Reassuring pregnancy outcomes in women with mild COL4A3-5-related disease (Alport syndrome) and genetic type of disease can aid personalized counseling

Individualized pre-pregnancy counseling and antenatal care for women with chronic kidney disease (CKD) require disease-specific data. Here, we investigated pregnancy outcomes and long-term kidney function in women with COL4A3-5 related disease (Alport Syndrome, (AS)) in a large multicenter cohort. The ALPART-network (mAternaL and fetal PregnAncy outcomes of women with AlpoRT syndrome), an international collaboration of 17 centers, retrospectively investigated COL4A3-5 related disease pregnancies after the 20th week. Outcomes were stratified per inheritance pattern (X-Linked AS (XLAS)), Autosomal Dominant AS (ADAS), or Autosomal Recessive AS (ARAS)). The influence of pregnancy on estimated glomerular filtration rate (eGFR)-slope was assessed in 192 pregnancies encompassing 116 women (121 with XLAS, 47 with ADAS, and 12 with ARAS). Median eGFR pre-pregnancy was over 90ml/min/1.73m2. Neonatal outcomes were favorable: 100% live births, median gestational age 39.0 weeks and mean birth weight 3135 grams. Gestational hypertension occurred during 23% of pregnancies (reference: 'general' CKD G1-G2 pregnancies incidence is 4-20%) and preeclampsia in 20%. The mean eGFR declined after pregnancy but remained within normal range (over 90ml/min/1.73m2). Pregnancy did not significantly affect eGFR-slope (pre-pregnancy β=-1.030, post-pregnancy β=-1.349). ARAS-pregnancies demonstrated less favorable outcomes (early preterm birth incidence 3/11 (27%)). ARAS was a significant independent predictor for lower birth weight and shorter duration of pregnancy, next to the classic predictors (pre-pregnancy kidney function, proteinuria, and chronic hypertension) though missing proteinuria values and the small ARAS-sample hindered analysis. This is the largest study to date on AS and pregnancy with reassuring results for mild AS, though inheritance patterns could be considered in counseling next to classic risk factors. Thus, our findings support personalized reproductive care and highlight the importance of investigating kidney disease-specific pregnancy outcomes.

Association between CYP4A11 and EPHX2 genetic polymorphisms and chronic kidney disease progression in hypertensive patients

BACKGROUND

There are evidence indicating that some metabolites of arachidonic acid produced by cytochromes P450 (CYP) and epoxide hydroxylase (EPHX2), such as hydroxyeicosatetraenoic acids (HETEs), epoxyeicosatrienoic acids (EETs) or dihydroxyeicosatrienoic acids (DHETEs), play an important role in blood pressure regulation and they could contribute to the development of hypertension (HT) and kidney damage. Therefore, the main aim of the study was to evaluate whether the genetic polymorphisms of CYP2C8, CYP2C9, CYP2J2, CYP4F2, CYP4F11 and EPHX2, responsible for the formation of HETEs, EETs and DHETEs, are related to the progression of impaired renal function in a group of patients with hypertension.

METHODS

151HT patients from a hospital nephrology service were included in the study. Additionally, a group of 87 normotensive subjects were involved in the study as control group. For HT patients, a general biochemistry analysis, estimated glomerular filtration rate and genotyping for different CYPs and EPHX2 variant alleles was performed.

RESULTS

CYP4A11 rs3890011, rs9332982 and EPHX2 rs41507953 polymorphisms, according to the dominant model, presented a high risk of impaired kidney function, with odds ratios (OR) of 2.07 (1.00-4.32; P=0.049) 3.02 (1.11-8.23; P=0.030) and 3.59 (1.37-9.41; P=0.009), respectively, and the EPHX2 rs1042032 polymorphism a greater risk according to the recessive model (OR=6.23; 95% CI=1.50-25.95; P=0.007). However, no significant differences in allele frequencies between HT patients and in normotensive subjects for any of the SNP analysed. In addition, the patients with diagnosis of dyslipidemia (n=90) presented higher frequencies of EPHX2 K55R (rs41507953) and *35A>G (rs1042032) variants than patients without dyslipidemia, 4% vs. 14% (P=0.005) and 16 vs. 27% (P=0.02), respectively.

CONCLUSIONS

In this study has been found higher odds of impaired renal function progression associated with rs3890011 and rs9332982 (CYP4A11) and rs41507953 and rs1042032 (EPHX2) polymorphisms, which may serve as biomarkers for improve clinical interventions aimed at avoiding or delaying, in chronic kidney disease patients, progress to end-stage kidney disease needing dialysis or kidney transplant.

Predicting chronic kidney disease progression with artificial intelligence

BACKGROUND

The use of tools that allow estimation of the probability of progression of chronic kidney disease (CKD) to advanced stages has not yet achieved significant practical importance in clinical setting. This study aimed to develop and validate a machine learning-based model for predicting the need for renal replacement therapy (RRT) and disease progression for patients with stage 3-5 CKD.

METHODS

This was a retrospective, closed cohort, observational study. Patients with CKD affiliated with a private insurer with five-year follow-up data were selected. Demographic, clinical, and laboratory variables were included, and the models were developed based on machine learning methods. The outcomes were CKD progression, a significant decrease in the estimated glomerular filtration rate (eGFR), and the need for RRT.

RESULTS

Three prediction models were developed-Model 1 (risk at 4.5 years, n = 1446) with a F1 of 0.82, 0.53, and 0.55 for RRT, stage progression, and reduction in the eGFR, respectively,- Model 2 (time- to-event, n = 2143) with a C-index of 0.89, 0.67, and 0.67 for RRT, stage progression, reduction in the eGFR, respectively, and Model 3 (reduced Model 2) with C-index = 0.68, 0.68 and 0.88, for RRT, stage progression, reduction in the eGFR, respectively.

CONCLUSION

The time-to-event model performed well in predicting the three outcomes of CKD progression at five years. This model can be useful for predicting the onset and time of occurrence of the outcomes of interest in the population with established CKD.

Prognostic Models in Nephrology: Where Do We Stand and Where Do We Go from Here? Mapping Out the Evidence in a Scoping Review

Prognostic models can strongly support individualized care provision and well-informed shared decision making. There has been an upsurge of prognostic research in the field of nephrology, but the uptake of prognostic models in clinical practice remains limited. Therefore, we map out the research field of prognostic models for kidney patients and provide directions on how to proceed from here. We performed a scoping review of studies developing, validating, or updating a prognostic model for patients with CKD. We searched all published models in PubMed and Embase and report predicted outcomes, methodological quality, and validation and/or updating efforts. We found 602 studies, of which 30.1% concerned CKD populations, 31.6% dialysis populations, and 38.4% kidney transplantation populations. The most frequently predicted outcomes were mortality ( n =129), kidney disease progression ( n =75), and kidney graft survival ( n =54). Most studies provided discrimination measures (80.4%), but much less showed calibration results (43.4%). Of the 415 development studies, 28.0% did not perform any validation and 57.6% performed only internal validation. Moreover, only 111 models (26.7%) were externally validated either in the development study itself or in an independent external validation study. Finally, in 45.8% of development studies no useable version of the model was reported. To conclude, many prognostic models have been developed for patients with CKD, mainly for outcomes related to kidney disease progression and patient/graft survival. To bridge the gap between prediction research and kidney patient care, patient-reported outcomes, methodological rigor, complete reporting of prognostic models, external validation, updating, and impact assessment urgently need more attention.

Association of baseline electrocardiographic left ventricular hypertrophy with future renal function decline in the general population

Electrocardiographic left ventricular hypertrophy (LVH) could predict adverse renal outcomes in patients with hypertension. This study aimed to investigate the association between electrocardiographic LVH and future decline in renal function in the general population using a dataset of population-based health checkups from 2010 to 2019 including 19,825 participants. Electrocardiographic LVH was defined according to the Minnesota code. Renal function decline was defined as a decrease of ≥ 25% in the estimated glomerular filtration rate from baseline to < 60 mL/min/1.73 m2. Electrocardiographic LVH was found in 1263 participants at the baseline visit. The mean follow-up period was 3.4 ± 1.9 years. The incidence rates of renal function decline were 0.30 and 0.78 per 100 person-years in the non-LVH group and LVH groups, respectively. Electrocardiographic LVH was associated with the risk for renal function decline in the adjusted analysis (hazard ratio 1.69, 95% confidence interval 1.14-2.50, P = 0.009). This association was comparable across subgroups stratified by age, sex, body mass index, diagnosed hypertension, systolic blood pressure, hemoglobin A1c, and urinary protein. This study underscores the usefulness of electrocardiographic LVH to detect high-risk individuals for renal function decline in the setting of health checkups in the general population.

