scholarly journals A Prediction Model for Bacteremia and Transfer to Intensive Care in Pediatric and Adolescent Cancer Patients With Febrile Neutropenia

2021 ◽  
Author(s):  
Muayad Alali ◽  
Anoop Mayampurath ◽  
Yangyang Dai ◽  
Allison H. Bartlett
Keyword(s):  
Logistic Regression ◽  
Intensive Care ◽  
High Risk ◽  
Random Forest ◽  
Febrile Neutropenia ◽  
Prediction Model ◽  
Cancer Patients ◽  
Low Risk ◽  
Adolescent Cancer ◽  
Time Of Presentation

Abstract Objectives:Febrile neutropenia (FN) is a common condition in children receiving chemotherapy. Our goal in this study was to develop a model for predicting blood stream infection (BSI) and transfer to intensive care (TIC) at time of presentation in pediatric cancer patients with FN. Methods: We conducted an observational cohort analysis of pediatric and adolescent cancer patients younger than 24 years admitted for fever and chemotherapy-induced neutropenia over a 7-year period. We excluded stem cell transplant recipients who developed FN after transplant and febrile non-neutropenic episodes. The primary outcome was onset of BSI, as determined by positive blood culture within 7 days of onset of FN. The secondary outcome was transfer to intensive care (TIC) within 14 days of FN onset. Predictor variables include demographics, clinical, and laboratory measures on initial presentation for FN. Data were divided into independent derivation (2009-2015) and prospective validation (2015-2016) cohorts. Prediction models were built for both outcomes using logistic regression and random forest and compared with Hakim model. Performance was assessed using area under the receiver operating characteristic curve (AUC) metrics. Results: A total of 505 FN episodes (FNEs) were identified in 230 patients. BSI was diagnosed in 106 (21%) and TIC occurred in 56 (10.6%) episodes. The most common oncologic diagnosis with FN was acute lymphoblastic leukemia (ALL), and the highest rate of BSI was in patients with AML. Patients who had BSI had higher maximum temperature, higher rates of prior BSI and higher incidence of hypotension compared with patients who did not have BSI. FN patients who were transferred to the intensive care (TIC) had higher temperature and higher incidence of hypotension at presentation compared to FN patients who didn’t have TIC. We compared 3 models: (1) random forest (2) logistic regression and (3) Hakim model. The areas under the curve for BSI prediction were (0.79, 0.65, and 0.64, P < 0.05) for models 1,2, and 3, respectively. And for TIC prediction were (0.88, 0.76, and 0.65, P < 0.05) respectively. The random forest model demonstrated higher accuracy in predicting BSI and TIC and showed a negative predictive value (NPV) of 0.91 and 0.97 for BSI and TIC respectively at the best cutoff point as determined by Youden’s Index. Likelihood ratios (LRs) (post-test probability) for RF model have potential utility of identifying low risk for BSI and TIC (0.24 and 0.12) and high-risk patients (3.5 and 6.8) respectively. Conclusions: Our prediction model has a good diagnostic performance in clinical practices for both BSI and TIC in FN patients at the time of presentation. The model can be used to identify a group of individuals at low risk for BSI who may benefit from early discharge and reduce length of stay, also it can identify FN patients at high risk of complications who might benefit from more intensive therapies at presentation.

Assessing the impact of a targeted electronic medical record intervention on growth factor usage in cancer patients.

2014 ◽  
Vol 32 (30_suppl) ◽  
pp. 262-262
Author(s):  
Jordan Bernens ◽  
Kara Hartman ◽  
Brendan F. Curley ◽  
Sijin Wen ◽  
Jame Abraham ◽  
...  
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Febrile Neutropenia ◽  
Cancer Patients ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Low Risk ◽  
Individual Risk ◽  
Individual Risk Factors ◽  
The Impact

