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Enregistrement W2737420799 · doi:10.2215/cjn.05940617

Reconfiguring Health Care Delivery to Improve AKI Outcomes

2017· letter· en· W2737420799 sur OpenAlexaboutno aff
Jay L. Koyner

Notice bibliographique

RevueClinical Journal of the American Society of Nephrology · 2017
Typeletter
Langueen
DomaineMedicine
ThématiqueAcute Kidney Injury Research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineIntensive care medicineStandardizationElectronic health recordMEDLINEHealth careHealth recordsMedical emergency

Résumé

récupéré en direct d'OpenAlex

AKI is the most common reason for inpatient nephrology consultation and has been shown to carry an increased risk of morbidity and mortality in a variety of clinical settings (1–3). These characteristics coupled with the often delayed recognition of AKI, the lack of specific pharmaceutical therapies, and primary reliance on supportive care as treatment represent major challenges and have potentially hindered progress improving the care of patients with AKI. Nephrologists have sought to transform the care of patients with AKI through changing these factors. Although little has changed with regard to novel therapies for AKI, the past several years have seen progress in the earlier recognition and standardization of supportive care in the setting of evolving AKI (4–10). Early recognition of AKI has been an area of intense investigation over the last decade, with efforts focused on blood and urinary biomarkers of AKI and electronic alerts. Electronic alerts have come in two flavors: those seeking to predict the development of AKI and adverse events before they happen (6,7) and those that notify the physician that AKI has just been diagnosed in a specific patient (4,10). Although clinical implementation of the former, predictive algorithms, has been limited, these complex risk scores show a large degree of promise owing to their utilization of the data available in the electronic health record, advancements in machine learning, and prior successes in other clinical settings (6,7,11). Conversely, electronic algorithms for the notification of recently diagnosed AKI have shown more progress and promise. Although an initial investigator-masked, parallel group, randomized, controlled trial of such alerts did not show improved outcomes, this trial did not link the alert to an AKI-focused specific intervention but rather, solely notified the primary treating physician and pharmacist to the presence of AKI (12). Since this trial, Selby and colleagues (4,13,14) have published a series of papers documenting their experiences in implementing a real-time electronic AKI alert and then linking the alert to an AKI-focused care bundle. They showed that a care bundle focused on history and physical examination, limited diagnostics, and treatment strategies, such as ensuring euvolemia and preventing fluid overload; identifying the underlying cause of AKI (stopping nephrotoxins, relieving obstruction, etc.); treating the complications of AKI; and dose adjusting medications facilitated improved patient outcomes. More specifically, those patients who had their care bundle completed within the first 24 hours (n=306 of 2997; 12% of the cohort) experienced better outcomes (fewer severe AKI events and lower mortality) (4). Care bundle completion provided a dose-response effect, with those having their bundle completed in >24 hours having better outcomes compared with those who never had the bundle completed. Thus, adherence to a simple kidney focused care bundle in the setting of early AKI may improve inpatient outcomes. This concept has been recently extended in a randomized, controlled trial using tissue inhibitor of metalloproteinase 2 and IGF binding protein 7 (Nephrocheck) instead of an electronic alert (5). In the Prevention of Cardiac Surgery-Associated AKI by Implementing the KDIGO Guidelines in High Risk Patients dentified by Biomarkers (PrevAKI) Trial, patients who had recently undergone cardiac surgery and were found to have [tissue inhibitor of metalloproteinase 2] × [IGF binding protein 7] levels >0.34 hours after cardiac surgery were randomized to receive a “[Kidney Disease Improving Global Outcomes–]based care bundle” consisting of optimization of volume status and hemodynamics, avoidance of nephrotoxins, and prevention of hyperglycemia. As with the electronic alert studies, the development and severity of AKI were significantly reduced in those randomized to the bundle compared with usual care. AKI of any stage occurred in 76 of 138 (55%) in the bundle group compared with 99 of 138 (71%) in the usual care (P=0.04), whereas the incidence of severe AKI (stage 2 or 3) occurred in 41 of 138 (30%) in the bundle group compared with 62 of 138 (45%) in the control group (P=0.01) (5). Thus, the timely identification of those with early postoperative AKI coupled with a systematic kidney protective care plan led to improved patient outcomes (5). In this issue of the Clinical Journal of the American Society of Nephrology, Acedillo et al. (15) report on a population-based retrospective cohort study, in which they examine the outcomes of patients who were discharged