Acute Kidney Injury in Trauma Patients Admitted to Critical Care: Development and Validation of a Diagnostic Prediction Model.
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ABSTRACT:
SUBMITTER: Haines RW
PROVIDER: S-EPMC5827665 | biostudies-literature | 2018 Feb
REPOSITORIES: biostudies-literature
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Acute Kidney Injury in Trauma Patients Admitted to Critical Care: Development and Validation of a Diagnostic Prediction Model.
Haines Ryan W RW Lin Shih-Pin SP Hewson Russell R Kirwan Christopher J CJ Torrance Hew D HD O'Dwyer Michael J MJ West Anita A Brohi Karim K Pearse Rupert M RM Zolfaghari Parjam P Prowle John R JR
Scientific reports 20180226 1
Acute Kidney Injury (AKI) complicating major trauma is associated with increased mortality and morbidity. Traumatic AKI has specific risk factors and predictable time-course facilitating diagnostic modelling. In a single centre, retrospective observational study we developed risk prediction models for AKI after trauma based on data around intensive care admission. Models predicting AKI were developed using data from 830 patients, using data reduction followed by logistic regression, and were ind ...[more]
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Project description:BackgroundAcute kidney injury (AKI) in pediatric critical care patients is diagnosed using elevated serum creatinine, which occurs only after kidney impairment. There are no treatments other than supportive care for AKI once it has developed, so it is important to identify patients at risk to prevent injury. This study develops a machine learning model to learn pre-disease patterns of physiological measurements and predict pediatric AKI up to 48 h earlier than the currently established diagnostic guidelines.MethodsEHR data from 16,863 pediatric critical care patients between 1 month to 21 years of age from three independent institutions were used to develop a single machine learning model for early prediction of creatinine-based AKI using intelligently engineered predictors, such as creatinine rate of change, to automatically assess real-time AKI risk. The primary outcome is prediction of moderate to severe AKI (Stage 2/3), and secondary outcomes are prediction of any AKI (Stage 1/2/3) and requirement of renal replacement therapy (RRT). Predictions generate alerts allowing fast assessment and reduction of AKI risk, such as: "patient has 90% risk of developing AKI in the next 48 h" along with contextual information and suggested response such as "patient on aminoglycosides, suggest check level and review dose and indication".ResultsThe model was successful in predicting Stage 2/3 AKI prior to detection by conventional criteria with a median lead-time of 30 h at AUROC of 0.89. The model predicted 70% of subsequent RRT episodes, 58% of Stage 2/3 episodes, and 41% of any AKI episodes. The ratio of false to true alerts of any AKI episodes was approximately one-to-one (PPV 47%). Among patients predicted, 79% received potentially nephrotoxic medication after being identified by the model but before development of AKI.ConclusionsAs the first multi-center validated AKI prediction model for all pediatric critical care patients, the machine learning model described in this study accurately predicts moderate to severe AKI up to 48 h in advance of AKI onset. The model may improve outcome of pediatric AKI by providing early alerting and actionable feedback, potentially preventing or reducing AKI by implementing early measures such as medication adjustment.
| S-EPMC8353807 | biostudies-literature
Project description:BACKGROUND:Injured patients are at risk of developing acute kidney injury (AKI), which is associated with increased morbidity and mortality. The aim of this study is to describe the incidence, timing, and severity of AKI in a large trauma population, identify risk factors for AKI, and report mortality outcomes. METHODS:A prospective observational study of injured adults, who met local criteria for trauma team activation, and were admitted to a UK Major Trauma Centre. AKI was defined by the Kidney Disease Improving Global Outcomes (KDIGO) criteria. Multivariable logistic regression and Cox proportional hazard modelling was used to analyse parameters associated with AKI and mortality. RESULTS:Of the 1410 patients enrolled in the study, 178 (12.6%) developed AKI. Age; injury severity score (ISS); admission systolic blood pressure, lactate and serum creatinine; units of Packed Red Blood Cells transfused in first 24 hours and administration of nephrotoxic therapy were identified as independent risk factors for the development of AKI. Patients that developed AKI had significantly higher mortality than those with normal renal function (47/178 [26.4%] versus 128/1232 [10.4%]; OR 3.09 [2.12 to 4.53]; p<0.0001). After adjusting for other clinical prognostic factors, AKI was an independent risk factor for mortality. CONCLUSIONS:AKI is a frequent complication following trauma and is associated with prolonged hospital length of stay and increased mortality. Future research is needed to improve our ability to rapidly identify those at risk of AKI, and develop resuscitation strategies that preserve renal function in trauma patients.
