Key result
Machine learning-directed interdisciplinary interventions in hemodialysis clinics were associated with a significant reduction in hospital admission rates compared to control clinics (P<0.01).
Why the study?
End stage kidney disease patients face high hospitalization rates, and the impact of ML risk-directed interventions in a Dialysis Hospitalization Reduction Program on clinic-wide hospitalization rates required evaluation.
Does machine learning model-based risk-directed interdisciplinary team intervention reduce hospitalization rates in outpatient hemodialysis patients?
Cohort
Yes
Does machine learning model-based risk-directed interdisciplinary team intervention reduce hospitalization rates in outpatient hemodialysis patients?
Machine learning model-based risk-directed interventions are associated with significantly lower hospitalization admission and day rates in hemodialysis patients.
May support ML-guided teams in hemodialysis clinics; leaves open confirmation in randomized trials.
• End Stage Kidney Disease patients have high rates of hospitalization. • ML models can direct root cause evaluations and interventions in high-risk patients. • ML based personalized interventions can improve hospitalization outcomes. • Psychosocial barriers and structured behavioral health intervention can improve outcomes. • ML models can be in integrated in clinical practice at the point of care to guide care teams. An integrated kidney disease company uses machine learning (ML) models that predict the 12-month risk of an outpatient hemodialysis (HD) patient having multiple hospitalizations to assist with directing personalized interdisciplinary interventions in a Dialysis Hospitalization Reduction Program (DHRP). We investigated the impact of risk directed interventions in the DHRP on clinic-wide hospitalization rates. We compared the hospital admission and day rates per-patient-year (ppy) from all hemodialysis patients in 54 DHRP and 54 control clinics identified by propensity score matching at baseline in 2015 and at the end of the pilot in 2018. We also used paired T test to compare the between group difference of annual hospitalization rate and hospitalization days rates at baseline and end of the pilot. The between group difference in annual hospital admission and day rates was similar at baseline (2015) with a mean difference between DHRP versus control clinics of −0.008 ± 0.09 ppy and −0.05 ± 0.96 ppy respectively. The between group difference in hospital admission and day rates became more distinct at the end of follow up (2018) favoring DHRP clinics with the mean difference being −0.155 ± 0.38 ppy and −0.97 ± 2.78 ppy respectively. A paired t -test showed the change in the between group difference in hospital admission and day rates from baseline to the end of the follow up was statistically significant (t-value = 2.73, p-value < 0.01) and (t-value = 2.29, p-value = 0.02) respectively. These findings suggest ML model-based risk-directed interdisciplinary team interventions associate with lower hospitalization rates and hospital day rate in HD patients, compared to controls.
No takes yet. Share an insight, caveat, or question.
Chaudhuri et al. (2021) conducted a cohort in End Stage Kidney Disease / Hemodialysis. Machine learning-directed personalized interdisciplinary interventions (DHRP) vs. Standard care in control clinics was evaluated on Annual hospital admission rate (MD -0.155, p=<0.01). Machine learning-directed interdisciplinary interventions in hemodialysis clinics were associated with a significant reduction in hospital admission rates compared to control clinics (P<0.01).
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: