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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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Nomogram displaying the points associated with each variable included in the final prediction model corrected for over fitting. The total score is converted to the probability that a relaparotomy is necessary and divided into three categories. The decision rule guides monitoring of the patient by timing the repetition of the prediction model and performance of a computed tomography scan if indicated.
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Figure 3: Nomogram displaying the points associated with each variable included in the final prediction model corrected for over fitting. The total score is converted to the probability that a relaparotomy is necessary and divided into three categories. The decision rule guides monitoring of the patient by timing the repetition of the prediction model and performance of a computed tomography scan if indicated.

Mentions: The final prediction model is displayed as a nomogram in Figure 3. If a patient scores points on all six variables this would result in the maximum score possible of 60 points corresponding with an 83% probability that a relaparotomy is necessary. The outcome of the prediction model is divided into three categories on which the decisional rule is based. Patients in category one (less then 20 points) have a low predicted probability of needing a relaparotomy, and these patients are reassessed with the prediction model after 24 hours. For patients in the second category (20–40 points) with an intermediate probability, the prediction model is repeated after 12 hours and performing a CT in these patients can be considered. Patients in the last category (more then 40 points) have a high predicted probability indicating that an abdominal CT should be performed and if negative the prediction model should be repeated within 12 hours (Figure 3).


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)

Nomogram displaying the points associated with each variable included in the final prediction model corrected for over fitting. The total score is converted to the probability that a relaparotomy is necessary and divided into three categories. The decision rule guides monitoring of the patient by timing the repetition of the prediction model and performance of a computed tomography scan if indicated.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Nomogram displaying the points associated with each variable included in the final prediction model corrected for over fitting. The total score is converted to the probability that a relaparotomy is necessary and divided into three categories. The decision rule guides monitoring of the patient by timing the repetition of the prediction model and performance of a computed tomography scan if indicated.
Mentions: The final prediction model is displayed as a nomogram in Figure 3. If a patient scores points on all six variables this would result in the maximum score possible of 60 points corresponding with an 83% probability that a relaparotomy is necessary. The outcome of the prediction model is divided into three categories on which the decisional rule is based. Patients in category one (less then 20 points) have a low predicted probability of needing a relaparotomy, and these patients are reassessed with the prediction model after 24 hours. For patients in the second category (20–40 points) with an intermediate probability, the prediction model is repeated after 12 hours and performing a CT in these patients can be considered. Patients in the last category (more then 40 points) have a high predicted probability indicating that an abdominal CT should be performed and if negative the prediction model should be repeated within 12 hours (Figure 3).

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