Acute kidney injury in heart failure: a population study

Differences in characteristics and risk factors for acute kidney injury between elderly and very elderly patients: a retrospective review

BACKGROUND

Few epidemiologic studies on acute kidney injury (AKI) have focused on the older adult population. This study aimed to clarify the characteristics and risk factors for AKI in this population.

METHODS

This retrospective observational study was performed with the clinical data of all outpatients and inpatients aged ≥ 65 years at the time of enrolment at Kochi Medical School Hospital between 1 January 1981 and 31 December 2021. The primary cohort was divided into those aged 65-74 and ≥ 75 years. The primary outcome was the occurrence of AKI.

RESULTS

Of 83,822 patients, 38,333 were included in the 65-74-year-old group, whereas 45,489 were included in the ≥ 75-year-old group. Prevalences of the first AKI event in the 65-74-year-old and ≥ 75-year-old groups were 11.9% and 12.4%, respectively. Overall, lower estimated glomerular filtration rate, lower albumin level, lower or higher level of serum uric acid, and histories of diabetes mellitus, chronic heart failure, ischaemic heart disease, non-ischaemic heart disease, cerebrovascular disease, cancer, and liver disease were independent risk factors for an AKI event. The risk factors for AKI unique to each cohort were using non-steroidal anti-inflammatory drugs (NSAIDs) and loop diuretics (L-DI), and histories of hypertension (HT) and vascular diseases (VD) in men aged 65-74 years; using NSAIDs, angiotensin-converting enzyme inhibitors (ACEIs), L-DI and other diuretics (O-DI), and histories of HT and VD in men aged ≥ 75 years; using NSAIDs and O-DI and not using angiotensin-receptor blockers (ARBs), and a history of HT in women aged 65-74 years; and use of L-DI and a history of VD in women aged ≥ 75 years. Presence of proteinuria was a risk factor for developing AKI.

CONCLUSIONS

Many AKI risk factors reported thus far are associated with AKI development. However, there are differences in the effects of the renin-angiotensin system inhibitors, ACEIs, and ARBs (ARBs may be protective). Additionally, the U-shaped relationship between AKI onset and uric acid levels differs between sexes in the elderly population, similar to other age groups, but this sex difference disappears in the very elderly population. Pre-existing chronic kidney disease is a risk factor for the development of AKI.

Association of suPAR, ST2, and galectin-3 with eGFR decline and mortality in patients with advanced heart failure with reduced ejection fraction

Patients with heart failure with reduced ejection fraction (HFrEF) are at risk for chronic kidney disease (CKD). Elevated levels of circulating biomarkers soluble urokinase plasminogen activator receptor (suPAR), galectin-3, soluble suppression of tumorigenicity 2 (ST2), and N-terminal prohormone B-type natriuretic peptide (NT-proBNP) are associated with CKD progression and mortality. The predictive value of these biomarkers in a population with HFrEF and kidney disease is relatively unknown. We sought to determine whether these biomarkers were associated with longitudinal trajectory of estimated glomerular filtration rate (eGFR) in HFrEF and assess their association with mortality using a joint model to account for competing risks of ventricular assist device (VAD) implantation and heart transplantation. We included participants from the Registry Evaluation of Vital Information for Ventricular Assist Devices in Ambulatory Life with repeated eGFR measures over 2 years. Of 309 participants, mean age was 59 years, median eGFR 60 ml/min/1.73 m2, 45 participants died, 33 received VAD, and 25 received orthotopic heart transplantation. Higher baseline serum standardized suPAR (β coefficient = -0.36 √(ml/min/1.73 m2), 95% confidence interval (-0.48 to -0.24), p < 0.001), standardized galectin-3 (-0.14 √(ml/min/1.73 m2) (-0.27 to -0.02), p = 0.02), and log NT-proBNP (-0.23 √(ml/min/1.73 m2) (-0.31 to -0.15), p < 0.001) were associated with eGFR decline. ST2 and log NT-proBNP were associated with mortality. Higher baseline suPAR, galectin-3, and NT-proBNP are associated with eGFR decline in patients with HFrEF. Only ST2 and NT-proBNP are associated with greater mortality after controlling for other factors including change in eGFR. These biomarkers may provide prognostic value for kidney disease progression in HFrEF and inform candidacy for advanced heart failure therapies.

A clinical score to predict recovery in end-stage kidney disease due to acute kidney injury

Background

Acute kidney injury (AKI) is a major contributor to end-stage kidney disease (ESKD). About one-third of patients with ESKD due to AKI recover kidney function. However, the inability to accurately predict recovery leads to improper triage of clinical monitoring and impacts the quality of care in ESKD.

Methods

Using data from the United States Renal Data System from 2005 to 2014 (n = 22 922), we developed a clinical score to predict kidney recovery within 90 days and within 12 months after dialysis initiation in patients with ESKD due to AKI. Multivariable logistic regressions were used to examine the effect of various covariates on the primary outcome of kidney recovery to develop the scoring system. The resulting logistic parameter estimates were transformed into integer point totals by doubling and rounding the estimates. Internal validation was performed.

Results

Twenty-four percent and 34% of patients with ESKD due to AKI recovered kidney function within 90 days and 12 months, respectively. Factors contributing to points in the two scoring systems were similar but not identical, and included age, race/ethnicity, body mass index, congestive heart failure, cancer, amputation, functional status, hemoglobin and prior nephrology care. Three score categories of increasing recovery were formed: low score (0-6), medium score (7-9) and high score (10-12), which exhibited 90-day recovery rates of 12%, 26% and 57%. For the 12-month scores, the low, medium and high groups consisted of scores 0-5, 6-8 and 9-11, with 12-month recovery rates of 16%, 33% and 62%, respectively. The internal validation assessment showed no overfitting of the models.

Conclusion

A clinical score derived from information available at incident dialysis predicts renal recovery at 90 days and 12 months in patients with presumed ESKD due to AKI. The score can help triage appropriate monitoring to facilitate recovery and begin planning long-term dialysis care for others.

