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Wilson T, Quan S, Cheema K, Zarnke K, Quinn R, de Koning L, Dixon E, Pannu N, James MT. Risk prediction models for acute kidney injury following major noncardiac surgery: systematic review. Nephrol Dial Transplant 2015; 31:231-40. [PMID: 26705194 DOI: 10.1093/ndt/gfv415] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2015] [Accepted: 11/10/2015] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Acute kidney injury (AKI) is a serious complication of major noncardiac surgery. Risk prediction models for AKI following noncardiac surgery may be useful for identifying high-risk patients to target with prevention strategies. METHODS We conducted a systematic review of risk prediction models for AKI following major noncardiac surgery. MEDLINE, EMBASE, BIOSIS Previews and Web of Science were searched for articles that (i) developed or validated a prediction model for AKI following major noncardiac surgery or (ii) assessed the impact of a model for predicting AKI following major noncardiac surgery that has been implemented in a clinical setting. RESULTS We identified seven models from six articles that described a risk prediction model for AKI following major noncardiac surgeries. Three studies developed prediction models for AKI requiring renal replacement therapy following liver transplantation, three derived prediction models for AKI based on the Risk, Injury, Failure, Loss of kidney function, End-stage kidney disease (RIFLE) criteria following liver resection and one study developed a prediction model for AKI following major noncardiac surgical procedures. The final models included between 4 and 11 independent variables, and c-statistics ranged from 0.79 to 0.90. None of the models were externally validated. CONCLUSIONS Risk prediction models for AKI after major noncardiac surgery are available; however, these models lack validation, studies of clinical implementation and impact analyses. Further research is needed to develop, validate and study the clinical impact of such models before broad clinical uptake.
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Affiliation(s)
- Todd Wilson
- Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada
| | - Samuel Quan
- Department of Medicine, University of Calgary, Calgary, AB, Canada
| | - Kim Cheema
- Department of Medicine, University of Calgary, Calgary, AB, Canada
| | - Kelly Zarnke
- Department of Medicine, University of Calgary, Calgary, AB, Canada
| | - Rob Quinn
- Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada Department of Medicine, University of Calgary, Calgary, AB, Canada
| | - Lawrence de Koning
- Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada Department of Medicine, University of Calgary, Calgary, AB, Canada
| | - Elijah Dixon
- Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada Department of Surgery, University of Calgary, Calgary, AB, Canada
| | - Neesh Pannu
- Department of Medicine, University of Alberta, Edmonton, AB, Canada
| | - Matthew T James
- Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada Department of Medicine, University of Calgary, Calgary, AB, Canada
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153
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Mattishent K, Kwok CS, Ashkir L, Pelpola K, Myint PK, Loke YK. Prognostic Tools for Early Mortality in Hemorrhagic Stroke: Systematic Review and Meta-Analysis. J Clin Neurol 2015; 11:339-48. [PMID: 26256658 PMCID: PMC4596099 DOI: 10.3988/jcn.2015.11.4.339] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2014] [Revised: 03/07/2015] [Accepted: 03/09/2015] [Indexed: 11/17/2022] Open
Abstract
Background and Purpose Several risk scores have been developed to predict mortality in intracerebral hemorrhage (ICH). We aimed to systematically determine the performance of published prognostic tools. Methods We searched MEDLINE and EMBASE for prognostic models (published between 2004 and April 2014) used in predicting early mortality (<6 months) after ICH. We evaluated the discrimination performance of the tools through a random-effects meta-analysis of the area under the receiver operating characteristic curve (AUC) or c-statistic. We evaluated the following components of the study validity: study design, collection of prognostic variables, treatment pathways, and missing data. Results We identified 11 articles (involving 41,555 patients) reporting on the accuracy of 12 different tools for predicting mortality in ICH. Most studies were either retrospective or post-hoc analyses of prospectively collected data; all but one produced validation data. The Hemphill-ICH score had the largest number of validation cohorts (9 studies involving 3,819 patients) within our systematic review and showed good performance in 4 countries, with a pooled AUC of 0.80 [95% confidence interval (CI)=0.77-0.85]. We identified several modified versions of the Hemphill-ICH score, with the ICH-Grading Scale (GS) score appearing to be the most promising variant, with a pooled AUC across four studies of 0.87 (95% CI=0.84-0.90). Subgroup testing found statistically significant differences between the AUCs obtained in studies involving Hemphill-ICH and ICH-GS scores (p=0.01). Conclusions Our meta-analysis evaluated the performance of 12 ICH prognostic tools and found greater supporting evidence for 2 models (Hemphill-ICH and ICH-GS), with generally good performance overall.
