MétaCan
Menu
← Back to cohort
Record W4386954198 · doi:10.1101/2023.09.21.23295890

Hospital-Acquired Acute Kidney Injury in Non-critical Medical Patients in a Developing Country Tertiary Hospital: Incidence and Predictors

2023· preprint· en· W4386954198 on OpenAlexaff
Nahom Dessalegn Mekonnen, Tigist Workneh Leulseged, Buure Ayderuss Hassen, Kidus Haile Yemaneberhan, Helen Surafeal Berhe, Nebiat Adane Mera, Anteneh Abera Beyene, Lidiya Zenebe Getachew, Birukti Gebreyohannes Habtezgi, Feven Negasi Abriha

Bibliographic record

VenuemedRxiv · 2023
Typepreprint
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsMedicine Hat College
Fundersnot available
KeywordsMedicineAcute kidney injuryIncidence (geometry)Interquartile rangeRelative riskInternal medicineRetrospective cohort studyEmergency medicineIntensive care medicineConfidence interval

Abstract

fetched live from OpenAlex

ABSTRACT Background Acute kidney injury (AKI) is a frequent complication in critical patients leading to worse prognosis. Although the consequences of AKI are worse among critical patients, AKI is also associated with less favorable outcomes in non-critical patients. Hence, understanding the magnitude of the problem in these patients is crucial, yet there is a scarcity of evidence in non-critical settings, especially in resource limited countries. Hence, the study aimed at determining the incidence and predictors of hospital acquired acute kidney injury (HAAKI) in non-critical medical patients who were admitted at a large tertiary hospital in Ethiopia. Methods A retrospective chart review study was conducted among 232 hospitalized non-critical medical patients admitted to St. Paul’s Hospital Millennium Medical College between January 2020 and January 2022. Data was characterized using frequency and median with interquartile range. To identify predictors of HAAKI, a log binomial regression model was fitted at a p value of ≤ 0.05. The magnitude of association was measured using adjusted relative risk (ARR) with its 95% CI. Results During the median follow-up duration of 11 days (IQR, 6-19 days), the incidence of HAAKI was estimated to be 6.0 per 100 person-day observation (95% CI= 5.5 to 7.2). Significant predictors of HAAKI were found to be having type 2 diabetes mellitus (ARR=2.36, 95% CI= 1.03, 5.39, p-value=0.042), and taking vancomycin (ARR=3.04, 95% CI= 1.38, 6.72, p-value=0.006) and proton pump inhibitors (ARR=3.80, 95% CI = 1.34,10.82, p-value=0.012). Conclusions HAAKI is a common complication in hospitalized non-critical medical patients, and is associated with a common medical condition and commonly prescribed medications. Therefore, it is important to remain vigilant in the prevention and timely identification of these cases and to establish a system of rational prescribing habits.

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)0.001
metaresearch head score (Gemma)0.002
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.326
Teacher spread0.312 · 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
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venuemedRxiv→Same topicAcute Kidney Injury Research→French-language works237,207→