Comparison of Surrogate Parameters between Cystic Fibrosis (CF) Patients in Frankfurt and Moscow during 1990-2015

Jean-Pascal Varescon1*, Christina Smaczny1, Olaf Eickmeier1, Gulja Babadjanova2, Yulia Philippova2, Stanislav Krasovskiy2, Elena Amelina2, and Thomas Otto Friedrich Wagner1

1Christiane Herzog CF-Zentrum, Pneumologie – Klinikum der Goethe- Universität Frankfurt am Main, Germany

2Pulmonology Scientific Research Institute – Lomonosov State University, Moscow

*Corresponding Author:
Jean-Pascal Varescon
Christiane Herzog CF-Zentrum
Pneumologie – Klinikum der Goethe-Universität Frankfurt am Main, Germany
Tel: +496963014232
E-mail: [email protected]

Received Date: January 13, 2021; Accepted Date: February 05, 2021; Published Date: February 12, 2021

Citation: Varescon JP, Smaczny C, Eickmeier O, Babadjanova G, Philippova Y, et al. (2021) Comparison of Surrogate Parameters between Cystic Fibrosis (CF) Patients in Frankfurt and Moscow during 1990-2015. J Rare Disord Diagn Ther Vol.7 No.2:1.

 
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Abstract

Background: Previous studies have demonstrated that CF (Cystic Fibrosis) prognosis is dependent of three major parameters: FEV1 (Forced Expiratory Pressure in one second), BMI (Body Mass Index) and need of intravenous antibiotic therapy. The CF centres of Frankfurt, Germany, and Moscow, Russia, care for cystic fibrosis patients. We decided to investigate and compare both centers from 1990 to 2015. No comparable study has been published so far.

Method: German patient data was collected from the national cystic fibrosis database “Muko.web”. Missing values were extracted from the Hospital Information System. Russian patient data were taken directly from the medical records in Moscow. In a descriptive statistical analysis with Bias and R Studio the values were compared.

Result: A total of 428 patients from Moscow (217 male, 211 female; 348 (81,3%) were P. aeruginosa positive) and 159 patients from Frankfurt (92 male, 67 female; 137 (86,2%) with P. aeruginosa positive) were compared with regard to P. aeruginosa positivity, BMI, FEV1 and need of intravenous antibiotic therapy. CF patients in Moscow stratified by age groups had lower BMI than CF patients in Frankfurt (age 16-18: p=0,003; age 19-22: p=0,004; age 23-29: p<0,001; age 30-35: p<0,001; age 36-66: p=0,024). In a matching pairs analysis including 100 patients from Frankfurt and 100 patients from Moscow for the year 2015 FEV1 was significantly lower in Moscow patients (p<0,001).

Conclusion: BMI, FEV1 and need of intravenous therapy have significant impact on survival and on quality of life of CF patients. A lower BMI and a lower FEV1 result in a worse survival and determine the prognosis. This study showed a significant difference in prognostic parameters between Frankfurt and Moscow in the crosssectional analysis for the year 2015. A further study should evaluate this difference to show whether this difference will be found over a longer period of time.

Keywords

Cystic fibrosis; BMI; FEV1; Intravenous Antibiotic Therapy; Lung Function; P. Aeruginosa; Surrogate Parameters

Introduction

Cystic Fibrosis (CF) is a disease characterized by a loss of function of the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) in different organs [1]. Previous work has showed that prognosis in CF is related to Body-Mass-Index (BMI), Forced Expiratory Volume in 1 second (FEV1) and need of intravenous antibiotic therapy [2], this is why these three parameters should be measured and monitored regularly. They have significant impact on survival and on quality of life of CF patients [3]. Disease progression in Cystic Fibrosis (CF) is marked by deterioration of a number of physiological systems [3] especially lung function is affected [2] progressively leading to pulmonary damage and in a final state to respiratory failure. The two centers of the Pulmonology Scientific Research Institute, Moscow and the University Hospital Frankfurt (Christiane Herzog CF-Zentrum) started a collaboration starting in July 2018. Both centers care for adult CF-patients, children as well as adults. A retrospective descriptive study was started to look for differences between patients treated in the Moscow CF center and the Frankfurt CF center from 1990 to 2015. If there was a significant and relevant difference this should be detected in a difference in the three surrogate markers of prognosis in CF: BMI [4], FEV1 [5] and the necessity of intravenous antibiotic therapy caused by exacerbations in CF [6]. BMI can be compared in stratified age classes. Higher BMI is related to better lung function test results (which improves consequently quality of life and survival) and in particular for underweight individuals a poorer prognosis has been reported [4]. FEV1 is the second parameter used to mark progression of CF lung disease progression and evaluate therapeutic efficacy [5]. Furthermore FEV1 is used as prognostic tool for mortality [7-9]. A third marker for the survival of CF patients is the need of intravenous antibiotic therapy as a result of a severe pulmonal exacerbation [6] or P. aeruginosa infection [10]. Exacerbations have a big consequence in terms of current morbidity as well as implications for long term morbidity and mortality [6-8]. P. aeruginosa presence is associated with faster rates of lung function decline in all age groups [10]. Need of intravenous antibiotic therapy consequently results in decreased survival. No comparable study has been published so far, i.e., there has been no published comparative descriptive study comparing CF patients in different settings in the past.

