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Bartoli A, Gallieni M, Cogliati C, Casella F, Calloni M, Melchionda C, Heidempergher M, Foschi A, Luca Brucato A, Rizzi G, Quici M, Gidaro A. A decision-making algorithm proposal for PICCs and midlines insertion in patients with advanced kidney disease: A pilot study. J Vasc Access 2024; 25:1151-1157. [PMID: 36726229 DOI: 10.1177/11297298231152499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
INTRODUCTION Kidney Disease Outcomes Quality Initiative clinical practice guidelines recommend avoiding placement of peripherally inserted vascular access devices in patients with an estimated glomerular filtration rate (eGFR) <45 ml/min. On the other hand, many patients with severe chronic kidney disease (CKD) have poor prognosis.This study carried out a global assessment of mortality at 2 years through Charlson Comorbidity Index (CCI) and Beclap score in patients with PICCs or Midlines, assuming that in those with an estimated high mortality rate at 2 years, it could be acceptable to implant a peripheral vascular access device (PVAD) despite the presence of CKD. METHODS We analyzed data on patients with PICCs or Midlines inserted from October 2018 to November 2019. CCI, Beclap score, and eGFR were calculated for each patient at the time of the catheter insertion. We then followed patients for 2 years to assess 2-year mortality for each. RESULTS One hundred and thirty-one patients were enrolled, 49 (37.4%) had eGFR<45 ml. The 2-year mortality rate was 57.3%. The cut off derived from ROC curve analysis of 15 for Beclap score and 5 for CCI, showed good sensitivity and specificity in predicting mortality of the total population, patients without an oncological disease and patients with eGFR<45 ml/min. CONCLUSION CCI and Beclap score are good predictors of mortality at 2 years.Physicians and nurses can use these tools in the evaluation of patients at risk for future dialysis, instead of relying exclusively on renal function to decide whether implanting PICCs, Midlines, or other vascular access devices.
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Affiliation(s)
- Arianna Bartoli
- Department of Biomedical and Clinical Sciences "Luigi Sacco," University of Milan, Luigi Sacco Hospital, Milan, Italy
| | - Maurizio Gallieni
- Department of Biomedical and Clinical Sciences "Luigi Sacco," University of Milan, Luigi Sacco Hospital, Milan, Italy
- Nephrology and Dialysis Unit, ASST Fatebenefratelli Sacco, Milan, Italy
| | - Chiara Cogliati
- Department of Biomedical and Clinical Sciences "Luigi Sacco," University of Milan, Luigi Sacco Hospital, Milan, Italy
| | - Francesco Casella
- Department of Biomedical and Clinical Sciences "Luigi Sacco," University of Milan, Luigi Sacco Hospital, Milan, Italy
| | - Maria Calloni
- Department of Biomedical and Clinical Sciences "Luigi Sacco," University of Milan, Luigi Sacco Hospital, Milan, Italy
| | - Chiara Melchionda
- Department of Biomedical and Clinical Sciences "Luigi Sacco," University of Milan, Luigi Sacco Hospital, Milan, Italy
| | | | - Antonella Foschi
- Department of Infectious Diseases, Luigi Sacco Hospital, Milan, Italy
| | - Antonio Luca Brucato
- Department of Biomedical and Clinical Sciences "Luigi Sacco," University of Milan, Luigi Sacco Hospital, Milan, Italy
| | - Giulia Rizzi
- Department of Biomedical and Clinical Sciences "Luigi Sacco," University of Milan, Luigi Sacco Hospital, Milan, Italy
| | - Massimiliano Quici
- Department of Biomedical and Clinical Sciences "Luigi Sacco," University of Milan, Luigi Sacco Hospital, Milan, Italy
| | - Antonio Gidaro
- Department of Biomedical and Clinical Sciences "Luigi Sacco," University of Milan, Luigi Sacco Hospital, Milan, Italy
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Hong J, Chu NM, Cockey SG, Long J, Cronin N, Ghildayal N, Hall RK, Huisingh-Scheetz M, Scherer J, Segev DL, McAdams-DeMarco MA. Frailty, but not cognitive impairment, improves mortality risk prediction among those with chronic kidney disease-a nationally representative study. BMC Nephrol 2024; 25:177. [PMID: 38778286 PMCID: PMC11112880 DOI: 10.1186/s12882-024-03613-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 05/15/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND Though older adults with chronic kidney disease (CKD) have a greater mortality risk than those without CKD, traditional risk factors poorly predict mortality in this population. Therefore, we tested our hypothesis that two common geriatric risk factors, frailty and cognitive impairment, and their co-occurrence, might improve mortality risk prediction in CKD. METHODS Among participants aged ≥ 60 years from National Health and Nutrition Examination Survey (2011-2014), we quantified associations between frailty (physical frailty phenotype) and global/domain-specific cognitive function (immediate-recall [CERAD-WL], delayed-recall [CERAD-DL], verbal fluency [AF], executive function/processing speed [DSST], and global [standardized-average of 4 domain-specific tests]) using linear regression, and tested whether associations differed by CKD using a Wald test. We then tested whether frailty, global cognitive impairment (1.5SD below the mean), or their combination improved prediction of mortality (Cox models, c-statistics) compared to base models (likelihood-ratios) among those with and without CKD. RESULTS Among 3,211 participants, 1.4% were cognitively impaired, and 10.0% were frail; frailty and cognitive impairment co-occurrence was greater among those with CKD versus those without (1.2%vs.0.1%). Frailty was associated with worse global cognitive function (Cohen's d = -0.26SD,95%CI -0.36,-0.17), and worse cognitive function across all domains; these associations did not differ by CKD (pinteractions > 0.05). Mortality risk prediction improved only among those with CKD when accounting for frailty (p[likelihood ratio test] < 0.001) but not cognitive impairment. CONCLUSIONS Frailty is associated with worse cognitive function regardless of CKD status. While CKD and frailty improved mortality prediction, cognitive impairment did not. Risk prediction tools should incorporate frailty to improve mortality prediction among those with CKD.