Risk Prediction Score for Chronic Kidney Disease in Healthy Adults and Adults With Type 2 Diabetes: Systematic Review

INTRODUCTION

Chronic kidney disease (CKD) is an important public health problem. In 2017, the global prevalence was estimated at 9.1%. Appropriate tools to predict the risk of developing CKD are necessary to prevent its progression. Type 2 diabetes is a leading cause of CKD; screening the population living with the disease is a cost-effective solution to prevent CKD. The aim of our study was to identify the existing prediction scores and their diagnostic accuracy for detecting CKD in apparently healthy populations and populations with type 2 diabetes.

METHODS

We conducted an electronic search in databases, including Medline/PubMed, Embase, Health Evidence, and others. For the inclusion criteria we considered studies with a risk predictive score in healthy populations and populations with type 2 diabetes. We extracted information about the models, variables, and diagnostic accuracy, such as area under the receiver operating characteristic curve (AUC), C statistic, or sensitivity and specificity.

RESULTS

We screened 2,359 records and included 13 studies for healthy population, 7 studies for patients with type 2 diabetes, and 1 for both populations. We identified 12 models for patients with type 2 diabetes; the range of C statistic was from 0.56 to 0.81, and the range of AUC was from 0.71 to 0.83. For healthy populations, we identified 36 models with the range of C statistics from 0.65 to 0.91, and the range of AUC from 0.63 to 0.91.

CONCLUSION

This review identified models with good discriminatory performance and methodologic quality, but they need more validation in populations other than those studied. This review did not identify risk models with variables comparable between them to enable conducting a meta-analysis.

A nomogram for predicting the 4-year risk of chronic kidney disease among Chinese elderly adults

BACKGROUND

Chronic kidney disease (CKD) has become a major public health problem across the globe, leading to various complications. This study aimed to construct a nomogram to predict the 4-year risk of CKD among Chinese adults.

METHODS

The study was based on the China Health and Retirement Longitudinal Study (CHARLS). A total of 3562 participants with complete information in CHARLS2011 and CHARLS2015 were included, and further divided into the training cohort and the validation cohort by a ratio of 7:3. Univariate and multivariate logistic regression analyses were used to select variables of the nomogram. The nomogram was evaluated by receiver-operating characteristic curve, calibration plots, and decision curve analysis (DCA).

RESULTS

In all, 2494 and 1068 participants were included in the training cohort and the validation cohort, respectively. A total of 413 participants developed CKD in the following 4 years. Five variables selected by multivariate logistic regression were incorporated in the nomogram, consisting of gender, hypertension, the estimated glomerular filtration rate (eGFR), hemoglobin, and Cystatin C. The area under curve was 0.809 and 0.837 in the training cohort and the validation cohort, respectively. The calibration plots showed agreement between the nomogram-predicted probability and the observed probability. DCA indicated that the nomogram had potential clinical use.

CONCLUSIONS

A predictive nomogram was established and internally validated in aid of identifying individuals at increased risk of CKD.

APOL1 Genotype, Proteinuria, and the Risk of Kidney Failure: A Secondary Analysis of the AASK (African American Study of Kidney Disease and Hypertension) and CRIC (Chronic Renal Insufficiency Cohort) Studies

Rationale & Objective

Patients with a high-risk Apolipoprotein L1 (APOL1) genotype are more likely to develop chronic kidney disease and kidney failure. It is unclear whether this increased risk is entirely mediated by the development of proteinuria.

Study Design

Retrospective observational study of the African American Study of Kidney Disease and Hypertension cohort and Chronic Renal Insufficiency Cohort.

Exposures & Predictors

Self-identified race (Black/non-Black) and presence of high-risk APOL1 genotype. The primary model was adjusted for age, sex, diabetes, estimated glomerular filtration rate, and urinary protein-creatinine ratio.

Outcomes

Time to kidney failure defined as time to dialysis or transplantation.

Analytical Approach

We used Cox proportional hazard models to study how proteinuria mediates the association between APOL1 and kidney failure. We modeled proteinuria at baseline and as a time-varying covariate.

Results

A high-risk APOL1 genotype was associated with a significantly higher risk of kidney failure, even for patients with minimal proteinuria (HR, 1.87; 95% CI, 1.23-2.84). The association was not significant among patients with high proteinuria (HR, 1.22; 95% CI, 0.93-1.61). When modeling proteinuria as a time-varying covariate, a high-risk APOL1 genotype was associated with higher kidney failure risk even among patients who never developed proteinuria (HR, 2.04; 95% CI, 1.10-3.77). Compared to non-Black patients, Black patients without the high-risk genotype did not have higher risk of kidney failure (HR, 0.96; 95% CI, 0.85-1.10).

Limitations

Two datasets were combined to increase statistical power. Limited generalizability beyond the study cohorts. Residual confounding common to observational studies.

Conclusions

A high-risk APOL1 genotype is significantly associated with increased kidney failure risk, especially among patients without baseline proteinuria. Although our results suggest that the risk is partially mediated through proteinuria, higher kidney failure risk was present even among patients who never developed proteinuria. Providers should consider screening for the high-risk APOL1 genotype, especially among Black patients without proteinuria in populations with chronic kidney disease.

A prediction model to assess the risk of egfr loss in patients with type 2 diabetes and preserved kidney function: The amd annals initiative

OBJECTIVE

To develop and validate a model for predicting 5-year eGFR-loss in type 2 diabetes mellitus (T2DM) patients with preserved renal function at baseline.

RESEARCH DESIGN AND METHODS

A cohort of 504.532 T2DM outpatients participating to the Medical Associations of Diabetologists (AMD) Annals Initiative was splitted into the Learning and Validation cohorts, in which the predictive model was respectively developed and validated. A multivariate Cox proportional hazard regression model including all baseline characteristics was performed to identify predictors of eGFR-loss. A weight derived from regression coefficients was assigned to each variable and the overall sum of weights determined the 0 to 8-risk score.

RESULTS

A set of demographic, clinical and laboratory parameters entered the final model. The eGFR-loss score showed a good performance in the Validation cohort. Increasing score values progressively identified a higher risk of GFR loss: a score ≥ 8 was associated with a HR of 13.48 (12.96-14.01) in the Learning and a HR of 13.45 (12.93-13.99) in the Validation cohort. The 5 years-probability of developing the study outcome was 55.9% higher in subjects with a score ≥ 8.

CONCLUSIONS

In the large AMD Annals Initiative cohort, we developed and validated an eGFR-loss prediction model to identify T2DM patients at risk of developing clinically meaningful renal complications within a 5-years time frame.

Prediction models used in the progression of chronic kidney disease: A scoping review

Objective

To provide a review of prediction models that have been used to measure clinical or pathological progression of chronic kidney disease (CKD).

Design

Scoping review.

Data sources

Medline, EMBASE, CINAHL and Scopus from the year 2011 to 17th February 2022.

Study selection

All English written studies that are published in peer-reviewed journals in any country, that developed at least a statistical or computational model that predicted the risk of CKD progression.

Data extraction

Eligible studies for full text review were assessed on the methods that were used to predict the progression of CKD. The type of information extracted included: the author(s), title of article, year of publication, study dates, study location, number of participants, study design, predicted outcomes, type of prediction model, prediction variables used, validation assessment, limitations and implications.

Results

From 516 studies, 33 were included for full-text review. A qualitative analysis of the articles was compared following the extracted information. The study populations across the studies were heterogenous and data acquired by the studies were sourced from different levels and locations of healthcare systems. 31 studies implemented supervised models, and 2 studies included unsupervised models. Regardless of the model used, the predicted outcome included measurement of risk of progression towards end-stage kidney disease (ESKD) of related definitions, over given time intervals. However, there is a lack of reporting consistency on details of the development of their prediction models.

Conclusions

Researchers are working towards producing an effective model to provide key insights into the progression of CKD. This review found that cox regression modelling was predominantly used among the small number of studies in the review. This made it difficult to perform a comparison between ML algorithms, more so when different validation methods were used in different cohort types. There needs to be increased investment in a more consistent and reproducible approach for future studies looking to develop risk prediction models for CKD progression.

Translational research in nephrology: prognosis

Translational research aims at reducing the gap between the results of studies focused on diagnosis, prognosis and therapy, and every day clinical practice. Prognosis is an essential component of clinical medicine. It aims at estimating the risk of adverse health outcomes in individuals, conditional to their clinical and non-clinical characteristics. There are three fundamental steps in prognostic research: development studies, in which the researcher identifies predictors, assigns the weights to each predictor, and assesses the model’s accuracy through calibration, discrimination and risk reclassification; validation studies, in which investigators test the model’s accuracy in an independent cohort of individuals; and impact studies, in which researchers evaluate whether the use of a prognostic model by clinicians improves their decision-making and patient outcome. This article aims at clarifying how to reduce the disconnection between the promises of prognostic research and the delivery of better individual health.

Nomogram to predict risk of incident chronic kidney disease in high-risk population of cardiovascular disease in China: community-based cohort study

AIMS

To develop a nomogram for incident chronic kidney disease (CKD) risk evaluation among community residents with high cardiovascular disease (CVD) risk.

