Prophetic

Bergin, Stephen P.; Coles, Adrian; Calvert, Sara B.; Farley, John; Powers, John H.; Zervos, Marcus J.; Sims, Matthew; Kollef, Marin H.; Durkin, Michael J.; Kabchi, Badih A.; Donnelly, Helen K.; Bardossy, Ana C.; Greenshields, Claire; Rubin, Daniel B.; Sun, Jie; Chiswell, Karen; Santiago, Joel Ferreira; Gu, Peidi; Tenaerts, Pamela; Fowler, Vance G.; Holland, David

doi:10.1016/j.chest.2020.06.034

Cited by 21 publications

(17 citation statements)

References 35 publications

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“…To convert white blood cell count to ×10 between reviewers, correspond variably with histology, and clinical audits suggest that a third or more of patients treated for NV-HAP do not have pneumonia. [40][41][42][43][44] Discharge diagnosis codes also do not provide reliable estimates of NV-HAP incidence and outcomes because they too are neither sensitive nor specific. 10,11 Even cases identified using formal CDC criteria are inconsistently confirmed on external review.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Incidence and Outcomes of Non–Ventilator-Associated Hospital-Acquired Pneumonia in 284 US Hospitals Using Electronic Surveillance Criteria

Jones,

Sarvet,

Ying

et al. 2023

JAMA Netw Open

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ImportanceNon–ventilator-associated hospital-acquired pneumonia (NV-HAP) is a common and deadly hospital-acquired infection. However, inconsistent surveillance methods and unclear estimates of attributable mortality challenge prevention.ObjectiveTo estimate the incidence, variability, outcomes, and population attributable mortality of NV-HAP.Design, Setting, and ParticipantsThis cohort study retrospectively applied clinical surveillance criteria for NV-HAP to electronic health record data from 284 US hospitals. Adult patients admitted to the Veterans Health Administration hospital from 2015 to 2020 and HCA Healthcare hospitals from 2018 to 2020 were included. The medical records of 250 patients who met the surveillance criteria were reviewed for accuracy.ExposuresNV-HAP, defined as sustained deterioration in oxygenation for 2 or more days in a patient who was not ventilated concurrent with abnormal temperature or white blood cell count, performance of chest imaging, and 3 or more days of new antibiotics.Main Outcomes and MeasuresNV-HAP incidence, length-of-stay, and crude inpatient mortality. Attributable inpatient mortality by 60 days follow-up was estimated using inverse probability weighting, accounting for both baseline and time-varying confounding.ResultsAmong 6 022 185 hospitalizations (median [IQR] age, 66 [54-75] years; 1 829 475 [26.1%] female), there were 32 797 NV-HAP events (0.55 per 100 admissions [95% CI, 0.54-0.55] per 100 admissions and 0.96 per 1000 patient-days [95% CI, 0.95-0.97] per 1000 patient-days). Patients with NV-HAP had multiple comorbidities (median [IQR], 6 [4-7]), including congestive heart failure (9680 [29.5%]), neurologic conditions (8255 [25.2%]), chronic lung disease (6439 [19.6%]), and cancer (5,467 [16.7%]); 24 568 cases (74.9%) occurred outside intensive care units. Crude inpatient mortality was 22.4% (7361 of 32 797) for NV-HAP vs 1.9% (115 530 of 6 022 185) for all hospitalizations; 12 449 (8.0%) were discharged to hospice. Median [IQR] length-of-stay was 16 (11-26) days vs 4 (3-6) days. On medical record review, pneumonia was confirmed by reviewers or bedside clinicians in 202 of 250 patients (81%). It was estimated that NV-HAP accounted for 7.3% (95% CI, 7.1%-7.5%) of all hospital deaths (total hospital population inpatient death risk of 1.87% with NV-HAP events included vs 1.73% with NV-HAP events excluded; risk ratio, 0.927; 95% CI, 0.925-0.929).Conclusions and RelevanceIn this cohort study, NV-HAP, which was defined using electronic surveillance criteria, was present in approximately 1 in 200 hospitalizations, of whom 1 in 5 died in the hospital. NV-HAP may account for up to 7% of all hospital deaths. These findings underscore the need to systematically monitor NV-HAP, define best practices for prevention, and track their impact.