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Affiliation(s)
- Jingyao Hong
- Department of Surgery, NYU Grossman School of Medicine and NYU Langone Health, New York, NY, USA
| | - Nadia M Chu
- Department of Surgery, Johns Hopkins University, Baltimore, MD, USA
| | - Samuel G Cockey
- Department of Surgery, NYU Grossman School of Medicine and NYU Langone Health, New York, NY, USA
| | - Jane Long
- Department of Surgery, NYU Grossman School of Medicine and NYU Langone Health, New York, NY, USA
| | - Nicolai Cronin
- Department of Surgery, NYU Grossman School of Medicine and NYU Langone Health, New York, NY, USA
| | - Nidhi Ghildayal
- Department of Surgery, NYU Grossman School of Medicine and NYU Langone Health, New York, NY, USA
| | - Rasheeda K Hall
- Department of Medicine, Duke University School of Medicine, Durham, NC, USA
- Durham Veterans Affairs Medical Center, Durham, NC, USA
| | - Megan Huisingh-Scheetz
- Department of Medicine, University of Chicago, Section of Geriatrics and Palliative Medicine, Chicago, IL, USA
| | - Jennifer Scherer
- Department of Population Health, NYU Grossman School of Medicine, New York, NY, USA
| | - Dorry L Segev
- Department of Surgery, NYU Grossman School of Medicine and NYU Langone Health, New York, NY, USA
- Department of Population Health, NYU Grossman School of Medicine, New York, NY, USA
| | - Mara A McAdams-DeMarco
- Department of Surgery, NYU Grossman School of Medicine and NYU Langone Health, New York, NY, USA.
- Department of Population Health, NYU Grossman School of Medicine, New York, NY, USA.
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Piscitelli P, D'Errico MM, Vigna C, Marchese N, Lamacchia O, Fontana A, Copetti M, Pontremoli R, Mirijello A, De Cosmo SA. Albuminuria improves R 2CHA 2DS 2-VASc score in predicting mortality in high cardiovascular risk population. Nutr Metab Cardiovasc Dis 2023; 33:1591-1598. [PMID: 37263913 DOI: 10.1016/j.numecd.2023.05.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 05/10/2023] [Indexed: 06/03/2023]
Abstract
BACKGROUND AND AIMS The CHA2DS2-VASc score estimates the risk of cardioembolism in patients with atrial fibrillation (AF). It also predicts vascular events and death in different clinical settings, even in the absence of AF. The R2CHA2DS2-VASc score, obtained by adding the glomerular filtration rate to CHA2DS2-VASc, shows a higher prediction ability for new events and all-cause mortality. The present study aims to assess whether the addition of albuminuria to R2CHA2DS2-VASc score further improves its discrimination ability in predicting all-cause mortality in a sample of high cardiovascular risk population. METHODS AND RESULTS Prospective, monocentric, observational study, evaluating a subset of 737 subjects consecutively undergoing to coronary angiography at Coronary Unit of Scientific Institute "Casa Sollievo della Sofferenza" from June 2016 to December 2018. The presence of albuminuria was significantly associated with all-cause mortality (p < 0.0001). Any one-point increase of Alb-R2CHA2DS2-VASc score increased mortality of about 1.5-fold (adjusted HR 1.49; 95%CI: 1.37-1.63; p < 0.0001). Considering tertiles of Alb-R2CHA2DS2-VASc, the third tertile showed a 9.5-fold increased risk of mortality (HR 9.52; 95% CI: 5.15-17.60, p < 0.001). Comparing the two scores, the Alb-R2CHA2DS2-VASc score (C-statistic = 0.751; 95%CI: 0.69-0.81) outperformed the R2-CHA2DS2-VASc score (C-statistic = 0.736; 95%CI: 0.68-0.961) in predicting mortality (delta C-statistic = 0.015; 95%CI: 0.001-0.029). The better prediction ability of the Alb-R2CHA2DS2-VASc score was also proven by an IDI of 0.024 (p < 0.0001) and a relative IDI of 24.11% (p < 0.0001), with an NRI = 0.608 (p < 0.00001). CONCLUSIONS The addition of albuminuria to R2CHA2DS2-VASc significantly and independently predicts the risk of all-cause mortality in a sample of high CV risk patients. Moreover, Alb-R2CHA2DS2-VASc outperforms R2CHA2DS2-VASc.
