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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.

[This corrects the article DOI: 10.3389/fimmu.2023.1128982.].

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.

In allogeneic hematopoietic stem cell transplantation (allo-HSCT), prognostic indicators effectively predict survival. The Disease conditions prior to transplantation dramatically affects the outcome of HSCT. Optimization of the pre-transplant risk assessment is critical for enhancing allo-HSCT decision-making. Inflammation and nutritional status play significant roles in cancer genesis and progression. As a combined inflammatory and nutritional status biomarker, the C-reactive protein/albumin ratio (CAR) can accurately forecast the prognosis in various malignancies. This research sought to examine the predictive value of CAR and develop a novel nomogram by combining biomarkers and evaluating their importance following HSCT. Analyses were conducted retroactively on a cohort of 185 consecutive patients who underwent haploidentical hematopoietic stem cell transplantation (haplo-HSCT) at Wuhan Union Medical College Hospital during the period from February 2017 to January 2019. Of these patients, 129 were randomly assigned to the training cohort, and the remaining 56 patients constituted the internal validation cohort. Univariate and multivariate analyses were carried out to examine the predictive significance of clinicopathological factors in the training cohort. Subsequently, the survival nomogram model was developed and compared with the disease risk comorbidity index (DRCI) using the concordance index (C-index), calibration curve, receiver operating characteristics (ROC) curve, and decision curve analysis (DCA). Patients were separated into low and high CAR groups using a cutoff of 0.087, which independently predicted overall survival (OS). Based on risk factors, CAR, the Disease Risk Index(DRI), and the Hematopoietic Cell Transplantation-specific Comorbidity Index(HCT-CI), the nomogram was developed to predict OS. The C-index and area under the ROC curve confirmed the improved predictive accuracy of the nomogram. The calibration curves revealed that the observed probabilities agreed well with those predicted by the nomogram in training, validation and entire cohort. It was confirmed by DCA that the nomogram offered greater net benefits than DRCI among all cohorts. CAR is an independent prognostic indicator for haplo-HSCT outcomes. Higher CAR was related to worse clinicopathologic characteristics and poorer prognoses in patients underwent haplo-HSCT. This research provided an accurate nomogram for predicting the OS of patients following haplo-HSCT, illustrating its potential clinical utility.

Purpose To improve the outcome of the acute respiratory distress syndrome (ARDS), one needs to identify potentially modifiable factors associated with mortality. Methods The large observational study to understand the global impact of severe acute respiratory failure (LUNG SAFE) was an international, multicenter, prospective cohort study of patients with severe respiratory failure, conducted in the winter of 2014 in a convenience sample of 459 ICUs from 50 countries across five continents. A pre-specified secondary aim was to examine the factors associated with outcome. Analyses were restricted to patients (93.1 %) fulfilling ARDS criteria on day 1–2 who received invasive mechanical ventilation. Results 2377 patients were included in the analysis. Potentially modifiable factors associated with increased hospital mortality in multivariable analyses include lower PEEP, higher peak inspiratory, plateau, and driving pressures, and increased respiratory rate. The impact of tidal volume on outcome was unclear. Having fewer ICU beds was also associated with higher hospital mortality. Non-modifiable factors associated with worsened outcome from ARDS included older age, active neoplasm, hematologic neoplasm, and chronic liver failure. Severity of illness indices including lower pH, lower PaO 2 /FiO 2 ratio, and higher non-pulmonary SOFA score were associated with poorer outcome. Of the 578 (24.3 %) patients with a limitation of life-sustaining therapies or measures decision, 498 (86.0 %) died in hospital. Factors associated with increased likelihood of limitation of life-sustaining therapies or measures decision included older age, immunosuppression, neoplasia, lower pH and increased non-pulmonary SOFA scores. Conclusions Higher PEEP, lower peak, plateau, and driving pressures, and lower respiratory rate are associated with improved survival from ARDS. Trial Registration: ClinicalTrials.gov NCT02010073.

