Clinical features, risk factors, and clinical burden of acute kidney injury in older adults

Development of Acute Kidney Injury Predictor Score in Intensive Care Unit Patients in Padang, Indonesia

OBJECTIVE

This study aims to develop and create a specialized acute kidney injury (AKI) predictor score for the intensive care unit (ICU) patients in Padang, Indonesia.

PATIENTS AND METHODS

This study was a prospective observational study on 352 ICU patients at three specialized hospitals in Padang City; Dr. M. Djamil General Hospital, Dr. Rasidin General Hospital, and Siti Rahmah Islamic Hospital. Data regarding demographics, clinical characteristics, laboratory results, and outcomes related to AKI were gathered. The factors that predict AKI were identified using multivariate logistic regression analysis to determine independent factors. The predictor scores were created using regression coefficients and then internally confirmed.

RESULTS

Out of a total of 352 patients, 128 individuals (36.4%) suffered from AKI. Factors that independently predict the occurrence of AKI include age over 60 years old, having a history of chronic kidney disease, having sepsis, need for vasopressors, and having creatinine level 1.3 mg/dL (IQR 1.0-1.8) upon admission to ICU. An area under the curve (AUC) of 0.85 (95% CI 0.80-0.90) indicated the strong performance of the constructed predictor score.

CONCLUSION

The constructed AKI predictor score a scale factor of 10, resulting in a range of 0-10 for the AKI predictor score. It demonstrates a good level of accuracy in predicting AKI in ICU patients in Padang. This score can be used by healthcare professionals to quickly identify and categorize individuals based on their risk level, facilitating timely intervention and personalized treatment.

A nomogram for predicting 28-day mortality in elderly patients with acute kidney injury receiving continuous renal replacement therapy: a secondary analysis based on a retrospective cohort study

BACKGROUND

Acute kidney injury (AKI) is a common and serious condition, particularly among elderly patients. It is associated with high morbidity and mortality rates, further compounded by the need for continuous renal replacement therapy in severe cases. To improve clinical decision-making and patient management, there is a need for accurate prediction models that can identify patients at a high risk of mortality.

METHODS

Data were extracted from the Dryad Digital Repository. Multivariate analysis was performed using least absolute shrinkage and selection operator (LASSO) logistic regression analysis to identify independent risk factors and construct a predictive nomogram for mortality within 28 days after continuous renal replacement therapy in elderly patients with acute kidney injury. The discrimination of the model was evaluated in the validation cohort using the area under the receiver operating characteristic curve (AUC), and calibration was evaluated using a calibration curve. The clinical utility of the model was assessed using decision curve analysis (DCA).

RESULTS

A total of 606 participants were enrolled and randomly divided into two groups: a training cohort (n = 424) and a validation cohort (n = 182) in a 7:3 proportion. A risk prediction model was developed to identify independent predictors of 28-day mortality in elderly patients with AKI. The predictors included age, systolic blood pressure, creatinine, albumin, phosphorus, age-adjusted Charlson Comorbidity Index (CCI), Acute Physiology and Chronic Health Evaluation II (APACHE II) score, and sequential organ failure assessment (SOFA) score. These predictors were incorporated into a logistic model and presented in a user-friendly nomogram. In the validation cohort, the model demonstrated good predictive performance with an AUC of 0.799. The calibration curve showed that the model was well calibrated. Additionally, DCA revealed significant net benefits of the nomogram for clinical application.

CONCLUSION

The development of a nomogram for predicting 28-day mortality in elderly patients with AKI receiving continuous renal replacement therapy has the potential to improve prognostic accuracy and assist in clinical decision-making.

Mortality prediction and influencing factors for intensive care unit patients with acute tubular necrosis: random survival forest and cox regression analysis

Background: Patients with acute tubular necrosis (ATN) not only have severe renal failure, but also have many comorbidities, which can be life-threatening and require timely treatment. Identifying the influencing factors of ATN and taking appropriate interventions can effectively shorten the duration of the disease to reduce mortality and improve patient prognosis. Methods: Mortality prediction models were constructed by using the random survival forest (RSF) algorithm and the Cox regression. Next, the performance of both models was assessed by the out-of-bag (OOB) error rate, the integrated brier score, the prediction error curve, and area under the curve (AUC) at 30, 60 and 90 days. Finally, the optimal prediction model was selected and the decision curve analysis and nomogram were established. Results: RSF model was constructed under the optimal combination of parameters (mtry = 10, nodesize = 88). Vasopressors, international normalized ratio (INR)_min, chloride_max, base excess_min, bicarbonate_max, anion gap_min, and metastatic solid tumor were identified as risk factors that had strong influence on mortality in ATN patients. Uni-variate and multivariate regression analyses were used to establish the Cox regression model. Nor-epinephrine, vasopressors, INR_min, severe liver disease, and metastatic solid tumor were identified as important risk factors. The discrimination and calibration ability of both predictive models were demonstrated by the OOB error rate and the integrated brier score. However, the prediction error curve of Cox regression model was consistently lower than that of RSF model, indicating that Cox regression model was more stable and reliable. Then, Cox regression model was also more accurate in predicting mortality of ATN patients based on the AUC at different time points (30, 60 and 90 days). The analysis of decision curve analysis shows that the net benefit range of Cox regression model at different time points is large, indicating that the model has good clinical effectiveness. Finally, a nomogram predicting the risk of death was created based on Cox model. Conclusion: The Cox regression model is superior to the RSF algorithm model in predicting mortality of patients with ATN. Moreover, the model has certain clinical utility, which can provide clinicians with some reference basis in the treatment of ATN and contribute to improve patient prognosis.

