Length of Hospital Stay after Total Knee Arthroplasty: A Correlation Study on 1200 Patients

Comparison of the effect of open-box versus closed-box prostheses on blood loss following total knee arthroplasty: a meta-analysis

Purpose

Postoperative blood loss is a common complication following total knee arthroplasty (TKA). The authors aimed to analyze the significance of open versus closed-box prostheses in reducing blood loss after TKA.

Methods

PubMed, Cochrane, Scopus, and Web of Science were searched. Observational studies and clinical trials comparing the effect of open-box versus closed-box prostheses on blood loss following TKA were included. The primary outcome was total blood loss following TKA. Secondary outcomes included average transfused units and total operation time. Continuous data were represented as mean difference (MD) and CI, while dichotomous data were presented as odds ratio (OR) and CI. RevMan software version 5.4 was used to conduct the analysis.

Results

Four studies with a total number of 687 patients were included. The pooled analysis showed a statistically significant association between closed-box and decreased total blood loss following TKA compared with open-box (MD=173.19, 95% CI=88.77-257.61, P value <0.0001). Similar findings were reported in unilateral TKA (MD=190.63, 95% CI=70.91-310.35, P value=0.002), and bilateral TKA (MD=160.79, 95% CI=61.70-359.86, P value=0.001). There was no significant difference between open and closed-box regarding average transfused units (MD=0.02, 95% CI=-0.07-0.11, P value=0.68), blood transfusion rate (OR=1.38, 95% CI=0.85-2.26, P value=0.20), length of stay (MD=0.06, 95% CI=-0.27 to 0.38, P value=0.74), and total operation time (MD=1.08, 95% CI=-4.62 to 6.79, P value=0.71).

Conclusion

Closed-box reduces the total blood loss following unilateral and bilateral TKA. More studies are warranted to explore the benefits of Closed-box in patients with high bleeding susceptibility.

Leg length discrepancy before total knee arthroplasty is associated with increased complications and earlier time to revision

INTRODUCTION

Leg length discrepancy (LLD) following total knee arthroplasty (TKA) is a common complaint, leading to decreased patient satisfaction. However, the effect of LLD diagnosis prior to TKA on outcomes and complications is not well defined. Thus, this study aimed to assess the effects that LLD has on rates of falls and implant complications, length of stay and readmissions, and implant survivorship following TKA.

METHODS

A retrospective review of a private insurance claims database was conducted from 2010 to 2021. All cases of TKA and those with a diagnosis of leg length discrepancy were identified. Patients undergoing TKA with a diagnosis of LLD were matched to control patients 1:5 based on demographic and comorbidity profiles. Two-year fall rates and implant complications, lengths of stay, 90-day readmissions, and time to revision were compared between cohorts.

RESULTS

A total of 1,378 LLD patients were matched to 6,889 control patients. The LLD group had significantly higher rates of falls, dislocation, mechanical loosening, periprosthetic fracture, and fibrosis when compared to the control group (all P < 0.01). Additionally, mean length of stay was significantly greater in the LLD group (4.9 days vs. 3.0 days, P < 0.001). There was no significant difference in 90-day readmission rates between groups (P = 0.178). Time to revision was significantly shorter in the LLD group (392 days vs. 928 days, P < 0.001).

CONCLUSIONS

Leg length discrepancy in patients undergoing TKA was associated with significantly increased fall risk, rates of implant complications, length of stay, and faster time to revision. The findings of this study may allow orthopedic surgeons to identify those patients at risk and allow for more educated patient counseling and operative planning.

LEVEL OF EVIDENCE

III, retrospective case-control study.

Identifying patients at risk of prolonged hospital length of stay after total knee arthroplasty: A real-world study on the creation and validation of a cloud estimator

Accurate prediction of the length of stay for patients undergoing total knee arthroplasty (TKA) is critical for efficient medical resource allocation. This study aimed to create a user-friendly model to assist this estimation process. A secondary analysis was conducted on 2676 patients who underwent elective primary TKA at a tertiary academic medical center in Singapore from January 2013 to June 2014. The eligible patients (n = 2600) were randomly divided into a training cohort (n = 2081) and a validation cohort (n = 519), at a ratio of 4:1. A prolonged hospital stay was defined as exceeding six days. Multivariable logistic regression was used to develop a prediction model, and an online calculator was created to facilitate its application. The model's discrimination power, goodness-of-fit, and clinical applicability were evaluated. Additionally, models using other statistical methods were developed for performance comparison. The model includes predictors such as age, operation duration, history of cerebrovascular accidents, creatinine levels, procedure site, the American Society of Anesthesiologists Physical status, hemoglobin levels, and primary anesthesia type. The model demonstrated robust discrimination power with a C statistic of 0.70 (95% confidence interval, 0.64 to 0.75), satisfactory goodness-of-fit (Hosmer-Lemeshow test, P=0.286), and was applicable when thresholds were between 0.08 and 0.52, based on decision curve analysis. A predictive model was developed that can be used to identify patients who are likely to require an extended stay following TKA. This could assist in planning bed availability and guiding therapeutic decisions.

