Significant fall risk factors in the personal history of in-patients with neurological dis­ease


Authors: M. Miertová 1;  I. Bóriková 1;  M. Grendár 2;  J. Madleňák 1;  M. Tomagová 1;  K. Žiaková 1
Authors‘ workplace: Ústav ošetrovateľstva, Jesseniova LF UK v Martine, Slovensko 1;  Martinské centrum pre biomedicínu (BioMed), Jesseniova LF UK v Martine, Slovensko 2
Published in: Cesk Slov Neurol N 2019; 82(6): 649-654
Category: Original Paper
doi: 10.14735/amcsnn2019649

Overview

Aim: To identify significant fall risk factors in in-patients with neurological dis­ease and to as­sess their predictive value.

Patients and methods: 298 in-patients were included into the prospective study. Fall risk factors were as­ses­sed through analysis of medical records, and fall risk score was identified through the Morse Fall Scale (MFS) screen­­ing dur­­ing admis­sion to the hospital. A multidimensional logistic regres­sion model was used to identify significant fall risk factors. The relative risk of fal­l­­ing was quantified us­­ing the odds ratio (OR). Receiver operat­­ing characteristic (ROC) curve with area under the curve (AUC) was used to as­sess the predictive value of selected fall risk factors.

Results: The most frequent fall risk factors were in the sample (N = 298): gait, balance and mobility disorders (80.9%), pharmacother­apy (57.0%), associated dis­ease (52.7%), and visual impairment (52.3%). The average fall risk score was at medium risk level (MFS score of 44.2 ± 21.2). The highest risk of fal­l­­ing was seen in risk factors: associated dis­ease (OR = 5.452; CI 1.693– 20.033; P = 0.007), medical dia­gnosis G35– G37 (OR = 4.597, CI 1.273– 17.481; P = 0.021), visual impairment (OR = 3.494; CI 1.281– 10.440; P = 0.019), and fall risk level according to the MFS at admis­sion (OR = 1.18; CI 1.135– 1.252; P < 0.001). The predictive value of risk factors expres­sed by the ROC curve was AUC = 0.934.

Conclusions: Identify­­ing fall risk factors is the first step in ef­fective prevent­­ion of this adverse event dur­­ing hospitalization. Targeted fall risk screen­­ing will al­low plan­n­­ing and implementation of interventions to minimize the risk of fal­ling.

The authors declare they have no potential conflicts of interest concerning drugs, products, or services used in the study.

The Editorial Board declares that the manu­script met the ICMJE “uniform requirements” for biomedical papers.


患有神经系统疾病的患者的个人病史中存在重要的跌倒危险因素

目的:确定患有神经系统疾病的住院患者的重大跌倒危险因素,并评估其预测价值。

患者和方法:298名住院患者纳入前瞻性研究。通过对病历的分析来评估跌倒风险因素,并在入院期间通过莫尔斯跌倒量表(MFS)筛查来确定跌倒风险评分。多维逻辑回归模型用于确定重大的跌倒风险因素。使用比值比(OR)量化跌倒的相对风险。受试者工作特征(ROC)曲线及其下的面积(AUC)用于评估所选跌倒危险因素的预测值。

结果:最常见的跌倒风险因素是样本(N = 298):步态,平衡和活动障碍(80.9%),药物治疗(57.0%),相关疾病(52.7%)和视力障碍(52.3%)。平均跌倒风险评分处于中等风险水平(MFS评分为44.2±21.2)。跌倒的最高风险发生于危险因素:相关疾病(OR = 5.452; CI 1.693-20.033; P = 0.007),医学诊断G35-G37(OR = 4.597,CI 1.273-17.481; P = 0.021),视力障碍(OR = 3.494; CI 1.281– 10.440; P = 0.019),并根据入院时的MFS下降风险水平(OR = 1.18; CI 1.135– 1.252; P <0.001)。 ROC曲线表示的危险因素的预测值为AUC = 0.934。

结论:识别跌倒危险因素是有效预防住院期间这种不良事件的第一步。有针对性的跌倒风险筛查将有助于规划和实施干预措施,以最大程度地降低跌倒的风险。

关键词:跌倒–危险因素–筛查–神经科–患者–住院

Keywords:

fall – risk factor – patient – screening – Neurology – hospitalization


Sources

1. Kobayashi K, Imagama S, Inagaki Y et al. Incidence and characteristics of accidental fal­ls in hospitalizations. Nagoya J Med Sci 2017; 79(3): 291– 298. doi: 10.18999/ nagjms.79.3.291.

2. Krobot A, Kolářová B, Kolář P et al. Neurorehabilitace chůze po cévní mozkové příhodě. Cesk Slov Neurol N 2017; 80/ 113(5): 521– 526. doi: 10.14735/ amcsn­n2017521.

3. Ken­ny RA, Rom­mero-Ortuno R, Kumar P. Fal­ls in older adults. Medicine 2017; 45(1): 28– 33. doi: 10.1016/ j.mpmed.2016.10.007. 

