Towards Prevention Of Acute Syndromes:Development And .
SciForschenISSN 2380-5498Open HUB for Scientific Researc hInternational Journal of Nephrology and Kidney FailureResearch ArticleOpen AccessVolume: 2.3Towards Prevention of Acute Syndromes:Development and Validation of an AutomatedElectronic Kidney Injury Clinical Prediction Score(Ecklips), Population-Based ProtocolTania khan1, Jonathan Williams1, Ashwinkumar Patel2, Ahmed Mattar1, AnjitKhurana1,Daniela Johnson1, Ankit Patel1, Amr Takieldeen1, David Carlson1,3 andAdil Ahmed1,4*North Central Texas Medical Foundation, Wichita Falls Family Practice Residency Program, Wichita Falls,Texas, USA2Department of Nephrology, United Regional Health Care System, Wichita Falls, Texas, USA3Department of psychology, Midwestern State University, Wichita Falls, Texas, USA4Institute for Health Informatics, University of Minnesota, Minneapolis, Minnesota, USA1Received date: 14 Mar 2016; Accepted date: 27May 2016; Published date: 31 May 2016.Citation: khan T, Williams J, Patel A, Mattar A,Khurana A, et al. (2016) Towards Prevention ofAcute Syndromes: Development and Validationof an Automated Electronic Kidney Injury ClinicalPrediction Score (Ecklips), Population-Based Protocol.Int J Nephrol Kidney Fail 2(3): doi http://dx.doi.org/10.16966/2380-5498.133Copyright: khan T, et al. This is an open-accessarticle distributed under the terms of the CreativeCommons Attribution License, which permitsunrestricted use, distribution, and reproductionin any medium, provided the original author andsource are credited.Corresponding author: Adil Ahmed, Institute for Health Informatics, University of Minnesota,Minneapolis, Minnesota, USA, Tel: 507-269-1799;E-mail: aahmed@wfresidency.org*AbstractTimely recognition and treatment of specific critical care syndromes are the key determinants of outcomes of critical illness, regardless of theunderlying cause. Acute kidney injury (AKI) is a prototypical example of a major health problem for which the efficacy of treatment is alreadylimited once the condition is fully established. The condition is rarely present at the time of hospital admission, but develops within hours or daysafter an initial insult. Due to this delay in diagnosis, both therapeutic and experimental interventions are currently instituted late in the course ofthe illness, which limits the potential for therapeutic impact.Timely recognition of patients at risk of AKI would allow for novel and potentially more meaningful therapeutic and preventative strategies.Advances in medical informatics and widespread implementation of electronic medical records provide the opportunity to improve early recognitionand treatment of specific critical care syndromes. In this study, we plan to develop a predictive model of AKI using probabilistic modeling. Ifsuccessfully validated, this model will allow for early recognition of patients at risk of AKI for the purpose of diagnosis, prognosis, and futureprevention trials.Keywords: Acute kidney injury; Electronic medical records; ModelingIntroductionAcute Kidney Injury (AKI) is one of the most prevalent clinicalcomplications among patients admitted to an intensive care unit (ICU).AKI incidence in the ICU has been reported to range from 5 to 80%[1]. Morbidity and mortality due to AKI correlates with the severityof renal dysfunction [2]. AKI is also an independent risk factor for thedevelopment of chronic kidney disease and end-stage renal disease [3].Over the last several decades, there has been significant confusionregarding the terminology used to describe renal failure. A systematicreview conducted in the early nineties showed that in 24 out of 28studies, no two studies used the same definition for renal failure [4]. Thisdefinitional ambiguity hindered researchers’ understanding of the trueimpact of this disease in critically ill patients and impeded epidemiologicand incidence studies [5]. In 2004, the Acute Dialysis Quality Initiative(ADQI) group proposed a standardized definition for the syndrome.AKI was defined by the risk of renal failure, injury to the kidney, failureof kidney function, loss of kidney function, and end-stage renal failure(RIFLE) [6]. In 2007, to increase the sensitivity and overcome some of thelimitations of the RIFLE criteria, AKI definitions were modified by theAcute Kidney Injury Network (AKIN) group [7] (Figure 1).The increased acceptance of standardized definitions (RIFLE andAKIN) of AKI by the scientific and medical communities has markeda new era in the study of AKI. These definitions have been used in thevast majority of recently published studies, with various objectives andoutcomes [2]. We are now at the point where we can strive to predict thedevelopment of AKI.Early diagnosis of AKI can potentially provide a wider therapeuticwindow for both prophylaxis and treatment of AKI and its relatedcomplications. Diagnosis of AKI at the time of hospital admission coulddecrease the chance of patients suffering a “second hit” from omissionsand delays in medical interventions. This, in turn, may decrease morbidityand mortality associated with AKI [8]. Therefore, the overall objective ofthis protocol is to develop and validate an automated electronic kidneyinjury clinical prediction model that can be integrated in the electronicmedical records.Specific Aims (SA)Specific aim 1Using data available in the electronic medical records (EMR) system,the first aim is to retrospectively develop and validate a rule-basedelectronic algorithm to identify patients with hospital-acquired AKI,defined as AKI development more than 12 hours after hospital admission,using the AKI criteria.Copyright: khan T, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permitsunrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
