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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.

The age-adjusted Charlson comorbidity index (ACCI) is a useful measure of comorbidity to standardize the evaluation of elderly patients and has been reported to predict mortality in various cancers. To our best knowledge, no studies have examined the relationship between the ACCI and survival of elderly patients with cancer. Therefore, the primary objective of this study was to investigate the relationship between the ACCI and survival of elderly patients with cancer. A total of 64 elderly patients (>80 years) with cancer between 2011 and 2021 were enrolled in this study. According to the ACCI, the age-adjusted comorbidity index was calculated by weighting individual comorbidities; patients with ACCI<11 were considered the low-ACCI group, whereas those with ACCI≥11 were considered the high-ACCI group. The correlations between the ACCI score and survival outcomes were statistically analyzed. There was a significant difference in overall survival (OS) and progression-free survival (PFS) between the high-ACCI group and the low-ACCI group (P<0.001). The median OS time of the high-ACCI group and the low-ACCI group were 13.9 (10.5-22.0) months and 51.9 (34.1-84.0) months, respectively. The 2-, 3-, and 5-year survival rates of the high-ACCI group were 28.1%, 18.8%, and 4.2%, respectively, whereas the 2-, 3-, and 5-year survival rates of the low-ACCI group were 77.3%, 66.4%, and 39.1%, respectively. Multivariate analysis showed that ACCI was independently associated with OS (HR=1.402, 95% CI: 1.226-1.604, P < 0.05) and PFS (HR=1.353, 95% CI: 1.085-1.688, P = 0.0073). The ACCI score is a significant independent predictor of prognosis in elderly patients with cancer.

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.

Objectives: To examine the comorbidity burden in patients with rheumatoid arthritis (RA) patients using a nationwide population-based cohort by assessing the Charlson Comorbidity Index (CCI), Elixhauser Comorbidity Index (ECI), Multimorbidity Index (MMI), and Rheumatic Disease Comorbidity Index (RDCI) scores and to investigate their predictive ability for all-cause mortality. Methods: We identified 24,767 RA patients diagnosed from 1998 to 2008 in Taiwan and followed up until 31 December 2013. The incidence of comorbidities was estimated in three periods (before, during, and after the diagnostic period). The incidence rate ratios were calculated by comparing during vs. before and after vs. before the diagnostic period. One- and 5-year mortality rates were calculated and discriminated by low and high-score groups and modified models for each index. Results: The mean score at diagnosis was 0.8 in CCI, 2.8 in ECI, 0.7 in MMI, and 1.3 in RDCI, and annual percentage changes are 11.0%, 11.3%, 9.7%, and 6.8%, respectively. The incidence of any increase in the comorbidity index was significantly higher in the periods of “during” and “after” the RA diagnosis (incidence rate ratios for different indexes: 1.33–2.77). The mortality rate significantly differed between the high and low-score groups measured by each index (adjusted hazard ratios: 2.5–4.3 for different indexes). CCI was slightly better in the prediction of 1- and 5-year mortality rates. Conclusions: Comorbidities are common before and after RA diagnosis, and the rate of accumulation accelerates after RA diagnosis. All four comorbidity indexes are useful to measure the temporal changes and to predict mortality.