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Affiliation(s)
- Pamela Piscitelli
- Unit of Internal Medicine, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, FG, Italy.
| | - Maria Maddalena D'Errico
- Unit of Internal Medicine, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, FG, Italy
| | - Carlo Vigna
- Unit of Cardiology, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, FG, Italy
| | - Nicola Marchese
- Unit of Cardiology, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, FG, Italy
| | - Olga Lamacchia
- Unit of Endocrinology, University of Foggia, Foggia, FG, Italy
| | - Andrea Fontana
- Biostatistics Unit, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Massimiliano Copetti
- Biostatistics Unit, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Roberto Pontremoli
- Dipartimento di Medicina Interna e Specialità Mediche, Università degli Studi di Genova, Italy; IRCCS Ospedale Policlinico San Martino, Genova, Italy
| | - Antonio Mirijello
- Unit of Internal Medicine, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, FG, Italy
| | - Salvatore A De Cosmo
- Unit of Internal Medicine, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, FG, Italy.
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Ye W, Ding X, Putnam N, Farej R, Singh R, Wang D, Kuo S, Kong SX, Elliott JC, Lott J, Herman WH. Development of clinical prediction models for renal and cardiovascular outcomes and mortality in patients with type 2 diabetes and chronic kidney disease using time-varying predictors. J Diabetes Complications 2022; 36:108180. [PMID: 35339377 DOI: 10.1016/j.jdiacomp.2022.108180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 03/10/2022] [Accepted: 03/11/2022] [Indexed: 11/25/2022]
Abstract
AIMS To develop a set of prediction models for end-stage kidney disease (ESKD), cardiovascular outcomes, and mortality in patients with type 2 diabetes (T2D) and chronic kidney disease (CKD) using commonly measured clinical variables. METHODS We studied 1432 participants with T2D and CKD enrolled in the Chronic Renal Insufficiency Cohort, followed for a median period of 7 years. We used Cox proportional-hazards models to model the six outcomes (ESKD, stroke, myocardial infarction (MI), congestive heart failure (CHF), death before ESKD, and all-cause mortality). We internally evaluated these models using concordance and calibration measures. RESULTS The newly developed six prediction models included 15 predictors: age at diabetes diagnosis, sex, blood pressure, body mass index, hemoglobin A1c, high density lipoprotein cholesterol, urine protein-to-creatinine ratio, estimated glomerular filtration rate, smoking status, and history of stroke, MI, CHF, ESKD, and amputation. The resulting models demonstrated good/strong discrimination (cross-validation C-index range: 0.70 to 0.90) and calibration. CONCLUSIONS This study provided an internally validated and useful tool for predicting individual adverse outcomes and mortality in patients with T2D and CKD. These models may inform optimal use of targeted health interventions.
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Affiliation(s)
- Wen Ye
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, United States of America.
