Intern Emerg Med (2013) 8:55–63 DOI 10.1007/s11739-011-0581-z
IM - ORIGINAL
Clinical validation of a new algorithm for computerized dosing of vitamin K antagonist therapy: a retrospective simulation study Michela Basileo • Carlo Micheluzzi • Marina Minozzi • Luigi Lazzaroni • Alfonso Iorio
Received: 15 November 2010 / Accepted: 17 March 2011 / Published online: 6 April 2011 SIMI 2011
Abstract The number of patients on oral anticoagulant therapy has increased in recent years, and this trend is expected to continue. The increased workload for physicians has led to the development of computerized systems to make organizational workflow more efficient. These programs may include algorithms to propose a weekly dosage and timing for the following visit. Before introducing a new algorithm in clinical practice, its safety and efficacy must be validated. We undertook a retrospective simulation study to test a new algorithm for the TAOnet system. The main outcome was the percentage of concordant and discordant proposals between manual- and algorithm-based prescriptions. Pairs of computerized and physician prescriptions were assessed. They were categorized as 0.1–5, 5.1–10 and [10% if the dose was different, and assigned as ‘‘algorithm better’’ or ‘‘manual better’’ dependent upon the subsequent international normalized
M. Basileo Department of Internal and Cardiovascular Medicine, University of Perugia, Perugia, Italy C. Micheluzzi EDP Progetti Srl, Bolzano, Italy M. Minozzi Department of Diagnosis and Treatment Service in Thromboembolic Disorders, University ‘‘La Sapienza’’, Roma, Italy L. Lazzaroni Roche Diagnostics Italy S.p.A., Milano, Italy A. Iorio (&) Department of Clinical Epidemiology and Biostatistics and Medicine, Hamilton Health Sciences, McMaster University, Hamilton, ON, Canada e-mail:
[email protected]
ratio value. In 61.0% of cases, the manual and computerized weekly dosage assignments were identical; in 15.3% of cases, the difference was between 0.1 and 5%; in 14.7 of cases, it was between 5.1 and 10%; and in 9.0% of cases, it was[10%. The algorithm did better in 43.9% of discordant pairs, generally due to less frequent under-dosing. In conclusion, the new algorithm proved to consistently overlap with the manual method. The algorithm is useful but must be tested in a multi-center, prospective, interventional study. Keywords Validation
VKA therapy Computerized algorithm
Background Oral anticoagulant therapy for the management and prevention of arterial and venous thromboembolic diseases is growing in importance. The number of patients who receive oral anticoagulation therapy with vitamin-K antagonists (VKAs) continues to increase worldwide. This is because of the aging population and because of its efficacy in preventing stroke in patients with atrial fibrillation and recurrent thromboembolism in patients with deep-vein thrombosis or pulmonary embolism [1]. Warfarin [(RS)-4-hydroxy-3-(3-oxo-1-phenylbutyl)-2Hchromen-2-one] is the most commonly used VKA because of favorable pharmacokinetic properties when compared with acenocoumarol (shorter half-life) or phenprocoumon (longer half-life) [2]. The management of warfarin is complex because of its: (1) intricate pharmacokinetic and pharmacodynamic properties; (2) narrow therapeutic range [3, 4]. Warfarin is among one of the top five medications associated with drug errors that cause harm, and has been
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shown to be one of the commonest causes of visits to the emergency department (ED) due to adverse events [5]. Patients taking VKAs are exposed to double risks if therapy is inadequate: (1) risk of hemorrhage if the treatment is excessive and the International Normalized Ratio (INR) is increased beyond the therapeutic range; (2) risk of recurrence of thrombotic events if the dosage of warfarin is too low and the INR results are sub-therapeutic [6]. It is therefore very important to adapt and modify the anticoagulant dosage whenever the measured INR is outside the predefined target range. Many factors have been reported to influence the dosing schemes of VKAs, including wide inter- and intra-individual variation in dosage requirements as well as unknown variations in concomitant medications, diet, increasing age, liver metabolic efficiency and vitamin K status [7, 8]. The most important features of care of patients on long-term anticoagulant therapy are the: (1) variation of dosage needed to maintain or restore the patient into the INR therapeutic range; (2) choice of timing for the following control visit. Computerized systems solve managerial problems related to the increasing number of patients referred to specialized clinics. This has led to good results in terms of a reduction in waiting times for checkups and an increase in the number of patients monitored simultaneously