scholarly journals Development and internal validation of a prediction model for hospital-acquired acute kidney injury

2019 ◽  
Author(s):  
Catalina Martin-Cleary ◽  
Luis Miguel Molinero-Casares ◽  
Alberto Ortiz ◽  
Jose Miguel Arce-Obieta
Keyword(s):  
Acute Kidney Injury ◽  
Risk Score ◽  
Bayesian Model Averaging ◽  
Characteristic Curve ◽  
Tertiary Care ◽  
Kidney Injury ◽  
Risk Scores ◽  
Internal Validation ◽  
Clinical Records ◽  
Hospital Acquired

Abstract Background Predictive models and clinical risk scores for hospital-acquired acute kidney injury (AKI) are mainly focused on critical and surgical patients. We have used the electronic clinical records from a tertiary care general hospital to develop a risk score for new-onset AKI in general inpatients that can be estimated automatically from clinical records. Methods A total of 47 466 patients met inclusion criteria within a 2-year period. Of these, 2385 (5.0%) developed hospital-acquired AKI. Step-wise regression modelling and Bayesian model averaging were used to develop the Madrid Acute Kidney Injury Prediction Score (MAKIPS), which contains 23 variables, all obtainable automatically from electronic clinical records at admission. Bootstrap resampling was employed for internal validation. To optimize calibration, a penalized logistic regression model was estimated by the least absolute shrinkage and selection operator (lasso) method of coefficient shrinkage after estimation. Results The area under the curve of the receiver operating characteristic curve of the MAKIPS score to predict hospital-acquired AKI at admission was 0.811. Among individual variables, the highest odds ratios, all >2.5, for hospital-acquired AKI were conferred by abdominal, cardiovascular or urological surgery followed by congestive heart failure. An online tool (http://www.bioestadistica.net/MAKIPS.aspx) will facilitate validation in other hospital environments. Conclusions MAKIPS is a new risk score to predict the risk of hospital-acquired AKI, based on variables present at admission in the electronic clinical records. This may help to identify patients who require specific monitoring because of a high risk of AKI.

scholarly journals AKI Risk Score (AKI-RiSc): Developing an Interpretable Clinical Score for Early Identification of Acute Kidney Injury at the Emergency Department

2021 ◽  
Author(s):  
Yukai Ang ◽  
Siqi Li ◽  
Marcus Eng Hock Ong ◽  
Feng Xie ◽  
Su Hooi Teo ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Emergency Department ◽  
Acute Kidney Injury ◽  
Risk Score ◽  
Early Identification ◽  
Kidney Injury ◽  
Clinical Score ◽  
Risk Scores ◽  
Singapore General Hospital ◽  
Hospitalised Patients

Abstract Acute kidney injury (AKI) in hospitalised patients is a common syndrome associated with poorer patient outcomes. Clinical risk scores can be used for the early identification of patients at risk of AKI. We conducted a retrospective study using electronic health records of Singapore General Hospital emergency department patients who were admitted from 2008 to 2016. The primary outcome was inpatient AKI of any stage within 7 days of admission based on Kidney Disease Improving Global Outcome (KDIGO) 2012 guidelines. A machine learning AutoScore algorithm was used to generate clinical scores from the study sample which was divided into training, validation and testing cohorts. Model performance was evaluated using area under the curve (AUC). Among the 119,468 admissions, 10,693 (9.0%) developed AKI. 8,491 were stage 1 (79.4%), 906 stage 2 (8.5%) and 1,296 stage 3 (12.1%). The AKI Risk Score (AKI-RiSc) was a summation of the integer scores of 6 variables: serum creatinine, serum bicarbonate, pulse, systolic blood pressure, and diastolic blood pressure. AUC of AKI-RiSc was 0.730 (95% CI: 0.713 – 0.747), outperforming an existing AKI Prediction Score model which achieved AUC of 0.665 (95% CI: 0.646 – 0.679) on the testing cohort. At a cut-off of 4 points, AKI-RiSc had a sensitivity of 82.5% and specificity of 46.7%. AKI-RiSc is a simple clinical score that can be easily implemented on the ground for early identification of AKI and potentially be applied in international settings.

scholarly journals AKI Risk Score (AKI-RiSc): Developing an Interpretable Clinical Score for Early Identification of Acute Kidney Injury for Patients Presenting to the Emergency Department