from the emergency department (ED) with AKI. In this study, which analyzed 9 years of emergency room visits in Ontario, Canada, the authors found 6346 ED discharges with serum creatinine–defined Kidney Disease Improving Global Outcomes AKI. Although 95% of these discharges only had stage 1 AKI, they showed that ED discharges with AKI were associated with lower long-term mortality compared with a matched cohort hospitalized after having AKI in the ED. One hundred and thirty of those discharged from the ED with AKI (3%) died within 30 days compared with 522 (12%) of those admitted with AKI (relative risk, 0.3; 95% confidence interval [95% CI], 0.2 to 0.3; P<0.01). Importantly, this effect was driven by the higher mortality in those patients admitted to the hospital with mild AKI (stages 1 and 2 AKI). There was no difference in the mortality for those who went on to develop stage 3 AKI, regardless of whether they were admitted or discharged home from the ED (7 of 44 died; 16% identical rates in both groups; relative risk, 1.0; 95% CI, 0.4 to 2.6; P>0.99). There was no statistical difference in the 30-day receipt of hospital-based RRT when comparing those discharged home with AKI with those admitted to the hospital, although there was a trend to more RRT in the hospitalized cohort (0.4% versus 0.8%; relative risk, 0.6; 95% CI, 0.3 to 1.0; P=0.06). ED discharges home with AKI were associated with higher mortality (relative risk, 1.6; 95% CI, 1.2 to 2.0; P=0.01) and higher rates of 30-day hospital-based RRT (relative risk, 2.7; 95% CI, 1.2 to 6.0; P=0.01) compared with a separate matched cohort of ED home discharges without AKI (n=6188) (15). Event rates were slightly lower in the discharged home without AKI cohort, with 136 (2%) and 87 (1%) of those with and without AKI experiencing 30-day mortality, respectively, and 19 (0.3%) and seven (0.1%) going on to receive hospital-based RRT, respectively. Although all of these analyses suffer from the inherent limitations of a retrospective cohort study, importantly, the results highlight two separate aspects of AKI care. First, the treating ED physicians were able to identify, manage, treat, and discharge home a cohort of patients with AKI in the setting of other acute on chronic medical issues and risk stratify them as lower risk for morbidity and mortality compared with the other patients who were admitted to the hospital from the ED with AKI. However, it also highlights that ED-based AKI, although it was almost exclusively stage 1, carries a significant degree of morbidity and mortality, which often go under-recognized by physicians all over the hospital, not just in the ED. Second, this study emphasizes that every patient with an incident case of AKI is an opportunity to improve patient care and prevent morbidity and mortality, regardless of the AKI setting or severity. It is important to acknowledge that, in individual patients, the link between AKI and adverse outcomes may not be causal, because the AKI (defined by a change in serum creatinine) may serve as a marker of another disease process that driving the risk for morbidity and mortality (e.g., cancer, cardiac disease, liver disease, or the impending need for a surgical procedure). Regardless of its underlying cause and severity, AKI needs to be identified, treated, and managed. Several of the aforementioned methods, including incorporating biomarkers of kidney injury, electronic medical record detection of AKI, and AKI care bundles, can facilitate improved patient outcomes in the setting of AKI. Some have advocated for and piloted programs around an AKI-focused rapid response team, which may improve patient outcomes across a variety of clinical settings (16,17). Over the last decade, this multidisciplinary rapid response team approach has been increasingly accepted and effective in improving patient care and outcomes (18–21). Identifying patients with early AKI (defined by creatinine, urine output, and/or biomarkers) and then adopting a multifaceted kidney-focused care bundle to be implemented by a multidisciplinary AKI-focused rapid response team may be the first steps in improving patient outcomes. Linking this AKI-focused inpatient care with the necessary nephrology outpatient follow-up will help us completely reconfigure the delivery of health care to those at risk for and diagnosed with hospital- and ED-based AKI (22). Disclosures J.L.K. has received research funds from Astute Medical and Satellite Healthcare and consulting fees from Astute Medical, Sphingotec, and Pfizer.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,005
score de la tête « metaresearch » (Gemma)0,014
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,011
Score d'incertitude au seuil0,037

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0050,014
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0030,003
Science ouverte0,0020,004
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0110,002

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,079
Tête enseignante GPT0,448
Écart entre enseignants0,369 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2017
Routes d'admission1
Résumé présentoui

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