| S-EPMC6347290 | biostudies-literature
Project description:BackgroundThe Kidney Disease: Improving Global Outcomes (KDIGO) Acute Kidney Injury (AKI) guidelines assign the same stage of AKI to patients whether they fulfil urine output criteria, serum creatinine criteria or both criteria for that stage. This study explores the validity of the KDIGO guidelines as a tool to stratify the risk of adverse outcomes in cardiac surgery patients.MethodsProspective data from consecutive adult patients admitted to the cardiac intensive care unit (CICU) following cardiac surgery between January 2013 and May 2015 were analysed. Patients were assigned to groups based on the criteria they met for each stage of AKI according to the KDIGO guidelines. Short and mid-term outcomes were compared between these groups.ResultsA total of 2267 patients were included with 772 meeting criteria for AKI-1 and 222 meeting criteria for AKI-2. After multivariable adjustment, patients meeting both urine output and creatinine criteria for AKI-1 were more likely to experience prolonged CICU stay (OR 4.9, 95%CI 3.3-7.4, p < 0.01) and more likely to require renal replacement therapy (OR 10.5, 95%CI 5.5-21.9, p < 0.01) than those meeting only the AKI-1 urine output criterion. Patients meeting both urine output and creatinine criteria for AKI-1 were at an increased risk of mid-term mortality compared to those diagnosed with AKI-1 by urine output alone (HR 2.8, 95%CI 1.6-4.8, p < 0.01). Patients meeting both urine output and creatinine criteria for AKI-2 were more likely to experience prolonged CICU stay (OR 16.0, 95%CI 3.2-292.0, p < 0.01) or require RRT (OR 11.0, 95%CI 4.2-30.9, p < 0.01) than those meeting only the urine output criterion. Patients meeting both urine output and creatinine criteria for AKI-2 were at a significantly increased risk of mid-term mortality compared to those diagnosed with AKI-2 by urine output alone (HR 3.6, 95%CI 1.4-9.3, p < 0.01).ConclusionsPatients diagnosed with the same stage of AKI by different KDIGO criteria following cardiac surgery have significantly different short and mid-term outcomes. The KDIGO criteria need to be revisited before they can be used to stratify reliably the severity of AKI in cardiac surgery patients. The utility of the criteria also needs to be explored in other settings.
| S-EPMC6020229 | biostudies-literature
Project description:BACKGROUND:Acute kidney injury (AKI) is still characterized by a high mortality rate. While most patients with AKI are admitted in conventional medical units, current available data are still obtained from studies designed for patients admitted in intensive care units (ICU). Our study aimed to elaborate and validate an in-hospital death prognosis score for AKI admitted in conventional medical care units. METHODS:We included two prospective cohorts of consecutive patients with AKI admitted between 2001 and 2004 (elaboration cohort (EC)) and between 2010 and 2014 (validation cohort (VC)). We developed a scoring system from clinical and biological parameters recorded at admission from the EC to predict in-hospital mortality. This score was then tested for validation in the VC. RESULTS:Three-hundred and twenty-three and 534 patients were included in the EC and VC cohorts, respectively. The proportion of in-hospital death were 15.5% (EC) and 8.9% (VC), mainly due to sepsis. The parameters independently associated with the in-hospital death in the EC were Glasgow score, oxygen requirement, fluid overload, blood diastolic pressure, multiple myeloma and prothrombin time. The in-hospital death prognosis score AUC was 0.845 +/-?0.297 (p?<?0.001) after validation in the VC. CONCLUSIONS:Our in-hospital death prognosis score is the first to be prospectively developed and validated for AKI admitted in a conventional medical care unit. Based on current parameters, easily collected at time of admission, this score could be a useful tool for physicians and nephrologists to determine the in-hospital death prognosis of this AKI population.