262 Background: Patients receiving chemotherapy are at risk for febrile neutropenia following treatment. The American Society of Clinical Oncology (ASCO) and National Comprehensive Cancer Network (NCCN) recommend screening patients for risk of febrile neutropenia and risk stratification based on likelihood of febrile neutropenia events. Prophylactic growth factors (G-CSF) should be in patients receiving high-risk regimens or intermediate-risk regimens with individual risk factors. The impact of electronic medical record system (EMR) implementation on compliance with G-CSF support guidelines has not been studied. Methods: At West Virginia University/Mary Babb Randolph Cancer Center we conducted an IRB approved retrospective chart review of cancer patients receiving chemotherapy from January 1, 2007 to August 1, 2008 (pre-EMR) and January 1, 2011 to December 31, 2011 (post-EMR). We reviewed the chemotherapy regimens and patient risk factors for developing febrile neutropenia, and determined if the G-CSF usage was consistent with guideline recommendations. Results: Compliance with prophylactic G-CSF guidelines was 75.6% in the post-EMR arm, compared to 67.5% in the pre-EMR arm (p=0.041, ch-square). The post EMR data of 1,042 new chemotherapy initiations showed: (see Table). The appropriateness of usage in high and low risk patients were the most compliant, as G-CSF orders were built into chemotherapy plans of high risk regimens and omitted from low risk regimens. Conclusions: Appropriate prophylactic G-CSF usage can be improved when orders are integrated into standard chemotherapy order sets in an EMR. An area of further improvement would include automatic identification of individual risk factors by the EMR. [Table: see text]

scholarly journals A Prediction Model for Metachronous Peritoneal Carcinomatosis in Patients with Stage T4 Colon Cancer after Curative Resection

Cancers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2808
Author(s):  
Tzong-Yun Tsai ◽  
Jeng-Fu You ◽  
Yu-Jen Hsu ◽  
Jing-Rong Jhuang ◽  
Yih-Jong Chern ◽  
...  
Keyword(s):  
Colon Cancer ◽  
High Risk ◽  
Prediction Model ◽  
Prediction Error ◽  
Area Under The Curve ◽  
Low Risk ◽  
Brier Score ◽  
Individual Risk ◽  
T4 Colon Cancer ◽  
Testing Set

(1) Background: The aim of this study was to develop a prediction model for assessing individual mPC risk in patients with pT4 colon cancer. Methods: A total of 2003 patients with pT4 colon cancer undergoing R0 resection were categorized into the training or testing set. Based on the training set, 2044 Cox prediction models were developed. Next, models with the maximal C-index and minimal prediction error were selected. The final model was then validated based on the testing set using a time-dependent area under the curve and Brier score, and a scoring system was developed. Patients were stratified into the high- or low-risk group by their risk score, with the cut-off points determined by a classification and regression tree (CART). (2) Results: The five candidate predictors were tumor location, preoperative carcinoembryonic antigen value, histologic type, T stage and nodal stage. Based on the CART, patients were categorized into the low-risk or high-risk groups. The model has high predictive accuracy (prediction error ≤5%) and good discrimination ability (area under the curve >0.7). (3) Conclusions: The prediction model quantifies individual risk and is feasible for selecting patients with pT4 colon cancer who are at high risk of developing mPC.

Accuracy of the CARG chemotherapy toxicity risk prediction tool in older Indian patients with cancer.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e24023-e24023
Author(s):  
Shreya Gattani ◽  
Vanita Noronha ◽  
Anant Ramaswamy ◽  
Renita Castelino ◽  
Vandhita Nair ◽  
...  
Keyword(s):  
High Risk ◽  
Cancer Patients ◽  
Risk Prediction ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Chemotherapy Toxicity ◽  
Patients With Cancer ◽  
Indian Patients ◽  
Grade 3

