Charlson Comorbidity Index can add prognostic information to Rapid Emergency Medicine Score as a predictor of long-term mortality.

OBJECTIVES: To investigate whether co-existing medical disorders, summed up in a comorbidity index, in nonsurgical patients attending the emergency department could predict short-term and long-term mortality, and whether the index could add prognostic information to the Rapid Emergency Medicine Score. METHODS: This was a prospective cohort study. In all, 885 nonsurgical patients, presenting to an adult emergency department and admitted to a medical department of a 1200-bed university hospital during 2 months, were enrolled consecutively. The Rapid Emergency Medicine Score (including blood pressure, oxygen saturation, respiratory rate, pulse rate, age and Glasgow coma scale) was calculated within 20 min in all those admitted to the emergency department. The history of coexisting disorders (Charlson Comorbidity Index) was collected from the medical records. RESULTS: In a univariate analysis, the Charlson Comorbidity Index could predict both short-term and long-term mortality in nonsurgical emergency department patients. An increase of one point in the 16-point Charlson Comorbidity Index scale was associated with a hazard ratio of 1.15 (95% CI 1.04-1.28, P<0.0001) for 7-day mortality and 1.28 (95% CI 1.23-1.33, P<0.0001) for 5-year mortality. The Rapid Emergency Medicine Score could also predict both short-term and long-term mortality (hazard ratio for an increase of one point in the 26-point Rapid Emergency Medicine Score scale was 1.33 (95% CI 1.28-1.39, P<0.0001) for 7-day mortality and 1.25 (95% CI 1.22-1.28, P<0.0001) for 5-year mortality. The Charlson Comorbidity Index could also add prognostic information to the Rapid Emergency Medicine Score as a predictor of long-term mortality, but it could not independently predict short-term (3-day, 7-day) mortality when forced into the same multivariate logistic model as the Rapid Emergency Medicine Score (hazard ratio for one point increase in the Charlson Comorbidity Index was 1.20 for 5-year mortality (95% CI 1.15-1.25, P<0.0001). CONCLUSION: Information on coexisting disorders (Charlson Comorbidity Index) can prognosticate both short-term and long-term mortality in the nonsurgical emergency department. It can also add prognostic information to the Rapid Emergency Medicine Score as a predictor of long-term mortality.     

Use of a self-report-generated Charlson Comorbidity Index for predicting mortality

BACKGROUND: The Charlson Comorbidity Index, a popular tool for risk adjustment, often is constructed from medical record abstracts or administrative data. Limitations in both sources have fueled interest in using patient self-report as an alternative. However, little data exist on whether self-reported Charlson Indices predict mortality. OBJECTIVES: We sought to determine whether a self-reported Charlson Index predicts mortality, its performance relative to indices derived from administrative data, and whether using study-specific weights instead of Charlson's original weights enhances model fit. METHODS: We surveyed 7761 patients admitted to a university medical service over the course of 4 years and extracted their administrative data. We constructed 6 different Charlson indices by using 2 weighting schemes (original Charlson weights and study-specific weights) and 3 different datasources (ICD-9CM data for index hospitalization, ICD-9CM data with a 1-year look-back period, and patient self-report of comorbidities.) Multivariate models were constructed predicting 1-year mortality, log total costs, and log length of stay. RESULTS: The 6 measures of the Charlson index all predicted 1-year mortality. Models with age and gender, with or without diagnosis-related group, had approximately the same predictive power regardless of which of the 6 Charlson indices were used. Nevertheless, there were small improvements in model fit using administrative data versus self-report, or study-specific versus original weights. All models obtained areas under the receiver operating curve of 0.70 to 0.77. CONCLUSIONS: Overall, self-reported Charlson indices predict 1-year mortality comparably with indices based on administrative data. Administrative data may offer some small improvements in predictive ability and may be preferred when readily available.     

