Acute Kidney InjuryHAAKIDNCT07866560
AKI and Delirium Risk Prediction and Cost Burden Study
An observational study of Acute Kidney Injury and Delirium, run by University of Nebraska at 1 site in United States.
Trial summary
The goal of this study is to learn how to better predict two common health problems that can happen during a hospital stay: acute kidney injury (AKI) and delirium. AKI is a sudden decline in kidney function. Delirium is a sudden change in thinking or awareness that can cause confusion. Read more about this study
- Who is it for?
- Ages 19 years and older
- Conditions the study is for
- Acute Kidney Injury, Delirium
- How many people
- 25,000 participants
- Where
- 1 siteUnited States
- Study dates
- 1 Sep 2026to 28 Sep 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 19 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Adults aged 19 years and older admitted general medical (non-ICU) ward at Nebraska medical center, Omaha, Nebraska, between January 1, 2019 to December 31, 2025.
Who may be able to join
- Age 19 years or older at the time of admission.
- Inpatient admission to the general medical (non-ICU) ward at Nebraska Medical Center, Omaha, Nebraska.
- Admission date between January 1, 2019 to December 31, 2025.
Who cannot join
- 1 . Admitted directly to Intensive Care unit. 2. Received Mechanical Ventilation at the time of admission. 3. Dependent on maintenance Dialysis at the time of admission (excluded from acute kidney injury analysis only).
- 4. Acute Kidney Injury present on admission (excluded from acute kidney injury analysis only) .
- 5. Delirium present on admission (excluded from Delirium analysis only) .
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Hospital-Acquired Acute Kidney Injury and Delirium Risk Prediction and Cost Burden
The goal of this study is to learn how to better predict two common health problems that can happen during a hospital stay: acute kidney injury (AKI) and delirium. AKI is a sudden decline in kidney function. Delirium is a sudden change in thinking or awareness that can cause confusion. Both conditions can make people stay in the hospital longer and may increase health care costs.
The two main questions this study aims to answer are:
Read more
- How do hospital acquired AKI and delirium affect how long people stay in the hospital and the cost of their care?
- Can early information from a patient's electronic health record help researchers predict who may develop AKI or delirium during a hospital stay?
Researchers will review information already collected in the electronic health records of adults who were admitted to the hospital. They will look at details such as age, health conditions, laboratory results, nursing and ancillary flowsheets, medications, and vital signs recorded during the first day of hospitalization.
Researchers will use this information to build a computer model that estimates a patient's risk of developing AKI or delirium later during the hospital stay. They will also study how these complications affect the length of the hospital stay and the cost of care.
This study will not change the care patients receive. Researchers will only use existing medical record data. The results may help doctors identify patients at higher risk earlier and guide steps that may prevent these complications in the future.
Read the detailed description
This study is a retrospective observational study designed to develop and validate early risk prediction models for two common hospital-acquired complications: acute kidney injury (AKI) and delirium. These complications frequently occur during hospitalization and are associated with prolonged hospital length of stay (LOS), increased intensive care unit (ICU) utilization, higher health care costs, and worse clinical outcomes. Early identification of patients at elevated risk may enable targeted preventive strategies and support future clinical decision support interventions.
The study will use routinely collected data from the electronic health record (EHR) system at Nebraska Medicine. The study has two primary objectives. The first primary objective is to develop and temporally validate prediction models that estimate the risk of hospital-acquired AKI and delirium that is accurate, well calibrated, clinically usable, and fair among important population subgroups, using information available early during hospitalization. The second primary objective is to quantify the hospital Length of Stay and direct cost burden attributable to these complications.
Data Sources and Predictor Variables:
Structured EHR data will be extracted for eligible inpatient encounters. Candidate predictors will include demographic characteristics, comorbid conditions, baseline kidney function, early laboratory values, vital signs, nursing and therapy flowsheets, medication exposures (including nephrotoxic and psychoactive medications), admission service, and indicators of physiologic instability recorded during the early hospitalization period.
Predictor variables will be defined using information available within the first 0-24 hours of admission to ensure temporal ordering between predictors and subsequent complications. Standardized clinical definitions and coding systems will be used where applicable.
Outcome Identification:
Hospital-acquired AKI will be identified using Kidney Disease Improving Global Outcomes (KDIGO) creatinine-based criteria derived from laboratory data in the EHR. Delirium will be identified using validated clinical screening results including Confusion Assessment Method (CAM) or Brief Confusion Assessment (bCAM) or CAM-ICU assessments when available, supplemented by occupational therapy and nursing flowsheets, and diagnostic codes documented in the medical record. Only complications occurring after the defined early hospitalization window will be included to preserve temporal validity.
Prediction Model Development:
Prediction models for hospital-acquired AKI and delirium will be developed using both regression-based and machine learning approaches.
Statistical methods will include:
- Regularized logistic regression (LASSO) to perform variable selection and reduce overfitting when evaluating multiple candidate predictors.
- Extreme Gradient Boosting (XGBoost) to capture nonlinear relationships and complex interactions between predictors.
- Additional model comparison approaches may include random forest or elastic net regression where appropriate.
Model performance will be evaluated using multiple complementary metrics:
- Discrimination: area under the receiver operating characteristic curve (AUROC) and precision-recall area under the curve.
- Calibration: calibration slope, calibration-in-the-large, and graphical calibration plots.
- Overall model performance: Brier score.
- Clinical utility: decision curve analysis evaluating net benefit across clinically relevant risk thresholds.
- Fairness: equitable performance across major population subgroups with subgroup analysis
Internal validation will be conducted using bootstrap resampling and k-fold cross-validation to estimate optimism-corrected model performance and assess potential overfitting.
