The American College of Surgeons National Surgical Quality Improvement Program Surgical Risk Calculator Has a Role in Predicting Discharge to Post-Acute Care in Total Joint Arthroplasty

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.

The utilization of artificial neural networks for the prediction of 90-day unplanned readmissions following total knee arthroplasty

BACKGROUND

A reliable predictive tool to predict unplanned readmissions has the potential to lower readmission rates through targeted pre-operative counseling and intervention with respect to modifiable risk factors. This study aimed to develop and internally validate machine learning models for the prediction of 90-day unplanned readmissions following total knee arthroplasty.

METHODS

A total of 10,021 consecutive patients underwent total knee arthroplasty. Patient charts were manually reviewed to identify patient demographics and surgical variables that may be associated with 90-day unplanned hospital readmissions. Four machine learning algorithms (artificial neural networks, support vector machine, k-nearest neighbor, and elastic-net penalized logistic regression) were developed to predict 90-day unplanned readmissions following total knee arthroplasty and these models were evaluated using ROC AUC statistics as well as calibration and decision curve analysis.

RESULTS

Within the study cohort, 644 patients (6.4%) were readmitted within 90 days. The factors most significantly associated with 90-day unplanned hospital readmissions included drug abuse, surgical operative time, and American Society of Anaesthesiologist Physical Status (ASA) score. The machine learning models all achieved excellent performance across discrimination (AUC > 0.82), calibration, and decision curve analysis.

CONCLUSION

This study developed four machine learning models for the prediction of 90-day unplanned hospital readmissions in patients following total knee arthroplasty. The strongest predictors for unplanned hospital readmissions were drug abuse, surgical operative time, and ASA score. The study findings show excellent model performance across all four models, highlighting the potential of these models for the identification of high-risk patients prior to surgery for whom coordinated care efforts may decrease the risk of subsequent hospital readmission.

LEVEL OF EVIDENCE

Level III, case-control retrospective analysis.

Neural network models accurately predict discharge disposition after revision total knee arthroplasty?

PURPOSE

Based on the rising incidence of revision total knee arthroplasty (TKA), bundled payment models may be applied to revision TKA in the near future. Facility discharge represents a significant cost factor for those bundled payment models; however, accurately predicting discharge disposition remains a clinical challenge. The purpose of this study was to develop and validate artificial intelligence algorithms to predict discharge disposition following revision total knee arthroplasty.

METHODS

A retrospective review of electronic patient records was conducted to identify patients who underwent revision total knee arthroplasty. Discharge disposition was defined as either home discharge or non-home discharge, which included rehabilitation and skilled nursing facilities. Four artificial intelligence algorithms were developed to predict this outcome and were assessed by discrimination, calibration and decision curve analysis.

RESULTS

A total of 2228 patients underwent revision TKA, of which 1405 patients (63.1%) were discharged home, whereas 823 patients (36.9%) were discharged to a non-home facility. The strongest predictors for non-home discharge following revision TKA were American Society of Anesthesiologist (ASA) score, Medicare insurance type and revision surgery for peri-prosthetic joint infection, non-white ethnicity and social status (living alone). The best performing artificial intelligence algorithm was the neural network model which achieved excellent performance across discrimination (AUC = 0.87), calibration and decision curve analysis.

CONCLUSION

This study developed four artificial intelligence algorithms for the prediction of non-home discharge disposition for patients following revision total knee arthroplasty. The study findings show excellent performance on discrimination, calibration and decision curve analysis for all four candidate algorithms. Therefore, these models have the potential to guide preoperative patient counselling and improve the value (clinical and functional outcomes divided by costs) of revision total knee arthroplasty patients.

LEVEL OF EVIDENCE

IV.

Life After BPCI: High Quality Total Knee and Hip Arthroplasty Care Can Still Exist Outside of a Bundled Payment Program

BACKGROUND

Concerns regarding target price methodology and financial penalties have led to withdrawal from Medicare bundled payment programs for total hip (THA) and knee arthroplasty (TKA), despite its early successful results. The purpose of this study was to determine whether there was any difference in patient comorbidities and outcomes following our institution's exit from the Bundled Payments for Care Improvement - Advanced (BPCI-A).

