Role of comorbidities in epilepsy surgery outcomes of older adults.

We lack knowledge about prognostic factors of resective epilepsy surgery (RES) in older adults (≥60 years), especially the role of comorbidities, which are a major consideration in managing the care of people with epilepsy (PWE). We analyzed a single-center cohort of 94 older adults (median age = 63.5 years, 52% females) who underwent RES between 2000 and 2021 with at least 6 months of postsurgical follow-up. Three fourths of the study cohort had lesional magnetic resonance imaging and underwent temporal lobectomy. Fifty-four (57%) PWE remained seizure-free during a median follow-up of 3.5 years. Cox proportional hazard multivariable analysis showed that aura (hazard ratio [HR] = .52, 95% confidence interval [CI] = .27-1.00), single ictal electroencephalographic pattern (HR = .33, 95% CI = .17-.660), and Elixhauser Comorbidity Index (HR = 1.05, 95% CI = 1.00-1.10) were independently associated with seizure recurrence at last follow-up. A sensitivity analysis using the Charlson Combined Score (HR = 1.38, 95% CI = 1.03-1.84, p = .027) confirmed the association of comorbidities with worse seizure outcome. Our findings provide a framework for a better informed discussion about RES prognosis in older adults. More extensive, multicenter cohort studies are needed to validate our findings and reduce hesitancy in pursuing RES in suitable older adults.

Utility of combining frailty and comorbid disease indices in predicting outcomes following craniotomy for adult primary brain tumors: A mixed-effects model analysis using the nationwide readmissions database.

OBJECTIVE: The escalating healthcare expenditures in the United States, particularly in neurosurgery, necessitate effective tools for predicting patient outcomes and optimizing resource allocation. This study explores the utility of combining frailty and comorbidity indices, specifically the Johns Hopkins Adjusted Clinical Groups (JHACG) frailty index and the Elixhauser Comorbidity Index (ECI), in predicting hospital length of stay (LOS), non-routine discharge, and one-year readmission in patients undergoing craniotomy for benign and malignant primary brain tumors. METHODS: Leveraging the Nationwide Readmissions Database (NRD) for 2016-2019, we analyzed data from 645 patients with benign and 30,991 with malignant tumors. Frailty, ECI, and frailty + ECI were assessed as predictors using generalized linear mixed-effects models. Receiver operating characteristic (ROC) curves evaluated predictive performance. RESULTS: Patients in the benign tumor cohort had a mean LOS of 8.1 ± 15.1 days, and frailty + ECI outperformed frailty alone in predicting non-routine discharge (AUC 0.829 vs. 0.820, p = 0.035). The malignant tumor cohort patients had a mean LOS of 7.9 ± 9.1 days. In this cohort, frailty + ECI (AUC 0.821) outperformed both frailty (AUC 0.744, p < 0.0001) and ECI alone (AUC 0.809, p < 0.0001) in predicting hospital LOS. Frailty + ECI (AUC 0.831) also proved superior to frailty (AUC 0.809, p < 0.0001) and ECI alone (AUC 0.827, p < 0.0001) in predicting non-routine discharge location for patients with malignant tumors. All indices performed comparably to one another as a predictor of readmission in both cohorts. CONCLUSION: This study highlights the synergistic predictive capacity of frailty + ECI, especially in malignant tumor cases, and further suggests that comorbid diseases may greatly influence perioperative outcomes more than frailty. Enhanced risk assessment could aid clinical decision-making, patient counseling, and resource allocation, ultimately optimizing patient outcomes.

Changes in multimorbidity among hospitalized adults in the US.