Analysis of Hospital Utilization and Cost Outcomes:
The study will also estimate the incremental hospital utilization and cost burden associated with incident hospital-acquired AKI and hospital-acquired delirium. It will also examine related outcomes, including transfer to an intensive care unit, hospital mortality, and 30-day readmission.
Because AKI and delirium may develop at different times during hospitalization, each condition will be analyzed as an event that can change a patient's hospital course. Follow-up will begin at hospital admission. Patients with the condition already present at admission will be excluded from the analysis of that condition.
These complications will be treated as time-varying exposures. To preserve temporal ordering between predictors, complications, and downstream outcomes, analyses will use a multistate analysis approach to represent movement through clinically relevant stages of hospitalization, such as admission without the complication, development of hospital-acquired AKI or delirium, transfer to an intensive care unit, discharge, or death. At each hospital day, patients who develop the complication will be compared with similar patients who are still hospitalized and have not yet developed it. This approach accounts for the timing of the complication and reduces bias that could occur if patients were classified only according to whether they experienced the complication at any time during hospitalization.
Comparisons will be made with patients who remain hospitalized and complication-free at the same time point using risk-set comparison methods. The analysis will estimate the difference in hospital days and costs between the observed course of care and an estimated comparison scenario in which the hospital-acquired complication did not occur. Hospital costs will be evaluated using daily cost information and statistical methods appropriate for healthcare cost data.
Outcome models will include:
- Hospital length of stay: negative binomial regression modeling residual LOS following complication onset.
- ICU transfer and 30-day readmission: multivariable logistic regression.
- Hospital costs: two-part modeling approach consisting of logistic regression for the probability of incurring costs and generalized linear models with gamma distribution and log link for positive cost values.
To reduce confounding, analyses will adjust for baseline covariates measured prior to complication onset. Differences in patient characteristics and clinical condition before the complication occurs will be addressed using multivariable adjustment and inverse probability weighting to standardize comparisons between exposed and unexposed patients and estimate incremental attributable outcomes. These methods will improve comparability between patients who develop hospital-acquired AKI or delirium and patients who remain free of the corresponding complication. Sensitivity analysis and subgroup analyses will be performed to assess the consistency and robustness of the findings.
Sensitivity and Subgroup Analysis:
Sensitivity analyses will evaluate robustness of findings using alternative modeling strategies for time-varying exposures, including marginal structural models with stabilized inverse probability weights. Additional analyses will assess potential model performance differences across clinically relevant subgroups such as age, baseline kidney disease status, and clinical service lines.
Data Quality and Validation:
Data quality procedures will include structured data extraction protocols, range checks, and consistency checks across related data fields. Variable definitions will follow standardized clinical definitions where possible, and analytic datasets will be validated against source EHR data to ensure completeness and accuracy.
Study Purpose:
This study uses existing clinical data and does not alter patient care. The findings will provide foundational evidence for the development of EHR-integrated risk prediction tools and clinical decision support strategies aimed at improving early identification and prevention of hospital-acquired complications such as AKI and delirium.
- Conditions
- Acute Kidney Injury, Delirium
- Being tested
- It is observational trial with no intervention being evaluated
- Keywords
- Hospital-Acquired Acute Kidney Injury, Hospital-Acquired Delirium, Temporal Validation, Decision Curve Analysis, Length of Stay, Hospital Costs, Electronic health Records, Multistate Model, Risk Prediction Model
- Medical subject headings
- Acute Kidney Injury, Delirium
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
It is observational trial with no intervention being evaluatedOther
Hospital-acquired acute kidney injury and hospital-acquired delirium will be identified retrospectively from electronic health record data using prespecified clinical definitions. Acute kidney injury will be defined using KDIGO-based criteria, and delirium will be identified using a prespecified phenotype incorporating available CAM-family assessments and other time-stamped structured clinical documentation and flowsheets. Patients with the relevant condition present on admission will be excluded from that outcome analysis. Neither exposure is assigned by the investigators; each will be analyzed separately as a time-varying complication according to its documented onset.
| Study group | Role | What they receive |
|---|---|---|
| Adult at or over age 19, admitted to Nebraska medical Center general medical ward | Adult acute-care inpatient admissions at Nebraska Medicine between January 1, 2019, and December 31, 2025, meeting the prespecified eligibility criteria. This is a retrospective observational study using existing electronic health record and hospital financial data. No intervention will be assigned by the investigators. The exposures of interest are incident hospital-acquired acute kidney injury and hospital-acquired delirium, which will be treated as time-varying complications. For each outcome, patients who develop the complication will be compared at the corresponding time with eligible patients who remain hospitalized and free of that complication. The cohort will also be used to develop prediction models using 2019-2023 admissions and temporally validate the locked models using 2024-2025 admissions. Receives: Other: It is observational trial with no intervention being evaluated |
What is being measured
The primary outcome is the main question the study is designed to answer.
Incremental Hospital Days Attributable to Hospital-Acquired Acute Kidney Injury
Difference in restricted mean hospital days per hospitalization between the observed course and a standardized hypothetical scenario in which hospital-acquired AKI did not occur, estimated using the prespecified multistate analysis. Reported in days, with a 95% confidence interval.
Measured: From inpatient admission to live discharge, in-hospital death, or day 30, whichever occurs first; assessed up to 30 days.
Incremental Direct Hospital Cost Attributable to Hospital-Acquired Acute Kidney Injury
Difference in cumulative direct hospital cost per hospitalization between the observed course and the standardized hypothetical AKI-prevention scenario. Reported in 2025 US dollars, with a 95% confidence interval.
Measured: From inpatient admission to live discharge, in-hospital death, or day 30, whichever occurs first; assessed up to 30 days.