METHODS

In this retrospective cohort study, 5730 non-CKD residents with high CVD risk participating the National Basic Public Health Service between January 2015 and December 2020 in Guangzhou were included. Endpoint was incident CKD defined as an estimated glomerular filtration rate (eGFR) less than 60 mL/min/1.73 m² during the follow-up period. The entire cohorts were randomly (2:1) assigned to a development cohort and a validation cohort. Predictors of incident CKD were selected by multivariable Cox regression and stepwise approach. A nomogram based on these predictors was developed and evaluated with concordance index (C-index) and area under curve (AUC).

RESULTS

During the median follow-up period of 4.22 years, the incidence of CKD was 19.09% (n=1094) in the entire cohort, 19.03% (727 patients) in the development cohort and 19.21% (367 patients) in the validation cohort. Age, body mass index, eGFR 60-89 mL/min/1.73 m², diabetes and hypertension were selected as predictors. The nomogram demonstrated a good discriminative power with C-index of 0.778 and 0.785 in the development and validation cohort. The 3-year, 4-year and 5-year AUCs were 0.817, 0.814 and 0.834 in the development cohort, and 0.830, 0.847 and 0.839 in the validation cohort.

CONCLUSION

Our nomogram based on five readily available predictors is a reliable tool to identify high-CVD risk patients at risk of incident CKD. This prediction model may help improving the healthcare strategies in primary care.

Derivation and External Validation of a Risk Index for Predicting Acute Kidney Injury Requiring Kidney Replacement Therapy After Noncardiac Surgery

IMPORTANCE

Severe acute kidney injury (AKI) is a serious postoperative complication. A tool for predicting the risk of AKI requiring kidney replacement therapy (KRT) after major noncardiac surgery might assist with patient counseling and targeted use of measures to reduce this risk.

OBJECTIVE

To derive and validate a predictive model for AKI requiring KRT after major noncardiac surgery.

DESIGN, SETTING, AND PARTICIPANTS

In this prognostic study, 5 risk prediction models were derived and internally validated in a population-based cohort of adults without preexisting kidney failure who underwent noncardiac surgery in Alberta, Canada, between January 1, 2004, and December 31, 2013. The best performing model and corresponding risk index were externally validated in a population-based cohort of adults without preexisting kidney failure who underwent noncardiac surgery in Ontario, Canada, between January 1, 2007, and December 31, 2017. Data analysis was conducted from September 1, 2019, to May 31, 2021.

EXPOSURES

Demographic characteristics, surgery type, laboratory measures, and comorbidities before surgery.

MAIN OUTCOMES AND MEASURES

Acute kidney injury requiring KRT within 14 days after surgery. Discrimination was assessed using the C statistic; calibration was assessed using calibration intercept and slope. Logistic recalibration was used to optimize model calibration in the external validation cohort.

RESULTS

The derivation cohort included 92 114 patients (52.2% female; mean [SD] age, 62.3 [18.0] years), and the external validation cohort included 709 086 patients (50.8% female; mean [SD] age, 61.0 [16.0] years). A total of 529 patients (0.6%) developed postoperative AKI requiring KRT in the derivation cohort, and 2956 (0.4%) developed postoperative AKI requiring KRT in the external validation cohort. The following factors were consistently associated with the risk of AKI requiring KRT: younger age (40-69 years: odds ratio [OR], 2.07 [95% CI, 1.69-2.53]; <40 years: OR, 3.73 [95% CI, 2.61-5.33]), male sex (OR, 1.55; 95% CI, 1.28-1.87), surgery type (colorectal: OR, 4.86 [95% CI, 3.28-7.18]; liver or pancreatic: OR, 6.46 [95% CI, 3.85-10.83]; other abdominal: OR, 2.19 [95% CI, 1.66-2.89]; abdominal aortic aneurysm repair: OR, 19.34 [95% CI, 14.31-26.14]; other vascular: OR, 7.30 [95% CI, 5.48-9.73]; thoracic: OR, 3.41 [95% CI, 2.07-5.59]), lower estimated glomerular filtration rate (OR, 0.97; 95% CI, 0.97-0.97 per 1 mL/min/1.73 m2 increase), lower hemoglobin concentration (OR, 0.99; 95% CI, 0.98-0.99 per 0.1 g/dL increase), albuminuria (mild: OR, 1.88 [95% CI, 1.52-2.33]; heavy: OR, 3.74 [95% CI, 2.98-4.69]), history of myocardial infarction (OR, 1.63; 95% CI, 1.32-2.03), and liver disease (mild: OR, 2.32 [95% CI, 1.66-3.24]; moderate or severe: OR, 4.96 [95% CI, 3.58-6.85]). In external validation, a final model including these variables showed excellent discrimination (C statistic, 0.95; 95% CI, 0.95-0.96), with sensitivity of 21.2%, specificity of 99.9%, positive predictive value of 38.1%, and negative predictive value of 99.7% at a predicted risk threshold of 10% or greater.

CONCLUSIONS AND RELEVANCE

The findings suggest that this risk model can predict AKI requiring KRT after noncardiac surgery using routine preoperative data. The model may be feasible for implementation in clinical perioperative risk stratification for severe AKI.

Distribution of estimated glomerular filtration rate and determinants of its age dependent loss in a German population-based study

Glomerular filtration rate (GFR) declines with age by approx. 1 ml/min/m² per year beginning in the third decade of life. At 70 years of age > 40 ml/min/m² of GFR will be lost. Thus, factors affecting loss of GFR have significant public health implications. Furthermore, the definition of chronic kidney disease based on GFR may not be appropriate for the elderly. We analyzed factors affecting absolute and relative change of eGFR over a 5 year period in 12,381 participants of the Gutenberg Health Study. We estimated GFR at baseline and after 5 years of follow-up by two different equations. Association with the decline of estimated GFR (eGFR) was assessed by multivariable regression analysis. We confirmed a median loss of eGFR per year of approx. 1 ml/min/m². Aside from albuminuria systolic blood pressure was most strongly associated with faster decline of eGFR followed by echocardiographic evidence of left ventricular diastolic dysfunction and reduced ejection fraction. White blood cell count showed a moderate association with eGFR loss. Diastolic blood pressure, serum uric acid and serum albumin were associated with slower GFR decline in multivariable analysis. Sensitivity analysis with exclusion of individuals taking diuretics, antihypertensive, antidiabetic, or lipid lowering drugs confirmed these associations.

Validation of the D: A: D Chronic Kidney Disease Risk Score Model Among People Living With HIV in the Asia-Pacific

BACKGROUND

We validated the Data collection on Adverse events of anti-HIV Drugs (D:A:D) full-risk and short-risk score models for chronic kidney disease (CKD) in the Asian HIV cohorts.

SETTINGS

A validation study among people living with HIV (PLHIV) aged ≥18 years among the cohorts in the Asia-Pacific region.

METHODS

PLHIV with a baseline estimated glomerular filtration rate > 60 mL/min/1.73 m were included for validation of the D:A:D CKD full version and short version without cardiovascular risk factors. Those with <3 estimated glomerular filtration rate measurements from baseline or previous exposure to potentially nephrotoxic antiretrovirals were excluded. Kaplan-Meier methods were used to estimate the probability of CKD development. The area under the receiver operating characteristics was also used to validate the risk score.

RESULTS

We included 5701 participants in full model {median 8.1 [interquartile range (IQR) 4.8-10.9] years follow-up} and 9791 in short model validation [median 4.9 (IQR 2.5-7.3) years follow-up]. The crude incidence rate of CKD was 8.1 [95% confidence interval (CI): 7.3 to 8.9] per 1000 person-years in the full model cohort and 10.5 (95% CI: 9.6 to 11.4) per 1000 person-years in the short model cohort. The progression rates for CKD at 10 years in the full model cohort were 2.7%, 8.9%, and 26.1% for low-risk, medium-risk, and high-risk groups, and 3.5%, 11.7%, and 32.4% in the short model cohort. The area under the receiver operating characteristics for the full-risk and short-risk score was 0.81 (95% CI: 0.79 to 0.83) and 0.83 (95% CI: 0.81 to 0.85), respectively.

CONCLUSION

The D:A:D CKD full-risk and short-risk score performed well in predicting CKD events among Asian PLHIV. These risk prediction models may be useful to assist clinicians in identifying individuals at high risk of developing CKD.

Improving Prognostic and Chronicity Evaluation of Chronic Kidney Disease with Contrast-Enhanced Ultrasound Index-Derived Peak Intensity

The capability of contrast-enhanced ultrasound (CEUS) to assess the prognosis and chronicity of chronic kidney disease (CKD) was evaluated in patients diagnosed with CKD in 2014 at Ren Ji Hospital, Shanghai, China. Time-intensity curves and quantitative indexes were created using QLab quantification software. Kidney biopsies were analyzed with α-smooth muscle actin immunohistochemistry. According to the renal chronicity score, patients were divided into four groups: minimal (n = 14), mild (n = 73), moderate (n = 49) and severe (n = 31). Multivariate logistic regression analysis revealed that the derived peak intensity (DPI) was independently associated with the renal chronicity score. Of 167 CKD patients (median follow-up: 30.4 ± 18.7 mo), 31 (18.6%) exhibited CKD progression, with a decline in the glomerular filtration rate of more than 25% or end-stage renal disease. Multivariate Cox regression analysis revealed that a lower DPI was independently associated with CKD progression. This study indicates that DPI is a reliable CEUS parameter for evaluating chronic renal changes and an independent prognostic factor of CKD.