Assessment of long-term physical function in acute respiratory distress syndrome (ARDS) patients: comparison of the Charlson Comorbidity Index and the Functional Comorbidity Index

OBJECTIVE: It is often important to adjust for the effect of comorbid diseases on patient outcomes. This study compares the association between physical function in acute respiratory distress syndrome patients with scores on two comorbidity indices, the Charlson Comorbidity Index, designed to predict mortality, and the Functional Comorbidity Index (FCI), which was designed to predict physical function. DESIGN: This is a prospective, longitudinal, observational study. A total of 73 survivors of acute respiratory distress syndrome were contacted at 3, 6, and 12 mos. Patient comorbidity was evaluated with the Charlson Comorbidity Index and the FCI. Physical function was measured using the Medical Outcomes Study 36-Item Short Form Health Survey Physical Function Subscale and the Physical Component Subscale scores. RESULT: Mean FCI and Charlson Comorbidity Index scores correlated fairly strongly (Spearman rho = 0.62, P < 0.001). FCI, but not the Charlson Comorbidity Index, scores correlated with the Physical Function Subscale and Physical Component Subscale scores. After controlling for other potentially confounding variables such as age and severity of illness through regression analysis, the FCI score was still significantly associated with both Physical Function Subscale and Physical Component Subscale scores. CONCLUSIONS: The FCI is a better method of measuring comorbidity with physical function as the outcome. This study illustrates the importance of choosing the most appropriate comorbidity index for the outcome of interest.     

Validation of the Charlson Comorbidity Index for predicting functional outcome of stroke

Tessier A, Finch L, Daskalopoulou SS, Mayo NE. Validation of the Charlson Comorbidity Index for predicting functional outcome of stroke.

Objective

To determine whether a separate comorbidity index is needed to predict functional outcome after stroke, we compared the predictability of the Charlson Comorbidity Index (CMI) and the Functional Comorbidity Index (FCI) to that of a stroke-specific comorbidity index with function quantified with a measure developed with a Rasch model as outcome.

Design

Two prospective inception cohort studies, in 1996 through 1998 and in 2002 through 2005, with up to 9 months of follow-up.

Setting

Participants enrolled in 2 studies were recruited from acute care hospitals in the Montreal area.

Participants

For study one, 1027 persons with a first stroke discharged into the community were eligible; the 437 who were interviewed a second time at 6 months were included in the analysis. In study two, 235 of 262 patients with stroke were enrolled.

Interventions

Not applicable.

Main Outcome Measures

To predict recovery, we developed 3 stroke-specific comorbidity algorithms based on the estimated strength of association between comorbidities and stroke function. The various indices were compared on the basis of their predictive ability with a c statistic.

Results

In study 1, the c statistics were .758, .763, .766, and .763 for the stroke-specific algorithms 1, 2, and 3 and the CMI, respectively. In study 2, the c statistics were .680, .700, .704, .714, and .714 for the algorithms 1, 2, and 3, the CMI, and the FCI, respectively.

Conclusions

For purposes of case-mix adjustment, the CMI seems to be more than adequate.     

Comorbidity as a predictor of stage of illness for patients with breast cancer

OBJECTIVE: The purpose of this research was to determine whether comorbidity affects the stage at which breast cancer is diagnosed. METHODS: Data from the Surveillance, Epidemiology and End Results (SEER) program of the National Cancer Institute (NCI) was merged with Medicare claims for 17,468 women diagnosed with breast cancer from 1993 to 1995. RESULTS: Women with cardiovascular disease, musculoskeletal disorders, mild-to-moderate gastrointestinal disease, and nonmalignant benign breast disease had a 13%, 7%, 14%, and 24% lower odds, respectively, of being diagnosed with advanced breast cancer. Women with diabetes, other endocrine disorders, psychiatric disorders, or hematologic disorders increased the odds of a late-stage diagnosis by 19%, 11%, 20%, and 19% respectively. Mammography screening and contact with the medical care system decreased the odds of late-stage diagnosis. DISCUSSION: Four hypotheses are suggested to explain this link between comorbid illness and stage at diagnosis: (1) the "surveillance" hypothesis, (2) the "physiological" hypothesis, (3) the "competing demand" hypothesis, and (4) the "death from other causes" hypothesis. CONCLUSIONS: Comorbidity may complicate the diagnostic decision-making process for breast cancer. The results suggest that contact with the medical care system improves the odds of early-stage diagnosis. Thus, barriers to access for people with chronic conditions may exacerbate those chronic conditions and increase the odds of late-stage breast cancer.     