METHODS

We reviewed consecutive 2,737 primary TKA and 2,009 primary THA patients following our withdraw from BPCI-A January 1, 2020-March 30, 2021 and compared them to 1,203 TKA and 1,088 THA patients from October 1, 2018-August 2, 2019 enrolled in BPCI-A. We compared patient demographics, comorbidities, discharge disposition, complications, and 90-day readmissions. Multivariate analysis was performed to identify if bundle participation was associated with complications or readmissions.

RESULTS

Post-bundle TKA had shorter length of stay (1.4 vs 1.8 days, P < .001). Both TKA and THA patients were significantly less likely to be discharged to a rehabilitation facility (5.6% vs 19.2%, P < .001 and 6.0% vs 10.0%, P < .001, respectively). Controlling for confounders, post-bundle TKA had lower complications (OR = 0.66, 95% CI 0.45-0.98, P = .037) but no difference in 90-day readmission (OR = 0.80, 95% CI 0.55-1.16, P = .224).

CONCLUSIONS

Since leaving BPCI-A, we have maintained high quality THA care and improved TKA care with reduced complications and length of stay under a fee-for-service model. Furthermore, we have lowered rehabilitation discharge for both TKA and THA patients. CMS should consider partnering with high performing institutions to develop new models for risk sharing.

Development of a Risk Stratification Scoring System to Predict General Surgical Complications for Patients Undergoing Foot and Ankle Surgery

Preventing postoperative complications is crucial for patients, surgeons, and health care facilities. We developed a risk stratification scoring system to optimize postoperative outcomes for patients undergoing foot and ankle surgery. A total of 35,580 patients who underwent foot and ankle procedures from 2005 to 2017 were identified as part of the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP). To assess the risk of a postoperative complication, we identified several independent risk factors associated with 30-day postoperative complications, then proceeded to develop a point-based risk scoring system. To validate our scoring system, we used it on a cohort of patients from the database who underwent foot and ankle surgery. Risk factors that correlated with postoperative complications included tobacco abuse, age (≥65 years), diabetes mellitus, hypertension, elevated creatinine level (≥1.3 mg/dL), hypoalbuminemia (<3.5 g/dL), chronic obstructive pulmonary disease (COPD), congestive heart failure (CHF), hyponatremia (<135 mEq/L), and anemia (hematocrit value, men <42%; women <38%). Point scores for each factor were: anemia, +10; hypoalbuminemia, +9; elevated creatinine level, +6; CHF, +4; diabetes mellitus, +3; hyponatremia, +3; COPD, +2; hypertension, +2; age, +1; and tobacco abuse, +1. For the validation cohort, we stratified patients according to risk as low (0-20 points), medium (21-30 points), and high (≥31 points) risk. In terms of having a postoperative complication, compared with low-risk patients, patients who were at medium risk had an odds ratio of 4.7 (95% CI, 2.8-7.9) and those at high risk had an odds ratio of 8.3 (95% CI, 4.8-14.5). [Orthopedics. 2022;45(3):139-144.].

The American College of Surgeons National Surgical Quality Improvement Program surgical risk calculator is not reliable in predicting complications and length of stay after primary total hip arthroplasty at an institution implementing clinical pathways

INTRODUCTION

The authors aimed to: (1) determine how length of stay (LOS) and complication rates changed over the past 10 years, in comparison to values estimated by the ACS-NSQIP surgical risk calculator, at a single private institution open to external surgeons; and (2) determine preoperative patient factors associated with complications.

METHODS

We retrospectively assessed 1018 consecutive patients who underwent primary elective THA over 10 years. We excluded 87 with tumours and 52 with incomplete records. Clinical data of the remaining 879 were used to determine real LOS and rate of 9 adverse events over time, as well as to estimate these values using the risk calculator. Its predictive reliability was represented on receiver operating characteristic curves. Multivariable analyses were performed to determine associations of complications with age, sex, ASA score, diabetes, hypertension, heart disease, smoking and BMI.

