The Kansas City Cardiomyopathy Questionnaire (KCCQ) is a heart failure health status measure and has been used in studies of patients with aortic stenosis. The Kansas City Cardiomyopathy Questionnaire (KCCQ) is a item self- questionnaire developed to independently measure the patient’s. To provide a better description of health related quality of life in patients with Congestive Heart Failure (CHF).
|Genre:||Health and Food|
|Published (Last):||26 January 2017|
|PDF File Size:||5.95 Mb|
|ePub File Size:||7.33 Mb|
|Price:||Free* [*Free Regsitration Required]|
Models are typically considered reasonable when the c -statistic is greater than 0. More recently, KCCQ score was used to assess the feasibility of reflecting the changes of acute HF during hospitalization and predicting day readmission.
KCCQ – Kansas City Cardiomyopathy Questionnaire
In the KCCQan overall summary score can be derived from the physical function, symptom frequency and severitysocial function and quality of life domains. Additional clinical studies need to be done in multiple centers with a larger sample size to validate our finding.
The authors found that it was feasible to use the KCCQ during acute HF hospitalizations and was sensitive to clinical improvement, but score changes during hospitalization did not predict day readmission.
Cardiovascular Quality and Outcomesvol. This study was performed in a single-community kcdq center, and further studies in other centers or multiple centers need to be done to validate our findings. Therefore, whether KCCQ score can be used to predict the short-term readmission has yet to be completely evaluated.
However, a significant difference between these two groups was noted on comparing gender, with male patients being more prone to being readmitted than female Construct validity was demonstrated with strong correlations to respective subscales of the SF December 16, Patients who were admitted to the HF unit were screened and enrolled for the study.
Responsiveness refers to the kcvq of a measure to track accurately a phenomenon when it does change. We then performed multivariate analysis to investigate how each clinical factor was associated with HF readmissions after controlling for the other factors.
The KCCQ score, lab test results on admission, and discharge medications were compared between the nonreadmitted and readmitted patients Table 2. Scores are transformed to a range ofin which higher scores reflect better health status. For each domain, the validity, reproducibility, responsiveness and interpretability have been independently established.
An alternative approach to interpreting clinical changes is to appreciate the prognostic significance of changes in scores. Previous studies have shown that KCCQ score correlated with survival and hospitalization in patients with HF [ 7 ] and was an independent predictor of poor prognosis in this patient population [ 8 ]. Heart failure is one of the most common diagnoses associated with readmission.
One possible interpretation could be that patients who have had a myocardial infarction are more likely to have wall motion abnormalities and fixed myocardial defects and thus a lower ejection fraction than those with nonobstructive coronary artery disease without an MI, leading to opposite contribution to HF readmission. In this analysis, we also used integrated discrimination improvement IDIdescribed by Quetsionnaire et al.
The Kansas City Cardiomyopathy Questionnaire (KCCQ)
Among these patients, the magnitude and direction of change was as follows: One is to examine the prognostic significance of KCCQ scores and the other is to benchmark score changes against clinical assessments of change. All values were two-tailed, and was set as the level of statistical significance for all tests. Cardiology Research and Practice. The KCCQ change scores were exquisitely reflective of clinical changes in heart failure both in terms of its directionality improvement versus deterioration and proportion-al-ity of change magnitude — as revealed in this figure:.
Cardiology Research and Practice
As mentioned above, there are multiple factors contributing to HF readmission; therefore, risk prediction models including and weighing all relevant factors were developed. Future research should include relevant physical examination findings and chest X-ray findings, which could be important in the risk prediction model. After the multivariate analysis, we further constructed five simplified prediction models and evaluated the importance of KCCQ score in the final model through comparing area under receiver operating characteristic curve ROC of each model.
Postdischarge readmission information was gathered through follow-up interview with the patient. I am considering using the Kansas City as a primary outcome measure in an RCT please can you advise on the numbers of patients needed to measure a significant change? We enrolled patients who met the study criteria. The study was conducted at Florida Hospital, Orlando Campus.
There was a problem providing the content you requested
However, this study was a relatively small study that included only 54 patients and was focused on KCCQ score differences during hospitalization between nonreadmission and admission groups [ 10 ]. Although new data showed reduction in Medicare hospital readmission rates [ 4 ], HF is still one of the most common diagnoses associated kcxq day readmission; an analysis of to Medicare claims-based data showed that questionnaide Details about how to license any of our instruments are in this FAQ.
Compared to readmitted patients, nonreadmitted patients had a higher ejection fraction on admission Comments Seng Khiong Jong — 14 May – Conversely, if risk prediction is no better than chance, the c -statistic is 0. This questionnaire identified the following clinically relevant domains: Heart failure HF is one of the most common diagnoses associated with hospital readmission.
This suggests that a mean difference over time of 5 points on the KCCQ Overall Summary Scale reflects a clinically significant change in heart failure status. It contributed to improving the c -statistics of a model based on age, gender, medications, laboratory data, and LVEF available at discharge from 0. Given that many other possible risk factors have not been included in this model, such as GFR and BNP, this model may not be perfect, although its c -statistics was greater than 0.
KCCQ score provided important prognostic information for predicting day readmission and it can significantly improve prediction reliability along with other critical components.
The c -statistic indicated that model 5 which included KCCQ score and all other potential predictors had the highest c -statistic value 0.