A Prediction Nomogram for Acute Kidney Injury in Very-Low-Birth-Weight Infants: A Retrospective Study

Prediction of postoperative residual primary ovarian neoplasm or metastatic lesion close to rectum of serous ovarian carcinoma based on clinical and MR T1-DEI features

BACKGROUND

The optimal primary debulking surgery outcome of serous ovarian carcinoma (SOC) is greatly affected by primary ovarian neoplasm or metastatic lesion close to the rectum.

PURPOSE

To study the risk factors affecting postoperative residual primary ovarian neoplasm or metastatic lesion close to the rectum of SOC.

MATERIAL AND METHODS

The clinical and MRI data of 164 patients with SOC eligible from institution A (training and test groups) and 36 patients with SOC eligible from institution B (external validation group) were collected and retrospectively analyzed. The clinical data included age, serum carbohydrate antigen 125 (CA-125), human epididymis protein 4, and neutrophil-to-lymphocyte ratio (NLR). Magnetic resonance imaging (MRI) data included ovarian mass distribution, maximum diameter of ovarian mass, ovarian mass features, degree of rectal invasion of the primary ovarian neoplasm or metastatic lesion, and amount of ascites. A model was established using multivariate logistic regression.

RESULTS

By univariate and multivariate logistic regressions, CA-125 (P = 0.024, odds ratio [OR] = 3.798, 95% confidence interval [CI] = 1.24-13.32), NLR (P = 0.037, OR = 3.543, 95% CI = 1.13-12.72), and degree of rectal invasion of the primary ovarian neoplasm or metastatic lesion (P < 0.001, OR = 37.723, 95% CI = 7.46-266.88) were screened as independent predictors. The area under the curve values of the model in the training, test, and external validation groups were 0.860, 0.764, and 0.778, respectively.

CONCLUSION

The clinical-radiological model based on T1-weighted dual-echo MRI can be used non-invasively to predict postoperative residual ovarian neoplasm or metastasis close to SOC in the rectum.

Circulating miR-129-3p in combination with clinical factors predicts vascular calcification in hemodialysis patients

Background

Vascular calcification (VC) commonly occurs and seriously increases the risk of cardiovascular events and mortality in patients with hemodialysis. For optimizing individual management, we will develop a diagnostic multivariable prediction model for evaluating the probability of VC.

Methods

The study was conducted in four steps. First, identification of miRNAs regulating osteogenic differentiation of vascular smooth muscle cells (VSMCs) in calcified condition. Second, observing the role of miR-129-3p on VC in vitro and the association between circulating miR-129-3p and VC in hemodialysis patients. Third, collecting all indicators related to VC as candidate variables, screening predictors from the candidate variables by Lasso regression, developing the prediction model by logistic regression and showing it as a nomogram in training cohort. Last, verifying predictive performance of the model in validation cohort.

Results

In cell experiments, miR-129-3p was found to attenuate vascular calcification, and in human, serum miR-129-3p exhibited a negative correlation with vascular calcification, suggesting that miR-129-3p could be one of the candidate predictor variables. Regression analysis demonstrated that miR-129-3p, age, dialysis duration and smoking were valid factors to establish the prediction model and nomogram for VC. The area under receiver operating characteristic curve of the model was 0.8698. The calibration curve showed that predicted probability of the model was in good agreement with actual probability and decision curve analysis indicated better net benefit of the model. Furthermore, internal validation through bootstrap process and external validation by another independent cohort confirmed the stability of the model.

Conclusion

We build a diagnostic prediction model and present it as an intuitive tool based on miR-129-3p and clinical indicators to evaluate the probability of VC in hemodialysis patients, facilitating risk stratification and effective decision, which may be of great importance for reducing the risk of serious cardiovascular events.

Inflammatory response biomarkers nomogram for predicting pneumonia in patients with spontaneous intracerebral hemorrhage

Objectives

Inflammatory response biomarkers are promising prognostic factors to improve the prognosis of stroke-associated pneumonia (SAP) after ischemic stroke. This study aimed to investigate the prognostic significance of inflammatory response biomarkers on admission in SAP after spontaneous intracerebral hemorrhage (SICH) and establish a corresponding nomogram.

Methods

The data of 378 patients with SICH receiving conservative treatment from January 2019 to December 2021 at Taizhou People's Hospital were selected. All eligible patients were randomized into the training (70%, 265) and validation cohorts (30%, 113). In the training cohort, multivariate logistic regression analysis was used to establish an optimal nomogram, including inflammatory response biomarkers and clinical risk factors. The area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve analysis (DCA) were used to evaluate the nomogram's discrimination, calibration, and performance, respectively. Moreover, this model was further validated in a validation cohort.

Results

A logistic regression analysis showed that intraventricular hemorrhage (IVH), hypertension, dysphagia, Glasgow Coma Scale (GCS), National Institute of Health Stroke Scale (NIHSS), systemic inflammation response index (SIRI), and platelet/lymphocyte ratio (PLR) were correlated with SAP after SICH (P < 0.05). The nomogram was composed of all these statistically significant factors. The inflammatory marker-based nomogram showed strong prognostic power compared with the conventional factors, with an AUC of 0.886 (95% CI: 0.841-0.921) and 0.848 (95% CI: 0.799-0.899). The calibration curves demonstrated good homogeneity between the predicted risks and the observed outcomes. In addition, the model has a significant net benefit for SAP, according to DCA. Also, internal validation demonstrated the reliability of the prediction nomogram. The length of hospital stay was shorter in the non-SAP group than in the SAP group. At the 3-month follow-up, clinical outcomes were worse in the SAP group (P < 0.001).