OBJECTIVE:We extend the literature on comorbidity measurement by developing 2 indices, based on the Elixhauser Comorbidity measures, designed to predict 2 frequently reported health outcomesin-hospital mortality and 30-day readmission in administrative data. The Elixhauser measures are commonly used in research as an adjustment factor to control for severity of illness. DATA SOURCES:We used a large analysis file built from all-payer hospital administrative data in the Healthcare Cost and Utilization Project State Inpatient Databases from 18 states in 2011 and 2012. METHODS:The final models were derived with bootstrapped replications of backward stepwise logistic regressions on each outcome. Odds ratios and index weights were generated for each Elixhauser comorbidity to create a single index score per record for mortality and readmissions. Model validation was conducted with c-statistics. RESULTS:Our index scores performed as well as using all 29 Elixhauser comorbidity variables separately. The c-statistic for our index scores without inclusion of other covariates was 0.777 (95% confidence interval, 0.776–0.778) for the mortality index and 0.634 (95% confidence interval, 0.633–0.634) for the readmissions index. The indices were stable across multiple subsamples defined by demographic characteristics or clinical condition. The addition of other commonly used covariates (age, sex, expected payer) improved discrimination modestly. CONCLUSIONS:These indices are effective methods to incorporate the influence of comorbid conditions in models designed to assess the risk of in-hospital mortality and readmission using administrative data with limited clinical information, especially when small samples sizes are an issue.

BACKGROUND:Comorbidity adjustment is an important component of health services research and clinical prognosis. When adjusting for comorbidities in statistical models, researchers can include comorbidities individually or through the use of summary measures such as the Charlson Comorbidity Index or Elixhauser score. We examined the conditions under which individual versus summary measures are most appropriate. METHODS:We provide an analytic proof of the utility of comorbidity summary measures when used in place of individual comorbidities. We compared the use of the Charlson and Elixhauser scores versus individual comorbidities in prognostic models using a SEER-Medicare data example. We examined the ability of summary comorbidity measures to adjust for confounding using simulations. RESULTS:We devised a mathematical proof that found that the comorbidity summary measures are appropriate prognostic or adjustment mechanisms in survival analyses. Once one knows the comorbidity score, no other information about the comorbidity variables used to create the score is generally needed. Our data example and simulations largely confirmed this finding. CONCLUSIONS:Summary comorbidity measures, such as the Charlson Comorbidity Index and Elixhauser scores, are commonly used for clinical prognosis and comorbidity adjustment. We have provided a theoretical justification that validates the use of such scores under many conditions. Our simulations generally confirm the utility of the summary comorbidity measures as substitutes for use of the individual comorbidity variables in health services research. One caveat is that a summary measure may only be as good as the variables used to create it.

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.

Some previously developed risk scores contained a mathematical error in their construction: risk ratios were added to derive weights to construct a summary risk score. This study demonstrates the mathematical error and derived different versions of the Charlson comorbidity score (CCS) using regression coefficient–based and risk ratio–based scoring systems to further demonstrate the effects of incorrect weighting on performance in predicting mortality. This retrospective cohort study included elderly people from the Clinical Practice Research Datalink. Cox proportional hazards regression models were constructed for time to 1-year mortality. Weights were assigned to 17 comorbidities using regression coefficient–based and risk ratio–based scoring systems. Different versions of CCS were compared using Akaike information criteria (AIC), McFadden's adjusted R2, and net reclassification improvement (NRI). Regression coefficient–based models (Beta, Beta10/integer, Beta/Schneeweiss, Beta/Sullivan) had lower AIC and higher R2 compared to risk ratio–based models (HR/Charlson, HR/Johnson). Regression coefficient–based CCS reclassified more number of people into the correct strata (NRI range, 9.02–10.04) compared to risk ratio–based CCS (NRI range, 8.14–8.22). Previously developed risk scores contained an error in their construction adding ratios instead of multiplying them. Furthermore, as demonstrated here, adding ratios fail to even work adequately from a practical standpoint. CCS derived using regression coefficients performed slightly better than in fitting the data compared to risk ratio–based scoring systems. Researchers should use a regression coefficient–based scoring system to develop a risk index, which is theoretically correct.