A Predictive Model for Knee Joint Replacement in Older Women

Abstract

Knee replacement (KR) is expensive and invasive. To date no predictive algorithms have been developed to identify individuals at high risk of surgery. This study assessed whether patient self-reported risk factors predict 10-year KR in a population-based study of 1,462 women aged over 70 years recruited for the Calcium Intake Fracture Outcome Study (CAIFOS). Complete hospital records of prevalent (1980-1998) and incident (1998-2008) total knee replacement were available via the Western Australian Data Linkage System. Potential risk factors were assessed for predicative ability using a modeling approach based on a pre-planned selection of risk factors prior to model evaluation. There were 129 (8.8%) participants that underwent KR over the 10 year period. Baseline factors including; body mass index, knee pain, previous knee replacement and analgesia use for joint pain were all associated with increased risk, (P < 0.001). These factors in addition to age demonstrated good discrimination with a C-statistic of 0.79 ± 0.02 as well as calibration determined by the Hosmer-Lemeshow Goodness-of-Fit test. For clinical recommendations, three categories of risk for 10-year knee replacement were selected; low < 5%; moderate 5 to < 10% and high ≥ 10% predicted risk. The actual risk of knee replacement was; low 16 / 741 (2.2%); moderate 32 / 330 (9.7%) and high 81 / 391 (20.7%), P < 0.001. Internal validation of this 5-variable model on 6-year knee replacements yielded a similar C-statistic of 0.81 ± 0.02, comparable to the WOMAC weighted score; C-statistic 0.75 ± 0.03, P = 0.064. In conclusion 5 easily obtained patient self-reported risk factors predict 10-year KR risk well in this population. This algorithm should be considered as the basis for a patient-based risk calculator to assist in the development of treatment regimens to reduce the necessity for surgery in high risk groups such as the elderly.

Full text

A Predictive Model for Knee Joint Replacement in Older
Women
Joshua R. Lewis1,2*, Satvinder S. Dhaliwal3, Kun Zhu1,2, Richard L. Prince1,2
1 School of Medicine and Pharmacology, University of Western Australia, Perth, Western Australia, Australia, 2 Department of Endocrinology and Diabetes, Sir
Charles Gairdner Hospital, Perth, Western Australia, Australia, 3 School of Public Health, Curtin University, Perth, Western Australia, Australia
Abstract
Knee replacement (KR) is expensive and invasive. To date no predictive algorithms have been developed to identify
individuals at high risk of surgery. This study assessed whether patient self-reported risk factors predict 10-year KR
in a population-based study of 1,462 women aged over 70 years recruited for the Calcium Intake Fracture Outcome
Study (CAIFOS). Complete hospital records of prevalent (1980-1998) and incident (1998-2008) total knee
replacement were available via the Western Australian Data Linkage System. Potential risk factors were assessed for
predicative ability using a modeling approach based on a pre-planned selection of risk factors prior to model
evaluation. There were 129 (8.8%) participants that underwent KR over the 10 year period. Baseline factors
including; body mass index, knee pain, previous knee replacement and analgesia use for joint pain were all
associated with increased risk, (P < 0.001). These factors in addition to age demonstrated good discrimination with a
C-statistic of 0.79 ± 0.02 as well as calibration determined by the Hosmer-Lemeshow Goodness-of-Fit test. For
clinical recommendations, three categories of risk for 10-year knee replacement were selected; low < 5%; moderate
5 to < 10% and high ≥ 10% predicted risk. The actual risk of knee replacement was; low 16 / 741 (2.2%); moderate
32 / 330 (9.7%) and high 81 / 391 (20.7%), P < 0.001. Internal validation of this 5-variable model on 6-year knee
replacements yielded a similar C-statistic of 0.81 ± 0.02, comparable to the WOMAC weighted score; C-statistic 0.75
± 0.03, P = 0.064. In conclusion 5 easily obtained patient self-reported risk factors predict 10-year KR risk well in this
population. This algorithm should be considered as the basis for a patient-based risk calculator to assist in the
development of treatment regimens to reduce the necessity for surgery in high risk groups such as the elderly.