by each physician [9]. Moreover, to make optimal use of the calculation power of computer systems, algorithms have been introduced to assist in the choice of optimal weekly dosage and planning the timing of the next visit. The use of algorithms improved the care of patients on anticoagulation therapy in terms of: accuracy of the therapy; time that patients spent within the therapeutic range of INR; and reduction of the incidence of adverse events [9]. A comparison of the results of physicians who use computer algorithms as support to their dosing decisions with those of physicians who use unassisted dosing shows that the use of computer algorithms leads to equal or improved quality of oral anticoagulant treatment [10–14]. Most computerized algorithms developed recently are based on an ‘‘empirical decision tree’’ that determines whether the same dosage can be maintained, dosage adjustments have to be made or intervention by a physician is required. Computerized programs using these algorithms can propose a weekly calendar of daily dosages of warfarin and the date for the next visit on the basis of the value of current, previous and target INRs and possibly other relevant patient characteristics [14, 15]. The equations used in the algorithms are based on a simple pharmacodynamic model that implies a linear function between the INR and dosage. The major disadvantages of these algorithms are that they do not generate a dosage proposal in all cases, and not all algorithms take
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into account the sensitivity of the patient to coumarin derivatives, (which may change over time), the half-life of the drug and the non-linearity of the dose–INR relationship [14]. One of the widely used computerized management program in Italy is the TAOnet system (EDP Progetti Srl, Bolzano, Italy; marketed by Roche Diagnostics Italy SpA, Milano, Italy). It is used in 36 anticoagulation centers in Italy, serving about 30,000 patients. Recently, an algorithm was newly developed for the TAOnet system. Before introducing an algorithm for dosing therapy into clinical practice, it is desirable to validate its safety and efficacy. There are few guidelines on how to validate a new algorithm for dosing VKA therapy. The only authoritative guideline is that of the International Society on Thrombosis and Haemostasis (ISTH) [16]. Our ultimate aim was to adopt the suggestion by Poller et al. [16] to choose the recommended number and type of centers and participants to run a prospective clinical validation study. Before embarking on such a trial, we wanted to assess the performance of the algorithm in a simulation study, the results of which are reported here. This first phase was a retrospective simulation, which allowed us to undertake a pilot evaluation and to plan the requested modification of the algorithm (if any) before the prospective evaluation. The second phase (which is ongoing) involved a multicenter, prospective intervention trial of the algorithm according to the model proposed by Poller et al. [16].
Methods TAOnet system The TAOnet system aims to support patient care in a distributed setting with a coordinating anticoagulation clinic connected through the Internet to peripheral access points. The TAOnet system permits the monitoring of patients with the same quality standards as those of anticoagulation clinics. The system is developed on an Oracle platform, and is organized as a centralized, Internet-supported program. The hardware is composed of a modular system using a central server and workstations in the anticoagulation clinic and in the peripheral sites. The TAOnet system is utilized by family doctors, nurses, pharmacies and retirement homes. Algorithm The algorithm is based on a simplified mathematical model executed in two main stages (Fig. 1). The first stage concerns the extraction and processing of therapeutic dosage and INR values relative to the patient. This is used to
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Calculate exponential average of the last 2 weekly doses that had determined INR values among ± 30% of the range and that were included among ± 30% of the average dose
Calculate exponencial average for the INR values selected
Calculate previous DELTAdose average > 0
Proposed dose= Mean dose-DELTAdose* ABS(target INR-actual INR)/ target INR
If the absolute value of the difference between proposed and previous doses is > of the mean of previous delta
If proposed dose > previous dose
Proposed dose= ((mean dose/mean INR)*target INR)((actual INR-Target INR)*(mean dose/mean INR)
Calculate DELTAdose between mean and proposed doses
Proposed dose= Previous dose + mean delta dose
Proposed dose= Previous dose - mean delta dose NO
If previous dose is > 0
YES NO NO
Actual and previous INR result to be lower than target (±15%)?
