wk4article1.pdf

RESEARCH ARTICLE Open Access

Fall prevention: is the STRATIFY tool the
right instrument in Italian Hospital
inpatient? A retrospective observational
study
Greta Castellini1,2* , Antonia Demarchi3, Monica Lanzoni3 and Silvana Castaldi1,3

Abstract

Background: Although several risk assessment tools are in use, uncertainties on their accuracy in detecting fall risk
already exist. Choosing the most accurate tool for hospital inpatient is still a challenge for the organizations. We
aimed to retrospectively assess the appropriateness of a fall risk prevention program with the STRATIFY assessment
tool in detecting acute-care inpatient fall risk.

Methods: Number of falls and near falls, occurred from January 2014 to March 2015, was collected through the
incident reporting web-system implemented in the hospital’s intranet. We reported whether the fall risk was assessed
with the STRATIFY assessment tool and, if so, which was the judgement. Primary outcome was the proportion of
inpatients identified as high risk of fall among inpatients who fell (True Positive Rate), and the proportion of inpatients
identified as low-risk that experienced a fall howsoever (False Negative Rate). Characteristics of population and fall
events were described among subgroups of low risk and high risk inpatients.

Results: We collected 365 incident reports from 40 hospital units, 349 (95.6%) were real falls and 16 (4.4%) were near
falls. The fall risk assessment score at patient’s admission had been reported in 289 (79%) of the overall incident reports.
Thus, 74 (20.3%) fallers were actually not assessed with the STRATIFY, even though the majority of them presented risk
recommended to be assessed. The True Positive Rate was 35.6% (n = 101, 95% CI 30% – 41.1%). The False Negative
Rate was 64.4% (n = 183, 95% CI 58.9%–70%) of fallers, nevertheless they incurred in a fall. The STRATIFY mean score
was 1.3 ± 1.4; the median was 1 (IQQ 0–2).

Conclusions: The prevention program using only the STRATIFY tool was found to be not adequate to screen our
inpatients population. The incorrect identification of patients’ needs leads to allocate resources to erroneous priorities
and to untargeted interventions, decreasing healthcare performance and quality.

Keywords: Accidental falls, Incident reporting, Patient safety, Risk assessment tools

Background
Falls and falls related injuries are a significant public health
issue. A fall is defined as “an event which results in a person
coming to rest inadvertently on the ground or floor or
other lower level”, [1]. The accidental fall is the most com-
mon adverse event among hospital inpatients and it occurs

from 0.3–19 per 1000 patient-days, [2, 3]. The National In-
stitute for Health and Care Excellence (NICE) has reported
nearly 209.000 falls between 1 October 2011 and 30 Sep-
tember 2012 in England with a relative cost of £2.3 billion a
year, [4]. The fall burden is remarkable: injuries, fractures,
anxiety, depression are some of the individual and social fall
consequences, [5]. Death, for instance, is the most common
unintentionally injury due to a fall in people over 65 years,
[6]. Due to significant physical, psychological and socio-
economic costs, fall prevention has been recognized as a
fundamental process for health care interventions. The

* Correspondence: [email protected]
1Department of Biomedical Sciences for Health, University of Milan, Via
Pascal, 36, 20133 Milan, Italy
2Unit of Clinical Epidemiology, IRCCS Istitute Orthopedic Galeazzi, Milan, Italy
Full list of author information is available at the end of the article

© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Castellini et al. BMC Health Services Research (2017) 17:656
DOI 10.1186/s12913-017-2583-7

http://crossmark.crossref.org/dialog/?doi=10.1186/s12913-017-2583-7&domain=pdf

http://orcid.org/0000-0002-3345-8187

mailto:[email protected]

http://creativecommons.org/licenses/by/4.0/

http://creativecommons.org/publicdomain/zero/1.0/

Joint Commission International has defined the reduction
of patient harm for falls as an indicator of care quality in a
hospital and a National Patient Safety Goal Standard, [7].
One of the common strategies in the fall reduction process
is to use a fall risk assessment tool to identify the popula-
tion at risk of fall, [8]. Therefore, it seems important to as-
sess risk of falling using ad hoc tools and to adopt the
perfect strategy to implement them into the organizational
routine. The choice of the most adequate and adaptable
tool for hospital inpatient population is always a challenge
for the organizations. Several studies have assessed the ac-
curacy of the existing risk assessment tools however, strong
evidence does not still exist and doubts on their usefulness
are real, [3, 9].
A frequently applied tool in hospitals prevention pro-