RESULTS

Over the 10-year period, real LOS and real complication rates decreased considerably, while LOS and complication rates estimated by the surgical risk calculator had little or no change. The difference between real and estimated LOS decreased over time. The overall estimated and real rates of any complication were respectively 3.3% and 2.8%. The risk calculator had fair reliability for predicting any complications (AUC 0.72). Overall estimated LOS was shorter than the real LOS in 764 (86.9%) patients. Multivariable analysis revealed risks of any complication to be greater in patients aged ⩾75 (OR = 4.36, p = 0.002), and with hypertension (OR = 3.13, p = 0.016).

CONCLUSIONS

Since the implementation of clinical pathways at our institution, real LOS and complication rates decreased considerably, while LOS and complication rates estimated by the surgical risk calculator had little or no change. The difference between real and estimated LOS decreased over time, which could lead some clinicians to reconsider their discharge criteria, knowing that advanced age and hypertension increased risks of encountering complications.

The ACS-NSQIP surgical risk calculator is a poor predictor of postoperative complications in patients undergoing oncologic distal femoral replacement

BACKGROUND

Distal femur replacement (DFR) has become a preferred reconstruction for tumors involving the femur but is associated with known complications. The ACS-NSQIP surgical risk calculator is an online tool developed to estimate postoperative complications in the first 30-days, however, has not been used in patients undergoing DFR. The purpose of this study was determining the utility of the ACS-NSQIP calculator to predict postoperative complications.

METHODS

56 (30 male, 26 female) patients who underwent DFR were analyzed using the CPT codes: 27,365 (Under Excision Procedures on the Femur and Knee Joint), 27,447 (Arthroplasty, knee, condyle and plateau), 27,486 (Revision of total knee arthroplasty, with or without allograft), 27,487 (Revision of total knee arthroplasty, with or without allograft) and 27,488 (Repair, Revision, and/or Reconstruction Procedures on the Femur [Thigh Region] and Knee Joint). The predicted rates of complications were compared to the observed rates.

RESULTS

Complications were noted in 30 (54%) of patients. The predicted risk of complications based off the CPT codes were: 27,356 (14%); 27,447 (5%); 27,486 (7%); 27,487 (8%) and 27,488 (12%). Based on ROC curves, the use of the ACS-NSQIP score were poor predictors of complications (27356, AUC 0.54); (27447, AUC 0.45); (27486, AUC 0.45); (27487, AUC 0.46); (27488, AUC 0.46).

CONCLUSIONS

Distal femur arthroplasty performed in the setting of oncologic orthopedics is a complex procedure in a "high risk" surgical group. The ACS-NSQIP does not adequately predict the incidence of complications in these patients and cannot be reliably used in the shared decision-making process.

Utility of the ACS-NSQIP surgical risk calculator in predicting postoperative complications in patients undergoing oncologic proximal femoral replacement

INTRODUCTION

Proximal femur replacement (PFR) in the setting of tumor resection is associated with a high rate of postoperative complication. The online American College of Surgeons-National Surgical Quality Improvement Program (ACS-NSQIP) surgical risk calculator is approved by the Center of Medicare and Medicaid services to estimate 30-day postoperative complications. This study was to determine if the ACS-NSQIP can predict postoperative complications following PFR.

METHODS

We reviewed 103 (61 male and 42 female) patients undergoing PFR using the Current Procedural Terminology (CPT) codes available in the calculator: 27125 (hemiarthroplasty), 27130 (total hip), 27132 (conversion to total hip), 27134 (revision total hip), 27137 (revision acetabulum), 27138 (revision femur), and 27365 (excision tumor hip). The predicted rates of complications were compared with the observed rates.

RESULTS

Complications occurred in 54 (52%) of patients, with the predicted risk based on CPT codes: 27125 (21.5%); 27130 (7.8%); 27132 (16.6%), 27134 (17.8%), 27137 (14.4%), 274138 (22.7%), and 27365 (16.2%). The calculator was a poor predictor of complications (27125, area under the curve [AUC] 0.576); (27130, AUC 0.489); (27132, AUC 0.490); (27134, AUC 00.489); (27137, AUC 0.489); (27138, AUC 0.471); and (27365, AUC 0.538).

