Falls are accidents wherein a part of the body inadvertently falls or descends to a lower position than its current position. According to the World Health Organization fact sheet reported in 2021, an estimated 684,000 fatal falls occur annually, making it the second leading cause of unintentional injury-related death after road traffic injuries. In addition, more than 80% of fall-related fatalities occur in low- and middle-income countries, with regions of the Western Pacific and South East Asia accounting for 60% of these deaths. Globally, mortality rates are the highest among adults older than 60 years.

Although not fatal, approximately 37.3 million severe falls requiring medical attention occur yearly.1 In the “Statistics of Korea Patient Safety Reporting & Learning System (KOPS),” 27,798 cases (45.8% of 60,567 cases) of safety accidents from July 2016 to July 2022 involved falls.2 Falls can cause varying degrees of damage ranging from scratches to death and can result in economic losses, such as additional medical expenses, extended hospital stays, and lawsuits due to medical negligence.3,4

Although falls can occur in any context or setting, hospital falls have been frequently reported and may have severe consequences for inpatients. After examining the electronic medical records of 1216 fall patients, Yoon et al5 reported that night shift hours, hospital rooms, department of internal medicine, and age of older than 71 years were risk factors for falls.6 The additional risk factors of falls in previous studies were reported as disorientation, frequency of urination, gait restriction, loss of endurance, and amount of medication administered by the caregiver within 72 hours before falling.7,8 Factors vary according to the individual patient’s environment, personnel, and hospital culture; therefore, understanding these factors helps in decision making regarding patient safety.9 In a previous study of fall risk factors in older patients admitted to a general hospital, including 360 fall and nonfall patients each, fall experience and gait disturbance were considered significant using the Johns Hopkins fall risk assessment tool (JHH).10

Therefore, prevention of hospital falls requires multifaceted evaluation of risk factors, customized intervention, and efforts by the entire medical institution, including nurses who play a key role in managing patients.8,11 Fall risk assessment tools are the earliest and most important strategies for fall prevention.12–14 The Morse Fall Scale, St Thomas’s Risk Assessment Tool in Falling Early Infants, and Hendrich II Fall Risk Model are the most reliable tools for predicting the risk of falling in patients.15,16 Although nurses regularly assess falls using internationally verified fall assessment tools, fall accidents continue to occur in clinical areas. Once a patient’s clinical data becomes unstable, the laboratory results are reported, or a nurse records the observations in a clinical flow sheet and nursing initial assessment record. Immediately fall risk factors are discovered, nurses automatically raise awareness of the risks of falls from patients’ electronic medical records. Therefore, it would also be particularly useful for nurses to provide fall care without assessing the risk of falls.

Risk factor analyses of various diseases and the proposal of an automated fall risk prediction model to prevent safety accidents (such as falls) have been reported with the initiation of the Clinical Data Warehouse (CDW). Particularly, it was stated that the predictive validity after the clinical application of the completed automated detection system of fall risk would help reduce the burdens on nurses.17–19

Therefore, this study aimed to identify risk factors for inpatient falls using the CDW analysis.

METHODS Study Setting and Data Collection

In this retrospective case-control study, we explored fall risk factors by analyzing the electronic medical records of fall and nonfall patients in 2017. Data were collected from June 1, 2018 to November 30, 2018, after obtaining approval from the institutional review board of Seoul National University Hospital (SNUH) (IRB no.: H-1804-154-942). The CDW system used in the hospital where this study was conducted was the SNUH Patient Research Environment (SUPREME). Based on the hospital research system, the fall and nonfall groups’ medical records (laboratory reports, clinical flow sheet, and nursing initial assessment) were retrospectively investigated to explore fall risk factors.

Patient Categories

This study’s participants were patients 15 years or older who were admitted to the hospital between January 1, 2017, and December 31, 2017; they were categorized into fall and control groups. Overall, 292 patients in the fall group were reported to the quality and patient safety team, which is the patient safety management system of the hospital, due to falls during hospitalization. The control group included 1168 patients (a multiple of 4) by pairing department, age, and sex with the fall group. The 1:4 selection ratio of the fall and control groups was selected based on a previous study.19

Patients’ data were obtained using the hospital’s 2.0 version of the CDW—which enabled advanced search scenarios, time series data search, basic visualization of patient distribution, privacy through the IRB system, and sharing of a web-based platform in 2018. User rights were automatically granted access to full-time doctors and nursing managers of hospitals for advanced clinical research. The collected data included all items of laboratory results, including blood tests and imaging, and those of the clinical flow sheet and nursing initial assessment, such as general characteristics and patient information.