Incremental Hospital Days Attributable to Hospital-Acquired Delirium
Difference in restricted mean hospital days per hospitalization between the observed course and a standardized hypothetical scenario in which hospital-acquired delirium did not occur, estimated using the prespecified multistate analysis. Reported in days, with a 95% confidence interval.
Measured: From inpatient admission to live discharge, in-hospital death, or day 30, whichever occurs first; assessed up to 30 days.
Incremental Direct Hospital Cost Attributable to Hospital-Acquired Delirium
Difference in cumulative direct hospital cost per hospitalization between the observed course and the standardized hypothetical delirium-prevention scenario. Reported in 2025 US dollars, with a 95% confidence interval.
Measured: From inpatient admission to live discharge, in-hospital death, or day 30, whichever occurs first; assessed up to 30 days.
Temporal Validation Performance of Hospital-Acquired Acute Kidney Injury Prediction Models
Locked HA-AKI prediction models will be evaluated in the 2024-2025 temporal-validation cohort. Using information available by hour 24, model will predict HA-AKI during hours 24-96. Performance will be assessed using AUROC for discrimination and calibration slope for agreement between predicted and observed risks. Estimates will be reported with 95% confidence intervals.
Measured: Prediction at hour 24 for incident HA-AKI during hours 24-96 after admission
Temporal Validation Performance of Hospital-Acquired Delirium Prediction Model
Locked hospital-acquired delirium prediction model will be evaluated in the 2024-2025 temporal-validation cohort. Using information available by hour 24, model will predict complication during hours 24-96. Performance will be assessed using AUROC for discrimination and calibration slope for agreement between predicted and observed risks. Estimates will be reported with 95% confidence intervals.
Measured: Prediction at hour 24 for incident hospital-acquired delirium during hours 24-96 after admission
Secondary outcomes (12)
Incidence of Hospital-Acquired Acute Kidney Injury
Number and percentage of eligible hospitalizations in which incident HA-AKI develops after the prespecified present-on-admission period. HA-AKI will be identified using the locked KDIGO-based definition and assigned to the earliest qualifying onset time. Results will be reported overall and by AKI stage.
Measured: From hour 24 after inpatient admission until live discharge, in-hospital death, or hospital day 30, whichever comes first, assessed up to 30 days
Incidence of Hospital-Acquired Delirium
Number and percentage of eligible hospitalizations in which incident hospital-acquired delirium develops after the prespecified present-on-admission period. Delirium will be identified using the locked electronic health record phenotype and assigned to the earliest qualifying onset time.
Measured: From hour 24 after inpatient admission until live discharge, in-hospital death, or hospital day 30, whichever comes first, assessed up to 30 days.
Cumulative Incidence of Intensive Care Unit (ICU) Transfer Following Hospital-Acquired Acute Kidney Injury (HA-AKI)
Percentage of participants with HA-AKI who are transferred to an ICU. This is estimated as the cumulative incidence by day 30 after HA-AKI onset, using a competing-risks method (Aalen-Johansen estimator) with in-hospital death as a competing event and live discharge as censoring. The analysis includes only participants who were not already receiving intensive care at HA-AKI onset.
Unit of Measure: Percentage of participants
Measured: From HA-AKI onset until ICU transfer, live discharge, in-hospital death, or hospital day 30, whichever comes first (assessed up to 30 days after admission)
Cumulative Incidence of Intensive Care Unit (ICU) Transfer Following Hospital-Acquired Delirium
Percentage of participants with hospital-acquired delirium who are transferred to an ICU. This is estimated as the cumulative incidence by day 30 after delirium onset, using a competing-risks method (Aalen-Johansen estimator) with in-hospital death as a competing event and live discharge as censoring. The analysis includes only participants who were not already receiving intensive care at delirium onset.
Unit of Measure: Percentage of participants
Measured: From hospital-acquired delirium onset until ICU transfer, live discharge, in-hospital death, or hospital day 30, whichever comes first (assessed up to 30 days after admission)
In-Hospital Mortality Associated with Hospital-Acquired Acute Kidney Injury and Delirium separately
In-hospital death will be evaluated separately for HA-AKI and hospital-acquired delirium. Death will be modeled as a terminal event distinct from live discharge. Standardized mortality probabilities will be reported under observed care and the corresponding hypothetical complication-prevention scenario, with 95% confidence intervals.
Measured: From inpatient admission until in-hospital death, live discharge, or hospital day 30, whichever comes first, assessed up to 30 days.
Live Discharge Following Hospital-Acquired Acute Kidney Injury and Delirium separately
The probability and timing of live hospital discharge will be evaluated separately for HA-AKI and hospital-acquired delirium. Live discharge and in-hospital death will be treated as distinct terminal outcomes in the multistate analysis. Standardized live-discharge probabilities will be reported with 95% confidence intervals.
Measured: From inpatient admission until live discharge, in-hospital death, or hospital day 30, whichever comes first, assessed up to 30 days
All-Cause 30-Day Hospital Readmission
Number and percentage of patients discharged alive who experience an all-cause inpatient readmission within 30 days after discharge from the index hospitalization. Readmission will be evaluated separately in relation to HA-AKI and hospital-acquired delirium. Post-discharge death will be handled as a competing event if sufficiently complete vital-status data are available.
Measured: Within 30 days after live discharge from the index hospitalization
Additional Temporal Validation Performance of the Prediction Models
Additional performance of the locked HA-AKI and delirium prediction models will be evaluated separately using area under the precision-recall curve, calibration-in-the-large, observed-to-expected ratio, flexible calibration curves, Brier score, and scaled Brier score. Outcome prevalence and predicted-risk distributions will also be reported, with patient-level 95% confidence intervals where applicable.