Objective: This investigation examines burden of comorbidity measured by the Charlson Comorbidity Index (CCI) and Elixhauser Comorbidity Index (ECI) among inpatients based on age, sex, and race. Methods: Cross-sectional analysis of 2012-2018 US NIS datasets. Participants were inpatients 55y+. ICD-9/10 codes for admitting diagnoses were used to calculate disease burden using the CCI and ECI. Unweighted mean CCI and ECI scores were compared across demographic variables. Results: An increase in mean CCI and ECI scores across age, sex, and races (p<.001) was identified. Compared to the youngest age group (55-59y), all age groups had higher mean CCI and ECI adjusting for time (p<.001). Increases were greatest in older age groups until age 80-84 for CCI and 85-89 for ECI. The female group had lower CCI adjusting for time (p<.001) compared to males. There was no difference between sex groups in mean ECI (p=.409). Compared with the White group, all other race groups had higher mean CCI adjusting for time (p<.001). Black inpatients had the highest CCI followed by Native American inpatients. Findings were similar for ECI, but with no difference between Hispanic and White groups (p=.434). Conclusions: Growing multimorbidity burden among adult inpatients across age, sex, and race supports the continued need for programs for preventing and reducing multimorbidity, especially among communities that experience health inequity including older, Black, and Native American patients.

Elixhauser comorbidity method in predicting death of Spanish inpatients with asplenia and pneumococcal pneumonia.

BACKGROUND: Pneumococcal pneumonia (PP) is a serious infection caused by Streptococcus pneumoniae (pneumococcus), with a wide spectrum of clinical manifestations. The aim of this study was to analyze the comorbidity factors that influenced the mortality in patients with asplenia according to PP. METHODS: Discharge reports from the Spanish Minimum Basic Data Set (MBDS) was used to retrospectively analyze patients with asplenia and PP, from 1997 to 2021. Elixhauser Comorbidity Index (ECI) was calculated to predict in-hospital mortality (IHM). RESULTS: 97,922 patients with asplenia were included and 381 cases of PP were identified. The average age for men was 63.87 years and for women 65.99 years. In all years, ECI was larger for splenectomized than for non-splenectomized patients, with men having a higher mean ECI than women. An association was found between risk factors ECI, splenectomy, age group, sex, pneumococcal pneumonia, and increased mortality (OR = 0.98; 95% CI: 0.97-0.99; p < 0.001). The IHM increased steadily with the number of comorbidities and index scores in 1997-2021. CONCLUSIONS: Asplenia remain a relevant cause of hospitalization in Spain. Comorbidities reflected a great impact in patients with asplenia and PP, which would mean higher risk of mortality.

Charlson and Elixhauser Comorbidity Indices for Prediction of Mortality and Hospital Readmission in Patients With Acute Pulmonary Embolism.

Several risk stratification systems aid clinicians in classifying pulmonary embolism (PE) severity and prognosis. We compared 2 clinical PE scoring systems, the PESI and sPESI scores, with 2 comorbidity indices, the Charlson Comorbidity Index (CCI) and the val Walraven Elixhauser Comorbidity Index (ECI), to determine the utility of each in predicting mortality and hospital readmission. Information was collected from 436 patients presenting with PE via retrospective chart review. The PESI, sPESI, CCI, and ECI scores were calculated for each patient. Multivariate analysis was used to determine each system's ability to predict in-hospital mortality, 90-day mortality, overall mortality, and all-cause hospital readmission. The impact of various demographic and clinical characteristics of each patient on these outcomes was also assessed. The PESI score was found to be an independent predictor of in-hospital mortality and 90-day mortality. The PESI score and the CCI were able to independently predict overall mortality. None of the 4 risk scores independently predicted hospital readmission. Other factors including hypoalbuminemia, serum BNP, coagulopathy, anemia, and diabetes were associated with increased mortality and readmission at various endpoints. The PESI score was the best tool for predicting mortality at any endpoint. The CCI may have utility in predicting long-term outcomes. Further work is needed to better determine the roles of the CCI and ECI in predicting patient outcomes in PE. The potential prognostic implications of low serum albumin and anemia at the time of PE also warrant further investigation.

Assessing the impact of transitioning to 11th revision of the International Classification of Diseases (ICD-11) on comorbidity indices.

