c4f90a564e0da.jpg"},{"id":96022734,"identity":"71bc8d89-7e6a-4701-a6c6-5b2460dfb51d","added_by":"auto","created_at":"2025-11-16 16:30:27","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":54014,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration for the fractures risk predictive nomogram\u003c/p\u003e\n\u003cp\u003eNote: The x-axis represents in calibration curve predicted fracture risk. The y-axis represents the actual diagnosed fractures. The solid line close to the 45 degrees diagonal line represents a better prediction;\u003c/p\u003e","description":"","filename":"fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7634641/v1/311d9c804111875fea2ed7d1.jpg"},{"id":96022742,"identity":"db11471f-464e-4f31-8448-f106d778df4b","added_by":"auto","created_at":"2025-11-16 16:30:28","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":49209,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis (DCA) for fractures risk predictive nomogram.\u003c/p\u003e","description":"","filename":"fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7634641/v1/6ad7d224294b05f9da920ceb.jpg"},{"id":102240959,"identity":"14035c59-2588-4fc9-859d-4a94d01a148c","added_by":"auto","created_at":"2026-02-09 16:57:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1635376,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7634641/v1/617cd353-b3a0-44d1-bf43-3027f85845c8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Constructing a Risk Predictive Model of Fractures by Falls for Elderly: A Retrospective Study Focus on Elderly Hospital Inpatient","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe World Health Organization (WHO) identified a fall as an event forcing a person to rest inadvertently on the ground, floor, or other lower level(1). Falls are the second leading cause of unintentional injury deaths worldwide, 684,000 individuals succumb to fall-related injuries every year(2, 3). Falling is considered one of the major problems that may affect the elderly, leading to multiple health issues(4). Falls and their consequent complications pose a major burden for the elderly, their caregivers, and the healthcare system. In Saudi Arabia, previous literature demonstrated that 31.6 to 57.7% of older individuals experienced at least one fall incident in the past year (5). Significant complications in the elderly accompany falls, such as soft tissue injuries, 5% suffer fractures and 1–2% have a hip fracture which is the one with the greatest functional impact, mortality, and hospital costs(6). The annual cost of falls in the United States is approximately $31 billion(7). The implications of falls are wide-ranging, from fractures and head injuries to prolonged hospitalizations(8). Hospital inpatient falls have been a major area of concern in the healthcare setting, especially for the elderly, as elderly patients are at increased risk of harm and significant morbidity secondary to inpatient falls(9). Orthopedic injuries play a central role in harm to patients following inpatient falls, with apparent higher mortality compared to community falls, with features of increased dependence at discharge(9).\u003c/p\u003e\n\u003cp\u003eFalls and their consequent complications pose a major burden for the elderly, their caregivers, and the healthcare system. Falls typically arise from the complex interplay of various factors rather than a singular cause. Walking dysfunction is the most common problem in post-stroke patients and involves an inability to use the ankle dorsiflexor, abnormal gait, and an increased risk of falls due to foot drop(10). Hemiparesis and physical deconditioning following a stroke leads to many individuals cannot get up after falling (11). Poor health and physiologic decreases in function as the major contributors to fall risk in older adults(12). As an important public health problem, osteoporosis is associated with high costs for the health system due to the several thousand fractures each year(13). There is consistent evidence that females have 1.3 times higher fall rates than males, and are more likely to develop bone fractures after falls (6). Other factors were also considered as potential risk factors for the fall which included and were not limited to hypoglycemia, loss of independence, ACS, arrhythmias, and greater fear of falling(14). Fractures of the laryngohyoid complex are associated with fatal falls(15). Most studies have focused on fall prevention and risk assessments, however, there is little research on the risk factors for fractures in elder hospitalized patients.\u003c/p\u003e\n\u003cp\u003eFall is one of the main causes of injury in old individuals leading to fractures, that might lead to prompt demise (15). Determining the risk factors for fractures of elderly hospital inpatients by falls is essential to help healthcare authorities develop effective prevention strategies and help enhance the quality of life among the elderly. Furthermore, there is no risk prediction model for fracture by falls for elderly hospital inpatients. Contemporary modeling techniques may be used to facilitate an unbiased assessment of the association between the risk of fracture and the predictor variables. Therefore, this study aimed to develop a new simple but accurate nomogram to predict elderly hospital inpatients’ risk of fracture by fall.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003ePatients\u003c/p\u003e\u003cp\u003eA retrospective review was performed on the patients who had sustained a fall during hospitalization. Patients admitted to a certain tertiary hospital from January 2022 to April 2024. The inclusion criterion was all patients above the age of 55 years who had sustained a fall during hospitalization. The exclusion criteria were:1.Patients sustaining falls while attending outpatient appointments or attending the accident and emergency department;2. Incomplete medical record. The patients were assigned into two groups: the case group of patients with fractures and the control group of patients without fractures.\u003c/p\u003e\u003cp\u003eEthical review\u003c/p\u003e\u003cp\u003e The full name of the ethics committee that reviewed my study is Nanchong Central Hospital,and approved all methodologies utilized in this study.All methods were performed in accordance with the relevant guidelines and regulations of Scientific Reports.Due to the retrospective nature of the study, Nanchong Central Hospital waived the need of obtaining informed consent. The datasets generated and/or analysed during the current study are not publicly available due patient privacy.