Materials and Methods

German patient data

German patient data were collected from the German national CF registry “muko.web” [11]. This registry was started 1995 under the name “Qualitätssicherung Mukoviszidose” has been renamed “muko.web”. In the year 2015 ninety German CF centers took part in data gathering within Muko.web describing in much detail 5331 patients (median age 20; 56,5% adults; 51,8% men; 80 died in 2015; median dying age 32) [11]. Data collected from muko.web for the study were height, weight, BMI, FEV1, Forced Vital Capacity (FVC), year of birth, year of death, gender and P. aeruginosa presence. In addition to this date of diagnosis of P. aeruginosa infection of CF-patients in Frankfurt from 1990 to 2015 were retrieved. These data were anonymized and gathered into an Excel table. Missing values - in particular those describing the utilization of intravenous antibiotic therapy (not listed in muko.web) - were completed with data from the Hospital Medical record Information System (Orbis, Agfa) of the Frankfurt University Hospital. German patients were coded with the letter “f” and were associated to group 01. They received a three-digit numerical code “XXX”.

Russian patient data

Russian patient data were collected directly from the medical files of the Pulmonology Scientific Research Institute, Moscow of the FMBA (Federal Medical-Biological Agency) of Russian Federation. Collected data were anonymized and regrouped in the same standardized table as in Frankfurt. Russian patients were coded with the letter “m” and were associated to group 02. In the same way as in Frankfurt they received a three-digit numerical code “XXX”.

Grouping of data

With this anonymized code data of both centers were aggregated in one data table. Gender information was coded with 01 for male patients and 02 for female patients. Body weight was expressed in kg (kilograms), body height in cm (centimeters), FEV1 in mL (milliliters), FVC in mL (milliliters). The presence of P. aeruginosa was coded with 01, the absence of P. aeruginosa with 02. Necessity of intravenous antibiotic treatment received the code 01 (02 coded not necessary intravenous antibiotic treatment).

Data analysis with Bias and RStudio

The complete data were biometrically analyzed with the program “Bias” [12]. After a descriptive approach of complete data, differences in BMI and FEV1 values were observed. Exemplarily BMI in the two centers was compared for 2015 after separating the sample in age classes [13,14]. FEV1 is an inconstant value, as it depends on age, height and sex category [15,16]. To compare FEV1 between both centers in 2015 a program was written in Rcode and executed with RStudio –a statistical programming tool, which can execute Rcode and analyze statistical data-. MatchIt [17] was used to create two new comparable samples. They were matched according to the parameters influencing FEV1 (height, age, sex category) [15,16]. The size of both samples was 100 patients and both samples were statistically not significant different (before matching p<0,001, after matching p=0,484). Afterward both new samples were compared for their FEV1 values in a new statistical biometrical analysis with “Bias” [12].

Comparison to normal population

At the end the descriptive data of the study were compared to normal population data in Germany [18] and the Russian Federation [19-21] taking in consideration epidemiological differences, which can influence CF-patients in both centers. Especially differences in BMI in both countries may have an influence on samples BMI.

Results

Description of the data range: Year of birth

The study totalized 428 (72,91%) patients from Moscow and 159 (27,09%) patients from Frankfurt, what conducted to a total of 587 analyzed patients. All of them were born in 1999 or earlier. The oldest patient of this study was born in 1949 (Figure 1). Average [22] year of birth was 1982 for Frankfurt, 1988 for Moscow and for the total cohort 1986. Standard deviation [22] was 11,10 for Frankfurt, 6,42 for Moscow and 8,45 for total cohort. Median [22] year of birth was 1983 for Frankfurt (1st quartile 1973, 3rd quartile 1992), 1989 for Moscow (1st quartile 1985, 3rd quartile 1993) and 1988 for total cohort (1st quartile 1983, 3rd quartile 1993). Minimum in Frankfurt was 1956. In Moscow it was 1949 (consequently 1949 for total cohort). In both centers maximum was 1999 (so same for total cohort. Finally range was 43 for Frankfurt and 50 for Moscow and total cohort.

raredisorders-birth-distribution

Figure 1: Differences in year of birth in both CF-populations. (A) Year of birth distribution of CF-patients in both centers described in dot plots. (B) Box plot of patients year of birth representing median, first quartile and third quartile. (C) Box plot of patient’s year of birth representing average and standard deviation.