Measured: Prediction at hour 24 for incident HA-AKI or hospital-acquired delirium during hours 24-96 after admission
Component-Specific Hospital Costs Attributable to Hospital-Acquired Acute Kidney Injury and Delirium
Incremental costs within reliable, mutually exclusive hospital cost categories will be estimated separately for HA-AKI and hospital-acquired delirium. Categories may include room and board, intensive care, pharmacy, laboratory testing, imaging, procedures, consultations, nursing or patient-care services, and renal replacement therapy. Results will be reported in 2025 US dollars with 95% confidence intervals.
Measured: From inpatient admission until live discharge, in-hospital death, or hospital day 30, whichever comes first, assessed up to 30 days.
Clinical Utility of the Hospital-Acquired Acute Kidney Injury and Delirium Prediction Models
Clinical utility will be evaluated separately for each model across prespecified risk thresholds using decision-curve net benefit. At selected thresholds, sensitivity, specificity, predictive values, likelihood ratios, false-positive and false-negative rates, alerts per 100 admissions, true-positive and false-positive alerts per 100 admissions, and number needed to evaluate will be reported.
Measured: Prediction at hour 24 for incident HA-AKI or hospital-acquired delirium during hours 24-96 after admission
Fairness and Subgroup Performance of the Prediction Models
Prediction performance will be evaluated within prespecified patient groups defined by age, sex, race, ethnicity, preferred language, insurance, service, initial care location, calendar period, CKD or eGFR for the AKI model, and cognitive impairment or dementia for the delirium model. Representation, missingness, prevalence, calibration, discrimination, prediction error, threshold-specific errors, alert burden, and net benefit will be reported with confidence intervals.
Measured: Prediction at hour 24 for incident HA-AKI or hospital-acquired delirium during hours 24-96 after admission
Severity of Hospital-Acquired Acute Kidney Injury and Delirium
HA-AKI severity will be described using the highest KDIGO stage reached during the index hospitalization. Delirium severity or motor subtype will be described when supported by sufficiently complete and validated electronic health record data. These analyses will characterize complication heterogeneity and will not be interpreted as baseline causal subgroup effects.
Measured: From complication onset until live discharge, in-hospital death, or hospital day 30, whichever comes first, assessed up to 30 days after admission.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 Oct 2026
- Study start1 Sep 2026
- Primary completion28 Apr 2027 (estimated)
- Study completion28 Sep 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (15)Papers the sponsor has linked to this study.
- Other trials for Acute Kidney InjuryStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 25,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of Nebraska
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Vasthala Juvvigunta, University of Nebraska
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07866560
- Sponsor's protocol ID
- 0056-26-EP
- Acronym
- HAAKID
- Status last verified
- Oct 2026
- First submitted
- 15 Sep 2026
- Last updated
- 8 Oct 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: De-identified individual participant data underlying the results reported in this study will be shared. This includes participant-level variables used in the analytic dataset, such as demographic characteristics, baseline comorbidities, laboratory values, medication exposures, and outcome variables including hospital-acquired acute kidney injury, delirium, length of stay, ICU transfer, readmission, and hospital cost outcomes.
Publications
Silver SA, Chertow GM. The Economic Consequences of Acute Kidney Injury. Nephron. 2017;137(4):297-301. doi: 10.1159/000475607. Epub 2017 Jun 9.
PubMed 28595193Koyner JL, Carey KA, Edelson DP, Churpek MM. The Development of a Machine Learning Inpatient Acute Kidney Injury Prediction Model. Crit Care Med. 2018 Jul;46(7):1070-1077. doi: 10.1097/CCM.0000000000003123.
PubMed 29596073van den Boogaard M, Pickkers P, Slooter AJ, Kuiper MA, Spronk PE, van der Voort PH, van der Hoeven JG, Donders R, van Achterberg T, Schoonhoven L. Development and validation of PRE-DELIRIC (PREdiction of DELIRium in ICu patients) delirium prediction model for intensive care patients: observational multicentre study. BMJ. 2012 Feb 9;344:e420. doi: 10.1136/bmj.e420.
PubMed 22323509Hshieh TT, Yue J, Oh E, Puelle M, Dowal S, Travison T, Inouye SK. Effectiveness of multicomponent nonpharmacological delirium interventions: a meta-analysis. JAMA Intern Med. 2015 Apr;175(4):512-20. doi: 10.1001/jamainternmed.2014.7779.
PubMed 25643002Rubin FH, Neal K, Fenlon K, Hassan S, Inouye SK. Sustainability and scalability of the hospital elder life program at a community hospital. J Am Geriatr Soc. 2011 Feb;59(2):359-65. doi: 10.1111/j.1532-5415.2010.03243.x.
PubMed 21314654Leslie DL, Marcantonio ER, Zhang Y, Leo-Summers L, Inouye SK. One-year health care costs associated with delirium in the elderly population. Arch Intern Med. 2008 Jan 14;168(1):27-32. doi: 10.1001/archinternmed.2007.4.
PubMed 18195192Zeng X, McMahon GM, Brunelli SM, Bates DW, Waikar SS. Incidence, outcomes, and comparisons across definitions of AKI in hospitalized individuals. Clin J Am Soc Nephrol. 2014 Jan;9(1):12-20. doi: 10.2215/CJN.02730313. Epub 2013 Oct 31.
PubMed 24178971Silver SA, Long J, Zheng Y, Chertow GM. Cost of Acute Kidney Injury in Hospitalized Patients. J Hosp Med. 2017 Feb;12(2):70-76. doi: 10.12788/jhm.2683.
