Machine learning for infection risk prediction in postoperative patients with non-mechanical ventilation and intravenous neurotargeted drugs

Abstract

Drug efficacy can be improved by understanding the effects of anesthesia on the neurovascular system. In this study, we used machine learning algorithms to predict the risk of infection in postoperative intensive care unit (ICU) patients who are on non-mechanical ventilation and are receiving hydromorphone analgesia. In this retrospective study, 130 patients were divided into high and low dose groups of hydromorphone analgesic pump patients admitted after surgery. The white blood cells (WBC) count and incidence rate of infection was significantly higher in the high hydromorphone dosage group compared to the low hydromorphone dosage groups (p < 0.05). Furthermore, significant differences in age (P = 0.006), body mass index (BMI) (P = 0.001), WBC count (P = 0.019), C-reactive protein (CRP) (P = 0.038), hydromorphone dosage (P = 0.014), and biological sex (P = 0.024) were seen between the infected and non-infected groups. The infected group also had a longer hospital stay and an extended stay in the intensive care unit compared to the non-infected group. We identified important risk factors for the development of postoperative infections by using machine learning algorithms, including hydromorphone dosage, age, biological sex, BMI, and WBC count. Logistic regression analysis was applied to incorporate these variables to construct infection prediction models and nomograms. The area under curves (AUC) of the model were 0.835, 0.747, and 0.818 in the training group, validation group, and overall pairwise column group, respectively. Therefore, we determined that hydromorphone dosage, age, biological sex, BMI, WBC count, and CRP are significant risk factors in developing postoperative infections.

Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools

“…65–70 Other models focused on the risk of developing pneumonia or respiratory failure, 83,85,125 acute kidney injury, 43,52,58,60–63,120–122 liver failure 117 or development of sepsis or surgical site infection. 74–76,87,99,131 Suhre et al . 78 analyzed the association between perioperative nausea and vomiting and cannabis use using a long-term survey.…”

“…Multivariate logistic regression 30 , 31 , 36 , 43 , 48 , 54 , 55 , 60 , 62 , 65 , 67 , 69 , 75 , 76 , 78 , 80 , 95 , 106 , 107 , 110 …”

“…Elective noncardiac surgery Mortality 30 days and/or 1 yr [26][27][28][29][30][31][32][33][34][35][36][37][38] In surgical patients with perioperative SarS-CoV-2 39 Morbidity Multiple postoperative complications 26,27,29,[40][41][42][43][44][45][46][47][48][49][50][51] acute and chronic pain 52-57 acute kidney failure 52,58-63 aSa score prediction 64 Delirium and cognitive decline [65][66][67][68][69][70] Cerebral/myocardial infarction 71 Difficult intubation prediciton 72 Ileus 73 Infection risk [74][75][76] Myocardial injury 77 Nausea and vomiting 78 Obstructive apnoea screening 79 Perioperative transfusion 80,81 Postoperative atrial fibrillation 82 respiratory failure and depression Liver failure 117 Major bleeding 118,119 Kidney failure…”

“…(1) Multivariate logistic regression 30,31,36,43,48,54,55,60,62,65,67,69,75,76,78,80,95,106,107,110 (2) Previously validated scores such as perioperative medicine-related scores (e.g., ASA status, POSSUM, Charlson Comorbidity Index, or National Surgical Quality Improvement Program calculator scores) 30,32,49,61,100,104 or other scores 58,62 (e.g., Bariclot tool, STOP-BANG score, Mallampati test, various frailty indexes, and the acute kidney injury score) 34,49,58,72,79,82,86,114,121 (3) Clinical assessment 42,52,88 Overall, the machine learning models described in these articles outperformed their technical or clinical comparator, with an average increase in AUC and accuracy between 0.2-0.3, except for that of Chen et al 38 where the ASA score alone, despite a lower AUC, had higher accuracy compared to neural network and logistic regression models.…”

Predictors of cognitive impairment in patients undergoing ileostomy for colorectal cancer: a retrospective analysis

“…The discernment of these risk factors and potential biomarkers bears clinical ramifications for the prognostication and management of postoperative cognitive impairment in patients undergoing ileostomy for colorectal cancer. Early detection methods and intervention for cognitive decline may contribute to delaying the onset and progression of neurovascular disorders such as Alzheimer’s disease ( Lenehan et al, 2015 ; Chen et al, 2022c ; Chen et al, 2022b ; Du et al, 2022 ). Moreover, our findings may facilitate the development of personalized treatment and care plans for these patients, taking into consideration their individual risk factors for cognitive impairment.…”