Integrating electronic health data records to develop and validate a predictive model of hospital-acquired acute kidney injury in non-critically ill patients
| Autor: | Maria J Torres, Jacqueline Del Carpio, Mercedes Ibarz, Iñaki Romero, Nacho Nieto, Maria Paz Marco, Elias Jatem, Natalia Ramos, Pamela Chang, Silvia Pico, Elisard Huertas, Jorge Gonzalez, Gloria Falcon, Judith de la Torre, Marina Canales, Bruno Montoro, Joana Prat, Alfons Segarra |
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| Rok vydání: | 2021 |
| Předmět: |
Transplantation
medicine.medical_specialty business.industry Critically ill Acute kidney injury prediction electronic health data records risk score medicine.disease Health data acute kidney injury Nephrology Medicine Original Article hospital-acquired AcademicSubjects/MED00340 business Intensive care medicine |
| Zdroj: | Clinical Kidney Journal |
| ISSN: | 2048-8513 2048-8505 |
| DOI: | 10.1093/ckj/sfab094 |
| Popis: | Background Models developed to predict hospital-acquired acute kidney injury (HA-AKI) in non-critically ill patients have a low sensitivity, do not include dynamic changes of risk factors and do not allow the establishment of a time relationship between exposure to risk factors and AKI. We developed and externally validated a predictive model of HA-AKI integrating electronic health databases and recording the exposure to risk factors prior to the detection of AKI. Methods The study set was 36 852 non-critically ill hospitalized patients admitted from January to December 2017. Using stepwise logistic analyses, including demography, chronic comorbidities and exposure to risk factors prior to AKI detection, we developed a multivariate model to predict HA-AKI. This model was then externally validated in 21 545 non-critical patients admitted to the validation centre in the period from June 2017 to December 2018. Results The incidence of AKI in the study set was 3.9%. Among chronic comorbidities, the highest odds ratios (ORs) were conferred by chronic kidney disease, urologic disease and liver disease. Among acute complications, the highest ORs were associated with acute respiratory failure, anaemia, systemic inflammatory response syndrome, circulatory shock and major surgery. The model showed an area under the curve (AUC) of 0.907 [95% confidence interval (CI) 0.902–0.908), a sensitivity of 82.7 (95% CI 80.7–84.6) and a specificity of 84.2 (95% CI 83.9–84.6) to predict HA-AKI, with an adequate goodness-of-fit for all risk categories (χ2 = 6.02, P = 0.64). In the validation set, the prevalence of AKI was 3.2%. The model showed an AUC of 0.905 (95% CI 0.904–0.910), a sensitivity of 81.2 (95% CI 79.2–83.1) and a specificity of 82.5 (95% CI 82.2–83) to predict HA-AKI and had an adequate goodness-of-fit for all risk categories (χ2 = 4.2, P = 0.83). An online tool (predaki.amalfianalytics.com) is available to calculate the risk of AKI in other hospital environments. Conclusions By using electronic health data records, our study provides a model that can be used in clinical practice to obtain an accurate dynamic and updated assessment of the individual risk of HA-AKI during the hospital admission period in non-critically ill patients. |
| Databáze: | OpenAIRE |
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