CONCLUSION

Oncologic PFR is known for complications. The ACS-NSQIP does not adequately predict the incidence of complications, and therefore cannot reliably be used in their shared decision-making process preoperative.

The association of preoperative blood markers with postoperative readmissions following arthroplasty

AIMS

While preoperative bloodwork is routinely ordered, its value in determining which patients are at risk of postoperative readmission following total knee arthroplasty (TKA) and total hip arthroplasty (THA) is unclear. The objective of this study was to determine which routinely ordered preoperative blood markers have the strongest association with acute hospital readmission for patients undergoing elective TKA and THA.

METHODS

Two population-based retrospective cohorts were assembled for all adult primary elective TKA (n = 137,969) and THA (n = 78,532) patients between 2011 to 2018 across 678 North American hospitals using the American College of Surgeons National Quality Improvement Programme (ACS-NSQIP) registry. Six routinely ordered preoperative blood markers - albumin, haematocrit, platelet count, white blood cell count (WBC), estimated glomerular filtration rate (eGFR), and sodium level - were queried. The association between preoperative blood marker values and all-cause readmission within 30 days of surgery was compared using univariable analysis and multivariable logistic regression adjusted for relevant patient and treatment factors.

RESULTS

The mean TKA age was 66.6 years (SD 9.6) with 62% being females (n = 85,163/137,969), while in the THA cohort the mean age was 64.7 years (SD 11.4) with 54% being female (n = 42,637/78,532). In both cohorts, preoperative hypoalbuminemia (< 35 g/l) was associated with a 1.5- and 1.8-times increased odds of 30-day readmission following TKA and THA, respectively. In TKA patients, decreased eGFR demonstrated the strongest association with acute readmission with a standardized odds ratio of 0.75 per two standard deviations increase (p < 0.0001).

CONCLUSION

In this population level cohort analysis of arthroplasty patients, low albumin demonstrated the strongest association with acute readmission in comparison to five other commonly ordered preoperative blood markers. Identification and optimization of preoperative hypoalbuminemia could help healthcare providers recognize and address at-risk patients undergoing TKA and THA. This is the most comprehensive and rigorous examination of the association between preoperative blood markers and readmission for TKA and THA patients to date. Cite this article: Bone Jt Open 2021;2(6):388-396.

Readmission, Complication, and Disposition Calculators in Total Joint Arthroplasty: A Systemic Review

BACKGROUND

Predictive tools are useful adjuncts in surgical planning. They help guide patient selection, candidacy for inpatient vs outpatient surgery, and discharge disposition as well as predict the probability of readmissions and complications after total joint arthroplasty (TJA). Surgeons may find it difficult due to significant variation among risk calculators to decide which tool is best suited for a specific patient for optimal decision-based care. Our aim is to perform a systematic review of the literature to determine the existing post-TJA readmission calculators and compare the specific elements that comprise their formula. Second, we intend to evaluate the pros and cons of each calculator.

METHODS

Using a Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols protocol, we conducted a systematic search through 3 major databases for publications addressing TJA risk stratification tools for readmission, discharge disposition, and early complications. We excluded those manuscripts that were not comprehensive for hips and knees, did not list discharge, readmission or complication as the primary outcome, or were published outside the North America.

RESULTS

Ten publications met our criteria and were compared on their sourced data, variable types, and overall algorithm quality. Seven of these were generated with single institution data and 3 from large administrative datasets. Three tools determined readmission risk, 5 calculated discharge disposition, and 2 predicted early complications. Only 4 prediction tools were validated by external studies. Seven studies utilized preoperative data points in their risk equations while 3 utilized intraoperative or postsurgical data to delineate risk.

CONCLUSION

The extensive variation among TJA risk calculators underscores the need for tools with more individualized stratification capabilities and verification. The transition to outpatient and same-day discharge TJA may preclude or change the need for many of these calculators. Further studies are needed to develop more streamlined risk calculator tools that predict readmission and surgical complications.