INR results to be into the target (±15%)?
YES
Actual INR result to be lower than previous?
Actual and previous INR result to be higher than target (±15%)?
NO
YES
NO
NO
YES New weekly dose= Previous dose + ¼ of warfarin
NO
Actual INR result to be higher than previous?
YES New weekly dose= Previous dose
New weekly dose= Previous dose - ¼ of warfarin New weekly dose proposed
Fig. 1 Description of the algorithm
calculate the sensitivity of the patient to changes in drug dosage. The second stage is a decision tree used to calculate the dosage of anticoagulant and therapy duration. The algorithm extracts dosage values that have effectively contributed and maintained the INR levels of patients within a therapeutic target range relative to the
diagnosis. In detail, the algorithm extracts values for the weekly dose of the preceding week and the dose of the last 2 days of each therapy. Exponential averages are then calculated for the dosage values and related INR values to give more weight to more recent controls. A mathematical function measures the sensitivity of the patient to the drug
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based on the calculated average dose and INR value. This returns a unitary value of anticoagulant (milligram) per unit value of INR. This is used to calculate the required dose to bring the patient into the target-range INR. To avoid sudden changes in weekly dosage, the proposed algorithm is corrected according to differences in drug dosage documented in the clinical history of the patient. The proposed dosage is an input value in the second stage of the algorithm: the decision tree. This stage checks some simple indicators related to dosage value (INR value and the weekly dosage of the preceding week). This is done to decide whether to maintain the previous dosage, or to eventually change it by steps of C1.25 mg/week using the value proposed by the first stage of the algorithm relative to the sensitivity of the patient. Study design The data used for the simulation phase of the study were selected from a data set of patients on anticoagulant therapy followed by a specialized anticoagulation clinic operating in Umbria, Italy. This clinic ‘‘manually’’ prescribes warfarin to attending patients. These data were inserted in an ad hoc database (Excel, Microsoft Corporation, NM, USA). In the database, all the information required by the algorithm to propose the weekly therapy dosage and the timing for the next control visit (actual INR value; previous INR values with relative date of controls and weekly dosages of warfarin) and the information needed for the study (patient identifier, sex, age, primary diagnosis, previous clinical events, concomitant diseases) were entered into the program. Patients: inclusion and exclusion criteria In this retrospective simulation study, all consecutive data of patients on stable anticoagulant therapy from 2001 to 2010 were included. Therapeutic quality control indices were assessed for participants. All follow-up data of patients who had the following characteristics were selected: (a) on continued treatment for C2 years, and (b) had C30 consecutive visits. All follow-up data of patients that were related to: (a) results of the first 3 weeks of treatment (induction period); (b) results from the 4 weeks before and after a clinical event; and (c) results out of the established INR range of acceptability were excluded. Data were not excluded according to diagnosis, age and gender.