PubMed 28182800Marcantonio ER. Delirium in Hospitalized Older Adults. N Engl J Med. 2017 Oct 12;377(15):1456-1466. doi: 10.1056/NEJMcp1605501.
PubMed 29020579Witlox J, Eurelings LS, de Jonghe JF, Kalisvaart KJ, Eikelenboom P, van Gool WA. Delirium in elderly patients and the risk of postdischarge mortality, institutionalization, and dementia: a meta-analysis. JAMA. 2010 Jul 28;304(4):443-51. doi: 10.1001/jama.2010.1013.
PubMed 20664045Coca SG, Yusuf B, Shlipak MG, Garg AX, Parikh CR. Long-term risk of mortality and other adverse outcomes after acute kidney injury: a systematic review and meta-analysis. Am J Kidney Dis. 2009 Jun;53(6):961-73. doi: 10.1053/j.ajkd.2008.11.034. Epub 2009 Apr 5.
PubMed 19346042Chertow GM, Burdick E, Honour M, Bonventre JV, Bates DW. Acute kidney injury, mortality, length of stay, and costs in hospitalized patients. J Am Soc Nephrol. 2005 Nov;16(11):3365-70. doi: 10.1681/ASN.2004090740. Epub 2005 Sep 21.
PubMed 16177006Inouye SK, Westendorp RG, Saczynski JS. Delirium in elderly people. Lancet. 2014 Mar 8;383(9920):911-22. doi: 10.1016/S0140-6736(13)60688-1. Epub 2013 Aug 28.
PubMed 23992774Susantitaphong P, Cruz DN, Cerda J, Abulfaraj M, Alqahtani F, Koulouridis I, Jaber BL; Acute Kidney Injury Advisory Group of the American Society of Nephrology. World incidence of AKI: a meta-analysis. Clin J Am Soc Nephrol. 2013 Sep;8(9):1482-93. doi: 10.2215/CJN.00710113. Epub 2013 Jun 6.
PubMed 23744003Hoste EAJ, Kellum JA, Selby NM, Zarbock A, Palevsky PM, Bagshaw SM, Goldstein SL, Cerda J, Chawla LS. Global epidemiology and outcomes of acute kidney injury. Nat Rev Nephrol. 2018 Oct;14(10):607-625. doi: 10.1038/s41581-018-0052-0.
PubMed 30135570
Study at a glance
From the registry record, last updated by the sponsor on 8 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Hospital-Acquired Acute Kidney Injury and Delirium Risk Prediction and Cost Burden
- ClinicalTrials.gov ID
- NCT07866560
- Acronym / sponsor ID
- HAAKID / 0056-26-EP
- Conditions
- Acute Kidney Injury, Delirium
- Keywords
- Hospital-Acquired Acute Kidney Injury, Hospital-Acquired Delirium, Temporal Validation, Decision Curve Analysis, Length of Stay, Hospital Costs, Electronic health Records, Multistate Model
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 25,000 participants (estimated)
- Lead sponsor
- University of NebraskaUniversity, hospital or other
- Study start
- 1 Sep 2026 (actual)
- Primary completion
- 28 Apr 2027 (estimated)
- Study completion
- 28 Sep 2027 (estimated)
- First posted
- 8 Oct 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
The goal of this study is to learn how to better predict two common health problems that can happen during a hospital stay: acute kidney injury (AKI) and delirium. AKI is a sudden decline in kidney function. Delirium is a sudden change in thinking or awareness that can cause confusion. Both conditions can make people stay in the hospital longer and may increase health care costs.
The two main questions this study aims to answer are:
- How do hospital acquired AKI and delirium affect how long people stay in the hospital and the cost of their care?
- Can early information from a patient's electronic health record help researchers predict who may develop AKI or delirium during a hospital stay?
Researchers will review information already collected in the electronic health records of adults who were admitted to the hospital. They will look at details such as age, health conditions, laboratory results, nursing and ancillary flowsheets, medications, and vital signs recorded during the first day of hospitalization.
Researchers will use this information to build a computer model that estimates a patient's risk of developing AKI or delirium later during the hospital stay. They will also study how these complications affect the length of the hospital stay and the cost of care.
This study will not change the care patients receive. Researchers will only use existing medical record data. The results may help doctors identify patients at higher risk earlier and guide steps that may prevent these complications in the future.
Detailed description
This study is a retrospective observational study designed to develop and validate early risk prediction models for two common hospital-acquired complications: acute kidney injury (AKI) and delirium. These complications frequently occur during hospitalization and are associated with prolonged hospital length of stay (LOS), increased intensive care unit (ICU) utilization, higher health care costs, and worse clinical outcomes. Early identification of patients at elevated risk may enable targeted preventive strategies and support future clinical decision support interventions.
The study will use routinely collected data from the electronic health record (EHR) system at Nebraska Medicine. The study has two primary objectives. The first primary objective is to develop and temporally validate prediction models that estimate the risk of hospital-acquired AKI and delirium that is accurate, well calibrated, clinically usable, and fair among important population subgroups, using information available early during hospitalization. The second primary objective is to quantify the hospital Length of Stay and direct cost burden attributable to these complications.
Data Sources and Predictor Variables:
Structured EHR data will be extracted for eligible inpatient encounters. Candidate predictors will include demographic characteristics, comorbid conditions, baseline kidney function, early laboratory values, vital signs, nursing and therapy flowsheets, medication exposures (including nephrotoxic and psychoactive medications), admission service, and indicators of physiologic instability recorded during the early hospitalization period.
Predictor variables will be defined using information available within the first 0-24 hours of admission to ensure temporal ordering between predictors and subsequent complications. Standardized clinical definitions and coding systems will be used where applicable.
