Husham Hasan Jassim1, Waleed Jawad Kadhim1, Khalid Ahmed Mahmood Aledresi2*
1Department of orthopedics, Basrah Teaching Hospital, Basrah Health Directorate, Basrah, Iraq
2Department of orthopedics, Al-sadder teaching hospital, Basrah, Iraq
*Correspondence author: Khalid Ahmed Mahmood Aledresi, Department of orthopedics, Al-sadder teaching hospital, Basrah, Iraq; Email: [email protected]
Published Date: 31-12-2024
Copyright© 2024 by Jassim HH, et al. All rights reserved. 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.
Abstract
Background: Tibial Plateau Fractures (TPF) consist for one percent of all treated fractures. Open Reduction Internal Fixation (ORIF) and reconstruction of articular surfaces are the mainstay of management. In this study, we sought to assess the risk of identifying cases with high risks for poor postoperative outcomes.
Methods: A total of 123 tibial plateau fractures treated at the Basrah Teaching Hospital were prospectively enrolled. The demographics, injury data, surgical management and SMFA scores were obtained. Variables included sex, age, BMI, comorbidity, open fracture, high-energy injury mechanisms, vascular or nerve injuries, residual TP depression, tibial spine fracture and OTA fracture types.
Results: Morbidity was recorded in 55(44.7%) patients. Seventy-one patients smoked during the study period. The high-energy mechanism of trauma was documented in 55.3% of cases, whereas low-energy was found in 44.7% of cases. Compartment syndrome was reported in 34.1% of cases. Open fractures were observed in 32.5% of the cases. The tibial spine was involved in 29.3% of cases. OTA class (C) was more frequently reported than class (B) (63.4% vs. 36.6%). Residual tibial plateau depression was reported in 43.1% of cases. The preoperative fracture depression range value was 1-39 mm while the postoperative fracture depression range was 0-5.5 mm.
Conclusion: SMFA score was used to assess the risk of postoperative dysfunction. Older age, obesity, severe comorbidity, tobacco smoking, alcohol consumption, high trauma energy, large postoperative depression, tibial spine involvement, female sex, complicated open fracture, type C fracture and high SMFA score are predictive risk factors for poor outcomes.
Conflict of Interest declaration:
The authors declare that they have no affiliations with or involvement in any organization or entity with any financial interest in the subject matter or materials discussed in this manuscript.
Keywords: TPF; Open-Reduction-Internal-Fixation; Articular Surfaces; Fracture Depression; OTA Class
Introduction
TPF accounted for one percent of all fractures treated, with annual incidence of 10.3/100,000 [1,2]. ORIF and reconstruction of articular surfaces are the mainstay [3-5]. In the literature, the most commonly evaluated outcome is the functional outcome [6]. The assessment was performed using questionnaires such as the SMFA, SF-36 and KOOS scores. Poor functional outcomes in the SMFA score are associated with poor clinical prognosis and vice-versa [7-9]. Other interesting tools include postoperative pain continuity, radiographic malalignment or fixation failure, intraoperative complications, posttraumatic arthrosis development and unplanned reoperation [6,10-12]. Several studies have determined the postoperative functional outcomes of TPF and have recorded the risk factors associated with poor outcomes. The commonest risk factors are older age, mal-alignment of the mechanical tibia axis, fractures classes and decline qualities among others [13-16]. The ability to investigate poor functional outcome risk factors is vital to the treatment of these injuries, as it may facilitate the initiation of aggressive and specialized physical therapies for higher-risk groups with poor overall outcomes. Here, we sought to assess the risk of identifying cases with a high risk of poor postoperative outcomes.
Methods
Study Design and Setting
In total, 123 tibial plateau fractures treated at Basrah Teaching Hospital were prospectively enrolled. Data on demographics, injury, surgical intervention and SMFA scores were collected.
Inclusion Criteria
- ≥18 years
- Tibial plateau fracture
Postoperative Therapy
All patients enrolled in a structured physical management program 14 days postoperatively
- Range of motion of the knee: active and graduated passive
- Strengthening of quadriceps and hamstrings: Graduated
- Weight-bearing: 3 months postoperatively
Outcomes
Poor Functional Outcome (PFO) was defined as any case with a functional SMFA (>10 points) above the median at the most recent follow-up visit. The most recent follow-up was at least 12 months postoperatively (>21.4). SMFA scores at this level have been revealed to be one SD > the mean in normative data and are linked with worse clinical outcomes and capability [7-9].
