Predictive models for identifying risk of readmission after index hospitalization for hip arthroplasty: A systematic review

Predictive Modeling of Medical- and Orthopaedic-Related 90-Day Readmissions Following Primary Total Hip Arthroplasty

BACKGROUND

The rate of unplanned hospital readmissions following total hip arthroplasty (THA) varies from 3 to 10%, representing a major economic burden. However, it is unknown if specific factors are associated with different types of complications (ie, medical or orthopaedic-related) that lead to readmissions. Therefore, this study aimed to: (1) determine the overall, medical-related, and orthopaedic-related 90-day readmission rate; and (2) develop a predictive model for risk factors affecting overall, medical-related, and orthopaedic-related 90-day readmissions following THA.

METHODS

A prospective cohort of primary unilateral THAs performed at a large tertiary academic center in the United States from 2016 to 2020 was included (n = 8,893 patients) using a validated institutional data collection system. Orthopaedic-related readmissions were specific complications affecting the prosthesis, joint, and surgical wound. Medical readmissions were due to any other cause requiring medical management. Multivariable logistic regression models were used to investigate associations between prespecified risk factors and 90-day readmissions, as well as medical and orthopaedic-related readmissions independently.

RESULTS

Overall, the rate of 90-day readmissions was 5.6%. Medical readmissions (4.2%) were found to be more prevalent than orthopaedic-related readmissions (1.4%). The area under the curve for the 90-day readmission model was 0.71 (95% confidence interval: 0.69 to 0.74). Factors significantly associated with medical-related readmissions were advanced age, Black race, education, Charlson Comorbidity Index, surgical approach, opioid overdose risk score, and nonhome discharge. In contrast, risk factors linked to orthopaedic-related readmissions encompassed body mass index, patient-reported outcome measure phenotype, nonosteoarthritis indication, opioid overdose risk, and nonhome discharge.

CONCLUSIONS

Of the overall 90-day readmissions following primary THA, 75% were due to medical-related complications. Our successful predictive model for complication-specific 90-day readmissions highlights how different risk factors may disproportionately influence medical versus orthopaedic-related readmissions, suggesting that patient-specific, tailored preventive measures could reduce postoperative readmissions in the current value-based health care setting.

An overview of reviews and synthesis across 440 studies examines the importance of hospital readmission predictors across various patient populations

OBJECTIVES

The scientific literature contains an abundance of prediction models for hospital readmissions. However, no review has yet synthesized their predictors across various patient populations. Therefore, our aim was to examine predictors of hospital readmissions across 13 patient populations.

STUDY DESIGN AND SETTING

An overview of systematic reviews was combined with a meta-analytical approach. Two thousand five hundred four different predictors were categorized using common ontologies to pool and examine their odds ratios and frequencies of use in prediction models across and within different patient populations.

RESULTS

Twenty-eight systematic reviews with 440 primary studies were included. Numerous predictors related to prior use of healthcare services (odds ratio; 95% confidence interval: 1.64; 1.42-1.89), diagnoses (1.41; 1.31-1.51), health status (1.35; 1.20-1.52), medications (1.28; 1.13-1.44), administrative information about the index hospitalization (1.23; 1.14-1.33), clinical procedures (1.20; 1.07-1.35), laboratory results (1.18; 1.11-1.25), demographic information (1.10; 1.06-1.14), and socioeconomic status (1.07; 1.02-1.11) were analyzed. Diagnoses were frequently used (in 37.38%) and displayed large effect sizes across all populations. Prior use of healthcare services showed the largest effect sizes but were seldomly used (in 2.57%), whereas demographic information (in 13.18%) was frequently used but displayed small effect sizes.

CONCLUSION

Diagnoses and patients' prior use of healthcare services showed large effects both across and within different populations. These results can serve as a foundation for future prediction modeling.

Continuity of primary care and emergency department visits following knee and hip replacement surgery: a retrospective cohort study

BACKGROUND

Continuity of primary care (CPC) improves patient well-being, but the association between CPC and surgical outcomes has not been well studied. The numbers of joint replacement procedures are expected to rise considerably in the coming years, so it is crucial to identify factors related to successful outcomes. The purpose of this study was to examine the association between CPC and emergency department (ED) visits after knee and hip replacement surgery.