Vitamin B6, Inflammation, and Cardiovascular Outcome in a Population-Based Cohort: The Prevention of Renal and Vascular End-Stage Disease (PREVEND) Study

Background: a large number of studies have linked vitamin B6 to inflammation and cardiovascular disease in the general population. However, it remains uncertain whether vitamin B6 is associated with cardiovascular outcome independent of inflammation. Methods: we measured plasma pyridoxal 5’-phosphate (PLP), as an indicator of vitamin B6 status, at baseline in a population-based prospective cohort of 6249 participants of the Prevention of Renal and Vascular End-stage Disease (PREVEND) study who were free of cardiovascular disease. As indicators of low-grade systemic inflammation, we measured high-sensitivity C-reactive protein and GlycA; Results: median plasma PLP was 37.2 (interquartile range, 25.1–57.0) nmol/L. During median follow-up for 8.3 (interquartile range, 7.8–8.9) years, 409 non-fatal and fatal cardiovascular events (composite outcome) occurred. In the overall cohort, log transformed plasma PLP was associated with the composite outcome, independent of adjustment for age, sex, smoking, alcohol consumption, body mass index (BMI), estimated glomerular filtration rate (eGFR), total cholesterol:high-density lipoprotein (HDL)-cholesterol ratio, and blood pressure (adjusted hazard ratio per increment of log plasma PLP, 0.66; 95% confidence interval (CI), 0.47–0.93). However, adjustment for high-sensitivity C-reactive protein and GlycA increased the hazard ratio by 9% and 12% respectively, to non-significant hazard ratios of 0.72 (95% confidence interval, 0.51–1.01) and 0.74 (95% confidence interval, 0.53–1.05). The association of plasma PLP with cardiovascular risk was modified by gender (adjusted P_interaction = 0.04). When stratified according to gender, in women the prospective association with cardiovascular outcome was independent of age, smoking, alcohol consumption, high-sensitivity C-reactive protein, and GlycA (adjusted hazard ratio, 0.50, 95% confidence interval, 0.27–0.94), while it was not in men (adjusted hazard, 0.99, 95% confidence interval, 0.65–1.51). Conclusions: in this population-based cohort, plasma PLP was associated with cardiovascular outcome, but this association was confounded by traditional risk factors and parameters of inflammation. Notably, the association of low plasma PLP with high risk of adverse cardiovascular outcome was modified by gender, with a stronger and independent association in women.

Risk scores for predicting incident chronic kidney disease among rural Chinese people: a village-based cohort study

Background

Few chronic kidney disease (CKD) risk prediction models have been investigated in low- and middle-income areas worldwide. We developed new risk scores for predicting incident CKD in low- and middle-income rural Chinese populations.

Methods

Data from the Handan Eye Study, which was a village-based cohort study and conducted from 2006 to 2013, were utilized as part of this analysis. The present study utilized data generated from 3266 participants who were ≥ 30 years of age. Two risk models for predicting incident CKD were derived using two-thirds of the sample cohort (selected randomly) using stepwise logistic regression, and were subsequently validated using data from the final third of the sample cohort. In addition, two simple point systems for incident CKD were generated according to the procedures described in the Framingham Study. CKD was defined as reduced renal function (estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73m²) or the presence of albuminuria (urinary albumin-to-creatinine ratio (UACR) ≥30 mg/g).

Results

The Simple Risk Score included waist circumference, systolic blood pressure (SBP), diabetes, sex, and education. The Best-fit Risk Score included urinary albumin-to-creatinine ratio, SBP, C-reactive protein, triglyceride, sex, education, and diabetes. In the validation sample, the areas under the receiver operating curve of the Simple Risk Score and Best-fit Risk Score were 0.717 (95% CI, 0.689–0.744) and 0.721 (95% CI, 0.693–0.748), respectively; the discrimination difference between the score systems was not significant (P = 0.455). The Simple Risk Score had a higher Youden index, sensitivity, and negative predictive value, with an optimal cutoff value of 14.

Conclusions

Our Simple Risk Score for predicting incident CKD in a low- and middle-income rural Chinese population will help identify individuals at risk for developing incident CKD.

Development of risk models for major adverse chronic renal outcomes among patients with type 2 diabetes mellitus using insurance claims: a retrospective observational study

Objective: To develop and validate models allowing the prediction of major adverse chronic renal outcomes (MACRO) in patients with type 2 diabetes mellitus (T2DM) using insurance claims data.Methods: The Optum Integrated Real World Evidence Electronic Health Records and Claims de-identified database (10/01/2006-09/30/2016) was used to identify T2DM patients ≥50 years old. Risk factors were assessed over a 12-month baseline period, and MACRO were subsequently assessed until the end of data availability, continuous enrollment, or death. Separate models were built for moderate-to-severe diabetic kidney disease (DKD), end-stage renal disease (ESRD), and renal death. A random split-sample approach was employed, where 70% of the sample served for model development (training set) and the remaining 30% served for validation (testing set). C-statistics were used to assess model performance.Results: A total of 160,031 patients were included. Risk factors associated with MACRO for all models included adapted diabetes complications severity index, heart failure, anemia, diabetic nephropathy, and CKD. C-statistics ranged between 0.70 (moderate-to-severe DKD) and 0.84 (renal death) in the testing set. A substantial proportion (e.g. 88.7% for moderate-to-severe DKD) of patients predicted to be at high-risk of MACRO did not have diabetic nephropathy, proteinuria, or CKD at baseline.Conclusions: The models developed using insurance claims data could reliably predict the risk of MACRO in patients with T2DM and enabled patients at higher-risk of DKD to be identified in the absence of baseline diabetic nephropathy, CKD, or proteinuria. These models could help establish strategies to reduce the risk of MACRO in T2DM patients.

Risk Stratification for Postoperative Acute Kidney Injury in Major Noncardiac Surgery Using Preoperative and Intraoperative Data

IMPORTANCE

Acute kidney injury (AKI) is one of the most common complications after noncardiac surgery. Yet current postoperative AKI risk stratification models have substantial limitations, such as limited use of perioperative data.

OBJECTIVE

To examine whether adding preoperative and intraoperative data is associated with improved prediction of noncardiac postoperative AKI.

DESIGN, SETTING, AND PARTICIPANTS

A prognostic study using logistic regression with elastic net selection, gradient boosting machine (GBM), and random forest approaches was conducted at 4 tertiary academic hospitals in the United States. A total of 42 615 hospitalized adults with serum creatinine measurements who underwent major noncardiac surgery between January 1, 2014, and April 30, 2018, were included in the study. Serum creatinine measurements from 365 days before and 7 days after surgery were used in this study.

MAIN OUTCOMES AND MEASURES

Postoperative AKI (defined by the Kidney Disease Improving Global Outcomes within 7 days after surgery) was the primary outcome. The area under the receiver operating characteristic curve (AUC) was used to assess discrimination.

RESULTS

Among 42 615 patients who underwent noncardiac surgery, the mean (SD) age was 57.9 (15.7) years, 23 943 (56.2%) were women, 27 857 (65.4%) were white, and the most frequent surgery types were orthopedic (15 718 [36.9%]), general (8808 [20.7%]), and neurologic (6564 [15.4%]). The rate of postoperative AKI was 10.1% (n = 4318). The progressive addition of clinical data improved model performance across all modeling approaches, with GBM providing the highest discrimination by AUC. In GBM models, the AUC increased from 0.712 (95% CI, 0.694-0.731) using prehospitalization variables to 0.804 (95% CI, 0.788-0.819) using preoperative variables (inclusive of prehospitalization variables) (P < .001 for AUC comparison). The AUC further increased to 0.817 (95% CI, 0.802-0.832) when adding intraoperative variables (P < .001 for comparison vs model using preoperative variables). However, the statistically significant improvements in discrimination did not appear to be clinically significant. In particular, the AKI rate among patients classified as high risk improved from 29.1% to 30.0%, a net of 15 patients were appropriately reclassified as high risk, and an additional 15 patients were appropriately reclassified as low risk.

CONCLUSIONS AND RELEVANCE

The findings of the study suggest that electronic health record data may be used to accurately stratify patients at risk of perioperative AKI, but the modest improvements from adding intraoperative data should be weighed against challenges in using intraoperative data.