Are We at the Bottom? BPCI Programs Now Disincentivize Providers Who Maintain Quality Despite Caring for Increasingly Complex Patients

BACKGROUND

The Bundled Payments for Care Improvement (BPCI) initiative has been successful at reducing Medicare costs after total joint arthroplasty (TJA). Target pricing is based on each institution's historical performance and is periodically reset. The purpose of this study was to examine the performance of our BPCI program accounting for patient complexity, quality, and resource utilization.

METHODS

We reviewed a consecutive series of 9195 Medicare patients undergoing primary TJA from 2015 to 2018. Demographics, comorbidities, and readmissions by year were compared. We then examined 90-day episode-of-care costs, changes in target price, and financial margins during the duration of the BPCI program using Medicare claims data.

RESULTS

Patients undergoing TJA in 2018 had a higher prevalence of diabetes and cardiac disease (all P < .001) as compared with those in 2015. From 2015 to 2018, there was a decrease in the rate of discharge to rehabilitation facilities (23% vs 14%, P < .001) and length of stay (2.1 vs 1.7 days, P < .001) with no difference in readmissions (6% vs 6%, P = .945). There was a reduction in postacute care costs ($6076 vs $4,890, P < .001) and 90-day episode-of-care costs ($19,954 vs $18,449, P < .001). However, the target price also decreased ($22,280 vs $18,971, P < .001), and the per-patient margin diminished ($2683 vs $522, P < .001).

CONCLUSION

Surgeons have maintained quality of care at a reduced cost despite increasing patient complexity. The target price adjustments resulted in declining margins during the course of our BPCI experience. Policy makers should consider changes to target price methodology to encourage participation in these successful cost-saving programs.

Risk-adjustment models in patients undergoing head and neck surgery with reconstruction

BACKGROUND

With the current focus on value-based outcomes and reimbursement models, perioperative risk adjustment is essential. Specialty surgical outcomes are not well predicted by the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP); the Head and Neck-Reconstructive Surgery NSQIP was created as a specialty-specific platform for patients undergoing head and neck surgery with flap reconstruction. This study aims to investigate risk prediction models in these patients.

METHODS

The Head and Neck-Reconstructive Surgery NSQIP collected data on patients undergoing head and neck surgery with flap reconstruction from August 1, 2012 to October 20, 2016. Multivariable logistic regression models were created for 9 outcomes (postoperative ventilator dependence, pneumonia, superficial recipient surgical site infection, presence of tracheostomy/nasoenteric (NE)/gastrostomy/gastrojejunostomy(G/GJ) tube 30 days postoperatively, conversion from NE to G/GJ tube, unplanned return to the operating room, length of stay > 7 days). External validation was completed with a more contemporary cohort.

RESULTS

A total of 1095 patients were included in the modelling cohort and 407 in the validation cohort. Models performed well predicting tracheostomy, NE, G/GJ tube presence at 30 days postoperatively and conversion from NE to G/GJ tube (c-indices = 0.75-0.91). Models for postoperative pneumonia, superficial recipient surgical site infection, ventilator dependence > 48 h, and length of stay > 7 days were fair (concordance [c]-indices = 0.63-0.69). The predictive model for unplanned return to the operating room was poor (c-index = 0.58).

CONCLUSIONS AND RELEVANCE

Reliable and discriminant risk prediction models were able to be created for postoperative outcomes using the specialty-specific Head and Neck-Reconstructive Surgery Specific NSQIP.

Compilation and Analysis of Web-Based Orthopedic Personalized Predictive Tools: A Scoping Review

Web-based personalized predictive tools in orthopedic surgery are becoming more widely available. Despite rising numbers of these tools, many orthopedic surgeons may not know what tools are available, how these tools were developed, and how they can be utilized. The aim of this scoping review is to compile and synthesize the profile of existing web-based orthopedic tools. We conducted two separate PubMed searches-one a broad search and the second a more targeted one involving high impact journals-with the aim of comprehensively identifying all existing tools. These articles were then screened for functional tool URLs, methods regarding the tool's creation, and general inputs and outputs required for the tool to function. We identified 57 articles, which yielded 31 unique web-based tools. These tools involved various orthopedic conditions (e.g., fractures, osteoarthritis, musculoskeletal neoplasias); interventions (e.g., fracture fixation, total joint arthroplasty); outcomes (e.g., mortality, clinical outcomes). This scoping review highlights the availability and utility of a vast array of web-based personalized predictive tools for orthopedic surgeons. Increased awareness and access to these tools may allow for better decision support, surgical planning, post-operative expectation management, and improved shared decision-making.

