Clinical physiological parameters for the prediction of gram-negative bacterial infection in the emergency department

Abstract Background Early detection and treatment of Gram-negative bacteria (GNB), major causative pathogens of sepsis, may benefit a patient’s outcome, since the mortality rate increases by 5–10% for each hour of delayed therapy. Unfortunately, GNB diagnosis is based on blood culture, which is time consuming. Therefore, an economic and effective GNB infection detection tool in the emergency department (ED) is warranted. Methods We conducted a retrospective case control-study in the ED of a university-affiliated medical center between January 01, 2014 and December 31, 2017. The inclusion criteria were as follows: (1) age ≥ 18; (2) clinical suspicion of bacterial infection; (3) positive bacterial culture of blood or sputum or urine. Descriptive statistics was performed on patient demographic characteristics, vital signs, laboratory data, infection sites, cultured microorganisms, and clinical outcomes. The accuracy of vital signs to predict GNB infection was identified via logistic regression and receiver operating characteristic (ROC) analysis. Results A total of 797 patients were included in this study; the mean age was 71.8 years and 51.3% were male. The results revealed that patients with body temperature ≥ 38.5°C, heart rate ≥ 110 beats per minute, respiratory rate ≥ 20 breaths per minute, and Glasgow coma scale (GCS) < 14, had a 2.3-, 1.4-, 1.9-, and 1.6-fold greater risk of GNB infection, respectively. The area under the curve values for ROC analysis of these measures were 0.70, 0.68, 0.69, and 0.67, respectively. Conclusion The four physiological parameters were rapid and reliable independent predictors for early detection of GNB infection.

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Clinical physiological parameters for the prediction of gram-negative bacterial infection in the emergency department

10.21203/rs.3.rs-27796/v1 ◽

2020 ◽

Author(s):

Chan-Peng Hsu ◽

Hsin-Yu Chen ◽

Wei-Lung Chen ◽

Jiann-Hwa Chen ◽

Chien-Cheng Huang ◽

...

Keyword(s):

Emergency Department ◽

Early Detection ◽

Bacterial Infection ◽

Roc Analysis ◽

Medical Center ◽

Laboratory Data ◽

Vital Signs ◽

Physiological Parameters ◽

Gram Negative ◽

Positive Bacterial Culture

Abstract Background: Early detection and treatment of Gram-negative bacteria (GNB), major causative pathogens of sepsis, may benefit a patient’s outcome, since the mortality rate increases by 5–10% for each hour of delayed therapy. Unfortunately, GNB diagnosis is based on blood culture, which is time consuming. Therefore, an economic and effective GNB infection detection tool in the emergency department (ED) is warranted.Methods: We conducted a retrospective case control-study in the ED of a university-affiliated medical center between January 01, 2014 and December 31, 2017. The inclusion criteria were as follows: (1) age ≥ 18; (2) clinical suspicion of bacterial infection; (3) positive bacterial culture of blood or sputum or urine. Descriptive statistics was performed on patient demographic characteristics, vital signs, laboratory data, infection sites, cultured microorganisms, and clinical outcomes. The accuracy of vital signs to predict GNB infection was identified via logistic regression and receiver operating characteristic (ROC) analysis.Results: A total of 797 patients were included in this study; the mean age was 71.8 years and 51.3% were male. The results revealed that patients with body temperature ≥ 38.5°C, heart rate ≥ 110 beats per minute, respiratory rate ≥ 20 breaths per minute, and Glasgow coma scale (GCS) < 14, had a 2.3-, 1.4-, 1.9-, and 1.6-fold greater risk of GNB infection, respectively. The area under the curve values for ROC analysis of these measures were 0.70, 0.68, 0.69, and 0.67, respectively.Conclusion: The four physiological parameters were rapid and reliable independent predictors for early detection of GNB infection.

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Clinical physiological parameters for the prediction of gram-negative bacterial infection in the emergency department

BMC Infectious Diseases ◽

10.1186/s12879-020-05758-1 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Chan-Peng Hsu ◽

Hsin-Yu Chen ◽

Wei-Lung Chen ◽

Jiann-Hwa Chen ◽

Chien-Cheng Huang ◽

...

