Keywords: Patient readmission - length of stay - nervous system diseases - multimorbidity -
aged - risk factors
Palavras-chave: Readmissão do paciente - tempo de internação - doenças do sistema nervoso - multimorbidade
- idoso - fatores de risco
Unplanned readmissions indicate suboptimal quality of patient care, cumulative costs,
increasing morbidity, and greater hospital mortality[1 ]. Patient readmissions and long length of stay (LOS) are associated with excessive
spending in health care, constituting a public health issue[2 ],[3 ]. In addition, the aging population is associated with the burden of neurological
disorders (NDs), which is a global phenomenon[4 ]. Long-term neurological conditions such as cerebrovascular diseases, epilepsy, dementia,
Parkinson's disease, neuromuscular disorders, and their comorbidities are associated
with patient readmissions and long LOS[1 ],[5 ],[6 ],[7 ],[8 ],[9 ],[10 ],[11 ]. Inappropriate readmissions of patients with long-term neurological conditions are
related to neglected opportunities to improve ambulatory care and inadequacies in
hospital procedures, such as discharge planning and patient transfers[12 ]. Moreover, in elders, hospital readmissions and long LOS are very common and closely
related[13 ]. In Brazil, the elderly population is increasing much faster than in developed countries,
exceeding 30 million persons aged 60 years or older in 2017[14 ]. In a previously published study we found high rates of hospital readmission (31%),
and long LOS (51%) during a two-year analysis of 798 elders hospitalized with NDs[15 ]. The present study intended to analyze demographic factors, NDs, and comorbidities
in the population of elderly inpatients to identify independent predictors of readmission
and long LOS.
METHODS
Patients
Patients aged 60 years or older successively admitted to the Hospital São Rafael (HSR)
between January 1, 2009, and December 31, 2010, and evaluated by neurologists were
enrolled. The HSR is a general tertiary teaching hospital in Salvador, Brazil, that
admits patients who are clients of several private health insurance companies, as
well as users of the Brazilian Unified Public Health System (SUS users).
Data were collected from electronic medical records, which made it easy to find patients
with NDs in discharge lists, as well as registries of all neurological procedures.
The Information Technology Department at this hospital (IT-HSR) recorded all admissions
during the period of the study. Further information regarding the HSR and the method
for capturing patients, including details concerning ND diagnostic assessments, can
be found in our previously-published studies of the same population[15 ],[16 ]. We enrolled elderly inpatients with NDs in this study using the following methods:
1) IT-HSR searches of NDs listed in discharge summaries and 2) IT-HSR identification
of registered neurological procedures. After the IT-HSR selection, the authors carefully
examined all of the written patient health records. We selected elderly inpatients
with neurological symptoms based on the following criteria: 1) elderly patients with
acute NDs admitted for treatment by neurologists, 2) patients admitted with acute
clinical disorders owing to an underlying chronic neurological disease that needed
care or follow-on therapy by a neurologist, and 3) medical or surgical patients who
suffered neurological complications during their hospital stay and required consultation
with a neurologist. This method ensured a corroborated data bank and captured all
inpatients with NDs and their comorbidities that might have influenced readmission
and long LOS. Furthermore, this process recognized patients with more than one ND
and a relevant patient multimorbidity, which represents the real world of caring for
elderly inpatients.
Exclusion criteria were as follows: 1) patients who were admitted by or consulted
with a neurologist but who had no NDs; 2) patients whose medical records lacked important
data; 3) patients with acute trauma, subarachnoid hemorrhage, central nervous system
(CNS) tumor, or other neurosurgical diseases, who were directly referred to a neurosurgeon;
and 4) patients who underwent neurological consultation solely for a diagnosis of
brain death due to head trauma, cardiovascular arrest, neurosurgical or oncological
condition that was beyond the responsibility of neurologists; and 5) patients transferred
to another hospital without an established diagnosis.
Demographics
The influences of age, sex, marital status, and socioeconomic level on patient readmission
and on long LOS were considered. Differences in patient readmission and long LOS with
regard to socioeconomic status were estimated comparing these outcomes among SUS users
with these outcomes in patients who were clients of private health insurance companies.