Description of the data range: Gender distribution, P. aeruginosa presence and more

In Moscow 217 (50,70%) male patients and 211 (49,30%) female patients were counted, while in Frankfurt 92 (57,86%) male patients and 67 (42,14%) female patients were registered. A performed Chi-square test with Yates´s correction for continuity, confirms both samples were comparable (p=0,147) for sex category distribution. 348 (81,31%) Moscow patients were infected with P. aeruginosa and 80 (18,69%) were not infected with this bacterium. In Frankfurt the number of P. aeruginosa infected patients was 137 (86,16%) while 22 CF patients were not infected (13,84%). In the same way as for the sex category distribution a Chi-square test with Yates´s correction for continuity [22] was performed (p=0,209). Subsequently both CFpatient populations were comparable for P. aeruginosa infections (p=0,209). 6 (3,77%) recorded patients died in Frankfurt (0 until 2015) and 114 (26,64%) recorded patients died in Moscow (68 (15,89%) until 2015).

Description of the data range: BMI, FEV1 and Necessity of intravenous antibiotic therapy evolution over time

Data were statistically analyzed and important values were calculated and entered into Table 1. Key values of BMI, FEV1 and necessity of intravenous antibiotic therapy were examined. Data were statistically analyzed and values were calculated and entered into Table 1. Key values of BMI, FEV1 and necessity of intravenous antibiotic therapy were examined. In summary the parameters in Table 1 are mostly better for Frankfurt patients than for Moscow patients. From 1990 to 1995 there were not sufficient data and consequently values and results cannot be considered to be representative. FEV1 values have to corrected by height, age and sex category [15,16], therefore, they are not directly comparable.

