[Risk predictors, scoring systems and prognostic models in anesthesia and intensive care. Part II. Intensive Care]

Incidence, Risk Factors and Outcomes of New Onset Supraventricular Arrhythmias in African American Patients with Severe Sepsis

PURPOSE

New onset supraventricular arrhythmias (SVA) are commonly reported in mixed intensive care settings. We sought to determine the incidence, risk factors and outcomes of new onset SVA in African American (AA) patients with severe sepsis admitted to medical intensive care unit (MICU).

METHODS

Patients admitted to MICU between January 2012 through December 2012 were studied. Patients with a previous history of arrhythmia or with new onset of ventricular arrhythmia were excluded. Data on risk factors, critical care interventions and outcomes were obtained.

RESULTS

One hundred and thirty-one patients were identified. New onset SVA occurred in 34 (26%) patients. Of those 34, 20 (59%) had atrial fibrillation (AF), 6 (18%) had atrial flutter and 8 (24%) had other forms of SVA. Compared with patients without SVA, patients with new onset SVA were older (69 ± 12 yrs vs 59 ± 13 yrs, P=.003), had congestive heart failure (47% vs 24%, P=.015) and dyslipidemia (41% vs 15%, P=.002). Additionally, they had a higher mean mortality prediction model (MPM II) score (65 ± 25 vs 49 ± 26, P=.001) and an increased incidence of respiratory failure (85% vs 55%, P=.001). Hospital mortality in patients with new onset SVA was 18 (53%) vs 30 (31%); P=.024; however, in a multivariate analysis, new onset SVA was associated with non-significantly increased odds (OR 2.58, 95% CI 0.86-8.05) for in-hospital mortality.

CONCLUSIONS

New onset SVA was prevalent in AA patients with severe sepsis and occurred more frequently with advanced age, increased severity of illness, congestive heart failure, and acute respiratory failure; it was associated with higher unadjusted in hospital mortality. However, after multiple adjustments, new onset SVA did not remain an independent predictor of mortality.

Prospective comparison of three risk score models at three different surgical intensive care units

PURPOSE

Although risk score models are of great value, their use is restricted because of the additional effort involved. The aim of this study was to compare three different score systems. Each of these requires a different degree of effort by the medical staff. One of the score systems is solely based on routine laboratory parameters. Data were collected on three different ICUs units, with each showing a large variety in patients' health conditions.

METHODS

Prospective data of 588 surgical patients were collected by means of Acute Physiology and Chronic Health Evaluation II (APACHE II), Dense Laboratory Whole Blood Applied Risk Estimation (DELAWARE), and Simplified Acute Physiology Score II (SAPS II) score systems. These patients were admitted to three different intensive care units over a period of 12 months. On the day of admission, predicted hospital survival and mortality were evaluated.

RESULTS

With a cutoff value of 0.6, the sensitivity of the APACHE II, DELAWARE, and SAPS II was at 0.19, 0.24, and 0.21; the specificity was at 0.98, 0.92, and 0.98; and the correct classification rate at 0.86, 0.83, and 0.86. The r(2) value was 0.35 for the APACHE II, 0.12 for the DELAWARE, and 0.21 for the SAPS II. The hospital mortality rate was overestimated in all three score systems.

CONCLUSIONS

The results of this first multicenter study comparing three risk score systems indicate that it is possible to establish a general risk score for surgical intensive care patients on admission date. Such a risk score is solely based on quality-controlled, low-cost routine laboratory parameters.

Long-term comparison of a routine laboratory parameter-based severity score with APACHE II and SAPS II

BACKGROUND

Risk score models predicting mortality have tremendous value, but because of the additional effort involved, their clinical use remains low. The aim of this study is to compare three different scores that each requires different levels of effort during admission and throughout treatment: the Acute Physiology and Chronic Health Evaluation II (APACHE II), the Simplified Acute Physiology Score II (SAPS II), and the Dense Laboratory Whole Blood Applied Risk Estimation (DELAWARE) score. Of the three, only the DELAWARE is based solely on routine laboratory parameters.

METHODS

Prospective data of the three scores were collected for 268 surgical patients admitted to the intensive care unit over 1 year. The predicted hospital mortality and survival were evaluated for the first 14 days.

