|
Abstract Objective To establish a nomogram model for pre-operative nutritional status of patients with gynecological tumors.
Method A total of 110 patients underwent elective surgery for gynecological tumors from January 2016 to December 2019 were selected
as the research objects three days before operation the patients were divided into well-nourished group 0 ~ 3 points n = 44 and
malnutrition group 4 ~ 35 points n = 66 according to the patient generated subjective global assessment scale PG-SGA . The clinical datas and blood biochemical indexes were compared between the two groups then multivariate Logistic regression analysis was used
to screen the risk factors of malnutrition. Result Univariate comparison found that the patients in malnutrition group were older more of
advanced tumors less of fat percentage middle and upper arm circumference MUAC handgrip strength hemoglobin and albumin
levels while higher of weight loss and percentage in recent one month C-reactive protein CRP levels the difference was statistically significant P < 0. 05 . Multivariate Logistic regression analysis showed that advanced tumors OR= 3. 001 95%CI = 2. 124-4. 125
higher of weight loss in recent 1 month OR= 5. 201 95%CI = 4. 526-6. 329 and CRP OR = 2. 012 95%CI = 1. 320-3. 020 less of
hemoglobin OR= 0. 562 95%CI = 0. 232-0. 859 and handgrip strength OR = 0. 324 95%CI = 0. 102-0. 667 were the independent
predictors to malnutrition in patients with gynecological tumors P < 0. 05 . The nomogram predictive model was established and the
area under the curve AUC of nomogram model for predicting malnutrition by receiver operating curve ROC curve was 0. 889. Conclusion Malnutrition is common in patients with gynecological tumors before elective surgery advanced tumors increase of weight loss
in recent one month and CRP decrease of hemoglobin and handgrip strength are the independent predictors of malnutrition establishment of a quantitative nomogram model with strong visual effect and simple operation has a high efficiency.
|
Received: 15 January 2022
|
|
|
|
|
|