A)
Year Number of Patient data BMI average BMI median
  Frankfurt Moscow Frankfurt Moscow Frankfurt Moscow
1990 2 0 21,52 - 21,52 -
1991 2 1 21,54 14,49 21,54 14,49
1992 1 2 21,50 16,27 21,50 16,27
1993 2 7 20,91 16,44 20,91 15,08
1994 1 11 20,02 17,34 20,02 17,16
1995 10 7 16,66 17,36 16,11 17,16
1996 30 10 18,84 16,91 18,52 17,39
1997 35 23 19,41 17,61 19,55 17,72
1998 46 38 19,89 16,34 19,66 16,45
1999 45 39 19,97 17,33 19,13 17,65
2000 30 45 20,29 16,98 19,09 16,53
2001 14 53 19,07 16,79 18,05 16,85
2002 16 64 18,78 17,41 18,02 17,54
2003 68 78 21,43 17,42 20,85 17,55
2004 75 103 21,48 17,49 20,76 17,57
2005 13 101 20,57 18,04 20,68 18,03
2006 13 124 20,95 18,02 21,27 17,96
2007 13 160 20,51 18,11 21,10 18,13
2008 91 179 21,69 18,38 21,01 18,55
2009 84 188 22,39 18,55 21,81 18,52
2010 132 192 21,20 18,76 20,70 18,69
2011 137 199 21,40 18,71 21,14 18,47
2012 131 250 21,80 18,79 21,62 18,51
2013 130 263 21,99 18,68 21,66 18,29
2014 133 278 22,12 18,78 21,73 18,52
2015 141 301 22,24 18,74 21,63 18,59
Year BMI standard deviation (SD) BMI maximum BMI minimum
  Frankfurt Moscow Frankfurt Moscow Frankfurt Moscow
1990 0,64 - 21,98 - 21,07 -
1991 1,13 - 22,34 14,49 20,75 14,49
1992 - 1,75 21,50 17,51 21,50 15,03
1993 1,26 2,88 21,80 20,93 20,02 13,22
1994 - 3,82 20,02 25,00 20,02 13,34
1995 2,29 2,37 20,64 22,21 13,68 15,43
1996 3,17 2,21 25,83 19,37 14,07 13,47
1997 3,18 2,49 28,22 22,77 13,71 13,34
1998 3,18 3,58 27,64 22,77 13,65 1,92
1999 3,52 2,51 31,11 22,94 14,88 12,63
2000 4,89 2,71 37,56 24,15 13,13 12,70
2001 4,02 2,69 27,76 22,76 14,60 12,40
2002 2,94 2,65 24,01 23,23 14,74 11,65
2003 3,76 2,70 33,30 23,61 13,98 11,65
2004 4,05 2,77 35,50 24,88 12,93 10,82
2005 3,22 2,76 26,35 25,86 13,73 12,02
2006 3,35 2,83 26,67 25,72 14,38 12,03
2007 3,58 2,73 24,97 25,62 13,89 12,73
2008 4,10 2,79 40,75 26,23 14,38 11,83
2009 4,41 2,78 44,29 26,03 15,34 12,80
2010 4,20 2,84 45,35 30,03 14,27 12,60
2011 4,18 2,90 45,52 31,99 14,35 12,47
2012 4,03 2,69 45,34 27,73 13,86 12,47
2013 4,04 2,80 44,47 27,73 13,86 10,85
2014 4,17 2,86 45,41 31,46 14,10 13,02
2015 4,13 2,78 46,60 31,46 14,17 11,33
Year BMI range BMI 1st quartile BMI 3rd quartile
  Frankfurt Moscow Frankfurt Moscow Frankfurt Moscow
1990 0,91 - - - - -
1991 1,59 0,00 - - - -
1992 0,00 2,48 - - - -
1993 1,78 7,71 - 14,49 - 18,42
1994 0,00 11,66 - 14,22 - 19,30
1995 6,96 6,78 15,21 15,64 17,29 17,70
1996 11,76 5,90 16,51 15,35 20,88 18,85
1997 14,51 9,43 17,38 15,89 20,87 19,12
1998 13,99 20,85 17,93 14,22 21,60 18,46
1999 16,23 10,32 17,79 15,41 21,72 19,00
2000 24,43 11,45 17,59 14,81 21,15 19,23
2001 13,16 10,36 15,90 14,66 21,14 18,67
2002 9,27 11,58 16,76 15,23 21,38 18,93
2003 19,33 11,96 19,03 15,23 22,92 19,11
2004 22,57 14,06 19,23 15,21 23,00 19,47
2005 12,61 13,85 19,33 16,37 22,01 19,68
2006 12,29 13,69 19,76 15,66 22,60 19,82
2007 11,08 12,89 20,48 16,28 22,92 19,91
2008 26,37 14,40 19,58 16,47 22,80 20,09
2009 28,95 13,22 20,03 16,71 23,46 19,93
2010 31,08 17,43 18,81 16,97 22,95 20,20
2011 31,17 19,53 19,05 16,93 23,13 20,45
2012 31,48 15,27 19,34 16,86 23,29 20,45
2013 30,61 16,88 19,58 16,82 23,69 20,43
2014 31,32 18,44 19,31 16,86 23,81 20,50
2015 32,43 20,13 19,31 16,82 24,14 20,32
B)
Year Number of Patient data FEV1 average FEV1 median
  Frankfurt Moscow Frankfurt Moscow Frankfurt Moscow
1990 2 0 3800 - 3800 -
1991 2 0 3590 - 3590 -
1992 1 1 4370 2820 4370 2820
1993 2 4 3600 1280 3600 1000
1994 1 8 2550 1706,25 2550 1210
1995 6 6 2136,67 1216,67 2030 1155
1996 27 6 1825,93 1783,33 1800 1685
1997 34 19 2150,88 1998,42 2070 1640
1998 42 24 2357,62 1783,33 2090 1350
1999 44 28 2244,32 2215,00 2205 1980
2000 28 25 2512,86 2193,20 2555 2300
2001 13 30 2333,85 2019,67 2120 1955
2002 17 37 2328,82 2026,76 2090 1920
