CN-121983298-A - Preoperative human N-terminal brain natriuretic peptide-based senile patient postoperative serious complication risk probability prediction nomogram model construction method

CN121983298ACN 121983298 ACN121983298 ACN 121983298ACN-121983298-A

Abstract

The invention relates to the field of biomedicine, in particular to a method for constructing a pre-operation human N-terminal brain natriuretic peptide-based senile patient postoperative serious complication risk prediction nomogram model. The invention provides 5 independent risk factors related to the occurrence of serious postoperative complications of elderly patients, and a risk probability prediction nomogram model is constructed by using multi-factor logistic regression so as to evaluate the risk of serious postoperative complications of elderly patients. The method comprises the steps of firstly collecting clinical data of an operation of an elderly patient, then screening risk factors by utilizing multi-single-factor logistic regression, then sorting the risk factors according to importance by utilizing an optimal subset regression method to obtain characteristic variables with higher predictive value, and finally establishing a multi-factor logistic regression risk probability prediction model and establishing a nomogram according to the prediction model. The invention can accurately predict the occurrence risk of serious postoperative complications of elderly patients based on preoperative N-terminal brain natriuretic peptide, can help clinicians to early identify high-risk elderly patients suffering from serious postoperative complications, guide doctors to perform individualized intervention, and reduce the occurrence of serious postoperative complications of elderly patients.

Inventors

ZHU YIHAO

Assignees

朱易豪

Dates

Publication Date: 20260505
Application Date: 20231029

Claims (4)

1. The method for constructing the pre-operation human N-terminal brain natriuretic peptide-based senile patient postoperative serious complication risk probability prediction nomogram model is characterized by comprising the following steps of: Collecting perioperative clinical data and follow-up data of an elderly patient, wherein the perioperative clinical data comprise gender, age, weight, height, operation position, operation duration, anesthesia duration, smoking history, drinking history, type and dosage of medicine used in operation, total transfusion quantity used in operation, blood transfusion quantity used in operation, hypertension combined before operation, diabetes combined before operation, american anesthesiologist association grade, activity tolerance, breath-hold test grade, urine quantity, total hospitalization day, intensive care unit hospitalization day and N-terminal brain natriuretic peptide concentration of preoperative person, and the follow-up data comprise whether serious complications occur within 30 days after operation; Step two, performing variable significance analysis on the perioperative clinical data by using single-factor logistic regression, and screening potential characteristic variables; Calculating importance of the potential feature variables screened in the second step by using optimal subset regression, and sequencing the potential feature variables screened in the second step according to the importance; Constructing risk probability prediction models of different variable combinations by adopting a multi-factor logistic regression method according to the potential feature variables reordered in the step three, wherein the model one only comprises a first potential feature variable, the model two comprises the first potential feature variable and a second potential feature variable, the model three comprises the first potential feature variable, the second potential feature variable and a third potential feature variable, and the like, and calculating AIC values of the risk probability prediction models of different variable combinations; Comparing the risk probability prediction models of different variable combinations in the fourth step by using AIC values, and taking the variable combination model with the minimum AIC value as an optimal risk probability prediction model; step six, evaluating the accuracy of the optimal risk probability prediction model in the step five by using an ROC curve method; and step seven, drawing the nomogram model of the optimal risk probability prediction model in the step five by using a nomogram method.
2. The method for constructing a model of a nomogram for predicting the risk of serious postoperative complications of elderly patients based on preoperative human N-terminal brain natriuretic peptide according to claim 1, wherein the selected potential characteristic variables include age, weight, length of anesthesia, smoking history, total amount of infusion during surgery, whether hypertension is combined before surgery, whether diabetes is combined before surgery, american society of anesthesiologists grading, activity tolerance, breath hold test grading, total hospital day, intensive care unit hospital day, preoperative human N-terminal brain natriuretic peptide concentration.
3. The method for constructing the pre-operative N-terminal brain natriuretic peptide-based senile patient postoperative serious complication risk probability prediction alignment model according to claim 1, wherein the selected potential characteristic variables are ranked according to importance in the order of pre-operative N-terminal brain natriuretic peptide concentration, age, american anesthesiologist association grade, anesthesia duration, weight, intra-operative blood transfusion amount, whether pre-operative hypertension is combined, whether pre-operative diabetes is combined, activity tolerance, total hospitalization day, intensive care unit hospitalization day, smoking history, intra-operative transfusion total amount, breath hold test grade.
4. The method for constructing the pre-operative human N-terminal brain natriuretic peptide-based senile patient postoperative serious complication risk probability prediction alignment chart model according to claim 1, wherein the optimal risk probability prediction model comprises characteristic variables of pre-operative human N-terminal brain natriuretic peptide concentration, age, american anesthesiologist association grade, anesthesia duration and body weight.