SciForschenOpen HUB for Scientific Researc hOpen AccessFigure 1: Correlation between the Relative Blood volume and the pulse rate (r -0.4, p 0.03)Specific aim 2Our second aim is to electronically identify at-risk patients for AKIdevelopment at the time of hospital admission, based upon the clinicalpredication model validated in Specific Aim 3. So doing will facilitateenrollment of patients into mechanistic studies, as well as future AKIprevention trials,Specific aim 3To prospectively validate a clinical prediction model using an EMRsyndrome surveillance tool for identifying patients at high risk ofdeveloping AKI in an independent cohort of consecutive hospitalizedpatients.Research Design and MethodsTo facilitate enrollment of patients into future mechanistic studiesand AKI prevention trials, we will retrospectively identify patients withhospital-acquired AKI.AKI outcomes will be used to develop a predictionmodel using the appropriate risk factors. Risk factors will be determinedat the time of hospital admission and all potential risk factors will beentered into the tool. Finally, the model will be automated and validatedprospectively in a sample population.AKI Detection in a Population-Based SampleSpecific aim 1 (SA#1)To develop and validate a rule-based electronic algorithm, to detectpatients with hospital acquired AKI in subset of ICU patients.SubjectsBoth derivation and validation cohorts will be adults’ 18 years of ageand admitted to an ICU hospital during the year 2015. Exclusion criteriaare listed in (Table 1).Study procedureFor SA#1, an electronic algorithm will be developed to predictdevelopment of AKI. The effectiveness of this algorithm will be comparedto two search strategies; manual searches and searches using ICD-9 codes.Patient eligibility will be reviewed by two blinded investigators, who willascertain the presence of AKI using the AKIN criteria [7]. Disagreementswill be resolved by a third reviewer. As reported in other studies, baselineCreatinine will be defined as the median value of Creatinine between6months and 7 days prior to hospital admission. For those patients withno record of a serum Creatinine in the EMR in the 6 months prior to ICUadmission, we will use the revised Modification of Diet in Renal Disease(MDRD) formula [9]. AKIN working group staging criteria will be usedto divide the outcome into three stages. Hospital-acquired AKI will bedefined as the onset of AKI in a hospitalized patient who either had noevidence of AKI (i.e., reduced urine output or elevated serum creatinine)at the time of hospital admission, or in whom AKIN criteria were satisfiedwithin 12 hours of hospital admission.Manual data will be considered as a reference standard and will bedivided into two datasets: derivation and validation cohorts. Data fromthe derivation cohort will be used to develop and refine the electronicalgorithm by reviewing all false positive and false negative cases,compared to manually-ascertained cases of AKI. For ICD-9 codes, we willbe using data from the hospital administrative database to identify ICD-9diagnosis codes for acute renal failure. The codes to be used are listed inthe Appendix (Table 1).OutcomesPrimary outcomes will be the sensitivity and specificity for each searchstrategy in both the derivation and validation cohorts, between the twoExclusion CriteriaAcute Kidney injury already presentat the time of hospital admissionAdmissiononly for comfort orhospice careLack of consent for use of medicalrecords for research ( 5%)Admission for cardiac or othermajor surgery surgeries, as well aslabor and deliveryChildren and pregnant womenHospital re-admissionHospital transferJustificationUnable to assess for developmentof outcomes of interestMissing predictor and outcomevariablesSelf-explanatoryDifferent risk factors and outcomeDifferent risk factors and outcomesComplexity of analysisExposure to healthcareinterventionsTable 1: Exclusion criteriaCitation: khan T, Williams J, Patel A, Mattar A, Khurana A, et al. (2016) Towards Prevention of Acute Syndromes: Development and Validation of anAutomated Electronic Kidney Injury Clinical Prediction Score (Ecklips), Population-Based Protocol. Int J Nephrol Kidney Fail 2(3): doi http://dx.doi.org/10.16966/2380-5498.1332