Regional variation in hip and knee arthroplasty rates in Switzerland: A population-based small area analysis

Background

Compared to other OECD countries, Switzerland has the highest rates of hip (HA) and knee arthroplasty (KA).

Objective

We assessed the regional variation in HA/KA rates and potential determinants of variation in Switzerland.

Methods

We conducted a population-based analysis using discharge data from all Swiss hospitals during 2013–2016. We derived hospital service areas (HSAs) by analyzing patient flows. We calculated age-/sex-standardized procedure rates and measures of variation (the extremal quotient [EQ, highest divided by lowest rate] and the systemic component of variation [SCV]). We estimated the reduction in variance of HA/KA rates across HSAs in multilevel regression models, with incremental adjustment for procedure year, age, sex, language, urbanization, socioeconomic factors, burden of disease, and the number of orthopedic surgeons.

Results

Overall, 69,578 HA and 69,899 KA from 55 HSAs were analyzed. The mean age-/sex-standardized HA rate was 265 (range 179–342) and KA rate was 256 (range 186–378) per 100,000 persons and increased over time. The EQ was 1.9 for HA and 2.5 for KA. The SCV was 2.0 for HA and 2.2 for KA, indicating a low variation across HSAs. When adjusted for procedure year and demographic, cultural, and sociodemographic factors, the models explained 75% of the variance in HA and 63% in KA across Swiss HSAs.

Conclusion

Switzerland has high HA/KA rates with a modest regional variation, suggesting that the threshold to perform HA/KA may be uniformly low across regions. One third of the variation remained unexplained and may, at least in part, represent differing physician beliefs and attitudes towards joint arthroplasty.

Analysis and Review of Automated Risk Calculators Used to Predict Postoperative Complications After Orthopedic Surgery

PURPOSE OF REVIEW

To discuss the automated risk calculators that have been developed and evaluated in orthopedic surgery.

RECENT FINDINGS

Identifying predictors of adverse outcomes following orthopedic surgery is vital in the decision-making process for surgeons and patients. Recently, automated risk calculators have been developed to quantify patient-specific preoperative risk associated with certain orthopedic procedures. Automated risk calculators may provide the orthopedic surgeon with a valuable tool for clinical decision-making, informed consent, and the shared decision-making process with the patient. Understanding how an automated risk calculator was developed is arguably as important as the performance of the calculator. Additionally, conveying and interpreting the results of these risk calculators with the patient and its influence on surgical decision-making are paramount. The most abundant research on automated risk calculators has been conducted in the spine, total hip and knee arthroplasty, and trauma literature. Currently, many risk calculators show promise, but much research is still needed to improve them. We recommend they be used only as adjuncts to clinical decision-making. Understanding how a calculator was developed, and accurate communication of results to the patient, is paramount.

Are high-risk patient and revision arthroplasty effective indications for closed-incisional negative-pressure wound therapy after total hip or knee arthroplasty? A systematic review and meta-analysis

To determine the effective indications of closed-incisional negative-pressure wound therapy (ciNPWT) following total hip or knee arthroplasty, this systematic review and meta-analysis was conducted. The systematic search was performed on MEDLINE, Embase, and Cochrane Library, and 11 studies were included. The studies comparing between ciNPWT and conventional dressings were categorised into following subgroups based on patient risk and revision procedures: routine vs high-risk patient; primary vs revision arthroplasty. Pooled estimates were calculated for wound complication and surgical site infection (SSI) rates in the subgroup analyses using Review Manager. In high-risk patients, the overall rates of wound complication (odds ratio [OR] = 0.38; 95% confidence interval [CI] 0.15-0.93; P = .030) and SSI (OR = 0.24; 95% CI = 0.09-0.64; P = .005) were significantly lower in the ciNPWT; however, there were no differences in routine patients. In cases involving revision arthroplasties, the overall rates of wound complication (OR = 0.33; 95% CI = 0.18-0.62; P < .001) and SSI (OR = 0.26; 95% CI = 0.11-0.66; P = .004) were significantly lower in the ciNPWT; however, there were no differences in cases involving primary arthroplasties. In summary, ciNPWT showed a positive effect in decreasing the rates of wound complication and SSI in high-risk patients and in revision arthroplasties.