2003 66 50 2439,39 2186,00 2320 2000
2004 71 67 2447,89 2221,34 2340 2020
2005 15 73 2366,67 2296,71 2350 2030
2006 13 87 2571,54 2202,41 2450 2040
2007 13 125 2728,46 2301,36 2480 2130
2008 93 155 2354,73 2247,81 2230 2010
2009 86 171 2488,72 2299,30 2450 2220
2010 133 169 2463,91 2270,77 2270 2120
2011 137 176 2446,93 2192,33 2300 2090
2012 133 234 2437,44 2136,54 2230 2030
2013 135 251 2450,67 2120,84 2370 2020
2014 135 275 2426,81 2057,35 2310 1900
2015 145 295 2460,34 1983,12 2290 1850
Year FEV1 Standard Deviation (SD) FEV1 maximum FEV1 minimum
  Frankfurt Moscow Frankfurt Moscow Frankfurt Moscow
1990 565,69 - 4200 - 3400 -
1991 1343,50 - 4540 - 2640 -
1992 - - 4370 2820 4370 2820
1993 1173,80 671,71 4430 2280 2770 840
1994 - 1290,10 2550 4390 2550 610
1995 638,55 647,79 3320 2420 1520 600
1996 512,72 1070,49 3090 3180 920 630
1997 726,81 1160,03 3960 4060 870 610
1998 883,31 1036,85 4400 3820 1090 570
1999 821,57 1126,85 4310 5020 130 580
2000 906,30 948,37 4080 3810 900 870
2001 1041,78 1000,76 4330 4330 560 570
2002 1053,90 905,15 4540 4060 600 660
2003 1027,37 1044,13 5820 4510 500 600
2004 908,67 1063,67 4870 4790 570 480
2005 1074,40 1141,05 4530 6490 920 460
2006 930,45 1036,96 4530 5980 960 610
2007 1053,31 1043,37 4590 5650 1340 510
2008 971,57 1114,60 5030 5870 580 380
2009 1055,56 1073,32 5440 6240 560 480
2010 1036,99 981,22 5390 5390 710 730
2011 1028,96 1036,40 5440 5080 650 420
2012 1023,65 1036,54 5130 6420 580 450
2013 1015,53 1034,53 5300 6400 690 500
2014 1042,62 990,51 5370 5130 730 192
2015 1112,38 985,01 5410 5220 600 520
Year FEV1 range FEV1 1st quartile FEV1 3rd quartile
  Frankfurt Moscow Frankfurt Moscow Frankfurt Moscow
1990 800 - - - - -
1991 1900 - - - - -
1992 0 0 - 2820 - 2820
1993 1660 1440 - 930 - 1350
1994 0 3780 - 870 - 1972,5
1995 1800 1820 1755 807,5 2200 1247,5
1996 2170 2550 1515 892,5 2060 2590
1997 3090 3450 1720 1090 2517,5 2975
1998 3310 3250 1772,5 975 2897,5 2472,5
1999 4180 4440 1775 1357,5 2607,5 3085
2000 3180 2940 1760 1230 3402,5 2740
2001 3770 3760 1520 1135 3000 2655
2002 3940 3400 1600 1280 3090 2540
2003 5320 3910 1755 1262,5 3157,5 2807,5
2004 4300 4310 1805 1405 2915 2865
2005 3610 6030 1805 1540 2790 2710
2006 3570 5370 2030 1500 2870 2695
2007 3250 5140 1920 1680 3440 2840
2008 4450 5490 1670 1370 2940 2985
2009 4880 5760 1732,5 1455 2980 3045
2010 4680 4660 1690 1560 2980 2970
2011 4790 4660 1680 1355 2960 2890
2012 4550 5970 1670 1332,5 3060 2820
2013 4610 5900 1690 1315 2975 2730
2014 4640 4938 1690 1230 2895 2785
2015 4810 4700 1600 1245 3280 2585
C)
Year Number of Patient data Necessity of intravenous antibiotic therapy
  Frankfurt Moscow Frankfurt Moscow  
1990 24 1 1 0  
1991 24 2 1 0  
1992 25 3 1 1  
1993 26 9 1 2  
1994 26 15 1 3  
1995 27 12 3 4  
1996 27 15 2 4  
1997 27 26 2 8  
1998 30 43 6 15  
1999 31 44 6 18  
2000 33 54 7 20  
2001 36 61 10 19  
2002 41 83 21 26  
2003 48 90 25 31  
2004 58 111 32 44  
2005 63 118 27 46  
2006 66 147 24 64  
2007 69 180 32 79  
2008 90 209 43 99  
2009 98 201 35 97  
2010 118 209 47 100  
2011 130 222 49 105  
2012 130 256 48 130  
2013 139 270 52 143  
2014 142 274 57 167  
2015 148 295 58 191  
Year Percentage of necessity of intravenous antibiotic therapy
  Frankfurt Moscow  
1990 4,17 0,00  
1991 4,17 0,00  
1992 4,00 33,33  
1993 3,85 22,22  
1994 3,85 20,00  
1995 11,11 33,33  
1996 7,41 26,67  
1997 7,41 30,77  
1998 20,00 34,88  
1999 19,35 40,91  
2000 21,21 37,04  
2001 27,78 31,15  
2002 51,22 31,33  
2003 52,08 34,44  
2004 55,17 39,64  
2005 42,86 38,98  
2006 36,36 43,54  
2007 46,38 43,89  
2008 47,78 47,37  
2009 35,71 48,26  
2010 39,83 47,85  
2011 37,69 47,30  
2012 36,92 50,78  
2013 37,41 52,96  
2014 40,14 60,95  
2015 39,19 64,75  

