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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.

Previous studies proposed comorbidity-based prediction tools to facilitate patient-level assessment of mortality risk, which are essential for confounder adjustment in epidemiologic studies. We compared established comorbidity indices using real-world administrative data of a broad surgical population. Adult patients undergoing surgical or interventional procedures between January 2005 and June 2020 at a tertiary academic medical center in Massachusetts, USA, were included. The Elixhauser Comorbidity Index (van Walraven modification), Combined Comorbidity Score, and Charlson Comorbidity Index were compared regarding the prediction of 30-day mortality. Age and sex were included in all models. Discriminative ability was quantified by the area under the receiver operating characteristic curve (AUROC), and calibration was assessed using the Brier score and reliability plots. A total of 514,282 patients were included, of which 5849 (1.1%) died within 30 days. A model including age and sex alone had an AUROC of 0.73 (95% CI 0.72-0.74). The Elixhauser Comorbidity Index–based model showed the best discriminative ability with an AUROC of 0.86 (95% CI 0.86-0.87) compared to models, including the Combined Comorbidity Score (AUROC, 0.85 [95% CI 0.84-0.85]) and the Charlson Comorbidity Index (AUROC, 0.82 [95% CI 0.81-0.83], P < .001, respectively). The Brier score was 0.011 for all scores. Overall, score performances were similar or improved after the implementation of the 10th Revision International Classification of Diseases (Clinical Modification) coding system. The primary findings were confirmed for in-hospital, 7-day, 90-day, 180-day, and 1-year mortality and when including score comorbidities as separate indicator variables (P < .001, respectively). Patient and procedural characteristics were predictive of mortality (AUROC, 0.91 [95% CI 0.91-0.91]), with confirmatory findings and slightly improved performances when adding comorbidity scores (AUROC, 0.93 [95% CI 0.93-0.93] for the Elixhauser Comorbidity Index; AUROC, 0.93 [95% CI 0.93-0.93] for the Combined Comorbidity Score; AUROC, 0.92 [95% CI 0.92-0.93] for the Charlson Comorbidity Index, P < .001, respectively). All 3 comorbidity indices predicted mortality with excellent discrimination; however, they showed only slightly improved performance when incorporated into a model including patient and procedural characteristics. When surgical data are unavailable and in surgical setting–specific subgroups, the Elixhauser Comorbidity Index consistently performed best. [Display omitted] •Comorbidity-based prediction tools enable patient-level assessment of mortality risk.•We compared established prediction tools using electronic health records.•Among 514,282 surgical patients, the Elixhauser Comorbidity Index performed best.•The Elixhauser Comorbidity Index may be used preferably for mortality prediction in broad surgical populations.

Background The Charlson and Elixhauser Comorbidity Indices are the most widely used comorbidity assessment methods in medical research. Both methods are adapted for use with the International Classification of Diseases, which 10th revision (ICD-10) is used by over a hundred countries in the world. Available Charlson and Elixhauser Comorbidity Index calculating methods are limited to a few applications with command-line user interfaces, all requiring specific programming language skills. This study aims to use Microsoft Excel to develop a non-programming and ICD-10 based dataset calculator for Charlson and Elixhauser Comorbidity Index and to validate its results with R- and SAS-based methods. Methods The Excel-based dataset calculator was developed using the program’s formulae, ICD-10 coding algorithms, and different weights of the Charlson and Elixhauser Comorbidity Index. Real, population-wide, nine-year spanning, index hip fracture data from the Estonian Health Insurance Fund was used for validating the calculator. The Excel-based calculator’s output values and processing speed were compared to R- and SAS-based methods. Results A total of 11,491 hip fracture patients’ comorbidities were used for validating the Excel-based calculator. The Excel-based calculator’s results were consistent, revealing no discrepancies, with R- and SAS-based methods while comparing 192,690 and 353,265 output values of Charlson and Elixhauser Comorbidity Index, respectively. The Excel-based calculator’s processing speed was slower but differing only from a few seconds up to four minutes with datasets including 6250–200,000 patients. Conclusions This study proposes a novel, validated, and non-programming-based method for calculating Charlson and Elixhauser Comorbidity Index scores. As the comorbidity calculations can be conducted in Microsoft Excel’s simple graphical point-and-click interface, the new method lowers the threshold for calculating these two widely used indices. Trial registration retrospectively registered.