Outcome Comparison in Hospitalized COVID-19 Patients With and Without AKI

Aim

Patients hospitalized with COVID-19 have a higher incidence of Acute Kidney Injury (AKI) compared with non-COVID patients. Previous observational studies showed AKI in hospitalized patients with COVID-19 was associated with significant increased mortality rate. We conducted a retrospective cohort study in a large mid-Atlantic health system to investigate whether COVID-19 associated AKI during hospitalization would lead to worse outcomes in a predominant Black patient population, compared to COVID-19 without AKI.

Methods

We reviewed health records of patients (aged≥18 years) admitted with symptomatic COVID-19 between March 5, 2020, and Jun 3, 2020, in 9 acute care facilities within the MedStar Health system. Patients were followed up until 3 months after discharge. Primary outcome was inpatient mortality. Secondary outcomes were need for ICU level of care, need for intubation, length of ICU stay, length of hospital stay, need for renal replacement therapy, recovery of renal function.

Results

Among 1107 patients admitted with symptomatic COVID-19, the AKI incidence rate was 35 %. African American patients made up 63 % of the total patient population and 74 % of the total AKI population. Inpatient mortality in the AKI group and the non-AKI group was 163 (41.9 %) and 71 (9.9 %), respectively. COVID-19 patients with AKI had significant higher risk of in-patient mortality (OR, 4.71 [95 % CI, 3.38-6.62], P < 0.001), ICU admission (OR, 4.27 [95 % CI, 3.21-5.72], P < 0.001) and need of intubation (OR, 6.18 [95 % CI, 4.45-8.68], P < 0.001).

Conclusions

AKI in hospitalized patients with COVID-19 was associated with higher mortality rate, need for intubation and ICU admission compared to COVID-19 patients without AKI group.

Incidence, mortality, and predictors of acute kidney injury in patients with heart failure: a systematic review

Acute kidney injury (AKI) is common in patients with heart failure (HF), but studies have been inconsistent about the incidence of AKI in patients with HF. We conducted a meta-analysis to examine the incidence of AKI and its impact on mortality in patients with HF. We also looked at inpatient variables that could predict the development of AKI to identify potential risk factors, so that these can be used as a starting point for intervention and prevention in this group. The Embase, Medline, PubMed, Cochrane libraries, and Web of Science databases were used for searching articles from the inception of the database to October 2022. The EndNote software was used for screening. Meta-analysis was performed using Stata 16.0 software to combine effect sizes. A total of 37 studies were included. Of all the 3 533 583 patients with HF, 774 887 had AKI, with a pooled incidence of 33% [95% confidence interval (CI): 32-35%]. The incidence rate of AKI in acute HF and chronic HF was 36% (95% CI: 31-40%) and 30% (95% CI: 24-35%), respectively. Eleven studies found that AKI patients had higher in-hospital mortality than non-AKI patients [risk ratio (RR): 3.65; 95% CI: 3.04-4.39, P < 0.001]. Mortality was assessed in five studies, and it was found that mortality remained high at 1-year follow-up after onset of AKI (RR: 1.85, 95% CI: 1.54-2.22, P < 0.001). Fifteen admission variables were included and analysed in 13 studies. The combined results showed that diabetes, hypertension, history of chronic kidney disease, chronic HF systolic, age, N-terminal pro-B-type natriuretic peptide, creatinine > 1.0 mg/dL, index estimated glomerular filtration rate < 60 mL/min/1.73 m2 , blood urea nitrogen > 24 mg/dL, intravenous dobutamine, and serum albumin were predictor factors for HF patients with AKI (P < 0.05). In this meta-analysis, AKI occurred in approximately 33% of HF patients during hospitalization and the risk of dying in the hospital was tripled. Even during 1-year long-term follow-up, the risk of death remained high, and multiple inpatient variables showed that HF patients tended to have AKI. Early intervention and treatment are important to reduce the incidence of AKI and improve the prognosis.

Construction and validation of a risk assessment model for acute kidney injury in patients with acute pancreatitis in the intensive care unit

BACKGROUND

To construct and validate a risk assessment model for acute kidney injury (AKI) in patients with acute pancreatitis (AP) in the intensive care unit (ICU).

METHODS

A total of 963 patients diagnosed with acute pancreatitis (AP) from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database was included. These patients were randomly divided into training set (N = 674) and validation set (N = 289) at a ratio of 7:3. Clinical characteristics were utilized to establish a nomogram for the prediction of AKI during ICU stay. These variables were selected by the least absolute shrinkage and selection operation (LASSO) regression and included in multivariate logistic regression analysis. Variables with P-values less than 0.05 were included in the final model. A nomogram was constructed based on the final model. The predicted accuracy of the nomogram was assessed by calculating the receiver operating characteristic curve (ROC) and the area under the curve (AUC). Moreover, calibration curves and Hosmer-Lemeshow goodness-of-fit test (HL test) were performed to evaluate model performance. Decision curve analysis (DCA) evaluated the clinical net benefit of the model.

RESULTS

A multivariable model that included 6 variables: weight, SOFA score, white blood cell count, albumin, chronic heart failure, and sepsis. The C-index of the nomogram was 0.82, and the area under the receiver operating characteristic curve (AUC) of the training set and validation set were 0.82 (95% confidence interval:0.79-0.86) and 0.76 (95% confidence interval: 0.70-0.82), respectively. Calibration plots showed good consistency between predicted and observed outcomes in both the training and validation sets. DCA confirmed the clinical value of the model and its good impact on actual decision-making.

CONCLUSION

We identified risk factors associated with the development of AKI in patients with AP. A risk prediction model for AKI in ICU patients with AP was constructed, and improving the treatment strategy of relevant factors in the model can reduce the risk of AKI in AP patients.

Outcomes Associated with Sodium-Glucose Cotransporter-2 Inhibitor Use in Acute Heart Failure Hospitalizations Complicated by AKI

Key Points

In a multicenter retrospective cohort study of adults hospitalized with acute heart failure, exposure to sodium-glucose cotransporter-2 inhibitor during AKI was associated with lower risk of 30-day mortality. Exposure to sodium-glucose cotransporter-2 inhibitor during acute heart failure–associated AKI was associated with no difference in time to renal recovery. The findings were reproducible in inverse probability-weighted analysis.