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Section: Discussionmentioning

confidence: 99%

“…Microbiologic confirmation of infection occurs in fewer than half of patients who undergo cultures . Clinical and radiographic interpretations vary between reviewers, correspond variably with histology, and clinical audits suggest that a third or more of patients treated for NV-HAP do not have pneumonia …”

Section: Discussionmentioning

confidence: 99%

Incidence and Outcomes of Non–Ventilator-Associated Hospital-Acquired Pneumonia in 284 US Hospitals Using Electronic Surveillance Criteria

Jones,

Sarvet,

Ying

et al. 2023

JAMA Netw Open

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show abstract

“…It can be acquired in community or hospital settings, as well as through the use of invasive mechanical ventilation [1,2]. Ventilatorassociated pneumonia (VAP) is notoriously di cult to diagnose due to the absence of a diagnostic gold standard, which can be attributed to the diversity of disease-causing pathogens, lung cultures that limit sampling to anatomical surfaces, clinical interpretation of pathogens extracted in lung samples, underlying health issues in individuals, and agreement of radiological scans with centers for disease control (CDC) diagnostic criteria [1][2][3][4][5]. VAP, which is de ned as pneumonia which develops after more than 48 hours following the initiation of invasive mechanical ventilation [4], is estimated to occur in 5%-67% of intubated patients, with the highest rates among those who are hospitalized due to physical trauma [6,7].…”

Section: Introductionmentioning

confidence: 99%

Predicting Ventilator-Associated Pneumonia with Machine Learning

Giang

Calvert

Barnes

et al. 2020

Preprint

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Objective Ventilator-associated pneumonia (VAP) is the most common and fatal nosocomial infection in intensive care units (ICUs). Existing methods for identifying VAP display low accuracy, and their use may delay antimicrobial therapy. VAP diagnostics derived from machine learning methods that utilize electronic health record data have not yet been explored. The objective of this study is to compare the performance of a variety of machine learning models trained to predict whether VAP will be diagnosed during the patient stay.Methods A retrospective study examined data from 6,129 adult ICU encounters lasting at least 48 hours following the initiation of mechanical ventilation. The gold standard was the presence of a diagnostic code for VAP. Five different machine learning models were trained to predict VAP 48 hours after initiation of mechanical ventilation. Model performance was evaluated with regard to area under the receiver operating characteristic curve (AUROC) on a 10% hold-out test set. Feature importance was measured in terms of Shapley values.Results The highest performing model achieved an AUROC value of 0.827. The most important features for the best-performing model were the length of time on mechanical ventilation, presence of antibiotics, sputum test frequency, and most recent Glasgow Coma Scale assessment.Discussion Supervised machine learning using patient electronic health record data is promising for VAP diagnosis and warrants further validation. Conclusion This tool has the potential to aid the timely diagnosis of VAP.

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“…It provides patients, caregivers, clinicians, and study staff more time to contemplate and discuss the proposed study and to complete enrollment without the pressure of having to treat immediately. This is important because investigators have reported that more than 50% of patients with HABP are excluded from treatment trials because they receive nonstudy antibiotics before investigators have time to enroll them . Advance consent could allow patients the opportunity to consider the risks and benefits of participating in a treatment trial when they are clinically stable rather than fighting respiratory distress or slipping into delirium.…”

mentioning

confidence: 99%

“…The obvious solution is to selectively enroll high-risk patients. The same study team has separately reported preliminary figures on the efficiency of limiting screening to intensive care unit patients on mechanical ventilation or high levels of supplemental oxygen alone; 11% of this population developed pneumonia, allowing for a much more promising enrollment to randomization ratio …”

mentioning

confidence: 99%

Tackling the Hospital-Acquired Pneumonia Enrollment Paradox

Klompas

2018

JAMA Netw Open

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Hospital-acquired bacterial pneumonia (HABP) is the most common hospital-acquired infection, the most common cause of hospital-onset sepsis, and a highly morbid complication for patients. 1 The estimated mortality rate is between 15% and 30% for both ventilated and nonventilated patients. 2,3 There is consequently a pressing need for better strategies to prevent and treat this condition. In practice, however, it is very difficult and expensive for investigators to enroll adequate numbers of patients into pneumonia treatment studies. Barriers include the limited amount of time available to

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Prophetic

Cited by 21 publications

References 35 publications

Incidence and Outcomes of Non–Ventilator-Associated Hospital-Acquired Pneumonia in 284 US Hospitals Using Electronic Surveillance Criteria

Incidence and Outcomes of Non–Ventilator-Associated Hospital-Acquired Pneumonia in 284 US Hospitals Using Electronic Surveillance Criteria

Predicting Ventilator-Associated Pneumonia with Machine Learning

Tackling the Hospital-Acquired Pneumonia Enrollment Paradox

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