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Association of comorbidities with postoperative in-hospital mortality: a retrospective cohort study.

Kork F, Balzer F, Krannich A, Weiss B, Wernecke KD, Spies C - Medicine (Baltimore) (2015)

Bottom Line: However, these scores have never been compared in a broad surgical population.The CCI was superior to the ASA PS in predicting postoperative mortality (AUROCCCI 0.865 vs AUROCASAPS 0.833, P < 0.001).It is capable of identifying those patients at especially high risk and may help reduce postoperative mortality.

View Article: PubMed Central - PubMed

Affiliation: From the Department of Anesthesiology and Intensive Care Medicine (FK, FB, BW, CS), Campus Charité Mitte and Campus Virchow-Klinikum; Department of Biostatistics (AK), Coordination Centre for Clinical Trials, Campus Virchow-Klinikum; and Department of Biometry and SOSTANA GmbH (KDW), Charité-University Medicine Berlin, Berlin, Germany.

ABSTRACT
The purpose of this article is to evaluate the American Society of Anesthesiologists Physical Status (ASA PS) and the Charlson comorbidity index (CCI) for the prediction of postoperative mortality. The ASA PS has been suggested to be equally good as the CCI in predicting postoperative outcome. However, these scores have never been compared in a broad surgical population. We conducted a retrospective cohort study in a German tertiary care university hospital. Predictive accuracy was compared using the area under the receiver-operating characteristic curves (AUROC). In a post hoc approach, a regression model was fitted and cross-validated to estimate the association of comorbidities and intraoperative factors with mortality. This model was used to improve prediction by recalibrating the CCI for surgical patients (sCCIs) and constructing a new surgical mortality score (SMS). The data of 182,886 patients with surgical interventions were analyzed. The CCI was superior to the ASA PS in predicting postoperative mortality (AUROCCCI 0.865 vs AUROCASAPS 0.833, P < 0.001). Predictive quality further improved after recalibration of the sCCI and construction of the new SMS (AUROCSMS 0.928 vs AUROCsCCI 0.896, P < 0.001). The SMS predicted postoperative mortality especially well in patients never admitted to an intensive care unit. The newly constructed SMS provides a good estimate of patient's risk of death after surgery. It is capable of identifying those patients at especially high risk and may help reduce postoperative mortality.

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Related in: MedlinePlus

Nomogram for predicting in-hospital mortality in surgical patients. Find the age, gender, priority, and location of surgery, and the comorbidities of the patient in the nomogram. Then draw a vertical line to the scale in the top row to determine the corresponding points for each item. Now add all points, find the sum in the second to last row (total of points), and draw a vertical line down: the intersection marks the probability of in-hospital death for this patient.
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Figure 3: Nomogram for predicting in-hospital mortality in surgical patients. Find the age, gender, priority, and location of surgery, and the comorbidities of the patient in the nomogram. Then draw a vertical line to the scale in the top row to determine the corresponding points for each item. Now add all points, find the sum in the second to last row (total of points), and draw a vertical line down: the intersection marks the probability of in-hospital death for this patient.

Mentions: For the regression model that served as a basis for the construction of the SMS, we also provide a nomogram as a paper-based alternative to manually estimate the probability of in-hospital death (Figure 3).


Association of comorbidities with postoperative in-hospital mortality: a retrospective cohort study.

Kork F, Balzer F, Krannich A, Weiss B, Wernecke KD, Spies C - Medicine (Baltimore) (2015)

Nomogram for predicting in-hospital mortality in surgical patients. Find the age, gender, priority, and location of surgery, and the comorbidities of the patient in the nomogram. Then draw a vertical line to the scale in the top row to determine the corresponding points for each item. Now add all points, find the sum in the second to last row (total of points), and draw a vertical line down: the intersection marks the probability of in-hospital death for this patient.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4554144&req=5

Figure 3: Nomogram for predicting in-hospital mortality in surgical patients. Find the age, gender, priority, and location of surgery, and the comorbidities of the patient in the nomogram. Then draw a vertical line to the scale in the top row to determine the corresponding points for each item. Now add all points, find the sum in the second to last row (total of points), and draw a vertical line down: the intersection marks the probability of in-hospital death for this patient.
Mentions: For the regression model that served as a basis for the construction of the SMS, we also provide a nomogram as a paper-based alternative to manually estimate the probability of in-hospital death (Figure 3).

Bottom Line: However, these scores have never been compared in a broad surgical population.The CCI was superior to the ASA PS in predicting postoperative mortality (AUROCCCI 0.865 vs AUROCASAPS 0.833, P < 0.001).It is capable of identifying those patients at especially high risk and may help reduce postoperative mortality.

View Article: PubMed Central - PubMed

Affiliation: From the Department of Anesthesiology and Intensive Care Medicine (FK, FB, BW, CS), Campus Charité Mitte and Campus Virchow-Klinikum; Department of Biostatistics (AK), Coordination Centre for Clinical Trials, Campus Virchow-Klinikum; and Department of Biometry and SOSTANA GmbH (KDW), Charité-University Medicine Berlin, Berlin, Germany.

ABSTRACT
The purpose of this article is to evaluate the American Society of Anesthesiologists Physical Status (ASA PS) and the Charlson comorbidity index (CCI) for the prediction of postoperative mortality. The ASA PS has been suggested to be equally good as the CCI in predicting postoperative outcome. However, these scores have never been compared in a broad surgical population. We conducted a retrospective cohort study in a German tertiary care university hospital. Predictive accuracy was compared using the area under the receiver-operating characteristic curves (AUROC). In a post hoc approach, a regression model was fitted and cross-validated to estimate the association of comorbidities and intraoperative factors with mortality. This model was used to improve prediction by recalibrating the CCI for surgical patients (sCCIs) and constructing a new surgical mortality score (SMS). The data of 182,886 patients with surgical interventions were analyzed. The CCI was superior to the ASA PS in predicting postoperative mortality (AUROCCCI 0.865 vs AUROCASAPS 0.833, P < 0.001). Predictive quality further improved after recalibration of the sCCI and construction of the new SMS (AUROCSMS 0.928 vs AUROCsCCI 0.896, P < 0.001). The SMS predicted postoperative mortality especially well in patients never admitted to an intensive care unit. The newly constructed SMS provides a good estimate of patient's risk of death after surgery. It is capable of identifying those patients at especially high risk and may help reduce postoperative mortality.

Show MeSH
Related in: MedlinePlus