Citation: Lewis JR, Dhaliwal SS, Zhu K, Prince RL (2013) A Predictive Model for Knee Joint Replacement in Older Women. PLoS ONE 8(12): e83665. doi:
10.1371/journal.pone.0083665
Editor: Alejandro Lucia, Universidad Europea de Madrid, Spain
Received May 5, 2013; Accepted November 6, 2013; Published December 11, 2013
Copyright: © 2013 Lewis et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This study was supported by research grants from the Healthway Health Promotion Foundation of Western Australia, the Australasian
Menopause Society, the Australian National Health and Medical Research Council (projects grants 254627, 303169 and 572604) and the Arthritis Australia
Barbara Cameron Memorial Grant. The salary of Dr. Lewis is supported by a Raine Medical Research Foundation Priming Grant. The funders had no role
in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
* E-mail: joshua.lewis@uwa.edu.au
Introduction
Osteoarthritis results in chronic pain, deformity, disability and
loss of quality of life with one third of all people above the age
of 85 suffering from this debilitating condition [1]. Despite being
a cost-effective treatment for osteoarthritis [2] and having
relatively low revision rates [3], knee replacements (KR) are a
major burden on healthcare systems worldwide [1,4].
Another major burden to the developed world’s healthcare
system is obesity [5] with the prevalence increasing by more
than 2.5 times over the past 20 years [6]. Lifestyle changes to
diet and physical activity have contributed to this rapid increase
of obese people with abdominal obesity increasing the risk of
serious diseases such as type II diabetes, metabolic syndrome
and cardiovascular disease [7]. Obesity is also strongly
associated with knee osteoarthritis and lower limb pain [8,9,10]
however there are conflicting findings as to whether obesity is
associated with knee joint replacement [11,12,13,14].
Few population-based longitudinal studies have investigated
the predictive value of self-reported and anthropometric
measures on KR and to date no predictive risk models for KR
exist. The development of a standardized predictive model for
knee replacement will allow testing of novel risk factors and to
assist in the development of treatment regimens to reduce the
necessity for this expensive and invasive surgery. We therefore
used 10-year procedure codes and hospital discharge data
from the Western Australia Data Linkage System (WADLS) in
conjunction with baseline data collected in the CAIFOS study to
identify easily obtained patient self-reported data that predicts
10-year knee joint replacement in a community based setting.
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Methods
The primary outcome was a knee replacement (KR)
procedure with pre-planned easily obtained self-reported
variables including baseline age, knee pain, physical activity,
calcium intervention group, prevalent knee replacement and
analgesia use for joint pain. Anthropometric variables were
then included individually into the multivariable receiver
operator curve (ROC) analysis to assess whether their
inclusion significantly improved predictive models.
Study participants
The participants were recruited in 1998 to a 5-year
prospective, randomized, controlled trial of oral calcium
supplements to prevent osteoporotic fracture [15]. Women
were recruited from the Western Australian general population
of women aged over 70 years by mail using the electoral roll.
Over 99% of Australians of this age are registered on the roll.
Of the 5,586 women who responded to a letter inviting
participation 1,510 women were willing and eligible and of
these 1,500 women were recruited for the study. Participants
were ambulant and did not have any medical conditions likely
to influence 5-year survival. They were excluded if they were
receiving bone-active agent, including hormone replacement
therapy. Participants were similar in terms of baseline disease
burden and medications compared to the whole population of
this age but they were more likely to be from higher socio-
economic groups [15]. In the 5 years of the trial, participants
received 1.2 g of elemental calcium as calcium carbonate daily
or a matched placebo. Participants were subsequently included
in a 5-year follow-up study of ageing. Complete clinical and
anthropometrical data were available in 1,478 participants of
these 16 participants had total knee replacements performed
on both knees and were excluded leaving 1,462 participants.
The human ethics committee of the University of Western
Australia approved the study and written informed consent was
obtained from all participants prior to recruitment.
Baseline measurements
At baseline, information was obtained from the patient on
their previous medical history and current medications, the
participants were asked to verify this information with their
General Practitioner where available. This data was then coded
using the International Classification of Primary Care – Plus
(ICPC-Plus) method [16]. The coding methodology allows
aggregation of different terms for similar pathologic entities as
defined by the ICD-10 coding system. Analgesic medications
used to treat joint pain at baseline included non-steroidal anti-
inflammatory drugs (NSAID’s) and paracetamol. Physical
activity level was assessed by questionnaire [17,18], and
calculated in kcal/day using a validated method utilising body
weight, questions on the number of hours and type of physical
activity and energy costs of such activities [19,20]. Because of
the randomisation to calcium or placebo for the first five years
of the study a variable named “calcium intervention group”
capturing this randomisation was included in the model.