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Affiliation(s)
- Katharina Mattishent
- Health Evidence Synthesis Group, Norwich Medical School, University of East Anglia, Norwich Research Park, Norwich, UK
| | - Chun Shing Kwok
- Institute of Cardiovascular Sciences, University of Manchester, Manchester, UK
| | - Liban Ashkir
- Health Evidence Synthesis Group, Norwich Medical School, University of East Anglia, Norwich Research Park, Norwich, UK
| | - Kelum Pelpola
- Department of Elderly Medicine, Southend University Hospital Trust, Westcliff-on-Sea, Essex, UK
| | - Phyo Kyaw Myint
- Epidemiology Group, Institute of Applied Health Sciences, School of Medicine & Dentistry, University of Aberdeen, Aberdeen, Scotland, UK
| | - Yoon Kong Loke
- Health Evidence Synthesis Group, Norwich Medical School, University of East Anglia, Norwich Research Park, Norwich, UK.
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159
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Lindström S, Schumacher FR, Cox D, Travis RC, Albanes D, Allen NE, Andriole G, Berndt SI, Boeing H, Bueno-de-Mesquita HB, Crawford ED, Diver WR, Ganziano JM, Giles GG, Giovannucci E, Gonzalez CA, Henderson B, Hunter DJ, Johansson M, Kolonel LN, Ma J, Le Marchand L, Pala V, Stampfer M, Stram DO, Thun MJ, Tjonneland A, Trichopoulos D, Virtamo J, Weinstein SJ, Willett WC, Yeager M, Hayes RB, Severi G, Haiman CA, Chanock SJ, Kraft P. Common genetic variants in prostate cancer risk prediction--results from the NCI Breast and Prostate Cancer Cohort Consortium (BPC3). Cancer Epidemiol Biomarkers Prev 2012; 21:437-44. [PMID: 22237985 PMCID: PMC3318963 DOI: 10.1158/1055-9965.epi-11-1038] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND One of the goals of personalized medicine is to generate individual risk profiles that could identify individuals in the population that exhibit high risk. The discovery of more than two-dozen independent single-nucleotide polymorphism markers in prostate cancer has raised the possibility for such risk stratification. In this study, we evaluated the discriminative and predictive ability for prostate cancer risk models incorporating 25 common prostate cancer genetic markers, family history of prostate cancer, and age. METHODS We fit a series of risk models and estimated their performance in 7,509 prostate cancer cases and 7,652 controls within the National Cancer Institute Breast and Prostate Cancer Cohort Consortium (BPC3). We also calculated absolute risks based on SEER incidence data. RESULTS The best risk model (C-statistic = 0.642) included individual genetic markers and family history of prostate cancer. We observed a decreasing trend in discriminative ability with advancing age (P = 0.009), with highest accuracy in men younger than 60 years (C-statistic = 0.679). The absolute ten-year risk for 50-year-old men with a family history ranged from 1.6% (10th percentile of genetic risk) to 6.7% (90th percentile of genetic risk). For men without family history, the risk ranged from 0.8% (10th percentile) to 3.4% (90th percentile). CONCLUSIONS Our results indicate that incorporating genetic information and family history in prostate cancer risk models can be particularly useful for identifying younger men that might benefit from prostate-specific antigen screening. IMPACT Although adding genetic risk markers improves model performance, the clinical utility of these genetic risk models is limited.