| S-EPMC6868787 | biostudies-literature
Project description:Eighty four out of 2151 militancy trauma patients sustained severe maxillofacial injury from Jan 1990 to March 1993. The resuscitation, stabilisation and intensive care of these patients was based on management priorities of primary resuscitation, care of airway, management of haemodynamics, oxygenation and monitoring. Anaesthesia was administered in a situation when the airway was likely to be compromised and the patients were critically sick. Initial ventilation and oxygenation was the most difficult and could be achieved with satisfactory seal around the face mask by applying water-soaked guaze pieces around the mouth and nose to "fill-in" the defects. Tracheal intubation could be accomplished with intravenous sedation by an experienced anaesthesiologist. Dental occlusion and wiring necessiated the placement of nasotracheal tube for 48-72 hours after surgery.
| S-EPMC5530267 | biostudies-other
Project description:Rationale & objectiveThere is limited evidence to guide follow-up after acute kidney injury (AKI). Knowledge gaps include which patients to prioritize, at what time point, and for mitigation of which outcomes. In this study, we sought to compare the net benefit of risk model-based clinical decisions following AKI.Study designExternal validation of 2 risk models of AKI outcomes: the Grampian -Aberdeen (United Kingdom) AKI readmissions model and the Alberta (Canada) kidney disease risk model of chronic kidney disease (CKD) glomerular (G) filtration rate categories 4 and 5 (CKD G4 and G5). Process mining to delineate existing care pathways.Setting & participantsValidation was based on data from adult hospital survivors of AKI from Grampian, 2011-2013.PredictorsKDIGO-based measures of AKI severity and comorbidities specified in the original models.OutcomesDeath or readmission within 90 days for all hospital survivors. Progression to new CKD G4-G5 for patients surviving at least 90 days after AKI.Analytical approachDecision curve analysis to assess the "net benefit" of use of risk models to guide clinical care compared to alternative approaches (eg, prioritizing all AKI, severe AKI, or only those without kidney recovery).Results26,575 of 105,461 hospital survivors in Grampian (mean age, 60.9 ± 19.8 [SD] years) were included for validation of the death or readmission model, and 9,382 patients (mean age, 60.9 ± 19.8 years) for the CKD G4-G5 model. Both models discriminated well (area under the curve [AUC], 0.77 and 0.86, respectively). Decision curve analysis showed greater net benefit for follow up of all AKI than only severe AKI in most cases. Both original and refitted models provided net benefit superior to any other decision strategy. In process mining of all hospital discharges, 41% of readmissions and deaths occurred among people recovering after AKI. 1,464 of 3,776 people (39%) readmitted after AKI had received no intervening monitoring.LimitationsBoth original models overstated risks, indicating a need for regular updating.ConclusionsFollow up after AKI has potential net benefit for preempting readmissions, death, and subsequent CKD progression. Decisions could be improved by using risk models and by focusing on AKI across a full spectrum of severity. The current lack of monitoring among many with poor outcomes indicates possible opportunities for implementation of decision support.
