Pulmonary function, genetic predisposition, and the risk of cirrhosis: A prospective cohort study.

OBJECTIVE: Pulmonary function is associated with the development of chronic liver disease. However, evidence of the association between pulmonary function and cirrhosis risk is still lacking. This study aimed to investigate the longitudinal associations of pulmonary function with the development of cirrhosis, and to explore whether genetic predisposition to cirrhosis could modify these associations. METHODS: Of 294,835 participants free of cirrhosis and had undergone spirometry at baseline from the UK Biobank were included. Cirrhosis diagnoses were ascertained through linked hospital records and death registries. Cox proportional hazard models were employed to investigate the longitudinal associations between pulmonary function, genetic predisposition, and cirrhosis risk. RESULTS: During a median follow-up of 12.0 years, 2598 incident cirrhosis cases were documented. Compared to individuals with normal spirometry findings, those with preserved ratio impaired spirometry (PRISm) findings (hazard ratio [HR] and 95% confidence interval [CI]: 1.32 [1.18, 1.48]) and airflow obstruction (HR [95%CI]: 1.19 [1.07, 1.31]) had a higher risk of developing cirrhosis after adjustments. These associations were consistent across all categories of genetic predisposition, with no observed modifying effect of genetic predisposition. In joint exposure analyses, the highest risk was observed in individuals with both a high genetic predisposition for cirrhosis and PRISm findings (HR [95% CI]: 1.74 [1.45, 2.08]). CONCLUSIONS: Our findings indicate that worse pulmonary function is a significant risk factor of cirrhosis, irrespective of genetic predisposition. Early identification and appropriate intervention for pulmonary function may lead to more effective healthcare resource utilization and reduce the burden associated with cirrhosis.

Perioperative administration of methylprednisolone was associated with postoperative pulmonary complications in elderly patients undergoing hip fracture surgery.

BACKGROUND: Postoperative pulmonary complications (PPCs) seriously affect the postoperative prognosis of elderly patients underwent hip fracture surgery. Although methylprednisolone is increasingly used, the association between perioperative methylprednisolone and PPCs is still controversial. The study aims to determine whether perioperative administration of methylprednisolone is associated with PPCs in elderly patients during hip fracture surgery. PATIENTS AND METHODS: In this retrospective cohort study, records of 584 patients (≥ 65 years) who underwent hip fracture surgery between January 2013 and October 2020 were extracted. Univariate and multivariate regression analysis were performed to identify the risk factors for PPCs. To further explore the association between administration of methylprednisolone and PPCs, 53 patients received methylprednisolone and 53 patients without methylprednisolone were matched for the confounding factors using propensity score matching (PSM) analysis. The odds ratios (OR) and 95% confidence intervals (CI) for the above variables were analyzed. RESULTS: The incidence of PPCs during postoperative hospitalization was 6.83% (38/556) among the elderly patients following hip fracture surgery. Patients with PPCs had higher postoperative mortality rate, longer hospital stay, more hospitalization cost, and higher incidence of cardiac arrest (all P < 0.05). Multivariate logistic regression analysis showed that age, hypertension, hypoglycemia, hypoproteinemia and perioperative methylprednisolone were independent risk factors for PPCs. Moreover, administration of methylprednisolone was significantly correlated with PPCs both before PSM adjustment (OR = 3.25; 95% CI, 1.67 to 6.33; P = 0.001) and after PSM adjustment (OR = 6.68; 95% CI, 1.40 to 31.82; P = 0.017). CONCLUSION: Perioperative administration of methylprednisolone is a risk factor for PPCs in elderly patients undergoing hip fracture surgery.

Development and validation of a practical machine learning model to predict sepsis after liver transplantation.

BACKGROUND: Postoperative sepsis is one of the main causes of mortality after liver transplantation (LT). Our study aimed to develop and validate a predictive model for postoperative sepsis within 7 d in LT recipients using machine learning (ML) technology. METHODS: Data of 786 patients received LT from January 2015 to January 2020 was retrospectively extracted from the big data platform of Third Affiliated Hospital of Sun Yat-sen University. Seven ML models were developed to predict postoperative sepsis. The area under the receiver-operating curve (AUC), sensitivity, specificity, accuracy, and f1-score were evaluated as the model performances. The model with the best performance was validated in an independent dataset involving 118 adult LT cases from February 2020 to April 2021. The postoperative sepsis-associated outcomes were also explored in the study. RESULTS: After excluding 109 patients according to the exclusion criteria, 677 patients underwent LT were finally included in the analysis. Among them, 216 (31.9%) were diagnosed with sepsis after LT, which were related to more perioperative complications, increased postoperative hospital stay and mortality after LT (all p < .05). Our results revealed that a larger volume of red blood cell infusion, ascitic removal, blood loss and gastric drainage, less volume of crystalloid infusion and urine, longer anesthesia time, higher level of preoperative TBIL were the top 8 important variables contributing to the prediction of post-LT sepsis. The Random Forest Classifier (RF) model showed the best overall performance to predict sepsis after LT among the seven ML models developed in the study, with an AUC of 0.731, an accuracy of 71.6%, the sensitivity of 62.1%, and specificity of 76.1% in the internal validation set, and a comparable AUC of 0.755 in the external validation set. CONCLUSIONS: Our study enrolled eight pre- and intra-operative variables to develop an RF-based predictive model of post-LT sepsis to assist clinical decision-making procedure.

Postoperative sepsis is one of the main causes of mortality after liver transplantation (LT).Our results revealed that a larger volume of red blood cell infusion, ascitic removal, blood loss and gastric drainage, less volume of crystalloid infusion and urine, longer anesthesia time, higher level of preoperative TBIL were the top 8 important variables contributing to the prediction of post-LT sepsis.The Random Forest Classifier (RF) model showed the best overall performance to predict sepsis after LT in our study, which could assist in the clinical decision-making procedure.