2021 ◽  
Author(s):  
Yukai Ang ◽  
Marcus Eng Hock Ong ◽  
Feng Xie ◽  
Su Hooi Teo ◽  
Lina Choong ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Emergency Department ◽  
Acute Kidney Injury ◽  
Risk Score ◽  
Early Identification ◽  
Kidney Injury ◽  
Clinical Score ◽  
Risk Scores ◽  
Singapore General Hospital ◽  
Hospitalised Patients

Background: Acute kidney injury (AKI) in hospitalised patients is a common syndrome associated with poorer patient outcomes. Clinical risk scores can be used for the early identification of patients at risk of AKI. Methods: We conducted a retrospective study using electronic health records of Singapore General Hospital emergency department patients who were admitted from 2008 to 2016. The primary outcome was inpatient AKI of any stage within 7 days of admission based on Kidney Disease Improving Global Outcome (KDIGO) 2012 guidelines. AutoScore, a machine learning based algorithm, was used to generate point based clinical scores from the study sample which was divided into training, validation and testing cohorts. Model performance was evaluated using area under the curve (AUC). Results: Among the 119,468 admissions, 10,693 (9.0%) developed AKI. 8,491 were stage 1 (79.4%), 906 stage 2 (8.5%) and 1,296 stage 3 (12.1%). The AKI Risk Score (AKI-RiSc) was a summation of the integer scores of 6 variables: serum creatinine, serum bicarbonate, pulse, systolic blood pressure, and diastolic blood pressure. AUC of AKI-RiSc was 0.730 (95% CI: 0.713 - 0.747), outperforming an existing AKI Prediction Score model which achieved AUC of 0.665 (95% CI: 0.646 - 0.679) when evaluated on the same test cohort. At a cut-off of 4 points, AKI-RiSc had a sensitivity of 82.5% and specificity of 46.7%. Conclusion: AKI-RiSc is a simple point based clinical score that can be easily implemented on the ground for early identification of AKI in high-risk patients and potentially be applied in healthcare settings internationally.

scholarly journals National Veterans Health Administration inpatient risk stratification models for hospital-acquired acute kidney injury

2015 ◽  
Vol 22 (5) ◽  
pp. 1054-1071 ◽  
Author(s):  
Robert M Cronin ◽  
Jacob P VanHouten ◽  
Edward D Siew ◽  
Svetlana K Eden ◽  
Stephan D Fihn ◽  
...  
Keyword(s):  
Risk Factors ◽  
Acute Kidney Injury ◽  
Logistic Regression ◽  
Random Forests ◽  
Characteristic Curve ◽  
Discrimination Performance ◽  
Kidney Injury ◽  
Health Administration ◽  
Stage 1 ◽  
Hospital Acquired

Abstract Objective Hospital-acquired acute kidney injury (HA-AKI) is a potentially preventable cause of morbidity and mortality. Identifying high-risk patients prior to the onset of kidney injury is a key step towards AKI prevention. Materials and Methods A national retrospective cohort of 1,620,898 patient hospitalizations from 116 Veterans Affairs hospitals was assembled from electronic health record (EHR) data collected from 2003 to 2012. HA-AKI was defined at stage 1+, stage 2+, and dialysis. EHR-based predictors were identified through logistic regression, least absolute shrinkage and selection operator (lasso) regression, and random forests, and pair-wise comparisons between each were made. Calibration and discrimination metrics were calculated using 50 bootstrap iterations. In the final models, we report odds ratios, 95% confidence intervals, and importance rankings for predictor variables to evaluate their significance. Results The area under the receiver operating characteristic curve (AUC) for the different model outcomes ranged from 0.746 to 0.758 in stage 1+, 0.714 to 0.720 in stage 2+, and 0.823 to 0.825 in dialysis. Logistic regression had the best AUC in stage 1+ and dialysis. Random forests had the best AUC in stage 2+ but the least favorable calibration plots. Multiple risk factors were significant in our models, including some nonsteroidal anti-inflammatory drugs, blood pressure medications, antibiotics, and intravenous fluids given during the first 48 h of admission. Conclusions This study demonstrated that, although all the models tested had good discrimination, performance characteristics varied between methods, and the random forests models did not calibrate as well as the lasso or logistic regression models. In addition, novel modifiable risk factors were explored and found to be significant.