\u003c/p\u003e\u003cp\u003eData collection\u003c/p\u003e\u003cp\u003eAll patients who had sustained a fall were identified using the hospital\u0026rsquo;s adverse event reporting system. Outcomes of these falls were recorded according to the definitions of fracture by Imaging and Clinical Diagnosis. Data for these patients were collated from electronic patient records, adverse event reporting systems, discharge summaries, and electronic care flow records, including their general information, clinical data, laboratory examination, and imaging findings. If patients underwent more than one laboratory examination, we used the result of the first examination during this hospitalization.\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eThe features of both the case and control groups were analyzed using SPSS software (Version 22.0, IBM, USA) and R software. Categorical variables were reported as frequencies and percentages, while grade data were also expressed as frequencies and percentages. Continuous variables with a normal distribution were presented as mean and standard deviation. Predictors were identified using the least absolute shrinkage and selection operator (LASSO) regression implemented in R software. A multivariable logistic regression analysis was conducted to construct a predictive model for fracture risk among elderly hospital inpatients, utilizing the predictors selected by LASSO. The odds ratio (OR) indicated the association between various factors and fracture risk. P-values below 0.05 were deemed statistically significant. A nomogram was developed based on the outcomes of the multivariable analyses, incorporating only those predictors with P-values less than 0.05.\u003c/p\u003e\u003cp\u003eThe C-index was utilized to assess the discriminative ability of the nomogram. Calibration curves were constructed to evaluate the concordance between the observed outcomes and the predicted probabilities of fracture, employing R software. A diagonal line with a 45-degree angle signifies a well-calibrated model. Bootstrapping with 1,000 resamples was conducted to obtain a more precise C-index for validation purposes.Decision curve analysis (DCA) was conducted to assess the clinical use of the fracture-risk predictive nomogram by quantifying the net benefits of different threshold probabilities in the data(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe general information on these patients is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The 103 patients who had sustained a fall aged from 56 to 92 years old and their mean age was 76.98\u0026thinsp;\u0026plusmn;\u0026thinsp;7.917 years old. Among them, 22 (21.4%) patients have suffered fractures, aged from 66 to 92 years old and their mean age was 78.18\u0026thinsp;\u0026plusmn;\u0026thinsp;5.086 years old, one patient had fractures in three parts, such as distal radius fracture, ulnar styloid process fracture, surgical neck fracture of the humerus; two patients had fractures in two parts, and other patients have fractures in one part (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The longest hospitalization time for patients with fractures was 98 days, the mean hospitalization time was 23.09\u0026thinsp;\u0026plusmn;\u0026thinsp;22.74 days. The longest hospitalization time for patients without fractures was 41 days, the mean hospitalization time was 15.38\u0026thinsp;\u0026plusmn;\u0026thinsp;9.12 days.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of Elderly Hospital Inpatient Who Falls\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003econtrol group(n\u0026thinsp;=\u0026thinsp;81)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ecase group (n\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (year)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e76.65\u0026thinsp;\u0026plusmn;\u0026thinsp;8.522\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78.18\u0026thinsp;\u0026plusmn;\u0026thinsp;5.086\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e55\u0026ndash;64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6(7.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0(0.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e65\u0026ndash;74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21(25.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3(13.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54(66.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19(86.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48(59.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8(36.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003efemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33(40.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14(63.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFall time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e08:01 AM-12:00 AM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7(8.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3(13.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12:01 PM-18:00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19(23.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10(45.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18:01\u0026thinsp;\u0026minus;\u0026thinsp;00:00AM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20(24.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6(27.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e00:01 AM-08:00 AM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35(43.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3(13.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;18.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11(13.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3(13.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18.5\u0026ndash;23.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44(54.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14(63.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;23.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26(32.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5(22.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndwelling catheterization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57(70.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16(72.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24(29.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6(27.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiuretics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45(55.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13(59.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36(44.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9(40.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlucocorticoids\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49(60. 5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14(63.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32(39.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8(36.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSedatives\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58(71.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19(86.