Background

Although sodium-glucose cotransporter-2 inhibitor (SGLT2i) use during acute heart failure (AHF) hospitalizations is associated with symptomatic improvement, reduction in rehospitalizations, and mortality, these medications are often withheld during AKI because of concerns about worsening GFR. We aimed to investigate the safety of SGLT2i exposure during AKI among patients hospitalized with AHF. We hypothesized that SGLT2i exposure would not worsen mortality but may prolong return of creatinine to baseline.

Methods

This was a retrospective study of adults hospitalized across five Yale New Haven Health System hospitals between January 2020 and May 2022 with AHF complicated by Kidney Disease Improving Global Outcomes–defined AKI. Patients with stage 5 CKD and those with potential contraindications to SGLT2i were excluded. We tested the association of SGLT2i use with kidney function recovery at 14 days and death at 30 days using time-varying, multivariable Cox-regression analyses.

Results

Of 3305 individuals hospitalized with AHF and AKI, 356 received SGLT2i after AKI diagnosis either as initiation or continuation. The rate of renal recovery was not significantly different among those exposed and unexposed to SGLT2i after AKI (adjusted hazard ratio, 0.94; 95% confidence interval, 0.79 to 1.11; P = 0.46). SGLT2i exposure was associated with lower risk of 30-day mortality (adjusted hazard ratio, 0.45; 95% confidence interval, 0.23 to 0.87; P = 0.02). Sensitivity analyses using an inverse probability-weighted time-varying Cox regression analysis and using alternate definitions of AHF with different NT-proBNP cutoffs yielded similar results. Rates of renal recovery were similar between the exposed and unexposed cohorts regardless of the proximity of SGLT2i exposure to AKI diagnosis.

Conclusion

In adults experiencing AHF-associated AKI, exposure to SGLT2i was associated with decreased mortality and no delay in renal recovery. Prospective studies are needed to elucidate the effect of SGLT2i exposure during AKI, particularly during heart failure hospitalizations.

Acute kidney injury in the critical care setting

Acute kidney injury is a sudden reduction in renal function which impairs the kidneys' ability to maintain fluid, electrolyte and acid-base balance. The syndrome often develops secondary to severe illness and is associated with a significant increase in morbidity and mortality rate in critically ill patients. This article gives an overview of the pathophysiology and aetiology of acute kidney injury, as well as the associated complications and clinical diagnostic signs. The authors also describe some common causes of the syndrome in critically ill patients, specifically sepsis, liver failure and cardiac failure, and discuss patient management in the critical care setting, with a focus on haemodynamic support and continuous renal replacement therapy.

Incidence of Post-laparotomy Acute Kidney Injury Among Abdominal Trauma Patients and Its Associated Risk Factors at King Abdulaziz Medical City, Riyadh

Background This research study investigates the prevalence of acute kidney injury (AKI) in trauma patients undergoing emergency laparotomies. AKI is a common complication in major surgeries and is associated with various adverse effects. The study aims to explore the relationship between AKI and other comorbidities in this specific context. Methodology This is a retrospective cohort study. All patients who had laparotomy after abdominal trauma at King Abdulaziz Medical City (KAMC) and met the inclusion criteria were included in the study. Nonprobability consecutive sampling was used. Data were collected by chart review using the Best-Care system at KAMC. Descriptive statistics were used to summarize and describe the characteristics of the study participants. Frequencies and percentages were calculated for categorical variables, such as comorbidities. For continuous variables, mean and standard deviations were calculated and tabulated. All statistical calculations were performed using IBM SPSS Statistics for Windows, Version 27.0 (IBM Corp., Armonk, NY, USA). Results This research study included 152 patients who underwent laparotomy, and the majority of patients (146, 96%) did not experience AKI. Several comorbidities were observed, with hypertension and diabetes being the most prevalent at 37 (24.3%) and 35 (23%), respectively. Intraoperative hypotension was experienced by 23 (15.1%) patients, while 129 (84.9%) did not have this issue. Norepinephrine was the most common vasopressor used (25.7%), followed by ephedrine and a combination of norepinephrine and epinephrine. Gender and age groups did not show significant associations with AKI, comorbidities like diabetes, heart failure, and chronic kidney disease (CKD) demonstrated significant relationships with AKI. There was no significant difference in eGFR and serum creatinine baseline levels between patients meeting AKI criteria and those who did not. Conclusions The low overall incidence of AKI in this patient population is encouraging. However, healthcare professionals must be aware of the significant impact of comorbidities such as diabetes, heart failure, and CKD on AKI development. Vigilant monitoring of postoperative kidney function, particularly serum creatinine levels within the first 48 hours, is essential for early detection and timely intervention. By understanding and addressing these risk factors, healthcare providers can take proactive steps to prevent and manage AKI in patients undergoing laparotomy, ultimately leading to improved patient outcomes and reduced healthcare costs.

Understanding the Mechanisms and Treatment of Heart Failure: Quantitative Systems Pharmacology Models with a Focus on SGLT2 Inhibitors and Sex-Specific Differences

Heart failure (HF), which is a major clinical and public health challenge, commonly develops when the myocardial muscle is unable to pump an adequate amount of blood at typical cardiac pressures to fulfill the body's metabolic needs, and compensatory mechanisms are compromised or fail to adjust. Treatments consist of targeting the maladaptive response of the neurohormonal system, thereby decreasing symptoms by relieving congestion. Sodium-glucose co-transporter 2 (SGLT2) inhibitors, which are a recent antihyperglycemic drug, have been found to significantly improve HF complications and mortality. They act through many pleiotropic effects, and show better improvements compared to others existing pharmacological therapies. Mathematical modeling is a tool used to describe the pathophysiological processes of the disease, quantify clinically relevant outcomes in response to therapies, and provide a predictive framework to improve therapeutic scheduling and strategies. In this review, we describe the pathophysiology of HF, its treatment, and how an integrated mathematical model of the cardiorenal system was built to capture body fluid and solute homeostasis. We also provide insights into sex-specific differences between males and females, thereby encouraging the development of more effective sex-based therapies in the case of heart failure.

