Hyponatraemia in Emergency Medical Admissions-Outcomes and Costs

Red Cell Distribution Width as a Prognostic Indicator in Acute Medical Admissions

The red cell distribution width (RDW) is the coefficient of variation of the mean corpuscular volume (MCV). We sought to evaluate RDW as a predictor of outcomes following acute medical admission. We studied 10 years of acute medical admissions (2002-2011) with subsequent follow-up to 2021. RDW was converted to a categorical variable, Q1 < 12.9 fl, Q2-Q4 ≥ 12.9 and <15.7 fL and Q5 ≥ 15.7 fL. The predictive value of RDW for 30-day in-hospital and long-term mortality was evaluated with logistic and Cox regression modelling. Adjusted odds ratios (aORs) were calculated and loss of life years estimated. There were 62,184 admissions in 35,140 patients. The 30-day in-hospital mortality (n = 3646) occurred in 5.9% of admissions. An additional 15,086 (42.9%) deaths occurred by December 2021. Admission RDW independently predicted 30-day in-hospital mortality aOR 1.93 (95%CI 1.79, 2.07). Admission RDW independently predicted long-term mortality aOR 1.04 (95%CI 1.02, 1.05). Median survival post-admission was 189 months. For those with admission RDW in Q5, observed survival half-life was 133 months-this represents a shortfall of 5.7 life years (33.9%). In conclusion, admission RDW independently predicts 30-day in-hospital and long-term mortality.

Hospital mortality and length of stay differences in emergency medical admissions related to 'on-call' specialty

BACKGROUND

The outcomes of acute medical admissions have been shown to be influenced by a variety of factors including system, patient, societal, and physician-specific differences.

AIM

To evaluate the influence of on-call specialty on outcomes in acute medical admissions.

METHODS

All acute medical admissions to our institution from 2015 to 2020 were evaluated. Admissions were grouped based on admitting specialty. Thirty-day in-hospital mortality and length of stay (LOS) were evaluated. Data was analysed using multivariable logistic regression and truncated Poisson regression modelling.

RESULTS

There were 50,347 admissions in 30,228 patients. The majority of admissions were under Acute Medicine (47.0%), and major medical subspecialties (36.1%); Elderly Care admitted 12.1%. Acute Medicine admissions were older at 72.9 years (IQR 57.0, 82.9) vs. 67.2 years (IQR 50.1, 80.2), had higher Acute Illness Severity (grades 4-6: 85.9% vs. 81.3%; p < 0.001), Charlson Index (> group 0; 61.5% vs. 54.6%; p < 0.001), and Comorbidity Score (40.7% vs. 36.7%; p < 0.001). Over time, there was a small (+ 8%) but significant increase in 30-day in-hospital mortality. Mortality rates for Acute Medicine, major medical specialties, and Elderly Care were not different at 5.1% (95% CI: 4.7, 5.5), 4.7% (95% CI: 4.3, 5.1), and 4.7% (95% CI: 3.9, 5.4), respectively. Elderly Care admissions had shorter LOS (7.8 days (95% CI: 7.6, 8.0)) compared with either Acute Medicine (8.7 days (95% CI: 8.6, 8.8)) or major medical specialties (8.7 days (95% CI: 8.6, 8.9)).

CONCLUSION

No difference in mortality and minor differences in LOS were observed. The prior pattern of improved outcomes year on year for emergency medical admissions appears ended.

Predicting Outcomes in Emergency Medical Admissions Using a Laboratory Only Nomogram

Background

We describe a nomogram to explain an Acute Illness Severity model, derived from emergency room triage and admission laboratory data, to predict 30-day in-hospital survival following an emergency medical admission.

Methods

For emergency medical admissions (96,305 episodes in 50,612 patients) between 2002 and 2016, the relationship between 30-day in-hospital mortality and admission laboratory data was determined using logistic regression. The previously validated Acute Illness Severity model was then transposed to a Kattan-style nomogram with a Stata user-written program.

Results

The Acute Illness Severity was based on the admission Manchester triage category and biochemical laboratory score; these latter were based on the serum albumin, sodium, potassium, urea, red cell distribution width, and troponin status. The laboratory admission data was predictive with an AUROC of 0.85 (95% CI: 0.85, 0.86). The sensitivity was 94.4%, with a specificity of 62.7%. The positive predictive value was 21.2%, with a negative predictive value of 99.1%. For the Kattan-style nomogram, the regression coefficients are converted to a 100-point scale with the predictor parameters mapped to a probability axis. The nomogram would be an easy-to-use tool at the bedside and for educational purposes, illustrating the relative importance of the contribution of each predictor to the overall score.

Conclusion

A nomogram to illustrate and explain the prognostic factors underlying an Acute Illness Severity Score system is described.

Dysnatremia in Relation to Frailty and Age in Community-dwelling Adults in the National Health and Nutrition Examination Survey

Background

Frailty represents an age-related state of increased risk of adverse health outcomes, reflecting some combination of increased damage and compromised repair processes. Our objectives were to establish whether frailty is associated with dysnatremia (a deviation of serum sodium from normal values), to determine whether frailty explains the previously established association between age and dysnatremia and to assay the impact of each on mortality.

Methods

The relationship between age, frailty, and dysnatremia was investigated across the adult life course in 8,911 respondents from the 2003-2004 and 2005-2006 cross-sectional National Health and Nutrition Examination Survey, on whom both laboratory and mortality data were available. A frailty index (FI) was calculated for each respondent and related to dysnatremia (serum sodium values outside a 136-144 mmol/L reference range).

Results

Dysnatremia was significantly related to both age and frailty; as the degree of frailty increased, so did the proportion with dysnatremia, for example, from 4.1% in those with FI less than 0.10, to 12.4% in those with FI 0.40 or more; p less than .001. Adjusted for frailty, the relationship between age and dysnatremia was no longer significant. In the age- and sex-adjusted Cox models, both frailty (hazard ratio [HR]: 1.05; 95% confidence interval [CI]: 1.04-1.05 for every 0.01 increase in FI) and dysnatremia (HR: 1.85; 95% CI: 1.51-2.26) were significant predictors of mortality; when hyponatremia was separated from hypernatremia in the Cox models, hypernatremia wasn't significant, but only 41 participants were identified as hypernatremic.

Conclusion

Increasing frailty is associated with dysnatremia and confounds the association between age and dysnatremia.