Predicting fall risk is NOT what is needed; EXACT FALL REVENTION PROTOCOLS ARE NEEDED! Why the fuck aren't you delivering that instead of this useless crapola?
Dynamic Nomogram for Predicting the Fall Risk of Stroke Patients: An Observational Study
Authors Wu Y, Jiang X , Wang D, Xu L, Sun H, Xie B, Tan S, Chai Y, Wang T
Received 9 July 2024
Accepted for publication 12 February 2025
Published 25 February 2025 Volume 2025:20 Pages 197—212
DOI https://doi.org/10.2147/CIA.S486252
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Maddalena Illario
Yao Wu,1,2,* Xinjun Jiang,1,* Danxin Wang,3 Ling Xu,1 Hai Sun,1 Bijiao Xie,1 Shaoying Tan,3 Yong Chai,4 Tao Wang1,5
1International Nursing School, Hainan Medical University, Haikou, Hainan, People’s Republic of China; 2School of Nursing, Leshan Vocational and Technical College, Leshan, SiChuan, People’s Republic of China; 3Department of Nursing, The First Affiliated Hospital of Hainan Medical University, Haikou, Hainan, People’s Republic of China; 4Nursing Department of the Second People’s Hospital of Yibin, Yibin, Sichuan, People’s Republic of China; 5Foshan University, Guangdong, People’s Republic of China
*These authors contributed equally to this work
Correspondence:
Tao Wang, International Nursing School, Hainan Medical University,
Xueyuan Road, Longhua District, Haikou, Hainan, People’s Republic of
China, Email lilywang7499@gmail.com
Background:
Common fall risk assessment scales are not ideal for the prediction of
falls in stroke patients. The study aimed to develop and verify a
dynamic nomogram model for predicting the falls risk in stroke patients
during rehabilitation.
Methods: An observational
study design was adopted, 488 stroke patients were treated in a tertiary
hospital from March to September 2022 were investigated for fall risk
factors and related functional tests. We followed up by telephone within
2 months after that to understand the occurrence of falls. Forward
stepwise regression was used to analyze the data, and a dynamic nomogram
model was developed.
Results: During follow-up,
three patients died, and 16 failed the follow-up, with a failure rate of
3.89%. Among 469 patients, 115 experienced falls, with a fall incidence
rate of 24.4% and a cumulative of 163 falls. The fall risk was higher
among patients aged 60– 69, and ≥ 80 years than among patients aged <
60 years. Patients with a fall history within the last 3 months, or a
Berg balance scale (BBS) score of < 40, or combined with anxiety had a
higher fall risk. The differentiation of the dynamic nomogram model was
evaluated. The area under the receiver operating characteristics curve
(AUC-ROC), sensitivity, specificity of the model was 0.756, 66.09% and
73.16%, respectively. The AUC-ROC of the model was 0.761 by using the
Bootstrap test, and the calibration curve coincided with the diagonal
dashed line with a slope of one. The Hosmer–Lemeshow good of fit test
value was χ²=2.040, and the decision curve analysis showed that the net benefit was higher than that of the two extreme curves.
Conclusion:
Independent fall risk factors in stroke patients are age, had a fall
history within the last 3 months, anxiety, and with the BBS score below
40 during rehabilitation. The dynamic nomogram prediction model for
stroke patients during rehabilitation has good differentiation,
calibration, and clinical utility. The prediction model is simple and
practical.
Keywords: stroke, fall, nomogram, prediction model
Introduction
In 2019, the annual number of new cases, total number of cases, and number of deaths from stroke were 12, 101 million, and 7 million, respectively.1 The mortality caused by stroke rank the second worldwide.2 Moreover, stroke is the leading cause of death and disability among adults in China, with an estimated 17.04 million people aged ≥40 years suffering from stroke. Meanwhile, disability-adjusted life years of stroke are much higher in China than in some developed countries.3
Stroke patients are more prone to falls compared to healthy person, due to reduced walking stability because of their inability to control trunk deflection and peak trunk velocity.4 Approximately 70% of stroke patients experience falls within 6 months after discharge,5 and approximately 27% of stroke patients have experienced recurrent falls within 1 year.6 Falls may not only lead to deterioration of clinical symptoms, increased dependence on care, increased financial burden, and even death,7,8 but they may cause fear of falling (FOF), anxiety, depression, and other adverse consequences.9,10 Studies have shown that the fastest early recovery of stroke patients occurs within 3 months,11 followed by 3–6 months, and recovery can be maximized in 90% of patients within 6 months.12 The adverse consequences of a fall can seriously affect the recovery and prognostic outcomes of patients. Therefore, it is particularly important for stroke patients to prevent fall during recovery.
Fall risk assessment is the first part of fall prevention. At present, although some researchers have used wearable inertial sensors or dual-force platforms to analyze the gait or posture of patients after stroke, so as to predict the fall risk of patients, most of the fall assessment tools used in stroke patients are traditional assessment scales due to high requirements for equipment.13,14 For example, the Morse Fall Scale (MFS),15 Hendrich II Fall Risk Assessment Model (HFRM),16 St Thomas’s Risk Assessment Tool (STRATIFY),17 and John Hopkins Fall Risk Assessment Tool.18 Unfortunately, there are few studies on the predictive effect of HFRM and JHFARAT in the fall risk assessment of stroke patients, MFS and STRATIFY also show poor predictive performance in the fall risk assessment of stroke patients. For example, in China, Xu et al19 retrospectively analyzed 2093 cases using the MFS and found that the MFS alone could not accurately predict fall risk in stroke patients, there was no significant difference in MFS between the fall group and the non-fall group (P>0.05). Smith J et al20 used the STRATIFY to assess the fall risks during rehabilitation in stroke patients; however, the results showed that STRATIFY underestimated the probability of falling in stroke patients.
The dynamic nomogram is an online scoring system generated based on a static column chart, which simplifies the traditional complex prediction-model formula into a visual form and can directly predict the probability of a fall in a patient at a certain point in time. It is fast and simple to operate with a clear interface. Presently, dynamic nomograms are used to predict the risk of various events and prognosis of patients, such as the risk of asthma, neonatal white matter damage, coronavirus-2019, and preoperative and postoperative peripheral lymphocyte difference in patients with hepatitis B virus-related hepatocellular cancer. Although, it is rarely used for fall risk assessment among stroke patients, there are also some nomogram prediction models for the prognosis of stroke patients at home and abroad that show good predictability and practicability, such as depression, mortality, nutritional risk, stroke-associated pneumonia, etc.21–24 Dynamic nomograms can help health professionals predict the individual probability of relevant events in patients.25–28 The dynamic nomogram is helpful for developing personalized measures for the treatment and prognosis of patients.
Summarily, a fall may hinder the rehabilitation progress of stroke patients, and the traditional fall risk assessment scale has a poor prediction effect on fall risk in stroke patient. A dynamic nomogram prediction model for the fall risk has not been developed, and a dynamic nomogram can quickly and intuitively predict fall risk in patients. Thus, the study aimed to develop and verify a dynamic nomogram fall risk prediction model for stroke patients during rehabilitation and provide a basis for the safe management of stroke patients.
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