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Faecal contamination of sacral pressure ulcers occurs frequently, so, theoretically, faecal diversion using colostomies is a useful procedure. We retrospectively analysed the data of adult patients for whom colostomies were created to enhance wound healing and compared patients with sacral pressure ulcers who received colostomies and those who did not during the same period. Patients' characteristics analysed included age, gender, comorbidities, WBC count, serum CRP level and microbial profile (before creating colostomy). Additionally, we examined whether the wound was closed, the recurrence rate after wound closure, and mortality outcomes. Regression analysis indicated that colostomy creation was associated with fewer species of gut microbiota cultured and lower rates of wound dehiscence after closure; no association was found between colostomy and mortality. Colostomies help promote wound healing of sacral pressure ulcers after closure by eradicating wound infection, and do not increase patients' mortality rates.
Unplanned pneumonia readmissions increase patient morbidity, mortality and healthcare costs. Among pneumonia patients, the middle-aged and elderly (≥45 years old) have a significantly higher risk of readmission compared with the young. Given that the 14-day readmission rate is considered a healthcare quality indicator, this study is the first to develop survival machine learning (ML) models using emergency department (ED) data to predict 14-day readmission risk following pneumonia-related admissions.
A retrospective multicentre cohort study.
This study used the Taipei Medical University Clinical Research Database, including data from patients at three affiliated hospitals.
11 989 hospital admissions for pneumonia among patients aged ≥45 years admitted from 2014 to 2021.
The dataset was randomly split into training (80%), validation (10%) and independent test (10%) sets. Input features included demographics, comorbidities, clinical events, vital signs, laboratory results and medical interventions. Four survival ML models—CoxNet, Survival Tree, Gradient Boosting Survival Analysis and Random Survival Forest—were developed and compared on the validation set. The best performance model was tested on the independent test set.
The RSF model outperformed the other models. Validation on an independent test set confirmed the model’s robustness (C-index=0.710; AUC=0.693). The most important predictive features included creatinine levels, age, haematocrit levels, Charlson Comorbidity Index scores, and haemoglobin levels, with their predictive value changing over time.
The RSF model effectively predicts 14-day readmission risk among pneumonia patients. The ED data-based model allows clinicians to estimate readmission risk before ward admission or discharge from the ED, enabling timely interventions. Accurately predicting short-term readmission risk might also further support physicians in designing the optimal healthcare programme and controlling individual medical status to prevent readmissions.
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