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

Recognition patterns of acute kidney injury in hospitalized patients

Background

Acute kidney injury (AKI) during hospitalization is associated with increased complications and mortality. Despite efforts to standardize AKI management, its recognition in clinical practice is limited.

Methods

To assess and characterize different patterns of AKI diagnosis, we collected clinical data, serum creatinine (sCr) levels, comorbidities and outcomes from adult patients using the Hospital Discharge Form (HDF). AKI diagnosis was based on administrative data and according to Kidney Disease: Improving Global Outcomes (KDIGO) criteria by evaluating sCr variations during hospitalization. Additionally, patients were categorized based on the timing of AKI onset.

Results

Among 56 820 patients, 42 900 (75.5%) had no AKI, 1893 (3.3%) had AKI diagnosed by sCr changes and coded in the HDF (full-AKI), 2529 (4.4%) had AKI reported on the HDF but not meeting sCr-based criteria (HDF-AKI) and 9498 (16.7%) had undetected AKI diagnosed by sCr changes but not coded in the HDF (KDIGO-AKI). Overall, AKI incidence was 24.5%, with a 68% undetection rate. Patients with KDIGO-AKI were younger and had a higher proportion of females, lower comorbidity burden, milder AKI stages, more frequent admissions to surgical wards and lower mortality compared with full-AKI patients. All AKI groups had worse outcomes than those without AKI, and AKI, even if undetected, was independently associated with mortality risk. Patients with AKI at admission had different profiles and better outcomes than those developing AKI later.

Conclusions

AKI recognition in hospitalized patients is highly heterogeneous, with a significant prevalence of undetection. This variability may be affected by patients' characteristics, AKI-related factors, diagnostic approaches and in-hospital patient management. AKI remains a major risk factor, emphasizing the importance of ensuring proper diagnosis for all patients.

Characteristics, Risk Factors, and Outcomes in Acute Kidney Injury Patients: A Retrospective Cross-Sectional Study, Palestine

Background

Acute kidney injury (AKI) is a major medical problem affecting patients' quality of life and healthcare costs.

Objectives

This study evaluated the severity, risk factors, and outcomes of patients diagnosed with acute kidney injury (AKI), including community-acquired AKI (CA-AKI) and hospital-acquired AKI (HA-AKI), who were admitted to tertiary institutions in Palestine.

Methods

This retrospective cross-sectional study was conducted at multiple tertiary care hospitals in Palestine by reviewing patient charts from January 2020 to March 2023. The study included all patients aged ≥18 years who were admitted to the hospital and diagnosed with AKI at admission (CA-AKI) or who developed AKI 48 hours after admission (HA-AKI). Patients with incomplete medical records and those with no reported creatinine levels during their stay, pregnant women, kidney transplant patients, and end-stage renal disease patients were excluded. Data were analyzed using SPSS v22.0. The incidence of AKI in each group was compared using the chi-squared test.

Results

This study included 259 participants. HA-AKI was present in 27.3% of the patients, while CA-AKI was 72.7%. The most common stage among patients was stage 3 (55.7%, HA-AKI) (42.9%, CA-AKI), and the most common comorbidity contributing to AKI was CKD. NSAIDs, ACE-I/ARBs, and DIURETICs were the most nephrotoxic drugs contributing to AKI. Patients with hyperphosphatemia, hyperkalemia, severe metabolic acidosis, or stage 3 AKI require renal replacement therapy. In addition, our findings revealed a significant association among AKI mortality, age, and heart disease.

Conclusion

CA-AKI was more prevalent than HA-AKI in Palestinian patients admitted for AKI. Risk factors for AKI included diabetes, CKD, and medications (antibiotics, NSAID, diuretics, and ACE-I/ARB). Preventive measures, medication management, and disease state management are necessary to minimize AKI during hospital admission or in the community.

Adverse clinical outcomes associated with drug-related hospitalizations in people with dementia

BACKGROUND

This study aimed to determine the clinical impact associated with adverse drug reactions (ADRs) in patients with dementia.

RESEARCH DESIGN AND METHODS

This case-control, propensity score-matched study utilized administrative data of people with dementia admitted to major public hospitals in Tasmania, Australia, from July 2010 to December 2019.

RESULTS

Acute renal failure constituted the most common ADR. The length of hospital stay was greater for people with an ADR index admission, versus non-ADR index admission (median [IQR]: 9 [4-18] versus 6 [2-12]; p < 0.001). In-hospital mortality and combined in-hospital and post-hospital mortality within 30, 60 and 90 days were higher for those whose index admission was ADR-related (in-hospital: HR 1.40, 95% CI 1.11-1.77, p-value <0.001; 30 days: HR 1.25, 95% CI 1.05-1.49, p-value <0.001; 60 days: HR 1.27, 95% CI 1.08-1.49, p-value <0.001; 90 days: HR 1.29, 95% CI 1.10-1.50, p-value <0.001). Subsequent ADR admission within 30, 60 and 90 days of index discharge was 9 to 10 times greater for people with dementia (30 days: OR 10.0, 95% CI 6.04-16.8, p-value <0.001; 60 days: OR 8.96, 95% CI 5.57-14.4, p-value <0.001; 90 days: OR 9.31, 95% CI 5.79-14.9, p-value <0.001).