OBJECTIVES: This study aimed to support the implementation of the 11th Revision of the International Classification of Diseases (ICD-11). We used common comorbidity indices as a case study for proactively assessing the impact of transitioning to ICD-11 for mortality and morbidity statistics (ICD-11-MMS) on real-world data analyses. MATERIALS AND METHODS: Using the MIMIC IV database and a table of mappings between the clinical modification of previous versions of ICD and ICD-11-MMS, we assembled a population whose diagnosis can be represented in ICD-11-MMS. We assessed the impact of ICD version on cross-sectional analyses by comparing the populations' distribution of Charlson and Elixhauser comorbidity indices (CCI, ECI) across different ICD versions, along with the adjustment in comorbidity weighting. RESULTS: We found that ICD versioning could lead to (1) alterations in the population distribution and (2) changes in the weight that can be assigned to a comorbidity category in a reweighting initiative. In addition, this study allowed the creation of the corresponding ICD-11-MMS codes list for each component of the CCI and the ECI. DISCUSSION: In common with the implementations of previous versions of ICD, implementation of ICD-11-MMS potentially hinders comparability of comorbidity burden on health outcomes in research and clinical settings. CONCLUSION: Further research is essential to enhance ICD-11-MMS usability, while mitigating, after identification, its adverse effects on comparability of analyses.

Influence of Comorbidity Burden, Socioeconomic Status, and Race and Ethnicity on Survival Disparities in Patients With Cancer.

PURPOSE: The purpose of this study was to assess the association of comorbidity burden with overall survival, accounting for racial/ethnic and socioeconomic differences in patients with cancer. METHODS: In this retrospective cohort study, patients newly diagnosed with cancer between 2010 and 2018 were identified from a large health plan in southern California. Cancer registry data were linked with electronic health records (EHR). Comorbidity burden was defined by the Elixhauser comorbidity index (ECI). Patients were followed through December 2019 to assess all-cause mortality. Association of comorbidity burden with all-cause mortality was evaluated using Cox proportional hazards model. Crude and adjusted hazard ratio (HR, 95%CI) were determined. RESULTS: Of 153,270 patients included in the analysis, 29% died during the ensuing 10-year follow-up. Nearly 49% were patients of color, and 32% had an ECI > 4. After adjusting for age, sex, race/ethnicity, cancer stage, smoking status, insurance payor, medical center, year of cancer diagnosis, and cancer treatments, we observed a trend demonstrating higher mortality risk by decreasing socioeconomic status (SES) (P-trend<.05). Compared to patients in the highest SES quintile, patients in the lowest, second lowest, middle, and second highest quintiles had 25%, 21%, 18%, and 11% higher risk of mortality, respectively [(HR, 95%CI): 1.25 (1.21-1.29), 1.21 (1.18-1.25), 1.18 (1.15-1.22), and 1.11 (1.07-1.14), respectively]. When we additionally adjusted for ECI, the adjusted HRs for SES were slightly attenuated; however, the trend persisted. Patients with higher comorbidity burden had higher mortality risk compared to patients with ECI score = 0 in the adjusted model [(HR, 95%CI): 1.22 (1.17-1.28), 1.48 (1.42-1.55), 1.80 (1.72-1.89), 2.24 (2.14-2.34), and 3.39 (3.25-3.53) for ECI = 1, 2, 3, 4, and >5, respectively]. CONCLUSIONS: Comorbidity burden affects overall survival in cancer patients irrespective of racial/ethnic and SES differences. Reducing comorbidity burden can reduce some, but not all, of the mortality risk associated with lower SES.

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.

Generating Older Adult Multimorbidity Trajectories Using Various Comorbidity Indices and Calculation Methods.

Background and Objectives: Older adult multimorbidity trajectories are helpful for understanding the current and future health patterns of aging populations. The construction of multimorbidity trajectories from comorbidity index scores will help inform public health and clinical interventions targeting those individuals that are on unhealthy trajectories. Investigators have used many different techniques when creating multimorbidity trajectories in prior literature, and no standard way has emerged. This study compares and contrasts multimorbidity trajectories constructed from various methods. Research Design and Methods: We describe the difference between aging trajectories constructed with the Charlson Comorbidity Index (CCI) and Elixhauser Comorbidity Index (ECI). We also explore the differences between acute (single-year) and chronic (cumulative) derivations of CCI and ECI scores. Social determinants of health can affect disease burden over time; thus, our models include income, race/ethnicity, and sex differences. Results: We use group-based trajectory modeling (GBTM) to estimate multimorbidity trajectories for 86,909 individuals aged 66-75 in 1992 using Medicare claims data collected over the following 21 years. We identify low-chronic disease and high-chronic disease trajectories in all 8 generated trajectory models. Additionally, all 8 models satisfied prior established statistical diagnostic criteria for well-performing GBTM models. Discussion and Implications: Clinicians may use these trajectories to identify patients on an unhealthy path and prompt a possible intervention that may shift the patient to a healthier trajectory.

