TY - JOUR
T1 - Symptom-based clusters in patients with advanced chronic organ failure identify different trajectories of symptom variations
AU - Finamore, Panaiotis
AU - Janssen, Daisy J. A.
AU - Schols, Jos M. G. A.
AU - Verstraeten, Els R. N.
AU - Antonelli Incalzi, Raffaele
AU - Wouters, Emiel F. M.
AU - Spruit, Martijn A.
N1 - Funding Information:
The authors acknowledge the research nurses, the doctors and the departments for their participation in this study: Maastricht University Medical Centre + (MUMC +), Maastricht, the Netherlands: Department of Respiratory Medicine, Department of Cardiology, Department of Internal Medicine; Laurentius Hospital, Roermond, the Netherlands: Department of Respiratory Medicine, Department of Internal Medicine; St Jans Gasthuis, Weert, the Netherlands: Department of Cardiology; Màxima Medical Centre, Veldhoven/Eindhoven, the Netherlands: Department of Internal Medicine, Department of Cardiology; Catharina Hospital, Eindhoven, the Netherlands: Department of Respiratory Medicine, Department of Internal Medicine; Zuyderland Medical Centre, Heerlen, the Netherlands: Department of Cardiology, Department of Internal Medicine; Zuyderland Medical Centre, Sittard-Geleen, the Netherlands: Department of Internal Medicine.
Funding Information:
This project was supported by: Proteion Thuis, Horn, The Netherlands; CIRO + , Centre of Expertise for Chronic Organ Failure, Horn, The Netherlands; Netherlands Asthma Foundation [Grant 3.4.06.082], Leusden, The Netherlands; Stichting Wetenschapsbevordering Verpleeghuiszorg (SWBV), Utrecht, The Netherlands. No funding source had any role in design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript. Acknowledgements
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2021/2
Y1 - 2021/2
N2 - Background Healthcare needs are complex and heterogeneous in advanced chronic organ failure. However, based on symptom clusters, groups of patients with similar quality of life, care dependency and life-sustaining treatment preferences can be identified. Aims To evaluate the stability of symptom-based clusters over time, and whether and to what extent the clusters are able to predict patients' 2-year survival and hospitalization rates. Methods This is a secondary analysis of a longitudinal observational study including 95 outpatients with chronic obstructive pulmonary disease (COPD) GOLD stage III-IV, 80 outpatients with chronic heart failure (CHF) NYHA stage III-IV and 80 outpatients with chronic renal failure (CRF) requiring dialysis. Patients were clustered into three groups applying K-means algorithm on baseline symptoms' severity and were then longitudinally evaluated. 2-year survival and hospital admissions during 1 year were estimated using Kaplan-Meier curves and Cox models. 1-year tendencies in symptom variation, using mixed linear models, and clusters comparison over time were performed. Results The three clusters were unable to predict patients' survival and hospital admissions. Noteworthy, they show different trajectories of symptom variation, with Cluster 1 patients experiencing a worsening of symptoms, associated with an increased care dependency, and Cluster 2 and Cluster 3 patients being stable or having a relief in some symptoms. Although Cluster 1 is becoming more similar to Cluster 2, the three clusters preserve the overall characteristics and differences. Discussion Symptom-based clusters might help to identify patients with different trajectories of symptom variations. Conclusion Symptom clusters do not predict survival and hospital admissions and are stable over time.
AB - Background Healthcare needs are complex and heterogeneous in advanced chronic organ failure. However, based on symptom clusters, groups of patients with similar quality of life, care dependency and life-sustaining treatment preferences can be identified. Aims To evaluate the stability of symptom-based clusters over time, and whether and to what extent the clusters are able to predict patients' 2-year survival and hospitalization rates. Methods This is a secondary analysis of a longitudinal observational study including 95 outpatients with chronic obstructive pulmonary disease (COPD) GOLD stage III-IV, 80 outpatients with chronic heart failure (CHF) NYHA stage III-IV and 80 outpatients with chronic renal failure (CRF) requiring dialysis. Patients were clustered into three groups applying K-means algorithm on baseline symptoms' severity and were then longitudinally evaluated. 2-year survival and hospital admissions during 1 year were estimated using Kaplan-Meier curves and Cox models. 1-year tendencies in symptom variation, using mixed linear models, and clusters comparison over time were performed. Results The three clusters were unable to predict patients' survival and hospital admissions. Noteworthy, they show different trajectories of symptom variation, with Cluster 1 patients experiencing a worsening of symptoms, associated with an increased care dependency, and Cluster 2 and Cluster 3 patients being stable or having a relief in some symptoms. Although Cluster 1 is becoming more similar to Cluster 2, the three clusters preserve the overall characteristics and differences. Discussion Symptom-based clusters might help to identify patients with different trajectories of symptom variations. Conclusion Symptom clusters do not predict survival and hospital admissions and are stable over time.
KW - Chronic obstructive pulmonary disease
KW - Congestive heart failure
KW - Chronic renal failure
KW - Dialysis
KW - Symptoms
KW - Cluster analysis
KW - OBSTRUCTIVE PULMONARY-DISEASE
KW - QUALITY-OF-LIFE
KW - STAGE RENAL-DISEASE
KW - HEART-FAILURE
KW - HEALTH SURVEY
KW - GO TEST
KW - CARE
KW - FATIGUE
KW - DYSPNEA
KW - DEPRESSION
U2 - 10.1007/s40520-020-01711-z
DO - 10.1007/s40520-020-01711-z
M3 - Article
C2 - 32951187
SN - 1594-0667
VL - 33
SP - 419
EP - 428
JO - Aging Clinical and Experimental Research
JF - Aging Clinical and Experimental Research
IS - 2
ER -