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Affiliation(s)
- Sara Lindström
- Program in Molecular and Genetic Epidemiology, Harvard School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA
| | - Fredrick R. Schumacher
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - David Cox
- Cancer Research Center of Lyon, Centre Léon Bérard, INSERM U1052, Lyon, France
- Department of Medicine and Department of Epidemiology and Biostatistics, School of Public Health, Imperial College, London, United Kingdom
| | - Ruth C. Travis
- Cancer Epidemiology Unit, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Naomi E. Allen
- Cancer Epidemiology Unit, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom
| | - Gerald Andriole
- Division of Urologic Surgery, Washington University School of Medicine, St Louis, MO, USA
| | - Sonja I. Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Heiner Boeing
- Department of Epidemiology, Deutsches Institut für Ernährungsforschung, Potsdam-Rehbrücke, Germany
| | - H. Bas Bueno-de-Mesquita
- National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
- Department of Gastroenterology and Hepatology, University Medical Centre Utrecht (UMCU), Utrecht, The Netherlands
| | - E. David Crawford
- Urologic Oncology, University of Colorado Health Sciences Center, Denver, CO, USA
| | - W. Ryan Diver
- Epidemiology Research Program, American Cancer Society, Atlanta, GA, USA
| | - J. Michael Ganziano
- Massachusetts Veterans Epidemiology and Research Information Center (MAVERIC) and Geriatric Research, Education, and Clinical Center (GRECC), Boston Veterans Affairs Healthcare System, Boston, MA, USA
- Division of Aging, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - Graham G. Giles
- Cancer Epidemiology Centre, Cancer Council Victoria and the Centre for Molecular, Genetic, Environmental, and Analytic Epidemiology, University of Melbourne, Melbourne, Australia
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia
| | - Edward Giovannucci
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA
- Department of Nutrition, Harvard School of Public Health, Boston, MA, USA
- Department of Medicine, Channing Laboratory, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Carlos A. Gonzalez
- Unit of Nutrition, Environment and Cancer, Catalan Institute of Oncology (IDIBELL, RETICC -RD06/0020), Barcelona, Spain
| | - Brian Henderson
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - David J. Hunter
- Program in Molecular and Genetic Epidemiology, Harvard School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA
- Department of Medicine, Channing Laboratory, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Mattias Johansson
- International Agency for Research on Cancer, Lyon, France
- Department of Surgical and Perioperative Sciences, Urology and Andrology, Umeå University, Sweden
| | | | - Jing Ma
- Department of Medicine, Channing Laboratory, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Loic Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Valeria Pala
- Department of Predictive Medicine, Nutritional Epidemiology Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Meir Stampfer
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA
- Department of Nutrition, Harvard School of Public Health, Boston, MA, USA
- Department of Medicine, Channing Laboratory, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Daniel O. Stram
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Michael J. Thun
- Epidemiology Research Program, American Cancer Society, Atlanta, GA, USA
| | - Anne Tjonneland
- Institute of Cancer Epidemiology, Danish Cancer Society, Copenhagen, Denmark
| | - Dimitrios Trichopoulos
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA
- Bureau of Epidemiologic Research, Academy of Athens, Greece
| | - Jarmo Virtamo
- Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland
| | - Stephanie J. Weinstein
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Walter C. Willett
- Department of Nutrition, Harvard School of Public Health, Boston, MA, USA
| | - Meredith Yeager
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Richard B. Hayes
- Division of Epidemiology, NYU Langone Medical Center, New York, NY, USA
| | - Gianluca Severi
- Cancer Epidemiology Centre, Cancer Council Victoria and the Centre for Molecular, Genetic, Environmental, and Analytic Epidemiology, University of Melbourne, Melbourne, Australia
| | - Christopher A. Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Stephen J. Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Peter Kraft
- Program in Molecular and Genetic Epidemiology, Harvard School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA
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