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.

Purpose The purpose of this study was to assess complications, reoperations, and their risk factors at 90 days and 2 years after high tibial osteotomy (HTO) and distal femoral osteotomy (DFO) in a national cohort. Methods The PearlDiver Mariner Dataset was queried using International Classification of Diseases (ICD) and Current Procedural Terminology (CPT) codes for HTO and DFO, complications, and subsequent surgery. Minimum follow-up was 2 years and complications were assessed at 90 days and 2 years. Hospital readmission in the first 90 days was also assessed. Univariate and multiple logistic regression were utilized to identify risk factors for complications and re-operation. Results The 90-day and 2-year complication rates after HTO ( n  = 1780) were 11.6% and 31.7%, compared to 21.5% ( p  < 0.0001) and 41.5% ( p  = 0.0001) after DFO ( n  = 446). Infection was the most frequent early (90-day) complication for both HTO and DFO cohorts, while hardware problems were most common at 2 years. Increasing Elixhauser Comorbidity Index (ECI) was associated with increased odds of infection, readmission, and hardware-associated complications in both cohorts. Gender and tobacco use were also associated with various complications after HTO. At 2 years, 23.7% of HTO patients and 26.2% of DFO patients had undergone subsequent surgery. Hardware removal occurred in 16.4% of HTO and 18.4% of DFO patients (n.s.), while 4.5% of HTO and 5.2% of DFO patients underwent total knee arthroplasty (TKA) within 2 years (n.s.). Conclusion HTO and DFO have substantial complication rates in the short and mid term, with a higher rate of overall complications observed after DFO as compared to the HTO cohort. After both procedures, roughly one quarter of patients will undergo subsequent surgery within 2 years. Patients with tobacco use and numerous medical co-morbidities may not be optimal candidates due to increased complication rates. Elixhauser Comorbidity Index (ECI) may be an useful tool for risk assessment prior to surgery. Level of evidence Retrospective cohort study, III.

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. 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. 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. 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. •Combined indices better predict outcomes in malignant brain tumor patients.•Frailty with comorbidity better predicts length of stay and non-routine discharge.•Comorbid diseases may greatly influence perioperative outcomes more than frailty.•Enhanced risk assessment can aid post-operative planning and patient counseling.

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.

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.

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.

Background Readmissions following exacerbations of chronic obstructive pulmonary disease (COPD) are prevalent and costly. Multimorbidity is common in COPD and understanding how comorbidity influences readmission risk will enable health systems to manage these complex patients. Objectives We compared two commonly used comorbidity indices published by Charlson and Elixhauser regarding their ability to estimate readmission odds in COPD and determine which one provided a superior model. Methods We analyzed discharge records for COPD from the Nationwide Readmissions Database spanning 2010 to 2016. Inclusion and readmission criteria from the Hospital Readmissions Reduction Program were utilized. Elixhauser and Charlson Comorbidity Index scores were calculated from published methodology. A mixed-effects logistic regression model with random intercepts for hospital clusters was fit for each comorbidity index, including year, patient-level, and hospital-level covariates to estimate odds of thirty-day readmissions. Sensitivity analyses included testing age inclusion thresholds and model stability across time. Results In analysis of 1.6 million COPD discharges, readmission odds increased by 9% for each half standard deviation increase of Charlson Index scores and 13% per half standard deviation increase of Elixhauser Index scores. Model fit was slightly better for the Elixhauser Index using information criteria. Model parameters were stable in our sensitivity analyses. Conclusions Both comorbidity indices provide meaningful information in prediction readmission odds in COPD with slightly better model fit in the Elixhauser model. Incorporation of comorbidity information into risk prediction models and hospital discharge planning may be informative to mitigate readmissions.