Keyword(s):

Emergency Department ◽

Bacterial Infection ◽

Bacterial Culture ◽

Medical Center ◽

Laboratory Data ◽

Vital Signs ◽

Area Under The Curve ◽

Physiological Parameters ◽

Roc Curve Analysis ◽

Gram Negative

Abstract Background Early detection and treatment of Gram-negative bacteria (GNB), major causative pathogens of sepsis (a potentially fatal condition caused by the body’s response to an infection), may benefit a patient’s outcome, since the mortality rate increases by 5–10% for each hour of delayed therapy. Unfortunately, GNB diagnosis is based on bacterial culture, which is time consuming. Therefore, an economic and effective GNB (defined as a positive blood, sputum, or urine culture) infection detection tool in the emergency department (ED) is warranted. Methods We conducted a retrospective cohort study in the ED of a university-affiliated medical center between January 01, 2014 and December 31, 2017. The inclusion criteria were as follows: (1) age ≥ 18; (2) clinical suspicion of bacterial infection; (3) bacterial culture from blood, sputum, or urine ordered and obtained in the ED. Descriptive statistics was performed on patient demographic characteristics, vital signs, laboratory data, infection sites, cultured microorganisms, and clinical outcomes. The accuracy of vital signs to predict GNB infection was identified via univariate logistic regression and receiver operating characteristic (ROC) curve analysis. Results A total of 797 patients were included in this study; the mean age was 71.8 years and 51.3% were male. The odds ratios of patients with body temperature ≥ 38.5 °C, heart rate ≥ 110 beats per minute, respiratory rate ≥ 20 breaths per minute, and Glasgow coma scale (GCS) < 14, in predicting GNB infection were found to be 2.3, 1.4, 1.9, and 1.6, respectively. The area under the curve values for ROC analysis of these measures were 0.70, 0.68, 0.69, and 0.67, respectively. Conclusion The four physiological parameters were rapid and reliable independent predictors for detection of GNB infection.

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Clinical physiological parameters for the prediction of gram-negative bacterial infection in the emergency department

10.21203/rs.3.rs-27796/v3 ◽

2020 ◽

Author(s):

Chan-Peng Hsu ◽

Hsin-Yu Chen ◽

Wei-Lung Chen ◽

Jiann-Hwa Chen ◽

Chien-Cheng Huang ◽

...

Keyword(s):

Emergency Department ◽

Bacterial Infection ◽

Bacterial Culture ◽

Medical Center ◽

Laboratory Data ◽

Vital Signs ◽

Area Under The Curve ◽

Physiological Parameters ◽

Roc Curve Analysis ◽

Gram Negative

Abstract Background: Early detection and treatment of Gram-negative bacteria (GNB), major causative pathogens of sepsis (a potentially fatal condition caused by the body's response to an infection), may benefit a patient’s outcome, since the mortality rate increases by 5–10% for each hour of delayed therapy. Unfortunately, GNB diagnosis is based on bacterial culture, which is time consuming. Therefore, an economic and effective GNB (defined as a positive blood, sputum, or urine culture) infection detection tool in the emergency department (ED) is warranted.Methods: We conducted a retrospective cohort study in the ED of a university-affiliated medical center between January 01, 2014 and December 31, 2017. The inclusion criteria were as follows: (1) age ≥ 18; (2) clinical suspicion of bacterial infection; (3) bacterial culture from blood, sputum, or urine ordered and obtained in the ED. Descriptive statistics was performed on patient demographic characteristics, vital signs, laboratory data, infection sites, cultured microorganisms, and clinical outcomes. The accuracy of vital signs to predict GNB infection was identified via univariate logistic regression and receiver operating characteristic (ROC) curve analysis.Results: A total of 797 patients were included in this study; the mean age was 71.8 years and 51.3% were male. The odds ratios of patients with body temperature ≥ 38.5°C, heart rate ≥ 110 beats per minute, respiratory rate ≥ 20 breaths per minute, and Glasgow coma scale (GCS) < 14, in predicting GNB infection were found to be 2.3, 1.4, 1.9, and 1.6, respectively. The area under the curve values for ROC analysis of these measures were 0.70, 0.68, 0.69, and 0.67, respectively.Conclusion: The four physiological parameters were rapid and reliable independent predictors for detection of GNB infection.