Length of stay and discharge dispositions following robotic arm-assisted total knee arthroplasty and unicompartmental knee arthroplasty versus conventional technique and predictors of delayed discharge

Aims

In-hospital length of stay (LOS) and discharge dispositions following arthroplasty could act as surrogate measures for improvement in patient pathways, and have major cost saving implications for healthcare providers. With the ever-growing adoption of robotic technology in arthroplasty, it is imperative to evaluate its impact on LOS. The objectives of this study were to compare LOS and discharge dispositions following robotic arm-assisted total knee arthroplasty (RO TKA) and unicompartmental arthroplasty (RO UKA) versus conventional technique (CO TKA and UKA).

Methods

This large-scale, single-institution study included patients of any age undergoing primary TKA (n = 1,375) or UKA (n = 337) for any cause between May 2019 and January 2023. Data extracted included patient demographics, LOS, need for post anaesthesia care unit (PACU) admission, anaesthesia type, readmission within 30 days, and discharge dispositions. Univariate and multivariate logistic regression models were also employed to identify factors and patient characteristics related to delayed discharge.

Results

The median LOS in the RO TKA group was 76 hours (interquartile range (IQR) 54 to 104) versus 82.5 (IQR 58 to 127) in the CO TKA group (p < 0.001) and 54 hours (IQR 34 to 77) in the RO UKA versus 58 (IQR 35 to 81) in the CO UKA (p = 0.031). Discharge dispositions were comparable between the two groups. A higher percentage of patients undergoing CO TKA required PACU admission (8% vs 5.2%; p = 0.040).

Conclusion

Our study showed that robotic arm assistance was associated with a shorter LOS in patients undergoing primary UKA and TKA, and no difference in the discharge destinations. Our results suggest that robotic arm assistance could be advantageous in partly addressing the upsurge of knee arthroplasty procedures and the concomitant healthcare burden; however, this needs to be corroborated by long-term cost-effectiveness analyses and data from randomized controlled studies.

A novel predictive model of hospital stay for Total Knee Arthroplasty patients

Objective

This study aimed to explore the main risk factors affecting Total Knee Arthroplasty (TKA) patients and develop a predictive nomogram of hospital stay.

Methods

In total, 2,622 patients undergoing TKA in Singapore were included in this retrospective cohort study. Hospital extension was defined based on the 75% quartile (Q3) of hospital stay. We randomly divided all patients into two groups using a 7:3 ratio of training and validation groups. We performed univariate analyses of the training group, in which variables with P-values < 0.05 were included and then subjected to multivariate analysis. The multivariable logistic regression analysis was applied to build a predicting nomogram, using variable P-values < 0.01. To evaluate the prediction ability of the model, we calculated the C-index. The ROC, Calibration, and DCA curves were drawn to assess the model. Finally, we verified the accuracy of the model using the validation group and by also using the C-index. The ROC curve, Calibration curve, and DCA curve were then applied to evaluate the model in the validation group.

Results

The final study included 2,266 patients. The 75% quartile (Q3) of hospital stay was six days. In total, 457 (20.17%) patients had hospital extensions. There were 1,588 patients in the training group and 678 patients in the validation group. Age, Hb, D.M., Operation Duration, Procedure Description, Day of Operation, Repeat Operation, and Blood Transfusion were used to build the prediction model. The C-index was 0.680 (95% CI: 0.734-0.626) in the training group and 0.710 (95% CI: 0.742-0.678) for the validation set. The calibration curve and DCA indicated that the hospital stay extension model showed good performance in the training and validation groups.

Conclusion

To identify patients' risk factors early, medical teams need to plan a patient's rehabilitation path as a whole. Its advantages lie in better resource allocation, maximizing medical resources, improving the functional recovery of patients, and reducing the overall cost of hospital stay and surgery, and will help clinicians in the future.