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Record W2324258136 · doi:10.1097/pcc.0b013e3182976cc6

Quality of Care Leads to Quality of Life After Trauma in Children*

2013· letter· en· W2324258136 on OpenAlexaboutno aff
Warwick Butt

Bibliographic record

VenuePediatric Critical Care Medicine · 2013
Typeletter
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsnot available
FundersNational Center for Advancing Translational Sciences
KeywordsMedicineQuality (philosophy)Trauma careMedical emergencyIntensive care medicine

Abstract

fetched live from OpenAlex

Intensive care was initially developed with a raison d' être of “save a life.” As extracorporeal organ replacement therapy rapidly evolved, this attitude matured into concern about the function and quality of life (QOL) of survivors of ICU (1). Recognition that medical errors caused morbidity and mortality raised concerns about patient safety and led to an increased focus on adverse events and the quality of care delivered (2). Organization and regionalization of pediatric critical care services improved survival in children admitted to intensive care (3); likewise the organization of trauma care saved lives (4). Even in countries with well-developed systems of care, ongoing development and refinement is necessary, as the implementation of the London Trauma System in April 2010 highlights (5). In this issue of Pediatric Critical Care Medicine, Cooper et al (6) compare quality improvement (QI) practices in 184 trauma centers (adult, mixed adult and pediatric, and pediatric) in the United States, Canada, Australia, and New Zealand. The number of quality indicators recorded and used for adult, mixed, and pediatric trauma centers was 27, 23, and 26, respectively. The most commonly used activities for QI included morbidity and mortality conferences and quality of care audits. Report cards were used in approximately half the centers and internal (80%, 81%, and 68%) and external (78%, 78%, and 68%) benchmarking were also used. When QI was classified by phases of care, structure indicators were measured in approximately one third or less (36%, 17%, and 21%), but patient outcome indicators were measured by most centers (89%, 83%, and 95%). All trauma centers that measured QI also measured processes of care. Gruen et al (7) have recently reviewed “trauma system performance” and how we may evaluate the provision of effective and safe care for patients following an acute traumatic injury. These include the following: 1) How is the trauma system organized? 2) Which part of the system is being evaluated (prehospital, hospital, rehabilitation, or prevention)? 3) What is the purpose of the evaluation? 4) Which phase of the “trauma care” is being evaluated (structure, process, or outcome)? and 5) What patient outcome is being measured (mortality, function, or QOL)? Once these have been considered, we then need to decide whether we will review the entire system or an individual hospital performance and then address another fundamental question, namely, what makes a good quality indicator? The National Quality Forum (8) considered these following criteria as useful for a measure of quality 1) importance (relevant to a large number of patients or a large improvement in a few); 2) scientific acceptability with both reliability and validity (reliability is the ability for the indicator to produce the same result on repeated measures and validity is that the indicator measures what it intended); 3) feasibility (able to be done); and 4) usability (can be easily understood by the intended groups). There are a number of differences between adult and pediatric trauma. The relationship between the volume of patients treated and the outcome for complex diseases is well established, and the volume of trauma presenting to stand-alone pediatric trauma centers is often small. The type of injuries that children suffer differs significantly from adults (9), albeit adolescents tend to have adult-type injuries (10). The prevalence of head injury in patients admitted to intensive care after trauma is much higher in children than adults (11). These differences immediately create debate about whether the best system of care for children is a stand-alone pediatric program or as part of an adult program (9). The marked variation in the care of children with splenic injuries depending on the type of treating trauma center highlights this (12). As stated by Cooper et al (6), no attempt was made by their study to evaluate the actual QIs themselves. However, they did note some other differences between adult and pediatric centers, including the focus on QI for safety, medical errors, and adverse events in adults compared to timeliness, diagnosis, and monitoring in children. What quality indicators should we use? In 2009, a scoping review by Stelfox et al (13) looked at what quality indicators existed for the evaluation of trauma care and found many QIs (1,572) had been proposed; these were divided into eight categories: American College of Surgeons-Committee on Trauma (ACS-COT) audit filters (42%), ACS-COT audit filters (19%), patient safety indicators (13%), trauma center/system indicators (10%), measures of outcome (7.5%), peer review (5.5%), general audit measures (2%), and guideline presence and use (1%). Indicators related to the phase of care were prehospital and hospital processes (60%) and outcomes (23%), posthospital and secondary prevention less than 5%. Many QIs are used but while audit filters that monitor the timeliness of specific actions (such as time to surgery), diagnostic tests (such as CT scan), or expected outcomes are inherently appealing, unfortunately, they are not supported by evidence of benefit (14). However, a recent study shows that delay in operative intervention (craniotomy, intracranial pressure monitoring, and abdominal surgery) led to a longer ICU and hospital stay with no difference in mortality (15); this may or may not be considered beneficial depending on what your outcome measure may be! We can group the care in hospital into the well-established, three phases of care, namely, the structure of the system (availability of highly trained staff, resources available for care, ongoing education and training, etc.), the processes of care (time to CT, blood transfusion or urgent surgery, etc.), and the outcome of care (mortality or long-term quality). Clearly for critical illness in children including trauma, we are most concerned with patient outcome. Initially survival but increasingly long-term QOL is used as the measured outcome. Good outcomes occur with mild injuries, use of a proxy, and short-term follow-up whereas a much poorer QOL occurs with severe injuries and longer term follow-up (16). Other “non quality of care” factors can have a major influence on long-term outcome. These include the availability or lack of community and family and social supports (17). Also changes in societal religious and legal attitudes over time may lead to an improvement in mortality but at the cost of increased survival of children with major cognitive impairment, motor dysfunction, and difficulties with higher executive functions (18). It is imperative that we evaluate the quality of healthcare offered to all children especially after trauma. This study is yet another effort by Cooper et al (6) to continue to raise the broad issues and complexities of quality indicators and how they are recorded and how they vary widely. They continue to highlight the need for more research to determine and then measure simple robust indicators of quality of care that will allow further improvement in treatments offered to critically ill patients and hopefully translate into improved outcomes (long-term QOL) for patients and their families.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.045
GPT teacher head0.377
Teacher spread0.332 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreCommentary

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".

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Citations1
Published2013
Admission routes1
Has abstractyes

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