Predicting Poor Outcomes After TKA in Those Awaiting Surgery
Predicting Poor Outcomes After TKA in Those Awaiting Surgery
This study employed a prospective longitudinal design with repeated measures. It was part of a broader study targeted at measuring the effects of wait time on patients undergoing TKA. It adheres to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for observational cohort studies (seep Additional file 1 http://www.biomedcentral.com/1471-2474/15/299/additional).
From 02/2006 to 09/2007, patients newly included on the waiting lists of the departments of orthopaedic surgery of three teaching hospitals in Quebec City, Canada (CHUL, HSFA and HDQ) were recruited. Follow-up of participants ended in 09/2010 because of the extensive wait times in the participating hospitals. All seven orthopaedic surgeons performing TKA in these three hospitals collaborated in the study.
Every week, patients newly enrolled on the surgical wait lists of the three hospitals were contacted by a research nurse by phone. Eligible subjects had to meet the following inclusion criteria: (1) age ≥ 40 years old; (2) scheduled for primary unilateral TKA; 3) understands, reads and speaks French. Patients were excluded if they were suffering from a severe cardiac condition, a severe degenerative disease (other than OA) such as Parkinson's disease, Alzheimer's disease, any type of dystrophies or other type of sclerosis with the potential to interfere with patient recovery following TKA or any severe mental disorder (severe depression, bipolar disorder, schizophrenia or dementia) that could interfere with the ability to answer the protocol questionnaires. Subjects with a previous joint arthroplasty (hip or knee) were also excluded. Those who suffered a major trauma to the knee in the previous year or underwent surgery urgently within 30 days of registration on the waiting list were further excluded.
Data were collected via a review of the patients' medical files and structured 45 minutes phone interviews conducted by three trained interviewers. The interviews were performed a few days after enrolment on the wait lists (mean ± SD: 12.6 ± 4.7 days) and six months after the TKA (mean ± SD: 188.7 ± 5.4 days). Patients were also interviewed before surgery; these results have been reported previously.
Pain, stiffness and function at enrolment and six months after surgery were measured with the Western Ontario and McMaster Osteoarthritis Index (WOMAC), a 24-question tool. The WOMAC has been found to have very good reliability, convergent construct validity and responsiveness, and has been used extensively with similar populations. The WOMAC score was transformed in order to obtain a score that varied from 0 to 100, 0 indicating no pain, no functional limitations nor knee joint stiffness. As there is no universal agreement on what is considered poor outcome following TKA surgery, it was defined as the last quintile of the six-month postoperative WOMAC score (i.e. WOMAC score > 40.4); a satisfactory outcome was defined by a WOMAC score in the first four other quintiles of the distribution (i.e. score ≤ 40.4).
Independent variables collected to be considered as potential predictors in the final predictive model included known important determinants of TKA outcomes reported in the literature Variables were measured at enrolment on the wait list and 6 months after TKA.
Initial diagnosis, anthropometric data and comorbidities were recorded from the subjects' medical files. The burden of comorbidities was assessed using the Cumulative Illness Rating Scale. At the initial interview, questions drawn from the questionnaire of the 1998 Quebec Health Survey were used to measure formal education, employment status, and household income. Social support was also measured with questions from the Quebec Health Survey. Marital status, household living status, and clinical variables such as duration of disease symptoms were also noted during the initial interview. Psychological distress was recorded with a modified version of the Psychological Symptom Index (PSI). The modified PSI includes 13 questions that measure depression and anxiety during the past week (range: 0–42). We also considered individual questions from validated questionnaires (i.e.: social support tool, PSI and WOMAC) to build the rule. This was done in an effort to simplify the number of items to include in the final PR.
Several surgical variables such as type of implant, bearing type, implant fixation, patella resurfacing and the number and type of in-hospital complications (wound infection, dislocation, knee ankylosis and manipulation, cardiovascular/pulmonary/circulatory complications, peripheral/central nervous system involvement, urinary infection, acute confusion, tendon and ligament rupture, blood transfusion) following TKA were recorded by reviewing the subjects' medical files. The same procedure was used to document hospital length of stay and discharge to a rehabilitation or recovery facility. The pre-surgery wait times were calculated from the data extracted from the wait list database of each hospital. Six months following the surgery, patients were asked about walking aid usage and the number of community physiotherapy treatment hours received since discharge from the hospital.
Less than 2% of the data of the WOMAC questionnaire was missing, and it was handled according to the recommendations of the tool's guidelines. Recursive partitioning analyses were used to build the PR. One of the most effective algorithm is Classification and Regression Trees. It relies on considering all combinations of the predictors in order to maximize homogeneity within nodes. The Gini heterogeneity coefficient was used as a criterion to build the models. Since the sample size was relatively small, we used all data in the training set. An automatic approach was first used to build PRs. Then, a set of eligible candidate predictors was created by manual adjustment based on statistical, clinical and ease of use considerations. For each resulting PR, sensitivity, specificity, Area Under the receiver operating characteristics – ROC – Curve (AUC), predictive value of positive and negative tests, as well as positive and negative likelihood ratios were calculated with their 95% confidence intervals. The simplest rule demonstrating the highest sensitivity with acceptable level of specificity was selected as the final tool. The accuracy of the proposed model using 1,000 bootstrap resamples was then calculated for internal validation. All analyses were carried out using SPSS Answer Tree 3.1 (SPSS Inc., Chicago) and SAS statistical suite software version 9.2 (SAS Institute Inc., Cary, NC, U.S.A.).
All participants signed an informed consent form. The study was approved annually by the Research Ethics Boards of all three hospitals (CHUL, HSFA and HSFA).