Incidence, severity, risk factors and outcomes of acute kidney injury in older adults: systematic review and meta-analysis

OBJECTIVES

Old age was identified as a strong risk factor for acute kidney injury (AKI). Our objectives were to provide estimates of AKI, risk factors and outcomes in patients ≥ 75 years for whom data are scarce.

METHODS

Observational studies and randomized controlled trials between 2005 and 2021 with patients of mean or median age ≥ 75 years, reporting AKI according to current definitions. Data on AKI incidence, risk factors and mortality were analyzed separately in unselected (UC) and acute heart failure (AHF) cohorts.

RESULTS

Twenty-six observational studies and 4 randomized controlled trials with 51,111 UC and 25,414 AHF patients were included. Ages averaged 79.4 and 79.8 years, respectively. Pooled risk ratios (RRs) of AKI rates were 26.29% (95% confidence intervals (CI) 13.20-41.97) (UC) and 24.21% (95% CI 20.03-28.65) (AHF). In both cohorts, AKI was associated with decreased estimated glomerular filtration rate at baseline, chronic kidney disease (UC: RR 1.80 (95% CI 1.15-2.80), AHF: RR 1.51 (95% CI 1.26-1.95) and hypertension (UC: RR 1.30 (95% CI 1.09-1.56), AHF: RR 1.07 (95% CI 1.05-1.09). RRs of AKI in patients on renin-angiotensin-inhibitors were 0.87 (95% CI 0.78-0.97) and 0.88 (95% CI 0.78-0.98) in UC and AHF, respectively. AKI was consistently associated with increased risk of in-hospital mortality (UC: RR 3.15 (95% CI 2.28-4.35), AHF: RR 4.28 (95% CI 2.53-7.24).

CONCLUSION

AKI is frequent in patients ≥ 75 years. While reduced renal function at baseline, CKD and hypertension were associated with AKI development, renin-angiotensin-inhibitors may be protective. Older AKI patients showed higher short-term mortality rates.

Machine Learning Approach to Understand Worsening Renal Function in Acute Heart Failure

Acute heart failure (AHF) is a common and severe condition with a poor prognosis. Its course is often complicated by worsening renal function (WRF), exacerbating the outcome. The population of AHF patients experiencing WRF is heterogenous, and some novel possibilities for its analysis have recently emerged. Clustering is a machine learning (ML) technique that divides the population into distinct subgroups based on the similarity of cases (patients). Given that, we decided to use clustering to find subgroups inside the AHF population that differ in terms of WRF occurrence. We evaluated data from the three hundred and twelve AHF patients hospitalized in our institution who had creatinine assessed four times during hospitalization. Eighty-six variables evaluated at admission were included in the analysis. The k-medoids algorithm was used for clustering, and the quality of the procedure was judged by the Davies–Bouldin index. Three clinically and prognostically different clusters were distinguished. The groups had significantly (p = 0.004) different incidences of WRF. Inside the AHF population, we successfully discovered that three groups varied in renal prognosis. Our results provide novel insight into the AHF and WRF interplay and can be valuable for future trial construction and more tailored treatment.

Levosimendan in acute heart failure with severely reduced kidney function, a propensity score matched registry study

Background

Patients with heart failure frequently present with kidney dysfunction. Kidney function is relevant, as prognosis declines with reduced kidney function and potentially beneficial drugs like levosimendan are contraindicated for missing safety data.

Materials and methods

A single-center retrospective registry study was conducted including all patients receiving levosimendan on a medical intensive care unit between January 2010 and December 2019. Exclusion criteria were a follow-up less than 24 h or missing glomerular filtration rate (eGFR) before administration of levosimendan. The first course of treatment was evaluated. Patients were stratified by eGFR before drug administration and the primary endpoint was a composite of supraventricular-, ventricular tachycardia and death within 7 days after administration of levosimendan. An internal control group was created by propensity score matching.

Results

A total of 794 patients receiving levosimendan were screened and 368 unique patients were included. Patients were predominantly male (73.6%) and median age was 63 years. Patients were divided by eGFR into three groups: >60 ml/min/1.73 m² (n = 110), 60–30 ml/min/1.73 m² (n = 130), and <30 ml/min/1.73 m² (n = 128). ICU survival was significantly lower in patients with lower eGFR (69.1, 57.7, and 50.8%, respectively, p = 0.016) and patients with lower eGFR were significantly older and had significantly more comorbidities. The primary combined endpoint was reached in 61.8, 63.1, and 69.5% of subjects, respectively (p = 0.396). A multivariate logistic regression model suggested only age (p < 0.020), extracorporeal membrane oxygenation (p < 0.001) or renal replacement therapy (p = 0.028) during day 1–7 independently predict the primary endpoint while kidney function did not (p = 0.835). A propensity score matching of patients with eGFR < 30 and >30 ml/min/1.73 m² based on these predictors of outcome confirmed the primary endpoint (p = 0.886).

Conclusion

The combined endpoint of supraventricular-, ventricular tachycardia and death within 7 days was reached at a similar rate in patients independently of kidney function. Prospective randomized trials are warranted to clarify if levosimendan can be used safely in severely reduced kidney function.

Dynamic nomogram for predicting acute kidney injury in patients with acute ischemic stroke: A retrospective study

Background

This study sought to develop and validate a dynamic nomogram chart to assess the risk of acute kidney injury (AKI) in patients with acute ischemic stroke (AIS).

Methods

These data were drawn from the Medical Information Mart for Intensive Care III (MIMIC-III) database, which collects 47 clinical indicators of patients after admission to the hospital. The primary outcome indicator was the occurrence of AKI within 48 h of intensive care unit (ICU) admission. Independent risk factors for AKI were screened from the training set using univariate and multifactorial logistic regression analyses. Multiple logistic regression models were developed, and nomograms were plotted and validated in an internal validation set. Based on the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) to estimate the performance of this nomogram.

