Development of a risk prediction model for dialysis disequilibrium syndrome in hemodialysis patients based on decision tree algorithm

YAN Shanshan; WANG Ya; YAO Dongfang

doi:10.7619/jcmp.20242224

Journal of Clinical Medicine in Practice > 2024 > 28(18): 51-55, 61. > DOI: 10.7619/jcmp.20242224

YAN Shanshan, WANG Ya, YAO Dongfang. Development of a risk prediction model for dialysis disequilibrium syndrome in hemodialysis patients based on decision tree algorithm[J]. Journal of Clinical Medicine in Practice, 2024, 28(18): 51-55, 61. DOI: 10.7619/jcmp.20242224

Citation:

PDF (1670 KB)

Development of a risk prediction model for dialysis disequilibrium syndrome in hemodialysis patients based on decision tree algorithm

1.
Department of Nephrology, the Fourth Medical Center of Chinese People's Liberation Army General Hospital, Beijing, 100048
2.
Department of Emergency Medicine, the Fourth Medical Center of Chinese People's Liberation Army General Hospital, Beijing, 100048

More Information

Received Date: May 26, 2024
Revised Date: July 21, 2024

Graphical Abstract

Abstract

Abstract

Objective
To establish a risk prediction model for dialysis disequilibrium syndrome in hemodialysis patients using the Chi-squared Automatic Interaction Detection (CHAID) decision tree algorithm.
Methods
A total of 200 hemodialysis patients were enrolled as study subjects. Patients who developed dialysis disequilibrium syndrome after hemodialysis were included in occurrence group, while those who did not develop dialysis disequilibrium syndrome were included in non-occurrence group. Clinical data were collected and analyzed, and univariate and multivariate Logistic regression analyses were performed to screen independent influencing factors of dialysis disequilibrium syndrome in hemodialysis patients. Based on these independent factors and the decision tree model, a risk prediction model for dialysis disequilibrium syndrome in hemodialysis patients was constructed.
Results
Among 200 hemodialysis patients, 40 developed dialysis disequilibrium syndrome, while 160 did not. Multivariate Logistic regression analysis revealed that age and urea nitrogen were independent risk factors for dialysis disequilibrium syndrome in hemodialysis patients (OR > 1, P < 0.05), while albumin was an independent protective factor against DDS (OR < 1, P < 0.05). The decision tree model showed that age, located at the first level, was a most important influencing factor for dialysis disequilibrium syndrome (the sample was divided into three subgroups, with an incidence rate of 95.0% in patients older than 56.5 years, which was significantly higher than that in patients aged ≤56.5 years). Albumin and urea nitrogen were influencing factors for patients aged > 55.5 to 56.5 and > 56.5 years, respectively, located at the second level.
Conclusion
Analyzing the independent influencing factors of dialysis disequilibrium syndrome and risk prediction model constructed based on the decision tree algorithm can predict the probability of dialysis disequilibrium syndrome in hemodialysis patients.
- hemodialysis,
- dialysis disequilibrium syndrome,
- risk factors,
- decision tree,
- chi-squared automatic interaction detection,
- prediction model

FullText(HTML)

References (20)