In-Hospital Research Search System: Clinical Data Warehouse

The medical records of patients in the fall and control groups were collected through the CDW system, including data regarding fall risk factors determined using the laboratory reports, clinical flow sheet, and nursing initial assessment. When using the CDW to establish the control group, age was selected based on the birth year. If less than 4 participants were selected, the range was expanded around the birth year, whereas the department of primary diagnosis was selected if a patient’s department was changed. In addition, if the control group contained many patients, those with complete data were selected, including hospitalization date, discharge date, and diagnosis. After analyzing the distribution of fall dates in the fall group, 179 of 292 cases were confirmed to have occurred 1 to 2 days before discharge. Finally, the laboratory results and clinical flow sheets were extracted from 2 days before discharge, while the nursing initial assessment on admission was collected at hospitalization.

Statistical Analysis

Data were analyzed using SAS version 9.4 (SAS Institute, Inc, Cary, NC). All extracted data were classified into continuous, descriptive, and categorical types. Among the extracted data, continuous variables are presented as mean and standard deviation (e.g., blood pressure, pulse, hemoglobin value, among others), whereas data of variables having a descriptive formula (e.g., computerized tomography [CT] and electrocardiography) are presented as frequency and percentage. In addition, the preexisting category was used when the result was expressed as a categorical variable in the electronic medical record (e.g., body mass index [BMI] and communication level). Therefore, to extensively explore factors affecting the occurrence of falls, this study primarily included analysis with logistic regression using univariate analysis.

Ethical Considerations

The bioethics review committee of Seoul National University Hospital in the Republic of Korea (IRB no. H-1804-154-942) approved this study, which focused on developing a fall risk prediction tool for inpatients. In addition, the requirement for informed consent was waived because of the study’s retrospective nature. All researchers accessed the data after receiving training on research ethics. The CDW system in the hospital can be accessed by one research director and co-researchers with IRB approval to extract data. Specifically, personal information identifying the patients was excluded from the extracted data, and only data necessary for analysis were obtained.

RESULTS General Characteristics of Participants

Table 1 shows the general characteristics of the participants. The average age of patients in the fall and control groups was 64.4 ± 1 and 64.1 ± 2 years, respectively, whereas 54% of the cohort were male. Among 292 falls, 171 (58.7%) and 73 (25.0%) falls occurred in the internal medicine department and hemato-oncology, respectively, demonstrating the highest incidence rates. This was followed by 33 (11.3%), 26 (23.9%), and 16 (5.5%) falls in the gastroenterology, general surgery, and neurosurgery department, respectively.

TABLE 1 - General and Clinical Characteristics of Study Participants (N = 1460) Characteristics Fall Group (n = 292) Control Group (n = 1168) Mean ± SD or n (%) Age, y 64.40 ± 14.90 64.10 ± 15.20 Sex  F 134 (45.9) 536 (45.9)  M 158 (54.1) 632 (54.1) Department (primary diagnosis)  Internal medicine 171 (58.7) 684 (58.7)  Gastroenterology 33 (11.3) 132 (11.3)  Infectious diseases 2 (0.7) 8 (0.7)  Internal medicine (hospitalist) 16 (5.5) 64 (5.5)  Endocrinology 1 (0.3) 4 (0.3)  Cardiology 14 (4.8) 56 (4.8)  Nephrology 18 (6.2) 72 (6.2)  Hemato-oncology 73 (25.0) 292 (25.0)  Pulmonary 14 (4.8) 56 (4.8)  Surgery 70 (23.9) 272 (23.8)  Plastic and reconstructive 1 (0.3) 4 (0.3)  Neurosurgery 16 (5.5) 64 (5.5)  General surgery 26 (8.9) 104 (8.9)  Breast cancer center 3 (1.0) 12 (1.0)  Orthopedic 11 (3.8) 44 (3.8)  Thoracic and cardiovascular 13 (4.5) 44 (3.8)  Urology 4 (1.4) 16 (1.4)  Obstetrics and gynecology 10 (3.4) 40 (3.4)  Neurology 15 (5.1) 60 (5.1)  Emergency medicine 10 (3.4) 48 (4.1)  Otorhinolaryngology 5 (1.7) 20 (1.7)  Rehabilitation medicine 2 (0.7) 8 (0.7)  Neuropsychiatry 5 (1.7) 20 (1.7)
Risk Factors for Falls Laboratory Reports

