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Predicting and comparing postoperative infections in different stratification following PCNL based on nomograms.

ABSTRACT: To discuss the mechanisms of infection complications in different degrees after percutaneous nephrolithotomy (PCNL) through predicting and comparing post-PCNL infections based on nomograms, a retrospective cohort study was conducted among 969 cases who underwent PCNL from Dec 5, 2016 to Dec 25, 2017 in Kunming, Yunnan Province. We examined clinical features, urine routine, blood routine, blood biochemistry, imaging studies and operative information and recorded the examination results before surgery for univariate and multivariate logistic regression. We applied receiver operating characteristic curves, calibration curves, accuracy, specificity, sensitivity, positive predictive value and negative predictive value to evaluate and compare the models. Nomograms were used to visualize the different degrees of postoperative infection complications. The risk scores of the three groups were compared by diabetes mellitus distribution. Our results suggest that the more severe the infection is, the more accurate the model predicts and that the occurrence of severe infection mostly is related to the patients' homeostasis. Hence, we developed an online post-PCNL sepsis dynamic nomogram which can achieve visualization and dynamically predict the incidence of sepsis in postoperative patients.

SUBMITTER: Jiang E

PROVIDER: S-EPMC7347600 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Predicting and comparing postoperative infections in different stratification following PCNL based on nomograms.

Jiang Enyan E Guo Haixiang H Yang Bowei B Li Pei P Mishra Prashant P Yang Tongxin T Li Yuhang Y Wang Haifeng H Jiang Yongming Y

Scientific reports 20200709 1

To discuss the mechanisms of infection complications in different degrees after percutaneous nephrolithotomy (PCNL) through predicting and comparing post-PCNL infections based on nomograms, a retrospective cohort study was conducted among 969 cases who underwent PCNL from Dec 5, 2016 to Dec 25, 2017 in Kunming, Yunnan Province. We examined clinical features, urine routine, blood routine, blood biochemistry, imaging studies and operative information and recorded the examination results before sur ...[more]

PMID: 32647305

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Project description:Treatment options and reliable predictive marker to determine patients at risk to develop postoperative LD and to define the optimal time point of liver resection are limited. Accordingly, there is an urgent need for an easily assessable preoperative test to predict postoperative liver function recovery, specifically as current markers are often expensive, time consuming and sometimes invasive. Emerging evidence suggests that microRNA (miRNA) signatures represent potent diagnostic, prognostic and treatment response biomarkers for several diseases. Using next-generation sequencing as an unbiased systematic approach 554 miRNAs were detected in preoperative plasma of 21 patients suffering from postoperative LD after liver resection and 27 matched controls. Subsequently, we identified a miRNA signature - consisting of miRNAs 151a-5p, 192-5p and 122-5p - that highly correlated with patients developing postoperative LD after liver resection. The predictive potential for postoperative LD was subsequently confirmed using real-time PCR in an independent validation cohort of 98 patients. Ultimately, a regression model of the two miRNA ratios 151a-5p to 192-5p and 122-5p to 151a-5p was found to reliably predict postoperative LD, severe morbidity, prolonged intensive care unit and hospital stay and even mortality prior to surgery with a remarkable accuracy, thereby outperforming established markers of postoperative LD. Ultimately, we documented that miRNA ratios closely followed liver function recovery after partial hepatectomy. Conclusion: Given the clinical relevance of predicting potentially fatal postoperative clinical outcome after liver resection, our data demonstrate the clinical utility of a novel miRNA-based biomarker to support the selection of patients undergoing partial hepatectomy. The dynamical changes during liver function recovery indicate a possible role in tailoring the optimal time point of surgery to each individual patient. Thereby, our data might help to tailor surgical strategies to the specific risk profile of individual patients.

Dataset Information

Predicting and comparing postoperative infections in different stratification following PCNL based on nomograms.

Publications

Predicting and comparing postoperative infections in different stratification following PCNL based on nomograms.

Similar Datasets

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