Intensive Care Unit Resource Utilization After Hip Fracture Surgery in Elderly Patients: Risk Factor Identification and Risk Stratification

The objective of this study was to develop a risk stratification index (RSI) system to guide intensive care unit (ICU) resource use for elderly patients after hip fracture surgery. The authors' first study cohort consisted of 302 elderly patients with hip fractures who had surgical treatment at their hospital. The authors conducted multivariate logistic regression analysis to investigate relevant risk factors for ICU resource utilization postoperatively. An RSI system was developed based on the significant risk factors from regression analysis. A second study cohort consisted of 205 elderly patients, among whom the authors applied the RSI system to guide ICU resource assignment. Among the first cohort of 302 hip fracture patients, 89 were transferred to ICU postoperatively, of whom 81 were planned to be transferred to ICU and 8 were not. Multivariate stepwise regression analysis revealed that age (≥80 years), preoperative pulmonary disease, perioperative anemia (hemoglobin <8 g/dL), and perioperative lactic acid level (>2 mmol/L) were independent risk factors for postoperative ICU management. The authors then constructed a weighted RSI with these risk factors. In addition, they manually added American Society of Anesthesiologists classification (III/IV) and types of anesthesia as additional risk factors based on their clinical experience. It was determined that an RSI score greater than 4 required postoperative ICU care. The RSI system was then prospectively applied to an independent cohort of 205 elderly surgical patients with hip fractures, among whom only 40 required ICU care. More importantly, there were no later transfers from the general ward to ICU after the application of RSI. The RSI system is effective for guiding postoperative ICU transfer without compromising patient care and minimizes unexpected transfers from the general ward to the postoperative ICU. [Orthopedics. 2020;43(3):e159-e165.].

A Weighted Index of Elixhauser Comorbidities for Predicting 90-day Readmission After Total Joint Arthroplasty

BACKGROUND

Evolving reimbursement models increasingly compel hospitals to assume costs for 90-day readmission after total joint arthroplasty. Although risk assessment tools exist, none currently reach the predictive performance required to accurately identify high-risk patients and modulate perioperative care accordingly. Although unlikely to perform adequately alone, the Elixhauser index is a set of 31 variables that may lend value in a broader model predicting 90-day readmission.

METHODS

Elixhauser comorbidities were examined in 10,022 primary unilateral total joint replacements, of which 4535 were hip replacements and 5487 were knee replacements, all performed between June 2013 and January 2018 at a single tertiary referral center. Data were extracted from electronic medical records using structured query language. After randomizing to derivation (80%) and validation (20%) subgroups, predictive models for 90-day readmission were generated and transformed into a system of weights based on each parameter's relative performance.

RESULTS

We observed 497 90-day readmissions (5.0%) during the study period, which demonstrated independent associations with 14 of the 31 Elixhauser comorbidity groups. A score created from the sum of each patient's weighted comorbidities did not lose substantial predictive discrimination (area under the curve: 0.653) compared to a comprehensive multivariable model containing all 31 unweighted Elixhauser parameters (area under the curve: 0.665). Readmission risk ranged from 3% for patients with a score of 0 to 27% for those with a score of 8 or higher.

CONCLUSIONS

The Elixhauser comorbidity score already meets or exceeds the predictive discrimination of available risk calculators. Although insufficient by itself, this score represents a valuable summary of patient comorbidities and merits inclusion in any broader model predicting 90-day readmission risk after total joint arthroplasty.

LEVEL OF EVIDENCE

III.

