Motor Vehicle Crashes in Diabetic Patients with Tight Glycemic Control: A Population-based Case Control Analysis


Background:
Complications from diabetes mellitus can compromise a driver's ability to safely operate a motor vehicle, yet little is known about whether euglycemia predicts normal driving risks among adults with diabetes. We studied the association between glycosylated hemoglobin (HbA1c) and the risk of a motor vehicle crash using a population-based case control analysis.

Methods and Findings:
We identified consecutive drivers reported to vehicle licensing authorities between January 1, 2005 to January 1, 2007 who had a diagnosis of diabetes mellitus and a HbA1c documented. The risk of a crash was calculated taking into account potential confounders including blood glucose monitoring, complications, and treatments. A total of 57 patients were involved in a crash and 738 were not involved in a crash. The mean HbA1c was lower for those in a crash than controls (7.4% versus 7.9%, unpaired t-test, p = 0.019), equal to a 26% increase in the relative risk of a crash for each 1% reduction in HbA1c (odds ratio = 1.26, 95% confidence interval 1.03–1.54). The trend was evident across the range of HbA1c values and persisted after adjustment for measured confounders (odds ratio = 1.25, 95% confidence interval 1.02–1.55). The two other significant risk factors for a crash were a history of severe hypoglycemia requiring outside assistance (odds ratio = 4.07, 95% confidence interval 2.35–7.04) and later age at diabetes diagnosis (odds ratio per decade = 1.29, 95% confidence interval 1.07–1.57).

Conclusions:
In this selected population, tighter glycemic control, as measured by the HbA1c, is associated with an increased risk of a motor vehicle crash.

: Please see later in the article for the Editors' Summary


Published in the journal: . PLoS Med 6(12): e32767. doi:10.1371/journal.pmed.1000192
Category: Research Article
doi: 10.1371/journal.pmed.1000192

Summary

Background:
Complications from diabetes mellitus can compromise a driver's ability to safely operate a motor vehicle, yet little is known about whether euglycemia predicts normal driving risks among adults with diabetes. We studied the association between glycosylated hemoglobin (HbA1c) and the risk of a motor vehicle crash using a population-based case control analysis.

Methods and Findings:
We identified consecutive drivers reported to vehicle licensing authorities between January 1, 2005 to January 1, 2007 who had a diagnosis of diabetes mellitus and a HbA1c documented. The risk of a crash was calculated taking into account potential confounders including blood glucose monitoring, complications, and treatments. A total of 57 patients were involved in a crash and 738 were not involved in a crash. The mean HbA1c was lower for those in a crash than controls (7.4% versus 7.9%, unpaired t-test, p = 0.019), equal to a 26% increase in the relative risk of a crash for each 1% reduction in HbA1c (odds ratio = 1.26, 95% confidence interval 1.03–1.54). The trend was evident across the range of HbA1c values and persisted after adjustment for measured confounders (odds ratio = 1.25, 95% confidence interval 1.02–1.55). The two other significant risk factors for a crash were a history of severe hypoglycemia requiring outside assistance (odds ratio = 4.07, 95% confidence interval 2.35–7.04) and later age at diabetes diagnosis (odds ratio per decade = 1.29, 95% confidence interval 1.07–1.57).

Conclusions:
In this selected population, tighter glycemic control, as measured by the HbA1c, is associated with an increased risk of a motor vehicle crash.

: Please see later in the article for the Editors' Summary

Introduction

Diabetic patients account for substantial amounts of driving. At a population disease prevalence of 5% to 7% for this diagnosis, general mobility statistics would suggest that diabetic patients drive about 250 million miles during the average day in the United States [1],[2]. Such distances are extraordinary—greater than traveling from the earth to the sun and back [3]. The exact figure could be either somewhat larger if diabetes correlates with a sedentary lifestyle that favors driving or somewhat smaller if diabetes is associated with incapacitating complications that leave the patient institutionalized [4]. The substantial driving distances are likely to continue into the future given societal reliance on road travel for work, recreation, leisure, and health care [5].