grams, mainly with old inpatients, is the St. Thomas Risk
Assessment Tool in Falling elderly inpatients (STRATIFY),
[10]. This tool comprises five items addressing risk factors:
past history of falling, patient agitation, visual impairment,
incontinence, transfer and mobility, [11]. The STRATIFY
score range from 0 to 5 points and the predictive cut off of
risk of falling is a score ≥ 2 points. Oliver et al. [11] identi-
fied that sensibility and specificity were, respectively, 92.4%
and 68.3% in the acute and rehabilitation settings, whereas
some authors declared that the STRATIFY’s predictive pro-
prieties fit best medical inpatients younger than 65 years old
and it’s unsuccessful in more elderly inpatients, [10]. This
tool has been object of studies performed in several settings
with deluding findings about its usefulness in screening
faller than non-fallers, [3, 12]. For instance, the STRATIFY
has been considered as not the best tool for screening in
traumatic brain injury rehabilitation as well its accuracy has
been doubted in the acute geriatric inpatient population,
[13, 14]. Due to lack of demonstrable efficacy, limitations
on the applicability of the instrument exist, [12, 15]. Never-
theless, the STRATIFY is suggested to be use in several hos-
pitals as, for example, a regional guideline based on Italian
Health Ministry’ recommendation proposed [16, 17].
Using an inaccurate tool may develop an untrue sense

of safety in both patients and healthcare personnel. It
can leave patient exposed to fall risk and to the potential
adverse effects of falling.
A recent observational study compared the effectiveness

of two assessment tools (Conley Scale and Hendrich Risk
Model) in an Italian hospital but the evaluation was limited
only to three inpatients populations, [9]. We aimed to
retrospectively assess whether the STRATIFY risk assess-
ment tool, implemented in a research and teaching hospital
in North Italy, was appropriately able to detect inpatients
fallers as at high risk and, consequently, to report all general
characteristics, intrinsic and extrinsic factors related to the
sample of fallers obtained. To our knowledge, this study is
the first in the Italian health care system that retrospectively
evaluates whether a preventive program with a known fall

risk assessment tool adequately detects all hospital inpatient
population who fell. The magnitude of the problem of falls
is recognized by researchers and the public health commu-
nity as a growing epidemic [18]. As a consequence, focusing
on how risk of falling can be detected, limited and managed
in hospitals seems to be crucial.

Methods
Study setting and data collection
The study was conducted in a research and teaching hos-
pital in a metropolitan area in the North part of Italy: Fon-
dazione IRCCS Ca′ Granda – Ospedale Maggiore
Policlinico, Milan. The Fondazione is a research and teach-
ing hospital with three emergency units (adult, paediatric
and obstetric), kidney, liver, lung, cornea and bone marrow
transplant centres, a Medical School, several post-graduate
schools and some 3 year courses for healthcare providers of
the Faculty of Medicine and Surgery of the University of
Milan. It also hosts a training centre for postgraduate
courses and first and second level Master’s Degrees.
A new incident reporting database has been imple-

mented in the hospital organization in 2014 [19]. The
reporting of the fall event is recorded contextual to the
event through an online template form. The incident
reporting form collects the following general information:
patient information (age, sex, type of hospital admission),
clinical details (diagnosis, pharmacological therapy, fall
risk assessment score) and fall event details (date, setting,
consequences, causes, risk factors).
The fall risk assessment had to be performed through

the STRATIFY tool at the time of hospitalization’s admis-
sion for patient older than 75 years in all units and for all
patients in Neurology and Neuro-surgery units. Over the
years, it has been recorded a higher number of fallers in
Neurology and Neurosurgery units therefore it has been
reinforced the fall prevention strategy accordingly, asses-
sing all inpatients irrespectively of the age. Moreover, the
fall risk assessment was strongly recommended for all in-
patients showing recent fall history, dementia, hip frac-
ture, diabetes type II, Parkinson’s diseases, arthritis,
depression, functional limitations with need of aids, al-
tered state of consciousness or cognitive dis s, visual
and balance deficits, assumption of more than three drugs,
incontinence, physical restraints, and osteoporosis.
The database was retrospectively searched for the events

occurred between January 2014 and March 2015. All data
in the database were anonymized so neither individual pa-
tient consent nor ethical approval were required.
Since we had access exclusively to data included in the

web incident reporting database, only data of patients
who experienced a fall, admitted from January 2014 to
March 2015, were available. Thus, data were limited to
this population. Falls occurring in all units of the hos-
pital were included in data collection.