| | - Xuemei Ding
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, United States of America
| | - Nathaniel Putnam
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, United States of America
| | - Ryan Farej
- Bayer HealthCare Pharmaceuticals Inc. (US), Bayer Boulevard Whippany, NJ, United States of America
| | - Rakesh Singh
- Bayer HealthCare Pharmaceuticals Inc. (US), Bayer Boulevard Whippany, NJ, United States of America
| | - Di Wang
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, United States of America
| | - Shihchen Kuo
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, United States of America
| | - Sheldon X Kong
- Bayer HealthCare Pharmaceuticals Inc. (US), Bayer Boulevard Whippany, NJ, United States of America
| | - Jay C Elliott
- Bayer HealthCare Pharmaceuticals Inc. (US), Bayer Boulevard Whippany, NJ, United States of America
| | - Jason Lott
- Bayer HealthCare Pharmaceuticals Inc. (US), Bayer Boulevard Whippany, NJ, United States of America
| | - William H Herman
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, United States of America
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Yeoh LY, Seow YY, Tan HC. Identifying high-risk hospitalised chronic kidney disease patient using electronic health records for serious illness conversation. ANNALS OF THE ACADEMY OF MEDICINE, SINGAPORE 2022; 51:161-169. [PMID: 35373239 DOI: 10.47102/annals-acadmedsg.2021427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
INTRODUCTION This study aimed to identify risk factors that are associated with increased mortality that could prompt a serious illness conversation (SIC) among patients with chronic kidney disease (CKD). METHODS The electronic health records of adult CKD patients admitted between August 2018 and February 2020 were retrospectively reviewed to identify CKD patients with >1 hospitalisation and length of hospital stay ≥4 days. Outcome measures were mortality and the duration of hospitalisation. We also assessed the utility of the Cohen's model to predict 6-month mortality among CKD patients. RESULTS A total of 442 patients (mean age 68.6 years) with median follow-up of 15.3 months were identified. The mean (standard deviation) Charlson Comorbidity Index [CCI] was 6.8±2.0 with 48.4% on chronic dialysis. The overall mortality rate until August 2020 was 36.7%. Mortality was associated with age (hazard ratio [HR] 1.51, 95% confidence interval [CI] 1.29-1.77), CCI≥7 (1.58, 1.08-2.30), lower serum albumin (1.09, 1.06-1.11), readmission within 30-day (1.96, 1.43-2.68) and CKD non-dialysis (1.52, 1.04-2.17). Subgroup analysis of the patients within first 6-month from index admission revealed longer hospitalisation stay for those who died (CKD-non dialysis: 5.5; CKD-dialysis: 8.0 versus 4 days for those survived, P<0.001). The Cohen's model demonstrated reasonable predictive ability to discriminate 6-month mortality (area under the curve 0.81, 95% CI 0.75-0.87). Only 24 (5.4%) CKD patients completed advanced care planning. CONCLUSION CCI, serum albumin and recent hospital readmission could identify CKD patients at higher risk of mortality who could benefit from a serious illness conversation.
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Affiliation(s)
- Lee Ying Yeoh
- Department of General Medicine, Sengkang General Hospital, Singapore
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Sheer R, Nair R, Pasquale MK, Evers T, Cockrell M, Gay A, Singh R, Schmedt N. Predictive Risk Models to Identify Patients at High-Risk for Severe Clinical Outcomes With Chronic Kidney Disease and Type 2 Diabetes. J Prim Care Community Health 2022; 13:21501319211063726. [PMID: 35068244 PMCID: PMC8796116 DOI: 10.1177/21501319211063726] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Introduction/Objective: Predictive risk models identifying patients at high risk for specific outcomes may provide valuable insights to providers and payers regarding points of intervention and modifiable factors. The goal of our study was to build predictive risk models to identify patients with chronic kidney disease (CKD) and type 2 diabetes (T2D) at high risk for progression to end stage kidney disease (ESKD), mortality, and hospitalization for cardiovascular disease (CVD), cerebrovascular disease (CeVD), and heart failure (HF). Methods: This was a retrospective observational cohort study utilizing administrative claims data in patients with CKD (stage 3-4) and T2D aged 65 to 89 years enrolled in a Medicare Advantage Drug Prescription plan offered by Humana Inc. between 1/1/2012 and 12/31/2017. Patients were enrolled ≥1 year pre-index and followed for outcomes, including hospitalization for CVD, CeVD and HF, ESKD, and mortality, 2 years post-index. Pre-index characteristics comprising demographic, comorbidities, laboratory values, and treatment (T2D and cardiovascular) were evaluated and included in the models. LASSO technique was used to identify predictors to be retained in the final models followed by logistic regression to generate parameter estimates and model performance statistics. Inverse probability censoring weighting was used to account for varying follow-up time. Results: We identified 169 876 patients for inclusion. Declining estimated glomerular filtration rate (eGFR) increased the risk of hospitalization for CVD (38.6%-61.8%) and HF (2-3 times) for patients with eGFR 15 to 29 mL/min/1.73 m2 compared to patients with eGFR 50 to 59 mL/min/1.73 m2. Patients with urine albumin-to-creatinine ratio (UACR) ≥300 mg/g had greater chance for hospitalization for CVD (2.0 times) and HF (4.9 times), progression to ESKD (2.9 times) and all-cause mortality (2.4 times) than patients with UACR <30 mg/g. Elevated hemoglobin A1c (≥8%) increased the chances for hospitalization for CVD (21.3%), CeVD (45.4%), and death (20.6%). Among comorbidities, history of HF increased the risk for ESKD, mortality, and hospitalization for CVD, CeVD, and HF. Conclusions: The predictive models developed in this study could potentially be used as decision support tools for physicians and payers, and the risk scores from these models can be applied to future outcomes studies focused on patients with T2D and CKD.