Information on knee joint pain frequency and site was collected
by a questionnaire at baseline. In the questionnaire, subjects
were asked to select one of five categories that best describes
the frequency of the pain they experienced at the knee over the
previous year: (1) never, (2) less than once a month, (3) once a
month to once a week, (4) once a week to once a day, and (5)
once a day or more. These categories were then transformed
into three categories; frequency < once a month (infrequent); ≥
one a month to < once a day (frequent) or ≥ once a day
(daily).At baseline weight was assessed using digital scales
with participants wearing light clothes and no shoes, height
was assessed using a stadiometer and the body mass index
(BMI) was calculated in kg/m2.
Previous knee replacement
Knee replacement procedures in patients with a primary
diagnosis of osteoarthritis were retrieved from the Western
Australian Data Linkage System (WADLS) hospitalisation
records for each of the study participants from 1980 to 1998,
when participants were entered into the CAIFOS study.
WADLS provides a complete validated record of every
participant’s primary diagnosis and up to 21 additional
diagnoses of hospitalizations and procedure codes within
Western Australia. Events were defined using primary
diagnosis and procedure codes from the International
Classification of Diseases, Injuries and Causes of Death
Clinical Modification (ICD-9-CM procedure code 81.54) [21].
Incident knee replacement
The primary outcome was KR procedures excluding those
with revisions of previous surgery and those with a primary
diagnosis other than osteoarthritis of the knee. Incident knee
replacement procedures were retrieved from the Western
Australian Data Linkage System (WADLS) hospitalization
record for each of the study participants from 1998, until 10
years after their baseline visit. Events were defined using
primary diagnosis and procedure codes from the International
Classification of Diseases, Injuries and Causes of Death
Clinical Modification (ICD-9-CM) [21] and the International
Statistical Classification of Diseases and Related Health
Problems, 10th Revision, Australian Modification (ICD-10- AM)
[22]. The procedure codes were classified according to the
Australian Institute of Health and Welfare intervention codes
(ICD-9-CM procedure code 81.54 and ICD-10-AM procedure
codes) for primary partial (49517-00) and total knee
replacements (49518-00, 49519-00, 49521-00, 49521-01,
49521-02, 49521-03, 49524-00, 49524-01, 49534-00), [1].
Internal validation
The model was re-tested at 48 months for a shortened
duration of 6 years (2002-2008) in the 1,119 participants who
attended the 2002 clinic visit and compared to WOMAC for
internal validation. Information on knee joint pain frequency
was collected by questionnaire at 60 months.
Western Ontario and Mcmaster University
Osteoarthritis Index (WOMAC)
The Western Ontario and Mcmaster University Osteoarthritis
Index (WOMAC) was completed at the 48 month clinic visit.
Prediction of Knee Replacement
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This scoring system assesses pain (0 to 20), stiffness (0 to 8)
and physical function (0 to 68), with higher scores indicating
more debilitating osteoarthritis [23].
Statistical analysis
The primary outcome was a KR procedure with a selection of
pre-planned patient self-reported data variables including age,
knee pain at baseline, physical activity, calcium intervention
group, prevalent knee replacement and analgesia use for joint
pain at baseline. Physical activity, height and calcium
intervention were non-significant in the model and were
excluded from further analyses. As age > 70 years was a
selection variable in the CAIFOS study it was retained despite
being non-significant in the model to provide age-adjusted
effects of predictor variables. Results are presented as either
an Odds Ratio (OR) or Hazard Ratio (HR) and associated 95%
confidence intervals. P values less than 0.05 in two tailed tests
were considered statistically significant. The data was analysed
using PASW software (version 18, SPSS Inc., Chicago, IL),
STATA (version 11 StataCorp LP, College Station, TX) and
SAS (Version 9, SAS Institute Inc., Chicago, IL).