On average, a population with a large amount of driving tends to have a large number of crashes. If diabetic drivers were identical to average American adults, the baseline risk of a serious crash would be about one in 20 per year [6]. This number would amount to about five diabetic drivers killed and another 50 incapacitated each day from motor vehicle crashes in the United States. Even for individuals who crash without injuries, the event can disrupt the ideals of regular exercise, a prudent diet, work productivity, and other elements of lifestyle [7]. Impairments from retinopathy, neuropathy, and hypoglycemia might make the average diabetic driver more prone to crashing than the prevailing population average [8].

Governmental policies sometimes restrict the licenses of diabetic drivers on grounds that the disease makes the individual unfit to drive [9]. Different states in the US have different regulations, yet even permissive regions require drivers who hold commercial licenses to document glycemic control [10][15]. The laws are based on the theory that glycemic control predicts lower driving risk either by preventing retinopathy and other complications or by indirectly distinguishing persons who are innately conscientious [16],[17]. Guidelines in Canada state, for example, “In general, a patient is considered fit to drive if it can be demonstrated that he or she is fastidious and knowledgeable about controlling his or her blood glucose levels …” [18]. In this study we tested whether glycemic control, as measured by glycosylated hemoglobin (HbA1c), was associated with the risk of a motor vehicle crash.

Methods

Patient Selection

We selected all drivers reported to the Ontario Ministry of Transportation Medical Advisory Board who had an underlying diagnosis of diabetes mellitus. This population-based sampling strategy included all licensed drivers in Ontario with the accrual interval spanning from January 1, 2005 to January 1, 2007, representing all years available for analysis. Candidates were identified from mandatory annual reviews submitted by drivers who held commercial licenses or mandatory reports submitted in the aftermath of a documented motor vehicle crash. We also included all other diabetic patients reviewed for any other reason such as those appealing a license suspension or those with notifiable medical conditions reported by physicians [19]. Individuals were excluded if no HbA1c was available; otherwise, all drivers were analyzed. This study was approved by the Research Ethics Board of Sunnybrook Health Sciences Center and conducted using privacy safeguards at the Institute for Clinical Evaluative Sciences.

Crash Outcome

We classified each individual according to the manner through which they came to the attention of the licensing authority. Individuals involved in a motor vehicle crash were defined as cases. Such cases were identified by the authorities responsible for investigating a crash. All other individuals who were not involved in a motor vehicle crash were defined as controls. Such controls are not a random sample of the population because they come to attention by reports submitted by others or because of legal requirements for having a valid driver's license. Controls ought to include all diabetic drivers who developed diabetes or obtained a license during the study period, but do not because of noncompliance with legislation or other reasons.

Glycemic Control

We obtained the medical record of each person's diabetes care from available files. These records reflect submissions from community physicians corresponding to each patient; the accuracy of these reports has never been validated although each is submitted and signed by a licensed physician [20]. We used the hemoglobin HbA1c as the primary measure of long term blood glucose control since it reflects glycemic control over 2 to 3 mo, is widely available with a liquid chromatography assay, and is the objective standard for traffic policy decisions around the world [21],[22]. In secondary analyses we also examined the patients' degree of monitoring, total years since diagnosis of diabetes, and specific complications. These secondary analyses were conducted for exploratory purposes and did not involve statistical power calculations in advance.

Missing Data

Missing data were handled using methods blind to outcome status. The type of diabetes was not always recorded in available documents; instead, we classified individuals on the basis of whether they had started insulin treatment before or after age 20 y. The duration of diabetes was also gauged by categorizing patients who had been on insulin for 20 y or more. Data on specific complications, monitoring, and treatments were accepted as recorded under the assumption that not documented implied not present. Information on diet, exercise, weight, compliance, alcohol, lifestyle, age of first licensing, driving patterns, commercial licenses, past infractions, diabetic education, visual acuity, and at-fault analysis was not recorded and deemed not possible to impute from sources.

Statistical Analysis

Our primary analysis compared the mean HbA1c among cases involved in a crash to controls who were not involved in a crash using an unpaired t-test with two-tailed statistics. Logistic regression was used to quantify associations using odds ratios and adjusting for baseline confounders using a step-wise forward selection procedure (models constrained to 12 events per covariate to avoid overfitting and used the c-statistic to gauge overall accuracy) [23]. Odds ratios are good approximations of relative risk for low probability events (such as the annual risk of a crash) [24]. A nonparametric test for trend was also conducted using the Cochran-Armitage method [25]. Data validation was conducted blind to outcome to correct HbA1c values outside the plausible range (4.0%–16.0%) for magnitude anomalies (e.g., 6.5% reported as 0.65 or 0.065). The sample size was estimated to provide 80% power to detect a 0.5% difference in HbA1c between the two groups of patients.