Table 1: (A) BMI biometrical descriptive statistic from 1990 to 2015 including number of patient data sets, average BMI, median BMI, SD (standard deviation) BMI, maximum BMI, minimum BMI, BMI range, 1st quartile BMI and 3rd quartile BMI. (B) FEV1 biometrical descriptive statistic from 1990 to 2015 including number of patient data, average FEV1, median FEV1, SD (standard deviation) FEV1, maximum FEV1, minimum FEV1, FEV1 range, 1st quartile FEV1 and 3rd quartile FEV1. (C) Biometrical descriptive statistical analysis of necessity of intravenous antibiotic therapy from 1990 to 2015 including number of patient data, number of necessity of intravenous antibiotic therapy and percentage of necessity of intravenous antibiotic therapy.

Statistical BMI comparison of both CF populations in 2015

To evaluate if there was a significant statistical BMI difference between patients in Frankfurt and Moscow year 2015 was analyzed exemplarily. Patients were categorized in age groups (Figures 2 and 3). In 2015 Moscow CF patients stratified by age groups had statistically significant lower BMI than Frankfurt CF patients in all age groups (age 16-18: p=0,003; age 19-22: p=0,004; age 23-29: p<0,001; age 30-35: p<0,001; age 36-66: p=0,024) [22-25].

raredisorders-frankfurt

Figure 2: Box plots representing BMI comparison of patients for 2015 with median, first quartile and third quartile. (A) Patients aged 16 to 18 years (Average BMI: Frankfurt (n=12): 19, 95; Moscow (n=25): 17, 90). (B) Patients aged 19 to 22 years (Average BMI: Frankfurt (n=19): 20, 87; Moscow (n=76): 18, 75). (C) Patients aged 23 to 29 years (Average BMI: Frankfurt (n=25): 22, 59; Moscow (n=139): 18, 66).

raredisorders-box-plots

Figure 3: Box plots representing BMI comparison of patients for 2015 with median, first quartile and third quartile. (A) Patients aged 30 to 35 years (Average BMI: Frankfurt (n=29): 22, 27; Moscow (n=44): 18, 97). (B) Patients older than 35 years (Average BMI: Frankfurt (n=56): 23, 03; Moscow (n=17): 19, 93).

Statistical FEV1 comparison of matched samples in 2015

To compare FEV1 in both centers a program run with RStudio [17] allowed isolation of two matched samples by height (before matching p=0,028, after matching p=0,876), age (before matching p<0,001, after matching p=0,484) and sex category (before matching p=0,088, after matching p=0,258) for 2015. Both included 100 patients (first sample with 100 Frankfurt patients and second sample with 100 Moscow patients) and were comparable after matching. Statistical analysis showed FEV1 was significantly lower for Moscow CF-patients (p<0,001) than for Frankfurt CF-patients in 2015 (Table 2 and Figure 4).

FEV1 Average Median SD Maximum Minimum Range 1st quartile 3rd quartile
Frankfurt 2497,90 2420,00 1143,19 5410,00 750,00 4660,00 1537,50 3325,00
Moscow 1908,70 1615,00 1044,80 5220,00 520,00 4700,00 1222,50 2487,50

Table 2: Biometrical statistic analysis of FEV1 in 2015 for both samples (n=100 CF-patients in Frankfurt and n=100 CF-patients in Moscow). Average, median, SD, maximum, minimum, range, first quartile and third quartile are higher in Frankfurt than in Moscow.

raredisorders-empirical-distribution

Figure 4: (A) Box plot representing FEV1 comparison of both samples for 2015 with median, first quartile and third quartile. (B) Empirical distribution function of FEV1 in both samples (Blue=Frankfurt, Red=Moscow) for 2015.