Results

Nomogram indicators include blood urea nitrogen (BUN), creatinine, red blood cell distribution width (RDW), heart rate (HR), Oxford Acute Severity of Illness Score (OASIS), the history of congestive heart failure (CHF), the use of vancomycin, contrast agent, and mannitol. The predictive model displayed well discrimination with the area under the ROC curve values of 0.8529 and 0.8598 for the training set and the validator, respectively. Calibration curves revealed favorable concordance between the actual and predicted incidence of AKI (p > 0.05). DCA indicates the excellent net clinical benefit of nomogram in predicting AKI.

Conclusion

In summary, we explored the incidence of AKI in patients with AIS during ICU stay and developed a predictive model to help clinical decision-making.

Real world data of anticoagulant treatment in non-valvular atrial fibrillation across renal function status

The objective is to assess the impact of anticoagulant treatment in non-valvular atrial fibrillation (AF) and different categories of renal dysfunction in real world. Electronic Health recordings of patients with diagnosis of AF and renal function collected throughout 5 years and classified according to KDIGO categories. Stroke, transitory ischemic attack (TIA), intracranial hemorrhage and all-cause mortality were identified. Anticoagulant treatments during the study period were classified in untreated (never received therapy), VKA, NOAC and Aspirin. The risk of events was calculated by Cox-proportional hazard models adjusted by confounders. A total of 65,734 patients with AF, mean age 73.3 ± 10.49 years old and 47% females and follow-up of 3.2 years were included. KDIGO classification were: G1 33,903 (51.6%), G2 17,456 (26.6%), G3 8024 (12.2%) and G4 6351 (9.7%). There were 8592 cases of stroke and TIA, 437 intracranial hemorrhage, and 9603 all-cause deaths (incidence 36, 2 and 38 per 10³ person/year, respectively). 4.1% of patients with CHA2DS2-VASc Score 2 or higher did not receive anticoagulant therapy. Risk of stroke, TIA, and all-cause mortality increased from G1 to G4 groups. Anticoagulant treatments reduced the risk of events in the four categories, but NOAC seemed to offer significantly better protection. Renal dysfunction increases the risk of events in AF and anticoagulant treatments reduced the risk of stroke and all-cause mortality, although NOAC were better than VKA. Efforts should be done to reduce the variability in the use of anticoagulants even in this high risk group.

Fibroblast Growth Factor 21 Predicts Short-Term Prognosis in Patients With Acute Heart Failure: A Prospective Cohort Study

BACKGROUND

Recent studies of fibroblast growth factor 21 (FGF21), first recognized as a regulator of glucose and lipid metabolism, have found that the level of in serum FGF21 is associated with the prognosis of many cardiovascular diseases, but its relationship to acute heart failure (AHF) patients remains unknown. Our study aimed to investigate whether circulating FGF21 could predict the short-term prognosis of AHF patients.

METHODS

Four hundred and two AHF patients and 19 healthy controls were recruited into the prospective cohort study, and blood samples of participants were collected, in tubes without anticoagulant, within the first 24 h after hospital admission. Serum FGF21 levels were detected by enzyme-linked immunosorbent assay (ELISA). All patients were followed-up at least 6 months after discharge. The primary endpoint was all-cause death, and secondary endpoint was a composite endpoint of death and heart failure readmission. Mortality and composite end point events were analyzed using Kaplan-Meier curves. ROC curves compared the difference between the FGF21 and NT-proBNP in predicting 3- and 6-months mortality. Time-to-event data were evaluated using Kaplan-Meier estimation and Cox proportional hazards models.

RESULTS

In the present study, the serum FGF21 concentrations were significantly higher in the 402 AHF patients enrolled, compared with the 19 healthy controls (p < 0.001). The average age was 70 (±12) years, and 58% were males. Participants were divided into two groups according to the median FGF21 level (262 pg/ml): a high FGF21 group (n = 201, FGF21 ≥ 262 pg/ml) and low FGF21 group (n = 201, FGF21 <262 pg/ml). FGF21 was positively correlated with NT-proBNP, BUN, AST, creatinine and cholesterol, and negatively correlated with ALB and HDL. After a median follow-up of 193 days, the high FGF21 group had higher mortality and composite endpoint events compared with the low FGF21 group (HR: 3.91, 95% CI 2.21-6.92, p <0.001), even after adjusting for NT-proBNP (HR: 3.17, 95% CI 1.72-5.81, p < 0.001). ROC analysis shows that FGF21 was better than NT-proBNP in predicting death at both 3 (AUC, 0.77 vs. 0.63, p < 0.001) and 6 months (AUC, 0.78 vs. 0.66).

CONCLUSION

High baseline FGF21 levels are associated with adverse clinical outcomes in AHF patients. Serum FGF21 might be a potential predictive biomarker of AHF patients.

Different Renal Function Patterns in Patients With Acute Heart Failure: Relationship With Outcome and Congestion

Background

The role of worsening renal function during acute heart failure (AHF) hospitalization is still debated. Very few studies have extensively evaluated the renal function (RF) trend during hospitalization by repetitive measurements.

Objectives

To investigate the prognostic relevance of different RF trajectories together with the congestion status in hospitalized patients.

Methods

This is a post hoc analysis of a multi-center study including 467 patients admitted with AHF who were screened for the Diur-AHF Trial. We recognized five main RF trajectories based on serum creatinine and estimated glomerular filtration rate (eGFR) behavior. According to the RF trajectories our sample was divided into 1-stable (S), 2-transient improvement (TI), 3-permanent improvement (PI), 4-transient worsening (TW), and 5-persistent worsening (PW). The primary outcome was the combined endpoint of 180 days including all causes of mortality and re-hospitalization.