Background Maternal critical illness occurs in 1.2 to 4.7 of every 1000 live births in the United States and approximately 1 in 100 women who become critically ill will die. Patient characteristics and comorbid conditions are commonly summarized as an index or score for the purpose of predicting the likelihood of dying; however, most such indices have arisen from non-pregnant patient populations. We sought to systematically review comorbidity indices used in health administrative datasets of pregnant women, in order to critically appraise their measurement properties and recommend optimal tools for clinicians and maternal health researchers. Methods We conducted a systematic search of MEDLINE and EMBASE to identify studies published from 1946 and 1947, respectively, to May 2017 that describe predictive validity of comorbidity indices using health administrative datasets in the field of maternal health research. We applied a methodological PubMed search filter to identify all studies of measurement properties for each index. Results Our initial search retrieved 8944 citations. The full text of 61 articles were identified and assessed for final eligibility. Finally, two eligible articles, describing three comorbidity indices appropriate for health administrative data remained: The Maternal comorbidity index, the Charlson comorbidity index and the Elixhauser Comorbidity Index. These studies of identified indices had a low risk of bias. The lack of an established consensus-building methodology in generating each index resulted in marginal sensibility for all indices. Only the Maternal Comorbidity Index was derived and validated specifically from a cohort of pregnant and postpartum women, using an administrative dataset, and had an associated c-statistic of 0.675 (95% Confidence Interval 0.647–0.666) in predicting mortality. Conclusions Only the Maternal Comorbidity Index directly evaluated measurement properties relevant to pregnant women in health administrative datasets; however, it has only modest predictive ability for mortality among development and validation studies. Further research to investigate the feasibility of applying this index in clinical research, and its reliability across a variety of health administrative datasets would be incrementally helpful. Evolution of this and other tools for risk prediction and risk adjustment in pregnant and post-partum patients is an important area for ongoing study.

Abstract 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.

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.

Assessing perioperative risk is essential for surgical decision-making. Our study compares the accuracy of comorbidity indices to predict morbidity and mortality. Analyzing the National Surgical Quality Improvement Program, 16 major procedures were identified and American Society of Anesthesiologists (ASA), Charlson Comorbidity Index and modified Frailty Index were calculated. We fit models with each comorbidity index for prediction of morbidity, mortality, and prolonged length of stay (pLOS). Decision Curve Analysis determined the effectiveness of each model. Of 650,437 patients, 11.7%, 6.0%, 17.0% and 0.75% experienced any, major complication, pLOS, and mortality, respectively. Each index was an independent predictor of morbidity, mortality, and pLOS (p < 0.05). While the indices performed similarly for morbidity and pLOS, ASA demonstrated greater net benefit for threshold probabilities of 1–5% for mortality. Models including readily available factors (age, sex) already provide a robust estimation of perioperative morbidity and mortality, even without considering comorbidity indices. All comorbidity indices show similar accuracy for prediction of morbidity and pLOS, while ASA, the score easiest to calculate, performs best in prediction of mortality. •Using comorbidity indices for prediction of perioperative morbidity and mortality.•ASA score, frailty index and CCI perform similarly for prediction of morbidity.•ASA score, the easiest to calculate, performs better for prediction of mortality.•Decision curve analysis determines the effectiveness of prediction models.

Clinical and demographic data, including, age, sex, disease duration, body mass index (BMI), pain, patient's global assessment, physician's global assessment, Bath Ankylosing Spondylitis Disease Activity Index, Ankylosing Spondylitis Disease Activity Score, Bath Ankylosing Spondylitis Functional Index, Bath Ankylosing Spondylitis Metrology Index, and Maastricht Enthesitis Score, were recorded. Additionally, the presence of comorbid conditions with SpA may decrease the tolerability of medications and indeed may influence the decision to use biological drugs.3 The extraarticular manifestations and comorbidities of SpA patients were found to increase disability and healthcare expenditures.4 The association of SpA with comorbid situations were previously evaluated.5\"8 Some of the recommendations/guidelines underline the importance of considering comorbid situations during the management of SpA.910 The main objective of this study was to evaluate the comorbid conditions of Turkish patients with SpA. The questionnaire contains questions about hypertension (HT), diabetes mellitus (DM) (including any complication related to DM), renal disease, chronic lung diseases (asthma or chronic obstructive pulmonary disease), pulmonary circulation disorders, thyroid dysfunction (hypo-or hyperthyroidism, any thyroid surgery, and consuming thyroid hormone replacement or suppressing medicine), cardiovascular system disorders (coronary artery disease, myocardial infarction, congestive heart failure, peripheral vascular events, and cardiac valve disease) gastrointestinal (GI) system disorders (peptic ulcer and GI bleeding), hepatic disorders, history of cancer, neurologic disorders (stroke, dementia, atlantoaxial instability, and spinal cord injury/cauda equina syndrome), psychiatric disorders (depression/psychosis). Three or more groups were compared by the Kruskal-Wallis test or analysis of variance (ANOVA) depending on their distribution.