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Normal gait, albumin and d-dimer levels identify low risk emergency department patients: a prospective observational cohort study with 365-day 100% follow-up

QJM ◽

10.1093/qjmed/hcz226 ◽

2019 ◽

Vol 113 (2) ◽

pp. 86-92

Author(s):

L Lyngholm ◽

C H Nickel ◽

J Kellett ◽

S Chang ◽

T Cooksley ◽

...

Keyword(s):

Emergency Department ◽

Laboratory Data ◽

Secondary Analysis ◽

Vital Signs ◽

Low Risk ◽

Ambulatory Care Setting ◽

Laboratory Findings ◽

Normal Gait ◽

Co Morbidity ◽

D Dimer

Abstract Background If survival could be reliably predicted many patients could be safely managed outside of hospital in an ambulatory care setting. Aim Comparison of common laboratory findings, co-morbidities, mobility and vital signs as predictors of mortality of acutely ill emergency department (ED) attendees. Design Prospective observational study. Methods Secondary analysis of 1334 consenting acutely ill patients attending a Danish ED. Results 67 (5%) out of 1334 patients died within 100 days. After logistic regression seven predictors of 100 days mortality remained significant: an albumin level ≤34 gm/l, D-dimer level >0.51 mg/l, an Asadollahi score (based on admission laboratory data and age) ≥12, a platelet count <159 X 1000/ml, impaired mobility on presentation, a respiratory rate ≥30 bpm and a Charlson co-morbidity index ≥3. Only 5 of the 442 without any of these variables died within 365 days. Only one of the 517 patients with a stable independent gait and normal d-dimer and albumin levels died within 100 days, none died within 30 days of assessment and 12 died within 365 days. Of the remaining 817 patients 66 (8%) died within 100 days. Conclusion These findings suggest that normal gait, albumin and d-dimer levels are the most parsimonious way of identifying low risk ED patients.

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Abstract WP409: Impact of Nursing Expertise and Staffing Ratio on Compliance with Post-tPA/Endovascular Patient Monitoring

Stroke ◽

10.1161/str.48.suppl_1.wp409 ◽

2017 ◽

Vol 48 (suppl_1) ◽

Author(s):

Kelly Montgomery ◽

Danielle Sindelar ◽

Julie Fussner ◽

Erin Supan ◽

Cathy Sila

Keyword(s):