Results

During the 2-y study interval a total of 3,900 individuals were reported to licensing authorities, of whom 795 were diabetic patients who had HbA1c values documented. Their mean age was 52 y, 84% were men, and the average patient had about a 20-y history of diabetes (Table 1). Most patients had end organ damage including retinopathy, nephropathy, and neuropathy. About 81% were treated with insulin, 27% with oral glucose-lowering medications, and 15% with neither insulin nor an oral medication. Overall, one in six lacked hypoglycemic awareness and one-third had a history of hypoglycemia that required outside assistance. The spread of HbA1c values was remarkable, ranging from 4.4% to 14.7%.

Tab. 1. Patient characteristics.
Patient characteristics.
Data are count (percentage) except where noted as mean (standard deviation).

Overall, 57 patients were involved in a crash (cases) and 738 were not involved in a crash (controls). In keeping with a potential adverse association, the mean HbA1c was lower among those who crashed than controls (7.4% versus 7.9%, p = 0.019). This association was equivalent to a 26% increase in the risk of a crash for each 1% reduction in HbA1c (odds ratio = 1.26, 95% confidence interval 1.03–1.54). The finding was evident across the range of HbA1c values and suggested that the risk of a crash in the bottom quartile was more than twice the risk in the top quartile (Figure 1). The absolute difference amounted to a net increase of 29 total crashes (95% confidence interval 16–46) had the risk in the highest quartile extended to all other quartiles.

Glycemic control and risk of a motor vehicle crash.
Fig. 1. Glycemic control and risk of a motor vehicle crash.
Relative risk of a motor vehicle crash for drivers at different levels of glycemic control. x-Axis shows glycemic control as measured by glycosylated hemoglobin concentration and grouped into approximate quartiles. Data in square brackets show individuals in each group as [number of cases/number of controls]. y-Axis shows relative risk of a crash expressed in odds-ratio calibrated using the top glycemic quartile as referent. Solid circles indicate point-estimates and vertical lines indicate standard error bars. p-Value tests for trend across all four quartiles. Overall results show a correlation between lower HbA1c levels and higher relative risk of a crash with no evidence of a U-shaped relationship.

The observed association between low HbA1c values and increased crash risks tended to be consistent for patients with different characteristics (Figure 2). The risk was observed for patients with longer and shorter durations of diabetes, regardless of whether measured as time since diagnosis or time since starting insulin. Moreover, the risk was observed for those treated with insulin, oral hypoglycemics, both, or neither. In addition, the risk extended to those with no mention of severe hypoglycemia, hypoglycemic unawareness, or other specific chronic complications. The largest single anomaly (yet not statistically significant and overlapping the main analysis) was the subgroup not treated with insulin or oral hypoglycemic medications.

Crash risk in different subgroups.
Fig. 2. Crash risk in different subgroups.
Each analysis examines correlation of lower HbA1c levels with higher risk of a crash. Results expressed as odds ratio (solid circle) and 95% confidence interval (horizontal line) per 1% point decrease in HbA1c. Analyses of chronic complication subgroups exclude patients reporting corresponding symptom. Results for full cohort appear at bottom and show an odds ratio of 1.26 with 95% confidence interval 1.03–1.54.

The observed association between low HbA1c values and increased crash risks persisted when adjusted for potential confounders. Analyses adjusting for age yielded approximately the same increase in the relative risk of a crash for each 1% reduction in HbA1c (odds ratio = 1.27, 95% confidence interval 1.04–1.55). Similarly, analyses adjusting for age, age at diagnosis, and age when insulin started also yielded a comparable increase in the risk of a crash (odds ratio = 1.26, 95% confidence interval 1.00–1.58). Analyses adjusting for both age, gender, and each separate complication also yielded about a 25% increase in the risk of a crash for each 1% reduction in HbA1c (odds ratio range 1.20–1.30). None of the statistical models yielded a contrary result although results in some models were not statistically significant.