Elderly patients who belong to families categorized as Brazilian B2 socioeconomic
class (average monthly income equal to or less than equivalent to US$1,475)[17 ] or lower socioeconomic classes use SUS[18 ], whereas elders belonging to the Brazilian middle class (class B1 with an average
family monthly income US$2,813) and from the Brazilian upper social classes prefer
to pay for private health services[17 ],[18 ],[19 ].
Neurological disorders
Diagnostic criteria for NDs were based on the Tenth Revision of the International
Statistical Classification of Diseases and Related Health Problems (ICD-10)[20 ]. We analyzed disease frequency, readmissions, and LOS of the following: cerebrovascular
disorders representing ischemic stroke, transient ischemic attack, and spontaneous
brain hemorrhage (excluding subarachnoid hemorrhage); epilepsy and acute symptomatic
seizures; movement disorders including hyperkinetic disorders (Huntington's chorea,
Parkinson's disease, and parkinsonism (but not cases of Parkinson's disease dementia
or Lewy bodies dementia); neuromuscular disorders; CNS infections; headaches; syncope
or near syncope; CNS toxic and metabolic disorders including alcoholism and other
toxic or metabolic encephalopathy as well as acute neurologic complications of water-electrolytic
balance disturbances and brain injury, represented in patients with sequelae of cardiovascular
arrest. In addition, CNS neoplasms were included if patients under the care of an
oncologist or neurosurgeon had consulted a neurologist for clinical treatment of NDs,
such as epilepsy, headache, or cognitive disorders. Neurocognitive disorders were
also included if cases of delirium and cases of dementia were diagnosed based on DSM-IV
criteria[21 ].
Comorbidities
Using the following criteria, we compiled important comorbidities that could influence
the studied outcomes.
1) Arterial hypertension was diagnosed according to the criteria of the Joint National
Committee on the Prevention, Detection, Evaluation and Treatment of High Blood Pressure[22 ]. 2) Dyslipidemias were defined according to the recommendations of the National
Cholesterol Education Program Expert Panel on the Detection, Evaluation and Treatment
of High Blood Cholesterol in Adults (Adult Treatment Panel III) and the results of
recent clinical trials[23 ]. 3) Diabetes mellitus was diagnosed based on the follow-up report of the 2007 guidelines
of the American Diabetes Association[24 ]. 4) Clinical disorders were classified according to the ICD-10 and included a) infections,
which represented not only in patients who were admitted with infections but also
patients with neurological symptoms who experienced infections during their hospitalization;
b) neoplasms; c) chronic and acute respiratory system diseases; d) musculoskeletal
diseases; e) genitourinary disorders; f) digestive disorders; g) endocrine and metabolic
disorders, including water-electrolyte imbalances (hepatic insufficiency was considered
a metabolic rather than a digestive disorder); h) circulatory system disorders (cardiac
and peripheral vascular disorders), excluding patients with cerebrovascular diseases;
and i) psychiatric disorders diagnosed based on DSM-IV criteria[21 ]. These psychiatric disorders were classified only as comorbidities of NDs, because
HSR does not admit patients with a primary psychiatric diagnosis; therefore, most
of the disorders were anxiety, depression, and bipolar disorder. 5) Patient multimorbidity
signified patients suffering from two or more morbidities[25 ].
Readmission and long LOS
Hospital readmissions during the two-year study period were recorded. The median LOS
for the entire population was nine days; hence, hospitalizations of nine days or longer
were conventionally considered long LOS.
Predictors of readmission and long LOS
Patients were dichotomized according to the incidence or no incidence of readmission.
The same process was used for categorizing patients according to occurrence of long
LOS. Univariate analysis of all patient characteristics was performed to find potential
predictors of hospital readmission as well as to detect predictors of long LOS. Multivariate
analysis of selected variables was executed thereafter.
Statistics
Quantitative variables with normal distribution were reported as their mean and standard
deviation, and for variables with non-normal distribution by their median and interquartile
interval. Normal variables were identified by graphic analysis and the Shapiro-Wilk
test. Categorical variables were reported as frequencies and percentages.