Results
Over the 10 years of the study there were 129 (8.8%)
individuals who had KR with a primary diagnosis of
osteoarthritis. Of these there were 119 total knee replacements
and 10 partial knee replacements. The effects of the selected
baseline characteristics of the participants by knee
replacement status are shown are shown in Table 1. In those
subsequently requiring KR there was a higher baseline
prevalence of knee pain, consumption of analgesia for joint
pain, previous knee replacement and higher body mass index.
Age, height, physical activity and calcium intervention group
were not significantly different between those with and without
knee replacement.
The selected variables were then entered into the model as
continuous variables, with the exception of prevalent knee
replacement and analgesia use for joint pain, which were
entered as dichotomous (yes / no) variables and knee pain at
baseline which was entered as three groups (infrequent /
frequent / daily). Calcium intervention and physical activity
were not significant in the model and were excluded from
further analyses (Table 2). Similarly including the 41
participants with clinical diagnosis of osteoarthritis of the knee
at baseline or socioeconomic status did not significantly
improve the model (improvement to the C-statistic + 0.007, P =
0.162 and + 0.001, P = 0.758 respectively).
Body mass index
The model was significantly improved with the addition of
body mass index (improvement to the C-statistic + 0.019, P =
0.011, Figure 1).The beta coefficients for the patient self-
reported variables in the final model are shown in Table 3.
Sensitivity analysis
The model was further examined in individuals without
previous knee replacement, without daily knee pain and without
analgesia for joint pain at baseline. In the 1,418 participants
without prevalent knee replacement there was good
discrimination with a C-statistic of 0.780 ± 0.020, similarly in
those without daily knee joint pain (n = 1,234) there was good
discrimination with a C-statistic of 0.762 ± 0.027 and in the 984
participants without analgesia use for joint pain at baseline the
C-statistic was 0.788 ± 0.031.
Table 1. Baseline characteristics.
Characteristics
No knee replacement
(n = 1,333)
10-year knee
replacement (n =
129) P value
Age (years) 75.2 ± 2.7 75.1 ± 2.5 0.695
Physical Activity
(Kcal) 141 ± 153 138 ± 169 0.806
Knee pain
Infrequent 901 (67.6) 35 (27.1) <0.001
Frequent 259 (19.4) 39 (30.2)
Daily 173 (13.0) 55 (42.6)
Analgesia use for
joint pain (yes) 402 (30.2) 76 (58.9) <0.001
Previous knee
replacement (yes) 31 (2.3) 13 (10.1) <0.001
Calcium intervention
(yes) 687 (51.5) 60 (46.5) 0.473
Anthropometric measures
Weight (kg) 67.9 ± 12.3 74.9 ± 13.0 <0.001
Height (cm) 158.8 ± 6.0 159.2 ± 6.0 0.385
Body Mass Index
(kg/m2)26.9 ± 4.7 29.5 ± 4.8 <0.001
All values are mean ± standard deviation for continuous variables or number and
percentage for categorical variables.
doi: 10.1371/journal.pone.0083665.t001
Table 2. Unstandardized regression coefficients and odds
ratio of individual variables tested for 10-year total knee
replacement prediction.
Clinical measures (n = 1,462) β X2OR* (95% CI) P value
Age (years) -0.013 0.154 0.99 (0.92-1.06) 0.695
Prevalent knee replacement (yes) 1.549 20.237 4.71 (2.40-9.22) <0.001
Analgesia use for joint pain (yes) 1.200 40.480 3.32 (2.29-4.81) <0.001
Knee pain (infrequent/frequent/
daily)1.043 86.817 2.84 (2.28-3.53) <0.001
Calcium intervention (yes) -0.151 0.783 0.86 (0.62-1.20) 0.376
Height (cm) 0.013 0.757 1.01 (0.98-1.05) 0.384
Weight (kg) 0.040 35.128 1.04 (1.03-1.05) <0.001
Body mass index (kg/m2) 0.100 33.071 1.11 (1.07-1.14) <0.001
* Odds Ratio (OR) with “no” category as the referent.
doi: 10.1371/journal.pone.0083665.t002
Prediction of Knee Replacement
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Calibration
The calibration of the final model was tested by separating
participants into deciles of predicted 10-year knee replacement
risk compared to the actual risk (Figure 2). This analysis
demonstrated good calibration with the actual risk of KR in
these categories, as demonstrated by the non-significant
Hosmer-Lemeshow test. For clinical recommendations, three
categories of risk for KR were selected, low (
< 5% over 10 years); moderate (5 to < 10% over 10 years)
and high (≥ 10% over 10 years). The actual risk of knee
replacement in the three groups was; low 16 / 741 (2.2%);
moderate 32 / 330 (9.7%) and high 81 / 391 (20.7%), see
Figure 3
.
