Development of comorbidity score for patients undergoing major surgery

Trends in the Adoption of Outpatient Joint Arthroplasties and Patient Risk: A Retrospective Analysis of 2019 to 2021 Medicare Claims Data

INTRODUCTION

Total joint arthroplasties (TJAs) have recently been shifting toward outpatient arthroplasty. This study aims to explore recent trends in outpatient total joint arthroplasty (TJA) procedures and examine whether patients with a higher comorbidity burden are undergoing outpatient arthroplasty.

METHODS

Medicare fee-for-service claims were screened for patients who underwent total hip, knee, or shoulder arthroplasty procedures between January 2019 and December 2022. The procedure was considered to be outpatient if the patient was discharged on the same date of the procedure. The Hierarchical Condition Category Score (HCC) and the Charlson Comorbidity Index (CCI) scores were used to assess patient comorbidity burden. Patient adverse outcomes included all-cause hospital readmission, mortality, and postoperative complications. Logistic regression analyses were used to evaluate if higher HCC/CCI scores were associated with adverse patient outcomes.

RESULTS

A total of 69,520, 116,411, and 41,922 respective total knee, hip, and shoulder arthroplasties were identified, respectively. Despite earlier removal from the inpatient-only list, outpatient knee and hip surgical volume did not markedly increase until the pandemic started. By 2022Q4, 16%, 23%, and 36% of hip, knee, and shoulder arthroplasties were discharged on the same day of surgery, respectively. Both HCC and CCI risk scores in outpatients increased over time ( P < 0.001).

DISCUSSION

TJA procedures are shifting toward outpatient surgery over time, largely driven by the COVID-19 pandemic. TJA outpatients' HCC and CCI risk scores increased over this same period, and additional research to determine the effects of this should be pursued.

LEVEL OF EVIDENCE

Level III, therapeutic retrospective cohort study.

Predictive Ability of Comorbidity Indices for Surgical Morbidity and Mortality: a Systematic Review and Meta-analysis

BACKGROUND

Several contemporary risk stratification tools are now being used since the development of the Charlson Comorbidity Index (CCI) in 1987. The purpose of this systematic review and meta-analysis was to compare the utility of commonly used co-morbidity indices in predicting surgical outcomes.

METHODS

A comprehensive review was performed to identify studies reporting an association between a pre-operative co-morbidity measurement and an outcome (30-day/in-hospital morbidity/mortality, 90-day morbidity/mortality, and severe complications). Meta-analysis was performed on the pooled data.

RESULTS

A total of 111 included studies were included with a total cohort size 25,011,834 patients. The studies reporting the 5-item Modified Frailty Index (mFI-5) demonstrated a statistical association with an increase in the odds of in-hospital/30-day mortality (OR:1.97,95%CI: 1.55-2.49, p < 0.01). The pooled CCI results demonstrated an increase in the odds for in-hospital/30-day mortality (OR:1.44,95%CI: 1.27-1.64, p < 0.01). Pooled results for co-morbidity indices utilizing a scale-based continuous predictor were significantly associated with an increase in the odds of in-hospital/30-day morbidity (OR:1.32, 95% CI: 1.20-1.46, p < 0.01). On pooled analysis, the categorical results showed a higher odd for in-hospital/30-day morbidity (OR:1.74,95% CI: 1.50-2.02, p < 0.01). The mFI-5 was significantly associated with severe complications (Clavien-Dindo ≥ III) (OR:3.31,95% CI:1.13-9.67, p < 0.04). Pooled results for CCI showed a positive trend toward severe complications but were not significant.

CONCLUSION

The contemporary frailty-based index, mFI-5, outperformed the CCI in predicting short-term mortality and severe complications post-surgically. Risk stratification instruments that include a measure of frailty may be more predictive of surgical outcomes compared to traditional indices like the CCI.

