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A decision rule to aid selection of patients with abdominal sepsis requiring a relaparotomy.

Kiewiet JJ, van Ruler O, Boermeester MA, Reitsma JB - BMC Surg (2013)

Bottom Line: However, acceptable sensitivity would require a low threshold for relaparotomy leading to an unacceptable rate of negative relaparotomies (63%).To construct a prediction model that will provide a definite answer whether or not to perform a relaparotomy seems a utopia.However, our prediction model can be used to stratify patients on their underlying risk and could guide further monitoring of patients with abdominal sepsis in order to identify patients with suspected ongoing peritonitis in a timely fashion.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Surgery, Academic Medical Center, Meibergdreef 9, Amsterdam 1105 AZ, The Netherlands. J.J.Kiewiet@amc.uva.nl

ABSTRACT

Background: Accurate and timely identification of patients in need of a relaparotomy is challenging since there are no readily available strongholds. The aim of this study is to develop a prediction model to aid the decision-making process in whom to perform a relaparotomy.

Methods: Data from a randomized trial comparing surgical strategies for relaparotomy were used. Variables were selected based on previous reports and common clinical sense and screened in a univariable regression analysis to identify those associated with the need for relaparotomy. Variables with the strongest association were considered for the prediction model which was constructed after backward elimination in a multivariable regression analysis. The discriminatory capacity of the model was expressed with the area under the curve (AUC). A cut-off analysis was performed to illustrate the consequences in clinical practice.

Results: One hundred and eighty-two patients were included; 46 were considered cases requiring a relaparotomy. A prediction model was build containing 6 variables. This final model had an AUC of 0.80 indicating good discriminatory capacity. However, acceptable sensitivity would require a low threshold for relaparotomy leading to an unacceptable rate of negative relaparotomies (63%). Therefore, the prediction model was incorporated in a decision rule were the interval until re-assessment and the use of Computed Tomography are related to the outcome of the model.

Conclusions: To construct a prediction model that will provide a definite answer whether or not to perform a relaparotomy seems a utopia. However, our prediction model can be used to stratify patients on their underlying risk and could guide further monitoring of patients with abdominal sepsis in order to identify patients with suspected ongoing peritonitis in a timely fashion.

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Receiver operating characteristic (ROC) curve showing sensitivity and 1 minus specificity for various cut-off values of the risk score of the multivariable prediction model before adjustment for over fitting. The area under this curve (AUC), a measure of discriminatory ability, is 0.83. After correction for over fitting the AUC is 0.80. The diagonal reference line indicates no discriminatory capacity (AUC 0.50).
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Figure 2: Receiver operating characteristic (ROC) curve showing sensitivity and 1 minus specificity for various cut-off values of the risk score of the multivariable prediction model before adjustment for over fitting. The area under this curve (AUC), a measure of discriminatory ability, is 0.83. After correction for over fitting the AUC is 0.80. The diagonal reference line indicates no discriminatory capacity (AUC 0.50).

Mentions: After backward elimination, the following six variables remained in the final prediction model and were associated with an increased risk of needing relaparotomy: heart rate, hemoglobin level, body temperature, no defecation, the extent of the contamination found at the initial laparotomy, and the need for administration of inotropic agents. The discriminatory capacity of the final model had an area under the curve of 0.83 (range; 0.71-0.91). After adjustment for overfitting using bootstrap techniques the AUC is 0.80 (range; 0.69-0.82), suggesting reasonable discriminatory capacity Figure 2. In other words, the probability that a randomly chosen patient who needs a relaparotomy will have a higher score than a randomly chosen patient who does not need a relaparotomy is 80%. Table 2 displays the odds ratios and the 95% confidence intervals of the final prediction model after the shrinkage factor is applied.


A decision rule to aid selection of patients with abdominal sepsis requiring a relaparotomy.

Kiewiet JJ, van Ruler O, Boermeester MA, Reitsma JB - BMC Surg (2013)

Receiver operating characteristic (ROC) curve showing sensitivity and 1 minus specificity for various cut-off values of the risk score of the multivariable prediction model before adjustment for over fitting. The area under this curve (AUC), a measure of discriminatory ability, is 0.83. After correction for over fitting the AUC is 0.80. The diagonal reference line indicates no discriminatory capacity (AUC 0.50).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3750491&req=5

Figure 2: Receiver operating characteristic (ROC) curve showing sensitivity and 1 minus specificity for various cut-off values of the risk score of the multivariable prediction model before adjustment for over fitting. The area under this curve (AUC), a measure of discriminatory ability, is 0.83. After correction for over fitting the AUC is 0.80. The diagonal reference line indicates no discriminatory capacity (AUC 0.50).
Mentions: After backward elimination, the following six variables remained in the final prediction model and were associated with an increased risk of needing relaparotomy: heart rate, hemoglobin level, body temperature, no defecation, the extent of the contamination found at the initial laparotomy, and the need for administration of inotropic agents. The discriminatory capacity of the final model had an area under the curve of 0.83 (range; 0.71-0.91). After adjustment for overfitting using bootstrap techniques the AUC is 0.80 (range; 0.69-0.82), suggesting reasonable discriminatory capacity Figure 2. In other words, the probability that a randomly chosen patient who needs a relaparotomy will have a higher score than a randomly chosen patient who does not need a relaparotomy is 80%. Table 2 displays the odds ratios and the 95% confidence intervals of the final prediction model after the shrinkage factor is applied.

Bottom Line: However, acceptable sensitivity would require a low threshold for relaparotomy leading to an unacceptable rate of negative relaparotomies (63%).To construct a prediction model that will provide a definite answer whether or not to perform a relaparotomy seems a utopia.However, our prediction model can be used to stratify patients on their underlying risk and could guide further monitoring of patients with abdominal sepsis in order to identify patients with suspected ongoing peritonitis in a timely fashion.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Surgery, Academic Medical Center, Meibergdreef 9, Amsterdam 1105 AZ, The Netherlands. J.J.Kiewiet@amc.uva.nl

ABSTRACT

Background: Accurate and timely identification of patients in need of a relaparotomy is challenging since there are no readily available strongholds. The aim of this study is to develop a prediction model to aid the decision-making process in whom to perform a relaparotomy.

Methods: Data from a randomized trial comparing surgical strategies for relaparotomy were used. Variables were selected based on previous reports and common clinical sense and screened in a univariable regression analysis to identify those associated with the need for relaparotomy. Variables with the strongest association were considered for the prediction model which was constructed after backward elimination in a multivariable regression analysis. The discriminatory capacity of the model was expressed with the area under the curve (AUC). A cut-off analysis was performed to illustrate the consequences in clinical practice.

Results: One hundred and eighty-two patients were included; 46 were considered cases requiring a relaparotomy. A prediction model was build containing 6 variables. This final model had an AUC of 0.80 indicating good discriminatory capacity. However, acceptable sensitivity would require a low threshold for relaparotomy leading to an unacceptable rate of negative relaparotomies (63%). Therefore, the prediction model was incorporated in a decision rule were the interval until re-assessment and the use of Computed Tomography are related to the outcome of the model.

Conclusions: To construct a prediction model that will provide a definite answer whether or not to perform a relaparotomy seems a utopia. However, our prediction model can be used to stratify patients on their underlying risk and could guide further monitoring of patients with abdominal sepsis in order to identify patients with suspected ongoing peritonitis in a timely fashion.

Show MeSH
Related in: MedlinePlus