Overall, 65 laboratory results were statistically significant in this study. Table 2 shows the significant variables among the tests where the laboratory results were presented as continuous variables. As the microalbumin/creatinine ratio in urine increased by 1, the fall occurrence rate decreased by 2.635 times. In contrast, the fall occurrence rate decreased by 1.903 times as the potassium concentration in the blood increased by 1. In addition, the levels of basophils (odds ratio [OR], 1.398; 95% confidence interval [CI], 1.030–1.898), fasting blood sugar (OR, 1.386; 95% CI, 1.042–1.844), phosphorus (OR, 1.299; 95% CI, 1.144–1.476), and calcium (OR, 1.261; 95% CI, 1.022–1.556) were statistically significant. As the red blood cell count and albumin level increased by 1, the fall occurrence rates decreased by 0.681 and 0.752, respectively. Many patients with severe oncology were hospitalized, and urinalysis was conducted 24 hours daily before chemotherapy. This outcome seemed to have occurred because the patients had abnormal levels of potassium or creatinine related to kidney function. Furthermore, the results of the electrolyte level, which is highly related to the high fall, seem to have been affected by the high rates of falls among oncology patients.

TABLE 2 - Results of the Univariate Analysis: Laboratory Findings (Continuous Variables) Items Fall Group Control Group 95% CI Mean ± SD LL UL OR P ANC 6522.24 ± 7374.60 5280.04 ± 5080.36 1.01 1.06 1.03 0.003 Albumin 3.37 ± 0.75 3.49 ± 0.61 0.61 0.92 0.75 0.006 Alkaline phosphatase 137.32 ± 133.63 100.77 ± 92.70 1.02 1.04 1.03 <0.001 BUN 26.38 ± 21.03 19.92 ± 16.31 1.01 1.03 1.02 <0.001 Basophil 0.63 ± 0.51 0.57 ± 0.39 1.03 1.90 1.40 0.032 Bilirubin, total 1.97 ± 4.69 1.06 ± 2.22 1.04 1.13 1.09 <0.001 CO2, total (serum) 24.01 ± 5.13 24.84 ± 3.91 0.93 0.99 0.96 0.010 Calcium 8.73 ± 0.70 8.63 ± 0.63 1.02 1.56 1.26 0.031 Eosinophil 2.27 ± 2.33 2.93 ± 2.73 0.84 0.95 0.89 <0.001 FBS (serum) 139.64 ± 58.51 98.64 ± 15.76 1.04 1.84 1.39 0.025 Fibrinogen 388.25 ± 131.75 362.44 ± 117.73 1.00 1.03 1.02 0.012 Folate 8.95 ± 5.27 6.04 ± 2.45 1.04 1.35 1.19 0.010 Glucose 135.1 ± 59.12 120.64 ± 46.45 1.03 1.08 1.05 <0.001 Hb 10.88 ± 1.88 11.23 ± 1.97 0.85 0.98 0.91 0.010 Hct 32.89 ± 5.97 34.29 ± 5.61 0.94 0.98 0.96 <0.001 Iron saturation 38.66 ± 25.86 27.73 ± 20.50 1.00 1.04 1.02 0.033 MPV 10 ± 1.44 10.38 ± 1.03 0.660 0.84 0.75 <0.001 Metamyelocyte 1.95 ± 2.20 2.89 ± 4.35 0.803 0.10 0.90 0.046 Microalbumin/creatinine ratio 1.97 ± 1.28 0.24 ± 0.60 1.21 5.76 2.64 0.015 O2CT 12.61 ± 3.59 13.68 ± 4.13 0.86 1.00 0.93 0.051 O2SAT (VBGA) 74.01 ± 16.32 58.39 ± 18.27 1.00 1.10 1.05 0.038 PCT 0.2 ± 0.10 0.23 ± 0.11 0.01 0.22 0.05 <0.001 PDW 12.53 ± 3.07 11.71 ± 2.34 1.07 1.18 1.13 <0.001 PT, % 85.99 ± 27.41 90.81 ± 22.75 0.87 0.98 0.92 0.012 PT, s 14.5 ± 7.67 13.1 ± 4.38 1.01 1.07 1.04 0.003 Phosphorus 3.7 ± 1.28 3.41 ± 0.90 1.14 1.48 1.30 <0.001 Potassium 4.35 ± 0.67 4.14 ± 0.50 1.50 2.42 1.90 <0.001 Protein (BF except CSF) 2.25 ± 1.39 3.3 ± 2.25 0.51 0.98 0.70 0.036 RBC 3.53 ± 0.69 3.7 ± 0.67 0.56 0.83 0.68 <0.001 RDW 15.94 ± 3.16 14.42 ± 2.50 1.15 1.26 1.20 <0.001 T3 60.46 ± 18.81 76.05 ± 16.66 0.91 0.10 0.95 0.028 Triglyceride 134.96 ± 68.39 98.93 ± 43.58 1.05 1.20 1.12 0.002 Uric acid 5.45 ± 2.47 4.77 ± 2.03 1.08 1.22 1.15 <0.001 WBC 135.15 ± 229.34 679.56 ± 975.34 0.70 0.96 0.82 0.015 eGFR 80.84 ± 50.52 89.52 ± 47.61 0.93 0.99 0.96 0.009 hs-CRP quantitation 4.3 ± 6.64 3.45 ± 4.80 1.00 1.05 1.03 0.031 pco 2 37.09 ± 9.93 42.89 ± 10.92 0.91 0.98 0.94 0.001 po 2 107.89 ± 57.08 91.08 ± 44.67 1.008 1.13 1.07 0.027 po 2 (VBGA) 47.87 ± 14.36 36.66 ± 9.79 0.999 1.17 1.08 0.052

