Literature DB >> 31362990

Prediction of Risk of Death for Patients Starting Dialysis: A Systematic Review and Meta-Analysis.

Ryan T Anderson1, Hailey Cleek2, Atieh S Pajouhi3, M Fernanda Bellolio4, Ananya Mayukha2, Allyson Hart5,6, LaTonya J Hickson7,8, Molly A Feely9, Michael E Wilson2,10, Ryan M Giddings Connolly3, Patricia J Erwin11, Abdul M Majzoub10, Navdeep Tangri12,13, Bjorg Thorsteinsdottir14,3,7.   

Abstract

BACKGROUND AND OBJECTIVES: Dialysis is a preference-sensitive decision where prognosis may play an important role. Although patients desire risk prediction, nephrologists are wary of sharing this information. We reviewed the performance of prognostic indices for patients starting dialysis to facilitate bedside translation. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Systematic review and meta-analysis following the PRISMA guidelines. We searched Ovid MEDLINE, Ovid Embase, Ovid Central Register of Controlled Trials, Ovid Cochrane Database of Systematic Reviews, and Scopus for eligible studies of patients starting dialysis published from inception to December 31, 2018. SELECTION CRITERIA: Articles describing validated prognostic indices predicting mortality at the start of dialysis. We excluded studies limited to prevalent dialysis patients, AKI and studies excluding mortality in the first 1-3 months. Two reviewers independently screened abstracts, performed full text assessment of inclusion criteria and extracted: study design, setting, population demographics, index performance and risk of bias. Pre-planned random effects meta-analysis was performed stratified by index and predictive window to reduce heterogeneity.
RESULTS: Of 12,132 articles screened and 214 reviewed in full text, 36 studies were included describing 32 prognostic indices. Predictive windows ranged from 3 months to 10 years, cohort sizes from 46 to 52,796. Meta-analysis showed discrimination area under the curve (AUC) of 0.71 (95% confidence interval, 0.69 to 073) with high heterogeneity (I 2=99.12). Meta-analysis by index showed highest AUC for The Obi, Ivory, and Charlson comorbidity index (CCI)=0.74, also CCI was the most commonly used (ten studies). Other commonly used indices were Kahn-Wright index (eight studies, AUC 0.68), Hemmelgarn modification of the CCI (six studies, AUC 0.66) and REIN index (five studies, AUC 0.69). Of the indices, ten have been validated externally, 16 internally and nine were pre-existing validated indices. Limitations include heterogeneity and exclusion of large cohort studies in prevalent patients.
CONCLUSIONS: Several well validated indices with good discrimination are available for predicting survival at dialysis start.
Copyright © 2019 by the American Society of Nephrology.

Entities:  

Keywords:  Acute Kidney Injury; Area Under Curve; Bias; Cohort Studies; Comorbidity; Humans; Nephrologists; Patient Selection; Prevalence; Prognosis; Renal Dialysis; Risk; dialysis; mortality; mortality risk; peritoneal dialysis

Year:  2019        PMID: 31362990      PMCID: PMC6682819          DOI: 10.2215/CJN.00050119

Source DB:  PubMed          Journal:  Clin J Am Soc Nephrol        ISSN: 1555-9041            Impact factor:   8.237


  68 in total

1.  Perceived mental health at the start of dialysis as a predictor of morbidity and mortality in patients with end-stage renal disease (CALVIDIA Study).

Authors:  Katia López Revuelta; Fernando J García López; Fernando de Alvaro Moreno; Jordi Alonso
Journal:  Nephrol Dial Transplant       Date:  2004-07-13       Impact factor: 5.992

2.  Charlson comorbidity index as a predictor of outcomes in incident peritoneal dialysis patients.

Authors:  L Fried; J Bernardini; B Piraino
Journal:  Am J Kidney Dis       Date:  2001-02       Impact factor: 8.860

3.  A simple comorbidity scale predicts clinical outcomes and costs in dialysis patients.

Authors:  S Beddhu; F J Bruns; M Saul; P Seddon; M L Zeidel
Journal:  Am J Med       Date:  2000-06-01       Impact factor: 4.965

4.  Quantifying comorbidity in peritoneal dialysis patients and its relationship to other predictors of survival.

Authors:  Simon J Davies; Louise Phillips; Patrick F Naish; Gavin I Russell
Journal:  Nephrol Dial Transplant       Date:  2002-06       Impact factor: 5.992

Review 5.  Communicating prognosis in the dialysis consent process: a patient-centered, guideline-supported approach.

Authors:  Donna M Michel; Alvin H Moss
Journal:  Adv Chronic Kidney Dis       Date:  2005-04       Impact factor: 3.620

6.  Adapting the Charlson Comorbidity Index for use in patients with ESRD.

Authors:  Brenda R Hemmelgarn; Braden J Manns; Hude Quan; William A Ghali
Journal:  Am J Kidney Dis       Date:  2003-07       Impact factor: 8.860

7.  Effect of coexistent diseases on survival of patients undergoing dialysis.

Authors:  A Nicolucci; D Cubasso; D Labbrozzi; E Mari; P Impicciatore; D A Procaccini; M Forcella; I Stella; M Querques; A Pappani
Journal:  ASAIO J       Date:  1992 Jul-Sep       Impact factor: 2.872

8.  Comparison of the Charlson Comorbidity Index and the Davies score as a predictor of outcomes in PD patients.

Authors:  Linda Fried; Judith Bernardini; Beth Piraino
Journal:  Perit Dial Int       Date:  2003 Nov-Dec       Impact factor: 1.756

9.  How to adjust for comorbidity in survival studies in ESRD patients: a comparison of different indices.

Authors:  Jeannette G van Manen; Johanna C Korevaar; Friedo W Dekker; Elisabeth W Boeschoten; Patrick M M Bossuyt; Raymond T Krediet
Journal:  Am J Kidney Dis       Date:  2002-07       Impact factor: 8.860

10.  Charlson Comorbidity Index is a predictor of outcomes in incident hemodialysis patients and correlates with phase angle and hospitalization.

