Performance of the APACHE II and SOFA Scores in Diabetic Ketoacidosis

Accurate prediction of severity and mortality in diabetic ketoacidosis (DKA) is important for allocation of resources. The APACHE II and SOFA scores are used to predict mortality in critically ill patients, however neither has been tested exclusively in DKA. We sought to determine if these scoring systems can accurately predict mortality in patients with DKA. This was an observational study of patients presenting to an urban tertiary care center with a diagnosis of DKA. Adult patients (age ≥ 18 years) with glucose > 250 mg/dL, bicarbonate ≤ 20 mEg/L, an anion gap ≥ 16 mEg/L, pH ≤ 7.30, and urine ketones were included. Predicted mortality based upon APACHE II and SOFA scores were compared to observed mortality. A total of 50 patients were included. There was no observed mortality in our population. The median APACHE II score was 10 (IQR: 6, 15) which predicted a mortality of 15% and the median SOFA score was 1 (IQR: 0, 2) which predicted a mortality of 0%. In summary, we found the APACHE II illness severity score does not accurately predict mortality in a population of patients with DKA, while the SOFA score appears to predict mortality in the same population.

Racial and Ethnic Disparities in Covid-19 Illness Severity Accounting for Concomitant Comorbidities: A Cohort Study

Background Multiple reports have highlighted important racial and ethnic differences in the degree to which Americans may be vulnerable to severe forms of Covid-19 illness. Whether or not racial or ethnic disparities are related to variations in the underlying burden of comorbidities or other predisposing factors remains unclear.Methods We identified patients diagnosed with Covid-19, based on a positive PCR for SARS-CoV-2, from the electronic health record of a large multi-hospital system located in Southern California. We developed an illness severity score, based on the level of care each patient required (not admitted to the hospital; required hospital admission but never required intensive care; required intensive level care but never intubation; and, required intubation during hospitalization) and assessed for associations with clinical and demographic factors for each patient using ordinal logistic regression.Results A total of 571 patients with Covid-19 were identified a majority of whom were male (56%), with a mean age of 55±21 years. There were 81 (14%) patient who identified as African American, and 101 (18%) as Hispanic. A total of 202 (36%) patients required hospitalization without need for intensive care, 43 (8%) required intensive care without intubation, and 64 (11%) required intubation while also receiving intensive care. Of the total sample, African American race (OR 2.33, 95% CI 1.44-3.78, P=0.001) and Hispanic ethnicity (OR 1.97, 95% CI 1.14-3.12, P=0.004) were associated with greater illness severity.Conclusions Racial and ethnic disparities in the severity of Covid-19 illness persist, even when controlling for baseline comorbidities. It remains unclear if these differences are related to variations in physiologic response to SARS-CoV-2, differential timing of presentation or disparities in care.

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.

The increased mortality associated with a weekend emergency admission is due to increased illness severity and altered case-mix

Background: A weekend emergency medical admission has been associated with a higher mortality. We have examined all weekend admissions to St James’ Hospital, Dublin between 2002 and 2009. Methods: We divided admissions by weekday or weekend (Saturday or Sunday) presentation. We utilised a multivariate logistic model, to determine whether a weekend admission was independently predictive of 30 day outcome. Results: There were 49337 episodes recorded in 25883 patients; 30-day inhospital mortality at the weekend (9.9% vs. 9.0%) had an unadjusted Odds Ratio of 1.11 (95% CI 0.99, 1.23: p=0.057). In the full risk (unlike the univariate) model, a weekend admission was not independently predictive (OR 1.05; 95% CI: 0.88, 1.24). The case-mix for a weekend admission differed; with more neurological diagnoses (22.8% vs 20.4% : p = 0.001) and less gastrointestinal disease (18.3% vs 21.1% : p = 0.001). A biochemistry only illness severity score predicted a higher mortality for weekend admissions. Conclusion: Patients admitted at the weekend had an approximate 11% increased 30-day in-hospital mortality, compared with a weekday admission. However, admission at the weekend was not independently predictive in a risk model that included Illness Severity (age and biochemical markers) and co-morbidity. Sicker patients, with a worse outcome, are admitted over the weekend; these considerations should inform the allocation of healthcare resources.