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Affiliation(s)
- Richard Sheer
- Humana Healthcare Research, Inc., Louisville, KY, USA
| | - Radhika Nair
- Humana Healthcare Research, Inc., Louisville, KY, USA
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Prouvot J, Pambrun E, Antoine V, Couchoud C, Vigneau C, Roche S, Francois M, Mariat C, Babici D, Prelipcean C, Moranne O. Low performance of prognostic tools for predicting death before dialysis in older patients with advanced CKD. J Nephrol 2021; 35:993-1004. [PMID: 34787796 DOI: 10.1007/s40620-021-01180-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 10/06/2021] [Indexed: 12/14/2022]
Abstract
INTRODUCTION Chronic kidney disease (CKD) is a disease which is spreading worldwide, especially among older patients. Several prognostic scores have been developed to predict death in older CKD patients, but they have not been validated. We aimed to evaluate the existing risk scores for predicting death before dialysis start, identified via an in-depth review, in a cohort of elderly patients with advanced CKD. METHODS We performed a review to identify scores predicting death, developed in and applicable to CKD patients. Each score was evaluated with an absolute risk calculation from the patients' baseline characteristics. We used a French prospective multicentre cohort of elderly patients (> 75 years) with advanced CKD [estimated glomerular filtration rate (eGFR) < 20 mL/min/1.73 m2], recruited from nephrological centres, with a 5-year follow-up. The outcome considered was death before initiating dialysis. Discrimination [area under curve (AUC)], calibration and Brier score were calculated for each score at its time frame. RESULTS Our review found 6 equations predicting death before dialysis in CKD patients. Four of these (GOLDFARB, BANSAL, GRAMS 2 and 4 years) were evaluated. The validation cohort (Parcours de Soins des Personnes Âgées Parcours de Soins des Personnes Âgées, PSPA) included 573 patients, with a median age of 82 years and a median eGFR of 13 mL/min/1.73 m2. At the end of follow-up, 287 (50%) patients had started dialysis and 238 (41%) patients had died before dialysis. The four equations evaluated showed average discrimination (AUC 0.61-0.70) and, concerning calibration, a global overestimation of the risk of death. DISCUSSION The available scores predicting death before dialysis showed low performance among older patients with advanced CKD in a French multicentre cohort, indicating the need to upgrade them or develop new scores for this population.
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Affiliation(s)
- Julien Prouvot
- IDESP, INSERM Université de Montpellier, Montpellier, France
- Service Néphrologie-Dialyses-Aphérèses, Hôpital Universitaire de Nîmes, CHU Caremeau, Place du Pr Debré, 30000, Nimes, France
| | - Emilie Pambrun
- Service Néphrologie-Dialyses-Aphérèses, Hôpital Universitaire de Nîmes, CHU Caremeau, Place du Pr Debré, 30000, Nimes, France
| | - Valery Antoine
- IDESP, INSERM Université de Montpellier, Montpellier, France
- Service de Gériatrie, Hôpital Universitaire de Nîmes, Nimes, France
| | - Cecile Couchoud
- Registre REIN, Agence de la Biomedecine, Saint-Denis La Plaine, France
- CNRS, UMR 5558, Laboratoire de Biométrie et Biologie Evolutive, Equipe Biostatistique Santé, Villeurbanne, France
| | - Cecile Vigneau
- CHU Pontchaillou, Service de Néphrologie-Dialyse-Transplantation, Université Rennes 1, IRSET, Rennes, France
| | - Sophie Roche
- Service de Nephrologie‑Dialyse, CH Macon, Macon, France
| | - Maud Francois
- Service de Néphrologie-Dialyse-Transplantation, CHU Tours, Tours, France
| | - Christophe Mariat
- Service de Néphrologie, Hôpital Nord, Centre Hospitalier Universitaire de Saint-Étienne, 42055, Saint-Étienne Cedex 02, France
| | - Daniela Babici
- Service Néphrologie-Dialyse, GHR MSA, Hôpital Emile Muller, Mulhouse, France
| | - Camelia Prelipcean
- Service Néphrologie-Dialyses-Aphérèses, Hôpital Universitaire de Nîmes, CHU Caremeau, Place du Pr Debré, 30000, Nimes, France
| | - Olivier Moranne
- IDESP, INSERM Université de Montpellier, Montpellier, France.