Description

Preoperative human N-terminal brain natriuretic peptide-based senile patient postoperative serious complication risk probability prediction nomogram model construction method Technical Field The invention relates to the technical field of biomedicine, in particular to a method for constructing a prediction alignment chart model of risk probability of serious postoperative complications of old patients based on preoperative human N-terminal brain natriuretic peptide. Background With the aging of population, more and more elderly patients receive surgical operation and anesthesia, and the elderly patients usually combine heart, kidney, brain and other system diseases, and the risk of postoperative complications in the perioperative period is far higher than that of adult patients in other age groups, especially postoperative serious complications (Clavien-Dindo grade is more than or equal to 3 grade), and the postoperative serious complications can increase death rate, hospitalization time and hospitalization cost, and reduce life quality. The existing postoperative adverse event risk prediction tool mainly depends on the technology, experience and subjective judgment of doctors, such as American anesthesia Association grading, heart function index grading and the like, and lacks objective, simple and visual risk layering tools. Therefore, there is an urgent need for an early, rapid, objective, postoperative complications risk assessment tool that can reflect the effects of surgery and anesthesia, to achieve early warning and guide perioperative clinical decisions. Human N-terminal brain natriuretic peptide is a family member of the natriuretic peptide that is synthesized and secreted by cardiomyocytes in response to stimuli such as volume expansion and pressure overload. In recent years, target-directed treatment of N-terminal brain natriuretic peptide of human and the predictive value of post-operative main cardiovascular adverse events, post-operative pulmonary complications and the like are attracting attention. The plasma N-terminal brain natriuretic peptide concentration is monitored in the perioperative period, so that the preoperative circulation and cardiac function states of patients can be evaluated, the occurrence of postoperative complications can be predicted, and a clinician is guided to intervene in advance and prevent the occurrence of serious complications. Studies show that preoperative high levels of human N-terminal brain natriuretic peptide are risk factors for the occurrence of postoperative complications in the thoracic surgery. In addition, if the post-operative period continues to rise within 5 days, the risk of post-operative early complications (Clavien-Dindo grade. Gtoreq.2 during hospitalization) and late complications (Clavien-Dindo grade. Gtoreq.2 grade occurring within 3 months post-operative) will increase significantly. It was found that in colorectal resected patients, serum levels of human N-terminal brain natriuretic peptide correlated with the amount of fluid treatment, and that an increase in fluid volume resulted in an increase in human N-terminal brain natriuretic peptide concentration, while preoperative levels of human N-terminal brain natriuretic peptide could predict the occurrence of post-operative atrial fibrillation, ventricular fibrillation, respiratory failure, pulmonary edema, and other cardiopulmonary complications. Therefore, the preoperative N-terminal brain natriuretic peptide is an effective biomarker for predicting postoperative complications of elderly patients, and a postoperative serious complications prediction model of elderly patients based on the preoperative N-terminal brain natriuretic peptide is not constructed at present, so that development of a postoperative serious complications risk prediction model of elderly patients based on the preoperative N-terminal brain natriuretic peptide is urgently needed, and the preoperative N-terminal brain natriuretic peptide can be conveniently and directly used by clinicians, so that early warning and intervention are realized, postoperative fatalities of elderly patients are improved, and medical expenses are reduced. Disclosure of Invention Aiming at the defects in the background art, the invention provides a method for constructing a postoperative serious complication risk prediction nomogram model of an elderly patient based on preoperative N-terminal brain natriuretic peptide, which solves the problem that the postoperative serious complication risk prediction nomogram model of the elderly patient based on preoperative N-terminal brain natriuretic peptide does not exist at present. The technical scheme of the invention is realized as follows: 1. A method for constructing a pre-operation human N-terminal brain natriuretic peptide-based prediction nomogram model of postoperative serious complication risk probability of an elderly patient comprises the following steps: Step one, collecting perioperativ