Emergency Department ◽

Medical Center ◽

Diagnostic Testing ◽

Vital Signs ◽

Quality Improvement Initiative ◽

Flow Sheet ◽

University Hospitals ◽

Patient Acuity ◽

Staffing Ratio ◽

Data Points

Introduction: In 2014, the Post-tPA/Endovascular nursing monitoring flow sheet was revised to harmonize with the NIHSS exam. The challenges to performance and documentation compliance posed by the intensity and frequency of assessments have been the focus of an ongoing quality improvement initiative. Methods: All Post-tPA/Endovascular monitoring flow sheets from June 2014- June 2016 at University Hospitals Case Medical Center were reviewed for presence of neurologic assessments, vital signs, and management of hypertension per protocol. Stroke staff conducted in-services on the enhanced assessments and modified NIHSS training and house staff mentored bedside RNs in performing the NIHSS. A tip sheet was developed for staff on the modified NIHSS and real time feedback was given on all outliers. Results: Of 459 patients, compliance with all of the 684 monitoring data points ranged from 67-100% in the Neuroscience ICU (Patient: RN ratio 2:1), 75-100% in the Neuro-Intermediate Unit (Patient: RN ratio 3:1) and 40-100% in the Emergency Department (Patient: RN ratio 4:1). Overall compliance to > 95% of data points was seen in all but 5 patients with missing flow sheets. Symptomatic hemorrhagic complications after IVtPA decreased from 6.5% to 2.7%. Root-cause analysis of missing data points revealed seven areas of opportunity: Interference by diagnostic testing (29%), during patient transportation (22%), and following endovascular treatment (15%) or due to travelling RNs (8%). Missing documentation was most frequent during the q15 minute phase due to the intensity of monitoring (11%)- with the Emergency Department the most vulnerable location- and less during the q30 minute (4%) or q1 hour (3%) assessments. Units with dedicated neuroscience nursing adjusted more rapidly to the revision compared to units that do not routinely perform such assessments. Conclusions: Optimum compliance with nursing assessments and monitoring occur when there is no interference with diagnostic testing or procedures, the patient needs were a high priority and the patient acuity was well matched to the RN staffing ratio. This data supports a care model where a neuroscience trained RN nurse transitions with the patient during the first 24 hours after tPA/Endovascular therapy.

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Transparent Machine Learning models for Rapid Risk Stratification in the Emergency Department: A multi-center evaluation

10.1101/2020.11.25.20238386 ◽

2020 ◽

Author(s):

William P.T.M. van Doorn ◽

Floris Helmich ◽

Paul M.E.L. van Dam ◽

Leo H.J. Jacobs ◽

Patricia M. Stassen ◽

...

Keyword(s):

Machine Learning ◽

Emergency Department ◽

Risk Stratification ◽

Diagnostic Performance ◽

Medical Center ◽

Laboratory Data ◽

Patient Specific ◽

Learning Models ◽

Laboratory Results ◽

Machine Learning Models

AbstractIntroductionRisk stratification of patients presenting to the emergency department (ED) is important for appropriate triage. Using machine learning technology, we can integrate laboratory data from a modern emergency department and present these in relation to clinically relevant endpoints for risk stratification. In this study, we developed and evaluated transparent machine learning models in four large hospitals in the Netherlands.MethodsHistorical laboratory data (2013-2018) available within the first two hours after presentation to the ED of Maastricht University Medical Centre+ (Maastricht), Meander Medical Center (Amersfoort), and Zuyderland (locations Sittard and Heerlen) were used. We used the first five years of data to develop the model and the sixth year to evaluate model performance in each hospital separately. Performance was assessed using area under the receiver-operating-characteristic curve (AUROC), brier scores and calibration curves. The SHapley Additive exPlanations (SHAP) algorithm was used to obtain transparent machine learning models.ResultsWe included 266,327 patients with more than 7 million laboratory results available for analysis. Models possessed high diagnostic performance with AUROCs of 0.94 [0.94-0.95], 0.98 [0.97-0.98], 0.88 [0.87-0.89] and 0.90 [0.89-0.91] for Maastricht, Amersfoort, Sittard and Heerlen, respectively. Using the SHAP algorithm, we visualized patient characteristics and laboratory results that drive patient-specific RISKINDEX predictions. As an illustrative example, we applied our models in a triage system for risk stratification that categorized 94.7% of the patients as low risk with a corresponding NPV of ≥99%.DiscussionDeveloped machine learning models are transparent with excellent diagnostic performance in predicting 31-day mortality in ED patients across four hospitals. Follow up studies will assess whether implementation of these algorithm can improve clinically relevant endpoints.

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Characteristics and Outcomes of Clinic Visits Immediately Preceding Sepsis Hospitalization

American Journal of Critical Care ◽

10.4037/ajcc2021456 ◽

2021 ◽

Vol 30 (2) ◽

pp. 135-139

Author(s):

H. Catherine Miller ◽

Vincent X. Liu ◽

Hallie C. Prescott

Keyword(s):