Table 3. Unstandardized regression coefficients and odds
ratio of the variables in the final 5-variable model for 10-
year total knee replacement prediction.
Clinical measures (n = 1,462) β X2OR* (95% CI) P value
Age (years) -0.002 0.004 1.00 (0.93-1.07) 0.949
Prevalent knee replacement (yes) 0.731 3.936 2.08 (1.01-4.28) 0.047
Analgesia use for joint pain (yes) 0.682 11.208 1.98 (1.33-2.95) 0.001
Knee pain (infrequent/frequent/
daily)0.854 51.260 2.35 (1.86-2.97) <0.001
Body mass index (kg/m2) 0.069 12.94 1.07 (1.03-1.11) <0.001
* Odds Ratio (OR) with “no” category as the referent.
doi: 10.1371/journal.pone.0083665.t003
Figure 1. Receiver operator characteristic (ROC) curve for the 5-variable predictive model. The model includes age, knee
joint pain at baseline, analgesia use for joint pain at baseline, prevalent knee joint replacement and body mass index; C-statistic
0.787 ± 0.019 (black line) or without body mass index C-statistic 0.768 ± 0.021 (red line).
doi: 10.1371/journal.pone.0083665.g001
Prediction of Knee Replacement
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Internal validation
The 5-variable model was then re-tested at 48 months in the
1,119 participants who attended the 48 and 60 month clinic
visit and filled in the questionnaires. There were 65 participants
that underwent KR in the subsequent 6 years. Discrimination
and calibration of the 5-variable model was then tested and
compared to the weighted WOMAC score. The model again
demonstrated good discrimination with a C statistic of 0.810 ±
0.023 and calibration demonstrated by the non-significant
Hosmer-Lemeshow goodness of fit test, P = 0.185. The C
statistic for the weighted WOMAC score was 0.748 ± 0.030,
(mean difference -0.062, P = 0.064), see Figure S1 and Table
S1.
Discussion
In this prospective population-based study of elderly women
we found that 5 simply evaluated variables including joint pain,
age, body mass index, previous knee replacement and
analgesia use for joint pain provided good clinical utility in
predicting future 10-year KR. This model also demonstrated
good discrimination with a C-statistic of 0.79 for 10-year KR.
This C-statistic is comparable to other 10-year risk prediction
models for major osteoporotic fracture (0.68) and hip fracture
(0.75) and coronary heart disease prediction in men (0.74) and
women (0.77) [24,25,26]. The 5-variable model was then
internally validated using the 48 month risk factors and
compared to the WOMAC score and found to be comparable to
the more complicated WOMAC test [23]. We developed this
community based risk calculator for the prediction of KR in the
elderly using non-radiographic self-reported variables primarily
as a “basic” model to test novel metabolic, biochemical and
hormonal risk factors as there is currently no prognostic model
available. Secondly we developed this model to allow clinical
researchers to identify high risk individuals from large
population based cohorts for randomized controlled trials of
Figure 2. Predicted 10-year risk vs. actual 10-year knee replacement.
Categorized by deciles of predicted risk (n = 1,462). Model calibration tested by Hosmer-Lemeshow Goodness-of-Fit test, P =
0.179.
doi: 10.1371/journal.pone.0083665.g002
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novel treatments and early intervention to prevent progression
to KR.
Interestingly in our cohort when clinical diagnosis of
osteoarthritis of the knee at baseline was added to the model it
did not significantly increase the discrimination perhaps due to
the small number of clinically diagnosed knee OA cases.
Similarly socioeconomic status was not associated with the
decision to undergo surgery which perhaps relates to
Australia’s national healthcare system that provides for the joint
replacement procedures irrespective of the ability of the patient
to pay for the surgery, similar to that of the US Medicare
system for people over the age of 65 [4]. However insurance
status may be an important variable in the decision to undergo
knee replacement surgery in other countries.