- Service Néphrologie-Dialyses-Aphérèses, Hôpital Universitaire de Nîmes, CHU Caremeau, Place du Pr Debré, 30000, Nimes, France.
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Thamcharoen N, Nissaisorakarn P, Cohen RA, Schonberg MA. Serious Illness Conversations in advanced kidney disease: a mixed-methods implementation study. BMJ Support Palliat Care 2021:bmjspcare-2020-002830. [PMID: 33731464 DOI: 10.1136/bmjspcare-2020-002830] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 02/20/2021] [Accepted: 02/24/2021] [Indexed: 02/02/2023]
Abstract
OBJECTIVE Advanced kidney disease is associated with a high risk of morbidity and mortality. Consequently, invasive treatments such as dialysis may not yield survival benefits. Advance care planning has been encouraged. However, whether such discussions are acceptable when done earlier, before end-stage kidney treatment decision-making occurs, is unclear. This pilot study aimed to explore whether use of the Serious Illness Conversation Guide to aid early advance care planning is acceptable, and to evaluate the information gained from these conversations. METHODS Patients with advanced kidney disease (stage 3B and above) and high mortality risk at 2 years were enrolled in this mixed-methods study from an academic nephrology clinic. Semi-structured interviews were conducted using the adapted Serious Illness Conversation Guide. Thematic analysis was used to assess patients' perceptions of the conversation. Participants completed a questionnaire assessing conversation acceptability. RESULTS Twenty-six patients participated, 50% were female. Participants felt that the conversation guide helped them reflect on their prognosis, goals of care and treatment preferences. Most did not feel that the conversation provoked anxiety (23/26, 88%) nor that it decreased hopefulness (24/26, 92%). Some challenges were elicited; patients expressed cognitive dissonance with the kidney disease severity due to lack of symptoms; had difficulty conceptualising their goals of care; and vocalised fear of personal failure without attempting dialysis. CONCLUSIONS Patients in this pilot study found the adapted Serious Illness Conversation Guide acceptable. This guide may be used with patients early in the course of advanced kidney disease to gather information for future advanced care planning.
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Affiliation(s)
- Natanong Thamcharoen
- Cheewabhibaln Palliative Care Center, King Chulalongkorn Memorial Hospital, Bangkok, Thailand
- Nephrology Division, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Pitchaphon Nissaisorakarn
- Nephrology Division, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Robert A Cohen
- Nephrology Division, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Mara A Schonberg
- Division of General Medicine and Primary Care, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
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Prouvot J, Pambrun E, Couchoud C, Vigneau C, Roche S, Allot V, Potier J, Francois M, Babici D, Prelipcean C, Moranne O. Low performance of prognostic tools for predicting dialysis in elderly people with advanced CKD. J Nephrol 2021; 34:1201-1213. [PMID: 33394346 DOI: 10.1007/s40620-020-00919-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2020] [Accepted: 11/13/2020] [Indexed: 12/23/2022]
Abstract
INTRODUCTION Clinical decision-making about care plans can be difficult for very elderly people with advanced chronic kidney disease (CKD). Current guidelines propose the use of prognostic tools predicting end stage renal disease (ESRD) to assist in a patient-centered shared decision-making approach. Our objective was to evaluate the existing risk model scores predicting ESRD, from data collected for a French prospective multicenter cohort of mainly octogenarians with advanced CKD. METHODS We performed a rapid review to identify the risk model scores predicting ESRD developed from CKD patient cohorts and evaluated them with data from a prospective multicenter French cohort of elderly (> 75 years) patients with advanced CKD (estimated glomerular filtration rate [eGFR] < 20 mL/min/1.75m2), followed up for 5 years. We evaluated these scores (in absolute risk) for discrimination, calibration and the Brier score. For scores using the same time frame, we made a joint calibration curve and compared areas under the curve (AUCs). RESULTS The PSPA cohort included 573 patients; their mean age was 83 years and their median eGFR was 13 mL/min/1.73 m2. At the end of follow-up, 414 had died and 287 had started renal replacement therapy (RRT). Our rapid review found 12 scores that predicted renal replacement therapy. Five were evaluated: the TANGRI 4-variable, DRAWZ, MARKS, GRAMS, and LANDRAY scores. No score performed well in the PSPA cohort: AUCs ranged from 0.57 to 0.65, and Briers scores from 0.18 to 0.25. CONCLUSIONS The low predictiveness for ESRD of the scores tested in a cohort of octogenarian patients with advanced CKD underlines the need to develop new tools for this population.