4. Yoo SH, Kim SR, Shin YS. A prediction model of fal­ls for patients with neurological disorder in acute care hospital. J Neurol Sci 2015; 356(1– 2): 113– 117. doi: 10.1016/ j.jns.2015.06.027.

5. Sung YH, Cho MS, Kwon IG et al. Evaluation of fal­lsby inpatients in acute care hospital in Korea us­­ing the Morse Fall Scale. Int J Nurs Pract 2014; 20(5): 510– 517. doi: 10.1111/ ijn.12192.

6. Bouldin ER, Andresen EM, Dunton NE et al. Fal­ls among adult patients hospitalized in the United States: prevalence and trends. J Patient Saf 2013; 9(1): 13– 17. doi: 10.1097/ PTS.0b013e3182699b64.

7. Hunderfund AN, Sweeney CM, Mandrekar JN et al. Ef­fect of multidisciplinary fall risk as­ses­sment on fal­ls among neurology inpatients. Mayo Clin Proc 2011; 86(1): 19– 24. doi: 10.4065/ mcp.2010.0441.

8. Zhao YL, Kim H. Older adult inpatient fal­ls in acute care hospitals: intrinsic, extrinsic, and environmental factors. J Gerontol Nurs 2015; 41(7): 29– 43. doi: 10.3928/ 00989134-20150616-05.

9. Camicioli R. Fal­ls in ag­­ing and neurological dis­ease. In: Albert ML, Knoefel JE (eds). Clinical neurology of aging. 3rd ed. New York: Oxford University Press 2011: 297– 313.

10. Tan KM, Tan MP. Stroke and fal­ls –  clash of the two titans in geriatrics. Geriatrics (Basel) 2016; 1(31): 1– 15. doi: 10.3390/ geriatrics1040031.

11. Rudzińska M, Bukowczan S, Stožek J et al. The incidence and risk factors of fal­ls in Parkinson’s dis­ease: prospective study. Neurol Neurochir Pol 2013; 47(5): 431– 437. doi: 10.5114/ ninp.2013.38223.

12. Valkovič P, Košutzká Z, Schmidt F. Posturálna instabi­lita, poruchy chôdze a pády pri Parkinsonovej chorobe. Cesk Slov Neurol N 2012; 75/ 108(2): 141– 153. 

13. Al­len NE, Sschwarzel AK, Can­n­­ing CG. Recur­rent fal­lsin Parkinson’s dis­ease: a systematic review. Parkinsons Dis 2013; 2013: 906274. doi: 10.1155/ 2013/ 906274.

14. Mazunder R, Murchison CH, Bourdette D et al. Fal­lsin people with multiple sclerosis compared with fal­lsin healthy controls. PLoS One 2014; 9(9): e107620. doi: 10.1371/ journal.pone.0107620.

15. Prevence pádů ve zdravotnickém zařízení. Cesta k dokonalosti a zvyšování kvality. Praha: GRADA Publish­­ing 2007: 172.

16. Remor CP, Cruz CB, Urbanetti JS. Analysis of fall risk factors in adults within the first 48 hours of hospitalization. Rev Gaucha Enferm 2014; 35(4): 28– 34. doi: 10.1590/ 1983-1447.2014.04.50716.

17. Renfro M, Mar­­ing J, Bainbridge D et al. Fall risk among older adult high-risk populations: a review of cur­rent screen­­ing and as­ses­sment tools. Curr Geri Rep 2016; 5(3): 160– 171. 

18. Han J, Xu L, Zhou CH et al. Stratify, Hendrich II fall risk model and Morse Fall Scale used in predict­­ing the risk of fal­l­­ing for elderly in-patients. Biomed Res 2017; 28 (special is­sue): S439– S442. 

19. Nas­sar N, Helou N, Madi CH. Predict­­ing fal­lsus­­ing two instruments (The Hendrich Fall Risk Scale and The Morse Fall Scale) in an Acute Care Sett­­ing in Lebanon. J Clin Nurs 2014; 23(11– 12): 1620– 1629. doi: 10.1111/ jocn.12278.

20. Sardo PM, Simões CS, Alvarelhão JJ et al. Fall risk as­ses­s­­-ment: retrospective analysis of Morse Fall Scale scores in Portuguese hospitalized adult patients. Appl Nurs Res 2016; 31: 34– 40. doi: 10.1016/ j.apnr.2015.11.013.

21. Gu YY, Balcaen K, Ni Y et al. Review on prevention of fal­ls in hospital settings. Chin Nurs Res 2016; 3(1): 7– 10. doi: 10.1016/ j.cnre.2015.11.002.

22. Bradley SM, Karani R, McGinn T et al. Predictors of serious injury among hospitalized patients evaluated for fal­ls. J Hosp Med 2010; 5(2): 63– 68. doi: 10.1002/ jhm.555.

23. Morse J. Prevent­­ing patient fal­ls. Establish­­ing a Fall Intervention Program. 2nd ed. New York: Springer Publish­­ing Company, LLC 2009.

24. Miake-Lye IM, Hempel S, Ganz DA et al. Inpatient fall prevention programs as a patient safety strategy. A systematic review. Ann Intern Med 2013; 158 (5 Pt 2): 390– 396. doi: 10.7326/ 0003-4819-158-5-201303051-00005.