SciForschenOpen HUB for Scientific Researc hOpen Accessstrategies. Positive (PPV) and negative predictive values (NPV) will becalculated along with percentage agreement and Cohen’s kappa statistics.Secondary outcomes will be the time to AKI between electronic andmanual strategies in the validation set. Time will be defined as the timebetween admission and the first time AKIN criteria are met. It, along withthe firing criteria (Urine Output vs. serum creatinine), will be comparedbetween the two methods.Development and Validation of an Electronic KidneyInjury Clinical Prediction Score (ecKLIPS)Specific aim 2Predisposing conditionsDemographic, clinical, and environmental predisposing conditions(Table 2) necessary for calculation of ecKLIPS will be collected based on theinformation present before or during the first 12 hours of hospitalization.The model will be developed using predisposing conditions and electronicalgorithms that have been validated in previous studies [10,11]. For allother predisposing factors, an electronic search strategy will be developedand validated using methodology similar to the one in SA#1. All datawill be extracted from the hospital electronic medical records andadministrative databases.Model development and outcome ascertainmentWe will develop and validate a clinical prediction model for identifyingpatients at high risk of developing AKI at the time of hospital admission.We will create a rule-based method for capturing electronic data, includingbaseline clinical, demographic, and environmental exposure informationthat is available on the first day of hospital admission. This will includedata about acute and chronic illnesses as well as data on interventions andoutcomes (e.g., patients and systems) relating to AKI.SubjectsEligible subjects will be consecutive hospital admissions of adultpatients 18 yearsof age, who will have been admitted to communitybased hospital over a 10 year period (retrospective validation cohort,2005-2014) with at least one predisposing condition at the time of hospitaladmission (e.g., sepsis, shock).Using the electronic tool developed and validated in the first Aim, wewill identify all patients with AKI during their hospital stay. To derivethe model, we will consider variables associated with the development orprevention of AKIin at least two prior published studies. Furthermore, acontent expert will review the selected risk factors and rank them basedon perceived importance. Hospital electronic medical records will providedatanecessary baseline and clinical information (e.g., demographics,clinical data, comorbid conditions, and laboratory data). Data collectionwill be accomplished by querying structured data from the EMR (e.g.,laboratory data, vitals, and medication tables , ICD 9 data).All data willbe restricted to 12 hours before AKI detection, unless specified otherwise.A sensitivity analysis will be performed using 6- and 24-hour time points.Multiple models will be developed using the derivation cohorts, differentdata points, and different time cutoffs.VariableAgeGenderRace/ EthnicityMeasurementDate of birth (DOB)Female/MaleHospital admission sourceNursing home, ED, Home, ClinicDiagnosisAdmissionPreferencesPredicted mortalityCharlson scoreDiabetes mellitusCOPDLiver cirrhosisImmunosuppressionHigh blood pressurePre-existing renal failurePre-existing HIV infectionPresence of seizuresCoronary artery diseaseRespiratory, ID, GI, CV, Trauma, Neuro, OtherMedical, Surgical, TraumaFull code/DNR/DNI/Comfort careAPACHE III score, APACHE IVMedian, oYes/NoWhite, Black, Asian, other Hispanic/Non-HispanicChemotherapy; Insulin; ACE inhibitor; ARB; Statins; Steroids; Diuretics; Aminoglycosides; Cephalosporin; Vancomycin; Amphotericin; NSAIDs;Benzodiazepines; Warfarin; Aspirin or PlavixPneumoniaYes/NoSIRSYes/No, SevereSepsisYes/No, te lung injuryYes/NoAcute pancreatitisYes/NoIV contrastYes/NoTraumaYes/NoBlood pressure; Heart rate; Respiratory rate; Body mass index; Temperature; SpO2/FIO2; GCSHct; WBC; Platelet count; Bicarbonate; Arterial blood gases; Anion gap; Potassium; Creatinine; LDH; Lactate; Albumin; Bilirubin; Glucose; Fibrinogen;D-dimer; INR; Blood and other cultures; CPK; Troponin; BNP; Cholesterol; or LipaseTable 2: Suggested risk factorsCitation: khan T, Williams J, Patel A, Mattar A, Khurana A, et al. (2016) Towards Prevention of Acute Syndromes: Development and Validation of anAutomated Electronic Kidney Injury Clinical Prediction Score (Ecklips), Population-Based Protocol. Int J Nephrol Kidney Fail 2(3): doi http://dx.doi.org/10.16966/2380-5498.1333