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23(28.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3(13.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOsteoporosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71(87.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20(90.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10(12.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2(9.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParkinson\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e77(95.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22(100.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4(4.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0(0.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCerebral infarction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51(63.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16(72.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30(37.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6(27.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40(49.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11(50.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41 (50.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11(50.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63(77.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14(63.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18(22.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8(36.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArrhythmias\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67(82.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18(81.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14(17.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4(18.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoronary heart disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64(79.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17(77.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17(21.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5(22.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypotension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80(98.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22(100.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1(1.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0(0.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleep disorders\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e76(93.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20(90.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5(6.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2(9.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOPD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53(65.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14(63.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28(34.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8(36.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCataract\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80(98.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22(100.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1(1.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0(0.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart failure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52(64.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15(68.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29(35.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7(31.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMalnutrition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e73(90.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18(81.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8(9.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4(18.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53(65.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14(63.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28(34.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8(36.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum potassium (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16(19.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1(4.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3.5\u0026ndash;5.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63(77.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17(77.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;5.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2(2.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4(18.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum calcium (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;1.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5(6.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5(22.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.97\u0026ndash;2.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32(39.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5(22.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2.12\u0026ndash;2.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26(32.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8(36.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;2.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18(22.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4(18.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eBMI\u0026thinsp;=\u0026thinsp;Body Mass Index; COPD\u0026thinsp;=\u0026thinsp;chronic obstructive pulmonary disease\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of Fractures by Falls on Elderly Hospital Inpatient\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003epatients\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFracture site\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatient 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSkull base fracture\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatient 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemoral neck fracture\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatient 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTemporal bone fracture\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatient 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemoral neck fracture\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatient 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFracture of parietal bone、Nasal bone fracture\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatient 6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemoral subtrochanteric fracture\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatient 7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemoral neck fracture\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatient 