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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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Inclusion of patients and outcome definition in the prediction study of patients requiring a relaparotomy.
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Figure 1: Inclusion of patients and outcome definition in the prediction study of patients requiring a relaparotomy.

Mentions: Of the 229 patients randomized a total of 182 patients where included in the present study. A flowchart of the inclusion is depicted in Figure 1. A total of 47 patients were excluded from the analysis because they had a relaparotomy more than three days after the initial laparotomy (n = 39), or they had no relaparotomy and deceased within 14 days after their initial laparotomy (n = 8). Of the 182 included patients in the analysis, 46 patients needed a relaparotomy and 136 did not. Demographic and outcome data are displayed in Table 1. Age and gender did not differ between the compared groups. However, as can be expected in patients with positive findings at relaparotomy indicating persistent peritonitis, the length of ICU admission and in-hospital mortality differed significantly from patients who did not need a relaparotomy.


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)

Inclusion of patients and outcome definition in the prediction study of patients requiring a relaparotomy.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Inclusion of patients and outcome definition in the prediction study of patients requiring a relaparotomy.
Mentions: Of the 229 patients randomized a total of 182 patients where included in the present study. A flowchart of the inclusion is depicted in Figure 1. A total of 47 patients were excluded from the analysis because they had a relaparotomy more than three days after the initial laparotomy (n = 39), or they had no relaparotomy and deceased within 14 days after their initial laparotomy (n = 8). Of the 182 included patients in the analysis, 46 patients needed a relaparotomy and 136 did not. Demographic and outcome data are displayed in Table 1. Age and gender did not differ between the compared groups. However, as can be expected in patients with positive findings at relaparotomy indicating persistent peritonitis, the length of ICU admission and in-hospital mortality differed significantly from patients who did not need a relaparotomy.

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