SciForschenOpen HUB for Scientific Researc hDerivation of the prediction scoreDuring the derivation of the rule,we will include all independentpredictors of AKI. Some of these predictors will be considered aspredisposing conditions, the rest as modifier conditions, depending on theexpert ranking. The magnitude by which each risk factor contributed to thedevelopment of AKI will be quantified according to the beta coefficientsinour derivation cohort and the magnitude previously described for eachfactor in at least two prior studies.Validation phaseTo be included in the cohort, patients in the validation cohort haveat least one predisposing condition. The variables needed to generateecKLIPS for these patients will be collected retrospectively usingautomated search described earlier and by compiling information fromthe EMR and by trained investigators.Prospective Validation of the AKI (ecKLIPS) ClinicalPrediction ModelSpecific aim 3Our third aim is to prospectively validate a clinical prediction modelusing an EMR syndrome surveillance tool for identifying patients at highrisk of developing AKI. Study subject and inclusion and exclusion criteriawill be similar to those in SA#2.Study procedureThe electronic surveillance tool built to detect at-risk patients willautomate the ecKLIPS and run through patient records at 15-60 minutesresolution from admission to the emergency room until discharge,or when a patient meets AKI criteria. Over a three month period, analert system using a threshold for high risk score will be prospectivelyimplemented in selected medical floors and ICUs.Identification of patients at risk prior to admission to the ICUTo identify patients at risk for AKI, an automated ecKLIPS modelwill run prospectively in the EMR databases and calculate the score togenerate e-mail and/or a pager alert to a research coordinator when thescore reaches the threshold for a high risk patient; a research coordinatorwill confirm and enroll the selected patients.The primary outcome is development of AKI at any time during thehospital stay. Secondary outcomes will be the number of interventionsin the alerted group, the proportion of patients who started renalreplacement therapy, reduction of median serum creatinine at the time ofhospital discharge, ICU and hospital mortality, and length of stay betweenthe two groups.Data collectionAll alerts will be collected in a separate electronic log, and the numberof interventions will be assessed within three hours of the alert time. Thesewill include ordering or administration of any of the following: fluids,defined as any bolus of 500 ml crystalloid or 250 ml colloid, diuretics,orvasopressors.Statistical ApproachThe purpose of Aim #1 is to validate that the data collected from theelectronic algorithm areconsistent with the manually-collected data.Consistency will be assessed using sensitivity, specificity, positive, andnegative predicted values.The purpose of Aims#2 and #3 is to develop and validate a predictivemodel of AKI using a population-based sample. Using AKIN guidelines,we plan to model AKI using a penalized, logistic regression model, LASSO,Open Accessto derive a parsimonious prediction rule. The explanatory variables will bederived using baseline patient risk factors, listed in Table 2. These factorsare biologically-plausible and are recorded in the EMR. Serial laboratoryrisk factors will be converted to either the worst value within the first 12hours of hospital admission, or by estimating the area under the curve forthe first 12 hours. Measurements used but not taken within the first 12hours will be considered missing. The “missingness” will be incorporatedinto statistical models with multiple imputation methods for the purposesof the sensitivity analysis.The penalization parameter for the penalized, logistic regression will bedetermined using cross validation. Both receiver operating characteristic(ROC) curve and area under the curve (AUC) will be used to assess theprediction rule discrimination ability. Calibration of the scoring rule willbe assessed using the Hosmer–Lemeshow test.Boosted tree models will be compared to the penalized, logisticregression scorecard to predict AKI. The maximum number of trees usedin the boosted model will be estimated using cross validation, dependingon the shrinkage parameter and the number of splits from each tree.The boosted models classification will be compared to the penalized,logistic regression using the AUC from the test sample. Prediction ruleperformance measurements for the validation cohort will include AUC,sensitivity, specificity, negative predictive value, positive predictive value,and positive and negative likelihood ratios at specific thresholds of the score.All data will be summarized as mean and standard deviation ormedian and interquartile range for continuous variables, and numberand percentage for categorical variables and compared using appropriatestatistical tests (i.e. t-test, chi square). P values will be two-tailed and willbe considered statistically significant if p 0.05. SAS (SAS , Cary, NC) andR (R Project for Statistical Computing, will be used for data analysis.Sample Size C
ci Forschen Open HUB for Scientific Research International ournal of ephrology and Kidney ailure Open Access Copyrit: han T, et al This is an openaccess article distriuted under the terms of the Creative Commons Attriution icense, which permits unrestricted use, distriution, and reproduction in any med
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