Outcome Identification:
Hospital-acquired AKI will be identified using Kidney Disease Improving Global Outcomes (KDIGO) creatinine-based criteria derived from laboratory data in the EHR. Delirium will be identified using validated clinical screening results including Confusion Assessment Method (CAM) or Brief Confusion Assessment (bCAM) or CAM-ICU assessments when available, supplemented by occupational therapy and nursing flowsheets, and diagnostic codes documented in the medical record. Only complications occurring after the defined early hospitalization window will be included to preserve temporal validity.
Prediction Model Development:
Prediction models for hospital-acquired AKI and delirium will be developed using both regression-based and machine learning approaches.
Statistical methods will include:
- Regularized logistic regression (LASSO) to perform variable selection and reduce overfitting when evaluating multiple candidate predictors.
- Extreme Gradient Boosting (XGBoost) to capture nonlinear relationships and complex interactions between predictors.
- Additional model comparison approaches may include random forest or elastic net regression where appropriate.
Model performance will be evaluated using multiple complementary metrics:
- Discrimination: area under the receiver operating characteristic curve (AUROC) and precision-recall area under the curve.
- Calibration: calibration slope, calibration-in-the-large, and graphical calibration plots.
- Overall model performance: Brier score.
- Clinical utility: decision curve analysis evaluating net benefit across clinically relevant risk thresholds.
- Fairness: equitable performance across major population subgroups with subgroup analysis
Internal validation will be conducted using bootstrap resampling and k-fold cross-validation to estimate optimism-corrected model performance and assess potential overfitting.
Analysis of Hospital Utilization and Cost Outcomes:
The study will also estimate the incremental hospital utilization and cost burden associated with incident hospital-acquired AKI and hospital-acquired delirium. It will also examine related outcomes, including transfer to an intensive care unit, hospital mortality, and 30-day readmission.
Because AKI and delirium may develop at different times during hospitalization, each condition will be analyzed as an event that can change a patient's hospital course. Follow-up will begin at hospital admission. Patients with the condition already present at admission will be excluded from the analysis of that condition.
These complications will be treated as time-varying exposures. To preserve temporal ordering between predictors, complications, and downstream outcomes, analyses will use a multistate analysis approach to represent movement through clinically relevant stages of hospitalization, such as admission without the complication, development of hospital-acquired AKI or delirium, transfer to an intensive care unit, discharge, or death. At each hospital day, patients who develop the complication will be compared with similar patients who are still hospitalized and have not yet developed it. This approach accounts for the timing of the complication and reduces bias that could occur if patients were classified only according to whether they experienced the complication at any time during hospitalization.
Comparisons will be made with patients who remain hospitalized and complication-free at the same time point using risk-set comparison methods. The analysis will estimate the difference in hospital days and costs between the observed course of care and an estimated comparison scenario in which the hospital-acquired complication did not occur. Hospital costs will be evaluated using daily cost information and statistical methods appropriate for healthcare cost data.
Outcome models will include:
- Hospital length of stay: negative binomial regression modeling residual LOS following complication onset.
- ICU transfer and 30-day readmission: multivariable logistic regression.
- Hospital costs: two-part modeling approach consisting of logistic regression for the probability of incurring costs and generalized linear models with gamma distribution and log link for positive cost values.
To reduce confounding, analyses will adjust for baseline covariates measured prior to complication onset. Differences in patient characteristics and clinical condition before the complication occurs will be addressed using multivariable adjustment and inverse probability weighting to standardize comparisons between exposed and unexposed patients and estimate incremental attributable outcomes. These methods will improve comparability between patients who develop hospital-acquired AKI or delirium and patients who remain free of the corresponding complication. Sensitivity analysis and subgroup analyses will be performed to assess the consistency and robustness of the findings.
Sensitivity and Subgroup Analysis:
Sensitivity analyses will evaluate robustness of findings using alternative modeling strategies for time-varying exposures, including marginal structural models with stabilized inverse probability weights. Additional analyses will assess potential model performance differences across clinically relevant subgroups such as age, baseline kidney disease status, and clinical service lines.
Data Quality and Validation:
Data quality procedures will include structured data extraction protocols, range checks, and consistency checks across related data fields. Variable definitions will follow standardized clinical definitions where possible, and analytic datasets will be validated against source EHR data to ensure completeness and accuracy.
Study Purpose:
This study uses existing clinical data and does not alter patient care. The findings will provide foundational evidence for the development of EHR-integrated risk prediction tools and clinical decision support strategies aimed at improving early identification and prevention of hospital-acquired complications such as AKI and delirium.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Adult at or over age 19, admitted to Nebraska medical Center general medical ward | – | Other: It is observational trial with no intervention being evaluatedAdult acute-care inpatient admissions at Nebraska Medicine between January 1, 2019, and December 31, 2025, meeting the prespecified eligibility criteria. This is a retrospective observational study using existing electronic health record and hospital financial data. No intervention will be assigned by the investigators. The exposures of interest are incident hospital-acquired acute kidney injury and hospital-acquired delirium, which will be treated as time-varying complications. For each outcome, patients who develop the complication will be compared at the corresponding time with eligible patients who remain hospitalized and free of that complication. The cohort will also be used to develop prediction models using 2019-2023 admissions and temporally validate the locked models using 2024-2025 admissions. |
Who may qualify
- Ages
- 19 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Adults aged 19 years and older admitted general medical (non-ICU) ward at Nebraska medical center, Omaha, Nebraska, between January 1, 2019 to December 31, 2025.
Key inclusion criteria (3 listed)
- Age 19 years or older at the time of admission.
