scholarly journals Prospective Implementation of a Risk Prediction Model for Bloodstream Infection Safely Reduces Antibiotic Usage in Febrile Pediatric Cancer Patients Without Severe Neutropenia

2020 ◽  
Vol 38 (27) ◽  
pp. 3150-3160
Author(s):  
Adam J. Esbenshade ◽  
Zhiguo Zhao ◽  
Alaina Baird ◽  
Emily A. Holmes ◽  
Daniel E. Dulek ◽  
...  
Keyword(s):  
High Risk ◽  
Bloodstream Infection ◽  
Risk Prediction ◽  
Pediatric Patients ◽  
Prediction Models ◽  
Low Risk ◽  
Severe Neutropenia ◽  
Patients With Cancer ◽  
Intravenous Antibiotics ◽  
Pediatric Cancer Patients

PURPOSE Management of febrile pediatric patients with cancer with an absolute neutrophil count of 500/µL or greater is unclear. The Esbenshade Vanderbilt (EsVan) risk prediction models have been shown to predict bloodstream infection (BSI) likelihood in this population, and this study sought to prospectively validate and implement these models in clinical practice. METHODS Data were prospectively collected on febrile pediatric patients with cancer with a central venous catheter from April 2015 to August 2019 at a single site, at which the models (EsVan: 2015 to 2017; EsVan2: October 2017 to 2019) were initially developed and subsequently implemented for clinical management in well-appearing nonseverely neutropenic individuals. It was recommended that patients with low BSI risk (< 10%) be discharged home without antibiotics, those with intermediate BSI risk (10%-39.9%) be administered an antibiotic before discharge, and those with high BSI risk (> 40%) be admitted on broad-spectrum antibiotics. Seven-day outcomes were then collected and EsVan models were prospectively validated and C-statistics estimated. RESULTS In 937 febrile, nonsevere neutropenia episodes, frequencies of low-, intermediate-, and high-risk episodes were 88.9%, 8.6%, and 2.3% respectively. BSI incidence was 4.2% (39 of 937). Within risk groups, low-risk BSI incidence was 1.9% (16 of 834) with BSI incidence of 13.6% and 54.5% for intermediate- and high-risk episodes, respectively. Empirical intravenous antibiotics were administered in 21.1% of low-risk episodes at presentation and at 7 days postpresentation, 72.3% of episodes never required intravenous antibiotics. There were no deaths or clinical decompensations attributable to antibiotic delay. For BSI detection, EsVan and EsVan2 models applied to the new cohort achieved C-statistics of 0.802 and 0.824, respectively. CONCLUSION Prospective, real-time clinical utilization of the EsVan models accurately predicts BSI risk and safely reduces unnecessary antibiotic use in febrile, nonseverely neutropenic pediatric patients with cancer.

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 P8 A single centre evaluation of risk prediction models and imaging modalities in acute appendicitis

BJS Open ◽  
2021 ◽  
Vol 5 (Supplement_1) ◽  
Author(s):  
James Ashcroft ◽  
Aminder A Singh ◽  
Siobhan Rooney ◽  
John Bennett ◽  
Richard Justin Davies ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Prediction ◽  
Prediction Models ◽  
Ct Imaging ◽  
Low Risk ◽  
Imaging Modalities ◽  
Risk Prediction Models ◽  
Suspected Appendicitis ◽  
High Risk Women ◽  
Low Risk Women

Abstract Objective Patients with suspected appendicitis remain a diagnostic challenge. This study aims to validate risk prediction models and to investigate diagnostic accuracy of ultrasonography (US) and computed tomography (CT) in adults undergoing an appendicectomy. Materials and Methods A retrospective case review of patients aged 16-45 undergoing an appendicectomy between January 2019 to January 2020 at a tertiary referral centre was performed. Primary outcomes were the accuracy of a high-risk appendicitis risk score and US and CT imaging modalities when compared to histological reports following appendicectomy. Results A total of 206 patients (107/205, 51.9% women) were included. Removal of histologically normal appendix was equally likely in men and women (13.1 versus 11.2%, relative risk 1.17, 95% c.i. 0.56 to 2.44; P =0.67). A high-risk appendicitis score correctly identified 84.0% (79/94) of cases in men and 85.9% (67/78) of cases in women. US was reported as equivocal in 85.7% (18/21) of low-risk women and 59.0% (23/39) of high-risk women. CT in low-risk women resulted in 25.0% (2/8) equivocal results whilst correctly diagnosing (5/6) or excluding (1/2) appendicitis in 75.0% of the total cohort (6/8). In high-risk women CT resulted in 3.8% (1/26) equivocal results whilst correctly detecting (22/23) or excluding (1/3) appendicitis in 88.5% of total high-risk patients (23/26). Conclusions This study suggests that risk prediction models may be useful in both women and men to identify appendicitis. US imaging gave high rates of equivocal results and should not be relied upon for the diagnosis of appendicitis but may be useful to exclude other differential diagnoses. CT imaging is a highly accurate diagnostic tool and could be considered in those at low-risk where clinical suspicion remains to reduce negative appendicectomy rates.

