A clinical predictive model for risk stratification of patients with severe acute lower gastrointestinal bleeding.

BACKGROUND: Lower gastrointestinal bleeding (LGIB) is a common presentation of surgical admissions, imposing a significant burden on healthcare costs and resources. There is a paucity of standardised clinical predictive tools available for the initial assessment and risk stratification of patients with LGIB. We propose a simple clinical scoring model to prognosticate patients at risk of severe LGIB and an algorithm to guide management of such patients. METHODS: A retrospective cohort study was conducted, identifying consecutive patients admitted to our institution for LGIB over a 1-year period. Baseline demographics, clinical parameters at initial presentation and treatment interventions were recorded. Multivariate logistic regression was performed to identify factors predictive of severe LGIB. A clinical management algorithm was developed to discriminate between patients requiring admission, and to guide endoscopic, angiographic and/or surgical intervention. RESULTS: 226/649 (34.8%) patients had severe LGIB. Six variables were entered into a clinical predictive model for risk stratification of LGIB: Tachycardia (HR ≥ 100), hypotension (SBP < 90 mmHg), anaemia (Hb < 9 g/dL), metabolic acidosis, use of antiplatelet/anticoagulants, and active per-rectal bleeding. The optimum cut-off score of ≥ 1 had a sensitivity of 91.9%, specificity of 39.8%, and positive and negative predictive Values of 45% and 90.2%, respectively, for predicting severe LGIB. The area under curve (AUC) was 0.77. CONCLUSION: Early diagnosis and management of severe LGIB remains a challenge for the acute care surgeon. The predictive model described comprises objective clinical parameters routinely obtained at initial triage to guide risk stratification, disposition and inpatient management of patients.

Time course response of inflammatory markers in pediatric appendicitis.

PURPOSE: We aim to evaluate the diagnostic value and time course response of the triple inflammatory markers: white blood cell count (WBC), neutrophil percentage (Neu), and C-reactive protein (CRP) in pediatric acute appendicitis. METHODS: A retrospective review of clinical data pertaining to 1391 patients admitted with suspicion for pediatric appendicitis from 2012 to 2017 was conducted. Triple inflammatory markers were acquired upon admission. Appendicitis was confirmed histologically post-appendectomy. The diagnostic value and time course response of these markers was trended in relation to the duration of abdominal pain on admission. RESULTS: 718 patients had histologically confirmed appendicitis. WBC and Neu demonstrate high sensitivity for early appendicitis at 94.6% and 80.0% at Day 1, while CRP demonstrates highest sensitivity of 97.9% at Day 4. The triple markers had poor overall diagnostic value when interpreted individually, however, had a high combined sensitivity of 99.7% and negative predictive value of 98.7% regardless of duration of disease. Overall negative appendectomy rate was 6.7% (n = 52). Among 19 patients with triple markers negative who underwent appendectomy, 17 (89.5%) were histologically normal. CONCLUSIONS: The triple inflammatory markers have limited diagnostic value when interpreted individually, but are strong discriminators of pediatric appendicitis when combined. Their high sensitivity and negative predictive value could potentially help patients avoid unnecessary admissions or costly imaging studies, and reduce negative appendectomy rates. In addition, their objective nature confers an advantage over existing clinical scoring systems which comprise subjective elements.

Development of a heart rate variability and complexity model in predicting the need for life-saving interventions amongst trauma patients.

BACKGROUND: Triage trauma scores are utilised to determine patient disposition, interventions and prognostication in the care of trauma patients. Heart rate variability (HRV) and heart rate complexity (HRC) reflect the autonomic nervous system and are derived from electrocardiogram (ECG) analysis. In this study, we aimed to develop a model incorporating HRV and HRC, to predict the need for life-saving interventions (LSI) in trauma patients, within 24 h of emergency department presentation. METHODS: We included adult trauma patients (≥ 18 years of age) presenting at the emergency department of Singapore General Hospital between October 2014 and October 2015. We excluded patients who had non-sinus rhythms and larger proportions of artefacts and/or ectopics in ECG analysis. We obtained patient demographics, laboratory results, vital signs and outcomes from electronic health records. We conducted univariate and multivariate analyses for predictive model building. RESULTS: Two hundred and twenty-five patients met inclusion criteria, in which 49 patients required LSIs. The LSI group had a higher proportion of deaths (10, 20.41% vs 1, 0.57%, p < 0.001). In the LSI group, the mean of detrended fluctuation analysis (DFA)-α1 (1.24 vs 1.12, p = 0.045) and the median of DFA-α2 (1.09 vs 1.00, p = 0.027) were significantly higher. Multivariate stepwise logistic regression analysis determined that a lower Glasgow Coma Scale, a higher DFA-α1 and higher DFA-α2 were independent predictors of requiring LSIs. The area under the curve (AUC) for our model (0.75, 95% confidence interval, 0.66-0.83) was higher than other scoring systems and selected vital signs. CONCLUSIONS: An HRV/HRC model outperforms other triage trauma scores and selected vital signs in predicting the need for LSIs but needs to be validated in larger patient populations.