Temporal and external validation of a prediction model for adverse outcomes among inpatients with diabetes

Adderley, Nicola J; Mallett, Susan; Marshall, Tom; Ghosh, Subrata; Rayman, Gerry; Bellary, Srikanth; Coleman, Jamie J.; Akiboye, Funke; Toulis, Konstantinos; Nirantharakumar, Krishnarajah

doi:10.1111/dme.13612

Cited by 4 publications

(2 citation statements)

References 20 publications

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“…However, the RPM-DKD established by logistic regression did not perform well. Furthermore, prediction models can become obsolete with change in population demography, better therapeutic options and care pathways, and improvement in data recording 34. In the future, it may be possible to build a DKD prediction model by deep learning methods in order to improve the prediction of DKD occurrence and progression; many studies have applied deep learning with proven success 35–39…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, prediction models can become obsolete with change in population demography, better therapeutic options and care pathways, and improvement in data recording. 34 In the future, it may be possible to build a DKD prediction model by deep learning methods in order to improve the prediction of DKD occurrence and progression; many studies have applied deep learning with proven success. [35][36][37][38][39] CONCLUSION Our independent external validation study revealed that, in patients with T2DM, the RPM-DKD cannot accurately predict the risk of DKD occurrence and progression.…”

Section: Future Researchmentioning

confidence: 99%

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External validation of the risk prediction model for early diabetic kidney disease in Taiwan population: a retrospective cohort study

et al. 2022

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ObjectivesThis study aims to independently and externally validate the Risk Prediction Model for Diabetic Kidney Disease (RPM-DKD) in patients with type 2 diabetes mellitus (T2DM).DesignThis is a retrospective cohort study.SettingOutpatient clinics at Lee’s United Clinics, Taiwan, China.ParticipantsA total of 2504 patients (average age 55.44 years, SD, 7.49 years) and 4455 patients (average age 57.88 years, SD, 8.80 years) were included for analysis in the DKD prediction and progression prediction cohorts, respectively.ExposureThe predicted risk for DKD and DKD progression for each patient were all calculated using the RPM-DKD.Primary and secondary outcome measuresThe primary outcome measure was overall incidence of DKD. Secondary outcomes included DKD progression. The discrimination, calibration and precision of the RPM-DKD score were assessed.ResultsThe DKD prediction cohort and progression prediction cohort consisted of patients with 2504 and 4455 T2DM, respectively. The RPM-DKD examined in this study showed moderately discriminative ability with area under the curve ranged from 0.636 to 0.681 for the occurrence of DKD and 0.620 to 0.654 for the progression of DKD. The Hosmer-Lemeshow χ2test indicted the RPM-DKD was not well calibrated for predicting the occurrence of DKD and overestimated the progression of DKD. The precision for predicting the occurrence and progression of DKD were 43.2% and 42.2%, respectively.ConclusionsOn external validation, the RPM-DKD cannot accurately predict the risk of DKD occurrence and progression in patients with T2DM.

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Section: Discussionmentioning

confidence: 99%