References

[1]	MANEWELL S M, RAO P, HANEMAN K, et al. Prevention and management of foot and lower limb health complications in adults undergoing dialysis: a scoping review[J]. J Foot Ankle Res, 2023, 16(1): 81. doi: 10.1186/s13047-023-00679-z
[2]	RADISIC G, LEU R L, DONNELLY F, et al. How can nurses support patients on hemodialysis who experience needle fear? an online educational intervention for nephrology nurses[J]. Nephrol Nurs J, 2023, 50(5): 423-428. doi: 10.37526/1526-744X.2023.50.5.423
[3]	HAYATI M, BAGHERZADEH R, MAHMUDPOUR M, et al. Effect of teaching health-promoting behaviors on the care burden of family caregivers of hemodialysis patients: a four-group clinical trial[J]. BMC Nurs, 2023, 22(1): 436. doi: 10.1186/s12912-023-01604-2
[4]	HNYNN SI P E, HERNÁNDEZ-ALAVA M, DUNN L, et al. The trajectory of a range of commonly captured symptoms with standard care in people with kidney failure receiving haemodialysis: consideration for clinical trial design[J]. BMC Nephrol, 2023, 24(1): 341. doi: 10.1186/s12882-023-03394-w
[5]	AUGUSTYNSKA J, LICHODZIEJEWSKA-NIEMIERKO M, NAUMNIK B, et al. Automated peritoneal dialysis with remote patient monitoring: clinical effects and economic consequences for Poland[J]. Value Health Reg Issues, 2024, 40: 53-62. doi: 10.1016/j.vhri.2023.09.011
[6]	郭志军, 高山林, 丁新国. 肾脏内科疾病诊断标准[M]. 北京: 科学技术文献出版社, 2009: 45-97.
[7]	LIU K H, LEE S H, LIN W R, et al. Recovery of severe dialysis disequilibrium syndrome with uncal herniation following therapy with mannitol, hyperventilation and hypertonic saline[J]. Clin Kidney J, 2022, 15(1): 165-167. doi: 10.1093/ckj/sfab165
[8]	RAINA R, DAVENPORT A, WARADY B, et al. Dialysis disequilibrium syndrome (DDS) in pediatric patients on dialysis: systematic review and clinical practice recommendations[J]. Pediatr Nephrol, 2022, 37(2): 263-274. doi: 10.1007/s00467-021-05242-1
[9]	GUNGOR O, AYDIN Z, INCI A, et al. Seizures in patients with kidney diseases: a neglected problem[J]. Nephrol Dial Transplant, 2023, 38(2): 291-299. doi: 10.1093/ndt/gfab283
[10]	GENENA K H, AHMED S, SZERLIP H M, et al. Half the V by 120: a practical approach to the prevention of the dialysis disequilibrium syndrome[J]. Hemodial Int, 2021, 25(4): 424-432. doi: 10.1111/hdi.12938
[11]	FLOREA B, ORASAN R, BUDUREA C, et al. EEG spectral changes induced by hemodialysis[J]. Clin Neurophysiol Pract, 2021, 6: 146-148. doi: 10.1016/j.cnp.2021.03.006
[12]	AMAECHI P K, JENSSEN F, KRISHNASAMI Z, et al. Excessive elevation of serum phosphate during tumor lysis syndrome: lessons from a particularly challenging case[J]. Clin Nephrol Case Stud, 2021, 9: 39-44. doi: 10.5414/CNCS110086
[13]	STAHL J L, WHELAN R S, SYMONS J M. Dialysis disequilibrium on CKRT: avoiding the steep slippery slope[J]. Pediatr Nephrol, 2021, 36(9): 2697-2702. doi: 10.1007/s00467-021-05026-7
[14]	CATO-ADDISON W B, FERGUSON L, STRACHAN R D, et al. Intra-dialytic intracranial pressure monitoring in a patient with lumbo-peritoneal shunt for idiopathic intracranial hypertension[J]. Br J Neurosurg, 2023, 37(3): 382-384. doi: 10.1080/02688697.2020.1774509
[15]	HONG C S, WANG K, FALCONE G J. The CSF diversion via lumbar drainage to treat dialysis disequilibrium syndrome in the critically ill neurological patient[J]. Neurocrit Care, 2020, 33(1): 312-316. doi: 10.1007/s12028-020-00972-w
[16]	ALI M, BAKHSH U. A vanishing complication of haemodialysis: Dialysis disequilibrium syndrome[J]. J Intensive Care Soc, 2020, 21(1): 92-95. doi: 10.1177/1751143718798585
[17]	STEWARD C. Dialysis disequilibrium syndrome in the neurointensive care unit: a case study[J]. Nephrol Nurs J, 2019, 46(6): 597-603.
[18]	MISTRY K. Dialysis disequilibrium syndrome prevention and management[J]. Int J Nephrol Renovasc Dis, 2019, 12: 69-77. doi: 10.2147/IJNRD.S165925
[19]	VIGIL D, KIEN C, GIBB J, et al. Symptomatic hyperglycemia in a patient with dialysis ascites[J]. Am J Med Sci, 2019, 357(6): 512-516. doi: 10.1016/j.amjms.2019.02.016
[20]	LUND A, DAMHOLT M B, WIIS J, et al. Intracranial pressure during hemodialysis in patients with acute brain injury[J]. Acta Anaesthesiol Scand, 2019, 63(4): 493-499. doi: 10.1111/aas.13298

Cited By

Get Citation

PDF

XML

Article views (77) PDF downloads (22)

Turn off MathJax

Article Contents

Abstract

References

Development of a risk prediction model for dialysis disequilibrium syndrome in hemodialysis patients based on decision tree algorithm

Abstract

References

Catalog

Export File

Citation

Format

Content