Machine learning approach to predict acute kidney injury after liver surgery.

BACKGROUND: Acute kidney injury (AKI) after surgery appears to increase the risk of death in patients with liver cancer. In recent years, machine learning algorithms have been shown to offer higher discriminative efficiency than classical statistical analysis. AIM: To develop prediction models for AKI after liver cancer resection using machine learning techniques. METHODS: We screened a total of 2450 patients who had undergone primary hepatocellular carcinoma resection at Changzheng Hospital, Shanghai City, China, from January 1, 2015 to August 31, 2020. The AKI definition used was consistent with the Kidney Disease: Improving Global Outcomes. We included in our analysis preoperative data such as demographic characteristics, laboratory findings, comorbidities, and medication, as well as perioperative data such as duration of surgery. Computerized algorithms used for model development included logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGboost), and decision tree (DT). Feature importance was also ranked according to its contribution to model development. RESULTS: AKI events occurred in 296 patients (12.1%) within 7 d after surgery. Among the original models based on machine learning techniques, the RF algorithm had optimal discrimination with an area under the curve value of 0.92, compared to 0.87 for XGBoost, 0.90 for DT, 0.90 for SVM, and 0.85 for LR. The RF algorithm also had the highest concordance-index (0.86) and the lowest Brier score (0.076). The variable that contributed the most in the RF algorithm was age, followed by cholesterol, and surgery time. CONCLUSION: Machine learning algorithms are highly effective in discriminating patients at high risk of developing AKI. The successful application of machine learning models may help guide clinical decisions and help improve the long-term prognosis of patients.

Two case reports and literature review for hepatic epithelioid angiomyolipoma: Pitfall of misdiagnosis.

BACKGROUND: Hepatic epithelioid angiomyolipoma (HEAML) is a rare liver disease and is easily misdiagnosed. Enhanced recognition of HEAML is beneficial to the differential diagnosis of rare liver diseases. CASE SUMMARY: We presented two cases of HEAML in Changzheng Hospital, Naval Medical University, and then collected and analyzed all reports about HEAML recorded in PubMed, MEDLINE, China Science Periodical Database, and VIP database from January 2000 to March 2018. A total of 409 cases of HEAML in 97 reports were collected, with a ratio of men to women of 1:4.84 and an age range from 12 years to 80 years (median 44 years). Among the patients with clinical symptoms mentioned, 61.93% (205/331) were asymptomatic, 34.74% (115/331) showed upper or right upper quadrant abdomen discomfort, while a few of them showed abdominal mass, gastrointestinal symptoms, low fever, or weight loss. The misdiagnosis rate of HEAML was as high as 40.34% (165/409) due to its nonspecific imaging findings. Most of the tumors were solitary and round in morphology, with clear boundaries. Ultrasound scan indicated low echo with internal nonuniformity and rich blood supply in most cases. Computer tomography/magnetic resonance imaging enhanced scan showed varied characteristics. The ratio of fast wash-in and fast wash-out, fast wash-in and slow wash-out, and delayed enhancement was roughly 4:5:1. A definite diagnosis of HEAML depended on the pathological findings of the epithelioid cells in lesions and the expression of human melanoma black 45, smooth muscle actin, melanoma antigen, and actin by immunohistochemical staining. HEAML had a relatively low malignant rate of 3.91%. However, surgical resection was the main treatment for HEAML, due to the difficulty diagnosing before operation. CONCLUSION: HEAML is a rare and easily misdiagnosed disease, and it should be diagnosed carefully, taking into account clinical course, imaging, pathological ,and immunohistochemical findings.

Changes and Clinical Significance of Glucose,Insulin and Insulin-Like Growth Factor-1 in Serum of Newborn Infants with Hypixic-Ischemic Encephalopathy / 实用儿科临床杂志

Objective To observe the changes and relationships of glucose (Glu),insulin(Ins) ,insulin-like growth factor-1 (IGF-1) in newborn infant with hypixic-ischemic encephalopathy (HIE) and the protective role which Ins,IGF-1 worked on neuron.Methods HIE 50 cases,the quantity of IGF-1 in serum was detected by radioimmunoassay,the quantity of Ins in serum was detected by chemiluminescent immunoassay,the quantity of glucose in serum was detected by the instrument with type of Rokangquan TM accutive.The umbilical vein blood 2 mL were drawn from 20 cases of controls after birth.The indexes were detected by the same methods.Results 1.In acute period,the level of serum IGF-1 reduced obviously in newborn infants with HIE,and Glu increased obviously.There was a negative correlation between IGF-1 and Glu.The changes related with neonate complications.In rehabilited pe riod,the Glu get to normal,the IGF-1 was a little higher than that in acute period.but it still was lower than that of control group.There was negative correlation between IGF-1 and Glu.2.The level of Ins in serum of newborn infants with HIE had no obvious changes compairing with control group in acute period and rehabilited period.It had no relations with Glu,Ins.Conclusions It approves that IGF-1 and Glu in serum of newborn infants with HIE have obviously changed.This result will provide an academic base on treatment of HIE.