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calculated to maintain the patient within his therapeutic range. In the present simulation study, the algorithm proposed a weekly dosage and timing for the following control after considering all previous INR values, the previous manual dosage of warfarin and time intervals among controls, as it would have done in real use. Hence, for each visit of each patient, we obtained pairs of manual and computerized weekly dosages of warfarin. The algorithm requires expression of an INR range within which it is allowed to produce a prescription. This range was set at 1–5. Outcome measures The main outcome was the percentage of concordant and discordant weekly dosage proposals between a manual specialized anticoagulation clinic and computer-assisted dosage system. In the case of discordant weekly dosage, the secondary outcomes were to evaluate: (a) the percentage of cases in which the effective dosage was proposed by the physician or by the computer algorithm program; and (b) the same percentage in the case of the patient’s INR value being outside ±1 point from the therapeutic range. Data analyses Pairs of dosage prescriptions were compared. Cases in which manual and computerized dosages were equal were not investigated further. In cases in which manual and computerized dosages were different, we categorized and ranked the amount of discordance. The difference between the computer and manual dosages was considered to be acceptable if it was \5% and to be a possible failure of the algorithm if it was [5%. Differences [5% were carefully analyzed and sub-classified into the ranges 5–10 and C10%. Each percentage range was quantified and counted. In cases in which manual and computerized dosages were different, we considered as ‘‘correct’’ the dosage modality (computer or manual) that maintained or reported the patient within the therapeutic range of INR. This judgment was possible because post hoc, we knew the subsequent INR value of the patient and hence if the patient had remained in or returned to the therapeutic range. Table 1 shows by examples our method of correctness assessment. To focus evaluation of the algorithm simulation to cases with likely clinical relevance, the same analysis was repeated for visits in which the INR value exceeded the patient’s therapeutic range by at least 1 point.
Algorithm simulation Statistical analyses The aim of the algorithm is to get the measured INR value and to determine whether the previous weekly manual dosage can be confirmed, or whether a new dosage must be
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The range of discordant weekly dosages were 0.1–5, 5.1–10 and [10%. The one-sample proportion test was
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Table 1 Rules for the assessment of the agreement between manual and computerized data
The dosage that was most likely to have maintained or retained the patient within his therapeutic range was considered as ‘‘correct’’. The INR range was defined for each patient on the basis of his primary diagnosis. The weekly dosage was increased (:) or strongly increased (::) or decreased (;) or strongly decreased (;;) compared with the previous dosage. For example, in the first row the decrease proposed by the physician was considered correct as compared to the strong decrease proposed by the computer, because it brought back the patient within the range. In the second row, the strong decrease proposed by the computer was considered better than the ineffective decrease proposed by the physician
used to assess the significance of differences among the proportion of dosages assigned as correct to the algorithm or to the manual method. Our null hypothesis was that there was no statistically significant difference between the algorithm dosing and manual dosing. If P \ 0.05, the hypothesis that the observed proportion of correct computer weekly dosage was equal to the correct manual dosage proportion value was rejected, and the alternative hypothesis that there was a significant difference between the two proportions was accepted. In this case, the test also provided a test of significant differences in the two possible directions of the difference (computer better or worse). Statistical analyses were done employing the commercially available software STATA 11.3 (StataCorp LP, College Station, TX, USA).
Results Characteristics of the overall population of the anticoagulation clinic Data were extracted from a data set of patients on anticoagulant therapy monitored by a specialized anticoagulation clinic in Umbria. Data were extracted in a completely anonymous format. In 2009, the clinic followed up 2,606 patients for 36,542 control visits. The mean INR was 2.57. The overall time in the therapeutic range was 68.4%. This value represented the mean number of days that a patient
stayed within the therapeutic range as calculated by the interpolation proposed by Rosendaal et al. [17]. The percentage of controls within the therapeutic range was 61.0%, those above the therapeutic range was 15.1% and those below the therapeutic range was 23.9%. Patient characteristics We selected 614 patients fulfilling our inclusion criteria from January 2001 to December 2009 (2,151 patient/ years). All patients were on stable anticoagulant treatment for C2 years and with C30 consecutive visits without treatment withdrawal or suspension due to interventional clinical events. The male:female ratio was 1. Overall, 600 patients (97.7%) were on warfarin therapy and 14 (2.3%) on acenocoumarol therapy without significant differences in mean age, mean time of treatment, mean number of visits per patient and mean number of visits per month. Patients treated with acenocoumarol were suffering from atrial fibrillation (11/14, 78.6%), arterial thrombosis (1/14, 7.1%), cardiopathy (1/14, 7.1%) and mechanical heart valve replacement (1/14, 7.1%).The mean age at study entry was 75 years (range 28–93 years). Overall, the mean time of treatment was 1,268 days; the mean number of visits per patient was 57 (for a total of 35,172 visits); and the mean number of visits per month was 1.4. Most patients were treated with VKA for atrial fibrillation. Table 2 shows the characteristics of the study population.