e24023 Background: Clinical judgement alone is inadequate in accurately predicting chemotherapy toxicity in older adult cancer patients. Hurria and colleagues developed and validated, the CARG score (range, 0–17) as a convenient and reliable tool for predicting chemotherapy toxicity in older cancer patients in America, however, its applicability in Indian patients is unknown. Methods: An observational retrospective and prospective study between 2018 and 2020 was conducted in the Department of Medical Oncology at Tata Memorial Hospital, Mumbai, India. The study was approved by the institutional ethics committee (IEC-III; Project No. 900596) and registered in the Clinical Trials Registry of India (CTRI/2020/04/024675). Written informed consent was obtained in the prospective part of the study. Patients aged ≥ 60 years and planned for systemic therapy were evaluated in the geriatric oncology clinic and their CARG score was calculated. Patients were stratified into low (0-4), intermediate (5-9) and high risk (10-17) based on the CARG scores. The CARG score was provided to the treating physicians, along with the results of the geriatric assessment. Chemotherapy-related toxicities were captured from the electronic medical record and graded as per the NCI CTCAE, version 4.0. Results: We assessed 130 patients, with a median age 69 years (IQR, 60 to 84); 72% patients were males. The common malignancies included gastrointestinal (52%) and lung (30%). Approximately 78% patients received polychemotherapy and 53% received full dose chemotherapy. Based on the CARG score, 28 (22%) patients belonged to low risk, 80 (61%) to intermediate risk and 22 (17%) to the high risk category. The AU-ROC of the CARG score in predicting grade 3-5 toxicities was 0.61 (95% CI, 0.51-0.71). The sensitivity and specificity of the CARG score in predicting grade 3-5 toxicities were 60.8% and 78.6%. Grade 3-5 toxicities occurred in 6/28 patients (21%) in the low risk group, compared to 62/102 patients (61%) in the intermediate /high risk group, p = 0.0002. There was also a significant difference in the time to development of grade 3-5 toxicities, which occurred at a median of 2.5 cycles (IQR, 1-3.8) in the intermediate /high risk group and at a median of 6 cycles (IQR, 3.5-8) in the low risk group, p = 0.0011. Conclusions: In older Indian patients with cancer, the CARG score reliably stratifies patients into low risk and intermediate/high risk categories, predicting both the occurrence and the time to occurrence of grade 3-5 toxicities from chemotherapy. The CARG score may aid the oncologist in estimating the risk-benefit ratio of chemotherapy. An important limitation was that we provided the CARG score to the treating oncologists prior to the start of chemotherapy, which may have resulted in alterations in the chemotherapy regimen and dose and may have impacted the CARG risk prediction model. Clinical trial information: CTRI/2020/04/024675.

scholarly journals P012: Mortality rate of cancer patients by type presenting to the intensive care unit with sepsis.

CJEM ◽  
2019 ◽  
Vol 21 (S1) ◽  
pp. S67
Author(s):  
S. Beckett ◽  
E. Karreman ◽  
R. Hughes
Keyword(s):  
Logistic Regression ◽  
Intensive Care ◽  
Mortality Rate ◽  
Cancer Patients ◽  
Solid Tumors ◽  
Predictive Factors ◽  
Poor Prognosis ◽  
Solid Tumours ◽  
Icu Mortality ◽  
Icu Admission

Introduction: Sepsis in cancer patients is associated with higher mortality rates than non-cancer patients. As a whole, hematological or solid tumor cancers have not demonstrated a prognostic link to sepsis survival rates in intensive care units (ICU), however poor-prognosis solid tumours (less than 25% 5-year survival) have not been investigated. This study examined ICU mortality rate and its predictive factors of patients with sepsis and poor-prognosis solid tumors in comparison to patients with higher prognosis solid tumours. Methods: A 6-year retrospective chart review of 79 patients with sepsis and solid tumour cancers and/or metastatic cancers admitted to the ICU was conducted. Information regarding mortality rate within 14 days, length of ICU stay, incidence of intubation, and other primary reasons for ICU admission was collected. Data was analysed using logistic regression. Results: Logistic regression results showed intubation as the only significant factor contributing to patient mortality (p &lt; .001), with the odds of mortality being 12.3 times higher for intubated than non-intubated patients. Five-year cancer survival rate was the second best predictor (p = .082), while age, sex, and metastasis were also not significant predictive factors for survival. Intubated patients with poor prognosis cancers had the lowest survival chance as further indicated by the 16 patients who met this criterion, of which 14 died within two weeks of ICU admission. Conclusion: The fact that poor prognosis cancers in sepsis were not significantly predictive of ICU mortality supports current literature regarding solid tumors in general, while intubation being a significant predictor for mortality in patients with sepsis and cancer regardless of type builds on previous research. A limitation of this study is the relative low number of included cases with poor-prognosis cancer types. Further evaluation is needed to understand the implications of our results for end-of-life care and ICU admission for patients with these characteristics.

scholarly journals Polygenic risk scores predict diabetic complications and their response to therapy

2019 ◽  
Author(s):  
J. Tremblay ◽  
M. Haloui ◽  
F. Harvey ◽  
R. Tahir ◽  
F.-C. Marois-Blanchet ◽  
...  
Keyword(s):  
High Risk ◽  
Prediction Model ◽  
Risk Group ◽  
Intensive Therapy ◽  
Cardiovascular Death ◽  
Response To Therapy ◽  
Low Risk ◽  
Number Needed To Treat ◽  
Genome Wide Association Studies ◽  
Increased Risk