Co-Management Reduces Mortality in Post-Sternotomy Mediastinitis

Background: Post-sternotomy mediastinitis (PSM) is one of the most feared complications of cardiac surgery. The impact of a multidisciplinary management approach on this pathology is yet unknown. Patients and Methods: A multidisciplinary approach based on a co-management model (CMM) of care was initiated in January 2018 because of the incorporation of a hospitalist unit on a cardiac surgery department. An observational retrospective cohort study was designed to evaluate the impact of the CMM of care compared to the standard model (SM) of care in patients diagnosed with PSM. Our primary and secondary outcomes were survival time and treatment failure rate (two or more surgical procedures needed to solve PSM or PSM-related death), respectively. Data related to patient death date were collected from the Spanish National Death Index. A multivariable Cox regression model was created using those variables believed to be clinically relevant. Results: Ninety-one patients developed PSM from January 2010 to June 2020. Regarding the pre-operative clinical status, surgical procedure, and PSM severity, both groups had similar baseline characteristics. Patients were followed for a mean of 27.54 ± 30.5 months. A total of 60.3% of the SM group and 11.1% of the CMM group (p < 0.001) died. Treatment failure occurred in 53 patients (72.6%) in the SM group versus 7 (38.6%) in the CMM group (p = 0.007). The CMM independently reduced overall mortality (hazard ratio [HR], 0.11; 95% confidence interval [CI]. 0.01-0.83) and treatment failure rate (HR, 0.01; 95% CI, 0.001-0.183). Gram-positive bacterial infection (HR, 3.73; 95% CI, .6-8.3), and complete osteosynthesis material removal (HR, 0.47; 95% CI, 0.24-0.91) also influenced mortality in our model. Conclusions: A co-management care model reduced overall mortality in patients diagnosed with post-sternotomy mediastinitis.

Patients From Medically Underserved Areas Are at Increased Risk for Nonhome Discharge and Emergency Department Return After Total Joint Arthroplasty

BACKGROUND

Maryland Health Enterprise Zones (MHEZs) were introduced in 2012 and encompass underserved areas and those with reduced access to healthcare providers. Across the United States many underserved and minority populations experience poorer total joint arthroplasty (TJA) outcomes seemingly because they reside in underserved areas. The purpose of this study is to identify and quantify the relationship between living in an MHEZ and TJA outcomes.

METHODS

Retrospective review of 11,451 patients undergoing primary TJA at a single institution from July 1, 2014 to June 30, 2020 was conducted. Patients were classified based on whether they resided in an MHEZ. Statistical analyses were used to compare outcomes for TJA patients who live in MHEZ and those who do not.

RESULTS

Of the 11,451 patients, 1057 patients lived in MHEZ and 10,394 patients did not. After risk adjustment, patients who live in an MHEZ were more likely to return to the emergency department within 90 days postoperatively and were less likely to be discharged home than those patients who do not live in an MHEZ.

CONCLUSION

Total joint arthroplasty patients residing in MHEZ appear to present with poorer overall health as measured by increased American Society of Anesthesiologists and Hierarchical Condition Categories scores, and they are less likely to be discharged home and more likely to return to the emergency department within 90 days. Several factors associated with these findings such as socioeconomic factors, household composition, housing type, disability, and transportation may be modifiable and should be targets of future population health initiatives.

Extracting Significant Comorbid Diseases from MeSH Index of PubMed

Text mining is an important research area to be explored in terms of understanding disease associations and have an insight in disease comorbidities. The reason for comorbid occurrence in any patient may be genetic or molecular interference from any other processes. Comorbidity and multimorbidity may be technically different, yet still are inseparable in studies. They have overlapping nature of associations and hence can be integrated for a more rational approach. The association rule generally used to determine comorbidity may also be helpful in novel knowledge prediction or may even serve as an important tool of assessment in surgical cases. Another approach of interest may be to utilize biological vocabulary resources like UMLS/MeSH across a patient health information and analyze the interrelationship between different health conditions. The protocol presented here can be utilized for understanding the disease associations and analyze at an extensive level.