Exploring the application of the Charlson Comorbidity Index to assess the patient population seen in a Veterans Affairs chiropractic residency program

ObjectiveChiropractic trainees require exposure to a diverse patient base, including patients with multiple medical conditions. The Veterans Affairs (VA) Chiropractic Residency Program aims for its doctor of chiropractic (DC) residents to gain experience managing a range of multimorbid cases, yet to our knowledge there are no published data on the comorbidity characteristics of patients seen by VA DC residents. We tested 2 approaches to obtaining Charlson Comorbidity Index (CCI) scores and compared CCI scores of resident patients with those of staff DCs at 1 VA medical center.MethodsTwo processes of data collection to calculate CCI scores were developed. Time differences and agreement between methods were assessed. Comparison of CCI distribution between resident DC and staff DCs was done using 100 Monte Carlo simulation iterations of Fisher''s exact test.ResultsBoth methods were able to calculate CCI scores (n = 22). The automated method was faster than the manual (13 vs 78 seconds per patient). CCI scores agreement between methods was good (κ = 0.67). We failed to find a significant difference in the distribution of resident DC and staff DC patients (mean p = .377; 95% CI, .375–.379).ConclusionCCI scores of a VA chiropractic resident''s patients are measurable with both manual and automated methods, although automated may be preferred for its time efficiency. At the facility studied, the resident and staff DCs did not see patients with significantly different distributions of CCI scores. Applying CCI may give better insight into the characteristics of DC trainee patient populations.     

The Impact of Charlson Comorbidity Index on De Novo Cardiac Implantable Electronic Device Procedural Outcomes in the United States

ObjectiveTo investigate the utility of Charlson comorbidity index (CCI) as a measure of comorbidity burden to predict procedural outcomes after de novo cardiac implantable electronic device (CIED) implantation.MethodsAll de novo CIED implantations in the United States National Inpatient Sample between 2015 and 2018 were retrospectively analyzed, stratified by CCI score (0=no comorbidity burden, 1=mild, 2=moderate, ≥3=severe). Multivariable logistic regression models were performed to examine the association between unit CCI score (scale) and in-hospital outcomes (major adverse cerebrovascular and cardiovascular events [MACCE]: composite of all-cause mortality, acute ischemic stroke, thoracic and cardiac complications, and device-related complications; and MACCE individual components).ResultsOf 474,475 CIED procedures, the distribution of CCI score was as follows: CCI=0 (17.7%), CCI=1 (21.8%), CCI=2 (18.7%), and CCI=3+ (41.8%). Charlson comorbidity index score was associated with increased odds ratios of MACCE (1.10; 95% CI, 1.09 to 1.11), all-cause mortality (1.23; 95% CI, 1.21 to 1.25), and acute stroke (1.45; 95% CI, 1.44 to 1.46). This finding was consistent across all CIED groups except the cardiac resynchronization therapy groups in which CCI was not associated with increased risk of mortality. A higher CCI score was not associated with increased odds of procedural (thoracic and cardiac) and device-related complications.ConclusionIn a nationwide cohort of CIED procedures, higher comorbidity burden as measured by CCI score was associated with an increased risk of in-hospital mortality and acute ischemic stroke, but not procedure-related (thoracic and cardiac) or device-related complications. Objective assessment of comorbidity burden is important to risk-stratify patients undergoing CIED implantation for better prognostication of their in-hospital survival.     

GRACE risk score as predictor of in-hospital mortality in patients with chest pain

Introduction  

Chest pain and chest discomfort are common problems in the acute care setting. Life-threatening causes of chest pain must be quickly differentiated from other less serious causes. There is a need to stratify risk rapidly in patients presenting to the emergency department (ED) with chest pain. This study evaluates the relationship between the GRACE risk score (GRS) and in-hospital mortality in patients presenting to the ED with chest pain of all causes.     