Results

We recruited 467 subjects with a mean congestion score of 3.5±1.08 and a median creatinine value of 1.28 (1.00-1.70) mg/dl, eGFR 50 (37-65) ml/min/m2 and NTpro B-type natriuretic peptide (BNP) 7,000 (4,200-11,700) pg/ml. A univariate analysis of the RF pattern demonstrated that TI and PW patterns were significantly related to poor prognosis [HR: 2.71 (1.81-4.05); p < 0.001; HR: 1.68 (1.15-2.45); p = 0.007, respectively]. Conversely, the TW pattern showed a significantly protective effect on outcome [HR:0.34 (0.19-0.60); p < 0.001]. Persistence of congestion and BNP reduction ≥ 30% were significantly related to clinical outcome at univariate analysis [HR: 2.41 (1.81-3.21); p < 0.001 and HR:0.47 (0.35-0.67); p < 0.001]. A multivariable analysis confirmed the independently prognostic role of TI, PW patterns, persistence of congestion, and reduced BNP decrease at discharge.

Conclusions

Various RF patterns during AHF hospitalization are associated with different risk(s). PW and TI appear to be the two trajectories related to worse outcome. Current findings confirm the importance of RF evaluation during and after hospitalization.

Identifying patients with heart failure in susceptible to de novo acute kidney injury: a machine learning approach (Preprint)

Background

Studies have shown that more than half of patients with heart failure (HF) with acute kidney injury (AKI) have newonset AKI, and renal function evaluation markers such as estimated glomerular filtration rate are usually not repeatedly tested during the hospitalization. As an independent risk factor, delayed AKI recognition has been shown to be associated with the adverse events of patients with HF, such as chronic kidney disease and death.

Objective

The aim of this study is to develop and assess of an unsupervised machine learning model that identifies patients with HF and normal renal function but who are susceptible to de novo AKI.

Methods

We analyzed an electronic health record data set that included 5075 patients admitted for HF with normal renal function, from which 2 phenogroups were categorized using an unsupervised machine learning algorithm called K-means clustering. We then determined whether the inferred phenogroup index had the potential to be an essential risk indicator by conducting survival analysis, AKI prediction, and the hazard ratio test.

Results

The AKI incidence rate in the generated phenogroup 2 was significantly higher than that in phenogroup 1 (group 1: 106/2823, 3.75%; group 2: 259/2252, 11.50%; P<.001). The survival rate of phenogroup 2 was consistently lower than that of phenogroup 1 (P<.005). According to logistic regression, the univariate model using the phenogroup index achieved promising performance in AKI prediction (sensitivity 0.710). The generated phenogroup index was also significant in serving as a risk indicator for AKI (hazard ratio 3.20, 95% CI 2.55-4.01). Consistent results were yielded by applying the proposed model on an external validation data set extracted from Medical Information Mart for Intensive Care (MIMIC) III pertaining to 1006 patients with HF and normal renal function.

Conclusions

According to a machine learning analysis on electronic health record data, patients with HF who had normal renal function were clustered into separate phenogroups associated with different risk levels of de novo AKI. Our investigation suggests that using machine learning can facilitate patient phengrouping and stratification in clinical settings where the identification of high-risk patients has been challenging.

Arrhythmias in patients with in-hospital alcohol withdrawal are associated with increased mortality: Insights from 1.5 million hospitalizations for alcohol withdrawal syndrome

Background

Atrial arrhythmias are commonly noted in patients with alcohol withdrawal syndrome (AWS), requiring inpatient admission.

Objective

The burden of arrhythmias and the association with in-hospital outcomes are incompletely defined in patients hospitalized with AWS.

Methods

The nationwide inpatient sample database was accessed from September 2015 to December 2018 to identify hospitalizations for AWS. We studied a cohort of patients with arrhythmias noted during hospitalization using the appropriate International Classification of Diseases, Tenth Revision billing codes. We compared patient characteristics, outcomes, and hospitalization costs between alcohol withdrawal hospitalizations with and without documented arrhythmias. Propensity score matching (PSM) and multivariate regression were performed to control confounders and develop odds ratios (OR), respectively.

Results

Among 1,511,155 hospitalization with AWS, 146,825 (9.72%) had concurrent arrhythmias. After PSM, we identified 135,540 cases in each group. Hospitalizations with AWS and concurrent arrhythmias had higher in-hospital mortality (4.19% vs 1.95%, OR 1.76, confidence interval [CI] 1.67–1.85, P < .0001). The most common arrhythmia was atrial fibrillation (66.7%). Arrhythmias in AWS were also associated with poorer in-hospital outcomes, including a higher risk of acute heart failure (8.40% vs 4.58%, OR 1.97, CI 1.90–2.05, P < .0001), acute kidney injury (21.32% vs 15.27%, OR 1.39, CI 1.36–1.43, P < .0001), and acute respiratory failure (9.19% vs 5.49%, OR 1.70, CI 1.64–1.76, P < .0001) requiring intubation. The length of hospital stay (6 days vs 4 days P < .0001) and cost of hospital care ($12,615 [$6683–$27,330] vs $7860 [$4482–$15,868], P < .0001) were higher in AWS with arrhythmias.

Conclusion

Arrhythmia in AWS is associated with higher in-hospital mortality and poorer in-hospital outcomes.

Health Care Analytics With Time-Invariant and Time-Variant Feature Importance to Predict Hospital-Acquired Acute Kidney Injury: Observational Longitudinal Study

Background

Acute kidney injury (AKI) develops in 4% of hospitalized patients and is a marker of clinical deterioration and nephrotoxicity. AKI onset is highly variable in hospitals, which makes it difficult to time biomarker assessment in all patients for preemptive care.

Objective

The study sought to apply machine learning techniques to electronic health records and predict hospital-acquired AKI by a 48-hour lead time, with the aim to create an AKI surveillance algorithm that is deployable in real time.

Methods

The data were sourced from 20,732 case admissions in 16,288 patients over 1 year in our institution. We enhanced the bidirectional recurrent neural network model with a novel time-invariant and time-variant aggregated module to capture important clinical features temporal to AKI in every patient. Time-series features included laboratory parameters that preceded a 48-hour prediction window before AKI onset; the latter’s corresponding reference was the final in-hospital serum creatinine performed in case admissions without AKI episodes.