- Inpatient admission to the general medical (non-ICU) ward at Nebraska Medical Center, Omaha, Nebraska.
- Admission date between January 1, 2019 to December 31, 2025.
Key exclusion criteria (3 listed)
- 1 . Admitted directly to Intensive Care unit. 2. Received Mechanical Ventilation at the time of admission. 3. Dependent on maintenance Dialysis at the time of admission (excluded from acute kidney injury analysis only).
- 4. Acute Kidney Injury present on admission (excluded from acute kidney injury analysis only) .
- 5. Delirium present on admission (excluded from Delirium analysis only) .
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| It is observational trial with no intervention being evaluatedHospital-acquired acute kidney injury and hospital-acquired delirium will be identified retrospectively from electronic health record data using prespecified clinical definitions. Acute kidney injury will be defined using KDIGO-based criteria, and delirium will be identified using a prespecified phenotype incorporating available CAM-family assessments and other time-stamped structured clinical documentation and flowsheets. Patients with the relevant condition present on admission will be excluded from that outcome analysis. Neither exposure is assigned by the investigators; each will be analyzed separately as a time-varying complication according to its documented onset. | Other | Adult at or over age 19, admitted to Nebraska medical Center general medical ward |
Primary outcome
Incremental Hospital Days Attributable to Hospital-Acquired Acute Kidney Injury
Time frame: From inpatient admission to live discharge, in-hospital death, or day 30, whichever occurs first; assessed up to 30 days.
Difference in restricted mean hospital days per hospitalization between the observed course and a standardized hypothetical scenario in which hospital-acquired AKI did not occur, estimated using the prespecified multistate analysis. Reported in days, with a 95% confidence interval.
Incremental Direct Hospital Cost Attributable to Hospital-Acquired Acute Kidney Injury
Time frame: From inpatient admission to live discharge, in-hospital death, or day 30, whichever occurs first; assessed up to 30 days.
Difference in cumulative direct hospital cost per hospitalization between the observed course and the standardized hypothetical AKI-prevention scenario. Reported in 2025 US dollars, with a 95% confidence interval.
Incremental Hospital Days Attributable to Hospital-Acquired Delirium
Time frame: From inpatient admission to live discharge, in-hospital death, or day 30, whichever occurs first; assessed up to 30 days.
Difference in restricted mean hospital days per hospitalization between the observed course and a standardized hypothetical scenario in which hospital-acquired delirium did not occur, estimated using the prespecified multistate analysis. Reported in days, with a 95% confidence interval.
Incremental Direct Hospital Cost Attributable to Hospital-Acquired Delirium
Time frame: From inpatient admission to live discharge, in-hospital death, or day 30, whichever occurs first; assessed up to 30 days.
Difference in cumulative direct hospital cost per hospitalization between the observed course and the standardized hypothetical delirium-prevention scenario. Reported in 2025 US dollars, with a 95% confidence interval.
Temporal Validation Performance of Hospital-Acquired Acute Kidney Injury Prediction Models
Time frame: Prediction at hour 24 for incident HA-AKI during hours 24-96 after admission
Locked HA-AKI prediction models will be evaluated in the 2024-2025 temporal-validation cohort. Using information available by hour 24, model will predict HA-AKI during hours 24-96. Performance will be assessed using AUROC for discrimination and calibration slope for agreement between predicted and observed risks. Estimates will be reported with 95% confidence intervals.
Temporal Validation Performance of Hospital-Acquired Delirium Prediction Model
Time frame: Prediction at hour 24 for incident hospital-acquired delirium during hours 24-96 after admission
Locked hospital-acquired delirium prediction model will be evaluated in the 2024-2025 temporal-validation cohort. Using information available by hour 24, model will predict complication during hours 24-96. Performance will be assessed using AUROC for discrimination and calibration slope for agreement between predicted and observed risks. Estimates will be reported with 95% confidence intervals.
12 secondary outcomes
Incidence of Hospital-Acquired Acute Kidney Injury
Time frame: From hour 24 after inpatient admission until live discharge, in-hospital death, or hospital day 30, whichever comes first, assessed up to 30 days
Incidence of Hospital-Acquired Delirium
Time frame: From hour 24 after inpatient admission until live discharge, in-hospital death, or hospital day 30, whichever comes first, assessed up to 30 days.
Cumulative Incidence of Intensive Care Unit (ICU) Transfer Following Hospital-Acquired Acute Kidney Injury (HA-AKI)
Time frame: From HA-AKI onset until ICU transfer, live discharge, in-hospital death, or hospital day 30, whichever comes first (assessed up to 30 days after admission)
Cumulative Incidence of Intensive Care Unit (ICU) Transfer Following Hospital-Acquired Delirium
Time frame: From hospital-acquired delirium onset until ICU transfer, live discharge, in-hospital death, or hospital day 30, whichever comes first (assessed up to 30 days after admission)
In-Hospital Mortality Associated with Hospital-Acquired Acute Kidney Injury and Delirium separately
Time frame: From inpatient admission until in-hospital death, live discharge, or hospital day 30, whichever comes first, assessed up to 30 days.
Live Discharge Following Hospital-Acquired Acute Kidney Injury and Delirium separately
Time frame: From inpatient admission until live discharge, in-hospital death, or hospital day 30, whichever comes first, assessed up to 30 days
All-Cause 30-Day Hospital Readmission
Time frame: Within 30 days after live discharge from the index hospitalization
Additional Temporal Validation Performance of the Prediction Models
Time frame: Prediction at hour 24 for incident HA-AKI or hospital-acquired delirium during hours 24-96 after admission
Component-Specific Hospital Costs Attributable to Hospital-Acquired Acute Kidney Injury and Delirium
Time frame: From inpatient admission until live discharge, in-hospital death, or hospital day 30, whichever comes first, assessed up to 30 days.