Two other patient characteristics were independent risk factors for a crash. A history of severe hypoglycemia that required outside help was associated with about a 4-fold increase in risk (odds ratio = 4.07, 95% confidence interval 2.35–7.04). In addition, older age of diabetes diagnosis (expressed as increase per decade) was also associated with an increase in risk (odds ratio = 1.29, 95% confidence interval 1.07–1.57). No other baseline characteristic (Table 1) was a significant predictor of risk in univariate analyses. Multivariate analysis that included both severe hypoglycemia requiring outside help and age at diabetes diagnosis had a mid-range overall accuracy (c-statistic = 0.65) and showed a persistent association of HbA1c with crash risk (odds ratio = 1.25, 95% confidence interval 1.02–1.55).

Discussion

We studied a selected sample of diabetic adults driving during a 2-y interval using a population-based approach. The main finding was that lower HbA1c levels were associated with an increased risk of a motor vehicle crash. The adverse association was observed across the range of HbA1c values, persisted after adjustment for independent confounders, yet was not as large as the relative risk associated with a history of severe hypoglycemia requiring outside assistance. The attributable risk was substantial, so that eliminating the association by extrapolating the risk observed at the highest HbA1c quartile to all drivers at all HbA1c quartiles would have eliminated about half of all observed crashes. These findings are difficult to explain with random chance, reverse-causality, or simple reporting bias.

A major limitation of our research relates to the nonrandomized design and sample selection. That is, adults with diabetes self-select how to control their glucose as well as how to drive a vehicle. One explanation for the association, therefore, could be that those who are stringent about controlling their blood glucose are paradoxically more careless about driving a vehicle. Another explanation could be that tightly controlled patients drive in more dangerous settings. A third explanation could be that unreported alcohol consumption influences both driving risk and glucose control (e.g., impaired liver glucogenesis). Many other biases are possible including Berkson's paradox, Neyman Bias, Hawthorn effects, restricted generalizability, imperfect compliance with the law, and spectrum bias [19],[26]. These limitations are unavoidable in trauma research except for studies that focus on volunteer samples, unnatural tasks, or hypothetical risks [27].

We have no data on baseline time spent driving, yet such data are unlikely to explain our findings. First, all individuals maintained valid licenses, remained active in the community, and were at risk for a crash. Second, no prior study shows diabetic adults drive substantially more than the prevailing average (or that small differences in HbA1c predict large differences in driving time) [28]. Third, research in other domains indicates time spent driving is a poor predictor of crash risk; for example, teenagers account for a large number of crashes despite a small amount of time spent driving and senior citizens have a heightened risk primarily explained by the very low distance drivers [29],[30]. No surprise, therefore, that license regulations account for fitness to drive but have no restrictions based on the amount of driving the person intends.

Our findings join a growing and contentious literature correlating low HbA1c values with adverse consequences in adults with diabetes mellitus. For example, three recent randomized trials found that intensive treatment regimens led to both lower HbA1c values and an increased incidence of severe hypoglycemia among diabetic patients [31][33]. These trials and our study do not prove that striving for a normal HbA1c is harmful; instead, the adverse association might indicate that customary treatments for achieving euglycemia are inexact and potentially hazardous to high level cognitive behavior [34][37]. Many patients, furthermore, are aware of their HbA1c results so that a double-blinded trial becomes unfeasible and susceptible to subtle confounders. Such behavioral factors are germane in clinical research since patients with a normalized HbA1c might develop a false sense of security whereas those with a high HbA1c might abandon their activities and ironically become protected from mobility related injury [38].

The basic implication of our study is to underscore the difficulty in judging fitness-to-drive in adults with severe diabetes mellitus [39]. This pitfall calls into question traffic laws that prevail in the United States, United Kingdom, Canada, Germany, Holland, Australia, and other countries that single out diabetic patients for specialized review. At a minimum, the data suggest that a patient's HbA1c level is neither necessary nor sufficient for determining fitness-to-drive. Whether a comprehensive medical review, functional performance assessment, formal driving test, detailed record of hypoglycemia episodes, or other measure could be more accurate and cost-effective remains a topic for future research. Unfortunately, most other measures of diabetes control are based on self-report that can be easily denied when applying for a driving license.