scholarly journals Risk prediction models for atherosclerotic cardiovascular disease: A systematic assessment with particular reference to Qatar

2021 ◽  
Vol 2021 (2) ◽  
Author(s):  
Aziz Sheikh ◽  
Ulugbek Nurmatov ◽  
Huda Amer Al-Katheeri ◽  
Rasmeh Ali Al Huneiti
Keyword(s):  
Risk Prediction ◽  
Prediction Models ◽  
Ethnically Diverse ◽  
Risk Of Bias ◽  
Low Risk ◽  
Atherosclerotic Cardiovascular Disease ◽  
Eligibility Criteria ◽  
Risk Prediction Models ◽  
Ascvd Risk

Background: Atherosclerotic cardiovascular disease (ASCVD) is a common disease in the State of Qatar and results in considerable morbidity, impairment of quality of life and mortality. The American College of Cardiology/American Heart Association Pooled Cohort Equations (PCE) is currently used in Qatar to identify those at high risk of ASCVD. However, it is unclear if this is the optimal ASCVD risk prediction model for use in Qatar's ethnically diverse population. Aims: This systematic review aimed to identify, assess the methodological quality of and compare the properties of established ASCVD risk prediction models for the Qatari population. Methods: Two reviewers performed head-to-head comparisons of established ASCVD risk calculators systematically. Studies were independently screened according to predefined eligibility criteria and critically appraised using Prediction Model Risk Of Bias Assessment Tool. Data were descriptively summarized and narratively synthesized with reporting of key statistical properties of the models. Results: We identified 20,487 studies, of which 41 studies met our eligibility criteria. We identified 16 unique risk prediction models. Overall, 50% (n = 8) of the risk prediction models were judged to be at low risk of bias. Only 13% of the studies (n = 2) were judged at low risk of bias for applicability, namely, PREDICT and QRISK3.Only the PREDICT risk calculator scored low risk in both domains. Conclusions: There is no existing ASCVD risk calculator particularly well suited for use in Qatar's ethnically diverse population. Of the available models, PREDICT and QRISK3 appear most appropriate because of their inclusion of ethnicity. In the absence of a locally derived ASCVD for Qatar, there is merit in a formal head-to-head comparison between PCE, which is currently in use, and PREDICT and QRISK3.

Algorithms for Individual Prediction of COVID-19 infection and Hospitalization among Symptomatic patients based on health data (Preprint)

2021 ◽  
Author(s):  
Rossella Murtas ◽  
Nuccia Morici ◽  
Chiara Cogliati ◽  
Massimo Puoti ◽  
Barbara Omazzi ◽  
...  
Keyword(s):  
High Risk ◽  
Prediction Models ◽  
Hospital Admissions ◽  
Health Data ◽  
Low Risk ◽  
Risk Prediction Models ◽  
Predictive Algorithms ◽  
Medium Risk ◽  
Risk Patients ◽  
At Home