Conclusion

SIRI and PLR at admission can be utilized as prognostic inflammatory biomarkers in patients with SICH in the upper brain treated with SAP. A nomogram covering SIRI and PLR can more accurately predict SAP in patients' supratentorial SICH. SAP can influence the length of hospital stay and the clinical outcome.

Risk factors of acute kidney injury in very low birth weight infants in a tertiary neonatal intensive care unit

OBJECTIVE

Acute kidney injury (AKI) in the neonatal period is associated with worst outcomes as increased mortality and increased length of hospital stay. Very low birth weight (VLBW) newborns are at higher risk for developing several other conditions that are associated with worst outcomes. Understanding the risk factors for AKI may help to prevent this condition and improve neonatal care for this population.

METHODS

This retrospective cohort study included 155 very low birth weight newborns admitted between 2015 and 2017. The authors compared the newborns who developed neonatal AKI with the non-AKI group and analyzed the main risk factors for developing AKI in the population. The authors also performed an analysis of the main outcomes defined as the duration of mechanical ventilation, length of stay, and death.

RESULTS

From the cohort, a total of 61 (39.4%) patients had AKI. The main risk factors associated with Neonatal AKI were necrotizing enterocolitis (aOR 7.61 [1.69 - 34.37]; p = 0.008), neonatal sepsis (aOR 2.91 [1.17 - 7.24], p = 0.021), and hemodynamic instability (aOR 2.99 [1.35 - 6.64]; p = 0.007). Neonatal AKI was also associated with an increase in the duration of mechanical ventilation in 9.4 days (p = 0.026) and in an increase in mortality 4 times (p = 0.009), after adjusting for the other variables.

CONCLUSION

The present results highlight the importance of minimizing sepsis and necrotizing enterocolitis, as well as the importance of identifying hemodynamic instability, to prevent AKI and diminish the burden of morbimortality in VLBW newborns.

A prediction nomogram for neonatal acute respiratory distress syndrome in late-preterm infants and full-term infants: A retrospective study

Background

Neonatal acute respiratory distress syndrome (ARDS) is a critical clinical disease with high disability and mortality rates. Early identification and treatment of neonatal ARDS is critical. This study aimed to build a perinatal prediction nomogram for early prediction of neonatal ARDS.

Methods

A prediction model was built including 243 late-preterm and full-term infants from Daping Hospital in Chongqing, China, hospitalised between Jan 1, 2018 and Dec 31, 2019. 80 patients from the Children's Hospital in Chongqing, China, hospitalised between Jan 1, 2018 and June 30, 2018 were considered for external validation. Multivariate logistic regression was performed to identify independent predictors and establish a nomogram to predict the occurrence of neonatal ARDS. Both discrimination and calibration were assessed by bootstrapping with 1000 resamples.

Findings

Multivariate logistic regression demonstrated that mother's education level (odds ratio [OR] 0·478, 95% confidence interval [CI] 0·324-0·704), premature rupture of membrane (OR 0·296, 95% CI 0·133-0·655), infectious disease within 7 days before delivery (OR 0·275, 95% CI 0·083-0·909), hospital level (OR 2·479, 95% CI 1·260-4·877), and Apgar 5-min score (OR 0·717, 95% CI 0·563-0·913) were independent predictors for neonatal ARDS in late-preterm and full-term infants, who experienced dyspnoea within 24 h after birth and required mechanical ventilation. The area under the curve and concordance index of the nomogram constructed from the above five factors were 0·760 and 0·757, respectively. The Hosmer-Lemeshow test showed that the model was a good fit (P = 0.320). The calibration curve of the nomogram was close to the ideal diagonal line. Furthermore, the decision curve analysis demonstrated significantly better net benefit in the model. The external validation proved the reliability of the prediction nomogram.

Interpretation

A nomogram based on perinatal factors was developed to predict the occurrence of neonatal ARDS in late-preterm and full-term infants who experienced dyspnoea within 24 h after birth and required mechanical ventilation. It provided clinicians with an accurate and effective tool for the early prediction and timely management of neonatal ARDS.

Funding

No funding was associated with this study.

Human Stem Cell and Organoid Models to Advance Acute Kidney Injury Diagnostics and Therapeutics

Acute kidney injury (AKI) is an increasingly common problem afflicting all ages, occurring in over 20% of non-critically ill hospitalized patients and >30% of children and >50% of adults in critical care units. AKI is associated with serious short-term and long-term consequences, and current therapeutic options are unsatisfactory. Large gaps remain in our understanding of human AKI pathobiology, which have hindered the discovery of novel diagnostics and therapeutics. Although animal models of AKI have been extensively studied, these differ significantly from human AKI in terms of molecular and cellular responses. In addition, animal models suffer from interspecies differences, high costs and ethical considerations. Static two-dimensional cell culture models of AKI also have limited utility since they have focused almost exclusively on hypoxic or cytotoxic injury to proximal tubules alone. An optimal AKI model would encompass several of the diverse specific cell types in the kidney that could be targets of injury. Second, it would resemble the human physiological milieu as closely as possible. Third, it would yield sensitive and measurable readouts that are directly applicable to the human condition. In this regard, the past two decades have seen a dramatic shift towards newer personalized human-based models to study human AKI. In this review, we provide recent developments using human stem cells, organoids, and in silico approaches to advance personalized AKI diagnostics and therapeutics.