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Affiliation(s)
- Julien Prouvot
- EA2415, Université de Montpellier, Montpellier, France.,Service de Nephrologie, Dialyse et Apherese, Hôpital Universitaire de Caremeau, Nimes, France
| | - Emilie Pambrun
- Service de Nephrologie, Dialyse et Apherese, Hôpital Universitaire de Caremeau, Nimes, France
| | - Cecile Couchoud
- Registre REIN, Agence de la Biomedecine, Saint-Denis La Plaine, France.,CNRS, UMR 5558, Laboratoire de Biométrie et Biologie Evolutive, Equipe Biostatistique Santé, Villeurbanne, France
| | - Cecile Vigneau
- CHU Pontchaillou, Service de Néphrologie-Dialyse-Transplantation, Université Rennes 1, IRSET 1085, Rennes, France
| | - Sophie Roche
- Service de Néphrologie-Dialyse, CH Macon, Macon, France
| | - Vincent Allot
- CHU Limoges, Service de Néphrologie, Dialyse, Transplantation, Limoges, France
| | - Jerome Potier
- Service de Néphrologie-Dialyse, CH St Brieuc, Saint Brieuc, France
| | - Maud Francois
- CHU Tours, Service de Néphrologie-Dialyse-Transplantation, Tours, France
| | - Daniela Babici
- Hôpital Emile Muller, Service Néphrologie-Dialyse, GHR MSA, Mulhouse, France
| | - Camelia Prelipcean
- Service de Nephrologie, Dialyse et Apherese, Hôpital Universitaire de Caremeau, Nimes, France
| | - Olivier Moranne
- EA2415, Université de Montpellier, Montpellier, France. .,Service de Nephrologie, Dialyse et Apherese, Hôpital Universitaire de Caremeau, Nimes, France.
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Evaluation of administrative case definitions for chronic kidney disease in children. Pediatr Res 2020; 87:569-575. [PMID: 31578037 DOI: 10.1038/s41390-019-0595-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Revised: 08/13/2019] [Accepted: 09/05/2019] [Indexed: 12/17/2022]
Abstract
INTRODUCTION Administrative data is increasingly used for chronic disease surveillance; however, its validity to define cases of chronic kidney disease (CKD) in children is unknown. We sought to evaluate the performance of case definitions for CKD in children. METHODS We utilized population-based administrative data from the Manitoba Center for Health Policy to evaluate the validity of algorithms based on a combination of hospital claims, outpatient physician visits, and pharmaceutical use over 1-3 years in children <18 years of age. Algorithms were compared with a laboratory-based definition (estimated glomerular filtration rate < 90 ml/min/1.73 m2 and/or presence of proteinuria). RESULTS All algorithms evaluated had very low sensitivity (0.20-0.39) and moderate positive predictive value (0.52-0.68). Algorithms had excellent specificity (0.98-0.99) and negative predictive value (0.96-0.97). Receiver operating characteristic (ROC) curves indicate fair accuracy (0.60-0.68). Sensitivity improved with increasing years of data. One or more physician claims and one or more prescriptions over 3 years had the highest sensitivity and ROC. CONCLUSIONS The sensitivity of administrative data algorithms for CKD is unacceptably low for a screening test. Specificity is excellent; therefore, children without CKD are correctly identified. Alternate data sources are required for population-based surveillance of this important chronic disease.
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Goldfarb-Rumyantzev A, Brown RS, Dong N, Sandhu GS, Vohra P, Gautam S. Developing and testing models to predict mortality in the general population. Inform Health Soc Care 2019; 45:188-203. [PMID: 31674845 DOI: 10.1080/17538157.2019.1656209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
We have previously proposed an approach using information collected from published reports to generate prediction models. The goal of this project was to validate this technique to develop and test various prediction models. A risk indicator (R) is calculated as a linear combination of the hazard ratios for the following predictors: age, male gender, diabetes, albuminuria, and either CKD, CVD or both. We developed a linear and two exponential expressions to predict the probability of the outcome of 2-year mortality and compared to actual outcome in the target dataset from NHANES. The risk indicator demonstrated good performance with area under ROC curve of 0.84. The linear and two exponential expressions generated similar predictions in the lower categories of risk indicator (R ≤ 6). However, in the groups with higher R value, the linear expression tends to predict lower, and the exponential expressions higher, probabilities than the observed outcome. A Combined model which averaged the linear and logistic expressions was shown to approximate the actual outcome data the best. A simple technique (named Woodpecker™) allows derivation functional prediction models and risk stratification tools from reports of clinical outcome studies and their application to new populations by using only summary statistics of the new population.