BACKGROUND The coronavirus disease 2019 (COVID-19) pandemic has generated a huge strain on the health care system worldwide. The metropolitan area of Milan, Italy was one of the most hit area in the world. OBJECTIVE Robust risk prediction models are needed to stratify individual patient risk for public health purposes METHODS Two predictive algorithms were implemented in order to foresee the probability of being a COVID-19 patient and the risk of being hospitalized. The predictive model for COVID-19 positivity was developed in 61.956 symptomatic patients, whereas the model for COVID-19 hospitalization was developed in 36.834 COVID-19 positive patients. Exposures considered were age, gender, comorbidities and symptoms associated with COVID-19 (vomiting, cough, fever, diarrhoea, myalgia, asthenia, headache, anosmia, ageusia, and dyspnoea). RESULTS The predictive models showed a good fit for predicting COVID-19 disease [AUC 72.6% (95% CI 71.6%-73.5%)] and hospitalization [AUC 79.8% (95% CI 78.6%-81%)]. Using these results, 118,804 patients with COVID-19 from October 25 to December 11, 2020 were stratified into low, medium and high risk for COVID-19 severity. Among the overall population, 67.030 (56%) were classified as low-risk, 43.886 (37%) medium-risk, and 7.888 (7%) high-risk, with 89% of the overall population being assisted at home, 9% hospitalized, and 2% dead. Among those assisted at home, most people (60%) were classified as low risk, whereas only 4% were classified at high risk. According to ordinal logistic regression, the OR of being hospitalised or dead was 5.0 (95% CI 4.6-5.4) in high-risk patients and 2.7 (95% CI 2.6-2.9) in medium-risk patients, as compared to low-risk patients. CONCLUSIONS A simple monitoring system, based on primary care datasets with linkage to COVID-19 testing results, hospital admissions data and death records may assist in proper planning and allocation of patients and resources during the ongoing COVID-19 pandemic.

Real-Time Risk Tool for Pharmacy Interventions

2020 ◽  
pp. 001857872097388
Author(s):  
Hanh L. Nguyen ◽  
Kristin S. Alvarez ◽  
Boryana Manz ◽  
Arun Nethi ◽  
Varun Sharma ◽  
...  
Keyword(s):  
High Risk ◽  
Prediction Models ◽  
Risk Model ◽  
Hospital Readmissions ◽  
Group Versus ◽  
Safety Net ◽  
Low Risk ◽  
Day Hospital ◽  
Significant Difference ◽  
Safety Net Hospital

Background: Adverse drug events (ADEs) result in excess hospitalizations. Thorough admission medication histories (AMHs) may prevent ADEs; however, the resources required oftentimes outweigh what is available in large hospital settings. Previous risk prediction models embedded into the Electronic Medical Record (EMR) have been used at hospitals to aid in targeting delivery of scarce resources. Objective: To determine if an AMH scoring tool used to allocate resources can decrease 30-day hospital readmissions. Design, Setting, and Participants: Propensity-matched cohort study, Medicine/Surgery patients in large academic safety-net hospital. Intervention or Exposure: Pharmacy-conducted AMHs identified by risk model versus standard of care AMH. Main Outcomes and Measures: A total of 30-day hospital readmissions and inpatient ADE prevention. Results: The model screened 87 240 hospitalizations between June 2017 and June 2019 and 4027 patients per group were included. There were significantly less 30 day readmissions among high-risk identified patients that received a pharmacy-conducted AMH compared to controls (11% vs 15%; P = 0.004) and no significant difference in readmission rates for low-risk patients. While there was significantly higher documentation of major ADE prevention in the pharmacy-led AMH group versus control (1656 vs 12; P < 0.001), there was no difference in electronically-detected inpatient ADEs between groups. Conclusions: A risk tool embedded into the EMR can be used to identify patients whom pharmacy teams can easily target for AMHs. This study showed significant reductions in readmissions for patients identified as high-risk. However, the same benefit in readmissions was not seen in those identified at low-risk, which supports allocating resources to those that will benefit the most.

scholarly journals 1756. Prediction of Bloodstream Infection Prior to Onset of Symptoms by Plasma Metagenomic Sequencing in Pediatric Patients With Relapsed or Refractory Malignancy (PREDSEQ)

2018 ◽  
Vol 5 (suppl_1) ◽  
pp. S60-S60 ◽  
Author(s):  
Kathryn Goggin ◽  
Yuki Inaba ◽  
Veronica Gonzalez-Pena ◽  
Kim J Allison ◽  
Ka Lok Chan ◽  
...  
Keyword(s):  
High Risk ◽  
Bloodstream Infection ◽  
Pediatric Patients ◽  
Clinical Samples ◽  
Metagenomic Sequencing ◽  
Research Support ◽  
Documented Infection ◽  
Risk Patients ◽  
Bacteria And Fungi ◽  
Refractory Malignancy