Bivariate comparisons between groups were performed using Student's t -test for numerical variables with normal distribution. Categorical variables were
compared by Pearson's chi-square or Fisher's exact test when necessary. We performed
a stepwise backward hierarchic logistic regression for multivariate analysis to increment
the power of prediction of the model. Independent variables were considered for multivariate
analysis if recognized as a biological plausibility associated with the main study
hypotheses and when bivariate tests showed a p-value < 0.25 according to the algorithm
proposed by Hosmer and Lemeshow[26 ].
The dimension of the model was measured by the Akaike information criterion. The Nagelkerke
R[2 ] was employed to estimate how much the model explained outcomes, and the omnibus
test of model coefficients (p-value) tested whether the explained variance in a set
of data was significantly greater than the unexplained variance, overall. Other tests
for the dimension of the multivariable model employed were: McFadden R[2 ], Cox and Snell R[2 ] (ML), McKelvey and Zavoina, and Effron.
Statistical analyses were performed using SPSS (v.25, Chicago, IL), R Program (v.3.4.4),
and Microsoft Excel 2016 software.
Ethics
The HSR Ethical Committee for Research approved this study (No. 8/11) on August 25,
2011. The HSR Ethical Committee for Research is certified by the National Committee
for Ethics in Research, according to the Brazilian Operational Manual for Ethics Committee
in Research.
RESULTS
Patient demographics
We selected 798 elders with NDs, admitted to HSR, to be studied. The mean age of these
individuals was 75.8 ± 9.1 years (median, 76 years). The interquartile interval was
68 years (25th percentile) to 82 years (75th percentile). Women formed 55% of this
population. Of this geriatric population, 464 (58%) were married and 713 (89%) were
patients of private health services.
Readmission
Over the two-year study period, 251 patients were readmitted to HSR, producing a readmission
rate of 31% (95%CI, 28%–35%). Among these patients, 101 (40%) were readmitted more
than once, resulting in an average of 1.8 ± 1.5 readmissions per patient.
Length of stay
The median hospital LOS for these elderly neurological inpatients was nine days, with
an interquartile range between 1-20 days; thus, 409 (51%) patients had an LOS of ≥
9 days (95%CI, 48% –55%).
NDs and comorbidities: frequency and association with patient readmission and long
LOS
Among our older adult inpatient population, 555 (70%) had primary NDs diagnosed by
neurologists and identified in discharge summaries. The remaining 243 (30%) patients
were admitted with clinical complications of underlying neurological chronic NDs or
developed neurological complications during their stay in the hospital. The latter
were captured by IT-HSR examination of the physicians’ billing codes. Overall, 312
(39%) patients were affected by more than one ND, totaling 668 additional NDs that
were classified as neurological comorbidities in this population. Therefore, we found
1,154 NDs among 798 elderly inpatients, which represented a mean of 1.32 ± 0.91 NDs
(95%CI, 1.23–1.38), with a range of 1-5 NDs per patient. Moreover, we observed a wide
range of diagnostic frequency of the NDs (0.3%-50.8%), with cerebrovascular disease
being the most common (50.8%; [Figure 1 ]). This study captured 2,679 comorbidities involving 90% of patients with two or
more comorbidities, constituting a mean of 3.35 ± 1.51 (95% CI, 3.25–3.47) and a range
of 0-9 comorbidities per patient. We found a wide range of diagnostic frequency for
comorbidities (5.6%-84.5%), with arterial hypertension (84.5%) and diabetes (57.5%)
being the most common comorbidities ([Figure 2 ]). Overall, for patient multimorbidity (NDs + comorbidities), the mean was 4.7 ±
1.7 morbidities per patient (median, 5.0).
Figure 1 Frequency of the most-common neurological disorders and respective rate of readmission
and long length of stay in elders admitted with neurological disorders in a tertiary
medical center.
Figure 2 Frequency of comorbidities and respective rates of readmission and long length of
stay in elders admitted with neurological disorders in a tertiary medical center.
Patient readmission rates were quite similar among all NDs and among their comorbidities.
