A Simpler Machine Learning Model for Acute Kidney Injury Risk Stratification in Hospitalized Patients

Background: Hospitalization-associated acute kidney injury (AKI), affecting one-in-five inpatients, is associated with increased mortality and major adverse cardiac/kidney endpoints. Early AKI risk stratification may enable closer monitoring and prevention. Given the complexity and resource utilization of existing machine learning models, we aimed to develop a simpler prediction model. Methods: Models were trained and validated to predict risk of AKI using electronic health record (EHR) data available at 24 h of inpatient admission. Input variables included demographics, laboratory values, medications, and comorbidities. Missing values were imputed using multiple imputation by chained equations. Results: 26,410 of 209,300 (12.6%) inpatients developed AKI during admission between 13 July 2012 and 11 July 2018. The area under the receiver operating characteristic curve (AUROC) was 0.86 for Random Forest and 0.85 for LASSO. Based on Youden’s Index, a probability cutoff of \u3e0.15 provided sensitivity and specificity of 0.80 and 0.79, respectively. AKI risk could be successfully predicted in 91% patients who required dialysis. The model predicted AKI an average of 2.3 days before it developed. Conclusions: The proposed simpler machine learning model utilizing data available at 24 h of admission is promising for early AKI risk stratification. It requires external validation and evaluation of effects of risk prediction on clinician behavior and patient outcomes

Development of Risk Prediction Equations for Incident Chronic Kidney Disease

IMPORTANCE ‐ Early identification of individuals at elevated risk of developing chronic kidney disease  could improve clinical care through enhanced surveillance and better management of underlying health  conditions.  OBJECTIVE – To develop assessment tools to identify individuals at increased risk of chronic kidney  disease, defined by reduced estimated glomerular filtration rate (eGFR).  DESIGN, SETTING, AND PARTICIPANTS – Individual level data analysis of 34 multinational cohorts from  the CKD Prognosis Consortium including 5,222,711 individuals from 28 countries. Data were collected  from April, 1970 through January, 2017. A two‐stage analysis was performed, with each study first  analyzed individually and summarized overall using a weighted average. Since clinical variables were  often differentially available by diabetes status, models were developed separately within participants  with diabetes and without diabetes. Discrimination and calibration were also tested in 9 external  cohorts (N=2,253,540). EXPOSURE Demographic and clinical factors.  MAIN OUTCOMES AND MEASURES – Incident eGFR <60 ml/min/1.73 m2.  RESULTS – In 4,441,084 participants without diabetes (mean age, 54 years, 38% female), there were  660,856 incident cases of reduced eGFR during a mean follow‐up of 4.2 years. In 781,627 participants  with diabetes (mean age, 62 years, 13% female), there were 313,646 incident cases during a mean follow‐up of 3.9 years. Equations for the 5‐year risk of reduced eGFR included age, sex, ethnicity, eGFR, history of cardiovascular disease, ever smoker, hypertension, BMI, and albuminuria. For participants  with diabetes, the models also included diabetes medications, hemoglobin A1c, and the interaction  between the two. The risk equations had a median C statistic for the 5‐year predicted probability of  0.845 (25th – 75th percentile, 0.789‐0.890) in the cohorts without diabetes and 0.801 (25th – 75th percentile, 0.750‐0.819) in the cohorts with diabetes. Calibration analysis showed that 9 out of 13 (69%) study populations had a slope of observed to predicted risk between 0.80 and 1.25. Discrimination was  similar in 18 study populations in 9 external validation cohorts; calibration showed that 16 out of 18 (89%) had a slope of observed to predicted risk between 0.80 and 1.25. CONCLUSIONS AND RELEVANCE – Equations for predicting risk of incident chronic kidney disease developed in over 5 million people from 34 multinational cohorts demonstrated high discrimination and  variable calibration in diverse populations

Successful kidney transplantation from a hepatitis B surface antigen-positive donor to an antigen-negative recipient using a novel vaccination regimen