Abstract Background Patients undergoing treatment for relapsed or refractory malignancies are at high risk of life-threatening bloodstream infection (BSI). A predictive screening test for BSI might allow pre-emptive therapy, but no validated test is currently available. We tested the hypothesis that plasma metagenomic next generation pathogen sequencing (NGS) would predict BSI before the onset of attributable symptoms. Methods We enrolled 31 pediatric patients receiving for treatment relapsed or refractory malignancy in an IRB-approved prospective cohort study (PREDSEQ) of predictive sequencing. Episodes of febrile neutropenia or documented infection were collected prospectively from the medical record. BSI was defined according to NHSN criteria. Control Samples were defined as samples collected ≥7 clear days before or after any fever or documented infection. Residual clinical samples were stored for NGS; after filtering human sequences, reads were aligned to a curated pathogen database, and organisms above a predefined threshold were reported (Karius Inc., Redwood City, CA). Only bacteria and fungi were included in this analysis. Results A total of 11 BSI episodes occurred in 9 participants (Table 1) during the study period. Predictive sensitivity of NGS in the 2 days before onset of infection (n = 9) was 78% (95% CI 45–94%), and diagnostic sensitivity on the day of infection (n = 11) was 82% (95% CI 52–95%). Specificity of NGS for development of fever or infection within 7 days (n = 16) was 81% (95% CI 57–93%). NGS was positive up to 6 days prior to onset of BSI. In samples collected before or during documented infections, NGS also identified additional bacteria and fungi that were not detected by standard clinical testing. Conclusion Plasma NGS shows promise for the detection of BSI prior to onset of symptoms in high-risk patients. Disclosures K. Goggin, Karius Inc.: Investigator, Research support. K. L. Chan, Karius Inc.: Employee, Salary. D. Hollemon, Karius Inc.: Employee, Salary. A. Ahmed, Karius, Inc.: Employee, Salary. D. Hong, Karius, Inc.: Employee, Salary. R. Hayden, Roche Molecular: Scientific Advisor, Consulting fee. Abbott Molecular: Scientific Advisor, Consulting fee. Quidel: Scientific Advisor, Consulting fee. C. Gawad, Karius Inc.: Investigator, Research support. J. Wolf, Karius Inc.: Investigator, Research support.

scholarly journals A risk prediction score to identify patients at low risk for COVID-19 infection

2021 ◽  
Author(s):  
WM Chew ◽  
CH Loh ◽  
A Jalali ◽  
G Fong ◽  
L Senthil Kumar ◽  
...  
Keyword(s):  
Risk Prediction ◽  
Respiratory Symptoms ◽  
Prediction Models ◽  
Clinical Symptoms ◽  
Characteristic Curve ◽  
Low Risk ◽  
Resource Utilisation ◽  
Logistic Regression Models ◽  
Cell Counts ◽  
Hospitalised Patients

Introduction: Singapore’s enhanced surveillance programme for COVID-19 identifies and isolates hospitalised patients with acute respiratory symptoms to prevent nosocomial spread. We developed risk prediction models to identify patients with low risk for COVID-19 from this cohort of hospitalised patients with acute respiratory symptoms. Methods: This was a single-centre retrospective observational study. Patients admitted to our institution’s respiratory surveillance wards from 10 February to 30 April 2020 contributed data for analysis. Prediction models for COVID-19 were derived from a training cohort using variables based on demographics, clinical symptoms, exposure risks and blood investigations fitted into logistic regression models. The derived prediction models were subsequently validated on a test cohort. Results: Of the 1,228 patients analysed, 52 (4.2%) were diagnosed with COVID-19. Two prediction models were derived, the first based on age, presence of sore throat, dormitory residence, blood haemoglobin level (Hb), and total white blood cell counts (TW), and the second based on presence of headache, contact with infective patients, Hb and TW. Both models had good diagnostic performance with areas under the receiver operating characteristic curve of 0.934 and 0.866, respectively. Risk score cut-offs of 0.6 for Model 1 and 0.2 for Model 2 had 100% sensitivity, allowing identification of patients with low risk for COVID-19. Limiting COVID-19 screening to only elevated-risk patients reduced the number of isolation days for surveillance patients by up to 41.7% and COVID-19 swab testing by up to 41.0%. Conclusion: Prediction models derived from our study were able to identify patients at low risk for COVID-19 and rationalise resource utilisation.

Sign in / Sign up

Export Citation Format

Share Document