Developing and Validating Risk Assessment Models of Clinical Outcomes in Modern Oncology

The identification of prognostic factors and building of risk assessment prognostic models will continue to play a major role in 21st century medicine in patient management and decision making. Investigators are often interested in examining the relationship between host, tumor-related, and environmental variables in predicting clinical outcomes. We make a distinction between static and dynamic prediction models. In static prediction modelling, typically variables collected at baseline are utilized in building models. On the other hand, dynamic predictive models leverage the longitudinal data of covariates collected during treatment or follow-up, and hence provide accurate predictions of patients prognoses. To date, most risk assessment models in oncology have been based on static models. In this article, we cover topics that are related to the analysis of prognostic factors, centering on factors that are both relevant at the time of diagnosis or initial treatment and during treatment. We describe the types of risk prediction and then provide a brief description of the penalized regression methods. We then review the state-of-the art methods for dynamic prediction and compare the strengths and the limitations of these methods. While static models will continue to play an important role in oncology, developing and validating dynamic models of clinical outcomes need to take a higher priority. It is apparent that a framework for developing and validating dynamic tools in oncology is still needed. One of the limitations in oncology that modelers may be constrained by the lack of access to the longitudinal biomarker data. It is highly recommended that the next generation of risk assessments consider the longitudinal biomarker data and outcomes so that prediction can be continually updated.

Chronic kidney disease progression in patients with resistant hypertension subject to 2 therapeutic strategies: Intensification with loop diuretics vs aldosterone antagonists

INTRODUCTION

Actualy, there are few data about glomerular filtration rate (eGFR) drop in patients with resistant hypertension and how diferent therapies can modify chronic kidney disease progression (CKD).

OBJECTIVE

To evaluate CKD progression in patients with resistant hypertension undergoing 2diferent therapies: treatment with spironolactone or furosemide.

METHODS

We included 30 patients (21M, 9W) with a mean age of 66.3±9.1 years, eGFR 55.8±16.5ml/min/1.73 m², SBP 162.8±8.2 and DBP 90.2±6.2mmHg: 15 patients received spironolactone and 15 furosemide and we followed up them a median of 32 months (28-41).

RESULTS

The mean annual eGFR decrease was -2.8±5.4ml/min/1.73 m². In spironolactone group was -2.1±4.8ml/min/1.73 m² and in furosemide group was -3.2±5.6ml/min/1.73 m², P<0.01. In patients received spironolactone, SBP decreased 23±9mmHg and in furosemide group decreased 16±3mmHg, P<.01. DBP decreased 10±8mmHg and 6±2mmHg, respectively (P<.01). Treatment with spironolactone reduced albuminuria from a serum albumin/creatine ratio of 210 (121-385) mg/g to 65 (45-120) mg/g at the end of follow-up, P<.01. There were no significant changes in the albumin/creatinine ratio in the furosemide group. The slower drop in kidney function was associated with lower SBP (P=.04), higher GFR (P=.01), lower albuminuria (P=.01), not diabetes mellitus (P=.01) and treatment with spironolactone (P=.02). Treatment with spironolactone (OR 2.13, IC 1.89-2.29) and lower albuminuria (OR 0.98, CI 0.97-0.99) maintain their independent predictive power in a multivariate model.

CONCLUSION

Treatment with spironolactone is more effective reducing BP and albuminuria in patients with resistant hypertension compared with furosemide and it is associated with a slower progression of CKD in the long term follow up.

Mild Albuminuria Is a Risk Factor for Faster GFR Decline in the Nondiabetic Population

INTRODUCTION

A minimal increase in the albumin-to-creatinine ratio (ACR) predicts cardiovascular disease and mortality, but whether it predicts kidney function loss in nondiabetic persons is unclear. We investigated the association between ACR in the optimal or high-normal range and the rate of glomerular filtration rate (GFR) decline in a cohort from the general population without diabetes, cardiovascular, or chronic kidney disease.

METHODS

In the Renal Iohexol Clearance Survey, we measured GFR using iohexol clearance in 1567 middle-aged nondiabetic individuals with an ACR <3.40 mg/mmol (30.0 mg/g) at baseline. The ACR was measured in unfrozen morning urine samples collected on 3 days before the GFR measurements. A total of 1278 (81%) participants had follow-up with GFR measurements after a median of 5.6 years.

RESULTS

The median ACR at baseline was 0.22 mg/mmol (interquartile range: 0.10-0.51 mg/mmol), the mean ± SD GFR was 104.0 ± 20.1 ml/min, and the mean ± SD GFR decline rate was -0.95 ± 2.23 ml/min per year. Higher baseline ACR levels were associated with a steeper GFR decline in adjusted linear mixed models. Study participants with ACR levels of 0.11 to 0.45 and 0.46 ± 3.40 mg/mmol had a 0.25 ml/min per year (95% confidence interval [95% CI]: -0.03 to 0.53) and 0.31 ml/min per year (95% CI: 0.02-0.60) steeper rate of decline than those with ACR ≤0.10 mg/mmol in multivariable-adjusted analyses. Among study participants with an ACR of <1.13 mg/mmol (defined as the optimal range), those with an ACR of 0.11 to 1.12 mg/mmol (n = 812) had a 0.28 ml/min per year (95% CI: 0.04-0.52) steeper rate of GFR decline than those with an ACR of ≤0.10 mg/mmol (n = 655).

CONCLUSION

A mildly increased ACR is an independent risk factor for faster GFR decline in nondiabetic individuals.

The Landscape of Diabetic Kidney Disease in the United States

PURPOSE OF REVIEW

The purposes of this review are to identify population characteristics of important risk factors for the development and progression of diabetic kidney disease (DKD) in the United States and to discuss barriers and opportunities to improve awareness, management, and outcomes in patients with DKD.

RECENT FINDINGS

The major risk factors for the development and progression of DKD include hyperglycemia, hypertension, and albuminuria. DKD disproportionately affects minorities and individuals with low educational and socioeconomic status. Barriers to effective management of DKD include the following: (a) limited patient and healthcare provider awareness of DKD, (b) lack of timely referrals of patients to a nephrologist, (c) low patient healthcare literacy, and (d) insufficient access to healthcare and health insurance. Increased patient and physician awareness of DKD has been shown to enhance patient outcomes. Multifactorial and multidisciplinary interventions targeting multiple risk factors and patient/physician education may provide better outcomes in patients with DKD.

The Patterns, Risk Factors, and Prediction of Progression in Chronic Kidney Disease: A Narrative Review

Chronic kidney disease (CKD) is a global public health problem that is associated with excess morbidity, mortality, and health resource utilization. The progression of CKD is defined by a decrease in glomerular filtration rate and leads to a variety of metabolic abnormalities including acidosis, hypertension, anemia, and mineral bone disorder. Lower glomerular filtration rate also bears a strong relationship with an increased risk of cardiovascular events, end-stage renal disease, and death. Patterns of CKD progression include linear and nonlinear trajectories, but kidney function can remain stable for years in some individuals. Addressing modifiable risk factors for the progression of CKD is needed to attenuate its associated morbidity and mortality. Developing effective risk prediction models for CKD progression is critical to identify patients who are more likely to benefit from interventions and more intensive monitoring. Accurate risk-prediction algorithms permit systems to best align health care resources with risk to maximize their effects and efficiency while guiding overall decision making.

Factors Associated with Chronic Kidney Disease and Their Clinical Utility in Primary Care Clinics in a Multi-Ethnic Southeast Asian Population

BACKGROUND

Chronic kidney disease (CKD) is a major global public health challenge. We investigated determinants of CKD and their clinical utility in an ethnically diverse Southeast Asian population.

METHODS

Electronic health records (EHR) of adults ≥40 years who visited any one of 4 government polyclinics in Singapore from January 1, 2012 to -December 31, 2015 were analyzed. CKD was defined as an estimated glomerular filtration rate <60 mL/min/1.73 m2 or 1+ dipstick proteinuria excretion, based on 2 measurements ≥3 months apart. CKD-associated factors and their clinical utility for predicting odds of CKD were investigated using multiple logistic regression analysis.

RESULTS

Based on the study criteria, 25.9% (95% CI 25.6-26.2) of the 88,765 eligible study individuals had CKD. The factors (OR and 95% CI) independently associated with CKD were older age ≥65 years (2.54 [2.44-2.64] vs. ≤65 years), respectively; men (1.13 [1.09-1.18]); Malay (1.27 [1.20-1.33]) and Indian (0.77 [0.71-0.83]) vs. Chinese ethnicity; overweight (body mass index [BMI] ≥27.5 kg/m2; 1.10 [1.04-1.16]) vs. normal weight (BMI 18 to <23 kg/m2); government (1.22 [1.15-1.31]) vs. private housing; and with hypertension (3.32 [3.09-3.56]), diabetes (6.93 [6.67-7.20]) or stroke (1.46 [1.36-1.56]) vs. without each co-morbidity, respectively. The area under the receiver operating characteristic curve (95% CI) for the model to predict the probability of CKD using hypertension, diabetes, and age was 0.808 (0.805-0.811). Only 28.5% (27.9-29.1%) of individuals with CKD had physician documentation of their CKD status. However, documentation of CKD status was associated with age ≥65 years (1.11 [1.04-1.20] vs. <65 years), men (1.35 [1.26-1.44]) vs. women, with vs. without hypertension (1.24 [1.07-1.44]), Indian (0.80 [0.69-0.92]) compared to Chinese ethnicity, ever smokers (0.89 [0.81-0.99]) vs. non-smokers, and those with vs. without stroke (0.83 [0.75-0.93]).