Castellini et al. BMC Health Services Research (2017) 17:656 Page 2 of 7

Outcomes
Having collected all inpatients that experienced a fall,
the primary outcome was to identify the proportion of
true high-risk fallers (True Positive Rate, TPR), i.e. inpa-
tients with STRATIFY score ≥ 2 that really experienced
a fall, and the proportion of false low-risk fallers, i.e. in-
patients identified as low-risk patient that experienced a
fall howsoever (False Negative Rate, FNR), with their
95% confidence intervals. We also reported details of
falls comprising extrinsic and intrinsic factors in all in-
patients fallen and in the two subgroups, low and high
risk, classified according to the STRATIFY.

Data analysis
Data were presented descriptively with Chi-square sig-
nificance testing for the comparison of extrinsic and in-
trinsic factors in the low/high risk subgroups and
significance testing for the proportions TPR and FNR.
For patients classified at high risk measures to prevent
the fall are put in place; it is expected that the TPR
should not be very high and it may not be considered as
the sensitivity of the STRATIFY test which quantify the
“a priori” proportion of true positive patients without
intervention. The second proportion instead allows to
quantify the false negative rate of STRATIFY in our
organization and to give the size of the phenomenon
which requires different evaluation criteria in to
have further reduction of falling risk. We generated an
excel report with all data for each patients and an an-
onymous ID. Mean, median and frequencies described
sample characteristics and fall events. Mean, median,
interquartile range and standard deviation were used to
synthetize the STRATIFY score and so the risk level of
falling. The significant level for all the tests is 0.05.

Results
We collected 399 incident reports received from 40 units.
After removal of duplicates, 365 reports of fall were in-
cluded in the analysis. The fall rate was 0.9 per 1000
patient-days in 2014.

Characteristics of inpatients who fell
Among the 365 falls reported, 56.7% (n = 207) of fallers
were men and 43.3% (n = 158) women. The median age
was 72 (Interquartile Range 59–82). Women fallers me-
dian age was 71 (Interquartile Range 54.25–81) while
men median age was 72 (Interquartile Range 59.5–82).
Half of the fallers were hospitalized in an ordinary ad-
mission (49.6%, n = 181) while 45.5% (n = 166) were ad-
mitted from the emergency department. The major
number of fallers was more likely to be admitted because
of internal medicine conditions (35.6%, n = 130),
followed by surgery (18.9%, n = 69), neurologic diseases
(15.9%, n = 58), cardiologic problems (8.2%, n = 30),

respiratory dis s (7.7%, n = 28), psychiatric condi-
tions (3.6%, n = 13), pediatric problems (3.6%, n = 13),
gynecologic conditions (2.5%, n = 9) and orthopedics
(0.1%, n = 2).

Fall risk assessment with the STRATIFY tool
The fall risk assessment with the STRATIFY was per-
formed in 289 (79.6%) out of 363 incident reports. For 2
out of 365 records was not possible to extrapolate
whether the STRATIFY assessment was done or not due
to lack of information, therefore a total number of 363
records was used for the analysis. Among the 289 re-
cords performed the STRATIFY assessment, 5 did not
report the score. Overall, the mean score of STRATIFY
assessment was 1.3 ± 1.4 and the median value was
equal to 1. Figure 1 showed the proportion of subjects
for each possible total score of the STRATIFY assess-
ment on 284 cases. The cumulative frequency showed
that the TPR, our primary outcome, was 35.6% (n = 101,
95% CI 30% – 41.1%) while the FNR, our secondary out-
come, 64.4% (n = 183, 95% CI 58.9% – 70%).
Of the 74 out of 363 (20.4%) fallers for which the risk

of falling was not evaluated, classifying those subjects
straight as at low risk, the 72.4% (n = 55) should have
been assessed instead since they have at least one criter-
ion (i.e. age > 75, previous fall, urinary incontinence) for
which the STRATIFY was strongly recommended by the
internal organizational procedure of the hospital for fall
prevention, as detailed above.