Transplanting a kidney from a hepatitis B surface antigen (HBsAg)-positive donor to an HBsAg-negative recipient who is naturally immune has been successful in countries endemic for hepatitis B virus (HBV). However, in most of these cases, the donors were deceased. We present a report of a successful HBsAg-discordant kidney transplantation in the United States; in this case, a living donor kidney was transplanted to a vaccinated recipient. The wife of a 58-year-old HBsAg-negative man volunteered to donate a kidney to her husband. She had chronic hepatitis B but undetectable HBV DNA. She tested positive for HBsAg and antibody to hepatitis B core antigen, but hepatitis B e antigen was undetectable. The recipient failed to develop an antibody response to 3 doses of intramuscular recombinant HBV vaccine given in consecutive months. Immunity was induced by using biweekly intradermal vaccine. However, antibody titer tapered to \u3c10 mIU/mL over 14 months. An intramuscular booster vaccine resulted in a prolonged anamnestic response, allowing for successful living unrelated donor transplantation. During the 10 years since transplantation, the patient has continued to have normal liver function, with undetectable HBsAg and HBV DNA. Antibody titers to HBsAg slowly decreased to 5.8 mIU/mL during the 10 years. Transplant function has been well preserved. This approach to inducing long-term immunity for transplantation merits further study in the United States

A Simpler Machine Learning Model for Acute Kidney Injury Risk Stratification in Hospitalized Patients

Background: Hospitalization-associated acute kidney injury (AKI), affecting one-in-five inpatients, is associated with increased mortality and major adverse cardiac/kidney endpoints. Early AKI risk stratification may enable closer monitoring and prevention. Given the complexity and resource utilization of existing machine learning models, we aimed to develop a simpler prediction model. Methods: Models were trained and validated to predict risk of AKI using electronic health record (EHR) data available at 24 h of inpatient admission. Input variables included demographics, laboratory values, medications, and comorbidities. Missing values were imputed using multiple imputation by chained equations. Results: 26,410 of 209,300 (12.6%) inpatients developed AKI during admission between 13 July 2012 and 11 July 2018. The area under the receiver operating characteristic curve (AUROC) was 0.86 for Random Forest and 0.85 for LASSO. Based on Youden&rsquo;s Index, a probability cutoff of &gt;0.15 provided sensitivity and specificity of 0.80 and 0.79, respectively. AKI risk could be successfully predicted in 91% patients who required dialysis. The model predicted AKI an average of 2.3 days before it developed. Conclusions: The proposed simpler machine learning model utilizing data available at 24 h of admission is promising for early AKI risk stratification. It requires external validation and evaluation of effects of risk prediction on clinician behavior and patient outcomes

Vignette-Based Reflections to Inform Genetic Testing Policies in Living Kidney Donors

Family history of kidney disease increases risk of end-stage kidney disease (ESKD) in donors. Pre-donation genetic testing is recommended in evaluation guidelines and regulatory policy. Collaborating across several institutions, we describe cases to illustrate the utility as well as practical issues in incorporating genetic testing in transplant protocols. Case 1 is from 2009, before pervasive genetic testing. A healthy 27-year-old Caucasian male had an uneventful donor evaluation for his mother, who had early onset ESKD of unclear cause. He participated in paired-exchange kidney donation, but developed progressive kidney disease and gout over the next 10 years. A uromodulin gene mutation (NM_003361.3(UMOD):c.377 G&gt;A p.C126Y) was detected and kidney biopsy showed tubulointerstitial kidney disease. The patient subsequently required kidney transplantation himself. Case 2 was a 36-year-old African American female who had an uneventful kidney donor evaluation. She underwent gene panel-based testing to rule out ApolipoproteinL1 risk variants, for which was negative. Incidentally, a sickle-cell trait (NM_000518.5(HBB):c.20A&gt;T p.Glu7Val) was noted, and she was declined for kidney donation. This led to significant patient anguish. Case 3 was a 26-year-old Caucasian female who underwent panel-based testing because the potential recipient, her cousin, carried a variant of uncertain significance in the hepatocyte nuclear factor-1-&beta; (HNF1B) gene. While the potential donor did not harbor this variant, she was found to have a likely pathogenic variant in complement factor I (NM_000204.4(CFI):c.1311dup:p.Asp438Argfs*8), precluding kidney donation. Our cases emphasize that while genetic testing can be invaluable in donor evaluation, transplant centers should utilize detailed informed consent, develop care pathways for secondary genetic findings, and share experience to develop best practices around genetic testing in donors