AbstractType 2 diabetes increases the risk of cardiovascular and renal complications, but early risk prediction can lead to timely intervention and better outcomes. Through summary statistics of meta-analyses of published genome-wide association studies performed in over 1.2 million of individuals, we combined 9 PRS gathering genomic variants associated to cardiovascular and renal diseases and their key risk factors into one logistic regression model, to predict micro- and macrovascular endpoints of diabetes. Its clinical utility in predicting complications of diabetes was tested in 4098 participants with diabetes of the ADVANCE trial followed during a period of 10 years and replicated it in three independent non-trial cohorts. The prediction model adjusted for ethnicity, sex, age at onset and diabetes duration, identified the top 30% of ADVANCE participants at 3.1-fold increased risk of major micro- and macrovascular events (p=6.3×10−21 and p=9.6×10−31, respectively) and at 4.4-fold (p=6.8×10−33) increased risk of cardiovascular death compared to the remainder of T2D subjects. While in ADVANCE overall, combined intensive therapy of blood pressure and glycaemia decreased cardiovascular mortality by 24%, the prediction model identified a high-risk group in whom this therapy decreased mortality by 47%, and a low risk group in whom the therapy had no discernable effect. Patients with high PRS had the greatest absolute risk reduction with a number needed to treat of 12 to prevent one cardiovascular death over 5 years. This novel polygenic prediction model identified people with diabetes at low and high risk of complications and improved targeting those at greater benefit from intensive therapy while avoiding unnecessary intensification in low-risk subjects.

scholarly journals Impact of Different Modules of 21-Gene Assay in Early Breast Cancer Patients

2021 ◽  
Vol 12 ◽  
Author(s):  
Mengdi Chen ◽  
Deyue Liu ◽  
Weilin Chen ◽  
Weiguo Chen ◽  
Kunwei Shen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Cancer Patients ◽  
Low Risk ◽  
Breast Cancer Patients ◽  
Younger Patients ◽  
Gene Assay ◽  
Risk Patients ◽  
Gene Modules ◽  
The Impact

BackgroundThe 21-gene assay recurrence score (RS) provides additional information on recurrence risk of breast cancer patients and prediction of chemotherapy benefit. Previous studies that examined the contribution of the individual genes and gene modules of RS were conducted mostly in postmenopausal patients. We aimed to evaluate the gene modules of RS in patients of different ages.MethodsA total of 1,078 estrogen receptor (ER)-positive and human epidermal growth factor receptor 2 (HER2)-negative breast cancer patients diagnosed between January 2009 and March 2017 from Shanghai Jiao Tong University Breast Cancer Data Base were included. All patients were divided into three subgroups: Group A, ≤40 years and premenopausal (n = 97); Group B, &gt;40 years and premenopausal (n = 284); Group C, postmenopausal (n = 697). The estrogen, proliferation, invasion, and HER2 module scores from RS were used to characterize the respective molecular features. Spearman correlation and analysis of the variance tests were conducted for RS and its constituent modules.ResultsIn patients &gt;40 years, RS had a strong negative correlation with its estrogen module (ρ = −0.76 and −0.79 in Groups B and C) and a weak positive correlation with its invasion module (ρ = 0.29 and 0.25 in Groups B and C). The proliferation module mostly contributed to the variance in young patients (37.3%) while the ER module contributed most in old patients (54.1% and 53.4% in Groups B and C). In the genetic high-risk (RS &gt;25) group, the proliferation module was the leading driver in all patients (ρ = 0.38, 0.53, and 0.52 in Groups A, B, and C) while the estrogen module had a weaker correlation with RS. The impact of ER module on RS was stronger in clinical low-risk patients while the effect of the proliferation module was stronger in clinical high-risk patients. The association between the RS and estrogen module was weaker among younger patients, especially in genetic low-risk patients.ConclusionsRS was primarily driven by the estrogen module regardless of age, but the proliferation module had a stronger impact on RS in younger patients. The impact of modules varied in patients with different genetic and clinical risks.

scholarly journals Development of an ensemble machine learning prognostic model to predict 60-day risk of major adverse cardiac events in adults with chest pain