8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHumerus fracture\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatient 9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eZygomatic fracture\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatient 10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntertrochanteric fracture of femur\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatient 11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eproximal humeral fractures\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatient 12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntertrochanteric fracture of femur\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatient 13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRadial fracture\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatient 14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClavicle fracture\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatient 15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRadial fracture, Ulnar styloid process fracture\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatient 16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntertrochanteric fracture of femur\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatient 17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDistal radius fracture, Ulnar styloid process fracture, Surgical neck fracture of humerus\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatient 18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemoral tuberosity fracture\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatient 19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePeriprosthetic fracture of the femur\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatient 20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUlnar olecranon fracture\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatient 21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHumerus fracture\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatient 22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemoral neck fracture\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAccording to LASSO regression by R software, 5 of 25 factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) were considered as the potential predictors namely fall time, gender, diabetes, serum potassium (K+), and serum calcium (Ca+). Multivariate logistic regression analysis of these predictors indicated significant differences in fall time (00:01\u0026ndash;08:00, P\u0026thinsp;=\u0026thinsp;0.04), gender (female, P\u0026thinsp;=\u0026thinsp;0.02), K+ (\u0026gt;\u0026thinsp;5.5mmol/L, P\u0026thinsp;=\u0026thinsp;0.003), Ca+ (1.97-2.11mmol/L, P\u0026thinsp;=\u0026thinsp;0.001) between the two groups (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). When assessing fall time in patients who had sustained a fall, more falls occurred between 00:01 AM and 08:00 AM (35 patients, 33.98%), and assessing fall time in patients whose fall led to fracture, more occurred between 12:01 PM and 18:00 (10 patients, 9.7%). These factors include fall time (00:01\u0026ndash;08:00, P\u0026thinsp;=\u0026thinsp;0.04), gender (female, P\u0026thinsp;=\u0026thinsp;0.02), K+ (\u0026gt;\u0026thinsp;5.5mmol/L, P\u0026thinsp;=\u0026thinsp;0.003), Ca+ (1.97-2.11mmol/L, P\u0026thinsp;=\u0026thinsp;0.001) were developed a fracture risk prediction nomogram which caused by falls in elderly hospital inpatient (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The scores in the nomogram are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The calibration results (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and the C-index values (0.87; 95% confidence interval: 0.82296\u0026ndash;0.91704) showed that the nomogram was very reliable. DCA results showed that within the range of 0.01\u0026ndash;0.67, the net benefit rate of the prediction nomogram was higher than that of those for \u0026ldquo;all\u0026rdquo; or \u0026ldquo;none\u0026rdquo; patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The C-index value of internal cross-validation was 0.7953076.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePotentially Prediction Factors of Fractures by Falls on Elderly Hospital Inpatient\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003emultivariate analysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOdds ratio (95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.31 (0.006\u0026ndash;9.722)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFall time (12:01 PM-18:00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.804 (0.255\u0026ndash;15.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.565\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFall time (18:01\u0026thinsp;\u0026minus;\u0026thinsp;00:00 AM)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.789\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.454 (0.047\u0026ndash;4.416)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.482\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFall time (00:01 AM-08:00 AM)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.535\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.079 (0.004\u0026ndash;0.851)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (female)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.442\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.233 (1.24\u0026ndash;16.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes (YES)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.471\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.602 (0.386\u0026ndash;6.372)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.502\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum potassium (3.5\u0026ndash;5.5 mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.159 (1.027\u0026ndash;718.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.106\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum potassium (\u0026gt;5.5 mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.305\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e547.53 (14.505-90731.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum calcium (1.97\u0026ndash;2.11 mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-4.243\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.014 (0.0007\u0026ndash;0.141)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum calcium (2.12\u0026ndash;2.24 mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-3.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.045 (0.003\u0026ndash;0.378)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum calcium (\u0026gt;2.24 mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-3.691\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.024(0.001\u0026ndash;0.251)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eβ is regression coefficient.