Data Collection
Variables included sex, age, BMI, comorbidity, open fracture, high-energy injury mechanisms, vascular or nerve injuries, residual TP depression, tibial spine fracture and OTA fracture types.
Statistics
SPSS ver.24 was used for data analysis. Frequency, range and percentage are described variables. Logistic regression was used to predict risk factors using odds ratios (OR). P <0.05 and 95% CI were considered significant.
Results
A total of 123 patients were prospectively followed for an average of 19.7 months prospectively followed. Based on our data, poor functional outcomes were observed at the long-term follow-up. The age of the patients ranged from 19 to 80 years. BMI ranged between 19.8 and 43.1 kg/m2. The male to female ratio was 2.15:1. Morbidity was recorded in 55(44.7%) patients. Seventy-one patients smoked during the study period. Approximately 14.6% of patients had alcoholism. The high-energy mechanism of trauma was documented in 55.3% of cases, whereas low-energy was found in 44.7% of cases. Compartment syndrome was reported in 34.1% of cases. Vascular and nerve injuries were recorded in 48.8% and 31.4% of the patients, respectively. Open fractures were observed in 32.5% of the cases. The tibial spine was involved in 29.3% of cases. OTA class (C) was more frequently reported than class (B) (63.4% vs. 36.6%). Residual tibial plateau depression was reported in 43.1% of cases. The preoperative fracture depression range value was 1-39 mm while the postoperative fracture depression range was (0 – 5.5) mm (Table 1).
Characteristic | No. | % | |
Age (years) range | 19-80 | – | |
BMI (kg/m2) range | 19.8-43.1 | – | |
Sex | Males | 84 | 68.3 |
Females | 39 | 31.7 | |
Comorbidity | Yes | 55 | 44.7 |
No | 68 | 55.3 | |
Smoking | Yes | 71 | 57.7 |
No | 52 | 42.3 | |
Alcohol | Yes | 18 | 14.6 |
No | 105 | 85.4 | |
Trauma mechanism | High Energy | 68 | 55.3 |
Low Energy | 55 | 44.7 | |
Compartment syndrome | Yes | 42 | 34.1 |
No | 81 | 65.9 | |
Vascular trauma | Positive | 60 | 48.8 |
Negative | 63 | 51.2 | |
Nerves injuries | Positive | 39 | 31.7 |
Negative | 84 | 68.3 | |
Open fractures | Positive | 40 | 32.5 |
Negative | 83 | 67.5 | |
Tibial spine involvement | Positive | 36 | 29.3 |
Negative | 87 | 70.7 | |
OTA class | B | 45 | 36.6 |
C | 78 | 63.4 | |
Residual of tibial plateau depression | Yes | 53 | 43.1 |
No | 70 | 56.9 | |
Preoperative fracture Depression | 1-39 mm | – | |
Postoperative fracture Depression | 0-5.5 mm | – |
Table 1: Demography.
At the long-term follow-up, poor outcomes were analyzed using the logistic regression models listed in Table 2. Old age (p=0.03), high BMI (p=0.04), comorbidity (p=0.02), smoking (p=0.01), alcoholism (p=0.03), trauma mechanism (p=0.01), postoperative depression (p=0.03), tibial spine involvement (p=0.02), female sex (p=0.03), open fracture (p=0.05), fracture type (p=0.001) and SMFA score (p=0.02) were predictive risk factors for poor outcomes.
Variables | OR | P-value | 95% CI | |
Age (years) | 0.78 | 0.03 | 1.068 | 5.46 |
BMI | 0.37 | 0.04 | 1886 | 2.809 |
Comorbidity | 0.5 | 0.02 | 2.022 | 4.378 |
Smoking | 1.02 | 0.01 | 1.231 | 5.998 |
Alcohol | 0.81 | 0.03 | 0.734 | 0.989 |
Trauma mechanism | 2. 7 | 0.01 | 1.777 | 7.656 |
Postoperative Depression | 0. 4 | 0.03 | 1.061 | 1.657 |
Tibial spine involvement | -1. 7 | 0.02 | 0.01 | 0.905 |
Female Gender | -0.119 | 0.03 | 0.197 | 1.922 |
Open fracture | 0.66 | 0.05 | 1.053 | 2.569 |
Fracture types | 2.706 | 0.001 | 1.898 | 2.514 |
SMFA Index | 0.41 | 0.02 | 1.035 | 1.727 |
Table 2: Logistic regression data.