25. Cumbler EU, Simpson JR, Rosenthal LD et al. Inpatient fal­ls: defin­­ing the problem and identify­­ing pos­sible solution. Part I: an evidence-based review. Neurohospitalist 2013; 3(3): 135– 143. doi: 10.1177/ 1941874412470665.

26. Kim KS, Kim JA, Choi YK et al. A comparative study of the validity of fall risk as­ses­sment scales in Korean hospitals. Asian Nurs Res (Korean Soc Nurs Sci) 2011; 5(1): 28– 37. doi: 10.1016/ S1976-1317(11)60011-X.

27. Morse JM, Morse RM, Tylko SJ. Development of a scale to identify the fal­l-prone patient. Canadian J Ag­­ing 1989; 8(4): 366– 377. doi: 10.1017/ S0714980800008576.

28. R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing. Vien­na: Austria 2018. [online]. Available from URL: https:/ / www.R-project.org/ .

29. Venables WN, Ripley BD. Modern Applied Statistics with S. 4th ed. New York: Springer 2002.

30. Fal­ls in older people: as­ses­s­­ing risk and prevention. NICE Clinical Guideline 161 (reviewed). Developed by the Centre for Clinical Practice at NICE, 2018. 31 p. [online]. Available from URL: https:/ / www.nice.org.uk/ guidance/ cg161/ chapter/ about-this-guideline.

31. Gunn H, Creanor S, Haas B et al. Risk factors for fall in multiple sclerosis: an observational study. Mult Scler 2013; 19(14): 1913– 1922. doi: 10.1177/ 1352458513488233.

32. Bednařík J, Ambler Z, Růžička E et al. Klinická neurologie –  část speciální I. Praha: TRITON 2010: 707.

33. Lunsford B, Wilson LD. As­ses­s­­ing your patients risk for fal­ling. American Nurse Today 2015; 10(7): 29– 31. 

34. Non­nekes J, Goselink RJ, Růžička E et al. Neurological disorders of gait, balance and posture: a sign-based approach. Nat Rev Neurol 2018; 14(3): 183– 189. doi: 10.1038/ nrneurol.2017.178.

35. Mion LC, Chandler AM, Waters TM et al. Is it pos­sible to identify risks for injurious fal­ls in hospital patients? Jt Comm J Qual Patient Saf 2012; 38(9): 408– 413. 

36. Gray-Miceli D, Quigley PA. Fal­ls prevention: as­ses­sment, dia­gnoses, and intervention strategies. In: Boltz M et al (eds). Evidence-based geriatric Nurs­­ing Protocols for Best Practice. 4th ed. New York: SpringerPublish­­ing Company 2012: 268– 297.

37. Marshall FJ. Approach to the elderly patient with gait disturbance. Neurol Clin Pract 2012; 2(2): 103– 111. doi: 10.1212/ CPJ.0b013e31825a7823.

38. Fehlberg EA, Lucero RJ, Weaver MT et al. As­sociations between hypernatremia, volume depletion and the risk of fal­ls in US hospitalised patients: a case-control study. BMJ Open 2017; 7(8): e017045. doi: 10.1136/ bmjopen-2017-017045.

39. Guil­laume D, Crawford S, Quigley P. Characteristics of the middle-age adult inpatient fal­l. Appl Nurs Res 2016; 31: 65– 71. doi: 10.1016/ j.apnr.2016.01.003.

40. Krasulová E, Blahová Dušánková J, Havrdová E. Roz­-troušená skleróza –  psychoneuroimunolo­gické onemoc­nění centrálního nervového systému. Psychiatr Prax 2009; 10(3): 121– 125.

41. Kurčová S, Menšíková K, Kaiserová M et al. Pre-motorické a non-motorické príznaky Parkinsonovej choroby –  taxonómia, klinická manifestácia a neuropatologické koreláty. Cesk Slov Neurol N 2016; 79/ 122(3): 255– 270. doi: 10.14735/ amcsn­n2016255.

42. Custodio N, Lira D, Her­rera-Perez E et al. Predictive model for fal­l­­ing in Parkinson dis­ease patients. eNeurological Sci 2016; 5: 20– 24. doi: 10.1016/ j.ensci.2016.11.003.

43. Dušek L, Pavlík T, Jarkovský J et al. Analýza dát v neurologii –  XXVIII. Hodnocení dia­gnostických testů –  křivky ROC. Cesk Slov Neurol N 2011; 74/ 107(4): 493– 499.

44. Pokorná A, Búřilová P, Šrombachová V et al. Centrální systém hlášení nežádoucích událostí –  Metodika nežádoucí událost PÁD. Plná verze metodiky 1/ 2017. Praha: Ústav zdravotnických informací a statistiky ČR 2017: 40.

45. Bittencourt VL, Graube SL, Stumm EM et al. Factors as­sociated with the risk of fal­ls in hospitalized adult patients. Rev Esc Enferm USP 2017; 51: e03237. doi: 10.1590/ S1980-220X2016037403237.

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Paediatric neurology Neurosurgery Neurology

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