Predictive validity of the Hendrich fall risk model II in an acute ...

More documents

Recommendations

Info

another geriatric unit of the Teaching Hospital at the end of January and the research protocol could not be followed as previously decided, the study stopped after 8 months, even if a longer survey period had been planned. The patients were screened with HFRM II at the moment of, or at the most within 24 h upon, admission at the geriatric unit by the trained nurses. The patients were followed-up from their admission until their first fall, discharge, death or transfer to another unit, and falls were reported on the appropriate fall incident report form. We defined a fall as ‘‘an event which results in a person coming to rest inadvertently on the ground or floor or other lower level’’ (Hauer et al., 2003). The inter-rater reliability was evaluated in the first period of the research. Two nurses applied consequently and independently the Hendrich II Model to a convenience sample of older patients admitted to the unit during the first 2 weeks of the study. The time of administration was evaluated asking all nurses to estimate the mean time needed to fill in the risk assessment tool after the routine assessment of the patients at the admission. 2.5. Statistic analysis Descriptive statistics were used for demographic data. To measure the reproducibility of results by more than one rater, the inter-rater agreement was calculated using kappa index at 95% CI. The likelihood of older patients to fall was examined creating a 2 2 table to record patients with high and low risk who fell or did not fall and the predictive validity was tested calculating sensitivity, specificity, predictive positive (PPV) and negative values (NPV). The sensitivity was calculated dividing the numbers of patients with high risk scores who fell by the total number of patients who fell, and specificity dividing the numbers of patients with low risk scores who did not fall by the total number of patients who did not experience a fall. The PPV was calculated dividing the number of older patients with high risk score by the total number of patients with high risk scores, while the NPV dividing the number of patients with low-risk scores who did not fall by the number of patients with low risk score. Associated 95% CIs were also reported. A receiver operating characteristic (ROC) analysis was also carried out to examine the relationship between sensitivity and specificity of the tool for different cut points in order to determine the optimal cutoff point. Moreover we calculated the incidence rate ratio during the period of study, and the fall index incidence ratio for days of hospital stay; to evaluate the risk factors that mainly affected the falls we calculated the odds ratio for each risk factor considered in the tool. The data was analyzed using SAS V9.0 for Windows (SAS Inc., Cary, NC, USA) and P < 0.05 was considered statistically significant. 2.6. Ethical considerations We asked the authorization to perform the study to the Teaching Hospital General Manager. The study protocol D. Ivziku et al. / International Journal of Nursing Studies 48 (2011) 468–474 471 was reviewed and approved by the University Hospital Review Board. At admission nurses gave patients, or their relatives if the patients were unable to decide for themselves, a brief description of the aim of the study, its risk and benefits, assuring that, independently of participation consent or tool scores, the best nursing care was going to be provided during their hospital stay. As the main aim of the study was a tool validation and posed only minimal risk for patients only oral consent was requested, as approved by review board. Anonymity and confidentiality was guaranteed, all the patients’ forms were retrieved in the unit daily by principal researcher and stored in a closed box. During the data analysis each patient was identified with a progressive number and only the main researcher could go back to the identification of the patients. 3. Results 3.1. Inter-rater reliability During the first 2 weeks of the study 24 elderly patients were admitted and the inter-rater reliability was estimated on them. The kappa index was 0.87 (95% CI 0.71– 1.00). Disagreements mainly concerned the classification of patients as depressed, the presence of dizziness, and the use of benzodiazepines (used at home, but not during the first 24 h of hospital stay). 3.2. Time of administration The average time needed to collect the data to fill in the tool was about 1 min, as estimated by each nurse: in fact, some information could be retrieved easily from the clinical record or asking the patients or relatives during the nursing assessment at the admission. The time needed to perform the get up and go test was variable, depending on the clinical general conditions of the patients and associated diseases. Sometimes was not performed specifically, because the nurse observed the patients during the examination and how she/he moves in the room. 3.3. Population A total of 179 patients met the inclusion criteria and gave consent to participate during the 8 months of the study. Of the 179 patients, 74 were male and 105 female. The mean age was 79.47 years (SD 9.5), with female older than male (81.7 vs 79.8). 3.4. Validity A total of 14 falls were reported on 179 patients, with a cumulative incidence of 7.8%, and a fall index of 7.5 per 1000 days of hospital stay. The mean score of the HFRM II was 5.78 (SD 3.52, range 0–15). Among the risk factors evaluated by the tool, vertigo (49%), incontinence (48%), depression (46%) and confusion (32%) were the most prevalent risk factors screened on the elderly (Table 1), consistently with the results of other