METHODS

Physician claims and hospital data from 2005 to 2020 in Nova Scotia were used in this retrospective study. To measure CPC, we used the Modified Modified Continuity Index (MMCI), which is the number of primary care providers adjusted for the total number of visits. The outcome was ED visits within 90 days of discharge. Logistic regression was used to test for associations between MMCI and the probability of an ED visit.

RESULTS

There were 28 574 knee and 16 767 hip procedures in the data set; 13.9% (95% confidence interval [CI] 13.5%-14.3%) and 13.5% (95% CI 13.0%-14.0%) of the patients, respectively, had an ED visit within 90 days. For patients who underwent knee procedures, the mean MMCI was 0.868 (95% CI 0.867-0.870); 10.7% (95% CI 10.4 %-11.1 %) had perfect continuity of care. For patients who underwent hip procedures, the corresponding measures were 0.864 (95% CI 0.862-0.866) and 13.5% (95% CI 13.0%-14.0%). There was a statistically significant negative association between greater continuity of care and the probability of an ED visit after controlling for confounders.

CONCLUSION

Having multiple primary care providers before surgery increased the likelihood of negative outcomes following knee or hip replacement surgery compared with having a single provider. Presurgical conversations should include primary care history to improve postsurgical outcomes.

Effect of Variation in Early Rehabilitation on Hospital Readmission After Hip Fracture

OBJECTIVE

Provision of early rehabilitation services during acute hospitalization after a hip fracture is vital for improving patient outcomes. The purpose of this study was to examine the association between the amount of rehabilitation services received during the acute care stay and hospital readmission in older patients after a hip fracture.

METHODS

Medicare claims data (2016-2017) for older adults admitted to acute hospitals for a hip fracture (n = 131,127) were used. Hospital-based rehabilitation (physical therapy, occupational therapy, or both) was categorized into tertiles by minutes per day as low (median = 17.5), middle (median = 30.0), and high (median = 48.8). The study outcome was risk-adjusted 7-day and 30-day all-cause hospital readmission.

RESULTS

The median hospital stay was 5 days (interquartile range [IQR] = 4-6 days). The median rehabilitation minutes per day was 30 (IQR = 21-42.5 minutes), with 17 (IQR = 12.6-20.6 minutes) in the low tertile, 30 (IQR = 12.6-20.6 minutes) in the middle tertile, and 48.8 (IQR = 42.8-60.0 minutes) in the high tertile. Compared with high therapy minutes groups, those in the low and middle tertiles had higher odds of a 30-day readmission (low tertile: odds ratio [OR] = 1.11, 95% CI = 1.06-1.17; middle tertile: OR = 1.07, 95% CI = 1.02-1.12). In addition, patients who received low rehabilitation volume had higher odds of a 7-day readmission (OR = 1.20; 95% CI = 1.10-1.30) compared with high volume.

CONCLUSION

Elderly patients with hip fractures who received less rehabilitation were at higher risk of readmission within 7 and 30 days.

IMPACT

These findings confirm the need to update clinical guidelines in the provision of early rehabilitation services to improve patient outcomes during acute hospital stays for individuals with hip fracture.

LAY SUMMARY

There is significant individual- and hospital-level variation in the amount of hospital-based rehabilitation delivered to older adults during hip fracture hospitalization. Higher intensity of hospital-based rehabilitation care was associated with a lower risk of hospital readmission within 7 and 30 days.

Predicting Length of Stay using machine learning for total joint replacements performed at a rural community hospital

Background

Predicting patient’s Length of Stay (LOS) before total joint replacement (TJR) surgery is vital for hospitals to optimally manage costs and resources. Many hospitals including in rural areas use publicly available models such as National Surgical Quality Improvement Program (NSQIP) calculator which, unfortunately, performs suboptimally when predicting LOS for TJR procedures.

Objective

The objective of this research was to develop a Machine Learning (ML) model to predict LOS for TJR procedures performed at a Perioperative Surgical Home implemented rural community hospital for better accuracy and interpretation than the NSQIP calculator.

