scholarly journals Charlson comorbidity index derived from chart review or administrative data: agreement and prediction of mortality in intensive care patients

2017 ◽  
Vol Volume 9 ◽  
pp. 311-320 ◽  
Author(s):  
Knut Stavem ◽  
Henrik Hoel ◽  
Stein Arve Skjaker ◽  
Rolf Haagensen
Keyword(s):  
Intensive Care ◽  
Administrative Data ◽  
Charlson Comorbidity Index ◽  
Chart Review ◽  
Intensive Care Patients ◽  
Prediction Of Mortality ◽  
Comorbidity Index

Revised Baux Score and updated Charlson comorbidity index are independently associated with mortality in burns intensive care patients

Burns ◽  
2015 ◽  
Vol 41 (7) ◽  
pp. 1420-1427 ◽  
Author(s):  
Jacob S. Heng ◽  
Olivia Clancy ◽  
Joanne Atkins ◽  
Jorge Leon-Villapalos ◽  
Andrew J. Williams ◽  
...  
Keyword(s):  
Intensive Care ◽  
Charlson Comorbidity Index ◽  
Intensive Care Patients ◽  
Comorbidity Index

PREDICTION OF MORTALITY FOLLOWING RADICAL PROSTATECTOMY BY CHARLSON COMORBIDITY INDEX

2008 ◽  
Vol 179 (4S) ◽  
pp. 559-559
Author(s):  
Paul Dluzniewski ◽  
Ryan Orosco ◽  
Elizabeth A Platz ◽  
Alan W Partin ◽  
Misop Han
Keyword(s):  
Radical Prostatectomy ◽  
Charlson Comorbidity Index ◽  
Prediction Of Mortality ◽  
Comorbidity Index

scholarly journals Factors associated with admission to the intensive care unit and mortality in patients with COVID-19, Colombia

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0260169
Author(s):  
Jorge Enrique Machado-Alba ◽  
Luis Fernando Valladales-Restrepo ◽  
Manuel Enrique Machado-Duque ◽  
Andrés Gaviria-Mendoza ◽  
Nicolás Sánchez-Ramírez ◽  
...  
Keyword(s):  
Intensive Care ◽  
Charlson Comorbidity Index ◽  
Severe Pneumonia ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Factors Associated ◽  
Intervention Measures ◽  
Point Increase ◽  
Comorbidity Index ◽  
Patient Prognosis

Introduction Coronavirus disease 2019 (COVID-19) has affected millions of people worldwide, and several sociodemographic variables, comorbidities and care variables have been associated with complications and mortality. Objective To identify the factors associated with admission to intensive care units (ICUs) and mortality in patients with COVID-19 from 4 clinics in Colombia. Methods This was a follow-up study of a cohort of patients diagnosed with COVID-19 between March and August 2020. Sociodemographic, clinical (Charlson comorbidity index and NEWS 2 score) and pharmacological variables were identified. Multivariate analyses were performed to identify variables associated with the risk of admission to the ICU and death (p<0.05). Results A total of 780 patients were analyzed, with a median age of 57.0 years; 61.2% were male. On admission, 54.9% were classified as severely ill, 65.3% were diagnosed with acute respiratory distress syndrome, 32.4% were admitted to the ICU, and 26.0% died. The factors associated with a greater likelihood of ICU admission were severe pneumonia (OR: 9.86; 95%CI:5.99–16.23), each 1-point increase in the NEWS 2 score (OR:1.09; 95%CI:1.002–1.19), history of ischemic heart disease (OR:3.24; 95%CI:1.16–9.00), and chronic obstructive pulmonary disease (OR:2.07; 95%CI:1.09–3.90). The risk of dying increased in those older than 65 years (OR:3.08; 95%CI:1.66–5.71), in patients with acute renal failure (OR:6.96; 95%CI:4.41–11.78), admitted to the ICU (OR:6.31; 95%CI:3.63–10.95), and for each 1-point increase in the Charlson comorbidity index (OR:1.16; 95%CI:1.002–1.35). Conclusions Factors related to increasing the probability of requiring ICU care or dying in patients with COVID-19 were identified, facilitating the development of anticipatory intervention measures that favor comprehensive care and improve patient prognosis.

scholarly journals PMU40 VALIDATION OF AN ICD-10 CODING ADAPTATION FOR THE CHARLSON COMORBIDITY INDEX IN UNITED STATES HEALTHCARE ADMINISTRATIVE DATA

2020 ◽  
Vol 23 ◽  
pp. S240
Author(s):  
J. Beyrer ◽  
J. Manjelievskaia ◽  
M. Bonafede ◽  
G.M. Lenhart ◽  
S. Nolot ◽  
...  
Keyword(s):  
United States ◽  
Administrative Data ◽  
Charlson Comorbidity Index ◽  
Icd 10 ◽  
Comorbidity Index ◽  
United States Healthcare

Development and Validation of the Modified Charlson Comorbidity Index in Incident Peritoneal Dialysis Patients: A National Population-Based Approach

