The development and implementation of a nurse practitioner sepsis screening team: Impact on transfer mortality
Bibliographic record
Abstract
Background: Sepsis is a potentially deadly but treatable condition that occurs as a result of the systemic manifestations of infection. Despite large healthcare expenditures, patient outcomes can be poor, and survivors may still suffer from permanent organ damage, cognitive impairment, and physical disability. Failure to recognize and implement early goal-directed therapy leads to increased mortality. A review of hospital mortality identified that sepsis among inbound transfer patients to acute care units significantly contributed to the overall hospital mortality. As part of a multipronged, multidisciplinary approach, a nurse practitioner sepsis screening team was implemented to improve early diagnosis and treatment of sepsis and decrease mortality in this high-risk population. Methods: A large academic medical facility located in the Texas Medical Center in Houston accepts a significant number of transfer patients requiring a higher level of care from other institutions. A nurse practitioner sepsis screening team was created to focus on this highly vulnerable group. A validated, electronic screening tool was utilized to screen patients and facilitate early identification and treatment of sepsis. The nurse practitioner team screened and evaluated 3,268 inbound transfer patients from 10/01/2009 to 06/30/2012. When a high suspicion for sepsis was appreciated, or another acute condition was identified, the nurse practitioner collaborated with the attending physician and initiated appropriate treatment. The data analyzed were part of an Institutional Review Board (IRB) approved prospectively collected data set. The data were collected over a 57 month period spanning from 09/30/2007 through 06/30/2012 on all inbound transfer patients to the facility, which include pre-screening baseline statistics. Basic demographics including the patient’s age, gender, and race were collected. The outcome variable was status at discharge from the facility (alive or dead). After verifying assumptions of the chi-square test were met, a Pearson’s chi-square was run against the data set. All data were analyzed using IBM Corp. Released 2012. IBM SPSS Statistics for Windows, Version 21.0. Armonk, NY: IBM Corp. Results: There was a significant association between inbound transfer patients who were evaluated upon arrival at this institution by the nurse practitioner sepsis screening team and mortality in this population regardless of their diagnoses (χ 2 (1) 115.04, p < .001). A patient not screened by the team was more likely to die during the hospitalization than a transfer patient that was screened. Conclusion: In this institution, the development and implementation of a nurse practitioner sepsis screening team has contributed to reducing mortality among the inbound acute care patient transfer population regardless of diagnoses. Further investigation is needed to understand the exact mechanisms that have contributed to this outcome.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".