The addition body mass index significantly improved the
predictive model for knee replacement. These findings confirm
and extend the findings of de Guia et al. [11] and others [12]
who reported younger patients who were overweight or obese
had significantly increased risk of knee replacement. Similarly
Grotle et al. [13] reported that body mass index was associated
with self-reported osteoarthritis of the knee over 10-years of
follow-up. However in a smaller study of participants with end-
stage knee osteoarthritis Zeni et al. [14] did not find any
association with body mass index and knee replacement.
These negative findings may have been due to the short term
follow up (2 years), cohort selection or the inclusion of both
men and women in the study. This is particularly likely as a
meta-analysis reported gender differences in osteoarthritis with
elderly women having a higher incidence and severity of
disease [27]. Thus we have demonstrated that body mass
index significantly improves prediction of knee replacements
over both 10-years and the inclusion of this “modifiable”
variable in the model will allow the public to assess their
reduction in KR risk with decreasing body weight.
The strengths of the current study include the long-term and
complete follow-up of the cohort using person-based linked
hospital procedure and discharge records for participants.
Western Australia is fortunate in having a system that captures
complete coded diagnostic data of all public and private
inpatient contacts and deaths, the Western Australian Data
Figure 3. Kaplan Meier hazard plot for 10-year knee joint replacement. Categorized into 3 categories of predicted risk of 10-
year knee replacement; red dotted line; low risk (< 5%); blue line moderate risk (5 - < 10%) and black line, high risk (≥ 10%), P <
0.001 when compared by the log-rank test.
doi: 10.1371/journal.pone.0083665.g003
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Linkage System (WADLS), a division of the Health Department
of Western Australia. The WADLS provides a comprehensive,
population-based linkage system that connects data from over
30 health-related data sets of residents of Western Australia
[28]. The use of this data system allowed complete
ascertainment of verified adverse events for patients in cohort
studies, independently of patient report. The validity of Western
Australian HMDS data has been exhaustively verified with over
250 publications [28]. This data in conjunction with
anthropometrical measures, medications history and
questionnaires regarding joint pain allowed detailed
assessment of the role of simply evaluated patient self-reported
knee joint replacement risk factors for KR over a long period of
follow up.
A limitation of the study was the lack of radiography at
baseline for the diseased knee joints which has in other studies
has been shown to be predictive of KR in longitudinal studies of
participants with osteoarthritic joint pain [14,29,30,31]. The
inclusion of radiography may have further improved the
discrimination of the model. Despite this and the inherent
variability in the decision to undergo surgery, simply assessed
measures predicted long term knee replacement well in this
high risk population. Further replication is needed to externally
validate the model in other population based cohorts and
countries where socioeconomic status may contribute to the
model. Despite this we see the development of a predictive
model as an important first step in the development of
strategies to reduce the burden of joint replacement surgery.
This early identification will allow targeted interventions in the
high risk group similar to other chronic disease risk calculators.
In conclusion widespread use of a population-based risk
calculator for early identification of 10-year KR risk may allow
identification of high-risk individuals who can then seek
radiological assessment and be targeted for improved early
treatment options. This model may allow clinical researchers to
identify high risk individuals from large population based
cohorts for randomized controlled trials of novel treatments to
prevent progression to knee replacement.
Supporting Information
Figure S1. Receiver operator characteristic (ROC) curve
for the 5-variable predictive model at 48 months compared
to the weighted WOMAC score. 5-variable model C-statistic
0.810 ± 0.023 (black line) and WOMAC 0.748 ± 0.030 (red
line).
(TIFF)
Table S1. Characteristics at 48 months.
(DOCX)
Acknowledgements
We thank all study participants for their cooperation. The
authors would also like to thank the staff at the Data Linkage
Branch, Hospital Morbidity Data collection and Registry of
Births, Deaths, and Marriages providing the data for this study.
Author Contributions
Conceived and designed the experiments: JRL SSD KZ RLP.
Performed the experiments: JRL KZ RLP. Analyzed the data:
JRL SSD KZ RLP. Contributed reagents/materials/analysis
tools: JRL KZ RLP. Wrote the manuscript: JRL SSD KZ RLP.
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