scholarly journals A validation study of UCSD-Mayo risk score in predicting hospital-acquired acute kidney injury in COVID-19 patients

Renal Failure ◽  
2021 ◽  
Vol 43 (1) ◽  
pp. 1115-1123
Author(s):  
Zhengying Fang ◽  
Chenni Gao ◽  
Yikai Cai ◽  
Lin Lu ◽  
Haijin Yu ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Risk Score ◽  
Validation Study ◽  
Kidney Injury ◽  
Hospital Acquired

P6531Evidence-practice gap in the preprocedural risk assessment for contrast-induced acute kidney injury

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
S Shoji ◽  
M Sawano ◽  
Y Shiraishi ◽  
N Ikemura ◽  
S Noma ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Acute Kidney Injury ◽  
Risk Score ◽  
Kidney Injury ◽  
Coronary Intervention ◽  
Acute Kidney Injury Network ◽  
Risk Scores ◽  
Risk Patients ◽  
Practice Gap ◽  
The Impact

Abstract Background Contrast-induced acute kidney injury (CI-AKI) is one of the frequently encountered and costly complications after percutaneous coronary intervention (PCI). Clinical practice guidelines strongly recommend that PCI patients should universally undergo preprocedural assessment for the risk of CI-AKI, and the contrast volume (CV) should be minimized to an achievable level, particularly among the high AKI risk patients. However, data on the CV use based on the comprehensive preprocedural risk assessment is still lacking. Purpose Our study aimed to 1) assess the impact of CV increase with the incidence of AKI among high AKI risk patients, and 2) retrospectively evaluate the used CV based on the preprocedural comprehensive risk assessment for patients undergoing PCI within multicenter longitudinal registry. Methods Between 2009 and 2018, 22,373 patients underwent PCI in 14 participating facilities, and consecutive patient data was registered. AKI was defined as a >0.3mg/dl absolute or >1.5-fold relative increase in post-PCI creatinine or new initiation of dialysis, based on the Acute Kidney Injury Network criteria. The post-procedural creatinine was defined as the highest value within 30 days after the indexed procedure. Congruent with the National Cardiovascular Data Registry (NCDR) definition, if more than 1 post-procedural creatinine level was measured, the highest value was used for determining AKI. We divided the patients into four groups according to quartile of NCDR AKI risk scores. Results Mean age of the patients were 68.7±11.1 years, and 79.1% were male. Mean CV use was 161.4±74.8ml. The incidence of CI-AKI was 8.9%, and was particularly high among high AKI risk patients (21.1%); CV (per 1ml linear increase) was directly associated with the occurrence of AKI (OR: 1.002 per unit in CV; 95% CI: 1.001–1.003; P<0.001) in these patients. CV during PCI decreased with the progression of chronic kidney disease (CKD), but it did not alter by the overall NCDR AKI risk score (Figure). After multivariable adjustment, CV was predicted by stage of CKD (−13.68ml; 95% CI: −12.05 to −15.30ml; P<0.001), but not by the value of pre-procedure prediction score (NCDR AKI risk score, P=0.575). CV according to CKD/NCDR AKI risk score Conclusions Higher CV was directly associated with the occurrence of AKI among higher AKI risk patients. However, CV use was largely influenced by the stage of renal disease, and not with overall patient risk presented by contemporary risk scores. Our results have identified an important evidence-practice gap and emphasizes the importance of total preprocedural assessment to minimize CV and prevent subsequent AKI. Acknowledgement/Funding KAKENHI (16KK0186, 16H05215, 25460630, 25460777), Bayer, Daiichi Sankyo, Bristol-Myers Squibb, Teikoku Seiyaku, Sumitomo Dainippon, AstraZeneka, Pfizer

scholarly journals Development of a practical prediction score for acute renal injury after surgery for Stanford type A aortic dissection

2020 ◽  
Vol 30 (5) ◽  
pp. 746-753
Author(s):  
Ning Dong ◽  
Hulin Piao ◽  
Yu Du ◽  
Bo Li ◽  
Jian Xu ◽  
...  
Keyword(s):  
Aortic Dissection ◽  
Risk Score ◽  
Scoring System ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Kidney Injury ◽  
Type A ◽  
Postoperative Management ◽  
Prediction Score ◽  
Improve Patient