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Table 2 Baseline characteristics of the population Number of patients by age group at entry (%) \50
11 (1.8)
50–59
32 (5.2)
60–69
92 (15.0)
70–79
289 (47.1)
C80
190 (30.9)
Number of patients by target INR range (%) 2–3 or lower range
557 (90.7)
2.5–3.5 or higher range
57 (9.3)
Number of patients by drug (%) Warfarin
600 (97.7)
Acenocumarol
14 (2.3)
Number of patients by clinical indication (%) Atrial fibrillation
469 (76.4)
Mechanical heart valve Arterial thrombosis
74 (12.1) 34 (5.5)
Cardiopathic diseases
30 (4.9)
Venous thrombosis
7 (1.1)
Mean days of treatment by clinical indication (per patient) Atrial fibrillation
1,258
Mechanical heart valve
1,309
Arterial thrombosis
1,390
Cardiopathic diseases
1,204
Venous thrombosis
1,168
Mean visit/month by clinical indication (per patient) Atrial fibrillation
1.4
Mechanical heart valve
1.5
Arterial thrombosis
1.4
Cardiopathic diseases
1.4
Venous thrombosis
1.5
We extracted and subsequently analyzed 35,172 visits. Overall, 4,210 clinical visits were excluded because the computerized algorithm did not provide a dosage or for the reasons given by the investigators as shown in Fig. 2. Data analyses In 61.0% (18,894/30,962) of the cases, the computerized and manual weekly dosage assignments were identical; in 15.3% (4,745/30,962) of the cases, the difference between the two prescriptions was between 0.1 and 5%; in 14.7 (4,536/30,962), it was between 5.1 and 10%; and in 9.0% (2,787/30,962), the difference was [10%. Table 3 shows the percentage of discordant computerized or manual weekly dosages of VKA therapy categorized as over-estimation and under-estimation of the dose. Overall, manual dosage was judged to be better than computerized dosage. In particular, the manual dosage was
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statistically judged to be more correct if the computerized dosage was lower than the manual dosage (Table 3). Conversely, if the computerized dosage was higher than the manual, the computerized dosage was judged to be more correct in a significantly higher number of cases. In 3.0% of the total visits (1,043/35,172), the INR value was ?1 or -1 point beyond the assigned therapeutic range. In 7.5% of cases (78/1,043), the two dosages were identical. In 51.6% (538/1,043) of cases, the manual dosage was different from the computer-assisted dosage, which in all cases was judged to be worse than the manual prescription (Table 4). Since the study population included two groups of patients with different therapeutic range (Table 2), we assessed in a sensitivity analysis if the target range had any effect on the performance of the algorithm, finding out no evidence of any effect (Fig. 3).