Characteristics, intrinsic and extrinsic factors of fall
Among the 365 incident reports, 349 (95.6%) were real
falls and 16 (4.4%) were classified as near falls (defined
as an event that could have resulted in a fall, but did
not). Figure 2 showed the distribution (%) of fallers on
the total number of inpatients admitted in each Unit
during the observed period. The top three Units with
the highest frequency of falls among admitted inpatients
were: Internal Medicine – Metabolic Unit (8.71%; 21/
241), Neurology (5.37%; 44/819) and Onco-hematology
Unit (4.82%; 21/436). While 12/365 fallers (3.3%) were
outpatients admitted at the emergency room.
The majority of fall events happened in the patient

room (63%, n = 230) followed by the toilette (22%,
n = 82) and the corridor of the ward (7%, n = 26). Dur-
ing the working days (77.3%, n = 282) 45% of falls hap-
pen in the night, while during holiday days (23%, n = 83)
falls occurred more during the morning shift (42.2%).
Fall injuries were more of slight entity, less than 3 days
of prognosis (38%, n = 113), severe entity was only in 9
events and no event occurred in death.
Tables 1 and 2 show the absolute and relative frequen-

cies of intrinsic and extrinsic risk factors in fallers inpa-
tients also in the two subgroups of low and high risk

Castellini et al. BMC Health Services Research (2017) 17:656 Page 3 of 7

according to STRATIFY. The Chi-square test for the fre-
quencies of intrinsic and extrinsic risk factors performed
to compare the two subgroups of low/high risk patients
are statistically significant (p < .05). Discussion Reduction of inpatient risk of falling is an important health policy issue due to physical, psychological and socio- economic costs fall related. A frequent and attractive ap- proach to prevent and reduce falls ‘number is to adopt and use a risk assessment tool. Despite the evidence [7, 16], a fall risk evaluation is performed only in 34% of the general elder population in contrast to the 70% of elderly receiving appropriate care for hypertension and heart failure, [20]. It has been highlighted how is problematic the transfer of the evidence supporting the effectiveness of fall prevention into clinical practice: a disparity between the wealth of the evi- dence and the neglect of falling exists, [21]. Overall, our fall rate was 0.9 per 1000 patient-days in 2014, a low rate in agreement with the range of pub- lished incidence rates of falls, [2, 9]. Collecting the num- ber of falls per unit, we discovered that the absolute frequencies of falls on the total of the inpatients admit- ted per unit varied substantially across them, reflecting those that Laket et al. [22] found: the units of internal medicine are those with the highest rate of falls among inpatients admitted. The presence of patients suffered from co-morbidities and acute medical conditions may Fig. 1 Fallers distribution per STRATIFY score Fig. 2 Fallers distribution on total inpatients admitted per each Unit Castellini et al. BMC Health Services Research (2017) 17:656 Page 4 of 7 be the reason of the high prevalence in units as internal medicine besides external and environmental risks that contributes to increase the hazards, [23]. Nevertheless, the reported rate among units could be underestimated: in a previous study was underlined that operators may not consider the web-based incident reporting system as an instrument for improving health care quality and therefore, be not supportive in the use of risk manage- ment system, [19]. The difference falls number rate among units may be linked to the aversion of recording adverse events bound up with the health professionals, without pro-active “risk management” behavior. This at- titude may reflect their wrong perception of low risk pa- tients and, consequently, it might have been affected the likelihood of reporting a fall. This study demonstrated that the risk assessment tool chosen by the organization, the STRATIFY, seems to be not adequate in detecting about 2/3 of fallers. The creator of the STRATIFY himself concluded that the elements of this tool (i.e. previous fall, agitation or stability) can vary between different inpatient populations (i.e. psychiatric, orthopedic, neurological), [24]. Oliver et al. suggested that case mix, ward design, type of patients, personnel skills may influence the validity and reliability of the risk assess- ment tool and so, a perspective validation is needed in any hospitals, [8, 11]. Indeed, according to several, the predict- ive accuracy and the external validity of the STRATIFY seems to be failed and not transferable to every hospital inpatient populations, [3, 25]. It should be also taken into consideration that this tool not assessed all the risks to- wards to patients are usually exposed. A non-appropriate screening tools leads to inappropriate