Death after surgery among patients with chronic disease: prospective study of routinely collected data in the English NHS

BACKGROUND

Five million surgeries take place in the NHS each year. Little is known about the prevalence of chronic diseases among these patients, and the association with postoperative outcomes.

METHODS

Analysis of routine data from all NHS hospitals in England including patients aged ≥18 yr undergoing non-obstetric surgery between January 1, 2010 and December 31, 2015. The primary outcome was death within 90 days after surgery. For each chronic disease, we adjusted for age, sex, presence of other diseases, emergency surgery, and year using logistic regression models. We defined high-risk diseases as those with an adjusted odds ratio (OR) for death ≥2 and report associated 2-yr survival.

RESULTS

We included 8 624 611 patients (median age, 53 [36-68] yr), of whom 6 913 451 (80.2%) underwent elective surgery and 1 711 160 (19.8%) emergency surgery. Overall, 2 311 600 (26.8%) patients had a chronic disease, of whom 109 686 (4.7%) died within 90 days compared with 24 136 (0.4%) of 6 313 011 without chronic disease. Respiratory disease (1 002 281 [11.6%]), diabetes mellitus (662 706 [7.7%]), and cancer (310 363; 3.6%) were the most common. Four chronic diseases accounted for 7.7% of patients but 59.0% of deaths: cancer (37 693 deaths [12.1%]; OR=8.3 [8.2-8.5]), liver disease (8638 deaths [10.3%]; OR=4.5 [4.4-4.7]), cardiac failure (26 604 deaths [12.6%]; OR=2.4 [2.4-2.5]), and dementia (19 912 deaths [17.9%]; OR=2.0 [1.9-2.0]). Two-year survival was 67.7% among patients with high-risk chronic disease, compared with 97.1% without.

CONCLUSION

One in four surgical patients has a chronic disease with an associated 10-fold increase in risk of postoperative death. Two-thirds of all deaths after surgery occur among patients with high-risk diseases (cancer, cardiac failure, liver disease, dementia).

Development of comorbidity score for patients undergoing major surgery

OBJECTIVE

To develop and validate a claims-based comorbidity score for patients undergoing major surgery, and compare its performance with established comorbidity scores.

DATA SOURCE

Five percent Medicare data from 2007 to 2014.

STUDY DESIGN

Retrospective cohort study of patients aged ≥65 years undergoing six major operations (N = 99 250).

DATA COLLECTION

One-year mortality was the primary outcome. Secondary outcomes were hospital mortality, 30-day mortality, 30-day readmission, and length of stay. The comorbidity score was developed in the derivation cohort (70 percent sample) using logistic regression model. The comorbidity score was calibrated and validated in the validation cohort (30 percent sample), and compared against the Charlson, Elixhauser, and Centers for Medicare and Medicaid Services Hierarchical Condition Categories (CMS-HCC) comorbidity scores using c-statistic, net reclassification improvement, and integrated discrimination improvement.

PRINCIPAL FINDINGS

In the validation cohort, the surgery-specific comorbidity score was well calibrated and performed better than the Charlson, Elixhauser, and CMS-HCC comorbidity scores for all outcomes; the performance was comparable to the CMS-HCC for 30-day readmission. For example, the surgery-specific comorbidity score (c-statistic = 0.792; 95% CI, 0.785-0.799) had greater discrimination than the Charlson (c-statistic = 0.747; 95% CI, 0.739-0.755), Elixhauser (c-statistic = 0.747; 95% CI, 0.735-0.755), or CMS-HCC (c-statistic = 0.755; 95% CI, 0.747-0.763) scores in predicting 1-year mortality. The net reclassification improvement and integrated discrimination improvement were greater for surgery-specific comorbidity score compared to the Charlson, Elixhauser, and CMS-HCC scores.

CONCLUSIONS

Compared to commonly used comorbidity measures, a surgery-specific comorbidity score better predicted outcomes in the surgical population.