46 research outputs found
Epidemiology, Trajectories and Outcomes of Acute Kidney Injury Among Hospitalized Patients: A Retrospective Multicenter Large Cohort Study
Background: Acute kidney injury (AKI) is a clinical syndrome affecting almost
one fifth of hospitalized patients, as well as more than half of the patients
who are admitted to the intensive care unit (ICU). Stratifying AKI patients
into groups based on severity and duration would facilitate more targeted
efforts for treating AKI. Methods: In a retrospective, multicenter and
longitudinal cohort study of 935,679 patients who were admitted between 2012
and 2020 to health centers included in OneFlorida+ Network, we analyzed the
impact of AKI trajectories which are rapidly reversed AKI, persistent AKI with
renal recovery, and persistent AKI without renal recovery on patients' clinical
outcomes, including hospital, 30-day, 1-year, and 3-year mortality, kidney
replacement therapy, new chronic kidney disease (CKD) within 90 days or 1-year
of discharge, CKD progression within 1-year of discharge, resource utilization,
hospital disposition, and major complications during hospitalization. As
analytical approaches, Kaplan-Meier estimators and survival curves, Cox
proportional-hazards regression model, logistic regression model,
Kruskal-Wallis test, analysis of variance, chi-square, Fisher's exact test were
used. Results: Among 2,187,254 encounters, 14% had AKI, of which 63%, 21%, and
16% had Stage 1, 2, and 3, respectively, as the worst AKI stage. Fraction of
patients with persistent AKI was 31%. Patients with AKI had worse clinical
outcomes and increased resource utilization compared to patients without the
condition. One-year mortality was 5 times greater for patients with persistent
AKI compared to those without AKI. Conclusions: Persistent AKI was associated
with prolonged hospitalization, increased ICU admission and mortality compared
to the other groups. This may emphasize the critical need for devising
strategies targeting effective management of AKI and prevention of persisting
AKI.Comment: 61 pages, 2 tables, 3 figures, 13 supplemental tables, 3 supplemental
figure
Acute kidney injury prediction for non-critical care patients: a retrospective external and internal validation study
Background: Acute kidney injury (AKI), the decline of kidney excretory
function, occurs in up to 18% of hospitalized admissions. Progression of AKI
may lead to irreversible kidney damage. Methods: This retrospective cohort
study includes adult patients admitted to a non-intensive care unit at the
University of Pittsburgh Medical Center (UPMC) (n = 46,815) and University of
Florida Health (UFH) (n = 127,202). We developed and compared deep learning and
conventional machine learning models to predict progression to Stage 2 or
higher AKI within the next 48 hours. We trained local models for each site (UFH
Model trained on UFH, UPMC Model trained on UPMC) and a separate model with a
development cohort of patients from both sites (UFH-UPMC Model). We internally
and externally validated the models on each site and performed subgroup
analyses across sex and race. Results: Stage 2 or higher AKI occurred in 3%
(n=3,257) and 8% (n=2,296) of UFH and UPMC patients, respectively. Area under
the receiver operating curve values (AUROC) for the UFH test cohort ranged
between 0.77 (UPMC Model) and 0.81 (UFH Model), while AUROC values ranged
between 0.79 (UFH Model) and 0.83 (UPMC Model) for the UPMC test cohort.