Authors:  B Di Iorio; N Cillo; M Cirillo; N Gaspare De Santo
Journal:  Int J Artif Organs       Date:  2004-04       Impact factor: 1.595

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  10 in total

1.  Older Patients with Advanced Chronic Kidney Disease and Their Perspectives on Prognostic Information: a Qualitative Study.

Authors:  Bjorg Thorsteinsdottir; Nataly R Espinoza Suarez; Susan Curtis; Annika T Beck; Ian Hargraves; Kevin Shaw; Susan P Y Wong; LaTonya J Hickson; Kasey R Boehmer; Brigid Amberg; Erin Dahlen; Cristina Wirtz; Robert C Albright; Ashok Kumbamu; Jon C Tilburt; Erica J Sutton
Journal:  J Gen Intern Med       Date:  2022-01-26       Impact factor: 6.473

2.  Primary kidney disease modifies the effect of comorbidities on kidney replacement therapy patients' survival.

Authors:  Jaakko Helve; Mikko Haapio; Per-Henrik Groop; Patrik Finne
Journal:  PLoS One       Date:  2021-08-20       Impact factor: 3.240

3.  Association of Body Mass Index and Waist Circumference with All-Cause Mortality in Hemodialysis Patients.

Authors:  Chang Seong Kim; Kyung-Do Han; Hong Sang Choi; Eun Hui Bae; Seong Kwon Ma; Soo Wan Kim
Journal:  J Clin Med       Date:  2020-04-29       Impact factor: 4.241

4.  Prediction of the Mortality Risk in Peritoneal Dialysis Patients using Machine Learning Models: A Nation-wide Prospective Cohort in Korea.

Authors:  Junhyug Noh; Kyung Don Yoo; Wonho Bae; Jong Soo Lee; Kangil Kim; Jang-Hee Cho; Hajeong Lee; Dong Ki Kim; Chun Soo Lim; Shin-Wook Kang; Yong-Lim Kim; Yon Su Kim; Gunhee Kim; Jung Pyo Lee
Journal:  Sci Rep       Date:  2020-05-04       Impact factor: 4.379

5.  Validation of prognostic indices for short term mortality in an incident dialysis population of older adults >75.

Authors:  Bjorg Thorsteinsdottir; LaTonya J Hickson; Rachel Giblon; Atieh Pajouhi; Natalie Connell; Megan Branda; Amrit K Vasdev; Rozalina G McCoy; Ladan Zand; Navdeep Tangri; Nilay D Shah
Journal:  PLoS One       Date:  2021-01-20       Impact factor: 3.240

6.  Serum uric acid level and all-cause and cardiovascular mortality in peritoneal dialysis patients: A systematic review and dose-response meta-analysis of cohort studies.

Authors:  Ting Kang; Youchun Hu; Xuemin Huang; Adwoa N Amoah; Quanjun Lyu
Journal:  PLoS One       Date:  2022-02-22       Impact factor: 3.240

7.  Validation of a United Kingdom Model to Predict Mortality in Incident Dialysis Patients in the Dialysis Outcomes and Practice Patterns Study Cohort: Introduction of a Clinical Risk Score.

Authors:  Martin Wagner; David M Kent; Ronald L Pisoni; Damian Fogarty; Gero von Gersdorff; Christoph Wanner; Navdeep Tangri
Journal:  Kidney Med       Date:  2022-01-25

8.  Perioperative Complications and Oncologic Outcomes after Radical Cystectomy in End-Stage Renal Disease Patients with Bladder Cancer Obtained Using a Standardized Reporting System.

Authors:  Yu-Liang Liu; Chun-Te Wu; Yu-Chao Hsu; Miao-Fen Chen; Chih-Shou Chen; Chung-Sheng Shi; Yun-Ching Huang
Journal:  Cancers (Basel)       Date:  2022-07-19       Impact factor: 6.575

9.  Physical Resilience Phenotype Trajectories in Incident Hemodialysis: Characterization and Mortality Risk Assessment.

Authors:  Melissa D Hladek; Jiafeng Zhu; Deidra C Crews; Mara A McAdams-DeMarco; Brian Buta; Ravi Varadhan; Tariq Shafi; Jeremy D Walston; Karen Bandeen-Roche
Journal:  Kidney Int Rep       Date:  2022-06-23

10.  Design of a consensus-based geriatric assessment tailored for older chronic kidney disease patients: results of a pragmatic approach.

Authors:  Carlijn G N Voorend; Hanneke Joosten; Noeleen C Berkhout-Byrne; Adry Diepenbroek; Casper F M Franssen; Willem Jan W Bos; Marjolijn Van Buren; Simon P Mooijaart
Journal:  Eur Geriatr Med       Date:  2021-04-19       Impact factor: 1.710

  10 in total

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