Discussion
The operative fixation aims of TPF are joint stabilization, tibial articular surface reconstruction and promotion of early range of knee motion. Smoking and alcohol consumption are modifiable risk factors. Smoking has deleterious effects on fractures healings [10]. Smoking and alcohol intake are known to be linked to poor wound healing, infections and nonunion in fractures [11]. Spine surgery and lower-extremity fractures are common non-modifiable factors [12,13]. Recently, Konda, et al., developed a targeted algorithm for direct resource usage in the context of orthopedic injuries [14,15]. In this study, older age, obesity, severe comorbidity, tobacco smoking, alcoholism ingestion, high trauma energy, large postoperative depression, tibial spine involvement, female sex, complicated open fracture, type C fracture and high SMFA score were predictive risk factors for poor outcomes. Although 10-point differences from the means of SMFA at (1 year) are reasonable thresholds for poorer outcomes [7-9], using greater thresholds (20-points) would provide greater specificity and PPV to differentiate cases with significant postoperative dysfunction.
Conclusion
The SMFA scores can be used to assess the risk of postoperative dysfunction. Older age, obesity, severe comorbidity, tobacco smoking, alcohol consumption, high trauma energy, large postoperative depression, tibial spine involvement, female sex, complicated open fracture, type C fracture and high SMFA score are predictive risk factors for poor outcomes.
Conflict of Interests
The authors declare that they have no conflict of interest in this paper.
References
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Article Type
Research Article
Publication History
Accepted Date: 09-12-2024
Accepted Date: 23-12-2024
Published Date: 31-12-2024
Copyright© 2024 by Jassim HH, et al. All rights reserved. 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.
Citation: Jassim HH, et al. The Prognosis of Management Following Tibial Plateau Fractures. J Ortho Sci Res. 2024;5(3):1-4.
Characteristic | No. | % | |
Age (years) range | 19-80 | – | |
BMI (kg/m2) range | 19.8-43.1 | – | |
Sex | Males | 84 | 68.3 |
Females | 39 | 31.7 | |
Comorbidity | Yes | 55 | 44.7 |
No | 68 | 55.3 | |
Smoking | Yes | 71 | 57.7 |
No | 52 | 42.3 | |
Alcohol | Yes | 18 | 14.6 |
No | 105 | 85.4 | |
Trauma mechanism | High Energy | 68 | 55.3 |
Low Energy | 55 | 44.7 | |
Compartment syndrome | Yes | 42 | 34.1 |
No | 81 | 65.9 | |
Vascular trauma | Positive | 60 | 48.8 |
Negative | 63 | 51.2 | |
Nerves injuries | Positive | 39 | 31.7 |
Negative | 84 | 68.3 | |
Open fractures | Positive | 40 | 32.5 |
Negative | 83 | 67.5 | |
Tibial spine involvement | Positive | 36 | 29.3 |
Negative | 87 | 70.7 | |
OTA class | B | 45 | 36.6 |
C | 78 | 63.4 | |
Residual of tibial plateau depression | Yes | 53 | 43.1 |
No | 70 | 56.9 | |
Preoperative fracture Depression | 1-39 mm | – | |
Postoperative fracture Depression | 0-5.5 mm | – |
Table 1: Demography.
Variables | OR | P-value | 95% CI | |
Age (years) | 0.78 | 0.03 | 1.068 | 5.46 |
BMI | 0.37 | 0.04 | 1886 | 2.809 |
Comorbidity | 0.5 | 0.02 | 2.022 | 4.378 |
Smoking | 1.02 | 0.01 | 1.231 | 5.998 |
Alcohol | 0.81 | 0.03 | 0.734 | 0.989 |
Trauma mechanism | 2. 7 | 0.01 | 1.777 | 7.656 |
Postoperative Depression | 0. 4 | 0.03 | 1.061 | 1.657 |
Tibial spine involvement | -1. 7 | 0.02 | 0.01 | 0.905 |
Female Gender | -0.119 | 0.03 | 0.197 | 1.922 |
Open fracture | 0.66 | 0.05 | 1.053 | 2.569 |
Fracture types | 2.706 | 0.001 | 1.898 | 2.514 |
SMFA Index | 0.41 | 0.02 | 1.035 | 1.727 |
Table 2: Logistic regression data.