UFH-UPMC Model achieved an AUROC of 0.81 (95% confidence interval [CI] [0.80,
0.83]) for UFH and 0.82 (95% CI [0.81,0.84]) for UPMC test cohorts; an area
under the precision recall curve values (AUPRC) of 0.6 (95% CI, [0.05, 0.06])
for UFH and 0.13 (95% CI, [0.11,0.15]) for UPMC test cohorts. Kinetic estimated
glomerular filtration rate, nephrotoxic drug burden and blood urea nitrogen
remained the top three features with the highest influence across the models
and health centers. Conclusion: Locally developed models displayed marginally
reduced discrimination when tested on another institution, while the top set of
influencing features remained the same across the models and sites
Clinical courses of acute kidney injury in hospitalized patients: a multistate analysis
Abstract Persistence of acute kidney injury (AKI) or insufficient recovery of renal function was associated with reduced long-term survival and life quality. We quantified AKI trajectories and describe transitions through progression and recovery among hospitalized patients. 245,663 encounters from 128,271 patients admitted to UF Health between 2012 and 2019 were retrospectively categorized according to the worst AKI stage experienced within 24-h periods. Multistate models were fit for describing characteristics influencing transitions towards progressed or regressed AKI, discharge, and death. Effects of age, sex, race, admission comorbidities, and prolonged intensive care unit stay (ICU) on transition rates were examined via Cox proportional hazards models. About 20% of encounters had AKI; where 66% of those with AKI had Stage 1 as their worst AKI severity during hospitalization, 18% had Stage 2, and 16% had Stage 3 AKI (12% with kidney replacement therapy (KRT) and 4% without KRT). At 3 days following Stage 1 AKI, 71.1% (70.5–71.6%) were either resolved to No AKI or discharged, while recovery proportion was 38% (37.4–38.6%) and discharge proportion was 7.1% (6.9–7.3%) following AKI Stage 2. At 14 days following Stage 1 AKI, patients with additional frail conditions stay had lower transition proportion towards No AKI or discharge states. Multistate modeling framework is a facilitating mechanism for understanding AKI clinical course and examining characteristics influencing disease process and transition rates
Details on output categories for CKD algorithm.
Standard race adjustments for estimating glomerular filtration rate (GFR) and reference creatinine can yield a lower acute kidney injury (AKI) and chronic kidney disease (CKD) prevalence among African American patients than non–race adjusted estimates. We developed two race-agnostic computable phenotypes that assess kidney health among 139,152 subjects admitted to the University of Florida Health between 1/2012–8/2019 by removing the race modifier from the estimated GFR and estimated creatinine formula used by the race-adjusted algorithm (race-agnostic algorithm 1) and by utilizing 2021 CKD-EPI refit without race formula (race-agnostic algorithm 2) for calculations of the estimated GFR and estimated creatinine. We compared results using these algorithms to the race-adjusted algorithm in African American patients. Using clinical adjudication, we validated race-agnostic computable phenotypes developed for preadmission CKD and AKI presence on 300 cases. Race adjustment reclassified 2,113 (8%) to no CKD and 7,901 (29%) to a less severe CKD stage compared to race-agnostic algorithm 1 and reclassified 1,208 (5%) to no CKD and 4,606 (18%) to a less severe CKD stage compared to race-agnostic algorithm 2. Of 12,451 AKI encounters based on race-agnostic algorithm 1, race adjustment reclassified 591 to No AKI and 305 to a less severe AKI stage. Of 12,251 AKI encounters based on race-agnostic algorithm 2, race adjustment reclassified 382 to No AKI and 196 (1.6%) to a less severe AKI stage. The phenotyping algorithm based on refit without race formula performed well in identifying patients with CKD and AKI with a sensitivity of 100% (95% confidence interval [CI] 97%–100%) and 99% (95% CI 97%–100%) and a specificity of 88% (95% CI 82%–93%) and 98% (95% CI 93%–100%), respectively. Race-agnostic algorithms identified substantial proportions of additional patients with CKD and AKI compared to race-adjusted algorithm in African American patients. The phenotyping algorithm is promising in identifying patients with kidney disease and improving clinical decision-making.</div
Administrative codes used for chronic kidney disease.
Administrative codes used for chronic kidney disease.</p
Logical Observation Identifier Names and Codes (LOINC) codes used for CKD A-staging.
Logical Observation Identifier Names and Codes (LOINC) codes used for CKD A-staging.</p
Reclassification of CKD status and CKD stages after race adjustment among African American patients who do not have CKD by medical history.
Reclassification of CKD status and CKD stages after race adjustment among African American patients who do not have CKD by medical history.</p
AKI characteristics using race-adjusted and race-agnostic algorithms.
AKI characteristics using race-adjusted and race-agnostic algorithms.</p