CONCLUSIONS

CKD prevalence in our Southeast Asian population is high and under-documented even in high-risk patients. Our findings highlight factors associated with CKD, and the predictive value of hypertension, diabetes, and advancing age as EHR-based screening targets for CKD. Our results also suggest that complementary educational efforts will be needed to increase physician detection and optimize the management of CKD, especially in high risk and marginalized groups across all clinics in Singapore, and possibly in the region.

The Michigan Risk Score to predict peripherally inserted central catheter-associated thrombosis

Essentials How best to quantify thrombosis risk with peripherally inserted central catheters (PICC) is unknown. Data from a registry were used to develop the Michigan Risk Score (MRS) for PICC thrombosis. Five risk factors were associated with PICC thrombosis and used to develop a risk score. MRS was predictive of the risk of PICC thrombosis and can be useful in clinical practice.

SUMMARY

Background Peripherally inserted central catheters (PICCs) are associated with upper extremity deep vein thrombosis (DVT). We developed a score to predict risk of PICC-related thrombosis. Methods Using data from the Michigan Hospital Medicine Safety Consortium, image-confirmed upper-extremity DVT cases were identified. A logistic, mixed-effects model with hospital-specific random intercepts was used to identify factors associated with PICC-DVT. Points were assigned to each predictor, stratifying patients into four classes of risk. Internal validation was performed by bootstrapping with assessment of calibration and discrimination of the model. Results Of 23 010 patients who received PICCs, 475 (2.1%) developed symptomatic PICC-DVT. Risk factors associated with PICC-DVT included: history of DVT; multi-lumen PICC; active cancer; presence of another CVC when the PICC was placed; and white blood cell count greater than 12 000. Four risk classes were created based on thrombosis risk. Thrombosis rates were 0.9% for class I, 1.6% for class II, 2.7% for class III and 4.7% for class IV, with marginal predicted probabilities of 0.9% (0.7, 1.2), 1.5% (1.2, 1.9), 2.6% (2.2, 3.0) and 4.5% (3.7, 5.4) for classes I, II, III, and IV, respectively. The risk classification rule was strongly associated with PICC-DVT, with odds ratios of 1.68 (95% CI, 1.19, 2.37), 2.90 (95% CI, 2.09, 4.01) and 5.20 (95% CI, 3.65, 7.42) for risk classes II, III and IV vs. risk class I, respectively. Conclusion The Michigan PICC-DVT Risk Score offers a novel way to estimate risk of DVT associated with PICCs and can help inform appropriateness of PICC insertion.

A Urinary Fragment of Mucin-1 Subunit α Is a Novel Biomarker Associated With Renal Dysfunction in the General Population

INTRODUCTION

Sequencing peptides included in the urinary proteome identifies the parent proteins and may reveal mechanisms underlying the pathophysiology of chronic kidney disease.

METHODS

In 805 randomly recruited Flemish individuals (50.8% women; mean age, 51.1 years), we determined the estimated glomerular filtration rate (eGFR) from serum creatinine using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. We categorized eGFR according to the National Kidney Foundation Kidney Disease Outcomes Quality Initiative guideline. We analyzed 74 sequenced urinary peptides with a detectable signal in more than 95% of participants. Follow-up measurements of eGFR were available in 597 participants.

RESULTS

In multivariable analyses, baseline eGFR decreased (P ≤ 0.022) with urinary fragments of mucin-1 (standardized association size expressed in ml/min/1.73 m², -4.48), collagen III (-2.84), and fibrinogen (-1.70) and was bi-directionally associated (P ≤ 0.0006) with 2 urinary collagen I fragments (+2.28 and -3.20). The eGFR changes over 5 years (follow-up minus baseline) resulted in consistent estimates (P ≤ 0.025) for mucin-1 (-1.85), collagen (-1.37 to 1.43) and fibrinogen (-1.45) fragments. Relative risk of having or progressing to eGFR <60 ml/min/1.73 m² was associated with mucin-1. Partial least-squares analysis confirmed mucin-1 as the strongest urinary marker associated with decreased eGFR, with a score of 2.47 compared with 1.80 for a collagen I fragment as the next contender. Mucin-1 predicted eGFR decline to <60 ml/min/1.73 m² over and above microalbuminuria (P = 0.011) and retained borderline significance (P = 0.05) when baseline eGFR was accounted for.

DISCUSSION

In the general population, mucin-1 subunit α, an extracellular protein that is shed from renal tubular epithelium, is a novel biomarker associated with renal dysfunction.

A Model to Predict Central-Line-Associated Bloodstream Infection Among Patients With Peripherally Inserted Central Catheters: The MPC Score

BACKGROUND Peripherally inserted central catheters (PICCs) are associated with central-line-associated bloodstream infections (CLABSIs). However, no tools to predict risk of PICC-CLABSI have been developed. OBJECTIVE To operationalize or prioritize CLABSI risk factors when making decisions regarding the use of PICCs using a risk model to estimate an individual's risk of PICC-CLABSI prior to device placement. METHODS Using data from the Michigan Hospital Medicine Safety consortium, patients that experienced PICC-CLABSI between January 2013 and October 2016 were identified. A Cox proportional hazards model with robust sandwich standard error estimates was then used to identify factors associated with PICC-CLABSI. Based on regression coefficients, points were assigned to each predictor and summed for each patient to create the Michigan PICC-CLABSI (MPC) score. The predictive performance of the score was assessed using time-dependent area-under-the-curve (AUC) values. RESULTS Of 23,088 patients that received PICCs during the study period, 249 patients (1.1%) developed a CLABSI. Significant risk factors associated with PICC-CLABSI included hematological cancer (3 points), CLABSI within 3 months of PICC insertion (2 points), multilumen PICC (2 points), solid cancers with ongoing chemotherapy (2 points), receipt of total parenteral nutrition (TPN) through the PICC (1 point), and presence of another central venous catheter (CVC) at the time of PICC placement (1 point). The MPC score was significantly associated with risk of CLABSI (P<.0001). For every point increase, the hazard ratio of CLABSI increased by 1.63 (95% confidence interval, 1.56-1.71). The area under the receiver-operating-characteristics curve was 0.67 to 0.77 for PICC dwell times of 6 to 40 days, which indicates good model calibration. CONCLUSION The MPC score offers a novel way to inform decisions regarding PICC use, surveillance of high-risk cohorts, and utility of blood cultures when PICC-CLABSI is suspected. Future studies validating the score are necessary. Infect Control Hosp Epidemiol 2017;38:1155-1166.

Proteinuria as an independent risk factor for contrast-induced acute kidney injury and mortality in patients with stroke undergoing cerebral angiography

BACKGROUND

The correlation between proteinuria and contrast-induced acute kidney injury (CI-AKI) in patients with cerebrovascular disease is still unknown.

OBJECTIVE

To determine whether proteinuria is a risk factor for CI-AKI and death in patients with stroke undergoing cerebral angiography.

METHODS

Data from 2015 patients with stroke undergoing cerebral angiography between January 2009 and December 2013 were retrospectively collected. Clinical parameters were obtained from the hospital's computerized database. All variables were analyzed by univariate analysis and multivariate logistic regression analysis.

RESULTS

CI-AKI was seen in 85 patients (4.2%). After adjustment for potential confounding risk factors, patients with proteinuria had a fivefold higher risk of CI-AKI than patients without proteinuria (OR=5.74; 95% CI 2.23 to 14.83; p<0.001). Other independent risk factors for CI-AKI were estimated glomerular filtration rate <60 mL/min/1.73 m2, anemia, and a high National Institute of Health Stroke Scale score. Proteinuria did not increase in-hospital mortality (OR=1.25; 95% CI 0.49 to 3.17; p=0.639) but did increase 1-year mortality (HR=2.30, 95% CI 1.55 to 3.41, p<0.001).

CONCLUSIONS

Proteinuria is an independent risk factor for CI-AKI and 1-year mortality in patients with stroke undergoing cerebral angiography. More attention should be paid to the development of CI-AKI in patients with stroke with proteinuria.

Development and validation of a predictive model for Chronic Kidney Disease progression in Type 2 Diabetes Mellitus based on a 13-year study in Singapore

AIMS

This study aims to develop and validate a predictive model for Chronic Kidney Disease (CKD) progression in Type 2 Diabetes Mellitus (T2DM).

METHODS

We conducted a prospective study on 1582 patients with T2DM from a Diabetes Centre in regional hospital in 2002-2014. CKD progression was defined as deterioration across eGFR categories with ⩾25% drop from baseline. The dataset was randomly split into development (70%) and validation (30%) datasets. Stepwise multivariable logistic regression was used to identify baseline predictors for model development. Model performance in the two datasets was assessed.