Comparing Risk Assessment Between Payers and Providers: Inconsistent Agreement in Medical Comorbidity Records for Patients Undergoing Total Joint Arthroplasty.

BACKGROUND: The proper risk adjustment for total hip arthroplasty (THA) and total knee arthroplasty (TKA) relies on an accurate assessment of comorbidity profiles by both the payer and the institution. The purpose of this study was to determine how strongly comorbidities tracked by our institution agreed with the same comorbidities reported by payers in patients undergoing THA and TKA. METHODS: All patients of a single payer undergoing primary THA and TKA at a single institution between January 5, 2021 and March 31, 2022 were included (n = 876). There were 8 commonly collected medical comorbidities obtained from institutional medical records and matched with patient records reported by the payer. Fleiss Kappa tests were used to determine agreement of payer data with institutional records. There were 4 medical risk calculations collected from our institutional records and compared with an insurance member risk score reported by the payer. RESULTS: Comorbidities reported by the institution differed significantly from those reported by payers, with Kappa varying between 0.139 and 0.791 for THA, and 0.062 and 0.768 for TKA. Diabetes was the only condition to demonstrate strong agreement for both procedures (THA; k = 0.791, TKA; k = 0.768). The insurance member risk score demonstrates the closest association with total cost and surplus for THA regardless of insurance type and for TKA procedures paid for with private commercial insurance. CONCLUSION: There is a lack of agreement between medical comorbidities within payer and institutional records for both THA and TKA. These differences may put institutions at a disadvantage within value-based care models and when optimizing patients perioperatively.

Surgical site infection in thoracic and lumbar fractures: incidence and risk factors in 11,401 patients from a nationwide administrative database.

BACKGROUND CONTEXT: The rate of surgical site infection (SSI) following elective spine surgery ranges from 0.5%‒10%. Published reports suggest a higher SSI rate in non-elective spine surgery such as spine trauma; however, there is a paucity of large database studies examining this issue. PURPOSE: The objective of this study was to investigate the incidence and risk factors of SSI in patients undergoing spine surgery for thoracic and lumbar fractures in a large population database. STUDY DESIGN/SETTING: This is a retrospective study utilizing the PearlDiver Patient Claims Database. PATIENT SAMPLE: Patients undergoing spine surgery for thoracic and lumbar fractures between 2015-2020 were identified in the PearlDiver Patient Claims Database using ICD-10 codes. Patients were excluded who had another surgery either 14 days before or 21 days after the index spine surgery, or pathologic fracture. OUTCOME MEASURES: Rate of surgical site infection. METHODS: Clinical data collected from the PearlDiver database based on ICD-10 codes included gender, age, diabetes, smoking status, obesity, Elixhauser Comorbidity Index (ECI), Charlson Comorbidity Index (CCI), and SSI. Univariate analysis was used to assess the association of potential risk factors and SSI. Multivariable analysis was used to identify independent risk factors of SSI. The authors have no conflicts of interest or funding sources to declare. RESULTS: A total of 11,401 patients undergoing spine surgery for thoracic and lumbar fractures met inclusion criteria, and 1,065 patients were excluded. 860 patients developed SSI (7.5%). Risk factors significantly associated with SSI in univariate analysis included diabetes (OR 1.50; 95% CI, 1.30‒1.73; p<.001), obesity (OR 1.66; 95% CI, 1.44‒1.92; p<.001), increased age (p<.001), ECI (p<.001), and CCI (p<.001). On multivariable analysis, obesity and ECI were independently associated with SSI (p<.001 and p<.001, respectively). CONCLUSIONS: Non-elective surgery for thoracic and lumbar fractures is associated with a 7.5% risk of SSI. Obesity and ECI are independent predictors of SSI in this population. Limitations include the reliance on accurate insurance coding which may not fully capture all SSI, and in particular superficial SSI. These findings provide a broad overview of the risk of SSI in this population at a national level and may also help counsel patients regarding risk.

