Developing and validating a model for predicting 7-day mortality of patients admitted from the emergency department: an initial alarm score by a prospective prediction model study

Mortality after major bleeding in Asian atrial fibrillation patients receiving different direct oral anticoagulants: a nationwide, propensity score study

In this research, we assessed mortality after major bleeding events in atrial fibrillation (AF) patients taking four direct oral anticoagulants (DOACs). Drawing data from the Taiwan National Health Insurance Research Database between 2016 and 2019, we focused on AF patients on DOACs who had major bleeding episodes. Using propensity score stabilized weighting, we established four comparable pseudo-DOAC groups. Among 2770 patients (460 dabigatran, 1322 rivaroxaban, 548 apixaban, 440 edoxaban), 85.3% were prescribed low-dose regimens. The 7-day mortality rate was 9.0%, surging to 16.0% by the 30th day. Compared with dabigatran, there was a distinct divergence in 7-day mortality of factor Xa inhibitors (p = 0.012), with hazard ratios of 1.83 (95% CI 1.11-3.00, p = 0.017) for rivaroxaban, 2.13 (95% CI 1.23-3.66, p = 0.007) for apixaban, and 2.41 (95% CI 1.39-4.19, p = 0.002) for edoxaban. This pattern remained consistent when analyzing the subgroup that received lower dosages of DOACs. In conclusion, factor Xa inhibitors were associated with a significantly higher risk of 7-day mortality following major bleeding events than dabigatran among AF patients.

A nomogram for predicting hospital mortality of critical ill patients with sepsis and cancer: a retrospective cohort study based on MIMIC-IV and eICU-CRD

OBJECTIVE

Sepsis remains a high cause of death, particularly in immunocompromised patients with cancer. The study was to develop a model to predict hospital mortality of septic patients with cancer in intensive care unit (ICU).

DESIGN

Retrospective observational study.

SETTING

Medical Information Mart for Intensive Care IV (MIMIC IV) and eICU Collaborative Research Database (eICU-CRD).

PARTICIPANTS

A total of 3796 patients in MIMIC IV and 549 patients in eICU-CRD were included.

PRIMARY OUTCOME MEASURES

The model was developed based on MIMIC IV. The internal validation and external validation were based on MIMIC IV and eICU-CRD, respectively. Candidate factors were processed with the least absolute shrinkage and selection operator regression and cross-validation. Hospital mortality was predicted by the multivariable logistical regression and visualised by the nomogram. The model was assessed by the area under the curve (AUC), calibration curve and decision curve analysis curve.

RESULTS

The model exhibited favourable discrimination (AUC: 0.726 (95% CI: 0.709 to 0.744) and 0.756 (95% CI: 0.712 to 0.801)) in the internal and external validation sets, respectively, and better calibration capacity than Acute Physiology and Chronic Health Evaluation IV in external validation.

CONCLUSIONS

Despite that the predicted model was based on a retrospective study, it may also be helpful to predict the hospital morality of patients with solid cancer and sepsis.

Prognostic tools for elderly patients with sepsis: in search of new predictive models

As a tool to support clinical decision-making, Mortality Prediction Models (MPM) can help clinicians stratify and predict patient risk. There are numerous scoring systems for patients with sepsis that predict sepsis-related mortality and the severity of sepsis. But there are currently no MPMs for adults with sepsis who meet the criteria of "good." Clinicians are unlikely to use complex MPMs that require extensive or expensive data collection to impede workflow. Machine learning applied to minimal medical records of patients diagnosed with sepsis can be a useful tool. Progress is needed in the development and validation of clinical decision support tools that can assist in patient risk stratification, prognosis, discussion of patient outcomes, and shared decision making.