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRisk Scores for Fractures by Falls on Elderly Hospital Inpatient\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRisk factor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eScore\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFall time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e08:01 AM-12:00 AM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12:01 PM-18:00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18:01\u0026thinsp;\u0026minus;\u0026thinsp;00:00 AM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e00:01 AM-08:00 AM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003efemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum potassium (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3.5\u0026ndash;5.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;5.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum calcium (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;1.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.97\u0026ndash;2.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2.12\u0026ndash;2.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;2.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAn increasing interest has recently been noticed regarding the problem of elderly falls and their consequences because of the increasing proportion of the elderly population. Elderly inpatient falls remain a considerable patient safety issue, with fractures playing a central role in harm to patients following these falls (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Fractures carried a variety of financial burdens, these combined with additional length of stay led to a notable pressure on hospital beds and additional medical costs. In this study, the longest hospitalization time for fracture patients was 98 days, the mean hospitalization time was 23.09\u0026thinsp;\u0026plusmn;\u0026thinsp;22.74 days, and the mean hospitalization time for patients with fractures was longer than patients without fractures. To some extent, it has increased the suffering of patients and the burden of medical insurance. Understanding the risk factors of fractures caused by falls in elderly inpatients might help establish evidence-based interventions and effective prevention strategies to reduce the fractures caused by falls.\u003c/p\u003e\u003cp\u003eAvailable Mendelian randomization studies found no conclusive effects of serum calcium levels on bone mineral density and fracture(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). It was reported that serum calcium levels\u0026thinsp;\u0026lt;\u0026thinsp;2.11 mmol/L were common in older patients with hip fractures(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). In this study, the fracture risk prediction nomogram caused by falls in elderly hospital inpatients we constructed shows serum calcium levels\u0026thinsp;\u0026lt;\u0026thinsp;1.97mmol/L get the risk score of graded 64 points, while serum calcium levels between 2.12mmol/L and 2.24mmol/L get the risk score of graded 17 points, serum calcium levels \u0026gt;2.24mmol/L gets the risk score of graded 10 points(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The important discovery is that serum calcium levels between 1.97mmol/L and 2.11mmol/L are a protective factor in this study, and get the risk score of graded 0 points (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eLiterature reported that ingestion of potassium (K+)-rich foods reduced the incidence of osteoporosis(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Another study found that hyperkalemia at admission is associated with increased 30-day mortality after a hip fracture(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). However, there is currently no research on the association between hyperkalemia and fractures. In our research, as the serum potassium increases, the probability of fracture increases. The serum potassium levels\u0026gt;5.5mmol/L is a risk factor for fractures caused by falls in elderly inpatients(P\u0026thinsp;=\u0026thinsp;0.003, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), and the fracture risk prediction nomogram we constructed shows serum potassium levels\u0026gt;5.5mmol/L gets the risk score of graded 100 points(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the future, research on the mechanisms related to hyperkalemia and fractures can be conducted.\u003c/p\u003e\u003cp\u003eIn this study, more falls occurred between 00:01 AM and 08:00 AM (35 patients, 33.98%), the reason may be that the patient went to the bathroom alone at night without turning on the lights and was unwilling to wake up the accompanying personnel. Based on this, our hospital ward is preparing to install induction night lights. In our research, more fractures by fall occurred between 12:01 PM and 18:00 (10 patients, 9.7%), and the fracture risk prediction nomogram we constructed shows fall time between 12:01 PM and 18:00 gets the risk score of graded 100 points (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). During this period, we can increase the number of nurses and nursing staff to provide better care for patients.\u003c/p\u003e\u003cp\u003eThere is consistent evidence that females have 1.3 times higher fall rates than males, and are more likely to develop bone fractures after falls(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). The reason may be that hormonal changes experienced by women as they age or after menopause may be linked to a more rapid decline in bone mass compared to men. Estrogen may regulate the metabolism and function of bone and skeletal muscle via estrogen receptors(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Most postmenopausal females have bone loss related to estrogen deficiency(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).In this study, the incidence of fractures in elderly females after falls is higher than that in males (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), and female is a risk factor for fractures caused by falls in elderly inpatients (P\u0026thinsp;=\u0026thinsp;0.02, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), gets the risk score of graded 21 points by our predictive nomogram in prediction of fractures, while male gets the risk score of graded 0 points. Therefore, we can encourage elderly females to enhance bone function through the implementation of a proper diet and inculcation of regular exercise through evaluating physical condition, to prevent fractures after falling.