Development of a predictive nomogram for acute respiratory distress syndrome in patients with acute pancreatitis complicated with acute kidney injury.

BACKGROUND: Acute respiratory distress syndrome (ARDS) is a common complication in patients with acute pancreatitis (AP), especially when patients complicated with acute kidney injury (AKI), resulting in increased duration of hospitalization and mortality. It is of potential clinical significance to develop a predictive model to identify the the high-risk patients. METHOD: AP patients complicated with AKI from January 2019 to March 2022 were enrolled in this study and randomly divided into training cohort and validation cohort at a ratio of 2:1. The Least absolute shrinkage and selection operator(LASSO) regression and machine learning algorithms were applied to select features. A nomogram was developed based on the multivariate logistic regression. The performance of the nomogram was evaluated by AUC, calibration curves, and decision curve analysis. RESULTS: A total of 292 patients were enrolled in the study, with 206 in the training cohort and 86 in the validation cohort. Multivariate logistic analysis showed that IAP (Odds Ratio (OR)=4.60, 95%CI:1.23-18.24, p = 0.02), shock (OR = 12.99, 95%CI:3.47-64.04, p < 0.001), CRP(OR= 26.19, 95%CI:9.37-85.57, p < 0.001), LDH (OR = 13.13, 95%CI:4.76-40.42, p < 0.001) were independent predictors of ARDS. The nomogram was developed based on IAP, shock, CRP and LDH. The nomogram showed good discriminative ability with an AUC value of 0.954 and 0.995 in the training and validation cohort, respectively. The calibration curve indicating good concordance between the predicted and observed values. The DCA showed favorable net clinical benefit. CONCLUSION: This study developed a simple model for predicting ARDS in AP patients complicated with AKI. The nomogram can help clinicians identify high-risk patients and optimize therapeutic strategies.

Development and validation of a predictive model for acute kidney injury in patients with moderately severe and severe acute pancreatitis.

BACKGROUND: Acute kidney injury is a serious complication of moderately severe and severe acute pancreatitis, which significantly increases mortality. There are currently no reliable tools for early identification of AKI especially severe AKI in these patients. We aim to develop a predictive model so that physicians can assess the risk of AKI and severe AKI, thus take further preventive measures. METHODS: Patients with a diagnosis of MSAP and SAP admitted to our hospital from January 2018 to December 2021 were retrospectively included in the study. The participants were divided into the training and validation cohorts randomly, in a 2:1 ratio. A clinical signature was built based on reproducible features, using the least absolute shrinkage and selection operator method and machine learning. Multivariate logistic regression analysis was used to develop the prediction model. Nomogram performance was determined by its discrimination, calibration, and clinical usefulness. RESULTS: A total of 996 eligible patients were enrolled. 698 patients were allocated in the training cohort and 298 in the validation cohort. AKI occurred in 148 patients (21%) in the training cohort and 54 (18%) in the validation cohort, respectively. The clinical features, including C-reactive protein, intra-abdominal pressure and serum cysC, were significantly associated with AKI as well as severe AKI. The nomogram showed favorable discrimination, calibration and clinical usefulness. CONCLUSIONS: The novel risk score model has good performance for predicting AKI and severe AKI in MSAP and SAP patients. Application of this model can help clinicians stratify patients for primary prevention, surveillance and early therapeutic intervention to improve care and prognosis.

Lactate and blood ammonia on admission as biomarkers to predict the prognosis of patients with acute mushroom poisoning and liver failure: a retrospective study.

The diagnosis of liver damage induced by mushroom poisoning is still challenging. This study aims to screen the early biological indexes that could predict acute mushroom poisoning with liver damage. The patients with acute mushroom poisoning and liver damage admitted to The First Affiliated Hospital of Dalian Medical University，China from July 2007 to August 2017 were analyzed retrospectively. A total of 66 patients were enrolled in this study, with 44 and 22 patients in the liver injury group and liver failure group, respectively. Ten patients in the liver failure group died, with a mortality of 45.5% in this group. Multivariable Cox regression showed that the blood ammonia (NH3) and lactic acid (Lac) at the time of admission were independently associated with the in-hospital time to death for patients with liver failure induced by mushroom poisoning. Lactate and blood ammonia at the time of admission could be used to predict the prognosis of patients with acute mushroom poisoning and liver failure.

Diagnostic accuracy of tablet-based software for the detection of concussion.

Despite the high prevalence of traumatic brain injuries (TBI), there are few rapid and straightforward tests to improve its assessment. To this end, we developed a tablet-based software battery ("BrainCheck") for concussion detection that is well suited to sports, emergency department, and clinical settings. This article is a study of the diagnostic accuracy of BrainCheck. We administered BrainCheck to 30 TBI patients and 30 pain-matched controls at a hospital Emergency Department (ED), and 538 healthy individuals at 10 control test sites. We compared the results of the tablet-based assessment against physician diagnoses derived from brain scans, clinical examination, and the SCAT3 test, a traditional measure of TBI. We found consistent distributions of normative data and high test-retest reliability. Based on these assessments, we defined a composite score that distinguishes TBI from non-TBI individuals with high sensitivity (83%) and specificity (87%). We conclude that our testing application provides a rapid, portable testing method for TBI.

G2019S LRRK2 and aging confer susceptibility to proteasome inhibitor-induced neurotoxicity in nigrostriatal dopaminergic system.

The leucine-rich repeat kinase 2 (LRRK2) mutation G2019S is one of the most common genetic causes in Parkinson's disease (PD). The penetrance of G2019S LRRK2 is incomplete and is age-dependent, therefore, it has been speculated that environmental toxins and aging could contribute to G2019S LRRK2-related PD pathogenesis. To prove this speculation, we performed a longitudinal investigation in mice bearing G2019S LRRK2 mutation. BAC G2019S LRRK2 transgenic (Tg) mice and their wildtype (Wt) littermates were treated with lactacystin, a specific proteasome inhibitor. The susceptibilities of mice to lactacystin-induced nigrostriatal dopaminergic (DAergic) degeneration were evaluated, at 5 and 12 months of age. We found that lactacystin treatment caused a greater decline of striatal DA content in the Tg mice at either 5 or 12 months of age than their age-matched Wt littermates. Moreover, the lactacystin-treated Tg or Wt mice at 12 months of age lose much more nigral tyrosine hydroxylase (TH)-positive neurons than the mice at 5 months of age, indicating an age-associated DAergic neurotoxicity. Additionally, stereotactic injection of lactacystin induced a dramatic increase of activated microglia in substantia nigra of mice at 12 months of age, compared with mice at 5 months of age. In summary, our study suggests that expression of the G2019S mutation in the mouse LRRK2 gene confers an age-associated high susceptibility to proteasome inhibition-induced nigrostriatal DAergic degeneration.