Abstract OBJECTIVES Acute kidney injury (AKI) is a common complication of cardiovascular surgery that is associated with increased mortality, especially after surgeries involving the aorta. Early detection and prevention of AKI in patients with aortic dissection may help improve outcomes. The objective of this study was to develop a practical prediction score for AKI after surgery for Stanford type A acute aortic dissection (TAAAD). METHODS This was a retrospective cohort study that included 2 independent hospitals. A larger cohort of 326 patients from The Second Hospital of Jilin University was used to identify the risk factors for AKI and to develop a risk score. The derived risk score was externally validated in a separate cohort of 102 patients from the other hospital. RESULTS The scoring system included the following variables: (i) age &gt;45 years; (ii) body mass index &gt;25 kg/m2; (iii) white blood cell count &gt;13.5 × 109/l; and (iv) lowest perioperative haemoglobin &lt;100 g/l, cardiopulmonary bypass duration &gt;150 min and renal malperfusion. On receiver operating characteristic curve analysis, the score predicted AKI with fair accuracy in both the derivation [area under the curve 0.778, 95% confidence interval (CI) 0.726–0.83] and the validation (area under the curve 0.747, 95% CI 0.657–0.838) cohorts. CONCLUSIONS We developed a convenient scoring system to identify patients at high risk of developing AKI after surgery for TAAAD. This scoring system may help identify patients who require more intensive postoperative management and facilitate appropriate interventions to prevent AKI and improve patient outcomes.

scholarly journals Risk score to stratify miscarriage risk levels in preconception women

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xin Hui Choo ◽  
Chee Wai Ku ◽  
Yin Bun Cheung ◽  
Keith M. Godfrey ◽  
Yap-Seng Chong ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Score ◽  
Smoking Status ◽  
Characteristic Curve ◽  
Lifestyle Changes ◽  
Risk Scores ◽  
Risk Level ◽  
Multivariable Logistic Regression Model ◽  
Spontaneous Miscarriage ◽  
Risk Levels

AbstractSpontaneous miscarriage is one of the most common complications of pregnancy. Even though some risk factors are well documented, there is a paucity of risk scoring tools during preconception. In the S-PRESTO cohort study, Asian women attempting to conceive, aged 18-45 years, were recruited. Multivariable logistic regression model coefficients were used to determine risk estimates for age, ethnicity, history of pregnancy loss, body mass index, smoking status, alcohol intake and dietary supplement intake; from these we derived a risk score ranging from 0 to 17. Miscarriage before 16 weeks of gestation, determined clinically or via ultrasound. Among 465 included women, 59 had miscarriages and 406 had pregnancy ≥ 16 weeks of gestation. Higher rates of miscarriage were observed at higher risk scores (5.3% at score ≤ 3, 17.0% at score 4–6, 40.0% at score 7–8 and 46.2% at score ≥ 9). Women with scores ≤ 3 were defined as low-risk level (< 10% miscarriage); scores 4–6 as intermediate-risk level (10% to < 40% miscarriage); scores ≥ 7 as high-risk level (≥ 40% miscarriage). The risk score yielded an area under the receiver-operating-characteristic curve of 0.74 (95% confidence interval 0.67, 0.81; p < 0.001). This novel scoring tool allows women to self-evaluate their miscarriage risk level, which facilitates lifestyle changes to optimize modifiable risk factors in the preconception period and reduces risk of spontaneous miscarriage.

scholarly journals Preoperative Albuminuria and Intraoperative Chloride Load: Predictors of Acute Kidney Injury Following Major Abdominal Surgery

2018 ◽  
Vol 7 (11) ◽  
pp. 431 ◽  
Author(s):  
Diamantina Marouli ◽  
Kostas Stylianou ◽  
Eleftherios Papadakis ◽  
Nikolaos Kroustalakis ◽  
Stavroula Kolyvaki ◽  
...  
Keyword(s):  
Risk Factors ◽  
Acute Kidney Injury ◽  
Abdominal Surgery ◽  
Care Center ◽  
Chronic Kidney Disease Stage ◽  
Tertiary Care ◽  
Kidney Injury ◽  
Major Abdominal Surgery ◽  
Disease Stage ◽  
Postoperative Aki