Methods
Study Design
This study employed a prospective longitudinal design with repeated measures. It was part of a broader study targeted at measuring the effects of wait time on patients undergoing TKA. It adheres to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for observational cohort studies (seep Additional file 1 http://www.biomedcentral.com/1471-2474/15/299/additional).
Settings
From 02/2006 to 09/2007, patients newly included on the waiting lists of the departments of orthopaedic surgery of three teaching hospitals in Quebec City, Canada (CHUL, HSFA and HDQ) were recruited. Follow-up of participants ended in 09/2010 because of the extensive wait times in the participating hospitals. All seven orthopaedic surgeons performing TKA in these three hospitals collaborated in the study.
Participants
Every week, patients newly enrolled on the surgical wait lists of the three hospitals were contacted by a research nurse by phone. Eligible subjects had to meet the following inclusion criteria: (1) age ≥ 40 years old; (2) scheduled for primary unilateral TKA; 3) understands, reads and speaks French. Patients were excluded if they were suffering from a severe cardiac condition, a severe degenerative disease (other than OA) such as Parkinson's disease, Alzheimer's disease, any type of dystrophies or other type of sclerosis with the potential to interfere with patient recovery following TKA or any severe mental disorder (severe depression, bipolar disorder, schizophrenia or dementia) that could interfere with the ability to answer the protocol questionnaires. Subjects with a previous joint arthroplasty (hip or knee) were also excluded. Those who suffered a major trauma to the knee in the previous year or underwent surgery urgently within 30 days of registration on the waiting list were further excluded.
Data Collection
Data were collected via a review of the patients' medical files and structured 45 minutes phone interviews conducted by three trained interviewers. The interviews were performed a few days after enrolment on the wait lists (mean ± SD: 12.6 ± 4.7 days) and six months after the TKA (mean ± SD: 188.7 ± 5.4 days). Patients were also interviewed before surgery; these results have been reported previously.
Dependent Variables
Pain, stiffness and function at enrolment and six months after surgery were measured with the Western Ontario and McMaster Osteoarthritis Index (WOMAC), a 24-question tool. The WOMAC has been found to have very good reliability, convergent construct validity and responsiveness, and has been used extensively with similar populations. The WOMAC score was transformed in order to obtain a score that varied from 0 to 100, 0 indicating no pain, no functional limitations nor knee joint stiffness. As there is no universal agreement on what is considered poor outcome following TKA surgery, it was defined as the last quintile of the six-month postoperative WOMAC score (i.e. WOMAC score > 40.4); a satisfactory outcome was defined by a WOMAC score in the first four other quintiles of the distribution (i.e. score ≤ 40.4).
Independent Variables
Independent variables collected to be considered as potential predictors in the final predictive model included known important determinants of TKA outcomes reported in the literature Variables were measured at enrolment on the wait list and 6 months after TKA.
Potential Predictors at Enrolment on Surgical Wait List
Initial diagnosis, anthropometric data and comorbidities were recorded from the subjects' medical files. The burden of comorbidities was assessed using the Cumulative Illness Rating Scale. At the initial interview, questions drawn from the questionnaire of the 1998 Quebec Health Survey were used to measure formal education, employment status, and household income. Social support was also measured with questions from the Quebec Health Survey. Marital status, household living status, and clinical variables such as duration of disease symptoms were also noted during the initial interview. Psychological distress was recorded with a modified version of the Psychological Symptom Index (PSI). The modified PSI includes 13 questions that measure depression and anxiety during the past week (range: 0–42). We also considered individual questions from validated questionnaires (i.e.: social support tool, PSI and WOMAC) to build the rule. This was done in an effort to simplify the number of items to include in the final PR.
Other Variables
Several surgical variables such as type of implant, bearing type, implant fixation, patella resurfacing and the number and type of in-hospital complications (wound infection, dislocation, knee ankylosis and manipulation, cardiovascular/pulmonary/circulatory complications, peripheral/central nervous system involvement, urinary infection, acute confusion, tendon and ligament rupture, blood transfusion) following TKA were recorded by reviewing the subjects' medical files. The same procedure was used to document hospital length of stay and discharge to a rehabilitation or recovery facility. The pre-surgery wait times were calculated from the data extracted from the wait list database of each hospital. Six months following the surgery, patients were asked about walking aid usage and the number of community physiotherapy treatment hours received since discharge from the hospital.
Statistical Analysis
Less than 2% of the data of the WOMAC questionnaire was missing, and it was handled according to the recommendations of the tool's guidelines. Recursive partitioning analyses were used to build the PR. One of the most effective algorithm is Classification and Regression Trees. It relies on considering all combinations of the predictors in order to maximize homogeneity within nodes. The Gini heterogeneity coefficient was used as a criterion to build the models. Since the sample size was relatively small, we used all data in the training set. An automatic approach was first used to build PRs. Then, a set of eligible candidate predictors was created by manual adjustment based on statistical, clinical and ease of use considerations. For each resulting PR, sensitivity, specificity, Area Under the receiver operating characteristics – ROC – Curve (AUC), predictive value of positive and negative tests, as well as positive and negative likelihood ratios were calculated with their 95% confidence intervals. The simplest rule demonstrating the highest sensitivity with acceptable level of specificity was selected as the final tool. The accuracy of the proposed model using 1,000 bootstrap resamples was then calculated for internal validation. All analyses were carried out using SPSS Answer Tree 3.1 (SPSS Inc., Chicago) and SAS statistical suite software version 9.2 (SAS Institute Inc., Cary, NC, U.S.A.).
Ethics
All participants signed an informed consent form. The study was approved annually by the Research Ethics Boards of all three hospitals (CHUL, HSFA and HSFA).
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