Emergency Severity Index as a predictor of in-hospital mortality in suspected sepsis patients in the emergency department

ObjectivesTo demonstrate the accuracy, sensitivity, and specificity of the Emergency Severity Index (ESI), quick Sepsis-related Organ Failure Assessment (qSOFA), Systemic Inflammatory Response Syndrome (SIRS) criteria, and National Early Warning Score (NEWS) for predicting in-hospital mortality and intensive care unit (ICU) admission in suspected sepsis patients.MethodsA retrospective cohort study conducted at a tertiary care hospital, Thailand. Suspected sepsis was defined by a combination of (1) hemoculture collection and (2) the initiation of intravenous antibiotics therapy during the emergency department (ED) visit. The accuracy of each scoring system for predicting in-hospital mortality and ICU admission was analyzed.ResultsA total of 8177 patients (median age: 62 years, 52.3% men) were enrolled in the study, 509 (6.2%) of whom died and 1810 (22.1%) of whom were admitted to the ICU. The ESI and NEWS had comparable accuracy for predicting in-hospital mortality (AUC of 0.70, 95% confidence interval [CI] 0.68 to 0.73 and AUC of 0.73, 95% CI 0.70 to 0.75) and ICU admission (AUC of 0.75, 95% CI 0.74 to 0.76 and AUC of 0.74, 95% CI 0.72 to 0.75). The ESI level 1–2 had the highest sensitivity for predicting in-hospital mortality (96.7%), and qSOFA ≥2 had the highest specificity (86.6%).ConclusionThe ESI was accurate and had the highest sensitivity for predicting in-hospital mortality and ICU admission in suspected sepsis patients in the ED. This confirms that the ESI is useful in both ED triage and predicting adverse outcomes in these patients.     

Plasma suPAR levels are associated with mortality, admission time, and Charlson Comorbidity Index in the acutely admitted medical patient: a prospective observational study

ABSTRACT: INTRODUCTION: Soluble urokinase plasminogen activator receptor (suPAR) is the soluble form of the membrane-bound receptor (uPAR) expressed predominantly on various immune cells. Elevated plasma suPAR concentration is associated with increased mortality in various patient groups, and it is speculated that suPAR is a low-grade inflammation marker reflecting on disease severity. The aim of this prospective observational study was to determine if the plasma concentration of suPAR is associated with admission time, re-admission, disease severity/Charlson Comorbidity Index Score, and mortality. METHODS: We included 543 patients with various diseases from a Danish Acute Medical Unit during a two month period. A triage unit ensured that only medical patients were admitted to the Acute Medical Unit. SuPAR was measured on plasma samples drawn upon admission. Patients were followed-up for three months after inclusion by their unique civil registry number and using Danish registries to determine admission times, readmissions, International Classification of Diseases, 10th Edition (ICD-10) diagnoses, and mortality. Statistical analysis was used to determine suPAR's association with these endpoints. RESULTS: Increased suPAR was significantly associated with 90-day mortality (4.87 ng/ml in survivors versus 7.29 ng/ml in non-survivors, P < 0.0001), higher Charlson Score (P < 0.0001), and longer admission time (P < 0.0001), but not with readmissions. The association with mortality remained when adjusting for age, sex, C-reactive protein (CRP), and Charlson Score. Furthermore, among the various Charlson Score disease groups, suPAR was significantly higher in those with diabetes, cancer, cardiovascular disease, and liver disease compared to those without comorbidities. CONCLUSIONS: SuPAR is a marker of disease severity, admission time, and risk of mortality in a heterogeneous cohort of patients with a variety of diseases. The independent value of suPAR suggests it could be of value in prognostic algorithms.     