Decreasing trend of inpatient mortality rates of aseptic versus septic revision total hip arthroplasty: an analysis of 681,034 cases.

BACKGROUND: While most revision total hip arthroplasties (rTHAs) are for aseptic loosening/instability, infection accounts for approximately 16%. The purpose of this study was to: (1) quantify mortality rates of aseptic versus septic rTHA; (2) determine if mortality rates have changed over the past 20 years; and (3) identify associated preoperative risk factors, focusing on the utility/validity of the Elixhauser comorbidity index (ECI). METHODS: ICD-9/ICD-10 codes were used to identify patients undergoing rTHA in the National Inpatient Sample database between 1998 and 2017. A total of 681,034 cases (576,143 aseptic THA and 104,891 septic THA) were identified. For each patient, demographic variables including age, sex, race, insurance type, ECI, and inhospital mortality were gathered. A logistic regression model was constructed to assess risk of inhospital mortality. RESULTS: From 1998 to 2017, inpatient mortality rates of aseptic and septic rTHA decreased from 0.83 to 0.45%, and from 2.58 to 1.24%, respectively. Septic rTHA was independently associated with higher odds of mortality relative to aseptic (odds ratio (OR): 2.305, 95% confidence interval (CI): (2.014, 2.638), p < 0.0001). Increased ECI was associated with higher odds of mortality at both medium (OR: 5.147, 95% CI: (4.433,5.977), p < 0.0001) and high index scores (OR: 13.714, 95% CI: (11.519,16.326), p < 0.0001). CONCLUSIONS: Mortality rates for both aseptic and septic rTHA have been declining over the past 20 years, potentially due to patient selection guidelines and advances in medical management. Our study confirms that the ECI is independently associated with increased inpatient mortality.

Risk Factors and Outcomes of Invasive Aspergillosis in Kidney Transplant Recipients: A Case-Control Study of United States Renal Data System Data.

BACKGROUND: Kidney transplant recipients are at increased risk for invasive aspergillosis (IA), a disease with poor outcomes and substantial economic burden. We aimed to determine risk factors for posttransplant IA by using a national database and to assess the association of IA with mortality and allograft failure. METHODS: Using the United States Renal Data System database, we performed a retrospective case-control study of patients who underwent kidney transplant from 1998 through 2017. To evaluate risk factors for IA, we performed conditional logistic regression analysis by comparing characteristics between IA-infected patients and their matched uninfected controls. We performed Cox regression analysis to evaluate the effects of IA on mortality and death-censored allograft failure. RESULTS: We matched 359 patients with IA to 1436 uninfected controls (1:4). IA was diagnosed at a median of 22.5 months (interquartile range, 5.4-85.2 months) after kidney transplant. Risk factors for IA were Black/African American race, duration of pretransplant hemodialysis, higher Elixhauser Comorbidity Index score, weight loss, chronic pulmonary disease, need for early posttransplant hemodialysis, and a history of cytomegalovirus infection. Receiving an allograft from a living donor was protective against IA. IA was a strong independent predictor of 1-year mortality (adjusted hazard ratio [aHR], 5.02 [95% confidence interval {CI}, 3.58-7.04], P < .001). Additionally, IA was associated with 1-year allograft failure (aHR, 3.37 [95% CI, 1.96-5.77], P < .001). CONCLUSIONS: Our findings emphasize the importance of timely transplant to mitigate the risk of posttransplant IA. An individualized approach to disease prevention is essential to decrease mortality and allograft failure.

Feasibility of measuring comorbidity indices based on clinical breast cancer records.