RESULTS

During median follow-up of 5.5years, 679 (42.9%) had CKD progression. Progression occurred in 467 (42.2%) and 212 patients (44.6%) in development and validation datasets respectively. Systolic blood pressure, HbA1c, estimated glomerular filtration rate and urinary albumin-to-creatinine ratio were associated with progression. Areas under receiving-operating-characteristics curve for the training and test datasets were 0.80 (95%CI, 0.77-0.83) and 0.83 (95%CI, 0.79-0.87). Observed and predicted probabilities by quintiles were not statistically different with Hosmer-Lemeshow χ² 0.65 (p=0.986) and 1.36 (p=0.928) in the two datasets. Sensitivity and specificity were 71.4% and 72.2% in development dataset, and 75.6% and 72.3% in the validation dataset.

CONCLUSIONS

A model using routinely available clinical measurements can accurately predict CKD progression in T2DM.

Cardiorenal Syndrome in Western Countries: Epidemiology, Diagnosis and Management Approaches

BACKGROUND

It is well established that a large number of hospitalized patients present various degrees of heart and kidney dysfunction; primary disease of the heart or kidney often involves dysfunction or injury to the other.

SUMMARY

Based on above-cited organ cross-talk, the term cardiorenal syndrome (CRS) was proposed. Although CRS was usually referred to as abruption of kidney function following heart injury, it is now clearly established that it can describe negative effects of an impaired renal function on the heart and circulation. The historical lack of clear syndrome definition and complexity of diseases contributed to a waste of precious time especially concerning diagnosis and therapeutic strategies. The effective classification of CRS proposed in a Consensus Conference by the Acute Dialysis Quality Group essentially divides CRS into two main groups, cardiorenal and renocardiac CRS, on the basis of primum movens of disease (cardiac or renal); both cardiorenal and renocardiac CRS are then divided into acute and chronic according to disease onset. Type 5 CRS integrates all cardiorenal involvement induced by systemic disease.

KEY MESSAGES

Prevalence and incidence data show a widespread increase of CRS also due to an increasing incidence of acute and chronic cardiovascular disease, such as acute decompensated heart failure, arterial hypertension and valvular heart disease. Patients with chronic kidney disease present various degrees of cardiovascular involvement especially due to chronic inflammatory status, volume and pressure overload and secondary hyperparathyroidism leading to a higher incidence of calcific heart disease. The following review will focus on the main aspects (epidemiology, risk factors, diagnostic tools and protocols, therapeutic approaches) of CRS in Western countries (Europe and United States).

An external validation of models to predict the onset of chronic kidney disease using population-based electronic health records from Salford, UK

BACKGROUND

Chronic kidney disease (CKD) is a major and increasing constituent of disease burdens worldwide. Early identification of patients at increased risk of developing CKD can guide interventions to slow disease progression, initiate timely referral to appropriate kidney care services, and support targeting of care resources. Risk prediction models can extend laboratory-based CKD screening to earlier stages of disease; however, to date, only a few of them have been externally validated or directly compared outside development populations. Our objective was to validate published CKD prediction models applicable in primary care.

METHODS

We synthesised two recent systematic reviews of CKD risk prediction models and externally validated selected models for a 5-year horizon of disease onset. We used linked, anonymised, structured (coded) primary and secondary care data from patients resident in Salford (population ~234 k), UK. All adult patients with at least one record in 2009 were followed-up until the end of 2014, death, or CKD onset (n = 178,399). CKD onset was defined as repeated impaired eGFR measures over a period of at least 3 months, or physician diagnosis of CKD Stage 3-5. For each model, we assessed discrimination, calibration, and decision curve analysis.

RESULTS

Seven relevant CKD risk prediction models were identified. Five models also had an associated simplified scoring system. All models discriminated well between patients developing CKD or not, with c-statistics around 0.90. Most of the models were poorly calibrated to our population, substantially over-predicting risk. The two models that did not require recalibration were also the ones that had the best performance in the decision curve analysis.

CONCLUSIONS

Included CKD prediction models showed good discriminative ability but over-predicted the actual 5-year CKD risk in English primary care patients. QKidney, the only UK-developed model, outperformed the others. Clinical prediction models should be (re)calibrated for their intended uses.

Primacy of lowered baseline glomerular filtration rate as a risk for incident chronic kidney disease: A longitudinal study in Japanese subjects

AIM

Risk profile for incident chronic kidney disease (CKD) in Japanese subjects has not been established. Our aim was to identify risk factors for CKD in Japanese.

METHODS

Consecutive 171 536 health examinees (median age 49 years and estimated glomerular filtration rate (eGFR) 78.2 mL/min per 1.73 m² ) without CKD were re-examined after a median period of 6.2 years. Results of Cox proportional hazards models in randomly assigned two thirds (Derivation cohort) were verified in the rest (Validation cohort). CKD was defined as eGFR <60 mL/min per 1.73 m² or positive dipstick proteinuria.

RESULTS

In the Derivation cohort, CKD developed in 1002 (5.8%) subjects. Seven variables such as lower eGFR, male gender, higher uric acid concentration, lower red cell count and higher age and systolic blood pressure were identified as significant risks for CKD, with lowered eGFR being an overwhelmingly strong risk: adjusted hazard ratio for those with the baseline eGFR <70 mL/min per 1.73 m² was as high as 90.1. Performance of prediction of CKD by the probability on the basis of the seven risk factors combined was only marginally preferable to eGFR alone. The area under the receiver operating characteristic curve (95% CI) for the prediction was 0.846 (0.826-0.864) and 0.822 (0.802-0.840) (P < 0.01), the kappa statistic was 0.263 and 0.250 (n.s.), and the mean absolute difference between "predicted probability" and "observed" CKD was 1.4% and 1.9% (P = 0.14) by the combined model and eGFR alone, respectively.

CONCLUSION

Seven risk factors for incident CKD were identified in Japanese health examinees. However, lowered baseline eGFR outweighed other risks to the degree that eGFR alone was suffice for CKD prediction.

Health Education and General Practitioner Training in Hypertension Management: Long-Term Effects on Kidney Function

BACKGROUND AND OBJECTIVES

In the Control of Blood Pressure and Risk Attenuation trial, a 2×2 factorial design study (2004-2007), the combined home health education and trained general practitioner intervention delivered over 2 years was more effective than no intervention (usual care) in lowering systolic BP among adults with hypertension in urban Pakistan. We aimed to assess the effectiveness of the interventions on kidney function.

DESIGN, PARTICIPANTS, SETTINGS, & METHODS

In 2012-2013, we conducted extended follow-up of a total of 1271 individuals aged ≥40 years with hypertension (systolic BP ≥140 mmHg, diastolic BP ≥90 mmHg, or receipt of antihypertensive treatment) and serum creatinine measurements with 2 years in-trial and 5 years of post-trial period in 12 randomly selected low-income communities in Karachi, Pakistan. The change in eGFR from baseline to 7 years was assessed among randomized groups using a generalized estimating equation method with multiple imputation of missing values.

RESULTS

At 7 years of follow-up, adjusted mean eGFR remained unchanged, with a change of -0.3 (95% confidence interval [95% CI], -3.5 to 2.9) ml/min per 1.73 m(2) among adults randomly assigned to the combined home health education plus trained general practitioner intervention compared with a significant decline of -3.6 (95% CI, -5.7 to -2.0) ml/min per 1.73 m(2) in those assigned to usual care (P=0.01, modified intention-to-treat analysis). The risk for the combined intervention of death from kidney failure or >20% decline in eGFR relative to usual care was significantly reduced (risk ratio, 0.47; 95% CI, 0.25 to 0.89).

CONCLUSIONS

The combined home health education plus trained general practitioner intervention is beneficial in preserving kidney function among adults with hypertension in communities in Karachi. These findings highlight the importance of scaling up simple strategies for renal risk reduction in low- and middle-income countries.

Risk Prediction for Early CKD in Type 2 Diabetes

BACKGROUND AND OBJECTIVES

Quantitative data for prediction of incidence and progression of early CKD are scarce in individuals with type 2 diabetes. Therefore, two risk prediction models were developed for incidence and progression of CKD after 5.5 years and the relative effect of predictors were ascertained.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

Baseline and prospective follow-up data of two randomized clinical trials, ONgoing Telmisartan Alone and in combination with Ramipril Global Endpoint Trial (ONTARGET) and Outcome Reduction with Initial Glargine Intervention (ORIGIN), were used as development and independent validation cohorts, respectively. Individuals aged ≥55 years with type 2 diabetes and normo- or microalbuminuria at baseline were included. Incidence or progression of CKD after 5.5 years was defined as new micro- or macroalbuminuria, doubling of creatinine, or ESRD. The competing risk of death was considered as an additional outcome state in the multinomial logistic models.