2021 ◽  
Author(s):  
Chris J. Kennedy ◽  
Dustin G. Mark ◽  
Jie Huang ◽  
Mark J. van der Laan ◽  
Alan E. Hubbard ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Chest Pain ◽  
Random Forest ◽  
Decision Trees ◽  
Low Risk ◽  
Major Adverse Cardiac Events ◽  
Risk Scores ◽  
Cardiac Events ◽  
Adverse Cardiac Events

Background: Chest pain is the second leading reason for emergency department (ED) visits and is commonly identified as a leading driver of low-value health care. Accurate identification of patients at low risk of major adverse cardiac events (MACE) is important to improve resource allocation and reduce over-treatment. Objectives: We sought to assess machine learning (ML) methods and electronic health record (EHR) covariate collection for MACE prediction. We aimed to maximize the pool of low-risk patients that are accurately predicted to have less than 0.5% MACE risk and may be eligible for reduced testing. Population Studied: 116,764 adult patients presenting with chest pain in the ED and evaluated for potential acute coronary syndrome (ACS). 60-day MACE rate was 1.9%. Methods: We evaluated ML algorithms (lasso, splines, random forest, extreme gradient boosting, Bayesian additive regression trees) and SuperLearner stacked ensembling. We tuned ML hyperparameters through nested ensembling, and imputed missing values with generalized low-rank models (GLRM). We benchmarked performance to key biomarkers, validated clinical risk scores, decision trees, and logistic regression. We explained the models through variable importance ranking and accumulated local effect visualization. Results: The best discrimination (area under the precision-recall [PR-AUC] and receiver operating characteristic [ROC-AUC] curves) was provided by SuperLearner ensembling (0.148, 0.867), followed by random forest (0.146, 0.862). Logistic regression (0.120, 0.842) and decision trees (0.094, 0.805) exhibited worse discrimination, as did risk scores [HEART (0.064, 0.765), EDACS (0.046, 0.733)] and biomarkers [serum troponin level (0.064, 0.708), electrocardiography (0.047, 0.686)]. The ensemble's risk estimates were miscalibrated by 0.2 percentage points. The ensemble accurately identified 50% of patients to be below a 0.5% 60-day MACE risk threshold. The most important predictors were age, peak troponin, HEART score, EDACS score, and electrocardiogram. GLRM imputation achieved 90% reduction in root mean-squared error compared to median-mode imputation. Conclusion: Use of ML algorithms, combined with broad predictor sets, improved MACE risk prediction compared to simpler alternatives, while providing calibrated predictions and interpretability. Standard risk scores may neglect important health information available in other characteristics and combined in nuanced ways via ML.

Adjuvant Chemotherapy Guidance for pT1-3N0-1 Breast Cancer Patients with HR+, HER2- subtype: a study based on SEER database

2021 ◽  
Author(s):  
juanjuan Qiu ◽  
Li Xu ◽  
Yu Wang ◽  
Jia Zhang ◽  
Jiqiao Yang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Prognostic Factors ◽  
High Risk ◽  
Cancer Patients ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Rank Test ◽  
Breast Cancer Patients ◽  
Group Score

Abstract Background Although the results of gene testing can guide early breast cancer patients with HR+, HER2- to decide whether they need chemotherapy, there are still many patients worldwide whose problems cannot be solved well by genetic testing. Methods 144 735 patients with HR+, HER2-, pT1-3N0-1 breast cancer from the Surveillance, Epidemiology, and End Results database were included from 2010 to 2015. They were divided into chemotherapy (n = 38 392) and no chemotherapy (n = 106 343) group, and after propensity score matching, 23 297 pairs of patients were left. Overall survival (OS) and breast cancer-specific survival (BCSS) were tested by Kaplan–Meier plot and log-rank test and Cox proportional hazards regression model was used to identify independent prognostic factors. A nomogram was constructed and validated by C-index and calibrate curves. Patients were divided into high- or low-risk group according to their nomogram score using X-tile. Results Patients receiving chemotherapy had better OS before and after matching (p < 0.05) but BCSS was not significantly different between patients with and without chemotherapy after matching: hazard ratio (HR) 1.005 (95%CI 0.897, 1.126). Independent prognostic factors were included to construct the nomogram to predict BCSS of patients without chemotherapy. Patients in the high-risk group (score > 238) can get better OS HR 0.583 (0.507, 0.671) and BCSS HR 0.791 (0.663, 0.944) from chemotherapy but the low-risk group (score ≤ 238) cannot. Conclusion The well-validated nomogram and a risk stratification model was built. Patients in the high-risk group should receive chemotherapy while patients in low-risk group may be exempt from chemotherapy.