Prevalence and Mortality Rates of Acute Kidney Injury among Critically Ill Patients: A Retrospective Study

Acute kidney injury (AKI) poses a significant challenge in critically ill patients. To determine the prevalence, risk factors, and mortality rate of AKI among nonsurgical critically ill patients in Jordan University Hospital, we conducted a retrospective study using a consecutive sampling method, including 457 nonsurgical critically ill patients admitted to the medical intensive care unit (MICU) from January to June 2021. The mean age was 63.8 ± 18 years, with 196 (42.8%) developing AKI during their stay in the MICU. Among AKI nonsurgical patients, pulmonary diseases (n = 52; 34.5%) emerged as the primary cause for admission, exhibiting the highest prevalence, followed by sepsis (n = 40; 20.4%). Furthermore, we found that older age (adjusted OR (AOR): 1.04; 95% confidence interval (CI): 1.04-1.06; p = 0.003), preadmission use of diuretics (AOR: 2.12; 95% CI: 1.06-4.25; p = 0.03), use of ventilators (2.19; 95% CI: 1.12-2.29; p = 0.02), and vasopressor use during MICU stay (AOR: 4.25; 95% CI: 2.1308.47; p = 0.001) were observed to have higher mortality rates. Prior utilization of statins before admission exhibited a significant association with reduced mortality rate (AOR: 0.42; 95% CI: 0.2-0.85; p = 0.02). Finally, AKI was associated with a higher mortality rate during MICU stay (AOR: 2.44; 95% CI: 1.07-5.56; p = 0.03). The prevalence of AKI among nonsurgical patients during MICU stay is higher than what has been reported previously in the literature, which highlights the nuanced importance of identifying more factors contributing to AKI in developing countries, and hence providing preventive measures and adhering to global strategies are recommended.

Incidence of hospital-acquired acute kidney injury and trajectories of glomerular filtration rate in older adults

BACKGROUND

In older adults, epidemiological data on incidence rates (IR) of hospital-acquired acute kidney injury (AKI) are scarce. Also, little is known about trajectories of kidney function before hospitalization with AKI.

METHODS

We used data from biennial face-to-face study visits from the prospective Berlin Initiative Study (BIS) including community-dwelling participants aged 70+ with repeat estimated glomerular filtration rate (eGFR) based on serum creatinine and cystatin C. Primary outcome was first incident of hospital-acquired AKI assessed through linked insurance claims data. In a nested case-control study, kidney function decline prior to hospitalization with and without AKI was investigated using eGFR trajectories estimated with mixed-effects models adjusted for traditional cardiovascular comorbidities.

RESULTS

Out of 2020 study participants (52.9% women; mean age 80.4 years) without prior AKI, 383 developed a first incident AKI, 1518 were hospitalized without AKI, and 119 were never hospitalized during a median follow-up of 8.8 years. IR per 1000 person years for hospital-acquired AKI was 26.8 (95% confidence interval (CI): 24.1-29.6); higher for men than women (33.9 (29.5-38.7) vs. 21.2 (18.1-24.6)). IR (CI) were lowest for persons aged 70-75 (13.1; 10.0-16.8) and highest for ≥ 90 years (54.6; 40.0-72.9). eGFR trajectories declined more steeply in men and women with AKI compared to men and women without AKI years before hospitalization. These differences in eGFR trajectories remained after adjustment for traditional comorbidities.

CONCLUSION

AKI is a frequent in-hospital complication in individuals aged 70 + showing a striking increase of IR with age. eGFR decline was steeper in elderly patients with AKI compared to elderly patients without AKI years prior to hospitalization emphasising the need for long-term kidney function monitoring pre-admission to improve risk stratification.

Impact of Diabetes on the Recurrence and Prognosis of Acute Kidney Injury in Older Male Patients: A 10-Year Retrospective Cohort Study

INTRODUCTION

While patients with diabetes are at higher risk of developing acute kidney injury (AKI), there are few studies on the recurrence of AKI in older adult patients. This study therefore aimed to examine the impact of diabetes on AKI recurrence and long-term outcomes in older male patients.