Background: Comorbidities have a significant impact on the treatment and outcome of breast cancer. However, data on such comorbidities from low-income countries are limited. Aim: To evaluate the feasibility and accuracy of comorbidity data extracted from medical records for estimating the prevalence of comorbidities among patients registered in the clinical breast cancer registry of the Islamic Republic of Iran. Methods: We collected data from the medical records of 400 patients on 30 comorbidities included in the Charlson Comorbidity Index (CCI) and Elixhauser Comorbidity Index (ECI). The sensitivity and specificity of comorbidity data extracted from medical records were calculated using data from interviews with 97 randomly selected patients. We studied the prevalence of comorbidities using the CCI and ECI. Data were analysed using SPSS version 24. Results: The mean age of patients was 51.69 SD 12.28 years. The sensitivity and specificity of medical records for detecting any comorbidity data contained in CCI versus non-comorbidity were 93.2% and 69.8%, respectively. However, for the comorbidity data included in ECI, both sensitivity (86.9%) and specificity (44.4%) were lower than in CCI. Hypertension (n = 144, 36.0%) and diabetes without chronic complications (n = 77, 19.3%) were the most prevalent comorbidities. A higher proportion of patients had no comorbidity with CCI (72.2%) than with ECI (44.8%). Conclusion: It is feasible to construct a comorbidity index using medical records with high accuracy, especially when we extract comorbidities using the CCI. Further studies are needed to understand the association between comorbidity index and breast cancer survival among Iranian patients.

Increasing Patient Complexity Is Associated With Longer In-Hospital Lengths of Stay and Higher Rates of Medical Complications Following Primary 1- to 2-Level Lumbar Fusion.

BACKGROUND: The Elixhauser Comorbidity Index (ECI) is a stratification tool to predict adverse surgical outcomes. No studies have explored the relationship between ECI and outcomes following primary 1- to 2-level lumbar fusion (1-2LF). The purpose was to determine whether an ECI score greater than 1 correlated with (1) longer in-hospital length of stay (LOS) and (2) greater odds of developing 90-day medical complications. METHODS: A retrospective review from 2004 to 2015 was performed using the Medicare Standard Analytical Files for patients undergoing primary LF. Patients with ECI scores from 2 to 5 served as the study cohorts (1 for each ECI score), and patients with an ECI score of 1 served as the control cohort. In-hospital LOS and 90-day medical complications were compared between cohorts. A P value of <0.001 was statistically significant. RESULTS: A total of 105,120 patients were equally distributed between the 5 cohorts. Patients with an ECI score of 2 (6.00 ± 4.51), ECI 3 (6.22 ± 4.67), ECI 4 (7.35 ± 5.05), or ECI 5 (8.99 ± 5.67) had longer in-hospital LOS compared with patients with an ECI score of 1 (4.28 ± 4.36) (all P < 0.001). Patients with an ECI score of 2 (OR: 1.17, 95% CI: 1.05-1.30, P = 0.003; 2.85% vs 2.45%), ECI 3 (OR: 1.22, 95% CI: 1.10-1.36, P < 0.001; 2.98% vs 2.45%), ECI 4 (OR: 1.26, 95% CI: 1.13-1.40, P < 0.001; 3.10% vs 2.45%), or ECI 5 (OR: 1.18, 95% CI: 1.06-1.31, P = 0.001; 2.89% vs 2.45%) had greater incidence and odds of 90-day medical complications such as pneumonia, deep vein thrombosis, cerebrovascular accidents, and myocardial infarctions than patients in the control group (all P < 0.0001). CONCLUSIONS: Increasing ECI score was associated with longer in-hospital LOS and increased 90-day medical complication rates following 1-2LF. This study is the first to establish a correlation between ECI score, in-hospital LOS, and complication rates following lumbar fusion. CLINICAL RELEVANCE: ECI score may assist physicians in adjusting pre- and postoperative care for complex patients undergoing 1-2LF.

Comorbidities and ethnic health disparities in the UK biobank.

