Quantifying the Effects of Prior Acetyl-Salicylic Acid on Sepsis-Related Deaths: An Individual Patient Data Meta-Analysis Using Propensity Matching.

OBJECTIVE: The primary objective was to conduct a meta-analysis on published observational cohort data describing the association between acetyl-salicylic acid (aspirin) use prior to the onset of sepsis and mortality in hospitalized patients. STUDY SELECTION: Studies that reported mortality in patients on aspirin with sepsis with a comparison group of patients with sepsis not on prior aspirin therapy were included. DATA SOURCES: Fifteen studies described hospital-based cohorts (n = 17,065), whereas one was a large insurance-based database (n = 683,421). Individual-level patient data were incorporated from all selected studies. DATA EXTRACTION: Propensity analyses with 1:1 propensity score matching at the study level were performed, using the most consistently available covariates judged to be associated with aspirin. Meta-analyses were performed to estimate the pooled average treatment effect of aspirin on sepsis-related mortality. DATA SYNTHESIS: Use of aspirin was associated with a 7% (95% CI, 2-12%; p = 0.005) reduction in the risk of death as shown by meta-analysis with considerable statistical heterogeneity (I = 61.6%). CONCLUSIONS: These results are consistent with effects ranging from a 2% to 12% reduction in mortality risk in patients taking aspirin prior to sepsis onset. This association anticipates results of definitive studies of the use of low-dose aspirin as a strategy for reduction of deaths in patients with sepsis.

Increased risk of urinary calculi in patients with migraine: a nationwide cohort study.

OBJECTIVE: Whether migraine is associated with urinary calculi is an unresolved issue, although topiramate, a migraine-preventive agent, is known to contribute to this complication. This study investigates the association between migraine and the risk of urinary calculi. METHODS: We identified a total of 147,399 patients aged ≥18 years with migraine diagnoses recorded in the Taiwan National Health Insurance Research Database between 2005 and 2009. Each patient was randomly matched with one individual without headache using propensity scores. All participants were followed from the date of enrollment until urinary calculi development, death, or the end of 2010. RESULTS: The risk of urinary calculi was greater in the migraine than the control cohort (adjusted hazard ratio (aHR), 1.58; 95% confidence interval (CI), 1.52-1.63; p < 0.001, irrespective of the influence of topiramate. The risk was higher in younger and female patients. The magnitude of the risk was proportional to the annual frequency of clinic visits for headache (≥6 vs. <3, aHR = 1.11; 95% CI, 1.04-1.17; p = 0.002), but did not differ between migraine patients with and without aura. CONCLUSIONS: Our study showed migraine was associated with an increased risk of urinary calculi, independent of topiramate use. A higher frequency of clinic visits was associated with a greater risk.

A bagging approach for improved predictive accuracy of intradialytic hypotension during hemodialysis treatment.

The primary objective of this study is to enhance the prediction accuracy of intradialytic hypotension in patients undergoing hemodialysis. A significant challenge in this context arises from the nature of the data derived from the monitoring devices and exhibits an extreme class imbalance problem. Traditional predictive models often display a bias towards the majority class, compromising the accuracy of minority class predictions. Therefore, we introduce a method called UnderXGBoost. This novel methodology combines the under-sampling, bagging, and XGBoost techniques to balance the dataset and improve predictive accuracy for the minority class. This method is characterized by its straightforward implementation and training efficiency. Empirical validation in a real-world dataset confirms the superior performance of UnderXGBoost compared to existing models in predicting intradialytic hypotension. Furthermore, our approach demonstrates versatility, allowing XGBoost to be substituted with other classifiers and still producing promising results. Sensitivity analysis was performed to assess the model's robustness, reinforce its reliability, and indicate its applicability to a broader range of medical scenarios facing similar challenges of data imbalance. Our model aims to enable medical professionals to provide preemptive treatments more effectively, thereby improving patient care and prognosis. This study contributes a novel and effective solution to a critical issue in medical prediction, thus broadening the application spectrum of predictive modeling in the healthcare domain.

Model for Predicting Complications of Hemodialysis Patients Using Data From the Internet of Medical Things and Electronic Medical Records.