In contrast, there was significant variation in the rates of long LOS for patients
with NDs as well as between their comorbidities. [Figure 1 ] shows all NDs with disease frequency exceeding 5% and their respective rates of
readmission and long LOS. [Figure 2 ] shows these findings for their comorbidities.
Predictors of readmission and long LOS
[Tables 1 ] and [2 ] compare the characteristics of patients who were readmitted to the hospital with
those who were not and also of patients who stayed ≥ 9 days (long LOS) and those who
stayed < 9 days. We found no statistically significant differences in the rates of
patient readmission among independent variables, except for female sex and arterial
hypertension (p < 0.05), which were associated with reduced readmission rates. However,
several variables were associated with a long LOS: age, SUS, delirium, arterial hypertension,
infection, neoplasm, and genitourinary and respiratory disorders. Female sex, syncope,
and headache were associated with a shorter LOS (p < 0.05).
Table 1
Univariate analysis of demographics and neurological disorders according to incidence
of readmission and occurrence of long length of stay in elderly inpatients admitted
in a tertiary medical center.
Variable
Readmission
Long LOS
Yes
No
p-value
Yes
No
p-value
All patients n = 798
n = 251
n = 547
n = 409
n = 389
Age
76.2 ± 9.3
75.5 ± 9.0
0.295
76.6 ± 8.7
74.9 ± 9. 4
0.011
Female
123 (49.0)
312 (57.0)
0.034
208 (50.9)
227(58.4)
0.033
Married
144 (57.4)
320 (58.5)
0.764
238 (58.2)
226 (58.1)
0.979
SUS
33 (13.1)
52 (9.5)
0.122
61 (14.8)
24 (6.2)
0.001
Cerebrovascular
130 (51.8)
275 (50.3)
0.690
216 (52.6)
189 (48.8)
0.233
Movement disorder
39 (15.5)
100 (18.3)
0.343
71 (17.4)
68 (17.5)
0.964
Epilepsy
41 (16.3
86 (15.7)
0.826
68 (16.5)
59 (15.2)
0.573
Syncope
35 (13.9)
87 (15.9)
0.475
42 (10.2)
80 (20.7)
0.001
Headache
39 (15.5)
63 (11.5)
0.114
36 (8.8)
66 (17.0)
0.001
Dementia
34 (13.5)
62 (11.3)
0.373
56 (13.7)
40 (10.3)
0.139
Delirium
25 (10.0)
61 (11.2)
0.712
59 (14.4)
27 (6.9)
0.001
Neuromuscular
21 (8.4)
34 (6.2)
0.265
28 (6.8)
27 (7.0)
0.958
Neurotoxic & metabolic
3 (1.2)
9 (1.6)
0.864
6 (1.5)
6 (1.6)
0.930
CNS infection
0 (0)
5 (0.9)
0.333
4 (1.0)
1 (0.3)
0.400
Brain injury
1 (0.4)
2 (0.4)
0.999
1 (0.2)
2 (0.5)
0.965
CNS neoplasm
0 (0)
2 (0.4)
0.999
1 (0.2)
1 (0.3)
0.999
Data are patient numbers, and inside the parentheses are percentages (%) within title
headings.
CNS: central nervous system; LOS: length of stay.
Table 2
Univariate analysis of comorbidities according to readmission and long length of stay
in 798 elderly neurological inpatients.