Vignette-Based Reflections to Inform Genetic Testing Policies in Living Kidney Donors

Family history of kidney disease increases risk of end-stage kidney disease (ESKD) in donors. Pre-donation genetic testing is recommended in evaluation guidelines and regulatory policy. Collaborating across several institutions, we describe cases to illustrate the utility as well as practical issues in incorporating genetic testing in transplant protocols. Case 1 is from 2009, before pervasive genetic testing. A healthy 27-year-old Caucasian male had an uneventful donor evaluation for his mother, who had early onset ESKD of unclear cause. He participated in paired-exchange kidney donation, but developed progressive kidney disease and gout over the next 10 years. A uromodulin gene mutation (NM_003361.3(UMOD):c.377 G>A p.C126Y) was detected and kidney biopsy showed tubulointerstitial kidney disease. The patient subsequently required kidney transplantation himself. Case 2 was a 36-year-old African American female who had an uneventful kidney donor evaluation. She underwent gene panel-based testing to rule out ApolipoproteinL1 risk variants, for which was negative. Incidentally, a sickle-cell trait (NM_000518.5(HBB):c.20A>T p.Glu7Val) was noted, and she was declined for kidney donation. This led to significant patient anguish. Case 3 was a 26-year-old Caucasian female who underwent panel-based testing because the potential recipient, her cousin, carried a variant of uncertain significance in the hepatocyte nuclear factor-1-β (HNF1B) gene. While the potential donor did not harbor this variant, she was found to have a likely pathogenic variant in complement factor I (NM_000204.4(CFI):c.1311dup:p.Asp438Argfs*8), precluding kidney donation. Our cases emphasize that while genetic testing can be invaluable in donor evaluation, transplant centers should utilize detailed informed consent, develop care pathways for secondary genetic findings, and share experience to develop best practices around genetic testing in donors

Development of Risk Prediction Equations for Incident Chronic Kidney Disease

Importance: Early identification of individuals at elevated risk of developing chronic kidney disease (CKD) could improve clinical care through enhanced surveillance and better management of underlying health conditions. Objective: To develop assessment tools to identify individuals at increased risk of CKD, defined by reduced estimated glomerular filtration rate (eGFR). Design, Setting, and Participants: Individual-level data analysis of 34 multinational cohorts from the CKD Prognosis Consortium including 5222711 individuals from 28 countries. Data were collected from April 1970 through January 2017. A 2-stage analysis was performed, with each study first analyzed individually and summarized overall using a weighted average. Because clinical variables were often differentially available by diabetes status, models were developed separately for participants with diabetes and without diabetes. Discrimination and calibration were also tested in 9 external cohorts (n = 2253540). Exposures: Demographic and clinical factors. Main Outcomes and Measures: Incident eGFR of less than 60 mL/min/1.73 m2. Results: Among 4441084 participants without diabetes (mean age, 54 years, 38% women), 660856 incident cases (14.9%) of reduced eGFR occurred during a mean follow-up of 4.2 years. Of 781627 participants with diabetes (mean age, 62 years, 13% women), 313646 incident cases (40%) occurred during a mean follow-up of 3.9 years. Equations for the 5-year risk of reduced eGFR included age, sex, race/ethnicity, eGFR, history of cardiovascular disease, ever smoker, hypertension, body mass index, and albuminuria concentration. For participants with diabetes, the models also included diabetes medications, hemoglobin A1c, and the interaction between the 2. The risk equations had a median C statistic for the 5-year predicted probability of 0.845 (interquartile range [IQR], 0.789-0.890) in the cohorts without diabetes and 0.801 (IQR, 0.750-0.819) in the cohorts with diabetes. Calibration analysis showed that 9 of 13 study populations (69%) had a slope of observed to predicted risk between 0.80 and 1.25. Discrimination was similar in 18 study populations in 9 external validation cohorts; calibration showed that 16 of 18 (89%) had a slope of observed to predicted risk between 0.80 and 1.25. Conclusions and Relevance: Equations for predicting risk of incident chronic kidney disease developed from more than 5 million individuals from 34 multinational cohorts demonstrated high discrimination and variable calibration in diverse populations. Further study is needed to determine whether use of these equations to identify individuals at risk of developing chronic kidney disease will improve clinical care and patient outcomes.