Objective: The goal of this study was to investigate the relationship between comorbidities and ethnic health disparities in a diverse, cosmopolitan population. Materials and Methods: We used the UK Biobank (UKB), a large progressive cohort study of the UK population. Study participants self-identified with 1 of 5 ethnic groups and participant comorbidities were characterized using the 31 disease categories captured by the Elixhauser Comorbidity Index. Ethnic disparities in comorbidities were quantified as the extent to which disease prevalence within categories varies across ethnic groups and the extent to which pairs of comorbidities co-occur within ethnic groups. Disease-risk factor comorbidity pairs were identified where one comorbidity is known to be a risk factor for a co-occurring comorbidity. Results: The Asian ethnic group shows the greatest average number of comorbidities, followed by the Black and then White groups. The Chinese group shows the lowest average number of comorbidities. Comorbidity prevalence varies significantly among the ethnic groups for almost all disease categories, with diabetes and hypertension showing the largest differences across groups. Diabetes and hypertension both show ethnic-specific comorbidities that may contribute to the observed disease prevalence disparities. Discussion: These results underscore the extent to which comorbidities vary among ethnic groups and reveal group-specific disease comorbidities that may underlie ethnic health disparities. Conclusion: The study of comorbidity distributions across ethnic groups can be used to inform targeted group-specific interventions to reduce ethnic health disparities.

Influence of center surgical aortic valve volume on outcomes of transcatheter aortic valve replacement.

Objective: The utilization of transcatheter aortic valve replacement (TAVR) technology has exceeded that of traditional surgical aortic valve replacement (SAVR). In addition, the role of minimum surgical volume requirements for TAVR centers has recently been disputed. The present work evaluated the association of annual institutional SAVR caseload on outcomes following TAVR. Methods: The 2012-2018 Nationwide Readmissions Database was queried for elective TAVR hospitalizations. The study cohort was split into early (Era 1: 2012-2015) and late (Era 2: 2016-2018) groups. Based on restricted cubic spline modeling of annual hospital SAVR caseload, institutions were dichotomized into low-volume and high-volume centers. Multivariable regressions were used to determine the influence of high-volume status on in-hospital mortality and perioperative complications following TAVR. Results: An estimated 181,740 patients underwent TAVR from 2012 to 2018. Nationwide TAVR volume increased from 5893 in 2012 to 49,983 in 2018. After adjustment for relevant patient and hospital factors, high-volume status did not alter odds of TAVR mortality in Era 1 (adjusted odds ratio, 0.94; P = .52) but was associated decreased likelihood of mortality in Era 2 (adjusted odds ratio, 0.83; P = .047). High-volume status did not influence the risk of perioperative complications during Era 1. However, during Era 2, patients at high-volume centers had significantly lower odds of infectious complications, relative to low-volume hospitals (adjusted odds ratio, 0.78; P = .002). Conclusions: SAVR experience is associated with improved TAVR outcomes in a modern cohort. Our findings suggest the need for continued collaboration between cardiologists and surgeons to maximize patient safety.

Single-level posterior lumbar fusions in patients with Ehlers Danlos Syndrome not found to be associated with increased postoperative adverse events or five-year reoperations.

Background: Ehlers Danlos Syndrome (EDS) is a rare connective tissue disorder that results from mutations in collagen genes. Potentially related to laxity and resultant degenerative changes, adult EDS patients may require posterior lumbar fusion (PLF). However, with low numbers, adequately powered outcome studies have been limited. The purpose of this study was to investigate risk of complications, readmissions and reoperations in adult patients with EDS following single-level PLF. Methods: A retrospective study using the 2010 to 2020 MSpine Pearldiver dataset was performed. Adult patients undergoing single-level PLF (excluding any with anterior procedures) with and without EDS for which at least 90-day follow up was available were identified. Any cases performed for trauma, tumor, or infection were excluded.Single-level PLF EDS patients were then matched 1:4 with PLF non-EDS patients based on age, sex, and Elixhauser Comorbidity Index (ECI). Rates of ninety-day any, severe, and minor adverse events as well as readmissions were tabulated and compared with chi-square tests. Multivariate logistical regression was then performed (controlling for age, sex, and ECI).Reoperation surgeries over five years were assessed, Kaplan-Meier survival curves generated, and curves of those with and without EDS were compared with log rank test. Results: In total, there were 170,100 single-level PLF case identified, of which 242 (0.14%) had EDS. After matching, there were 957 without EDS and 239 with EDS. On multivariate regression, there were no significant differences in 90-day any, severe, or minor adverse events, or readmissions (p>0.05 for each). Over five years, there were also not significant differences in rates of reoperation (p> 0.05). Conclusions: For EDS patients undergoing PLF, the current study identified similar 90-day adverse events and 5-year reoperation rates compared to those without EDS. These findings may be useful for patient counseling and surgical planning for those with this rare condition.