scholarly journals Data Science for Extubation Prediction and Value of Information in Surgical Intensive Care Unit

2019 ◽  
Vol 8 (10) ◽  
pp. 1709 ◽  
Author(s):  
Tsung-Lun Tsai ◽  
Min-Hsin Huang ◽  
Chia-Yen Lee ◽  
Wu-Wei Lai
Keyword(s):  
Intensive Care Unit ◽  
Logistic Regression ◽  
Intensive Care ◽  
Prediction Model ◽  
Value Of Information ◽  
Data Science ◽  
Apache Ii ◽  
Surgical Intensive Care Unit ◽  
Surgical Intensive Care ◽  
Science Framework

Besides the traditional indices such as biochemistry, arterial blood gas, rapid shallow breathing index (RSBI), acute physiology and chronic health evaluation (APACHE) II score, this study suggests a data science framework for extubation prediction in the surgical intensive care unit (SICU) and investigates the value of the information our prediction model provides. A data science framework including variable selection (e.g., multivariate adaptive regression splines, stepwise logistic regression and random forest), prediction models (e.g., support vector machine, boosting logistic regression and backpropagation neural network (BPN)) and decision analysis (e.g., Bayesian method) is proposed to identify the important variables and support the extubation decision. An empirical study of a leading hospital in Taiwan in 2015–2016 is conducted to validate the proposed framework. The results show that APACHE II and white blood cells (WBC) are the two most critical variables, and then the priority sequence is eye opening, heart rate, glucose, sodium and hematocrit. BPN with selected variables shows better prediction performance (sensitivity: 0.830; specificity: 0.890; accuracy 0.860) than that with APACHE II or RSBI. The value of information is further investigated and shows that the expected value of experimentation (EVE), 0.652 days (patient staying in the ICU), is saved when comparing with current clinical experience. Furthermore, the maximal value of information occurs in a failure rate around 7.1% and it reveals the “best applicable condition” of the proposed prediction model. The results validate the decision quality and useful information provided by our predicted model.

scholarly journals Semiautomated Glasgow-Blatchford Bleeding Score helps direct bed placement for patients with upper gastrointestinal bleeding

2020 ◽  
Vol 7 (1) ◽  
pp. e000479
Author(s):  
Drew B Schembre ◽  
Robson E Ely ◽  
Janice M Connolly ◽  
Kunjali T Padhya ◽  
Rohit Sharda ◽  
...  
Keyword(s):  
Intensive Care ◽  
High Risk ◽  
Length Of Stay ◽  
Gastrointestinal Bleeding ◽  
Transfusion Requirement ◽  
Upper Gastrointestinal Bleeding ◽  
Low Risk ◽  
Risk Patients ◽  
Bleeding Score ◽  
Upper Gastrointestinal

ObjectiveThe Glasgow-Blatchford Bleeding Score (GBS) was designed to identify patients with upper gastrointestinal bleeding (UGIB) who do not require hospitalisation. It may also help stratify patients unlikely to benefit from intensive care.DesignWe reviewed patients assigned a GBS in the emergency room (ER) via a semiautomated calculator. Patients with a score ≤7 (low risk) were directed to an unmonitored bed (UMB), while those with a score of ≥8 (high risk) were considered for MB placement. Conformity with guidelines and subsequent transfers to MB were reviewed, along with transfusion requirement, rebleeding, length of stay, need for intervention and death.ResultsOver 34 months, 1037 patients received a GBS in the ER. 745 had an UGIB. 235 (32%) of these patients had a GBS ≤7. 29 (12%) low-risk patients were admitted to MBs. Four low-risk patients admitted to UMB required transfer to MB within the first 48 hours. Low-risk patients admitted to UMBs were no more likely to die, rebleed, need transfusion or require more endoscopic, radiographic or surgical procedures than those admitted to MBs. No low-risk patient died from GIB. Patients with GBS ≥8 were more likely to rebleed, require transfusion and interventions to control bleeding but not to die.ConclusionA semiautomated GBS calculator can be incorporated into an ER workflow. Patients with a GBS ≤7 are unlikely to need MB care for UGIB. Further studies are warranted to determine an ideal scoring system for MB admission.

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