Emergency Department ◽

Medical Center ◽

Academic Medical Center ◽

Vital Signs ◽

Time Lapse ◽

Clinic Visit ◽

Illness Severity ◽

Multiple Parameters ◽

Qsofa Score ◽

Clinic Visits

Background Existing sepsis quality improvement initiatives focus on recognition and treatment of sepsis upon hospital admission. Yet many patients are evaluated in the clinic within 1 day of sepsis hospitalization. Objectives To determine the circumstances of clinic visits that precede sepsis hospitalization, including illness severity, whether patients are referred to the hospital, and time lapse and change in illness severity between clinic and hospital evaluation. Methods In a retrospective cohort study at a tertiary academic medical center, data from electronic medical records were collected for all adult patients evaluated in an outpatient clinic within 1 day of sepsis hospitalization in 2017. Results Of 1450 patients hospitalized with sepsis, 118 had an established outpatient provider and a clinic visit within 1 day of admission and thus were included. During the clinic visit, 47 patients (39.8%) had a quick Sequential Organ Failure Assessment (qSOFA) score ≥1, and 59 (50.0%) had vital sign abnormalities. Most (74, 62.7%) were sent directly to the emergency department or hospital. Upon emergency department/hospital presentation, 62 patients (52.5%) had a worsening qSOFA score and/ or vital signs and 27 (22.9%) had worsening of multiple parameters. Median time lapse from clinic to emergency department/hospital evaluation was 3.2 hours. Conclusions One in 10 patients hospitalized for sepsis had been evaluated in a clinic within 1 day of admission. At that clinic visit, most patients had an elevated qSOFA score or abnormal vital signs and a majority were sent directly to the emergency department/hospital. Half experienced clinical deterioration between the clinic visit and arrival in the emergency department/hospital.

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The Causative Organisms of Bacterial Meningitis and their Antimicrobial Resistance Profiles in Iranian Children in 2011-2016

Infectious Disorders - Drug Targets ◽

10.2174/1871526519666181123130101 ◽

2020 ◽

Vol 20 (2) ◽

pp. 229-236

Author(s):

Sepideh Keshavarz Valian ◽

Shima Mahmoudi ◽

Babak Pourakbari ◽

Maryam Banar ◽

Mohammad Taghi Haghi Ashtiani ◽

...

Keyword(s):

Antimicrobial Resistance ◽

Bacterial Meningitis ◽

Medical Center ◽

Blood Cultures ◽

Gram Positive ◽

Gram Negative ◽

Number Of Patients ◽

Resistance Patterns ◽

Iranian Children ◽

Surgery Department

Objective: The study aimed to describe the identity and antimicrobial resistance patterns of the causative agents of bacterial meningitis in children referred to Children’s Medical Center (CMC) Hospital, Tehran, Iran. Methods: This retrospective study was performed at CMC Hospital during a six-year period from 2011 to 2016. The microbiological information of the patients with a diagnosis of bacterial meningitis was collected and the following data were obtained: patients’ age, sex, hospital ward, the results of CSF and blood cultures, and antibiotic susceptibility profiles of isolated organisms. Results: A total of 118 patients with bacterial meningitis were admitted to CMC hospital. Sixty-two percent (n=73) of the patients were male. The median age of the patients was ten months (interquartile range [IQR]: 2 months-2 years) and the majority of them (n=92, 80%) were younger than two years of age. The highest number of patients (n=47, 40%) were admitted to the surgery department. Streptococcus epidermidis was the most frequent isolated bacterium (n=27/127, 21%), followed by Klebsiella pneumoniae (n=20/127, 16%), and Staphylococcus aureus (n=16/127, 12.5%). Blood culture was positive in 28% (n=33/118) of patients. Ampicillin-sulbactam and imipenem were the most effective antibiotics against Gram-negative bacteria isolated from CSF cultures. In the case of Gram-positive organisms, ampicillinsulbactam, vancomycin, and linezolid were the best choices. Imipenem was the most active drug against Gram-negative blood pathogens. Also, ampicillin and vancomycin had the best effect on Gram-positive bacteria isolated from blood cultures. Conclusion: Results of this study provide valuable information about the antibiotic resistance profiles of the etiologic agents of childhood meningitis, which can be used for prescription of more effective empirical therapies.

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