| S-EPMC8234511 | biostudies-literature
Project description:ImportanceThe Simple Postoperative AKI Risk (SPARK) index is a prediction model for postoperative acute kidney injury (PO-AKI) in patients undergoing noncardiac surgery. External validation has not been performed.ObjectiveTo externally validate the SPARK index.Design, setting, and participantsThis single-center retrospective cohort study included adults who underwent noncardiac surgery under general anesthesia from 2007 to 2011. Those with obstetric or urological surgery, estimated glomerular filtration rate (eGFR) of less than 15 mL/min/1.73 m2, preoperative dialysis, or an expected surgical duration of less than 1 hour were excluded. The study was conducted at Nara Medical University Hospital. Data analysis was conducted from January to July 2021.ExposuresRisk factors for AKI included in SPARK index.Main outcomes and measuresPO-AKI, defined as an increase in serum creatinine of at least 0.3 mg/dL within 48 hours or 150% compared with preoperative baseline value or urine output of less than 0.5 mL/kg/h for at least 6 hours within 1 week after surgery, and critical AKI, defined as either AKI stage 2 or greater and/or any AKI connected to postoperative death or requiring kidney replacement therapy before discharge. The discrimination and calibration of the SPARK index were examined with area under the receiver operating characteristic curves (AUC) and calibration plots, respectively.ResultsAmong 5135 participants (2410 [46.9%] men), 303 (5.9%) developed PO-AKI, and 137 (2.7%) developed critical AKI. Compared with the SPARK cohort, participants in our cohort were older (median [IQR] age, 56 [44-66] years vs 63 [50-73] years), had lower baseline eGFR (median [IQR], 82.1 [71.4-95.1] mL/min/1.73 m2 vs 78.2 [65.6-92.2] mL/min/1.73 m2), and had a higher prevalence of comorbidities (eg, diabetes: 3956 of 51 041 [7.8%] vs 802 [15.6%]). The incidence of PO-AKI and critical AKI increased as the scores on the SPARK index increased. For example, 10 of 593 participants (1.7%) in SPARK class A, indicating lowest risk, experienced PO-AKI, while 53 of 332 (16.0%) in SPARK class D, indicating highest risk, experienced PO-AKI. However, AUCs for PO-AKI and critical AKI were 0.67 (95% CI, 0.63-0.70) and 0.62 (95% CI, 0.57-0.67), respectively, and the calibration was poor (PO-AKI: y = 0.24x + 3.28; R2 = 0.86; critical AKI: y = 0.20x + 2.08; R2 = 0.51). Older age, diabetes, expected surgical duration, emergency surgery, renin-angiotensin-aldosterone system blockade use, and hyponatremia were not associated with PO-AKI in our cohort, resulting in overestimation of the predicted probability of AKI in our cohort.Conclusions and relevanceIn this study, the incidence of PO-AKI increased as the scores on the SPARK index increased. However, the predicted probability might not be accurate in cohorts with older patients with more comorbidities.
| S-EPMC8524308 | biostudies-literature
Project description:ImportanceSome patients will develop chronic kidney disease after a hospitalization with acute kidney injury; however, no risk-prediction tools have been developed to identify high-risk patients requiring follow-up.ObjectiveTo derive and validate predictive models for progression of acute kidney injury to advanced chronic kidney disease.Design, setting, and participantsData from 2 population-based cohorts of patients with a prehospitalization estimated glomerular filtration rate (eGFR) of more than 45 mL/min/1.73 m2 and who had survived hospitalization with acute kidney injury (defined by a serum creatinine increase during hospitalization > 0.3 mg/dL or > 50% of their prehospitalization baseline), were used to derive and validate multivariable prediction models. The risk models were derived from 9973 patients hospitalized in Alberta, Canada (April 2004-March 2014, with follow-up to March 2015). The risk models were externally validated with data from a cohort of 2761 patients hospitalized in Ontario, Canada (June 2004-March 2012, with follow-up to March 2013).ExposuresDemographic, laboratory, and comorbidity variables measured prior to discharge.Main outcomes and measuresAdvanced chronic kidney disease was defined by a sustained reduction in eGFR less than 30 mL/min/1.73 m2 for at least 3 months during the year after discharge. All participants were followed up for up to 1 year.ResultsThe participants (mean [SD] age, 66 [15] years in the derivation and internal validation cohorts and 69 [11] years in the external validation cohort; 40%-43% women per cohort) had a mean (SD) baseline serum creatinine level of 1.0 (0.2) mg/dL and more than 20% had stage 2 or 3 acute kidney injury. Advanced chronic kidney disease developed in 408 (2.7%) of 9973 patients in the derivation cohort and 62 (2.2%) of 2761 patients in the external validation cohort. In the derivation cohort, 6 variables were independently associated with the outcome: older age, female sex, higher baseline serum creatinine value, albuminuria, greater severity of acute kidney injury, and higher serum creatinine value at discharge. In the external validation cohort, a multivariable model including these 6 variables had a C statistic of 0.81 (95% CI, 0.75-0.86) and improved discrimination and reclassification compared with reduced models that included age, sex, and discharge serum creatinine value alone (integrated discrimination improvement, 2.6%; 95% CI, 1.1%-4.0%; categorical net reclassification index, 13.5%; 95% CI, 1.9%-25.1%) or included age, sex, and acute kidney injury stage alone (integrated discrimination improvement, 8.0%; 95% CI, 5.1%-11.0%; categorical net reclassification index, 79.9%; 95% CI, 60.9%-98.9%).Conclusions and relevanceA multivariable model using routine laboratory data was able to predict advanced chronic kidney disease following hospitalization with acute kidney injury. The utility of this model in clinical care requires further research.
