Evidence map›Paper›PMID 33546651›Full record

ArticleBMC pulmonary medicine2021

A scoring system derived from electronic health records to identify patients at high risk for noninvasive ventilation failure.

Mihaela S Stefan, Aruna Priya, Penelope S Pekow, Jay S Steingrub, Nicholas S Hill, Tara Lagu, Karthik Raghunathan, Anusha G Bhat, Peter K Lindenauer

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Article in BMC pulmonary medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Mihaela S StefanInstitute for Healthcare Delivery and Population Science, University of Massachusetts Medical School - Baystate, Springfield, MA, USA. Mihaela.Stefan@baystatehealth.org.
Aruna PriyaInstitute for Healthcare Delivery and Population Science, University of Massachusetts Medical School - Baystate, Springfield, MA, USA.
Penelope S PekowInstitute for Healthcare Delivery and Population Science, University of Massachusetts Medical School - Baystate, Springfield, MA, USA.
Jay S SteingrubDivision of Pulmonary and Critical Care, Department of Medicine, University of Massachusetts Medical School - Baystate, Springfield, MA, USA.
Nicholas S HillDivision of Pulmonary and Critical Care, Tufts University School of Medicine, Boston, MA, USA.
Tara LaguInstitute for Healthcare Delivery and Population Science, University of Massachusetts Medical School - Baystate, Springfield, MA, USA.
Karthik RaghunathanDivision of Veterans Affairs, Department of Anesthesiology, Duke University Medical Center, Durham, NC, USA.
Anusha G BhatDepartment of Medicine, University of Massachusetts Medical School - Baystate, Springfield, MA, USA.
Peter K LindenauerInstitute for Healthcare Delivery and Population Science, University of Massachusetts Medical School - Baystate, Springfield, MA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo develop and validate a clinical risk prediction score for noninvasive ventilation (NIV) failure defined as intubation after a trial of NIV in non-surgical patients.

designRetrospective cohort study of a multihospital electronic health record database. PATIENTS: Non-surgical adult patients receiving NIV as the first method of ventilation within two days of hospitalization. MEASUREMENT: Primary outcome was intubation after a trial of NIV. We used a non-random split of the cohort based on year of admission for model development and validation. We included subjects admitted in years 2010-2014 to develop a risk prediction model and built a parsimonious risk scoring model using multivariable logistic regression. We validated the model in the cohort of subjects hospitalized in 2015 and 2016. MAIN

resultsOf all the 47,749 patients started on NIV, 11.7% were intubated. Compared with NIV success, those who were intubated had worse mortality (25.2% vs. 8.9%). Strongest independent predictors for intubation were organ failure, principal diagnosis group (substance abuse/psychosis, neurological conditions, pneumonia, and sepsis), use of invasive ventilation in the prior year, low body mass index, and tachypnea. The c-statistic was 0.81, 0.80 and 0.81 respectively, in the derivation, validation and full cohorts. We constructed three risk categories of the scoring system built on the full cohort; the median and interquartile range of risk of intubation was: 2.3% [1.9%-2.8%] for low risk group; 9.3% [6.3%-13.5%] for intermediate risk category; and 35.7% [31.0%-45.8%] for high risk category.

conclusionsIn patients started on NIV, we found that in addition to factors known to be associated with intubation, neurological, substance abuse, or psychiatric diagnoses were highly predictive for intubation. The prognostic score that we have developed may provide quantitative guidance for decision-making in patients who are started on NIV.

Indexed as

Clinical Decision RulesNoninvasive VentilationAgedAsthmaBlack or African AmericanCohort StudiesElectronic Health RecordsFemaleHeart FailureHospital MortalityHumansIntubation, IntratrachealLength of StayMaleMiddle AgedMyocardial InfarctionAcute respiratory failureIntubationMechanical ventilationnoninvasive ventilation failurePredictive score

Identifiers

PMID33546651
PMCPMC7863252

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