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Affiliation(s)
| | - Robert S Brown
- Division of Nephrology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
| | - Ning Dong
- Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Gurprataap S Sandhu
- Division of Internal Medicine, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA
| | - Parag Vohra
- Lahey Health, Beverly Hospital, Beverly, Massachusetts, USA
| | - Shiva Gautam
- Department of Internal Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
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Inaguma D, Morii D, Kabata D, Yoshida H, Tanaka A, Koshi-Ito E, Takahashi K, Hayashi H, Koide S, Tsuboi N, Hasegawa M, Shintani A, Yuzawa Y. Prediction model for cardiovascular events or all-cause mortality in incident dialysis patients. PLoS One 2019; 14:e0221352. [PMID: 31437231 PMCID: PMC6705850 DOI: 10.1371/journal.pone.0221352] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2019] [Accepted: 08/05/2019] [Indexed: 11/18/2022] Open
Abstract
Some variables including age, comorbidity of diabetes, and so on at dialysis initiation are associated with patient prognosis. Cardiovascular (CV) events are a major cause of death, and adequate models that predict prognosis in dialysis patients are warranted. Therefore, we created models using some variables at dialysis initiation. We used a database of 1,520 consecutive dialysis patients (median age, 70 years; 492 women [32.4%]) from a multicenter prospective cohort study. We established the primary endpoint as a composite of the incidence of first CV events or all-cause death. A multivariable Cox proportional hazard regression model was used to construct a model. We considered a complex and a simple model. We used area under the receiver operating characteristic curve (AUROC) to assess and compare the predictive performances of the prediction models and evaluated the improvement in discrimination using the complex model versus the simple model using net reclassification improvement (NRI). We then assessed integrated discrimination improvement (IDI) to evaluate improvements in average sensitivity and specificity. Of 392 deaths, 152 were CV-related. Totally, 506 CV events occurred during the follow-up period (median 1,285 days). Finally, 692 patients reached the primary endpoint. Baseline data were set at dialysis initiation. AUROC for the primary endpoint was 0.737 (95% confidence interval [CI], 0.712–0.761) in the simple model and 0.765 (95% CI, 0.741–0.788) in the complex model. There were significant intergroup differences in NRI (0.44; 95% CI, 0.34–0.53; p < 0.001) and IDI (0.02; 95% CI, 0.02–0.03; p < 0.001). We prepared a Shiny R application for each model to automatically calculate the predicted occurrence probability (https://statacademy.shinyapps.io/App_inaguma_20190717/). The complex model made more accurate predictions than the simple model. However, the intergroup difference was not significant. Hence, the simple model was more useful than the complex model. The tool was useful in a real-world clinical setting because it required only routinely available variables. Moreover, we emphasized that the tool could predict the incidence of CV events or all-cause mortality for individual patients. In the future, we must confirm its external validity in other prospective cohorts.
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Affiliation(s)
- Daijo Inaguma
- Department of Nephrology, Fujita Health University School of Medicine, Toyoake, Japan
- The Aichi Cohort Study of Prognosis in Patients Newly Initiated into Dialysis (AICOPP) Group, Aichi, Japan
- * E-mail:
| | - Daichi Morii
- Department of Medical Statistics, Osaka City University Graduate School of Medicine, Osaka, Japan
| | - Daijiro Kabata
- Department of Medical Statistics, Osaka City University Graduate School of Medicine, Osaka, Japan
| | - Hiroyuki Yoshida
- Department of Nephrology, Fujita Health University School of Medicine, Toyoake, Japan
| | - Akihito Tanaka
- The Aichi Cohort Study of Prognosis in Patients Newly Initiated into Dialysis (AICOPP) Group, Aichi, Japan
- Department of Nephrology, Nagoya University School of Medicine, Nagoya, Japan
| | - Eri Koshi-Ito
- Department of Nephrology, Fujita Health University School of Medicine, Toyoake, Japan
| | - Kazuo Takahashi
- Department of Nephrology, Fujita Health University School of Medicine, Toyoake, Japan
| | - Hiroki Hayashi
- Department of Nephrology, Fujita Health University School of Medicine, Toyoake, Japan
| | - Shigehisa Koide
- Department of Nephrology, Fujita Health University School of Medicine, Toyoake, Japan
- The Aichi Cohort Study of Prognosis in Patients Newly Initiated into Dialysis (AICOPP) Group, Aichi, Japan
| | - Naotake Tsuboi
- Department of Nephrology, Fujita Health University School of Medicine, Toyoake, Japan
| | - Midori Hasegawa
- Department of Nephrology, Fujita Health University School of Medicine, Toyoake, Japan
| | - Ayumi Shintani
- Department of Medical Statistics, Osaka City University Graduate School of Medicine, Osaka, Japan
| | - Yukio Yuzawa
- Department of Nephrology, Fujita Health University School of Medicine, Toyoake, Japan
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