An observational study in Acute Kidney Injury and Delirium, sponsored by University of Nebraska. Active, not recruiting at 1 site in United States. Open to participants aged 19 Years and older. Per ClinicalTrials.gov, last updated 2026-10-08.
Sponsored by University of Nebraska · Observational
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:
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.
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:
Model performance will be evaluated using multiple complementary metrics:
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:
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.
1,641 studies on the registry are indexed under Acute Kidney Injury; 380 are open to participants now.
This study's planned enrollment of 25,000 is above the median of 156 across 795 observational studies indexed under Acute Kidney Injury.
Browse Acute Kidney Injury studies →University of Nebraska is the lead sponsor of 495 studies on the registry; 68 are open to participants now.
Of its 83 completed or terminated interventional studies of FDA-regulated products, 52 (63%) have results posted.
Counted across the registry records on this site, refreshed daily.
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.
Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
Exclusion Criteria:
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) .
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.
Other: It is observational trial with no intervention being evaluated
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.
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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: Prediction at hour 24 for incident hospital-acquired delirium during hours 24-96 after admission
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.
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
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.
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)
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
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
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
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
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.
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
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.
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
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.
Time frame: 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.
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
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.
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
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.
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
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.
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
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.
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.
Internal Validation Performance of the Prediction Models
Internal validation of the HA-AKI and delirium prediction models will use patient-level bootstrap resampling of the 2019-2023 development cohort. Apparent and optimism-corrected AUROC, area under the precision-recall curve, calibration intercept and slope, Brier score, and scaled Brier score will be reported. The complete model-development process will be repeated within resampling when computationally feasible.
Time frame: Prediction at hour 24 for incident HA-AKI or hospital-acquired delirium during hours 24-96 after admission
Supportive Encounter-Level Length-of-Stay and Cost Associations
Supportive adjusted regression analyses will estimate associations between HA-AKI or hospital-acquired delirium and total or post-onset hospital length of stay and encounter-level direct hospital cost. These analyses will complement the primary time-resolved multistate analyses but will not be interpreted as fully time-resolved attributable effects.
Time frame: From inpatient admission (total length of stay) or complication onset (post-onset length of stay) until live discharge, in-hospital death, or hospital day 30, whichever comes first, assessed up to 30 days
Prediction Performance at Alternative Prediction Origins and Horizons
As sensitivity analyses, prediction performance may be assessed using alternative prediction origins, including hours 12 and 48, and an alternative outcome horizon through day 7 or discharge. These analyses represent separate prediction use cases and will not be described as validation of the primary hour-24 models.
Time frame: Predictions at hours 12 or 48 for outcomes during the prespecified subsequent period through day 7 or discharge
Attributable Burden Within Prespecified Patient Subgroups
Incremental restricted mean hospital days and, when estimable, direct hospital costs will be reported within prespecified baseline subgroups. Subgroups include age, sex, race, ethnicity, service, initial care location, calendar period, baseline severity, CKD or eGFR for HA-AKI, and cognitive impairment or dementia for delirium. Exposure-by-subgroup interactions and standardized contrasts will be reported with confidence intervals.
Time frame: From inpatient admission until live discharge, in-hospital death, or hospital day 30, whichever comes first, assessed up to 30 days.
Plan to share: 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.
Supporting information: Study protocol, Statistical analysis plan, Clinical study report, Analytic code
Time frame: Data will be available beginning 6 months after publication of the primary study results and will remain available for 5 years.
Access criteria: Data will be shared with qualified researchers who provide a methodologically sound proposal and who agree to a data use agreement designed to protect participant confidentiality. Requests will be reviewed by the study investigators and the institutional data governance committee.
Started Sep 2026 (actual). Primary completion is estimated for Apr 2027, 7 months after the start.
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