| S-EPMC5820711 | biostudies-literature
Project description:IntroductionAcute kidney injury (AKI) is common among hospitalized patients and has a significant impact on morbidity and mortality. Although early prediction of AKI has the potential to reduce adverse patient outcomes, it remains a difficult condition to predict and diagnose. The purpose of this study was to evaluate the ability of a machine learning algorithm to predict for AKI as defined by Kidney Disease: Improving Global Outcomes (KDIGO) stage 2 or 3 up to 48 hours in advance of onset using convolutional neural networks (CNNs) and patient electronic health record (EHR) data.MethodsA CNN prediction system was developed to use EHR data gathered during patients' stays to predict AKI up to 48 hours before onset. A total of 12,347 patient encounters were retrospectively analyzed from the Medical Information Mart for Intensive Care III (MIMIC-III) database. An XGBoost AKI prediction model and the sequential organ failure assessment (SOFA) scoring system were used as comparators. The outcome was AKI onset. The model was trained on routinely collected patient EHR data. Measurements included area under the receiver operating characteristic (AUROC) curve, positive predictive value (PPV), and a battery of additional performance metrics for advance prediction of AKI onset.ResultsOn a hold-out test set, the algorithm attained an AUROC of 0.86 and PPV of 0.24, relative to a cohort AKI prevalence of 7.62%, for long-horizon AKI prediction at a 48-hour window before onset.ConclusionA CNN machine learning-based AKI prediction model outperforms XGBoost and the SOFA scoring system, revealing superior performance in predicting AKI 48 hours before onset, without reliance on serum creatinine (SCr) measurements.
| S-EPMC8116756 | biostudies-literature
Project description:: The ideal circumstances for whether and when to start RRT remain unclear. The outcome predictive ability of acute kidney injury (AKI) biomarkers measuring at dialysis initializing need more validation. This prospective, multi-center observational cohort study enrolled 257 patients with AKI undergoing renal replacement therapy (RRT) shortly after admission. At the start of RRT, blood and urine samples were collected for relevant biomarker measurement. RRT dependence and all-cause mortality were recorded up to 90 days after discharge. Areas under the receiver operator characteristic (AUROC) curves and a multivariate generalized additive model were applied to predict outcomes. One hundred and thirty-five (52.5%) patients died within 90 days of hospital discharge. Plasma c-terminal FGF-23 (cFGF-23) had the best discriminative ability (AUROC, 0.687) as compared with intact FGF-23 (iFGF-23) (AUROC, 0.504), creatinine-adjusted urine neutrophil gelatinase-associated lipocalin (AUROC, 0.599), and adjusted urine cFGF-23 (AUROC, 0.653) regardless whether patients were alive or not on day 90. Plasma cFGF-23 levels above 2050 RU/mL were independently associated with higher 90-day mortality (HR 1.76, p = 0.020). Higher cFGF-23 levels predicted less weaning from dialysis in survivors (HR, 0.62, p = 0.032), taking mortality as a competing risk. Adding cFGF-23 measurement to the AKI risk predicting score signi?cantly improved risk strati?cation and 90-day mortality prediction (total net reclassification improvement = 0.148; p = 0.002). In patients with AKI who required RRT, increased plasma cFGF-23 levels correlated with higher 90-day overall mortality after discharge and predicted worse kidney recovery in survivors. When coupled to the AKI risk predicting score, cFGF-23 significantly improved mortality risk prediction. This observation adds evidence that cFGF-23 could be used as an optimal timing biomarker to initiate RRT.
| S-EPMC6112021 | biostudies-literature