Turkish journal of surgery · 2025
A predictive tool for postoperative ileus after ileostomy closure: Model development and validation.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
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Ошибки и ограничения
История · было → стало
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Khare Atul, Kothari Reena, Kateha Dinesh, Verma Amrendra, Agarwal Pawan, Sharma Dhananjaya
Аннотация
OBJECITVE: Postoperative ileus (POI) is a significant complication after ileostomy closure, which results in recurrent vomiting, dehydration, delay in starting enteral feeding, and even anastomotic breakdown. We aimed to develop a prediction model for POI occurrence after ileostomy closure.
MATERIAL AND METHODS: One hundred consecutive patients undergoing ileostomy closure were studied prospectively and data of various demographic and clinical variables were recorded in a predesigned proforma. The final prediction model was developed using logistic regression and internally validated in the next 50 patients.
RESULTS: Factors associated with POI were age, body mass index, tobacco or alcohol addiction, comorbidity, anemia, thrombocytopenia, renal dysfunction, as shown by creatinine level, hypoproteinemia, hypernatremia, and hypokalemia. The mean score of those who developed POI was higher (p=0.002) than those who did not. A cut-off at score 8 had a sensitivity of 85.71%, specificity of 73.12%, and area under the curve was 0.8241 (SE 0.1123). The predictive model was validated in the next 50 consecutive patients and showed good sensitivity (80%) and specificity (93.3%).
CONCLUSION: Our predictive model can determine the occurrence of POI with accuracy.
MATERIAL AND METHODS: One hundred consecutive patients undergoing ileostomy closure were studied prospectively and data of various demographic and clinical variables were recorded in a predesigned proforma. The final prediction model was developed using logistic regression and internally validated in the next 50 patients.
RESULTS: Factors associated with POI were age, body mass index, tobacco or alcohol addiction, comorbidity, anemia, thrombocytopenia, renal dysfunction, as shown by creatinine level, hypoproteinemia, hypernatremia, and hypokalemia. The mean score of those who developed POI was higher (p=0.002) than those who did not. A cut-off at score 8 had a sensitivity of 85.71%, specificity of 73.12%, and area under the curve was 0.8241 (SE 0.1123). The predictive model was validated in the next 50 consecutive patients and showed good sensitivity (80%) and specificity (93.3%).
CONCLUSION: Our predictive model can determine the occurrence of POI with accuracy.