472 Table 1 Frequency of risk factors on the studied population. Items N % Vertigo 87 49 Incontinence 86 48 Depression 82 46 Sex (male) 74 41 Confusion 58 32 Benzodiazepines 50 28 Get up and go (score 3) 36 20 Antiepileptics 13 0.7 Table 2 Psychometric properties of HFRM II. Values CI 95% Sensitivity 0.86 0.67–1.04 Specificity 0.43 0.34–0.51 PPV 0.11 0.051–0.17 NPV 0.97 0.94–1.01 studies that reported these factors more prevalent in the hospitalized elderly population (Oliver et al., 2004). Of 179 patients included in the study, 106 were classified at risk (HFRM II 5) and 73 not at risk of falling. Of 106 patients classified at risk 12 fell (11%), and of 73 classified not at risk 2 older people fell (2.7%). 87.5% patients fell within the first 10 days of hospitalization, indicating that the elderly recovering from acute illnesses are more frail and subject to fall-prone in the first days of the hospitalization. The mean age of the fallers was 81 (SD 8.5); the ratio of males and females was equal in the fallers (7 females and 7 males) with a quite similar mean age (males 81.14 and females 80.7). The sensitivity of HFRM II was 86% and the specificity 43%. The positive predictive value was 11% identifying 12 patients that fell on 106 at risk patients. The negative predictive value was 97.26% and allowed to identify 71/73 of the patients that did not fall. The 95% confidence intervals showed a narrower range for the specificity (0.34–0.51) and a broader range for sensitivity (0.67–1.04). In Table 2 the psychometric properties of the Hendrich II model are described in detail. The analysis of ROC curve has been carried out to find the best cutoff and best sensitivity and specificity values. The best cutoff point was found for values equal or higher than 5, comparable to that indicated in the development study (Hendrich et al., 2003) and in Kim’s study (Kim et al., 2007). The area under the curve (AUC) was 0.71 (95% CI 0.60– 0.85), showing a moderate discriminating power of the tool (Fig. 1), similarly to the values found in Kim et al.’s study (0.73, 95% CI 67–80). 3.5. Risk factors in hospitalized elderly To evaluate the risk factors that affected the falls in our population we calculated the odds ratio for each risk factor (Table 3). The only two risk factors that were significantly related to falls in our study were confusion (OR: 4.26; 95% CI 1.35– D. Ivziku et al. / International Journal of Nursing Studies 48 (2011) 468–474 13.36) and depression (OR: 3.22; 95% CI 0.97–10.71), consistently with weight attributed to these risk factors in the tool: in fact Hendrich recognized a score of 4 to the confusion and of 2 to the depression. 4. Discussion In the present study the predictive properties as well as reliability and feasibility of the HFRM II were assessed in a geriatric unit of a Teaching Hospital. Our results showed that the time needed to complete the HFRM II was about 1 min, as indicated by Hendrich (2007). The nurses found it easy and quick to use because no additional data and time were needed to assess the risk at the patients admission: the tool could be compiled using the data collected at the moment of admission in the nursing assessment record and observing the patient movements in the room, not increasing the nursing workload, already heavy in geriatric units, and not adding burden to the patient. In acute care hospitals these factors are important if we want that fall risk assessment tools be extensively used in clinical practice, as the time dedicated to the patient assessment is affected by severity of patients admitted to the unit, number of nurses in staff and complexity of nursing administration duties, such as the bulk of nursing records and other documentations to write out (Dempsey, 2004). Compared to Hendrich et al. (2003) and Kim studies (2007), we found higher values of sensitivity (86% vs. 74.9% and 70%) and lower values of specificity (43% vs. 73.9% and 61%) (Table 4). Table 3 Relative risks of the risk factors. Fig. 1. ROC curve. Risk factors OR CI P value Confusion 4.26 1.35–13.36 0.009 Depression 3.22 0.97–10.71 0.03 Incontinence 1.08 0.36–3.24 0.21 Vertigo 0.77 0.25–2.34 0.19 Sex (male) 1.46 0.49–4.36 0.17 Benzodiazepines 1.03 0.30–3.46 0.23 Antiepileptics 0.98 0.11–8.14 0.39 Get up and Go (score 3) 1.95 0.64–5.87 0.10
Page 1 and 2: Predictive validity of the Hendrich
Page 3: 470 developmental study (Hendrich e
Page 7: 474 assessments and interventions i

Predictive validity of the Hendrich fall risk model II in an acute ...

Create successful ePaper yourself

Delete template?

Save as template?