The role of frailty in the clinical management of neurofibromatosis type 1: a mixed-effects modeling study using the Nationwide Readmissions Database.

OBJECTIVE: Frailty embodies a state of increased medical vulnerability that is most often secondary to age-associated decline. Recent literature has highlighted the role of frailty and its association with significantly higher rates of morbidity and mortality in patients with CNS neoplasms. There is a paucity of research regarding the effects of frailty as it relates to neurocutaneous disorders, namely, neurofibromatosis type 1 (NF1). In this study, the authors evaluated the role of frailty in patients with NF1 and compared its predictive usefulness against the Elixhauser Comorbidity Index (ECI). METHODS: Publicly available 2016-2017 data from the Nationwide Readmissions Database was used to identify patients with a diagnosis of NF1 who underwent neurosurgical resection of an intracranial tumor. Patient frailty was queried using the Johns Hopkins Adjusted Clinical Groups frailty-defining indicator. ECI scores were collected in patients for quantitative measurement of comorbidities. Propensity score matching was performed for age, sex, ECI, insurance type, and median income by zip code, which yielded 60 frail and 60 nonfrail patients. Receiver operating characteristic (ROC) curves were created for complications, including mortality, nonroutine discharge, financial costs, length of stay (LOS), and readmissions while using comorbidity indices as predictor values. The area under the curve (AUC) of each ROC served as a proxy for model performance. RESULTS: After propensity matching of the groups, frail patients had an increased mean ± SD hospital cost ($85,441.67 ± $59,201.09) compared with nonfrail patients ($49,321.77 ± $50,705.80) (p = 0.010). Similar trends were also found in LOS between frail (23.1 ± 14.2 days) and nonfrail (10.7 ± 10.5 days) patients (p = 0.0020). For each complication of interest, ROC curves revealed that frailty scores, ECI scores, and a combination of frailty+ECI were similarly accurate predictors of variables (p > 0.05). Frailty+ECI (AUC 0.929) outperformed using only ECI for the variable of increased LOS (AUC 0.833) (p = 0.013). When considering 1-year readmission, frailty (AUC 0.642) was outperformed by both models using ECI (AUC 0.725, p = 0.039) and frailty+ECI (AUC 0.734, p = 0.038). CONCLUSIONS: These findings suggest that frailty and ECI are useful in predicting key complications, including mortality, nonroutine discharge, readmission, LOS, and higher costs in NF1 patients undergoing intracranial tumor resection. Consideration of a patient's frailty status is pertinent to guide appropriate inpatient management as well as resource allocation and discharge planning.

All Patient Refined-Diagnosis Related Groups' (APR-DRGs) Severity of Illness and Risk of Mortality as predictors of in-hospital mortality.

The aims of this study were to assess All-Patient Refined Diagnosis-Related Groups' (APR-DRG) Severity of Illness (SOI) and Risk of Mortality (ROM) as predictors of in-hospital mortality, comparing with Charlson Comorbidity Index (CCI) and Elixhauser Comorbidity Index (ECI) scores. We performed a retrospective observational study using mainland Portuguese public hospitalizations of adult patients from 2011 to 2016. Model discrimination (C-statistic/ area under the curve) and goodness-of-fit (R-squared) were calculated. Our results comprised 4,176,142 hospitalizations with 5.9% in-hospital deaths. Compared to the CCI and ECI models, the model considering SOI, age and sex showed a statistically significantly higher discrimination in 49.6% (132 out of 266) of APR-DRGs, while in the model with ROM that happened in 33.5% of APR-DRGs. Between these two models, SOI was the best performer for nearly 20% of APR-DRGs. Some particular APR-DRGs have showed good discrimination (e.g. related to burns, viral meningitis or specific transplants). In conclusion, SOI or ROM, combined with age and sex, perform better than more widely used comorbidity indices. Despite ROM being the only score specifically designed for in-hospital mortality prediction, SOI performed better. These findings can be helpful for hospital or organizational models benchmarking or epidemiological analysis.