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Several simple rules predicted complications in high risk patients with diabetes
  1. Steven Smith, MD
  1. Mayo Clinic, Rochester, Minnesota, USA

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 QUESTION: What is the accuracy of a prediction rule for identifying patients with diabetes mellitus who are at high short term risk for macro- and microvascular events, infectious disease, and metabolic complications?

    Design

    A cohort of patients, randomly split into derivation and validation datasets.

    Setting

    Kaiser Permanente health maintenance organization (HMO) in Oakland, California, USA.

    Patients

    57 722 members of the HMO who were ≥ 19 years of age, had diabetes, and were continuously enrolled in the health plan during the 2 year baseline period. The derivation dataset included 28 838 patients (mean age 61 y, 53% men), and the validation dataset included 28 884 patients (mean age 61 y, 52% men).

    Description of prediction guide

    A “best” model and 4 simpler approaches were derived: the previous events strategy (identifies patients with previous events or related outpatient diagnoses during the baseline period), the first 3 variables of the “best” model, the numerical risk score (a summed score obtained by replacing significant model coefficients with integer values: 1.0 for a significant multivariate odds ratio [OR] between 1.1 and 1.49, 2.0 for an OR between 1.50 and 1.99, and 3.0 for an OR ≥2, with corresponding negative numbers for significant ORs < 1.0), and ranking on the basis of average HbA1c concentration during baseline.

    Main outcome measures

    Identification of patients at high short term risk for macro- and microvascular, infectious, and metabolic complications.

    Main results

    Comparisons of the test properties of the various models for predicting each type of complication are summarised in the table.

    Test properties of 5 models for predicting complications in diabetes (validation dataset)*

    Conclusion

    Simple prediction rules were better than HbA1c concentrations for identifying patients with diabetes who were at high short term risk for complications.

    
 
 QUESTION: What is the accuracy of a prediction rule for identifying patients with diabetes mellitus who are at high short term risk for macro- and microvascular events, infectious disease, and metabolic complications?

    Commentary

    The Diabetes Control and Complications Trial, the UK Prospective Diabetes Study, and other large randomised trials have shown that long term metabolic control in patients with diabetes can reduce costs and complications. Despite this evidence, translating the beneficial effects of treatment to the real world of clinical practice has been a major challenge for the healthcare community. Selby et al suggest that interventions targeting patients admitted to hospital with diabetes and patients with related diagnoses will have the greatest opportunity and power to show a short term effect on care for people with diabetes.

    For patients who had not been admitted to hospital recently, clinical predictors included an elevated creatinine concentration, the use of >1 antihypertensive medication, and the use of insulin. In the absence of additional clinical information, these predictors may act as surrogates for the duration of the diagnosis.1 This and other meta-data analysis strategies may hold promise in the quest for the optimal information systems and decision support. In the meantime, although only a few health systems may have integrated datasets that could identify patients at high risk for diabetes complications, every health system could easily identify patients admitted to hospital with diabetes and plan, implement, and refine risk reduction strategies targeting this group. An earlier report suggests that clinical systems fail to diagnose or document a previous diagnosis of diabetes in patients admitted to hospital.2 Reorganisation of clinical systems across the continuum of care can be effective in the absence of high technology information systems.

    References

    View Abstract

    Footnotes

    • Source of funding: in part, Pfizer Pharmaceuticals.

    • For correspondence: Dr J V Selby, Division of Research, Kaiser Permanente, Oakland, CA, USA. jvs{at}dor.kaiser.org.

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