Analysis of variation in length of stay (LOS) after ischemic and hemorrhagic stroke using the Charlson Comorbidity Index (CCI)

[Purpose] The purpose of this study was to understand factors present at baseline that affect outcome and healthcare utilization post-stroke. We investigated the association between the Charlson Comorbidity Index (CCI) score and functional outcome (length of stay) after hemorrhagic and ischemic stroke. [Subjects and Methods] Data from the Korean National Hospital Discharge In-depth Injury Survey for 6 years, from 2005 to 2010, were used. The t-test and analysis of variance were carried out to compare average differences in the length of stay with the general characteristics in accordance with CCI. Multiple regression analysis was carried out using dummy variables to look at factors affecting stroke patients’ length of stay. [Results] Independent variables with significant relationships with the log-transformed length of stay included gender, type of insurance, the size of city of residence, the number of beds in the hospital, the location of the medical institution, hospitalization path, receipt of physical therapy, treatment involving brain surgery, death, the type of stroke, and CCI. [Conclusion] The results of the present study suggests that CCI independently influences the length of stay after ischemic and hemorrhagic stroke and that variables with significant relationships with the log-transformed length of stay need to be continuously managed.Key words: Stroke, CCI, Length of stay     

Computed tomography severity score as a predictor of disease severity and mortality in COVID-19 patients: A systematic review and meta-analysis

BackgroundPrediction of outcomes in severe COVID-19 patients using chest computed tomography severity score (CTSS) may enable more effective clinical management and early, timely ICU admission. We conducted a systematic review and meta-analysis to determine the predictive accuracy of the CTSS for disease severity and mortality in severe COVID-19 subjects.MethodsThe electronic databases PubMed, Google Scholar, Web of Science, and the Cochrane Library were searched to find eligible studies that investigated the impact of CTSS on disease severity and mortality in COVID-19 patients between 7 January 2020 and 15 June 2021. Two independent authors looked into the risk of bias using the Quality in Prognosis Studies (QUIPS) tool.ResultsSeventeen studies involving 2788 patients reported the predictive value of CTSS for disease severity. The pooled sensitivity, specificity, and summary area under the curve (sAUC) of CTSS were 0.85 (95% CI 0.78–0.90, I² =83), 0.86 (95% CI 0.76–0.92, I² =96) and 0.91 (95% CI 0.89–0.94), respectively. Six studies involving 1403 patients reported the predictive values of CTSS for COVID-19 mortality. The pooled sensitivity, specificity, and sAUC of CTSS were 0.77 (95% CI 0.69–0.83, I² = 41), 0.79 (95% CI 0.72–0.85, I² = 88), and 0.84 (95% CI 0.81–0.87), respectively.DiscussionEarly prediction of prognosis is needed to deliver the better care to patients and stratify them as soon as possible. Because different CTSS thresholds have been reported in various studies, clinicians are still determining whether CTSS thresholds should be used to define disease severity and predict prognosis.ConclusionEarly prediction of prognosis is needed to deliver optimal care and timely stratification of patients.  CTSS has strong discriminating power for the prediction of disease severity and mortality in patients with COVID-19.     

End-of-life communication and adjustment: pre-loss communication as a predictor of bereavement-related outcomes

Although bereavement-related emotional distress usually remits on its own over time, approximately 20% of bereaved individuals experience chronic emotional difficulties following the loss (Prigerson & Jacobs, 2001). Although several factors have been shown to be associated with poor outcomes post-loss, few studies have examined the relationship between pre-loss communication and bereavement-related depression, and none have examined its relationship to complicated grief. The present study examined how pre-loss communication between the bereaved and the deceased is related to post-loss outcomes within a sample of 60 members of online bereavement support groups. Results indicated that developing acceptance of an expected loss is strongly associated with bereavement outcomes. This finding persists even after controlling for relationship closeness and time elapsed post-loss.     

Student selection of tertiary nursing courses: efficacy of the Anderson score as a performance predictor

Selection of students for undergraduate nursing courses in Victorian Institutes of Higher Education is based almost exclusively on their Victorian Certificate of Education (VCE) Anderson score. Major aims of the selection process should be maximization of academic achievement as a basis for professional development and minimization of attrition. This paper examines the efficacy of the Anderson score as a selection instrument, using statistical methods to correlate Anderson score and other VCE data with academic performance in the first year of a Diploma of Applied Science (Nursing) course. Further statistical analysis is performed on upper and lower half groupings to show the selective value of the Anderson score in the region where it is likely to be applied.