RESULTS

With a cutoff value of 0.65, the sensitivity of the DELAWARE was 71.6%, the specificity, 92.5%, and the correct classification rate, 87.3%. The APACHE II and SAPS II showed values of 41.2%/96.8%/86.2% and 62.7%/87.1%/82.5%, respectively. The r2 value was 0.884 for the DELAWARE, 0.876/0.814 for the APACHE II and SAPS II. Hospital mortality rate was overestimated by 20% to 65% in all scores. The discriminatory ability of the APACHE II and SAPS II increased throughout the course of treatment.

CONCLUSIONS

The routine laboratory-based DELAWARE provides a reliable, valid risk assessment of the surgical intensive care patient at admission. It also provides additional information without added effort or poor interobserver reliability, which leads to better data comparability. We have to state that until now the data have been collected in a single-center and their general validity is therefore limited. By the end of treatment, the SAPS II and APACHE II had increased discriminatory ability and are therefore useful as process parameters.

A laboratory-based risk score for medical intensive care patients

BACKGROUND

Established general risk score models for intensive care patients incorporate several clinical and laboratory data. However, the collection, documentation and classification of clinical data are time-consuming, incur labor-related costs, and are dependent on the experience of the examiner. Therefore, in the present study a general score for medical intensive care patients based solely on routine laboratory parameters is presented.

METHODS

Parameter selection was performed using stepwise logistic regression analysis. The maximum likelihood estimate of variable influence on mortality provided a relative weighting for each variable. The new score was compared to two established risk models (Acute Physiology And Chronic Health Evaluation II, APACHE II; and Simplified Acute Physiology Score II, SAPS II).

RESULTS

The study included 528 medical intensive care patients with a mean age of 65.4+/-0.7 years. The in-hospital mortality was 16.5% (87/528). Multiple logistic regression analysis revealed eight parameters with significant prognostic power: alanine aminotransferase, cholesterol, creatinine, leukocytes, sodium, thrombocytes, urea, and age. These parameters were used to build a new laboratory score called Critical Risk Evaluation by Early Keys (CREEK). The area under the receiver operating characteristics curve was 0.857 (0.814-0.900). Pearson correlation analysis showed significant correlation between CREEK and APACHE II (r=0.550) and SAPS II (r=0.516; p<0.001; n=387). The areas under curve of the APACHE II and the SAPS II were 0.869 and 0.874, respectively.

CONCLUSIONS

We show that a general risk score for medical intensive care patients on admission based solely on routine laboratory parameters is feasible. The quality of risk estimation using CREEK is comparable to established risk models. Furthermore, this new score is based on quality controlled low-cost laboratory parameters that are routinely measured on admission to the intensive care unit. Therefore, no additional costs are involved.

The simplified acute physiology score to predict outcome in patients with subarachnoid hemorrhage

OBJECTIVE

Current prognosticators for patients with subarachnoid hemorrhage (SAH) do not take into account signs of extracerebral organ dysfunction. This may explain the only moderate predictive value of these prognosticators. We assessed the prognostic value of the simplified acute physiology score (SAPS) II in SAH patients.

METHODS

In a retrospective cohort study of 148 consecutive SAH patients, we related the SAPS II calculated within 24 hours after admission to clinical and initial computed tomographic imaging characteristics using the Mann-Whitney U test. We compared the prognostic value of the SAPS II with that of the World Federation of Neurosurgical Surgeons score, the patient's age, and the amount of blood showing in computed tomographic imaging for the occurrence of delayed cerebral ischemia using Cox proportional hazards modeling or, for poor outcome (death or dependence), logistic regression modeling.

RESULTS

In the univariate analysis, the SAPS II was the strongest prognosticator; in the multivariate model, the SAPS II was the only independent predictor for outcome (odds ratio, 1.08; 95% confidence interval, 1.06-1.11]). Patients in the highest tertile of SAPS II had a significantly higher risk of poor outcome than those in the lowest tertile (odds ratio, 30.9; 95% confidence interval, 9.9-96.7]). The SAPS II was also the only independent predictor for the occurrence of delayed cerebral ischemia (hazard ratio, 1.020; 95% confidence interval, 1.002-1.039]).

CONCLUSION

The SAPS II is a useful and reliable prognosticator in SAH patients. This score may provide more information than specific SAH scales in predicting poor outcome or the occurrence of delayed cerebral ischemia in some circumstances.