A Novel Risk Calculator Predicts 90-Day Readmission Following Total Joint Arthroplasty

BACKGROUND

A reliable prediction tool for 90-day adverse events not only would provide patients with valuable estimates of their individual risk perioperatively, but would also give health-care systems a method to enable them to anticipate and potentially mitigate postoperative complications. Predictive accuracy, however, has been challenging to achieve. We hypothesized that a broad range of patient and procedure characteristics could adequately predict 90-day readmission after total joint arthroplasty (TJA).

METHODS

The electronic medical records on 10,155 primary unilateral total hip (4,585, 45%) and knee (5,570, 55%) arthroplasties performed at a single institution from June 2013 to January 2018 were retrospectively reviewed. In addition to 90-day readmission status, >50 candidate predictor variables were extracted from these records with use of structured query language (SQL). These variables included a wide variety of preoperative demographic/social factors, intraoperative metrics, postoperative laboratory results, and the 30 standardized Elixhauser comorbidity variables. The patient cohort was randomly divided into derivation (80%) and validation (20%) cohorts, and backward stepwise elimination identified important factors for subsequent inclusion in a multivariable logistic regression model.

RESULTS

Overall, subsequent 90-day readmission was recorded for 503 cases (5.0%), and parameter selection identified 17 variables for inclusion in a multivariable logistic regression model on the basis of their predictive ability. These included 5 preoperative parameters (American Society of Anesthesiologists [ASA] score, age, operatively treated joint, insurance type, and smoking status), duration of surgery, 2 postoperative laboratory results (hemoglobin and blood-urea-nitrogen [BUN] level), and 9 Elixhauser comorbidities. The regression model demonstrated adequate predictive discrimination for 90-day readmission after TJA (area under the curve [AUC]: 0.7047) and was incorporated into static and dynamic nomograms for interactive visualization of patient risk in a clinical or administrative setting.

CONCLUSIONS

A novel risk calculator incorporating a broad range of patient factors adequately predicts the likelihood of 90-day readmission following TJA. Identifying at-risk patients will allow providers to anticipate adverse outcomes and modulate postoperative care accordingly prior to discharge.

LEVEL OF EVIDENCE

Prognostic Level IV. See Instructions for Authors for a complete description of levels of evidence.

Development of a Risk Stratification Scoring System to Predict General Surgical Complications in Hand Surgery Patients

PURPOSE

Avoidance of postoperative complications is important to both patients and surgeons. In an attempt to optimize postoperative outcomes, a risk stratification scoring system has been created to aid in optimizing risk factors for general surgical complications in hand surgery patients.

METHODS

Patients were identified who underwent hand procedures as part of the American College of Surgeons National Surgical Quality Improvement Program. Independent risk factors associated with postoperative complications within 30 days of surgery were identified and used to develop a point-scoring system to estimate the relative risk for sustaining complications. For validation, the system was tested on a subset of patients from the database who had undergone hand surgery.

RESULTS

A total of 49,272 patients were identified as having undergone hand surgery from 2005 to 2015. The incidence of postoperative complications within 30 days of hand surgery was 2.3%. Risk factors associated with postoperative complications were male sex, tobacco abuse, congestive heart failure, anemia (male hematocrit less than 42; female less than 38), elevated creatinine (greater than 1.3 mg/dL), hypoalbuminemia (less than 3.5 g/dL), and hyponatremia (less than 135 mEq/L). Point scores derived for each of these factors were: hypoalbuminemia, +5; congestive heart failure, +2; anemia, +2; elevated creatinine, +2; male sex, +1; tobacco abuse, +1; and hyponatremia, +1. In the validation cohort, patients categorized as low-risk (0-3) using the point-scoring system had a 2.4% rate of 30-day complications; patients categorized as medium risk (4-7) had a 10.4% complication rate (relative risk = 4.3; 95% confidence interval, 3.1-5.9 compared with low risk) and high risk (≥8), 28.9% (relative risk = 11.9; 95% confidence interval, 9.0-15.7).

CONCLUSIONS

This point-scoring system predicts risk for general postoperative complications after hand surgery. These data may help surgeons identify areas of clinical concern with patients to reduce the risk for sustaining postoperative complications.

TYPE OF STUDY/LEVEL OF EVIDENCE

Prognostic II.