Electrocardiography and Outcome in Patients with Diabetes Mellitus on Maintenance Hemodialysis

Krane, Vera; Heinrich, Fritz; Meesmann, Malte; Olschewski, Manfred; Lilienthal, Jürgen; Angermann, Christiane; Störk, Stefan; Bauersachs, Johann; Wanner, Christoph; Frantz, Stefan; Investigators, Dialysis Study

doi:10.2215/cjn.02020408

Cited by 54 publications

(42 citation statements)

References 28 publications

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“…Furthermore, compared with our study, in the Spanish study the Cornell criteria used a different definition of EKG-LVH in that it did not include the QRS duration to calculate the voltage-duration product; thus, headto-head comparison is difficult. Krane et al reported the prevalence of EKG-LVH diagnosed by Sokolow-Lyon criteria among 1253 patients participating in the German Diabetes and Dialysis 4D study (14). All of these patients were on hemodialysis and had type 2 diabetes mellitus; therefore, the patient population was substantially different from the study presented here.…”

Section: Adjusted For Ckd+ Other+cornellmentioning

confidence: 65%

“…Krane et al reported the association of EKG-LVH diagnosed by Sokolow-Lyon criteria with cardiovascular events and mortality (14). These 1253 patients participating in the German Diabetes and Dialysis 4D study were all on hemodialysis and had type 2 diabetes mellitus; therefore, the patient population was substantially different from the study presented here.…”

Section: Prognostic Value Of Ekg-lvhmentioning

confidence: 72%

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Determinants and Prognostic Significance of Electrocardiographic Left Ventricular Hypertrophy Criteria in Chronic Kidney Disease

Agarwal¹,

Light²

2011

Clinical Journal of the American Society of Nephrology

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SummaryBackground and objectives The diagnosis of left ventricular hypertrophy (LVH) has prognostic value in the general population. However, among those with chronic kidney disease (CKD), the determinants of electrocardiographic (EKG) LVH and its prognostic value are not clear.Design, setting, participants, & measurements A cross-sectional study was performed among 387 consenting consecutive patients from a veterans hospital with a longitudinal follow-up. ResultsThe overall prevalence of EKG-LVH by the Sokolow-Lyon criteria was 8% and by the Cornell voltage-duration product was 11%. Compared with non-CKD controls, CKD patients had unadjusted odds ratio (OR) for LVH by Cornell criteria of 2.52 (95% CI 1.18 to 5.42). Significance was lost after adjustment. The unadjusted OR for LVH by Sokolow-Lyon criteria was 2.24 (95% CI 0.95 to 5.33). This OR remained statistically insignificant after multivariate adjustment. Anemia, proteinuria, and 24-hour ambulatory systolic BP were associated with EKG-LVH regardless of diagnostic criteria. After a 7.5-year median follow-up, the hazard ratio for all-cause mortality was not associated with EKG-LVH diagnosed by the Sokolow-Lyon criteria; however, multivariable adjustments made EKG-LVH significant. A statistically significant relationship was seen between mortality and Cornell criteria; however, multivariable adjustments made EKG-LVH nonsignificant. ConclusionsThe Sokolow-Lyon and Cornell EKG-LVH criteria cannot be used interchangeably to diagnose LVH or determine prognosis. Among those with CKD, ambulatory systolic BP predicts all-cause mortality. Moreover, the duration and severity of BP elevation presumably reflected in EKG-LVH diagnosed by Sokolow-Lyon criteria is also of prognostic significance; the Cornell criteria do not carry independent prognostic information.

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Section: Adjusted For Ckd+ Other+cornellmentioning

confidence: 65%

Section: Prognostic Value Of Ekg-lvhmentioning

confidence: 72%

Determinants and Prognostic Significance of Electrocardiographic Left Ventricular Hypertrophy Criteria in Chronic Kidney Disease

Agarwal¹,

Light²

2011

Clinical Journal of the American Society of Nephrology

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“…The HEMO Study participants were subject to exclusion criteria, such as unstable angina or New York Heart Association class IV congestive heart failure; therefore, generalization of our findings to the HD population in the United States should be done with caution. Data regarding ejection fraction, left ventricular hypertrophy, and QT interval on electrocardiography, known risk factors for SCD (particularly in the general population), were not available for all patients (36)(37)(38). In addition, cardiac biomarkers, such as troponins Hazard ratios for continuous predictors are given per 1 SD increase.…”