Intelligent models for predicting hemodialysis-related complications, i.e., hypotension and the deterioration of the quality or obstruction of the AV fistula, based on machine learning (ML) methods were established to offer early warnings to medical staff and give them enough time to provide pre-emptive treatment. A novel integration platform collected data from the Internet of Medical Things (IoMT) at a dialysis center and inspection results from electronic medical records (EMR) to train ML algorithms and build models. The selection of the feature parameters was implemented using Pearson's correlation method. Then, the eXtreme Gradient Boost (XGBoost) algorithm was chosen to create the predictive models and optimize the feature choice. 75% of collected data are used as a training dataset and the other 25% are used as a testing dataset. We adopted the prediction precision and recall rate of hypotension and AV fistula obstruction to measure the effectiveness of the predictive models. These rates were sufficiently high at approximately 71%-90%. In the context of hemodialysis, hypotension and the deterioration of the quality or obstruction of the arteriovenous (AV) fistula affect treatment quality and patient safety and may lead to a poor prognosis. Our prediction models with high accuracies can provide excellent references and signals for clinical healthcare service providers. Clinical and Translational Impact Statement-With the integrated dataset collected from IoMT and EMR, the superior predictive results of our models for complications of hemodialysis patients are demonstrated. We believe, after enough clinical tests are implemented as planned, these models can assist the healthcare team in making appropriate preparations in advance or adjusting the medical procedures to avoid these adverseevents.

Angiotensin receptor blockers are associated with lower mortality than ACE inhibitors in predialytic stage 5 chronic kidney disease: A nationwide study of therapy with renin-angiotensin system blockade.

Dual renin angiotensin system (RAS) blockade using angiotensin-receptor blockers (ARBs) in combination with angiotensin converting enzyme inhibitors (ACEIs) is reported to improve proteinuria in both diabetic and non-diabetic patients. However, its renoprotective effect and safety remain uncertain in patients with advanced chronic kidney disease (CKD). From January 1, 2000 through June 30, 2009, we enrolled 14,117 pre-dialytic stage 5 CKD patients with serum creatinine >6mg/dL and hematocrit <28% under the treatment with erythropoiesis stimulating agents and RAS blockade. We used Cox proportional hazards regression models to estimate the hazard ratios (HRs) against the commencement of long-term dialysis and all-cause mortality for ACEI/ARB users. Over a median follow-up of 7 months, 9,867 patients (69.9%) required long-term dialysis and 2,805 (19.9%) died before progression to end-stage renal disease requiring dialysis. In comparison with the ARB-only users, dual blockade with ACEIs and ARBs was associated with a significantly higher risk of (1) death in all CKD patients (HR = 1.49, [95%CI, 1.30-1.71]; P = 0.02) and in diabetic subgroup (HR = 1.58, [95%CI, 1.34-1.86]; P = 0.02); (2) composite endpoint of long-term dialysis or death in diabetic subgroup (HR = 1.10, [95%CI, 1.01-1.20]; P = 0.04); (3) hyperkalemia-associated hospitalization in non-diabetic subgroup (HR, 2.74, [95%CI, 1.05-7.15]; P = 0.04). However, ACEIs users were associated with higher mortality than ARBs users in all CKD patients (HR = 1.17, [95%CI, 1.07-1.27]; P = 0.03) and in diabetic subgroup (HR = 1.32, [95%CI, 1.18-1.48]; P = 0.03). Monotherapy of RAS blockade, especially ARB, is more effective and safer than dual RAS blockade in pre-dialytic stage 5 CKD patients.

Association of prior antiplatelet agents with mortality in sepsis patients: a nationwide population-based cohort study.

BACKGROUND: Antiplatelet agents are widely used for cardiovascular disea ses, but their pleiotropic effects in sepsis are controversial. OBJECTIVE: To investigate the association between antiplatelet agents and the survival benefit for sepsis patients. DESIGN: A nationwide population-based cohort and nested case-control study. SETTING: Taiwan National Health Insurance database. PARTICIPANTS: All patients (age ≥18 years) who were hospitalized for sepsis between January 2000 and December 2010. MEASUREMENTS: Conditional logistic regression was used to adjust for confounding. Adjusted odd ratios (ORs) were used to compare the mortality rate due to sepsis in antiplatelet drug users and nonusers. RESULTS: Of 683,421 included patients, 229,792 (33.6 %) patients died during hospitalization for sepsis, and the rest (64.4 %) survived to discharge. Use of antiplatelet agents before admission was associated with a lower risk of mortality in sepsis patients (aOR 0.82, 95 % confidence interval [CI] 0.81-0.83, P < 0.001). By using another case-control study design, the beneficial effect was more significant in current users (aOR 0.78, 95 % CI 0.76-0.79) than in recent users (aOR 0.88, 95 % CI 0.85-0.91), but was not significant in past users (aOR 1.00, 95 % CI 0.98-1.02). LIMITATIONS: Observational study. CONCLUSIONS: Prior use of antiplatelet agents was associated with a survival benefit in sepsis patients.