fall prevention strat- egies and, consequently, to an unbalanced distribution of organizational resources in terms of educational pro- grams, personnel and care assistance. Our study revealed the inappropriateness of the use of STRATIFY as the only tool for fall prevention program implemented in our hospital as the Italian guideline re- quired. The different distribution of intrinsic and extrin- sic risk factors in the low/high risk subgroup, which generally are more in the second one, confirms the dif- ferent causes for low risk patient fallers. However, the latter also suggests that possible other causes not already considered must be identified and monitored to prevent falls in low risk STRATIFY subgroup or to implement the use of other tool. For the high risk group the forbid- den use of open footwear seems a simple intervention to prevent falls. Despite the definition of specific require- ments for assessing inpatients at the admission, a per- centage of fallers were not assessed for fall risk, i.e. STRATIFY was not performed, although they must have been assessed having risks factors as those recommend to be assessed. This can reflect a failure in the preven- tion program regarding its adherence by the health Table 1 Distribution of intrinsic risk factors in the total sample, in low risk and high risk patients’ subgroups Intrinsic risk factors Absolut and relative frequency in the 365 fallers n(%) Absolut and relative frequency in 183 low risk patients n(%) Absolut and relative frequency in 101 high risk patient n(%) Cognitive impairment 130 (35.6) 66 (36.1) 45 (44.6) Balance dis s 102 (28.0) 53 (29.0) 33 (32.7) Neuromuscular and musculoskeletal dis s 100 (27.4) 49 (26.8) 37 (36.6) Past falls 81 (22.2) 31 (16.9) 30 (29.7) Incontinence 26 (7.1) 13 (7.1) 12 (11.9) Visus impairment 25 (6.9) 14 (7.7) 9 (8.9) Hearing impairment 15 (4.1) 6 (3.3) 6 (5.9) Table 2 Distribution of extrinsic risk factors in the total sample, in low risk and high risk patient’s subgroups Extrinsic risk factors Absolut and relative frequency in the 365 fallers n(%) Absolut and relative frequency in 183 low risk patients n(%) Absolut and relative frequency in 101 high risk patient n(%) Open footwear 73 (20) 26 (14.2) 33 (32.7) Lack of lighting 58 (15.9) 35 (19.1) 19 (18.8) Physical restraints 47 (12.9) 24 (13.1) 9 (8.9) Distance from toilette 37 (10.1) 17 (9.3) 15 (14.9) Walking aids (i.e. walker) 35 (9.6) 17 (9.3) 14 (13.9) Inappropriate furniture 20 (5.5) 12 (6.6) 7 (6.9) Wet floor 13 (3.6) 10(5.5) 2 (2.0) Irregular floor 6 (1.6) 3 (1.6) 3 (3.0) Castellini et al. BMC Health Services Research (2017) 17:656 Page 5 of 7 personnel. Even with the organization’ adherence to the national guidelines, [16], it seems to be a lack not only in the management system but also in the guideline rec- ommendation. The most recent NICE guideline recom- mends to not limit the risk assessment at using fall predictive tools (i.e. STRATIFY or Morse assessment tool, [26]) and it suggests to consider a multidimensional fall assessment followed by a multifactorial intervention, [4]. Assessing all risk factors (patient-related and envir- onmental) at early admission time should enable to cor- rectly allocate the patient in low, moderate and high risk of falling, so that a cost-effective prevention program in- cluding physiotherapy and occupational therapy could be implemented. Gait and balance evaluation, daily living activities and home environment appraisal, along with assessing cognition and medication use, should be espe- cially evaluated in those who are more inclined to be at high risk of falling. It should be remembered that patient ‘conditions can be altered during in-hospital stay, thus the assessment has to be a dynamic process. In agreement with the NICE recommendations, a recent review suggest to organize the screening evaluation in three steps: (1) develop a fall management strategy and policy, (2) include a multifactorial assessment, and (3) al- locate patients to interventions as physiotherapy or occu- pational therapy, [15]. In the viewpoint of a multifactorial assessment, the functional ability, gait and balance are key factors for developing an adequate preventive interven- tion. There are several tools used to assess them: the Time Up and Go Test (TUG), [27], the Short Physical Perform- ance Battery, [28], the Tinetti Performance Oriented Mo- bility Assessment [29], the 6 Min Walking Test or the literature-based FRAT-up assessment, [30, 31]. Some of these tools are implemented in national guideline as the Best Practice Guidelines for Australian Residential Aged Care Facilities’ and in national preventive program as the STEADI program, developed by the Centre for Disease Control and Prevention in USA (https://www.safetyand- quality.gov.au/our-work/falls-prevention/falls-prevention- resources, https://www.cdc.gov/steadi/index.html). On