Discussion We found that the weekly dosage of VKAs proposed by the new algorithm to be incorporated in the TAOnet systems was equal to the manual dosage in 61.0% of cases. When considering as clinically acceptable a difference \5% of the weekly dose, the percentage of equivalent prescriptions rose to 76.3%. Despite the lack of authoritative guidance for the definition of goodness of a new algorithm in a simulated comparison like the one we did, the performance we found can be considered, in our opinion, to be satisfactory enough to make the algorithm worthy to be tested in a prospective clinical trial. In fact, three out of four prescriptions correctly done are well likely to get an INR time in a range higher that that required by the ISTH guidance [16]. A discrepancy in the proposed dosage of [10% was relatively rare, and found in \1 in ten cases. The tendency of the algorithm was to dose higher with respect to the physician [61.5% (7,417/12,068) vs. 38.5% (4,650/12,068) of discrepant pairs]. When the algorithm proposal was higher than the manual proposal, the algorithm was, in general, correct. Conversely, when considering prescriptions for patients outside of their therapeutic range by [1 INR unit, the algorithm failed more frequently than that proposed by the physician. Both the aforementioned characteristics of the algorithm under study are likely to be useful in clinical practice if, as it is universally advised, the dosage proposal generated by the algorithm is considered as a suggestion to the supervising physician. This physician in turn knows that: (1) nine out of ten times he will be just called to accept the proposal; (2) the algorithm will partly mitigate the well-known tendency of the physicians to under-dose warfarin; (3) the physician will be left with more time to focus on patients out of range. For the sake of
Intern Emerg Med (2013) 8:55–63 Fig. 2 Flowchart of the study. In a further analysis, 1,043 visits in which INR value was exceeded for at least 1 point of patient’s therapeutic range were considered separately. A total of 427/1,043 cases were withdrawn because: corresponding to the induction period (13.4% 140/1,043), the INR test value was outside the established range of 1–5 (26.8%, 280/1,043) or corresponding to that at last visit (0.7%, 7/1,043)
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35172
4210
Total number of clinic visits analyzed by the computerized algorithm
Total number of clinic visits withdrawal by the computerized algorithm because: 3668
30962
Corresponding to induction period
284
INR test value was outside of established range 1-5
178
Corresponding at last visit
61
There was no manual prescription
19
There was not dosage proposed by computer
Total clinic visits with weekly dosage calculated by both computerized algorithm and physician
18894 Number of computer and manual concordant dosages 12068 Number of computer and manual discordant dosages
Table 3 Percentage of discordant weekly VKA dosage between manual and computer-assisted methods with the differentiation between overand under-estimation dosage Dosage difference (%)
% (N) of discordant weekly dosages (dosec - dosep) Dosec - dosep [ 0
Dosec - dosep \ 0 Computer-assisted right dosage
Total
Computer-assisted right dosage
Manual right dosage
Manual right dosage
Computer-assisted right dosage
Manual right dosage
0.1–5
48.6 (862/1,774)
51.4 (912/1,774)
35.8 (1,064/2,971)
64.2* (1,907/2,971)
40.6 (1,926/4,745)
59.4* (2,819/4,745)
5.1–10
56.0 (933/1,666)
44.0* (733/1,666)
39.4 (1,131/2,870)
60.6* (1,739/2,870)
45.5 (2,064/4,536)
54.5* (2,472/4,536)
[10
60.7 (735/1,211)
39.3* (476/1,211)
36.3 (572/1,576)
63.7* (1,004/1,576)
46.9 (1,307/2,787)
53.1* (1,480/2,787)
* P \ 0.05
patient safety, while waiting for a newer version of the algorithm with improved performance, a warning for the physicians dealing with results likely to need a validation has been added to the program. The number of patients on oral anticoagulant therapy has increased in recent years and is expected to continue to
increase. To meet the growing worldwide demand for oral anticoagulation, computer systems to manage patients have been developed. Subsequently, algorithms have been introduced to aid the physician to choose the optimal weekly dosage and the planning of visits [7]. Adoption of computerized systems in clinical practice allows a
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Table 4 Percentage of discordant weekly VKA dosage between manual and computer method observed among the case in which the INR value was outside the therapeutic range for ? or -1 unit Dosage difference (%)
% (N) of discordant weekly dosages (dosec - dosep) Dosec - dosep [ 0
Dosec - dosep \ 0
Total
Computer-assisted right dosage
Manual right dosage
Computer-assisted right dosage
Manual right dosage
Computer-assisted right dosage
Manual right dosage
0.1–5
30.6 (30/98)
69.4* (68/98)
25.0 (3/12)
75.0 (9/12)
30.0 (33/110)
70.0* (77/110)
5.1–10
37.6 (59/157)
62.4* (98/157)
25.0 (4/16)
75.0* (12/16)
36.4 (63/173)
63.6* (110/173)
[10
51.7 (106/209)
49.3 (103/209)
21.7 (10/46)
78.3* (36/46)
45.5 (116/255)
54.5 (139/255)
* P \ 0.05
Fig. 3 Percentage of discordant weekly VKA dosage (left) and number of patients with discordance greater than 1 INR points (right) between different target INR groups (2–3 and 2.5–3.5). There was no statistically significant difference for any comparisons