Discussion

Data described both CF-populations in Frankfurt and Moscow. At first glance values of BMI, FEV1 and the necessity of intravenous antibiotic therapy were better in Frankfurt than in Moscow. An evaluation of both CF-populations for 2015 revealed BMI was significantly higher in Frankfurt, than in Moscow. A high BMI is a positive predictor for a better outcome [26,27] and decreased mortality [26]. Epidemiological analysis of normal German [18] and Russian [19-21] population didn´t explain this severe gap (referred to 3.6.). In the same way both FEV1 populations of 2015 obtained with the R-program to get comparable samples showed Frankfurt CF-patients have a better FEV1 than Moscow CF-patients. A better FEV1 is associated with a better outcome [26] and a lower mortality. Subsequently these data indicate Frankfurt patients should have a better outcome than Moscow patients. An effort to increase BMI and FEV1 will improve the CF prognosis in Moscow.

Comparison with BMI and FEV1 of normal population

At first we had to analyze epidemiological available data of normal population to see if both are reasonably comparable. Latest data from the German federal office of statistics [28] shows a mean BMI of 26.0 for German population in 2017. Russian data [29,30] are not equally detailed and latest data was published in 2014. Mean BMI in the Russian population was 26.5. In the same year mean BMI was 26.3 in Germany, this might mean that the Russian population has a higher mean BMI than the German population, however both populations can be considered comparable. Consequently a possible gap in BMI in both CF-populations (referred to 3.4.) cannot be explained by epidemiological data of the normal population. A comparison of FEV1% between Germany and the Russian Federation [31] shows a difference for patients categorized in age groups. Average and mean values seem higher in Germany for children and for adults. According to ECFSPR annual report of 2017 [31] the FEV1% of Germany and the Russian Federation are different. German data seem to resemble the pooled data very closely, while the Russian data seem to be lower than pooled data and German data. This is the reason why we expected differences between both centers we wanted to analyze.

Limitations of the study

The data quality of our study should be discussed. First of all, it should be mentioned that data were not available from every patient every year. This is why the significance of the data should be nuanced. As an example, in 2015 for the entire cohort, only 486 out of 587 entries (82.79%) were found for the BMI, only 440 out of 587 (74.96%) entries were found for FEV1 and only 492 out of 587 (83.82%) entries were found for intravenous antibiotic therapy. This shows that a significant amount of data is missing and that the quality of the data is negatively affected. Moreover, the data was collected on one hand by doctors and clinic employees, which makes human bias in the data collection possible. On the other hand, this clinically collected data is entered manually into the computer system, which makes further errors possible and can explain missing data. Deviations due to anomalies were also found in the patient's follow-up data. These have also affected the quality of the data and thus reduced the representativeness of the data.

Possible explanation for the observed differences

In our study we could observe the Russian cohort is significantly younger than the German cohort. Average age was 33.57 for Frankfurt, 25.59 for Moscow and for the total cohort 28.14. According to ECFSPR in 2017 mean average age was 22.4 [31] years in Germany and 12.4 [31] years in Russian Federation what confirms our results. In Moscow 217 (50.70%) male patients and 211 (49.30%) female patients were counted, while in Frankfurt 92 (57.86%) male patients and 67 (42.14%) female patients were registered. According to ECFSPR in 2017 in Germany around 52% were male patients and 48% were female. In Russian Federation the percentage was similar with about 51% male patients and 49% female patients [31]. These results were comparable with our study for Moscow. In Frankfurt the relative amount of male patients was higher than the German average. The observed gender gap could have influenced our results.

According to the number of death patient totalized in our study, there were less deaths in Frankfurt than in Moscow. This can be partially explained by new therapies [32], a better organization [33] and a medicine that becomes more and more detailed and precise due to the economic possibilities and the research. This means that German patients are in a transition phase, where life expectancy increases. Patients in Russia yet are not in this phase. This may be linked to a possible delay in the use of more modern equipment and therapies, as well as probably lower or unevenly distributed financial means. Moreover the economic structures are different between both countries as well as the regional structures of Frankfurt and Moscow. Russia evaluated recently with the independence from the Soviet Union in 1991 and inherited an extensive centralized system. In 1993 a mandatory health insurance (MHI) was introduced to open up an earmarked stream of funding for health care, but faced lots of fiscal constraints [34]. In Germany, the health system is build up in a different way. The state is organized federally and multiple adapted health care centers were created. This was also reflected in CF management. Since 1995, the German Cystic Fibrosis Quality Assessment project has collected demographic data and outcome parameters, what aims to develop tools for quality management and improve health care [35]. This could also partly explain our results, but has to be confirmed in further studies, where economical, management and organization can be monitored.