CONCLUSION

Safe prescribing and vigilant monitoring of ADRs is pivotal to mitigate adverse outcomes in people with dementia.

Analysis of pharmacovigilance databases for spontaneous reports of adverse drug reactions related to substandard and falsified medical products: A descriptive study

Introduction: The public health threat of substandard and falsified medicines has been well known in the last two decades, and several studies focusing on the identification of products affected and preventing consumption have been published. However, the number of these products reaching patients and causing health consequences and adverse drug reactions is not a well-researched area.

Objectives: Our aim was to identify and describe the characteristics of cases that are related to adverse drug reactions potentially originating from counterfeit medication using publicly available pharmacovigilance data.

Methods: A descriptive study was performed based on pharmacovigilance data retrieved from Individual Case Safety Reports (ICSRs) identified in the European Medicines Agency’s EudraVigilance and FDA Adverse Event Reporting System (FAERS) databases in April 2022 using selected MedDRA preferred terms: counterfeit product administered, product counterfeit, product label counterfeit, product packaging counterfeit, suspected counterfeit product, adulterated product, product tampering, and suspected product tampering. ICSRs were analyzed by age and gender, by year of reporting, region of origin, reporter’s profession, and severity of the outcome. The disproportionality method was used to calculate pharmacovigilance signal measures.

Results: A total of 5,253 cases in the FAERS and 1,049 cases in the EudraVigilance database were identified, generally affecting middle-aged men with a mean age of 51.055 (±19.62) in the FAERS and 64.18% of the cases between 18 and 65 years, while the male to female ratios were 1.18 and 1.5. In the FAERS database, we identified 138 signals with 95% confidence interval including sildenafil (n = 314; PRR, 12.99; ROR, 13.04; RRR, 11.97), tadalafil (n = 200; PRR, 11.51; ROR, 11.55; RRR, 10.94), and oxycodone (n = 190; PRR, 2.47; ROR, 2.14; RRR, 2.47). While in the EV data 31, led by vardenafil (n = 16, PRR = 167.19; 101.71–274.84; 95% CI, RRR = 164.66; 100.17–270.66; 95% CI, ROR = 169.47; 103.09–278.60; 95% CI, p &lt; 0.001), entecavir (n = 46, PRR = 161.26, RRR = 154.24, ROR = 163.32, p &lt; 0.001), and tenofovir (n = 20, PRR = 142.10, RRR = 139.42, ROR = 143.74, p &lt; 0.001).

Conclusion: The application of pharmacovigilance datasets to identify potential counterfeit medicine ADRs can be a valuable tool in recognition of potential risk groups of consumers and the affected active pharmaceutical ingredients and products. However, the further development and standardization of ADR reporting, pharmacovigilance database analysis, and prospective and real-time collection of potential patients with health consequences are warranted in the future.

Klotho restoration via ACE2 activation: A potential therapeutic strategy against acute kidney injury-diabetes comorbidity

Acute kidney injury (AKI) is a collection of clinical syndromes with persistent increases in morbidity and mortality rates. Hyperglycemia is a risk factor for AKI development. Renin-angiotensin-aldosterone system (RAS) disequilibrium and Klotho downregulation also play a pivotal role in the pathogenesis of AKI. Moreover, the relationship between Klotho and ACE2 (a component of non-conventional RAS) regulation in AKI remains an unexplored area of research. Hence, in this study, we investigated ACE2 and Klotho regulation in AKI using ischemic Wistar rats and NRK52E cells under normal and hyperglycemic conditions. Our findings suggested that hyperglycemia exacerbates renal ischemia-reperfusion injury (IRI)/hypoxia-reperfusion injury (HRI) induced AKI. Systemic and renal Klotho deficiency is a novel hallmark of AKI. Additionally, ACE2 is a protective component of the RAS, and its inhibition/deficiency leads to inflammation, apoptosis, Klotho downregulation, and thus AKI development. However, ACE2 activation resulted in the amelioration of AKI. Importantly, ACE2 plays an important role in Klotho upregulation, which might act as an intermediate for ACE2-mediated reno-protection. In conclusion, ACE2 activator i.e. DIZE restored endogenous ACE2-Ang-(1-7)-Klotho level, inhibited apoptosis and inflammation, and ameliorates IRI/HRI induced AKI under diabetic and non-diabetic conditions. Hence, in future, targeting ACE2-Ang-(1-7)-Klotho axis may prove a novel therapeutic strategy against AKI, where further preclinical and clinical investigations are required to verify the clinical potential of this finding.

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

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 CA‐ and HA‐AKIs and (ultimately) reducing healthcare costs.

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

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.

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

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.