2017 ◽  
Vol 37 (1) ◽  
pp. 94-102 ◽  
Author(s):  
Hyunjeong Cho ◽  
Myoung-Hee Kim ◽  
Hyo Jin Kim ◽  
Jae Yoon Park ◽  
Dong-Ryeol Ryu ◽  
...  
Keyword(s):  
Peritoneal Dialysis ◽  
Charlson Comorbidity Index ◽  
Clinical Decision Making ◽  
Proportional Hazards Model ◽  
Cox Regression ◽  
Relative Weight ◽  
Cox Proportional Hazards Model ◽  
Net Reclassification Improvement ◽  
Prediction Of Mortality ◽  
Comorbidity Index

Background The utility of applying the Charlson comorbidity index (CCI) to peritoneal dialysis (PD) patients is disputed because the relative weight of each comorbidity in PD patients may be different from those in other chronic diseases. We aimed to develop and validate a modified CCI in incident PD patients (mCCI-IPD) for better risk stratification and prediction of mortality. Methods The mCCI-IPD was developed using data from all Korean adult incident PD patients between 2005 and 2008 ( n = 7,606). Multivariate Cox regression was used to determine new weights for the individual comorbidities in the CCI. The prognostic performance of the mCCI-IPD was validated in an independent cohort ( n = 664) through c-statistics and continuous net reclassification improvement (cNRI). Results A total of 75.5% of the patients in the development cohort had 1 or more comorbidities. The Cox proportional hazards model provided reassigned severity weights for the 11 comorbidities that significantly predicted mortality. In the validation cohort, the CCI and mCCI-IPD scores were both correlated with survival and showed no differences in their c-statistics. However, multivariate analyses using cNRI revealed that the mCCI-IPD provided a 38.2% improvement in mortality risk assessment compared with the CCI (95% confidence interval [CI], 15.3 – 61.0; p < 0.001). These significant reclassification improvements were observed consistently in subjects with events (cNRIEvent, 28.2% [95% CI, 6.9 – 49.5; p = 0.009]) and without events (cNRINon-event, 10.0% [95% CI, 1.7 – 18.2; p = 0.019]). Conclusions Compared with the CCI, the mCCI-IPD showed better performance in mortality prediction for incident PD patients. Therefore, this tool may be used as a preferred index for statistical analysis and clinical decision-making.

Predicting Patient Participation to an ICU Follow-Up Program

2021 ◽  
Author(s):  
Danielle Prevedello ◽  
Claire Steckelmacher ◽  
Marianne Devroey ◽  
Jacques Creteur ◽  
Jean-Charles Preiser
Keyword(s):  
Intensive Care ◽  
Charlson Comorbidity Index ◽  
Multivariable Analysis ◽  
Ventilatory Support ◽  
University Hospital ◽  
Multivariable Logistic Regression Analysis ◽  
Number Of Patients ◽  
Comorbidity Index

Abstract Objective: Survivors of intensive care often present long-term sequelae, including cognitive impairment and psychological discomfort. Follow-up programs have therefore been developed to assess and manage these long-term complications. Studying the effectiveness of such programs can be limited by the number of patients lost during follow-up. The aim of this study was therefore to evaluate patient characteristics predictive of participation to an intensive care unit (ICU) follow-up program. Design: In this prospective, nested, case-control study, all patients with an ICU stay of at least five days were invited to participate in an ICU follow-up program. Having attended 2 follow-up sessions at ICU and hospital discharge, they were given an appointment for their 3-month follow up. Patients were divided into two groups (“participants and “non-participants”) according to whether or not they attended this appointment. Multivariable logistic regression analysis was used to identify independent predictors of participation.Settings: An ICU follow-up program from a mixed ICU at a university hospital.Participants: All patients selected to participate to the ICU follow-up program were included in this study. They were allocated into two groups depending on their attendance in the follow-up program. Intervention: NoneMain results: Of the 199 patients included during the study period, 80 (40.2%) were classified as “participants”. These patients had a lower Charlson Comorbidity Index, a longer ICU length of stay (LOS), more frequently received ventilatory support for at least 24 hours and more frequently received extracorporeal membrane oxygenation (ECMO) than non-participant patients. In the multivariable analysis, ICU LOS longer than 10 days was associated with a 3.3 times increased likelihood of participating in the follow-up; a lower Charlson Comorbidity Index also predicted an increased likelihood of participating to the ICU follow-up clinic.Conclusions: Fewer comorbidities and longer intensive care LOS were independent predictors of participation in ICU follow-up.

The Pitt Bacteremia Score, Charlson Comorbidity Index and Chronic Disease Score are useful tools for the prediction of mortality in patients with Candida bloodstream infection

Mycoses ◽  
2017 ◽  
Vol 60 (10) ◽  
pp. 676-685 ◽  
Author(s):  
María Paz Vaquero-Herrero ◽  
Silvio Ragozzino ◽  
Fabián Castaño-Romero ◽  
María Siller-Ruiz ◽  
Rebeca Sánchez González ◽  
...  
Keyword(s):  
Chronic Disease ◽  
Bloodstream Infection ◽  
Charlson Comorbidity Index ◽  
Disease Score ◽  
Chronic Disease Score ◽  
Prediction Of Mortality ◽  
Comorbidity Index ◽  
Candida Bloodstream Infection
Sign in / Sign up

Export Citation Format

Share Document