the contrary, Italian regional and national guidelines suggest to use predictive tool as the STRATIFY tool or the Morse scale being not specific to functional ability and equilib- rium skills. The lack of updated preventive strategy in- cluding more “functional” evaluation maybe affect the success of the fall screening in our hospitals and so under- estimate the burden of fall and its consequences. Further research is needed to determine the optimal assessment and interventions for in-patients Italian population but also to implement a national standard program that fill the gap between recent evidence and still neglect in clinical practice. It is known that cost- effective programs would be more supportive if leader- ship is involved and powerful monitoring strategies are developed in to ensure cooperation among staff from different disciplines [32]. Therefore, future studies should discover an easy, quick and multidimensional tool that may go beyond the limited resources in terms of time, personnel and money but always aim to the evi- dence based interventions. Limitation Our observational study was a retrospective evaluation just of people who fell and its nature could be recog- nized as a limitation. We did not have the opportunity and the resources to select controls. Missing data could have been occurred because of the complexity of the management database system as well as the compliance of the several health professionals involved in managing it. It has been known that about 25% of fall events are not reported in incident reports but they are in medical charts [33], and so even our incident reporting system may be not effective as it is expected to be. A little sam- ple of our medical charts obtained by the wards with the higher number of falls were reviewed to assess this and data obtained confirmed the published data. Conclusions Evaluation of prevention programs should be considered as a feedback to improve the efficacy and the effectiveness of organization’s efforts in ensuring patient ‘safety. Our risk management program was found to be in line with the lower values of published incidence rates of falls but to be failure in adequately detecting all in-patient who actually experienced a fall. The multifactorial nature of falls requires the assessment of multiple domains including gait, balance, functional mobility, home/hospital environment and cogni- tive function. Multidimensional tool is needed in to supply the STRATIFY’s limitations. Accordingly, it is essen- tial to involve several healthcare professionals in developing the most adequate and appropriate risk assessment strategy: identifying incorrect needs of patients lead to allocate re- sources for erroneous priorities and untargeted interven- tions, decreasing healthcare performance and quality. Abbreviations FNR: False Negative Rate; TPR: True Positive Rate Acknowledgments None. Funding None. Availability of data and materials Dataset available from the corresponding author on reasonable request. Authors’ contributions SC, AD and GC provided concept/idea/research design. GC collected and analysed the data and drafted the manuscript. ML provided statistical analysis support. All authors critically revised, read and approved the final manuscript. Castellini et al. BMC Health Services Research (2017) 17:656 Page 6 of 7 https://www.safetyandquality.gov.au/our-work/falls-prevention/falls-prevention-resources https://www.safetyandquality.gov.au/our-work/falls-prevention/falls-prevention-resources https://www.safetyandquality.gov.au/our-work/falls-prevention/falls-prevention-resources https://www.cdc.gov/steadi/index.html Ethics approval and consent to participate All data in the database were anonymized so neither individual patient consent nor ethical approval were required. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Author details 1Department of Biomedical Sciences for Health, University of Milan, Via Pascal, 36, 20133 Milan, Italy. 2Unit of Clinical Epidemiology, IRCCS Istitute Orthopedic Galeazzi, Milan, Italy. 3Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy. Received: 14 June 2016 Accepted: 30 August 2017 References 1. WHO: Global Report on Falls Prevention in Older Age. 2007. 2. Halfon P, Eggli Y, Van Melle G, Vagnair A. Risk of falls for hospitalized patients: a predictive model based on routinely available data. J Clin Epidemiol. 2001;54:1258–66. 3. da Costa BR, Rutjes AW, Mendy A, Freund-Heritage R, Vieira ER. Can falls risk prediction tools correctly identify fall-prone elderly rehabilitation inpatients? A systematic review and meta-analysis. PLoS One. 2012;7:e41061. 4. Falls: the assessment and prevention of falls in older people. NICE (National Institute for Health and Care Excellence) clinical guideline 161. 2013. 5. Rubenstein LZ, …