Use of a hospital administrative database to identify and characterize community-acquired, hospital-acquired and drug-induced acute kidney injury

Rey, Amayelle; Gras‐Champel, Valérie; Balcaen, Thibaut; Choukroun, Gabriel; Masmoudi, Kamel; Liabeuf, Sophie

doi:10.1007/s40620-021-01174-z

Cited by 9 publications

(16 citation statements)

References 42 publications

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“…In addition, inflammation often interacts with coagulation dysfunction and then influences the development of sepsis. The decreased expression of tissue factors cannot initiate the exogenous coagulation pathway, which results in coagulation disorders and accelerates the formation of vascular damage [ 12 – 14 ].…”

Section: Introductionmentioning

confidence: 99%

Account of Deep Learning-Based Ultrasonic Image Feature in the Diagnosis of Severe Sepsis Complicated with Acute Kidney Injury

Zhijia

2022

Computational and Mathematical Methods in Medicine

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This study was aimed at analyzing the diagnostic value of convolutional neural network models on account of deep learning for severe sepsis complicated with acute kidney injury and providing an effective theoretical reference for the clinical use of ultrasonic image diagnoses. 50 patients with severe sepsis complicated with acute kidney injury and 50 healthy volunteers were selected in this study. They all underwent ultrasound scans. Different deep learning convolutional neural network models dense convolutional network (DenseNet121), Google inception net (GoogLeNet), and Microsoft’s residual network (ResNet) were used for training and diagnoses. Then, the diagnostic results were compared with professional image physicians’ artificial diagnoses. The results showed that accuracy and sensitivity of the three deep learning algorithms were significantly higher than professional image physicians’ artificial diagnoses. Besides, the error rates of the three algorithm models for severe sepsis complicated with acute kidney injury were significantly lower than professional physicians’ artificial diagnoses. The areas under curves (AUCs) of the three algorithms were significantly higher than AUCs of doctors’ diagnosis results. The loss function parameters of DenseNet121 and GoogLeNet were significantly lower than that of ResNet, with the statistically significant difference ( P < 0.05 ). There was no significant difference in training time of ResNet, GoogLeNet, and DenseNet121 algorithms under deep learning, as the convergence was reached after 700 times, 700 times, and 650 times, respectively ( P > 0.05 ). In conclusion, the value of the three algorithms on account of deep learning in the diagnoses of severe sepsis complicated with acute kidney injury was higher than professional physicians’ artificial judgments and had great clinical value for the diagnoses and treatments of the disease.

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Section: Introductionmentioning

confidence: 99%

Account of Deep Learning-Based Ultrasonic Image Feature in the Diagnosis of Severe Sepsis Complicated with Acute Kidney Injury

Zhijia

2022

Computational and Mathematical Methods in Medicine

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“…Based on a cohort of hospitalized patients suffering from AKIs based on PMSI extraction and chart review (IRA-PMSI), we have recently described different characteristics between hospitalacquired AKI (henceforth referred to as HA-AKI) and community-acquired AKI (henceforth referred to as CA-AKI). Drug-induced AKIs accounted for 28.6% of the AKIs [7]. Indeed, the main avoidable risk factor is the use of certain medications [8,9].…”

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confidence: 99%

Risk factors for and characteristics of community‐ and hospital‐acquired drug‐induced acute kidney injuries

Rey

Gras‐Champel

Choukroun

et al. 2022

Fundamemntal Clinical Pharma

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Drugs constitute one of the leading causes of acute kidney injuries (AKIs) and can appear in community (CA-AKI) or hospital (HA-AKI) population. The objectives of the present study of a cohort of hospitalized patients with AKI were to describe the characteristics of drug-induced AKIs and the patients' short-term outcomes and assess risk factors for drug-induced AKIs overall, CA-AKIs, and HA-AKIs. Based on a cohort of 1557 hospitalized patients suffering from AKIs based on PMSI extraction and chart review (IRA-PMSI), drug-induced AKIs were identified by applying the Naranjo adverse drug reaction (ADR) probability scale. Multivariate logistic regression was used to identify factors associated with CA-AKIs and/or HA-AKIs. When considering the 1557 patients who experienced an AKI, 445 (28.6%) of the injuries were drug-induced (180 CA-AKIs (40.4%) and 265 HA-AKIs (59.6%)). Antibiotics, diuretics, and contrast agents were significantly more likely to be involved in HA-AKIs, whereas antineoplastic, lipid-lowering drugs, antidiabetics, and immunosuppressive were significantly more likely to be involved in CA-AKIs. Female sex (odds ratio [OR] [95%CI] = 1.3 [1.04-1.67]), chronic kidney disease (CKD) (OR = 1.8 [1.40-2.67]), and a history of ADRs of any type (OR = 1.3 [1.05-1.73]) were significant risk factors for drug-induced AKIs. CKD was a risk factor for both CA-AKI and HA-AKI. In view of the long-term impact of AKI on the kidneys and the differences between our CA-AKI and HA-AKI subgroups, our present results are interesting for optimizing treatments, limiting the occurrence of CAand HA-AKIs and (ultimately) reducing healthcare costs. K E Y W O R D S acute kidney injury, community-and hospital-acquired, drug-induced, pharmacovigilance, PMSI, risk factors 1 | INTRODUCTION Acute kidney injury (AKI) is a common, treatable, reversible syndrome that can be immediately life-threatening and can have serious long-term consequences [1]. The main impact is on the kidney itself: the development of chronic kidney disease (CKD), accelerated progression to end-stage renal disease (ESRD), and failure to recover kidney function [2, 3]. The risk factors for AKI have been well studied; they include older age, female

