Establishment of a nomogram model for predicting adverse outcomes in advanced-age pregnant women with preterm preeclampsia

Predicting intensive care need in women with preeclampsia using machine learning - a pilot study

BACKGROUND

Predicting severe preeclampsia with need for intensive care is challenging. To better predict high-risk pregnancies to prevent adverse outcomes such as eclampsia is still an unmet need worldwide. In this study we aimed to develop a prediction model for severe outcomes using routine biomarkers and clinical characteristics.

METHODS

We used machine learning models based on data from an intensive care cohort with severe preeclampsia (n=41) and a cohort of preeclampsia controls (n=40) with the objective to find patterns for severe disease not detectable with traditional logistic regression models.

RESULTS

The best model was generated by including the laboratory parameters aspartate aminotransferase (ASAT), uric acid and body mass index (BMI) with a cross-validation accuracy of 0.88 and an area under the curve (AUC) of 0.91. Our model was internally validated on a test-set where the accuracy was lower, 0.82, with an AUC of 0.85.

CONCLUSION

The clinical routine blood parameters ASAT and uric acid as well as BMI, were the parameters most indicative of severe disease. Aspartate aminotransferase reflects liver involvement, uric acid might be involved in several steps of the pathophysiologic process of preeclampsia, and obesity is a well-known risk factor for development of both severe and non-severe preeclampsia likely involving inflammatory pathways..[Figure: see text].

Chromosome 1p36 candidate gene ZNF436 predicts the prognosis of neuroblastoma: a bioinformatic analysis

BACKGROUND

Genetic 1p deletion is reported in 30% of all neuroblastoma and is associated with the unfavorable prognosis of neuroblastoma. The expressions and prognosis of 1p candidate genes in neuroblastoma are unclear.

METHODS

Public neuroblastoma cohorts were obtained for secondary analysis. The prognosis of 1p candidate genes in neuroblastoma was determined using Kaplan-Meier and cox regression analysis. The prediction of the nomogram model was determined using timeROC.

RESULTS

First, we confirmed the bad prognosis of 1p deletion in neuroblastoma. Moreover, zinc finger protein 436 (ZNF436) located at 1p36 region was down-regulated in 1p deleted neuroblastoma and higher ZNF436 expression was associated with the longer event free survival and overall survival of neuroblastoma. The expression levels of ZNF436 were lower in neuroblastoma patients with MYCN amplification or age at diagnosis ≥ 18months, or with stage 4 neuroblastoma. ZNF436 had robust predictive values of MYCN amplification and overall survival of neuroblastoma. Furthermore, the prognostic significance of ZNF436 in neuroblastoma was independent of MYCN amplification and age of diagnosis. Combinations of ZNF436 with MYCN amplification or age of diagnosis achieved better prognosis. At last, we constructed a nomogram risk model based on age, MYCN amplification and ZNF436. The nomogram model could predict the overall survival of neuroblastoma with high specificity and sensitivity.

CONCLUSIONS

Chromosome 1p36 candidate gene ZNF436 was a prognostic maker of neuroblastoma.

Analysis of the Nursing Effect of Anesthesia Care Integration Combined with Preventive Nursing on Older Patients with Lumbar Disc Herniation During the Perioperative Period

Objective

This study explored the nursing effect of anesthesia care integration combined with preventive nursing on older patients with perioperative lumbar disc herniation (LDH).

Methods

Clinical data of 100 older patients with LDH who were admitted to our hospital between May 2017 and May 2022 were used, and there were no patients who had not had surgery between January and May 2020 because of the COVID-19 pandemic. Based on the different nursing methods, the patients were divided into control and observation groups, with 50 cases each. The control group received anesthesia care integration, whereas the observation group received anesthesia care integration combined with preventive nursing. Lumbar spine function, pain score, anesthesia recovery assessment, and nursing effects were compared between the two groups.

Results

The scores of the anesthesia recovery assessment of the two groups were compared, and the vital signs of the observation group during recovery from anesthesia were significantly better than those of the control group (P<0.05). After nursing care, the Japanese Orthopaedic Association (JOA) score of the observation group was significantly higher than that of the control group; however, the numerical scale (NRS) score of the observation group was significantly lower than that of the control group (P<0.05). After nursing care, the physical comfort, emotional state, psychological support, self-care ability, and pain scores were higher in the observation group than in the control group; however, the NRS score of the observation group was significantly lower than that of the control group (P<0.05).

Conclusion

Anesthesia care integration combined with preventive nursing has a positive effect on older patients with perioperative LDH, and it significantly improves lumbar spine function, reduces pain, shortens recovery time, and benefits physical and mental health.