considerable reduction of workload and a decrease in waiting time for patients due to more efficient organization of work flow [7]. Furthermore, implementation of distributed network over the Internet allows important financial savings as compared to traditional management [9]. Most comparison studies show that computerized decision support systems for anticoagulant therapy are safe and effective, achieving levels of therapeutic quality similar to those obtained by experienced medical staff [11, 18, 19]. Among computer systems, those equipped with an algorithm perform at the top of the scale [8]. The present study aimed to provide the first step in the validation procedure of a new algorithm before its adoption in routine use in clinical practice. Comprehensive guidelines on how to validate such algorithms for dosing VKA therapy are lacking. We planned to follow the guidelines published by the ISTH [16], but we preferred to start safely with a preliminary simulation study to assess the basic performance of the algorithm. One of the major strengths of the present study is the number of patient years accounted for. We selected 614 patients with the same characteristics of the overall VKAtreated population in our center in terms of gender, age, primary diagnosis and mean INR for a total of 2,131 person years. Furthermore, selection of patients was aimed at obtaining unbiased results so that only stable patients were
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enrolled, and that only visits with INR values close to their therapeutic range (i.e., 1–5) were analyzed. As a consequence of these two strengths of the present study, algorithm assessment produced consistent results. Actually, the design of the present study was aimed at not discarding the new algorithm based on unsatisfactory performance in unstable patients, which was very likely to occur just by chance and would have required a much larger sample size to be ruled out with sufficient power. Thus, we preferred to leave this for the next development step and aimed at showing that the algorithm performed well when used to dose patients on stable VKAs. The present study also had limitations. First, it was retrospective. This was a convenient choice to effectively assess the algorithm before proceeding to a second multicenter prospective interventional phase carried out on the basis of methods proposed by Poller et al. [20] to fully validate the computerized algorithm. Second, the participants were mainly elderly, and this would require additional testing in patients of different ages before the results of this study were transferred to clinical practice. However, oral anticoagulant therapy has proven to be most effective in the prevention of cardioembolism, which is more frequent among the elderly. Third, the induction period and the periods close to clinically relevant events were excluded from the analysis to minimize bias. Patients starting
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treatment (who are usually unstable) and results temporally close to the period of suspension of treatment could represent a source of variability that, in this phase of the study, could make the analysis too complex. We acknowledge that other studies have shown the validity of computerized systems during the induction period [11]. Fourth, since the majority of patients enrolled in the study had atrial fibrillation and consequently target INR range of 2–3, the study conclusions were especially valid for this population. In conclusion, these results indicate that the new algorithm successfully passed the first phase of the study. The new algorithm could suggest the appropriate VKA dosage in 9/10 prescriptions. However, this was a single-center, retrospective study, so conclusions should not be generalized before testing the algorithm in a multicenter prospective interventional study. Most of the information to evaluate algorithm performance (i.e., the proportion of time for which patients were maintained within the locally assigned target INR ranges; incidence of clinical events; number of rejected computerized dosages) will be obtained in the second multicenter, prospective, interventional phase of the study. What is known on this topic? •
• •
For the safety of patients receiving anticoagulant therapy, it is critical to establish the correct dose of warfarin, balancing the risk of hemorrhage and of recurrence of thrombotic events. Computerized systems help in managing outpatient clinics and the increased demand for VKA treatment. Most comparison studies between computer-assisted and manual dosing methods have shown that computerized decision support systems for anticoagulant therapy are safe and effective.
What does this article add? •
• •
The first evidence of the effectiveness of a new algorithm to be used with the TAOnet management system. A preliminary retrospective validation of a VKA management algorithm. A practical example on how to start the validation process of a new computerized algorithm for dosing therapy before embarking on more complex and costly assessments.
Conflict of interest
None.
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