Methods

A total of 158 TJR patients were collected and analyzed from a rural community hospital located in Montana. A random forest (RF) model was used to predict patient’s LOS. For interpretation, permuted feature importance and partial dependence plot methods were used to identify the important variables and their relationship with the LOS.

Results

The root mean square error for the RF model (0.7) was lower than the NSQIP calculator (1.21). The five most important variables for predicting LOS were BMI, Duke Activity Status-Index, diabetes, patient’s household income, and patient’s age.

Conclusion

This pilot study is the first of its kind to develop an ML model to predict LOS for TJR procedures that were performed at a small-scale rural community hospital. This pilot study contributes an approach for rural hospitals, making them more independent by developing their own predictions instead of relying on public models.

A Review of Artificial Intelligence and Robotics in Transformed Health Ecosystems

Health care is shifting toward become proactive according to the concept of P5 medicine-a predictive, personalized, preventive, participatory and precision discipline. This patient-centered care heavily leverages the latest technologies of artificial intelligence (AI) and robotics that support diagnosis, decision making and treatment. In this paper, we present the role of AI and robotic systems in this evolution, including example use cases. We categorize systems along multiple dimensions such as the type of system, the degree of autonomy, the care setting where the systems are applied, and the application area. These technologies have already achieved notable results in the prediction of sepsis or cardiovascular risk, the monitoring of vital parameters in intensive care units, or in the form of home care robots. Still, while much research is conducted around AI and robotics in health care, adoption in real world care settings is still limited. To remove adoption barriers, we need to address issues such as safety, security, privacy and ethical principles; detect and eliminate bias that could result in harmful or unfair clinical decisions; and build trust in and societal acceptance of AI.

Effects of Comorbidities on Pain and Function After Total Hip Arthroplasty

Background

The growing number of patients undergoing total hip arthroplasty (THA) and postoperative outcomes receive increasing attention from doctors and patients. This study aimed to elucidate the effects of comorbidities on postoperative function, pain, complications, readmission rate, and mortality.

Methods

We included consecutive patients who underwent primary unilateral THA between 2017 and 2019. The Charlson comorbidity index (CCI) and the WOMAC and SF-36 (physical function, body pain) scales were assessed preoperatively and at 3, 6, 12, and 24 months postoperatively. The complications, 30-day readmission, and mortality rates assessed the impact of comorbidities and their changes over time on the WOMAC and SF-36 scores during follow-up. We used mixed model linear regression to examine the association of worsening comorbidity post-THA with change in WOMAC and SF-36 scores in the subsequent follow-up periods, controlling for age, length of follow-up, and repeated observations.

Results

This study included 468 patients, divided into four groups based on comorbidity burden (CCI-0, 1, 2, and ≥3). The physiological function recovery and pain scores in the CCI ≥ 3 group were inferior to the other groups and took longer than the other groups (6 vs. 3 months) to reach their best level. The four groups preoperative waiting times were 2.41 ± 0.74, 2.97 ± 0.65, 3.80 ± 0.53, and 5.01 ± 0.71 days, respectively. The complications, 30-day readmission, and 1-year mortality rates for the overall and the CCI ≥ 3 group were 1.92% and 4.69%, 0.85% and 2.01%, and 0.43% and 1.34%, respectively, with no mortality in the other groups.

Conclusion

Patients with higher CCI were more susceptible to physical function and pain outcome deterioration, experienced longer waiting time before surgery, took longer to recover, and had higher rates of complications, 30-day readmission, and mortality after THA. Older age in the group led to a greater impact.

Stepping toward outpatient total hip arthroplasty with a rapid discharge protocol

With total hip arthroplasty no longer an inpatient only procedure, this study retrospectively evaluated the percentage of same day, outpatient (<24 h) and inpatient (>24 h) discharges between 543 standard discharge (SDP) and 372 rapid discharge (RDP) patients. Outpatient discharges increased from 31.9% with SDP to 87.4% with RDP (p < 0.001) and 16.4% achieving same day discharge. Patients not achieving outpatient discharge were more commonly older (odds ratio (OR):1.052, p = 0.003), female (OR:2.715, p = 0.003) and a higher comorbidity classification (OR:2.751, p = 0.002). The 12.6% of patients failing to achieve outpatient discharge suggests that careful patient selection may be necessary to obtain 100% success.