Section: Discussionmentioning

confidence: 99%

Predictors of Sudden Cardiac Death

Shastri

Tangri

Tighiouart

et al. 2012

Clinical Journal of the American Society of Nephrology

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SummaryBackground and objectives There are few data on risk factors for sudden cardiac death (SCD) in patients undergoing hemodialysis (HD). The study objective was to identify predictors associated with various causes of death in the Hemodialysis (HEMO) Study and to develop a prediction model for SCD using a competing risk approach.Design, setting, participants, & measurements In this analysis of 1745 HEMO participants, all-cause mortality was classified as SCD, non-SCD, and noncardiac death. Predictors for each cause of death were evaluated using cause-specific Cox proportional hazards models, and a competing risk approach was used to calculate absolute risk predictions for SCD.Results During a median follow-up of 2.5 years, 808 patients died. Rates of SCD, non-SCD, and noncardiac death were 22%, 17%, and 61%, respectively. Predictors of various causes of death differ somewhat in HD patients. Age, diabetes, peripheral vascular disease, ischemic heart disease, serum creatinine, and alkaline phosphatase were independent predictors of SCD. The 3-year C-statistic for SCD was 0.75 (95% confidence interval, 0.70-0.79), and calibration was good (x 2 =1.1; P=0.89). At years 3 and 5 of follow-up, the standard Cox model overestimated the risk for SCD as compared with the competing risk approach on the relative scale by 25% and 46%, respectively, and on the absolute scale by 2% and 6%, respectively.Conclusions Predictors of various causes of death differ in HD patients. The proposed prediction model for SCD accounts for competing causes of death. External validation of this model is required.

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“…Berbagai kelainan yang dapat dideteksi dengan EKG antara lain sinus bradikardia dan takikardia, aritmia, deviasi aksis QRS, blok atrioventrikular, infark miokard, hipertrofi ventrikel kiri, blok cabang serabut kiri dan kanan (left/right bundle branch block, LBBB/ RBBB), dan interval QT terkoreksi. 27,28 Krane meneliti pada tahun 2009 mengenai berbagai gambaran EKG tersebut pada pasien PGK dan DM yang akan menjalani HD dan menilai apakah kelainan tersebut memiliki nilai prognostik terhadap kejadian kardiovaskular. Ia mendapatkan bahwa LVH dan aritmia merupakan kelainan yang berhubungan dengan peningkatan mortalitas.…”

Section: Penelitian Tahun Nilai Batas Hemoglobin (G/dl)unclassified

Pengembangan Model Prediksi Mortalitas 3 Bulan Pertama pada Pasien Penyakit Ginjal Kronik yang Menjalani Hemodialisis

Umami¹,

Lydia²,

Nainggolan³

et al. 2017

JPDI

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ABSTRAKPendahuluan. Mortalitas pasien yang menjalani hemodialisis (HD) paling tinggi pada tiga bulan pertama. Data mengenai insidens dan prediktor mortalitas dini pada pasien HD sangat terbatas. Suatu model prediksi dapat menjadi alat bantu yang sederhana untuk mengetahui pasien yang berisiko tinggi sehingga pada akhirnya upaya pencegahan dapat dilakukan. Penelitian ini bertujuan untuk mengetahui insidens dan prediktor mortalitas 3 bulan pada pasien hemodialisis baru dan membuat suatu model prediksi.Metode. Penelitian dengan disain kohort retrospektif terhadap 246 pasien PGTA yang baru menjalani HD di Unit HD RSCM antara Januari 2011-Januari 2012. Dilakukan analisis chi-square untuk mendapatkan nilai OR (Odds Ratio) terhadap variabel usia, pembiayaan, jenis HD, akses pembuluh darah, anemia, hipoalbuminemia, kelainan EKG, kardiomegali, komorbid, waktu rujukan ke nefrologis, dan kepatuhan. Prediktor yang bermakna kemudian dimasukkan pada model regresi logistik untuk mendapatkan sistem skor.Hasil. Sebanyak 78 (31,7%) dari 246 pasien meninggal dalam 3 bulan pertama. Terdapat 5 variabel yang berhubungan dengan terjadinya mortalitas 3 bulan yaitu usia > 60 tahun, hemoglobin <8 g/dl, albumin serum <3,5 g/dl, kelainan EKG, dan akses femoral. Skor prediksi untuk prediktor usia, hemoglobin, albumin serum, kelainan EKG, dan akses pembuluh darah berturut-turut sebesar 1, 3, 1, 3, 1. Jumlah skor kemudian dikategorikan menjadi risiko rendah (skor 0-3), sedang (skor 4-6), dan tinggi (skor 7-9). Tiap kelompok memiliki prediksi mortalitas 3 bulan berturut-turut sebesar 1,23%, 26,69%, dan 86,04%.Simpulan. Insidens mortalitas 3 bulan pada pasien HD baru sebesar 31,7%. Usia > 60 tahun, hemoglobin <8 g/dl, albumin serum <3,5 g/dl, kelainan EKG, dan akses femoral merupakan prediktor yang bermakna terhadap terjadinya mortalitas dalam 3 bulan pertama HD. Kata kunci. Hemodialisis, insidens, mortalitas 3 bulan, sistem skor.

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Electrocardiography and Outcome in Patients with Diabetes Mellitus on Maintenance Hemodialysis

Cited by 54 publications

References 28 publications

Determinants and Prognostic Significance of Electrocardiographic Left Ventricular Hypertrophy Criteria in Chronic Kidney Disease

Determinants and Prognostic Significance of Electrocardiographic Left Ventricular Hypertrophy Criteria in Chronic Kidney Disease

Predictors of Sudden Cardiac Death

Pengembangan Model Prediksi Mortalitas 3 Bulan Pertama pada Pasien Penyakit Ginjal Kronik yang Menjalani Hemodialisis

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