\u003c/p\u003e\u003cp\u003eOlder individuals with hypertension and diabetes mellitus are more vulnerable to falls(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). It was found that the prevalence of hypertension was not associated with fracture(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). In this study, hypertension was not a potential predictor for fractures. It was reported that type 2 diabetes mellitus was associated with increased fracture risk, which resulted in an increased risk of disability in women(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). According to LASSO regression by R software, diabetes was considered as the potential predictor for fractures in this study, but, multivariate logistic regression analysis has no significant differences in diabetes (P\u0026thinsp;=\u0026thinsp;0.5). Osteoporosis in patients with diabetes may be associated with fractures after falling. Next, we will expand the sample size to further study the correlation between diabetes, osteoporosis, and fall fractures.\u003c/p\u003e\u003cp\u003eOsteoporosis is one of the leading causes of morbidity and mortality in older people, and its prevalence ranks the highest among non-communicable diseases worldwide, at about 0.83%(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Osteoporosis is contributing to an increasing number of osteoporotic vertebral fractures(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Fractures of the hip, spine, and distal forearm are regarded as typical osteoporotic fractures(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). In this study, out of 103 elderly people who fell, 40 elderly people with osteoporosis had 8 fractures after falling, of which 6 were females. Osteoporosis was not a potential predictor for fractures in this study, however, the incidence of fractures after falls in female osteoporosis patients was significantly higher than that in males. Therefore, for elderly female patients, regular physical activities, a sufficient intake of calcium, and a normal vitamin D level are important for bone health which is an important measure to prevent a fracture.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eFalls and their consequent complications such as fractures pose a major burden for the elderly, their caregivers, and the healthcare system. Prevention of falls and their consequent complications have been becoming increasingly challenging. A risk prediction model of fracture by falls for elderly hospital inpatient has been developed in this study and it boasts a relatively high accuracy in early identification of patients who have a high risk of fracture. It may help clinicians develop strategies to prevent fractures and improve care quality. In addition, the prediction model may help elderly hospital inpatients identify the risk factors of fracture and have timely prevention.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflicts of interest\u003c/h2\u003e\u003cp\u003eThe authors have stated explicitly that there are no conflicts of interest in connection with this article. We thank all the people who have provided us with support and help during the writing of this article.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was supported by Sichuan Provincial Nursing Research Project Plan (No. H23025) and This work was supported by Sichuan Provincial Grassroots Health Development Research Center(No.SWFZ22-C-92).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eWenqiang Wang collected data and drafted the manuscript. Zonghan Du provided research ideas. Peng Xie guided the research process and manuscript revisions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlanazi, A. \u0026amp; Salih, S. 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(2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eImamudeen, N. et al. Management of Osteoporosis and Spinal Fractures: Contemporary Guidelines and Evolving Paradigms. \u003cem\u003eClin. Med. Res.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e (2), 95\u0026ndash;106 (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Predictive model, Fractures, Falls, Elderly Inpatient","lastPublishedDoi":"10.21203/rs.3.rs-7634641/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7634641/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eObjective\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe purpose of this study is to create a nomogram to evaluate the risk of fractures by falls in elderly hospital inpatients.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe data of elderly patients who had sustained a fall were accessed from the hospital's adverse event reporting system and electronic patient records between January 2022 and April 2024. The collected data included general information, clinical data, laboratory examination results, and imaging findings. The Least Absolute Shrinkage and Selection Operator (LASSO) regression model and multivariate logistic regression analysis were conducted to develop a risk-predictive model for fractures. The C-index was used for the internal validation of the model.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003e103 patients\u0026thinsp;\u0026gt;\u0026thinsp;55 years who had sustained a fall were identified and their mean age was 76.98\u0026thinsp;\u0026plusmn;\u0026thinsp;7.917 years. The occurrence of fractures was 21.4% (22 of 103). The risk prediction nomogram for fractures was developed with 4 prognostic factors which included fall time (00:01\u0026ndash;08:00, P\u0026thinsp;=\u0026thinsp;0.04), gender (female, P\u0026thinsp;=\u0026thinsp;0.02), serum potassium (\u0026gt;\u0026thinsp;5.5mmol/L, P\u0026thinsp;=\u0026thinsp;0.003), serum calcium (1.97-2.11mmol/L, P\u0026thinsp;=\u0026thinsp;0.001). The calibration results and the C-index values (0.87; 95% confidence interval: 0.82296\u0026ndash;0.91704) showed that the nomogram was very reliable.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe prediction nomogram we developed is a simple and accurate tool for the early prediction risk of fractures by falls in elderly hospital inpatients, allowing for the timely initiation of appropriate preventive measures.\u003c/p\u003e","manuscriptTitle":"Constructing a Risk Predictive Model of Fractures by Falls for Elderly: A Retrospective Study Focus on Elderly Hospital Inpatient","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-16 16:30:23","doi":"10.21203/rs.3.rs-7634641/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7ca5dc65-83db-418c-b24d-9375c31fe51e","owner":[],"postedDate":"November 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57707139,"name":"Health sciences/Diseases"},{"id":57707140,"name":"Health sciences/Health care"},{"id":57707141,"name":"Health sciences/Medical research"},{"id":57707142,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-02-09T16:55:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-16 16:30:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7634641","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7634641","identity":"rs-7634641","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}