The significant variables are displayed in this table.

ANC, absolute neutrophil count; BUN, serum urea nitrogen; eGFR, estimated glomerular filtration rate; FBS, fasting blood sugar; Hb, hemoglobin; Hct, hematocrit; hs-CRP quantitation, high sensitivity C-reactive protein quantitation; LL, lower limit; MPV, mean platelet volume; O2CT, oxygen content; O2SAT (VBGA), oxygen saturation (venous blood gas analysis); PCT, procalcitonine; PDW, platelet distribution width; pco2, partial pressure of carbon dioxide; po2, partial pressure of oxygen; po2 (VBGA), partial pressure of oxygen (venous blood gas analysis); Protein (BF except CSF), protein (body fluid except cerebrospinal fluid); PT, prothrombin time; RBC, red blood cell count; RDW, red cell distribution width; T3, triiodothyronine; UL, upper limit; WBC, white blood cell count.

As shown in Table 3, patients who requested potassium, protein, and creatinine levels exam through the 24-hour urinalysis had increased risk of falls by 12.85, 30.56, and 31.38 times, respectively. In addition, significant differences were found between the genital (OR, 14.12; 95% CI, 10.00–19.93) and respiratory (OR, 9.52; 95% CI, 6.74–13.43) specific cultures and the abdominal CT (OR, 9.47; 95% CI, 6.82–13.16) and blood culture (OR, 9.15; 95% CI, 6.73–12.44) results.

TABLE 3 - Results of the Univariate Analysis: Laboratory Findings (Binary Variables) Items Fall Group Control Group 95% CI Yes No Yes No LL UL OR P n (%) HBs Ag 67 (22.9) 225 (77.1) 96 (8.2) 1072 (91.8) 2.36 4.69 3.33 <0.001 Bacteria 137 (46.9) 155 (53.1) 95 (8.1) 1073 (91.9) 7.31 13.63 9.98 <0.001 Anti-HCV 55 (18.8) 237 (81.2) 90 (7.7) 1078 (92.3) 1.93 4.00 2.78 <0.001 Creatinine (24-h urine) 289 (99.0) 3 (1.0) 881 (75.4) 287 (24.6) 9.98 98.64 31.38 <0.001 Echocardiography 74 (25.3) 218 (74.7) 87 (7.4) 1081 (92.6) 3.00 5.94 4.22 <0.001 RPR (auto) 35 (12.0) 257 (88.0) 86 (7.4) 1082 (92.6) 1.13 2.60 1.71 0.011 Protein (24-h urine) 288 (98.6) 4 (1.4) 820 (70.2) 348 (29.8) 11.30 82.62 30.56 <0.001 CT (abdomen) 117 (40.1) 175 (59.9) 77 (6.6) 1091 (93.4) 6.82 13.16 9.47 <0.001 Respiratory specimen culture 106 (36.3) 186 (63.7) 66 (5.7) 1102 (94.3) 6.74 13.43 9.52 <0.001 Genitourinary specimen culture 129 (44.2) 163 (55.8) 62 (5.3) 1106 (94.7) 10.00 19.93 14.12 <0.001 X-ray (chest) 259 (88.7) 33 (11.3) 605 (51.8) 563 (48.2) 5.00 10.68 7.30 <0.001 Casts (urine) 126 (43.2) 166 (56.8) 57 (4.9) 1111 (95.1) 10.40 21.05 14.79 <0.001 Potassium (24-h urine) 116 (39.7) 176 (60.3) 57 (4.9) 1111 (95.1) 9.01