Important and new aspect of our study

Our study is the first one comparing CF patients between both centers of Frankfurt and Moscow. It has confirmed expected differences between surrogate parameters of prognosis (BMI, FEV1 and need of intravenous therapy) in CF patients of both centers. A higher BMI and FEV1 in one CF centre (Frankfurt) are positive prognostic parameters for survival compared to the values in Moscow. An effort to increase BMI and FEV1 in Moscow will certainly improve lifetime prognosis. This opens new ways to research possible causes of BMI gaps and FEV1 gaps and to close them. Our data indicates that in daily CF therapy routine BMI should be checked more often. Moreover higher BMI values should be targeted in CF patients. In a same way FEV1 should be monitored even more regularly and control intervals should be reduced. Our results permit to evaluate differences in therapy schemes and the use of various CF medications, in particular CFTR modulator therapies in further studies. Moreover our results show that the necessity of intravenous antibiotic treatment has also to be reexamined. The observed better results in Frankfurt for intravenous therapies in CF patients have to be proved statistically. The relation between the necessity of intravenous antibiotic therapy and a worse outcome for CF-patients has to be discussed. A study published in 2015 questioned the link between both and put other antibiotic treatments (oral therapy or inhaled therapy) on the same acting level [36]. Our work will also revolutionize research, as the focus of comparative and new founding studies should concentrate on the BMI, the FEV1 and the need of intravenous therapies. Furthermore regional development aspects and organizational differences have to be included and considered more often. The health-policy could also navigate on these findings to elaborate new health plans and new goal achievements based in particular on a higher BMI, a higher FEV1 and an evaluation of antibiotic use depending on the region and the available resources. Furthermore, we showed the reasons of these gaps in surrogate markers for CF prognosis have to be investigated. One possible cause could be a possible difference in delta F508 mutation or other CF-specific gene mutation distribution. Moreover epidemiologic reasons should be regarded in a larger scale and also compared and evaluated in another study. The socioeconomic differences between both countries should also be taken in consideration. Our study indicates in particular that different types of drugs, modes of application, frequency of application, treatment regimens and the availability of medication could play a role in CF prognosis.

Conclusion

We have identified that Frankfurt CF patients values for surrogate parameters of CF outcome were better than those in Moscow patients in a short time. Further studies should verify this difference on a longer lapse of time including larger data spectrum. First, this will allow to establish a hypothesis explaining this difference. Secondly, this could help to refine therapeutic approaches and to definite new recommendations.

Acknowledgement

This research was supported by the Christiane Herzog CFZentrum, Pneumologie – Klinikum der Goethe-Universität Frankfurt am Main – and the Pulmonology Scientific Research Institute – Lomonosov State University Moscow –. We are thankful to our colleagues who provided expertise that greatly assisted the research. We are also grateful to Dr. Ümniye Balaban for assistance with a statistical help, and Prof. Dr. med. Gernot Rohde who moderated this paper and in that line improved the manuscript significantly.

Statement of Ethics

There are no legal or ethical concerns. The ethics committee issued a positive assessment. See document “Ethical approval Varescon Study”. Study number 183/17.

Conflict of Interest

The authors have no conflicts of interest to declare.

Funding Sources

None.

Author Contributions

Jean-Pascal Varescon: Conceptualization, data curation, methodology, software, formal analysis, investigation, resources, writing, review – original draft, visualization, project administration. Approval for submission, agreement to be accountable for all aspects of the work is ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated or resolved.

T.O.F. Wagner: Supervision, conceptualization, writing – review & editing, project administration, final approval for submission, agreement to be accountable for all aspects of the work is ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated or resolved.

Gulja Babadjanova: Resources acquisition, Conceptualization, Methodology, review, final approval for submission, agreement to be accountable for all aspects of the work is ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated or resolved.

Christina Smaczny: Methodology and analysis, Resources acquisition, Conceptualization, review, final approval for submission, agreement to be accountable for all aspects of the work is ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated or resolved.

Olaf Eickmeier: Resources, review, final approval for submission, agreement to be accountable for all aspects of the work is ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated or resolved.

Yulia Philippova: Data curation, Resources, review, final approval for submission, agreement to be accountable for all aspects of the work is ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated or resolved.

Stanislav Krasovskiy: Resources, Data curation, review, final approval for submission, agreement to be accountable for all aspects of the work is ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated or resolved.

Elena Amelina: Resources, review, final approval for submission, agreement to be accountable for all aspects of the work is ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated or resolved.

Prof. Dr. med. Gernot Rohde: Substantial contributions to the conception of the work.

Dr. Ümniye Balaban: Statistical analysis help.

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