Variable
Readmission
Long LOS
Yes
No
p-value
Yes
No
p-value
All patients n = 798
n = 251
n = 547
n = 409
n = 389
Arterial hypertension
200 (79.7)
474 (86.7)
0.012
359 (87.8)
315 (81.0)
0.008
Diabetes
142 (56.6)
317 (58.0)
0.714
240 (58.7)
219 (56.3)
0.496
Dyslipidemia
117 (46.6)
246 (45.0)
0.948
173 (42.3)
190 (50.4)
0.101
Infection
118 (47.0)
242 (44.2)
0.465
235 (57.5)
125 (32.1)
0.001
Neoplasm
56 (22.3)
125 (22.9)
0.865
108 (26.4)
73 (18.8)
0.010
Endocrine/metabolic
41 (16.3)
81 (14.8)
0.578
69 (16.9)
53 (13.6)
0.203
Circulatory
24 (9.6)
78 (14.3)
0.065
59 (14.4)
43 (11.1)
0.154
Genitourinary
34 (13.5)
58 (10.6)
0.227
61 (14.9)
31 (8.0)
0.002
Respiratory
25 (10.0)
61 (11.2)
0.614
73 (17.8)
13 (3.3)
0.001
Musculoskeletal
24 (9.6)
62 (11.3)
0.453
44 (10.5)
43 (11.1)
0.806
Digestive
21 (8.4)
37 (6.8)
0.418
33 (8.1)
25 (6.4)
0.372
Trauma
18 (7.2)
35 (6.4)
0.684
23 (5.6)
30 (7.7)
0.236
Psychiatric
16 (6.4)
29 (5.3)
0.542
25 (6.1)
20 (5.1)
0.552
Data are patient numbers, and inside the parentheses are percentages (%) within title
headings.
CNS: central nervous system; LOS: length of stay.
Multivariate analysis
Despite female sex (p = 0.045) and arterial hypertension (p = 0.0016) being associated
with each other in a multiple model, their logistic model was poor, according to statistics
of model dimension effect: pseudo R[2 ], Cox-Snell R[2 ] (R[2 ] = 0.013), Nagelkerke R[2 ] (R[2 ] = 0.18), and Hosmer-Lemeshow (c2
HL [2] = 19.859), p = 0.007. Thus, these findings provided unsatisfactory data for female
sex and arterial hypertension in the final adjustment and, consequently, they did
not predict a decrease in readmissions.
[Table 3 ] shows multivariate analysis for long LOS: adjusted odds ratio (Exp b) with 95% CI
for the three blocks (demographics, comorbidities, and NDs) as well as p-values of
variables’ coefficients, according to Wald statistics. It follows that this study
demonstrated that SUS users, respiratory disorders, infection, genitourinary disorders,
and arterial hypertension predicted long LOS, whereas dyslipidemia, headache, and
syncope were associated with a decreased probability of long LOS. Note that dyslipidemia
was included in the multivariable analysis because of its relevance and in accordance
with the Hosmer-Lemeshow criteria.
Table 3
Multivariate analysis for long length of stay: adjusted odds ratio and final p-values
according to Wald statistics.
Variables
OR (CI95%)
p-value
Demographics
Unified Health System [SUS]
2452 (1.429–4.208)
0.001
Comorbidities
Respiratory
5795 (3.031–11.081)
< 0.001
Infections
2585 (1.88–3.555)
< 0.001
Genitourinary
1720 (1.044–2.834)
0.033
Arterial Hypertension
2056 (1.316–3.212)
0.002
Dyslipidemia
0.717 (0.523–0.984)
0.039
Neurological disorders
Headache
0.571 (0.356–0.918)
0.021
Syncope
0.548 (0.349–0.861)
0.009
LOS: length of stay; CI: confidence interval
Accordingly, in this study, no NDs were recognized as independent risk factors for
long LOS, but syncope and headache were confirmed as associated with shorter LOS.
[Figure 3 ] shows the model receiver operating characteristic and analysis of the area under
the curve, which quantified the power of prediction of this model. [Table 4 ] shows summary statistics related to the variables of hierarchical logistic regression
for models according to the variables block for long LOS. Additional description of
the statistical analysis is available on request.
Figure 3 Receiver operating characteristic curve (ROC) for the final model.
Table 4
Hierarchical logistic regression for long length of stay related variables. The summary
of statistics for models according to variable blocks.
Statistics
Final model
Akaike information criterion
939.03
McFadden R2
0.133
Adj.McFadden
0.114
Cox and Snell R2 (ML)
0.168
McKelvey.Zavoina
0.231
Effron
0.170
Nagelkerke R2 (Cragg and Uhler's)
0.224
Omnibus Test of Model Coefficientsª (p-value)
< 0.001
Hosmer-Lemeshow test (p-value)
0.170
DISCUSSION
Patient readmission and long LOS are major undesirable outcomes for elderly inpatients.