Zdroje

1. Fatality analysis reporting system encyclopedia Washington (D.C.) National Highway Traffic Safety Administration Available: http://www-fars.nhtsa.dot.gov/Main/index.aspx. Accessed 15 October 2009

2. Centers for Disease Control and Prevention 2005 National diabetes fact sheet: general information and national estimates on diabetes in the United States, 2005. Atlanta: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention. Available: http://www.diabetes.org/uedocuments/NationalDiabetesFactSheetRev.pdf. Accessed 15 October 2009

3. Wikipedia, the Free Encyclopedia. Astronomical unit. Available: http://en.wikipedia.org/wiki/Astronomical_unit. Accessed 15 October 2009

4. EvansL

2004 Traffic safety Bloomfield Hills (Michigan) Science Serving Society 444

5. StegL

VlekC

SlotegraafG

2001 Instrumental-reasoned and symbolic-affective motives for using a motor car. Transportation Research Part F: Psychology and Behaviour 4 151 169

6. RedelmeierDA

WeinsteinMC

1999 Cost-effectiveness of regulations against using a cellular telephone while driving. Med Decis Making 19 1 8

7. WinstonFK

Kassam-AdamsN

Vivarelli-O'NeillC

FordJ

NewmanE

2002 Acute stress disorder symptoms in children and their parents after pediatric traffic injury. Pediatrics 109 e90

8. MarshallSC

2008 The role of reduced fitness to drive due to medical impairments in explaining crashes involving older drivers. Traffic Inj Prev 9 291 298

9. LangfordJ

BraitmanK

CharltonJ

EberhardJ

O'NeillD

2008 Licensing authorities' options for managing older driver safety—practical advice from the researchers. Traffic Inj Prev 9 278 281

10. DistillerLA

KramerBD

1996 Driving and diabetics on insulin therapy. S Afr Med J 86 1018 1020

11. FlanaganDE

WatsonJ

EverettJ

CavanD

KerrD

2000 Driving and insulin–consensus, conflict or confusion? Diabet Med 17 316 320

12. CundyT

DruryP

2000 Vocational driving, diabetes and insulin use. N Z Med J 113 317 318

13. PolakBC

van RijnLJ

KorverC

2003 Fitness to drive in people with diabetes mellitus; a recommendation from the Health Council of the Netherlands. Ned Tijdschr Geneeskd 147 1243

14. MarcinkiewiczA

SzoslandD

2007 Medical certification for diabetic drivers in the selected European Union member states. Med Pr 58 541 546

15. JornayvazFR

RagusoCA

PhilippeJ

2007 Diabetes mellitus and driving. Rev Med Suisse 3 1437 1438, 1440–1441

16. GillG

DurstonJ

JohnstonR

MacLeodK

WatkinsP

2002 Insulin-treated diabetes and driving in the UK. Diabet Med 19 435 439

17. American Diabetes Association 2004 Standards of medical care in diabetes. Diabetes Care 27 S15 S35

18. Canadian Medical Association 2006 Section 17: Endocrine and metabolic disorders. 74 81 Determining medical fitness to operate motor vehicles. CMA driver's guide, 7th edition Available: http://www.cma.ca/multimedia/CMA/Content_Images/Inside_cma/WhatWePublish/Drivers_Guide/Section17_e.pdf. Accessed 15 October 2009

19. RedelmeierDA

VenkateshV

StanbrookMB

2008 Mandatory reporting by physicians of patients potentially unfit to drive. Open Medicine 2 8 17

20. Ontario Ministry of Transportation Driver Improvement Office 2009 Medical review section. Available: http://www.mto.gov.on.ca/english/dandv/driver/medical-review/. Accessed 15 October 2009

21. CarpinelliA

MoscaA

BoniniP

1986 Evaluation of a new semi-automated high-performance liquid chromatography method for glycosylated haemoglobins. J Automat Chem 8 192 196

22. ManleyS

JohnWG

MarshallS

2004 Introduction of IFCC reference method for calibration of HbA1c: implications for clinical care. Diabetic Med 21 673 676

23. PeduzziP

ConcatoJ

KemperE

HolfordTR

FeinsteinAR

1996 A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol 49 1373 1379

24. HulleySB

CummingsSR

1988 Designing clinical research Baltimore Williams and Wilkins 250

25. NeuhauserM

HothornLA

1999 An exact Cochran-Armitage test for trend when dose-response shapes are a priori unknown. Comput Stat Data Anal 30 403 412

