Enhancing the performance of predictive models for Hospital mortality by adding nursing data

Antonello Cocchieri, Maurizio Zega, Gianfranco Sanson, John Welton, Ercole Vellone, Massimo Maurici, Rosaria Alvaro, Fabio D'Agostino

Risultato della ricerca: Contributo in rivistaArticolo in rivista

4 Citazioni (Scopus)

Abstract

Background: Mortality is the most considered outcome for assessing the quality of hospital care. However, hospital mortality depends on diverse patient characteristics; thus, complete risk stratification is crucial to correctly estimate a patient's prognosis. Electronic health records include standard medical data; however, standard nursing data, such as nursing diagnoses (which were considered essential for a complete picture of the patient condition) are seldom included. Objective: To explore the independent predictive power of nursing diagnoses on patient hospital mortality and to investigate whether the inclusion of this variable in addition to medical diagnostic data can enhance the performance of risk adjustment tools. Methods: Prospective observational study in one Italian university hospital. Data were collected for six months from a clinical nursing information system and the hospital discharge register. The number of nursing diagnoses identified by nurses within 24 h after admission was used to express the nursing dependency index (NDI). Eight logistic regression models were tested to predict patient mortality, by adding to a first basic model considering patient's age, sex, and modality of hospital admission, the level of comorbidity (CCI), and the nursing and medical condition as expressed by the NDI and the All Patient Refined-Diagnosis Related Group weight (APR-DRGw), respectively. Results: Overall, 2301 patients were included. The addition of the NDI to the model increased the explained variance by 20%. The explained variance increased by 56% when the APR-DRGw, CCI, and NDI were included. Thus, the latter model was nearly highly accurate (c = 0.89, 95% confidence interval: 0.87–0.92). Conclusion: Nursing diagnoses have an independent power in predicting hospital mortality. The explained variance in the predictive models improved when nursing data were included in addition to medical data. These findings strengthen the need to include standardized nursing data in electronic health records.
Lingua originaleEnglish
pagine (da-a)79-85
Numero di pagine7
RivistaInternational Journal of Medical Informatics
Volume125
DOI
Stato di pubblicazionePubblicato - 2019

Keywords

  • Aged
  • Comorbidity
  • Diagnosis-related groups
  • Electronic Health Records
  • Female
  • Hospital Mortality
  • Hospital mortality
  • Hospitalization
  • Hospitals, University
  • Humans
  • Italy
  • Logistic Models
  • Male
  • Middle Aged
  • Nursing Diagnosis
  • Nursing dependency index
  • Nursing diagnosis
  • Patient Discharge
  • Predictive models
  • Prognosis
  • Prospective Studies
  • Risk adjustment

Fingerprint

Entra nei temi di ricerca di 'Enhancing the performance of predictive models for Hospital mortality by adding nursing data'. Insieme formano una fingerprint unica.

Cita questo