Literature DB >> 11423689

Identifying patients at risk for hemodialysis underprescription.

J B Leon1, A R Sehgal.   

Abstract

Underprescription of hemodialysis is an important barrier to adequate delivery of dialysis. We sought to determine which patient factors are associated with hemodialysis underprescription and to examine variation in prescription across facilities. For 721 randomly selected patients from all 22 chronic hemodialysis units in northeast Ohio, we calculated prescribed Kt/V based on dialyzer urea clearance at prescribed blood and dialysate flow (K), prescribed treatment time (t), and anthropometric volume (V). A minimum 'prescribed Kt/V' of 1.3 has been recommended to ensure an adequate 'delivered Kt/V' of 1.2. Using this criterion, 15% of patients had a low prescribed Kt/V. Prescribed Kt was strongly related to patient anthropometric volume but not to other patient characteristics (age, gender, race, cause of renal failure, number of years on dialysis, number of comorbid conditions). A 10-liter increase in V was associated with an 8.3-liter increase in prescribed Kt. However, a 13-liter increase in prescribed Kt would be needed to maintain a prescribed Kt/V of 1.3. As a result, the proportion of patients with low prescriptions increased from 2% of patients with V <35 liters to 42% of patients with V > or =50 liters. In addition, the prevalence of low prescriptions varied dramatically across facilities (range 0-47%) even after accounting for volumes of individual patients. Of the 109 patients with low prescription, 75% would achieve a prescribed Kt/V of 1.3 with less than 30 min of additional treatment time. In conclusion, large patients and patients at specific facilities are at increased risk for underprescription of hemodialysis. Most patients with low prescriptions would achieve a prescribed Kt/V of 1.3 with a modest increase in treatment time. Copyright 2001 S. Karger AG, Basel

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Year:  2001        PMID: 11423689     DOI: 10.1159/000046248

Source DB:  PubMed          Journal:  Am J Nephrol        ISSN: 0250-8095            Impact factor:   3.754


  1 in total

1.  Dialysis adequacy predictions using a machine learning method.

Authors:  Hyung Woo Kim; Seok-Jae Heo; Jae Young Kim; Annie Kim; Chung-Mo Nam; Beom Seok Kim
Journal:  Sci Rep       Date:  2021-07-29       Impact factor: 4.379

  1 in total

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