We studied a relatively large population of elderly inpatients with a high mean age
(25% of patients were aged 82 years or older). Our method of capturing data was uncommon,
because health systems are largely configured for individual disorders (i.e., patient
multimorbidity was frequent in this population, which is common among elders)[27 ]. Eighty-five (11%) of the studied inpatients were SUS users. As SUS users belong
to a lower socioeconomic class than users of private health services, we were able
to compare outcomes of these two socioeconomically distinct populations. All patient
characteristics were associated with a fairly high uniform rate of hospital readmission
including NDs. Notwithstanding the high rate of patient readmission in this population,
neither the isolated diagnosis of NDs nor their comorbidities were recognized as predictors
for readmission. Other risk factors for readmission in this population should be examined,
such as lack of appropriate follow-up (post-discharge suboptimal care) and social
or additional factors as cited for other patients[28 ],[29 ]. As expected, headache, syncope, and dyslipidemia were associated with reduced risk
of long LOS[30 ]
–
[32 ]. Being an SUS user was a significant predictor of long LOS, because these patients
are likely to be more severely affected by chronic disorders owing to inequalities
in access to health care[33 ]. Long LOS has been noted in other low-income populations of neurological patients[34 ]. Hence, strategies for better access to primary care could ultimately reduce the
LOS in Brazilian hospitals, as only 24% of Brazil's population use private health
services[35 ]. Despite the fact that no ND predicted a long LOS, several comorbidities such as
respiratory disorder, infection, genitourinary disorder, and arterial hypertension
were associated with long LOS in this population. Patients suffering from acute and
chronic respiratory disorders represented only 11% of comorbidities, but the vast
majority (85%) was associated with long LOS, indicating respiratory disorders are
a strong predictor for this outcome. These results corroborate that long LOS is common
in patients admitted with acute or chronic respiratory disorders[36 ]. Infections were numerous, affecting almost half of the patients, and predicted
long LOS in this population. Infections are common in neurological units, particularly
urinary infection by gram-negative bacteria, blood infections, skin and soft tissue
infections, as well as respiratory system infections[37 ]. Genitourinary disorders were also frequent among this elderly population, especially
kidney diseases. Kidney disorders are common causes of long LOS, principally when
associated with sepsis and cardiovascular disorders[38 ]. Arterial hypertension was the most common comorbidity and a strong predictor for
long LOS in this population, and it may be due to patients (elders) suffering from
long-term arterial hypertension[39 ]. Arterial hypertension is a long-lasting disorder, and its control depends on adequate
primary care, which is underprovided in Bahia, northeastern Brazil[40 ]. Comorbidities were the leading risk for long LOS in these elderly neurological
patients; thus, patient multimorbidity needs to be better studied in this population.
Patient multimorbidity is a matter of concern to health services, as health systems
are configured for individual disease rather than multimorbidity[27 ].
Overall, the studied variables accounted for 22% of the predictors of long LOS, indicating
that a prospective study is needed that includes other risks, such as patient multimorbidity,
nutrition status, and cognitive and functional scores, as stated for other elderly
populations[41 ].
Limitations of this study
This was a retrospective study, and we examined only the outcome of readmission to
the same hospital over a maximum two-year period. Furthermore, these patients were
studied before restructuring of and improvements to the HSR neurology department,
which recently incorporated a neurological intensive care unit.
Strengths of this study
The number of elderly inpatients in this sample and the method used for searching
NDs and comorbidities could be considered relatively robust; for more than 20 years,
HSR has been one of the most important reference centers for neurology in northeast
Brazil.
CONCLUSIONS
This study found no predictors for patient readmission among analyzed characteristics
in elderly neurological inpatients. We suggest collaboration involving hospital neurology
teams, home care stakeholders, and outpatient coordinators to study other possible
risk factors and post-discharge measures to prevent patient readmissions.
Social deprivation and several comorbidities accounted for 22% of the predictors of
long LOS in this particular population. Conversely, NDs were not a risk factor for
long LOS. Therefore, improvements in the model of risk for long LOS ought to be attempted,
including study of other patient characteristics such as patient multimorbidity and
functional and cognitive scores as identified in other elderly populations.