26. SackettDL

1979 Bias in analytic research. J Chron Dis 32 52 63

27. FildesBN

2008 Future directions for older driver research. Traffic Inj Prev 9 387 393

28. DanaeiG

DingEL

MozaffarianD

TaylorB

RehmJ

2009 The preventable causes of death in the United States: comparative risk assessment of dietary, lifestyle, and metabolic risk factors. PLoS Med 6 e1000058 doi:10.1371/journal.pmed.1000058

29. WilliamsAF

2009 Licensing age and teenage driver crashes: a review of the evidence. Traffic Inj Prev 10 9 15

30. EberhardJ

2008 Older drivers' “high per mile crash involvement”: the implications for licensing authorities. Traffic Inj Prev 9 284 290

31. Action to Control Cardiovascular Risk in Diabetes Study Group 2008 Effects of intensive glucose lowering in type 2 diabetes. N Engl J Med 358 2545 2559

32. ADVANCE Collaborative Group 2008 Intensive blood glucose control and vascular outcomes in patients with type 2 diabetes. N Engl J Med 358 2560 2572

33. DuckworthW

AbrairaC

MoritzT

RedaD

EmanueleN

2009 Glucose control and vascular complications in veterans with type 2 diabetes. N Engl J Med 360 129 139

34. WeingerK

KinsleyBT

LevyCJ

BajajM

SimonsonDC

1999 The perception of safe driving ability during hypoglycemia in patients with type 1 diabetes mellitus. Am J Med 107 246 253

35. ClarkeWL

CoxDJ

Gonder-FrederickLA

KovatchevB

1999 Hypoglycemia and the decision to drive a motor vehicle by persons with diabetes. JAMA 282 750 754

36. CoxDJ

Gonder-FrederickLA

KovatchevBP

JulianDM

ClarkeWL

2000 Progressive hypoglycemia's impact on driving simulation performance. Occurrence, awareness and correction. Diabetes Care 23 163 170

37. StorkAD

van HaeftenTW

VenemanTF

2007 The decision not to drive during hypoglycemia in patients with type 1 and type 2 diabetes according to hypoglycemia awareness. Diabetes Care 30 2822 2826

38. KennedyRL

HenryJ

ChapmanAJ

NayarR

GrantP

2002 Accidents in patients with insulin-treated diabetes: increased risk of low-impact falls but not motor vehicle crashes–a prospective register-based study. J Trauma 52 660 666

39. BeggIS

YaleJF

HouldenRL

RoweRC

McSherryJ

2003 Canadian Diabetes Association's clinical practice guidelines for diabetes and private and commercial driving. Can J Diabetes 27 128 148

Štítky
Interní lékařství

Článek vyšel v časopise

PLOS Medicine


2009 Číslo 12

Nejčtenější v tomto čísle

Tomuto tématu se dále věnují…


Kurzy

Zvyšte si kvalifikaci online z pohodlí domova

Zánětlivá bolest zad a axiální spondylartritida – Diagnostika a referenční strategie
nový kurz
Autoři: MUDr. Monika Gregová, Ph.D., MUDr. Kristýna Bubová

Inhibitory karboanhydrázy v léčbě glaukomu
Autoři: as. MUDr. Petr Výborný, CSc., FEBO

Příběh jedlé sody
Autoři: MUDr. Ladislav Korábek, CSc., MBA

Krvácení v důsledku portální hypertenze při jaterní cirhóze – od pohledu záchranné služby až po závěrečný hepato-gastroenterologický pohled
Autoři: PhDr. Petr Jaššo, MBA, MUDr. Hynek Fiala, Ph.D., prof. MUDr. Radan Brůha, CSc., MUDr. Tomáš Fejfar, Ph.D., MUDr. David Astapenko, Ph.D., prof. MUDr. Vladimír Černý, Ph.D.

Rozšíření možností lokální terapie atopické dermatitidy v ordinaci praktického lékaře či alergologa
Autoři: MUDr. Nina Benáková, Ph.D.

Všechny kurzy
Kurzy Doporučená témata Časopisy
Přihlášení
Zapomenuté heslo

Nemáte účet?  Registrujte se

Zapomenuté heslo

Zadejte e-mailovou adresu se kterou jste vytvářel(a) účet, budou Vám na ni zaslány informace k nastavení nového hesla.

Přihlášení

Nemáte účet?  Registrujte se