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“…Using the same capturerecapture method, Rolland et al found that 20.0% of AKIs were drug-induced AKI (Rolland et al, 2021). However, cases from the hospital database were identify by using AKI ICD-10 codes as the main diagnosis or an associated diagnosis-leading to underestimation of the number of cases (relative to the identification process applied in the present study (Rey et al, 2021)). AC, community-acquired; HA, hospital-acquired; FPVD, french national pharmacovigilance database; CI, confidence interval; AKI, acute kidney injury; N, total number of drug-induced AKIs; X, number of non-identified drug-induced AKIs.…”

Section: Key Pointsmentioning

confidence: 76%

“…But AKI represents an event that induces additional costs for hospitals and so prompts them to code it (Chertow et al, 2005). Also, we previously demonstrated using a set of ICD 10 codes for AKI that the codes found corresponded to those mostly used to code AKIs (Rey et al, 2021). Finally, we did not have information about length of treatment and suspected drugs'rechallenge.…”

Section: Strengths and Limitsmentioning

confidence: 95%

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Use of the Capture-Recapture Method to Estimate the Frequency of Community- and Hospital-Acquired Drug-Induced Acute Kidney Injuries in French Databases

Rey

Gras²,

Moragny³

et al. 2022

Front. Pharmacol.

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Drug-induced acute kidney injury (AKI) can occur both in primary care (i.e., community-acquired AKI (CA-AKI)) and in hospital settings (i.e., hospital-acquired AKI (HA-AKI)). The reported prevalence of these events varies markedly from one study to another, mainly due to differences in the study design. To estimate the frequency of drug-induced AKIs (both CA-AKIs and HA-AKIs) observed in a French university hospital, we applied the capture-recapture method to 1) the French national pharmacovigilance database (FPVD) and 2) a cohort of hospitalized patients with drug-induced AKIs (documented by analyzing the French national hospital discharge database and the patients’ electronic medical records). Drug-induced AKIs were determined according to the Naranjo algorithm and then categorized as CA-AKIs or HA-AKIs. A total number of 1,557 episodes of AKI were record during the study period, of them, the estimated total number of drug-induced AKIs was 593 [95% confidence interval (CI): 485–702], and the estimated prevalence was 38.1% [95%CI: 35.67–40.50]. The prevalences of HA-AKIs and CA-AKIs were similar (39.4% [36.24–42.54] and 37.4% [33.67–41.21], respectively). Only 6.1% of the drug-induced AKIs were recorded in the FPVD, and the proportions of recorded HA-AKIs and CA-AKI differed markedly (3.0% vs. 10.5%, respectively). One of the most frequently involved drug classes were antibiotics in the HA-AKI subgroup (13.0%) and antineoplastics in the CA-AKI subgroup (8.3%). Application of the capture-recapture method to two incomplete data sources can improve the ability to identify and quantify adverse drug reactions like AKIs. The frequency of drug-induced AKI is relatively high and is probably underestimated. The clinical management of an AKI might depend on where it originated.

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Use of a hospital administrative database to identify and characterize community-acquired, hospital-acquired and drug-induced acute kidney injury

Cited by 9 publications

References 42 publications

Account of Deep Learning-Based Ultrasonic Image Feature in the Diagnosis of Severe Sepsis Complicated with Acute Kidney Injury

Account of Deep Learning-Based Ultrasonic Image Feature in the Diagnosis of Severe Sepsis Complicated with Acute Kidney Injury

Risk factors for and characteristics of community‐ and hospital‐acquired drug‐induced acute kidney injuries

Use of the Capture-Recapture Method to Estimate the Frequency of Community- and Hospital-Acquired Drug-Induced Acute Kidney Injuries in French Databases

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