Development and validation of a risk model for cognitive impairment in the older Chinese inpatients: An analysis based on a 5-year database


      • A risk model for cognitive impairment in the older Chinese inpatients was built.
      • Age, diabetes, depression and educational level were included in this model.
      • The prediction model had a high sensitivity (86.2%) and a low specificity (59.4%).
      • It is helpful to “identify” high risk patients rather than “rule out” those at low risk.


      Early diagnosis of cognitive impairment is important but difficult. Prediction models may work as an efficient way to identify high risk individuals for this disease. This study aimed to develop a simple and convenient model to identify high-risk individuals of cognitive impairment in the older Chinese inpatients. We enrolled 1300 inpatients aged 60 years or above from the department of geriatrics of the First Affiliated Hospital of Chongqing Medical University during 2013 to 2017. The model for cognitive impairment was established in the developing cohort of 1100 participants and tested in another validating cohort of 200 participants. Logistic regression analyses were used to identify the candidate variables of cognitive impairment. Receiver operating curve was adopted to validate the model. Logistic regression analyses showed that increasing age, diabetes, depression and low educational level were independently associated with cognitive impairment. The model was generated in the following way: P model = ey/(1 + ey), where y = −6.874 + 0.088 * age + 0.317 * diabetes + 0.647 * depression + 0.345 * education level. The value of P model indicates the probability of cognitive impairment for each patient. The present model proved to be a reliable marker for identifying people at high risk of cognitive impairment (area under curve = 0.790, 95% CI = 0.728–0.852, p < 0.001). It had a high sensitivity (86.2%) but a relatively low specificity (59.4%). It may be helpful to “recognize” those at high risk of cognitive impairment rather than “rule out” those at low risk of this disease.


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