Background: Postoperative Acute Kidney Injury (AKI) is a common and serious complication associated with significant morbidity and mortality. While several pre- and intra-operative risk factors for AKI have been recognized in cardiac surgery patients, relatively few data are available regarding the incidence and risk factors for perioperative AKI in other surgical operations. The aim of the present study was to determine the risk factors for perioperative AKI in patients undergoing major abdominal surgery. Methods: This was a prospective, observational study of patients undergoing major abdominal surgery in a tertiary care center. Postoperative AKI was diagnosed according to the Acute Kidney Injury Network criteria within 48 h after surgery. Patients with chronic kidney disease stage IV or V were excluded. Logistic regression analysis was used to evaluate the association between perioperative factors and the risk of developing postoperative AKI. Results: Eleven out of 61 patients developed postoperative AKI. Four intra-operative variables were identified as predictors of AKI: intra-operative blood loss (p = 0.002), transfusion of fresh frozen plasma (p = 0.004) and red blood cells (p = 0.038), as well as high chloride load (p = 0.033, cut-off value > 500 mEq). Multivariate analysis demonstrated an independent association between AKI development and preoperative albuminuria, defined as a urinary Albumin to Creatinine ratio ≥ 30 mg·g−1 (OR = 6.88, 95% CI: 1.43–33.04, p = 0.016) as well as perioperative chloride load > 500 mEq (OR = 6.87, 95% CI: 1.46–32.4, p = 0.015). Conclusion: Preoperative albuminuria, as well as a high intraoperative chloride load, were identified as predictors of postoperative AKI in patients undergoing major abdominal surgery.

scholarly journals The ratio of CRP to prealbumin levels predict mortality in patients with hospital-acquired acute kidney injury

2011 ◽  
Vol 12 (1) ◽  
Author(s):  
Qionghong Xie ◽  
Ying Zhou ◽  
Zhongye Xu ◽  
Yanjiao Yang ◽  
Dingwei Kuang ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Kidney Injury ◽  
Hospital Acquired

Acute Kidney Injury in Pediatric Acute Respiratory Distress Syndrome

2020 ◽  
pp. 088506662094404
Author(s):  
Shubhi Kaushik ◽  
Sindy Villacres ◽  
Ruth Eisenberg ◽  
Shivanand S. Medar
Keyword(s):  
Acute Kidney Injury ◽  
Acute Respiratory Distress Syndrome ◽  
Respiratory Distress Syndrome ◽  
Respiratory Distress ◽  
Distress Syndrome ◽  
Invasive Mechanical Ventilation ◽  
Tertiary Care ◽  
Kidney Injury ◽  
Oxygenation Index ◽  
Significant Difference

Objectives: To describe the incidence of and risk factors for acute kidney injury (AKI) in children with acute respiratory distress syndrome (ARDS) and study the effect of AKI on patient outcomes. Design: A single-center retrospective study. Setting: A tertiary care children’s hospital. Patients: All patients less than 18 years of age who received invasive mechanical ventilation (MV) and developed ARDS between July 2010 and July 2013 were included. Acute kidney injury was defined using p-RIFLE (risk, injury, failure, loss, and end-stage renal disease) criteria. Interventions: None. Measurements and Main Results: One hundred fifteen children met the criteria and were included in the study. Seventy-four children (74/115, 64%) developed AKI. The severity of AKI was risk in 34 (46%) of 74, injury in 19 (26%) of 74, and failure in 21 (28%) of 74. The presence of AKI was associated with lower Pao 2 to Fio 2 (P/F) ratio ( P = .007), need for inotropes ( P = .003), need for diuretics ( P = .004), higher oxygenation index ( P = .03), higher positive end-expiratory pressure (PEEP; P = .01), higher mean airway pressure ( P = .008), and higher Fio 2 requirement ( P = .03). Only PEEP and P/F ratios were significantly associated with AKI in the unadjusted logistic regression model. Patients with AKI had a significantly longer duration of hospital stay, although there was no significant difference in the intensive care unit stay, duration of MV, and mortality. Recovery of AKI occurred in 68% of the patients. A multivariable model including PEEP, P/F ratio, weight, need for inotropes, and need for diuretics had a better receiver operating characteristic (ROC) curve with an AUC of 0.75 compared to the ROC curves for PEEP only and P/F ratio only for the prediction of AKI. Conclusions: Patients with ARDS have high rates of AKI, and its presence is associated with increased morbidity and mortality.

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