Results

The cohort was of mean age 53 (SD 25) years, of whom 29%, 12%, 12%, and 53% had diabetes, ischemic heart disease, cancers, and baseline eGFR <90 mL/min/1.73 m², respectively. There were 911 AKI episodes in 869 patients. We derived and validated an algorithm in the testing dataset with an AUROC of 0.81 (0.78-0.85) for predicting AKI. At a 15% prediction threshold, our model generated 699 AKI alerts with 2 false positives for every true AKI and predicted 26% of AKIs. A lowered 5% prediction threshold improved the recall to 60% but generated 3746 AKI alerts with 6 false positives for every true AKI. Representative interpretation results produced by our model alluded to the top-ranked features that predicted AKI that could be categorized in association with sepsis, acute coronary syndrome, nephrotoxicity, or multiorgan injury, specific to every case at risk.

Conclusions

We generated an accurate algorithm from electronic health records through machine learning that predicted AKI by a lead time of at least 48 hours. The prediction threshold could be adjusted during deployment to optimize recall and minimize alert fatigue, while its precision could potentially be augmented by targeted AKI biomarker assessment in the high-risk cohort identified.

End-stage Renal Disease and Long-term Survival Among Survivors of Extracorporeal Membrane Oxygenation

We aimed to investigate the prevalence and associated factors of newly diagnosed end-stage renal disease (ESRD) requiring renal-replacement therapy (RRT) among survivors of extracorporeal membrane oxygenation (ECMO) and determine whether newly diagnosed ESRD is associated with poorer long-term survival outcomes. All adult patients who underwent ECMO between 2005 and 2018 were included, and ECMO survivors were those who survived more than 365 days after ECMO support. ECMO survivors with a history of pre-ECMO RRT were excluded. A total of 5,898 ECMO survivors were included in the analysis. At the 1-year post-ECMO follow-up, 447 patients (7.6%) were newly diagnosed with ESRD requiring RRT. Preexisting renal disease (odds ratio [OR]: 2.83), increased duration of continuous RRT during hospitalization (OR: 1.16), the cardiovascular group (vs. respiratory group; OR: 1.78), and the postcardiac arrest group (vs. respiratory group; OR: 2.52) were associated with newly diagnosed ESRD. Moreover, patients with newly diagnosed ESRD were associated with a 1.56-fold higher risk of 3-year all-cause mortality than those in the control group (hazard ratio: 1.56). At the 1-year post-ECMO follow-up, 7.6% of ECMO survivors were newly diagnosed with ESRD requiring RRT. Moreover, post-ECMO ESRD was associated with poorer long-term survival among ECMO survivors.

In Vivo and In Vitro Evaluation of Urinary Biomarkers in Ischemia/Reperfusion-Induced Kidney Injury

Oxidative stress plays an important role in the pathophysiology of acute kidney injury (AKI). Previously, we reported that vanin-1, which is involved in oxidative stress, is associated with renal tubular injury. This study was aimed to determine whether urinary vanin-1 is a biomarker for the early diagnosis of AKI in two experimental models: in vivo and in vitro. In a rat model of AKI, ischemic AKI was induced in uninephrectomized rats by clamping the left renal artery for 45 min and then reperfusing the kidney. On Day 1 after renal ischemia/reperfusion (I/R), serum creatinine (SCr) in I/R rats was higher than in sham-operated rats, but this did not reach significance. Urinary N-acetyl-β-D-glucosaminidase (NAG) exhibited a significant increase but decreased on Day 2 in I/R rats. In contrast, urinary vanin-1 significantly increased on Day 1 and remained at a significant high level on Day 2 in I/R rats. Renal vanin-1 protein decreased on Days 1 and 3. In line with these findings, immunofluorescence staining demonstrated that vanin-1 was attenuated in the renal proximal tubules of I/R rats. Our in vitro results confirmed that the supernatant from HK-2 cells under hypoxia/reoxygenation included significantly higher levels of vanin-1 as well as KIM-1 and NGAL. In conclusion, our results suggest that urinary vanin-1 might be a potential novel biomarker of AKI induced by I/R.

Sodium-Glucose Cotransporter-2 Inhibitor Use is Associated with a Reduced Risk of Heart Failure Hospitalization in Patients with Heart Failure with Preserved Ejection Fraction and Type 2 Diabetes Mellitus: A Real-World Study on a Diverse Urban Population

BACKGROUND

Limited evidence-based therapies exist for the management of heart failure with preserved ejection fraction (HFpEF). Sodium-glucose cotransporter-2 inhibitor (SGLT2i) use in patients with systolic heart failure (HFrEF) and type-2-diabetes mellitus (T2DM) is associated with improved cardiovascular (CV) and renal outcomes.

OBJECTIVE

We sought to examine whether there is an association of SGLT2i use with improved CV outcomes in patients with HFpEF.

PATIENTS AND METHODS

We conducted a single-center, retrospective review of patients with HFpEF and T2DM. The cohort was divided into two groups based on prescription of a SGLT2i or sitagliptin. The primary outcome was heart failure hospitalization (HFH); secondary outcomes were all-cause hospitalization and acute kidney injury (AKI).

RESULTS

After propensity score matching, there were 250 patients (89 in the SGLT2i group, 161 in the sitagliptin group), with a mean follow-up of 295 days. Univariate Cox regression analysis showed that the SGLT2i group had a reduced risk of HFH versus the sitagliptin group (hazard ratio (HR) 0.13; 95% confidence interval (CI) (0.05-0.36); p < 0.001). The SGLT2i group had a decreased risk of all-cause hospitalization (HR 0.48; 95% CI (0.33-0.70); p < 0.001) and SGLT2i had a lower risk of AKI (HR 0.39; 95% CI (0.20-0.74); p = 0.004).

CONCLUSIONS

The use of SGLT2is is associated with a reduced incidence of HFH and AKI in patients with HFpEF and T2DM.

Nomogram to predict the risk of acute kidney injury in patients with diabetic ketoacidosis: an analysis of the MIMIC-III database

BACKGROUND

This study aimed to develop and validate a nomogram for predicting acute kidney injury (AKI) during the Intensive Care Unit (ICU) stay of patients with diabetic ketoacidosis (DKA).