METHODS

This retrospective cohort study included older male patients who experienced AKI during hospitalization from July 2007 to August 2011. Medical records of all patients were followed up for 10 years. Patients with AKI were classified into groups with and without diabetes. We analyzed differences in common geriatric comorbidities, AKI recurrence frequency, and severity between the two groups, identified risk factors affecting recurrence frequency, and assessed outcomes.

RESULTS

Of all 266 patients, 128 had diabetes and 138 did not. The AKI recurrence rate was significantly higher in the group with diabetes (80.5 vs. 66.7%; P = 0.011). There was a significantly higher proportion of AKI caused by infections in patients with diabetes (43.3 vs. 33.2%, P = 0.006). The proportion of patients with an AKI recurrence frequency ≥ 3 was significantly higher in the group with diabetes (44.7 vs. 29.4%, P = 0.027). Diabetes and coronary heart disease were independent risk factors for AKI recurrence (P < 0.05), diabetes control was associated with multiple AKI recurrences (P = 0.016), and no significant difference was found between the groups regarding the 10-year prognosis (P = 0.522). However, a subgroup analysis showed that patients with multiple AKI recurrences within 2 years had the worst survival outcome (P = 0.004).

CONCLUSIONS

Older male patients with diabetes are prone to AKI recurrence after initial onset of AKI. Diabetes is an independent risk factor for AKI recurrence, and active diabetes control (HbA1c < 7%) may thus reduce the recurrence of AKI and improve the very poor outcomes of patients with multiple recurrences of AKI within 2 years.

Severe acute kidney injury predicting model based on transcontinental databases: a single-centre prospective study

OBJECTIVES

There are many studies of acute kidney injury (AKI) diagnosis models lack of external validation and prospective validation. We constructed the models using three databases to predict severe AKI within 48 hours in intensive care unit (ICU) patients.

DESIGN

A retrospective and prospective cohort study.

SETTING

We studied critically ill patients in our database (SHZJU-ICU) and two other public databases, the Medical Information Mart for Intensive Care (MIMIC) and AmsterdamUMC databases, including basic demographics, vital signs and laboratory results. We predicted the diagnosis of severe AKI in patients in the next 48 hours using machine-learning algorithms with the three databases. Then, we carried out real-time severe AKI prediction in the prospective validation study at our centre for 1 year.

PARTICIPANTS

All patients included in three databases with uniform exclusion criteria.

PRIMARY AND SECONDARY OUTCOME MEASURES

Effect evaluation index of prediction models.

RESULTS

We included 58 492 patients, and a total of 5257 (9.0%) patients met the definition of severe AKI. In the internal validation of the SHZJU-ICU and MIMIC databases, the best area under the receiver operating characteristic curve (AUROC) of the model was 0.86. The external validation results by AmsterdamUMC database were also satisfactory, with the best AUROC of 0.86. A total of 2532 patients were admitted to the centre for prospective validation; 358 positive results were predicted and 344 patients were diagnosed with severe AKI, with the best sensitivity of 0.72, the specificity of 0.80 and the AUROC of 0.84.

CONCLUSION

The prediction model of severe AKI exhibits promises as a clinical application based on dynamic vital signs and laboratory results of multicentre databases with prospective and external validation.

External validation of the Madrid Acute Kidney Injury Prediction Score

Background

The Madrid Acute Kidney Injury Prediction Score (MAKIPS) is a recently described tool capable of performing automatic calculations of the risk of hospital-acquired acute kidney injury (HA-AKI) using data from from electronic clinical records that could be easily implemented in clinical practice. However, to date, it has not been externally validated. The aim of our study was to perform an external validation of the MAKIPS in a hospital with different characteristics and variable case mix.

Methods

This external validation cohort study of the MAKIPS was conducted in patients admitted to a single tertiary hospital between April 2018 and September 2019. Performance was assessed by discrimination using the area under the receiver operating characteristics curve and calibration plots.

Results

A total of 5.3% of the external validation cohort had HA-AKI. When compared with the MAKIPS cohort, the validation cohort showed a higher percentage of men as well as a higher prevalence of diabetes, hypertension, cardiovascular disease, cerebrovascular disease, anaemia, congestive heart failure, chronic pulmonary disease, connective tissue diseases and renal disease, whereas the prevalence of peptic ulcer disease, liver disease, malignancy, metastatic solid tumours and acquired immune deficiency syndrome was significantly lower. In the validation cohort, the MAKIPS showed an area under the curve of 0.798 (95% confidence interval 0.788–0.809). Calibration plots showed that there was a tendency for the MAKIPS to overestimate the risk of HA-AKI at probability rates ˂0.19 and to underestimate at probability rates between 0.22 and 0.67.

Conclusions

The MAKIPS can be a useful tool, using data that are easily obtainable from electronic records, to predict the risk of HA-AKI in hospitals with different case mix characteristics.