Automated tools for identifying the causes of anaemia in general practices are particularly advantageous for patients who do not fit the typical profile.

BACKGROUND: The Dutch guideline algorithm for the analysis of anaemia in patients of general practitioners (GPs) was programmed in a Clinical Decision Support system (CDS-anaemia) to support the process of diagnosing the cause of anaemia in the laboratory. This research aims to assess the supplementary benefit provided by the automated algorithm in various demographic categories, including different sexes, age groups and severities of anaemia, in comparison to the manual diagnostic approach employed by GPs. METHODS: This was a retrospective cohort study of 5399 primary care patients where the cause of anaemia was diagnosed by GPs with or without the aid of CDS-anaemia within the age groups 18-44, 45-64, 65-79 and 80 and older. Anaemia was defined according to the Dutch College of General Practitioners (DCGP) guideline. Causes of anaemia were based on the DCGP guidelines with the corresponding blood tests. By calculation of rate ratios and percentage differences of the determined cause of anaemia we evaluated the effect of the diagnostic algorithm. RESULTS AND CONCLUSION: The percentage patients in which an underlying cause of anaemia was found increased 34 and 46 percentage points in males and females, respectively, when GPs were supported by CDS-anaemia compared to GPs who were not supported by CDS-anaemia. The highest increase in percentage points when CDS-anaemia was used, was found in younger- and middle-aged males and mild or moderate anaemia.

[The cause of anemia is underdiagnosed in primary care]. / Onderdiagnostiek naar anemie in de eerste lijn.

OBJECTIVE: To determine if general practitioners can diagnose the cause of anemia, based on the requested laboratory tests. DESIGN: Retrospective observational study. METHOD: The research population consisted of 20.004 adult patients with established anemia, who had blood samples analyzed by Atalmedial in 2019. The cause of anemia was found when criteria based on the NHG-standard were met. We considered the NHG-guideline to be followed when hemoglobin was requested in the first diagnostic request and when the correct combination of blood tests was requested in the second diagnostic request. Descriptive statistics and multilevel regression analysis were performed. RESULTS: A possible cause of anemia has been found in 38,7% of the patients within two diagnostic requests, regardless of the adherence to the NHG-guideline. The chance of finding a cause of anemia was smaller in men than women of the same age, whereas the chance was highest in women over the age of 80 and between 18 and 44. The NHG-guideline for anemia was followed in 11.794 (59%) of the patients in the first diagnostic request. 19,3% (11,4% of total) of these patients also had a second diagnostic request. In 10,4% (1,2% of total) of these patients, the NHG-guideline was adhered to in the second diagnostic request. CONCLUSION: A cause of anemia is, based on laboratory tests, often not diagnosed in daily practice in the primary care. The reason for this is insufficient laboratory follow-up after initial testing when no cause of anemia is found. The NHG-guideline for anemia is poorly adhered to.

The effectiveness of an automated algorithm as a tool for investigating the cause of anaemia in undiagnosed patients from general practitioners.

BACKGROUND: The Dutch guideline algorithm for the analysis of anaemia in patients of general practitioners (GPs) was programmed in a Clinical Decision Support system (CDS-anaemia) to support the process of diagnosing the cause of anaemia in the laboratory. This study investigates the diagnostic yield of the automated anaemia algorithm compared to that of the manual work up by the GP. METHODS: This retrospective population-based study consisted of 2697 people ≥18 years. Anaemia was defined according to the Dutch College of General Practitioners (DCGP) guideline. Causes of anaemia were based on the DCGP guidelines with the corresponding blood tests. The number of blood tests and causes of anaemia were measured in two separate periods in both the (CDS-anaemia) pilot group and a control group in which routine care was provided. RESULTS: Patients from GPs supported by CDS-anaemia had higher chances of having more anaemia-related blood tests being performed. This resulted in finding significantly more causes of anaemia in the pilot group compared to the control group with respect to iron deficiency (resp. 31.3% vs 14.5%), possible iron deficiency (resp. 11.4% vs 2.8%), iron deficiency in acute phase (2.6% vs 0.5%), chronic disease/infection/inflammation (23.5% vs 1.9%), vitamin B12 deficiency (4.5% vs 1.9%), possible vitamin B12 deficiency (16.8% vs 8.7%), folate deficiency (3.3% vs 0.9%) and possible bone marrow disorder (3.8% vs 0.0%); p < 0.05. CONCLUSIONS: This study suggests that an automated-algorithm support can effectively aid in the diagnostic work-up of anaemia in primary care to find more causes of anaemia.

Dynamics in N-terminal pro-brain natriuretic peptide concentration in patients with non-ST-elevation acute coronary syndrome.

BACKGROUND: Although there is growing evidence that N-terminal pro-brain natriuretic peptide (NT-proBNP) can be used as a powerful tool in risk prediction in patients with non-ST-elevation acute coronary syndrome (NSTEACS), the dynamic variation of serum concentrations in time is poorly understood. To gain insight into the dynamics of NT-proBNP, a study was performed using serial serum samples in patients admitted with NSTEACS. METHODS: A total of 24 patients admitted with NSTEACS was included in this study. Serial samples were taken at baseline, 8 hours, 16 hours, 24 hours, and 36 hours after admittance. RESULTS: A highly dynamic pattern in serial measurements of NT-proBNP was observed. Although an increase in NT-proBNP serum levels already existed 8 hours after admittance, it did not reach significance as compared with baseline. The samples obtained 16, 24, and 36 hours after admission were all significantly increased as compared with the values at admission (P < .01), generally leading to a > 2-fold increase with peak values at 16 to 24 hours after admittance. Furthermore, considerable differences in NT-proBNP concentrations between patients were observed. CONCLUSIONS: It was shown that NT-proBNP is a highly dynamic cardiac peptide. Strategic sampling at 16 to 24 hours after admittance could prove representative regarding the assessment of risk prediction and subsequent clinical decision making.