METHODS

A total of 760 patients diagnosed with DKA from the Medical Information Mart for Intensive Care III (MIMIC-III) database were included and randomly divided into a training set (70%, n = 532) and a validation set (30%, n = 228). Clinical characteristics of the data set were utilized to establish a nomogram for the prediction of AKI during ICU stay. The least absolute shrinkage and selection operator (LASSO) regression was utilized to identified candidate predictors. Meanwhile, a multivariate logistic regression analysis was performed based on variables derived from LASSO regression, in which variables with P < 0.1 were included in the final model. Then, a nomogram was constructed applying these significant risk predictors based on a multivariate logistic regression model. The discriminatory ability of the model was determined by illustrating a receiver operating curve (ROC) and calculating the area under the curve (AUC). Moreover, the calibration plot and Hosmer-Lemeshow goodness-of-fit test (HL test) were conducted to evaluate the performance of our newly bullied nomogram. Decision curve analysis (DCA) was performed to evaluate the clinical net benefit.

RESULTS

A multivariable model that included type 2 diabetes mellitus (T2DM), microangiopathy, history of congestive heart failure (CHF), history of hypertension, diastolic blood pressure (DBP), urine output, Glasgow coma scale (GCS), and respiratory rate (RR) was represented as the nomogram. The predictive model demonstrated satisfied discrimination with an AUC of 0.747 (95% CI, 0.706-0.789) in the training dataset, and 0.712 (95% CI, 0.642-0.782) in the validation set. The nomogram showed well-calibrated according to the calibration plot and HL test (P > 0.05). DCA showed that our model was clinically useful.

CONCLUSION

The nomogram predicted model for predicting AKI in patients with DKA was constructed. This predicted model can help clinical physicians to identify the patients with high risk earlier and prevent the occurrence of AKI and intervene timely to improve prognosis.

Chronic kidney disease as a cardiovascular risk factor

: Chronic kidney disease (CKD) is a public health threat with impact in cardiovascular risk. All forms of cardiovascular disease and mortality are more common in CKD. Treatment of cardiovascular risk factors, hypertension, dyslipidemia and diabetes is essential for cardiovascular and kidney protection. CKD is a marker of high or very high cardiovascular risk and its presence require early treatment and specific goals. Lifestyle is a pivotal factor, stopping smoking, reducing weight in the overweight or obese, starting regular physical exercise and healthy dietary pattern are recommended. Office BP should be lowered towards 130/80 mmHg or even lower if tolerated with sodium restriction and single pill combination, including angiotensin system blocker. Out-of-office BP monitoring, mainly 24-h assessment, is recommended. Diabetes requires treatment from the moment of diagnosis, but prediabetes benefits with lifestyle changes and metformin in patients stage 2 and 3a. iSGLT2 and GLP-1RA are initially recommended in T2D patients with high or very high cardiovascular risk. Concerning dyslipidemia, for patients in stage 4, LDL-C 55 mg/dl or less (1.4 mmol/l) and an LDL-C reduction of 50% or less from baseline is recommended. In stage 3, LDL-C goal is 70 mg/dl or less (1.8 mmol/l) and an LDL-C. reduction of at least 50% from baseline. Statins are the lipid-lowering therapy of choice with or without ezetimibe. Higher doses of statins are required as GFR declines. Available evidence suggests that combined PCSK9 inhibitors with maximally tolerated dose of statins may have an emerging role in treatment of dyslipidemia in CKD patients.

Acute kidney injury in heart failure: a population study

Aims

The objective of the present study is to assess the prognostic value of acute kidney injury (AKI) in the evolution of patients with heart failure (HF) using real‐world data.

Methods and results

Patients with a diagnosis of HF and with serial measurements of renal function collected throughout the study period were included. Estimated glomerular filtration rate (GFR) was calculated with the CKD‐EPI (Chronic Kidney Disease Epidemiology Collaboration). AKI was defined when a sudden drop in creatinine with posterior recovery was recorded. According to the Risk, Injury, Failure, Loss, and End‐Stage Renal Disease (RIFLE) scale, AKI severity was graded in three categories: risk [1.5‐fold increase in serum creatinine (sCr)], injury (2.0‐fold increase in sCr), and failure (3.0‐fold increase in sCr or sCr > 4.0 mg/dL). AKI incidence and risk of hospitalization and mortality after the first episode were calculated by adjusting for potential confounders. A total of 30 529 patients with HF were included. During an average follow‐up of 3.2 years, 5294 AKI episodes in 3970 patients (13.0%) and incidence of 3.3/100 HF patients/year were recorded. One episode was observed in 3161 (10.4%), two in 537 (1.8%), and three or more in 272 (0.9%). They were more frequent in women with diabetes and hypertension. The incidence increases across the GFR levels (Stages 1 to 4: risk 7.6%, 6.8%, 11.3%, and 12.5%; injury 2.1%, 2.0%, 3.3%, and 5.5%; and failure 0.9%, 0.6%. 1.4%, and 8.0%). A total of 3817 patients with acute HF admission were recorded during the follow‐up, with incidence of 38.4/100 HF patients/year, 3101 (81.2%) patients without AKI, 545 (14.3%) patients with one episode, and 171 (4.5%) patients with two or more. The number of AKI episodes [one hazard ratio (HR) 1.05 (0.98–1.13); two or more HR 2.01 (1.79–2.25)] and severity [risk HR 1.05 (0.97–1.04); injury HR 1.41 (1.24–1.60); and failure HR 1.90 (1.64–2.20)] increases the risk of hospitalization. A total of 10 560 deaths were recorded, with incidence of 9.3/100 HF patients/year, 8951 (33.7%) of subjects without AKI episodes, 1180 (11.17%) of subjects with one episode, and 429 (4.06%) with two or more episodes. The number of episodes [one HR 1.05 (0.98–1.13); two or more HR 2.01 (1.79–2.25)] and severity [risk 1.05 confidence interval (CI) (0.97–1.14), injury 1.41 (CI 1.24–1.60), and failure 1.90 (CI 1.64–2.20)] increases mortality risk.

Conclusions

The study demonstrated the worse prognostic value of sudden renal function decline in HF patients and pointed to those with more future risk who require review of treatment and closer follow‐up.