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Record W2138220644 · doi:10.3747/pdi.2012.00095

Hyponatremia in Peritoneal Dialysis: Epidemiology in a Single Center and Correlation with Clinical and Biochemical Parameters

2013· article· en· W2138220644 on OpenAlex
Chrysostomos Dimitriadis, Nigar Sekercioglu, Chrysoula Pipili, Dimitrios G. Oreopoulos, Joanne M. Bargman

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenuePeritoneal Dialysis International · 2013
Typearticle
Languageen
FieldMedicine
TopicElectrolyte and hormonal disorders
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsPeritoneal dialysisMedicineHyponatremiaEpidemiologyDialysisIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Hyponatremia in peritoneal dialysis (PD) patients has previously been associated with water overload and weight gain, or with malnutrition and intracellular potassium depletion. Although there is a sizable literature about transmembrane sodium and water removal in PD, there are few reports about the incidence and characteristics of hyponatremia in the clinical setting. AIM: We evaluated the incidence and factors associated with hyponatremia in PD patients in a single PD unit. METHODS: We retrospectively evaluated the records of all patients (n = 198) who were treated with PD in the Home PD Unit of the University Health Network at Toronto General Hospital during 2010. We identified 166 patients who had a minimum follow-up of 60 days during 2010 and at least 2 consecutive sodium measurements at least a month apart. We examined baseline differences between patients who developed hyponatremia and those who did not, and clinical and biochemical factors that correlated with mean sodium values. In the 24 patients who developed hyponatremia, we examined paired differences between the normonatremic and hyponatremic periods. Finally, we investigated any possible correlations of change in serum sodium with clinical and biochemical characteristics before and during the hyponatremic period. RESULTS: The incidence of hyponatremia was 14.5%. In multivariate analysis, serum sodium correlated significantly and independently with residual renal function (RRF: r = 0.463, p = 0.0001) and negatively with the daily volume of instilled icodextrin (r = -0.476, p = 0.0001). Residual renal function was significantly lower in patients with hyponatremia than in those with normal serum sodium (1.97 ± 2.3 mL/min vs 4.31 ± 5.01 mL/min, p = 0.033). The mean paired difference in body weight was -1.113 kg and the median difference was -0.55 kg (range: -8.5 kg to +4.2 kg). Impressively, hyponatremia was not associated with an increase in body weight in most patients who developed this complication (13 of 16 for whom comparative weights were known). Moreover, the mean paired change in serum sodium (ΔNa) from normonatremia to hyponatremia was, contrary to our expectations, significantly correlated with a decrease in body weight (r = 0.584, p = 0.017). The ΔNa was also significantly correlated with serum potassium (r = 0.526, p = 0.008), the greatest drop in serum sodium being associated with lower serum potassium in the hyponatremic period, as predicted. CONCLUSIONS: Hyponatremia is seen more often than expected in a clinical setting. Serum sodium is strongly correlated with RRF, hyponatremia being associated with lower RRF. In patients who experienced hyponatremia, the fall in serum sodium was associated with a decrease, not an increase, in body weight and was correlated with serum potassium, suggesting that sodium and potassium depletion-and, by inference, malnutrition-may be important contributors in the clinical setting.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.683

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.310
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it