Increased dose of diuretics correlates with severity of heart failure and renal dysfunction and does not lead to reduction of mortality and rehospitalizations due to acute decompensation of heart failure; data from AHEAD registry
Bibliographic record
Abstract
Kontext: Diuretika jsou doporuÄena ke kontrole symptomĹŻ kongesce a retence tekutin u pacientĹŻ se srdeÄnĂm selhĂĄnĂm, ale jejich vliv doposud nebyl zkoumĂĄn v randomizovanĂ˝ch klinickĂ˝ch studiĂch. ExistujĂ konfliktnĂ data o moĹžnĂŠm pozitivnĂm a negativnĂm ĂşÄinku v zĂĄvislosti na dĂĄvce kliÄkovĂŠho diuretika. CĂlem naĹĄĂ analĂ˝zy je vyhodnotit, zda by relativnÄ malĂŠ zvýťenĂ dĂĄvky furosemidu mohlo snĂĹžit vĂ˝skyt rehospitalizacĂ pro akutnĂ dekompenzaci a/nebo celkovou mortalitu.Metody a vĂ˝sledky: Celkem jsme vyhodnotili 1 119 pacientĹŻ pĹijatĂ˝ch pro akutnĂ dekompenzaci srdeÄnĂho selhĂĄnĂ, kteĹĂ byli propuĹĄtÄni do domĂĄcĂho oĹĄetĹovĂĄnĂ ve stabilizovanĂŠm stavu. VĹĄichni pĹeĹživĹĄĂ pacienti byli sledovĂĄni po dobu nejmĂŠnÄ dvou let. PrimĂĄrnĂm cĂlovĂ˝m ukazatelem byla kombinace opakovanĂŠ hospitalizace pro akutnĂ srdeÄnĂ selhĂĄnĂ a celkovĂŠ mortality. PrimĂĄrnĂ analĂ˝za prokĂĄzala vĂ˝znamnĂŠ rozdĂly v charakteristikĂĄch a prognĂłze mezi pacienty, kteĹĂ nemuseli uĹžĂvat ŞådnĂĄ kliÄkovĂĄ diuretika, a tÄmi, jimĹž bylo nutno podĂĄvat furosemid v dĂĄvce > 125 mg. Srovnali jsme proto skupinu pacientĹŻ uĹžĂvajĂcĂch furosemid v nĂzkĂ˝ch dĂĄvkĂĄch (10-40 mg) se skupinou uĹžĂvajĂcĂch pouze vysokĂŠ dĂĄvky furosemidu (41-125 mg). VyĹĄĹĄĂ dĂĄvka diuretika korelovala se zĂĄvaĹžnostĂ onemocnÄnĂ (niŞťà systolickĂ˝ krevnĂ tlak, NYHA III, niŞťà ejekÄnĂ frakce levĂŠ komory, vyĹĄĹĄĂ hodnoty kreatininu). DlouhodobÄ byly mortalita a poÄty opakovanĂ˝ch hospitalizacĂ niŞťà ve skupinÄ s niŞťĂmi dĂĄvkami diuretik (p = 0,037, resp. p = 0,036), avĹĄak po adjustaci pĂĄrovĂĄnĂm podle propensity skĂłre (propensity score matching) byla incidence primĂĄrnĂho cĂlovĂŠho ukazatele v obou skupinĂĄch srovnatelnĂĄ.ZĂĄvÄr: DĂĄvka kliÄkovĂŠho diuretika doporuÄenĂĄ pacientĹŻm s akutnĂm srdeÄnĂm selhĂĄnĂm pĹi jejich propuĹĄtÄnĂ z nemocnice koreluje se zĂĄvaĹžnostĂ srdeÄnĂho selhĂĄnĂ. VyĹĄĹĄĂ dĂĄvka furosemidu (41-125 mg) ve srovnĂĄnĂ s niŞťà dĂĄvkou (10-40 mg) mĂĄ po adjustaci obou skupin pacientĹŻ pomocĂ propensity skĂłre neutrĂĄlnĂ vliv na vĂ˝skyt kombinovanĂŠho cĂlovĂŠho ukazatele mortality a/nebo rehospitalizacĂ pro akutnĂ dekompenzaci srdeÄnĂho selhĂĄnĂ. V ŞådnĂŠm pĹĂpadÄ tato relativnÄ vyĹĄĹĄĂ dĂĄvka nemÄla pozitivnĂ ĂşÄinky na snĂĹženĂ rizika rehospitalizacĂ/mortality.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".