Clinical Utility of the Hospital-Acquired Acute Kidney Injury and Delirium Prediction Models
Time frame: Prediction at hour 24 for incident HA-AKI or hospital-acquired delirium during hours 24-96 after admission
Fairness and Subgroup Performance of the Prediction Models
Time frame: Prediction at hour 24 for incident HA-AKI or hospital-acquired delirium during hours 24-96 after admission
Severity of Hospital-Acquired Acute Kidney Injury and Delirium
Time frame: From complication onset until live discharge, in-hospital death, or hospital day 30, whichever comes first, assessed up to 30 days after admission.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 8 Oct 2026.
- Background: PubMed 28595193Silver SA, Chertow GM. The Economic Consequences of Acute Kidney Injury. Nephron. 2017;137(4):297-301. doi: 10.1159/000475607. Epub 2017 Jun 9.
- Background: PubMed 29596073Koyner JL, Carey KA, Edelson DP, Churpek MM. The Development of a Machine Learning Inpatient Acute Kidney Injury Prediction Model. Crit Care Med. 2018 Jul;46(7):1070-1077. doi: 10.1097/CCM.0000000000003123.
- Background: PubMed 22323509van den Boogaard M, Pickkers P, Slooter AJ, Kuiper MA, Spronk PE, van der Voort PH, van der Hoeven JG, Donders R, van Achterberg T, Schoonhoven L. Development and validation of PRE-DELIRIC (PREdiction of DELIRium in ICu patients) delirium prediction model for intensive care patients: observational multicentre study. BMJ. 2012 Feb 9;344:e420. doi: 10.1136/bmj.e420.
- Background: PubMed 25643002Hshieh TT, Yue J, Oh E, Puelle M, Dowal S, Travison T, Inouye SK. Effectiveness of multicomponent nonpharmacological delirium interventions: a meta-analysis. JAMA Intern Med. 2015 Apr;175(4):512-20. doi: 10.1001/jamainternmed.2014.7779.
- Background: PubMed 21314654Rubin FH, Neal K, Fenlon K, Hassan S, Inouye SK. Sustainability and scalability of the hospital elder life program at a community hospital. J Am Geriatr Soc. 2011 Feb;59(2):359-65. doi: 10.1111/j.1532-5415.2010.03243.x.
- Background: PubMed 18195192Leslie DL, Marcantonio ER, Zhang Y, Leo-Summers L, Inouye SK. One-year health care costs associated with delirium in the elderly population. Arch Intern Med. 2008 Jan 14;168(1):27-32. doi: 10.1001/archinternmed.2007.4.
- Background: PubMed 24178971Zeng X, McMahon GM, Brunelli SM, Bates DW, Waikar SS. Incidence, outcomes, and comparisons across definitions of AKI in hospitalized individuals. Clin J Am Soc Nephrol. 2014 Jan;9(1):12-20. doi: 10.2215/CJN.02730313. Epub 2013 Oct 31.
- Background: PubMed 28182800Silver SA, Long J, Zheng Y, Chertow GM. Cost of Acute Kidney Injury in Hospitalized Patients. J Hosp Med. 2017 Feb;12(2):70-76. doi: 10.12788/jhm.2683.
- Background: PubMed 29020579Marcantonio ER. Delirium in Hospitalized Older Adults. N Engl J Med. 2017 Oct 12;377(15):1456-1466. doi: 10.1056/NEJMcp1605501.
- Background: PubMed 20664045Witlox J, Eurelings LS, de Jonghe JF, Kalisvaart KJ, Eikelenboom P, van Gool WA. Delirium in elderly patients and the risk of postdischarge mortality, institutionalization, and dementia: a meta-analysis. JAMA. 2010 Jul 28;304(4):443-51. doi: 10.1001/jama.2010.1013.
- Background: PubMed 19346042Coca SG, Yusuf B, Shlipak MG, Garg AX, Parikh CR. Long-term risk of mortality and other adverse outcomes after acute kidney injury: a systematic review and meta-analysis. Am J Kidney Dis. 2009 Jun;53(6):961-73. doi: 10.1053/j.ajkd.2008.11.034. Epub 2009 Apr 5.
- Background: PubMed 16177006Chertow GM, Burdick E, Honour M, Bonventre JV, Bates DW. Acute kidney injury, mortality, length of stay, and costs in hospitalized patients. J Am Soc Nephrol. 2005 Nov;16(11):3365-70. doi: 10.1681/ASN.2004090740. Epub 2005 Sep 21.
- A systematic review (2004-2012) of large cohort studies was conducted to estimate the world incidence of AKI and its stages of severity and associated mortality, and to describe geographic variations according to countries, regions, and their economies.Link provided by the sponsor.
- KDIGO clinical Practice GuidelinesLink provided by the sponsor.
- Individual participant dataSharing planned: Yes. De-identified individual participant data underlying the results reported in this study will be shared. This includes participant-level variables used in the analytic dataset, such as demographic characteristics, baseline comorbidities, laboratory values, medication exposures, and outcome variables including hospital-acquired acute kidney injury, delirium, length of stay, ICU transfer, readmission, and hospital cost outcomes.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Acute Kidney Injury and Delirium, run by University of Nebraska at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: under way, not looking for participants, as of the sponsor's last update on 8 Oct 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Incremental Hospital Days Attributable to Hospital-Acquired Acute Kidney Injury (time frame: From inpatient admission to live discharge, in-hospital death, or day 30, whichever occurs first; assessed up to 30 days.). The study lists 6 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07866560, which also lists 15 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 8 Oct 2026.