RESULTS

Of the 6766 ONTARGET participants with diabetes, 1079 (15.9%) experienced incidence or progression of CKD, and 1032 (15.3%) died. The well calibrated, parsimonious laboratory prediction model incorporating only baseline albuminuria, eGFR, sex, and age exhibited an externally validated c-statistic of 0.68 and an R(2) value of 10.6%. Albuminuria, modeled to depict the difference between baseline urinary albumin/creatinine ratio and the threshold for micro- or macroalbuminuria, was mostly responsible for the predictive performance. Inclusion of clinical predictors, such as glucose control, diabetes duration, number of prescribed antihypertensive drugs, previous vascular events, or vascular comorbidities, increased the externally validated c-statistic and R(2) value only to 0.69 and 12.1%, respectively. Explained variation was largely driven by renal and not clinical predictors.

CONCLUSIONS

Albuminuria and eGFR were the most important factors to predict onset and progression of early CKD in individuals with type 2 diabetes. However, their predictive ability is modest. Inclusion of demographic, clinical, and other laboratory predictors barely improved predictive performance.

Prediction models and risk assessment for silicosis using a retrospective cohort study among workers exposed to silica in China

This study aims to develop a prognostic risk prediction model for the development of silicosis among workers exposed to silica dust in China. The prediction model was performed by using retrospective cohort of 3,492 workers exposed to silica in an iron ore, with 33 years of follow-up. We developed a risk score system using a linear combination of the predictors weighted by the LASSO penalized Cox regression coefficients. The model's predictive accuracy was evaluated using time-dependent ROC curves. Six predictors were selected into the final prediction model (age at entry of the cohort, mean concentration of respirable silica, net years of dust exposure, smoking, illiteracy, and no. of jobs). We classified workers into three risk groups according to the quartile (Q1, Q3) of risk score; 203 (23.28%) incident silicosis cases were derived from the high risk group (risk score ≥ 5.91), whilst only 4 (0.46%) cases were from the low risk group (risk score < 3.97). The score system was regarded as accurate given the range of AUCs (83-96%). This study developed a unique score system with a good internal validity, which provides scientific guidance to the clinicians to identify high-risk workers, thus has important cost efficient implications.

Comparison of methods for renal risk prediction in patients with type 2 diabetes (ZODIAC-36)

Background

Patients with diabetes are at high risk of death prior to reaching end-stage renal disease, but most models predicting the risk of kidney disease do not take this competing risk into account. We aimed to compare the performance of Cox regression and competing risk models for prediction of early- and late-stage renal complications in type 2 diabetes.

Methods

Patients with type 2 diabetes participating in the observational ZODIAC study were included. Prediction models for (micro)albuminuria and 50% increase in serum creatinine (SCr) were developed using Cox regression and competing risk analyses. Model performance was assessed by discrimination and calibration.

Results

During a total follow-up period of 10 years, 183 out of 640 patients (28.6%) with normoalbuminuria developed (micro)albuminuria, and 22 patients (3.4%) died without developing (micro)albuminuria (i.e. experienced the competing event). Seventy-nine out of 1,143 patients (6.9%) reached the renal end point of 50% increase in SCr, while 219 (19.2%) died without developing the renal end point. Performance of the Cox and competing risk models predicting (micro)albuminuria was similar and differences in predicted risks were small. However, the Cox model increasingly overestimated the risk of increase in SCr in presence of a substantial number of competing events, while the performance of the competing risk model was quite good.

Conclusions

In this study, we demonstrated that, in case of substantial numbers of competing events, it is important to account for the competing risk of death in renal risk prediction in patients with type 2 diabetes.

Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration

The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) Statement includes a 22-item checklist, which aims to improve the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. This explanation and elaboration document describes the rationale; clarifies the meaning of each item; and discusses why transparent reporting is important, with a view to assessing risk of bias and clinical usefulness of the prediction model. Each checklist item of the TRIPOD Statement is explained in detail and accompanied by published examples of good reporting. The document also provides a valuable reference of issues to consider when designing, conducting, and analyzing prediction model studies. To aid the editorial process and help peer reviewers and, ultimately, readers and systematic reviewers of prediction model studies, it is recommended that authors include a completed checklist in their submission. The TRIPOD checklist can also be downloaded from www.tripod-statement.org.

Presence of early CKD-related metabolic complications predict progression of stage 3 CKD: a case-controlled study

BACKGROUND

Only a subset of patients who enter stage 3 chronic kidney disease (CKD) progress to stage 4. Identifying which patients entering stage 3 are most likely to progress could improve outcomes, by allowing more appropriate referrals for specialist care, and spare those unlikely to progress the adverse effects and costliness of an unnecessarily aggressive approach. We hypothesized that compared to non-progressors, patients who enter stage 3 CKD and ultimately progress have experienced greater loss of renal function, manifested by impairment of metabolic function (anemia, worsening acidosis and mineral abnormalities), than is reflected in the eGFR at entry to stage 3. The purpose of this case-controlled study was to design a prediction model for CKD progression using laboratory values reflecting metabolic status.

METHODS

Using data extracted from the electronic health record (EHR), two cohorts of patients in stage 3 were identified: progressors (eGFR declined >3 ml/min/1.73 m2/year; n=117) and non-progressors (eGFR declined <1 ml/min/1.713 m2; n=364). Initial laboratory values recorded a year before to a year after the time of entry to stage 3, reflecting metabolic complications (hemoglobin, bicarbonate, calcium, phosphorous, and albumin) were obtained. Average values in progressors and non-progressors were compared. Classification algorithms (Naïve Bayes and Logistic Regression) were used to develop prediction models of progression based on the initial lab data.

RESULTS

At the entry to stage 3 CKD, hemoglobin, bicarbonate, calcium, and albumin values were significantly lower and phosphate values significantly higher in progressors compared to non-progressors even though initial eGFR values were similar. The differences were sufficiently large that a prediction model of progression could be developed based on these values. Post-test probability of progression in patients classified as progressors or non-progressors were 81% (73% - 86%) and 17% (13% - 23%), respectively.

CONCLUSIONS

Our studies demonstrate that patients who enter stage 3 and ultimately progress to stage 4 manifest a greater degree of metabolic complications than those who remain stable at the onset of stage 3 when eGFR values are equivalent. Lab values (hemoglobin, bicarbonate, phosphorous, calcium and albumin) are sufficiently different between the two cohorts that a reasonably accurate predictive model can be developed.

Survival Analysis with Electronic Health Record Data: Experiments with Chronic Kidney Disease

This paper presents a detailed survival analysis for chronic kidney disease (CKD). The analysis is based on the EHR data comprising almost two decades of clinical observations collected at New York-Presbyterian, a large hospital in New York City with one of the oldest electronic health records in the United States. Our survival analysis approach centers around Bayesian multiresolution hazard modeling, with an objective to capture the changing hazard of CKD over time, adjusted for patient clinical covariates and kidney-related laboratory tests. Special attention is paid to statistical issues common to all EHR data, such as cohort definition, missing data and censoring, variable selection, and potential for joint survival and longitudinal modeling, all of which are discussed alone and within the EHR CKD context.

Cardiorenal syndrome

Cardiorenal syndrome (CRS) includes a broad spectrum of diseases within which both the heart and kidneys are involved, acutely or chronically. An effective classification of CRS in 2008 essentially divides CRS in two main groups, cardiorenal and renocardiac CRS, based on primum movens of disease (cardiac or renal); both cardiorenal and renocardiac CRS are then divided into acute and chronic, according to onset of disease. The fifth type of CRS integrates all cardiorenal involvement induced by systemic disease. This article addresses the pathophysiology, diagnosis, treatment, and outcomes of the 5 distinct types of CRS.

Progressive chronic kidney disease in primary care: modifiable risk factors and predictive model

OBJECTIVE

To determine the incidence and prevalence of CKD and of progressive GFR decline, to identify modifiable risk factors of and to develop a predictive risk model for progressive GFR decline in high risk primary care patients.

METHODS

Retrospective observational study of 803 patients with or at high risk of CKD exclusively managed in primary care. Baseline data was collected in 2003, follow-up data in 2006. High risk was defined as inadequately controlled hypertension or diabetes, and GFR<60, progressive GFR decline as annual GFR decline >2.5mlmin(-1) 1.73m(-2).

RESULTS

CKD was present in 25.4% at baseline and developed in further 13.7% during follow-up, 42.5% demonstrated progressive GFR decline. Obesity, proteinuria, heart failure, inadequate hypertension and diabetes control, lacking angiotensin-converting-enzyme-inhibitors or angiotensin-receptor-blockers, radio contrast, and dual renin-angiotensin-aldosterone-system blockade were identified as modifiable, independent risk factors of progressive GFR decline. The risk model, containing 7 readily obtainable variables, showed good discriminative ability.

CONCLUSIONS

High risk primary care patients demonstrated high CKD prevalence and incidence, and rate of progressive GFR decline. Identified risk factors can be modified in primary care. Our risk model may aid primary care physicians to predict patients at high risk of progressive GFR decline.